Miracle-dz/UniFAR-FTUs huggingface dataset:
Syntaxin clusters and cholesterol affect the mobility of Syntaxin1a

Syntaxin1a (Syx1a) is essential for stimulated exocytosis in neuroendocrine cells. The vesicle docking process involves the formation of nanoscale Syx1a domains on the plasma membrane and the Syx1a clusters disintegrate during the fusion process. Syx1a nanodomains are both static yet empty and refill; the process by which these clusters maintain this balance is unclear. In this work, the dynamics of the Syx1a molecules is elucidated relative to the cluster position through a labeling strategy that allows both the bulk position of the Syx clusters to be visualized concurrent with the trajectories of single Syx1a molecules on the surface of PC12 cells. Single Syx1a molecules were tracked in time relative to cluster positions to decipher how Syx1a moves within a cluster and when clusters are not present. Syx1a is mobile on the plasma membrane, more mobile at the center of clusters, and less mobile near the edges of clusters; this depends on the presence of the N-terminal Habc domain and cholesterol, which are essential for proper exocytosis. Simulations of the dynamics observed at clusters support a model where clusters are maintained by a large cage (r = 100 nm) within which Syx1a remains highly mobile within the cluster (r = 50 nm). The depletion of cholesterol dramatically reduces the mobility of Syx1a within clusters and less so over the rest of the plasma membrane. This suggests that fluidity of Syx1a supramolecular clusters is needed for function. STATEMENT OF SIGNIFICANCE Syntaxin1a (Syx1a) is essential for exocytosis where the vesicle docking process involves the formation of nanoscale Syx1a domains on the plasma membrane. Syx1a nanodomains are both static yet empty and refill; the process by which these clusters maintain this balance is unclear. In this work, the dynamics of the Syx1a molecules was elucidated relative to the cluster position on the surface of PC12 cells. Single molecules were tracked relative to clusters and Syx1a is mobile on the plasma membrane, more so within a cluster and less mobile near the edges of clusters. This depends on the presence of the N-terminal domain and cholesterol, which are essential for proper exocytosis. This suggests that fluidity of Syx1a clusters is needed for function.
Protein Structure Refinement via DeepTracer and AlphaFold2

Understanding the structures of proteins has numerous applications, such as vaccine development. It is a slow and labor-intensive task to manually build protein structures from experimental electron density maps, therefore, machine learning approaches have been proposed to automate this process. However, most of the experimental maps are not atomic resolution, so they are insufficient for computer vision-based machine learning methods to precisely determine the protein structure. On the other hand, methods that utilize evolutionary information from protein sequences to predict structures, like AlphaFold2, have recently achieved groundbreaking accuracy but often require manual effort to refine the results. We propose DeepTracer-Refine, an automated method to refine AlphaFold structures by aligning them to DeepTracer’s predicted structure. We tested our method on 39 multi-domain proteins and we improved the average residue coverage from 78.2% to 90.0% and average lDDT score from 0.67 to 0.71. We also compared DeepTracer-Refine against another method, Phenix’s AlphaFold refinement, to demonstrate that our method not only performs better when the initial AlphaFold model is less precise but also exceeds Phenix in run-time performance.
GSK3α/β restrains IFNγ-inducible costimulatory molecule expression in alveolar macrophages, limiting CD4+ T cell activation

Macrophages play a crucial role in eliminating respiratory pathogens. Both pulmonary resident alveolar macrophages (AMs) and recruited macrophages contribute to detecting, responding to, and resolving infections in the lungs. Despite their distinct functions, it remains unclear how these macrophage subsets regulate their responses to infection, including how activation by the cytokine IFNγ is regulated. This shortcoming prevents the development of therapeutics that effectively target distinct lung macrophage populations without exacerbating inflammation. We aimed to better understand the transcriptional regulation of resting and IFNγ-activated cells using a new ex vivo model of AMs from mice, fetal liver-derived alveolar-like macrophages (FLAMs), and immortalized bone marrow-derived macrophages (iBMDMs). Our findings reveal that IFNγ robustly activates both macrophage types; however, the profile of activated IFNγ-stimulated genes varies greatly between these cell types. Notably, FLAMs show limited expression of costimulatory markers essential for T cell activation upon stimulation with only IFNγ. To understand cell type-specific differences, we examined how the inhibition of the regulatory kinases GSK3α/β alters the IFNγ response. GSK3α/β controlled distinct IFNγ responses, and in AM-like cells, we found GSK3α/β restrained the induction of type I IFN and TNF, thus preventing the robust expression of costimulatory molecules and limiting CD4+ T cell activation. Together, these data suggest that the capacity of AMs to respond to IFNγ is restricted in a GSK3α/β-dependent manner and that IFNγ responses differ across distinct macrophage populations. These findings lay the groundwork to identify new therapeutic targets that activate protective pulmonary responses without driving deleterious inflammation.
Toward dynamic energy-efficient operation of cellular network infrastructure

