Miracle-dz/UniFAR-FTUs huggingface dataset:
Decision-level fusion method for diagnosis of major depressive disorder

The extraction of features from the electroencephalography (EEG) of patients with major depressive disorder (MDD) and the subsequent construction of classifiers using machine learning methods to fuse decision information from multiple classifiers at the decision level is an effective method to improve the final classification performance. However, the current multi-classifier fusion approach is plagued by the output error probability of the support vector machine (SVM) affecting the accuracy of the final decision. Thus, to counteract the influence of the error probability of the classifier outputs on the classification results, this study proposed a decision-level strategy based on SVM classification confidence. Specifically, multiple classifiers were trained and the confidence of the classifier classification outputs was constructed as confidence feature vectors to be input into the SVM for the final decision. Compared with a single classifier or the traditional multi-classifier fusion method, the proposed method exhibited a stronger ability to identify depression with an accuracy of 86.79%.
Mutant Bisexual and Wild Male Flowers Were Compared by Integrated Proteome and Transcriptome Analyses to Provide Insight into the Sex Conversion of Idesia polycarpa Maxim

Idesia polycarpa is a dioecious tree; in field surveys, there are rare sex conversions in I. polycarpa individuals with bisexual flowers. To identify the molecular mechanisms underlying sex conversion in this species, an integrative analysis of the proteome and transcriptome profiles of I. polycarpa male and bisexual flowers at key developmental stages was conducted in this study using isobaric tags for relative and absolute quantification and RNA-seq technology. A total of 15,003 proteins were identified; the differentially expressed proteins (DEPs) were enriched in metabolic pathways, biosynthesis of secondary metabolites, and flavonoid metabolism pathways in all comparison groups. A total of 290,442 unigenes were obtained; these were compared with seven databases, revealing 196,366 annotated unigenes. In general, the expression of proteins and genes tended to be positively correlated, with Spearman correlation coefficients in the ranges of 0.152–0.262 (all genes and all proteins) and 0.497–0.778 (DEPs and DEGs). The integrative analysis of DEPs and DEGs between male and bisexual flowers revealed that the most significantly enriched pathways were flavonoid pathways, metabolic pathways, and the biosynthesis of secondary metabolites. Finally, four co-expressed proteins and transcripts and one gene associated with the flavonoid biosynthesis pathway were screened out. The proteins identified were p-coumaroyl shikimate 3′-hydroxylase, and shikimate/quinate hydroxycinnamoyl transferase, and the gene was caffeoyl-CoA O-methyltransferase. The analysis has revealed key potential proteins and genes involved in sex conversion at the molecular level and has provided a basis for future investigations of artificial regulation of sex differentiation in I. polycarpa.
Scalable Prediction of Potato Pests and Diseases with Insights from Mobile Sensing for Rabi 2021–22

Large scale adoption of digital technologies in farming is expanding the reach of precision agriculture. The Indo-Gangetic region in northern India is a major potato growing belt where West Bengal is one of the prominent states with Early Blight, Late Blight, Septoria Leaf Spot, and Thrips being some key disease and pest conditions. Forecasting stress across the region requires models that understand the crop, its stress behaviour dynamics and variation of ambient conditions across the zone of interest. For rapidly changing conditions on the ground, predicting the likelihood of stress could be combined with detection of stress incidents through images to course-correct timely during the season thereby mitigating risks. We present the work on region-level stress forecast in potato supported by automated detection of reported conditions for the Rabi season of 2021–22 in West Bengal. The results with various proposed approaches are presented and contrasted to address the related scenarios.
Comparative Analysis of Optimisers Used in CNN for Brain Tumor Detection

