Miracle-dz/UniFAR-FTUs huggingface dataset:
fMRI Brain Decoding and Its Applications in Brain–Computer Interface: A Survey

Brain neural activity decoding is an important branch of neuroscience research and a key technology for the brain–computer interface (BCI). Researchers initially developed simple linear models and machine learning algorithms to classify and recognize brain activities. With the great success of deep learning on image recognition and generation, deep neural networks (DNN) have been engaged in reconstructing visual stimuli from human brain activity via functional magnetic resonance imaging (fMRI). In this paper, we reviewed the brain activity decoding models based on machine learning and deep learning algorithms. Specifically, we focused on current brain activity decoding models with high attention: variational auto-encoder (VAE), generative confrontation network (GAN), and the graph convolutional network (GCN). Furthermore, brain neural-activity-decoding-enabled fMRI-based BCI applications in mental and psychological disease treatment are presented to illustrate the positive correlation between brain decoding and BCI. Finally, existing challenges and future research directions are addressed.
ANALYSIS OF IMAGE INFORMATION WHEN EMPLOYING THE DIFFUSION WEIGHTED IMAGING (DWI) SEQUENCES WITH ‘B’ VALUE VARIATION FOR INTRACRANIAL TUMOR CASE

Background: One variation of pulse sequence used in MRI Brain examination is Diffusion Weighted Image (DWI). In the DWI sequence, the value of 'b' which the operator must choose when setting the parameters, affects the signal intensity. In radiology installations, radiographers often use a 'b' value of 1000 s/mm2 with various pathologies. The purpose of this study was to determine the effect of setting the value of 'b' (1000.1500.2000 s/mm2) on image information and to determine the best setting of the three selected 'b' values in generating DWI signals for cases of intracranial tumors.Methods: This research is experimental study. This research uses MR GE 1.5 Tesla. 6 radiographic images were created with three 'b' value settings. Three radiologists then assessed areas of white matter, gray matter, proc. coronoid, basal ganglia and tumor lesions. The results were then analyzed using the Friedman statistical test.Results: The results showed that there were differences in signal intensity and image quality between the three setting values of 'b' with p value 0.005. The mean rank indicates that the best setting 'b' value in producing high signal intensity in Basal ganglia, Proc. coronoid and tumor lesions is 1500 s/mm2 (Mean rank: 2.75 and 2.42). then for white matter and gray matter the best 'b' value setting is 1000 s/mm2 (average rating: 2.50). Conclusion: There is a significant difference in MRI Brain image information with variations in the "b" values of 1000 s/mm2, 1500 s/mm2 and 2000 s/mm2 with pulse sequence Diffusion Weighted Imaging (DWI) using GE 1.5 Tesla MRI modality in patients with intracranial tumors (p 0.05). Keyword : DWI , ‘b’ value, Brain, Tumor, image information
Choosing Proper Relationship Between Teacher and Students: Interpersonal, Contractual and Pedagogical Relationship

Choosing proper relationship is an important matter to be considered particularly in the school setting. Education is intellectual and character and spiritual formation. All kinds of relationship must be tailored toward such aim. The relationship between teachers and students must help the students grow intellectually and spiritually or morally. Based on my presentation, the nature of the relationship between teachers and students must be pedagogical in nature, asymmetrical relationship or between the unequal. The teacher is there to help the students learn and grow intellectually and spiritually.
Caring for bliss moderates the association between mindfulness, self-compassion, and well-being in college-attending emerging adults

ABSTRACT Ample evidence suggests that mindfulness and self-compassion are related to well-being, but little is known about the role of a new construct, caring for bliss, in these relationships. This study examined the potential moderating role of caring for bliss on the effect of mindfulness and self-compassion on well-being in college-attending emerging adults (n = 683). Well-being was examined in terms of positive (flourishing) and negative (depressive symptoms) indicators. Results show that caring for bliss enhanced the effect of high self-compassion on flourishing and compensated for the effect of low mindfulness on flourishing. Caring for bliss also buffered the effect of low mindfulness on depressive symptoms. No moderating effect was found for self-compassion on depressive symptoms. The present study highlights the importance of caring for bliss as a new human strength among college-attending emerging adults that can enhance and buffer the effect of mindfulness and self-compassion on well-being.
Has the tectonic regime of the Baltic Shield always remained the same?

