bluuebunny/crossref_metadata_2025_split huggingface dataset:
Implementing a Blended Learning Approach to Foreign Language Teaching at SPbPU
Authors: Ivanova Ekaterina , Polyakova Marina , Abakumova Maria 
Published: October 2020

Abstract
One of the most significant trends in higher education today is combining conventional classroom instruction with online learning, or blended learning. Implementing blended learning for foreign language teaching has proven effective due to a variety of factors. Universities in Russia also switch to this new teaching model and design online courses to accompany and support traditional full-time teaching. In accordance with the SPbPU Provision on online-learning, a blended course of General English was launched in the Institute of Industrial Management, Economics and Trade (IIMET). The paper describes the aspects of course development and its structure. With the aim of evaluating the pilot blended project, we have conducted a survey to define the complexity level and elicit the most difficult aspects of the course, give account of students’ motivation level and ability to work self-consistently and responsibly, and measure the satisfaction level with the course. Test results show that much bigger number of students in pilot groups got excellent and good grades which increased by 40% compared to the results of control groups. Although some aspects of the experimental course were rather challenging and caused some complications, the majority of students opted for blended learning model, as it gives more flexibility, convenience and allows students to achieve good learning outcomes.
The Higher School Community Resilience as a Predictor of Efficient System of Education
Authors: Valieva F 
Published: October 2020

Abstract
Over the last two decades, terms connected with efficient functioning emerged within the education paradigm. The scientific research is aimed at investigating the education community’s resilience as a predictor of efficient educational system. Resilience may be defined as the power or ability to recover readily from adversity, stresses or the like to the original form or elasticity. The study included short theoretical analysis of different schools of thought, self-assessment of students and teachers abilities to discern and identify the constructs of resilience phenomenon. The survey sample included about 150 participants. The set of questionnaires the respondents were asked to complete consisted of three sections, including assessment, discerning the resilient behaviour and respondents’ personal information. The empirical study findings included the description of the sample in terms of resilience level; correlation analysis identifying the relationships between factors stated and resilience subscales as well as among all variables; factor analysis reducing the number of variables and extracting principal factors associated with resilience, personal data. Comparison of the results of the three groups identified differences not only on the level of resilience, but also on the characteristics the research project participants endued with teachers and students.
The Disadvantages of Using Scientometric Indicators in the Digital Age
Authors: Grinäv Andrei V. 
Published: October 2020

Abstract
Scientometric data is now widely used in solving personnel issues, grants, and financing universities, as well as monitoring their activities. However, in the course of the study using a variety of theoretical and practical methods, it was found that the main scientometric indicators, such as the total number of publications, citation index, and Hirsch Index, even in the aggregate, can rarely give a relatively objective picture of the real scholarly contribution of a particular researcher or team. Therefore, the problem of expanding the list of basic scientometric indicators and the use of new scientometric instruments (as an example, the quartile index developed by the author) becomes more urgent than ever. It is necessary to abandon the indexing of “Collider publications,” self-citations, and negative links and go from the calculation of single citations to the full accounting of all references to the author’s work. In the era of digital technology it will not be difficult to implement at least some of such recommendations. After all, the more diverse the scientometric indicators are, the more fully and more accurately they will reflect the publication activity of an individual author, team, or university as a whole.
Tracking Primary Dendritic Arm Evolution through Single Crystal CMSX-10® Ni-Base Superalloy
Authors: Strickland J , Nenchev B , Perry S , Tassenberg K , Dong HB 
Published: October 2020

