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Csontos AA, Šugar D. Dataset of geomagnetic absolute measurements performed by Declination and Inclination Magnetometer (DIM) and nuclear magnetometer during the joint Croatian-Hungarian repeat station campaign in Adriatic region. Data Brief 2024; 54:110276. [PMID: 38516282 PMCID: PMC10950720 DOI: 10.1016/j.dib.2024.110276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 02/12/2024] [Accepted: 02/23/2024] [Indexed: 03/23/2024] Open
Abstract
The highest level of the present-day geomagnetic recordings is presented by the absolute controlled magnetic measurements. This quality is permanently fulfilled in geomagnetic observatories (GO) only. The absolute records are based on nuclear magnetometers for the intensity measurement and on DIM (Declination and Inclination Magnetometer) instruments for the direction determination of the ambient magnetic field. Although the density of GOs is not dense enough for every scientific purpose (i.e., modelling the secular variation in detail), repeat station (RS) networks were founded in a lot of countries on the Earth. Regarding to the main task of RS measurements, almost the same requirements must be fulfilled during the fieldwork as in the GOs. Consequently, the environmental and instrumental expectations are pre-defined as well as the procedure of measurement which are generally absolute readings. After the reduction of the absolute measurements to the simultaneous record of the nearest GO is performed, the data can be represented to a relevant yearly mean value. The present article shortly summarises the environmental and the instrumental background of geomagnetic absolute measurements on reoccupations of two different RS in Croatia. In 2010 tree-days-long reoccupations were performed on the selected stations with applying an on-site magnetic variometer. One of the results of data processing pointed out the appearance of a geomagnetic sea-side effect on both of RSs. The phenomenon is the geomagnetic influence of the induced current system in the seawater near to the shore. The anomalous lateral currents are the consequence of the high conductivity contrast between the mainland and the (moving) salinity water and the induction effect of the fluctuating external geomagnetic field. The calculation steps of the geomagnetic elements will be also detailed regarding the recorded samples of a presented dataset as well as the diagnostic values (i.e., offset value and misalignment error of fluxgate probe).
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Affiliation(s)
- András Attila Csontos
- HUN-REN Institute of Earth Physics and Space Science, Csatkai E. u. 6-8, Sopron H-9400, Hungary
| | - Danijel Šugar
- University of Zagreb, Faculty of Geodesy, Institute for Geomatics, Savska cesta 144A, Zagreb HR-10000, Croatia
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Rosenthal K, Lindley MR, Turner MA, Ratcliffe E, Hunsicker E. Current data processing methods and reporting standards for untargeted analysis of volatile organic compounds using direct mass spectrometry: a systematic review. Metabolomics 2024; 20:42. [PMID: 38491298 PMCID: PMC10942920 DOI: 10.1007/s11306-024-02104-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 02/16/2024] [Indexed: 03/18/2024]
Abstract
INTRODUCTION Untargeted direct mass spectrometric analysis of volatile organic compounds has many potential applications across fields such as healthcare and food safety. However, robust data processing protocols must be employed to ensure that research is replicable and practical applications can be realised. User-friendly data processing and statistical tools are becoming increasingly available; however, the use of these tools have neither been analysed, nor are they necessarily suited for every data type. OBJECTIVES This review aims to analyse data processing and analytic workflows currently in use and examine whether methodological reporting is sufficient to enable replication. METHODS Studies identified from Web of Science and Scopus databases were systematically examined against the inclusion criteria. The experimental, data processing, and data analysis workflows were reviewed for the relevant studies. RESULTS From 459 studies identified from the databases, a total of 110 met the inclusion criteria. Very few papers provided enough detail to allow all aspects of the methodology to be replicated accurately, with only three meeting previous guidelines for reporting experimental methods. A wide range of data processing methods were used, with only eight papers (7.3%) employing a largely similar workflow where direct comparability was achievable. CONCLUSIONS Standardised workflows and reporting systems need to be developed to ensure research in this area is replicable, comparable, and held to a high standard. Thus, allowing the wide-ranging potential applications to be realised.
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Affiliation(s)
- K Rosenthal
- School of Sport, Exercise & Health Sciences, Loughborough University, Loughborough, UK.
| | - M R Lindley
- School of Health Sciences, Faculty of Medicine and Health, University of New South Wales, Sydney, Australia
| | - M A Turner
- Department of Chemistry, Loughborough University, Loughborough, UK
| | - E Ratcliffe
- Department of Chemical Engineering, Loughborough University, Loughborough, UK
| | - E Hunsicker
- Department of Mathematical Sciences, Loughborough University, Loughborough, UK
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Padhy MR, Vigneshwari S, Venkat Ratnam M. Implementation of Adaptive-Bayesian DStoch technique for obtaining winds from MST radar covering higher altitudes. Heliyon 2024; 10:e26316. [PMID: 38420412 PMCID: PMC10900930 DOI: 10.1016/j.heliyon.2024.e26316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 02/02/2024] [Accepted: 02/09/2024] [Indexed: 03/02/2024] Open
Abstract
It is challenging to estimate winds accurately from higher altitudes using VHF-MST radar. The current study introduces the Adaptive-Bayesian Deterministic Stochastics Technique (ADStoch), which implements an Empirical Bayesian 1D prediction method using stochastics to analyze radar signals. A new and robust estimator for empirical wavelet shrinkage with Gaussian prior of the nonzero mean for wavelet coefficients is presented, which makes the current prior different from other priors. The mean parameters and the prior covariance hyperparameters follow a pseudo maximum likelihood method for computation. Details on the implemented algorithm developed from scratch using C# are also presented. This technique outperforms contemporary techniques discussed in this context that can recover signals buried in noise established based on the analysis of moment and quality. The estimated Wind is cross-validated for accuracy with the observed wind from the GPS radiosonde operated simultaneously. This technique can consistently extract 3D wind that can reach the range of 25.5 km-28.2 km, improving the conventional maximum altitude of 21.2 km in real time for the MST radar. It is concluded that the ADStoch analysis technique can effectively obtain VHF-MST radar signals at significantly higher altitudes, which is helpful in various scientific investigations.
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Jia W, Liu H, Ma Y, Huang G, Liu Y, Zhao B, Xie D, Huang K, Wang R. Reproducibility in nontarget screening (NTS) of environmental emerging contaminants: Assessing different HLB SPE cartridges and instruments. Sci Total Environ 2024; 912:168971. [PMID: 38042181 DOI: 10.1016/j.scitotenv.2023.168971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 11/25/2023] [Accepted: 11/27/2023] [Indexed: 12/04/2023]
Abstract
Non-targeted screening (NTS) methods are integral in environmental research for detecting emerging contaminants. However, their efficacy can be influenced by variations in hydrophilic-lipophilic balance (HLB) solid phase extraction (SPE) cartridges and high-resolution mass spectrometry (HRMS) instruments across different laboratories. In this study, we scrutinized the influence of five HLB SPE cartridges (Nano, Weiqi, CNW, Waters, and J&K) and four LC-HRMS platforms (Agilent, Waters, Thermo, and AB SCIEX) on the identification of emerging environmental contaminants. Our results demonstrate that 87.6 % of the target compounds and over 59.6 % of the non-target features were consistently detected across all tested HLB cartridges, with an overall 71.2 % universally identified across the four LC-HRMS systems. Discrepancies in detection rates were primarily attributable to variations in retention time stability, mass stability of precursors and fragments, system cleanliness affecting fold change and p-values, and fragment response. These findings confirm the necessity of refining parameter criteria for NTS. Moreover, our study confirms the efficacy of the PyHRMS tool in analyzing and processing data from multiple instrumental platforms, reinforcing its utility for multi-platform NTS. Overall, our findings underscore the reliability and robustness of NTS methods in identifying potential water contaminants, while also highlighting factors that may influence these outcomes.
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Affiliation(s)
- Wenhao Jia
- Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province (Hainan University), Haikou 570228, China
| | - He Liu
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China
| | - Yini Ma
- Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province (Hainan University), Haikou 570228, China
| | - Guolong Huang
- Zhejiang GenPure Eco-Tech Co., Ltd., Hangzhou 310020, Zhejiang, China
| | - Yaxiong Liu
- Guangdong Institute for Drug Control, Guangzhou 510663, Guangdong, China
| | - Bo Zhao
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China; Guangxi Key Laboratory of Emerging Contaminants Monitoring, Early Warning and Environmental Health Risk Assessment, Nanning 530028, China
| | - Danping Xie
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China; Guangxi Key Laboratory of Emerging Contaminants Monitoring, Early Warning and Environmental Health Risk Assessment, Nanning 530028, China
| | - Kaibo Huang
- Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province (Hainan University), Haikou 570228, China.
| | - Rui Wang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China; Guangxi Key Laboratory of Emerging Contaminants Monitoring, Early Warning and Environmental Health Risk Assessment, Nanning 530028, China.
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Roach J, Mital R, Haffner JJ, Colwell N, Coats R, Palacios HM, Liu Z, Godinho JLP, Ness M, Peramuna T, McCall LI. Microbiome metabolite quantification methods enabling insights into human health and disease. Methods 2024; 222:81-99. [PMID: 38185226 DOI: 10.1016/j.ymeth.2023.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 10/27/2023] [Accepted: 12/13/2023] [Indexed: 01/09/2024] Open
Abstract
Many of the health-associated impacts of the microbiome are mediated by its chemical activity, producing and modifying small molecules (metabolites). Thus, microbiome metabolite quantification has a central role in efforts to elucidate and measure microbiome function. In this review, we cover general considerations when designing experiments to quantify microbiome metabolites, including sample preparation, data acquisition and data processing, since these are critical to downstream data quality. We then discuss data analysis and experimental steps to demonstrate that a given metabolite feature is of microbial origin. We further discuss techniques used to quantify common microbial metabolites, including short-chain fatty acids (SCFA), secondary bile acids (BAs), tryptophan derivatives, N-acyl amides and trimethylamine N-oxide (TMAO). Lastly, we conclude with challenges and future directions for the field.
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Affiliation(s)
- Jarrod Roach
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Rohit Mital
- Department of Biology, University of Oklahoma
| | - Jacob J Haffner
- Department of Anthropology, University of Oklahoma; Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma
| | - Nathan Colwell
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Randy Coats
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Horvey M Palacios
- Department of Anthropology, University of Oklahoma; Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma
| | - Zongyuan Liu
- Department of Chemistry and Biochemistry, University of Oklahoma
| | | | - Monica Ness
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Thilini Peramuna
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Laura-Isobel McCall
- Department of Chemistry and Biochemistry, University of Oklahoma; Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma; Department of Chemistry and Biochemistry, San Diego State University.
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Xu Y, Nam KH. Data of the subatomic resolution structure of glucose isomerase complexed with xylitol inhibitor. Data Brief 2024; 52:109916. [PMID: 38235177 PMCID: PMC10792680 DOI: 10.1016/j.dib.2023.109916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/30/2023] [Accepted: 11/30/2023] [Indexed: 01/19/2024] Open
Abstract
Glucose isomerase (GI) is a crucial enzyme in industrial processes, including the production of high-fructose corn syrup, biofuels, and other renewable chemicals. Understanding the mechanisms of GI inhibition by GI inhibitors can offer valuable insights into enhancing production efficiency. We previously reported the subatomic resolution structure of Streptomyces rubiginosus GI (SruGI) complexed with a xylitol inhibitor, determined at 0.99 Å resolution, was reported. Structural analysis showed that the xylitol inhibitor is partially bound to the M1 binding site at the SruGI active site, enabling it to distinguish the xylitol-bound and -free state of SruGI. This structural information demonstrates that xylitol binding to the M1 site causes a conformational change in the metal binding site and the substrate binding channel of SruGI. Herein, detailed information on data collection and processing procedures of the subatomic resolution structure of the SruGI complexed with xylitol was reported.
