Miracle-dz/UniFAR-FTUs huggingface dataset:
Process-specific somatic mutation distributions vary with three-dimensional genome structure

Somatic mutations arise during the life history of a cell. Mutations occurring in cancer driver genes may ultimately lead to the development of clinically detectable disease. Nascent cancer lineages continue to acquire somatic mutations throughout the neoplastic process and during cancer evolution (Martincorena and Campbell, 2015). Extrinsic and endogenous mutagenic factors contribute to the accumulation of these somatic mutations (Zhang and Pellman, 2015). Understanding the underlying factors generating somatic mutations is crucial for developing potential preventive, therapeutic and clinical decisions. Earlier studies have revealed that DNA replication timing (Stamatoyannopoulos et al., 2009) and chromatin modifications (Schuster-Böckler and Lehner, 2012) are associated with variations in mutational density. What is unclear from these early studies, however, is whether all extrinsic and exogenous factors that drive somatic mutational processes share a similar relationship with chromatin state and structure. In order to understand the interplay between spatial genome organization and specific individual mutational processes, we report here a study of 3000 tumor-normal pair whole genome datasets from more than 40 different human cancer types. Our analyses revealed that different mutational processes lead to distinct somatic mutation distributions between chromatin folding domains. APOBEC- or MSI-related mutations are enriched in transcriptionally-active domains while mutations occurring due to tobacco-smoke, ultraviolet (UV) light exposure or a signature of unknown aetiology (signature 17) enrich predominantly in transcriptionally-inactive domains. Active mutational processes dictate the mutation distributions in cancer genomes, and we show that mutational distributions shift during cancer evolution upon mutational processes switch. Moreover, a dramatic instance of extreme chromatin structure in humans, that of the unique folding pattern of the inactive X-chromosome leads to distinct somatic mutation distribution on X chromosome in females compared to males in various cancer types. Overall, the interplay between three-dimensional genome organization and active mutational processes has a substantial influence on the large-scale mutation rate variations observed in human cancer.
An Energy Efficient Network Architecture and Spectrum Sharing Technique for Cognitive Radio Based Smart Grid Communications

With the introduction of an automated electric power system known as smart grid, issues relating to power quality, shortages and inefficiency have been solved. Two vital foundations necessary for the sustainability and the stability of Smart Grids (SG) are a resourceful and reliable communication between users in a smart grid network and an energy efficient Smart Grid Communication Network (SGCN) architecture. On the other hand, Cognitive radios (CR) are equipped with the capability of exploiting unused licensed spectrum resources for effective spectrum utilization. The objective of this paper is to integrate cognitive radio into smart grid communication network in order to make the network a lot smarter and provide spectrum sharing solutions in SGCN. In this paper, an energy efficient SGCN architecture that is layered into the Home Area Network (HAN), Neighbourhood Area Network (HAN) and the Wide Area Network (WAN) has been proposed. Furthermore, in order to achieve a fair spectrum sharing amongst users in the network, CR users in the HANs estimate the need of its neighbouring users to transmit data. Then it uses this information to predict the vacant channels that will be utilized by any node in the network using a Markov chain process. In the NANs, a model is also proposed where every user sensor node access the spectrum based on the buffer occupancy estimates of its neighboring nodes. Simulation results show that there is a higher throughput in the network and also an increase in the quantity of data packets successfully transmitted using the proposed model.
The Development of an Interactive Animation to Prevent Social Media Fraud

Technology is the creation, invention, methods or systems of something new that has the purpose to overcome human’s limitation, so that the creation can help human to complete the task that they cannot do. However, the world is getting advanced day-by-day, and so do the online frauds, where it is getting more types due to the ease of these technologies. People would use these advanced technologies to scam others, especially through social media. Thus, the number of online scam seems to be rising, and it is an appropriate action that someone does to bring benefits only for themselves. This paper is about the development of an interactive animation to increase the awareness of social media frauds among people and ways to prevent it. Subsequently, we created an animation that can raise the awareness of social media fraud among people, and hence they can also learn effective ways to prevent social media fraud from happening.
Amide hydrolyzing potential of amidase from halotolerant bacterium Brevibacterium sp. IIIMB2706

