scSTAR reveals hidden heterogeneity with a real-virtual cell pair structure across conditions in single-cell RNA sequencing data.

Cell-state transition can reveal additional information from single-cell ribonucleic acid (RNA)-sequencing data in time-resolved biological phenomena. However, most of the current methods are based on the time derivative of the gene expression state, which restricts them to the short-term evolution of cell states. Here, we present single-cell State Transition Across-samples of RNA-seq data (scSTAR), which overcomes this limitation by constructing a paired-cell projection between biological conditions with an arbitrary time span by maximizing the covariance between two feature spaces using partial least square and minimum squared error methods. In mouse ageing data, the response to stress in CD4+ memory T cell subtypes was found to be associated with ageing. A novel Treg subtype characterized by mTORC activation was identified to be associated with antitumour immune suppression, which was confirmed by immunofluorescence microscopy and survival analysis in 11 cancers from The Cancer Genome Atlas Program. On melanoma data, scSTAR improved immunotherapy-response prediction accuracy from 0.8 to 0.96.

Deciphering genotype-by-environment interaction of grass pea genotypes under rain-fed conditions and emphasizing the role of monthly rainfall.

Rainfed regions have inconsistent spatial and temporal rainfall. So, these regions could face water deficiency during critical stages of crop growth. In this regard, multi-environment trials could play a key role in introducing stable genotypes with good performance across several rainfed regions. Grass pea, as a potential forage crop, is a resilient plant that could grow in unsuitable circumstances. In this study, agro-morphological attributes of 16 grass pea genotypes were examined in four semi-warm rain-fed regions during the years 2018-2021. The MLM analysis of variance showed a significant genotype-by-environment interaction (GEI) for dry yield, seed yield, days to maturity, days to flowering, and plant height of grass pea. The PLS (partial least squares) regression revealed that rainfall in the grass pea establishment stage (October and November) is meaningful. For grass pea cultivation, monthly rainfall during plant growth is important, especially in May, with an aim for seed yield. Regarding dry yield, G5, G10, G11, G12, G13, and G15 were selected as good performers and stable genotypes using DY × WAASB biplots, while SY × WAASB biplot manifested G2, G3, G12, and G13 as superior genotypes with stable seed yield. Considering equal weights for yield as well as the WAASB stability index (50/50), G13 was selected as the best one. Among test environments, E2 and E11 played a prominent role in distinguishing the above genotypes from other ones. In this study, MTSI (multi-trait stability index) analysis was applied to select a stable genotype, considering all measured agro-morphological traits simultaneously. Henceforth, the G5 and G15 grass pea genotypes were discerningly chosen due to their commendable performance in the WAASBY plot. In this context, G13 did not emerge as the winner based on MTSI; however, it exhibited an MTSI value in close proximity to the outer boundary of the circle. Consequently, upon comprehensive consideration of all traits, it is deduced that G5, G13, and G15 can be appraised as promising superior genotypes with stability across diverse environmental conditions.

A comparative analysis of univariate versus multivariate eco-friendly spectrophotometric manipulations for resolving severely overlapped spectra of vonoprazan and amoxicillin new combination.

Vonoprazan and amoxicillin are pharmacological combinations that demonstrate synergistic effects in treating Helicobacter pylori (H. pylori), a global public health concern associated with peptic ulcer disease and gastric cancer. Four spectrophotometric methods were developed, including two univariate techniques (Fourier self-deconvolution and ratio difference) and two multivariate chemometric approaches (partial least squares and principal component regression). These methods provide innovative solutions for effectively resolving and accurately quantifying the overlapping spectra of vonoprazan and amoxicillin. The concentration ranges covered were 3-60 µg ml-1 for vonoprazan and 5-140 µg ml-1 for amoxicillin. To assess the environmental sustainability of the methodologies, various measures such as the Green Analytical Procedure Index (GAPI), National Environmental Method Index (NEMI), Analytical GREEnness Calculator, and Analytical Eco-scale, as well as RGB12 and hexagon toll were implemented. The validation of the developed techniques was carried out in compliance with ICH standards. The present study is highly significant because it is the first time that the mixture has been determined using the current approaches. The comparative analysis demonstrated no significant difference in terms of accuracy and precision compared to reference HPLC method (p = 0.05). The established spectrophotometric methods offer a straightforward, rapid, and cost-effective alternative to complex analytical techniques for determining the vonoprazan and amoxicillin mixture. They show potential for routine analysis in research laboratories and pharmaceutical industries.

