bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reactively-sputtered super-hydrophilic ultra-thin titania films deposited at 120 °C
Authors: Kaidatzis Andreas , Mouti Nafsika , Arfanis Michalis , Papadimitropoulos Giorgos , Mitterer Christian , Falaras Polycarpos , Giannakopoulos Konstantinos 
Published: October 2023

Abstract
We investigate super-hydrophilic TiO2 (titania) films for concentrated solar-thermal power applications. Reactive magnetron sputtering has been used to deposit 8 to 12 nm thick titania thin films onto borosilicate microscope glass slides, low-Fe extra-clear architectural glass, or Si(100) wafers with a 500 nm thick thermal SiO2 layer. The effects of deposition temperature and O2 fraction of the O2/Ar working gas were investigated. We demonstrate the importance of the O2 fraction for obtaining optically transparent, super-hydrophilic (contact angle below 1°) thin films. In particular, we show that as the O2 fraction increases, contact angle decreases, obtaining super-hydrophilic titania thin films at deposition temperatures as low as 120 °C. Our work enables to develop low thermal budget cost-efficient industrial synthesis processes, paving the way for commercial applications.
Revealing the changes in water footprint at the provincial level and their drivers in the Yellow River Basin, China
Authors: Xia Qing , Tian Guiliang , Hu Hao , Wu Zheng 
Published: October 2023

Abstract
Water scarcity has become the most significant limiting factor for sustainable economic and social development in the Yellow River Basin (YRB). Despite this, most current studies on water resources in the YRB from a water footprint (WF) perspective have focused on WF measurements and have explored the influencing factors of WF changes in certain industries, while the impact mechanisms driving regional WF changes remain unclear. To bridge this research gap, the WF of nine provinces in the YRB for 2012 and 2017 were quantified using an environmentally extended multi-regional input-output model (EE-MRIO), and the driving forces of regional WF changes were explored via structural decomposition analysis (SDA). The results showed that the WF of the YRB increased by 3.8% to 113.64 billion m3 between 2012 and 2017. With rapid economic development and enhanced inter-regional trade links, the external WF has played an important role in meeting local water demand. Technological advances and production structure adjustments contribute to the reduction of the WF, thus promoting the sustainable use and management of the YRB’s water resources. Both consumption patterns and final demand per capita have dominated the YRB’s WF growth, particularly in the economically developed middle and lower reaches, where urban household consumption drove the largest WF, accounting for over 40%. Therefore, in the future, continuous optimization of the consumption structure and guidance of green consumption awareness are expected to contribute more to the reduction in WF. The findings of this study reveal the primary causes of WF changes in the YRB and offer a theoretical justification for the formulation of water conservation and sustainable utilization policies.
The phenotypic and genotypic association of grip strength with frailty, physical performance and functional limitations over time in older adults
Authors: Stringa Najada , van Schoor Natasja M , Hoogendijk Emiel O , Milaneschi Yuri , Huisman Martijn 
Published: October 2023

Abstract

Objectives
To replicate the phenotypic associations of grip strength with frailty, physical performance and functional limitations in older adults for longer follow-up periods and to examine whether these associations are due to shared genetic factors.

Methods
In total 2,262 participants 55 years and older with follow-up data up to 23 years (Nobservations = 8,262) from the Longitudinal Aging Study Amsterdam were included. Weighted polygenic risk scores for grip strength (PRS-GS) were built using the genome-wide meta-analysis results from UK Biobank as reference. Grip strength was measured two times on each hand using a dynamometer. Frailty index (FI) and frailty phenotype were operationalised following standard procedures. Performance tests included a timed walk test, a repeated chair stands test and put on–take off cardigan test. Functional limitations were assessed using a questionnaire with six items.

Results
Higher grip strength was phenotypically associated with lower FI (b = −0.013, 95% CI (−0.016, −0.009)), better physical performance (b = 0.040, 95% CI (0.026, 0.054)) and less functional limitations (OR = 0.965, 95% CI (0.954, 0.977)) over time for follow-up periods up to 23 years. However, PRS-GS was not associated with any of the traits.

