bluuebunny/crossref_metadata_2025_split huggingface dataset:
Microbiome differential abundance methods produce disturbingly different results across 38 datasets
Authors: Nearing Jacob T. , Douglas Gavin M. , Hayes Molly , MacDonald Jocelyn , Desai Dhwani , Allward Nicole , Jones Casey M. A. , Wright Robyn , Dhanani Akhilesh , Comeau André M. , Langille Morgan G. I. 
Published: May 2021

AbstractIdentifying differentially abundant microbes is a common goal of microbiome studies. Multiple methods have been applied for this purpose, which are largely used interchangeably in the literature. Although it has been observed that these tools can produce different results, there have been very few large-scale comparisons to describe the scale and significance of these differences. In addition, it is challenging for microbiome researchers to know which differential abundance tools are appropriate for their study and how these tools compare to one another. Here, we have investigated these questions by analyzing 38 16S rRNA gene datasets with two sample groups for differential abundance testing. We tested for differences in amplicon sequence variants and operational taxonomic units (referred to as ASVs for simplicity) between these groups with 14 commonly used differential abundance tools. Our findings confirmed that these tools identified drastically different numbers and sets of significant ASVs, however, for many tools the number of features identified correlated with aspects of the tested study data, such as sample size, sequencing depth, and effect size of community differences. We also found that the ASVs identified by each method were dependent on whether the abundance tables were prevalence-filtered before testing. ALDEx2 and ANCOM produced the most consistent results across studies and agreed best with the intersect of results from different approaches. In contrast, several methods, such as LEfSe, limma voom, and edgeR, produced inconsistent results and in some cases were unable to control the false discovery rate. In addition to these observations, we were unable to find supporting evidence for a recent recommendation that limma voom, corncob, and DESeq2 are more reliable overall compared with other methods. Although ALDEx2 and ANCOM are two promising conservative methods, we argue that those researchers requiring more sensitive methods should use a consensus approach based on multiple differential abundance methods to help ensure robust biological interpretations.
LYRUS: A Machine Learning Model for Predicting the Pathogenicity of Missense Variants
Authors: Lai Jiaying , Yang Jordan , Gamsiz Uzun Ece D. , Rubenstein Brenda M. , Sarkar Indra Neil 
Published: May 2021

AbstractSingle amino acid variations (SAVs) are a primary contributor to variations in the human genome. Identifying pathogenic SAVs can aid in the diagnosis and understanding of the genetic architecture of complex diseases, such as cancer. Most approaches for predicting the functional effects or pathogenicity of SAVs rely on either sequence or structural information. Nevertheless, previous analyses have shown that methods that depend on only sequence or structural information may have limited accuracy. Recently, researchers have attempted to increase the accuracy of their predictions by incorporating protein dynamics into pathogenicity predictions. This study presents < Lai Yang Rubenstein Uzun Sarkar > (LYRUS), a machine learning method that uses an XGBoost classifier selected by TPOT to predict the pathogenicity of SAVs. LYRUS incorporates five sequence-based features, six structure-based features, and four dynamics-based features. Uniquely, LYRUS includes a newly-proposed sequence co-evolution feature called variation number. LYRUS’s performance was evaluated using a dataset that contains 4,363 protein structures corresponding to 20,307 SAVs based on human genetic variant data from the ClinVar database. Based on our dataset, the LYRUS classifier has a higher accuracy, specificity, F-measure, and Matthews correlation coefficient (MCC) than alternative methods including PolyPhen2, PROVEAN, SIFT, Rhapsody, EVMutation, MutationAssessor, SuSPect, FATHMM, and MVP. Variation numbers used within LYRUS differ greatly between pathogenic and neutral SAVs, and have a high feature weight in the XGBoost classifier employed by this method. Applications of the method to PTEN and TP53 further corroborate LYRUS’s strong performance. LYRUS is freely available and the source code can be found at https://github.com/jiaying2508/LYRUS.
Data-driven modeling predicts gene regulatory network dynamics during the differentiation of multipotential progenitors
Authors: Handzlik Joanna Elzbieta , Manu  
Published: May 2021