Abstract
A novel packing pattern and local primary dendrite arm spacing (PDAS) algorithm, Shape-Limited Primary Spacing (SLPS), is applied to single crystal CMSX-10® Ni-base superalloy. The characterisation technique is rapid, repeatable and comparable, enabling the standardisation of single crystal microstructure analysis. It is deduced, that the increase in local PDAS range observed within the last to solidify sections of the CMSX-10® sample used this study, is due to lateral macrosegregation. SLPS enables quantification of the influence of the processing parameters on resultant packing patterns and local PDAS distribution. Through refinement of local PDAS, deleterious non-hexagonal pattern formation can be reduced. SLPS permits the optimisation of single crystal microstructure and the development of superior quality components.
Experimental analysis on tribological properties and lubricating mechanisms of oil-based Al2O3 nanofluids applied on steel-brass frictional pairs
Authors: Yang Jinlin , Wang Bingxu , Barber Gary C. , Hu Ming , Na Tyrer , Yang Fan 
Published: October 2020

Abstract
The present research studied the tribological properties of Al2O3 nanofluids applied on steel-brass contacts. It was found that the coefficient of friction could be stabilized and reduced by approximately 40.7% after adding Al2O3 nanoparticles. The wear width and wear depth could be reduced by up to 39.6% and 69.3% while using 1 wt% Al2O3 nanofluid. By examining the worn surfaces, Al2O3 nanoparticles were embedded into brass by external load to form a hard case, which could protect the brass surfaces from wear loss. In addition, some black spots with low shear resistance containing high carbon content were formed which were the result of the presence of oleic acid. In summary, the excellent lubricating performance of Al2O3 nanofluids was mainly associated with the physical embedment of Al2O3 nanoparticles and tribo-films of oleic acid. The results obtained in this research can be used to enhance the performance and extend the service life of some engineering components such as journal bearings with inner brass bushings.
Enhanced mechanical properties are possible with urethane dimethacrylate-based experimental restorative dental composite
Authors: Khan Aftab Ahmed , AlKhureif Abdulaziz Abdullah , Mohamed Badreldin A , Bautista Leonel S J 
Published: October 2020

Abstract
This laboratory study aimed to replace the traditional bisphenol A-glycidyl methacrylate (bisGMA) resin system with a urethane dimethacrylate (UDMA)-based resin system to enhance the physicomechanical properties of dental restorative composite (DRC). We evaluated surface roughness, porosity %, nanohardness, elastic modulus, and compressive strength (CS) in the control group (the bisGMA-resin system) and two experimental groups (UDMA-based resin systems with 20 wt.% SiO2 or Al2O3, each 30 μm in size spherical fillers). Cylindrically shaped samples 4 mm in diameter and 6 mm in height were fabricated using light curing (n = 10). One-way analysis of variance method was used to statistically analyze the results (p ≤ 0.05). The data suggest that both UDMA-based resin systems showed increased surface roughness (Ra) and porosity % in the sample fabrication compared with the control. However, the nanohardness in both SiO2-based UDMA composite (0.20 ± 0.09 GPa) and Al2O3-based UDMA composite (0.22 ± 0.07 GPa) was not significantly higher compared with the control (0.19 ± 0.05 GPa). Likewise, while improved CS values were observed in both SiO2-based UDMA composite (15.5 ± 1.9 MPa) and Al2O3-based UDMA composite (16.2 ± 0.7 MPa) compared to the control (14.5 ± 4.5 MPa), this increase was not statistically significant. The newly developed UDMA-based resin formulation with Al2O3 micro-filler showed promising physicomechanical properties and may be appropriate for use as DRC.
Effect of weak Mn doping on optical properties of ZnGeP2 single crystal grown by vertical gradient freezing method
Authors: Yang Deng-Hui , Cao Xin-Ling 
Published: October 2020

Abstract
ZnGeP2 single crystals weakly doped by manganese (Mn) were grown by vertical gradient freezing method from polycrystalline batch which was synthesized by the modified two temperature vapor phase transport method. Energy Disperse Spectroscopy (EDS), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and Fourier Transform Infrared Spectrometry (FTIR) were employed to characterize the doping single crystals. The un-doped and Mn doped single crystals show the same color. XRD results manifest that the obtained Mn doped ZnGeP2 has tetragonal structure with slight lattice distortion and has no segregation. The EDS results indicate a good stoichiometry of ZnGeP2 weakly doped with Mn. From the XPS results, the doped single crystal with no impurity phases were successfully obtained by the vertical gradient freezing method. FTIR spectra show a slight decrease in infrared transmittance of Mn doped single crystals.
Deciphering the mechanism of hafnium oxide nanoparticles perturbation in the bio-physiological microenvironment of catalase
Authors: Ahmad Farooq , Muhmood Tahir , Mahmood Asif 
Published: October 2020

