Identifying Putative Biomarkers of Foodborne Pathogens Using a Metabolomic Approach.

Metabolomics is the study of low molecular weight biochemical molecules (typically <1500 Da) in a defined biological organism or system. In case of food systems, the term "food metabolomics" is often used. Food metabolomics has been widely explored and applied in various fields including food analysis, food intake, food traceability, and food safety. Food safety applications focusing on the identification of pathogen-specific biomarkers have been promising. This chapter describes a nontargeted metabolite profiling workflow using gas chromatography coupled with mass spectrometry (GC-MS) for characterizing three globally important foodborne pathogens, Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella enterica, from selective enrichment liquid culture media. The workflow involves a detailed description of food spiking experiments followed by procedures for the extraction of polar metabolites from media, the analysis of the extracts using GC-MS, and finally chemometric data analysis using univariate and multivariate statistical tools to identify potential pathogen-specific biomarkers.

Detection of serum alterations in polysubstance use patients by FT-Raman spectroscopy.

Substance use disorders pose significant health risks and treatment challenges due to the diverse interactions between substances and their impact on physical and mental health. The chemical effects of multiple substance use on bodily fluids are not yet fully understood. Therefore, this study aimed to investigate the chemical changes induced by a combination of substances compared to a control group. Analysis of FT-Raman spectra revealed structural alterations in the amide III, I, and C = O functional groups of lipids in subjects treated with opioids, alcohol and cannabis (polysubstance group). These changes were evident in the form of peak shifts compared to the control group. Additionally, an imbalance in the amide-lipid ratio was observed, indicating perturbations in serum protein and lipid levels. Furthermore, a 2D plot of two-track two-dimensional correlation spectra (2T2D-COS) demonstrated a shift towards dominance of lipid vibrations in the polysubstance use groups, contrasting with the predominance of the amide fraction in the control group. This observation suggests distinct molecular changes induced by multiple substance use, potentially contributing to the pathophysiology of substance use disorders. Principal Component Analysis (PCA) was utilized to visualize the data structure and identify outliers. Subsequently, Partial Least Squares Discriminant Analysis (PLS-DA) was employed to classify the polysubstance use and control groups. The PLS-DA model demonstrated high classification accuracy, achieving 100.00 % in the training dataset and 94.74 % in the test dataset. Furthermore, receiver operating characteristic (ROC) analysis yielded perfect AUC values of 1.00 for both the training and test sets, underscoring the robustness of the classification model. This study highlights the quantitative and qualitative changes in serum protein and lipid levels induced by polysubstance use groups, as evidenced by FT-Raman spectroscopy. The findings underscore the importance of understanding the chemical effects of polysubstance use on bodily fluids for improved diagnosis and treatment of substance use disorders. Moreover, the successful classification of spectral data using machine learning techniques emphasizes the potential of these approaches in clinical applications for substance abuse monitoring and management.

Transforming Motor Imagery Analysis: A Novel EEG Classification Framework Using AtSiftNet Method.

This paper presents an innovative approach for the Feature Extraction method using Self-Attention, incorporating various Feature Selection techniques known as the AtSiftNet method to enhance the classification performance of motor imaginary activities using electrophotography (EEG) signals. Initially, the EEG signals were sorted and then denoised using multiscale principal component analysis to obtain clean EEG signals. However, we also conducted a non-denoised experiment. Subsequently, the clean EEG signals underwent the Self-Attention feature extraction method to compute the features of each trial (i.e., 350×18). The best 1 or 15 features were then extracted through eight different feature selection techniques. Finally, five different machine learning and neural network classification models were employed to calculate the accuracy, sensitivity, and specificity of this approach. The BCI competition III dataset IV-a was utilized for all experiments, encompassing the datasets of five volunteers who participated in the competition. The experiment findings reveal that the average accuracy of classification is highest among ReliefF (i.e., 99.946%), Mutual Information (i.e., 98.902%), Independent Component Analysis (i.e., 99.62%), and Principal Component Analysis (i.e., 98.884%) for both 1 and 15 best-selected features from each trial. These accuracies were obtained for motor imagery using a Support Vector Machine (SVM) as a classifier. In addition, five-fold validation was performed in this paper to assess the fair performance estimation and robustness of the model. The average accuracy obtained through five-fold validation is 99.89%. The experiments' findings indicate that the suggested framework provides a resilient biomarker with minimal computational complexity, making it a suitable choice for advancing Motor Imagery Brain-Computer Interfaces (BCI).