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Affiliation(s)
- Yongbin Xu
- Department of Bioengineering, College of Life Science, Dalian Minzu University, Dalian 116600, China
- Key Laboratory of Biotechnology and Bioresources Utilization of Ministry of Education, College of Life Science, Dalian Minzu University, Dalian 116600, China
| | - Ki Hyun Nam
- College of General Education, Kookmin University, Seoul 02707, South Korea
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Liu L, Li Y, Liu N, Luo J, Deng J, Peng W, Bai Y, Zhang G, Zhao G, Yang N, Li C, Long X. Establishment of machine learning-based tool for early detection of pulmonary embolism. Comput Methods Programs Biomed 2024; 244:107977. [PMID: 38113803 DOI: 10.1016/j.cmpb.2023.107977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 09/11/2023] [Accepted: 12/11/2023] [Indexed: 12/21/2023]
Abstract
BACKGROUND AND OBJECTIVES Pulmonary embolism (PE) is a complex disease with high mortality and morbidity rate, leading to increasing society burden. However, current diagnosis is solely based on symptoms and laboratory data despite its complex pathology, which easily leads to misdiagnosis and missed diagnosis by inexperienced doctors. Especially, CT pulmonary angiography, the gold standard method, is not widely available. In this study, we aim to establish a rapid and accurate screening model for pulmonary embolism using machine learning technology. Importantly, data required for disease prediction are easily accessed, including routine laboratory data and medical record information of patients. METHODS We extracted features from patients' routine laboratory results and medical records, including blood routine, biochemical group, blood coagulation routine and other test results, as well as symptoms and medical history information. Samples with a feature loss rate greater than 0.8 were deleted from the original database. Data from 4723 cases were retained, 231 of which were positive for pulmonary embolism. 50 features were retained through the positive and negative statistical hypothesis testing which was used to build the predictive model. In order to avoid identification as majority-class samples caused by the imbalance of sample proportion, we used the method of Synthetic Minority Oversampling Technique (SMOTE) to increase the amount of information on minority samples. Five typical machine learning algorithms were used to model the screening of pulmonary embolism, including Support Vector Machines, Logistic Regression, Random Forest, XGBoost, and Back Propagation Neural Networks. To evaluate model performance, sensitivity, specificity and AUC curve were analyzed as the main evaluation indicators. Furthermore, a baseline model was established using the characteristics of the pulmonary embolism guidelines as a comparison model. RESULTS We found that XGBoost showed better performance compared to other models, with the highest sensitivity and specificity (0.99 and 0.99, respectively). Moreover, it showed significant improvement in performance compared to the baseline model (sensitivity and specificity were 0.76 and 0.76 respectively). More important, our model showed low missed diagnosis rate (0.46) and high AUC value (0.992). Finally, the calculation time of our model is only about 0.05 s to obtain the possibility of pulmonary embolism. CONCLUSIONS In this study, five machine learning classification models were established to assess the likelihood of patients suffering from pulmonary embolism, and the XGBoost model most significantly improved the precision, sensitivity, and AUC for pulmonary embolism screening. Collectively, we have established an AI-based model to accurately predict pulmonary embolism at early stage.
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Affiliation(s)
- Lijue Liu
- School of Automation, Central South University, Changsha, Hunan 410083, China; Xiangjiang Laboratory, Changsha 410205, China; Hunan Zixing Intelligent Medical Technology Co., Ltd, Changsha, Hunan 410000, China
| | - Yaming Li
- School of Automation, Central South University, Changsha, Hunan 410083, China
| | - Na Liu
- Xiangya Hospital, Central South University, Xiangya Road 87#, Changsha 410008, China
| | - Jingmin Luo
- Xiangya Hospital, Central South University, Xiangya Road 87#, Changsha 410008, China
| | - Jinhai Deng
- Hunan Zixing Intelligent Medical Technology Co., Ltd, Changsha, Hunan 410000, China; Richard Dimbleby Laboratory of Cancer Research, School of Cancer & Pharmaceutical Sciences, King's College London, London SE1 1UL, UK
| | - Weixiong Peng
- Hunan Zixing Intelligent Medical Technology Co., Ltd, Changsha, Hunan 410000, China; Department of Electrical and Electronic Engineering, College of Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, Guangdong 518055, China
| | - Yongping Bai
- Xiangya Hospital, Central South University, Xiangya Road 87#, Changsha 410008, China
| | - Guogang Zhang
- Department of Cardiovascular Medicine, The Third Xiangya Hospital, Central South University, Tongzipo Road 138#, Changsha 410008,China.
| | - Guihu Zhao
- Xiangya Hospital, Central South University, Xiangya Road 87#, Changsha 410008, China
| | - Ning Yang
- Xiangya Hospital, Central South University, Xiangya Road 87#, Changsha 410008, China
| | - Chuanchang Li
- Xiangya Hospital, Central South University, Xiangya Road 87#, Changsha 410008, China
| | - Xueying Long
- Xiangya Hospital, Central South University, Xiangya Road 87#, Changsha 410008, China
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Khan S, Alzaabi A, Ratnarajah T, Arslan T. Novel statistical time series data augmentation and machine learning based classification of unobtrusive respiration data for respiration Digital Twin model. Comput Biol Med 2024; 168:107825. [PMID: 38061156 DOI: 10.1016/j.compbiomed.2023.107825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 11/29/2023] [Accepted: 12/04/2023] [Indexed: 01/10/2024]
Abstract
Digital Twin (DT), a concept of Healthcare (4.0), represents the subject's biological properties and characteristics in a digital model. DT can help in monitoring respiratory failures, enabling timely interventions, personalized treatment plans to improve healthcare, and decision-support for healthcare professionals. Large-scale implementation of DT technology requires extensive patient data for accurate monitoring and decision-making with Machine Learning (ML) and Deep Learning (DL). Initial respiration data was collected unobtrusively with the ESP32 Wi-Fi Channel State Information (CSI) sensor. Due to limited respiration data availability, the paper proposes a novel statistical time series data augmentation method for generating larger synthetic respiration data. To ensure accuracy and validity in the augmentation method, correlation methods (Pearson, Spearman, and Kendall) are implemented to provide a comparative analysis of experimental and synthetic datasets. Data processing methodologies of denoising (smoothing and filtering) and dimensionality reduction with Principal Component Analysis (PCA) are implemented to estimate a patient's Breaths Per Minute (BPM) from raw respiration sensor data and the synthetic version. The methodology provided the BPM estimation accuracy of 92.3% from raw respiration data. It was observed that out of 27 supervised classifications with k-fold cross-validation, the Bagged Tree ensemble algorithm provided the best ML-supervised classification. In the case of binary-class and multi-class, the Bagged Tree ensemble showed accuracies of 89.2% and 83.7% respectively with combined real and synthetic respiration dataset with the larger synthetic dataset. Overall, this provides a blueprint of methodologies for the development of the respiration DT model.
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Affiliation(s)
- Sagheer Khan
- School of Engineering, The University of Edinburgh, Edinburgh EH9 3FF, UK.
| | - Aaesha Alzaabi
- School of Engineering, The University of Edinburgh, Edinburgh EH9 3FF, UK
| | | | - Tughrul Arslan
- School of Engineering, The University of Edinburgh, Edinburgh EH9 3FF, UK; Advanced Care Research Centre (ACRC), The University of Edinburgh, Edinburgh, EH16 4UX, UK
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Maia RN, Mitra S, Baiz CR. Extracting accurate infrared lineshapes from weak vibrational probes at low concentrations. MethodsX 2023; 11:102309. [PMID: 37577166 PMCID: PMC10416016 DOI: 10.1016/j.mex.2023.102309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 07/31/2023] [Indexed: 08/15/2023] Open
Abstract
Fourier-transform infrared (FTIR) spectroscopy using vibrational probes is an ideal tool to detect changes in structure and local environments within biological molecules. However, challenges arise when dealing with weak infrared probes, such as thiocyanates, due to their inherent low signal strengths and overlap with solvent bands. In this protocol we demonstrate:•A streamlined approach for the precise extraction of weak infrared absorption lineshapes from a strong solvent background.•A protocol combining a spectral filter, background modeling, and subtraction.•Our methodology successfully extracts the CN stretching mode peak from methyl thiocyanate at remarkably low concentrations (0.25 mM) in water, previously a challenge for FTIR spectroscopy.This approach offers valuable insights and tools for more accurate FTIR measurements using weak vibrational probes. This enhanced precision can potentially enable new approaches to enhance our understanding of protein structure and dynamics in solution.
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Affiliation(s)
- Raiza N.A. Maia
- Department of Chemistry, University of Texas at Austin, Austin, TX 78712-1224, USA
| | - Sunayana Mitra
- Department of Chemistry, University of Texas at Austin, Austin, TX 78712-1224, USA
| | - Carlos R. Baiz
- Department of Chemistry, University of Texas at Austin, Austin, TX 78712-1224, USA
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Kwon DY, Kim J, Park S, Hong S. Advancements of remote data acquisition and processing in unmanned vehicle technologies for water quality monitoring: An extensive review. Chemosphere 2023; 343:140198. [PMID: 37717916 DOI: 10.1016/j.chemosphere.2023.140198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 08/28/2023] [Accepted: 09/14/2023] [Indexed: 09/19/2023]
Abstract
Regular water quality monitoring is becoming desirable due to the increase in water pollution caused by both climate change and the generation of industrial chemicals. Unmanned vehicles have emerged as key technologies for remote data acquisition, providing fast and accurate methods for water quality monitoring. However, current research on unmanned vehicles has not systematically examined their features and limitations, which are crucial for identifying future research directions and applications of unmanned vehicle technologies. Therefore, this study extensively reviews the advancements in remote data acquisition and processing using unmanned vehicle technologies for water quality monitoring to provide valuable insights for future research. First, the types of unmanned vehicles and their application ranges for water quality monitoring are summarized. Among the unmanned vehicle technologies, unmanned aerial vehicles are considered primary platforms for water quality monitoring due to their wide data acquisition range and their ability to accommodate diverse sensors and samplers. Also, the types of samplers and sensors mounted on the unmanned vehicles are analyzed based on their characteristics. It is concluded that spectral sensors offer the most cost-effective approach for acquiring real-time water quality data. Furthermore, algorithms that convert image data into water quality data are examined, focusing on data preprocessing, analysis, and validation. The findings reveal a close relationship between the analysis of spectral characteristics of each water quality parameter and the wavelength ranges of red and red-edge. Lastly, future research directions for unmanned vehicle technologies are further suggested based on the summarized technological limitations.
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Affiliation(s)
- Da Yun Kwon
- School of Civil, Environmental and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Jungbin Kim
- School of Civil, Environmental and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea; Department of Environmental Science, College of Science, Mathematics and Technology, Wenzhou-Kean University, 88 Daxue Road, Ouhai, Wenzhou, 325060, Zhejiang Province, China
| | - Seongyeol Park
- School of Civil, Environmental and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Seungkwan Hong
- School of Civil, Environmental and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.
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Sánchez-Cachero A, Jiménez-Moreno M, Fariñas NR, Martín-Doimeadios RCR. Critical evaluation of key parameters in single particle ICP-MS data processing for the correct determination of platinum nanoparticles in complex environmental and biological matrices. Mikrochim Acta 2023; 190:476. [PMID: 37993653 DOI: 10.1007/s00604-023-06032-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 10/04/2023] [Indexed: 11/24/2023]
Abstract
There is an urgent need for the harmonization of critical parameters in single particle inductively coupled plasma mass spectrometry (SP-ICP-MS) and they have been deeply studied and optimized in the present work using platinum nanoparticles (PtNPs) as a representative case of study. Special attention has been paid to data processing in order to achieve an adequate discrimination between signals. Thus, a comparison between four different algorithms has been performed and the method for transport efficiency calculation has also been thorougly evaluated (finding the use of a well-characterized solution of the same targeted analyte (30 nm PtNPs) as adequate). The best results have been obtained after the application of a deconvolution approach for the data processing and using 5 ms as dwell time and 40,000 data points for data acquisition. Under the optimized conditions, a correct discrimination between NP events and background signal up to 100 or 750 ng L-1 of added ionic Pt was reached for 30 and 50 nm PtNPs, respectively. The suitability of the developed method for the characterization of PtNPs in relevant environmental (water samples) and biological (cell culture media) matrices has also been demonstrated.
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Affiliation(s)
- Armando Sánchez-Cachero
- Department of Analytical Chemistry and Food Technology, Environmental Sciences Institute (ICAM), University of Castilla-La Mancha, Avda. Carlos III s/n, 45071, Toledo, Spain
| | - María Jiménez-Moreno
- Department of Analytical Chemistry and Food Technology, Environmental Sciences Institute (ICAM), University of Castilla-La Mancha, Avda. Carlos III s/n, 45071, Toledo, Spain
| | - Nuria Rodríguez Fariñas
- Department of Analytical Chemistry and Food Technology, Environmental Sciences Institute (ICAM), University of Castilla-La Mancha, Avda. Carlos III s/n, 45071, Toledo, Spain
| | - Rosa Carmen Rodríguez Martín-Doimeadios
- Department of Analytical Chemistry and Food Technology, Environmental Sciences Institute (ICAM), University of Castilla-La Mancha, Avda. Carlos III s/n, 45071, Toledo, Spain.
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Gaida M, Stefanuto PH, Focant JF. Theoretical modeling and machine learning-based data processing workflows in comprehensive two-dimensional gas chromatography-A review. J Chromatogr A 2023; 1711:464467. [PMID: 37871505 DOI: 10.1016/j.chroma.2023.464467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 10/15/2023] [Accepted: 10/17/2023] [Indexed: 10/25/2023]
Abstract
In recent years, comprehensive two-dimensional gas chromatography (GC × GC) has been gradually gaining prominence as a preferred method for the analysis of complex samples due to its higher peak capacity and resolution power compared to conventional gas chromatography (GC). Nonetheless, to fully benefit from the capabilities of GC × GC, a holistic approach to method development and data processing is essential for a successful and informative analysis. Method development enables the fine-tuning of the chromatographic separation, resulting in high-quality data. While generating such data is pivotal, it does not necessarily guarantee that meaningful information will be extracted from it. To this end, the first part of this manuscript reviews the importance of theoretical modeling in achieving good optimization of the separation conditions, ultimately improving the quality of the chromatographic separation. Multiple theoretical modeling approaches are discussed, with a special focus on thermodynamic-based modeling. The second part of this review highlights the importance of establishing robust data processing workflows, with a special emphasis on the use of advanced data processing tools such as, Machine Learning (ML) algorithms. Three widely used ML algorithms are discussed: Random Forest (RF), Support Vector Machine (SVM), and Partial Least Square-Discriminate Analysis (PLS-DA), highlighting their role in discovery-based analysis.
