Miracle-dz/UniFAR-FTUs huggingface dataset:
The N-Terminal Region of Middle East Respiratory Syndrome Coronavirus Accessory Protein 8b Is Essential for Enhanced Virulence of an Attenuated Murine Coronavirus

Multiple coronaviruses (CoV) cause severe respiratory infections and become global public health threats such as SARS-CoV, MERS-CoV, and SARS-CoV-2. Each coronavirus contains different numbers of accessory proteins which show high variability among different CoVs. ABSTRACT Middle East respiratory syndrome coronavirus (MERS-CoV) is a beta coronavirus that emerged in 2012, causing severe pneumonia and renal failure. MERS-CoV encodes five accessory proteins. Some of them have been shown to interfere with host antiviral immune response. However, the roles of protein 8b in innate immunity and viral virulence was rarely studied. Here, we introduced individual MERS-CoV accessory protein genes into the genome of an attenuated murine coronavirus (Mouse hepatitis virus, MHV), respectively, and found accessory protein 8b could enhance viral replication in vivo and in vitro and increase the lethality of infected mice. RNA-seq analysis revealed that protein 8b could significantly inhibit type I interferon production (IFN-I) and innate immune response in mice infected with MHV expressing protein 8b. We also found that MERS-CoV protein 8b could initiate from multiple internal methionine sites and at least three protein variants were identified. Residues 1-23 of protein 8b was demonstrated to be responsible for increased virulence in vivo. In addition, the inhibitory effect on IFN-I of protein 8b might not contribute to its virulence enhancement as aa1-23 deletion did not affect IFN-I production in vitro and in vivo. Next, we also found that protein 8b was localized to the endoplasmic reticulum (ER)/Golgi membrane in infected cells, which was disrupted by C-terminal region aa 88-112 deletion. This study will provide new insight into the pathogenesis of MERS-CoV infection. IMPORTANCE Multiple coronaviruses (CoV) cause severe respiratory infections and become global public health threats such as SARS-CoV, MERS-CoV, and SARS-CoV-2. Each coronavirus contains different numbers of accessory proteins which show high variability among different CoVs. Accessory proteins are demonstrated to play essential roles in pathogenesis of CoVs. MERS-CoV contains 5 accessory proteins (protein 3, 4a, 4b, 5, 8b), and deletion of all four accessory proteins (protein 3, 4a, 4b, 5), significantly affects MERS-CoV replication and pathogenesis. However, whether ORF8b also regulates MERS-CoV infection is unknown. Here, we constructed mouse hepatitis virus (MHV) recombinant virus expressing MERS-CoV protein 8b and demonstrated protein 8b could significantly enhance the virulence of MHV, which is mediated by N-terminal domain of protein 8b. This study will shed light on the understanding of pathogenesis of MERS-CoV infection.
Clonal Hematopoiesis Mutations in Patients with Lung Cancer Are Associated with Lung Cancer Risk Factors

Analysis of whole-exome sequencing data uncovers correlations between clonal hematopoiesis and lung cancer risk factors, identifies genetic variants correlated with clonal hematopoiesis, and highlights hundreds of potential novel clonal hematopoiesis mutations. Clonal hematopoiesis (CH) is a phenomenon caused by expansion of white blood cells descended from a single hematopoietic stem cell. While CH can be associated with leukemia and some solid tumors, the relationship between CH and lung cancer remains largely unknown. To help clarify this relationship, we analyzed whole-exome sequencing (WES) data from 1,958 lung cancer cases and controls. Potential CH mutations were identified by a set of hierarchical filtering criteria in different exonic regions, and the associations between the number of CH mutations and clinical traits were investigated. Family history of lung cancer (FHLC) may exert diverse influences on the accumulation of CH mutations in different age groups. In younger subjects, FHLC was the strongest risk factor for CH mutations. Association analysis of genome-wide genetic variants identified dozens of genetic loci associated with CH mutations, including a candidate SNP rs2298110, which may promote CH by increasing expression of a potential leukemia promoter gene OTUD3. Hundreds of potentially novel CH mutations were identified, and smoking was found to potentially shape the CH mutational signature. Genetic variants and lung cancer risk factors, especially FHLC, correlated with CH. These analyses improve our understanding of the relationship between lung cancer and CH, and future experimental studies will be necessary to corroborate the uncovered correlations. Significance: Analysis of whole-exome sequencing data uncovers correlations between clonal hematopoiesis and lung cancer risk factors, identifies genetic variants correlated with clonal hematopoiesis, and highlights hundreds of potential novel clonal hematopoiesis mutations.
Blast Effects Evaluation Using TNT Equivalent

