The Effect of Fermentation on the Chemical Constituents of Gastrodia Tuber Hallimasch Powder (GTHP) Estimated by UHPLC-Q-Orbitrap HRMS and HPLC

OBJECTIVE

To compare the effect of fermentation on the chemical constituents of Gastrodia Tuder Halimasch Powder (GTHP), to establish its fingerprinting and multicomponent content determination, and to provide a basis for the processing, handling, and clinical application of this herb.

METHODS

Ultra-high-performance liquid chromatography-quadrupole-Orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS) was used to conduct a preliminary analysis of the chemical constituents in GTHP before and after fermentation. High-performance liquid chromatography (HPLC) was used to determine some major differential components of GTHP and establish fingerprints. Cluster analysis (CA), and principal component analysis (PCA) were employed for comprehensive evaluation.

RESULTS

Seventy-nine compounds were identified, including flavonoids, organic acids, nucleosides, terpenoids, and others. The CA and PCA results showed that ten samples were divided into three groups. Through standard control and HPLC analysis, 10 compounds were identified from 22 peaks, namely uracil, guanosine, adenosine, 5-hydroxymethylfurfural (5-HMF), daidzin, genistin, glycitein, daidzein, genistein, and ergosterol. After fermentation, GTHP exhibited significantly higher contents of uracil, guanosine, adenosine, 5-hydroxymethylfurfural, and ergosterol and significantly lower genistein and daidzein contents.

CONCLUSIONS

The UHPLC-Q-Orbitrap HRMS and HPLC methods can effectively identify a variety of chemical components before and after the fermentation of GTHP. This study provides a valuable reference for further research on the rational clinical application and quality control improvement of GTHP.

Molecular modeling of novel 2-aminopyridine derivatives as potential JAK2 inhibitors: a rational strategy for promising anticancer agents

Janus kinase 2(JAK2) is a potential target for anticancer drugs in the treatment of numerous myeloproliferative diseases due to its central role in the JAK/STAT signaling cascade. In this study, the binding behavior of 2 amino-pyridine derivatives as JAK2 inhibitors was investigated by using multifaceted strategies including 3D-QSAR, molecular docking, Fingerprint analysis, MD simulations, and MM-PBSA calculations. A credible COMFA (q2 = 0.606 and r2 = 0.919) and COMSIA (q2 = 0.641 and r2 = 0.992) model was developed, where the internal and external validation revealed that the obtained 3D-QSAR models could be capable of predicting bioactivities of JAK2 inhibitors. The structural criteria provided by the contour maps of model were used to computationally develop more potent 100 new JAK2 inhibitors. Docking studies were conducted on the model data set and newly developed compounds (in-house library) to demonstrate their binding mechanism and highlight the key interacting residues within JAK2 active site. The selected docked complexes underwent MD simulation (100 ns), which contributed in the further study of the binding interactions. Binding free energy analyses (MMGB/PBSA) revealed that key residues such as Glu930, Leu932 (hinge region), Asp939 (solvent accessible region), Arg980, Asn981and Asp994 (catalytic site) have a significantly facilitate ligand-protein interactions through H-bonding and van der Waals interactions. The preliminary in-silico ADMET evaluation revealed encouraging results for all the modeled and in-house library compounds. The findings of this research have the potential to offer valuable recommendations for the advancement of novel, potent, and efficacious JAK2 inhibitors. Overall, this work has successfully employed a wide range of computer-based methodologies to understand the interaction dynamics between 2-amino-pyridine derivatives and the JAK2 enzyme, which is a crucial target in myeloproliferative disorders.Communicated by Ramaswamy H. Sarma.

Attention Mechanism and LSTM Network for Fingerprint-Based Indoor Location System

The demand for precise indoor localization services is steadily increasing. Among various methods, fingerprint-based indoor localization has become a popular choice due to its exceptional accuracy, cost-effectiveness, and ease of implementation. However, its performance degrades significantly as a result of multipath signal attenuation and environmental changes. In this paper, we propose an indoor localization method based on fingerprints using self-attention and long short-term memory (LSTM). By integrating a self-attention mechanism and LSTM network, the proposed method exhibits outstanding positioning accuracy and robustness in diverse experimental environments. The performance of the proposed method is evaluated under two different experimental scenarios, which involve 2D and 3D moving trajectories, respectively. The experimental results demonstrate that our approach achieves an average localization error of 1.76 m and 2.83 m in the respective scenarios, outperforming the existing state-of-the-art methods by 42.67% and 31.64%.

Innovative non-targeted liquid chromatography fingerprinting approach for authenticating tigernuts under Protected Designation of Origin quality seal

BACKGROUND

Tigernut is a typical foodstuff from a specific region of Valencia (Spain) called 'L'Horta Nord', where it is commercialized under a Protected Designation of Origin (PDO) as Chufa de Valencia ('Valencia's tigernut'). PDO-recognized tigernuts present unique characteristics associated with their particular production region. Increasing demand and the associated expansion of its cultivation area has made necessary an exhaustive quality control to check the geographical origin and quality seal.

RESULTS

In this work, a new multivariate analytical method capable of authenticating the PDO quality seal of tigernut samples was developed. Tigernut fat fraction was extracted under optimal conditions, applying the methodology of design of experiments. The analytical method combined fingerprinting methodology and chemometric tools to observe the natural grouping of samples using the exploratory analysis method and to develop classification models (partial least squares-discriminatory analysis; PLS-DA) to discriminate between two sample categories: (i) PDO tigernuts; and (ii) NON-PDO tigernuts.

CONCLUSION

The built PLS-DA model demonstrated 100% accuracy, high sensitivity and specificity, revealing that the tigernut fat fraction can be applied to authenticate the PDO quality seal. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

[Genetic diversity analysis and fingerprinting of 175 Chimonanthus praecox germplasm based on SSR molecular marker]

The elucidation of resources pertaining to the Chimonanthus praecox varieties and the establishment of a fingerprint serve as crucial underpinnings for advancing scientific inquiry and industrial progress in relation to C. praecox. Employing the SSR molecular marker technology, an exploration of the genetic diversity of 175 C. praecox varieties (lines) in the Yanling region was conducted, and an analysis of the genetic diversity among these varieties was carried out using the UPDM clustering method in NTSYSpc 2.1 software. We analyzed the genetic structure of 175 germplasm using Structure v2.3.3 software based on a Bayesian model. General linear model (GLM) association was utilized to analyze traits and markers. The genetic diversity analysis revealed a mean number of alleles (Na) of 6.857, a mean expected heterozygosity (He) of 0.496 3, a mean observed heterozygosity (Ho) of 0.503 7, a mean genetic diversity index of Nei՚s of 0.494 9, and a mean Shannon information index of 0.995 8. These results suggest that the C. praecox population in Yanling exhibits a rich genetic diversity. Additionally, the population structure and the UPDM clustering were examined. In the GLM model, a total of fifteen marker loci exhibited significant (P < 0.05) association with eight phenotypic traits, with the explained phenotypic variation ranging from 14.90% to 36.03%. The construction of fingerprints for C. praecox varieties (lines) was accomplished by utilizing eleven primer pairs with the highest polymorphic information content, resulting in the analysis of 175 SSR markers. The present study offers a thorough examination of the genetic diversity and SSR molecular markers of C. praecox in Yanling, and establishes a fundamental germplasm repository of C. praecox, thereby furnishing theoretical underpinnings for the selection and cultivation of novel and superior C. praecox varieties, varietal identification, and resource preservation and exploitation.

Real-Time Three-Dimensional Pedestrian Localization System Using Smartphones

Robust and accurate three-dimensional localization is essential for personal navigation, emergency rescue, and worker monitoring in indoor environments. For localization technology to be employed in various applications, it is necessary to reduce infrastructure dependence and limit the maximum error bound. This study aims to accurately estimate the location of various people using smartphones in a building with a cloud platform-based localization system. The proposed technology is modularized in a hierarchical structure to sequentially estimate the floor and location. This system comprises four localization modules: course level detection, fine level detection (FLD), fine location tracking (FLT), and level change detection (LCD). Each module operates organically according to the current user status. The position estimation range is defined as a total of three phases, and an appropriate location estimation module suitable for the corresponding phase operates to estimate the user's location gradually and precisely. When the user's floor is determined by an FLD, the two-dimensional position of the user is estimated by an FLT module that tracks the user's position by comparing the received signal strength indicator vector sequence and radio map. Also, LCD recognizes the user's floor change and converts the user's phase. To verify the proposed technology, various experiments were conducted in a six-story building, and an average accuracy of less than 2 m was obtained.

