bluuebunny/crossref_metadata_2025_split huggingface dataset:
One-step synthesis of super-absorbent nanocomposite hydrogel based on bentonite
Authors: Le Giang H , Thanh Duong A , My Pham T H , Pham Trang TT , Quan Trang T T , Nguyen Tung N , Nguyen Quang K , Ngo Quoc Anh 
Published: December 2022

Abstract
The nanoparticles of bentonite can be easily synthesized by hydrothermal treatment with microwave-assisted method. The products were characterized by XRD, FTIR, BET, SEM, EDS, and DLS. Results have revealed that the nano-bentonite was in nanometer scale with particles size of 200–300 nm. The SEM analysis of the nano-bentonite dispersion showed that a decrease in agglomeration and surface roughness. The nano-bentonite possessed high surface area (152.4 m2.g−1) and pore volume (0.25 cm3.g−1). Additionally, nanocomposite hydrogels (NB/PAA) were synthesized through the free-radical polymerization of acrylic acid (PAA) in the presence of the as-synthesized nano-bentonite (NB). The presence of NB in hydrogel matrix may improve the tensile strength of hydrogel. The tensile strength of the hydrogel increases from 0.294 to 1.609 MPa when NB content was 1.0% (NB10/PAA). Under the optimum conditions, the nanocomposite hydrogels (NB10/PAA) exhibited high water absorption capacity of 420.0

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These findings contribute to the sustainable development and green chemistry.
The Global Glimm Property
Authors: Thiel Hannes , Vilalta Eduard 
Published: December 2022

It is known that a

C
∗

C^*

-algebra with the Global Glimm Property is nowhere scattered (it has no elementary ideal-quotients), and the Global Glimm Problem asks if the converse holds. We provide a new approach to this long-standing problem by showing that a

C
∗

C^*

-algebra has the Global Glimm Property if and only if it is nowhere scattered and its Cuntz semigroup is ideal-filtered (the Cuntz classes generating a given ideal are downward directed) and has property (V) (a weak form of being sup-semilattice ordered).

We show that ideal-filteredness and property (V) are automatic for

C
∗

C^*

-algebras that have stable rank one or real rank zero, thereby recovering the solutions to the Global Glimm Problem in these cases. We also use our approach to solve the Global Glimm Problem for new classes of

C
∗

C^*

-algebras.
Revana: a comprehensive tool for regulatory variant analysis and visualization of cancer genomes
Authors: Ulrich Elias , Pfister Stefan M , Jäger Natalie 
Published: December 2022

Abstract

Motivation
As non-coding driver mutations move more into the focus of cancer research, a comprehensive and easy-to-use software solution for regulatory variant analysis and data visualization is highly relevant. The interpretation of regulatory variants in large tumor genome cohorts requires specialized analysis and visualization of multiple layers of data, including for example breakpoints of structural variants, enhancer elements and additional available gene locus annotation, in the context of changes in gene expression.

Results
We introduce a user-friendly tool, Revana (REgulatory Variant ANAlysis), that can aggregate and visually represent regulatory variants from cancer genomes in a gene-centric manner. It requires whole-genome and RNA sequencing data of a cohort of tumor samples and creates interactive HTML reports summarizing the most important regulatory events.

Availability and implementation
Revana is implemented in R and JavaScript. It is available for download as an R package under &lt;https://github.com/KiTZ-Heidelberg/revana&gt;. Sample results can be viewed under &lt;https://github.com/KiTZ-Heidelberg/revana-demo-report&gt; and a short walkthrough is available under &lt;https://github.com/KiTZ-Heidelberg/revana-demo-data&gt;.