This paper shows blast parameters of high explosives commonly used in explosive ordnance of Serbian armed forces regarding TNT equivalents. Primary blast wave parameters, overpressure, impulse and positive phase duration are calculated using modified Sadovskiy equations and the modified K-B equation. TNT equivalents of the observed explosives are determined using thermochemical calculations based on BKW EOS parameters. The calculated blast wave parameters show significant influence of used explosives concerning the TNT equivalent.
TreadWill: Development and pragmatic randomized controlled trial of an unguided, computerized cognitive behavioral therapy intervention in a lower middle-income country

Most individuals vulnerable to depression do not receive adequate or timely treatment globally. Unguided computerized cognitive behavioral therapy (cCBT) has the potential to bridge this treatment gap. However, the real-world effectiveness of unguided cCBT interventions, particularly in low- and middle-income countries (LMICs), remains inconclusive. In this study, we report the design and development of a new unguided cCBT intervention, TreadWill, and its pragmatic evaluation. TreadWill was designed to be fully automated, engaging, easy to use, and accessible in LMICs. To evaluate its effectiveness and engagement level, we performed a double-blind, fully-remote, randomized controlled trial with 598 participants in India. The use of TreadWill significantly reduced depression-related and anxiety-related symptoms. Compared with a plain-text version with the same therapeutic content, the full-featured version of TreadWill showed significantly higher engagement. Overall, our study provides a new resource and evidence for the use of unguided cCBT as a scalable intervention in LMICs.
Severe COVID-19 induces molecular signatures of aging in the human brain

Coronavirus disease 2019 (COVID-19) is predominantly an acute respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and remains a significant threat to public health. COVID-19 is accompanied by neurological symptoms and cognitive decline, but the molecular mechanisms underlying this effect remain unclear. As aging induces distinct molecular signatures in the brain associated with cognitive decline in healthy populations, we hypothesized that COVID-19 may induce molecular signatures of aging. Here, we performed whole transcriptomic analysis of human frontal cortex, a critical area for cognitive function, in 12 COVID-19 cases and age- and sex-matched uninfected controls. COVID-19 induces profound changes in gene expression, despite the absence of detectable virus in brain tissue. Pathway analysis shows downregulation of genes involved in synaptic function and cognition and upregulation of genes involved in immune processes. Comparison with five independent transcriptomic datasets of aging human frontal cortex reveals striking similarities between aged individuals and severe COVID-19 patients. Critically, individuals below 65 years of age exhibit profound transcriptomic changes not observed among older individuals in our patient cohort. Our data indicate that severe COVID-19 induces molecular signatures of aging in the human brain and emphasize the value of neurological follow-up in recovered individuals.
A libP2P Implementation of the Bitcoin Block Exchange Protocol

The progressive decentralization of web services brought a need for new instruments to support the development of P2P protocols and applications. The only tools available are network or event simulators, which help the development in artificial or extremely controlled environments. They usually provide low-level support with completely unstructured networks. LibP2P is a library that tries to solve all such problems, letting the users easily deploy nodes on the Internet. It also comes with a set of functionalities, to solve the most important problems of P2P networks, such as NAT traversal, peer and content discovery and routing, and much more. In this paper, we employ LibP2P to implement min-bitcoin, a minimal version of the protocol used by the Bitcoin network to exchange the blocks. We evaluate our implementation to assess the applicability of LibP2P in a real-life blockchain scenario. The evaluation uncovers that setting up communication channels is time-consuming, but data transfers are fast. Additionally, LibP2P efficiently manages cases in which nodes are behind a NAT, under VPN, or in geographically distant places.
Confidence limits for the averted infections ratio estimated via the counterfactual placebo incidence rate

Abstract Objectives The averted infections ratio (AIR) is a novel measure for quantifying the preservation-of-effect in active-control non-inferiority clinical trials with a time-to-event outcome. In the main formulation, the AIR requires an estimate of the counterfactual placebo incidence rate. We describe two approaches for calculating confidence limits for the AIR given a point estimate of this parameter, a closed-form solution based on a Taylor series expansion (delta method) and an iterative method based on the profile-likelihood. Methods For each approach, exact coverage probabilities for the lower and upper confidence limits were computed over a grid of values of (1) the true value of the AIR (2) the expected number of counterfactual events (3) the effectiveness of the active-control treatment. Results Focussing on the lower confidence limit, which determines whether non-inferiority can be declared, the coverage achieved by the delta method is either less than or greater than the nominal coverage, depending on the true value of the AIR. In contrast, the coverage achieved by the profile-likelihood method is consistently accurate. Conclusions The profile-likelihood method is preferred because of better coverage properties, but the simpler delta method is valid when the experimental treatment is no less effective than the control treatment. A complementary Bayesian approach, which can be applied when the counterfactual incidence rate can be represented as a prior distribution, is also outlined.
Inheritance of the wood properties of the Japanese red pine (Pinus densiflora Siebold et Zucc.) from the open-pollinated families selected as resistance to the pine wood nematode

