bluuebunny/crossref_metadata_2025_split huggingface dataset:
Developing a nanostructured surface layer on AISI 316 stainless steel by ultrasonic surface nanocrystallization and evaluating its tribological properties
Authors: Ballam Masoud Hosseini , Karimzadeh Fathallah , Enayati Mohammad Hossein , Sanati Alireza 
Published: March 2021

Abstract
Austenitic stainless steels have received a lot of attention for a wide range of applications, including petrochemical, automotive industry, building architecture, and bioengineering; however, their poor mechanical properties such as high wear rate are always challenging. In this research, ultrasonic surface nanocrystallization treatment (USNT) has been used for surface modification and improvement of tribological characteristics of AISI 316 stainless steel. For this purpose, 375 N accompanied with 20 kHz vibration frequency was applied on the surface to provide static and dynamic forces simultaneously, increase dislocations density, and fabricate a nanocrystalline surface. After this process, the untreated and USN-treated specimens were characterized by optical microscopy, scanning electron microscopy, transmission electron microscopy, x-ray diffraction analysis, Vickers microhardness measurements, and surface profilometry. Also, a reciprocating pin-on-plate test was used to evaluate the tribological features of the surface. After the wear test, the untreated sample showed approximately twice weight loss, in comparison with the USN-treated specimen. The friction coefficient during 500 m sliding was reduced from around 1 (for the untreated specimen) to 0.4 after USNT. Also, the SEM investigations showed that the abrasive wear is decreased after USNT. The improved tribological properties of AISI 316 in this research was attributed to the residual compressive stress, nanocrystallization, strain-induced phase transformation, microhardness enhancement, and surface roughness reduction after USNT.
Investigation of magnesium addition in ZnO matrix using group II heptahydrate
Authors: Hussain Hadba , Albrithen Hamad A , Alshammari Abeer , Alyamani Ahmed , Bano Nargis , Alyemni Sarah Nasser , AlAhmary Shareefah Ayed , Alanzi Ali , Almutairy Baderah Awad 
Published: March 2021

Abstract
ZnO and MgZnO nanoparticles were prepared by the co-precipitation method utilizing zinc sulfate heptahydrate and magnesium sulfate heptahydrate; structural measurements were also carried out. An x-ray diffraction (XRD) study indicated that no peaks for other possible phases such as MgO or MgZn intermetallic compounds indicating pure wurtzite structure. All nanoparticles crystallized in a hexagonal wurtzite structure with different orientation diffraction peaks; the main peaks were (100), (002), and (101). Grain size (D) increased with increasing Mg concentrations. A scanning electron microscopy (SEM) analysis revealed that nanoparticle size increased by increasing the Mg concentration in a good qualitative with Scherrer equation and not only the size even the grain shape changed. In addition, optical measurements were taken infer that the band gap energy (Eg), extracted from Tauc’s plot, decreases with increasing of the Mg concentration doped, and found to be between 3.255 eV and 3.169 eV. The photoluminescence (PL) emission spectra show two peaks at the ultraviolet and green regions.
Effect of (Ti, Nb)(C, N) solid-solution powder on microstructure and mechanical properties of Ti(C, N)-based cermets: key experiment aided by thermodynamic calculations
Authors: Lv Jian , Zhang Cong , Zhang Weibin , Long Jianzhan , Yue Yang , Zheng Zhenghui , Du Yong 
Published: March 2021