Conclusion
The phenotypic associations between grip strength, frailty, physical performance and functional limitations were replicated for follow-up periods up to 23 years. However, the associations between the traits could not be explained by shared genetics potentially indicating a more relevant involvement of non-genetic factors.
NetAllergen, a random forest model integrating MHC-II presentation propensity for improved allergenicity prediction
Authors: Li Yuchen , Sackett Peter Wad , Nielsen Morten , Barra Carolina 
Published: October 2023

Abstract

Motivation
Allergy is a pathological immune reaction towards innocuous protein antigens. Although only a narrow fraction of plant or animal proteins induce allergy, atopic disorders affect millions of children and adults and cost billions in healthcare systems worldwide. In silico predictors can aid in the development of more innocuous food sources. Previous allergenicity predictors used sequence similarity, common structural domains, and amino acid physicochemical features. However, these predictors strongly rely on sequence similarity to known allergens and fail to predict protein allergenicity accurately when similarity diminishes.

Results
To overcome these limitations, we collected allergens from AllergenOnline, a curated database of IgE-inducing allergens, carefully removed allergen redundancy with a novel protein partitioning pipeline, and developed a new allergen prediction method, introducing MHC presentation propensity as a novel feature. NetAllergen outperformed a sequence similarity-based BLAST baseline approach, and previous allergenicity predictor AlgPred 2 when similarity to known allergens is limited.

Availability and implementation
The web service NetAllergen and the datasets are available at https://services.healthtech.dtu.dk/services/NetAllergen-1.0/.
PanKmer: k-mer-based and reference-free pangenome analysis
Authors: Aylward Anthony J , Petrus Semar , Mamerto Allen , Hartwick Nolan T , Michael Todd P 
Published: October 2023

Abstract

Summary
Pangenomes are replacing single reference genomes as the definitive representation of DNA sequence within a species or clade. Pangenome analysis predominantly leverages graph-based methods that require computationally intensive multiple genome alignments, do not scale to highly complex eukaryotic genomes, limit their scope to identifying structural variants (SVs), or incur bias by relying on a reference genome. Here, we present PanKmer, a toolkit designed for reference-free analysis of pangenome datasets consisting of dozens to thousands of individual genomes. PanKmer decomposes a set of input genomes into a table of observed k-mers and their presence–absence values in each genome. These are stored in an efficient k-mer index data format that encodes SNPs, INDELs, and SVs. It also includes functions for downstream analysis of the k-mer index, such as calculating sequence similarity statistics between individuals at whole-genome or local scales. For example, k-mers can be “anchored” in any individual genome to quantify sequence variability or conservation at a specific locus. This facilitates workflows with various biological applications, e.g. identifying cases of hybridization between plant species. PanKmer provides researchers with a valuable and convenient means to explore the full scope of genetic variation in a population, without reference bias.

Availability and implementation
PanKmer is implemented as a Python package with components written in Rust, released under a BSD license. The source code is available from the Python Package Index (PyPI) at https://pypi.org/project/pankmer/ as well as Gitlab at https://gitlab.com/salk-tm/pankmer. Full documentation is available at https://salk-tm.gitlab.io/pankmer/.
ggkegg: analysis and visualization of KEGG data utilizing the grammar of graphics
Authors: Sato Noriaki , Uematsu Miho , Fujimoto Kosuke , Uematsu Satoshi , Imoto Seiya 
Published: October 2023

Abstract

Summary
The Kyoto Encyclopedia of Genes and Genomes (KEGG) database serves as a valuable systems biology resource and is widely utilized in diverse research fields. However, existing software does not allow flexible visualization and network analyses of the vast and complex KEGG data. We developed ggkegg, an R package that integrates KEGG information with ggplot2 and ggraph. ggkegg enables enhanced visualization and network analyses of KEGG data. We demonstrate the utility of the package by providing examples of its application in single-cell, bulk transcriptome, and microbiome analyses. ggkegg may empower researchers to analyze complex biological networks and present their results effectively.