A new species of Opsariichthys (Teleostei, Cypriniformes, Xenocyprididae) from Southeast China.

Opsariichthysiridescens sp. nov. is described from the Qiantang and Oujiang rivers in Zhejiang Province and a tributary of the Yangtze River adjacent to the Qiantang River. It is distinguished from congeners by the following combination of morphological features: no obvious anterior notch on the tip of the upper lip; 45-52 lateral-line scales; 18-21 pre-dorsal scales; two rows of pharyngeal teeth; a maxillary extending to or slightly beyond the vertical anterior margin of the orbit in adult males; a pectoral fin extending to the pelvic fin in adult males; nuptial tubercles on the cheeks and lower jaw of males, which are usually united basally to form a plate; uniform narrow pale pink cross-bars on trunk and two widening significantly on caudal peduncle. Its validity was also supported by its distinct Cyt b gene sequence divergence from all congeners and its monophyly recovered in a Cyt b gene-based phylogenetic analysis.

Early warning of Aspergillus contamination in maize by gas chromatography-ion mobility spectrometry.

Introduction: As one of the main grain crops in China, maize is highly susceptible to Aspergillus infection during processing, storage and transportation due to high moisture at harvest, which results in the loss of quality. The aim of this study is to explore the early warning marker molecules when Aspergillus infects maize kernels. Methods: Firstly, strains MA and MB were isolated from moldy maize and identified by morphological characterization and 18S rRNA gene sequence analysis to be Aspergillus flavus (A. flavus) and Aspergillus niger (A. niger). Next, fresh maize was moldy by contaminated with strains MA and MB. The volatile organic compounds (VOCs) during the contamination process of two fungal strains were analyzed by gas chromatography-ion mobility spectrometry (GC-IMS). A total of 31 VOCs were detected in maize contaminated with strain MA, a total of 32 VOCs were detected in maize contaminated with strain MB, including confirmed monomers and dimers. Finally, heat maps and principal component analysis (PCA) showed that VOCs produced in different growth stages of Aspergillus had great differences. Combined with the results of GC-IMS, total fungal colony counts and fungal spores, it was concluded that the Aspergillus-contaminated maize was in the early stage of mold at 18 h. Results: Therefore, the characteristic VOCs butan-2-one, ethyl acetate-D, Benzaldehyde, and pentan-2-one produced by maize at 18 h of storage can be used as early mildew biomarkers of Aspergillus infection in maize. Discussion: This study provided effective marker molecules for the development of an early warning and monitoring system for the degree of maize mildew in granaries.

Predicting stroke occurrences: a stacked machine learning approach with feature selection and data preprocessing.

Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. This study investigates the efficacy of machine learning techniques, particularly principal component analysis (PCA) and a stacking ensemble method, for predicting stroke occurrences based on demographic, clinical, and lifestyle factors. We systematically varied PCA components and implemented a stacking model comprising random forest, decision tree, and K-nearest neighbors (KNN).Our findings demonstrate that setting PCA components to 16 optimally enhanced predictive accuracy, achieving a remarkable 98.6% accuracy in stroke prediction. Evaluation metrics underscored the robustness of our approach in handling class imbalance and improving model performance, also comparative analyses against traditional machine learning algorithms such as SVM, logistic regression, and Naive Bayes highlighted the superiority of our proposed method.

Real-space heterogeneous reconstruction, refinement, and disentanglement of CryoEM conformational states with HetSIREN.