The Potential of NIR Spectroscopy and Chemometrics to Discriminate Roast Degrees and Predict Volatiles in Coffee

This study aimed to establish a rapid and practical method for monitoring and predicting volatile compounds during coffee roasting using near-infrared (NIR) spectroscopy coupled with chemometrics. Washed Arabica coffee beans from Ethiopia and Congo were roasted to industry-validated light, medium, and dark degrees. Concurrent analysis of the samples was performed using gas chromatography-mass spectrometry (GC-MS) and NIR spectroscopy, generating datasets for partial least squares (PLS) regression analysis. The results showed that NIR spectroscopy successfully differentiated the differently roasted samples, similar to the discrimination achieved by GC-MS. This finding highlights the potential of NIR spectroscopy as a rapid tool for monitoring and standardizing the degree of coffee roasting in the industry. A PLS regression model was developed using Ethiopian samples to explore the feasibility of NIR spectroscopy to indirectly measure the volatiles that are important in classifying the roast degree. For PLSR, the data underwent autoscaling as a preprocessing step, and the optimal number of latent variables (LVs) was determined through cross-validation, utilizing the root mean squared error (RMSE). The model was further validated using Congo samples and successfully predicted (with R2 values > 0.75 and low error) over 20 volatile compounds, including furans, ketones, phenols, and pyridines. Overall, this study demonstrates the potential of NIR spectroscopy as a practical and rapid method to complement current techniques for monitoring and predicting volatile compounds during the coffee roasting process.

Extraction solvent selection for Cannabis sativa L. by efficient exploration of cannabinoid selectivity and phytochemical diversity

INTRODUCTION

Cannabis sativa L. is attracting worldwide attention due to various health-promoting effects. Extraction solvent type is critical for the recovery of bioactive compounds from the plant, especially cannabinoids. However, the choice of solvent is varied and not adequately warranted elsewhere, causing confusion in involved fields.

OBJECTIVE

The present work aimed to investigate the effect of extraction solvent on C. sativa (hemp) with regard to cannabinoid recovery and phytochemical profile of the extracts, considering most of the related solvents.

METHODOLOGY

The majority of solvents reported for C. sativa (n = 14) were compared using a representative hemp pool. Quantitative results for major and minor cannabinoids were rapidly and reliably obtained using ultrahigh-performance liquid chromatography coupled with photodiode array detection (UPLC-PDA). In parallel, high-performance thin-layer chromatographic (HPTLC) fingerprinting was employed, involving less toxic mobile phase than in relevant reports. Various derivatisation schemes were applied for more comprehensive comparison of extracts.

RESULTS

Differential selectivity towards cannabinoids was observed among solvents. MeOH was found particularly efficient for most cannabinoids, in addition to solvent systems such as n-Hex/EtOH 70:30 and ACN/EtOH 80:20, while EtOH was generally inferior. For tetrahydrocannabinol (THC)-type compounds, EtOAc and n-Hex/EtOAc 60:40 outperformed n-Hex, despite its use in the official EU method. Solvents that tend to extract more lipids or more polar compounds were revealed based on HPTLC results.

CONCLUSION

Combining the observations from UPLC quantitation and HPTLC fingerprinting, this work allowed comprehensive evaluation of extraction solvents, in view of robust quality assessment and maximised utilisation of C. sativa.

Hour-Level Persistent Multicolor Phosphorescence Enabled by Carbon Dot-Based Nanocomposites Through a Multi-Confinement-Based Approach

Hour-level persistent room temperature phosphorescence (RTP) phenomena based on multi-confinement carbon dots (CDs) are reported. The CDs-based system reported here (named Si-CDs@B2 O3 ) can be efficiently synthesized by a simple pyrolysis method compared to the established persistent RTP systems. The binding modes of CDs, silica (SiO2 ), and boron oxide (B2 O3 ) are deduced from a series of characterizations including XRD, FT-IR, and TEM characterization. Further studies show that the formation of covalent bonds between B2 O3 , SiO2 , and CDs play a key role in activating the persistent RTP and preventing its quenching. This is a rare example of a persistent RTP system that exhibits hourly persistent RTP under environmental conditions. Finally, the applications of Si-CDs@B2 O3 are demonstrated for anti-counterfeiting, long-duration phosphorescence imaging, and fingerprinting. This synthetic strategy is expected to provide strong technical support for the preparation of persistent RTP CDs and pave the way for the synthesis of persistent RTP CDs in the future.

Improved Screening of Monoclonal Gammopathy Patients by MALDI-TOF Mass Spectrometry

Monoclonal gammopathies are a group of blood diseases characterized by presence of abnormal immunoglobulins in peripheral blood and/or urine of patients. Multiple myeloma and plasma cell leukemia are monoclonal gammopathies with unclear etiology, caused by malignant transformation of bone marrow plasma cells. Mass spectrometry with matrix-assisted laser desorption/ionization and time-of-flight detection is commonly used for investigation of the peptidome and small proteome of blood plasma with high accuracy, robustness, and cost-effectivity. In addition, mass spectrometry coupled with advanced statistics can be used for molecular profiling, classification, and diagnosis of liquid biopsies and tissue specimens in various malignancies. Despite the fact there have been fully optimized protocols for mass spectrometry of normal blood plasma available for decades, in monoclonal gammopathy patients, the massive alterations of biophysical and biochemical parameters of peripheral blood plasma often limit the mass spectrometry measurements. In this paper, we present a new two-step extraction protocol and demonstrated the enhanced resolution and intensity (>50×) of mass spectra obtained from extracts of peripheral blood plasma from monoclonal gammopathy patients. When coupled with advanced statistics and machine learning, the mass spectra profiles enabled the direct identification, classification, and discrimination of multiple myeloma and plasma cell leukemia patients with high accuracy and precision. A model based on PLS-DA achieved the best performance with 71.5% accuracy (95% confidence interval, CI = 57.1-83.3%) when the 10× repeated 5-fold CV was performed. In summary, the two-step extraction protocol improved the analysis of monoclonal gammopathy peripheral blood plasma samples by mass spectrometry and provided a tool for addressing the complex molecular etiology of monoclonal gammopathies.

A reductionist paradigm for high-throughput behavioural fingerprinting in Drosophila melanogaster

Understanding how the brain encodes behaviour is the ultimate goal of neuroscience and the ability to objectively and reproducibly describe and quantify behaviour is a necessary milestone on this path. Recent technological progresses in machine learning and computational power have boosted the development and adoption of systems leveraging on high-resolution video recording to track an animal pose and describe behaviour in all four dimensions. However, the high temporal and spatial resolution that these systems offer must come as a compromise with their throughput and accessibility. Here, we describe coccinella, an open-source reductionist framework combining high-throughput analysis of behaviour using real-time tracking on a distributed mesh of microcomputers (ethoscopes) with resource-lean statistical learning (HCTSA/Catch22). Coccinella is a reductionist system, yet outperforms state-of-the-art alternatives when exploring the pharmacobehaviour in Drosophila melanogaster.

Thermal desorption direct analysis in real-time high-resolution mass spectrometry and machine learning allow the rapid authentication of ground black pepper and dried oregano: A proof-of-concept study

Thermal desorption direct analysis in real-time high-resolution mass spectrometry (TD-DART-HRMS) approaches have gained popularity for fast screening of a variety of samples. With rapid volatilization of the sample at increasing temperatures outside the mass spectrometer, this technique can provide a direct readout of the sample content with no sample preparation. In this study, TD-DART-HRMS's utility for establishing spice authenticity was examined. To this aim, we directly analyzed authentic (typical) and adulterated (atypical) samples of ground black pepper and dried oregano in positive and negative ion modes. We analyzed a set of authentic ground black pepper samples (n = 14) originating from Brazil, Sri Lanka, Madagascar, Ecuador, Vietnam, Costa Rica, Indonesia, Cambodia, and adulterated samples (n = 25) consisting of mixtures of ground black pepper with this spice's nonfunctional by-products (pinheads or spent) or with different exogenous materials (olive kernel, green lentils, black mustard seeds, red beans, gypsum plaster, garlic, papaya seeds, chili, green aniseed, or coriander seeds). TD-DART-HRMS facilitated the capture of informative fingerprinting of authentic dried oregano (n = 12) originating from Albania, Turkey, and Italy and those spiked (n = 12) with increasing percentages of olive leaves, sumac, strawberry tree leaves, myrtle, and rock rose. A predictive LASSO classifier was built, after merging by low-level data fusion, the positive and negative datasets for ground black pepper. Fusing multimodal data allowed retrieval of more comprehensive information from both datasets. The resultant classifier achieved on the withheld test set accuracy, sensitivity, and specificity of 100%, 75%, and 90%, respectively. On the contrary, the sole TD-(+)DART-HRMS spectra of the oregano samples allowed construction of a LASSO classifier that predicted the adulteration of the oregano with excellent statistical indicators. This classifier achieved, on the withheld test set, 100% each for accuracy, sensitivity, and specificity.

