TMBstable: a variant caller controls performance variation across heterogeneous sequencing samples.

In cancer genomics, variant calling has advanced, but traditional mean accuracy evaluations are inadequate for biomarkers like tumor mutation burden, which vary significantly across samples, affecting immunotherapy patient selection and threshold settings. In this study, we introduce TMBstable, an innovative method that dynamically selects optimal variant calling strategies for specific genomic regions using a meta-learning framework, distinguishing it from traditional callers with uniform sample-wide strategies. The process begins with segmenting the sample into windows and extracting meta-features for clustering, followed by using a pre-trained meta-model to select suitable algorithms for each cluster, thereby addressing strategy-sample mismatches, reducing performance fluctuations and ensuring consistent performance across various samples. We evaluated TMBstable using both simulated and real non-small cell lung cancer and nasopharyngeal carcinoma samples, comparing it with advanced callers. The assessment, focusing on stability measures, such as the variance and coefficient of variation in false positive rate, false negative rate, precision and recall, involved 300 simulated and 106 real tumor samples. Benchmark results showed TMBstable's superior stability with the lowest variance and coefficient of variation across performance metrics, highlighting its effectiveness in analyzing the counting-based biomarker. The TMBstable algorithm can be accessed at https://github.com/hello-json/TMBstable for academic usage only.

Advancing the accuracy of SARS-CoV-2 phosphorylation site detection via meta-learning approach.

The worldwide appearance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has generated significant concern and posed a considerable challenge to global health. Phosphorylation is a common post-translational modification that affects many vital cellular functions and is closely associated with SARS-CoV-2 infection. Precise identification of phosphorylation sites could provide more in-depth insight into the processes underlying SARS-CoV-2 infection and help alleviate the continuing COVID-19 crisis. Currently, available computational tools for predicting these sites lack accuracy and effectiveness. In this study, we designed an innovative meta-learning model, Meta-Learning for Serine/Threonine Phosphorylation (MeL-STPhos), to precisely identify protein phosphorylation sites. We initially performed a comprehensive assessment of 29 unique sequence-derived features, establishing prediction models for each using 14 renowned machine learning methods, ranging from traditional classifiers to advanced deep learning algorithms. We then selected the most effective model for each feature by integrating the predicted values. Rigorous feature selection strategies were employed to identify the optimal base models and classifier(s) for each cell-specific dataset. To the best of our knowledge, this is the first study to report two cell-specific models and a generic model for phosphorylation site prediction by utilizing an extensive range of sequence-derived features and machine learning algorithms. Extensive cross-validation and independent testing revealed that MeL-STPhos surpasses existing state-of-the-art tools for phosphorylation site prediction. We also developed a publicly accessible platform at https://balalab-skku.org/MeL-STPhos. We believe that MeL-STPhos will serve as a valuable tool for accelerating the discovery of serine/threonine phosphorylation sites and elucidating their role in post-translational regulation.

Enhancing trauma care through innovative trauma and disaster team response training: A blended learning approach in Tanzania.

BACKGROUND: In Tanzania, inadequate infrastructures and shortages of trauma-response training exacerbate trauma-related fatalities. McGill University's Centre for Global Surgery introduced the Trauma and Disaster Team Response course (TDTR) to address these challenges. This study assesses the impact of simulation-based TDTR training on care providers' knowledge/skills and healthcare processes to enhance patient outcomes. METHODS: The study used a pre-post-interventional design. TDTR, led by Tanzanian instructors at Muhimbili Orthopedic Institute from August 16-18, 2023, involved 22 participants in blended online and in-person approaches with simulated skills sessions. Validated tools assessed participants' knowledge/skills and teamwork pre/post-interventions, alongside feedback surveys. Outcome measures included evaluating 24-h emergency department patient arrival-to-care time pre-/post-TDTR interventions, analyzed using parametric and non-parametric tests based on data distributions. RESULTS: Participants' self-assessment skills significantly improved (median increase from 34 to 58, p < 0.001), along with teamwork (median increase from 44.5 to 87.5, p < 0.003). While 99% of participants expressed satisfaction with TDTR meeting their expectations, 97% were interested in teaching future sessions. The six-month post-intervention arrival-to-care time significantly decreased from 29 to 13 min, indicating a 55.17% improvement (p < 0.004). The intervention led to fewer ward admissions (35.26% from 51.67%) and more directed to operating theaters (29.83% from 16.85%), suggesting improved patient management (p < 0.018). CONCLUSION: The study confirmed surgical skills training effectiveness in Tanzanian settings, highlighting TDTR's role in improving teamwork and healthcare processes that enhanced patient outcomes. To sustain progress and empower independent trauma educators, ongoing refresher sessions and expanding TDTR across low- and middle-income countries are recommended to align with global surgery goals.