An abnormal cell growth in the brain is referred to as a brain tumour. It can affect persons of all ages and can be either malignant or non-cancerous, though older adults are more likely to develop them. Headaches, convulsions, altered vision or hearing, limb weakness, and trouble speaking or understanding are some symptoms. Artificial Neural Networks (ANNs) are used in DL, to identify brain tumours in medical images. It helps the doctors to find tumors more quickly than with conventional approaches by accurately and swiftly analysing vast amounts of complex data. Although deep learning models like CNNs and DBNs have demonstrated great accuracy in the diagnosis of brain tumours, additional study is required to confirm their efficacy in actual clinical settings.
TMAO Promotes NLRP3 Inflammasome Activation of Microglia Aggravating Neurological Injury in Ischemic Stroke Through FTO/IGF2BP2

Objective Stroke is a kind of cerebrovascular disease with high mortality. TMAO has been shown to aggravate stroke outcomes, but its mechanism remains unclear. Materials and Methods Mice were fed with 0.12% TMAO for 16 weeks. Then, mice were made into MCAO/R models. Neurological score, infarct volume, neuronal damage and markers associated with inflammation were assessed. Since microglia played a crucial role in ischemic stroke, microglia of MCAO/R mice were isolated for high-throughput sequencing to identify the most differentially expressed gene following TMAO treatment. Afterward, the downstream pathways of TMAO were investigated using primary microglia. Results TMAO promoted the release of inflammatory cytokines in the brain of MCAO/R mice and promoted the activation of OGD/R microglial inflammasome, thereby exacerbating ischemic stroke outcomes. FTO/IGF2BP2 inhibited NLRP3 inflammasome activation in OGD/R microglia by downregulating the m6A level of NLRP3. TMAO can inhibit the expression of FTO and IGF2BP2, thus promoting the activation of NLRP3 inflammasome in OGD/R microglia. In conclusion, these results demonstrated that TMAO promotes NLRP3 inflammasome activation of microglia aggravating neurological injury in ischemic stroke through FTO/IGF2BP2. Conclusion Our results demonstrated that TMAO promotes NLRP3 inflammasome activation of microglia aggravating neurological injury in ischemic stroke through FTO/IGF2BP2. These findings explained the molecular mechanism of TMAO aggravating ischemic stroke in detail and provided molecular mechanism for clinical treatment.
How Do University Students Perceive Inequality, Relationships and Power in University Life in the COVID-19 Era?

This study examined university students’ perceptions of inequality, relationships and power following the COVID-19 outbreak. We used a qualitative research method, inductive content analysis (ICA), to analyse their perceptions of inequality in their personal life, insiders and outsiders that show superiority in relationships and people with strong and weak characteristics of power structures. We extracted superordinate concepts, such as those in the individual, interaction and social/environmental dimensions, as the perceptions of inequality, insiders and outsiders and people with strong and weak characteristics. First, we found that university students experience inequalities when they perceive that individuals must cope independently with changes brought about by COVID-19. Second, the results showed that individuals can become insiders or outsiders depending on how they act during the COVID-19 pandemic. Finally, we demonstrated that strong individuals are less affected by COVID-19-related changes than weak individuals. Therefore, improving university students’ quality of life requires plans based on the students’ perceptions of inequality.
Supporting Underrepresented Students in Health Sciences Using a Fuzzy Cognitive Mapping Approach

Background: The Students in Training in Academia, Health, and Research (STAHR) Program at the University of Missouri-Kansas City (UMKC) strives to increase the number of students from low-income families that have experienced educational challenges due to poverty who are prepared to enter, persist, and graduate from a health sciences degree program at UMKC. Students in the program participated in fuzzy cognitive mapping (FCM) sessions to ensure that all voices of the program were heard to improve program implementation, student success, and contribute to an equitable educational environment. Methods: Fuzzy Cognitive Mapping sessions for the 2020-2021 cohort of students (n=52) were conducted to assess the strengths and weaknesses in program implementation, especially through the beginning of the COVID-19 pandemic. Students’ maps were coded by a team of researchers and then confirmed using confirmatory factor analysis. Results: Statistical analyses reveal that mentorship, workshops, and social supports helped students to work toward their goal of obtaining a professional health sciences degree, while a lack of time, remote learning, and outside stressors inhibited their opportunities for success. Conclusions: The findings from mixed methods analyses of mapping data demonstrate the value of this innovative approach to the field, especially when looking to incorporate student voice.
The development of a competitive multiplayer electromyography-based biofeedback video game