[Prediction of quality markers and medicinal value of sea buckthorn leaves based on network pharmacology, content determination, and activity evaluation]

The leaves of sea buckthorn(Hippophae rhamnoides), considered as common food raw materials, have records of medicinal use and diverse pharmacological activities, showing a potential medicinal value. However, the active substances in the sea buckthorn leaves and their mechanisms of action remain unclear. In addition, due to the extensive source and large variety variations, the quality evaluation criteria of sea buckthorn leaves remain to be developed. To solve the problems, this study predicted the main active components, core targets, key pathways, and potential pharmacological effects of sea buckthorn leaves by network pharmacology and molecular docking. Furthermore, ultra-performance liquid chromatography with diode-array detection(UPLC-DAD) was employed to determine the content of active components and establish the chemical fingerprint, on the basis of which the quality markers of sea buckthorn leaves were predicted and then verified by the enzyme activity inhibition method. The results indicated that sea buckthorn leaves had potential therapeutic effects on a variety of digestive tract diseases, metabolic diseases, tumors, and autoimmune diseases, which were consistent with the ancient records and the results of modern pharmacological studies. The core targets of sea buckthorn leaves included PTPN11, AKT1, PIK3R1, ESR1, and SRC, which were mainly involved in the PI3K-AKT, MAPK, and HIF-1 signaling pathways. In conclusion, the active components of sea buckthorn leaves are associated with the rich flavonoids and tannins, among which quercitrin, narcissoside, and ellagic acid can be used as the quality markers of sea buckthorn leaves. The findings provide a reference for the quality control and further development and utilization of sea buckthorn leaves as medicinal materials.

Low-Field Benchtop NMR to Discover Early-Onset Sepsis: A Proof of Concept

Low-field (LF) benchtop NMR is a new family of instruments available on the market, promising for fast metabolic fingerprinting and targeted quantification of specific metabolites despite a lack of sensitivity and resolution with respect to high-field (HF) instruments. In the present study, we evaluated the possibility to use the urinary metabolic fingerprint generated using a benchtop LF NMR instrument for an early detection of sepsis in preterm newborns, considering a cohort of neonates previously investigated by untargeted metabolomics based on Mass Spectrometry (MS). The classifier obtained behaved similarly to that based on MS, even if different classes of metabolites were taken into account. Indeed, investigating the regions of interest mainly related to the development of sepsis by a HF NMR instrument, we discovered a set of relevant metabolites associated to sepsis. The set included metabolites that were not detected by MS, but that were reported as relevant in other published studies. Moreover, a strong correlation between LF and HF NMR spectra was observed. The high reproducibility of the NMR spectra, the interpretability of the fingerprint in terms of metabolites and the ease of use make LF benchtop NMR instruments promising in discovering early-onset sepsis.

Molecular fingerprinting by multi-locus sequence typing identifies microevolution and nosocomial transmission of Candida glabrata in Kuwait

Backgrounds

Candida glabrata is a frequently isolated non-albicans Candida species and invasive C. glabrata infections in older patients are associated with high mortality rates. Opportunistic Candida infections in critically ill patients may be either endogenous or nosocomial in origin and this distinction is critical for effective intervention strategies. This study performed multi-locus sequence typing (MLST) to study genotypic relatedness among clinical C. glabrata isolates in Kuwait.

Methods

Candida glabrata isolates (n = 91) cultured from 91 patients were analyzed by MLST. Repeat isolates (n = 16) from 9 patients were also used. Antifungal susceptibility testing for fluconazole, voriconazole, caspofungin and amphotericin B (AMB) was determined by Etest. Genetic relatedness was determined by constructing phylogenetic tree and minimum spanning tree by using BioNumerics software.

Results

Resistance to fluconazole, voriconazole and AMB was detected in 7, 2 and 10 C. glabrata isolates, respectively. MLST identified 28 sequence types (STs), including 12 new STs. ST46 (n = 33), ST3 (n = 8), ST7 (n = 6) and ST55 (n = 6) were prevalent in ≥4 hospitals. Repeat isolates obtained from same or different site yielded identical ST. No association of ST46 with source of isolation or resistance to antifungals was apparent. Microevolution and cross-transmission of infection was indicated in two hospitals that yielded majority (57 of 91, 67%) of C. glabrata.

Conclusion

Our data suggest that C. glabrata undergoes microevolution in hospital environment and can be nosocomially transmitted to other susceptible patients. Thus, proper infection control practices during routine procedures on C. glabrata-infected patients may prevent transmission of this pathogen to other hospitalized patients.

Quantification and Detection of Ground Garlic Adulteration Using Fourier-Transform Near-Infrared Reflectance Spectra

This study demonstrates the rapid and cost-effective possibility of quantifying adulterant amounts (corn flour or corn starch) in ground and dried garlic samples. Prepared mixtures with different concentrations of selected adulterant were effectively characterized using Fourier-transform near-infrared reflectance spectra (FT-NIR), and multivariate calibration models were developed using two methods: principal component regression (PCR) and partial least squares regression (PLSR). They were constructed for optimally preprocessed FT-NIR spectra, and PLSR models generally performed better regarding model fit and predictions than PCR. The optimal PLSR model, built to estimate the amount of corn flour present in the ground and dried garlic samples, was constructed for the first derivative spectra obtained after Savitzky-Golay smoothing (fifteen sampling points and polynomial of the second degree). It demonstrated root mean squared errors for calibration and validation samples equal to 1.8841 and 1.8844 (i.e., 1.88% concerning the calibration range), respectively, and coefficients of determination equal to 0.9955 and 0.9858. The optimal PLSR model constructed for spectra after inverse scattering correction to assess the amount of corn starch had root mean squared errors for calibration and validation samples equal to 1.7679 and 1.7812 (i.e., 1.77% and 1.78% concerning the calibration range), respectively, and coefficients of determination equal to 0.9961 and 0.9873. It was also possible to discriminate samples adulterated with corn flour or corn starch using partial least squares discriminant analysis (PLS-DA). The optimal PLS-DA model had a very high correct classification rate (99.66%), sensitivity (99.96%), and specificity (99.36%), calculated for external validation samples. Uncertainties of these figures of merit, estimated using the Monte Carlo validation approach, were relatively small. One-class classification partial least squares models, developed to detect the adulterant type, presented very optimistic sensitivity for validation samples (above 99%) but low specificity (64% and 45.33% for models recognizing corn flour or corn starch adulterants, respectively). Through experimental investigation, chemometric data analysis, and modeling, we have verified that the FT-NIR technique exhibits the required sensitivity to quantify adulteration in dried ground garlic, whether it involves corn flour or corn starch.

A Reaction-Based Optical Fingerprinting Strategy for the Recognition of Fat-Soluble Samples: Discrimination of Motor Oils

Optical "fingerprints" are widely used for chemometrics-assisted recognition of samples of different types. An emerging trend in this area is the transition from obtaining "static" spectral data to reactions analyzed over time. Indicator reactions are usually carried out in aqueous solutions; in this study, we developed reactions that proceed in an organic solvent, thereby making it possible to recognize fat-soluble samples. In this capacity, we used 5W40, 10W40, and 5W30 motor oils from four manufacturers, with six samples in total. The procedure involved mixing a dye, sample, and reagents (HNO3, HCl, or tert-butyl hydroperoxide) in an ethanolic solution in a 96-well plate and measuring absorbance or near-infrared fluorescence intensity every several minutes for 20-55 min. The obtained photographic images were processed by linear discriminant analysis (LDA) and the k-nearest neighbors algorithm (kNN). Discrimination accuracy was evaluated by a validation procedure. A reaction of oxidation of a dye by nitric acid allowed us to recognize all six samples with 100% accuracy for LDA. Merging of data from the four reactions that did not provide complete discrimination ensured an accuracy of 93% for kNN. The newly developed indicator systems have good prospects for the discrimination of other fat-soluble samples. Overall, the results confirm the viability of the kinetics-based discrimination strategy.