Abstract Pine wilt disease is one of the most serious tree diseases occurring worldwide. Clones of Pinus densiflora Siebold et Zucc with pine wood nematode resistance were selected. In addition to resistance, wood quality is also an important criterion in the breeding program of P. densiflora because of its use as construction lumber. However, little information is available on the wood qualities of the progenies of resistant clones. The repeatabilities of the wood properties were investigated for 11 open-pollinated families of P. densiflora selected for their pine wood nematode resistance. Oven-dry density, latewood tracheid length, the microfibril angle (MFA) of the S2 layer in latewood tracheids, modulus of elasticity (MOE), and modulus of rupture (MOR) were measured in the third or fourth annual ring from the pith. No significant correlations were found between the wood properties and the stem diameter or tree height. However, significant correlations were found between oven-dry density and MOE or MOR, which suggests that oven-dry density is a good indicator for selecting wood with higher bending properties. Among the measured wood properties, oven-dry density had the highest repeatability (R=0.47), followed by MOR (R=0.33), tracheid length (R=0.21), and MFA (R=0.14). MOE had the lowest value (R=0.01). The 11 families examined were classified into three groups according to their growth characteristics, wood properties, and resistance to pine wilt disease. On the basis of the results, we conclude that genetic improvement of wood properties especially for wood density and MOR is possible for the resistant P. densiflora.
Array element auto-tiling based on capacitive displacement sensor

Array element tiling is one of the key technologies for the coherent beam combination in a high-power laser facility. In this paper, we proposed a method of the array element auto-tiling based on capacitive displacement sensor. The method was verified on a double-pass tiled-grating compressor in XG-III laser facility. The research showed that the method is an effective way to control the misalignment errors automatically, with high precision and long-term stability.
Developing static reactive power compensators in a power system simulator for power education

This paper describes a newly installed laboratory module microcomputer-based static reactive power compensator (SVC) in detail to teach students how an SVC affects system voltage, load balancing, power factor, and transmission line losses. The SVC is merged into an old power system simulator for extensive power engineering education. The structure of the SVC is thyristor controlled reactors with fixed capacitors (TCR-FC). Two control algorithms, feedback control and feedforward control, are developed and compared. For the purpose of program flexibility and portability, a VME-Bus based microcomputer is used to synthesize the controller of the SVC. Several suggested experiments are given to show the effects of the SVC on distribution system compensation. The SVC greatly promotes the performance of the power system simulator.
How the Design and Assessment of Instruction/Learning Strategies Influence the Academic Performance of Civil Engineering Students at Universidad Industrial De Santander, Colombia

AbstractThis paper discusses the findings and experiences drawn from a study on the design of content and instruction strategies for an introductory course in construction management at the Universidad Industrial de Santander in Colombia. The research aimed at boosting the students’ performance through the assessment of instruction strategies and course content that can match the education demands needed to support the economical and social development of the country. The study adopted a sequential and exploratory research design with a longitudinal approach. The researchers found that Colombian students prefer courses that combine theory with practice in class, such as workshops, as instruction strategies. In addition, the results showed a degree of improvement in the academic performance of the students. Such improvement was statistically significant with a higher average level of academic achievement and a more consistent achievement of learning outcomes among the enrolled students.
Laminar-Flow Instrumentation for Wind-Tunnel and Flight Experiments

This article reports on selection, adaptation, and integration of advanced measuring techniques for laminar wing testing in wind-tunnel and flight experiments. The aim was to perform high quality experiments with results of sufficient detail and resolution which can be used for theoretical code development and verification as well as for design criteria of laminar wing concepts. Results from various wind-tunnel and flight tests fully support and confirm the quality and potential of the testing techniques employed.
Intelligent Power Distribution Visual Monitoring System

In the power system of the urban platform, due to the dense urban population, the real-time monitoring and early warning power distribution cabinet system is of great significance to the safety of urban residents and reducing property losses. Therefore, combined with the dense distribution of urban electrical equipment, we designed a brand-new Internet of Things monitoring system that accurately locates the location of each power distribution cabinet in the city. Through the MQTT protocol, it realizes the data transmission between the microcontroller, server and database. On the web side, we use the current mainstream front-end vue. js and back-end SpringBoot framework. Display data in the form of 2D (echarts) and 3D (unity). The data is updated more timely, the display is more diverse, and the readability becomes stronger. Finally, we evaluated the practicability of the system in combination with the specific use situation. When the temperature and humidity reach a critical value, the system will send an early warning signal to the monitor to achieve the final effect.
Leveraging Human Insights by Combining Multi-Objective Optimization with Interactive Evolution