Single-particle analysis by Cryo-electron microscopy (CryoEM) provides direct access to the conformation of each macromolecule. However, the image's signal-to-noise ratio is low, and some form of classification is usually performed at the image processing level to allow structural modeling. Classical classification methods imply the existence of a discrete number of structural conformations. However, new heterogeneity algorithms introduce a novel reconstruction paradigm, where every state is represented by a lower number of particles, potentially just one, allowing the estimation of conformational landscapes representing the different structural states a biomolecule explores. In this work, we present a novel deep learning-based method called HetSIREN. HetSIREN can fully reconstruct or refine a CryoEM volume in real space based on the structural information summarized in a conformational latent space. The unique characteristics that set HetSIREN apart start with the definition of the approach as a real space-based only method, a fact that allows spatially focused analysis, but also the introduction of a novel network architecture specifically designed to make use of meta-sinusoidal activations, with proven high analytics capacities. Continuing with innovations, HetSIREN can also refine the pose parameters of the images at the same time that it conditions the network with prior information/constraints on the maps, such as Total Variation and L 1 denoising, ultimately yielding cleaner volumes with high-quality structural features. Finally, but very importantly, HetSIREN addresses one of the most confusing issues in heterogeneity analysis, as it is the fact that real structural heterogeneity estimation is entangled with pose estimation (and to a lesser extent with CTF estimation), in this way, HetSIREN introduces a novel encoding architecture able to decouple pose and CTF information from the conformational landscape, resulting in more accurate and interpretable conformational latent spaces. We present results on computer-simulated data, public data from EMPIAR, and data from experimental systems currently being studied in our laboratories. An important finding is the sensitivity of the structure and dynamics of the SARS-CoV-2 Spike protein on the storage temperature.

SERS-based Ag NCs@PDMS flexible substrate combined with chemometrics for rapid detection of foodborne pathogens on egg surface.

An innovative method is introduced based on the combination of label-free surface-enhanced Raman scattering with advanced multivariate analysis. This technique allows both quantitative and qualitative assessment of Salmonella typhimurium and Escherichia coli on eggshells. Using silver nanocubes embedded in polydimethylsiloxane, we consistently achieved Raman spectra of bacteria. The stability of the Ag NCs@PDMS substrate is confirmed using rhodamine 6G over 30 days under standard conditions. Principal component analysis (PCA) effectively distinguishes between S. typhimurium and E. coli spectra. Partial least squares regression (PLS) models were developed for quantitative determination of bacteria on egg surfaces, yielding accurate results with minimal error. The S. typhimurium model achieves Rc2 = 0.9563 and RMSEC = 0.601 in calibration, and Rv2 = 0.9113 and RMSEV = 0.907 in validation. Similarly, the E. coli model achieves Rc2 = 0.9877 and RMSEC = 0.322 in calibration, and Rv2 = 0.9606 and RMSEV = 0.579 in validation. Recoveries validate PLS predictions by inoculating egg surfaces with varying bacterial amounts. Our study demonstrates the feasibility of SERS-PLS for quantitative determination of S. typhimurium and E. coli on eggshells, promising enhanced food safety protocols.

Real-Time Monitoring of Molecules in Aqueous Solution via a Surface-Functionalized Ag-Anodic Aluminum Oxide Surface-Enhanced Raman Scattering Platform.

Real-time monitoring of molecular species in aqueous solutions is crucial for diverse scientific applications, from biomedical diagnostics to environmental analysis. In this study, we investigate the selective detection and discrimination of specific molecules in aqueous solution samples using a Ag-coated anodized aluminum oxide (Ag-AAO) surface functionalized with thiol molecules. Our investigation harnesses the power of surface-enhanced Raman scattering (SERS) synergized with principal component analysis (PCA) to elucidate the distinctive signatures of aqueous dopamine and l-tyrosine molecules. By scrutinizing the Raman spectra of surface-treated molecules, we unveil nuanced variations driven by the unique functional groups of the thiol molecules and their dynamic interactions with the target molecules in solution. Notably, we observe different alterations in the SERS spectra of Ag-AAO surface-functionalized boronic acid molecules for detection of dopamine and l-tyrosine, even at a concentration as low as 10-8 M. Moreover, the spectral PCA elucidates the discrimination of dopamine and l-tyrosine within the aqueous environment attributed to the different molecular interactions near SERS-active hotspots. Our findings facilitate real-time monitoring of minute analytes with exceptional molecular selectivity, ushering in an era of precise chemical analysis in aqueous solutions.