Environmental RNA as a Noninvasive Tool for Assessing Toxic Effects in Fish: A Proof-of-concept Study Using Japanese Medaka Exposed to Pyrene

Although environmental RNA (eRNA) is emerging as a noninvasive tool to assess the health status of aquatic macroorganisms, the potential of eRNA in assessing chemical hazards remain largely untested. In this study, we investigated the ability of eRNA to detect changes in gene expression in Japanese medaka fish (Oryzias latipes) in response to sublethal pyrene exposure, as a model toxic chemical. We performed standardized acute toxicity tests and collected eRNA from tank water and RNA from fish tissue after 96 h of exposure. Our results showed that over 1000 genes were detected in eRNA and the sequenced read counts of these genes correlated with those in fish tissue (r = 0.50). Moreover, eRNA detected 86 differentially expressed genes in response to pyrene, some of which were shared by fish RNA, including the suppression of collagen fiber genes. These results suggest that eRNA has the potential to detect changes in gene expression in fish in response to environmental stressors without the need for sacrificing or causing pain to fish. However, we also found that the majority of sequenced reads of eRNA (>99%) were not mapped to the reference medaka genome and they originated from bacteria and fungi, resulting in low sequencing depth. In addition, eRNA, in particular nuclear genes, was highly degraded with a median transcript integrity number (TIN) of <20. These limitations highlight the need for future studies to improve the analytical methods of eRNA application.

Authentication of Cocoa Products Based on Profiling and Fingerprinting Approaches: Assessment of Geographical, Varietal, Agricultural and Processing Features

Cocoa and its derivative products, especially chocolate, are highly appreciated by consumers for their exceptional organoleptic qualities, thus being often considered delicacies. They are also regarded as superfoods due to their nutritional and health properties. Cocoa is susceptible to adulteration to obtain illicit economic benefits, so strategies capable of authenticating its attributes are needed. Features such as cocoa variety, origin, fair trade, and organic production are increasingly important in our society, so they need to be guaranteed. Most of the methods dealing with food authentication rely on profiling and fingerprinting approaches. The compositional profiles of natural components -such as polyphenols, biogenic amines, amino acids, volatile organic compounds, and fatty acids- are the source of information to address these issues. As for fingerprinting, analytical techniques, such as chromatography, infrared, Raman, and mass spectrometry, generate rich fingerprints containing dozens of features to be used for discrimination purposes. In the two cases, the data generated are complex, so chemometric methods are usually applied to extract the underlying information. In this review, we present the state of the art of cocoa and chocolate authentication, highlighting the pros and cons of the different approaches. Besides, the relevance of the proposed methods in quality control and the novel trends for sample analysis are also discussed.

A Performance Improvement for Indoor Positioning Systems Using Earth's Magnetic Field

Although most indoor positioning systems use radio waves, such as Wi-Fi, Bluetooth, or RFID, for application in department stores, exhibition halls, stations, and airports, the accuracy of such technology is easily affected by human shadowing and multipath propagation delay. This study combines the earth's magnetic field strength and Wi-Fi signals to obtain the indoor positioning information with high availability. Wi-Fi signals are first used to identify the user's area under several kinds of environment partitioning methods. Then, the signal pattern comparison is used for positioning calculations using the strength change in the earth's magnetic field among the east-west, north-south, and vertical directions at indoor area. Finally, the k-nearest neighbors (KNN) method and fingerprinting algorithm are used to calculate the fine-grained indoor positioning information. The experiment results show that the average positioning error is 0.57 m in 12-area partitioning, which is almost a 90% improvement in relation to that of one area partitioning. This study also considers the positioning error if the device is held at different angles by hand. A rotation matrix is used to convert the magnetic sensor coordinates from a mobile phone related coordinates into the geographic coordinates. The average positioning error is decreased by 68%, compared to the original coordinates in 12-area partitioning with a 30-degree pitch. In the offline procedure, only the northern direction data are used, which is reduced by 75%, to give an average positioning error of 1.38 m. If the number of reference points is collected every 2 m for reducing 50% of the database requirement, the average positioning error is 1.77 m.

Leveraging Deep Learning for IoT Transceiver Identification

With the increasing demand for Internet of Things (IoT) network applications, the lack of adequate identification and authentication has become a significant security concern. Radio frequency fingerprinting techniques, which utilize regular radio traffic as the identification source, were then proposed to provide a more secured identification approach compared to traditional security methods. Such solutions take hardware-level characteristics as device fingerprints to mitigate the risk of pre-shared key leakage and lower computational complexity. However, the existing studies suffer from problems such as location dependence. In this study, we have proposed a novel scheme for further exploiting the spectrogram and the carrier frequency offset (CFO) as identification sources. A convolutional neural network (CNN) is chosen as the classifier. The scheme addressed the location-dependence problem in the existing identification schemes. Experimental evaluations with data collected in the real world have indicated that the proposed approach can achieve 80% accuracy even if the training and testing data are collected on different days and at different locations, which is 13% higher than state-of-the-art approaches.

Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results

Functional magnetic resonance (fMRI) is an invaluable tool in studying cognitive processes in vivo. Many recent studies use functional connectivity (FC), partial correlation connectivity (PC), or fMRI-derived brain networks to predict phenotypes with results that sometimes cannot be replicated. At the same time, FC can be used to identify the same subject from different scans with great accuracy. In this paper, we show a method by which one can unknowingly inflate classification results from 61% accuracy to 86% accuracy by treating longitudinal or contemporaneous scans of the same subject as independent data points. Using the UK Biobank dataset, we find one can achieve the same level of variance explained with 50 training subjects by exploiting identifiability as with 10,000 training subjects without double-dipping. We replicate this effect in four different datasets: the UK Biobank (UKB), the Philadelphia Neurodevelopmental Cohort (PNC), the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP), and an OpenNeuro Fibromyalgia dataset (Fibro). The unintentional improvement ranges between 7% and 25% in the four datasets. Additionally, we find that by using dynamic functional connectivity (dFC), one can apply this method even when one is limited to a single scan per subject. One major problem is that features such as ROIs or connectivities that are reported alongside inflated results may confuse future work. This article hopes to shed light on how even minor pipeline anomalies may lead to unexpectedly superb results.

Robust phase determination in complex solid solutions using diffuse multiple scattering

A novel methodology is presented for identifying and distinguishing between structural phases in multi-phasic systems, such as piezoelectric materials like PMN-PT [Pb(Mg1/3Nb2/3)O3-PbTiO3], PIN-PMN-PT [Pb(In1/2Nb1/2)O3-Pb(Mg1/3Nb2/3)O3-PbTiO3] and PZT [Pb(Zr,Ti)O3], using diffuse multiple scattering and Kossel line diffraction techniques. The method exploits the splitting of triple line intersections from special coplanar reflections combined with logical constraints to generate a splitting fingerprint for robust crystallographic phase determination and discrimination.

Comparing Magnetic Resonance Fingerprinting (MRF) and the MAGiC Sequence for Simultaneous T1 and T2 Quantitative Measurements in the Female Pelvis: A Prospective Study

The aim of this study was to explore the potential of magnetic resonance fingerprinting (MRF), an emerging quantitative MRI technique, in measuring relaxation values of female pelvic tissues compared to the conventional magnetic resonance image compilation (MAGiC) sequence. The study included 32 female patients who underwent routine pelvic MRI exams using anterior and posterior array coils on a 3T clinical scanner. Our findings demonstrated significant correlations between MRF and MAGiC measured T1 and T2 values (p < 0.0001) for various pelvic tissues, including ilium, femoral head, gluteus, obturator, iliopsoas, erector spinae, uterus, cervix, and cutaneous fat. The tissue contrasts generated from conventional MRI and synthetic MRF also showed agreement in bone, muscle, and uterus for both T1-weighted and T2-weighted images. This study highlights the strengths of MRF in providing simultaneous T1 and T2 mapping. MRF offers distinct tissue contrast and has the potential for accurate diagnosis of female pelvic diseases, including tumors, fibroids, endometriosis, and pelvic inflammatory disease. Additionally, MRF shows promise in monitoring disease progression or treatment response. Overall, the study demonstrates the potential of MRF in the field of female pelvic organ imaging and suggests that it could be a valuable addition to the clinical practice of pelvic MRI exams. Further research is needed to establish the clinical utility of MRF and to develop standardized protocols for its implementation in clinical practice.

