bluuebunny/crossref_metadata_2025_split huggingface dataset:
Morphogenesis and cell ordering in confined bacterial biofilms
Authors: Zhang Qiuting , Li Jian , Nijjer Japinder , Lu Haoran , Kothari Mrityunjay , Alert Ricard , Cohen Tal , Yan Jing 
Published: February 2021

AbstractBiofilms are aggregates of bacterial cells surrounded by an extracellular matrix. Much progress has been made in studying biofilm growth on solid substrates; however, little is known about the biophysical mechanisms underlying biofilm development in three-dimensional confined environments, in which the biofilm-dwelling cells must push against and even damage the surrounding environment to proliferate. Here, combining single-cell imaging, mutagenesis, and rheological measurement, we reveal the key morphogenesis steps ofVibrio choleraebiofilms embedded in hydrogels as they grow by four orders of magnitude from their initial size. We show that the morphodynamics and cell ordering in embedded biofilms are fundamentally different from those of biofilms on flat surfaces. Treating embedded biofilms as inclusions growing in an elastic medium, we quantitatively show that the stiffness contrast between the biofilm and its environment determines biofilm morphology and internal architecture, selecting between spherical biofilms with no cell ordering and oblate ellipsoidal biofilms with high cell ordering. When embedded in stiff gels, cells self-organize into a bipolar structure that resembles the molecular ordering in nematic liquid crystal droplets.In vitrobiomechanical analysis shows that cell ordering arises from stress transmission across the biofilm-environment interface, mediated by specific matrix components. Our imaging technique and theoretical approach are generalizable to other biofilm-forming species, and potentially to biofilms embedded in mucus or host tissues as during infection. Our results open an avenue to understand how confined cell communities grow by means of a compromise between their inherent developmental program and the mechanical constraints imposed by the environment.Significance StatementBiofilms are microbial cities in which bacterial cells reside in a polymeric matrix. They are commonly found inside soft confining environments such as food matrices and host tissues, against which bacteria must push to proliferate. Here, by combining single-cell live imaging and mechanical characterization, we show that the confining environment determines the dynamics of biofilm shape and internal structure. This self-organized evolution of biofilm architecture is caused by force transmission between the environment and the biofilm, mediated by the extracellular matrix secreted by the cells. Our findings lead to new ways to understand how bacterial communities develop under mechanical constraints, and potentially to new strategies for preventing and controlling biofilm growth in three-dimensional environments.
Doublecortin engages the microtubule lattice through a cooperative binding mode involving its C-terminal domain
Authors: Rafiei Atefeh , Lee Linda , Crowder D. Alex , Saltzberg Daniel J. , Sali Andrej , Brouhard Gary J. , Schriemer David C. 
Published: February 2021

AbstractDoublecortin (DCX) is a microtubule (MT) associated protein that regulates MT structure and function during neuronal development and mutations in DCX lead to a spectrum of neurological disorders. The structural properties of MT-bound DCX remain poorly resolved. Here, we describe the molecular architecture of the DCX-MT complex through an integrative modeling approach that combines data from X-ray crystallography, cryo-EM and a high-fidelity chemical crosslinking method. We demonstrate that DCX interacts with MTs through its N-terminal domain and induces a lattice-dependent self-association involving both the C-terminal structured domain and the C-tails, in a conformation that favors an open, domain-swapped state. The networked state can accommodate multiple different attachment points on the MT lattice, all of which orient the C-tails away from the lattice. As numerous disease mutations cluster in the C-terminus, and regulatory phosphorylations cluster in the C-tail, our study shows that lattice-driven self-assembly is an important property of DCX.
Genomic architecture of 5S rDNA cluster and its variations within and between species
Authors: Ding Qiutao , Li Runsheng , Ren Xiaoliang , Chan Lu-yan , Ho Vincy W. S. , Xie Dongying , Ye Pohao , Zhao Zhongying 
Published: February 2021