Abstract Till date, amidases from halophiles and halotolerant micro-organisms have not been much explored. In the present study, Brevibacterium sp. IIIMB2706 strain was isolated from salt fields of Gujarat, India, using propionitrile as a nitrogen source in the mineral base media and explored for its amidase activity. Amidase from Brevibacterium sp. IIIMB2706 exhibited substrate affinity towards isobutyramide, propionamide and butyramide. The optimum temperature and pH required for its maximum activity was 45 °C and 7.0, respectively. Effect of salt concentration on amidase activity was also studied and maximum activity was observed in presence of 50 g L−1 NaCl with significant activity up to 200 g L−1 NaCl which justifies its halotolerant nature. Various organic solvents compatibility profile showed that the enzyme was highly active in presence of 10% methyl alcohol. Henceforth, halotolerant enzymes may find application in industrial processes where substrate requires organic solvents for solubilization.
Mechanism of Corporate Governance and Transparency of Indonesian Companies (Case Study of Manufacturing Companies Listed on the Indonesia Stock Exchange in 2015-2018)

This study aims to examine the influence of corporate governance mechanisms to transparency. Corporate governance mechanisms examined in this study include internal systems including from commissioners, and managerial ownership. Foreign ownership, debt financing, quality audits and company size, firm age as control variables. The population of this research is manufacturing companies listed in the Indonesia Stock Exchange period 2015 - 2018. The research sample is determined by the purposive sampling method with a total sample of 103 annual reports. This study data analysis uses Structural Equation Modeling-Partial Least Square (PLS-SEM). The results showed: the internal governance, debt financing, quality audit
Pan-viral protection against arboviruses by targeting inoculation site-based skin macrophages

Arthropod-borne viruses (arboviruses) are important human pathogens for which there are no specific antiviral medicines. The large number of genetically-distinct arbovirus species, coupled with the unpredictable nature of their outbreaks, has made developing virus-specific anti-viral medicines challenging. Instead, we have defined and targeted a key aspect of the host innate immune response to virus at the arthropod bite that is common to all arbovirus infections, potentially circumventing the need for virus-specific therapies at this site. Using mouse models and human skin explants, we identify innate immune responses by dermal macrophages in the skin as a key determinant of disease severity. Post-exposure treatment of the inoculation site by a topical innate immune agonist significantly suppressed both the local and subsequent systemic course of infection and improved clinical outcome in mice to infection with a variety of arboviruses from the Alphavirus, Flavivirus and Orthobunyavirus genuses. In the absence of treatment, anti-viral interferon expression to virus in the skin was restricted to dermal dendritic cells. In contrast, targeting the more populous skin-resident macrophages with an immune agonist elicited protective responses in key cellular targets of virus that otherwise replicated virus to high levels. By defining and targeting a key aspect of the innate immune response to virus at the mosquito bite site, we have shown that it is possible to improve outcome to infection by targeting pathways activated at the site of inoculation, and thereby identified a putative new strategy for limiting disease following infection with a variety of genetically-distinct arboviruses. One-sentence summary We demonstrate that activation of innate immune responses to arbovirus at the mosquito bite is a limiting factor for preventing efficient systemic dissemination of virus and that therapeutic targeting of skin-resident macrophages can have defining inhibitory effects on the later systemic course.
Sonochemical synthesis of gold nanoparticles via palm oil fronds extracts for cytotoxicity assay