Some Design Considerations in Passive Indoor Positioning Systems

User location is becoming an increasingly common and important feature for a wide range of services. Smartphone owners increasingly use location-based services, as service providers add context-enhanced functionality such as car-driving routes, COVID-19 tracking, crowdedness indicators, and suggestions for nearby points of interest. However, positioning a user indoors is still problematic due to the fading of the radio signal caused by multipath and shadowing, where both have complex dependencies on the indoor environment. Location fingerprinting is a common positioning method where Radio Signal Strength (RSS) measurements are compared to a reference database of previously stored RSS values. Due to the size of the reference databases, these are often stored in the cloud. However, server-side positioning computations make preserving the user's privacy problematic. Given the assumption that a user does not want to communicate his/her location, we pose the question of whether a passive system with client-side computations can substitute fingerprinting-based systems, which commonly use active communication with a server. We compared two passive indoor location systems based on multilateration and sensor fusion using an Unscented Kalman Filter (UKF) with fingerprinting and show how these may provide accurate indoor positioning without compromising the user's privacy in a busy office environment.

Diversity analyses in two ornamental and large-genome Ranunculaceae species based on a low-cost Klenow NGS-based protocol

Persian buttercup (Ranunculus asiaticus L.) and poppy anemone (Anemone coronaria L.) are ornamental, outcrossing, perennial species belonging to the Ranunculaceae family, characterized by large and highly repetitive genomes. We applied K-seq protocol in both species to generate high-throughput sequencing data and produce a large number of genetic polymorphisms. The technique entails the application of Klenow polymerase-based PCR using short primers designed by analyzing k-mer sets in the genome sequence. To date the genome sequence of both species has not been released, thus we designed primer sets based on the reference the genome sequence of the related species Aquilegia oxysepala var. kansuensis (Brühl). A whole of 11,542 SNPs were selected for assessing genetic diversity of eighteen commercial varieties of R. asiaticus, while 1,752 SNPs for assessing genetic diversity in six cultivars of A. coronaria. UPGMA dendrograms were constructed and in R. asiaticus integrated in with PCA analysis. This study reports the first molecular fingerprinting within Persian buttercup, while the results obtained in poppy anemone were compared with a previously published SSR-based fingerprinting, proving K-seq to be an efficient protocol for the genotyping of complex genetic backgrounds.

Development of an Automated Online Flow Cytometry Method to Quantify Cell Density and Fingerprint Bacterial Communities

Cell density is an important factor in all microbiome research, where interactions are of interest. It is also the most important parameter for the operation and control of most biotechnological processes. In the past, cell density determination was often performed offline and manually, resulting in a delay between sampling and immediate data processing, preventing quick action. While there are now some online methods for rapid and automated cell density determination, they are unable to distinguish between the different cell types in bacterial communities. To address this gap, an online automated flow cytometry procedure is proposed for real-time high-resolution analysis of bacterial communities. On the one hand, it allows for the online automated calculation of cell concentrations and, on the other, for the differentiation between different cell subsets of a bacterial community. To achieve this, the OC-300 automation device (onCyt Microbiology, Zürich, Switzerland) was coupled with the flow cytometer CytoFLEX (Beckman Coulter, Brea, USA). The OC-300 performs the automatic sampling, dilution, fixation and 4',6-diamidino-2-phenylindole (DAPI) staining of a bacterial sample before sending it to the CytoFLEX for measurement. It is demonstrated that this method can reproducibly measure both cell density and fingerprint-like patterns of bacterial communities, generating suitable data for powerful automated data analysis and interpretation pipelines. In particular, the automated, high-resolution partitioning of clustered data into cell subsets opens up the possibility of correlation analysis to identify the operational or abiotic/biotic causes of community disturbances or state changes, which can influence the interaction potential of organisms in microbiomes or even affect the performance of individual organisms.

A generalizable connectome-based marker of in-scan sustained attention in neurodiverse youth

Difficulty with attention is an important symptom in many conditions in psychiatry, including neurodiverse conditions such as autism. There is a need to better understand the neurobiological correlates of attention and leverage these findings in healthcare settings. Nevertheless, it remains unclear if it is possible to build dimensional predictive models of attentional state in a sample that includes participants with neurodiverse conditions. Here, we use 5 datasets to identify and validate functional connectome-based markers of attention. In dataset 1, we use connectome-based predictive modeling and observe successful prediction of performance on an in-scan sustained attention task in a sample of youth, including participants with a neurodiverse condition. The predictions are not driven by confounds, such as head motion. In dataset 2, we find that the attention network model defined in dataset 1 generalizes to predict in-scan attention in a separate sample of neurotypical participants performing the same attention task. In datasets 3-5, we use connectome-based identification and longitudinal scans to probe the stability of the attention network across months to years in individual participants. Our results help elucidate the brain correlates of attentional state in youth and support the further development of predictive dimensional models of other clinically relevant phenotypes.

Carbocyanine-Based Optical Sensor Array for the Discrimination of Proteins and Rennet Samples Using Hypochlorite Oxidation

Optical sensor arrays are widely used in obtaining fingerprints of samples, allowing for solutions of recognition and identification problems. An approach to extending the functionality of the sensor arrays is using a kinetic factor by conducting indicator reactions that proceed at measurable rates. In this study, we propose a method for the discrimination of proteins based on their oxidation by sodium hypochlorite with the formation of the products, which, in turn, feature oxidation properties. As reducing agents to visualize these products, carbocyanine dyes IR-783 and Cy5.5-COOH are added to the reaction mixture at pH 5.3, and different spectral characteristics are registered every several minutes (absorbance in the visible region and fluorescence under excitation by UV (254 and 365 nm) and red light). The intensities of the photographic images of the 96-well plate are processed by principal component analysis (PCA) and linear discriminant analysis (LDA). Six model proteins (bovine and human serum albumins, γ-globulin, lysozyme, pepsin, and proteinase K) and 10 rennet samples (mixtures of chymosin and pepsin from different manufacturers) are recognized by the proposed method. The method is rapid and simple and uses only commercially available reagents.

Positioning by Multicell Fingerprinting in Urban NB-IoT Networks

Narrowband Internet of Things (NB-IoT) has quickly become a leading technology in the deployment of IoT systems and services, owing to its appealing features in terms of coverage and energy efficiency, as well as compatibility with existing mobile networks. Increasingly, IoT services and applications require location information to be paired with data collected by devices; NB-IoT still lacks, however, reliable positioning methods. Time-based techniques inherited from long-term evolution (LTE) are not yet widely available in existing networks and are expected to perform poorly on NB-IoT signals due to their narrow bandwidth. This investigation proposes a set of strategies for NB-IoT positioning based on fingerprinting that use coverage and radio information from multiple cells. The proposed strategies were evaluated on two large-scale datasets made available under an open-source license that include experimental data from multiple NB-IoT operators in two large cities: Oslo, Norway, and Rome, Italy. Results showed that the proposed strategies, using a combination of coverage and radio information from multiple cells, outperform current state-of-the-art approaches based on single cell fingerprinting, with a minimum average positioning error of about 20 m when using data for a single operator that was consistent across the two datasets vs. about 70 m for the current state-of-the-art approaches. The combination of data from multiple operators and data smoothing further improved positioning accuracy, leading to a minimum average positioning error below 15 m in both urban environments.

Fast and Noise-Resilient Magnetic Field Mapping on a Low-Cost UAV Using Gaussian Process Regression

This study presents a comprehensive approach to mapping local magnetic field anomalies with robustness to magnetic noise from an unmanned aerial vehicle (UAV). The UAV collects magnetic field measurements, which are used to generate a local magnetic field map through Gaussian process regression (GPR). The research identifies two categories of magnetic noise originating from the UAV's electronics, adversely affecting map precision. First, this paper delineates a zero-mean noise arising from high-frequency motor commands issued by the UAV's flight controller. To mitigate this noise, the study proposes adjusting a specific gain in the vehicle's PID controller. Next, our research reveals that the UAV generates a time-varying magnetic bias that fluctuates throughout experimental trials. To address this issue, a novel compromise mapping technique is introduced, enabling the map to learn these time-varying biases with data collected from multiple flights. The compromise map circumvents excessive computational demands without sacrificing mapping accuracy by constraining the number of prediction points used for regression. A comparative analysis of the magnetic field maps' accuracy and the spatial density of observations employed in map construction is then conducted. This examination serves as a guideline for best practices when designing trajectories for local magnetic field mapping. Furthermore, the study presents a novel consistency metric intended to determine whether predictions from a GPR magnetic field map should be retained or discarded during state estimation. Empirical evidence from over 120 flight tests substantiates the efficacy of the proposed methodologies. The data are made publicly accessible to facilitate future research endeavors.