A machine learning approach for predicting and localizing the failure and damage point in sewer networks due to pipe properties.

As a basic infrastructure, sewers play an important role in the innards of every city and town to remove unsanitary water from all kinds of livable and functional spaces. Sewer pipe failures (SPFs) are unwanted and unsafe in many ways, as the disturbance that they cause is undeniable. Sewer pipes meet manholes frequently, unlike water distribution systems, as in sewers, water movement is due to gravity and manholes are needed in every intersection as well as through pipe length. Many studies have been focused on sewer pipe failures and so on, but few investigations have been done to show the effect of manhole proximity on pipe failure. Predicting and localizing the sewer pipe failures is affected by different parameters of sewer pipe properties, such as material, age, slope, and depth of the sewer pipes. This study investigates the applicability of a support vector machine (SVM), a supervised machine learning (ML) algorithm, for the development of a prediction model to predict sewer pipe failures and the effects of manhole proximity. The results show that SVM with an accuracy of 84% can properly approximate the manhole effects on sewer pipe failures.

A comparison of two learning approach inventories and their utility in predicting examination performance and study habits.

The revised two-factor Study Process Questionnaire and the Approaches and Study Skills Inventory for Students are two instruments commonly used to measure student learning approach. Although they are designed to measure similar constructs, it is unclear whether the metrics they provide differ in terms of their real-world classification of learning approach. The purpose of this study is to compare outcomes of these two inventories in a study population from an undergraduate (baccalaureate) human anatomy course. The three central goals of this study are to compare the inventories in terms of 1) how students are classified, 2) the relationship between examination performance, time spent studying, and learning approach, and 3) instrument reliability. Results demonstrate that student classifications of corresponding scales of each inventory are significantly correlated, suggesting they measure similar constructs. Although the inventories had similar reliability, neither was consistently strong in predicting examination performance or study habits. Overall, these results suggest that the two inventories are comparable in terms of how they measure learning approach, but the lack of correspondence between learning approach scores and measurement outcomes questions their validity as tools that can be used universally in classrooms.NEW & NOTEWORTHY Although learning approach inventories have been used extensively in education research, there has been no direct comparison of how student classification differs between instruments or how classification influences the interpretation of how learning approach impacts student performance. This is especially relevant in light of recent research questioning the validity of the Study Process Questionnaire (LoGiudice AB, Norman GR, Manzoor S, Monteiro S. Adv Health Sci Educ Theory Pract 28: 47-63, 2023; Johnson SN, Gallagher ED, Vagnozzi AM. PLoS One 16: e0250600, 2021).

Assessment and evaluation of online education and virtual simulation technology in dental education: a cross-sectional survey.