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Affiliation(s)
- Meriem Gaida
- Organic and Biological Analytical Chemistry Group (OBiAChem), MolSys Research Unit, Liège University, Belgium
| | - Pierre-Hugues Stefanuto
- Organic and Biological Analytical Chemistry Group (OBiAChem), MolSys Research Unit, Liège University, Belgium
| | - Jean-François Focant
- Organic and Biological Analytical Chemistry Group (OBiAChem), MolSys Research Unit, Liège University, Belgium
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13
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Ma W, Wang C, Sun X, Lin X, Niu L, Wang Y. MBGA-Net: A multi-branch graph adaptive network for individualized motor imagery EEG classification. Comput Methods Programs Biomed 2023; 240:107641. [PMID: 37327754 DOI: 10.1016/j.cmpb.2023.107641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 05/21/2023] [Accepted: 05/31/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE The development of deep learning has led to significant improvements in the decoding accuracy of Motor Imagery (MI) EEG signal classification. However, current models are inadequate in ensuring high levels of classification accuracy for an individual. Since MI EEG data is primarily used in medical rehabilitation and intelligent control, it is crucial to ensure that each individual's EEG signal is recognized with precision. METHODS We propose a multi-branch graph adaptive network (MBGA-Net), which matches each individual EEG signal with a suitable time-frequency domain processing method based on spatio-temporal domain features. We then feed the signal into the relevant model branch using an adaptive technique. Through an enhanced attention mechanism and deep convolutional method with residual connectivity, each model branch more effectively harvests the features of the related format data. RESULTS We validate the proposed model using the BCI Competition IV dataset 2a and dataset 2b. On dataset 2a, the average accuracy and kappa values are 87.49% and 0.83, respectively. The standard deviation of individual kappa values is only 0.08. For dataset 2b, the average classification accuracies obtained by feeding the data into the three branches of MBGA-Net are 85.71%, 85.83%, and 86.99%, respectively. CONCLUSIONS The experimental results demonstrate that MBGA-Net could effectively perform the classification task of motor imagery EEG signals, and it exhibits strong generalization performance. The proposed adaptive matching technique enhances the classification accuracy of each individual, which is beneficial for the practical application of EEG classification.
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Affiliation(s)
- Weifeng Ma
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, PR China
| | - Chuanlai Wang
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, PR China
| | - Xiaoyong Sun
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, PR China
| | - Xuefen Lin
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, PR China
| | - Lei Niu
- Faculty of Artificial Intelligence Education, Central China Normal University Wollongong Joint Institude, Wuhan 430079, PR China
| | - Yuchen Wang
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, PR China.
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Jaroszewicz MJ, Altenhof AR, Schurko RW, Frydman L. An automated multi-order phase correction routine for processing ultra-wideline NMR spectra. J Magn Reson 2023; 354:107528. [PMID: 37632988 DOI: 10.1016/j.jmr.2023.107528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 07/25/2023] [Accepted: 07/26/2023] [Indexed: 08/28/2023]
Abstract
Efficient acquisition of wideline solid-state nuclear magnetic resonance (NMR) spectra with patterns affected by large inhomogeneous broadening is accomplished with the use of broadband pulse sequences. These specialized pulse sequences often use frequency-swept pulses, which feature time-dependent phase and amplitude modulations that in turn deliver broad and uniform excitation across large spectral bandwidths. However, the resulting NMR spectra are often affected by complex frequency-dependent phase dispersions, owing to the interplay between the frequency-swept excitations and anisotropic resonance frequencies. Such phase distortions necessitate the use of multi-order non-linear corrections in order to obtain absorptive, distortion-free patterns with uniform phasing. Performing such corrections is often challenging due to the complex interdependence of the linear and non-linear phase contributions, and how these may affect the NMR signal. Hence, processing of these data usually involves calculating the spectra in magnitude mode wherein the phase information is discarded. Herein, we present a fully automated phasing routine that is capable of processing and phase correcting such wideline NMR spectra. Its performance is corroborated via processing of NMR data acquired using both the WURST-CPMG (Wideband, Uniform-Rate, Smooth Truncation with Carr-Purcell Meiboom-Gill acquisition) and BRAIN-CP (BRoadband Adiabatic Inversion Cross Polarization) pulse sequences for a variety of nuclei (i.e., 119Sn, 195Pt, 35Cl, 87Rb, and 14N). Based on both simulated and experimental NMR datasets, it is demonstrated that automatic phase corrections up to and including second order can be readily achieved without a priori information regarding the nature of the phase-distorted NMR datasets, and independently of the exact manner in which time-domain NMR data are collected and subsequently processed. In addition, it is shown that NMR spectra acquired at both single and multiple transmitter frequencies that are processed with this automated phasing routine have improved signal-to-noise properties than those processed with conventional magnitude calculations, along with powder patterns that better match those of ideal NMR spectra, even for datasets possessing low signal-to-noise ratios and/or affected by spectral artifacts.
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Affiliation(s)
- Michael J Jaroszewicz
- Department of Chemical and Biological Physics, Weizmann Institute, Rehovot 7610001, Israel.
| | - Adam R Altenhof
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, FL 32306, USA; National High Magnetic Field Laboratory, 1800 East Paul Dirac Drive, Tallahassee, FL 32310, USA
| | - Robert W Schurko
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, FL 32306, USA; National High Magnetic Field Laboratory, 1800 East Paul Dirac Drive, Tallahassee, FL 32310, USA.
| | - Lucio Frydman
- Department of Chemical and Biological Physics, Weizmann Institute, Rehovot 7610001, Israel; National High Magnetic Field Laboratory, 1800 East Paul Dirac Drive, Tallahassee, FL 32310, USA.
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15
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Meisburger SP, Ando N. Processing macromolecular diffuse scattering data. Methods Enzymol 2023; 688:43-86. [PMID: 37748832 DOI: 10.1016/bs.mie.2023.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
Diffuse scattering is a powerful technique to study disorder and dynamics of macromolecules at atomic resolution. Although diffuse scattering is always present in diffraction images from macromolecular crystals, the signal is weak compared with Bragg peaks and background, making it a challenge to visualize and measure accurately. Recently, this challenge has been addressed using the reciprocal space mapping technique, which leverages ideal properties of modern X-ray detectors to reconstruct the complete three-dimensional volume of continuous diffraction from diffraction images of a crystal (or crystals) in many different orientations. This chapter will review recent progress in reciprocal space mapping with a particular focus on the strategy implemented in the mdx-lib and mdx2 software packages. The chapter concludes with an introductory data processing tutorial using Python packages DIALS, NeXpy, and mdx2.
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Affiliation(s)
- Steve P Meisburger
- Cornell High Energy Synchrotron Source, Cornell University, Ithaca, NY, United States.
| | - Nozomi Ando
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, United States.
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16
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Zhang B, Hu S, Li M. Comparative study of multiple machine learning algorithms for risk level prediction in goaf. Heliyon 2023; 9:e19092. [PMID: 37636440 PMCID: PMC10448475 DOI: 10.1016/j.heliyon.2023.e19092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 08/09/2023] [Accepted: 08/10/2023] [Indexed: 08/29/2023] Open
Abstract
With the acceleration of the mining process, the goaf has become one of the main sources of danger in underground mines, seriously threatening the safe production of mines. To make an accurate prediction of the risk level of the goaf quickly, this paper optimizes the features of the goaf by correlation analysis and feature importance and constructs a combination of feature parameters for the risk level prediction of the goaf to solve the problem of redundancy of evaluation indexes. Multiple machine learning algorithms are applied to 121 sets of goaf data respectively, and the optimal algorithm and the best combination of feature parameters are obtained by evaluating the mining area with multiple indicators such as accuracy and kappa coefficient. The best combination of features parameters are ground-water, goaf layout, volume of goaf, goaf volume, span-height ratio, and mining disturbance, and the optimal algorithm is Extra Tree (ET), which needles the goaf risk level prediction problem with the accuracy of 94%. This model can be used to solve the problem of how to quickly and accurately predict the risk level of the goaf.
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Affiliation(s)
- Bin Zhang
- School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, Hubei, 430070, China
| | - Shaohua Hu
- School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, Hubei, 430070, China
| | - Moxiao Li
- School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, Hubei, 430070, China
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17
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Renner G, Reuschenbach M. Critical review on data processing algorithms in non-target screening: challenges and opportunities to improve result comparability. Anal Bioanal Chem 2023; 415:4111-4123. [PMID: 37380744 PMCID: PMC10328864 DOI: 10.1007/s00216-023-04776-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 04/23/2023] [Accepted: 05/15/2023] [Indexed: 06/30/2023]
Abstract
Non-target screening (NTS) is a powerful environmental and analytical chemistry approach for detecting and identifying unknown compounds in complex samples. High-resolution mass spectrometry has enhanced NTS capabilities but created challenges in data analysis, including data preprocessing, peak detection, and feature extraction. This review provides an in-depth understanding of NTS data processing methods, focusing on centroiding, extracted ion chromatogram (XIC) building, chromatographic peak characterization, alignment, componentization, and prioritization of features. We discuss the strengths and weaknesses of various algorithms, the influence of user input parameters on the results, and the need for automated parameter optimization. We address uncertainty and data quality issues, emphasizing the importance of incorporating confidence intervals and raw data quality assessment in data processing workflows. Furthermore, we highlight the need for cross-study comparability and propose potential solutions, such as utilizing standardized statistics and open-access data exchange platforms. In conclusion, we offer future perspectives and recommendations for developers and users of NTS data processing algorithms and workflows. By addressing these challenges and capitalizing on the opportunities presented, the NTS community can advance the field, improve the reliability of results, and enhance data comparability across different studies.
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Affiliation(s)
- Gerrit Renner
- Instrumental Analytical Chemistry, University of Duisburg-Essen, Universitätsstr. 5, Essen, D-45141, NRW, Germany.
- Centre for Water and Environmental Research (ZWU), University of Duisburg-Essen, Universitätsstr. 2, Essen, D-45141, NRW, Germany.
| | - Max Reuschenbach
- Instrumental Analytical Chemistry, University of Duisburg-Essen, Universitätsstr. 5, Essen, D-45141, NRW, Germany
- Centre for Water and Environmental Research (ZWU), University of Duisburg-Essen, Universitätsstr. 2, Essen, D-45141, NRW, Germany
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18
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Zhang J, Mazurowski MA, Allen BC, Wildman-Tobriner B. Multistep Automated Data Labelling Procedure (MADLaP) for thyroid nodules on ultrasound: An artificial intelligence approach for automating image annotation. Artif Intell Med 2023; 141:102553. [PMID: 37295897 DOI: 10.1016/j.artmed.2023.102553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 02/14/2023] [Accepted: 04/11/2023] [Indexed: 06/12/2023]
Abstract
Machine learning (ML) for diagnosis of thyroid nodules on ultrasound is an active area of research. However, ML tools require large, well-labeled datasets, the curation of which is time-consuming and labor-intensive. The purpose of our study was to develop and test a deep-learning-based tool to facilitate and automate the data annotation process for thyroid nodules; we named our tool Multistep Automated Data Labelling Procedure (MADLaP). MADLaP was designed to take multiple inputs including pathology reports, ultrasound images, and radiology reports. Using multiple step-wise 'modules' including rule-based natural language processing, deep-learning-based imaging segmentation, and optical character recognition, MADLaP automatically identified images of a specific thyroid nodule and correctly assigned a pathology label. The model was developed using a training set of 378 patients across our health system and tested on a separate set of 93 patients. Ground truths for both sets were selected by an experienced radiologist. Performance metrics including yield (how many labeled images the model produced) and accuracy (percentage correct) were measured using the test set. MADLaP achieved a yield of 63 % and an accuracy of 83 %. The yield progressively increased as the input data moved through each module, while accuracy peaked part way through. Error analysis showed that inputs from certain examination sites had lower accuracy (40 %) than the other sites (90 %, 100 %). MADLaP successfully created curated datasets of labeled ultrasound images of thyroid nodules. While accurate, the relatively suboptimal yield of MADLaP exposed some challenges when trying to automatically label radiology images from heterogeneous sources. The complex task of image curation and annotation could be automated, allowing for enrichment of larger datasets for use in machine learning development.