Fingerprinting of Plum (Prunus domestica) Genotypes in Lithuania Using SSR Markers

This study's aim was to evaluate the genetic diversity of European plum (Prunus domestica) cultivars and hybrids in Lithuania using SSR markers. In total, 107 plum genotypes (including 68 European plum cultivars and 39 hybrids) from the genetic resources collection of the Institute of Horticulture of the Lithuanian Research Centre for Agriculture and Forestry (LRCAF IH) were evaluated using nine microsatellite markers (SSRs) previously published and suggested by the European Cooperative Programme for Plant Genetic Resources (ECPGR). Up to six alleles per locus with each primer pair were generated for some genotypes due to the hexaploidy of plums. The number of alleles in each primer ranged from 18 to 30, with an average of 24.33. The highest number of alleles was generated with the PacA33 primer pair (30). The most informative primer, according to the PIC value, was BPPCT007. Sixty-two unique alleles (representing 39.5% of all polymorphic alleles) have been detected in the plum germplasm developed in Lithuania. According to UPGMA cluster analysis, 58 European plum genotypes were separated into eight groups without any relation to fruit color or shape. By genetic diversity (UPGMA) and structure (Bayesian) analysis, European plum hybrids were grouped into clusters according to their pedigree.

Machine Learning Assists IoT Localization: A Review of Current Challenges and Future Trends

The widespread use of the internet and the exponential growth in small hardware diversity enable the development of Internet of things (IoT)-based localization systems. We review machine-learning-based approaches for IoT localization systems in this paper. Because of their high prediction accuracy, machine learning methods are now being used to solve localization problems. The paper's main goal is to provide a review of how learning algorithms are used to solve IoT localization problems, as well as to address current challenges. We examine the existing literature for published papers released between 2020 and 2022. These studies are classified according to several criteria, including their learning algorithm, chosen environment, specific covered IoT protocol, and measurement technique. We also discuss the potential applications of learning algorithms in IoT localization, as well as future trends.

Listeria monocytogenes Isolates from Meat Products and Processing Environment in Poland Are Sensitive to Commonly Used Antibiotics, with Rare Cases of Reduced Sensitivity to Ciprofloxacin

Antibiotic resistance is a global health problem, causing not only an increased mortality rate of bacterial infections but also economic losses due to, among other reasons, the need for longer hospital stays. Listeria monocytogenes is one of the foodborne pathogens with the ability to induce a serious illness called listeriosis, with approximately 20-30% fatal outcomes. The treatment regimen of listeriosis in humans includes the administration of antibiotics (in most cases, ampicillin or trimethoprim with sulfamethoxazole in case of allergies to β-lactams), so the resistance of this pathogen to antibiotics can potentially lead to increased mortality. The antibiotic sensitivity status of n = 153 L. monocytogenes isolates originating from meat food samples (raw and processed) and meat-processing environment (both contacting and non-contacting with food) collected between October 2020 and November 2021 in Poland was examined in this study. Susceptibility to antibiotics was determined using the disc diffusion method on Mueller-Hinton agar plates. All collected samples were susceptible to 9 antibiotics: ampicillin (10 µg), chloramphenicol (30 µg), erythromycin (15 µg), gentamicin (10 µg), penicillin (10 IU), streptomycin (10 µg), sulfamethoxazole/trimethoprim (1.25/23.75 µg), tetracycline (30 µg) and vancomycin (30 µg). Some of the isolates (n = 10; 6.5%) showed reduced susceptibility to ciprofloxacin (5 µg), which was classified as an intermediate response. All these ten isolates were collected from surfaces contacting with food in food-processing facilities.

Ternary eluent compositions in supercritical fluid chromatography improved fingerprinting of therapeutic peptides

Currently, little information has been published on the application of ternary eluent compositions in supercritical fluid chromatography for separating peptides. This work investigates the benefits of adding acetonitrile to methanol as the modifier. Three cyclic antibiotic peptides (bacitracin, colistin, and daptomycin) ranging between 1000 and 2000 Da were chosen as model substances. The ternary mixture of carbon dioxide, methanol, and acetonitrile is optimized to increase the resolution of the peptide's fingerprint. In addition, varying compositions of methanol and acetonitrile were found to change the elution order of the analytes, which is a valuable tool during method development. An individual gradient method using two Torus 2-PIC columns (each 100 × 3.0 mm, 1.7 μm), carbon dioxide, and a modifier consisting of acetonitrile/methanol/water/methanesulfonic acid (60:40:2:0.1, v:v:v:v) was optimized for each of the peptides. Subsequently, a generic method development protocol applicable to polypeptides is proposed.

A Survey of the Performance-Limiting Factors of a 2-Dimensional RSS Fingerprinting-Based Indoor Wireless Localization System

A receive signal strength (RSS) fingerprinting-based indoor wireless localization system (I-WLS) uses a localization machine learning (ML) algorithm to estimate the location of an indoor user using RSS measurements as the position-dependent signal parameter (PDSP). There are two stages in the system's localization process: the offline phase and the online phase. The offline phase starts with the collection and generation of RSS measurement vectors from radio frequency (RF) signals received at fixed reference locations, followed by the construction of an RSS radio map. In the online phase, the instantaneous location of an indoor user is found by searching the RSS-based radio map for a reference location whose RSS measurement vector corresponds to the user's instantaneously acquired RSS measurements. The performance of the system depends on a number of factors that are present in both the online and offline stages of the localization process. This survey identifies these factors and examines how they impact the overall performance of the 2-dimensional (2-D) RSS fingerprinting-based I-WLS. The effects of these factors are discussed, as well as previous researchers' suggestions for minimizing or mitigating them and future research trends in RSS fingerprinting-based I-WLS.

A Comparison of the Stability of Refined Edible Vegetable Oils under Frying Conditions: Multivariate Fingerprinting Approach

The stability of highly consumed vegetable refined oils after discontinuous frying of potatoes was compared. Both those vegetable oils containing additives and those that did not were considered. Vegetable oil samples were evaluated using refractive index, anisidine and peroxide values, UV absorbance and dielectric constant-based determination of the content of total polar compounds. Chemical changes caused over the frying time were monitored and multivariate modelling of the data was carried out. A new gas chromatographic-mass spectroscopy method was intended to record a fingerprint of both polar and non-polar compound fractions. Multivariate models of chromatographic fingerprints were also developed, and the results obtained from both approaches were verified to be statistically similar. In addition, multivariate modelling also allows to differentiate among vegetable oils according to oxidation performance. Indeed, it was initially observed that olive oils presented the highest natural thermo-oxidative stability compared to other seed oils, although it should be noted that these differences were not significant when regarding olive pomace oils and seed oils containing synthetic additives.

Approximate B 1 + scaling of the SSFP steady state

PURPOSE

It is shown that the steady state of rapid, TR-periodic steady-state free precession (SSFP) sequences at small to moderate flip angles exhibits a universal, approximate scaling law with respect to variations of B 1 + $$ {B}_1^{+} $$ . Implications for the accuracy and precision of relaxometry experiments are discussed.

METHODS

The approximate scaling law is derived from and numerically tested against known analytical solutions. To assess the attainable estimator precision in a typical relaxometry experiment, we calculate the Cramér-Rao bound (CRB) and perform Monte Carlo (MC) simulations.

RESULTS

The approximate universal scaling holds well up to moderate flip angles. For pure steady state relaxometry, we observe a significant precision penalty for simultaneous estimation of R 1 $$ {R}_1 $$ and B 1 + $$ {B}_1^{+} $$ , whereas good R 2 $$ {R}_2 $$ estimates can be obtained without even knowing the correct actual flip angle.

CONCLUSION

Simultaneous estimation of R 1 $$ {R}_1 $$ and B 1 + $$ {B}_1^{+} $$ from a set of SSFP steady states alone is not advisable. Apart from separate B 1 + $$ {B}_1^{+} $$ measurements, the problem can be addressed by adding transient state information, but, depending on the situation, residual effects due to the scaling may still require some attention.