BACKGROUND: The global outbreak of coronavirus disease (COVID-19) has led medical universities in China to conduct online teaching. This study aimed to assess the effectiveness of a blended learning approach that combines online teaching and virtual reality technology in dental education and to evaluate the acceptance of the blended learning approach among dental teachers and students. METHODS: The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist was followed in this study. A total of 157 students' perspectives on online and virtual reality technology education and 54 teachers' opinions on online teaching were collected via questionnaires. Additionally, 101 students in the 2015-year group received the traditional teaching method (TT group), while 97 students in the 2017-year group received blended learning combining online teaching and virtual reality technology (BL group). The graduation examination results of students in the two groups were compared. RESULTS: The questionnaire results showed that most students were satisfied with the online course and the virtual simulation platform teaching, while teachers held conservative and neutral attitudes toward online teaching. Although the theoretical score of the BL group on the final exam was greater than that of the TT group, there was no significant difference between the two groups (P = 0.805). The skill operation score of the BL group on the final exam was significantly lower than that of the TT group (P = 0.004). The overall score of the BL group was lower than that of the TT group (P = 0.018), but the difference was not statistically significant (P = 0.112). CONCLUSIONS: The blended learning approach combining online teaching and virtual reality technology plays a positive role in students' learning and is useful and effective in dental education.

Uncertainty-based saltwater intrusion prediction using integrated Bayesian machine learning modeling (IBMLM) in a deep aquifer.

Data-driven machine learning approaches are promising to substitute physically based groundwater numerical models and capture input-output relationships for reducing computational burden. But the performance and reliability are strongly influenced by different sources of uncertainty. Conventional researches generally rely on a stand-alone machine learning surrogate approach and fail to account for errors in model outputs resulting from structural deficiencies. To overcome this issue, this study proposes a flexible integrated Bayesian machine learning modeling (IBMLM) method to explicitly quantify uncertainties originating from structures and parameters of machine learning surrogate models. An Expectation-Maximization (EM) algorithm is combined with Bayesian model averaging (BMA) to find out maximum likelihood and construct posterior predictive distribution. Three machine learning approaches representing different model complexity are incorporated in the framework, including artificial neural network (ANN), support vector machine (SVM) and random forest (RF). The proposed IBMLM method is demonstrated in a field-scale real-world "1500-foot" sand aquifer, Baton Rouge, USA, where overexploitation caused serious saltwater intrusion (SWI) issues. This study adds to the understanding of how chloride concentration transport responds to multi-dimensional extraction-injection remediation strategies in a sophisticated saltwater intrusion model. Results show that most IBMLM exhibit r values above 0.98 and NSE values above 0.93, both slightly higher than individual machine learning, confirming that the IBMLM is well established to provide better model predictions than individual machine learning models, while maintaining the advantage of high computing efficiency. The IBMLM is found useful to predict saltwater intrusion without running the physically based numerical simulation model. We conclude that an explicit consideration of machine learning model structure uncertainty along with parameters improves accuracy and reliability of predictions, and also corrects uncertainty bounds. The applicability of the IBMLM framework can be extended in regions where a physical hydrogeologic model is difficult to build due to lack of subsurface information.

Enhancing the prediction for shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage using a machine learning approach.

Early and reliable prediction of shunt-dependent hydrocephalus (SDHC) after aneurysmal subarachnoid hemorrhage (aSAH) may decrease the duration of in-hospital stay and reduce the risk of catheter-associated meningitis. Machine learning (ML) may improve predictions of SDHC in comparison to traditional non-ML methods. ML models were trained for CHESS and SDASH and two combined individual feature sets with clinical, radiographic, and laboratory variables. Seven different algorithms were used including three types of generalized linear models (GLM) as well as a tree boosting (CatBoost) algorithm, a Naive Bayes (NB) classifier, and a multilayer perceptron (MLP) artificial neural net. The discrimination of the area under the curve (AUC) was classified (0.7 ≤ AUC < 0.8, acceptable; 0.8 ≤ AUC < 0.9, excellent; AUC ≥ 0.9, outstanding). Of the 292 patients included with aSAH, 28.8% (n = 84) developed SDHC. Non-ML-based prediction of SDHC produced an acceptable performance with AUC values of 0.77 (CHESS) and 0.78 (SDASH). Using combined feature sets with more complex variables included than those incorporated in the scores, the ML models NB and MLP reached excellent performances, with an AUC of 0.80, respectively. After adding the amount of CSF drained within the first 14 days as a late feature to ML-based prediction, excellent performances were reached in the MLP (AUC 0.81), NB (AUC 0.80), and tree boosting model (AUC 0.81). ML models may enable clinicians to reliably predict the risk of SDHC after aSAH based exclusively on admission data. Future ML models may help optimize the management of SDHC in aSAH by avoiding delays in clinical decision-making.