SummaryCellular differentiation during hematopoiesis is guided by gene regulatory networks (GRNs) thought to be organized as a hierarchy of bistable switches, with antagonism between Gata1 and PU.1 driving red- and white-blood cell differentiation. We utilized high temporal-resolution gene-expression data from in vitro erythrocyte-neutrophil differentiation and a predictive data-driven dynamical modeling framework to learn the architecture and dynamics of gene regulation in a comprehensive twelve-gene GRN. The inferred genetic architecture is densely interconnected rather than hierarchical. The analysis of model dynamics revealed that neutrophil differentiation is driven by C/EBPα and Gfi1 rather than PU.1. This prediction is supported by the sequence of gene up-regulation in an independent mouse bone marrow scRNA-Seq dataset. These results imply that neutrophil differentiation is not driven by the Gata1-PU.1 switch but by neutrophil-specific genes instead. More generally, this work shows that data-driven dynamical modeling can uncover the causality of developmental events that might otherwise be obscured.
Discovery of novel ID2 antagonists from pharmacophore-based virtual screening as potential therapeutics for glioma
Authors: Zhong Genshen , Wang Yichun , Wang Qi , Wu Minna , Liu Yichuang , Sun Shitao , Li Zhenli , Hao Jinle , Dou Peiyuan , Lin Bin 
Published: May 2021

AbstractGlioma, especially the most aggressive type glioblastoma multiforme, is one of the central nervous system malignant cancer with a poor prognosis. Traditional treatments are mainly surgery combined with radiotherapy and chemotherapy, which is still not satisfactory. Therefore, it is of great clinical significance to find new therapeutic agents. Served as an inhibitor of differentiation, protein ID2 (inhibitor of DNA binding 2) plays an important role in neurogenesis, neovascularization and malignant development of gliomas. It has been shown that ID2 affects the malignant progression of gliomas through different mechanisms. In this study, a pharmacophore-based virtual screening was carried out and 16 hit compounds were purchased for pharmacological evaluations on their ID2 inhibitory activities. Based on the cytotoxicity of these small-molecule compounds, two compounds were shown to effectively inhibit the viability of glioma cells in the low micromolar range. Among them, AK-778-XXMU was chosen for further study due to its better solubility in water. A SPR assay proved the high affinity between AK-778-XXMU and ID2 protein with the KD value as 129 nM. The plausible binding mode in the biding site of ID2 was studied by molecular docking. Subsequently, the cancer-suppressing potency of the compound was characterized both in vitro and in vivo. The data demonstrated that compound AK-778-XXMU is a potent ID2 antagonist which has the potential to be developed as a therapeutic agent against glioma.HighlightsTwo pharmacophores were built from the first-in-class pan-ID antagonists AGX51A pharmacophore-based virtual screening was carried out and 16 hit compounds were purchased for pharmacological evaluations in glioma inhibitionCompound AK-778-XXMU was identified to be a potent ID2 antagonist in the low submicromolar range (KD: 159 nM)
High-precision mapping of nuclear pore-chromatin interactions reveals new principles of genome organization at the nuclear envelope
Authors: Tyagi Swati , Capitanio Juliana S. , Xu Jiawei , Chen Fei , Sharma Rahul , Huang Jialiang , Hetzer Martin W. 
Published: May 2021

ABSTRACTThe role of nuclear pore complexes (NPCs) in genome organization remains poorly characterized due to technical limitations in probing genome-wide protein-DNA interactions specific to the nuclear periphery. Here, we developed a new sensitive method, NPC-DamID, which combinesin vitroreconstitution of nuclear import and DamID technology. The fixation-free method identifies chromatin interactions at the NPCs in intact nuclei from cells and tissues. We found that NPCs are preferentially associated with common and hierarchically arranged super-enhancers (SEs) across multiple cell types. We also uncovered phase-separated condensates at NPCs that compartmentalize and concentrate transcriptional coactivators and structural proteins at SE-regulated genes. Our results support NPCs as anchoring sites for SE regulatory hubs and cell-type-specific transcriptional control.
Dynamic Interactions of Fully Glycosylated SARS-CoV-2 Spike Protein with Various Antibodies
Authors: Cao Yiwei , Choi Yeol Kyo , Frank Martin , Woo Hyeonuk , Park Sang-Jun , Yeom Min Sun , Seok Chaok , Im Wonpil 
Published: May 2021