Supplementary information
Supplementary data are available at Bioinformatics online.
Asteroid: a new algorithm to infer species trees from gene trees under high proportions of missing data
Authors: Morel Benoit , Williams Tom A , Stamatakis Alexandros 
Published: December 2022

AbstractMotivationMissing data and incomplete lineage sorting (ILS) are two major obstacles to accurate species tree inference. Gene tree summary methods such as ASTRAL and ASTRID have been developed to account for ILS. However, they can be severely affected by high levels of missing data.ResultsWe present Asteroid, a novel algorithm that infers an unrooted species tree from a set of unrooted gene trees. We show on both empirical and simulated datasets that Asteroid is substantially more accurate than ASTRAL and ASTRID for very high proportions (&gt;80%) of missing data. Asteroid is several orders of magnitude faster than ASTRAL for datasets that contain thousands of genes. It offers advanced features such as parallelization, support value computation and support for multi-copy and multifurcating gene trees.Availability and implementationAsteroid is freely available at https://github.com/BenoitMorel/Asteroid.Supplementary informationSupplementary data are available at Bioinformatics online.
DFinder: a novel end-to-end graph embedding-based method to identify drug–food interactions
Authors: Wang Tao , Yang Jinjin , Xiao Yifu , Wang Jingru , Wang Yuxian , Zeng Xi , Wang Yongtian , Peng Jiajie 
Published: December 2022

AbstractMotivationDrug–food interactions (DFIs) occur when some constituents of food affect the bioaccessibility or efficacy of the drug by involving in drug pharmacodynamic and/or pharmacokinetic processes. Many computational methods have achieved remarkable results in link prediction tasks between biological entities, which show the potential of computational methods in discovering novel DFIs. However, there are few computational approaches that pay attention to DFI identification. This is mainly due to the lack of DFI data. In addition, food is generally made up of a variety of chemical substances. The complexity of food makes it difficult to generate accurate feature representations for food. Therefore, it is urgent to develop effective computational approaches for learning the food feature representation and predicting DFIs.ResultsIn this article, we first collect DFI data from DrugBank and PubMed, respectively, to construct two datasets, named DrugBank-DFI and PubMed-DFI. Based on these two datasets, two DFI networks are constructed. Then, we propose a novel end-to-end graph embedding-based method named DFinder to identify DFIs. DFinder combines node attribute features and topological structure features to learn the representations of drugs and food constituents. In topology space, we adopt a simplified graph convolution network-based method to learn the topological structure features. In feature space, we use a deep neural network to extract attribute features from the original node attributes. The evaluation results indicate that DFinder performs better than other baseline methods.Availability and implementationThe source code is available at https://github.com/23AIBox/23AIBox-DFinder.Supplementary informationSupplementary data are available at Bioinformatics online.
A pairwise strategy for imputing predictive features when combining multiple datasets
Authors: Wu Yujie , Ren Boyu , Patil Prasad 
Published: December 2022

AbstractMotivationIn the training of predictive models using high-dimensional genomic data, multiple studies’ worth of data are often combined to increase sample size and improve generalizability. A drawback of this approach is that there may be different sets of features measured in each study due to variations in expression measurement platform or technology. It is often common practice to work only with the intersection of features measured in common across all studies, which results in the blind discarding of potentially useful feature information that is measured in individual or subsets of studies.ResultsWe characterize the loss in predictive performance incurred by using only the intersection of feature information available across all studies when training predictors using gene expression data from microarray and sequencing datasets. We study the properties of linear and polynomial regression for imputing discarded features and demonstrate improvements in the external performance of prediction functions through simulation and in gene expression data collected on breast cancer patients. To improve this process, we propose a pairwise strategy that applies any imputation algorithm to two studies at a time and averages imputed features across pairs. We demonstrate that the pairwise strategy is preferable to first merging all datasets together and imputing any resulting missing features. Finally, we provide insights on which subsets of intersected and study-specific features should be used so that missing-feature imputation best promotes cross-study replicability.Availability and implementationThe code is available at https://github.com/YujieWuu/Pairwise_imputation.Supplementary informationSupplementary information is available at Bioinformatics online.
Imbalanced classification for protein subcellular localization with multilabel oversampling
Authors: Rana Priyanka , Sowmya Arcot , Meijering Erik , Song Yang 
Published: December 2022

Abstract

Motivation
Subcellular localization of human proteins is essential to comprehend their functions and roles in physiological processes, which in turn helps in diagnostic and prognostic studies of pathological conditions and impacts clinical decision-making. Since proteins reside at multiple locations at the same time and few subcellular locations host far more proteins than other locations, the computational task for their subcellular localization is to train a multilabel classifier while handling data imbalance. In imbalanced data, minority classes are underrepresented, thus leading to a heavy bias towards the majority classes and the degradation of predictive capability for the minority classes. Furthermore, data imbalance in multilabel settings is an even more complex problem due to the coexistence of majority and minority classes.