Rapid quality assessment and traceability of ginger powder from Northeast India and Indian market based on near infrared spectroscopic fingerprinting.

INTRODUCTION: Ginger (Zingiber officinale Rosc.) varies widely due to varying concentrations of phytochemicals and geographical origin. Rapid non-invasive quality and traceability assessment techniques ensure a sustainable value chain. OBJECTIVE: The objective of this study is the development of suitable machine learning models to estimate the concentration of 6-gingerol and check traceability based on the spectral fingerprints of dried ginger samples collected from Northeast India and the Indian market using near-infrared spectrometry. METHODS: Samples from the market and Northeast India underwent High Performance Liquid Chromatographic analysis for 6-gingerol content estimation. Near infrared (NIR) Spectrometer acquired spectral data. Quality prediction utilized partial least square regression (PLSR), while fingerprint-based traceability identification employed principal component analysis and t-distributed stochastic neighbor embedding (t-SNE). Model performance was assessed using RMSE and R2 values across selective wavelengths and spectral fingerprints. RESULTS: The standard normal variate pretreated spectral data over the wavelength region of 1,100-1,250 nm and 1,325-1,550 nm showed the optimal calibration model with root mean square error of calibration and R2 C (coefficient of determination for calibration) values of 0.87 and 0.897 respectively. A lower value (0.24) of root mean square error of prediction and a higher value (0.973) of R2 P (coefficient of determination for prediction) indicated the effectiveness of the developed model. t-SNE performed better clustering of samples based on geographical location, which was independent of gingerol content. CONCLUSION: The developed NIR spectroscopic model for Indian ginger samples predicts the 6-gingerol content and provides geographical traceability-based identification to ensure a sustainable value chain, which can promote efficiency, cost-effectiveness, consumer confidence, sustainable sourcing, traceability, and data-driven decision-making.

Investigating important and necessary conditions to analyse traffic noise levels at intersections in mid-sized cities.

Traffic noise is a major problem for urban residents, especially near intersections. In order to effectively manage and control traffic noise, there is a need for a better understanding of noise-influencing variables at intersections. In this way, the study aims to identify and distinguish the important and necessary conditions corresponding to the particular traffic noise level. Using 342 h of field data from 19 intersections in Kanpur, the current research has used the Partial Least Square-Structural Equation Modelling (PLS-SEM) and Necessary Condition Analysis (NCA). The study determines that traffic volume, honking, speed, and median width are important factors. Traffic volume and honking are positively affecting traffic noise level, while speed and median width have a negative effect. Further investigation reveals that only traffic volume and honking are necessary to achieve a particular traffic noise level. Policymakers can use these findings to manage and control traffic noise at intersections.

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.

Developing Prediction Models Using Near-Infrared Spectroscopy to Quantify Cannabinoid Content in Cannabis Sativa.