Abstract
Nanoparticles (NPs) are extensively being used in state-of-the-art nano-based therapies, modern electronics, and consumer products, so can be released into the environment with enhancement interaction with humans. Hence, the exposures to these multifunctional NPs lead to changes in protein structure and functionality, raising serious health issues. This study thoroughly investigated the interaction and adsorption of catalase (CAT) with HfO2-NPs by circular dichroism (CD), Fourier transform infrared (FTIR), absorption, and fluorescence spectroscopic techniques. The results indicate that HfO2 NPs cause fluorescence quenching in CAT by a static quenching mechanism. The negative values of Vant Hoff thermodynamic expressions (ΔH

o
, ΔS

o
, and ΔG

o
) corroborate the spontaneity and exothermic nature of static quenching driven by van der Waals forces and hydrogen bonding. Also, FTIR, UV-CD, and UV–visible spectroscopy techniques confirmed that HfO2 NPs binding could induce microenvironment perturbations leading to secondary and tertiary conformation changes in CAT. Furthermore, synchronous fluorescence spectroscopy confirmed the significant changes in the microenvironment around tryptophan (Trp) residue caused by HfO2 NPs. The time depending denaturing of CAT biochemistry through HfO2-NPs was investigated by assaying catalase activity elucidates the potential toxic action of HfO2-NPs at the macromolecular level. Briefly, this provides an empathetic knowledge of the nanotoxicity and likely health effects of HfO2 NPs exposure.
Association of Mammography Screening With a Reduction in Breast Cancer Mortality: A Modeling Study Using Population-Based Data From 2 French Departments
Authors: Seigneurin Arnaud , Exbrayat Catherine , Molinié Florence , Croisier Lucie , Poncet Florence , Berquet Karine , Delafosse Patricia , Colonna Marc 
Published: October 2020

Abstract
Meta-analyses of randomized controlled trials that started from 1963 to 1991 reported a decrease of breast cancer mortality, associated with mammography screening. However, the effectiveness of population-based screening programs conducted currently might have changed due to the higher effectiveness of treatments for late-stage cancers and the better diagnostic performance of mammography. The main objective of this study was to predict the reduction of breast cancer mortality associated with mammography screening in the current French setting. We compared breast cancer mortality in 2 simulated cohorts of women, which differed from each other solely in a 70% biennial participation in screening from 50 to 74 years old. The microsimulation model used for predictions was calibrated with incidence rates of breast cancer according to stage that were observed in Isère and Loire-Atlantique departments, France, in 2007–2013. The model predicted a decrease of breast cancer mortality associated with mammography screening of 18% (95% CI: 5, 31) and 17% (95% CI: 3, 29) for models calibrated with data from Isère and Loire-Atlantique departments, respectively. Our results highlight the interest in biennial mammography screening from ages 50 to 74 years old to decrease breast cancer mortality in the current setting, despite improvements in treatment effectiveness.
Hypercrisy and standing to self-blame
Authors: Tierney Hannah 
Published: October 2020