Results
Our studies reveal that based on the extent of concurrence of majority and minority classes, oversampling of minority samples through appropriate data augmentation techniques holds promising scope for boosting the classification performance for the minority classes. We measured the magnitude of data imbalance per class and the concurrence of majority and minority classes in the dataset. Based on the obtained values, we identified minority and medium classes, and a new oversampling method is proposed that includes non-linear mixup, geometric and colour transformations for data augmentation and a sampling approach to prepare minibatches. Performance evaluation on the Human Protein Atlas Kaggle challenge dataset shows that the proposed method is capable of achieving better predictions for minority classes than existing methods.

Availability and implementation
Data used in this study are available at https://www.kaggle.com/competitions/human-protein-atlas-image-classification/data. Source code is available at https://github.com/priyarana/Protein-subcellular-localisation-method.

Supplementary information
Supplementary data are available at Bioinformatics online.
PRAWNS: compact pan-genomic features for whole-genome population genomics
Authors: Javkar Kiran , Rand Hugh , Strain Errol , Pop Mihai 
Published: December 2022

AbstractMotivationScientists seeking to understand the genomic basis of bacterial phenotypes, such as antibiotic resistance, today have access to an unprecedented number of complete and nearly complete genomes. Making sense of these data requires computational tools able to perform multiple-genome comparisons efficiently, yet currently available tools cannot scale beyond several tens of genomes.ResultsWe describe PRAWNS, an efficient and scalable tool for multiple-genome analysis. PRAWNS defines a concise set of genomic features (metablocks), as well as pairwise relationships between them, which can be used as a basis for large-scale genotype–phenotype association studies. We demonstrate the effectiveness of PRAWNS by identifying genomic regions associated with antibiotic resistance in Acinetobacter baumannii.Availability and implementationPRAWNS is implemented in C++ and Python3, licensed under the GPLv3 license, and freely downloadable from GitHub (https://github.com/KiranJavkar/PRAWNS.git).Supplementary informationSupplementary data are available at Bioinformatics online.
Constrained sense-making in families with parental mental illness and the importance of intra-family communication for positive outcomes
Authors: Yates Scott , Gatsou Lina 
Published: December 2022

Abstract
Recent work in parental mental illness (PMI) has focused attention on mechanisms by which it impacts the lives of family members and is transmitted intergenerationally. This includes arguments for attending to how recovery and experiences of the illness in families are shaped relationally. In this article, we contribute to this area through presenting the results of in-depth qualitative research studying how family communication around PMI shapes families’ experiences of illness and recovery. This comprises thematic analysis of thirty-six semi-structured interviews and eight focus groups with families and multi-agency professionals who worked with them. Findings demonstrate a complex interplay of drivers of lack of communication and of the impacts of this on family relationships and well-being. Notably, families found it difficult to communicate around mental health due to guilt and shame felt by the unwell parent, a belief that concealing the illness will protect children and lack of capacity in families for communication in general. This lack of communication affects how families make sense of one another’s experiences and behaviours and impacts individual coping and family relationships. Fostering positive communication around the illness in social work settings has the potential to improve relationships, reduce perceived burdens of illness and aid recovery.
Fragment Ends of Circulating Microbial DNA as Signatures for Pathogen Detection in Sepsis
Authors: Wang Guangya , Lam W K Jacky , Ling Lowell , Ma Mary-Jane L , Ramakrishnan Saravanan , Chan Don C T , Lee Wing-Shan , Cheng Suk Hang , Chan Rebecca W Y , Yu Stephanie C Y , Tse Irene O L , Wong Wai Tat , Jiang Peiyong , Chiu Rossa W K , Allen Chan K C , Lo Y M Dennis 
Published: December 2022

Abstract

Background
Nuclear-derived cell-free DNA (cfDNA) molecules in blood plasma are nonrandomly fragmented, bearing a wealth of information related to tissues of origin. DNASE1L3 (deoxyribonuclease 1 like 3) is an important player in shaping the fragmentation of nuclear-derived cfDNA molecules, preferentially generating molecules with 5 CC dinucleotide termini (i.e., 5 CC-end motif). However, the fragment end properties of microbial cfDNA and its clinical implication remain to be explored.