ABSTRACT Recent theories on the growth of continental crust stress its formation in pre-Archean and Archean times with minor additions at later times. The Earth has gone through a unidirectional evolution including surface changes, the genesis of life and an important loss of energy. The energy loss drives tectonic processes, but at a rate declining with available energy and thus with time. Semiquantitative modelling of energy-changes and transport indicates important differences between today’s Earth and the Paleoproterozoic Earth. Actualistic but non-uniformitarian arguments suggest that principal tectonic processes have changed over time. The Baltic Shield displays a fundamental difference in conditions between the c. 0.9 Ga high-pressure metamorphism including the occurrence of eclogite in the western Sveconorwegian Orogen and c. 2 Ga low-pressure metamorphism in the eastern Svecokarelian Orogen. This suggests that modern platetectonic processes were operative at c. 0.9 Ga but leaves the question whether it was active during the Svecokarelian orogeny open. The lack of eclogite in the Svecokarelian Orogen is explained with a change with time of the geotherm slope. The occurrence of two generations of granitic rocks of different compositions in the Svecokarelian is easily explained by an increased mantle heat flow in a stratified continental crust. In contrast to calc-alkaline platetectonic compositions, alkali-calcic rocks occur in a quarter-circle around the Svecokarelian core. The question arises whether modern plate-processes successively replaced proto-plate processes during Paleo- and Mesoproterozoic times.
Evidence for “Pushed Out” and “Opt Out” Factors in Women’s Career Inclusion Across the World of Work in the United States

There is an ongoing debate over the extent to which women “opt out” and/or are “pushed out” of various occupations (Kossek et al., 2017). To advance this debate, we explore the correspondence of women’s interests in stereotypically masculine work activities with the work activities of their occupations/occupational-aspirations. We examine 42,631 responses to a survey of employed and unemployed persons in the United States and analyze associations along all six of Holland’s (1997) interest/work-activity dimensions. Overall, we find support for a “pushed out” perspective as women’s interests in hands-on/practical, analytic/scientific, and managerial/sales-related work activities are less strongly associated with being employed in occupations with those activities – in comparison to similarly interested men. However, these effect sizes are small and we find support for “opt out” dynamics in relation to hands-on/practical occupations. Altogether, our results indicate the need to continue looking beyond women’s vocational interests as explanations of their underrepresentation.
An Improved Whale Optimization Algorithm Based on Aggregation Potential Energy for QoS-Driven Web Service Composition

With more complex user needs, the web service composition (WSC) has become a key research area in the current circumstance. The swarm intelligence algorithms are proved to solve this problem well. However, no researchers have applied the whale optimization algorithm (WOA) to the WSC problem. In this work, we propose a logarithmic energy whale optimization algorithm (LEWOA) based on aggregation potential energy and logarithmic convergence factor to solve this problem. Firstly, the improved algorithm uses a chaotic strategy to enhance the initial swarm diversity. After that, a logarithmic convergence factor is applied to obtain the nonlinear search step. Furthermore, aggregation potential energy as the spatial evaluation is employed in the swarm intelligence algorithms for the first time. Finally, the aggregation potential energy is used to dynamically adjust the nonlinear weight, which improves the search efficiency and prevents the algorithm from falling into local optimization. The experimental results of the benchmark functions show that the LEWOA has better optimization ability and convergence speed than other swarm intelligence algorithms. In the second experiment of the WSC optimization, the effectiveness and superiority of the LEWOA are verified.
Activating RAC1 variants in the switch II region cause a developmental syndrome and alter neuronal morphology

