Miracle-dz/UniFAR-FTUs huggingface dataset:
Network study of responses to unusualness and psychological stress during the COVID-19 outbreak in Korea

The dramatic changes in people’s daily lives caused by the 2019 coronavirus (COVID-19) pandemic have had a huge impact on their emotions and behaviors. This study aimed to examine psychosocial responses to COVID-19 using network analysis. A total of 1,500 urban residents of South Korea, selected from an online public panel, were surveyed using self-rating questionnaires addressing daily life changes, fear of infection, and distress related to COVID-19. Participants also completed a 10-item Perceived Stress Scale survey. We constructed regularized partial correlation networks, estimated global and local network metrics, tested network accuracy and stability, and compared the estimated networks between men and women. The network of the psychosocial responses consisted of 24 nodes that were classified into five groups: ‘fear of infection’, ‘difficulty with outside activities’, ‘economic loss’, ‘altered eating and sleeping’, and ‘adaptive stress’. The node centralities indicated that ‘distress in obtaining daily necessities’ and ‘concern about harming others’ were the most important issues in people’s responses to COVID-19. These nodes were connected by a negative edge, reflecting individual- and community-level issues, respectively. The overall level of perceived stress was linked to the network by the connection node ‘anger toward others or society’, which was associated with economic problems in men, but with distress from changes in daily activities in women. The results suggest that two contrasting feelings—personal insecurity regarding basic needs and a collectivistic orientation—play roles in the response to unusual experiences and distress due to COVID-19. This study also showed that public anger could arise from the psychological stress under the conditions imposed by COVID-19.
Factors associated with electrical stimulation‐induced performance fatigability are dependent upon stimulation location

What is the central question of this study? How does peripheral nerve stimulation (PNS) compare with neuromuscular electrical stimulation (NMES) used clinically to reduce muscle atrophy? What is the main finding and its importance? NMES resulted in progressive increases in M‐wave duration and delay of muscle relaxation throughout a single stimulation protocol, findings not observed with PNS. This suggests PNS recruits from a wider pool of muscle fibres/motor units, providing a more favourable alternative to NMES for rehabilitation intervention.
BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides

MOTIVATION The identification of bitter peptides through experimental approaches is an expensive and time-consuming endeavor. Due to the huge number of newly available peptide sequences in the post-genomic era, the development of automated computational models for the identification of novel bitter peptides is highly desira-ble. RESULTS In this work, we present BERT4Bitter, a bidirectional encoder representation from transformers (BERT)-based model for predicting bitter peptides directly from their amino acid sequence without using any structural information. To the best of our knowledge, this is the first time a BERT-based model has been employed to identify bitter peptides. Compared to widely used machine learning models, BERT4Bitter achieved the best performance with accuracy of 0.861 and 0.922 for cross-validation and independent tests, respectively. Furthermore, extensive empirical benchmarking experiments on the independent dataset demonstrated that BERT4Bitter clearly outperformed the existing method with improvements of > 8% accuracy and >16% Matthews coefficient correlation, highlighting the effectiveness and robustness of BERT4Bitter. We believe that the BERT4Bitter method proposed herein will be a useful tool for rapidly screening and identifying novel bitter peptides for drug development and nutritional research. AVAILABILITY The user-friendly web server of the proposed BERT4Bitter is freely accessible at: http://pmlab.pythonanywhere. com/BERT4Bitter. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
MAGNETIC FIELD EFFECTS ON LAMINAR FILM CONDENSATION ON A FINITE-SIZE HORIZONTAL WAVY DISK

ABSTRACT This work aims to study the laminar film condensation heat transfer with magnetic field effects on a finite-size horizontal wavy disk. Mathematical analysis is used to obtain the dimensionless governing equations and boundary conditions, and the cubic spline collocation method is then used to calculate the flow and heat transfer characteristics along the wavy disk. The effects of the important parameters, such as Hartmann number, condensation parameter, wave number, amplitude-wavelength ratio, and Pr/Ja on the heat transfer characteristics have been successfully examined. The magnetic field effect increases the critical condensate thickness and thus decreases the condensation heat transfer. Odd wave number effects decrease the critical condensate thickness and thus enhance the condensation heat transfer.
Competitive Retention of Sulfamethoxazole (SMX) and Sulfamethazine (SMZ) from Synthetic Solutions in a Strong Anionic Ion Exchange Resin

