bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ibuprofen Adsorption Study by Langmuir, Freundlich, Temkin and Dubinin-Radushkevich Models Using Nano Zinc Oxide from Mild Hydrothermal Condition
Authors: Ulfa Maria , Iswanti Yuli 
Published: July 2020

Abstract
Ibuprofen in the aquatic area as inflammatory drug residue is dangerous for environmental sustainability. The removal of ibuprofen at the aquatic area is carried out on nano zinc oxide (NZO) as an adsorbent. The NZO adsorbent was synthesized by gelatin self-assembly as a green method and characterized by FT-IR and XRD techniques. The residual ibuprofen model was prepared by dissolution of ibuprofen in water-hexane at ratio 10:0; 1:9; 5:5, 9:1 and 0:10. The optimized batch of experimental parameters were 100 ppm for 55 min at room temperature. Adsorption data was investigated by Langmuir, Freundlich, Temkin and Dubinin-Radushkevich models. The result shows that Temkin model is the best model for describing the adsorption mechanism between ibuprofen as adsorbate and zinc oxide as an adsorbent. The adsorption capacity of 9:1 is the highest on that ibuprofen adsorption indicated that the composition of both solvents is favorable for ibuprofen dissolution due to polar-nonpolar interaction which is reaching 92% removal of ibuprofen. In the future, zinc oxide as adsorbent may be used for the removal of ibuprofen from any aquatic core area due to the economic and environmental reason.
Intracellular delivery of nanoparticles fabricated from monoclonal and polyclonal antibodies against influenza A and hepatitis B virus antigens
Authors: Morozova O.V. , Pavlova E.R. , Isaeva E.I. , Sokolova A.I. , Obraztsova E.A. , Ivleva E.A. , Klinov D.V. 
Published: July 2020

Abstract
Specific antibodies (AB) may serve as molecular tools for both targeted, etiotropic and immunomodulation therapy of the infectious and oncological diseases. Despite their ability to bind with extracellular virions and isolated viral antigens AB may not pass through host cell membranes to inhibit intracellular infectious agents. Nanoparticles (NP) were fabricated from polyclonal and monoclonal AB against the influenza A structural and non-structural proteins as well as against the hepatitis B virus surface antigen (HBsAg). ELISA and immunofluorescent analysis revealed the ligand-binding activity of the AB NP comparable with the properties of the original AB. Fluorescent microscopy and quantitation showed efficient cellular uptake of the labeled AB NP and their degradation in 5 days posttreatment. Intracellular AB NP induced interferon (IFN) gene expression but not interleukin (IL) 4 and IL10 transcription. The cytokines mediated immunomodulation potential of the protein NP.
Analysis of different microcracks shapes and the effect of each shape on performance of PV modules
Authors: Bdour Mathhar , Al-Sadi Aseel 
Published: July 2020

Abstract
Photovoltaic solar panels became the world’s largest distributed Renewable technology through its easy manufacturing and installation, moreover, the cost of photovoltaic panels falls continuously which provided a safe environment to investors to invest in such systems. The Photovoltaic solar panel made from different layers starting from anti reflected coated glass layer, encapsulation material, solar cells matrix another encapsulant layer and back sheet. This structure does not provide the needed protection to the solar cells encapsulated inside and there will be a possibility of microcracks happen at different stages of the project life cycle starting from the production, transportation, installation, cleaning and operation, and maintenance stage. Through this research, modules from different projects were tested to see the microcracks and their effect. Results show that those cracks can cause a power drop up to 3.21% depending on the type of the module. Furthermore, microcracks are classified as minor and major ones based on their shape, dendritic and vertical shapes are resulted to be the most severe ones.
Study of printed polymeric flexible energy harvesting elements – impact of the electrode materials and patterns
Authors: Aleksandrova M , Aepuru R , Dobrikov G 
Published: July 2020

Abstract
In this paper is presented study of flexible pressure piezoelectric energy harvesting developed with an screen-printing of polyvinylidene fluoride-trifluoroethylene (P(VDF-TrFE)) ink on three different metal electrodes (silver, gold and aluminium) with three different topologies – rectangular, meander and side comb. The influence of the electrode type and pattern is studied in terms of polymer coating distribution and morphology, piezoelectric voltage generation and stability at multiple bending, as well as piezoelectric coefficient. The elements are studied at low mass loading of up to 100 g and frequency up to 50 Hz. They produce voltage between 916 and 362 mV from active area of 1 cm2 and piezoelectric polymer film of 3 μm, according to the electrode metal nature and shape. It was found that the silver electrode with meander shape is the optimal for electrical performance and mechanical stability giving superior piezoelectric coefficient of 0.332 V.m/N to the reported values and only 8.6 % decrease of the piezoelectric voltage at 1500 repeating bending cycles.
Laser induced resistivity of chiral single wall carbon nanotubes
Authors: Twum Anthony , Mensah Samuel Yeboah , Edziah Raymond , Arthur Augustine 
Published: July 2020

