The most common scientific analysis of archaeological ceramics aims to determine the raw material source and/or production technology. Scientists and archaeologists widely use XRF-based techniques as a tool in a provenance study. After conducting XRF analysis, the results are often analyzed using multivariate analysis in addition to interpretation and conclusions. Various multivariate techniques have already been applied in archaeological ceramics provenance studies to reveal different raw material sources, identify imported pieces, or determine different production recipes. This study aims to evaluate the results of multivariate analysis in the provenance study of ceramics that belong to three cultures that settled in the same area during various prehistoric periods. Portable energy-dispersive X-ray fluorescence spectrometry (pEDXRF) was used to determine the elemental composition of the ceramic material. The ceramic material was prepared in two different ways. The ceramic body material was ground into powder, homogenized, and then pressed into tablets. After that, the same fragments are polished in suitable places. Quantitative and qualitative analyses were performed on the tablets and polished pieces. The results were subjected to both unsupervised and supervised multivariate analysis. Based on the results, it was concluded that qualitative analysis of the well-prepared shards\' surface using EDXRF spectrometry could be utilized in provenance studies, even when the ceramic assemblages were made of similar raw materials.

This study investigated whether modes of variance in trial-to-trial whole-body kinematic variability identified by principal component analysis (PCA) were consistent across data pre-processing conditions generated from a common dataset. Comparisons made included 1) when trajectory data was expressed in a global vs. local reference frame; 2) when the number of landmarks used to represent whole-body motion differed, and; 3) whether input trajectory data were normalized to participant stature. Varying data pre-processing conditions prior to PCA does not bias the total variance identified. However, it can influence how modes of variance are dispersed across PCs, which in turn, can influence interpretation.

The study introduces a new online spike encoding algorithm for spiking neural networks (SNN) and suggests new methods for learning and identifying diagnostic biomarkers using three prominent deep learning neural network models: deep BiLSTM, reservoir SNN, and NeuCube. EEG data from datasets related to epilepsy, migraine, and healthy subjects are employed. Results reveal that BiLSTM hidden neurons capture biological significance, while reservoir SNN activities and NeuCube spiking dynamics identify EEG channels as diagnostic biomarkers. BiLSTM and reservoir SNN achieve 90 and 85% classification accuracy, while NeuCube achieves 97%, all methods pinpointing potential biomarkers like T6, F7, C4, and F8. The research bears implications for refining online EEG classification, analysis, and early brain state diagnosis, enhancing AI models with interpretability and discovery. The proposed techniques hold promise for streamlined brain-computer interfaces and clinical applications, representing a significant advancement in pattern discovery across the three most popular neural network methods for addressing a crucial problem. Further research is planned to study how early can these diagnostic biomarkers predict an onset of brain states.

Osteoarthritis (OA) is a degenerative joint disease primarily affecting the cartilage. The therapeutic potential of the Dipsacus asper-Achyranthes bidentate herb pair for OA has been acknowledged, yet its precise mechanism remains elusive. In this study, we conducted a comprehensive analysis of metabolomic changes and therapeutic outcomes in osteoarthritic rats, employing a gas chromatography-mass spectrometry-based metabolomics approach in conjunction with histopathological and biochemical assessments. The rats were divided into six groups: control, model, positive control, Dipsacus asper treated, Achyranthes bidentata treated, and herb pair treated groups. Compared to the model group, significant reductions in levels of tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), interleukin-6 (IL-6), and iNOS were observed in the treated groups. Multivariate statistical analyses were employed to investigate metabolite profile changes in serum samples and identify potential biomarkers, revealing 45 differential biomarkers, with eighteen validated using standard substances. These analytes exhibited excellent linearity across a wide concentration range (R2>0.9990), with intra- and inter-day precision RSD values below 4.69% and 4.83%, respectively. Recoveries of the eighteen analytes ranged from 93.97% to 106.59%, with RSD values under 5.72%, underscoring the method\'s reliability. Treatment with the herbal pair effectively restored levels of unsaturated fatty acids such as linoleic acid and arachidonic acid, along with glucogenic amino acids. Additionally, levels of phosphoric acid and citric acid were reversed, indicating restoration of energy metabolism. Collectively, these findings highlight the utility of metabolomic analysis in evaluating therapeutic efficacy and elucidating the underlying molecular mechanisms of herb pairs in OA treatment.

