Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating the Resistance Levels and Mechanisms to Penoxsulam and Cyhalofop-Butyl in Barnyardgrass (Echinochloa crus-galli) from Ningxia Province, China

Abstract Barnyardgrass [Echinochloa crus-galli (L.) P. Beauv. ] is a noxious grass weed that infests rice fields and causes huge crop yield losses. In this study, we collected 12 E. crus-galli populations from rice fields of Ningxia Province in China and investigated the resistance levels to the acetolactate synthase (ALS) inhibitor penoxsulam and the acetyl-CoA carboxylase (ACCase) inhibitor cyhalofop-butyl. The results showed that eight populations exhibited resistance to penoxsulam and four populations evolved resistance to cyhalofop-butyl. Moreover, all four cyhalofop-butyl–resistant populations (NX3, NX4, NX6, and NX7) displayed multiple herbicide resistance to both penoxsulam and cyhalofop-butyl. The alternative herbicides bispyribac-sodium, metamifop, and fenoxaprop-p-ethyl cannot effectively control the multiple herbicide–resistant (MHR) plants. To characterize the molecular mechanisms of resistance, we amplified and sequenced the target site–encoding genes in resistant and susceptible populations. Partial sequences of three ALS genes and six ACCase genes were examined. A Trp-574-Leu mutation was detected in EcALS1 and EcALS3 in two high-level (65.84- and 59.30-fold) penoxsulam-resistant populations, NX2 and NX10, respectively. In addition, one copy (EcACC4) of ACCase genes encodes a truncated aberrant protein due to a frameshift mutation in E. crus-galli populations. None of the amino acid substitutions that are known to confer herbicide resistance were detected in ALS and ACCase genes of MHR populations. Our study reveals the wide spread of MHR E. crus-galli populations in Ningxia Province that exhibit resistance to several ALS and ACCase inhibitors. Non–target site based mechanisms are likely to be involved in E. crus-galli resistance to the herbicides, at least in four MHR populations.
Managing the adverse effect of supply chain risk on corporate reputation: The mediating role of corporate social responsibility practices

Within the last decade, research has focussed on corporate social responsibility (CSR) practices as a strategic tool that enables firms to improve stakeholder perception, brand image and corporate reputation. However, one area that remains understudied is the role played by CSR practices in managing corporate reputation under conditions of supply chain risk. To answer this research question, we conduct a literature review and develop the corresponding hypothesis. We test our hypothesis using quantitative analysis of both primary and secondary data collected from organizations dispersed globally. The results highlight that under conditions of supply chain network disruption risk, CSR practices play an important role in enabling firms to manage the negative impact of such risk drivers on corporate reputation. The study further suggests that CSR practices positively impact supply chain risk management (SCRM) practices and are a necessary condition for SCRM practices to be effective in improving corporate reputation of organizations.
Industry robotic motion and pose recognition method based on camera pose estimation and neural network

To control industry robots and make sure they are working in a correct status, an efficient way to judge the motion of the robot is important. In this article, an industry robotic motion and pose recognition method based on camera pose estimation and neural network are proposed. Firstly, industry robotic motion recognition based on the neural network has been developed to estimate and optimize motion of the robotics only by a monoscope camera. Secondly, the motion recognition including key flames recording and pose adjustment has been proposed and analyzed to restore the pose of the robotics more accurately. Finally, a KUKA industry robot has been used to test the proposed method, and the test results have demonstrated that the motion and pose recognition method can recognize the industry robotic pose accurately and efficiently without inertial measurement unit (IMU) and other censers. Below in the same algorithm, the error of the method introduced in this article is better than the traditional method using IMU and has a better merit of reducing cumulative error.
DUDMap: 3D RGB-D mapping for dense, unstructured, and dynamic environment

Simultaneous localization and mapping (SLAM) problem has been extensively studied by researchers in the field of robotics, however, conventional approaches in mapping assume a static environment. The static assumption is valid only in a small region, and it limits the application of visual SLAM in dynamic environments. The recently proposed state-of-the-art SLAM solutions for dynamic environments use different semantic segmentation methods such as mask R-CNN and SegNet; however, these frameworks are based on a sparse mapping framework (ORBSLAM). In addition, segmentation process increases the computational power, which makes these SLAM algorithms unsuitable for real-time mapping. Therefore, there is no effective dense RGB-D SLAM method for real-world unstructured and dynamic environments. In this study, we propose a novel real-time dense SLAM method for dynamic environments, where 3D reconstruction error is manipulated for identification of static and dynamic classes having generalized Gaussian distribution. Our proposed approach requires neither explicit object tracking nor object classifier, which makes it robust to any type of moving object and suitable for real-time mapping. Our method eliminates the repeated views and uses consistent data that enhance the performance of volumetric fusion. For completeness, we compare our proposed method using different types of high dynamic dataset, which are publicly available, to demonstrate the versatility and robustness of our approach. Experiments show that its tracking performance is better than other dense and dynamic SLAM approaches.
Foreign Direct Investment and Growth Nexus: Further Evidence from Africa’s Largest Economy