Abstract : Deceptive fitness landscapes are a growing concern for evolutionary computation. Recent work has shown that combining human insights with short-term evolution has a synergistic effect that accelerates the discovery of solutions. While humans provide rich insights, they fatigue easily. Previous work reduced the number of human evaluations by evolving a diverse set of candidates via intermittent searches for novelty. While successful at evolving solutions for a deceptive maze domain, this approach lacks the ability to measure what the human evaluator identi es as important. The key insight here is that multi-objective evolutionary algorithms foster diversity, serving as a surrogate for novelty, while measuring user preferences. This approach, called Pareto Optimality-Assisted Interactive Evolutionary Computation (POA-IEC), allows users to identify candidates that they feel are promising. Experimental results reveal that POA-IEC finds solutions in fewer evaluations than previous approaches, and that the non-dominated set is significantly more novel than the dominated set. In this way, POA-IEC simultaneously leverages human insights while quantifying their preferences.
Grassmannian Dimensionality Reduction for Optimized Universal Manifold Embedding Representation of 3D Point Clouds

Consider a 3-D object and the orbit of equivalent objects turned out by the rigid transformation group. The set of possible observations on these equivalent objects is generally a manifold in the ambient space of observations. It has been shown that the rigid transformation universal manifold embedding (RTUME) provides a mapping from the orbit of observations on some object to a single low dimensional linear subspace of Euclidean space. This linear subspace is invariant to the geometric transformations and hence is a representative of the orbit. In the classification set-up the RTUME subspace extracted from an experimental observation is tested against a set of subspaces representing the different object manifolds, in search for the nearest class. We clarify the way in which level-set functions, computed at each quantization level in an observation, serve as a basis for the invariant subspaces in RTUME. In the presence of observation noise and random sampling patterns of the point clouds, the observations do not lie strictly on the manifold and the resulting RTUME subspaces are noisy. Inspired by the ideas of Locality Preserving Projections and Grassmannian dimensionality reduction, we derive an optimal companding of the level-set functions yielding the Grassmannian dimensionality reduction universal manifold embedding (GDRUME). We evaluate the proposed method in a classification task on a noisy version of the ModelNet40 dataset and compare its performance to that of PointNet classification DNN. We show that in the presence of noise, GDRUME provides highly accurate classification results, while the performance of PointNet is poor.
C4M: A Partition-Robust Consensus Algorithm for Blockgraph in Mesh Network

Blockchain designed for Mobile Ad hoc Networks (MANET) and mesh networks is an emerging research topic which has to cope with the network partition problem. However, existing consensus algorithms used in blockchains have been designed to work in a fully connected network with reliable communication. As this assumption does not hold anymore in mobile wireless networks, we describe in this paper the problem of network partitions and its impact on blockchain. Then, we propose a new consensus algorithm called Consensus for Mesh (C4M) which inspires from RAFT as a solution to this problem. The C4M consensus algorithm is integrated in Blockgraph, a blockchain solution for MANET and mesh networks. We implemented our solution in NS-3 to analyze its performances through simulations. The simulation results show that the heartbeat interval and the election timeout have a great impact on the leader election time, especially in case of topology changes.
In Silico lead identification by virtual screening and in vitro anti-cancer activities by MTT assay

Computational tools have been widely utilized to design potent leads against specific targets as a part of drug discovery process. Moreover, virtual screening has been reported as an efficient concept in filtering actives from a huge library of compounds. In this paper, a computational approach employing virtual screening of Drug Bank database was implemented to dock 1400 drugs against caspase-3 to evaluate the binding affinities of ligands with the receptor. Docking analysis with Molegro Virtual Docker revealed top 10 drugs and hence consensus re-scoring was employed to obtain true hits. Classes were generated using rank-sum technique and the top five hits (Olmesartan, Verteporfin, Saprisartan, Atorvastatin and Lapatinib) were tested experimentally to determine the inhibitory effects on growth of HeLa cells in vitro. It was observed that proliferation of HeLa cells could be significantly inhibited by Atorvastatin in a concentration dependent manner.
SS-SFDA : Self-Supervised Source-Free Domain Adaptation for Road Segmentation in Hazardous Environments

We present a novel approach for unsupervised road segmentation in adverse weather conditions such as rain or fog. This includes a new algorithm for source-free domain adaptation (SFDA) using self-supervised learning. More-over, our approach uses several techniques to address various challenges in SFDA and improve performance, including online generation of pseudo-labels and self-attention as well as use of curriculum learning, entropy minimization and model distillation. We have evaluated the performance on 6 datasets corresponding to real and synthetic adverse weather conditions. Our method outperforms all prior works on unsupervised road segmentation and SFDA by at least 10.26%, and improves the training time by 18−180×. Moreover, our self-supervised algorithm exhibits similar accuracy performance in terms of mIOU score as compared to prior supervised methods.