Abstract RAC1 is a highly conserved Rho GTPase critical for many cellular and developmental processes. De novo missense RAC1 variants cause a highly variable neurodevelopmental disorder. Some of these variants have previously been shown to have a dominant negative effect. Most previously reported patients with this disorder have either severe microcephaly or severe macrocephaly. Here, we describe eight patients with pathogenic missense RAC1 variants affecting residues between Q61 and R68 within the switch II region of RAC1. These patients display variable combinations of developmental delay, intellectual disability, brain anomalies such as polymicrogyria and cardiovascular defects with normocephaly or relatively milder micro- or macrocephaly. Pulldown assays, NIH3T3 fibroblast spreading assays and staining for activated PAK1/2/3 and WAVE2 suggest that these variants increase RAC1 activity and over-activate downstream signalling targets. Axons of neurons isolated from Drosophila embryos expressing the most common of the activating variants are significantly shorter, with an increased density of filopodial protrusions. In vivo, these embryos exhibit frequent defects in axonal organization. Class IV dendritic arborization neurons expressing this variant exhibit a significant reduction in the total area of the dendritic arbour, increased branching and failure of self-avoidance. RNAi knock down of the WAVE regulatory complex component Cyfip significantly rescues these morphological defects. These results establish that activating substitutions affecting residues Q61–R68 within the switch II region of RAC1 cause a developmental syndrome. Our findings reveal that these variants cause altered downstream signalling, resulting in abnormal neuronal morphology and reveal the WAVE regulatory complex/Arp2/3 pathway as a possible therapeutic target for activating RAC1 variants. These insights also have the potential to inform the mechanism and therapy for other disorders caused by variants in genes encoding other Rho GTPases, their regulators and downstream effectors.
Significance of neotectonics in assessing the relative tectonic activity in and around the Mat Fault of Eastern Himalayan Fold Belt, Mizoram, north‐east India

The Mat Fault situated in the Eastern Himalayan Fold Belt was mapped around Mizoram and is oriented NNW to SSE. This region has experienced a number of earthquakes in the recent past that damaged settlements and triggered numerous landslides. A set of geomorphic parameters were analysed on both sides of the Mat Fault to understand the tectonic setting. The six geomorphic parameters include: elongation ratio, hypsometric analysis, asymmetry factor, transverse topographic symmetry factor, stream length‐gradient index, and valley floor‐to‐width ratio, and they were calculated for 20 fourth‐order sub‐watersheds. Out of 20 sub‐watersheds, 10 sub‐watersheds were selected from the northern block and 10 from the southern block. Hypsometric analyses show that all the sub‐watersheds have attained a mature stage of geomorphic cycle of erosion; and from the analysis of hypsometric curves, it is found that 16 of the sub‐watersheds might have been uplifted due to tectonic activity. The asymmetry and transverse topographic symmetry factor values also suggest tilting in different directions possibly due to obstruction by faulting. The anomaly of high stream length index values is also observed in different sub‐watersheds indicating neotectonic activity. Low values of the valley floor‐to‐width ratio (<0.5) suggest V‐shaped valley topography, which indicate vertical incision due to tectonic activity. On evaluating relative tectonic activity among 20 sub‐watersheds, it is found that 80% of sub‐watersheds in the northern block show relatively stronger tectonic activity than the southern block. The present work suggests that the study area is tectonically active, and the possibility that they were produced by seismic activity.
SoMoGym: A Toolkit for Developing and Evaluating Controllers and Reinforcement Learning Algorithms for Soft Robots

