bluuebunny/crossref_metadata_2025_split huggingface dataset:
Still WALKing-FOR: 2-year sustainability of the ‘WALK FOR’ intervention
Authors: Gil Efrat , Zisberg Anna , Shadmi Efrat , Gur-Yaish Nurit , Shulyaev Ksenya , Chayat Yehudith , Agmon Maayan 
Published: June 2023

Abstract

Background
low mobility of hospitalised older adults is associated with adverse outcomes and imposes a significant burden on healthcare and welfare systems. Various interventions have been developed to reduce this problem; at present, however, their methodologies and outcomes vary and information is lacking about their long-term sustainability. This study aimed to evaluate the 2-year sustainability of the WALK-FOR (walking for better outcomes and recovery) intervention implemented by teams in acute care medical units.

Methods
a quasi-experimental three-group comparative design (N = 366): pre-implementation, i.e. control group (n = 150), immediate post-implementation (n = 144) and 2-year post-implementation (n = 72).

Results
mean participant age was 77.6 years (± 6 standard deviation [SD]) and 45.3% were females. We conducted an analysis of variance test to evaluate the differences in primary outcomes: number of daily steps and self-reported mobility. Levels of mobility improved significantly from the pre-implementation (control) group to the immediate and 2-year post-implementation groups. Daily step count: pre-implementation (median: 1,081, mean: 1,530 SD = 1,506), immediate post-implementation (median: 2,225, mean: 2,724. SD = 1,827) and 2-year post-implementation (median: 1,439, mean: 2,582, SD = 2,390) F = 15.778 P &lt; 0.01. Self-reported mobility: pre-implementation (mean:10.9, SD = 3.5), immediate post-implementation (mean: 12.4, SD = 2.2), 2-year post-implementation (mean: 12.7, SD = 2.2), F = 16.250, P &lt; 0.01.

Conclusions
the WALK-FOR intervention demonstrates 2-year sustainability. The theory-driven adaptation and reliance on local personnel produce an effective infrastructure for long-lasting intervention. Future studies should evaluate sustainability from a wider perspective to inform further in-hospital intervention development and implementation.
Association of Lung Function With Cognitive Decline and Incident Dementia in the Atherosclerosis Risk in Communities Study
Authors: Shrestha Srishti , Zhu Xiaoqian , London Stephanie J , Sullivan Kevin J , Lutsey Pamela L , Windham B Gwen , Griswold Michael E , Mosley Jr Thomas H 
Published: June 2023

Abstract
We examined the associations between lung function and incident dementia and cognitive decline in 12,688 participants in the ARIC Study who provided lung function measurements in 1990–1992. Cognitive tests were administered up to 7 times, and dementia was ascertained through 2019. We used shared parameter models to jointly fit proportional hazard models and linear mixed-effect models to estimate lung-function–associated dementia rate and cognitive change, respectively. Higher forced expiratory volume in 1 second (FEV1) and forced vital capacity (FVC) were associated with reduced dementia (n = 2,452 persons developed dementia); hazard ratios per 1-L increase in FEV1 and FVC were 0.79 (95% confidence interval (CI): 0.71, 0.89) and 0.81 (95% CI: 0.74, 0.89), respectively. Each 1-L increase in FEV1 and FVC was associated with a 0.08–standard deviation (SD) (95% CI: 0.05, 0.12) and a 0.05-SD (95% CI: 0.02, 0.07) attenuation of 30-year cognitive decline, respectively. A 1% increase in FEV1/FVC ratio was associated with 0.008-SD (95% CI: 0.004, 0.012) less cognitive decline. We observed statistical interaction between FEV1 and FVC, suggesting that cognitive declines depended on values of specific FEV1 and FVC (as compared with FEV1, FVC, or FEV1/FVC ratio models that suggested linear incremental associations). Our findings may have important implications for reducing the burden of cognitive decline that is attributable to environmental exposures and associated lung function impairment.
Effects of spaced k-mers on alignment-free genotyping
Authors: Häntze Hartmut , Horton Paul 
Published: June 2023

Abstract

Motivation
Alignment-free, k-mer based genotyping methods are a fast alternative to alignment-based methods and are particularly well suited for genotyping larger cohorts. The sensitivity of algorithms, that work with k-mers, can be increased by using spaced seeds, however, the application of spaced seeds in k-mer based genotyping methods has not been researched yet.

Results
We add a spaced seeds functionality to the genotyping software PanGenie and use it to calculate genotypes. This significantly improves sensitivity and F-score when genotyping SNPs, indels, and structural variants on reads with low (5×) and high (30×) coverage. Improvements are greater than what could be achieved by just increasing the length of contiguous k-mers. Effect sizes are particularly large for low coverage data. If applications implement effective algorithms for hashing of spaced k-mers, spaced k-mers have the potential to become an useful technique in k-mer based genotyping.

Availability and implementation
The source code of our proposed tool MaskedPanGenie is openly available on https://github.com/hhaentze/MaskedPangenie.
A gated graph transformer for protein complex structure quality assessment and its performance in CASP15
Authors: Chen Xiao , Morehead Alex , Liu Jian , Cheng Jianlin 
Published: June 2023

Abstract

Motivation
Proteins interact to form complexes to carry out essential biological functions. Computational methods such as AlphaFold-multimer have been developed to predict the quaternary structures of protein complexes. An important yet largely unsolved challenge in protein complex structure prediction is to accurately estimate the quality of predicted protein complex structures without any knowledge of the corresponding native structures. Such estimations can then be used to select high-quality predicted complex structures to facilitate biomedical research such as protein function analysis and drug discovery.

Results
In this work, we introduce a new gated neighborhood-modulating graph transformer to predict the quality of 3D protein complex structures. It incorporates node and edge gates within a graph transformer framework to control information flow during graph message passing. We trained, evaluated and tested the method (called DProQA) on newly-curated protein complex datasets before the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) and then blindly tested it in the 2022 CASP15 experiment. The method was ranked 3rd among the single-model quality assessment methods in CASP15 in terms of the ranking loss of TM-score on 36 complex targets. The rigorous internal and external experiments demonstrate that DProQA is effective in ranking protein complex structures.

Availability and implementation
The source code, data, and pre-trained models are available at https://github.com/jianlin-cheng/DProQA.
CellBRF: a feature selection method for single-cell clustering using cell balance and random forest
Authors: Xu Yunpei , Li Hong-Dong , Lin Cui-Xiang , Zheng Ruiqing , Li Yaohang , Xu Jinhui , Wang Jianxin 
Published: June 2023

Abstract

Motivation
Single-cell RNA sequencing (scRNA-seq) offers a powerful tool to dissect the complexity of biological tissues through cell sub-population identification in combination with clustering approaches. