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Monteiro HBN, Tartakovsky DM. A meshless stochastic method for Poisson-Nernst-Planck equations. J Chem Phys 2024; 161:054106. [PMID: 39087897 DOI: 10.1063/5.0223018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Accepted: 07/16/2024] [Indexed: 08/02/2024] Open
Abstract
A plethora of biological, physical, and chemical phenomena involve transport of charged particles (ions). Its continuum-scale description relies on the Poisson-Nernst-Planck (PNP) system, which encapsulates the conservation of mass and charge. The numerical solution of these coupled partial differential equations is challenging and suffers from both the curse of dimensionality and difficulty in efficiently parallelizing. We present a novel particle-based framework to solve the full PNP system by simulating a drift-diffusion process with time- and space-varying drift. We leverage Green's functions, kernel-independent fast multipole methods, and kernel density estimation to solve the PNP system in a meshless manner, capable of handling discontinuous initial states. The method is embarrassingly parallel, and the computational cost scales linearly with the number of particles and dimension. We use a series of numerical experiments to demonstrate both the method's convergence with respect to the number of particles and computational cost vis-à-vis a traditional partial differential equation solver.
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Affiliation(s)
- Henrique B N Monteiro
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, California 94305, USA
| | - Daniel M Tartakovsky
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, California 94305, USA
- Department of Energy Science and Engineering, Stanford University, Stanford, California 94305, USA
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2
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Rafiei F, Shekhar M, Rahnev D. The neural network RTNet exhibits the signatures of human perceptual decision-making. Nat Hum Behav 2024:10.1038/s41562-024-01914-8. [PMID: 38997452 DOI: 10.1038/s41562-024-01914-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 05/13/2024] [Indexed: 07/14/2024]
Abstract
Convolutional neural networks show promise as models of biological vision. However, their decision behaviour, including the facts that they are deterministic and use equal numbers of computations for easy and difficult stimuli, differs markedly from human decision-making, thus limiting their applicability as models of human perceptual behaviour. Here we develop a new neural network, RTNet, that generates stochastic decisions and human-like response time (RT) distributions. We further performed comprehensive tests that showed RTNet reproduces all foundational features of human accuracy, RT and confidence and does so better than all current alternatives. To test RTNet's ability to predict human behaviour on novel images, we collected accuracy, RT and confidence data from 60 human participants performing a digit discrimination task. We found that the accuracy, RT and confidence produced by RTNet for individual novel images correlated with the same quantities produced by human participants. Critically, human participants who were more similar to the average human performance were also found to be closer to RTNet's predictions, suggesting that RTNet successfully captured average human behaviour. Overall, RTNet is a promising model of human RTs that exhibits the critical signatures of perceptual decision-making.
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Affiliation(s)
- Farshad Rafiei
- School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA.
| | - Medha Shekhar
- School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA
| | - Dobromir Rahnev
- School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA
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3
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De Carlo A, Ronchi D, Piastra M, Tosca EM, Magni P. Predicting ADMET Properties from Molecule SMILE: A Bottom-Up Approach Using Attention-Based Graph Neural Networks. Pharmaceutics 2024; 16:776. [PMID: 38931898 PMCID: PMC11207804 DOI: 10.3390/pharmaceutics16060776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 05/08/2024] [Accepted: 05/30/2024] [Indexed: 06/28/2024] Open
Abstract
Understanding the pharmacokinetics, safety and efficacy of candidate drugs is crucial for their success. One key aspect is the characterization of absorption, distribution, metabolism, excretion and toxicity (ADMET) properties, which require early assessment in the drug discovery and development process. This study aims to present an innovative approach for predicting ADMET properties using attention-based graph neural networks (GNNs). The model utilizes a graph-based representation of molecules directly derived from Simplified Molecular Input Line Entry System (SMILE) notation. Information is processed sequentially, from substructures to the whole molecule, employing a bottom-up approach. The developed GNN is tested and compared with existing approaches using six benchmark datasets and by encompassing regression (lipophilicity and aqueous solubility) and classification (CYP2C9, CYP2C19, CYP2D6 and CYP3A4 inhibition) tasks. Results show the effectiveness of our model, which bypasses the computationally expensive retrieval and selection of molecular descriptors. This approach provides a valuable tool for high-throughput screening, facilitating early assessment of ADMET properties and enhancing the likelihood of drug success in the development pipeline.
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Affiliation(s)
| | | | | | | | - Paolo Magni
- Dipartimento di Ingegneria Industriale e dell’Informazione, Università degli Studi di Pavia, 27100 Pavia, Italy; (A.D.C.); (D.R.); (M.P.); (E.M.T.)
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Kim JSG, Moon S, Park J, Kim T, Chung S. Development of a machine vision-based weight prediction system of butterhead lettuce ( Lactuca sativa L.) using deep learning models for industrial plant factory. FRONTIERS IN PLANT SCIENCE 2024; 15:1365266. [PMID: 38903437 PMCID: PMC11188371 DOI: 10.3389/fpls.2024.1365266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 05/10/2024] [Indexed: 06/22/2024]
Abstract
Introduction Indoor agriculture, especially plant factories, becomes essential because of the advantages of cultivating crops yearly to address global food shortages. Plant factories have been growing in scale as commercialized. Developing an on-site system that estimates the fresh weight of crops non-destructively for decision-making on harvest time is necessary to maximize yield and profits. However, a multi-layer growing environment with on-site workers is too confined and crowded to develop a high-performance system.This research developed a machine vision-based fresh weight estimation system to monitor crops from the transplant stage to harvest with less physical labor in an on-site industrial plant factory. Methods A linear motion guide with a camera rail moving in both the x-axis and y-axis directions was produced and mounted on a cultivating rack with a height under 35 cm to get consistent images of crops from the top view. Raspberry Pi4 controlled its operation to capture images automatically every hour. The fresh weight was manually measured eleven times for four months to use as the ground-truth weight of the models. The attained images were preprocessed and used to develop weight prediction models based on manual and automatic feature extraction. Results and discussion The performance of models was compared, and the best performance among them was the automatic feature extraction-based model using convolutional neural networks (CNN; ResNet18). The CNN-based model on automatic feature extraction from images performed much better than any other manual feature extraction-based models with 0.95 of the coefficients of determination (R2) and 8.06 g of root mean square error (RMSE). However, another multiplayer perceptron model (MLP_2) was more appropriate to be adopted on-site since it showed around nine times faster inference time than CNN with a little less R2 (0.93). Through this study, field workers in a confined indoor farming environment can measure the fresh weight of crops non-destructively and easily. In addition, it would help to decide when to harvest on the spot.
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Affiliation(s)
- Jung-Sun Gloria Kim
- Department of Biosystems Engineering, Seoul National University, Seoul, Republic of Korea
- Integrated Major in Global Smart Farm, Seoul National University, Seoul, Republic of Korea
| | - Seongje Moon
- Department of Biosystems Engineering, Seoul National University, Seoul, Republic of Korea
| | - Junyoung Park
- Department of Biosystems Engineering, Seoul National University, Seoul, Republic of Korea
- Integrated Major in Global Smart Farm, Seoul National University, Seoul, Republic of Korea
| | - Taehyeong Kim
- Department of Biosystems Engineering, Seoul National University, Seoul, Republic of Korea
- Integrated Major in Global Smart Farm, Seoul National University, Seoul, Republic of Korea
- Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea
| | - Soo Chung
- Department of Biosystems Engineering, Seoul National University, Seoul, Republic of Korea
- Integrated Major in Global Smart Farm, Seoul National University, Seoul, Republic of Korea
- Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea
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5
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Okui N. Unsupervised Machine Learning Reveals a Vulvodynia-Predominant Subtype in Bladder Pain Syndrome/Interstitial Cystitis. Cureus 2024; 16:e62585. [PMID: 39027744 PMCID: PMC11257649 DOI: 10.7759/cureus.62585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/17/2024] [Indexed: 07/20/2024] Open
Abstract
Background Bladder pain syndrome/interstitial cystitis (BPS/IC) is a chronic condition characterized by pelvic pain and urinary symptoms. Despite its significant impact on patients' quality of life, the heterogeneity of BPS/IC symptoms and the presence of comorbidities such as vulvodynia may not be adequately captured by validated questionnaires. Identifying vulvodynia in BPS/IC patients is crucial for providing appropriate treatment options. This study aimed to identify subtypes of BPS/IC patients using unsupervised machine learning and to investigate the prevalence of vulvodynia in each subtype. Methods We conducted a prospective cross-sectional study of 123 BPS/IC patients and 64 age-matched controls. Hierarchical clustering was performed using data from validated questionnaires, including the Numerical Rating Scale-11, Interstitial Cystitis Symptom Index (ICSI), Interstitial Cystitis Problem Index (ICPI), Pelvic Pain and Urgency/Frequency scores, Overactive Bladder Questionnaire Short Form (OABq SF), Overactive Bladder Symptom Score (OABSS), and Pelvic Floor Distress Inventory-20. The optimal number of clusters was determined using the elbow method, and the characteristics of each cluster were analyzed. All participants underwent a vulvodynia swab test to assess vulvodynia symptoms. Results Unsupervised machine learning revealed three distinct clusters of BPS/IC patients. Clusters 0 and 2 differed significantly, with Cluster 2 characterized by significantly higher vulvodynia scores compared to other clusters (P < 0.001). In contrast, Cluster 2 had lower bladder pain scores (ICSI and ICPI) and overactive bladder symptom scores (OABq SF and OABSS) compared to other clusters. Clusters 0 and 1 were characterized by a predominance of bladder pain and urinary frequency symptoms, with Cluster 0 exhibiting more severe symptoms. Conclusions Our study identified distinct subtypes of BPS/IC patients using unsupervised machine learning, with Cluster 2 representing a vulvodynia-predominant subtype. This finding, along with the potential of targeted therapies such as non-ablative erbium YAG laser for vulvodynia, underscores the importance of assessing extravesical symptoms, particularly vulvodynia, for the diagnosis and treatment of BPS/IC. A tailored approach, including laser therapy for vulvodynia-predominant patients, may be necessary for optimal management of BPS/IC. The vulvodynia swab test plays a crucial role in assessing vulvodynia symptoms, underlining the limitations of validated questionnaires in capturing the full spectrum of BPS/IC symptoms. A comprehensive evaluation of patients, including the vulvodynia swab test, is essential for accurate subtyping and management of BPS/IC. Further research with larger sample sizes and investigation of the relationship between identified subtypes and other clinical data is warranted to advance our understanding and management of BPS/IC.
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Affiliation(s)
- Nobuo Okui
- Dentistry, Kanagawa Dental University, Yokosuka, JPN
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Jiang F, Guo Y, Ma H, Na S, Zhong W, Han Y, Wang T, Huang J. GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity. Brief Bioinform 2024; 25:bbae343. [PMID: 39007599 PMCID: PMC11247411 DOI: 10.1093/bib/bbae343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 05/15/2024] [Accepted: 07/01/2024] [Indexed: 07/16/2024] Open
Abstract
The interaction between T-cell receptors (TCRs) and peptides (epitopes) presented by major histocompatibility complex molecules (MHC) is fundamental to the immune response. Accurate prediction of TCR-epitope interactions is crucial for advancing the understanding of various diseases and their prevention and treatment. Existing methods primarily rely on sequence-based approaches, overlooking the inherent topology structure of TCR-epitope interaction networks. In this study, we present $GTE$, a novel heterogeneous Graph neural network model based on inductive learning to capture the topological structure between TCRs and Epitopes. Furthermore, we address the challenge of constructing negative samples within the graph by proposing a dynamic edge update strategy, enhancing model learning with the nonbinding TCR-epitope pairs. Additionally, to overcome data imbalance, we adapt the Deep AUC Maximization strategy to the graph domain. Extensive experiments are conducted on four public datasets to demonstrate the superiority of exploring underlying topological structures in predicting TCR-epitope interactions, illustrating the benefits of delving into complex molecular networks. The implementation code and data are available at https://github.com/uta-smile/GTE.