Biofeedback training is a method of combining information technology with physiological therapy and rehabilitation. Electromyography (EMG) is a muscle biopotential measurement technique that is widely used for monitoring the activity of muscles. This method has been applied to biofeedback training and lets individuals learn to control their muscles properly through the visualization of electrical signals. Video games have been incorporated into EMG biofeedback training to make the sessions more enjoyable and challenging. Competition has been considered one of the key factors enhancing self-improvement to overcome challenges in video games. This work proposes the development of a real-time EMG biofeedback system that is integrated into a multiplayer competitive video game. The development consisted of the implementation of a low-cost EMG data acquisition device and EMG biofeedback software. The system was designed with the aim of demonstrating a method of acquiring EMG signals from the antagonistic forearm muscles of two players and utilizing them to myoelectrically control a virtual table tennis video game. A multiprocessing architecture has been applied to biofeedback software development to achieve the goal of a real-time system. This allowed the software to simultaneously handle the EMG data stream, process the data, control the video game, and record the data to memory storage. The system evaluations show that, with this development, the competitive EMG biofeedback system was able to respond to the players’ controls with an average response time of 14 msec, and the myoelectric control parameters are adjustable to associate with the player’s muscle strength.
Verkenning van de inzet van topic modelling bij het analyseren van schrijfopdrachten

In het hoger onderwijs wordt regelmatig gebruik gemaakt van tussenopdrachten die studenten zelfstandig dienen uit te voeren. In dit artikel gaan we specifiek in op de flipped classroom, waarbij studenten schriftelijke opdrachten voltooien als voorbereiding op een begeleide werkgroepbijeenkomst. Deze opdrachten vormen potentieel een waardevolle bron van informatie voor de docent, die hieruit informatie kan destilleren over het begrip van de stof van de studenten. In de praktijk komen docenten er echter niet altijd aan toe om de opdrachten voorafgaand aan de werkgroepbijeenkomst te bekijken. In deze studie verkennen we daarom de perceptie van docenten omtrent de mogelijkheid om schriftelijke opdrachten te analyseren met een text mining techniek, namelijk topic modelling (TM), waarbij de meest genoemde onderwerpen uit de opdrachten gedestilleerd worden. Een team van drie docenten uit een bachelorcursus gaven middels interviews hun mening over de potentie van TM. De docenten zagen potentie voor TM op drie manieren, namelijk 1) op basis van TM met individuele studenten in gesprek gaan, 2) de uitkomsten van TM als werkvorm aan studenten voorleggen, en 3) de uitkomsten van TM met het gehele docententeam bespreken. Aan de andere kant vraagt de inzet van TM wel een serieuze tijdsinvestering. Een afweging van de voor- en nadelen moet dus in elke situatie gemaakt worden.
Insights into cognitive and behavioral comorbidities of SLC6A1-related epilepsy: five new cases and literature review