Abstract
Using the Boltzmann transport equation within the semi-classical approximation with constant relaxation time, we theoretically studied the dynamics of electrons in chiral single wall nanotubes (SWNTs) subjected to a temperature gradient (∇T) in the presence of a combined direct current and high frequency alternating fields. We obtained an expression for the resistivity (ρ

c
) of the SWNTs which varies with temperature and depends among others on material’s chiral angle (θ
h), dc field strength (Eo) and ac field amplitude (Es). Our results show that chiral SWNTs exhibit metallic behavior with resistivity increasing approximately linearly with temperature over a wide temperature range well above 100 K. Based on the low chiral resistivity obtained for the SWNTs at room temperatures, we propose these materials as good candidates for possible optoelectronic applications.
Social Distancing as a Health Behavior: County-Level Movement in the United States During the COVID-19 Pandemic Is Associated with Conventional Health Behaviors
Authors: Bourassa Kyle J , Sbarra David A , Caspi Avshalom , Moffitt Terrie E 
Published: July 2020

Abstract

Background
Social distancing—when people limit close contact with others outside their household—is a primary intervention available to combat the COVID-19 pandemic. The importance of social distancing is unlikely to change until effective treatments or vaccines become widely available. However, relatively little is known about how best to promote social distancing. Applying knowledge from social and behavioral research on conventional health behaviors (e.g., smoking, physical activity) to support public health efforts and research on social distancing is promising, but empirical evidence supporting this approach is needed.

Purpose
We examined whether one type of social distancing behavior—reduced movement outside the home—was associated with conventional health behaviors.

Method
We examined the association between GPS-derived movement behavior in 2,858 counties in USA from March 1 to April 7, 2020 and the prevalence of county-level indicators influenced by residents’ conventional health behaviors.

Results
Changes in movement were associated with conventional health behaviors, and the magnitude of these associations were similar to the associations among the conventional health behaviors. Counties with healthier behaviors—particularly less obesity and greater physical activity—evidenced greater reduction in movement outside the home during the initial phases of the pandemic in the USA.

Conclusions
Social distancing, in the form of reduced movement outside the home, is associated with conventional health behaviors. Existing scientific literature on health behavior and health behavior change can be more confidently used to promote social distancing behaviors during the COVID-19 pandemic.
Bayesian Pathway Analysis for Complex Interactions
Authors: Baurley James W , Kjærsgaard Anders , Zwick Michael E , Cronin-Fenton Deirdre P , Collin Lindsay J , Damkier Per , Hamilton-Dutoit Stephen , Lash Timothy L , Ahern Thomas P 
Published: July 2020

AbstractModern epidemiologic studies permit investigation of the complex pathways that mediate effects of social, behavioral, and molecular factors on health outcomes. Conventional analytical approaches struggle with high-dimensional data, leading to high likelihoods of both false-positive and false-negative inferences. Herein, we describe a novel Bayesian pathway analysis approach, the algorithm for learning pathway structure (ALPS), which addresses key limitations in existing approaches to complex data analysis. ALPS uses prior information about pathways in concert with empirical data to identify and quantify complex interactions within networks of factors that mediate an association between an exposure and an outcome. We illustrate ALPS through application to a complex gene-drug interaction analysis in the Predictors of Breast Cancer Recurrence (ProBe CaRe) Study, a Danish cohort study of premenopausal breast cancer patients (2002–2011), for which conventional analyses severely limit the quality of inference.
How to Write About Superheroes
Authors: Burt Stephanie 
Published: July 2020

AbstractEighty years after the first Superman comic, scholars are catching up to the importance, and to the popularity, of superheroes in comics and in other media. Recent monographs and edited collections examine racial politics, disabilities, other identities, and reception history across a range of decades and of superhero characters. Most of these worthy works remain within the limits of critique, judging the comics on how well they handle one or another theme; the result is a picture of superhero comics that cannot do justice to the genre. To them and to their like, the academic critics of the future might add—what vernacular comics critics already contribute—additional attention to what one or another character does best, to the transformative potential of even minor superhero work, and to how commercially produced superhero comics at their best handle narrative form.One superhero symbol can work many ways, as Neal Curtis’s examination of Truth and other Captain America stories shows: Cap has repeatedly fought off right-wing doubles, alternatively costumed versions of himself who aspired to make America white again.
DNA Features Viewer: a sequence annotation formatting and plotting library for Python
Authors: Zulkower Valentin , Rosser Susan 
Published: July 2020

Abstract

Motivation
Although the Python programming language counts many Bioinformatics and Computational Biology libraries; none offers customizable sequence annotation visualizations with layout optimization.