ABSTRACTThe spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents a public health crisis, and the vaccines that can induce highly potent neutralizing antibodies are essential for ending the pandemic. The spike (S) protein on the viral envelope mediates human angiotensin-converting enzyme 2 (ACE2) binding and thus is the target of a variety of neutralizing antibodies. In this work, we built various S trimer-antibody complex structures on the basis of the fully glycosylated S protein models described in our previous work, and performed all-atom molecular dynamics simulations to get insight into the structural dynamics and interactions between S protein and antibodies. Investigation of the residues critical for S-antibody binding allows us to predict the potential influence of mutations in SARS-CoV-2 variants. Comparison of the glycan conformations between S-only and S-antibody systems reveals the roles of glycans in S-antibody binding. In addition, we explored the antibody binding modes, and the influences of antibody on the motion of S protein receptor binding domains. Overall, our analyses provide a better understanding of S-antibody interactions, and the simulation-based S-antibody interaction maps could be used to predict the influences of S mutation on S-antibody interactions, which will be useful for the development of vaccine and antibody-based therapy.
Predicted UV-C Sensitivity of Human and Non-human Vertebrate (+) ssRNA Viruses
Authors: Pendyala Brahmaiah , Patras Ankit 
Published: May 2021

AbstractEpidemic and pandemic infectious diseases caused by RNA viruses constitute a significant hazard to human and animal health. Disinfection is an essential aspect of infection prevention and control measures. In this study, we estimated UV-C sensitivity of 83 human and veterinary pathogenic (+) ssRNA viruses by developed pyrimidine dinucleotide frequency-based genomic model. The data showed that the avian infectious bronchitis virus (genus: γ-coronavirus) with an estimated D90 value of 17.8 J/m2 was highly UV sensitive, whereas Salivirus NG-J1 (genus: salivirus) with a D90 value of 346.4 J/m2 was highly UV resistant. Overall, the trend of UV-C sensitivity of (+) ssRNA virus families followed as Coronaviridae < Flaviviridae < Togadoviridae < Arteriviridae, Matonaviridae, Astroviridae < Caciviridae < Picornaviridae < Nodaviridae < Herpeviridae. The results revealed that the enveloped viral families (Coronaviridae, Flaviviridae, Togadoviridae Arteriviridae, and Matonaviridae) are more UV-C sensitive than other nonenveloped families. Further validation of the model estimated UV sensitivity with literature available experimental data showed good agreement of predicted values. The estimates presented here could make it possible to reasonably predict UV-C disinfection efficiency of human and veterinary pathogenic viruses, which need specific biosafety requirements and/or difficult to cultivate in lab conditions.
Willingness to pay for traditional, complementary and alternative medicine (TCAM) among Malaysian adults
Authors: Koh M.Z.Y. , Goh Yen-Nee 
Published: May 2021

Purpose
Health plays a crucial role in the daily lives and supporting health is the important role of medicine. With the availability of traditional, complementary and alternative medicine (TCAM), the demands and willingness to pay among users are increasing. Hence, this study aims to determine the psychological factors influencing the willingness to pay for TCAM among Malaysian adults.

Design/methodology/approach
In total, 300 completed self-administered questionnaires were collected from Malaysian adults using a purposive sampling method through intercepts at public health-care facilities. A structural equation modelling approach using partial least square was used to test the hypotheses.

Findings
The findings show that attitude, subjective norms, perceived price and knowledge have a significant impact on willingness to pay for TCAM. Surprisingly, there was no relationship found between perceived behavioural control and health consciousness on willingness to pay for TCAM.