An Analysis of Capsaicin, Dihydrocapsaicin, Vitamin C and Flavones in Different Tissues during the Development of Ornamental Pepper.

As a fruit and vegetable crop, the ornamental pepper is not just highly ornamental but also rich in nutritional value. The quality of ornamental pepper fruits is given in their contents of capsaicin, vitamin C (VC), flavonoids and total phenols. The study concentrated on the accumulation of capsaicin and dihydrocapsaicin in different tissues of 18 peppers during fruit growth and development. The results showed that the pericarp and placenta contained significantly higher levels of capsaicin than dihydrocapsaicin. Additionally, the placenta contained significantly higher levels of both capsaicin and dihydrocapsaicin compared to the pericarp. The content of capsaicin was in the range of 0-6.7915 mg·g-1, the range of dihydrocapsaicin content was 0-5.329 mg·g-1. Interestingly, we found that the pericarp is rich in VC (5.4506 mg·g-1) and the placenta is high in flavonoids (4.8203 mg·g-1) and total phenols (119.63 mg·g-1). The capsaicin is the most important component using the correlation analysis and principal component analysis. The qPCR results substantiated that the expression of genes in the placenta was significantly higher than that in the pericarp and that the expression of genes in green ripening stage was higher than that in red ripening stage. This study could be utilized to select the best ripening stages and tissues to harvest peppers according to the use of the pepper and to the needs of producers. It not only provides a reference for quality improvement and processing for consumers and market but also provides a theoretical basis for high-quality pepper breeding.

Insights on the comparative affinity of ribonucleic acids with plant-based beta carboline alkaloid, harmine: Spectroscopic, calorimetric and computational evaluation.

Small molecules as ligands target multifunctional ribonucleic acids (RNA) for therapeutic engagement. This study explores how the anticancer DNA intercalator harmine interacts various motifs of RNAs, including the single-stranded A-form poly (rA), the clover leaf tRNAphe, and the double-stranded A-form poly (rC)-poly (rG). Harmine showed the affinity to the polynucleotides in the order, poly (rA) > tRNAphe > poly (rC)·poly (rG). While no induced circular dichroism change was detected with poly (rC)poly (rG), significant structural alterations of poly (rA) followed by tRNAphe and occurrence of concurrent initiation of optical activity in the attached achiral molecule of alkaloid was reported. At 25 °C, the affinity further showed exothermic and entropy-driven binding. The interaction also highlighted heat capacity (ΔC o p ) and Gibbs energy contribution from the hydrophobic transfer (ΔG hyd) of binding with harmine. Molecular docking calculations indicated that harmine exhibits higher affinity for poly (rA) compared to tRNAphe and poly (rC)·poly (rG). Subsequent molecular dynamics simulations were conducted to investigate the binding mode and stability of harmine with poly(A), tRNAphe, and poly (rC)·poly (rG). The results revealed that harmine adopts a partial intercalative binding with poly (rA) and tRNAphe, characterized by pronounced stacking forces and stronger binding free energy observed with poly (rA), while a comparatively weaker binding free energy was observed with tRNAphe. In contrast, the stacking forces with poly (rC)·poly (rG) were comparatively less pronounced and adopts a groove binding mode. It was also supported by ferrocyanide quenching analysis. All these findings univocally provide detailed insight into the binding specificity of harmine, to single stranded poly (rA) over other RNA motifs, probably suggesting a self-structure formation in poly (rA) with harmine and its potential as a lead compound for RNA based drug targeting.

Deep learning-based electricity theft prediction in non-smart grid environments.