Numerous antibiotics such as sulfonamides have been found in effluents from drug manufacturers. Removal of pharmaceuticals by adsorption and ion exchange comprise some of the most promising techniques for the retention of these compounds from wastewaters due to their low cost, easy regeneration, and selective removal of pollutants. This article studies the removal of sulfamethoxazole (SMX) and sulfamethazine (SMZ), the most common sulfonamide antibiotics detected in municipal sewage. Lewatit MP500 (Lanxess Chemical) ion exchange resin was used to remove SMX, SMZ present in solutions. Batch experiments were carried out in cylindrical stirred tanks. Binary system solutions of equal mass concentration were prepared containing 30-250 mg/L of each compound, SMX and SMZ, and tested with an L/S ratio (mL solution/g resin) = 1000 obtained in previous experiments. Also, the adsorption of SMX mixed with trimethoprim (TMP) in batch experiments was analyzed due to the fact that both compounds are usually administered together. Equilibrium and kinetics were studied in order to characterize the operation. The adsorption equilibrium constants for SMX and SMZ were determined using a modified extended Langmuir model for multicomponent equilibrium sorption. Kinetics was analyzed using the pore diffusion model. Finally, several loading and elution cycles were carried out in fixed bed to obtain the breakthrough curves, comparing the experimental data to predicted values from a fixed bed model.
Lightweight user authentication scheme for roaming service in GLOMONET with privacy preserving

With the development of information technology and the Internet, users can conveniently use roaming services without time and space restrictions. This roaming service is initiated by establishing a session key between a home node, which exists in a home network, and a mobile node, which exists in a foreign network. However, in the process of verifying a legitimate user and establishing a session key, various security threats and privacy exposure issues can arise. This study demonstrates that the authentication scheme for the roaming service proposed in the existing Global Mobility Network (GLOMONET) environment has several vulnerabilities and, hence, is impractical. In addition, the scheme does not satisfy the privacy of the session key or user’s identity or password. Accordingly, we propose a new lightweight authentication scheme to compensate for these vulnerabilities and secure a high level of privacy, such as non-traceability. In addition, formal and informal analyses are conducted to examine the safety of the proposed scheme. Based on the results of our analyses, we prove that the proposed scheme is highly secure and applicable to the actual GLOMONET environment.
Isomerization of 4,6-Dinitrobenzofuroxan—A DFT Study

The present study concentrates on the isomerization process of 4,6-dinitrobenzofuroxan and the monoionic forms of its bicyclic isomers. The ionic forms shed some light on the stability of those isomers in an ionizing electric field or static charging. To achieve the goals of the study, certain density functional theory (DFT) calculations have been carried out at the restricted and unrestricted levels of B3LYP using 6-31G(d) and 6-311 + G(d,p) basis sets. The stabilities and some other quantum chemical properties of the isomers and the ionic forms were obtained. It was found that upon charging, the bicyclic isomers undergo ring opening of the five-membered ring to yield stable structures.
Utilizing bacteriophage to define the minimum residence time within a plug flow reactor

As part of a viral mitigating strategy for continuous bioprocessing, that utilizes a plug flow reactor (PFR) for continuous viral inactivation (CVI), understanding the minimum residence time as a function of reactor scale and operational conditions is critical. An empirical‐based model was utilized to calculate the minimum duration a virus particle experiences within a plug flow reactor as a function of reactor design and operational conditions. This empirical model's calculations were challenged by pulse injecting the bacteriophage ΦX‐174 in non‐inactivating conditions and monitoring the discharge of the PFR with infectivity assays. The initial proposed empirical model, with the constraint of requiring an operational Dean number of >100, proved to be effective at calculating first breakthrough of ΦX‐174 but only for the appropriate Dean number conditions. With the knowledge gained from the first empirical model, a second was generated to eliminate the Dean number constraint. This second modified empirical model proved to be successful at calculating the first breakthrough at all Dean number's tested, however CVI operation at the lower Dean's number will lead to an increased asymmetry (i.e., increased tailing) in the residence time distribution.
Mutation analysis and prenatal diagnosis of a family with congenital contractural arachnodactyly