Results
DNA Features Viewer is a sequence annotation plotting library which optimizes plot readability while letting users tailor other visual aspects (colors, labels, highlights etc.) to their particular use case.

Availability and implementation
Open-source code and documentation are available on Github under the MIT license (https://github.com/Edinburgh-Genome-Foundry/DnaFeaturesViewer).

Supplementary information
Supplementary data are available at Bioinformatics online.
EvoLSTM: context-dependent models of sequence evolution using a sequence-to-sequence LSTM
Authors: Lim Dongjoon , Blanchette Mathieu 
Published: July 2020

Abstract

Motivation
Accurate probabilistic models of sequence evolution are essential for a wide variety of bioinformatics tasks, including sequence alignment and phylogenetic inference. The ability to realistically simulate sequence evolution is also at the core of many benchmarking strategies. Yet, mutational processes have complex context dependencies that remain poorly modeled and understood.

Results
We introduce EvoLSTM, a recurrent neural network-based evolution simulator that captures mutational context dependencies. EvoLSTM uses a sequence-to-sequence long short-term memory model trained to predict mutation probabilities at each position of a given sequence, taking into consideration the 14 flanking nucleotides. EvoLSTM can realistically simulate mammalian and plant DNA sequence evolution and reveals unexpectedly strong long-range context dependencies in mutation probabilities. EvoLSTM brings modern machine-learning approaches to bear on sequence evolution. It will serve as a useful tool to study and simulate complex mutational processes.

Availability and implementation
Code and dataset are available at https://github.com/DongjoonLim/EvoLSTM.

Supplementary information
Supplementary data are available at Bioinformatics online.
Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions
Authors: Yan Zichao , Hamilton William L , Blanchette Mathieu 
Published: July 2020

Abstract

Motivation
RNA-protein interactions are key effectors of post-transcriptional regulation. Significant experimental and bioinformatics efforts have been expended on characterizing protein binding mechanisms on the molecular level, and on highlighting the sequence and structural traits of RNA that impact the binding specificity for different proteins. Yet our ability to predict these interactions in silico remains relatively poor.

Results
In this study, we introduce RPI-Net, a graph neural network approach for RNA-protein interaction prediction. RPI-Net learns and exploits a graph representation of RNA molecules, yielding significant performance gains over existing state-of-the-art approaches. We also introduce an approach to rectify an important type of sequence bias caused by the RNase T1 enzyme used in many CLIP-Seq experiments, and we show that correcting this bias is essential in order to learn meaningful predictors and properly evaluate their accuracy. Finally, we provide new approaches to interpret the trained models and extract simple, biologically interpretable representations of the learned sequence and structural motifs.

Availability and implementation
Source code can be accessed at https://www.github.com/HarveyYan/RNAonGraph.

Supplementary information
Supplementary data are available at Bioinformatics online.
Artificial-cell-type aware cell-type classification in CITE-seq
Authors: Lian Qiuyu , Xin Hongyi , Ma Jianzhu , Konnikova Liza , Chen Wei , Gu Jin , Chen Kong 
Published: July 2020

Abstract

Motivation
Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq), couples the measurement of surface marker proteins with simultaneous sequencing of mRNA at single cell level, which brings accurate cell surface phenotyping to single-cell transcriptomics. Unfortunately, multiplets in CITE-seq datasets create artificial cell types (ACT) and complicate the automation of cell surface phenotyping.

Results
We propose CITE-sort, an artificial-cell-type aware surface marker clustering method for CITE-seq. CITE-sort is aware of and is robust to multiplet-induced ACT. We benchmarked CITE-sort with real and simulated CITE-seq datasets and compared CITE-sort against canonical clustering methods. We show that CITE-sort produces the best clustering performance across the board. CITE-sort not only accurately identifies real biological cell types (BCT) but also consistently and reliably separates multiplet-induced artificial-cell-type droplet clusters from real BCT droplet clusters. In addition, CITE-sort organizes its clustering process with a binary tree, which facilitates easy interpretation and verification of its clustering result and simplifies cell-type annotation with domain knowledge in CITE-seq.

Availability and implementation
http://github.com/QiuyuLian/CITE-sort.

Supplementary information
Supplementary data is available at Bioinformatics online.
Copy number evolution with weighted aberrations in cancer
Authors: Zeira Ron , Raphael Benjamin J 
Published: July 2020

Abstract

Motivation
Copy number aberrations (CNAs), which delete or amplify large contiguous segments of the genome, are a common type of somatic mutation in cancer. Copy number profiles, representing the number of copies of each region of a genome, are readily obtained from whole-genome sequencing or microarrays. However, modeling copy number evolution is a substantial challenge, because different CNAs may overlap with one another on the genome. A recent popular model for copy number evolution is the copy number distance (CND), defined as the length of a shortest sequence of deletions and amplifications of contiguous segments that transforms one profile into the other. In the CND, all events contribute equally; however, it is well known that rates of CNAs vary by length, genomic position and type (amplification versus deletion).