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Affiliation(s)
- Jikai Zhang
- Department of Electrical and Computer Engineering, Duke University, Room 10070, 2424 Erwin Rd, Durham, NC 27705, United States.
| | - Maciej A Mazurowski
- Department of Radiology, Duke University Medical Center, Durham, NC, United States; Department of Electrical and Computer Engineering, Department of Biostatistics and Bioinformatics, Department of Computer Science, Duke University, Room 9044, 2424 Erwin Rd, Durham, NC 27705, United States
| | - Brian C Allen
- Department of Radiology, Duke University Medical Center, Duke University, Dept of Radiology, Box 3808, Durham, NC 27710, United States
| | - Benjamin Wildman-Tobriner
- Department of Radiology, Duke University Medical Center, Duke University, Dept of Radiology, Box 3808, Durham, NC 27710, United States
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Wörtge D, Parziale M, Claussen J, Mohebbi B, Stapf S, Blümich B, Augustine M. Quantitative stray-field T 1 relaxometry with the matrix pencil method. J Magn Reson 2023; 351:107435. [PMID: 37060888 DOI: 10.1016/j.jmr.2023.107435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 03/17/2023] [Accepted: 04/01/2023] [Indexed: 05/29/2023]
Abstract
The matrix pencil method (MPM) is tested as an approach to quantitatively process multiexponential low-field nuclear magnetic resonance T1 relaxometry data. The data is obtained by measuring T1 saturation recovery curves in the highly inhomogeneous magnetic field of a stray-field sensor. 0.9% brine solutions, doped with different concentrations of a Gd3+ containing contrast agent, serve as test liquids. Relaxation-times as a function of contrast-agent concentration along with the T1 relaxation curves for combinations of multiple different test liquids are measured, and the results from processing using MPM as well as inverse Laplace transformation as a benchmark are compared. The relaxation-time resolution limits of both procedures are probed by gradually reducing the difference between the relaxation-times of two liquids measured simultaneously. The sensitivity to quantify the relative contribution of each component to the magnetization build-up curve is explored by changing their volume ratio. Furthermore, the potential to resolve systems with more than two components is tested. For the systems under test, MPM shows superior performance in separating two or three relaxation components, respectively and effectively quantifying the time constants.
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Affiliation(s)
- Dennis Wörtge
- Institut für Technische Physik, TU Ilmenau, PO Box 100 565, 98684 Ilmenau, Germany; P&G Service GmbH., German Inovation Center, Sulzacher Straße 40, 65824 Schwalbach am Taunus, Germany.
| | - Matthew Parziale
- Dept. of Chemistry, University of California Davis, 69 Chemistry Building, 95616 Davis, CA, USA
| | - Jan Claussen
- P&G Service GmbH., German Inovation Center, Sulzacher Straße 40, 65824 Schwalbach am Taunus, Germany
| | - Behzad Mohebbi
- P&G Service GmbH., German Inovation Center, Sulzacher Straße 40, 65824 Schwalbach am Taunus, Germany
| | - Siegfried Stapf
- Institut für Technische Physik, TU Ilmenau, PO Box 100 565, 98684 Ilmenau, Germany
| | - Bernhard Blümich
- Institut für Technische und Makromolekulare Chemie, RWTH Aachen University, Worringerweg 2, 52074 Aachen, Germany
| | - Matthew Augustine
- Dept. of Chemistry, University of California Davis, 69 Chemistry Building, 95616 Davis, CA, USA
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Doetsch JN, Dias V, Lopes I, Redinha R, Barros H. Record linkage of routine and cohort data of children in Portugal: challenges and opportunities when using record linkage as a tool for scientific research. Med Law Rev 2023; 31:247-271. [PMID: 36240458 DOI: 10.1093/medlaw/fwac040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Linking records could serve as a useful tool for scientific research and as a facilitator for local policymaking. This article examines the challenges and opportunities for researchers to lawfully link routinely collected health and education data with cohort data of children when using it as a tool for scientific research in Portugal. Such linking can be lawfully conducted in Portugal if three requirements are met. First, data processing pursues a legitimate purpose, such as scientific research. Secondly, data linking complies with the legal obligations of research entities and researchers, acting as data controllers or processors, and it respects the rights of children as data subjects. Finally, data linking is based on the explicit written consent of those with parental responsibility for the child. So far, the implementation of the General Data Protection Regulation in Portugal has not facilitated record linkage. It is argued that further harmonised implementation of that Regulation across European Union and European Economic Area Member States, establishing a minimum shared denominator for record linkage in scientific research for the common good, including without explicit consent, is needed.
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Affiliation(s)
- Julia Nadine Doetsch
- EPIUnit-Instituto de Saúde Pública da Universidade do Porto (ISPUP), 4050-600 Porto, Portugal
- Laboratory for Integrative and Translational Research in Population Health (ITR), Porto, Portugal
| | - Vasco Dias
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Campus da Faculdade de Engenharia da Universidade do Porto, 4200-465 Porto, Portugal
| | - Inês Lopes
- Faculdade de Direito da Universidade do Porto (FDUP), 4050-123 Porto, Portugal
- Centro de Investigação Jurídico Económica (CIJE), 4050-123, Porto, Portugal
| | - Regina Redinha
- Faculdade de Direito da Universidade do Porto (FDUP), 4050-123 Porto, Portugal
- Centro de Investigação Jurídico Económica (CIJE), 4050-123, Porto, Portugal
| | - Henrique Barros
- EPIUnit-Instituto de Saúde Pública da Universidade do Porto (ISPUP), 4050-600 Porto, Portugal
- Laboratory for Integrative and Translational Research in Population Health (ITR), Porto, Portugal
- Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina, Universidade do Porto (FMUP), Porto, Portugal
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Zhang J, Zheng N, Liu M, Yao D, Wang Y, Wang J, Xin J. Multi-weight susceptible-infected model for predicting COVID-19 in China. Neurocomputing 2023; 534:161-170. [PMID: 36923265 PMCID: PMC9993734 DOI: 10.1016/j.neucom.2023.02.065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/10/2023] [Accepted: 02/26/2023] [Indexed: 03/17/2023]
Abstract
The mutant strains of COVID-19 caused a global explosion of infections, including many cities of China. In 2020, a hybrid AI model was proposed by Zheng et al., which accurately predicted the epidemic in Wuhan. As the main part of the hybrid AI model, ISI method makes two important assumptions to avoid over-fitting. However, the assumptions cannot be effectively applied to new mutant strains. In this paper, a more general method, named the multi-weight susceptible-infected model (MSI) is proposed to predict COVID-19 in Chinese Mainland. First, a Gaussian pre-processing method is proposed to solve the problem of data fluctuation based on the quantity consistency of cumulative infection number and the trend consistency of daily infection number. Then, we improve the model from two aspects: changing the grouped multi-parameter strategy to the multi-weight strategy, and removing the restriction of weight distribution of viral infectivity. Experiments on the outbreaks in many places in China from the end of 2021 to May 2022 show that, in China, an individual infected by Delta or Omicron strains of SARS-CoV-2 can infect others within 3-4 days after he/she got infected. Especially, the proposed method effectively predicts the trend of the epidemics in Xi'an, Tianjin, Henan, and Shanghai from December 2021 to May 2022.
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Affiliation(s)
- Jun Zhang
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China.,School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Nanning Zheng
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Mingyu Liu
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China.,Qian Xuesen College, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Dingyi Yao
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China.,Qian Xuesen College, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Yusong Wang
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Jianji Wang
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Jingmin Xin
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
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Dos Santos EKP, Canuto GAB. Optimizing XCMS parameters for GC-MS metabolomics data processing: a case study. Metabolomics 2023; 19:26. [PMID: 36976375 DOI: 10.1007/s11306-023-01992-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 03/05/2023] [Indexed: 03/29/2023]
Abstract
BACKGROUND AND AIMS Optimizing metabolomics data processing parameters is a challenging and fundamental task to obtain reliable results. Automated tools have been developed to assist this optimization for LC-MS data. GC-MS data require substantial modifications in processing parameters, as the chromatographic profiles are more robust, with more symmetrical and Gaussian peaks. This work compared an automated XCMS parameter optimization using the Isotopologue Parameter Optimization (IPO) software with manual optimization of GC-MS metabolomics data. Additionally, the results were compared to online XCMS platform. METHODS GC-MS data from control and test groups of intracellular metabolites from Trypanosoma cruzi trypomastigotes were used. Optimizations were performed on the quality control (QC) samples. RESULTS The results in terms of the number of molecular features extracted, repeatability, missing values, and the search for significant metabolites showed the importance of optimizing the parameters for peak detection, alignment, and grouping, especially those related to peak width (fwhm, bw) and noise ratio (snthresh). CONCLUSION This is the first time that a systematic optimization using IPO has been performed on GC-MS data. The results demonstrate that there is no universal approach for optimization but automated tools are valuable at this stage of the metabolomics workflow. The online XCMS proves to be an interesting processing tool, helping, above all, in the choice of parameters as a starting point for adjustments and optimizations. Although the tools are easy to use, there is still a need for technical knowledge about the analytical methods and instruments used.
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Ding R, Yu L, Wang C, Zhong S, Gu R. Quality assessment of traditional Chinese medicine based on data fusion combined with machine learning: A review. Crit Rev Anal Chem 2023:1-18. [PMID: 36966435 DOI: 10.1080/10408347.2023.2189477] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/27/2023]
Abstract
The authenticity and quality of traditional Chinese medicine (TCM) directly impact clinical efficacy and safety. Quality assessment of traditional Chinese medicine (QATCM) is a global concern due to increased demand and shortage of resources. Recently, modern analytical technologies have been extensively investigated and utilized to analyze the chemical composition of TCM. However, a single analytical technique has some limitations, and judging the quality of TCM only from the characteristics of the components is not enough to reflect the overall view of TCM. Thus, the development of multi-source information fusion technology and machine learning (ML) has further improved QATCM. Data information from different analytical instruments can better understand the connection between herbal samples from multiple aspects. This review focuses on the use of data fusion (DF) and ML in QATCM, including chromatography, spectroscopy, and other electronic sensors. The common data structures and DF strategies are introduced, followed by ML methods, including fast-growing deep learning. Finally, DF strategies combined with ML methods are discussed and illustrated for research on applications such as source identification, species identification, and content prediction in TCM. This review demonstrates the validity and accuracy of QATCM-based DF and ML strategies and provides a reference for developing and applying QATCM methods.
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Affiliation(s)
- Rong Ding
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Lianhui Yu
- Chengdu Pushi Pharmaceutical Technology Co., Ltd, Chengdu, China
| | - Chenghui Wang
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Shihong Zhong
- School of Pharmacy, Southwest Minzu University, Chengdu, China
| | - Rui Gu
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
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Zanuttini B, Henry J, Couronne C, Ouali A, Robert V, Zatylny-Gaudin C. PepTraq: a toolbox for in silico data mining and fast sequence filtering. Amino Acids 2023. [PMID: 36884076 DOI: 10.1007/s00726-023-03251-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 02/14/2023] [Indexed: 03/09/2023]
Abstract
The development of de novo sequencing tools has led to the massive production of genomes and transcriptomes from many unconventional animal models. To tackle this huge flow of data, PepTraq brings together many functionalities generally scattered in multiple tools, so that sequences can be filtered on the basis of multiple criteria. It is particularly suitable for the identification of non-annotated transcripts, re-annotation, extraction of secretomes, neuropeptidomes, targeted search for peptides and proteins, preparing specific proteomics/peptidomics fasta files for mass spectrometry (MS) applications, MS data processing, etc. PepTraq is developed in Java, and is available as a desktop application that can be downloaded from https://peptraq.greyc.fr . It is also available as a web application at the same URL for processing small files (10-20 MB). The source code is open under a CeCILL-B licence.
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25
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Meola A, Winkler M, Weinrich S. Metaheuristic optimization of data preparation and machine learning hyperparameters for prediction of dynamic methane production. Bioresour Technol 2023; 372:128604. [PMID: 36634878 DOI: 10.1016/j.biortech.2023.128604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/06/2023] [Accepted: 01/08/2023] [Indexed: 06/17/2023]
Abstract
Machine learning algorithms provide detailed description of the anaerobic digestion process, but the impact of data preparation procedures and hyperparameter optimization has rarely been investigated. A genetic algorithm was developed for optimizing data preparation and model hyperparameters to simulate dynamic methane production from steady-state anaerobic digestion of agricultural residues at full-scale. A long short-term memory neural network was used as prediction model. Results indicate that batch size, learning rate and number of neurons are the most important model parameters for accurate description of methane production rates, whereas combination of hyperparameter and data preparation optimization shows best model efficiencies, with a root mean square scaled error of 76.5 %. Mass of solid feed, time and mass of volatile solids are the most relevant input features. This study provides fundamental steps for optimal prediction of dynamic biomethane production, as a reliable basis for improving bioconversion efficiency during anaerobic digestion of agricultural residues.
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Affiliation(s)
- Alberto Meola
- DBFZ, Deutsches Biomasseforschungszentrum gemeinnützige GmbH, Torgauer Straße 116, Leipzig 04347, Germany; Faculty of Mathematics and Computer Science, Leipzig University, Augustusplatz 10, Leipzig 04109, Germany
| | - Manuel Winkler
- DBFZ, Deutsches Biomasseforschungszentrum gemeinnützige GmbH, Torgauer Straße 116, Leipzig 04347, Germany
| | - Sören Weinrich
- DBFZ, Deutsches Biomasseforschungszentrum gemeinnützige GmbH, Torgauer Straße 116, Leipzig 04347, Germany.