Abstract
In a 2020 article in Analysis, Lippert-Rasmussen argues that the moral equality account of the hypocrite’s lack of standing to blame fails. To object to this account, Lippert-Rasmussen considers the contrary of hypocrisy: hypercrisy. In this article, I show that if hypercrisy is a problem for the moral equality account, it is also a problem for Lippert-Rasmussen’s own account of why hypocrites lack standing to blame. I then reflect on the hypocrite’s and hypercrite’s standing to self-blame, which reveals that the challenge hypercrisy poses for accounts of standing is different from the challenge Lippert-Rasmussen articulates.
AlgPred 2.0: an improved method for predicting allergenic proteins and mapping of IgE epitopes
Authors: Sharma Neelam , Patiyal Sumeet , Dhall Anjali , Pande Akshara , Arora Chakit , Raghava Gajendra P S 
Published: October 2020

AbstractAlgPred 2.0 is a web server developed for predicting allergenic proteins and allergenic regions in a protein. It is an updated version of AlgPred developed in 2006. The dataset used for training, testing and validation consists of 10 075 allergens and 10 075 non-allergens. In addition, 10 451 experimentally validated immunoglobulin E (IgE) epitopes were used to identify antigenic regions in a protein. All models were trained on 80% of data called training dataset, and the performance of models was evaluated using 5-fold cross-validation technique. The performance of the final model trained on the training dataset was evaluated on 20% of data called validation dataset; no two proteins in any two sets have more than 40% similarity. First, a Basic Local Alignment Search Tool (BLAST) search has been performed against the dataset, and allergens were predicted based on the level of similarity with known allergens. Second, IgE epitopes obtained from the IEDB database were searched in the dataset to predict allergens based on their presence in a protein. Third, motif-based approaches like multiple EM for motif elicitation/motif alignment and search tool have been used to predict allergens. Fourth, allergen prediction models have been developed using a wide range of machine learning techniques. Finally, the ensemble approach has been used for predicting allergenic protein by combining prediction scores of different approaches. Our best model achieved maximum performance in terms of area under receiver operating characteristic curve 0.98 with Matthew’s correlation coefficient 0.85 on the validation dataset. A web server AlgPred 2.0 has been developed that allows the prediction of allergens, mapping of IgE epitope, motif search and BLAST search (https://webs.iiitd.edu.in/raghava/algpred2/).
Text mining approaches for dealing with the rapidly expanding literature on COVID-19
Authors: Wang Lucy Lu , Lo Kyle 
Published: October 2020

AbstractMore than 50 000 papers have been published about COVID-19 since the beginning of 2020 and several hundred new papers continue to be published every day. This incredible rate of scientific productivity leads to information overload, making it difficult for researchers, clinicians and public health officials to keep up with the latest findings. Automated text mining techniques for searching, reading and summarizing papers are helpful for addressing information overload. In this review, we describe the many resources that have been introduced to support text mining applications over the COVID-19 literature; specifically, we discuss the corpora, modeling resources, systems and shared tasks that have been introduced for COVID-19. We compile a list of 39 systems that provide functionality such as search, discovery, visualization and summarization over the COVID-19 literature. For each system, we provide a qualitative description and assessment of the system’s performance, unique data or user interface features and modeling decisions. Many systems focus on search and discovery, though several systems provide novel features, such as the ability to summarize findings over multiple documents or linking between scientific articles and clinical trials. We also describe the public corpora, models and shared tasks that have been introduced to help reduce repeated effort among community members; some of these resources (especially shared tasks) can provide a basis for comparing the performance of different systems. Finally, we summarize promising results and open challenges for text mining the COVID-19 literature.
Next generation sequencing of SARS-CoV-2 genomes: challenges, applications and opportunities
Authors: Chiara Matteo , D’Erchia Anna Maria , Gissi Carmela , Manzari Caterina , Parisi Antonio , Resta Nicoletta , Zambelli Federico , Picardi Ernesto , Pavesi Giulio , Horner David S , Pesole Graziano 
Published: October 2020