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Affiliation(s)
- Feng Jiang
- Department of Computer Science and Engineering, University of Texas at Arlington, 701 S. Nedderman Drive, TX 76019, United States
| | - Yuzhi Guo
- Department of Computer Science and Engineering, University of Texas at Arlington, 701 S. Nedderman Drive, TX 76019, United States
| | - Hehuan Ma
- Department of Computer Science and Engineering, University of Texas at Arlington, 701 S. Nedderman Drive, TX 76019, United States
| | - Saiyang Na
- Department of Computer Science and Engineering, University of Texas at Arlington, 701 S. Nedderman Drive, TX 76019, United States
| | - Wenliang Zhong
- Department of Computer Science and Engineering, University of Texas at Arlington, 701 S. Nedderman Drive, TX 76019, United States
| | - Yi Han
- Public Health, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, TX 75390, United States
| | - Tao Wang
- Public Health, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, TX 75390, United States
| | - Junzhou Huang
- Department of Computer Science and Engineering, University of Texas at Arlington, 701 S. Nedderman Drive, TX 76019, United States
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Liu Y, Ghosh TK, Lin G, Chen M. Unbiasing Enhanced Sampling on a High-Dimensional Free Energy Surface with a Deep Generative Model. J Phys Chem Lett 2024; 15:3938-3945. [PMID: 38568182 DOI: 10.1021/acs.jpclett.3c03515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Biased enhanced sampling methods that utilize collective variables (CVs) are powerful tools for sampling conformational ensembles. Due to their large intrinsic dimensions, efficiently generating conformational ensembles for complex systems requires enhanced sampling on high-dimensional free energy surfaces. While temperature-accelerated molecular dynamics (TAMD) can trivially adopt many CVs in a simulation, unbiasing the simulation to generate unbiased conformational ensembles requires accurate modeling of a high-dimensional CV probability distribution, which is challenging for traditional density estimation techniques. Here we propose an unbiasing method based on the score-based diffusion model, a deep generative learning method that excels in density estimation across complex data landscapes. We demonstrate that this unbiasing approach, tested on multiple TAMD simulations, significantly outperforms traditional unbiasing methods and can generate accurate unbiased conformational ensembles. With the proposed approach, TAMD can adopt CVs that focus on improving sampling efficiency and the proposed unbiasing method enables accurate evaluation of ensemble averages of important chemical features.
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Affiliation(s)
- Yikai Liu
- Department of Mechanical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Tushar K Ghosh
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47906, United States
| | - Guang Lin
- Department of Mechanical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Ming Chen
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47906, United States
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8
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Xavier P, Rodrigues PM, Silva CLM. Shelf-Life Management and Ripening Assessment of 'Hass' Avocado ( Persea americana) Using Deep Learning Approaches. Foods 2024; 13:1150. [PMID: 38672823 PMCID: PMC11049431 DOI: 10.3390/foods13081150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 03/21/2024] [Accepted: 04/04/2024] [Indexed: 04/28/2024] Open
Abstract
Avocado production is mostly confined to tropical and subtropical regions, leading to lengthy distribution channels that, coupled with their unpredictable post-harvest behavior, render avocados susceptible to significant loss and waste. To enhance the monitoring of 'Hass' avocado ripening, a data-driven tool was developed using a deep learning approach. This study involved monitoring 478 avocados stored in three distinct storage environments, using a 5-stage Ripening Index to classify each fruit's ripening phase based on their shared characteristics. These categories were paired with daily photographic records of the avocados, resulting in a database of labeled images. Two convolutional neural network models, AlexNet and ResNet-18, were trained using transfer learning techniques to identify distinct ripening indicators, enabling the prediction of ripening stages and shelf-life estimations for new unseen data. The approach achieved a final prediction accuracy of 88.8% for the ripening assessment, with 96.7% of predictions deviating by no more than half a stage from their actual classifications when considering the best side of the samples. The average shelf-life estimates based on the attributed classifications were within 0.92 days of the actual shelf-life, whereas the predictions made by the models had an average deviation of 0.96 days from the actual shelf-life.
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Affiliation(s)
| | | | - Cristina L. M. Silva
- CBQF—Centro de Biotecnologia e Química Fina—Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Rua Diogo Botelho 1327, 4169-005 Porto, Portugal; (P.X.); (P.M.R.)
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9
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Yousefzadeh N, Tran C, Ramirez-Zamora A, Chen J, Fang R, Thai MT. Neuron-level explainable AI for Alzheimer's Disease assessment from fundus images. Sci Rep 2024; 14:7710. [PMID: 38565579 PMCID: PMC10987553 DOI: 10.1038/s41598-024-58121-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 03/26/2024] [Indexed: 04/04/2024] Open
Abstract
Alzheimer's Disease (AD) is a progressive neurodegenerative disease and the leading cause of dementia. Early diagnosis is critical for patients to benefit from potential intervention and treatment. The retina has emerged as a plausible diagnostic site for AD detection owing to its anatomical connection with the brain. However, existing AI models for this purpose have yet to provide a rational explanation behind their decisions and have not been able to infer the stage of the disease's progression. Along this direction, we propose a novel model-agnostic explainable-AI framework, called Granula ̲ r Neuron-le v ̲ el Expl a ̲ iner (LAVA), an interpretation prototype that probes into intermediate layers of the Convolutional Neural Network (CNN) models to directly assess the continuum of AD from the retinal imaging without the need for longitudinal or clinical evaluations. This innovative approach aims to validate retinal vasculature as a biomarker and diagnostic modality for evaluating Alzheimer's Disease. Leveraged UK Biobank cognitive tests and vascular morphological features demonstrate significant promise and effectiveness of LAVA in identifying AD stages across the progression continuum.
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Affiliation(s)
- Nooshin Yousefzadeh
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, Florida, USA
| | - Charlie Tran
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA
| | | | - Jinghua Chen
- Department of Ophthalmology, University of Florida, Gainesville, FL, USA
| | - Ruogu Fang
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA.
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA.
- Center for Cognitive Aging and Memory, University of Florida, Gainesville, FL, USA.
| | - My T Thai
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, Florida, USA.
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10
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Kouris AD, Christopoulos A, Vlachopoulos K, Christopoulou A, Dimitrakopoulos PG, Zevgolis YG. Spatiotemporal Patterns of Reptile and Amphibian Road Fatalities in a Natura 2000 Area: A 12-Year Monitoring of the Lake Karla Mediterranean Wetland. Animals (Basel) 2024; 14:708. [PMID: 38473093 DOI: 10.3390/ani14050708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 02/19/2024] [Accepted: 02/22/2024] [Indexed: 03/14/2024] Open
Abstract
The pervasive expansion of human-engineered infrastructure, particularly roads, has fundamentally reshaped landscapes, profoundly affecting wildlife interactions. Wildlife-vehicle collisions, a common consequence of this intricate interplay, frequently result in fatalities, extending their detrimental impact within Protected Areas (PAs). Among the faunal groups most susceptible to road mortality, reptiles and amphibians stand at the forefront, highlighting the urgent need for global comprehensive mitigation strategies. In Greece, where road infrastructure expansion has encroached upon a significant portion of the nation's PAs, the plight of these road-vulnerable species demands immediate attention. To address this critical issue, we present a multifaceted and holistic approach to investigating and assessing the complex phenomenon of herpetofauna road mortality within the unique ecological context of the Lake Karla plain, a rehabilitated wetland complex within a PA. To unravel the intricacies of herpetofauna road mortality in the Lake Karla plain, we conducted a comprehensive 12-year investigation from 2008 to 2019. Employing a combination of statistical modeling and spatial analysis techniques, we aimed to identify the species most susceptible to these encounters, their temporal and seasonal variations, and the ecological determinants of their roadkill patterns. We documented a total of 340 roadkill incidents involving 14 herpetofauna species in the Lake Karla's plain, with reptiles, particularly snakes, being more susceptible, accounting for over 60% of roadkill occurrences. Moreover, we found that environmental and road-related factors play a crucial role in influencing roadkill incidents, while spatial analysis techniques, including Kernel Density Estimation, the Getis-Ord Gi*, and the Kernel Density Estimation plus methods revealed critical areas, particularly in the south-eastern region of Lake Karla's plain, offering guidance for targeted interventions to address both individual and collective risks associated with roadkill incidents.
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Affiliation(s)
- Alexandros D Kouris
- Biodiversity Conservation Laboratory, Department of Environment, University of the Aegean, 81132 Mytilene, Greece
| | - Apostolos Christopoulos
- Department of Zoology and Marine Biology, Faculty of Biology, National and Kapodistrian University of Athens, 15772 Athens, Greece
| | - Konstantinos Vlachopoulos
- Department of Agriculture, Crop Production and Rural Environment, University of Thessaly, 38446 Volos, Greece
| | | | - Panayiotis G Dimitrakopoulos
- Biodiversity Conservation Laboratory, Department of Environment, University of the Aegean, 81132 Mytilene, Greece
| | - Yiannis G Zevgolis
- Biodiversity Conservation Laboratory, Department of Environment, University of the Aegean, 81132 Mytilene, Greece
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11
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Lin J, Smith GC, Gliske SV, Zochowski M, Shedden K, Stacey WC. High frequency oscillation network dynamics predict outcome in non-palliative epilepsy surgery. Brain Commun 2024; 6:fcae032. [PMID: 38384998 PMCID: PMC10881100 DOI: 10.1093/braincomms/fcae032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 12/28/2023] [Accepted: 02/05/2024] [Indexed: 02/23/2024] Open
Abstract
High frequency oscillations are a promising biomarker of outcome in intractable epilepsy. Prior high frequency oscillation work focused on counting high frequency oscillations on individual channels, and it is still unclear how to translate those results into clinical care. We show that high frequency oscillations arise as network discharges that have valuable properties as predictive biomarkers. Here, we develop a tool to predict patient outcome before surgical resection is performed, based on only prospective information. In addition to determining high frequency oscillation rate on every channel, we performed a correlational analysis to evaluate the functional connectivity of high frequency oscillations in 28 patients with intracranial electrodes. We found that high frequency oscillations were often not solitary events on a single channel, but part of a local network discharge. Eigenvector and outcloseness centrality were used to rank channel importance within the connectivity network, then used to compare patient outcome by comparison with the seizure onset zone or a proportion within the proposed resected channels (critical resection percentage). Combining the knowledge of each patient's seizure onset zone resection plan along with our computed high frequency oscillation network centralities and high frequency oscillation rate, we develop a Naïve Bayes model that predicts outcome (positive predictive value: 100%) better than predicting based upon fully resecting the seizure onset zone (positive predictive value: 71%). Surgical margins had a large effect on outcomes: non-palliative patients in whom most of the seizure onset zone was resected ('definitive surgery', ≥ 80% resected) had predictable outcomes, whereas palliative surgeries (<80% resected) were not predictable. These results suggest that the addition of network properties of high frequency oscillations is more accurate in predicting patient outcome than seizure onset zone alone in patients with most of the seizure onset zone removed and offer great promise for informing clinical decisions in surgery for refractory epilepsy.
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Affiliation(s)
- Jack Lin
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - Garnett C Smith
- Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Stephen V Gliske
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Michal Zochowski
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Physics and Biophysics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kerby Shedden
- Department of Statistics and Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - William C Stacey
- Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
- Division of Neurology, Ann Arbor VA Health System, Ann Arbor, MI 48109, USA
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12
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Zhou P, Zhang H, Liu L, Pan Y, Liu Y, Sang X, Liu C, Chen Z. Sustainable planning in Wuhan City during COVID-19: an analysis of influential factors, risk profiles, and clustered patterns. Front Public Health 2023; 11:1241029. [PMID: 38152666 PMCID: PMC10751330 DOI: 10.3389/fpubh.2023.1241029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 11/21/2023] [Indexed: 12/29/2023] Open
Abstract
The outbreak of novel coronavirus pneumonia (COVID-19) is closely related to the intra-urban environment. It is important to understand the influence mechanism and risk characteristics of urban environment on infectious diseases from the perspective of urban environment composition. In this study, we used python to collect Sina Weibo help data as well as urban multivariate big data, and The random forest model was used to measure the contribution of each influential factor within to the COVID-19 outbreak. A comprehensive risk evaluation system from the perspective of urban environment was constructed, and the entropy weighting method was used to produce the weights of various types of risks, generate the specific values of the four types of risks, and obtain the four levels of comprehensive risk zones through the K-MEANS clustering of Wuhan's central urban area for zoning planning. Based on the results, we found: ①the five most significant indicators contributing to the risk of the Wuhan COVID-19 outbreak were Road Network Density, Shopping Mall Density, Public Transport Density, Educational Facility Density, Bank Density. Floor Area Ration, Poi Functional Mix ②After streamlining five indicators such as Proportion of Aged Population, Tertiary Hospital Density, Open Space Density, Night-time Light Intensity, Number of Beds Available in Designated Hospitals, the prediction accuracy of the random forest model was the highest. ③The spatial characteristics of the four categories of new crown epidemic risk, namely transmission risk, exposure risk, susceptibility risk and Risk of Scarcity of Medical Resources, were highly differentiated, and a four-level integrated risk zone was obtained by K-MEANS clustering. Its distribution pattern was in the form of "multicenter-periphery" gradient diffusion. For the risk composition of the four-level comprehensive zones combined with the internal characteristics of the urban environment in specific zones to develop differentiated control strategies. Targeted policies were then devised for each partition, offering a practical advantage over singular COVID-19 impact factor analyses. This methodology, beneficial for future public health crises, enables the swift identification of unique risk profiles in different partitions, streamlining the formulation of precise policies. The overarching goal is to maintain regular social development, harmonizing preventive measures and economic efforts.
