Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on the design of analytical communication and information model for teaching resources with cloud‐sharing platform

The recent growth in the adoption by several organizations of cloud computing services has created the challenge to determine their performance of information model. The ability of cloud computing systems to provide the required services in line with the quality of service and service level agreement constraints is a major problem for cloud service providers and consumers. To that end, it is possible to develop the means of ensuring that the desired levels of success are achieved in simulated environments. In the eventual stage of commissioning and running cloud computing, this will reduce the disrupted service and performance degradation problems using analytical communication and information model of cloud technology. However, several modelers and simulators for simulating cloud computing systems are available in conventional research. This paper, therefore, provides a critical assessment with state of the art cloud computing modeling and simulation frameworks. It compares the prominent frameworks of simulation regarding application programming interface features, programming flexibility, operating system requirements, services supported, and licensing needs with 95% reliability. It offers recommendations to the researchers, administrators, and managers of cloud computing systems regarding the choice of the most appropriate framework.
An ecological explanation for hyperallometric scaling of reproduction

In wild populations, large individuals have disproportionately higher reproductive output than smaller individuals. Some theoretical models explain this pattern – termed reproductive hyperallometry – by individuals allocating a greater fraction of available energy towards reproductive effort as they grow. Here, we propose an ecological explanation for this observation: differences between individuals in rates of resource assimilation, where greater assimilation causes both increased reproduction and body size, resulting in reproductive hyperallometry at the level of the population. We illustrate this effect by determining the relationship between size and reproduction in wild and lab-reared Trinidadian guppies. We show that (i) reproduction increased disproportionately with body size in the wild but not in the lab, where resource competition was eliminated and food availability restricted; (ii) in the wild, hyperallometry was greatest during the wet season, when resource competition is strongest; and (iii) detection of hyperallometric scaling of reproduction at the population level was inevitable if individual differences in assimilation were ignored. We propose that ecologically-driven variation in assimilation – caused by size-dependent resource competition, niche expansion, and chance – contributes substantially to hyperallometric scaling of reproduction in natural populations. We recommend that mechanistic models incorporate such ecologically-caused variation when seeking to explain reproductive hyperallometry.
TMP- SSurface2: A Novel Deep Learning-Based Surface Accessibility Predictor for Transmembrane Protein Sequence

Transmembrane protein (TMP) is an important type of membrane protein that is involved in various biological membranes related biological processes. As major drug targets, TMPs’ surfaces are highly concerned to form the structural biases of their material-bindings for drugs or other biological molecules. However, the quantity of determinate TMP structures is still far less than the requirements, while artificial intelligence technologies provide a promising approach to accurately identify the TMP surfaces, merely depending on their sequences without any feature-engineering. For this purpose, we present an updated TMP surface residue predictor TMP-SSurface2 which achieved an even higher prediction accuracy compared to our previous version. The method uses an attention-enhanced Bidirectional Long Short Term Memory (BiLSTM) network, benefiting from its efficient learning capability, some useful latent information is abstracted from protein sequences, thus improving the Pearson correlation coefficients (CC) value performance of the old version from 0.58 to 0.66 on an independent test dataset. The results demonstrate that TMP-SSurface2 is efficient in predicting the surface of transmembrane proteins, representing new progress in transmembrane protein structure modeling based on primary sequences. TMP-SSurface2 is freely accessible at https://github.com/NENUBioCompute/TMP-SSurface-2.0.
Individual Magnetoencephalography Response Profiles to Short-Duration L-Dopa in Parkinson’s Disease

Clinical responses to dopamine replacement therapy for individuals with Parkinson’s disease (PD) are often difficult to predict. We characterized changes in MDS-UPDRS motor factor scores resulting from a short-duration L-Dopa response (SDR), and investigated how the inter-subject clinical differences could be predicted from motor cortical magnetoencephalography (MEG). MDS-UPDRS motor factor scores and resting-state MEG recordings were collected during SDR from twenty individuals with a PD diagnosis. We used a novel subject-specific strategy based on linear support vector machines to quantify motor cortical oscillatory frequency profiles that best predicted medication state. Motor cortical profiles differed substantially across individuals and showed consistency across multiple data folds. There was a linear relationship between classification accuracy and SDR of lower limb bradykinesia, although this relationship did not persist after multiple comparison correction, suggesting that combinations of spectral power features alone are insufficient to predict clinical state. Factor score analysis of therapeutic response and novel subject-specific machine learning approaches based on subject-specific neuroimaging provide tools to predict outcomes of therapies for PD.
Comparative Study on Relationship Between Inconsistent Online-Offline Social Performance and Self-Efficacy of University Students Based on Types of Social Activity