The operation of cellular network infrastructure incurs significant electrical energy consumption. From the perspective of cellular network operators, reducing this consumption is not only a matter of showing environmental responsibility, but also of substantially reducing their operational expenditure. We discuss how dynamic operation of cellular base stations, in which redundant base stations are switched off during periods of low traffic such as at night, can provide significant energy savings. We quantitatively estimate these potential savings through a first-order analysis based on real cellular traffic traces and information regarding base station locations in a part of Manchester, United Kingdom. We also discuss a number of open issues pertinent to implementing such energy-efficient dynamic base station operation schemes, such as various approaches to ensure coverage, and interoperator coordination.
Secure service provision in smart grid communications

The smart grid provides a platform for thirdparty service providers to remotely monitor and manage energy usage for consumers. At the same time, the involvement of service providers brings a new set of security threats to the smart grid. In this article, we first identify the cyber security challenges on service provision in the smart grid. Then we present two main security issues related to service provision and provide potential solutions. The first one is to establish a secure communication procedure among the electric utility, consumers, and service providers. The second one is to provide a privacy-preserving yet accountable authentication framework among the smart grid entities without relying on any trusted third party. Finally, we suggest directions of future work on secure service provision by describing several open issues.
Numerical Visualization of Plunging Water Jet using Volume of Fluid Model

A plunging liquid jet is defined as a moving column of liquid passing through a gaseous headspace, air in our case, before impinging a free surface of receiving liquid pool. The mechanism of air entrainment due to plunging liquid jets is very complex and the complete mechanism of air entrainment is not fully understood so far. The present paper is an unsteady numerical simulation of air entrainment by water jet plunging, using the Volume Of Fluid (VOF) model. The piece wise linear interface construction algorithm (PLIC) for interface tracking is used, to describe the phase distributions of entirely immiscible air and liquid phases. The aim of this work is to investigate the performance and accuracy of the VOF method in predicting the initial impact between the descending jet and water free surface, air entrainment and the developing flow region under free surface. Three scale models based on geometric similarities (Froude number and dimensionless free jet length) are used for validation according to Chanson (2004) experience. The simulations show with accuracy, the air cavity formation steps, caused by the initial jet impact, its deep stretching under the pool free surface, until breakdown due to the shear created by a toroidal vortex. In terms of, air entrainment estimation, bubble dispersion and radial distribution of air volume fraction, large-scale models present a good agreement with the experience. However, for the smallest scale model, the results lead to suggest that air entrainment is governed by more parameters than the geometric similarities.
ERTOS: Enclaves in Real-Time Operating Systems

With the growing popularity of edge computing and Internet of Things (IoT) devices, there is an increased need for secure computation on embedded devices. Typically, embedded devices have a heterogeneous environment and do not have general security protections compared to hosts on the cloud. As we see more third-party libraries and applications being run on embedded devices, we face the risk of system compromise that even the device’s RTOS kernel cannot protect. There is a need for creating Trusted Execution Environments (TEEs) on embedded devices; however, many current TEEs have expensive hardware requirements. We propose using Keystone, a framework for creating customizable TEEs, on RISC-V architectures. The hardware requirement for creating TEEs in Keystone are generally available on standard RISC-V devices as RISC-V already provides PMP registers, the basis of Keystone’s isolation. We propose using Keystone with FreeRTOS to implement a module in FreeRTOS for creating efficient and dynamic TEEs on embedded devices. We introduce ERTOS, a new module to FreeRTOS that allows the creation of secure tasks that can be attested and strongly isolated from other tasks using Keystone’s security monitor. ERTOS exposes an easy-to-use API that allows developers to create and run enclave-protected tasks. ERTOS adds negligible performance overhead for computation-intensive tasks inside an enclave and introduces optimizations to allow inter-task communication to be more efficient.
Effects of vocal variation on the output of an automatic speaker recognition system

Automatic speaker recognition (ASR) is increasingly used in forensic voice comparison cases. State-of-the-art systems utilise deep learning to convert acoustic features into compact speaker embeddings (e.g. x-vectors; Snyder et al. 2018). Embeddings from known and unknown voice samples are compared to generate a score, which in turn is calibrated to compute a numerical likelihood ratio (LR). Impressive performance of state-of-the-art systems has been reported with forensically realistic data (Morrison and Enzinger 2019), with marked improvements over previous generations of systems (e.g. i-vectors and GMM-UBM). Despite this progress, still relatively little is known about why certain voices perform better or worse within an ASR system, in part due to the abstract relationship between input and output, especially in state-of-the-art DNN-based systems. This issue is particularly important in the context of forensic voice comparison, where it is important for the practitioner to understand whether the output of an ASR system is reasonable given the input, and to explain the output to an end-user (e.g. a court). In this study, we examine the effects of vocal variation on ASR output. We collected controlled recordings of six phoneticians reading the same text whilst systematically varying aspects of their speech production. Variations included modal voice, a range of laryngeal voice qualities and supralaryngeal vocal settings, high and low pitch, accent guises, and miscellaneous disguise techniques. Each speaker produced three repetitions of each vocal condition in each of
MIGSA : Massive and Integrative Gene Set Analysis