Soft robotsoffer a host of benefits over traditional rigid robots, including inherent compliance that lets them passively adapt to variable environments and operate safely around humans and fragile objects. However, that same compliance makes it hard to use model-based methods in planning tasks requiring high precision or complex actuation sequences. Reinforcement learning (RL) can potentially find effective control policies, but training RL using physical soft robots is often infeasible, and training using simulations has had a high barrier to adoption. To accelerate research in control and RL for soft robotic systems, we introduce SoMoGym (Soft Motion Gym), a software toolkit that facilitates training and evaluating controllers for continuum robots. SoMoGym provides a set of benchmark tasks in which soft robots interact with various objects and environments. It allows evaluation of performance on these tasks for controllers of interest, and enables the use of RL to generate new controllers. Custom environments and robots can likewise be added easily. We provide and evaluate baseline RL policies for each of the benchmark tasks. These results show that SoMoGym enables the use of RL for continuum robots, a class of robots not covered by existing benchmarks, giving them the capability to autonomously solve tasks that were previously unattainable.
Machine learning classification for D0 meson signal extraction in d+Au collisions

In heavy-ion collisions at large particle colliders, such as LHC or RHIC, heavy-flavour (charm and beauty) quarks are produced mainly through initial hard scatterings. Therefore, they can serve as the probes of properties of the hot medium created in such collisions. Additionally, in small collision systems, such as d/p+Au collisions, cold nuclear matter effects can also affect the charm quark production with respect to p+p collisions. Hadrons, that contain heavy-flavour quarks, could not be directly detected, thus they are measured via reconstruction of their decay products. However, due to a large number of particles produced in such collisions, separation of the decay products from combinatorial background is challenging and advanced statistical analysis is needed. In this article, we exploit D0 (D0)→K- π+ (K+ π-) decay in order to investigate performance of several machine learning algorithms with different implementation approaches to find the most effective way how to separate signal from random combinatorial background. For this study, we use HIJING and STAR detector simulation of d+Au collisions at √(sNN)=200 GeV embedded to the collisions recorded with the STAR. In this paper we compare deep neural network implemented using Keras with TensorFlow backend, random forest model implemented using scikit-learn and boosted decision trees implemented by means of the Toolkit for Multivariate Data Analysis with ROOT. Described methods might be applied on reconstruction of any two-body decay in high-energy physics experiments.
Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases

Abstract Motivation Gene expression data are commonly used at the intersection of cancer research and machine learning for better understanding of the molecular status of tumour tissue. Deep learning predictive models have been employed for gene expression data due to their ability to scale and remove the need for manual feature engineering. However, gene expression data are often very high dimensional, noisy and presented with a low number of samples. This poses significant problems for learning algorithms: models often overfit, learn noise and struggle to capture biologically relevant information. In this article, we utilize external biological knowledge embedded within structures of gene interaction graphs such as protein–protein interaction (PPI) networks to guide the construction of predictive models. Results We present Gene Interaction Network Constrained Construction (GINCCo), an unsupervised method for automated construction of computational graph models for gene expression data that are structurally constrained by prior knowledge of gene interaction networks. We employ this methodology in a case study on incorporating a PPI network in cancer phenotype prediction tasks. Our computational graphs are structurally constructed using topological clustering algorithms on the PPI networks which incorporate inductive biases stemming from network biology research on protein complex discovery. Each of the entities in the GINCCo computational graph represents biological entities such as genes, candidate protein complexes and phenotypes instead of arbitrary hidden nodes of a neural network. This provides a biologically relevant mechanism for model regularization yielding strong predictive performance while drastically reducing the number of model parameters and enabling guided post-hoc enrichment analyses of influential gene sets with respect to target phenotypes. Our experiments analysing a variety of cancer phenotypes show that GINCCo often outperforms support vector machine, Fully Connected Multi-layer Perceptrons (MLP) and Randomly Connected MLPs despite greatly reduced model complexity. Availability and implementation https://github.com/paulmorio/gincco contains the source code for our approach. We also release a library with algorithms for protein complex discovery within PPI networks at https://github.com/paulmorio/protclus. This repository contains implementations of the clustering algorithms used in this article. Supplementary information Supplementary data are available at Bioinformatics online.
DBT affects sleep in both circadian and non-circadian neurons