AbstractRibosomal genes (rDNAs) are arranged in purely tandem repeats, preventing them from being reliably assembled onto chromosome. The uncertainty of rDNA genomic structure presents a significant barrier for studying their function and evolution. Here, we generate ultra-long Nanopore and short NGS reads to delineate the architecture and variation of the 5S rDNA cluster in the different strains ofC. elegansandC. briggsae. We classify the individual rDNA units into 25 types based on the unique sequence variations in each unit ofC. elegans(N2). We next perform manual assembly of the cluster using the long reads that carry these units, which led to an assembly of rDNA cluster consisting of up to 167 5S rDNA units. The ordering and copy number of various rDNA units are indicative of separation time between strains. Surprisingly, we observed a drastically lower level of variation in the 5S rDNA cluster in theC. elegansCB4856 andC. briggsaeAF16 strains thanC. elegansN2 strain, suggesting a unique mechanism in maintaining the rDNA cluster stability in the N2. Single-copy transgenes landed into the rDNA cluster shows the expected expression in the soma, supporting that rDNA genomic environment is transcriptionally compatible with RNA polymerase II. Delineating the structure and variation of rDNA cluster paves the way for its functional and evolutionary studies.
Pyrazinamide action is driven by the cell envelope stress response in Mycobacterium tuberculosis
Authors: Thiede Joshua M. , Dillon Nicholas A. , Howe Michael D. , Aflakpui Ranee , Modlin Samuel J. , Hoffner Sven E. , Valafar Faramarz , Minato Yusuke , Baughn Anthony D. 
Published: February 2021

ABSTRACTPyrazinamide (PZA) plays a crucial role in first-line tuberculosis drug therapy. Unlike other antimicrobial agents, PZA is only active against Mycobacterium tuberculosis at low pH. The basis for this conditional drug susceptibility remains undefined. In this study, we utilized a genome-wide approach to interrogate potentiation of PZA action. We find that mutations in numerous genes involved in central metabolism as well as cell envelope maintenance and stress response are associated with PZA resistance. Further, we demonstrate that constitutive activation of the cell envelope stress response can drive PZA susceptibility independent of environmental pH. Consequently, treatment with peptidoglycan synthesis inhibitors, such as beta-lactams and D-cycloserine, potentiate PZA action through triggering this response. These findings illuminate a regulatory mechanism for conditional PZA susceptibility and reveals new avenues for enhancing potency of this important drug through targeting activation of the cell envelope stress response.
Sensitive, reliable, and robust circRNA detection from RNA-seq with CirComPara2
Authors: Gaffo Enrico , Buratin Alessia , Molin Anna Dal , Bortoluzzi Stefania 
Published: February 2021

AbstractCircular RNAs (circRNAs) are a large class of covalently closed RNA molecules that originate by a process called back-splicing. CircRNAs are emerging as functional RNAs involved in the regulation of biological processes as well as in disease and cancer mechanisms. Current computational methods for circRNA identification from RNA-seq experiments are characterised by low discovery rates and performance dependent on the analysed data set. We developed a new automated computational pipeline, CirComPara2 (https://github.com/egaffo/CirComPara2), that consistently achieves high recall rates without losing precision by combining multiple circRNA detection methods. In our benchmark analysis, CirComPara2 outperformed state-of-the-art circRNA discovery tools and proved to be a reliable and robust method for comprehensive transcriptomics characterisation.
Dynamic selective auditory attention detection using RNN and reinforcement learning
Authors: Geravanchizadeh Masoud , Roushan Hossein 
Published: February 2021