Availability and implementation
The package and user documentation are available at: https://github.com/noriakis/ggkegg.
CAAStools: a toolbox to identify and test Convergent Amino Acid Substitutions
Authors: Barteri Fabio , Valenzuela Alejandro , Farré Xavier , de Juan David , Muntané Gerard , Esteve-Altava Borja , Navarro Arcadi 
Published: October 2023

Abstract

Motivation
Coincidence of Convergent Amino Acid Substitutions (CAAS) with phenotypic convergences allow pinpointing genes and even individual mutations that are likely to be associated with trait variation within their phylogenetic context. Such findings can provide useful insights into the genetic architecture of complex phenotypes.

Results
Here we introduce CAAStools, a set of bioinformatics tools to identify and validate CAAS in orthologous protein alignments for predefined groups of species representing the phenotypic values targeted by the user.

Availability and implementation
CAAStools source code is available at http://github.com/linudz/caastools, along with documentation and examples.
scHiCDiff: detecting differential chromatin interactions in single-cell Hi-C data
Authors: Liu Huiling , Ma Wenxiu 
Published: October 2023

Abstract

Summary
Here, we presented the scHiCDiff software tool that provides both nonparametric tests and parametirc models to detect differential chromatin interactions (DCIs) from single-cell Hi-C data. We thoroughly evaluated the scHiCDiff methods on both simulated and real data. Our results demonstrated that scHiCDiff, especially the zero-inflated negative binomial model option, can effectively detect reliable and consistent single-cell DCIs between two conditions, thereby facilitating the study of cell type-specific variations of chromatin structures at the single-cell level.

Availability and implementation
scHiCDiff is implemented in R and freely available at GitHub (https://github.com/wmalab/scHiCDiff).
PC_ali: a tool for improved multiple alignments and evolutionary inference based on a hybrid protein sequence and structure similarity score
Authors: Bastolla Ugo , Abia David , Piette Oscar 
Published: October 2023

Abstract

Motivation
Evolutionary inference depends crucially on the quality of multiple sequence alignments (MSA), which is problematic for distantly related proteins. Since protein structure is more conserved than sequence, it seems natural to use structure alignments for distant homologs. However, structure alignments may not be suitable for inferring evolutionary relationships.

Results
Here we examined four protein similarity measures that depend on sequence and structure (fraction of aligned residues, sequence identity, fraction of superimposed residues, and contact overlap), finding that they are intimately correlated but none of them provides a complete and unbiased picture of conservation in proteins. Therefore, we propose the new hybrid protein sequence and structure similarity score PC_sim based on their main principal component. The corresponding divergence measure PC_div shows the strongest correlation with divergences obtained from individual similarities, suggesting that it infers accurate evolutionary divergences. We developed the program PC_ali that constructs protein MSAs either de novo or modifying an input MSA, using a similarity matrix based on PC_sim. The program constructs a starting MSA based on the maximal cliques of the graph of these PAs and it refines it through progressive alignments along the tree reconstructed with PC_div. Compared with eight state-of-the-art multiple structure or sequence alignment tools, PC_ali achieves higher or equal aligned fraction and structural scores, sequence identity higher than structure aligners although lower than sequence aligners, highest score PC_sim, and highest similarity with the MSAs produced by other tools and with the reference MSA Balibase.

Availability and implementation
https://github.com/ugobas/PC_ali.
Determining epitope specificity of T-cell receptors with transformers
Authors: Khan Abdul Rehman , Reinders Marcel J T , Khatri Indu 
Published: October 2023