Cannabis is commercially cultivated for both therapeutic and recreational purposes in a growing number of jurisdictions. The main cannabinoids of interest are cannabidiol (CBD) and delta-9 tetrahydrocannabidiol (THC), which have applications in different therapeutic treatments. The rapid, nondestructive determination of cannabinoid levels has been achieved using near-infrared (NIR) spectroscopy coupled to high-quality compound reference data provided by liquid chromatography. However, most of the literature describes prediction models for the decarboxylated cannabinoids, e.g., THC and CBD, rather than naturally occurring analogues, tetrahydrocannabidiolic acid (THCA) and cannabidiolic acid (CBDA). The accurate prediction of these acidic cannabinoids has important implications for quality control for cultivators, manufacturers and regulatory bodies. Using high-quality liquid chromatography-mass spectroscopy (LCMS) data and NIR spectra data, we developed statistical models including principal component analysis (PCA) for data quality control, partial least squares regression (PLS-R) models to predict cannabinoid concentrations for 14 different cannabinoids and partial least squares discriminant analysis (PLS-DA) models to characterise cannabis samples into high-CBDA, high-THCA and even-ratio classes. This analysis employed two spectrometers, a scientific grade benchtop instrument (Bruker MPA II-Multi-Purpose FT-NIR Analyzer) and a handheld instrument (VIAVI MicroNIR Onsite-W). While the models from the benchtop instrument were generally more robust (99.4-100% accuracy prediction), the handheld device also performed well (83.1-100% accuracy prediction) with the added benefits of portability and speed. In addition, two cannabis inflorescence preparation methods were evaluated: finely ground and coarsely ground. The models generated from coarsely ground cannabis provided comparable predictions to that of the finely ground but represent significant timesaving in terms of sample preparation. This study demonstrates that a portable NIR handheld device paired with LCMS quantitative data can provide accurate cannabinoid predictions and potentially be of use for the rapid, high-throughput, nondestructive screening of cannabis material.

Colorimetric Paper-Based Analytical Devices (PADs) Backed by Chemometrics for Pd(II) Detection.

This paper presents the development of cheap and selective Paper-based Analytical Devices (PADs) for selective Pd(II) determination from very acidic aqueous solutions. The PADs were obtained by impregnating two cm-side squares of filter paper with an azoic ligand, (2-(tetrazolylazo)-1,8 dihydroxy naphthalene-3,6,-disulphonic acid), termed TazoC. The so-obtained orange TazoC-PADs interact quickly with Pd(II) in aqueous solutions by forming a complex purple-blue-colored already at pH lower than 2. The dye complexes no other metal ions at such an acidic media, making TazoC-PADs highly selective to Pd(II) detection. Besides, at higher pH values, other cations, for example, Cu(II) and Ni(II), can interact with TazoC through the formation of stable and pink-magenta-colored complexes; however, it is possible to quantify Pd(II) in the presence of other cations using a multivariate approach. To this end, UV-vis spectra of the TazoC-PADs after equilibration with the metal ions solutions were registered in the 300-800 nm wavelength range. By applying Partial Least Square regression (PLS), the whole UV-vis spectra of the TazoC-PADs were related to the Pd(II) concentrations both when present alone in solution and also in the presence of Cu(II) and Ni(II). Tailored PLS models obtained with matrix-matched standard solutions correctly predicted Pd(II) concentrations in unknown samples and tap water spiked with the metal cation, making the method promising for quick and economical sensing of Pd(II).

Can Gait Characteristics Be Represented by Physical Activity Measured with Wrist-Worn Accelerometers?

Wearable accelerometers allow for continuous monitoring of function and behaviors in the participant's naturalistic environment. Devices are typically worn in different body locations depending on the concept of interest and endpoint under investigation. The lumbar and wrist are commonly used locations: devices placed at the lumbar region enable the derivation of spatio-temporal characteristics of gait, while wrist-worn devices provide measurements of overall physical activity (PA). Deploying multiple devices in clinical trial settings leads to higher patient burden negatively impacting compliance and data quality and increases the operational complexity of the trial. In this work, we evaluated the joint information shared by features derived from the lumbar and wrist devices to assess whether gait characteristics can be adequately represented by PA measured with wrist-worn devices. Data collected at the Pfizer Innovation Research (PfIRe) Lab were used as a real data example, which had around 7 days of continuous at-home data from wrist- and lumbar-worn devices (GENEActiv) obtained from a group of healthy participants. The relationship between wrist- and lumbar-derived features was estimated using multiple statistical methods, including penalized regression, principal component regression, partial least square regression, and joint and individual variation explained (JIVE). By considering multilevel models, both between- and within-subject effects were taken into account. This work demonstrated that selected gait features, which are typically measured with lumbar-worn devices, can be represented by PA features measured with wrist-worn devices, which provides preliminary evidence to reduce the number of devices needed in clinical trials and to increase patients' comfort. Moreover, the statistical methods used in this work provided an analytic framework to compare repeated measures collected from multiple data modalities.