The study investigated the effect of foreign direct investment (FDI) on economic growth in Nigeria, which is currently Africa’s largest economy, and also determined the long-run relationship between FDI and economic growth in Nigeria from 1981 to 2017. The study adopted the autoregressive distributed lag modelling approach and ordinary least square in the analysis. The empirical results revealed that FDI has a positive and significant relationship with economic growth in Nigeria within the period under review. The study concluded and recommended that Nigerian Government should formulate policies that will attract more FDI in all sectors of the economy especially in the service and manufacturing sectors, so as to improve the infrastructural facilities and production of goods in the country and also expand its labour force. Finally, there is need to improve the educational policy of the country in order to raise the stock of human capital in the country that will make useful policies for the attraction for productive FDIs in the country. JEL Classification: E22, F21, F23, F43
Enterprise e-banking satisfaction and continuance in business operations

This article aims at examining key determinants and implications of enterprise e-banking satisfaction and continuance in business operations. The empirical results unveil that firms’ satisfaction significantly mediates the effects of trustworthiness, utilitarian value, and usability on firms’ continuance intention to use enterprise e-banking services. The empirical results also suggest that it is fundamental for commercial banks to provide trustworthy and reliable enterprise e-banking services that can efficiently and effectively facilitate firms in different industries to perform banking works and transactions online. The present study makes important contributions to research and practice. The empirically grounded findings enrich the theoretical understanding of the implementation of enterprise e-banking systems and services in business operations and provide managerial insights for the development and enhancement of digital banking and financial services.
A novel live cell imaging assay reveals regulation of endosome maturation

Cell-cell communication is an essential process in life, with endosomes acting as key organelles for regulating uptake and secretion of signaling molecules. Endocytosed material is accepted by the sorting endosome where it either is sorted for recycling or remains in the endosome as it matures to be degraded in the lysosome. Investigation of the endosome maturation process has been hampered by the small size and rapid movement of endosomes in most cellular systems. Here, we report an easy versatile live-cell imaging assay to monitor endosome maturation kinetics, which can be applied to a variety of mammalian cell types. Acute ionophore treatment led to enlarged early endosomal compartments that matured into late endosomes and fused with lysosomes to form endolysosomes. Rab5-to-Rab7 conversion and PI(3)P formation and turn over were recapitulated with this assay and could be observed with a standard widefield microscope. We used this approach to show that Snx1- and Rab11-dependent endosomal recycling occurred throughout endosome maturation and was uncoupled from Rab conversion. In contrast, efficient endosomal acidification was dependent on Rab conversion. The assay provides a powerful tool to further unravel various aspects of endosome maturation.
Analysis of expression Quantitative Trait Loci for NLGN4X in relation to language and neurodevelopmental function: an exploratory analysis using FUSION and GTEx workflows

Language disorders in children are highly heritable, but progress in identifying genetic variants that contribute to language phenotypes has been slow. Here we applied a novel approach by identifying SNPs that are associated with gene expression in the brain, taking as our focus a gene on the X chromosome, NLGN4X, which has been postulated to play a role in neurodevelopmental disorders affecting language and communication. We found no significant associations between expression quantitative trait loci (eQTLs) and phenotypes of nonword repetition, general language ability or neurodevelopmental disorder in two samples of twin children, who had been selected for a relatively high rate of language problems. We report here our experiences with two methods, FUSION and GTEx, for eQTL analysis. It is likely that our null result represents a true negative, but for the interest of others interested in using these methods, we note specific challenges encountered in applying this approach to our data: a) complications associated with studying a gene on the X chromosome; b) lack of agreement between expression estimates from FUSION and GTEx; c) software compatibility issues with different versions of the R programming language.
Making high-dimensional molecular distribution functions tractable through Belief Propagation on Factor Graphs