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Affiliation(s)
| | | | - Lanjun Liu
- School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan, China
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13
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Zhang Q, Black MS. Exposure hazards of particles and volatile organic compounds emitted from material extrusion 3D printing: Consolidation of chamber study data. ENVIRONMENT INTERNATIONAL 2023; 182:108316. [PMID: 37952412 DOI: 10.1016/j.envint.2023.108316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 10/31/2023] [Accepted: 11/07/2023] [Indexed: 11/14/2023]
Abstract
Ultrafine particles and volatile organic compounds (VOCs) have been detected from material extrusion 3D printing, which is widely used in non-industrial environments. This study consolidates data of 447 particle emission and 58 VOC emission evaluations from a chamber study using a standardized testing method with various 3D printing scenarios. The interquartile ranges of the observed emission rates were 109-1011 #/h for particles and 0.2-1.0 mg/h for total VOC. Print material contributed largely to the variations of particle and total VOC emissions and determined the most abundantly emitted VOCs. Printing conditions and filament specifications, included printer brand, print temperature and speed, build plate heating setup, filament brand, color and composite, also affected emissions and resulted in large variations observed in emission profiles. Multiple regression showed that particle emissions were more impacted by various print conditions than VOC emissions. According to indoor exposure modeling, personal and residential exposure scenarios were more likely to result in high exposure levels, often exceeding recommended exposure limits. Hazardous VOCs commonly emitted from 3D printing included aromatics, aldehydes, alcohols, ketones, esters and siloxanes, among which were various carcinogens, irritants and developmental and reproductive toxins. Therefore, 3D printing emits a complex mixture of ultrafine particles and various hazardous chemicals, exposure to which may exceed recommended exposure limits and potentially induce acute, chronic, or developmental health effects for users depending on exposure scenarios.
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Affiliation(s)
- Qian Zhang
- Chemical Insights Research Institute, UL Research Institutes, Marietta, GA 30067, USA.
| | - Marilyn S Black
- Chemical Insights Research Institute, UL Research Institutes, Marietta, GA 30067, USA
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14
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Huang J, Lu H, Jin H, Zhang L. Urban resilience in China's eight urban agglomerations: evolution trends and driving factors. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023:10.1007/s11356-023-30971-1. [PMID: 38012489 DOI: 10.1007/s11356-023-30971-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 11/05/2023] [Indexed: 11/29/2023]
Abstract
Improving urban resilience (UR) and enhancing urban anti-risk ability are important foundations for promoting the high-quality development of new urbanization. This research employs the time-varying entropy method to evaluate the resilience level of 138 cities within China's eight urban agglomerations (UAs) between 2005 and 2019. Additionally, the Dagum Gini coefficient and the kernel density estimation method are utilized to examine the spatial disparities and distribution dynamics of UR across the eight UAs. The results of this investigation indicate that (1) the collective UR performance of the eight UAs has experienced an upward trend. However, a notable spatial disparity exits, which is primarily attributed to the differences among the UAs. (2) The overall UR development of the eight UAs has a certain gradient effect, and the UR within each UA has different degrees of polarization characteristics. (3) For the eight UAs as a whole, per capita savings deposits, capitalization of foreign capital, and per capita fiscal expenditure are the three most important driving factors. Within each UA, there was heterogeneity in the main influencing factors. The interplay between any two factors amplifies their individual driving effects on the spatial differentiation of UR.
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Affiliation(s)
- Jie Huang
- Business School, Xinyang Normal University, Xinyang, 464000, Henan, China
- Research Institute of the Economic and Social Development in the Dabie Mountains, Xinyang Normal University, Xinyang, 464000, Henan, China
| | - Hongyang Lu
- Business School, Xinyang Normal University, Xinyang, 464000, Henan, China
| | - Huali Jin
- School of Economics, Shandong University of Finance and Economics, Jinan, 250000, Shandong, China
| | - Long Zhang
- Business School, Xinyang Normal University, Xinyang, 464000, Henan, China.
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15
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Ge F, Arif M, Yan Z, Alahmadi H, Worachartcheewan A, Yu DJ, Shoombuatong W. MMPatho: Leveraging Multilevel Consensus and Evolutionary Information for Enhanced Missense Mutation Pathogenic Prediction. J Chem Inf Model 2023; 63:7239-7257. [PMID: 37947586 PMCID: PMC10685454 DOI: 10.1021/acs.jcim.3c00950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 10/21/2023] [Accepted: 10/23/2023] [Indexed: 11/12/2023]
Abstract
Understanding the pathogenicity of missense mutation (MM) is essential for shed light on genetic diseases, gene functions, and individual variations. In this study, we propose a novel computational approach, called MMPatho, for enhancing missense mutation pathogenic prediction. First, we established a large-scale nonredundant MM benchmark data set based on the entire Ensembl database, complemented by a focused blind test set specifically for pathogenic GOF/LOF MM. Based on this data set, for each mutation, we utilized Ensembl VEP v104 and dbNSFP v4.1a to extract variant-level, amino acid-level, individuals' outputs, and genome-level features. Additionally, protein sequences were generated using ENSP identifiers with the Ensembl API, and then encoded. The mutant sites' ESM-1b and ProtTrans-T5 embeddings were subsequently extracted. Then, our model group (MMPatho) was developed by leveraging upon these efforts, which comprised ConsMM and EvoIndMM. To be specific, ConsMM employs individuals' outputs and XGBoost with SHAP explanation analysis, while EvoIndMM investigates the potential enhancement of predictive capability by incorporating evolutionary information from ESM-1b and ProtT5-XL-U50, large protein language embeddings. Through rigorous comparative experiments, both ConsMM and EvoIndMM were capable of achieving remarkable AUROC (0.9836 and 0.9854) and AUPR (0.9852 and 0.9902) values on the blind test set devoid of overlapping variations and proteins from the training data, thus highlighting the superiority of our computational approach in the prediction of MM pathogenicity. Our Web server, available at http://csbio.njust.edu.cn/bioinf/mmpatho/, allows researchers to predict the pathogenicity (alongside the reliability index score) of MMs using the ConsMM and EvoIndMM models and provides extensive annotations for user input. Additionally, the newly constructed benchmark data set and blind test set can be accessed via the data page of our web server.
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Affiliation(s)
- Fang Ge
- School
of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, 9 Wenyuanlu, Nanjing 210023, China
- Center
for Research Innovation and Biomedical Informatics, Faculty of Medical
Technology, Mahidol University, Bangkok 10700, Thailand
| | - Muhammad Arif
- College
of Science and Engineering, Hamad Bin Khalifa
University, Doha 34110, Qatar
- Department
of Community Medical Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Zihao Yan
- School
of Computer Science and Engineering, Nanjing
University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China
| | - Hanin Alahmadi
- College of
Computer Science and Engineering, Taibah
University, Madinah 344, Saudi Arabia
| | - Apilak Worachartcheewan
- Department
of Community Medical Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Dong-Jun Yu
- School
of Computer Science and Engineering, Nanjing
University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China
| | - Watshara Shoombuatong
- Center
for Research Innovation and Biomedical Informatics, Faculty of Medical
Technology, Mahidol University, Bangkok 10700, Thailand
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16
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Candela E, Goizueta C, Sandon L, Muñoz-Antoli C, Periago MV. The Relationship Between Soil-Transmitted Helminth Infections and Environmental Factors in Puerto Iguazú, Argentina: Cross-Sectional Study. JMIR Public Health Surveill 2023; 9:e41568. [PMID: 37934580 PMCID: PMC10664009 DOI: 10.2196/41568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 07/26/2023] [Accepted: 09/19/2023] [Indexed: 11/08/2023] Open
Abstract
BACKGROUND Soil-transmitted helminths (STHs) are widely distributed throughout the world. Various factors, including the environment, socioeconomic characteristics, and access to water and sanitation, play an important role in the spread and persistence of these parasites within communities. They, in turn, affect the growth and development of members of the community, especially children. Studies in the northern provinces of Argentina have shown variable prevalence of STHs, but the factors associated with their presence have not been completely elucidated. OBJECTIVE This cross-sectional study aimed to identify the socioeconomic and environmental factors related to STH infection in indigenous villages located in Puerto Iguazú (Misiones), Argentina. METHODS Between 2018 and 2019, stool samples were collected from individuals ≥1 year residing in 3 villages: Mini-Marangatú, Yriapú, and Fortín Mbororé. Standard parasitological methods were used to determine STH prevalence. Standardized questionnaires were used to assess participants' habits, customs, and household characteristics, and environmental data were obtained through satellite imagery. Multilinear regression with Akaike information criterion stepwise variables was used to explore relevant associations. RESULTS A total of 342 individuals from the 3 villages participated in this study. The prevalence of STHs varied across villages: 89.6% (43/48), in Mini-Marangatú, 80.8% (101/125) in Yriapú, and 68.5% (115/169) in Fortín Mbororé. Notably, there was a significant difference in hookworm infection among the villages (P=.02). The analysis highlighted the significant influence of specific environmental factors on STH presence and spatial distribution, particularly in relation to hookworm infection. Vegetation patterns represented by the Vegetation Heterogeneity Index, created ad hoc for this study, emerged as a critical factor, with 2 significant predictors related to it (P=.002 and P=.004) alongside impervious surface density with a significant predictor (P<.001). The multilinear regression model yielded a high F test score (F108=4.75, P<.001), indicating a strong fit (R2=0.5465). Furthermore, socioeconomic factors, including walking barefoot in houses with dirt floors and overcrowding, were significantly correlated with hookworm infection intensity (P<.001 and P=.001, respectively). We also used the multilinear regression model to calculate hookworm infection intensity (F110=21.15, P<.001; R2=0.4971). CONCLUSIONS Our study underscores the complexity of STH transmission, as villages with similar living conditions and environmental characteristics displayed varied STH prevalence and spatial distribution. Specific environmental factors, such as vegetation pattern and impervious surface density, played major roles in STH presence, demonstrating the crucial relationship between environmental factors and hookworm infection distribution. Moreover, our findings emphasize the significant influence of socioeconomic factors on hookworm infection intensity. By gaining insights into this complex interplay, our research contributes to a better understanding of STH transmission characteristics, thereby informing targeted public health interventions for effective control.
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Affiliation(s)
- Ernesto Candela
- Department of Pharmacy Pharmaceutical Technology and Parasitology, Universitat de València, Burjassot, Spain
| | | | | | - Carla Muñoz-Antoli
- Department of Pharmacy Pharmaceutical Technology and Parasitology, Universitat de València, Burjassot, Spain
| | - Maria Victoria Periago
- Mundo Sano Foundation, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
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17
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de Gelder E, Adjenughwure K, Manders J, Snijders R, Paardekooper JP, Op den Camp O, Tejada A, De Schutter B. PRISMA: A novel approach for deriving probabilistic surrogate safety measures for risk evaluation. ACCIDENT; ANALYSIS AND PREVENTION 2023; 192:107273. [PMID: 37689004 DOI: 10.1016/j.aap.2023.107273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 08/07/2023] [Accepted: 08/26/2023] [Indexed: 09/11/2023]
Abstract
Surrogate Safety Measures (SSMs) are used to express road safety in terms of the safety risk in traffic conflicts. Typically, SSMs rely on assumptions regarding the future evolution of traffic participant trajectories to generate a measure of risk, restricting their applicability to scenarios where these assumptions are valid. In response to this limitation, we present the novel Probabilistic RISk Measure derivAtion (PRISMA) method. The objective of the PRISMA method is to derive SSMs that can be used to calculate in real time the probability of a specific event (e.g., a crash). The PRISMA method adopts a data-driven approach to predict the possible future traffic participant trajectories, thereby reducing the reliance on specific assumptions regarding these trajectories. Since the PRISMA is not bound to specific assumptions, the PRISMA method offers the ability to derive multiple SSMs for various scenarios. The occurrence probability of the specified event is based on simulations and combined with a regression model, this enables our derived SSMs to make real-time risk estimations. To illustrate the PRISMA method, an SSM is derived for risk evaluation during longitudinal traffic interactions. Since there is no known method to objectively estimate risk from first principles, i.e., there is no known risk ground truth, it is very difficult, if not impossible, to objectively compare the relative merits of two SSMs. Instead, we provide a method for benchmarking our derived SSM with respect to expected risk trends. The application of the benchmarking illustrates that the SSM matches the expected risk trends. Whereas the derived SSM shows the potential of the PRISMA method, future work involves applying the approach for other types of traffic conflicts, such as lateral traffic conflicts or interactions with vulnerable road users.