Sex Determination of Human Nails Based on Attenuated Total Reflection Fourier Transform Infrared Spectroscopy in Forensic Context.

This study reports on the successful use of a machine learning approach using attenuated total reflectance Fourier transform infrared (ATR FT-IR) spectroscopy for the classification and prediction of a donor's sex from the fingernails of 63 individuals. A significant advantage of ATR FT-IR is its ability to provide a specific spectral signature for different samples based on their biochemical composition. The infrared spectrum reveals unique vibrational features of a sample based on the different absorption frequencies of the individual functional groups. This technique is fast, simple, non-destructive, and requires only small quantities of measured material with minimal-to-no sample preparation. However, advanced multivariate techniques are needed to elucidate multiplex spectral information and the small differences caused by donor characteristics. We developed an analytical method using ATR FT-IR spectroscopy advanced with machine learning (ML) based on 63 donors' fingernails (37 males, 26 females). The PLS-DA and ANN models were established, and their generalization abilities were compared. Here, the PLS scores from the PLS-DA model were used for an artificial neural network (ANN) to create a classification model. The proposed ANN model showed a greater potential for predictions, and it was validated against an independent dataset, which resulted in 92% correctly classified spectra. The results of the study are quite impressive, with 100% accuracy achieved in correctly classifying donors as either male or female at the donor level. Here, we underscore the potential of ML algorithms to leverage the selectivity of ATR FT-IR spectroscopy and produce predictions along with information about the level of certainty in a scientifically defensible manner. This proof-of-concept study demonstrates the value of ATR FT-IR spectroscopy as a forensic tool to discriminate between male and female donors, which is significant for forensic applications.

Evaluation of the effect of granule size of raw tableting materials on critical quality attributes of tablets during the continuous tablet manufacturing process using near-infrared spectroscopy.

OBJECTIVE: The effects of granule size of raw materials on tablet hardness (TH) and weight (TW) in the continuous tablet manufacturing process (CTMP) were investigated using near-infrared spectroscopy (NIRS). METHODS: Granule materials of different sizes were prepared by extrusion granulation from a standard granule formula powder containing lactose/starch and 4.5% acetaminophen. Large-, small-, and medium-sized granules were sequentially filled in a hopper, and tablets were produced continuously using a single-shot tableting machine. After arranging approximately 500 tablets in order, the tablets were subjected to NIRS. A total of 450 NIRS datasets were divided into three groups of 150 each (calibration, validation 1, and validation 2 datasets). RESULTS: The best fitted calibration models for predicting TH and TW were obtained, with sufficient accuracy, based on NIRS using the partial least squares regression, and comprised both physical and chemical information. The regression and loading vectors of the calibration models suggested that the models used to predict TH and TW involve physical information based on geometrical factors of the tablet and chemical information related to binder-related intermolecular interactions. CONCLUSIONS: The changes in the predicted value profiles of TH and TW using NIRS reflected the changes in the measured values depending on the raw granule size during CTMP.

Correlates of dental visits in children with hearing loss: An application of the theory of planned behaviour.

BACKGROUND: Maintenance of oral health of children with special needs requires the involvement of caregivers who are also responsible for ensuring adherence to professional recommendations, including dental visits. AIM: This study aimed at exploring the correlates of dental visits of children with hearing loss (CWHL) in Indonesia using the theory of planned behaviour (TPB). DESIGN: This cross-sectional study involved purposive sampling methods, conducted via an online survey administered to mothers of CWHL who were aged 5-12 years. Constructs of the TPB model were collected, including the mother's attitudes, subjective norms, perceived behaviour control (PBC) and intention towards dental visits for CWHL. Dental visits were measured by asking whether their children had a dental visit within the last 12 months. Data were analysed using SPSS for descriptive and bivariate analyses. The significance level was set as p < .05. Partial least square structural equation modelling (PLS-SEM) was used to analyse measurement and structural TPB models. RESULTS: A total of 254 mothers participated in this study. The final TPB model explained 35.4% and 9.2% of the variance in mothers' intention and behaviour towards their children's dental visits, respectively. Of all the included constructs from the TPB model, only PBC was significantly associated with intention and behaviour (p < .05). The mother's attitude and subjective norms did not significantly predict intention (p > .05). Intention did not significantly predict the mother's behaviour towards children's dental visits (p > .05). CONCLUSION: The TPB model revealed a construct associated with dental visit intention and behaviour in CWHL. This study suggested that effective promotion intervention should focus on the mother's PBC to increase parents' adherence to preventive dental visits in CWHL in Indonesia.