Abstract
The preparation of Ti(C, N)-based cermets using (Ti, Me)(C, N) solid-solution powder instead of the traditional Ti(C, N) + MeC mixtures has gradually become a popular research subject. By using a (Ti, Nb)(C, N) solid-solution powder and Ti(C, N) + NbC powder, the microstructures and mechanical properties of Ti(C, N)-based cermets were investigated in the present work with the guidance of thermodynamic calculations. The thermodynamic calculations indicated that the (Ti, Nb)(C, N) was thermodynamically less stable than the Ti(C, N), which led to a difference in the microstructures and properties of the cermets prepared using (Ti, Nb)(C, N) and Ti(C, N) + NbC. The cermet prepared using the (Ti, Nb)(C, N) solid-solution powder had a no/weak core–rim structure. Moreover, the surface binder-rich phases of the cermets prepared by the (Ti, Nb)(C, N) solid solution was thinner under vacuum sintering, which was beneficial for improving the surface quality of the cermets. The hardness values of the cermets prepared using (Ti, Nb)(C, N) were greater than those of the cermets prepared using Ti(C, N) + NbC, and the fracture toughness underwent no noticeable change.
Controllable preparation of trifold nanorods and nanospheres of cadmium telluride via solvothermal process
Authors: Kang Weifeng , Li Shuo , Wang Cong , Zhang Linhui , Zhong Binnian 
Published: March 2021

Abstract
In this study, trifold CdTe nanorods and CdTe nanospheres were prepared by the environmentally benign solvothermal process where K2TeO3 and CdCl2 acting as the raw materials, L-ascorbic acid as the reducing agent, ethylene glycol as the solvent and polyvinylpyrrolidone as the surfactant, respectively. The morphology of the products can be regulated by the amount of KOH. Specifically, trifold CdTe nanorods could be prepared with 0.1 g of KOH before the reaction; while CdTe nanospheres were obtained with 0.5 g of KOH. The higher the amount of KOH added, the stronger the reducibility of L-ascorbic acid. For higher reducibility, CdTe crystal nuclei which was continuously generated will first nucleate and grow in the gaps between the rods of the trifold nanorods because of the special morphology and structure of the trifold nanorods. Therefore, with the increasing of the number of CdTe crystal nuclei caused by the accelerated reaction rate, the trifold nanorods will gradually evolve into nanospheres.
Extracting electron scattering cross sections from swarm data using deep neural networks
Authors: Jetly Vishrut , Chaudhury Bhaskar 
Published: March 2021

Abstract
Electron-neutral scattering cross sections are fundamental quantities in simulations of low temperature plasmas used for many technological applications today. From these microscopic cross sections, several macro-scale quantities (called ‘swarm’ parameters) can be calculated. However, measurements as well as theoretical calculations of cross sections are challenging. Since the 1960s, researchers have attempted to solve the inverse swarm problem of obtaining cross sections from swarm data; but the solutions are not necessarily unique. To address these issues, we examine the use of deep learning models which are trained using the previous determinations of elastic momentum transfer, ionization and excitation cross sections for different gases available on the LXCat website and their corresponding swarm parameters calculated using the BOLSIG+ solver for the numerical solution of the Boltzmann equation for electrons in weakly ionized gases. We implement artificial neural network (ANN), convolutional neural network (CNN) and densely connected convolutional network (DenseNet) for this investigation. To the best of our knowledge, there is no study exploring the use of CNN and DenseNet for the inverse swarm problem. We test the validity of predictions by all these trained networks for a broad range of gas species and we deduce that DenseNet effectively extracts both long and short term features from the swarm data and hence, it predicts cross sections with significantly higher accuracy compared to ANN. Further, we apply Monte Carlo dropout as Bayesian approximation to estimate the probability distribution of the cross sections to determine all plausible solutions of this inverse problem.
Data-Driven Model Building for Life-Course Epidemiology
Authors: Petersen Anne H , Osler Merete , Ekstrøm Claus T 
Published: March 2021