Molecular dynamics (MD) simulations provide a wealth of high-dimensional data at all-atom and femtosecond resolution but deciphering mechanistic information from this data is an ongoing challenge in physical chemistry and biophysics. Theoretically speaking, joint probabilities of the equilibrium distribution contain all thermodynamic information, but they prove increasingly difficult to compute and interpret as the dimensionality increases. Here, inspired by tools in probabilistic graphical modeling, we develop a factor graph trained through belief propagation that helps factorize the joint probability into an approximate tractable form that can be easily visualized and used. We validate the study through the analysis of the conformational dynamics of two small peptides with 5 and 9 residues. Our validations include testing the conditional dependency predictions through an intervention scheme inspired by Judea Pearl. Secondly we directly use the belief propagation based approximate probability distribution as a high-dimensional static bias for enhanced sampling, where we achieve spontaneous back-and-forth motion between metastable states that is up to 350 times faster than unbiased MD. We believe this work opens up useful ways to thinking about and dealing with high-dimensional molecular simulations.
Distinct epicardial gene regulatory programmes drive development and regeneration of the zebrafish heart

Unlike the adult mammalian heart, which has limited regenerative capacity, the zebrafish heart can fully regenerate following injury. Reactivation of cardiac developmental programmes is considered key to successfully regenerating the heart, yet the regulatory elements underlying the response triggered upon injury and during development remain elusive. Organ-wide activation of the epicardium is essential for zebrafish heart regeneration and is considered a potential regenerative source to target in the mammalian heart. Here we compared the transcriptome and epigenome of the developing and regenerating zebrafish epicardium by integrating gene expression profiles with open chromatin ATAC-seq data. By generating gene regulatory networks associated with epicardial development and regeneration, we inferred genetic programmes driving each of these processes, which were largely distinct. We identified wt1a, wt1b, and the AP-1 subunits junbb, fosab and fosb as central regulators of the developing network, whereas hif1ab, zbtb7a, tbx2b and nrf1 featured as putative central regulators of the regenerating epicardial network. By interrogating developmental gene regulatory networks that drive cell-specific transcriptional heterogeneity, we tested novel subpopulation-related epicardial enhancers in vivo. Taken together, our work revealed striking differences between the regulatory blueprint deployed during epicardial development and regeneration. These findings challenge the dogma that heart regeneration is essentially a reactivation of developmental programmes, and provide important insights into epicardial regulation that can assist in developing therapeutic approaches to enable tissue regeneration in the adult mammalian heart.
Architecture violations detection and visualization in the continuous integration pipeline

New code in projects can introduce violations that deviate the code implementation from the intended architecture. This process is known as architecture erosion. In this article, we propose an approach for recovering the implemented architecture, and detecting violations when comparing it with the intended architecture. Given a code repository, the continuous integration pipeline calls the solution to detect the incidences of architecture violations as well as some quality and social metrics. This data is presented in metric‐centered views that help development teams to manage architecture erosion. Our approach is based on model‐driven engineering techniques since models serve to represent the code, and a model‐based pattern language helps us to automate the search for violation occurrences and execute corresponding actions (e.g., creation/assignment of issues). We confirm the approach benefits in a real project implemented by a software developing company, in a sample project available on the internet, and in a software development course, including 20 projects, where every single project decreases its architecture violations density through time.
Magnetic trapping and thermal isolation of NH molecules using the buffer gas techniques

Thermal isolation of trapped NH molecules is accomplished. We cool NH molecules to 500 mK using helium vapor buffer gas and magnetically trap them in the 4 Tesla field of an anti-Helmholtz superconducting magnet. The helium is introduced using a fast-acting cryogenic valve, allowing us to abruptly switch off the flow. The buffer gas rapidly exits the trapping region through a large diameter orifice. We trap 105 molecules with lifetimes exceeding 20 s. The prospect of extending the loading and trapping techniques to observe atom-molecule collisions is investigated.
Substrate access mechanism in a novel membrane-bound phospholipase A of Pseudomonas aeruginosa concordant with specificity and regioselectivity

PlaF is a cytoplasmic membrane-bound phospholipase A1 from Pseudomonas aeruginosa that alters the membrane glycerophospholipid (GPL) composition and fosters the virulence of this human pathogen. PlaF activity is regulated by a dimer-to-monomer transition followed by tilting of the monomer in the membrane. However, how substrates reach the active site and how the characteristics of the active site tunnels determine the activity, specificity, and regioselectivity of PlaF for natural GPL substrates has remained elusive. Here, we combined unbiased and biased all-atom molecular dynamics (MD) simulations and configurational free energy computations to identify access pathways of GPL substrates to the catalytic center of PlaF. Our results map out a distinct tunnel through which substrates access the catalytic center. PlaF variants with bulky tryptophan residues in this tunnel revealed decreased catalysis rates due to tunnel blockage. The MD simulations suggest that GPLs preferably enter the active site with the sn-1 acyl chain first, which agrees with the experimentally demonstrated PLA1 activity of PlaF. We propose that the acyl chain-length specificity of PlaF is determined by the structural features of the access tunnel, which results in favorable free energy of binding of medium-chain GPLs. The suggested egress route conveys fatty acid products to the dimerization interface and, thus, contributes to understanding the product feedback regulation of PlaF by fatty acid-triggered dimerization. These findings open up opportunities for developing potential PlaF inhibitors, which may act as antibiotics against P. aeruginosa.
Software defect prediction with imbalanced distribution by radius‐synthetic minority over‐sampling technique