Results
We introduce a weighted CND that allows events to have varying weights, or probabilities, based on their length, position and type. We derive an efficient algorithm to compute the weighted CND as well as the associated transformation. This algorithm is based on the observation that the constraint matrix of the underlying optimization problem is totally unimodular. We show that the weighted CND improves phylogenetic reconstruction on simulated data where CNAs occur with varying probabilities, aids in the derivation of phylogenies from ultra-low-coverage single-cell DNA sequencing data and helps estimate CNA rates in a large pan-cancer dataset.

Availability and implementation
Code is available at https://github.com/raphael-group/WCND.

Supplementary information
Supplementary data are available at Bioinformatics online.
DNA Chisel, a versatile sequence optimizer
Authors: Zulkower Valentin , Rosser Susan 
Published: July 2020

Abstract

Motivation
Accounting for biological and practical requirements in DNA sequence design often results in challenging optimization problems. Current software solutions are problem-specific and hard to combine.

Results
DNA Chisel is an easy-to-use, easy-to-extend sequence optimization framework allowing to freely define and combine optimization specifications via Python scripts or Genbank annotations.

Availability and implementation
The framework is available as a web application (https://cuba.genomefoundry.org/sculpt_a_sequence) or open-source Python library (see at https://github.com/Edinburgh-Genome-Foundry/DNAChisel for code and documentation).

Supplementary information
Supplementary data are available at Bioinformatics online.
TreeSAPP: the Tree-based Sensitive and Accurate Phylogenetic Profiler
Authors: Morgan-Lang Connor , McLaughlin Ryan , Armstrong Zachary , Zhang Grace , Chan Kevin , Hallam Steven J 
Published: July 2020

Abstract

Motivation
Microbial communities drive matter and energy transformations integral to global biogeochemical cycles, yet many taxonomic groups facilitating these processes remain poorly represented in biological sequence databases. Due to this missing information, taxonomic assignment of sequences from environmental genomes remains inaccurate.

Results
We present the Tree-based Sensitive and Accurate Phylogenetic Profiler (TreeSAPP) software for functionally and taxonomically classifying genes, reactions and pathways from genomes of cultivated and uncultivated microorganisms using reference packages representing coding sequences mediating multiple globally relevant biogeochemical cycles. TreeSAPP uses linear regression of evolutionary distance on taxonomic rank to improve classifications, assigning both closely related and divergent query sequences at the appropriate taxonomic rank. TreeSAPP is able to provide quantitative functional and taxonomic classifications for both assembled and unassembled sequences and files supporting interactive tree of life visualizations.

Availability and implementation
TreeSAPP was developed in Python 3 as an open-source Python package and is available on GitHub at https://github.com/hallamlab/TreeSAPP.

Supplementary information
Supplementary data are available at Bioinformatics online.
ProVision: a web-based platform for rapid analysis of proteomics data processed by MaxQuant
Authors: Gallant James Luke , Heunis Tiaan , Sampson Samantha Leigh , Bitter Wilbert 
Published: July 2020

Abstract

Summary
Proteomics is a powerful tool for protein expression analysis and is becoming more readily available to researchers through core facilities or specialized collaborations. However, one major bottleneck for routine implementation and accessibility of this technology to the wider scientific community is the complexity of data analysis. To this end, we have created ProVision, a free open-source web-based analytics platform that allows users to analyze data from two common proteomics relative quantification workflows, namely label-free and tandem mass tag-based experiments. Furthermore, ProVision allows the freedom to interface with the data analysis pipeline while maintaining a user-friendly environment and providing default parameters for fast statistical and exploratory data analysis. Finally, multiple customizable quality control, differential expression plots as well as enrichments and protein–protein interaction prediction can be generated online in one platform.

Availability and implementation
Quick start and step-by-step tutorials as well as tutorial data are fully incorporated in the web application. This application is available online at https://provision.shinyapps.io/provision/ for free use. The source code is available at https://github.com/JamesGallant/ProVision under the GPL version 3.0 license.
Naked mole rat (HETEROCEPHALUS GLABER)
Authors: Wicks Ian 
Published: July 2020

Abstract
Enriching BioScience's role as a Forum for Integrating the Life Sciences, Arts in Science provides an occasional venue for poems, visual art, and other forms of artistic expression that explore and enliven our understanding of life. Through the contributions in this section, we hope to share with our readers the passion for nature that science inspires.
This contribution is from Ian Wicks, Head of the Inflammation Division at the Walter and Eliza Hall Institute of Medical Research and Professor/Director of the Rheumatology Unit at the Royal Melbourne Hospital and University of Melbourne.