One of the major focus of scientific community todays is the synthesis of nanoparticles using various bio agents, which are biodegradable for their subsequent bio-application. One pot green synthesis of gold nanoparticles (AuNPs) was successfully achieved using palm oil fronds extracts as stabilizing agent and ultrasound radiation as reducing agent. The functional groups such as OH–, CO – stretch, NH – stretching and other function groups were found to be responsible for stabilizing the AuNPs. The size and morphology of the as-synthesized AuNPs was investigated using HRTEM. The XRD patterns display the peaks of face centred cubic crystal structures at (111), (200), (220), (311) and (222). The cytotoxicity of the bio-synthesized was investigated using human embryonic kidney 293 cells (HEK-293 cells) and breast cancer cell (MCF-7 cells) in vitro study, where more than 50 % of the cells were viable after exposure from 0 mg/ml (negative control) to 1 mg/ml, which indicates the material suitability in future medical application.
From miRNA Target Gene Network to miRNA Function: miR-375 Might Regulate Apoptosis and Actin Dynamics in the Heart Muscle via Rho-GTPases-Dependent Pathways

MicroRNAs (miRNAs) are short single-stranded non-coding RNA molecules, which are involved in regulation of main biological processes, such as apoptosis, cell proliferation and differentiation, through sequence-specific interaction with target mRNAs. In this study we propose a workflow for predicting miRNAs function by analyzing the structure of the network of their target genes. This workflow was applied to study the functional role of miR-375 in the heart muscle (myocardium), since this miRNA was previously shown to be associated with heart diseases and data on its function in myocardium are mostly unclear. We identified PIK3CA, RHOA, MAPK3, PAFAH1B1, CTNNB1, MYC, PRKCA, ERBB2, and CDC42 as key genes in the miR-375 regulated network and predicted the possible function of miR-375 in the heart muscle, consisting mainly in the regulation of the Rho-GTPases-dependent signalling pathways. We implemented our algorithm for miRNA function prediction into Python module, which is available at GitHub (https://github.com/GJOsmak/miRNET)
Modeling enamel matrix secretion in mammalian teeth

The most mineralized tissue of the mammalian body is tooth enamel. Especially in species with thick enamel, three-dimensional (3D) tomography data has shown that the distribution of enamel varies across the occlusal surface of the tooth crown. Differences in enamel thickness among species and within the tooth crown have been used to examine taxonomic affiliations, life history, and functional properties of teeth. Before becoming fully mineralized, enamel matrix is secreted on the top of a dentine template, and it remains to be explored how matrix thickness is spatially regulated. To provide a predictive framework to examine enamel distribution, we introduce a computational model of enamel matrix secretion that maps the dentine topography to the enamel surface topography. Starting from empirical enamel-dentine junctions, enamel matrix deposition is modeled as a diffusion-limited free boundary problem. Using laboratory microCT and synchrotron tomographic data of pig molars that have markedly different dentine and enamel surface topographies, we show how diffusion-limited matrix deposition accounts for both the process of matrix secretion and the final enamel distribution. Simulations reveal how concave and convex dentine features have distinct effects on enamel surface, thereby explaining why the enamel surface is not a straightforward extrapolation of the dentine template. Human molar simulations show that even subtle variation in dentine topography can be mapped to the enamel surface features. Mechanistic models of extracellular matrix deposition can be used to predict occlusal morphologies of teeth.
Proving Server Faults: RPCs for Distributed Systems in Byzantine Networks

Distributed systems are often designed to recover from downed nodes. Unfortunately, it is challenging to create recovery mechanisms that work in Byzantine networks, where the attacker controls some of the nodes and links. Often times an adversarial node can lie about an honest node being offline, and there is no way to verify this claim or detect the liar. To resolve this challenge, we design rRPC, a robust remote procedure call library for distributed systems running in Byzantine networks. rRPC ensures that either the call succeeds or the online caller/callee can create a third-party verifiable proof that the other party is faulty. A distributed system can use these proofs to identify and remove a faulty node automatically. We implement a prototype of rRPC, and use 20 - 100 EC2 VMs to evaluate its performance as a standalone library and in the context of a distributed mix-net system. Our results quantitatively show that rRPC's overhead is low and induces about 1% increase in latency in the common case.
Impact of social media on the trait of empathy