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Affiliation(s)
- Erwin de Gelder
- TNO, Integrated Vehicle Safety, Helmond, The Netherlands; Delft University of Technology, Delft Center for Systems and Control, Delft, The Netherlands.
| | | | - Jeroen Manders
- TNO, Integrated Vehicle Safety, Helmond, The Netherlands
| | - Ron Snijders
- TNO, Monitoring & Control Services, Groningen, The Netherlands
| | - Jan-Pieter Paardekooper
- TNO, Integrated Vehicle Safety, Helmond, The Netherlands; Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | | | - Arturo Tejada
- TNO, Integrated Vehicle Safety, Helmond, The Netherlands; Eindhoven University of Technology, Dynamics and Control Group, Eindhoven, The Netherlands
| | - Bart De Schutter
- Delft University of Technology, Delft Center for Systems and Control, Delft, The Netherlands
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18
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Dong YW. Roles of multi-level temperature-adaptive responses and microhabitat variation in establishing distributions of intertidal species. J Exp Biol 2023; 226:jeb245745. [PMID: 37909420 DOI: 10.1242/jeb.245745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2023]
Abstract
How intertidal species survive their harsh environment and how best to evaluate and forecast range shifts in species distribution are two important and closely related questions for intertidal ecologists and global change biologists. Adaptive variation in responses of organisms to environmental change across all levels of biological organization - from behavior to molecular systems - is of key importance in setting distribution patterns, yet studies often neglect the interactions of diverse types of biological variation (e.g. differences in thermal optima owing to genetic and acclimation-induced effects) with environmental variation, notably at the scale of microhabitats. Intertidal species have to cope with extreme and frequently changing thermal stress, and have shown high variation in thermal sensitivities and adaptive responses at different levels of biological organization. Here, I review the physiological and biochemical adaptations of intertidal species to environmental temperature on multiple spatial and temporal scales. With fine-scale datasets for the thermal limits of individuals and for environmental temperature variation at the microhabitat scale, we can map the thermal sensitivity for each individual in different microhabitats, and then scale up the thermal sensitivity analysis to the population level and, finally, to the species level by incorporating physiological traits into species distribution models. These more refined mechanistic models that include consideration of physiological variations have higher predictive power than models that neglect these variations, and they will be crucial to answering the questions posed above concerning adaptive mechanisms and the roles they play in governing distribution patterns in a rapidly changing world.
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Affiliation(s)
- Yun-Wei Dong
- Ministry Key Laboratory of Mariculture, Fisheries College, Ocean University of China, Qingdao 266001, China
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19
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Zhang L, Zhang YY, Ma LX, Dong YW. Evaluation of species thermal sensitivity with individual-based physiological performance. MARINE ENVIRONMENTAL RESEARCH 2023; 192:106212. [PMID: 37812948 DOI: 10.1016/j.marenvres.2023.106212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 09/22/2023] [Accepted: 09/26/2023] [Indexed: 10/11/2023]
Abstract
Ignoring intraspecific variations can prevent us from accurately assessing species' thermal sensitivity to global warming. Individual-based physiological performance provides a feasible solution to depict species' thermal sensitivity using a bottom-up approach. We measured the cardiac performance of intertidal bivalves (1159 individuals from multiple populations of six bivalves), determined the upper thermal limit of each individual, calculated the proportions of individuals suffering sublethal/lethal heat stress, and mapped sensitive regions to high temperatures. Results showed that high inter-individual variations of physiological performance existed in levels of populations and species, and species' thermal sensitivity was positively related to the intraspecific variations of heat tolerance. This bottom-up approach scaled up from individual, population to species emphasizes the importance of individual-based physiology performance in assessing thermal sensitivity across different hierarchical levels and enables better evaluating and forecasting of species responses to global warming.
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Affiliation(s)
- Liang Zhang
- Ministry Key Laboratory of Mariculture, Fisheries College, Ocean University of China, Qingdao, 266001, China
| | - Yu-Yang Zhang
- Ministry Key Laboratory of Mariculture, Fisheries College, Ocean University of China, Qingdao, 266001, China
| | - Lin-Xuan Ma
- Ministry Key Laboratory of Mariculture, Fisheries College, Ocean University of China, Qingdao, 266001, China
| | - Yun-Wei Dong
- Ministry Key Laboratory of Mariculture, Fisheries College, Ocean University of China, Qingdao, 266001, China.
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20
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Havumaki J, Warren JL, Zelner J, Menzies NA, Calderon R, Contreras C, Lecca L, Becerra MC, Murray M, Cohen T. Spatially-targeted tuberculosis screening has limited impact beyond household contact tracing in Lima, Peru: A model-based analysis. PLoS One 2023; 18:e0293519. [PMID: 37903091 PMCID: PMC10615320 DOI: 10.1371/journal.pone.0293519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 10/15/2023] [Indexed: 11/01/2023] Open
Abstract
Mathematical models have suggested that spatially-targeted screening interventions for tuberculosis may efficiently accelerate disease control, but empirical data supporting these findings are limited. Previous models demonstrating substantial impacts of these interventions have typically simulated large-scale screening efforts and have not attempted to capture the spatial distribution of tuberculosis in households and communities at a high resolution. Here, we calibrate an individual-based model to the locations of case notifications in one district of Lima, Peru. We estimate the incremental efficiency and impact of a spatially-targeted interventions used in combination with household contact tracing (HHCT). Our analysis reveals that HHCT is relatively efficient with a median of 40 (Interquartile Range: 31.7 to 49.9) household contacts required to be screened to detect a single case of active tuberculosis. However, HHCT has limited population impact, producing a median incidence reduction of only 3.7% (Interquartile Range: 5.8% to 1.9%) over 5 years. In comparison, spatially targeted screening (which we modeled as active case finding within high tuberculosis prevalence areas 100 m2 grid cell) is far less efficient, requiring evaluation of ≈12 times the number of individuals as HHCT to find a single individual with active tuberculosis. Furthermore, the addition of the spatially targeted screening effort produced only modest additional reductions in tuberculosis incidence over the 5 year period (≈1.3%) in tuberculosis incidence. In summary, we found that HHCT is an efficient approach for tuberculosis case finding, but has limited population impact. Other screening approaches which target areas of high tuberculosis prevalence are less efficient, and may have limited impact unless very large numbers of individuals can be screened.
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Affiliation(s)
- Joshua Havumaki
- Department of Epidemiology of Microbial Diseases, Yale University, New Haven, CT, United States of America
| | - Joshua L. Warren
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States of America
| | - Jon Zelner
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, United States of America
- Center for Social Epidemiology and Population Health, University of Michigan School of Public Health, Ann Arbor, MI, United States of America
| | - Nicolas A. Menzies
- Department of Global Health and Population, Harvard T. H. Chan, School of Public Health, Boston, MA, United States of America
| | - Roger Calderon
- Socios en Salud Sucursal Peru, Lima, Peru
- Programa Acadêmico de Tuberculose, Faculdade de Medicina, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | | | - Leonid Lecca
- Department of Global Health and Population, Harvard T. H. Chan, School of Public Health, Boston, MA, United States of America
- Socios en Salud Sucursal Peru, Lima, Peru
| | - Mercedes C. Becerra
- Department of Global Health and Population, Harvard T. H. Chan, School of Public Health, Boston, MA, United States of America
| | - Megan Murray
- Department of Global Health and Population, Harvard T. H. Chan, School of Public Health, Boston, MA, United States of America
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases, Yale University, New Haven, CT, United States of America
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21
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Mortezazadeh S, Demeler B. Systematic noise removal from analytical ultracentrifugation data with UltraScan. EUROPEAN BIOPHYSICS JOURNAL : EBJ 2023; 52:203-213. [PMID: 36786920 PMCID: PMC10423743 DOI: 10.1007/s00249-023-01631-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 12/29/2022] [Accepted: 01/23/2023] [Indexed: 02/15/2023]
Abstract
A method for removing time- and radially invariant noise from sedimentation velocity and sedimentation equilibrium experiments performed in an analytical ultracentrifuge is presented. The method averages repeat radial incident light measurements as a function of the photomultiplier response at different wavelengths to remove the majority of the time-invariant noise contributions from intensity data measurements. The results of this method are compared to traditional absorbance data generated with a buffer reference and the Beckman Optima AUC data acquisition program, and with the standard UltraScan refinement workflow. The method avoids the amplification of stochastic noise inherent in the absorbance scan subtraction traditionally employed in sedimentation velocity and equilibrium data. In addition, the collection of intensity data frees up the reference channel for additional samples, doubling the capacity of the instrument. In comparison to absorbance data, the residual mean square deviation of a fitted sedimentation velocity experiment without additional noise correction by UltraScan was improved by a factor of 4.5 when using the new method. This improvement benefits sedimentation equilibrium experiments as well as analytical buoyant density equilibrium experiments where routine time-invariant noise correction calculations cannot be performed.
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Affiliation(s)
- Saeed Mortezazadeh
- Department of Chemistry and Biochemistry, University of Lethbridge, Lethbridge, AB, T1K3M4, Canada
| | - Borries Demeler
- Department of Chemistry and Biochemistry, University of Lethbridge, Lethbridge, AB, T1K3M4, Canada.
- Department of Chemistry and Biochemistry, University of Montana, Missoula, MT, 59812, United States.
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22
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Bazzani A, Giovannozzi M, Montanari CE, Turchetti G. Performance analysis of indicators of chaos for nonlinear dynamical systems. Phys Rev E 2023; 107:064209. [PMID: 37464644 DOI: 10.1103/physreve.107.064209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 06/07/2023] [Indexed: 07/20/2023]
Abstract
The efficient detection of chaotic behavior in orbits of a complex dynamical system is an active domain of research. Several indicators have been proposed, and new ones have recently been developed in view of improving the performance of chaos detection by means of numerical simulations. The challenge is to predict chaotic behavior based on the analysis of orbits of limited length. In this paper the performance analysis of past and recent indicators of chaos, in terms of predictive power, is carried out in detail using the dynamical system characterized by a symplectic Hénon-like cubic polynomial map.
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Affiliation(s)
- A Bazzani
- Dipartimento di Fisica e Astronomia, Università di Bologna, via Irnerio 46, 40126 Bologna, Italy
| | - M Giovannozzi
- Beams Department, CERN, Esplanade des Particules 1, 1211 Geneva 23, Switzerland
| | - C E Montanari
- Dipartimento di Fisica e Astronomia, Università di Bologna, via Irnerio 46, 40126 Bologna, Italy
- Beams Department, CERN, Esplanade des Particules 1, 1211 Geneva 23, Switzerland
| | - G Turchetti
- Dipartimento di Fisica e Astronomia, Università di Bologna, via Irnerio 46, 40126 Bologna, Italy
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23
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Deng H, Zhou S, He Y, Lan Z, Zou Y, Mao X. Efficient Calibration of Groundwater Contaminant Transport Models Using Bayesian Optimization. TOXICS 2023; 11:toxics11050438. [PMID: 37235252 DOI: 10.3390/toxics11050438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 04/21/2023] [Accepted: 05/04/2023] [Indexed: 05/28/2023]
Abstract
Numerical modeling is a significant tool to understand the dynamic characteristics of contaminants transport in groundwater. The automatic calibration of highly parametrized and computationally intensive numerical models for the simulation of contaminant transport in the groundwater flow system is a challenging task. While existing methods use general optimization techniques to achieve automatic calibration, the large numbers of numerical model evaluations required in the calibration process lead to high computing overhead and limit the efficiency of model calibration. This paper presents a Bayesian optimization (BO) method for efficient calibration of numerical models of groundwater contaminant transport. A Bayes model is built to fully represent calibration criteria and derive the objective function for model calibration. The efficiency of model calibration is made possible by the probabilistic surrogate model and the expected improvement acquisition function in BO. The probabilistic surrogate model approximates the computationally expensive objective function with a closed-form expression that can be computed efficiently, while the expected improvement acquisition function proposes the most promising model parameters to improve the fitness to the calibration criteria and reduce the uncertainty of the surrogate model. These schemes allow us to find the optimized model parameters effectively by using a small number of numerical model evaluations. Two case studies for the calibration of the Cr(VI) transport model demonstrate that the BO method is effective and efficient in the inversion of hypothetical model parameters, the minimization of the objective function, and the adaptation of different model calibration criteria. Specifically, this promising performance is achieved within 200 numerical model evaluations, which substantially reduces the computing budget for model calibration.