Synergistic Inhibiting Effect of Phytochemicals in Rheum palmatum on Tyrosinase Based on Metabolomics and Isobologram Analyses.

Tyrosinase (TYR) plays a key role in the enzymatic reaction that is responsible for a range of unwanted discoloration effects, such as food browning and skin hyperpigmentation. TYR inhibitors could, therefore, be candidates for skin care products that aim to repair pigmentation problems. In this study, we used a metabolomics approach combined with the isobologram analysis to identify anti-TYR compounds within natural resources, and evaluate their possible synergism with each other. Rheum palmatum was determined to be a model plant for observing the effect, of which seven extracts with diverse phytochemicals were prepared by way of pressurized solvent extraction. Each Rheum palmatum extract (RPE) was profiled using nuclear magnetic resonance spectroscopy and its activity of tyrosinase inhibition was evaluated. According to the orthogonal partial least square analysis used to correlate phytochemicals in RPE with the corresponding activity, the goodness of fit of the model (R2 = 0.838) and its predictive ability (Q2 = 0.711) were high. Gallic acid and catechin were identified as the active compounds most relevant to the anti-TYR effect of RPE. Subsequently, the activity of gallic acid and catechin were evaluated individually, and when combined in various ratios by using isobologram analysis. The results showed that gallic acid and catechin in the molar ratios of 9:5 and 9:1 exhibited a synergistic inhibition on TYR, with a combination index lower than 0.77, suggesting that certain combinations of these compounds may prove effective for use in cosmetic, pharmaceutical, and food industries.

Towards a real-time release of blends and tablets using NIR and Raman spectroscopy at commercial scales.

Near Infrared and Raman spectroscopy-based Process Analytical Technology tools were used for monitoring blend uniformity (BU) and content uniformity (CU) for solid oral formulations. A quantitative Partial Least Square model was developed to monitor BU as real-time release testing at a commercial scale. The model having the R2, and root mean square error of 0.9724 and 2.2047, respectively can predict the target concentration of 100% with a 95% confidence interval of 101.85-102.68% even after one year. The tablets from the same blends were investigated for CU using NIR and Raman techniques both in reflection and transmission mode. Raman reflection technique was found to be the best and the PLS model was developed using tablets compressed at different concentrations, hardness, and speed. The model with R2 and RMSE of 0.9766 and 1.9259, respectively was used for the quantification of CU. Both the BU and CU models were validated for accuracy, precision, specificity, linearity, and robustness. The accuracy was proved against the HPLC method with a relative standard deviation of less than 3%. The equivalency for BU by NIR and CU by Raman was evaluated using Schuirmann's Two One-sided tests and found equivalent to HPLC within a 2% acceptable limit.

Spectral characteristics of the correlation between elemental arsenic and vegetation stress in the Yueliangbao gold mining.

Some soils in the Yueliangbao gold mining area have been contaminated by heavy metals, resulting in variations in vegetation. Hyperspectral remote sensing provides a new perspective for heavy metal inversion in vegetation. In this paper, we collected ground truth spectral data of three dominant vegetation species, Miscanthus floridulus, Equisetum ramosissimum and Eremochloa ciliaris, from contaminated and healthy non-mining areas of the Yueliangbao gold mining region, and determined their heavy metal contents. Firstly, we compared the spectral characteristics of vegetation in the mining and non-mining areas by removing the envelope and derivative transformation. Secondly, we extracted their characteristic identification bands using the Mahalanobis distance and PLS-DA method. Finally, we constructed the inverse model by selecting the vegetation index (such as the PRI, DCNI, MTCI, etc.) related to the characteristic band combined with the heavy metal content. Compared to previous studies, we found that the pollution level in the Yueliangbao gold mining area had greatly reduced, but arsenic metal pollution remained a serious issue. Miscanthus floridulus and Eremochloa ciliaris in the mining area exhibited obvious arsenic stress, with a large "red-edge blue shift" (9 and 6 nm). The extracted characteristic wavebands were around 550 and 680-740 nm wavelengths, and correlation analysis showed significant correlations between vegetation index and arsenic, allowing us to construct a prediction model for arsenic and realize the calculation of heavy metal content using vegetation spectra. This provides a methodological basis for monitoring vegetation pollution in other gold mining areas.

