Miracle-dz/UniFAR-FTUs huggingface dataset:
A Bending Model for Nodal Discretizations of Yarn‐Level Cloth

To deploy yarn‐level cloth simulations in production environments, it is paramount to design very efficient implementations, which mitigate the cost of the extremely high resolution. To this end, nodal discretizations aligned with the regularity of the fabric structure provide an optimal setting for efficient GPU implementations. However, nodal discretizations complicate the design of robust and controllable bending. In this paper, we address this challenge, and propose a model of bending that is both robust and controllable, and employs only nodal degrees of freedom. We extract information of yarn and fabric orientation implicitly from the nodal degrees of freedom, with no need to augment the model explicitly. But most importantly, and unlike previous formulations that use implicit orientations, the computation of bending forces bears no overhead with respect to other nodal forces such as stretch. This is possible by tracking optimal orientations efficiently. We demonstrate the impact of our bending model in examples with controllable anisotropy, as well as ironing, wrinkling, and plasticity.
Designing Ambient Multisensory Notification Devices: Managing Disruptions in the Home

An increasing number of technologies are battling for our attention constantly throughout the day. The majority of distractions come in the form of notifications from mobile instant messaging services, which under certain circumstances can make us feel overwhelmed and stressed. The goal of this paper is to add to the growing body of research that aims to find solutions and coping mechanisms for the problems associated with excessive notifications. Instead of focusing on software solutions on the mobile device itself, we explore the use of multisensory ambient information displays as notification delivery systems in the home context. Our goal was to use the attention periphery to create sufficient awareness of incoming messages without overwhelming the user. We conducted a three-part investigation that began with a laboratory study with 23 participants exploring initial impressions with notifications across a variety of sensory channels and modalities. Insights from this study were then used in a design workshop that helped us develop four different ambient notification prototypes. We present qualitative findings from a subsequent study in which seven participants used those devices for a period of four days in their homes. Finally, we compare the results of different notification modalities and provide implications for the design of ambient multisensory notifications in the home context.
C-HVAC: A practical tool for assessing ventilation capacity for HVAC systems during the COVID-19 Pandemic

Although many health organizations are advising commercial and public sectors to maximize the outdoor air circulation and ventilation rates, such actions may not be completely feasible and impose a significant energy load on the HVAC systems. While the social distancing measures are being implemented, the aim of this paper is to present a feasible and practical solution for various stakeholders to calculate the maximum number of people that can occupy buildings during the COVID-19 pandemic when the HVAC systems of buildings work based on outside air or a combination of outside and recirculated air. As a result, C-HVAC, as an easy-to-use tool, was developed using thermodynamic principles that can calculate the maximum number of people that can occupy buildings during the COVID-19 pandemic based on ASHRAE's Building Readiness recommendations. The current paper is part of ongoing research on preventing the spread of highly infectious airborne diseases such as COVID-19 in indoor environments. The beta version of the tool is made publicly available through this article.
FIRED: A Fully-labeled hIgh-fRequency Electricity Disaggregation Dataset

As more and more homes are equipped with smart electricity meters, home owners gain the ability to monitor their total electricity consumption on a daily or hourly basis. Techniques such as load forecasting, load disaggregation, and activity recognition try to provide even better insights into our electricity consumption, highlight saving potential or improve our daily living. To develop and evaluate these techniques, publicly available datasets are used. We identified a lack of high frequency fully labeled electricity datasets in the residential domain and present the FIRED dataset. It contains 52 days of 8 kHz aggregated current and voltage readings of the 3-phase supply of a typical residential apartment in Germany. The dataset also contains synchronized ground truth data as 2 kHz readings of 21 individual appliances, as well as room temperature readings and fully labeled state changes of the lighting system, resulting in a complete and versatile residential electricity dataset.
PIANX – A Platform for Piano Players to Alleviate Music Performance Anxiety Using Mixed Reality