Social behavior is closely linked to self-efficacy, which is the individual’s confidence or belief that they can successfully complete a task in a given situation. The advent of social media classified social behavior as online and offline sociality, and has cultivated inconsistency in online and offline social behavior of university students, an issue that has come to prominence in scholarly research. However, the relationship between this inconsistency and self-efficacy is worthy of investigation because this particular confluence of behavioral concepts has been rarely been researched. In this paper, online and offline social behavior is integrated, a typology for university student social activities established, and the correlation between different types of social activity and student self-efficacy investigated, with a specific focus on those with notable inconsistencies in their social performance. The following findings are reported. First, as online social networking has become the dominant form of social interaction, the types of social activity have increased, with one-third of university students showing inconsistent online and offline social behavior. However, different types of social activities have varied effects on the self-efficacy of university students, with differences between general self-efficacy, which is significantly above academic self-efficacy, and social self-efficacy. These effects are also different for students with inconsistent online and offline social performance; those who are active online show higher self-efficacy than those who are active offline. This study shows online social network interactions to be more closely related to student self-efficacy than offline interactions.
PulseNet international: the missing link between PFGE and WGS

Introduction DNA-based surveillance of bacterial diseases has been using pulsed field gel electrophoresis (PFGE) since 1996. Currently, the international surveillance network (PulseNet international) is turning toward whole genome sequencing (WGS). ATCGs of WGS are compared using several sequence alignment methods. While patterns of horizontal lines of PFGE profiles are being compared using relative positioning of bands within a range of tolerance. A recently suggested image analysis algorithm and a deployed database (geltowgs.uofk.edu) collectively invented a promising method for comparing PFGE to in-silico obtained digestion models (DMs) derived from WGS. The database requires a parameter that determines PFGE resolution. Here, the author suggests a new method for calculating this factor. Epidemiological and molecular conclusions returned by the database are evaluated. Methodology two PFGE profiles representing XbaI digests of E. coli and Salmonella enterica analyzed by the suggested image analysis algorithm were submitted to the database after calculating resolution of PFGE using Dice percentage of difference between the closest PFGE bands in length. E. coli and Salmonella enterica test subjects were compared to 489 and 401 DMs respectively. The three data sets returned were analyzed. Results and conclusions according to modified PFGE evaluation criteria; a single DM is possibly related to E. coli test subject. It belonged to the same serovar. No epidemiologically related DM was shown for S. enterica test subject. Conclusions mentioned earlier could never be made ignoring co-migration. Standardization of both; suggested image analysis and database algorithms will deepen our understanding of bacterial epidemiology by means of possible qualitative approach built upon identification of fragment sequences and their locations within chromosomes.
Expansion-Assisted Iterative-FISH defines lateral hypothalamus spatio-molecular organization

Determining the spatial organization and morphological characteristics of molecularly defined cell types is a major bottleneck for characterizing the architecture underpinning brain function. We developed Expansion-Assisted Iterative Fluorescence In Situ Hybridization (EASI-FISH) to survey gene expression in brain tissue, as well as a turnkey computational pipeline to rapidly process large EASI-FISH image datasets. EASI-FISH was optimized for thick brain sections (300 µm) to facilitate reconstruction of spatio-molecular domains that generalize across brains. Using the EASI-FISH pipeline, we investigated the spatial distribution of dozens of molecularly defined cell types in the lateral hypothalamic area (LHA), a brain region with poorly defined anatomical organization. Mapping cell types in the LHA revealed nine novel spatially and molecularly defined subregions. EASI-FISH also facilitates iterative re-analysis of scRNA-Seq datasets to determine marker-genes that further dissociated spatial and morphological heterogeneity. The EASI-FISH pipeline democratizes mapping molecularly defined cell types, enabling discoveries about brain organization. Highlights - EASI-FISH enables robust gene expression profiling in thick brain slices - A turnkey analysis pipeline for facile analysis of large EASI-FISH image datasets - EASI-FISH reveals novel subregions of the lateral hypothalamus - Identification of rare cell types based on morphological and spatial heterogeneity
cinaR: A comprehensive R package for the differential analyses and functional interpretation of ATAC-seq data