In developing countries, smart grids are nonexistent, and electricity theft significantly hampers power supply. This research introduces a lightweight deep-learning model using monthly customer readings as input data. By employing careful direct and indirect feature engineering techniques, including Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), UMAP (Uniform Manifold Approximation and Projection), and resampling methods such as Random-Under-Sampler (RUS), Synthetic Minority Over-sampling Technique (SMOTE), and Random-Over-Sampler (ROS), an effective solution is proposed. Previous studies indicate that models achieve high precision, recall, and F1 score for the non-theft (0) class, but perform poorly, even achieving 0 %, for the theft (1) class. Through parameter tuning and employing Random-Over-Sampler (ROS), significant improvements in accuracy, precision (89 %), recall (94 %), and F1 score (91 %) for the theft (1) class are achieved. The results demonstrate that the proposed model outperforms existing methods, showcasing its efficacy in detecting electricity theft in non-smart grid environments.

Development of a Neural Network for Target Gas Detection in Interdigitated Electrode Sensor-Based E-Nose Systems.

In this study, a neural network was developed for the detection of acetone, ethanol, chloroform, and air pollutant NO2 gases using an Interdigitated Electrode (IDE) sensor-based e-nose system. A bioimpedance spectroscopy (BIS)-based interface circuit was used to measure sensor responses in the e-nose system. The sensor was fed with a sinusoidal voltage at 10 MHz frequency and 0.707 V amplitude. Sensor responses were sampled at 100 Hz frequency and converted to digital data with 16-bit resolution. The highest change in impedance magnitude obtained in the e-nose system against chloroform gas was recorded as 24.86 Ω over a concentration range of 0-11,720 ppm. The highest gas detection sensitivity of the e-nose system was calculated as 0.7825 Ω/ppm against 6.7 ppm NO2 gas. Before training with the neural network, data were filtered from noise using Kalman filtering. Principal Component Analysis (PCA) was applied to the improved signal data for dimensionality reduction, separating them from noise and outliers with low variance and non-informative characteristics. The neural network model created is multi-layered and employs the backpropagation algorithm. The Xavier initialization method was used for determining the initial weights of neurons. The neural network successfully classified NO2 (6.7 ppm), acetone (1820 ppm), ethanol (1820 ppm), and chloroform (1465 ppm) gases with a test accuracy of 87.16%. The neural network achieved this test accuracy in a training time of 239.54 milliseconds. As sensor sensitivity increases, the detection capability of the neural network also improves.

Real-Time Indoor Visible Light Positioning (VLP) Using Long Short Term Memory Neural Network (LSTM-NN) with Principal Component Analysis (PCA).

New applications such as augmented reality/virtual reality (AR/VR), Internet-of-Things (IOT), autonomous mobile robot (AMR) services, etc., require high reliability and high accuracy real-time positioning and tracking of persons and devices in indoor areas. Among the different visible-light-positioning (VLP) schemes, such as proximity, time-of-arrival (TOA), time-difference-of-arrival (TDOA), angle-of-arrival (AOA), and received-signal-strength (RSS), the RSS scheme is relatively easy to implement. Among these VLP methods, the RSS method is simple and efficient. As the received optical power has an inverse relationship with the distance between the LED transmitter (Tx) and the photodiode (PD) receiver (Rx), position information can be estimated by studying the received optical power from different Txs. In this work, we propose and experimentally demonstrate a real-time VLP system utilizing long short-term memory neural network (LSTM-NN) with principal component analysis (PCA) to mitigate high positioning error, particularly at the positioning unit cell boundaries. Experimental results show that in a positioning unit cell of 100 × 100 × 250 cm3, the average positioning error is 5.912 cm when using LSTM-NN only. By utilizing the PCA, we can observe that the positioning accuracy can be significantly enhanced to 1.806 cm, particularly at the unit cell boundaries and cell corners, showing a positioning error reduction of 69.45%. In the cumulative distribution function (CDF) measurements, when using only the LSTM-NN model, the positioning error of 95% of the experimental data is >15 cm; while using the LSTM-NN with PCA model, the error is reduced to <5 cm. In addition, we also experimentally demonstrate that the proposed real-time VLP system can also be used to predict the direction and the trajectory of the moving Rx.