We present PIANX, a platform to assist piano players in alleviating Music Performance Anxiety (MPA). Our work is motivated by the ability of Virtual Reality (VR) to create environments closely resembling the real world. For musicians, settings such as auditions or concerts are of particular interest, since they allow practicing in situations which evoke stress as a result of stage fright. Current approaches are limited: while they provide a virtual scene, realistic haptic feedback (i.e. playing on a real piano) and an authentic representation of their hands is missing. We close this gap with the design of a Mixed Reality platform, consisting of a MIDI (Musical Instrument Digital Interface) stage piano and an HTC Vive Pro VR headset. The platform offers (a) two approaches to finger tracking and visualization – a virtual representation based on LeapMotion hand tracking (baseline) and a real representation using see-through VR; in addition, it provides (b) three different settings in which users can practice (home, audition, concert hall) and (c) a mechanism for real time feedback. We created a series of videos demonstrating the system and collected feedback from 23 participants in an online study, assessing their views towards our platform. Results reveal key insights for the design of virtual MPA training platforms from a scientific and consumer perspective.
A Divergence‐free Mixture Model for Multiphase Fluids

We present a novel divergence free mixture model for multiphase flows and the related fluid‐solid coupling. The new mixture model is built upon a volume‐weighted mixture velocity so that the divergence free condition is satisfied for miscible and immiscible multiphase fluids. The proposed mixture velocity can be solved efficiently by adapted single phase incompressible solvers, allowing for larger time steps and smaller volume deviations. Besides, the drift velocity formulation is corrected to ensure mass conservation during the simulation. The new approach increases the accuracy of multiphase fluid simulation by several orders. The capability of the new divergence‐free mixture model is demonstrated by simulating different multiphase flow phenomena including mixing and unmixing of multiple fluids, fluid‐solid coupling involving deformable solids and granular materials.
Spatial Mapping of Riverbed Grain-Size Distribution Using Machine Learning

Recent alluvial sediments in riverbeds play a significant role in controlling hydrologic exchange flows (HEFs) in river systems. The alluvial layer is usually associated with strong heterogeneity in physical properties (e.g., permeability and hydraulic conductivity), which affects local HEFs and therefore biogeochemical processes. The spatial distribution of these physical properties needs to be determined to inform the numerical models used to reveal the realistic hydro-biogeochemical behaviors. Such information can be obtained based on the intrinsic link between sediment grain-size distribution and hydraulic properties where sediment texture information is available. However, grain-size measurements are usually spatially sparse and do not have adequate coverage and resolution, particularly for a relatively large domain such as the Hanford Reach of the Columbia River. In this paper, we adopted machine learning (ML) approaches for categorizing and mapping the spatial distributions of riverbed substrate grain size and filling in missing areas of substrate data using the ML models along the reach. Such ML models for substrate size mapping were trained at 13,372 locations using measured substrate sizes along with observed and simulated attributes, including bathymetric attributes (e.g., elevation, slope, and aspect ratio) from LIDAR and bathymetric surveys, and hydrodynamic properties (e.g., water depth, velocity, shear stress, and their statistical moments). An ensemble bagging-based ML technique, Random Forest, was adopted to identify the most influential factors as predictors to develop the predictive models with over-fitting issues addressed. The models were evaluated with respect to each individual substrate size class and the lumped group, and then used to generate the final substrate size maps covering all the grid cells in the numerical modeling domain.
An ArcA-Modulated Small RNA in Pathogenic Escherichia coli K1

Escherichia coli K1 is the leading cause of meningitis in newborns. Understanding the molecular basis of E. coli K1 pathogenicity will help develop treatment of meningitis and prevent neurological sequelae. E. coli K1 replicates in host blood and forms a high level of bacteremia to cause meningitis in human. However, the mechanisms that E. coli K1 employs to sense niche signals for survival in host blood are poorly understood. We identified one intergenic region in E. coli K1 genome that encodes a novel small RNA, sRNA-17. The expression of sRNA-17 was downregulated by ArcA in microaerophilic blood. The ΔsRNA-17 strain grew better in blood than did the wild-type strain and enhanced invasion frequency in human brain microvascular endothelial cells. Transcriptome analyses revealed that sRNA-17 regulates tens of differentially expressed genes. These data indicate that ArcA downregulates the sRNA-17 expression to benefit bacterial survival in blood and penetration of the blood–brain barrier. Our findings reveal a signaling mechanism in E. coli K1 for host adaptation.
Proof of Concept of Culturomics Use of Time of Care