While the term task load (TL) refers to external task demands, the amount of work, or the number of tasks to be performed, mental workload (MWL) refers to the individual\'s effort, mental capacity, or cognitive resources utilized while performing a task. MWL in multitasking scenarios is often closely linked with the quantity of tasks a person is handling within a given timeframe. In this study, we challenge this hypothesis from the perspective of electroencephalography (EEG) using a deep learning approach. We conducted an EEG experiment with 50 participants performing NASA Multi-Attribute Task Battery II (MATB-II) under 4 different task load levels. We designed a convolutional neural network (CNN) to help with two distinct classification tasks. In one setting, the CNN was used to classify EEG segments based on their task load level. In another setting, the same CNN architecture was trained again to detect the presence of individual MATB-II subtasks. Results show that, while the model successfully learns to detect whether a particular subtask is active in a given segment (i.e., to differentiate between different subtasks-related EEG patterns), it struggles to differentiate between the two highest levels of task load (i.e., to distinguish MWL-related EEG patterns). We speculate that the challenge comes from two factors: first, the experiment was designed in a way that these two highest levels differed only in the quantity of work within a given timeframe; and second, the participants\' effective adaptation to increased task demands, as evidenced by low error rates. Consequently, this indicates that under such conditions in multitasking, EEG may not reflect distinct enough patterns to differentiate higher levels of task load.

Pain assessment is a critical aspect of healthcare, influencing timely interventions and patient well-being. Traditional pain evaluation methods often rely on subjective patient reports, leading to inaccuracies and disparities in treatment, especially for patients who present difficulties to communicate due to cognitive impairments. Our contributions are three-fold. Firstly, we analyze the correlations of the data extracted from biomedical sensors. Then, we use state-of-the-art computer vision techniques to analyze videos focusing on the facial expressions of the patients, both per-frame and using the temporal context. We compare them and provide a baseline for pain assessment methods using two popular benchmarks: UNBC-McMaster Shoulder Pain Expression Archive Database and BioVid Heat Pain Database. We achieved an accuracy of over 96% and over 94% for the F1 Score, recall and precision metrics in pain estimation using single frames with the UNBC-McMaster dataset, employing state-of-the-art computer vision techniques such as Transformer-based architectures for vision tasks. In addition, from the conclusions drawn from the study, future lines of work in this area are discussed.

BACKGROUND: Lung ultrasound (LUS) is a noninvasive tool for examining respiratory distress patients. The lung ultrasound score (LUSS) can be used to quantify and monitor lung aeration loss with good reliability.
OBJECTIVE: Assess the reliability of a new LUSS among raters with different levels of experience and determine how well the same raters agree on identifying patterns of LUS abnormalities.
METHODS: Forty LUS examinations of dogs and cats and 320 videos were reviewed from a digital database.
METHODS: Retrospective reliability study with post hoc analysis. Protocolized LUS were randomly selected; intrarater and interrater reliability of the LUSS and pattern recognition agreement among 4 raters with different levels of experience in LUS were tested.
RESULTS: The intrarater intraclass correlation coefficient (ICC) single measurement, absolute agreement, and 2-way mixed effects model was 0.967 for the high-experience rater (H-Exp), 0.963 and 0.952 for the medium-experience raters (M-Exp-1; M-Exp-2), and 0.950 for the low-experience rater (L-Exp). The interrater ICC average measurement, absolute agreement, and 2-way random effects model among the observers was 0.980. The Fleiss\' kappa (k) values showed almost perfect agreement (k = 1) among raters in identifying pleural effusion and translobar tissue-like pattern, strong agreement for A-lines (k = 0.881) and B-lines (k = 0.806), moderate agreement (k = 0.693) for subpleural loss of aeration, and weak agreement (k = 0.474) for irregularities of the pleural line.
CONCLUSIONS: Our results indicate excellent intra- and interrater reliability for LUS scoring and pattern identification, providing a foundation for the use of the LUSS in emergency medicine and intensive care.