AbstractVarious next generation sequencing (NGS) based strategies have been successfully used in the recent past for tracing origins and understanding the evolution of infectious agents, investigating the spread and transmission chains of outbreaks, as well as facilitating the development of effective and rapid molecular diagnostic tests and contributing to the hunt for treatments and vaccines. The ongoing COVID-19 pandemic poses one of the greatest global threats in modern history and has already caused severe social and economic costs. The development of efficient and rapid sequencing methods to reconstruct the genomic sequence of SARS-CoV-2, the etiological agent of COVID-19, has been fundamental for the design of diagnostic molecular tests and to devise effective measures and strategies to mitigate the diffusion of the pandemic.Diverse approaches and sequencing methods can, as testified by the number of available sequences, be applied to SARS-CoV-2 genomes. However, each technology and sequencing approach has its own advantages and limitations. In the current review, we will provide a brief, but hopefully comprehensive, account of currently available platforms and methodological approaches for the sequencing of SARS-CoV-2 genomes. We also present an outline of current repositories and databases that provide access to SARS-CoV-2 genomic data and associated metadata. Finally, we offer general advice and guidelines for the appropriate sharing and deposition of SARS-CoV-2 data and metadata, and suggest that more efficient and standardized integration of current and future SARS-CoV-2-related data would greatly facilitate the struggle against this new pathogen. We hope that our ‘vademecum’ for the production and handling of SARS-CoV-2-related sequencing data, will contribute to this objective.
Self-supervised feature extraction from image time series in plant phenotyping using triplet networks
Authors: Marin Zapata Paula A , Roth Sina , Schmutzler Dirk , Wolf Thomas , Manesso Erica , Clevert Djork-Arné 
Published: October 2020

Abstract

Motivation
Image-based profiling combines high-throughput screening with multiparametric feature analysis to capture the effect of perturbations on biological systems. This technology has attracted increasing interest in the field of plant phenotyping, promising to accelerate the discovery of novel herbicides. However, the extraction of meaningful features from unlabeled plant images remains a big challenge.

Results
We describe a novel data-driven approach to find feature representations from plant time-series images in a self-supervised manner by using time as a proxy for image similarity. In the spirit of transfer learning, we first apply an ImageNet-pretrained architecture as a base feature extractor. Then, we extend this architecture with a triplet network to refine and reduce the dimensionality of extracted features by ranking relative similarities between consecutive and non-consecutive time points. Without using any labels, we produce compact, organized representations of plant phenotypes and demonstrate their superior applicability to clustering, image retrieval and classification tasks. Besides time, our approach could be applied using other surrogate measures of phenotype similarity, thus providing a versatile method of general interest to the phenotypic profiling community.

Availability and implementation
Source code is provided in https://github.com/bayer-science-for-a-better-life/plant-triplet-net.

Supplementary information
Supplementary data are available at Bioinformatics online.
Leveraging heterogeneous network embedding for metabolic pathway prediction
Authors: M A Basher Abdur Rahman , Hallam Steven J 
Published: October 2020

AbstractMotivationMetabolic pathway reconstruction from genomic sequence information is a key step in predicting regulatory and functional potential of cells at the individual, population and community levels of organization. Although the most common methods for metabolic pathway reconstruction are gene-centric e.g. mapping annotated proteins onto known pathways using a reference database, pathway-centric methods based on heuristics or machine learning to infer pathway presence provide a powerful engine for hypothesis generation in biological systems. Such methods rely on rule sets or rich feature information that may not be known or readily accessible.ResultsHere, we present pathway2vec, a software package consisting of six representational learning modules used to automatically generate features for pathway inference. Specifically, we build a three-layered network composed of compounds, enzymes and pathways, where nodes within a layer manifest inter-interactions and nodes between layers manifest betweenness interactions. This layered architecture captures relevant relationships used to learn a neural embedding-based low-dimensional space of metabolic features. We benchmark pathway2vec performance based on node-clustering, embedding visualization and pathway prediction using MetaCyc as a trusted source. In the pathway prediction task, results indicate that it is possible to leverage embeddings to improve prediction outcomes.Availability and implementationThe software package and installation instructions are published on http://github.com/pathway2vec.Supplementary informationSupplementary data are available at Bioinformatics online.