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26
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Notter MP, Herholz P, Da Costa S, Gulban OF, Isik AI, Gaglianese A, Murray MM. fMRIflows: A Consortium of Fully Automatic Univariate and Multivariate fMRI Processing Pipelines. Brain Topogr 2023; 36:172-91. [PMID: 36575327 DOI: 10.1007/s10548-022-00935-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 12/18/2022] [Indexed: 12/28/2022]
Abstract
How functional magnetic resonance imaging (fMRI) data are analyzed depends on the researcher and the toolbox used. It is not uncommon that the processing pipeline is rewritten for each new dataset. Consequently, code transparency, quality control and objective analysis pipelines are important for improving reproducibility in neuroimaging studies. Toolboxes, such as Nipype and fMRIPrep, have documented the need for and interest in automated pre-processing analysis pipelines. Recent developments in data-driven models combined with high resolution neuroimaging dataset have strengthened the need not only for a standardized preprocessing workflow, but also for a reliable and comparable statistical pipeline. Here, we introduce fMRIflows: a consortium of fully automatic neuroimaging pipelines for fMRI analysis, which performs standard preprocessing, as well as 1st- and 2nd-level univariate and multivariate analyses. In addition to the standardized pre-processing pipelines, fMRIflows provides flexible temporal and spatial filtering to account for datasets with increasingly high temporal resolution and to help appropriately prepare data for advanced machine learning analyses, improving signal decoding accuracy and reliability. This paper first describes fMRIflows' structure and functionality, then explains its infrastructure and access, and lastly validates the toolbox by comparing it to other neuroimaging processing pipelines such as fMRIPrep, FSL and SPM. This validation was performed on three datasets with varying temporal sampling and acquisition parameters to prove its flexibility and robustness. fMRIflows is a fully automatic fMRI processing pipeline which uniquely offers univariate and multivariate single-subject and group analyses as well as pre-processing.
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27
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Cai Z, Fu P, Xu L, Deng L, Peng Y. Systematic identification and characterization of viral small RNAs from small RNA-Seq data. J Med Virol 2023; 95:e28617. [PMID: 36840404 DOI: 10.1002/jmv.28617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 02/03/2023] [Accepted: 02/21/2023] [Indexed: 02/26/2023]
Abstract
Virus-encoded small RNAs (vsRNAs) have been reported to play an important role in viral infection. Unfortunately, there is still a lack of a systematic characterization and resource of vsRNAs. Herein, we identified a total of 19,734 high-confidence vsRNAs including 2,746 miRNAs in 64 viral species from more than 800 samples of public small RNA-Seq data. The number of vsRNAs identified in viruses varied from 1 to 2,489 with a median of 170. The length distribution of vsRNAs peaked at 21 and 22 nt. Plant viruses were found to express larger number and higher levels of vsRNAs than those of animal viruses. Besides, the number of vsRNAs identified increased as the viral infection persisted. Interestingly, the vsRNA showed strong expression specificity as little overlap was observed among vsRNAs identified in different strains of a virus, or in different hosts, cells, or tissues infected by the same virus. Little conservation was observed among vsRNAs of different viruses. The viral miRNAs were found to interact with host genes involved in multiple biological processes related to organization, development, action potential, polarity establishment, methylation, immune response, gene regulation, localization, and so on. To facilitate the usage of vsRNAs, a database named vsRNAdb was built to organize and store vsRNAs which is available at http://www.computationalbiology.cn/vsRNAdb/#/. Overall, the study deepens our understanding about the diversity and complexity of vsRNAs and provides a rich resource for further studies of vsRNAs. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Zena Cai
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha, 410082, China
| | - Ping Fu
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha, 410082, China
| | - Lihua Xu
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha, 410082, China
| | - Li Deng
- Internal Medicine-Neurology, The Third Hospital of Changsha, Changsha, 410015, China
| | - Yousong Peng
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha, 410082, China
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Wang Y, Adam ML, Zhao Y, Zheng W, Gao L, Yin Z, Zhao H. Machine Learning-Enhanced Flexible Mechanical Sensing. Nanomicro Lett 2023; 15:55. [PMID: 36800133 PMCID: PMC9936950 DOI: 10.1007/s40820-023-01013-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 01/08/2023] [Indexed: 05/31/2023]
Abstract
To realize a hyperconnected smart society with high productivity, advances in flexible sensing technology are highly needed. Nowadays, flexible sensing technology has witnessed improvements in both the hardware performances of sensor devices and the data processing capabilities of the device's software. Significant research efforts have been devoted to improving materials, sensing mechanism, and configurations of flexible sensing systems in a quest to fulfill the requirements of future technology. Meanwhile, advanced data analysis methods are being developed to extract useful information from increasingly complicated data collected by a single sensor or network of sensors. Machine learning (ML) as an important branch of artificial intelligence can efficiently handle such complex data, which can be multi-dimensional and multi-faceted, thus providing a powerful tool for easy interpretation of sensing data. In this review, the fundamental working mechanisms and common types of flexible mechanical sensors are firstly presented. Then how ML-assisted data interpretation improves the applications of flexible mechanical sensors and other closely-related sensors in various areas is elaborated, which includes health monitoring, human-machine interfaces, object/surface recognition, pressure prediction, and human posture/motion identification. Finally, the advantages, challenges, and future perspectives associated with the fusion of flexible mechanical sensing technology and ML algorithms are discussed. These will give significant insights to enable the advancement of next-generation artificial flexible mechanical sensing.
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Affiliation(s)
- Yuejiao Wang
- Applied Mechanics Laboratory, Department of Engineering Mechanics, Tsinghua University, Beijing, 100084, People's Republic of China
| | - Mukhtar Lawan Adam
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People's Republic of China
| | - Yunlong Zhao
- Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen, 361102, People's Republic of China
| | - Weihao Zheng
- School of Mechano-Electronic Engineering, Xidian University, Xi'an , 710071, People's Republic of China
| | - Libo Gao
- Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen, 361102, People's Republic of China.
| | - Zongyou Yin
- Research School of Chemistry, Australian National University, Canberra, ACT, 2601, Australia.
| | - Haitao Zhao
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People's Republic of China.
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29
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Seong D, Choi YH, Shin SY, Yi BK. Deep learning approach to detection of colonoscopic information from unstructured reports. BMC Med Inform Decis Mak 2023; 23:28. [PMID: 36750932 PMCID: PMC9903463 DOI: 10.1186/s12911-023-02121-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 01/23/2023] [Indexed: 02/09/2023] Open
Abstract
BACKGROUND Colorectal cancer is a leading cause of cancer deaths. Several screening tests, such as colonoscopy, can be used to find polyps or colorectal cancer. Colonoscopy reports are often written in unstructured narrative text. The information embedded in the reports can be used for various purposes, including colorectal cancer risk prediction, follow-up recommendation, and quality measurement. However, the availability and accessibility of unstructured text data are still insufficient despite the large amounts of accumulated data. We aimed to develop and apply deep learning-based natural language processing (NLP) methods to detect colonoscopic information. METHODS This study applied several deep learning-based NLP models to colonoscopy reports. Approximately 280,668 colonoscopy reports were extracted from the clinical data warehouse of Samsung Medical Center. For 5,000 reports, procedural information and colonoscopic findings were manually annotated with 17 labels. We compared the long short-term memory (LSTM) and BioBERT model to select the one with the best performance for colonoscopy reports, which was the bidirectional LSTM with conditional random fields. Then, we applied pre-trained word embedding using large unlabeled data (280,668 reports) to the selected model. RESULTS The NLP model with pre-trained word embedding performed better for most labels than the model with one-hot encoding. The F1 scores for colonoscopic findings were: 0.9564 for lesions, 0.9722 for locations, 0.9809 for shapes, 0.9720 for colors, 0.9862 for sizes, and 0.9717 for numbers. CONCLUSIONS This study applied deep learning-based clinical NLP models to extract meaningful information from colonoscopy reports. The method in this study achieved promising results that demonstrate it can be applied to various practical purposes.
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Affiliation(s)
- Donghyeong Seong
- grid.264381.a0000 0001 2181 989XSamsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, 06355 Republic of Korea
| | - Yoon Ho Choi
- grid.264381.a0000 0001 2181 989XDepartment of Digital Health, SAIHST, Sungkyunkwan University, Seoul, 06355 Republic of Korea
| | - Soo-Yong Shin
- grid.264381.a0000 0001 2181 989XDepartment of Digital Health, SAIHST, Sungkyunkwan University, Seoul, 06355 Republic of Korea ,grid.414964.a0000 0001 0640 5613Research Institute for Future Medicine, Samsung Medical Center, Seoul, 06351 Republic of Korea
| | - Byoung-Kee Yi
- Department of Artificial Intelligence Convergence, Kangwon National University, 1 Kangwondaehak-Gil, Chuncheon-si, Gangwon-do, 24341, Republic of Korea.
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30
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Wang SY, Weng TI, Chen JY, Lee NC, Lee KC, Lai ML, Chien YH, Hwu WL, Chen GY. An automated workflow on data processing (AutoDP) for semiquantitative analysis of urine organic acids with GC-MS to facilitate diagnosis of inborn errors of metabolism. Clin Chim Acta 2023; 540:117230. [PMID: 36682441 DOI: 10.1016/j.cca.2023.117230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 12/29/2022] [Accepted: 01/13/2023] [Indexed: 01/22/2023]
Abstract
Determination of urine organic acids (UOAs) is essential to understand the disease progress of inborn errors of metabolism (IEM) and often relies on GC-MS analysis. However, the efficiency of analytical reports is sometimes restricted by data processing due to labor-intensive work if no proper tool is employed. Herein, we present a simple and rapid workflow with an R-based script for automated data processing (AutoDP) of GC-MS raw files to quantitatively analyze essential UOAs. AutoDP features automatic quality checks, compound identification and confirmation with specific fragment ions, retention time correction from analytical batches, and visualization of abnormal UOAs with age-matched references on chromatograms. Compared with manual processing, AutoDP greatly reduces analytical time and increases the number of identifications. Speeding up data processing is expected to shorten the waiting time for clinical diagnosis, which could greatly benefit clinicians and patients with IEM. In addition, with quantitative results obtained from AutoDP, it would be more feasible to perform retrospective analysis of specific UOAs in IEM and could provide new perspectives for studying IEM.
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31
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Tardif M, Fremy E, Hesse AM, Burger T, Couté Y, Wieczorek S. Statistical Analysis of Quantitative Peptidomics and Peptide-Level Proteomics Data with Prostar. Methods Mol Biol 2023; 2426:163-196. [PMID: 36308690 DOI: 10.1007/978-1-0716-1967-4_9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Prostar is a software tool dedicated to the processing of quantitative data resulting from mass spectrometry-based label-free proteomics. Practically, once biological samples have been analyzed by bottom-up proteomics, the raw mass spectrometer outputs are processed by bioinformatics tools, so as to identify peptides and quantify them, notably by means of precursor ion chromatogram integration. From that point, the classical workflows aggregate these pieces of peptide-level information to infer protein-level identities and amounts. Finally, protein abundances can be statistically analyzed to find out proteins that are significantly differentially abundant between compared conditions. Prostar original workflow has been developed based on this strategy. However, recent works have demonstrated that processing peptide-level information is often more accurate when searching for differentially abundant proteins, as the aggregation step tends to hide some of the data variabilities and biases. As a result, Prostar has been extended by workflows that manage peptide-level data, and this protocol details their use. The first one, deemed "peptidomics," implies that the differential analysis is conducted at peptide level, independently of the peptide-to-protein relationship. The second workflow proposes to aggregate the peptide abundances after their preprocessing (i.e., after filtering, normalization, and imputation), so as to minimize the amount of protein-level preprocessing prior to differential analysis.
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Affiliation(s)
- Marianne Tardif
- Univ. Grenoble Alpes, INSERM, CEA, UMR BioSanté U1292, CNRS, Grenoble, France
| | - Enora Fremy
- Univ. Grenoble Alpes, INSERM, CEA, UMR BioSanté U1292, CNRS, Grenoble, France
| | - Anne-Marie Hesse
- Univ. Grenoble Alpes, INSERM, CEA, UMR BioSanté U1292, CNRS, Grenoble, France
| | - Thomas Burger
- Univ. Grenoble Alpes, CNRS, INSERM, CEA, Grenoble, France
| | - Yohann Couté
- Univ. Grenoble Alpes, INSERM, CEA, UMR BioSanté U1292, CNRS, Grenoble, France
| | - Samuel Wieczorek
- Univ. Grenoble Alpes, INSERM, CEA, UMR BioSanté U1292, CNRS, Grenoble, France.
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32
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Luo G, Xiao L, Luo S, Liao G, Shao R. A study on multi-exponential inversion of nuclear magnetic resonance relaxation data using deep learning. J Magn Reson 2023; 346:107358. [PMID: 36525932 DOI: 10.1016/j.jmr.2022.107358] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 12/04/2022] [Accepted: 12/06/2022] [Indexed: 06/17/2023]
Abstract
Nuclear magnetic resonance (NMR) is a powerful tool for formation evaluation in the oil industry to determine parameters, such as pore structure, fluid saturation, and permeability of porous materials, which are critical to reservoir engineering. The inversion of the measured relaxation data is an ill-posed problem and may lead to deviations of inversion results, which may degrade the accuracy of further data analysis and evaluation. This paper proposes a deep learning method for multi-exponential inversion of NMR relaxation data to improve accuracy. Simulated NMR data are first constructed using a priori knowledge based on the signal parameters and Gaussian distribution. These data are then used to train the neural network designed to consider noise characteristics, signal decay characteristics, signal energy variations, and non-negative features of the T2 spectra. With the validation from simulated data, the models introduced by multi-scale convolutional neural network (CNN) and attention mechanism outperform other approaches in terms of denoising and T2 inversion. Finally, NMR measurements of rock cores are used to compare the effectiveness of the attention multi-scale convolutional neural network (ATT-CNN) model in practical applications. The results demonstrate that the proposed method based on deep learning has better performance than the regularization method.