Culturomics, a high throughput culture method with rapid identification of the colonies by Matrix Assisted Laser Desorption Ionization/Time Of Flight Mass Spectrometry (MALDI-TOF MS), has demonstrated its contribution to the exploration of the gut microbiota over the past 10 years. However, the cost, work time and workload, considerably limit its use on a large scale or emergency context. Here, by testing two different stool samples, including a stool sample from a patient requiring rapid immunotherapy treatment, we tested a new fast culturomic protocol using two pre-incubation media, blood culture bottle and YCFA modified medium. Both media were supplemented with 2 ml of rumen fluid filtered at 0.2 μm and 2 ml of defibrinated and sterile sheep blood. Unlike the standard culturomics, subculturing of blood culture bottle were performed at reduced incubation time (3 h, 6 h, 9 h, 24 h) and at a longer incubation time (3 days, 7 days, and 10 days) at 37°C. By testing 5,200 colonies per MALDI-TOF MS and obtaining a comparable number of cultured bacterial species (131 to 143) in a stool sample, this new protocol reduced the number of colonies tested by 57%, working time by 78.6% and cost by 72.2%. In addition, we highlighted that the proportion of strict anaerobic species has increased by 24%, known to be the preferential targets for biotherapy, including Faecalibacterium prausnitzii, Akkermansia muciniphila, Christensenella minuta, and Phascolarctobacterium faecium. Finally, this work showed that some bacterial species grew earlier but disappeared with prolonged incubation times.
Silent Counterattack: The Impact of Workplace Bullying on Employee Silence

The purpose of this paper is to explore the relationship between workplace bullying (WB) and employee silence (ES) as well as its mechanism. This paper collects data from 322 employees of three Chinese enterprises in two waves, with a 2 months interval between the two waves. Moreover, this paper uses confirmatory factor analysis, a bootstrapping mediation test, a simple slope test, and other methods to verify the hypothesis. We find that: (1) WB is positively correlated with ES; (2) psychological safety (PS) and affective commitment mediated the relationship between WB and ES, respectively, and these two variables have a chain mediating effect in the above relationship; and (3) a forgiveness climate moderates this chain mediating effect by weakening the negative impact of WB on PS. Our findings can effectively guide organizations to ultimately adjust their management style, pay attention to employees’ cognitive and emotional resources, and formulate some measures to curb WB in organizations.
A Systematic Review of the Impact of Viral Respiratory Epidemics on Mental Health: An Implication on the Coronavirus Disease 2019 Pandemic

Background: The twenty-first century viral respiratory epidemics have taught us valuable lessons. Our systematic review examined the impact of these epidemics, including coronavirus disease 2019 (COVID-19), on mental health among different population groups, drawing on their insights for recommendations for the current COVID-19 pandemic. Methods: Searches were performed on PubMed, Embase, PsycINFO, Web of Science, Scopus, CINAHL, and Cochrane on April 4, 2020. Studies that had undefined mental health outcomes or did not use a validated scale for measure were excluded. Quality assessment was carried out via the Newcastle–Ottawa Scale. Results: We included 95 studies, most of which were conducted in Hong Kong (31.6%) and China (21.4%). A total of 30 (30.9%) studies are on the general public, 41 (42.2%) on healthcare workers, and 26 (26.6%) on patients and quarantined individuals. Furthermore, 36 (37.1%) of the studies are of high quality, 48 (49.5%) are of moderate quality, and 13 (13.4%) are of low quality. The most significant mental health outcomes reported include anxiety, depression, and post-traumatic stress disorder symptoms. The subgroups identified to have a higher risk of psychiatric symptoms among the general public include females, the elderly, individuals with chronic illness, migrant workers, and students. Long-term mental health impact was reported in some healthcare workers and epidemic patients, even up to 3 years in the former. Interestingly, when compared to non-quarantined groups, quarantine was not significantly associated with worse mental health outcomes. Conclusion: Important implications for the COVID-19 pandemic were highlighted. Respiratory epidemics pose a significant psychological morbidity onto many population groups. Psychological support for vulnerable groups, including healthcare workers and patients, should be implemented to prevent them from spiraling into clinical psychiatric conditions.
Classifying Parkinson’s Disease Patients With Syntactic and Socio-emotional Verbal Measures