"What medical students with better academic results do: a cross-sectional analysis".

BACKGROUND: With university material doubling over time, medical students need to learn how to become successful life-long learners. Overall a Deep Approach (DA) to learning, and Self-Regulation (SR) skills are among the elements with a potential to accelerate learning, and Student Engagement (SE) has been associated with better university outcomes. However, specific recommendations concerning what students should do are lacking. The aim of this study was to identify above-average students' specific attitudes and strategies toward learning. METHODS: A cross-sectional analysis of the answers to the validated questionnaires Revised Study Process Questionnaire (R-SPQ-2F), SE, and Motivated Strategies for Learning Questionnaire (MSLQ) of 155 s and third-year students included in a prospective interventional study in the University of Navarre in September 2020 was performed. Students were stratified according to their standardized average mean in above-average (mean > 0) and below-average (mean ≤ 0). RESULTS: Overall, 67.1% of students scored higher in DA than in Surface Approach (SA) and had very high Intrinsic Value (IV, median 5.9). A higher proportion of above-average students had DA > SA score (72.7% vs 57.1%, p = 0.05), and showed higher scores in SR (median 4.9 vs 4.3, p = 0.007) compared to below-average, while the latter scored higher in SA (median 24.5 vs 23, p = 0.04), and surface motive (median 11 vs 9, p = 0.007). No differences were found in SE, and both groups had average scores in the cooperative dimension. Differences were rooted to hard work, interest over material and prioritizing understanding over rote-learning motives and aligned strategies. CONCLUSIONS: Curricula design and assessment should be aligned to promote DA and SR skills among learners. Furthermore, it is paramount that teachers help instill students with interest over material and encourage understanding and hard work, since are traits associated with better results. More studies concerning metacognition and other promising traits for becoming life-long learners and prepared professionals should be made.

Meta-Learner-Based Approach for Detecting Attacks on Internet of Things Networks.

The significant surge in Internet of Things (IoT) devices presents substantial challenges to network security. Hackers are afforded a larger attack surface to exploit as more devices become interconnected. Furthermore, the sheer volume of data these devices generate can overwhelm conventional security systems, compromising their detection capabilities. To address these challenges posed by the increasing number of interconnected IoT devices and the data overload they generate, this paper presents an approach based on meta-learning principles to identify attacks within IoT networks. The proposed approach constructs a meta-learner model by stacking the predictions of three Deep-Learning (DL) models: RNN, LSTM, and CNN. Subsequently, the identification by the meta-learner relies on various methods, namely Logistic Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). To assess the effectiveness of this approach, extensive evaluations are conducted using the IoT dataset from 2020. The XGBoost model showcased outstanding performance, achieving the highest accuracy (98.75%), precision (98.30%), F1-measure (98.53%), and AUC-ROC (98.75%). On the other hand, the SVM model exhibited the highest recall (98.90%), representing a slight improvement of 0.14% over the performance achieved by XGBoost.

Towards an Optimized Distributed Message Queue System for AIoT Edge Computing: A Reinforcement Learning Approach.

The convergence of artificial intelligence and the Internet of Things (IoT) has made remarkable strides in the realm of industry. In the context of AIoT edge computing, where IoT devices collect data from diverse sources and send them for real-time processing at edge servers, existing message queue systems face challenges in adapting to changing system conditions, such as fluctuations in the number of devices, message size, and frequency. This necessitates the development of an approach that can effectively decouple message processing and handle workload variations in the AIoT computing environment. This study presents a distributed message system for AIoT edge computing, specifically designed to address the challenges associated with message ordering in such environments. The system incorporates a novel partition selection algorithm (PSA) to ensure message order, balance the load among broker clusters, and enhance the availability of subscribable messages from AIoT edge devices. Furthermore, this study proposes the distributed message system configuration optimization algorithm (DMSCO), based on DDPG, to optimize the performance of the distributed message system. Experimental evaluations demonstrate that, compared to the genetic algorithm and random searching, the DMSCO algorithm can provide a significant improvement in system throughput to meet the specific demands of high-concurrency AIoT edge computing applications.