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Affiliation(s)
- Hao Deng
- School of Geosciences and Info-Physics, Central South University, South Lushan Road, Changsha 410083, China
- Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Ministry of Education, Changsha 410083, China
- Key Laboratory of Non-Ferrous Resources and Geological Hazard Detection, Changsha 410083, China
| | - Shengfang Zhou
- School of Geosciences and Info-Physics, Central South University, South Lushan Road, Changsha 410083, China
| | - Yong He
- School of Geosciences and Info-Physics, Central South University, South Lushan Road, Changsha 410083, China
- Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Ministry of Education, Changsha 410083, China
- Key Laboratory of Non-Ferrous Resources and Geological Hazard Detection, Changsha 410083, China
| | - Zeduo Lan
- School of Geosciences and Info-Physics, Central South University, South Lushan Road, Changsha 410083, China
| | - Yanhong Zou
- School of Geosciences and Info-Physics, Central South University, South Lushan Road, Changsha 410083, China
- Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Ministry of Education, Changsha 410083, China
- Key Laboratory of Non-Ferrous Resources and Geological Hazard Detection, Changsha 410083, China
| | - Xiancheng Mao
- School of Geosciences and Info-Physics, Central South University, South Lushan Road, Changsha 410083, China
- Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Ministry of Education, Changsha 410083, China
- Key Laboratory of Non-Ferrous Resources and Geological Hazard Detection, Changsha 410083, China
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van der Zwaard S, Otter RTA, Kempe M, Knobbe A, Stoter IK. Capturing the Complex Relationship Between Internal and External Training Load: A Data-Driven Approach. Int J Sports Physiol Perform 2023; 18:634-642. [PMID: 37080541 DOI: 10.1123/ijspp.2022-0493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/27/2023] [Accepted: 03/06/2023] [Indexed: 04/22/2023]
Abstract
BACKGROUND Training load is typically described in terms of internal and external load. Investigating the coupling of internal and external training load is relevant to many sports. Here, continuous kernel-density estimation (KDE) may be a valuable tool to capture and visualize this coupling. AIM Using training load data in speed skating, we evaluated how well bivariate KDE plots describe the coupling of internal and external load and differentiate between specific training sessions, compared to training impulse scores or intensity distribution into training zones. METHODS On-ice training sessions of 18 young (sub)elite speed skaters were monitored for velocity and heart rate during 2 consecutive seasons. Training session types were obtained from the coach's training scheme, including endurance, interval, tempo, and sprint sessions. Differences in training load between session types were assessed using Kruskal-Wallis or Kolmogorov-Smirnov tests for training impulse and KDE scores, respectively. RESULTS Training impulse scores were not different between training session types, except for extensive endurance sessions. However, all training session types differed when comparing KDEs for heart rate and velocity (both P < .001). In addition, 2D KDE plots of heart rate and velocity provide detailed insights into the (subtle differences in) coupling of internal and external training load that could not be obtained by 2D plots using training zones. CONCLUSION 2D KDE plots provide a valuable tool to visualize and inform coaches on the (subtle differences in) coupling of internal and external training load for training sessions. This will help coaches design better training schemes aiming at desired training adaptations.
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Affiliation(s)
- Stephan van der Zwaard
- Leiden Institute of Advanced Computer Science, Leiden University, Amsterdam,the Netherlands
- Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam,the Netherlands
| | - Ruby T A Otter
- School of Sports Studies, Hanze University of Applied Sciences, Groningen,the Netherlands
- Department of Biomedical Sciences of Cells & Systems, Section of Anatomy & Medical Physiology, University of Groningen, University Medical Center Groningen, Groningen,the Netherlands
| | - Matthias Kempe
- Department of Human Movement Sciences, University of Groningen, University Medical Center Groningen, Groningen,the Netherlands
| | - Arno Knobbe
- Leiden Institute of Advanced Computer Science, Leiden University, Amsterdam,the Netherlands
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25
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Lindwall G, Gerlee P. Fast and precise inference on diffusivity in interacting particle systems. J Math Biol 2023; 86:64. [PMID: 36991271 PMCID: PMC10060353 DOI: 10.1007/s00285-023-01902-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 03/07/2023] [Accepted: 03/09/2023] [Indexed: 03/31/2023]
Abstract
Particle systems made up of interacting agents is a popular model used in a vast array of applications, not the least in biology where the agents can represent everything from single cells to animals in a herd. Usually, the particles are assumed to undergo some type of random movements, and a popular way to model this is by using Brownian motion. The magnitude of random motion is often quantified using mean squared displacement, which provides a simple estimate of the diffusion coefficient. However, this method often fails when data is sparse or interactions between agents frequent. In order to address this, we derive a conjugate relationship in the diffusion term for large interacting particle systems undergoing isotropic diffusion, giving us an efficient inference method. The method accurately accounts for emerging effects such as anomalous diffusion stemming from mechanical interactions. We apply our method to an agent-based model with a large number of interacting particles, and the results are contrasted with a naive mean square displacement-based approach. We find a significant improvement in performance when using the higher-order method over the naive approach. This method can be applied to any system where agents undergo Brownian motion and will lead to improved estimates of diffusion coefficients compared to existing methods.
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Dynamic evaluation of low-carbon development in China's power industry and the impact of carbon market policies. Heliyon 2023; 9:e13467. [PMID: 36814617 PMCID: PMC9939597 DOI: 10.1016/j.heliyon.2023.e13467] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 01/29/2023] [Accepted: 02/01/2023] [Indexed: 02/05/2023] Open
Abstract
At the 75th session of the United Nations General Assembly, China clearly put forward the goals of "carbon peak" in 2030 and "carbon neutrality" in 2060. Achievement of carbon targets. Therefore, the goal of this paper is to analyze the low-carbon development level of China's power industry and study the impact of carbon market policies on the low-carbon development level of the power industry. Based on this, this paper first constructs the low-carbon development evaluation index system of the power industry around the connotation of low-carbon development in the power industry and uses the global principal component analysis model to measure the low-carbon development level of China's power industry. Then, the dynamic change trend and spatial distribution characteristics of the low-carbon development level of China's power industry are analyzed using kernel density estimation and the K-means clustering method. Finally, propensity score matching and difference-in-difference methods are used to analyze the impact of carbon market policies on the low-carbon development level of China's power industry. The results show that, first, the low-carbon development level of China's power industry generally shows an upward trend and a polarized development trend. Second, the low-carbon development level of China's power industry has regional effects and gradient effects. The low-carbon development level of the power industry from high to low is the eastern region, central region and western region. Third, carbon market policies can help improve the low-carbon development level of China's power industry. The research results provide some reference and guidance for the evaluation of the low-carbon development level of China's power industry and the improvement of carbon market policies.
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Zhang Z, Wan Z, Xu S, Wu H, Liu L, Chen Z, Zeng J. Environmental Adaptation in the Process of Human-Land Relationship in Southeast China's Ethnic Minority Areas and Its Significance on Sustainable Development. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:2737. [PMID: 36768101 PMCID: PMC9915183 DOI: 10.3390/ijerph20032737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/29/2023] [Accepted: 02/01/2023] [Indexed: 06/18/2023]
Abstract
The relationship between regional human development and geographic environment is the basis for dynamic social change, and studying the evolution of human-land relations in typical regions can provide background knowledge for global change studies. This study is based on GIS and spatio-temporal statistical techniques, combined with the analysis of toponymic cultural landscapes, to study ethnic minority regions of southeastern China. The results show that: (1) The geographical environment of the region will affect the naming of villages, and the orientation and family name are the most common; the frequency of plants, pit (keng), animals, and flat (ping) is also very high. (2) Han settlements and She settlements have obvious spatial differentiation, and in general the Han distribution area is lower than that of the She. Han settlements are mainly distributed in plain areas along rivers with elevations less than 200 m; She settlements are mainly distributed in hilly areas (200~500 m) and low mountain areas (500~800 m). (3) The results of quadrat analysis and nearest neighbor index analysis show that both Han and She settlements are clustered in the spatial distribution pattern, and the distribution of She settlements is more clustered than that of Han, with more dense settlements at a certain spatial scale. The regional cultural landscape is the result of the development and evolution of human-land relationship, and the comprehensive analysis of cultural landscape can understand the process of human-land relationship in a small region. The settlements in the region are indicative of the geographic environment in terms of village naming, spatial pattern, elevation differentiation and relationship with rivers, which can reflect the environmental adaptation process of human activities.
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Affiliation(s)
- Zhi Zhang
- School of Ecology and Environment, Yuzhang Normal University, Nanchang 330103, China
| | - Zhiwei Wan
- School of Geography and Environmental Engineering, Gannan Normal University, Ganzhou 341000, China
| | - Shan Xu
- School of Geography and Environmental Engineering, Gannan Normal University, Ganzhou 341000, China
| | - Hong Wu
- Development Research Center of the State Council, Beijing 100010, China
| | - Lingyue Liu
- School of Geography and Environmental Engineering, Gannan Normal University, Ganzhou 341000, China
| | - Zhao Chen
- National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Key Laboratory of Integrated Agro-Environmental Pollution Control and Management, Institute of Eco-Environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou 510650, China
| | - Ji Zeng
- School of Geography and Environmental Engineering, Gannan Normal University, Ganzhou 341000, China
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Qin J, Gao F, Wang Z, Wong DC, Zhao Z, Relton SD, Fang H. A novel temporal generative adversarial network for electrocardiography anomaly detection. Artif Intell Med 2023; 136:102489. [PMID: 36710067 DOI: 10.1016/j.artmed.2023.102489] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 11/28/2022] [Accepted: 01/09/2023] [Indexed: 01/15/2023]
Abstract
Cardiac abnormality detection from Electrocardiogram (ECG) signals is a common task for cardiologists. To facilitate efficient and objective detection, automated ECG classification by using deep learning based methods have been developed in recent years. Despite their impressive performance, these methods perform poorly when presented with cardiac abnormalities that are not well represented, or absent, in the training data. To this end, we propose a novel one-class classification based ECG anomaly detection generative adversarial network (GAN). Specifically, we embedded a Bi-directional Long-Short Term Memory (Bi-LSTM) layer into a GAN architecture and used a mini-batch discrimination training strategy in the discriminator to synthesis ECG signals. Our method generates samples to match the data distribution from normal signals of healthy group so that a generalised anomaly detector can be built reliably. The experimental results demonstrate our method outperforms several state-of-the-art semi-supervised learning based ECG anomaly detection algorithms and robustly detects the unknown anomaly class in the MIT-BIH arrhythmia database. Experiments show that our method achieves the accuracy of 95.5% and AUC of 95.9% which outperforms the most competitive baseline by 0.7% and 1.7% respectively. Our method may prove to be a helpful diagnostic method for helping cardiologists identify arrhythmias.
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Affiliation(s)
- Jing Qin
- College of Software Engineering, Dalian University, Dalian, China.
| | - Fujie Gao
- College of Information Engineering, Dalian University, Dalian, China.
| | - Zumin Wang
- College of Information Engineering, Dalian University, Dalian, China.
| | - David C Wong
- Department of Computer Science and Centre for Health Informatics, University of Manchester, Manchester, UK.
| | - Zhibin Zhao
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.
| | - Samuel D Relton
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK.
| | - Hui Fang
- Department of Computer Science, Loughborough University, Loughborough, UK.