Frontostriatal disorders, such as Parkinson’s disease (PD), are characterized by progressive disruption of cortico-subcortical dopaminergic loops involved in diverse higher-order domains, including language. Indeed, syntactic and emotional language tasks have emerged as potential biomarkers of frontostriatal disturbances. However, relevant studies and models have typically considered these linguistic dimensions in isolation, overlooking the potential advantages of targeting multidimensional markers. Here, we examined whether patient classification can be improved through the joint assessment of both dimensions using sentential stimuli. We evaluated 31 early PD patients and 24 healthy controls via two syntactic measures (functional-role assignment, parsing of long-distance dependencies) and a verbal task tapping social emotions (envy, Schadenfreude) and compared their classification accuracy when analyzed in isolation and in combination. Complementarily, we replicated our approach to discriminate between patients on and off medication. Results showed that specific measures of each dimension were selectively impaired in PD. In particular, joint analysis of outcomes in functional-role assignment and Schadenfreude improved the classification accuracy of patients and controls, irrespective of their overall cognitive and affective state. These results suggest that multidimensional linguistic assessments may better capture the complexity and multi-functional impact of frontostriatal disruptions, highlighting their potential contributions in the ongoing quest for sensitive markers of PD.
LongShengZhi Capsule Attenuates Alzheimer-Like Pathology in APP/PS1 Double Transgenic Mice by Reducing Neuronal Oxidative Stress and Inflammation

Alzheimer’s disease (AD) is the most common form of dementia in the elderly. It may be caused by oxidative stress, inflammation, and cerebrovascular dysfunctions in the brain. LongShengZhi Capsule (LSZ), a traditional Chinese medicine, has been approved by the China Food and Drug Administration for treatment of patients with cardiovascular/cerebrovascular disease. LSZ contains several neuroprotective ingredients, including Hirudo, Astmgali Radix, Carthami Flos (Honghua), Persicae Semen (Taoren), Acori Tatarinowii Rhizoma (Shichangpu), and Acanthopanax Senticosus (Ciwujia). In this study, we aimed to determine the effect of LSZ on the AD process. Double transgenic mice expressing the amyloid-β precursor protein and mutant human presenilin 1 (APP/PS1) to model AD were treated with LSZ for 7 months starting at 2 months of age. LSZ significantly improved the cognition of the mice without adverse effects, indicating its high degree of safety and efficacy after a long-term treatment. LSZ reduced AD biomarker Aβ plaque accumulation by inhibiting β-secretase and γ-secretase gene expression. LSZ also reduced p-Tau expression, cell death, and inflammation in the brain. Consistently, in vitro, LSZ ethanol extract enhanced neuronal viability by reducing L-glutamic acid-induced oxidative stress and inflammation in HT-22 cells. LSZ exerted antioxidative effects by enhancing superoxide dismutase and glutathione peroxidase expression, reduced Aβ accumulation by inhibiting β-secretase and γ-secretase mRNA expression, and decreased p-Tau level by inhibiting NF-κB-mediated inflammation. It also demonstrated neuroprotective effects by regulating the Fas cell surface death receptor/B-cell lymphoma 2/p53 pathway. Taken together, our study demonstrates the antioxidative stress, anti-inflammatory, and neuroprotective effects of LSZ in the AD-like pathological process and suggests it could be a potential medicine for AD treatment.
Functional Change in Experimental Allodynia After Glutamate-Induced Pain in the Human Masseter Muscle