Effects of robot-assisted digital storytelling on hospitalized children's communication during the COVID-19 pandemic.

This study proposed a robot-assisted digital storytelling approach to reduce hospitalized children's anxiety about intravenous injections and to improve their therapeutic communication and therapeutic engagement. In order to verify the effectiveness of the robot-assisted digital storytelling approach, a randomized controlled study was implemented. A total of 47 children from a regional hospital were randomly assigned to an experimental group (n = 21) and a control group (n = 26). The experimental group adopted the robot-assisted digital storytelling approach in health education for intravenous injections, while the control group received video-based health education. The study results indicated that the proposed robot-assisted digital storytelling approach not only reduced the children's anxiety, but also had positive effects on children's communication about intravenous injections, emotions during hospitalization, and therapeutic engagement. As a consequence, it is suggested that educators and researchers consider adopting robot-assisted digital storytelling to facilitate nursing clinical health education for children.

Comprehensive characterization of costimulatory molecule gene for diagnosis, prognosis and recognition of immune microenvironment features in sepsis.

The present study, which involved 10 GEO datasets and 3 ArrayExpress datasets, comprehensively characterized the potential effects of CMGs in sepsis. Based on machine learning algorithms (Lasso, SVM and ANN), the CMG classifier was constructed by integrating 6 hub CMGs (CD28, CD40, LTB, TMIGD2, TNFRSF13C and TNFSF4). The CMG classifier exhibit excellent diagnostic values across multiple datasets and time points, and was able to distinguish sepsis from other critical diseases. The CMG classifier performed better in predicting mortality than other clinical characteristics or endotypes. More importantly, from clinical specimens, the CMG classifier showed more superior diagnostic values than PCT and CRP. Alternatively, the CMG classifier/hub CMGs is significantly correlated with immune cells infiltration (B cells, T cells, Tregs, and MDSC), pivotal immune and molecular pathways (inflammation-promoting, complement and coagulation cascades), and several cytokines. Collectively, CMG classifier was a robust tool for diagnosis, prognosis and recognition of immune microenvironment features in sepsis.

Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns.

BACKGROUND: Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)-infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work. OBJECTIVE: This study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in AАL systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems. METHODS: This study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI. RESULTS: We included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assistance and recognition were the most common target domains of the models. Ambient sensing, mobile technology, and robotic devices mainly implemented the models. Older adults were the primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern. CONCLUSIONS: This study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should involve health care professionals and caregivers as designers and users, comply with health-related regulations, improve transparency and privacy, integrate with health care technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines. TRIAL REGISTRATION: PROSPERO (International Prospective Register of Systematic Reviews) CRD42022347590; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022347590.

2D nanomaterial sensing array using machine learning for differential profiling of pathogenic microbial taxonomic identification.