Abstract

Summary
T-cell receptors (TCRs) on T cells recognize and bind to epitopes presented by the major histocompatibility complex in case of an infection or cancer. However, the high diversity of TCRs, as well as their unique and complex binding mechanisms underlying epitope recognition, make it difficult to predict the binding between TCRs and epitopes. Here, we present the utility of transformers, a deep learning strategy that incorporates an attention mechanism that learns the informative features, and show that these models pre-trained on a large set of protein sequences outperform current strategies. We compared three pre-trained auto-encoder transformer models (ProtBERT, ProtAlbert, and ProtElectra) and one pre-trained auto-regressive transformer model (ProtXLNet) to predict the binding specificity of TCRs to 25 epitopes from the VDJdb database (human and murine). Two additional modifications were performed to incorporate gene usage of the TCRs in the four transformer models. Of all 12 transformer implementations (four models with three different modifications), a modified version of the ProtXLNet model could predict TCR–epitope pairs with the highest accuracy (weighted F1 score 0.55 simultaneously considering all 25 epitopes). The modification included additional features representing the gene names for the TCRs. We also showed that the basic implementation of transformers outperformed the previously available methods, i.e. TCRGP, TCRdist, and DeepTCR, developed for the same biological problem, especially for the hard-to-classify labels. We show that the proficiency of transformers in attention learning can be made operational in a complex biological setting like TCR binding prediction. Further ingenuity in utilizing the full potential of transformers, either through attention head visualization or introducing additional features, can extend T-cell research avenues.

Availability and implementation
Data and code are available on https://github.com/InduKhatri/tcrformer.
MosaiCatcher v2: a single-cell structural variations detection and analysis reference framework based on Strand-seq
Authors: Weber Thomas , Cosenza Marco Raffaele , Korbel Jan 
Published: October 2023

Abstract

Summary
Single-cell DNA template strand sequencing (Strand-seq) allows a range of various genomic analysis including chromosome length haplotype phasing and structural variation (SV) calling in individual cells. Here, we present MosaiCatcher v2, a standardized workflow and reference framework for single-cell SV detection using Strand-seq. This framework introduces a range of functionalities, including: an automated upstream Quality Control (QC) and assembly sub-workflow that relies on multiple genome assemblies and incorporates a multistep normalization module, integration of the single-cell nucleosome occupancy and genetic variation analysis SV functional characterization and of the ArbiGent SV genotyping modules, platform portability, as well as a user-friendly and shareable web report. These new features of MosaiCatcher v2 enable reproducible computational processing of Strand-seq data, which are increasingly used in human genetics and single-cell genomics, toward production environments. MosaiCatcher v2 is compatible with both container and conda environments, ensuring reproducibility and robustness and positioning the framework as a cornerstone in computational processing of Strand-seq data.

Availability and implementation
MosaiCatcher v2 is a standardized workflow, implemented using the Snakemake workflow management system. The pipeline is available on GitHub: https://github.com/friendsofstrandseq/mosaicatcher-pipeline/ and on the snakemake-workflow-catalog: https://snakemake.github.io/snakemake-workflow-catalog/?usage=friendsofstrandseq/mosaicatcher-pipeline. Strand-seq example input data used in the publication can be found in the Data availability statement. Additionally, a lightweight dataset for test purposes can be found on the GitHub repository.
Automated calibration of consensus weighted distance-based clustering approaches using sharp
Authors: Bodinier Barbara , Vuckovic Dragana , Rodrigues Sabrina , Filippi Sarah , Chiquet Julien , Chadeau-Hyam Marc 
Published: October 2023

Abstract

Motivation
In consensus clustering, a clustering algorithm is used in combination with a subsampling procedure to detect stable clusters. Previous studies on both simulated and real data suggest that consensus clustering outperforms native algorithms.

Results
We extend here consensus clustering to allow for attribute weighting in the calculation of pairwise distances using existing regularized approaches. We propose a procedure for the calibration of the number of clusters (and regularization parameter) by maximizing the sharp score, a novel stability score calculated directly from consensus clustering outputs, making it extremely computationally competitive. Our simulation study shows better clustering performances of (i) approaches calibrated by maximizing the sharp score compared to existing calibration scores and (ii) weighted compared to unweighted approaches in the presence of features that do not contribute to cluster definition. Application on real gene expression data measured in lung tissue reveals clear clusters corresponding to different lung cancer subtypes.