Summary ATAC-seq is a frequently used assay to study chromatin accessibility levels. Differential chromatin accessibility analyses between biological groups and functional interpretation of these differential regions are essential in ATAC-seq data analyses. Although distinct methods and analyses pipelines are developed for this purpose, a stand-alone R package that combines state-of-the art differential and functional enrichment analyses pipelines is missing. To fill this gap, we developed cinaR (Chromatin Analyses in R), which is a single wrapper function and provides users with various data analyses and visualization options, including functional enrichment analyses with gene sets curated from multiple sources. Availability and implementation cinaR is an R/CRAN package which is under GPL-3 License and its source code is freely accessible at https://CRAN.R-project.org/package=cinaR. Gene sets are available at https://CRAN.R-project.org/package=cinaRgenesets. Bone marrow ATAC-seq data is available at https://www.ncbi. nlm.nih. gov/geo/query/acc.cgi? acc=GSE165120 Contact onur.karakaslar@jax.org or duygu.ucar@jax.org
A Neuromotor to Acoustical Jaw-Tongue Projection Model With Application in Parkinson’s Disease Hypokinetic Dysarthria

Aim The present work proposes the study of the neuromotor activity of the masseter-jaw-tongue articulation during diadochokinetic exercising to establish functional statistical relationships between surface Electromyography (sEMG), 3D Accelerometry (3DAcc), and acoustic features extracted from the speech signal, with the aim of characterizing Hypokinetic Dysarthria (HD). A database of multi-trait signals of recordings from an age-matched control and PD participants are used in the experimental study. Hypothesis: The main assumption is that information between sEMG and 3D acceleration, and acoustic features may be quantified using linear regression methods. Methods Recordings from a cohort of eight age-matched control participants (4 males, 4 females) and eight PD participants (4 males, 4 females) were collected during the utterance of a diadochokinetic exercise (the fast repetition of diphthong [aI]). The dynamic and acoustic absolute kinematic velocities produced during the exercises were estimated by acoustic filter inversion and numerical integration and differentiation of the speech signal. The amplitude distributions of the absolute kinematic and acoustic velocities (AKV and AFV) are estimated to allow comparisons in terms of Mutual Information. Results The regression results show the relationships between sEMG and dynamic and acoustic estimates. The projection methodology may help in understanding the basic neuromotor muscle activity regarding neurodegenerative speech in remote monitoring neuromotor and neurocognitive diseases using speech as the vehicular tool, and in the study of other speech-related disorders. The study also showed strong and significant cross-correlations between articulation kinematics, both for the control and the PD cohorts. The absolute kinematic variables presents an observable difference for the PD participants compared to the control group. Conclusion Kinematic distributions derived from acoustic analysis may be useful biomarkers toward characterizing HD in neuromotor disorders providing new insights into PD.
A Systematic Review of Tissue Engineering Scaffold in Tendon Bone Healing in vivo

Background: Tendon-bone healing is an important factor in determining the success of ligament reconstruction. With the development of biomaterials science, the tissue engineering scaffold plays an extremely important role in tendon-bone healing and bone tissue engineering. Materials and Methods: Electronic databases (PubMed, Embase, and the Web of Science) were systematically searched for relevant and qualitative studies published from 1 January 1990 to 31 December 2019. Only original articles that met eligibility criteria and evaluated the use of issue engineering scaffold especially biomaterials in tendon bone healing in vivo were selected for analysis. Results: The search strategy identified 506 articles, and 27 studies were included for full review including two human trials and 25 animal studies. Fifteen studies only used biomaterials like PLGA, collage, PCL, PLA, and PET as scaffolds to repair the tendon-bone defect, on this basis, the rest of the 11 studies using biological interventions like cells or cell factors to enhance the healing. The adverse events hardly ever occurred, and the tendon bone healing with tissue engineering scaffold was effective and superior, which could be enhanced by biological interventions. Conclusion: Although a number of tissue engineering scaffolds have been developed and applied in tendon bone healing, the researches are mainly focused on animal models which are with limitations in clinical application. Since the efficacy and safety of tissue engineering scaffold has been proved, and can be enhanced by biological interventions, substantial clinical trials remain to be done, continued progress in overcoming current tissue engineering challenges should allow for successful clinical practice.
Transgenerational Plasticity in Flower Color Induced by Caterpillars

Variation in flower color due to transgenerational plasticity could stem directly from abiotic or biotic environmental conditions. Finding a link between biotic ecological interactions across generations and plasticity in flower color would indicate that transgenerational effects of ecological interactions, such as herbivory, might be involved in flower color evolution. We conducted controlled experiments across four generations of wild radish (Raphanus sativus, Brassicaceae) plants to explore whether flower color is influenced by herbivory, and to determine whether flower color is associated with transgenerational chromatin modifications. We found transgenerational effects of herbivory on flower color, partly related to chromatin modifications. Given the presence of herbivory in plant populations worldwide, our results are of broad significance and contribute to our understanding of flower color evolution.
Generation of human blastocyst-like structures from pluripotent stem cells