AbstractThe cocktail party phenomenon describes the ability of the human brain to focus auditory attention on a particular stimulus while ignoring other acoustic events. Selective auditory attention detection (SAAD) is an important issue in the development of brain-computer interface systems and cocktail party processors. This paper proposes a new dynamic attention detection system to process the temporal evolution of the input signal. The proposed dynamic SAAD is modeled as a sequential decision-making problem, which is solved by recurrent neural network (RNN) and reinforcement learning methods of Q-learning and deep Q-learning. Among different dynamic learning approaches, the evaluation results show that the deep Q-learning approach with RNN as agent provides the highest classification accuracy (94.2%) with the least detection delay. The proposed SAAD system is advantageous, in the sense that the detection of attention is performed dynamically for the sequential inputs. Also, the system has the potential to be used in scenarios, where the attention of the listener might be switched in time in the presence of various acoustic events.
TransPi – a comprehensive TRanscriptome ANalysiS PIpeline forde novotranscriptome assembly
Authors: Rivera-Vicéns R.E. , Garcia-Escudero C.A. , Conci N. , Eitel M. , Wörheide G. 
Published: February 2021

AbstractThe use of RNA-Seq data and the generation ofde novotranscriptome assemblies have been pivotal for studies in ecology and evolution. This is distinctly true for non-model organisms, where no genome information is available. Nevertheless, studies of differential gene expression, DNA enrichment baits design, and phylogenetics can all be accomplished with the data gathered at the transcriptomic level. Multiple tools are available for transcriptome assembly, however, no single tool can provide the best assembly for all datasets. Therefore, a multi assembler approach, followed by a reduction step, is often sought to generate an improved representation of the assembly. To reduce errors in these complex analyses while at the same time attaining reproducibility and scalability, automated workflows have been essential in the analysis of RNA-Seq data. However, most of these tools are designed for species where genome data is used as reference for the assembly process, limiting their use in non-model organisms. We present TransPi, a comprehensive pipeline forde novotranscriptome assembly, with minimum user input but without losing the ability of a thorough analysis. A combination of different model organisms, k-mer sets, read lengths, and read quantities were used for assessing the tool. Furthermore, a total of 49 non-model organisms, spanning different phyla, were also analyzed. Compared to approaches using single assemblers only, TransPi produces higher BUSCO completeness percentages, and a concurrent significant reduction in duplication rates. TransPi is easy to configure and can be deployed seamlessly using Conda, Docker and Singularity.
Development of distance education in Chinese higher education in perspectives of accessibility, quality and equity under COVID-19
Authors: Chen Ying , Hou Angela Yung Chi , Huang Lei 
Published: February 2021

PurposeThis paper aimed to explore the development of distance education (DE) in Chinese higher education as well as the three significant themes: accessibility, quality and equity in Chinese DE and the performance of these three themes in Chinese DE.Design/methodology/approachDocument analysis was used as the major research method in this study to examine the development and challenges in terms of accessibility, quality and equity in Chinese DE. In this study, national-level official policy documents and reports from the Chinese government were collected and analyzed. Also, scientific articles from CNKI were analyzed to find out the evolution of the Frontier topics on Chinese DE in accessibility, quality and equity.FindingsThere are three major findings. First, the Chinese government has shown its positive attitude toward DE in higher education. Second, compared with the other two themes: quality and equity; the number of articles in the accessibility of DE was lower than the other two themes and the problems of accessing online courses were insufficiently caused by two reasons due to lack of basic ICT literacy and poor Internet infrastructure. Third, there was a gap between Chinese policies and research articles mainly because of the unbalanced development of accessibility in policy management and research articles.Research limitations/implicationsAlthough this paper has summarized the development of the DE in Chinese higher education, it was clear that accessibility, quality and equity were three critical issues in DE. However, there are still other essential factors that contribute to the development of DE that requires further investigation, such as learner satisfaction, different features of learning platforms and instructional strategy.Practical implicationsThe findings of this paper can be used to identify the attitude toward the DE of the Chinese government. Besides, the accessibility could be a research and practical focus for Chinese DE in the future.Originality/valueThis study showed some significant policies released by the Chinese government toward DE in higher education and revealed the achievements, trends and challenges in the accessibility, quality and equity of Chinese DE.
Income smoothing and firm value in a regulated market: the moderating effect of market risk
Authors: Abogun Segun , Adigbole Ezekiel Aiyenijo , Olorede Titilope Esther 
Published: February 2021