Availability and implementation
The R package sharp (version ≥1.4.3) is available on CRAN at https://CRAN.R-project.org/package=sharp.
EVRC: reconstruction of chromosome 3D structure models using error-vector resultant algorithm with clustering coefficient
Authors: Wang Xiao , Gu Wei-Cheng , Li Jie , Ma Bin-Guang 
Published: October 2023

Abstract

Motivation
Reconstruction of 3D structure models is of great importance for the study of chromosome function. Software tools for this task are highly needed.

Results
We present a novel reconstruction algorithm, called EVRC, which utilizes co-clustering coefficients and error-vector resultant for chromosome 3D structure reconstruction. As an update of our previous EVR algorithm, EVRC now can deal with both single and multiple chromosomes in structure modeling. To evaluate the effectiveness and accuracy of the EVRC algorithm, we applied it to simulation datasets and real Hi-C datasets. The results show that the reconstructed structures have high similarity to the original/real structures, indicating the effectiveness and robustness of the EVRC algorithm. Furthermore, we applied the algorithm to the 3D conformation reconstruction of the wild-type and mutant Arabidopsis thaliana chromosomes and demonstrated the differences in structural characteristics between different chromosomes. We also accurately showed the conformational change in the centromere region of the mutant compared with the wild-type of Arabidopsis chromosome 1. Our EVRC algorithm is a valuable software tool for the field of chromatin structure reconstruction, and holds great promise for advancing our understanding on the chromosome functions.

Availability and implementation
The software is available at https://github.com/mbglab/EVRC.
Luteinizing hormone stimulates ingression of mural granulosa cells within the mouse preovulatory follicle
Authors: Owen Corie M , Jaffe Laurinda A 
Published: October 2023

Abstract
Luteinizing hormone (LH) induces ovulation by acting on its receptors in the mural granulosa cells that surround a mammalian oocyte in an ovarian follicle. However, much remains unknown about how activation of the LH receptor modifies the structure of the follicle such that the oocyte is released and the follicle remnants are transformed into the corpus luteum. The present study shows that the preovulatory surge of LH stimulates LH receptor-expressing granulosa cells, initially located almost entirely in the outer layers of the mural granulosa, to rapidly extend inwards, intercalating between other cells. The cellular ingression begins within 30 min of the peak of the LH surge, and the proportion of LH receptor-expressing cell bodies in the inner half of the mural granulosa layer increases until the time of ovulation, which occurs at about 10 h after the LH peak. During this time, many of the initially flask-shaped cells appear to detach from the basal lamina, acquiring a rounder shape with multiple filipodia. Starting at about 4 h after the LH peak, the mural granulosa layer at the apical surface of the follicle where ovulation will occur begins to thin, and the basolateral surface develops invaginations and constrictions. Our findings raise the question of whether LH stimulation of granulosa cell ingression may contribute to these changes in the follicular structure that enable ovulation.
Robustness of quantifying mediating effects of genetically regulated expression on complex traits with mediated expression score regression
Authors: Lin Chen , Liu Wei , Jiang Wei , Zhao Hongyu 
Published: October 2023

AbstractGenetic association signals have been mostly found in noncoding regions through genome-wide association studies (GWAS), suggesting the roles of gene expression regulation in human diseases and traits. However, there has been limited success in colocalizing expression quantitative trait locus (eQTL) with disease-associated variants. Mediated expression score regression (MESC) is a recently proposed method to quantify the proportion of trait heritability mediated by genetically regulated gene expressions (GReX). Applications of MESC to GWAS results have yielded low estimation of mediated heritability for many traits. As MESC relies on stringent independence assumptions between cis-eQTL effects, gene effects, and nonmediated SNP effects, it may fail to characterize the true relationships between those effect sizes, which leads to biased results. Here, we consider the robustness of MESC to investigate whether the low fraction of mediated heritability inferred by MESC reflects biological reality for complex traits or is an underestimation caused by model misspecifications. Our results suggest that MESC may lead to biased estimates of mediated heritability with misspecification of gene annotations leading to underestimation, whereas misspecification of SNP annotations may lead to overestimation. Furthermore, errors in eQTL effect estimates may lead to underestimation of mediated heritability.