Originality/value
The findings of this study are expected to provide better insights into TCAM use among Malaysian adults. The results are also important to encourage health-care institutions and practitioners to educate the general public on the safety of TCAM to ensure more health benefits to the users.
The cubic S-curve relationship between board independence and intellectual capital efficiency: does firm size matter?
Authors: Kweh Qian Long , Lu Wen-Min , Ting Irene Wei Kiong , Thi My Le Hanh 
Published: May 2021

PurposeFirst, this study assesses firms’ efficiency of transforming intellectual capital (IC) components into firm performance. Second, this study examines (1) cubic S-curve relationship between board independence and IC efficiency and (2) how firm size moderates the cubic S-curve relationship.Design/methodology/approachThis study employs a stochastic nonparametric envelopment of data (StoNED) framework to estimate IC efficiency, which is derived from the estimation process of transforming structural, relational and human capitals into accounting- and market-based performance indicators. This study conducts regression analyses on 1,104 firm-year observations of Taiwanese semiconductor firms over the period of 2011–2018.FindingsStoNED results suggest that sample firms' IC efficiency can be relatively improved by approximately 80%. Regression results indicate that a cubic S-curve relationship between board independence and IC efficiency exists, and firm size moderates the nonlinear effects.Practical implicationsOverall, this study highlights the importance of examining the nonlinear effect of board independence on IC efficiency from the perspective of agency theory, and the moderating effect from firm size, which may suggest availability of resources from the resource-based view of the firm.Originality/valueThis study contributes to the literature through the innovative application of an efficiency-based tool for evaluating IC efficiency. The cubic S-curve relationship between board independence and IC efficiency also points to the policy concerning the appropriate number of independent directors on board.
How a doer persuade a donor? Investigating the moderating effects of behavioral biases in donor acceptance of donation crowdfunding
Authors: P. Theerthaana , Lysander Manohar Hansa 
Published: May 2021

Purpose
The concept of donation crowdfunding has been drawing enormous attention as it connects donors worldwide in a shorter time at a relatively lower cost. This paper aims to integrate two unified theories, namely, behavioral finance and unified theory of acceptance and use of technology model, to investigate on the motivators and deterrents that influence prospective donors to adopt and use donation crowdfunding. The study also substantiates the significance of donors’ behavioral biases through the moderating effect in the crowdfunding adoption process.

Design/methodology/approach
The study used survey method for data collection and the data set was obtained from the sample of respondents belonging to India and Bangladesh. The proposed structural equation modeling is tested using SPSS 23.0 and AMOS 23.0.

Findings
The study reveals that performance expectancy, effort expectancy, facilitating conditions and trust significantly enhance the intention to adopt donation crowdfunding. Also, biases including overconfidence bias, herding bias and regret aversion bias are found to have significant moderating effects on the relationship between the behavioral intention to adopt donation crowdfunding and use behavior.

Practical implications
By investigating motivators and deterrents of the adoption of donation crowdfunding, the study renders lucrative insights for the donation crowdfunders in devising a donation fundraising campaign that motivates the prospective donors to provide financial contribution.

Originality/value
The study establishes its novelty in explaining the adoption behavior of donation crowdfunding with behavioral bias moderators as a theoretical paradigm. Furthermore, the unified theory of acceptance and use of technology model is extended by introducing, the variable “trust,” while studying the adoption behavior of donation crowdfunding.
Synthesis and evaluation of epoxy resin modified by hyperbranched polyester of caprylic acid and hexanoic acid
Authors: Shi Jiahao , Weng Ling , Wang Xiaoming , Sun Xue , Du Shuqiang , Gao Feng , Zhang Xiaorui 
Published: May 2021