Congenital contractural arachnodactyly (CCA) is a rare autosomal dominant condition caused by mutations in the fibrillin 2 gene (FBN2). The primary clinical symptoms of CCA include multiple flexion contractures, arachnodactyly, dolichostenomelia, scoliosis, abnormal pinnae, muscular hypoplasia, and crumpled ears.
Accelerated atherosclerosis caused by serum amyloid A response in lungs of ApoE−/− mice

Airway exposure to eg particulate matter is associated with cardiovascular disease including atherosclerosis. Acute phase genes, especially Serum Amyloid A3 (Saa3), are highly expressed in the lung following pulmonary exposure to particles. We aimed to investigate whether the human acute phase protein SAA (a homolog to mouse SAA3) accelerated atherosclerotic plaque progression in Apolipoprotein E knockout (ApoE−/−) mice. Mice were intratracheally (i.t.) instilled with vehicle (phosphate buffered saline) or 2 µg human SAA once a week for 10 weeks. Plaque progression was assessed in the aorta using noninvasive ultrasound imaging of the aorta arch as well as by en face analysis. Additionally, lipid peroxidation, SAA3, and cholesterol were measured in plasma, inflammation was determined in lung, and mRNA levels of the acute phase genes Saa1 and Saa3 were measured in the liver and lung, respectively. Repeated i.t. instillation with SAA caused a significant progression in the atherosclerotic plaques in the aorta (1.5‐fold). Concomitantly, SAA caused a statistically significant increase in neutrophils in bronchoalveolar lavage fluid (625‐fold), in pulmonary Saa3 (196‐fold), in systemic SAA3 (1.8‐fold) and malondialdehyde levels (1.14‐fold), indicating acute phase response (APR), inflammation and oxidative stress. Finally, pulmonary exposure to SAA significantly decreased the plasma levels of very low‐density lipoproteins ‐ low‐density lipoproteins and total cholesterol, possibly due to lipids being sequestered in macrophages or foam cells in the arterial wall. Combined these results indicate the importance of the pulmonary APR and SAA3 for plaque progression.
Enriched Environment Promotes Adult Hippocampal Neurogenesis through FGFRs

The addition of new neurons to existing neural circuits in the adult brain remains of great interest to neurobiology because of its therapeutic implications. The premier model for studying this process has been the hippocampal dentate gyrus in mice, where new neurons are added to mature circuits during adulthood. Notably, external factors such as an enriched environment (EE) and exercise markedly increase hippocampal neurogenesis. Here, we demonstrate that EE acts by increasing fibroblast growth factor receptor (FGFR) function autonomously within neurogenic cells to expand their numbers in adult male and female mice. FGFRs activated by EE signal through their mediators, FGFR substrate (FRS), to induce stem cell proliferation, and through FRS and phospholipase Cγ to increase the number of adult-born neurons, providing a mechanism for how EE promotes adult neurogenesis. SIGNIFICANCE STATEMENT How the environment we live in affects cognition remains poorly understood. In the current study, we explore the mechanism underlying the effects of an enriched environment on the production of new neurons in the adult hippocampal dentate gyrus, a brain area integral in forming new memories. A mechanism is provided for how neural precursor cells in the adult mammalian dentate gyrus respond to an enriched environment to increase their neurogenic output. Namely, an enriched environment acts on stem and progenitor cells by activating fibroblast growth factor receptor signaling through phospholipase Cγ and FGF receptor substrate proteins to expand the pool of precursor cells.
Large-Scale Subspace Clustering by Independent Distributed and Parallel Coding