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29
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Wei Z, Chen YC. Skeleton Clustering: Dimension-Free Density-Aided Clustering. J Am Stat Assoc 2023. [DOI: 10.1080/01621459.2023.2174122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Affiliation(s)
- Zeyu Wei
- Department of Statistics, University of Washington
| | - Yen-Chi Chen
- Department of Statistics, University of Washington
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30
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Wang W, Wu Z, Dietschreit JCB, Gómez-Bombarelli R. Learning pair potentials using differentiable simulations. J Chem Phys 2023; 158:044113. [PMID: 36725529 DOI: 10.1063/5.0126475] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Learning pair interactions from experimental or simulation data is of great interest for molecular simulations. We propose a general stochastic method for learning pair interactions from data using differentiable simulations (DiffSim). DiffSim defines a loss function based on structural observables, such as the radial distribution function, through molecular dynamics (MD) simulations. The interaction potentials are then learned directly by stochastic gradient descent, using backpropagation to calculate the gradient of the structural loss metric with respect to the interaction potential through the MD simulation. This gradient-based method is flexible and can be configured to simulate and optimize multiple systems simultaneously. For example, it is possible to simultaneously learn potentials for different temperatures or for different compositions. We demonstrate the approach by recovering simple pair potentials, such as Lennard-Jones systems, from radial distribution functions. We find that DiffSim can be used to probe a wider functional space of pair potentials compared with traditional methods like iterative Boltzmann inversion. We show that our methods can be used to simultaneously fit potentials for simulations at different compositions and temperatures to improve the transferability of the learned potentials.
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Affiliation(s)
- Wujie Wang
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, USA
| | - Zhenghao Wu
- Eduard-Zintl-Institut für Anorganische und Physikalische Chemie, Technische Universität Darmstadt, Alarich-Weiss-Str. 8, 64287 Darmstadt, Germany
| | - Johannes C B Dietschreit
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, USA
| | - Rafael Gómez-Bombarelli
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, USA
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31
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Jankowska MM, Yang JA, Luo N, Spoon C, Benmarhnia T. Accounting for space, time, and behavior using GPS derived dynamic measures of environmental exposure. Health Place 2023; 79:102706. [PMID: 34801405 PMCID: PMC9129269 DOI: 10.1016/j.healthplace.2021.102706] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 10/21/2021] [Accepted: 10/25/2021] [Indexed: 10/19/2022]
Abstract
Time-weighted spatial averaging approaches (TWSA) are an increasingly utilized method for calculating exposure using global positioning system (GPS) mobility data for health-related research. They can provide a time-weighted measure of exposure, or dose, to various environments or health hazards. However, little work has been done to compare existing methodologies, nor to assess how sensitive these methods are to mobility data inputs (e.g., walking vs driving), the type of environmental data being assessed as the exposure (e.g., continuous surfaces vs points of interest), and underlying point-pattern clustering of participants (e.g., if a person is highly mobile vs predominantly stationary). Here we contrast three TWSA approaches that have been previously used or recently introduced in the literature: Kernel Density Estimation (KDE), Density Ranking (DR), and Point Overlay (PO). We feed GPS and accelerometer data from 602 participants through each method to derive time-weighted activity spaces, comparing four mobility behaviors: all movement, stationary time, walking time, and in-vehicle time. We then calculate exposure values derived from the various TWSA activity spaces with four environmental layer data types (point, line, area, surface). Similarities and differences across TWSA derived exposures for the sample and between individuals are explored, and we discuss interpretation of TWSA outputs providing recommendations for researchers seeking to apply these methods to health-related studies.
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Affiliation(s)
| | - Jiue-An Yang
- Population Sciences, Beckman Research Institute, City of Hope, USA
| | - Nana Luo
- Scripps Institute of Oceanography, University of California San Diego, USA
| | - Chad Spoon
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, USA
| | - Tarik Benmarhnia
- Scripps Institute of Oceanography, University of California San Diego, USA
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32
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Replacing Histogram with Smooth Empirical Probability Density Function Estimated by K-Moments. SCI 2022. [DOI: 10.3390/sci4040050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Whilst several methods exist to provide sample estimates of the probability distribution function at several points, for the probability density of continuous stochastic variables, only a gross representation through the histogram is typically used. It is shown that the newly introduced concept of knowable moments (K-moments) can provide smooth empirical representations of the distribution function, which in turn can yield point and interval estimates of the density function at a large number of points or even at any arbitrary point within the range of the available observations. The proposed framework is simple to apply and is illustrated with several applications for a variety of distribution functions.
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33
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Saeed S, Chouinard L, Sajid S. Robust Synchronization of Ambient Vibration Time Histories Based on Phase Angle Compensations and Kernel Density Function. SENSORS (BASEL, SWITZERLAND) 2022; 22:8835. [PMID: 36433438 PMCID: PMC9698187 DOI: 10.3390/s22228835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/03/2022] [Accepted: 11/12/2022] [Indexed: 06/16/2023]
Abstract
The output-only modal analysis is ubiquitously used for structural health monitoring of civil engineering systems. The measurements for such applications require the use of multiple data acquisition systems (DAS) to avoid complicated meshes of cables in high-rise buildings, avoid traffic constriction on a bridge during measurements, or to avoid having limited channels in a single DAS. Nevertheless, such requirements introduce time synchronization problems which potentially lead to erroneous structural dynamic characterization and hence misleading or inconclusive structural health monitoring results. This research aims at proposing a system-identification-based time synchronization algorithm for output-only modal analysis using multiple DAS. A new procedure based on the compensation of the phase angle shifts is proposed to identify and address the time synchronization issue in ambient vibration data measured through multiple DAS. To increase the robustness of the proposed algorithm to the inherent inconsistencies in these datasets, the kernel density function is applied to rank multiple time-shift estimates that are sometimes detected by the algorithm when inaccuracies exist in the data arising from low signal-to-noise ratio and/or presence of colored noise in the ambient excitations. First, the synchronized ambient vibration dataset of a full-scale bridge is artificially de-synchronized and used to present a proof of concept for the proposed algorithm. Next, the algorithm is applied to ambient vibration data of a 30-story, reinforced concrete building, where the synchronization of the data could not be achieved using two DAS despite best efforts. The application of the proposed time synchronization algorithm is shown to both detect and correct the time synchronization discrepancies in the output-only modal analysis.
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Affiliation(s)
- Salman Saeed
- National Institute of Urban Infrastructure Planning, University of Engineering & Technology, Peshawar 25120, Pakistan
| | - Luc Chouinard
- Department of Civil Engineering, McGill University, Montreal, QC H3A 0C3, Canada
| | - Sikandar Sajid
- Department of Civil Engineering, McGill University, Montreal, QC H3A 0C3, Canada
- Department of Civil Engineering, University of Engineering & Technology, Peshawar 25120, Pakistan
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Lee J, Kim T, Park J, Park J. Smartphone Sensor-Based Human Motion Characterization with Neural Stochastic Differential Equations and Transformer Model. SENSORS (BASEL, SWITZERLAND) 2022; 22:7480. [PMID: 36236580 PMCID: PMC9572335 DOI: 10.3390/s22197480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 09/27/2022] [Accepted: 09/28/2022] [Indexed: 06/16/2023]
Abstract
With many conveniences afforded by advances in smartphone technology, developing advanced data analysis methods for health-related information from smartphone users has become a fast-growing research topic in the healthcare field. Along these lines, this paper addresses smartphone sensor-based characterization of human motions with neural stochastic differential equations (NSDEs) and a Transformer model. NSDEs and modeling via Transformer networks are two of the most prominent deep learning-based modeling approaches, with significant performance yields in many applications. For the problem of modeling dynamical features, stochastic differential equations and deep neural networks are frequently used paradigms in science and engineering, respectively. Combining these two paradigms in one unified framework has drawn significant interest in the deep learning community, and NSDEs are among the leading technologies for combining these efforts. The use of attention has also become a widely adopted strategy in many deep learning applications, and a Transformer is a deep learning model that uses the mechanism of self-attention. This concept of a self-attention based Transformer was originally introduced for tasks of natural language processing (NLP), and due to its excellent performance and versatility, the scope of its applications is rapidly expanding. By utilizing the techniques of neural stochastic differential equations and a Transformer model along with data obtained from smartphone sensors, we present a deep learning method capable of efficiently characterizing human motions. For characterizing human motions, we encode the high-dimensional sequential data from smartphone sensors into latent variables in a low-dimensional latent space. The concept of the latent variable is particularly useful because it can not only carry condensed information concerning motion data, but also learn their low-dimensional representations. More precisely, we use neural stochastic differential equations for modeling transitions of human motion in a latent space, and rely on a Generative Pre-trained Transformer 2 (GPT2)-based Transformer model for approximating the intractable posterior of conditional latent variables. Our experiments show that the proposed method can yield promising results for the problem of characterizing human motion patterns and some related tasks including user identification.
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35
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Thiruthummal AA, Kim EJ. Monte Carlo Simulation of Stochastic Differential Equation to Study Information Geometry. ENTROPY (BASEL, SWITZERLAND) 2022; 24:e24081113. [PMID: 36010777 PMCID: PMC9407417 DOI: 10.3390/e24081113] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/03/2022] [Accepted: 08/09/2022] [Indexed: 05/28/2023]
Abstract
Information Geometry is a useful tool to study and compare the solutions of a Stochastic Differential Equations (SDEs) for non-equilibrium systems. As an alternative method to solving the Fokker-Planck equation, we propose a new method to calculate time-dependent probability density functions (PDFs) and to study Information Geometry using Monte Carlo (MC) simulation of SDEs. Specifically, we develop a new MC SDE method to overcome the challenges in calculating a time-dependent PDF and information geometric diagnostics and to speed up simulations by utilizing GPU computing. Using MC SDE simulations, we reproduce Information Geometric scaling relations found from the Fokker-Planck method for the case of a stochastic process with linear and cubic damping terms. We showcase the advantage of MC SDE simulation over FPE solvers by calculating unequal time joint PDFs. For the linear process with a linear damping force, joint PDF is found to be a Gaussian. In contrast, for the cubic process with a cubic damping force, joint PDF exhibits a bimodal structure, even in a stationary state. This suggests a finite memory time induced by a nonlinear force. Furthermore, several power-law scalings in the characteristics of bimodal PDFs are identified and investigated.
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Wang J, Du G, Liu M. Spatiotemporal characteristics and influencing factors of carbon emissions from civil buildings: Evidence from urban China. PLoS One 2022; 17:e0272295. [PMID: 35925953 PMCID: PMC9352032 DOI: 10.1371/journal.pone.0272295] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 07/15/2022] [Indexed: 11/18/2022] Open
Abstract
Carbon emissions from civil buildings refer to the carbon emissions generated during the operation of civil buildings. With the continuous development of the urban economy and the improvement of people’s living standards, this part of carbon emission puts tremendous pressure on China to achieve the goal of carbon peaking and carbon neutral. In the context of rapid urbanization, studying the spatiotemporal characteristics and influencing factors of the carbon emissions from civil buildings have strong practical significance for China to achieve the "dual carbon" goal. Based on the emission data from 104 prefecture-level cities in China, we examine the spatiotemporal characteristics of the civil building carbon emissions from the perspectives of temporal evolution trend, spatial distribution and its dynamic evolution, spatial difference and its decomposition, and spatial autocorrelation characteristics. Finally, we reveal the influencing factors of the carbon emissions from civil buildings using static panel data models and spatial dynamic panel data models. The results of the study show that: (1) During the sample period, the carbon emissions from civil buildings have increased year by year. The civil building carbon emissions have become an important source of China’s overall carbon emissions. Realizing energy saving and emission reduction in the operational stage of civil buildings is crucial to realizing China’s "dual carbon" goal. (2) According to the estimated results, there is a significant inverted U-shaped non-linear relationship between urbanization and civil building carbon emissions. Most Chinese cities are located in the upward part of the inverted U-shaped curve at present. Thus, the traditional economic growth model characterized by high energy consumption and high emission during rapid urbanization should be abandoned to reduce the carbon emissions from civil buildings. (3) Technological progress and fixed asset investment can effectively reduce the carbon emissions from civil buildings. At the same time, the level of marketization and social consumption expenditure positively affect the carbon emissions from civil buildings. It is necessary to improve the relevant market mechanisms, policy subsidies, and other means to encourage the application of green energy-saving technologies in civil buildings. Also, it is needed to guide the urban residents’ consumption structure and lifestyle in a low-carbon direction, to reduce the energy consumption and carbon emissions during the operation of civil buildings.