AbstractLife-course epidemiology is useful for describing and analyzing complex etiological mechanisms for disease development, but existing statistical methods are essentially confirmatory, because they rely on a priori model specification. This limits the scope of causal inquiries that can be made, because these methods are suited mostly to examine well-known hypotheses that do not question our established view of health, which could lead to confirmation bias. We propose an exploratory alternative. Instead of specifying a life-course model prior to data analysis, our method infers the life-course model directly from the data. Our proposed method extends the well-known Peter-Clark (PC) algorithm (named after its authors) for causal discovery, and it facilitates including temporal information for inferring a model from observational data. The extended algorithm is called temporal PC. The obtained life-course model can afterward be perused for interesting causal hypotheses. Our method complements classical confirmatory methods and guides researchers in expanding their models in new directions. We showcase the method using a data set encompassing almost 3,000 Danish men followed from birth until age 65 years. Using this data set, we inferred life-course models for the role of socioeconomic and health-related factors on development of depression.
Discovering novel cancer bio-markers in acquired lapatinib resistance using Bayesian methods
Authors: Azad A K M , Alyami Salem A 
Published: March 2021

Abstract
Signalling transduction pathways (STPs) are commonly hijacked by many cancers for their growth and malignancy, but demystifying their underlying mechanisms is difficult. Here, we developed methodologies with a fully Bayesian approach in discovering novel driver bio-markers in aberrant STPs given high-throughput gene expression (GE) data. This project, namely ‘PathTurbEr’ (Pathway Perturbation Driver) uses the GE dataset derived from the lapatinib (an EGFR/HER dual inhibitor) sensitive and resistant samples from breast cancer cell lines (SKBR3). Differential expression analysis revealed 512 differentially expressed genes (DEGs) and their pathway enrichment revealed 13 highly perturbed singalling pathways in lapatinib resistance, including PI3K-AKT, Chemokine, Hippo and TGF-$\beta $ singalling pathways. Next, the aberration in TGF-$\beta $ STP was modelled as a causal Bayesian network (BN) using three MCMC sampling methods, i.e. Neighbourhood sampler (NS) and Hit-and-Run (HAR) sampler that potentially yield robust inference with lower chances of getting stuck at local optima and faster convergence compared to other state-of-art methods. Next, we examined the structural features of the optimal BN as a statistical process that generates the global structure using $p_1$-model, a special class of Exponential Random Graph Models (ERGMs), and MCMC methods for their hyper-parameter sampling. This step enabled key drivers identification that drive the aberration within the perturbed BN structure of STP, and yielded 34, 34 and 23 perturbation driver genes out of 80 constituent genes of three perturbed STP models of TGF-$\beta $ signalling inferred by NS, HAR and MH sampling methods, respectively. Functional-relevance and disease-relevance analyses suggested their significant associations with breast cancer progression/resistance.
STREME: accurate and versatile sequence motif discovery
Authors: Bailey Timothy L 
Published: March 2021

Abstract

Motivation
Sequence motif discovery algorithms can identify novel sequence patterns that perform biological functions in DNA, RNA and protein sequences—for example, the binding site motifs of DNA- and RNA-binding proteins.

Results
The STREME algorithm presented here advances the state-of-the-art in ab initio motif discovery in terms of both accuracy and versatility. Using in vivo DNA (ChIP-seq) and RNA (CLIP-seq) data, and validating motifs with reference motifs derived from in vitro data, we show that STREME is more accurate, sensitive and thorough than several widely used algorithms (DREME, HOMER, MEME, Peak-motifs) and two other representative algorithms (ProSampler and Weeder). STREME’s capabilities include the ability to find motifs in datasets with hundreds of thousands of sequences, to find both short and long motifs (from 3 to 30 positions), to perform differential motif discovery in pairs of sequence datasets, and to find motifs in sequences over virtually any alphabet (DNA, RNA, protein and user-defined alphabets). Unlike most motif discovery algorithms, STREME reports a useful estimate of the statistical significance of each motif it discovers. STREME is easy to use individually via its web server or via the command line, and is completely integrated with the widely used MEME Suite of sequence analysis tools. The name STREME stands for ‘Simple, Thorough, Rapid, Enriched Motif Elicitation’.

Availability and implementation
The STREME web server and source code are provided freely for non-commercial use at http://meme-suite.org.