PurposeEpoxy resin (EP) is a kind of thermosetting resin, and its application is usually limited by poor toughness. In this case, a type of new flexible chain blocking hyperbranched polyester (HBP) was designed and synthesized. The purpose of this study is to enhance the toughness and dielectric properties of EP.Design/methodology/approachP-toluene sulfonic acid was used as the catalyst, with dimethy propionic acid as the branch unit and pentaerythritol as the core in the experiment. Then,n-hexanoic acid andn-caprylic acid were, respectively, put to gain HBP with an-hexanoic acid andn-caprylic acid capped structure. The microstructure, mechanical properties, insulation properties and dielectric properties of the composite were characterized for the purpose of finding the appropriate proportion of HBP.FindingsHBP enhanced the toughness of epoxy-cured products by interpenetrating polymer network structure between the flexible chain of HBP and the EP molecular chain. Meanwhile, HBP reduced the ε and tgδ of the epoxy anhydride-cured product by reducing the number of polar groups per unit volume of the EP through free volumes.Research limitations/implicationsYet EP is a kind of thermosetting resin, which is widely used in coating, aerospace, electronics, polymer composites and military fields, but it is usually limited by poor toughness. In a word, it is an urgent priority to develop new EP with better toughness and mechanical properties.Originality/valueAt present, HBP has been applied as a new kind of toughening strategy and as a modifier for EP. According to the toughening mechanism of HBP modified EP, the free volume of HBP creates a space for the EP molecule to move around when loaded. Moreover, the free volume could cause the dielectric constant of EP to diminish by reducing the content of polarizable groups. Meanwhile, the addition of HBP with flexible chains grafted to the EP could develop an interpenetrating network structure, thus further enhancing the toughness of EP
Household‐level poverty, consumption poverty thresholds, income inequality and quality of lives in sub‐Saharan Africa
Authors: Ekeocha Davidmac O. , Iheonu Chimere O. 
Published: May 2021

AbstractPoverty and income inequality are the twin greatest menaces in sub‐Saharan Africa (SSA) with significant prevalence. This study decomposed their significance and probabilities in determining the quality of life (QOL) of individuals and households in SSA while controlling for household demographics. The household‐level poverty (HLP) index and the indexes for QOL indicators were constructed from the national demographic and health surveys (DHS) for SSA countries. Data on consumption poverty thresholds and income inequality are from the global consumption and income project (GCIP) over 1985–2015. Findings reveal that, while the HLP index elicits more rural poverty across regions, consumption poverty thresholds are more urbanized and persistent across regions, except for Central Africa. The results for income inequality were found to be Gini −0.4396 and Atkinson index −0.4437, and more urbanized across regions. Furthermore, we found that SSA has made giant strides towards achieving sustainable development goals (SDGs) 6 and 3 but no information on the quality of healthcare. However, abysmal likelihoods exist for SDGs 4, 7 and 8 in SSA. Similarly, abysmal probabilities were also found for households’ access to adequate housing, a good environment and insurance coverage. Indeed, SDG‐4 is more significant for achieving SDG‐8. HLP stand as a significant bottleneck to achieving SDG‐7 and adequate housing, while income inequality mars SDG‐3 targets and sustainable environmental conditions in SSA. Policy options are discussed.
Prognostic tools for survival prediction in advanced cancer patients: A systematic review
Authors: Pobar Isaiah , Job Mary , Holt Tanya , Hargrave Catriona , Hickey Brigid 
Published: May 2021

SummarySurvival prediction for palliative cancer patients by physicians is often optimistic. Patients with a very short life expectancy (<4 weeks) may not benefit from radiation therapy (RT), as the time to maximal symptom relief after treatment can take 4–6 weeks. We aimed to identify a prognostic tool (or tools) to predict survival of less than 4 weeks and less than 3 months in patients with advanced cancer to guide the choice of radiation dose and fractionation. We searched Embase, Medline (EBSCOhost) and CINAHL (EBSCOhost) clinical databases for literature published between January 2008 and June 2018. Seventeen studies met the inclusion criteria and were included in the review. Prediction accuracy at less than 4 weeks and less than 3 months were compared across the prognostic tools. Reporting of prediction accuracy among the different studies was not consistent: the Palliative Prognostic Score (PaP), Palliative Prognostic Index (PPI) and Number of Risk Factors (NRF) best‐predicted survival duration of less than 4 weeks. The PPI, performance status with Palliative Prognostic Index (PS‐PPI), NRF and Survival Prediction Score (SPS) may predict 3‐month survival. We recommend PPI and PaP tools to assess the likelihood of a patient surviving less than 4 weeks. If predicted to survive longer and RT is justified, the NRF tool could be used to determine survival probability less than 3 months which can then help clinicians select dose and fractionation. Future research is needed to verify the reliability of survival prediction using these prognostic tools in a radiation oncology setting.