Relationship between amide ratio assessed by Fourier-transform infrared spectroscopy: A biomarker candidate for polycythemia vera disease.

The study utilized Fourier transform infrared (FTIR) spectroscopy coupled with chemometrics to investigate protein composition and structural changes in the blood serum of patients with polycythemia vera (PV). Principal component analysis (PCA) revealed distinct biochemical properties, highlighting elevated absorbance of phospholipids, amides, and lipids in PV patients compared to healthy controls. Ratios of amide I/amide II and amide I/amide III indicated alterations in protein structures. Support vector machine analysis and receiver operating characteristic curves identified amide I as a crucial predictor of PV, achieving 100% accuracy, sensitivity, and specificity, while amide III showed a lower predictive value (70%). PCA analysis demonstrated effective differentiation between PV patients and controls, with key wavenumbers including amide II, amide I, and CH lipid vibrations. These findings underscore the potential of FTIR spectroscopy for diagnosing and monitoring PV.

Assessment of heat stress contributing factors in the indoor environment among vulnerable populations in Klang Valley using principal component analysis (PCA).

Rising global temperatures can lead to heat waves, which in turn can pose health risks to the community. However, a notable gap remains in highlighting the primary contributing factors that amplify heat-health risk among vulnerable populations. This study aims to evaluate the precedence of heat stress contributing factors in urban and rural vulnerable populations living in hot and humid tropical regions. A comparative cross-sectional study was conducted, involving 108 respondents from urban and rural areas in Klang Valley, Malaysia, using a face-to-face interview and a validated questionnaire. Data was analyzed using the principal component analysis, categorizing factors into exposure, sensitivity, and adaptive capacity indicators. In urban areas, five principal components (PCs) explained 64.3% of variability, with primary factors being sensitivity (health morbidity, medicine intake, increased age), adaptive capacity (outdoor occupation type, lack of ceiling, longer residency duration), and exposure (lower ceiling height, increased building age). In rural, five PCs explained 71.5% of variability, with primary factors being exposure (lack of ceiling, high thermal conductivity roof material, increased building age, shorter residency duration), sensitivity (health morbidity, medicine intake, increased age), and adaptive capacity (female, non-smoking, higher BMI). The order of heat-health vulnerability indicators was sensitivity > adaptive capacity > exposure for urban areas, and exposure > sensitivity > adaptive capacity for rural areas. This study demonstrated a different pattern of leading contributors to heat stress between urban and rural vulnerable populations.

A bayesian spatio-temporal dynamic analysis of food security in Africa.

Exploring the factors influencing Food Security and Nutrition (FSN) and understanding its dynamics is crucial for planning and management. This understanding plays a pivotal role in supporting Africa's food security efforts to achieve various Sustainable Development Goals (SDGs). Utilizing Principal Component Analysis (PCA) on data from the FAO website, spanning from 2000 to 2019, informative components are derived for dynamic spatio-temporal modeling of Africa's FSN Given the dynamic and evolving nature of the factors impacting FSN, despite numerous efforts to understand and mitigate food insecurity, existing models often fail to capture this dynamic nature. This study employs a Bayesian dynamic spatio-temporal approach to explore the interconnected dynamics of food security and its components in Africa. The results reveal a consistent pattern of elevated FSN levels, showcasing notable stability in the initial and middle-to-late stages, followed by a significant acceleration in the late stage of the study period. The Democratic Republic of Congo and Ethiopia exhibited particularly noteworthy high levels of FSN dynamicity. In particular, child care factors and undernourishment factors showed significant dynamicity on FSN. This insight suggests establishing regional task forces or forums for coordinated responses to FSN challenges based on dynamicity patterns to prevent or mitigate the impact of potential food security crises.

A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management.