Software defect prediction, which can identify the defect‐prone modules, is an effective technology to ensure the quality of software products. Due to the importance in software maintenance, many learning‐based software defect prediction models are presented in recent years. Actually, the defects usually occupy a very small proportions in software source codes; thus, the imbalanced distributions between defect‐prone modules and non‐defect‐prone modules increase the learning difficulty of the classification task. To address this issue, we present a random over‐sampling mechanism used to generate minority‐class samples from high‐dimensional sampling space to deal with the imbalanced distributions in software defect prediction, in which two constraints are applied to provide a robust way to generate new synthetic samples, that is, scaling the random over‐sampling scope to a reasonable area and distinguishing the majority‐class samples in a critical region. Based on nine open datasets of software projects, we experimentally verify that our presented method is effective on predict the defect‐prone modules, and the effect is superior to the traditional imbalanced processing methods.
Toward an autonomic approach for Internet of Things service placement using gray wolf optimization in the fog computing environment

Divers and the huge amount of data produced by the Internet of Things (IoT) applications on the one hand, and inherent limitations of local equipment to handle these data, on the other hand, leads to present emerging closer technologies to the end‐users such as fog computing environment. Nevertheless, despite the numerous advantages of such an environment, it still needs state‐of‐the‐art approaches to cope with some inherent limitations. In the literature, resource placement strategies are generally proposed to address such problems, in which the IoT applications are mapped to fog nodes. However, despite its importance, different approaches attempt to enhance the overall system's performance and users' expectations: none of such approaches is satisfactory. In this article, to deploy IoT applications on fog nodes, an autonomic IoT service placement approach based on the gray wolf optimization scheme is proposed, enhancing the system's performance while considering execution costs. Besides, the autonomic concepts help make an appropriate automanagement system that fits better the fog environment's dynamic behavior. Simulation results demonstrate that the proposed approach outperforms the other approaches and converges to the solution in near‐optimal application deployment on fog nodes in respect of the performance of performing services that are 93.7%, the performance of the average waiting time for performed services that are 100%, the remaining services sent to an extra provisioned period that is zero.
RobustECD: Enhancement of Network Structure for Robust Community Detection

Community detection, which focuses on clustering vertex interactions, plays a significant role in network analysis. However, it also faces numerous challenges like missing data and adversarial attack. How to further improve the performance and robustness of community detection for real-world networks has raised great concerns. In this paper, we explore robust community detection by enhancing network structure, with two generic algorithms presented: one is named robust community detection via genetic algorithm (RobustECD-GA), in which the modularity and the number of clusters are combined in a fitness function to find the optimal structure enhancement scheme; the other is called robust community detection via similarity ensemble (RobustECD-SE), integrating multiple information of community structures captured by various vertex similarities, which scales well on large-scale networks. Comprehensive experiments on real-world networks demonstrate, by comparing with two traditional enhancement strategies, that the new methods help six representative community detection algorithms achieve more significant performance improvement. Moreover, experiments on the corresponding adversarial networks indicate that the new methods could also optimize the network structure to a certain extent, achieving stronger robustness against adversarial attack.
Using an Advanced Text Index Structure for Corpus Exploration in Digital Humanities

With suitable index structures many corpus exploration tasks can be solved in an efficient way without rescanning the text repository in an online manner. In this paper we show that symmetric compacted directed acyclic word graphs (SCDAWGs) - a refinement of suffix trees - offer an ideal basis for corpus exploration, helping to answer many of the questions raised in DH research in an elegant way. From a simplified point of view, the advantages of SCDAWGs rely on two properties. First, needing linear computation time, the index offers a joint view on the similarities (in terms of common substrings) and differences between all text. Second, structural regularities of the index help to mine interesting portions of texts (such as phrases and concept names) and their relationship in a language independent way without using prior linguistic knowledge. As a demonstration of the power of these principles we look at text alignment, text reuse in distinct texts or between distinct authors, automated detection of concepts, temporal distribution of phrases in diachronic corpora, and related problems.