Forensic Pathological Diagnosis of Acute and Old Myocardial Infarction Using Fourier Transform Infrared Spectroscopy.

OBJECTIVES: Fourier transform infrared spectroscopy (FTIR) was used to analyze myocardial infarction tissues at different stages of pathological change to achieve the forensic pathology diagnosis of acute and old myocardial infarction. METHODS: FTIR spectra data of early ischemic myocardium, necrotic myocardium, and myocardial fibrous tissue in the left ventricular anterior wall of the sudden death group of atherosclerotic heart disease and the myocardium of the normal control group were collected using hematoxylin-eosin (HE) and immunohistochemistry (IHC) staining as a reference, and the data were analyzed using multivariate statistical analysis. RESULTS: The mean normalized spectra of control myocardium, early ischemic myocardium and necrotic myocardium were relatively similar, but the mean second derivative spectra were significantly different. The peak intensity of secondary structure of proteins in early ischemic myocardium was significantly higher than in other types of myocardium, and the peak intensity of the α-helix in necrotic myocardium was the lowest. The peaks of amide Ⅰ and amide Ⅱ in the mean normalized spectra of myocardial fibrous tissue significantly shifted towards higher wave numbers, the peak intensities of amide Ⅱ and amide Ⅲ were higher than those of other types of myocardium, and the peak intensities at 1 338, 1 284, 1 238 and 1 204 cm-1 in the mean second derivative spectra were significantly enhanced. Principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) showed that FTIR could distinguish different types of myocardium. CONCLUSIONS: FTIR technique has the potential to diagnose acute and old myocardial infarction, and provides a new basis for the analysis of the causes of sudden cardiac death.

Metabonomics Analysis of Brain Stem Tissue in Rats with Primary Brain Stem Injury Caused Death.

OBJECTIVES: To explore the potential biomarkers for the diagnosis of primary brain stem injury (PBSI) by using metabonomics method to observe the changes of metabolites in rats with PBSI caused death. METHODS: PBSI, non-brain stem brain injury and decapitation rat models were established, and metabolic maps of brain stem were obtained by LC-MS metabonomics method and annotated to the HMDB database. Partial least square-discriminant analysis (PLS-DA) and random forest methods were used to screen potential biomarkers associated with PBSI diagnosis. RESULTS: Eighty-six potential metabolic markers associated with PBSI were screened by PLS-DA. They were modeled and predicted by random forest algorithm with an accuracy rate of 83.3%. The 818 metabolic markers annotated to HMDB database were used for random forest modeling and prediction, and the accuracy rate was 88.9%. According to the importance in the identification of cause of death, the most important metabolic markers that were significantly up-regulated in PBSI group were HMDB0038126 (genipinic acid, GA), HMDB0013272 (N-lauroylglycine), HMDB0005199 [(R)-salsolinol] and HMDB0013645 (N,N-dimethylsphingosine). CONCLUSIONS: GA, N-lauroylglycine, (R)-salsolinol and N,N-dimethylsphingosine are expected to be important metabolite indicators in the diagnosis of PBSI caused death, thus providing clues for forensic medicine practice.

Encouraging teacher participation in Professional Learning Communities: exploring the Facilitating or restricting factors that Influence collaborative activities.