PurposeThis study aims to examine the impact of income smoothing on the value of firms in a regulated security market, moderated by market risk. This is based on the prevalence of accounting scandals resulting in the collapse of firms which has been attributed to the opportunistic behaviors of managers.Design/methodology/approachThe ex post facto research design was employed, and as such, data were gathered from secondary sources. The quantitative approach was also used in the study. Furthermore, the system generalized method of moments (Blundell–Bond) panel estimation technique was used for analyzing the data. Income smoothing was measured using the accrual based methods, while firm value was measured using share price.FindingsThe study found that income smoothing has a negative significant impact on firm value. The study also revealed that market risk is a significant variable that defines the relationship between income smoothing and firm value.Originality/valueTesting the moderating effect of market risk on the relationship between income smoothing and firm value is unique to this study, particularly from a regulated security market and emerging economy.
Corporation financial distress prediction with deep learning: analysis of public listed companies in Malaysia
Authors: Halim Zulkifli , Shuhidan Shuhaida Mohamed , Sanusi Zuraidah Mohd 
Published: February 2021

PurposeIn the previous study of financial distress prediction, deep learning techniques performed better than traditional techniques over time-series data. This study investigates the performance of deep learning models: recurrent neural network, long short-term memory and gated recurrent unit for the financial distress prediction among the Malaysian public listed corporation over the time-series data. This study also compares the performance of logistic regression, support vector machine, neural network, decision tree and the deep learning models on single-year data.Design/methodology/approachThe data used are the financial data of public listed companies that been classified as PN17 status (distress) and non-PN17 (not distress) in Malaysia. This study was conducted using machine learning library of Python programming language.FindingsThe findings indicate that all deep learning models used for this study achieved 90% accuracy and above with long short-term memory (LSTM) and gated recurrent unit (GRU) getting 93% accuracy. In addition, deep learning models consistently have good performance compared to the other models over single-year data. The results show LSTM and GRU getting 90% and recurrent neural network (RNN) 88% accuracy. The results also show that LSTM and GRU get better precision and recall compared to RNN. The findings of this study show that the deep learning approach will lead to better performance in financial distress prediction studies. To be added, time-series data should be highlighted in any financial distress prediction studies since it has a big impact on credit risk assessment.Research limitations/implicationsThe first limitation of this study is the hyperparameter tuning only applied for deep learning models. Secondly, the time-series data are only used for deep learning models since the other models optimally fit on single-year data.Practical implicationsThis study proposes recommendations that deep learning is a new approach that will lead to better performance in financial distress prediction studies. Besides that, time-series data should be highlighted in any financial distress prediction studies since the data have a big impact on the assessment of credit risk.Originality/valueTo the best of authors' knowledge, this article is the first study that uses the gated recurrent unit in financial distress prediction studies based on time-series data for Malaysian public listed companies. The findings of this study can help financial institutions/investors to find a better and accurate approach for credit risk assessment.
The competing value framework model of organizational culture, innovation and performance
Authors: Zeb Ali , Akbar Fazal , Hussain Khawar , Safi Adnan , Rabnawaz Muhammad , Zeb Faheem 
Published: February 2021