Fish Fingerprinting: Identifying Crude Oil Pollutants using Bicyclic Sesquiterpanes (Bicyclanes) in the Tissues of Exposed Fish

In the present study, we investigated the possibility of identifying the source oils of exposed fish using ratios of bicyclic sesquiterpane (bicyclane) chemical biomarkers. In the event of an oil spill, identification of source oil(s) for assessment, or for litigation purposes, typically uses diagnostic ratios of chemical biomarkers to produce characteristic oil "fingerprints." Although this has been applied in identifying oil residues in sediments, water, and sessile filtering organisms, so far as we are aware this has never been successfully demonstrated for oil-exposed fish. In a 35-day laboratory trial, juvenile Lates calcarifer (barramundi or Asian seabass) were exposed, via the diet (1% w/w), to either a heavy fuel oil or to Montara, an Australian medium crude oil. Two-dimensional gas chromatography with high-resolution mass spectrometry and gas chromatography-mass spectrometry were then used to measure selected ratios of the bicyclanes to examine whether the ratios were statistically reproducibly conserved in the fish tissues. Six diagnostic bicyclane ratios showed high correlation (r²  > 0.98) with those of each of the two source oils. A linear discriminatory analysis model showed that nine different petroleum products could be reproducibly discriminated using these bicyclane ratios. The model was then used to correctly identify the bicyclane profiles of each of the two exposure oils in the adipose tissue extracts of each of the 18 fish fed oil-enriched diets. From our initial study, bicyclane biomarkers appear to show good potential for providing reliable forensic fingerprints of the sources of oil contamination of exposed fish. Further research is needed to investigate the minimum exposure times required for bicyclane bioaccumulation to achieve detectable concentrations in fish adipose tissues and to determine bicyclane depuration rates once exposure to oil has ceased. Environ Toxicol Chem 2023;42:7-18. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Crowdsourced Reconstruction of Cellular Networks to Serve Outdoor Positioning: Modeling, Validation and Analysis

Positioning via outdoor fingerprinting, which exploits the radio signals emitted by cellular towers, is fundamental in many applications. In most cases, the localization performance is affected by the availability of information about the emitters, such as their coverage. While several projects aim at collecting cellular network data via crowdsourcing observations, none focuses on information about the structure of the networks, which is paramount to correctly model their topology. The difficulty of such a modeling is exacerbated by the inherent differences among cellular technologies, the strong spatio-temporal nature of positioning, and the continuously evolving configuration of the networks. In this paper, we first show how to synthesize a detailed conceptual schema of cellular networks on the basis of the signal fingerprints collected by devices. We turned it into a logical one, and we exploited that to build a relational spatio-temporal database capable of supporting a crowdsourced collection of data. Next, we populated the database with heterogeneous cellular observations originating from multiple sources. In addition, we illustrate how the developed system allows us to properly deal with the evolution of the network configuration, e.g., by detecting cell renaming phenomena and by making it possible to correct inconsistent measurements coming from mobile devices, fostering positioning tasks. Finally, we provide a wide range of basic, spatial, and temporal analyses about the arrangement of the cellular network and its evolution over time, demonstrating how the developed system can be used to reconstruct and maintain a deep knowledge of the cellular network, possibly starting from crowdsourced information only.

Development of Non-Targeted Mass Spectrometry Method for Distinguishing Spelt and Wheat

Food fraud, even when not in the news, is ubiquitous and demands the development of innovative strategies to combat it. A new non-targeted method (NTM) for distinguishing spelt and wheat is described, which aids in food fraud detection and authenticity testing. A highly resolved fingerprint in the form of spectra is obtained for several cultivars of spelt and wheat using liquid chromatography coupled high-resolution mass spectrometry (LC-HRMS). Convolutional neural network (CNN) models are built using a nested cross validation (NCV) approach by appropriately training them using a calibration set comprising duplicate measurements of eleven cultivars of wheat and spelt, each. The results reveal that the CNNs automatically learn patterns and representations to best discriminate tested samples into spelt or wheat. This is further investigated using an external validation set comprising artificially mixed spectra, samples for processed goods (spelt bread and flour), eleven untypical spelt, and six old wheat cultivars. These cultivars were not part of model building. We introduce a metric called the D score to quantitatively evaluate and compare the classification decisions. Our results demonstrate that NTMs based on NCV and CNNs trained using appropriately chosen spectral data can be reliable enough to be used on a wider range of cultivars and their mixes.

Improving Fingerprint-Based Positioning by Using IEEE 802.11mc FTM/RTT Observables

Received signal strength (RSS) has been one of the most used observables for location purposes due to its availability at almost every wireless device. However, the volatile nature of RSS tends to yield to non-reliable location solutions. IEEE 802.11mc enabled the use of the round trip time (RTT) for positioning, which is expected to be a more consistent observable for location purposes. This approach has been gaining support from several companies such as Google, which introduced that feature in the Android O.S. As a result, RTT estimation is now available in several recent off-the-shelf devices, opening a wide range of new approaches for computing location. However, RTT has been traditionally addressed to multilateration solutions. Few works exist that assess the feasibility of the RTT as an accurate feature in positioning methods based on classification algorithms. An attempt is made in this paper to fill this gap by investigating the performance of several classification models in terms of accuracy and positioning errors. The performance is assessed using different AP layouts, distinct AP vendors, and different frequency bands. The accuracy and precision of the RTT-based position estimation is always better than the one obtained with RSS in all the studied scenarios, and especially when few APs are available. In addition, all the considered ML algorithms perform pretty well. As a result, it is not necessary to use more complex solutions (e.g., SVM) when simpler ones (e.g., nearest neighbor classifiers) achieve similar results both in terms of accuracy and location error.

Dual Biomimetic Recognition-Driven Plasmonic Nanogap-Enhanced Raman Scattering for Ultrasensitive Protein Fingerprinting and Quantitation

Protein assays with fingerprints and high sensitivity are essential for biomedical research and applications. However, the prevailing methods mainly rely on indirect or labeled immunoassays, failing to provide fingerprint information. Herein, we report a dual biomimetic recognition-driven plasmonic nanogap-enhanced Raman scattering (DBR-PNERS) strategy for ultrasensitive protein fingerprinting and quantitation. A pair of molecularly imprinted nanoantennas were rationally engineered for specifically trapping a target protein into well-defined plasmonic nanogaps through dual-terminal recognition for ultrahigh Raman signal amplification. Meanwhile, a Raman-active small molecule was embedded into the nanoantenna as an internal standard to provide a ratiometric assay for robust quantitation. DBR-PNERS exhibited several significant merits over existing approaches, including fingerprinting, ultrahigh sensitivity, quantitation robustness, speed, sample consumption, and so on. Therefore, it can be a promising tool for a protein assay and holds a great perspective in important applications.

Characterization and Classification of Spanish Honey by Non-Targeted LC-HRMS (Orbitrap) Fingerprinting and Multivariate Chemometric Methods

A non-targeted LC-HRMS fingerprinting methodology based on a C18 reversed-phase mode under universal gradient elution using an Orbitrap mass analyzer was developed to characterize and classify Spanish honey samples. A simple sample treatment consisting of honey dissolution with water and a 1:1 dilution with methanol was proposed. A total of 136 honey samples belonging to different blossom and honeydew honeys from different botanical varieties produced in different Spanish geographical regions were analyzed. The obtained LC-HRMS fingerprints were employed as sample chemical descriptors for honey pattern recognition by principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The results demonstrated a superior honey classification and discrimination capability with respect to previous non-targeted HPLC-UV fingerprinting approaches, with them being able to discriminate and authenticate the honey samples according to their botanical origins. Overall, noteworthy cross-validation multiclass predictions were accomplished with sensitivity and specificity values higher than 96.2%, except for orange/lemon blossom (BL) and rosemary (RO) blossom-honeys. The proposed methodology was also able to classify and authenticate the climatic geographical production region of the analyzed honey samples, with cross-validation sensitivity and specificity values higher than 87.1% and classification errors below 10.5%.