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Affiliation(s)
- Jing Wang
- Institute of Finance and Economics Research, Shanghai University of Finance and Economics, Shanghai, China
- * E-mail:
| | - Guangjie Du
- Institute of Finance and Economics Research, Shanghai University of Finance and Economics, Shanghai, China
| | - Mohan Liu
- Institute of Finance and Economics Research, Shanghai University of Finance and Economics, Shanghai, China
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Mohammadi A, Bergquist R, Fathi G, Pishgar E, de Melo SN, Sharifi A, Kiani B. Homicide rates are spatially associated with built environment and socio-economic factors: a study in the neighbourhoods of Toronto, Canada. BMC Public Health 2022; 22:1482. [PMID: 35927698 PMCID: PMC9351166 DOI: 10.1186/s12889-022-13807-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 07/14/2022] [Indexed: 11/26/2022] Open
Abstract
Objectives Homicide rate is associated with a large variety of factors and therefore unevenly distributed over time and space. This study aims to explore homicide patterns and their spatial associations with different socioeconomic and built-environment conditions in 140 neighbourhoods of the city of Toronto, Canada. Methods A homicide dataset covering the years 2012 to 2021 and neighbourhood-based indicators were analysed using spatial techniques such as Kernel Density Estimation, Global/Local Moran’s I and Kulldorff’s SatScan spatio-temporal methodology. Geographically weighted regression (GWR) and multi-scale GWR (MGWR) were used to analyse the spatially varying correlations between the homicide rate and independent variables. The latter was particularly suitable for manifested spatial variations between explanatory variables and the homicide rate and it also identified spatial non-stationarities in this connection. Results The adjusted R2 of the MGWR was 0.53, representing a 4.35 and 3.74% increase from that in the linear regression and GWR models, respectively. Spatial and spatio-temporal high-risk areas were found to be significantly clustered in downtown and the north-western parts of the city. Some variables (e.g., the population density, material deprivation, the density of commercial establishments and the density of large buildings) were significantly associated with the homicide rate in different spatial ways. Conclusion The findings of this study showed that homicide rates were clustered over time and space in certain areas of the city. Socioeconomic and the built environment characteristics of some neighbourhoods were found to be associated with high homicide rates but these factors were different for each neighbourhood. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-022-13807-4.
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Affiliation(s)
- Alireza Mohammadi
- Department of Geography and Urban Planning, Faculty of Social Sciences, University of MohagheghArdabili, Ardabil, Iran.
| | - Robert Bergquist
- Ingerod, Brastad, SE-454 94 Sweden. Formerly UNICEF/UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases (TDR), World Health Organization, Geneva, Switzerland
| | - Ghasem Fathi
- Department of Geography and Urban Planning, Faculty of Social Sciences, University of MohagheghArdabili, Ardabil, Iran
| | - Elahe Pishgar
- Department of Human Geography, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
| | - Silas Nogueira de Melo
- Department of Geography, State University of Maranhão, CidadeUniversitária Paulo VI, São Luís, 65055-000, Brazil
| | - Ayyoob Sharifi
- Graduate School of Humanities and Social Sciences, and Network for Education and Research on Peace and Sustainability, Hiroshima University, Higashi-Hiroshima, 739-8530, Japan
| | - Behzad Kiani
- Centre de Recherche en Santé Publique, Université de Montréal, 7101, Avenue du Parc, Montréal, Canada.
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Pensky M. Discussion of “Confidence Intervals for Nonparametric Empirical Bayes Analysis”. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2022.2096039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- Marianna Pensky
- Department of Mathematics, University of Central Florida, Orlando, FL
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Liu W, He MZ, Dambach P, Schwertz R, Chen S, Yu F, Marx M. Trends of overweight and obesity among preschool children from 2013 to 2018: a cross-sectional study in Rhine-Neckar County and the City of Heidelberg, Germany. BMC Public Health 2022; 22:941. [PMID: 35538526 PMCID: PMC9092815 DOI: 10.1186/s12889-022-13302-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 04/20/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Early childhood overweight and obesity is a growing public health concern worldwide. Few recent studies have addressed how time trends varied by sociodemographic characteristics at the regional level using large and high-quality data. This study determines how time trends vary in the prevalence of early childhood overweight and obesity by age, gender, and migration background at the regional level. METHODS We used a Kernel-density curve to describe the BMI distribution, and evaluated the trends of overweight and obesity by age, gender, and migration background using logistic regression. RESULTS Mean BMI and the overall prevalence of overweight and obesity increased among preschool children aged 4-6 years in the Rhine-Neckar County and the City of Heidelberg. After adjusting for age, sex, and migration background, trends of overweight significantly increased only among male children in the age 5 year group with migration background (P < 0.05), and an upward trend of obesity was observed only among male children in the age 5 year group and female children in the age 6 year group with migration background (P < 0.05). CONCLUSIONS BMI distribution as well as general prevalence of overweight and obesity are still increasing among preschool children. Children with migration backgrounds, particularly male children in the age 5 year groups and female children in the age 6 year group should be prioritized. Health promotion strategies for children with migration backgrounds will help address this challenge.
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Affiliation(s)
- Weina Liu
- grid.7700.00000 0001 2190 4373Faculty of Medicine and University Hospital, Heidelberg Institute of Global Health (HIGH), Heidelberg University, Im Neuenheimer Feld 130, Heidelberg, Germany ,Jiangsu Province Center for Disease Control and Prevention, Nanjing, China
| | - Mike Z. He
- grid.59734.3c0000 0001 0670 2351Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Peter Dambach
- grid.7700.00000 0001 2190 4373Faculty of Medicine and University Hospital, Heidelberg Institute of Global Health (HIGH), Heidelberg University, Im Neuenheimer Feld 130, Heidelberg, Germany
| | - Rainer Schwertz
- Public Health Authority of Rhein-Neckar-Kreis, Heidelberg, Germany
| | - Simiao Chen
- grid.7700.00000 0001 2190 4373Faculty of Medicine and University Hospital, Heidelberg Institute of Global Health (HIGH), Heidelberg University, Im Neuenheimer Feld 130, Heidelberg, Germany ,grid.506261.60000 0001 0706 7839Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Fengyun Yu
- grid.7700.00000 0001 2190 4373Faculty of Medicine and University Hospital, Heidelberg Institute of Global Health (HIGH), Heidelberg University, Im Neuenheimer Feld 130, Heidelberg, Germany ,grid.506261.60000 0001 0706 7839Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Michael Marx
- grid.7700.00000 0001 2190 4373Faculty of Medicine and University Hospital, Heidelberg Institute of Global Health (HIGH), Heidelberg University, Im Neuenheimer Feld 130, Heidelberg, Germany
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Llerena Caña JP, García Herrero J, Molina López JM. Error Reduction in Vision-Based Multirotor Landing System. SENSORS 2022; 22:s22103625. [PMID: 35632053 PMCID: PMC9285663 DOI: 10.3390/s22103625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 04/27/2022] [Accepted: 05/06/2022] [Indexed: 02/04/2023]
Abstract
New applications are continuously appearing with drones as protagonists, but all of them share an essential critical maneuver—landing. New application requirements have led the study of novel landing strategies, in which vision systems have played and continue to play a key role. Generally, the new applications use the control and navigation systems embedded in the aircraft. However, the internal dynamics of these systems, initially focused on other tasks such as the smoothing trajectories between different waypoints, can trigger undesired behaviors. In this paper, we propose a landing system based on monocular vision and navigation information to estimate the helipad global position. In addition, the global estimation system includes a position error correction module by cylinder space transformation and a filtering system with a sliding window. To conclude, the landing system is evaluated with three quality metrics, showing how the proposed correction system together with stationary filtering improves the raw landing system.
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Assessing the Uncertainties of Simulation Approaches for Solar Thermal Systems Coupled to Industrial Processes. ENERGIES 2022. [DOI: 10.3390/en15093333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Industrial energy accounts for a large percentage of global consumption and, thus, it is a target for decarbonization by renewable and in particular solar energy adoption. Low uncertainty simulation tools can reduce the financial risk of solar projects, fostering the transition to a sustainable energy system. Several simulation tools are readily available to developers; differences exist in the format of input data and complexity of physical and numerical models. These tools can provide a variety of results from technical to financial and sensitivity analysis, often producing significant differences in yield assessment and uncertainty levels. IEA SHC Task 64/SolarPACES Task IV—Subtask C aims to address the lack of standard simulation tools for Solar Heating of Industrial Processes (SHIP) plants. This article describes the collaborative work developed by the researchers participating in the task. The identification and classification of several currently available simulation tools are performed on the basis of their capabilities and simulation approaches. A case study of solar heat supply to a copper mining operation is defined, allowing a comparison of the results produced by equivalent simulation tools. The proposed methodology identifies the main sources of differences among the simulation tools, the assessment of the deviation considering a series of statistical metrics for different time scales, and identifies their limitations and bias. The effects of physical characteristics of SHIP plants and different simulation approaches are discussed and quantified. The obtained results allow us to develop a basic guideline for a standardized yield assessment procedure with known uncertainties. Creating this common framework could partially reduce the risk perceived by the finance industry regarding SHIP systems.
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Spatio-Temporal Evolution and Future Simulation of Agricultural Land Use in Xiangxi, Central China. LAND 2022. [DOI: 10.3390/land11040587] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Researches on agricultural land use would help the stakeholders to make better decisions about agricultural resources. However, studies on agricultural land have been lacking. In this context, Xiangxi was chosen as a typical region, and five indicators (Kernel Density, change importance, etc.) and two models (gray forecasting model and GeoSoS-FLUS) were used, to explore the spatio-temporal evolution trends and simulate the future scenarios of agricultural land use. The results were as follows: (1) Xiangxi was dominated by agricultural land, and nearly 50% of total extent was forestry land. Extent of agricultural land decreased by about 56.89 km2 or 3.74% from 2000 to 2018; (2) The density of each agricultural land in the study area had considerable spatial heterogeneity, and showed a main trend of shrinkage, especially in the south regions; (3) In 2030, the spatial pattern and composition of agricultural land in Xiangxi will maintain the existing status, while both of the area and proportion of agricultural land will decline, with a loss of 241.34 km2 or 2.85% decrease from 2000. Nevertheless, the study believed that the slight shrinkage of the agricultural land in Xiangxi is in line with the objective law. At the same time, the study suggested to strengthen the scientific management and rational utilization of agricultural land, with emphasis on arable land and fishery land in the south, especially the administrative center.