An integrated custom cross-response sensing array has been developed combining the algorithm module's visible machine learning approach for rapid and accurate pathogenic microbial taxonomic identification. The diversified cross-response sensing array consists of two-dimensional nanomaterial (2D-n) with fluorescently labeled single-stranded DNA (ssDNA) as sensing elements to extract a set of differential response profiles for each pathogenic microorganism. By altering the 2D-n and different ssDNA with different sequences, we can form multiple sensing elements. While interacting with microorganisms, the competition between ssDNA and 2D-n leads to the release of ssDNA from 2D-n. The signals are generated from binding force driven by the exfoliation of either ssDNA or 2D-n from the microorganisms. Thus, the signal is distinguished from different ssDNA and 2D-n combinations, differentiating the extracted information and visualizing the recognition process. Fluorescent signals collected from each sensing element at the wavelength around 520 nm are applied to generate a fingerprint. As a proof of concept, we demonstrate that a six-sensing array enables rapid and accurate pathogenic microbial taxonomic identification, including the drug-resistant microorganisms, under a data size of n = 288. We precisely identify microbial with an overall accuracy of 97.9%, which overcomes the big data dependence for identifying recurrent patterns in conventional methods. For each microorganism, the detection concentration is 105 ~ 108 CFU/mL for Escherichia coli, 102 ~ 107 CFU/mL for E. coli-ß, 103 ~ 108 CFU/mL for Staphylococcus aureus, 103 ~ 107 CFU/mL for MRSA, 102 ~ 108 CFU/mL for Pseudomonas aeruginosa, 103 ~ 108 CFU/mL for Enterococcus faecalis, 102 ~ 108 CFU/mL for Klebsiella pneumoniae, and 103 ~ 108 CFU/mL for Candida albicans. Combining the visible machine learning approach, this sensing array provides strategies for precision pathogenic microbial taxonomic identification. • A molecular response differential profiling (MRDP) was established based on custom cross-response sensor array for rapid and accurate recognition and phenotyping common pathogenic microorganism. • Differential response profiling of pathogenic microorganism is derived from the competitive response capacity of 6 sensing elements of the sensor array. Each of these sensing elements' performance has competitive reaction with the microorganism. • MRDP was applied to LDA algorithm and resulted in the classification of 8 microorganisms.

Dietary Patterns Associated with Diabetes in an Older Population from Southern Italy Using an Unsupervised Learning Approach.

Dietary behaviour is a core element in diabetes self-management. There are no remarkable differences between nutritional guidelines for people with type 2 diabetes and healthy eating recommendations for the general public. This study aimed to evaluate dietary differences between subjects with and without diabetes and to describe any emerging dietary patterns characterizing diabetic subjects. In this cross-sectional study conducted on older adults from Southern Italy, eating habits in the "Diabetic" and "Not Diabetic" groups were assessed with FFQ, and dietary patterns were derived using an unsupervised learning algorithm: principal component analysis. Diabetic subjects (n = 187) were more likely to be male, slightly older, and with a slightly lower level of education than subjects without diabetes. The diet of diabetic subjects reflected a high-frequency intake of dairy products, eggs, vegetables and greens, fresh fruit and nuts, and olive oil. On the other hand, the consumption of sweets and sugary foods was reduced compared to non-diabetics (23.74 ± 35.81 vs. 16.52 ± 22.87; 11.08 ± 21.85 vs. 7.22 ± 15.96). The subjects without diabetes had a higher consumption of red meat, processed meat, ready-to-eat dishes, alcoholic drinks, and lower vegetable consumption. The present study demonstrated that, in areas around the Mediterranean Sea, older subjects with diabetes had a healthier diet than their non-diabetic counterparts.

Edge-to-Cloud IIoT for Condition Monitoring in Manufacturing Systems with Ubiquitous Smart Sensors.

The Industrial Internet of Things (IIoT) connects industrial assets to ubiquitous smart sensors and actuators to enhance manufacturing and industrial processes. Data-driven condition monitoring is an essential technology for intelligent manufacturing systems to identify anomalies from malfunctioning equipment, prevent unplanned downtime, and reduce the operation costs by predictive maintenance without interrupting normal machine operations. However, data-driven condition monitoring requires massive data collected from smart sensors to be transmitted to the cloud for further processing, thereby contributing to network congestion and affecting the network performance. Furthermore, unbalanced training data with very few labelled anomalies limit supervised learning models because of the lack of sufficient fault data for the training process in anomaly detection algorithms. To address these issues, we proposed an IIoT-based condition monitoring system with an edge-to-cloud architecture and computed the relative wavelet energy as feature vectors on the edge layer to reduce the network traffic overhead. We also proposed an unsupervised deep long short-term memory (LSTM) network module for anomaly detection. We implemented the proposed IIoT condition monitoring system for a manufacturing machine in a real shop site to evaluate our proposed solution. Our experimental results verify the effectiveness of our approach which can not only reduce the network traffic overhead for the IIoT but also detect anomalies accurately.