Background: Glutamate, as well as nerve growth factor (NGF), is involved in nociception from peripheral tissues, such as muscles. However, the potential interaction between glutamate and NGF still remains unclear. This study investigated the interaction between glutamate-induced masseter muscle pain and NGF-induced allodynia on pain perception and jaw function in healthy individuals, and any possible sex differences in the response. Materials and Methods: Thirty pain-free adult participants (15 men and 15 women, mean age ± SD: 24 ± 4 years) participated in this study consisting of three sessions (Day 0, Day 3, and Day 4). NGF (5 μg/mL, 1.0 mL) was injected into the masseter muscle on Day 0 to induce muscle allodynia. On Day 3, glutamate (1M, 0.2 mL) was injected into the same masseter muscle. Before and after injections on Day 0 and 3, and post-injection (Day 4), spontaneous pain, temporal summation pain, as well as functional pain and fatigue in response to chewing were assessed with validated scales, and the pressure pain threshold (PPT) was recorded. Results: Spontaneous pain intensity was significantly higher after glutamate than NGF (P < 0.001). PPTs, temporal summation pain and functional measures were all reduced 3 days after NGF injection (P's < 0.001). Injection of glutamate on Day 3 did not further affect PPTs or temporal summation pain and there were no sex differences in the effects (P > 0.189). Chewing pain (P = 0.022) and fatigue increased after glutamate injection to a higher degree in the women than men (P = 0.037). Conclusion: Taken together, while glutamate injected into the NGF-sensitized muscle was painful, it did not alter muscle tenderness in women vs. men. However, pain and fatigue evoked by jaw function were higher in women after glutamate injection. This suggest that sex differences reported for masseter myalgia, mimicked by glutamate and NGF mediated pain in this study, may be greater for measures of perceived jaw function, which should be considered in a clinical evaluation.
Diversity and Geographic Distribution of Ligninolytic Fungi Associated With Castanopsis sieboldii Leaf Litter in Japan

The diversity and geographic pattern of ligninolytic fungi were investigated within the distribution range of an evergreen tree, Castanopsis sieboldii (Fagaceae), in Japan. Fungal isolates obtained from 18 sites in subtropical and temperate regions in Japan were classified into 50 operational taxonomic units in Ascomycota and Basidiomycota according to the base sequence of the rDNA internal transcribed spacer region. Ordination by nonmetric multidimensional scaling showed the separation of fungal compositions between the study sites which was significantly related to the latitude, longitude, and mean annual temperature (MAT) of the study sites. We applied variation partitioning to separate the magnitude of the climatic, spatial, and leaf property factors and found the roles of MAT and spatial factors in structuring fungal assemblages, suggesting the importance of both niche processes and such non-niche processes as priority effect and dispersal limitation. The bleached area on leaf litter was greater at sites with higher MAT and precipitation located at lower latitudes and at sites where some major ligninolytic fungi occurred at greater relative frequencies, indicating that not only the climatic conditions but also the biogeographic patterns of distribution of ligninolytic fungi influence the decomposition of lignin in leaf litter.
Phenotypic, Transcriptomic, and Metabolomic Signatures of Root-Specifically Overexpressed OsCKX2 in Rice

Cytokinins are crucial signaling molecules that regulate plant growth and development. OsCKX2 irreversibly degrades nucleobase cytokinins by encoding cytokinin oxidase/dehydrogenase to control grain production in rice. In this study, OsCKX2 was specifically overexpressed in roots using RCc3 promoter to investigate the effects of root-source cytokinins on the growth of rice. OsCKX2 overexpressed (OE) rice showed retarded growth with lower cytokinin levels and biomass production. Shoot-specific transcriptome analysis between OsCKX2 OE rice and wild type (WT) revealed differentially expressed genes (DEGs) associated with cell division, cell wall structure, phytohormone signaling, and assimilation and catabolism. Metabolome analysis indicated that a majority of differential primary metabolites, such as amino acids and organic acids, increased, while lipids decreased in OsCKX2 OE rice. Integration of transcriptomic and metabolomic data showed that several DEGs and differential metabolites were related to glycolysis and tricarboxylic acid cycle (TCA). To conclude, reduced cytokinin levels via root-specific overexpression of OsCKX2 resulted in developmental defects, which confirmed the importance of root-source cytokinins in plant growth and morphogenesis.