Subspace clustering is a popular method to discover underlying low-dimensional structures of high-dimensional multimedia data (e.g., images, videos, and texts). In this article, we consider a large-scale subspace clustering (LS2C) problem, that is, partitioning million data points with a millon dimensions. To address this, we explore an independent distributed and parallel framework by dividing big data/variable matrices and regularization by both columns and rows. Specifically, LS2C is independently decomposed into many subproblems by distributing those matrices into different machines by columns since the regularization of the code matrix is equal to a sum of that of its submatrices (e.g., square-of-Frobenius/ $\ell _{1}$ -norm). Consensus optimization is designed to solve these subproblems in a parallel way for saving communication costs. Moreover, we provide theoretical guarantees that LS2C can recover consensus subspace representations of high-dimensional data points under broad conditions. Compared with the state-of-the-art LS2C methods, our approach achieves better clustering results in public datasets, including a million images and videos.
Prediction of regional wildfire activity in the probabilistic Bayesian framework of Firelihood.

• Modelling wildfire activity is crucial for informing science-based risk management and understanding the spatio-temporal dynamics of fire-prone ecosystems worldwide. Models help disentangle the relative influences of different factors, understand wildfire predictability and provide insights into specific events. Here, we develop Firelihood, a two-component Bayesian hierarchically structured probabilistic model of daily fire activity, which is modelled as the outcome of a marked point process: individual fires are the points (occurrence component), and fire sizes are the marks (size component). The space-time Poisson model for occurrence is adjusted to gridded fire counts using the integrated nested Laplace approximation (INLA) combined with the Stochastic Partial Differential Equation (SPDE) approach. The size model is based on piecewise-estimated Pareto and Generalized-Pareto distributions, adjusted with INLA. The Fire Weather Index (FWI) and Forest Area are the main explanatory variables. Temporal and spatial residuals are included to improve the consistency of the relationship between weather and fire occurrence. The posterior distribution of the Bayesian model provided 1000 replications of fire activity that were compared with observations at various temporal and spatial scales in Mediterranean France. The number of fires larger than 1ha across the region was coarsely reproduced at the daily scale, and was more accurately predicted on a weekly basis or longer. The regional weekly total number of larger fires (10 to 100 ha) was predicted as well, but the accuracy degraded with size, as the model uncertainty increased with event rareness. Local predictions of fire numbers or burnt areas also required a longer aggregation period to maintain model accuracy. The estimation of fires larger than 1ha was also consistent with observations during the extreme fire season of the 2003 unprecedented heat wave, but the model systematically underrepresented large fires and burnt areas, which suggests that the FWI does not consistently rate the actual danger of large fire occurrence during heat waves. Firelihood enabled a novel analysis of the stochasticity underlying fire hazard, and offers a variety of applications, including fire hazard predictions for management and projections in the context of climate change.
Computational insights into modulating the performance of MXene based electrode materials for rechargeable batteries

MXene, a still-growing large family of two-dimensional (2D) materials, has aroused enormous attention in the scientific community. Owing to their high specific surface area, good electronic conductivity, stability, and hydrophilicity, MXene has found a wide application involving electromagnetic interference shielding, sensors, catalysis, and energy storage, etc. In the field of energy storage, MXenes are promising electrode materials for various metal-ion batteries and they are also effective anchoring materials for Li−S batteries. One of the most unique features of MXene is its abundant compositions, which renders us large room to modulate its properties. Besides, other effective approaches applicable to traditional 2D materials can also be used to optimize the performance of MXene. Theoretical calculations have played a significant role in predicting and screening high-performance MXene based electrode materials. So far, theoretical researchers have made much progress in optimizing the performance of MXene as electrode materials for various rechargeable batteries. In the present review, started by a brief introduction of the involved mechanism and basic calculation methods, we comprehensively overview the latest theoretical studies of modulating the performance of MXene based electrode materials for rechargeable batteries.
Identification and Predictions Regarding the Biosynthesis Pathway of Polyene Macrolides Produced by Streptomyces roseoflavus Men-myco-93-63