Methods
We performed end motif analysis on microbial cfDNA fragments in plasma samples from patients with sepsis. A sequence context-based normalization method was used to minimize the potential biases for end motif analysis.

Results
The end motif profiles of microbial cfDNA appeared to resemble that of nuclear cfDNA (Spearman correlation coefficient: 0.82, P value 0.001). The CC-end motif was the most preferred end motif in microbial cfDNA, suggesting that DNASE1L3 might also play a role in the fragmentation of microbe-derived cfDNA in plasma. Of note, differential end motifs were present between microbial cfDNA originating from infection-causing pathogens (enriched at the CC-end) and contaminating microbial DNA potentially derived from reagents or the environment (nearly random). The use of fragment end signatures allowed differentiation between confirmed pathogens and contaminating microbes, with an area under the receiver operating characteristic curve of 0.99. The performance appeared to be superior to conventional analysis based on microbial cfDNA abundance alone.

Conclusions
The use of fragmentomic features could facilitate the differentiation of underlying contaminating microbes from true pathogens in sepsis. This work demonstrates the potential usefulness of microbial cfDNA fragmentomics in metagenomics analysis.
Males armed with big weapons win fights at limited cost in ant-mimicking jumping spiders
Authors: Yu Guocheng , Wong Boon Hui , Painting Christina J , Li Hongze , Yu Long , Zhang Zengtao , Zhang Shichang , Li Daiqin 
Published: December 2022

Abstract
A core assumption of sexual selection theory is that sexually selected weapons, specialized morphological structures used directly in male contests, can improve an individual’s reproductive success but only if the bearer can overcome associated costs, the negative effects on the bearer’s fitness components. However, recent studies have shown that producing and wielding exaggerated weapons may not necessarily be costly. Rather, some traits can be selected for supporting, or compensating for, the expense of producing and wielding such exaggerated weapons. In the ant-mimicking jumping spider Myrmarachne gisti, exaggerated chelicerae are borne only by adult males and not females, showing sexual dimorphism and steep positive allometry with body size. Here, we determine the potential benefits of bearing exaggerated chelicerae during male contests and explore the potential for costs in terms of prey-capture efficiency and compensation between chelicera size and neighboring trait size. While males with longer chelicerae won most of their male-male contests, we found no significant differences in prey-capture efficiency between males and females regardless of whether prey was winged or flightless. Males’ elongated chelicerae thus do not impede their efficiency at capturing prey. Furthermore, we found that the sizes of all neighboring traits are positively correlated with chelicera size, suggesting that these traits may be under correlational selection. Taken together, our findings suggest that M. gisti males armed with the exaggerated chelicerae that function as weapons win more fights at limited cost for performance in prey capture and compensate for neighboring structures.
Emerging molecular imaging targets and tools for myocardial fibrosis detection
Authors: Barton Anna K , Tzolos Evangelos , Bing Rong , Singh Trisha , Weber Wolfgang , Schwaiger Markus , Varasteh Zohreh , Slart Riemer H J A , Newby David E , Dweck Marc R 
Published: December 2022

AbstractMyocardial fibrosis is the heart’s common healing response to injury. While initially seeking to optimize the strength of diseased tissue, fibrosis can become maladaptive, producing stiff poorly functioning and pro-arrhythmic myocardium. Different patterns of fibrosis are associated with different myocardial disease states, but the presence and quantity of fibrosis largely confer adverse prognosis. Current imaging techniques can assess the extent and pattern of myocardial scarring, but lack specificity and detect the presence of established fibrosis when the window to modify this process may have ended. For the first time, novel molecular imaging methods, including gallium-68 (68Ga)-fibroblast activation protein inhibitor positron emission tomography (68Ga-FAPI PET), may permit highly specific imaging of fibrosis activity. These approaches may facilitate earlier fibrosis detection, differentiation of active vs. end-stage disease, and assessment of both disease progression and treatment–response thereby improving patient care and clinical outcomes.
Robust nonparametric analysis of dynamic profits, prices and productivity: An application to French meat-processing firms
Authors: Ang Frederic , Kerstens Pieter Jan 
Published: December 2022