With steady rise in the use of social networking sites, Changes in the behavior of people are evident, especially in the psychological characteristic or trait of empathy. The current study is a quantitative study which explores two variables, i.e. the influence of SNS (Independent Variable) and empathy (Dependent Variable). For the fulfillment of the aim, a total of 100 responses were recorded and the data was compared between two groups, i.e. Male and Female. The age of the sample ranges from 18 to 40. The data was collected by using Davis’s Interpersonal Reactivity Index which scores on four constructs (empathetic concern, personal distress, fantasy, perspective taking) and the questionnaire was distributed through the medium of various social media apps and sites such Facebook, WhatsApp, Instagram, and Others. The data was compared by dividing it into two categories, i.e. usage of SNS for less or more hours. Furthermore, T-test was applied to check the same. Thus, our study laid a groundwork for understanding empathy’s role in facilitating interactions on social media.
Development of Advanced Hydrocarbon Fuels at Marshall Space Flight Center

This was a small-scale, hot-fire test series to make initial measurements of performance differences of five new liquid fuels relative to rocket propellant-1 (RP-1). The program was part of a high-energy-density materials development at Marshall Space Flight Center (MSFC), and the fuels tested were quadricyclane, 1-7 octodiyne, AFRL-1, biclopropylidene, and competitive impulse noncarcinogenic hypergol (CINCH) (di-methyl-aminoethyl-azide). All tests were conducted at MSFC. The first four fuels were provided by the U.S. Air Force Research Laboratory (AFRL), Edwards Air Force Base, CA. The U.S. Army, Redstone Arsenal, Huntsville, AL, provided the CINCH. The data recorded in all hot-fire tests were used to calculate specific impulse and characteristic exhaust velocity for each fuel, then compared to RP-1 at the same conditions. This was not an exhaustive study, comparing each fuel to RP-1 at an array of mixture ratios, nor did it include important fuel parameters, such as fuel handling or long-term storage. The test hardware was designed for liquid oxygen (lox)/RP-1, then modified for gaseous oxygen/RP-1 to avoid two-phase lox at very small flow rates. All fuels were tested using the same thruster/injector combination designed for RP-1. The results of this test will be used to determine which fuels will be tested in future test programs.
Exploring Environmental Coverages of Species: A New Variable Selection Methodology for Rulesets from the Genetic Algorithm for Ruleset Prediction

Variable selection for, and determination of variable importance within, species distribution models (SDMs) remain an important area of research with continuing challenges. Most SDM algorithms provide normally exhaustive searches through variable space, however, selecting variables to include in models is a first challenge. The estimation of the explanatory power of variables and the selection of the most appropriate variable set within models can be a second challenge. Although some SDMs incorporate the variable selection rubric inside the algorithms, there is no integrated rubric to evaluate the variable importance in the Genetic Algorithm for Ruleset Production (GARP). Here, we designed a novel variable selection methodology based on the rulesets generated from a GARP experiment. The importance of the variables in a GARP experiment can be estimated based on the consideration of the prevalence of each environmental variable in the dominant presence rules of the best subset of models and its coverage. We tested the performance of this variable selection method based on simulated species with both weak and strong responses to simulated environmental covariates. The variable selection method generally performed well during the simulations with over 2/3 of the trials correctly identifying most covariates. We then predict the distribution of Bacillus anthracis (the bacterium that causes anthrax) in the continental United States (US) and apply our variable selection procedure as a real-world example. We found that the distribution of B. anthracis was primarily determined by organic content, soil pH, calcic vertisols, vegetation, sand fraction, elevation, and seasonality in temperature and moisture.
Small-Molecule In Vitro Inhibitors of the Coronavirus Spike – ACE2 Protein-Protein Interaction as Blockers of Viral Attachment and Entry for SARS-CoV-2