Streptomyces roseoflavus Men-myco-93-63 is a biocontrol strain that has been studied in our laboratory for many years and exhibits a good inhibitory effect in many crop diseases. Therefore, the identification of antimicrobial metabolites is necessary and our main objective. ABSTRACT A group of polyene macrolides mainly composed of two constituents was isolated from the fermentation broth of Streptomyces roseoflavus Men-myco-93-63, which was isolated from soil where potato scabs were repressed naturally. One of these macrolides was roflamycoin, which was first reported in 1968, and the other was a novel compound named Men-myco-A, which had one methylene unit more than roflamycoin. Together, they were designated RM. This group of antibiotics exhibited broad-spectrum antifungal activities in vitro against 17 plant-pathogenic fungi, with 50% effective concentrations (EC50) of 2.05 to 7.09 μg/ml and 90% effective concentrations (EC90) of 4.32 to 54.45 μg/ml, which indicates their potential use in plant disease control. Furthermore, their biosynthetic gene cluster was identified, and the associated biosynthetic assembly line was proposed based on a module and domain analysis of polyketide synthases (PKSs), supported by findings from gene inactivation experiments. IMPORTANCE Streptomyces roseoflavus Men-myco-93-63 is a biocontrol strain that has been studied in our laboratory for many years and exhibits a good inhibitory effect in many crop diseases. Therefore, the identification of antimicrobial metabolites is necessary and our main objective. In this work, chemical, bioinformatic, and molecular biological methods were combined to identify the structures and biosynthesis of the active metabolites. This work provides a new alternative agent for the biological control of plant diseases and is helpful for improving both the properties and yield of the antibiotics via genetic engineering.
Ecological responses to variation in seasonal snow cover

Seasonal snow is among the most important factors governing the ecology of many terrestrial ecosystems, but rising global temperatures are changing snow regimes and driving widespread declines in the depth and duration of snow cover. Loss of the insulating snow layer will fundamentally change the environment. Understanding how individuals, populations, and communities respond to different snow conditions is thus essential for predicting and managing future ecosystem change. We synthesized 365 studies that examined ecological responses to variation in winter snow conditions. This research encompasses a broad range of methods (experimental manipulations, measurement of natural snow gradients, and long‐term monitoring), locations (35 countries), study organisms (plants, mammals, arthropods, birds, fish, lichen, and fungi), and response measures. Earlier snowmelt was consistently associated with advanced spring phenology in plants, mammals, and arthropods. Reduced snow depth often increased mortality or physical injury in plants, although there were few clear effects on animals. Neither snow depth nor snowmelt timing had clear or consistent directional effects on body size of animals or biomass of plants. However, because 96% of studies were from the northern hemisphere, the generality of these trends across ecosystems and localities is also unclear. We identified substantial research gaps for several taxonomic groups and response types; research on wintertime responses was notably scarce. Future research should prioritize examination of the mechanisms underlying responses to changing snow conditions and the consequences of those responses for seasonally snow‐covered ecosystems.
Detection of the Intention of Direction Changes During Gait Through EEG Signals

Brain-Computer Interfaces (BCIs) are becoming an important technological tool for the rehabilitation process of patients with locomotor problems, due to their ability to recover the connection between brain and limbs by promoting neural plasticity. They can be used as assistive devices to improve the mobility of handicapped people. For this reason, current BCIs have to be improved to allow an accurate and natural use of external devices. This work proposes a novel methodology for the detection of the intention to change the direction during gait based on event-related desynchronization (ERD). Frequency and temporal features of the electroencephalographic (EEG) signals are characterized. Then, a selection of the most influential features and electrodes to differentiate the direction change intention from the walking is carried out. Best results are obtained when combining frequency and temporal features with an average accuracy of [Formula: see text]%, which are promising to be applied for future BCIs.