With the ease of using information technology tools, the explosive growth of smartphone applications (apps), and the rise of learning communities on social media, the acceptance of learning communities has become one of the most significant challenges for higher education institutions in Taiwan. In order to better understand teachers' collaborative performance inlearning communities, this study employs the cognitive dimension (opportunism) andinternal tension dimension (e.g. rising expectation, relationship burden) as restrictive factors; on the other hand, it uses emotional support, sense of belonging, and interpersonal altruism as facilitating factors; and community interaction, relationship performance, and collaborative performance as endrogenous factors. With a cross-sectional survey method and a quantitative approach, this study further dives into the collaborative performance of professional learning communities. A total of 157 teachers (87 male and 70 female) were surveyed, and a structural equation modeling approach was used. It was found that social media learning communities have done better than previous courses of field learning in unrestraining learning styles and increasing the breadth of knowledge. Facilitating and restrictive factors led to the rearrangement of the entire knowledge contribution process, enabling new configurations of individuals, members, and community. Moreover, community interactions are important drivers of relationship and collaboration performance supported by empirical data. The findings offer guidelines for policymakers and educators who evaluate teachers' collaborative performance and relationship performance to promote teaching efficiency and effectiveness by incorporating cyberethics in educational activities.

Distinct Immunological Features of Inflammatory Demyelinating Diseases of the Central Nervous System.

OBJECTIVE: The immunological features between neuromyelitis optica spectrum disorder (NMOSD), multiple sclerosis (MS), and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), lacked systemic comparisons. Accordingly, we aimed to investigate immunological differences between NMOSD, MS, and MOGAD. METHODS: Patients with MOGAD, MS, and NMOSD who received immunological tests including cytokine profiles and cytometry analysis of the lymphocyte subgroups were retrospectively reviewed and divided into training and validation sets. Discriminatory models based on immunological data were established to identify optimal classifiers using orthogonal partial least square discriminant analysis (OPLS-DA). Constructed models were tested in another independent cohort. RESULTS: OPLS-DA of the immunological data from 50 patients (26 NMOSD, 14 MS, and 10 MOGAD) demonstrated the discriminatory values of a relatively low level of T-lymphocyte subsets, especially the CD4+ T cells, in MOGAD; a decreased NK cell, eosinophil, and lymphocyte level; an elevated neutrophil-to-lymphocyte ratio in NMOSD; and a declined IFN-γ-producing CD4+ T cells/Th with an increased IL-8 concentration in MS. All the models (NMOSD vs. MS, NMOSD vs. MOGAD, and MS vs. MOGAD) exhibited a significant predictive value and accuracy (>85%). CONCLUSIONS: NMOSD, MS, and MOGAD may be different in pathogenesis, and several immunological biomarkers can serve as potential classifiers clinically.

Neural underpinnings of default mode network on empathy revealed by intracranial stereoelectroencephalography.

BACKGROUND: Empathy is the ability to understand and share the feelings of others. It is fundamental to emotional intelligence and social iterations. Neuroimaging studies have demonstrated that empathy activates brain regions associated with the social cognition network. AIM: To explore the neural underpinnings of empathy revealed by stereoelectroencephalography utilizing recurrence quantification analysis (RQA). METHODS: This retrospective cohort included 38 epilepsy patients with stereoelectroencephalography implantation. RQA metrics were applied to parameterize the network organization of default mode network (DMN) brain regions. The relationships between DMN, seizure burden activity, and empathy, as measured using the Interpersonal Reactivity Index, were examined using partial least-square regression and mediation analysis. RESULTS: RQA metrics with DMN (R2  = 0.75, PBonferroni  < 0.001) and its subsystems (medial temporal subsystem: R2  = 0.53, PBonferroni  < 0.001; core subsystem: R2  = 0.70, PBonferroni  < 0.001; dorsal medial subsystem: R2  = 0.48, PBonferroni  < 0.001) were positively correlated with empathy scores. Of 13 RQA metrics, the mean diagonal line length, entropy of the diagonal line lengths, trapping time, maximal vertical line length, and recurrence time of second type were found to be statistically higher in patient cohorts with reportedly high empathy. Furthermore, DMN characteristics (b path: F = 3.69, P = 0.04), rather than seizure burdens (direct effect: t = 0.33, P = 0.74, c' = - 0.007), mediated empathy status. CONCLUSION: The present study used various RQA metrics to parameterize the network organization of DMN and determine the neural underpinning of DMN for empathy modulation.