Introduction SLC6A1 pathogenic variants have been associated with epilepsy and neurodevelopmental disorders. The clinical phenotype includes different seizure types, intellectual disability, and psychiatric symptoms affecting mood and behavior. Few data regarding neuropsychological features have been described, and details on cognitive profiles are often missing due to the lack of standardized tests. Methods We retrospectively reviewed the neuropsychological assessments of five subjects carrying heterozygous missense genetic variants in SLC6A1. We also collected data on epileptic features, EEGs, and brain MRIs. Additionally, we reviewed neuropsychological data from 204 previously reported patients with SLC6A1 pathogenic variants. Results In our series, at the last evaluation (median 12.6 years), three patients had borderline intellectual functioning, one patient had mild cognitive impairment, and one patient presented with a moderate cognitive disability. Three out of five patients underwent at least two neuropsychological evaluations, which revealed a worsening of cognitive functions over time. We detected attention deficits in all patients. In addition, we observed anxiety, disruptive behavior disorder, emotional instability, and hetero aggressiveness. We also performed a literature review that highlighted that most of the patients with SLC6A1 pathogenic variants have mild-to-moderate intellectual disability and that one-third of cases have autistic traits. Discussion Based on the literature review and the detailed description of our cases, we conclude that patients with SLC6A1-related epilepsy mostly present with mild-to-moderate intellectual disability, often associated with attention disorders. Such symptoms may worsen over time. Periodic standardized neuropsychological tests may be useful tools to follow development over time, and patient-specific rehabilitation programs could be tailored consistently.
Improving wheelchair‐driving using a sensor system to control wheelchair‐veer and variable‐switches as an alternative to digital‐switches or joysticks

Purpose – The purpose of this paper is to present powered‐wheelchair transducers and systems that provide more control, reduced veer on slopes, and improved energy conservation, while reducing effort. They are especially significant for people with movement disorders who lack sufficient hand‐grasp and release ability or sufficient targeting skill to use joysticks.Design/methodology/approach – Laboratory test rigs are created to test proportional switches and teach potential users. Then, trials are conducted with a rolling road and in real situations. Caster angle‐measurement is selected to provide feedback to minimize drift away from a chosen course and an electronic solution was created to match driver control to caster‐steering‐position. A case study is described as an example.Findings – Results and advantages are presented from changing from using a set of digital‐switches to a set of new variable‐switches and then adding a sensor system to prevent veer on slopes. Systems have been tested for nearly tw.
Changing face of socio-economic vulnerability and COVID-19: An analysis of country wealth during the first two years of the pandemic

There are numerous academic studies on the relationship between population wealth and the incidence of COVID-19. However, research developed shows contradictory results on their relationship. In accordance with this question, this work pursues two objectives: on the one hand, to check whether wealth and disease incidence have a unidirectional and stable relationship. And on the other hand, to find out if the country’s statistical production capacity is masking the real incidence of the COVID-19 pandemic. In order to achieve this objective, an ecological study has been designed at international level with the countries established as study units. The analytical strategy utilized involves the consecutive application of cross-sectional analysis, specifically employing multivariate linear regression daily throughout the first two years of the pandemic (from 03/14/2020 to 03/28/2022). The application of multiple cross-sectional analysis has shown that country wealth has a dynamic relationship with the incidence of COVID-19. Initially, it appears as a risk factor and, in the long term, as a protective element. In turn, statistical capacity appears as an explanatory variable for the number of published COVID-19 cases and deaths. Therefore, the inadequate statistical production capacity of low income countries may be masking the real incidence of the disease.
Construction Site Modeling Objects Using Artificial Intelligence and BIM Technology: A Multi-Stage Approach

This study presents a multi-stage approach for building object models (BOMs) on a construction site, aimed at creating an “evolutionary” digital twin. The integration of building information modeling (BIM) and artificial intelligence is used to achieve this goal, with the use of photo modeling using moving cameras and the potential integration of IoT technologies also discussed. A comprehensive artificial intelligence system, combining Convolutional Neural Network (CNN) and Feed Forward Neural Network (FFNN) architectures, has been developed to detect, categorize, and evaluate BIM projects throughout their life cycle. The scalability prospects for point cloud and mesh models, as well as the use of big data technology to optimize the representation of the digital twin, are also addressed. The study determines the effectiveness of construction site conformance detection during the construction of a BIM model, providing consistency and a quantitative evaluation of the processes taking place on the construction site. The findings of this research can be used to enhance BIM modeling methods and concepts toward a multi-stage representation of the digital twin of the construction site.
Facility-Specific Radiation Exposure Risks and Their Implications for Radiation Workers at Department of Energy Laboratories