Increasingly, information technology facilitates the storage and management of data useful for risk analysis and event prediction. Studies on data extraction related to occupational health and safety are increasingly available; however, due to its variability, the construction sector warrants special attention. This review is conducted under the research programs of the National Institute for Occupational Accident Insurance (Inail). OBJECTIVES: The research question focuses on identifying which data mining (DM) methods, among supervised, unsupervised, and others, are most appropriate for certain investigation objectives, types, and sources of data, as defined by the authors. METHODS: Scopus and ProQuest were the main sources from which we extracted studies in the field of construction, published between 2014 and 2023. The eligibility criteria applied in the selection of studies were based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA). For exploratory purposes, we applied hierarchical clustering, while for in-depth analysis, we used principal component analysis (PCA) and meta-analysis. RESULTS: The search strategy based on the PRISMA eligibility criteria provided us with 63 out of 2234 potential articles, 206 observations, 89 methodologies, 4 survey purposes, 3 data sources, 7 data types, and 3 resource types. Cluster analysis and PCA organized the information included in the paper dataset into two dimensions and labels: "supervised methods, institutional dataset, and predictive and classificatory purposes" (correlation 0.97-8.18 × 10-1; p-value 7.67 × 10-55-1.28 × 10-22) and the second, Dim2 "not-supervised methods; project, simulation, literature, text data; monitoring, decision-making processes; machinery and environment" (corr. 0.84-0.47; p-value 5.79 × 10-25--3.59 × 10-6). We answered the research question regarding which method, among supervised, unsupervised, or other, is most suitable for application to data in the construction industry. CONCLUSIONS: The meta-analysis provided an overall estimate of the better effectiveness of supervised methods (Odds Ratio = 0.71, Confidence Interval 0.53-0.96) compared to not-supervised methods.

Estimation of Time-Since-Deposition of bloodstains on different surfaces using ATR-FTIR Spectroscopy and Chemometrics.

Blood is commonly discovered at crime scenes in various forms, including stains, dried residue, pools, and fingerprints on assorted surfaces. Estimating the age of bloodstains is a crucial aspect of reconstructing crime scenes. This research aimed to investigate how the nature of different surfaces affects the estimation of bloodstain age, utilizing a reliable and non-destructive approach. The study employed ATR-FTIR spectroscopy in conjunction with Chemometric techniques such as PCA (Principal Component Analysis) and OPLSR (Orthogonal Signal Correction Partial Least Square Regression Analysis) to analyze spectral data and develop regression models for estimating bloodstain age on cement, metal, and wooden surfaces for up to eleven days. The chemometric models for bloodstains on all three substrates demonstrated strong performance, with predictive Root Mean Square Error (RMSE) values ranging from 1.1 to 1.43 and R2 values from 0.84 to 0.89. Notably, the model developed for metal surfaces was found to be the most accurate with minimal prediction error. The findings of the study showed that the porosity of the substrates upon which bloodstains were found had a discernible influence on the age-related transformations observed in bloodstains; the majority of which occured within the spectral range of 2800 cm- 1 to 3500 cm- 1.

ToF-SIMS evaluation of PEG-related mass peaks and applications in PEG detection in cosmetic products.

Polyethylene glycols (PEGs) are used in industrial, medical, health care, and personal care applications. The cycling and disposal of synthetic polymers like PEGs pose significant environmental concerns. Detecting and monitoring PEGs in the real world calls for immediate attention. This study unveils the efficacy of time-of-flight secondary ion mass spectrometry (ToF-SIMS) as a reliable approach for precise analysis and identification of reference PEGs and PEGs used in cosmetic products. By comparing SIMS spectra, we show remarkable sensitivity in pinpointing distinctive ion peaks inherent to various PEG compounds. Moreover, the employment of principal component analysis effectively discriminates compositions among different samples. Notably, the application of SIMS two-dimensional image analysis visually portrays the spatial distribution of various PEGs as reference materials. The same is observed in authentic cosmetic products. The application of ToF-SIMS underscores its potential in distinguishing PEGs within intricate environmental context. ToF-SIMS provides an effective solution to studying emerging environmental challenges, offering straightforward sample preparation and superior detection of synthetic organics in mass spectral analysis. These features show that SIMS can serve as a promising alternative for evaluation and assessment of PEGs in terms of the source, emission, and transport of anthropogenic organics.