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Affiliation(s)
- Gang Luo
- College of Artificial Intelligence, China University of Petroleum, 102249 Beijing, China
| | - Lizhi Xiao
- College of Artificial Intelligence, China University of Petroleum, 102249 Beijing, China.
| | - Sihui Luo
- College of Petroleum Engineering, China University of Petroleum, 102249 Beijing, China
| | - Guangzhi Liao
- College of Artificial Intelligence, China University of Petroleum, 102249 Beijing, China
| | - Rongbo Shao
- College of Artificial Intelligence, China University of Petroleum, 102249 Beijing, China
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33
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Sugimoto M, Aizawa Y, Tomita A. Data Processing and Analysis in Liquid Chromatography-Mass Spectrometry-Based Targeted Metabolomics. Methods Mol Biol 2023; 2571:241-255. [PMID: 36152165 DOI: 10.1007/978-1-0716-2699-3_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Mass spectrometry (MS)-based metabolomics provides high-dimensional datasets; that is, the data include various metabolite features. Data analysis begins by converting the raw data obtained from the MS to produce a data matrix (metabolite × concentrations). This is followed by several steps, such as peak integration, alignment of multiple data, metabolite identification, and calculation of metabolite concentrations. Each step yields the analytical results and the accompanying information used for the quality assessment of the anterior steps. Thus, the measurement quality can be analyzed through data processing. Here, we introduce a typical data processing procedure and describe a method to utilize the intermediate data as quality control. Subsequently, commonly used data analysis methods for metabolomics data, such as statistical analyses, are also introduced.
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Affiliation(s)
- Masahiro Sugimoto
- Institute of Medical Science, Tokyo Medical University, Tokyo, Japan.
- Institute for Advanced Biosciences, Yamagata, Japan.
| | - Yumi Aizawa
- Institute of Medical Science, Tokyo Medical University, Tokyo, Japan
| | - Atsumi Tomita
- Institute of Medical Science, Tokyo Medical University, Tokyo, Japan
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34
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Kuhn L, Vincent T, Hammann P, Zuber H. Exploring Protein Interactome Data with IPinquiry: Statistical Analysis and Data Visualization by Spectral Counts. Methods Mol Biol 2023; 2426:243-265. [PMID: 36308692 DOI: 10.1007/978-1-0716-1967-4_11] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Immunoprecipitation mass spectrometry (IP-MS) is a popular method for the identification of protein-protein interactions. This approach is particularly powerful when information is collected without a priori knowledge and has been successively used as a first key step for the elucidation of many complex protein networks. IP-MS consists in the affinity purification of a protein of interest and of its interacting proteins followed by protein identification and quantification by mass spectrometry analysis. We developed an R package, named IPinquiry, dedicated to IP-MS analysis and based on the spectral count quantification method. The main purpose of this package is to provide a simple R pipeline with a limited number of processing steps to facilitate data exploration for biologists. This package allows to perform differential analysis of protein accumulation between two groups of IP experiments, to retrieve protein annotations, to export results, and to create different types of graphics. Here we describe the step-by-step procedure for an interactome analysis using IPinquiry from data loading to result export and plot production.
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Affiliation(s)
- Lauriane Kuhn
- Plateforme protéomique Strasbourg Esplanade du CNRS, Université de Strasbourg, Strasbourg, France
| | - Timothée Vincent
- Institut de biologie moléculaire des plantes, CNRS, Université de Strasbourg, Strasbourg, France
| | - Philippe Hammann
- Plateforme protéomique Strasbourg Esplanade du CNRS, Université de Strasbourg, Strasbourg, France
| | - Hélène Zuber
- Institut de biologie moléculaire des plantes, CNRS, Université de Strasbourg, Strasbourg, France.
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35
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Seillier L, Peifer M. Reconstructing Phylogenetic Relationship in Bladder Cancer: A Methodological Overview. Methods Mol Biol 2023; 2684:113-132. [PMID: 37410230 DOI: 10.1007/978-1-0716-3291-8_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/07/2023]
Abstract
Bladder cancer (BC) expresses itself as a highly heterogeneous disease both at the histological and molecular level, often occurring as synchronous or metachronous multifocal disease with high risk of recurrence and potential to metastasize. Multiple sequencing studies focusing on both non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) gave insights into the extent of both inter- and intrapatient heterogeneity, but many questions on clonal evolution in BC remain unanswered. In this review article, we provide an overview over the technical and theoretical concepts linked to reconstructing evolutionary trajectories in BC and propose a set of tools and established software for phylogenetic analysis.
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Affiliation(s)
| | - Martin Peifer
- Department of Translational Genomics, University of Cologne, Cologne, Germany
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36
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Peralbo-Molina Á, Solà-Santos P, Perera-Lluna A, Chicano-Gálvez E. Data Processing and Analysis in Mass Spectrometry-Based Metabolomics. Methods Mol Biol 2023; 2571:207-239. [PMID: 36152164 DOI: 10.1007/978-1-0716-2699-3_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Metabolomics is the latest of the omics sciences. It attempts to measure and characterize metabolites-small chemical compounds <1500 Da-on cells, tissue, or biofluids, which are usually products of biological reactions. As metabolic reactions are closer to the phenotype, metabolomics has emerged as an attractive science for various areas of research, including personalized medicine. However, due to the complexity of data obtained and the absence of curated databases for metabolite identification, data processing is the major bottleneck in this area since most technicians lack the required bioinformatics expertise to process datasets in a reliable and fast manner. The aim of this chapter is to describe the available tools for data processing that makes an inexperienced researcher capable of obtaining reliable results without having to undergo through huge parametrization steps.
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Affiliation(s)
- Ángela Peralbo-Molina
- IMIBIC Mass Spectrometry and Molecular Imaging Unit, Maimonides, Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba (UCO), Córdoba, Spain.
| | - Pol Solà-Santos
- B2SLab, Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, Spain
- Networking Biomedical Research Centre in the Subject Area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - Alexandre Perera-Lluna
- B2SLab, Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, Spain
- Networking Biomedical Research Centre in the Subject Area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - Eduardo Chicano-Gálvez
- IMIBIC Mass Spectrometry and Molecular Imaging Unit, Maimonides, Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba (UCO), Córdoba, Spain
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Han JW, Park J, Lee H. Analysis of the effect of an artificial intelligence chatbot educational program on non-face-to-face classes: a quasi-experimental study. BMC Med Educ 2022; 22:830. [PMID: 36457086 PMCID: PMC9713176 DOI: 10.1186/s12909-022-03898-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 11/16/2022] [Indexed: 05/30/2023]
Abstract
BACKGROUND Education and training are needed for nursing students using artificial intelligence-based educational programs. However, few studies have assessed the effect of using chatbots in nursing education. OBJECTIVES This study aimed to develop and examine the effect of an artificial intelligence chatbot educational program for promoting nursing skills related to electronic fetal monitoring in nursing college students during non-face-to-face classes during the COVID-19 pandemic. DESIGN This quasi-experimental study used a nonequivalent control group non-synchronized pretest-posttest design. METHODS The participants were 61 junior students from a nursing college located in G province of South Korea. Data were collected between November 3 and 16, 2021, and analyzed using independent t-tests. RESULTS The experimental group-in which the artificial intelligence chatbot program was applied-did not show statistically significant differences in knowledge (t = -0.58, p = .567), clinical reasoning competency (t = 0.75, p = .455), confidence (t = 1.13, p = .264), and feedback satisfaction (t = 1.72, p = .090), compared with the control group; however, its participants' interest in education (t = 2.38, p = .020) and self-directed learning (t = 2.72, p = .006) were significantly higher than those in the control group. CONCLUSION The findings of our study highlighted the potential of artificial intelligence chatbot programs as an educational assistance tool to promote nursing college students' interest in education and self-directed learning. Moreover, such programs can be effective in enhancing nursing students' skills in non-face-to face-situations caused by the ongoing COVID-19 pandemic.
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Affiliation(s)
- Jeong-Won Han
- College of Nursing Science, Kyung Hee University, 26 Kyunghee-Daero, Dongdaemun-Gu, Seoul, 02447, Republic of Korea
| | - Junhee Park
- College of Nursing Science, Dongnam Health University, 50, Cheoncheon-Ro 74Beon-Gil, Jangan-Gu, Suwon-Si, Gyeonggi-Do, 16323, Republic of Korea
| | - Hanna Lee
- Department of Nursing, Gangneung-Wonju National University, 150 Namwon-Ro, Heungeop-Myeon, Wonju-Si, Gangwon-Do, 26403, Republic of Korea.
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38
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Kronik OM, Liang X, Nielsen NJ, Christensen JH, Tomasi G. Obtaining clean and informative mass spectra from complex chromatographic and high-resolution all-ions-fragmentation data by nonnegative parallel factor analysis 2. J Chromatogr A 2022; 1682:463501. [PMID: 36155072 DOI: 10.1016/j.chroma.2022.463501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 08/30/2022] [Accepted: 09/12/2022] [Indexed: 10/14/2022]
Abstract
A major challenge in processing of complex data obtained from chromatography hyphenated to mass spectrometry is to resolve chromatographically co-eluting compounds. In this study, we present a workflow for the resolution of ultra-high pressure liquid chromatography high-resolution mass spectrometry data obtained by the broadband data-independent acquisition MSE operation (UHPLCHRMSE). The workflow is based on a recently introduced algorithm for Parallel Factor Analysis 2 (PARAFAC2) that allows to enforce non-negativity on all the model coefficients. The workflow was tested on three sets of UHPLC-HRMSE measurements from a Lupinus angustifolius L. crop field study, which included plant tissue samples, soil samples and samples from drainage water as well as stream water close to the field. The three datasets included 93, 59, and 75 chromatographic runs in total for the plant, soil and water batches, respectively. Nonnegative-PARAFAC2 models were fitted on the summed high and low energy (HE and LE) traces on chromatographic intervals corresponding to spiked standard for the three sample sets independently. In soil and plant samples, 13 out of 14 spiked standards were resolved by NN-PARAFAC2 even in presence of chromatographic co-elution, and their mass spectral loadings could be matched to a reference spectrum. In contrast, only seven spiked standards were correctly resolved and matched for the water samples because a higher chromatographic baseline rendered the data noisier. The results show that the workflow we present can provide improved mass spectral selectivity for data-independent acquisition compared to using the raw mass spectra and can be used to match fragment ions from the HE trace, and precursor and adduct ions from the LE trace even in presence of co-eluting compounds.
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Affiliation(s)
- Oskar Munk Kronik
- Department of Plant and Environmental Science, University of Copenhagen, Thorvaldsensvej 40, Frederiksberg DK-1871, Denmark.
| | - Xiaomeng Liang
- Department of Plant and Environmental Science, University of Copenhagen, Thorvaldsensvej 40, Frederiksberg DK-1871, Denmark
| | - Nikoline Juul Nielsen
- Department of Plant and Environmental Science, University of Copenhagen, Thorvaldsensvej 40, Frederiksberg DK-1871, Denmark
| | - Jan H Christensen
- Department of Plant and Environmental Science, University of Copenhagen, Thorvaldsensvej 40, Frederiksberg DK-1871, Denmark
| | - Giorgio Tomasi
- Department of Plant and Environmental Science, University of Copenhagen, Thorvaldsensvej 40, Frederiksberg DK-1871, Denmark
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Daud SNSS, Sudirman R. Wavelet Based Filters for Artifact Elimination in Electroencephalography Signal: A Review. Ann Biomed Eng 2022; 50:1271-1291. [PMID: 35994164 DOI: 10.1007/s10439-022-03053-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/10/2022] [Indexed: 11/26/2022]
Abstract
Electroencephalography (EEG) is a diagnostic test that records and measures the electrical activity of the human brain. Research investigating human behaviors and conditions using EEG has increased from year to year. Therefore, an efficient approach is vital to process the EEG dataset to improve the output signal quality. The wavelet is one of the well-known approaches for processing the EEG signal in time-frequency domain analysis. The wavelet is better than the traditional Fourier Transform because it has good time-frequency localized properties and multi-resolution analysis where the transient information of an EEG signal can be extracted efficiently. Thus, this review article aims to comprehensively describe the application of the wavelet method in denoising the EEG signal based on recent research. This review begins with a brief overview of the basic theory and characteristics of EEG and the wavelet transform method. Then, several wavelet-based methods commonly applied in EEG dataset denoising are described and a considerable number of the latest published EEG research works with wavelet applications are reviewed. Besides, the challenges that exist in current EEG-based wavelet method research are discussed. Finally, alternative solutions to mitigate the issues are recommended.