AbstractAppropriately considering adjustment costs, this paper develops a robust nonparametric framework to analyse profits, prices and productivity in a dynamic context. Dynamic profit change is decomposed into a dynamic Bennet price indicator and a dynamic Bennet quantity indicator. The latter is decomposed into explanatory factors. It is shown to be a superlative indicator for the dynamic Luenberger indicator. The application focuses on 1,638 observations of French meat-processing firms for the years 2012–2019. Using m-out-of-n bootstrapped data envelopment analysis, we obtain robust estimates and confidence intervals. The components of dynamic productivity growth fluctuate substantially. However, these fluctuations are often statistically insignificant.
Rational hapten design and establishment of broad-spectrum indirect competitive enzyme-linked immunosorbent assay for benzimidazoles monitoring in milk
Authors: Wang Zile , Zhang Liang , Yang YanHong , Zhang Huixia , Zhang Weichunbai , Zheng Pimiao , Jiang Haiyang 
Published: December 2022

AbstractObjectivesBenzimidazoles (BZs) are commonly used for the treatment of soil-transmitted helminth infections in veterinary clinics; however, misuse and overdosing of BZs will cause residual problems and have the potential to damage human health through the food chain. Thus, the existence of BZs in foods needs more attention. This study aims to establish a broad-spectrum immunoassay for rapid detection and to simultaneously monitor BZs in milk.Materials and MethodsBased on structure analysis, a ‘zero epitope loss’ strategy, which introduced a spacer arm into the imino group of the imidazole ring of albendazole, was first adopted for hapten modification to obtain an ultra-sensitive and broad-spectrum antibody. An indirect competitive enzyme-linked immunosorbent assay (icELISA) was established for the detection of 18 BZs in milk sample with a single-step pretreatment. A quantitative structure–activity relationship model was constructed to interpret and predict the recognition.ResultsThe antibody could recognize 20 BZs and the half-inhibitory concentrations ranged from 0.054 to 417.58 ng/mL, the limits of detection of icELISA ranged from 0.4 to 89.4 ng/mL, and the mean recovery rates ranged from 76.49% to 120.40%, with a coefficient of variation &lt;20%. Substituent R1 of BZs was considered to be the main influencing factor for recognition, and the comparative molecular field analysis model (q2=0.724, r2=0.998) was finally chosen for further prediction.ConclusionsThe results indicated that the established icELISA could simultaneously identify 18 BZs, with good accuracy and precision, which was suitable for rapid detection of BZs in milk.
Chromosome-Scale Genome Assembly and Annotation of Allotetraploid Annual Bluegrass (Poa annua L.)
Authors: Robbins Matthew D , Bushman B Shaun , Huff David R , Benson Christopher W , Warnke Scott E , Maughan Chase A , Jellen Eric N , Johnson Paul G , Maughan Peter J 
Published: December 2022

Abstract
Poa annua L. is a globally distributed grass with economic and horticultural significance as a weed and as a turfgrass. This dual significance, and its phenotypic plasticity and ecological adaptation, have made P. annua an intriguing plant for genetic and evolutionary studies. Because of the lack of genomic resources and its allotetraploid (2n = 4x = 28) nature, a reference genome sequence would be a valuable asset to better understand the significance and polyploid origin of P. annua. Here we report a genome assembly with scaffolds representing the 14 haploid chromosomes that are 1.78 Gb in length with an N50 of 112 Mb and 96.7% of BUSCO orthologs. Seventy percent of the genome was identified as repetitive elements, 91.0% of which were Copia- or Gypsy-like long-terminal repeats. The genome was annotated with 76,420 genes spanning 13.3% of the 14 chromosomes. The two subgenomes originating from Poa infirma (Knuth) and Poa supina (Schrad) were sufficiently divergent to be distinguishable but syntenic in sequence and annotation with repetitive elements contributing to the expansion of the P. infirma subgenome.