The MIGSA package allows to perform a massive and integrative gene set analysis over several experiments and gene sets simultaneously. It provides a common gene expression analytic framework that grants a comprehensive and coherent analysis. Only a minimal user parameter setting is required to perform both singular and gene set enrichment analyses in an integrative manner by means of enhanced versions of the best available methods, i.e. dEnricher and mGSZ respectively. One of the greatest strengths of this big omics data tool is the availability of several functions to explore, analyze and visualize its results in order to facilitate the data mining task over huge information sources. The MIGSA package also allows to easily load the most updated gene sets collections from several repositories.
Déformation et convection d’une ou plusieurs capsules en écoulement dans un tube cylindrique

Une capsule est une petite particule constituee d'un milieu interne liquide entoure d'une fine membrane elastique. Les capsules sont souvent amenees a s'ecouler en espace confine, et notamment dans des tubes cylindriques. Elles peuvent subir une dissolution partielle de leur membrane et deviennent alors pregonflees. Dans le tube, les capsules prennent une forme stationnaire en ‘parachute' ou en ‘slug'. Le changement de courbure a l'arriere d'une capsule depend beaucoup du pregonflement et du nombre capillaire, et peu du rapport de taille entre la capsule et le tube. La mise au point d'une methode numerique, basee sur les equations de Stokes et reposant sur une formulation par integrales de frontieres, permet de caracteriser la mecanique de capsules pregonflees a membrane d'alginate. Lorsque deux capsules sont en interaction, elles sont espacees de moins d'un rayon de pore. La perte de charge creee par un train de capsules est la somme de celles creees par chacune des capsules isolement.
Combined effect of two transgenes performing host delivered RNAi against Meloidogyne incognita and its parasitic potential

In present scenario, RKNs are the major threat to the agriculture, causing both direct and indirect damage. Root-knot nematodes ( Meloidogyne incognita ) are exceedingly evolved obligate sedentary plant endoparasites with highly complex parasitism strategy, which makes it hard to manage them. Developing resistance in plants is a promising way to deal with RKNs problem. RNA interference (RNAi) is a novel method through which plants can be modified to develop resistance against specific pest. Splicing factor and integrase are two housekeeping genes in Meloidogyne incognita . Host induced gene silencing (HIGs) of two genes ( splicing factor and integrase) was performed simultaneously in the target organism through crossing the parents which were already transformed to express the dsRNA of each gene individually. The resultant progeny, expressing dsRNA for both of the genes was subjected to bioassay studies. The results showed a significant reduction in pathogenic and reproductive capability of Meloidogyne incognita . The number of galls, number of females and number of egg masses were recorded were in the range of 71.81-74.39%, 76.73-78.76% and 67.13-70.27% respectively as compared to the untransformed events. The results here clearly show the enhanced effect of combinatorial gene silencing as compared to the single gene silencing and no gene silencing at all.
Snow Distribution and Heat Flow in the Taiga

The trees of the taiga intercept falling snow and cause it to become distributed in an uneven fashion. Around aspen and birch, cone-shaped accumulations form. Beneath large spruce trees, the snow cover is depleted, forming a bowl-shaped depression called a tree well. Small spruce trees become covered with snow, creating cavities that funnel cold air to the snow/ground interface. The depletion of snow under large spruce trees results in greater heat loss from the ground. A finite difference model suggests that heat flow from tree wells can be more than twice that of undisturbed snow. In forested watersheds, this increase can be a significant percentage of the total winter energy exchange.
Not Your Average Firm: a quantile regression approach to the firm level investment

The large majority of the work published on ﬁrm investment is done in the neoclas-sical frame of a rational optimizing ﬁrm attempting to achieve optimal size. While this frame addresses one important consideration in ﬁrm investment, it has two important shortcomings that this paper will address. First, it doesn’t have a clear interpretation of how the cash-ﬂows are aﬀecting the ﬁrm investment decisions. Second, the standard approach operates on an “average ﬁrm,” which in fact is signiﬁcantly diﬀerent from a ﬁrm with modal investment behavior. This study employs a Bayesian quantile regression model that yields two signiﬁcant results. First concerning the relative responsiveness of these two neglected factors, it determines that the ﬁrms with higher investment rates have higher responsiveness to the valuation ratio and lower responsiveness to the proﬁt rate. Second and of broader political economic note, it ﬁnds a decline in the responsiveness of ﬁrm investment to these factors that is consistent with the widely discussed macroeconomic “secular stagnation” of the US economy, and within that consistency, that the decline varies across sectors, and is more pronounced in ﬁrms with higher investment rates.
MOLECULAR APPROACHES IN ASSESSING THE SOIL MICROBIAL DIVERSITY OF MALAYSIAN OIL PALM PLANTATIONS: A REVIEW