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Affiliation(s)
| | - Rubita Sudirman
- School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru, 81310, Johor, Malaysia
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Chen Z, Cheng Z, Duan Y, Zhang Q, Zhang N, Gu F, Wang Y, Zhou Y, Wang H, Liang D, Zheng H, Hu Z. FDG PET Scan Durations via Effective Data Processing. Med Phys 2022; 50:2121-2134. [PMID: 35950784 DOI: 10.1002/mp.15893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 07/20/2022] [Accepted: 07/25/2022] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND Total-body dynamic PET (dPET) imaging using 18 F-fluorodeoxyglucose (18 F-FDG) has received widespread attention in clinical oncology. However, the conventionally required scan duration of approximately 1 hour seriously limits the application and promotion of this imaging technique. In this study, we investigated the possibility and feasibility of shortening the total-body dynamic scan duration to 30 min post-injection (PI) with the help of a novel Patlak data processing algorithm for accurate Ki estimations of tumor lesions. METHODS Total-body dPET images acquired by uEXPLORER (United Imaging Healthcare Inc.) using 18 F-FDG of 15 patients with different tumor types were analyzed in this study. Dynamic images were reconstructed into 25 frames with a specific temporal dividing protocol for the scan data acquired 1 hour PI. Patlak analysis-based Ki parametric imaging was conducted based on the imaging data corresponding to the first 30 min PI, during which a Patlak data processing method based on cubit Hermite interpolation (THI) was applied. The resultant Ki images acquired by 30-min dynamic PET data and the standard 1-hour Ki images were compared in terms of visual imaging effect, region signal-to-noise ratio (SNR), and Ki estimation accuracy to evaluate the performance of the proposed Ki imaging method with a shortened scan duration. RESULTS With the help of Patlak data processing, acceptable Ki parametric images were obtained from dynamic PET data acquired with a scan duration of 30 min PI. Compared with Ki images obtained from unprocessed Patlak data, the resulting images from the proposed method performed better in terms of noise reduction. Moreover, Bland-Altman (BA) plot and Person correlation coefficient (PPC) analysis showed that that 30-min Ki images obtained from the processed Patlak data had higher accuracy for tumor lesions. CONCLUSION Satisfactory Ki parametric images with high tumor accuracy can be acquired from dynamic imaging data corresponding to the first 30 min PI. Patlak data processing can help achieve higher Ki imaging quality and higher accuracy regarding tumor lesion Ki values. Clinically, it is possible to shorten the dynamic scan duration of 18 F-FDG PET to 30 min to acquire an accurate tumor Ki and further effective tumor detection with uEXPLORER scanners. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Zixiang Chen
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Science.,University of Chinese Academy of Sciences
| | - Zhaoping Cheng
- Department of PET/CT, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital
| | - Yanhua Duan
- Department of PET/CT, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital
| | - Qiyang Zhang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Science.,National Innovation Center for High Performance Medical Devices
| | - Na Zhang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Science.,United Imaging Research Institute of Innovative Medical Equipment
| | - Fengyun Gu
- Central Research Institute, United Imaging Healthcare Group
| | - Ying Wang
- Central Research Institute, United Imaging Healthcare Group
| | - Yun Zhou
- Central Research Institute, United Imaging Healthcare Group
| | - Haining Wang
- United Imaging Research Institute of Innovative Medical Equipment
| | - Dong Liang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Science.,United Imaging Research Institute of Innovative Medical Equipment
| | - Hairong Zheng
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Science
| | - Zhanli Hu
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Science.,United Imaging Research Institute of Innovative Medical Equipment
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O'Brien MW, Petterson JL, Johns JA, Mekary S, Kimmerly DS. The impact of different step rate threshold methods on physical activity intensity in older adults. Gait Posture 2022; 94:51-57. [PMID: 35247825 DOI: 10.1016/j.gaitpost.2022.02.030] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 02/04/2022] [Accepted: 02/24/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND Older adults benefit most from engaging in higher-intensity physical activity, which is often determined using step rate thresholds. Fixed step rate thresholds that correspond to moderate (MPA) and vigorous-intensity physical activity (VPA) have been developed for heuristic activity promotion. The activPAL monitor uses step rate thresholds to determine activity intensity. Stepping thresholds may also vary based on body mass index (BMI) or aerobic fitness level in older adults. Despite the various thresholds used in the literature, it is unclear whether they produce similar outcomes. RESEARCH QUESTION How does time spent in physical activity intensities compare between different step rate thresholds in older adults? METHODS Thirty-eight participants (24♀; 67 ± 4 years; BMI: 26.6 ± 4.4 kg/m2) wore an activPAL monitor 24-hr/day for up to 7-d (total: 205-d). Aerobic fitness (V̇O2max: 23 ± 8 ml/kg/min) was determined via indirect calorimetry during a maximal, graded cycling test. Time spent in each intensity category (light-physical-activity [LPA], MPA, VPA) was determined using the fixed (MPA/VPA) 100/130, 110/130, and activPAL step rate thresholds (74/212), as well as BMI-adjusted absolute (108.5 ± 2.5/134.0 ± 4.8) and BMI-adjusted relative (40%/60% V̇O2max; 111.4 ± 14.7/132.0 ± 19.0) cut-offs. Times spent in each intensity category were compared between methods. RESULTS The activPAL and 100/130 thresholds yielded less LPA and more MPA than all other methods. The activPAL had no time spent in VPA at all. The BMI-adjusted absolute and relative thresholds produced statistically equivalent time in LPA and MPA (via equivalence testing), but not VPA. No two methods yielded similar time spent in LPA, MPA, or VPA. SIGNIFICANCE The choice of step rate threshold has a major impact on physical activity intensity outcomes in older adults. Inherently, strategies that adjust for older adults' body size and/or aerobic fitness level provide a more individualized data processing strategy than fixed thresholds that assume the same threshold for all older adults.
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Affiliation(s)
- Myles W O'Brien
- Division of Kinesiology, School of Health and Human Performance, Faculty of Health, Dalhousie University, Halifax, Nova Scotia, Canada.
| | - Jennifer L Petterson
- Division of Kinesiology, School of Health and Human Performance, Faculty of Health, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Jarrett A Johns
- Division of Kinesiology, School of Health and Human Performance, Faculty of Health, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Said Mekary
- Department of Family Medicine, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Derek S Kimmerly
- Division of Kinesiology, School of Health and Human Performance, Faculty of Health, Dalhousie University, Halifax, Nova Scotia, Canada
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Ben-Tal Y, Boaler PJ, Dale HJA, Dooley RE, Fohn NA, Gao Y, García-Domínguez A, Grant KM, Hall AMR, Hayes HLD, Kucharski MM, Wei R, Lloyd-Jones GC. Mechanistic analysis by NMR spectroscopy: A users guide. Prog Nucl Magn Reson Spectrosc 2022; 129:28-106. [PMID: 35292133 DOI: 10.1016/j.pnmrs.2022.01.001] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 01/05/2022] [Accepted: 01/06/2022] [Indexed: 06/14/2023]
Abstract
A 'principles and practice' tutorial-style review of the application of solution-phase NMR in the analysis of the mechanisms of homogeneous organic and organometallic reactions and processes. This review of 345 references summarises why solution-phase NMR spectroscopy is uniquely effective in such studies, allowing non-destructive, quantitative analysis of a wide range of nuclei common to organic and organometallic reactions, providing exquisite structural detail, and using instrumentation that is routinely available in most chemistry research facilities. The review is in two parts. The first comprises an introduction to general techniques and equipment, and guidelines for their selection and application. Topics include practical aspects of the reaction itself, reaction monitoring techniques, NMR data acquisition and processing, analysis of temporal concentration data, NMR titrations, DOSY, and the use of isotopes. The second part comprises a series of 15 Case Studies, each selected to illustrate specific techniques and approaches discussed in the first part, including in situ NMR (1/2H, 10/11B, 13C, 15N, 19F, 29Si, 31P), kinetic and equilibrium isotope effects, isotope entrainment, isotope shifts, isotopes at natural abundance, scalar coupling, kinetic analysis (VTNA, RPKA, simulation, steady-state), stopped-flow NMR, flow NMR, rapid injection NMR, pure shift NMR, dynamic nuclear polarisation, 1H/19F DOSY NMR, and in situ illumination NMR.
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Affiliation(s)
- Yael Ben-Tal
- School of Chemistry, Joseph Black Building, David Brewster Road, Edinburgh, EH9 3FJ, United Kingdom
| | - Patrick J Boaler
- School of Chemistry, Joseph Black Building, David Brewster Road, Edinburgh, EH9 3FJ, United Kingdom
| | - Harvey J A Dale
- School of Chemistry, Joseph Black Building, David Brewster Road, Edinburgh, EH9 3FJ, United Kingdom
| | - Ruth E Dooley
- School of Chemistry, Joseph Black Building, David Brewster Road, Edinburgh, EH9 3FJ, United Kingdom; Evotec (UK) Ltd, 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire OX14 4RZ, United Kingdom
| | - Nicole A Fohn
- School of Chemistry, Joseph Black Building, David Brewster Road, Edinburgh, EH9 3FJ, United Kingdom
| | - Yuan Gao
- School of Chemistry, Joseph Black Building, David Brewster Road, Edinburgh, EH9 3FJ, United Kingdom
| | - Andrés García-Domínguez
- School of Chemistry, Joseph Black Building, David Brewster Road, Edinburgh, EH9 3FJ, United Kingdom
| | - Katie M Grant
- School of Chemistry, Joseph Black Building, David Brewster Road, Edinburgh, EH9 3FJ, United Kingdom
| | - Andrew M R Hall
- School of Chemistry, Joseph Black Building, David Brewster Road, Edinburgh, EH9 3FJ, United Kingdom
| | - Hannah L D Hayes
- School of Chemistry, Joseph Black Building, David Brewster Road, Edinburgh, EH9 3FJ, United Kingdom
| | - Maciej M Kucharski
- School of Chemistry, Joseph Black Building, David Brewster Road, Edinburgh, EH9 3FJ, United Kingdom
| | - Ran Wei
- School of Chemistry, Joseph Black Building, David Brewster Road, Edinburgh, EH9 3FJ, United Kingdom
| | - Guy C Lloyd-Jones
- School of Chemistry, Joseph Black Building, David Brewster Road, Edinburgh, EH9 3FJ, United Kingdom.
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Elliott J, Khandare S, Butt AA, Smallcomb M, Vidt ME, Simon JC. Automated Tissue Strain Calculations Using Harris Corner Detection. Ann Biomed Eng 2022; 50:564-574. [PMID: 35334018 DOI: 10.1007/s10439-022-02946-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 03/09/2022] [Indexed: 11/28/2022]
Abstract
The elastic modulus, or slope of the stress-strain curve, is an important metric for evaluating tissue functionality, particularly for load-bearing tissues such as tendon. The applied force can be tracked directly from a mechanical testing system and converted to stress using the tissue cross-sectional area; however, strain can only be calculated in post-processing by tracking tissue displacement from video collected during mechanical testing. Manual tracking of Verhoeff stain lines pre-marked on the tissue is time-consuming and highly dependent upon the user. This paper details the development and testing of an automated processing method for strain calculations using Harris corner detection. The automated and manual methods were compared in a dataset consisting of 97 rat tendons (48 Achilles tendons, 49 supraspinatus tendons), divided into ten subgroups for evaluating the effects of different therapies on tendon mechanical properties. The comparison showed that average percent differences between the approaches were 0.89% and -2.10% for Achilles and supraspinatus tendons, respectively. The automated approach reduced processing time by 83% and produced similar results to the manual method when comparing the different subgroups. This automated approach to track tissue displacements and calculate elastic modulus improves post-processing time while simultaneously minimizing user dependency.
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Affiliation(s)
- Jake Elliott
- Graduate Program in Acoustics, Pennsylvania State University, 201E Applied Science Building, University Park, PA, 16802, USA.
| | - Sujata Khandare
- Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Ali A Butt
- Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Molly Smallcomb
- Graduate Program in Acoustics, Pennsylvania State University, 201E Applied Science Building, University Park, PA, 16802, USA
| | - Meghan E Vidt
- Biomedical Engineering, Pennsylvania State University, University Park, PA, USA.,Physical Medicine & Rehabilitation, Penn State College of Medicine, Hershey, PA, USA
| | - Julianna C Simon
- Graduate Program in Acoustics, Pennsylvania State University, 201E Applied Science Building, University Park, PA, 16802, USA.,Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
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Liedtke J, Depelteau JS, Briegel A. How advances in cryo-electron tomography have contributed to our current view of bacterial cell biology. J Struct Biol X 2022; 6:100065. [PMID: 35252838 PMCID: PMC8894267 DOI: 10.1016/j.yjsbx.2022.100065] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 02/17/2022] [Accepted: 02/23/2022] [Indexed: 12/13/2022] Open
Abstract
Advancements in the field of cryo-electron tomography have greatly contributed to our current understanding of prokaryotic cell organization and revealed intracellular structures with remarkable architecture. In this review, we present some of the prominent advancements in cryo-electron tomography, illustrated by a subset of structural examples to demonstrate the power of the technique. More specifically, we focus on technical advances in automation of data collection and processing, sample thinning approaches, correlative cryo-light and electron microscopy, and sub-tomogram averaging methods. In turn, each of these advances enabled new insights into bacterial cell architecture, cell cycle progression, and the structure and function of molecular machines. Taken together, these significant advances within the cryo-electron tomography workflow have led to a greater understanding of prokaryotic biology. The advances made the technique available to a wider audience and more biological questions and provide the basis for continued advances in the near future.