Sleep is a very important behavior observed in almost all animals. Importantly, sleep is subject to both circadian and homeostatic regulation. The circadian rhythm determines the daily alternation of the sleep-wake cycle, while homeostasis mediates the rise and dissipation of sleep pressure during the wake and sleep period. As an important kinase, dbt plays a central role in both circadian rhythms and development. We investigated the sleep patterns of several ethyl methanesulfonate-induced dbt mutants and discuss the possible reasons why different sleep phenotypes were shown in these mutants. In order to reduce DBT in all neurons in which it is expressed, CRISPR-Cas9 was used to produce flies that expressed GAL4 in frame with the dbt gene at its endogenous locus, and knock-down of DBT with this construct produced elevated sleep during the day and reduced sleep at night. Loss of sleep at night is mediated by dbt loss during the sleep/wake cycle in the adult, while the increased sleep during the day is produced by reductions in dbt during development and not by reductions in the adult. Additionally, using targeted RNA interference, we uncovered the contribution of dbt on sleep in different subsets of neurons in which dbt is normally expressed. Reduction of dbt in circadian neurons produced less sleep at night, while lower expression of dbt in noncircadian neurons produced increased sleep during the day. Importantly, independently of the types of neurons where dbt affects sleep, we demonstrate that the PER protein is involved in DBT mediated sleep regulation.
Research of Phase-Shifted SVPWM Based on H-Bridge Cascaded Multilevel Inverter

Phase-shifted PWM is specially for cascaded inverter.The carrier phase-shifted SPWM has high equivalent carrier frequency and low harmonic.And the output between power units is balanced,and it is easy for expansion.SVPWM has low harmonic and high voltage utilization.The control method is simple and easy for digital realization.Based on the configuration of H-bridge cascaded multilevel inverter,this paper combines SVPWM and carrier phase-shifted SPWM.A novel algorithm of SVPWM is proposed.It hasn't any trigonometric function and irrational number calculations,which simplifies the calculation process and leads the calculation results more accurate.Simulation verification is presented to confirm the capability of the novel algrithm.
Mental Health Surveillance at federal state level – Reporting on psychiatry in Bavaria

In Germany, mental health reporting is organised at the federal, federal state and municipal level. At federal level, a number of concepts and approaches are implemented. In 2020 and 2021, in accordance with Article 4 of the Mental Health Assistance Act the first Bavarian Psychiatry Report was prepared. Important data bases include the billing and care data of Bavaria’s Association of Statutory Health Insurance Physicians and the administrative data of the Bavarian districts. The aim is to enhance coordination between these federal state projects and Mental Health Surveillance at national level, in particular regarding the use of health care data.
Using video consultation technology between care homes and health and social care professionals: a scoping review and interview study during COVID-19 pandemic

Abstract Background the COVID-19 pandemic disproportionately affected care home residents’ and staffs’ access to health care and advice. Health and social care professionals adapted rapidly to using video consultation (videoconferencing) technology without guidance. We sought to identify enablers and barriers to their use in supporting care home residents and staff. Methods a scoping review of the evidence on remote consultations between healthcare services and care homes. Interviews with English health and social care professionals about their experiences during the pandemic. Findings were synthesised using the non-adoption, abandonment, scale-up, spread, sustainability framework. Results 18 papers were included in the review. Twelve interviews were completed. Documented enablers and barriers affecting the uptake and use of technology (e.g. reliable internet; reduced travelling) resonated with participants. Interviews demonstrated rapid, widespread technology adoption overcame barriers anticipated from the literature, often strengthening working relationships with care homes. Novel implementation issues included using multiple platforms and how resident data were managed. Healthcare professionals had access to more bespoke digital platforms than their social care counterparts. Participants alternated between platforms depending on individual context or what their organisation supported. All participants supported ongoing use of technologies to supplement in-person consultations. Conclusions the evidence on what needs to be in place for video consultations to work with care homes was partly confirmed. The pandemic context demolished many documented barriers to engagement and provided reassurance that residents’ assessments were possible. It exposed the need to study further differing resident requirements and investment in digital infrastructure for adequate information management between organisations.