Inhibitors of the protein-protein interaction (PPI) between the SARS-CoV-2 spike protein and ACE2, which acts as a ligand-receptor pair that initiates the viral attachment and cellular entry of this coronavirus causing the ongoing COVID-19 pandemic, are of considerable interest as potential antiviral agents. While blockade of such PPIs with small molecules is more challenging than with antibodies, small-molecule inhibitors (SMIs) might offer alternatives that are less strain- and mutation-sensitive, suitable for oral or inhaled administration, and more controllable / less immunogenic. Here, we report the identification of SMIs of this PPI by screening our compound-library that is focused on the chemical space of organic dyes. Among promising candidates identified, several dyes (Congo red, direct violet 1, Evans blue) and novel drug-like compounds (DRI-C23041, DRI-C91005) inhibited the interaction of hACE2 with the spike proteins of SARS-CoV-2 as well as SARS-CoV with low micromolar activity in our cell-free ELISA-type assays (IC50s of 0.2-3.0 μM); whereas, control compounds, such as sunset yellow FCF, chloroquine, and suramin, showed no activity. Protein thermal shift assays indicated that the SMIs identified here bind SARS-CoV-2-S and not ACE2. Selected promising compounds inhibited the entry of a SARS-CoV-2-S expressing pseudovirus into ACE2-expressing cells in concentration-dependent manner with low micromolar IC50s (6-30 μM). This provides proof-of-principle evidence for the feasibility of small-molecule inhibition of PPIs critical for coronavirus attachment/entry and serves as a first guide in the search for SMI-based alternative antiviral therapies for the prevention and treatment of diseases caused by coronaviruses in general and COVID-19 in particular.
Issues and Challenges in Cloud Storage Architecture: A Survey

From home appliances to industrial enterprises, the Information and Communication Technology (ICT) industry is revolutionizing the world. We are witnessing the emergence of new technologies (e.g, Cloud computing, Fog computing, Internet of Things (IoT), Artificial Intelligence (AI) and Block-chain) which proves the growing use of ICT (e,g. business, education, health, and home appliances), resulting in massive data generation. It is expected that more than 175 ZB data will be processed annually by 75 billion devices by 2025. The 5G technology (i.e. mobile communication technology) dramatically increases network speed, enabling users to upload ultra high definition videos in real-time, which will generate a massive stream of big data. Furthermore, smart devices, having artificial intelligence, will act like a human being (e.g, a self-driving vehicle, etc) on the network, will also generate big data. This sudden shift and massive data generation created serious challenges in storing and managing heterogeneous data at such a large scale. This article presents a state-of-the-art review of the issues and challenges involved in storing heterogeneous big data, their countermeasures (i.e, from security and management perspectives), and future opportunities of cloud storage. These challenges are reviewed in detail and new dynamics for researchers in the field of cloud storage are discovered.
DSAC: Distributional Soft Actor Critic for Risk-Sensitive Reinforcement Learning

In this paper, we present a new reinforcement learning (RL) algorithm called Distributional Soft Actor Critic (DSAC), which exploits the distributional information of accumulated rewards to achieve better performance. Seamlessly integrating SAC (which uses entropy to encourage exploration) with a principled distributional view of the underlying objective, DSAC takes into consideration the randomness in both action and rewards, and beats the state-of-the-art baselines in several continuous control benchmarks. Moreover, with the distributional information of rewards, we propose a unified framework for risk-sensitive learning, one that goes beyond maximizing only expected accumulated rewards. Under this framework we discuss three specific risk-related metrics: percentile, mean-variance and distorted expectation. Our extensive experiments demonstrate that with distribution modeling in RL, the agent performs better for both risk-averse and risk-seeking control tasks.
Petri Nets Generating Array Languages

Petri net models have been characterised to create array languages. In array generating Petri nets arrays are utilised as tokens in the initial places, instead of black dots. Triangular exhibits and triangular models are generally found in the literature in writing on picture preparing and scene investigation. Picture age should be possible from multiple points of view in formal dialects. Petri net models to create rectangular exhibits and hexagonal picture languages are also seen in the literature. We use this information from the literature in Petri nets generating diamond arrays. The catenation of upper cone arrays and lower cone arrays to a diamond array results in a similar diamond array.