RSSI Fingerprint Height Based Empirical Model Prediction for Smart Indoor Localization

Smart indoor living advances in the recent decade, such as home indoor localization and positioning, has seen a significant need for low-cost localization systems based on freely available resources such as Received Signal Strength Indicator by the dense deployment of Wireless Local Area Networks (WLAN). The off-the-shelf user equipment (UE's) available at an affordable price across the globe are well equipped with the functionality to scan the radio access network for hearable single strength; in complex indoor environments, multiple signals can be received at a particular reference point with no consideration of the height of the transmitter and possible broadcasting coverage. Most effective fingerprinting algorithm solutions require specialized labor, are time-consuming to carry out site surveys, training of the data, big data analysis, and in most cases, additional hardware requirements relatively increase energy consumption and cost, not forgetting that in case of changes in the indoor environment will highly affect the fingerprint due to interferences. This paper experimentally evaluates and proposes a novel technique for Received Signal Indicator (RSSI) distance prediction, leveraging transceiver height, and Fresnel ranging in a complex indoor environment to better suit the path loss of RSSI at a particular Reference Point (RP) and time, which further contributes greatly to indoor localization. The experimentation in different complex indoor environments of the corridor and office lab during work hours to ascertain real-life and time feasibility shows that the technique's accuracy is greatly improved in the office room and the corridor, achieving lower average prediction errors at low-cost than the comparison prediction algorithms. Compared with the conventional prediction techniques, for example, with Access Point 1 (AP1), the proposed Height Dependence Path-Loss (HEM) model at 0 dBm error attains a confidence probability of 10.98%, higher than the 2.65% for the distance dependence of Path-Loss New Empirical Model (NEM), 4.2% for the Multi-Wall dependence on Path-Loss (MWM) model, and 0% for the Conventional one-slope Path-Loss (OSM) model, respectively. Online localization, amongst the hearable APs, it is seen the proposed HEM fingerprint localization based on the proposed HEM prediction model attains a confidence probability of 31% at 3 m, 55% at 6 m, 78% at 9 m, outperforming the NEM with 26%, 43%, 62%, 62%, the MWM with 23%, 43%, 66%, respectively. The robustness of the HEM fingerprint using diverse predicted test samples by the NEM and MWM models indicates better localization of 13% than comparison fingerprints.

Low-Density Reference Fingerprinting SNP Dataset of CIMMYT Maize Lines for Quality Control and Genetic Diversity Analyses

CIMMYT maize lines (CMLs), which represent the tropical maize germplasm, are freely available worldwide. All currently released 615 CMLs and fourteen temperate maize inbred lines were genotyped with 180 kompetitive allele-specific PCR single nucleotide polymorphisms to develop a reference fingerprinting SNP dataset that can be used to perform quality control (QC) and genetic diversity analyses. The QC analysis identified 25 CMLs with purity, identity, or mislabeling issues. Further field observation, purification, and re-genotyping of these CMLs are required. The reference fingerprinting SNP dataset was developed for all of the currently released CMLs with 152 high-quality SNPs. The results of principal component analysis and average genetic distances between subgroups showed a clear genetic divergence between temperate and tropical maize, whereas the three tropical subgroups partially overlapped with one another. More than 99% of the pairs of CMLs had genetic distances greater than 0.30, showing their high genetic diversity, and most CMLs are distantly related. The heterotic patterns, estimated with the molecular markers, are consistent with those estimated using pedigree information in two major maize breeding programs at CIMMYT. These research findings are helpful for ensuring the regeneration and distribution of the true CMLs, via QC analysis, and for facilitating the effective utilization of the CMLs, globally.

Off-Line SPE LC-LRMS Polyphenolic Fingerprinting and Chemometrics to Classify and Authenticate Spanish Honey

The feasibility of non-targeted off-line SPE LC-LRMS polyphenolic fingerprints to address the classification and authentication of Spanish honey samples based on both botanical origin (blossom and honeydew honeys) and geographical production region was evaluated. With this aim, 136 honey samples belonging to different botanical varieties (multifloral and monofloral) obtained from different Spanish geographical regions with specific climatic conditions were analyzed. Polyphenolic compounds were extracted by off-line solid-phase extraction (SPE) using HLB (3 mL, 60 mg) cartridges. The obtained extracts were then analyzed by C18 reversed-phase LC coupled to low-resolution mass spectrometry in a hybrid quadrupole-linear ion trap mass analyzer and using electrospray in negative ionization mode. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were employed to assess the pattern recognition capabilities of the obtained fingerprints to address honey classification and authentication. In general, a good sample discrimination was accomplished by PLS-DA, being able to differentiate both blossom-honey and honeydew-honey samples according to botanical varieties. Multiclass predictions by cross-validation for the set of blossom-honey samples showed sensitivity, specificity, and classification ratios higher than 60%, 85%, and 87%, respectively. Better results were obtained for the set of honeydew-honey samples, exhibiting 100% sensitivity, specificity, and classification ratio values. The proposed fingerprints also demonstrated that they were good honey chemical descriptors to deal with climatic and geographical issues. Characteristic polyphenols of each botanical variety were tentatively identified by LC-MS/MS in multiple-reaction monitoring mode to propose possible honey markers for future experiments (i.e., naringin for orange/lemon blossom honeys, syringic acid in thyme honeys, or galangin in rosemary honeys).

A Blockchain and Fingerprinting Traceability Method for Digital Product Lifecycle Management

The rise of digitalization, sensory devices, cloud computing and internet of things (IoT) technologies enables the design of novel digital product lifecycle management (DPLM) applications for use cases such as manufacturing and delivery of digital products. The verification of the accomplishment/violations of agreements defined in digital contracts is a key task in digital business transactions. However, this verification represents a challenge when validating both the integrity of digital product content and the transactions performed during multiple stages of the DPLM. This paper presents a traceability method for DPLM based on the integration of online and offline verification mechanisms based on blockchain and fingerprinting, respectively. A blockchain lifecycle registration model is used for organizations to register the exchange of digital products in the cloud with partners and/or consumers throughout the DPLM stages as well as to verify the accomplishment of agreements at each DPLM stage. The fingerprinting scheme is used for offline verification of digital product integrity and to register the DPLM logs within digital products, which is useful in either dispute or violation of agreements scenarios. We built a DPLM service prototype based on this method, which was implemented as a cloud computing service. A case study based on the DPLM of audios was conducted to evaluate this prototype. The experimental evaluation revealed the ability of this method to be applied to DPLM in real scenarios in an efficient manner.

IoT Device Identification Using Directional Packet Length Sequences and 1D-CNN

With the large-scale application of the Internet of Things (IoT), security issues have become increasingly prominent. Device identification is an effective way to secure IoT environment by quickly identifying the category or model of devices in the network. Currently, the passive fingerprinting method used for IoT device identification based on network traffic flow mostly focuses on protocol features in packet headers but does not consider the direction and length of packet sequences. This paper proposes a device identification method for the IoT based on directional packet length sequences in network flows and a deep convolutional neural network. Each value in a packet length sequence represents the size and transmission direction of the corresponding packet. This method constructs device fingerprints from packet length sequences and uses convolutional layers to extract deep features from the device fingerprints. Experimental results show that this method can effectively recognize device identity with accuracy, recall, precision, and f1-score over 99%. Compared with methods using traditional machine learning and feature extraction techniques, our feature representation is more intuitive, and the classification model is effective.

The Volatile Profile of Brussels Sprouts (Brassica oleracea Var. gemmifera) as Affected by Pulsed Electric Fields in Comparison to Other Pretreatments, Selected to Steer (Bio)Chemical Reactions

Pulsed electric fields (PEF) at low field strength is considered a non-thermal technique allowing membrane permeabilization in plant-based tissue, hence possibly impacting biochemical conversions and the concomitant volatile profile. Detailed studies on the impact of PEF at low field strength on biochemical conversions in plant-based matrices are scarce but urgently needed to provide the necessary scientific basis allowing to open a potential promising field of applications. As a first objective, the effect of PEF and other treatments that aim to steer biochemical conversions on the volatile profile of Brussels sprouts was compared in this study. As a second objective, the effect of varying PEF conditions on the volatile profile of Brussels sprouts was elucidated. Volatile fingerprinting was used to deduce whether and which (bio)chemical reactions had occurred. Surprisingly, PEF at 1.01 kV/cm and 2.7 kJ/kg prior to heating was assumed not to have caused significant membrane permeabilization since similar volatiles were observed in the case of only heating, as opposed to mixing. A PEF treatment with an electrical field strength of 3.00 kV/cm led to a significantly higher formation of certain enzymatic reaction products, being more pronounced when combined with an energy input of 27.7 kJ/kg, implying that these PEF conditions could induce substantial membrane permeabilization. The results of this study can be utilized to steer enzymatic conversions towards an intended volatile profile of Brussels sprouts by applying PEF.