UNASSIGNED: In this study, we aimed to apply laboratory blood analysis to identify the hematological (based on hemoglobin concentration, erythrocytes, hematocrit, and RDW count) profiles associated with the most prevalent forms of digestive tract malignancies. Furthermore, we aimed to evaluate how these profiles contributed to distinguishing these tumors at diagnosis.
UNASSIGNED: We collected data from the date of ICD-10 diagnostic coding for C15 esophagus, C16 stomach, C18 colon, and C19 rectum tumors of 184 individuals. The statistical analysis and data visualization approaches, notably the heat map and principal component analysis (PCA), allowed for creating a summary hematological profile and identifying the most associated parameters for each pathologic state. Univariate and multivariate data modeling and ROC analysis were performed in both SPSS and Python.
UNASSIGNED: Our data reveal unique patterns based on tumor development anatomical location, clustering the C18 colon and C19 rectum from the C15 esophagus and C16 stomach. We found a significant difference between C16 stomach carcinoma and the other tumors, which substantially correlated with raised RDW in conjunction with low hemoglobin concentration, erythrocytes, and hematocrit counts. In contrast, C18 colon carcinoma had the higher red blood cell count, allowing for the best classification metrics in the test set of the binary logistic regression (LR) model, accounting for an AUC of 0.77 with 94% sensitivity and 52% specificity.
UNASSIGNED: This study emphasizes the significance of adding hematological patterns in diagnosing these malignancies, which could path further investigations regarding profiling and monitoring at the point of care.

Drop-on-demand printing using colloidal or pigmented inks is prone to the clogging of printing nozzles, which can lead to positional deviations and inconsistently printed patterns (e.g., data matrix codes, DMCs). However, if such deviations are detected early, they can be useful for determining the state of the print head and planning maintenance operations prior to reaching a printing state where the printed DMCs are unreadable. To realize this predictive maintenance approach, it is necessary to accurately quantify the positional deviation of individually printed dots from the actual target position. Here, we present a comparison of different methods based on affinity transformations and clustering algorithms for calculating the target position from the printed positions and, subsequently, the deviation of both for complete DMCs. Hence, our method focuses on the evaluation of the print quality, not on the decoding of DMCs. We compare our results to a state-of-the-art decoding algorithm, adopted to return the target grid positions, and find that we can determine the occurring deviations with significantly higher accuracy, especially when the printed DMCs are of low quality. The results enable the development of decision systems for predictive maintenance and subsequently the optimization of printing systems.

Hypericum perforatum L. (St. John\'s wort) is one of the world\'s most consumed medicinal plants for treating depression and psychiatric disorders. Counterfeiting can occur in the medicinal plant trade, either due to the lack of active ingredients or the addition of substances not mentioned on the labels, often without therapeutic value or even harmful to health. Hence, 43 samples of St. John\'s wort commercially acquired in different Brazilian regions and other countries were analyzed by paper spray ionization mass spectrometry (PS-MS) and modeled by principal component analysis. Hence, samples (plants, capsules, and tablets) were extracted with ethanol in a solid-liquid extraction. For the first time, PS-MS analysis allowed the detection of counterfeit H. perforatum samples containing active principles typical of other plants, such as Ageratum conyzoides and Senna spectabilis. About 52.3% of the samples were considered adulterated for having at least one of these two species in their composition. Furthermore, out of 35 samples produced in Brazil, only 13 were deemed authentic, having only H. perforatum. Therefore, there is a clear need to improve these drugs\' quality control in Brazil.