PurposeInnovation is the basic input to organizational endurance; therefore, the study of processes that support innovation should be of interest to practitioners and researchers alike. Consequently, there is increasing attention for the supplementary research examination of the influencing elements of innovativeness.Design/methodology/approachThe data were collected in 2018 using adapted questionnaires that were tested. The respondents were 446 employees of Pakistan Electric Power Company (PEPCO). Bivariate correlations and hierarchical regression were used for the data analysis.FindingsThe findings showed that the competing value framework (CVF) model of organizational culture may promote innovativeness that translates to the progress of PEPCO, which deserved barrier for everyone relying on the values made by the culture of the organization. The adhocracy culture is considered to be statistically suitable for the prediction of performance and enhancement of innovation in the organization. Based on the above findings, it may be deduced that innovation mediated the relationship between some particular types of organizational culture and performance.Practical implicationsThe CVF model provides a supportive framework for the development of procedures that promote innovation in the organization. The focus of the CVF model highlighted employees' behavior and function of organizational culture, which can restrain or stimulate performance. This study reported and developed a basis for an empirical model based on the CVF model.Originality/valueThis paper found that the CVF model and innovation are mega sources of innovation at PEPCO. This work should be of interest in the area of innovation and performance improvement. There are very few empirical research studies on the relationship between organizational culture, innovation and performance, specifically in the context of developing countries. This is one of the very few studies conducted to empirically examine the influence of CVF model on performance through the mediating role of innovation in PEPCO.
Does humane orientation matter? A cross-cultural study of job characteristics needs-supplies fit/misfit and affective organizational commitment
Authors: Kim Si Hyun , Wagstaff M. Fernanda , Laffranchini Giacomo 
Published: February 2021

PurposeDrawing from job characteristic theory and person-environment fit theory, the authors examine the relationship between job characteristics needs-supplies fit/misfit and affective organizational commitment across countries and how humane orientation moderates this relationship.Design/methodology/approachTo test the authors’ hypotheses, the authors conducted a number of multilevel polynomial regressions with three-dimensional surface analyses on a sample of 19,049 employees from 24 countries drawn from the International Social Survey Program (ISSP) 2005.FindingsResults indicate that job characteristics needs-supplies fit is positively related to affective organizational commitment, while job characteristics needs-supplies misfit is negatively related to affective organizational commitment. In addition, results reveal that humane orientation is relevant to increasing affective organizational commitment when external rewards job characteristics needs are higher than external rewards job characteristics supplies.Originality/valueThese results weaken the universality of job characteristics and call for a departure from a one-size-fits-all approach to human resources.
CEO overconfidence and future firm risk in China: the moderating role of institutional investors
Authors: Ali Zulfiqar , Tauni Muhammad Zubair 
Published: February 2021

PurposeThe purpose of this paper is to determine how CEO overconfidence influences firm’s future risk in a sample of Chinese listed firms. It further examines the moderating effect of institutional investors on the association between CEO overconfidence and future firm risk.Design/methodology/approachThe initial sample consists of Chinese A-share issuing firms listed on Shanghai and Shenzhen Stock Exchanges during the period starting from 2000 to 2017. This study classifies a CEO as overconfident if the forecasted profits of the firm are greater than the actual profits for majority of the time during the tenure of the CEO. Ordinary least squares regression is used as the primary estimation method for generating the results, however, firm fixed effects and two-stage least squares regressions have also been used for verifying the robustness of the results.FindingsThe results demonstrate that CEO overconfidence leads to an escalation in firm’s risk level over the subsequent years. However, the intensity of this positive association is weaker in state-owned firms. Analysis of the moderating effect of institutional investors reveals that only active institutional investors, specifically mutual funds and foreign institutional investors, play their governance role in reducing the effect of CEO overconfidence on firm’s risk level. Furthermore, the moderating effect of active institutional investors is weaker in state-owned firms.Research limitations/implicationsThe empirical evidence obtained by this study suggests that CEOs should exercise extreme diligence in decision-making. They must analyze a situation based on realistic facts and figures, rather than having misperception about their excessive abilities in controlling the outcomes of a situation. The findings also imply that regulators and policymakers should formulate strategies for motivating mutual funds and foreign investors to increase their shareholding in Chinese firms.Originality/valueTo the best of the authors’ knowledge, this is the first study that examines the impact of CEO overconfidence on future firm risk, not the current firm risk. Besides, literature regarding the role of external governance mechanisms in the context of behavioral biases is extremely scant. This study contributes to the literature by analyzing how the association between CEO overconfidence and firm’s future risk is influenced by the institutional investors’ ownership.