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Silva I, Fleming CH, Noonan MJ, Alston J, Folta C, Fagan WF, Calabrese JM. Autocorrelation‐informed home range estimation: A review and practical guide. Methods Ecol Evol 2021. [DOI: 10.1111/2041-210x.13786] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Inês Silva
- Center for Advanced Systems Understanding (CASUS) Görlitz Germany
- Helmholtz‐Zentrum Dresden‐Rossendorf (HZDR) Dresden Germany
| | - Christen H. Fleming
- Department of Biology University of Maryland College Park MD USA
- Smithsonian's National Zoo and Conservation Biology Institute Front Royal VA USA
| | - Michael J. Noonan
- Department of Biology University of British Columbia Okanagan Kelowna BC Canada
| | - Jesse Alston
- Center for Advanced Systems Understanding (CASUS) Görlitz Germany
- Helmholtz‐Zentrum Dresden‐Rossendorf (HZDR) Dresden Germany
| | - Cody Folta
- Department of Biology University of Maryland College Park MD USA
| | - William F. Fagan
- Department of Biology University of Maryland College Park MD USA
| | - Justin M. Calabrese
- Center for Advanced Systems Understanding (CASUS) Görlitz Germany
- Helmholtz‐Zentrum Dresden‐Rossendorf (HZDR) Dresden Germany
- Department of Biology University of Maryland College Park MD USA
- Helmholtz Centre for Environmental Research—UFZ Leipzig Germany
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He YL, Ye X, Huang DF, Huang JZ, Zhai JH. Novel kernel density estimator based on ensemble unbiased cross-validation. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.09.045] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Li X, Giles DK, Andaloro JT, Long R, Lang EB, Watson LJ, Qandah I. Comparison of UAV and fixed-wing aerial application for alfalfa insect pest control: evaluating efficacy, residues, and spray quality. PEST MANAGEMENT SCIENCE 2021; 77:4980-4992. [PMID: 34216079 DOI: 10.1002/ps.6540] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 07/03/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Integrating unmanned aerial vehicles (UAV) as a new method of pesticide application into existing commercial crop protection systems requires extensive research and comparison to conventional, proven application technology. Pest control expressed as efficacy against target pests, and spray quality expressed as coverage and chemical residue are three key criteria. We investigated and compared these quantitative parameters between a multi-rotor UAV and conventional piloted airplanes in two commercial alfalfa production systems. RESULTS Effective and equivalent control of leaf-feeding insect pests was achieved by both methods of aerial application when delivering chlorantraniliprole at the same labeled use rate in different spray volumes (46.8 and 93.5 L ha-1 ) on commercially grown alfalfa in California. Residue levels and spray coverage were also comparable and consistent between the UAV and airplane applications across three sampling techniques, specifically residue levels on alfalfa, insecticide recovery from filter paper, and spray coverage on water sensitive cards. Differences in droplet size and deposit characteristics were more variable for the UAV than airplanes based on analysis of deposition images. CONCLUSION The results of this study provide confidence supporting the use of small-scale multi-rotor UAVs for pesticide application on agricultural crops. According to the parameters tested, UAV application quality and crop protection performance were comparable to that of the conventional fixed wing airplane application. However, the droplet spectrum and the short-term fate of droplets from unmanned aerial spray system require further optimization for effective and efficient crop protection with minimal risk to the environment.
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Affiliation(s)
- Xuan Li
- Stine Research Center, FMC Corporation, Newark, USA
| | - D Ken Giles
- Department of Biological and Agricultural Engineering, University of California at Davis, Davis, CA, USA
| | | | - Rachael Long
- University of California Cooperative Extension, Woodland, CA, USA
| | | | | | - Issa Qandah
- Technical Service Center, FMC Corporation, Clovis, CA, USA
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Glielmo A, Husic BE, Rodriguez A, Clementi C, Noé F, Laio A. Unsupervised Learning Methods for Molecular Simulation Data. Chem Rev 2021; 121:9722-9758. [PMID: 33945269 PMCID: PMC8391792 DOI: 10.1021/acs.chemrev.0c01195] [Citation(s) in RCA: 116] [Impact Index Per Article: 38.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Indexed: 12/21/2022]
Abstract
Unsupervised learning is becoming an essential tool to analyze the increasingly large amounts of data produced by atomistic and molecular simulations, in material science, solid state physics, biophysics, and biochemistry. In this Review, we provide a comprehensive overview of the methods of unsupervised learning that have been most commonly used to investigate simulation data and indicate likely directions for further developments in the field. In particular, we discuss feature representation of molecular systems and present state-of-the-art algorithms of dimensionality reduction, density estimation, and clustering, and kinetic models. We divide our discussion into self-contained sections, each discussing a specific method. In each section, we briefly touch upon the mathematical and algorithmic foundations of the method, highlight its strengths and limitations, and describe the specific ways in which it has been used-or can be used-to analyze molecular simulation data.
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Affiliation(s)
- Aldo Glielmo
- International
School for Advanced Studies (SISSA) 34014 Trieste, Italy
| | - Brooke E. Husic
- Freie
Universität Berlin, Department of Mathematics
and Computer Science, 14195 Berlin, Germany
| | - Alex Rodriguez
- International Centre for Theoretical
Physics (ICTP), Condensed Matter and Statistical
Physics Section, 34100 Trieste, Italy
| | - Cecilia Clementi
- Freie
Universität Berlin, Department for
Physics, 14195 Berlin, Germany
- Rice
University Houston, Department of Chemistry, Houston, Texas 77005, United States
| | - Frank Noé
- Freie
Universität Berlin, Department of Mathematics
and Computer Science, 14195 Berlin, Germany
- Freie
Universität Berlin, Department for
Physics, 14195 Berlin, Germany
- Rice
University Houston, Department of Chemistry, Houston, Texas 77005, United States
| | - Alessandro Laio
- International
School for Advanced Studies (SISSA) 34014 Trieste, Italy
- International Centre for Theoretical
Physics (ICTP), Condensed Matter and Statistical
Physics Section, 34100 Trieste, Italy
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Abstract
AbstractIn many real world settings,
imbalanced data impedes model performance of learning algorithms, like neural networks, mostly for rare cases. This is especially problematic for tasks focusing on these rare occurrences. For example, when estimating precipitation, extreme rainfall events are scarce but important considering their potential consequences. While there are numerous well studied solutions for classification settings, most of them cannot be applied to regression easily. Of the few solutions for regression tasks, barely any have explored cost-sensitive learning which is known to have advantages compared to sampling-based methods in classification tasks. In this work, we propose a sample weighting approach for imbalanced regression datasets called DenseWeight and a cost-sensitive learning approach for neural network regression with imbalanced data called DenseLoss based on our weighting scheme. DenseWeight weights data points according to their target value rarities through kernel density estimation (KDE). DenseLoss adjusts each data point’s influence on the loss according to DenseWeight, giving rare data points more influence on model
training compared to common data points. We show on multiple differently distributed datasets that DenseLoss significantly improves model performance for rare data points through its density-based weighting scheme. Additionally, we compare DenseLoss to the state-of-the-art method SMOGN, finding that our method mostly yields better performance. Our approach provides more control over model training as it enables us to actively decide on the trade-off between focusing on common or rare cases through a single hyperparameter, allowing the training of better models for rare data points.
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Dobruch-Sobczak KS, Piotrzkowska-Wróblewska H, Karwat P, Klimonda Z, Markiewicz-Grodzicka E, Litniewski J. Quantitative Assessment of the Echogenicity of a Breast Tumor Predicts the Response to Neoadjuvant Chemotherapy. Cancers (Basel) 2021; 13:3546. [PMID: 34298759 PMCID: PMC8307405 DOI: 10.3390/cancers13143546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 06/25/2021] [Accepted: 07/12/2021] [Indexed: 11/16/2022] Open
Abstract
The aim of the study was to improve monitoring the treatment response in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). The IRB approved this prospective study. Ultrasound examinations were performed prior to treatment and 7 days after four consecutive NAC cycles. Residual malignant cell (RMC) measurement at surgery was the standard of reference. Alteration in B-mode ultrasound (tumor echogenicity and volume) and the Kullback-Leibler divergence (kld), as a quantitative measure of amplitude difference, were used. Correlations of these parameters with RMC were assessed and Receiver Operating Characteristic curve (ROC) analysis was performed. Thirty-nine patients (mean age 57 y.) with 50 tumors were included. There was a significant correlation between RMC and changes in quantitative parameters (KLD) after the second, third and fourth course of NAC, and alteration in echogenicity after the third and fourth course. Multivariate analysis of the echogenicity and KLD after the third NAC course revealed a sensitivity of 91%, specificity of 92%, PPV = 77%, NPV = 97%, accuracy = 91%, and AUC of 0.92 for non-responding tumors (RMC ≥ 70%). In conclusion, monitoring the echogenicity and KLD parameters made it possible to accurately predict the treatment response from the second course of NAC.
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Affiliation(s)
- Katarzyna Sylwia Dobruch-Sobczak
- Ultrasound Department, Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, 02-106 Warsaw, Poland; (H.P.-W.); (P.K.); (Z.K.); (J.L.)
- Radiology Department II, Maria Sklodowska-Curie National Research Institute of Oncology, 15 Wawelska St., 02-034 Warsaw, Poland
| | - Hanna Piotrzkowska-Wróblewska
- Ultrasound Department, Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, 02-106 Warsaw, Poland; (H.P.-W.); (P.K.); (Z.K.); (J.L.)
| | - Piotr Karwat
- Ultrasound Department, Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, 02-106 Warsaw, Poland; (H.P.-W.); (P.K.); (Z.K.); (J.L.)
| | - Ziemowit Klimonda
- Ultrasound Department, Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, 02-106 Warsaw, Poland; (H.P.-W.); (P.K.); (Z.K.); (J.L.)
| | - Ewa Markiewicz-Grodzicka
- Department of Oncology and Radiotherapy, Maria Sklodowska-Curie National Research Institute of Oncology, 15 Wawelska St., 02-034 Warsaw, Poland;
| | - Jerzy Litniewski
- Ultrasound Department, Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, 02-106 Warsaw, Poland; (H.P.-W.); (P.K.); (Z.K.); (J.L.)
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MohammadEbrahimi S, Mohammadi A, Bergquist R, Dolatkhah F, Olia M, Tavakolian A, Pishgar E, Kiani B. Epidemiological characteristics and initial spatiotemporal visualisation of COVID-19 in a major city in the Middle East. BMC Public Health 2021; 21:1373. [PMID: 34247616 PMCID: PMC8272989 DOI: 10.1186/s12889-021-11326-2] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 06/18/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) emerged initially in China in December 2019 causing the COVID-19 disease, which quickly spread worldwide. Iran was one of the first countries outside China to be affected in a major way and is now under the spell of a fourth wave. This study aims to investigate the epidemiological characteristics of COVID-19 cases in north-eastern Iran through mapping the spatiotemporal trend of the disease. METHODS The study comprises data of 4000 patients diagnosed by laboratory assays or clinical investigation from the beginning of the disease on Feb 14, 2020, until May 11, 2020. Epidemiological features and spatiotemporal trends of the disease in the study area were explored by classical statistical approaches and Geographic Information Systems. RESULTS Most common symptoms were dyspnoea (69.4%), cough (59.4%), fever (54.4%) and weakness (19.5%). Approximately 82% of those who did not survive suffered from dyspnoea. The highest Case Fatality Rate (CFR) was related to those with cardiovascular disease (27.9%) and/or diabetes (18.1%). Old age (≥60 years) was associated with an almost five-fold increased CFR. Odds Ratio (OR) showed malignancy (3.8), nervous diseases (2.2), and respiratory diseases (2.2) to be significantly associated with increased CFR with developments, such as hospitalization at the ICU (2.9) and LOS (1.1) also having high correlations. Furthermore, spatial analyses revealed a geographical pattern in terms of both incidence and mortality rates, with COVID-19 first being observed in suburban areas from where the disease swiftly spread into downtown reaching a peak between 25 February to 06 March (4 incidences per km2). Mortality peaked 3 weeks later after which the infection gradually decreased. Out of patients investigated by the spatiotemporal approach (n = 727), 205 (28.2%) did not survive and 66.8% of them were men. CONCLUSIONS Older adults and people with severe co-morbidities were at higher risk for developing serious complications due to COVID-19. Applying spatiotemporal methods to identify the transmission trends and high-risk areas can rapidly be documented, thereby assisting policymakers in designing and implementing tailored interventions to control and prevent not only COVID-19 but also other rapidly spreading epidemics/pandemics.
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Affiliation(s)
- Shahab MohammadEbrahimi
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Alireza Mohammadi
- Department of Geography and Urban Planning, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran.
| | - Robert Bergquist
- Ingerod, Brastad, Sweden
- (Formerly with the UNICEF/UNDP/World Bank/WHO Special Program for Research and Training in Tropical Diseases, World Health Organization), Geneva, Switzerland
| | - Fatemeh Dolatkhah
- Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Microbiology and Virology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mahsa Olia
- Department of Anaesthesiology, School of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Ayoub Tavakolian
- Department of Emergency Medicine, Faculty of Medicine, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Elahe Pishgar
- Department of Human Geography and Logistics, Faculty of Earth Science, Shahid Beheshti University, Tehran, Iran
| | - Behzad Kiani
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
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Wulkow N, Telgmann R, Hungenberg K, Schütte C, Wulkow M. Deterministic and Stochastic Parameter Estimation for Polymer Reaction Kinetics I: Theory and Simple Examples. MACROMOL THEOR SIMUL 2021. [DOI: 10.1002/mats.202100017] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Niklas Wulkow
- Freie Universität Berlin Arnimallee 14 Berlin 14195 Germany
| | - Regina Telgmann
- Computing in Technology GmbH Harry‐Wilters‐Ring 27 Rastede 26180 Germany
| | | | | | - Michael Wulkow
- Computing in Technology GmbH Harry‐Wilters‐Ring 27 Rastede 26180 Germany
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