Cerebral blood flow alterations in migraine patients with and without aura: An arterial spin labeling study.

BACKGROUND: Migraine aura is a transient, fully reversible visual, sensory, or other central nervous system symptom that classically precedes migraine headache. This study aimed to investigate cerebral blood flow (CBF) alterations of migraine with aura patients (MwA) and without aura patients (MwoA) during inter-ictal periods, using arterial spin labeling (ASL). METHODS: We evaluated 88 migraine patients (32 MwA) and 44 healthy control subjects (HC) who underwent a three-dimensional pseudo-continuous ASL MRI scanning. Voxel-based comparison of normalized CBF was conducted between MwA and MwoA. The relationship between CBF variation and clinical scale assessment was further analyzed. The mean CBF values in brain regions showed significant differences were calculated and considered as imaging features. Based on these features, different machine learning-based models were established to differentiate MwA and MwoA under five-fold cross validation. The predictive ability of the optimal model was further tested in an independent sample of 30 migraine patients (10 MwA). RESULTS: In comparison to MwoA and HC, MwA exhibited higher CBF levels in the bilateral superior frontal gyrus, bilateral postcentral gyrus and cerebellum, and lower CBF levels in the bilateral middle frontal gyrus, thalamus and medioventral occipital cortex (all p values < 0.05). These variations were also significantly correlated with multiple clinical rating scales about headache severity, quality of life and emotion. On basis of these CBF features, the accuracies and areas under curve of the final model in the training and testing samples were 84.3% and 0.872, 83.3% and 0.860 in discriminating patients with and without aura, respectively. CONCLUSION: In this study, CBF abnormalities of MwA were identified in multiple brain regions, which might help better understand migraine-stroke connection mechanisms and may guide patient-specific decision-making.

Lecture Capture: Friend or Foe?

Lecture capture (LC)-a recording of the live lecture provided as a supplementary resource-is accepted as a standard provision in UK higher education. Previous research has shown it to be very popular with students, although there have been conflicting findings in terms of its impact on attendance and attainment, and suggestions that student engagement with this resource varies depending on their own preferences and approaches. The aim of the present study was to determine the impact of LC on students in a wider sense, encompassing pedagogic and pastoral aspects of student development. This mixed-methods study analyzed focus group and questionnaire data from first- and second-year veterinary students at one UK university. Results demonstrated the student belief that LC is important for learning and well-being but highlighted the facilitation of passive and surface learning that this resource offers. More worryingly, this study identified a group of students for whom this resource may be particularly unhelpful. This group, relied excessively upon LC for learning, felt overwhelmed by their workload despite working fewer hours, and subsequently achieved poorer exam results. A key theme in this negative relationship appeared to be low self-efficacy. The findings enable educators to consider how resources are provided and to encourage implementing mechanisms to help students make better choices, and take control of their learning.

Developing a mental health index using a machine learning approach: Assessing the impact of mobility and lockdown during the COVID-19 pandemic.

Governments worldwide have implemented stringent restrictions to curtail the spread of the COVID-19 pandemic. Although beneficial to physical health, these preventive measures could have a profound detrimental effect on the mental health of the population. This study focuses on the impact of lockdowns and mobility restrictions on mental health during the COVID-19 pandemic. We first develop a novel mental health index based on the analysis of data from over three million global tweets using the Microsoft Azure machine learning approach. The computed mental health index scores are then regressed with the lockdown strictness index and Google mobility index using fixed-effects ordinary least squares (OLS) regression. The results reveal that the reduction in workplace mobility, reduction in retail and recreational mobility, and increase in residential mobility (confinement to the residence) have harmed mental health. However, restrictions on mobility to parks, grocery stores, and pharmacy outlets were found to have no significant impact. The proposed mental health index provides a path for theoretical and empirical mental health studies using social media.