Supplementary information
Supplementary data are available at Bioinformatics online.
A convolutional neural network and graph convolutional network-based method for predicting the classification of anatomical therapeutic chemicals
Authors: Zhao Haochen , Li Yaohang , Wang Jianxin 
Published: March 2021

Abstract

Motivation
The Anatomical Therapeutic Chemical (ATC) system is an official classification system established by the World Health Organization for medicines. Correctly assigning ATC classes to given compounds is an important research problem in drug discovery, which can not only discover the possible active ingredients of the compounds, but also infer theirs therapeutic, pharmacological and chemical properties.

Results
In this article, we develop an end-to-end multi-label classifier called CGATCPred to predict 14 main ATC classes for given compounds. In order to extract rich features of each compound, we use the deep Convolutional Neural Network and shortcut connections to represent and learn the seven association scores between the given compound and others. Moreover, we construct the correlation graph of ATC classes and then apply graph convolutional network on the graph for label embedding abstraction. We use all label embedding to guide the learning process of compound representation. As a result, by using the Jackknife test, CGATCPred obtain reliable Aiming of 81.94%, Coverage of 82.88%, Accuracy 80.81%, Absolute True 76.58% and Absolute False 2.75%, yielding significantly improvements compared to exiting multi-label classifiers.

Availability and implementation
The codes of CGATCPred are available at https://github.com/zhc940702/CGATCPred and https://zenodo.org/record/4552917.
SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization
Authors: Yu Yue , Huang Kexin , Zhang Chao , Glass Lucas M , Sun Jimeng , Xiao Cao 
Published: March 2021

Abstract

Motivation
Thanks to the increasing availability of drug–drug interactions (DDI) datasets and large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using machine learning models becomes possible. However, it remains largely an open problem how to effectively utilize large and noisy biomedical KG for DDI detection. Due to its sheer size and amount of noise in KGs, it is often less beneficial to directly integrate KGs with other smaller but higher quality data (e.g. experimental data). Most of existing approaches ignore KGs altogether. Some tries to directly integrate KGs with other data via graph neural networks with limited success. Furthermore most previous works focus on binary DDI prediction whereas the multi-typed DDI pharmacological effect prediction is more meaningful but harder task.

Results
To fill the gaps, we propose a new method SumGNN: knowledge summarization graph neural network, which is enabled by a subgraph extraction module that can efficiently anchor on relevant subgraphs from a KG, a self-attention based subgraph summarization scheme to generate reasoning path within the subgraph, and a multi-channel knowledge and data integration module that utilizes massive external biomedical knowledge for significantly improved multi-typed DDI predictions. SumGNN outperforms the best baseline by up to 5.54%, and performance gain is particularly significant in low data relation types. In addition, SumGNN provides interpretable prediction via the generated reasoning paths for each prediction.

Availability and implementation
The code is available in Supplementary Material.

Supplementary information
Supplementary data are available at Bioinformatics online.
Do colour morphs of wall lizards express different personalities?
Authors: Sreelatha Lekshmi B , Carretero Miguel Angel , Pérez I De Lanuza Guillem , Klomp Danielle A , Boratyński Zbyszek 
Published: March 2021

Abstract
Colour morphs sometimes have different behavioural strategies which may be maintained by frequency or density dependence mechanisms. We investigated temporal changes in behavioural reaction to a novel environment among colour morphs (yellow, orange, white) of the European wall lizard (Podarcis muralis). Adult males were given two 15 min experimental trials, and their locomotion was highly consistent between the two trials. Boldness, freezing and escape behaviour were less repeatable. Colour morphs differed in their locomotion and freezing behaviour. Boldness was similar among the morphs, whereas escape behaviour was lowest in yellow morph. Consequently, yellow morph males tended to explore novel environments quickly and thus were more likely to move to potentially safe areas. Orange and white males showed more fear when exposed to a novel environment. Whether such alternative behavioural strategies can contribute to the maintenance of variable fitness optima among the morphs and ultimately to the maintenance of polymorphism remains open to further investigation.
Acute submassive pulmonary embolism after SARS-CoV-2 infection: a case report of reinfection or prolonged hypercoagulable state
Authors: Pow Timothy , Allen Sorcha , Brailovsky Yevgeniy , Darki Amir 
Published: March 2021