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Affiliation(s)
- Janine Liedtke
- Department of Microbial Sciences, Institute of Biology, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands.,Centre for Microbial Cell Biology, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands
| | - Jamie S Depelteau
- Department of Microbial Sciences, Institute of Biology, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands.,Centre for Microbial Cell Biology, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands
| | - Ariane Briegel
- Department of Microbial Sciences, Institute of Biology, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands.,Centre for Microbial Cell Biology, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands
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Abstract
The thermal shift assay (TSA)—also known as differential scanning fluorimetry (DSF), thermofluor, and Tm shift—is one of the most popular biophysical screening techniques used in fragment-based ligand discovery (FBLD) to detect protein–ligand interactions. By comparing the thermal stability of a target protein in the presence and absence of a ligand, potential binders can be identified. The technique is easy to set up, has low protein consumption, and can be run on most real-time polymerase chain reaction (PCR) instruments. While data analysis is straightforward in principle, it becomes cumbersome and time-consuming when the screens involve multiple 96- or 384-well plates. There are several approaches that aim to streamline this process, but most involve proprietary software, programming knowledge, or are designed for specific instrument output files. We therefore developed an analysis workflow implemented in the Konstanz Information Miner (KNIME), a free and open-source data analytics platform, which greatly streamlined our data processing timeline for 384-well plates. The implementation is code-free and freely available to the community for improvement and customization to accommodate a wide range of instrument input files and workflows. ![]()
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Affiliation(s)
- Errol L G Samuel
- Center for Drug Discovery, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA. .,Department of Pharmacology and Chemical Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
| | - Secondra L Holmes
- Center for Drug Discovery, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.,Department of Pharmacology and Chemical Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Damian W Young
- Center for Drug Discovery, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA. .,Department of Pharmacology and Chemical Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA. .,Department of Pathology and Immunology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
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Liu Z, Chen Z, Song K. SpinSPJ: a novel NMR scripting system to implement artificial intelligence and advanced applications. BMC Bioinformatics 2021; 22:581. [PMID: 34875998 PMCID: PMC8650269 DOI: 10.1186/s12859-021-04492-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 11/24/2021] [Indexed: 12/02/2022] Open
Abstract
Background Software for nuclear magnetic resonance (NMR) spectrometers offer general functionality of instrument control and data processing; these applications are often developed with non-scripting languages. NMR users need to flexibly integrate rapidly developing NMR applications with emerging technologies. Scripting systems offer open environments for NMR users to write custom programs. However, existing scripting systems have limited capabilities for both extending the functionality of NMR software’s non-script main program and using advanced native script libraries to support specialized application domains (e.g., biomacromolecules and metabolomics). Therefore, it is essential to design a novel scripting system to address both of these needs. Result Here, a novel NMR scripting system named SpinSPJ is proposed. It works as a plug-in in the Java based NMR spectrometer software SpinStudioJ. In the scripting system, both Java based NMR methods and original CPython based libraries are supported. A module has been developed as a bridge to integrate the runtime environments of Java and CPython. The module works as an extension in the CPython environment and interacts with Java via the Java Native Interface. Leveraging this bridge, Java based instrument control and data processing methods of SpinStudioJ can be called with the CPython style. Compared with traditional scripting systems, SpinSPJ better supports both extending the non-script main program and implementing advanced NMR applications with a rich variety of script libraries. NMR researchers can easily call functions of instrument control and data processing as well as developing complex functionality (such as multivariate statistical analysis, deep learning, etc.) with CPython native libraries. Conclusion SpinSPJ offers a user-friendly environment to implement custom functionality leveraging its powerful basic NMR and rich CPython libraries. NMR applications with emerging technologies can be easily integrated. The scripting system is free of charge and can be downloaded by visiting http://www.spinstudioj.net/spinspj. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04492-y.
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Affiliation(s)
- Zao Liu
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Center for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071, People's Republic of China.,Zhongke-Niujin MR Tech Co. Ltd, Wuhan, 430075, People's Republic of China
| | - Zhiwei Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Research, Xiamen University, Xiamen, 361005, People's Republic of China.
| | - Kan Song
- Zhongke-Niujin MR Tech Co. Ltd, Wuhan, 430075, People's Republic of China.
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Zamora Obando HR, Duarte GHB, Simionato AVC. Metabolomics Data Treatment: Basic Directions of the Full Process. Adv Exp Med Biol 2021; 1336:243-264. [PMID: 34628635 DOI: 10.1007/978-3-030-77252-9_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
The present chapter describes basic aspects of the main steps for data processing on mass spectrometry-based metabolomics platforms, focusing on the main objectives and important considerations of each step. Initially, an overview of metabolomics and the pivotal techniques applied in the field are presented. Important features of data acquisition and preprocessing such as data compression, noise filtering, and baseline correction are revised focusing on practical aspects. Peak detection, deconvolution, and alignment as well as missing values are also discussed. Special attention is given to chemical and mathematical normalization approaches and the role of the quality control (QC) samples. Methods for uni- and multivariate statistical analysis and data pretreatment that could impact them are reviewed, emphasizing the most widely used multivariate methods, i.e., principal components analysis (PCA), partial least squares-discriminant analysis (PLS-DA), orthogonal partial least square-discriminant analysis (OPLS-DA), and hierarchical cluster analysis (HCA). Criteria for model validation and softwares used in data processing were also approached. The chapter ends with some concerns about the minimal requirements to report metadata in metabolomics.
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Affiliation(s)
- Hans Rolando Zamora Obando
- Department of Analytical Chemistry, Institute of Chemistry, University of Campinas, Campinas, SP, Brazil
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48
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Doetsch JN, Dias V, Indredavik MS, Reittu J, Devold RK, Teixeira R, Kajantie E, Barros H. Record linkage of population-based cohort data from minors with national register data: a scoping review and comparative legal analysis of four European countries. Open Res Eur 2021; 1:58. [PMID: 37645179 PMCID: PMC10445839 DOI: 10.12688/openreseurope.13689.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/20/2021] [Indexed: 08/31/2023]
Abstract
Background: The GDPR was implemented to build an overarching framework for personal data protection across the EU/EEA. Linkage of data directly collected from cohort participants, potentially serving as a prominent tool for health research, must respect data protection rules and privacy rights. Our objective was to investigate law possibilities of linking cohort data of minors with routinely collected education and health data comparing EU/EEA member states. Methods: A legal comparative analysis and scoping review was conducted of openly accessible published laws and regulations in EUR-Lex and national law databases on GDPR's implementation in Portugal, Finland, Norway, and the Netherlands and its connected national regulations purposing record linkage for health research that have been implemented up until April 30, 2021. Results: The GDPR does not ensure total uniformity in data protection legislation across member states offering flexibility for national legislation. Exceptions to process personal data, e.g., public interest and scientific research, must be laid down in EU/EEA or national law. Differences in national interpretation caused obstacles in cross-national research and record linkage: Portugal requires written consent and ethical approval; Finland allows linkage mostly without consent through the national Social and Health Data Permit Authority; Norway when based on regional ethics committee's approval and adequate information technology safeguarding confidentiality; the Netherlands mainly bases linkage on the opt-out system and Data Protection Impact Assessment. Conclusions: Though the GDPR is the most important legal framework, national legislation execution matters most when linking cohort data with routinely collected health and education data. As national interpretation varies, legal intervention balancing individual right to informational self-determination and public good is gravely needed for health research. More harmonization across EU/EEA could be helpful but should not be detrimental in those member states which already opened a leeway for registries and research for the public good without explicit consent.
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Affiliation(s)
- Julia Nadine Doetsch
- Laboratory for Integrative and Translational Research in Population Health (ITR), Porto, 4050-600, Portugal
- EPIUnit, Instituto de Saúde Pública da, Universidade do Porto (ISPUP), Porto, 4050-600, Portugal
| | - Vasco Dias
- INESC TEC -Institute for Systems and Computer Engineering, Technology and Science, Campus da Faculdade de Engenharia da Universidade do Porto, Porto, 4050-091, Portugal
| | - Marit S. Indredavik
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, NTNU – Norwegian University of Science and Technology, Trondheim, NO-7491, Norway
| | - Jarkko Reittu
- Finnish Institute for Health and Welfare, Legal Services, Helsinki, Finland
- University of Helsinki, Faculty of Law, Helsinki, Finland
| | - Randi Kallar Devold
- Faculty of Medicine and Health Sciences, NTNU – Norwegian University of Science and Technology, Trondheim, NO-7491, Norway
| | - Raquel Teixeira
- Laboratory for Integrative and Translational Research in Population Health (ITR), Porto, 4050-600, Portugal
- EPIUnit, Instituto de Saúde Pública da, Universidade do Porto (ISPUP), Porto, 4050-600, Portugal
| | - Eero Kajantie
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, NTNU – Norwegian University of Science and Technology, Trondheim, NO-7491, Norway
- Finnish Institute for Health and Welfare, Population Health Unit, Helsinki and Oulu, Finland
- PEDEGO Research Unit, MRC Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland
- Children’s Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Henrique Barros
- Laboratory for Integrative and Translational Research in Population Health (ITR), Porto, 4050-600, Portugal
- EPIUnit, Instituto de Saúde Pública da, Universidade do Porto (ISPUP), Porto, 4050-600, Portugal
- Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina, Universidade do Porto (FMUP), Porto, Portugal
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49
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Ozcan T. A new composite approach for COVID-19 detection in X-ray images using deep features. Appl Soft Comput 2021; 111:107669. [PMID: 34248447 DOI: 10.1016/j.asoc.2021.107669] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 05/23/2021] [Accepted: 06/25/2021] [Indexed: 11/23/2022]
Abstract
The new type of coronavirus, COVID 19, appeared in China at the end of 2019. It has become a pandemic that is spreading all over the world in a very short time. The detection of this disease, which has serious health and socio-economic damages, is of vital importance. COVID-19 detection is performed by applying PCR and serological tests. Additionally, COVID detection is possible using X-ray and computed tomography images. Disease detection has an important position in scientific researches that includes artificial intelligence methods. The combined models, which consist of different phases, are frequently used for classification problems. In this paper, a new combined approach is proposed to detect COVID-19 cases using deep features obtained from X-ray images. Two main variances of the approach can be presented as single layer-based (SLB) and feature fusion-based (FFB). SLB model consists of pre-processing, deep feature extraction, post-processing, and classification phases. On the other side, the FFB model consists of pre-processing, deep feature extraction, feature fusion, post-processing, and classification phases. Four different SLB and six different FFB models were developed according to the number and binary combination of layers used in the feature extraction phase. Each model is employed for binary and multi-class classification experiments. According to experimental results, the accuracy performance for COVID-19 and no-findings classification of the proposed FFB3 model is 99.52%, which is better than the best performance accuracy (of 98.08%) in the literature. Concurrently, for multi-class classification, the proposed FFB3 model has an accuracy performance of 87.64% outperforming the best existing work (which reported an 87.02% classification performance). Various metrics, including sensitivity, specificity, precision, and F1-score metrics are used for performance analysis. For all performance metrics, the FFB3 model recorded a higher success rate than existing work in the literature. To the best of our knowledge, these accuracy rates are the best in the literature for the dataset and data split type (five-fold cross-validation). Composite models (SLBs and FFBs), which are generated in this paper, are successful ways to detect COVID-19. Experimental results show that feature extraction, pre-processing, post-processing, and hyperparameter tuning are the steps are necessary to obtain a higher success. For prospective works, different types of pre-trained models and other hyperparameter tuning methods can be implemented.
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Musy L, Bulanadi R, Gaponenko I, Paruch P. Hystorian: A processing tool for scanning probe microscopy and other n-dimensional datasets. Ultramicroscopy 2021; 228:113345. [PMID: 34214695 DOI: 10.1016/j.ultramic.2021.113345] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 06/20/2021] [Indexed: 11/16/2022]
Abstract
Research in materials science increasingly depends on the correlation of information from multiple characterisation techniques, acquired in ever larger datasets. Efficient methods of processing and storing these complex datasets are therefore crucial. Reliably keeping track of data processing is also essential to conform with the goals of open science. Here, we introduce Hystorian, a generic materials science data analysis Python package built at its core to improve the traceability, reproducibility, and archival ability of data processing. Proprietary data formats are converted into open hierarchical data format (HDF5) files, with both datasets and subsequent workflows automatically stored into a single location, thus allowing easy management of multiple data types. At present, Hystorian provides a basic scanning probe microscopy and x-ray diffraction analysis toolkit, and is readily extensible to suit user needs. It is also able to wrap over any existing processing functions, making it easy to append in an extant workflow.
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Affiliation(s)
- Loïc Musy
- University of Geneva, Department of Quantum Matter Physics, 1204, Geneva, Switzerland.
| | - Ralph Bulanadi
- University of Geneva, Department of Quantum Matter Physics, 1204, Geneva, Switzerland.
| | - Iaroslav Gaponenko
- University of Geneva, Department of Quantum Matter Physics, 1204, Geneva, Switzerland
| | - Patrycja Paruch
- University of Geneva, Department of Quantum Matter Physics, 1204, Geneva, Switzerland
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