Human blastocysts are comprised of the first three cell lineages of the embryo: trophectoderm, epiblast and primitive endoderm, all of which are essential for early development and organ formation. However, due to ethical concerns and restricted access to human blastocysts, a comprehensive understanding of early human embryogenesis is still lacking. To bridge this knowledge gap, a reliable model system that recapitulates early stages of human embryogenesis is needed. Here we developed a three-dimensional (3D), two-step induction protocol for generating blastocyst-like structures (EPS-blastoids) from human extended pluripotent stem (EPS) cells. Morphological and single-cell transcriptomic analyses revealed that EPS-blastoids contain key cell lineages and are transcriptionally similar to human blastocysts. Furthermore, EPS-blastoids are similar with human embryos that were cultured for 8 or 10 days in vitro, in terms of embryonic structures, cell lineages and transcriptomic profiles. In conclusion, we developed a scalable system to mimic human blastocyst development, which can potentially facilitate the study of early implantation failure that induced by developmental defects at early stage.
Development of an extended chemical mechanism for global- through-urban applications

Abstract The interactions between climate and air quality are receiving increasing attention due to their high relevancy to climate change. Coupled climate and air quality models are being developed to study these interactions. These models need to address the transport and chemistry of atmospheric species over a large range of scales and atmospheric conditions. In particular, the chemistry mechanism is a key component of such models because it needs to include the relevant reactions to simulate the chemistry of the lower troposphere, the upper troposphere, and the lower stratosphere, as well as the chemistry of polluted, rural, clean, and marine environments. This paper describes the extension of an existing chemistry mechanism for urban/regional applications, the 2005 version of the Carbon Bond Mechanism (CB05), to include the relevant atmospheric chemistry for global and global–through–urban applications. Updates to the mechanism include the most important gas–phase reactions needed for the lower stratosphere as well as reactions involving mercury species, and a number of heterogeneous reactions on aerosol particles, cloud droplets, and Polar Stratospheric Clouds (PSCs). The extended mechanism, referred to as CB05 for Global Extension (CB05_GE), is tested for a range of atmospheric conditions using a zero–dimensional box–model. A comparison of results from the extended mechanism with those from the original starting mechanism for both clean and polluted conditions in the lower troposphere shows that the extended mechanism preserves the fidelity of the original mechanism under those conditions. Simulations of marine Arctic conditions, upper tropospheric conditions, and lower stratospheric conditions with the box model illustrate the importance of halogen chemistry and heterogeneous reactions (on aerosol surfaces as well as PSCs for stratospheric conditions) for predicting ozone and elemental mercury depletion events that are often observed during these conditions. Depletions that are comparable to observed depletions are predicted by the box model for very clean conditions (extremely low or zero concentrations of aldehydes and other VOCs) because, in the absence of continuous sources of active halogens, these conditions result in less conversion of active chlorine and bromine to more stable products, such as HCl and HBr.
Multiple resource limitations explain biomass-precipitation relationships in grasslands

Interannual variability in grassland primary production is strongly driven by precipitation, nutrient availability and herbivory, but there is no general consensus on the mechanisms linking these variables. If grassland biomass is limited by the single most limiting resource at a given time, then we expect that nutrient addition will not affect biomass production at arid sites. We conducted a distributed experiment manipulating nutrients and herbivores at 44 grassland sites in 8 regions around the world, spanning a broad range in aridity. We estimated the effects of 5-11 years of nutrient addition and herbivore exclusion treatments on precipitation sensitivity of biomass (proportional change in biomass relative to proportional change in rainfall among years), and the biomass in the driest year (to measure treatment effects when water was most limiting) at each site. Grazer exclusion did not interact with nutrients to influence driest year biomass or sensitivity. Nutrient addition increased driest year biomass by 74% and sensitivity by 0.12 (proportional units), and that effect did not change across the range of aridity spanned by our sites. Grazer exclusion did not interact with nutrients to influence sensitivity or driest year biomass. At almost half of our sites, the previous year's rainfall explained as much variation in biomass as current year precipitation. Overall, our distributed fertilization experiment detected co-limitation between nutrients and water governing grasslands, with biomass sensitivity to precipitation being limited by nutrient availability irrespective of site aridity and herbivory. Our findings refute the classical ideas that grassland plant performance is limited by the single most limiting resource at a site. This suggests that nutrient eutrophication will destabilize grassland ecosystems through increased sensitivity to precipitation variation.