Abstract

Background
Coronavirus disease 19 (COVID-19) reinfection has been a topic of discussion with data still emerging. Viral antibodies are known to develop upon initial infection; however, it is unclear the amount of protection this confers against reinfection. Additionally, COVID-19-associated coagulopathy (CAC) is a well-documented phenomenon; however, there are no high-quality studies to support the treatment of outpatients beyond standard indications of venous thromboembolism (VTE) prophylaxis. This case describes a patient with either COVID-19 reinfection or prolonged course of CAC resulting in pulmonary embolism (PE).

Case summary
A 40-year-old healthy man presented with fever and cough. He tested positive for COVID-19 and was sent home to self-quarantine. His symptoms resolved and repeat COVID-19 testing returned negative. Two months later, he developed dyspnoea on exertion and syncope. Computed tomography with PE protocol demonstrated acute bilateral PE, and repeat COVID-19 testing returned positive. He was escalated to catheter-directed thrombolysis, but prior to the procedure went into cardiopulmonary arrest. Cardiopulmonary resuscitation was initiated and full-dose systemic alteplase was administered. Cardiothoracic surgery was consulted for consideration of veno-arterial extracorporeal membrane oxygenation; however, return of spontaneous circulation was unable to be achieved.

Discussion
This case raises the question of COVID-19 reinfection and prolonged risk of VTE due to CAC. We believe the patient was reinfected with COVID-19 provoking his PE; however, a single COVID-19 infection causing a prolonged course of CAC is possible. Until better data exists, decisions regarding outpatient prophylaxis must be individualized to weigh the risks of bleeding against the risk of thrombosis.
Fathers’ Job Flexibility and Mothers’ Return to Employment
Authors: Buchler Sandra , Lutz Katharina 
Published: March 2021

AbstractOne of the main drivers of gender inequality is the unequal distribution of paid work between men and women, in particular, after the birth of a child. In this study, we examine how a man’s employment flexibility, specifically his weekly hours of employment and schedule autonomy, influence his female partner’s return to employment after the birth of a first child using the German Socio-Economic Panel. Given women’s placement as primary but not solitary carers of young children, it is plausible that women’s attachment to the labour force will increase if their male partner is in a position to take on more of the responsibility for unpaid work. Results indicate that a father’s employment flexibility facilities a mother’s part-time employment. A return to full-time hours, however, is less contingent on the father’s employment circumstances or other external factors. The findings highlight the importance of employing couple-level analyses in examinations of the gender division of labour.
Signal time–frequency representation and decomposition using partial fractions
Authors: Ursin Bjorn , Porsani Milton J 
Published: March 2021

Summary
The Z-transform of a complex time signal (or the analytic signal of a real signal) is equal to the Z-transform of a prediction error divided by the Z-transform of the prediction error operator. This inverse is decomposed into a sum of partial fractions, which are used to obtain impulse response operators formed by non-causal filters that complex-conjugate symmetric coefficients. The time components are obtained by convolving the filters with the original signal, and the peak frequencies, corresponding to the poles of the prediction error operator, are used for mapping the time components into frequency components. For non-stationary signals, this decomposition is done in sliding time windows, and the signal component values, in the middle of each window, are attributed to the peak value of its frequency response that corresponds to the pole of this partial fraction component. The result is an exact, but non-unique, time–frequency representation of the input signal. A sparse signal decomposition can be obtained by summing along the frequency axis in patches with similar characteristics in the time–frequency domain. The peak amplitude frequency of each new time component is obtained by computing a scalar prediction error operator in sliding time windows, resulting in a sparse time–frequency representation. In both cases, the result is a time–frequency matrix where an estimate of the frequency content of the input signal can be obtained by summation over the time variable. The performance of the new method is demonstrated with excellent results on a synthetic time signal, the LIGO gravitational wave signal and seismic field data.