Soil microorganisms are vital to the overall function, stability and sustainability of the ecosystem. Microbial communities are key bio-indicators for sustainable agro-ecosystem management and for evaluating oil palm soil health in Malaysia. The development of effective methods for studying the diversity, distribution and behaviour of soil microorganisms is essential for a broader understanding of soil quality and fertility. In the past, culture-dependent, Denaturing Gradient Gel Electrophoresis (DGGE) and Terminal-Restriction Fragment Length Polymorphism (T-RFLP) were used to assess soil microbial community, abundance and diversity in the oil palm soils. However, these methods have significant shortcomings and limitations in microbial ecology monitoring. In recent years, high-throughput next-generation sequencing has dramatically increased the resolution and detectable spectrum of microbial from environmental samples, and has played a significant role in microbial ecology studies. Next-generation sequencing using 454-pyrosequencing, Ion Torrent, Illumina and PacBio platforms are rapid and flexible. These sequencing platforms are powerful metagenomic tools for analysing microbial communities, including their structure, metabolic potential, diversity and their impact on ecosystem functions. In this review, the major development of molecular approaches used in the characterisation of soil microbial community dynamics and their functions in Malaysian oil palm plantations are also discussed.
Recommendation System for E-Learning Resources by Neural Networks A Preview

With several learning resources available online, a recommendation system can recommend appropriate resources based on the student’s need. Automatic multimedia learning resources recommendation has become an increasingly relevant problem: it allows students to discover new learning resources that match their needs, and enables the e-learning system to target the learning resources to the right students. Automatic multimedia learning resources recommendation has become an increasingly relevant problem, it allows students to discover new learning resources that match their tastes, and enables the e-learning system to target the learning resources to the right students. The task of recommendation algorithms in e-learning systems is to give student a personalized and suitable learning service With the increase in e-learning applications and its allied benefits, optimizing e-learning resources based on the requirements of the users has become a challenging task due to the large number of resources. Different individuals prefer different learning resources especially based on their age, country, neighborhood and requirements. There is no specific way to tailor make the recommendation system which can help users get to the best resources within the least amount of time. However, collaborative learning has come up as an effective technique in machine learning based approaches for the design of recommendation systems.
Hypoglycaemic and hypolipidaemic effects of low GI and medium GL Indian diets in type 2 diabetics for a period of 4 weeks: A prospective study

This prospective study reports significant hypoglycaemic and hypolipidaemic effects in type 2 diabetic subjects who were provided the complete diet plan to be on low glycaemic index (GI) and low-medium glycaemic load (GL) Indian vegetarian snacks and mixed meals for 4 continuous weeks. Five millilitres of fasting blood sample drawn at weekly intervals for 4 weeks were analysed for blood glucose, HbA1c and lipid profile. Four weeks later mean blood glucose level of 173.6 mg% decreased to 137.8 mg%, HbA1c of 8% also decreased to 7.1% which reflected the blood glucose level during the study period and hence correlated well with the fall in blood glucose level. Triglyceride level of 244.5 mg% decreased to 164.7 mg% (p < 0.0001) and total cholesterol of 173.5 mg% decreased to 134.6 mg% (p < 0.0001). High-density lipoprotein cholesterol of 33 mg% increased to 39.8 mg% (p < 0.003), very low density lipoprotein (VLDL) cholesterol of 48.9 mg% decreased to 32.9 mg% (p < 0.0001) and low-density lipoprotein cholesterol of 90.1 mg% decreased to 64.3 (p < 0.009). This significant outcome can be improved further if compliance to low GI and low-to-medium GL diet is continued. This may achieve desired glycaemic control and that's limit oxidative stress.
NO SIGNIFICANT RISK LEVEL ( NSRL ) FOR THE PROPOSITION 65 CARCINOGEN BROMOFORM

The cancer potency of bromoform was estimated from dose-response data for gastrointestinal tumors among female rats exposed orally to bromoform by gavage (NTP, 1989). The cancer potency estimate corresponds to the upper 95 percent confidence bound on the linear term of the multistage model fit to cancer dose-response data in animals. The potency derivation takes into account body size differences between humans and experimental animals. The Proposition 65 “no significant risk level” (NSRL) is defined in regulation as the daily intake level posing a 10 lifetime risk of cancer. The cancer potency estimate and the corresponding NSRL are given in Table 1.