This research develops a new framework for evaluating the occupational risks of exposure to hazardous substances in any setting where As Low As Reasonably Achievable (ALARA) practices are mandated or used. The evaluation is performed by developing a hypothesis-test-based procedure for evaluating the homogeneity of various epidemiological cohorts, and thus the appropriateness of the application of aggregate data-pooling techniques to those cohorts. A statistical methodology is then developed as an alternative to aggregate pooling for situations in which individual cohorts show heterogeneity between them and are thus unsuitable for pooled analysis. These methods are then applied to estimate the all-cancer mortality risks incurred by workers at four Department-of-Energy nuclear weapons laboratories. Both linear, nothreshold and dose-bin averaged risks are calculated and it is further shown that aggregate analysis tends to overestimate the risks with respect to those calculated by the methods developed in this work.
CXCL8 Promotes Endothelial-to-Mesenchymal Transition of Endothelial Cells and Protects Cells from Erastin-Induced Ferroptosis via CXCR2-Mediated Activation of the NF-κB Signaling Pathway

CXCL8-CXCR1/CXCR2 signaling pathways might form complex crosstalk among different cell types within the ovarian tumor microenvironment, thereby modulating the behaviors of different cells. This study aimed to investigate the expression pattern of CXCL8 in the ovarian tumor microenvironment and its impact on both endothelial-to-mesenchymal transition (EndMT) and ferroptosis of endothelial cells. The human monocytic cell line THP-1 and the human umbilical vein endothelial cell line PUMC-HUVEC-T1 were used to conduct in vitro studies. Erastin was used to induce ferroptosis. Results showed that tumor-associated macrophages are the major source of CXCL8 in the tumor microenvironment. CXCL8 treatment promoted the nucleus entrance of NF-κB p65 and p65 phosphorylation via CXCR2 in endothelial cells, suggesting activated NF-κB signaling. Via the NF-κB signaling pathway, CXCL8 enhanced TGF-β1-induced EndMT of PUMC-HUVEC-T1 cells and elevated their expression of SLC7A11 and GPX4. These trends were drastically weakened in groups with CXCR2 knockdown or SB225002 treatment. TPCA-1 reversed CXCL8-induced upregulation of SLC7A11 and GPX4. CXCL8 protected endothelial cells from erastin-induced ferroptosis. However, these protective effects were largely canceled when CXCR2 was knocked down. In summary, CXCL8 can activate the NF-κB signaling pathway in endothelial cells in a CXCR2-dependent manner. The CXCL8-CXCR2/NF-κB axis can enhance EndMT and activate SLC7A11 and GPX4 expression, protecting endothelial cells from ferroptosis.
Platform Ontologies for the Model-Driven Architecture

The only study to integrate a platform-driven decision framework with the model-driven architecture (MDA), this in-depth, practical guide explains how to combine three technology areasMDA, ontologies, and software product linesin order to integrate several platform-specific software products into a single MDA. Further exploring concepts such as knowledge-representation systems, artificial intelligence, and the "semantic web," this complete resource reveals why these technologies are sohighly relevant today and speculates on how they will impact future software development.
A Novel Multiphysics Simulation Method for Packaging Analysis of Chips Based on MOOSE

In this paper, we present a fully-coupled electromagnetic-thermal-mechanical multiphysics simulation method for the packaging analysis of chips by overcoming the challenges resulting from the miniaturization of packaging structure and inclusion of thermodynamic and mechanical coupling effects. By using the multiphysics object-oriented simulation environment (MOOSE), we achieve a parallel high-performance simulation which employs a hybrid scheme of combining the finite element method (FEM) and the finite-difference time-domain (FDTD) method to minimize computational costs. Numerical experiments demonstrate that the method has a superior efficiency compared to the COMSOL while it can give a consistent result.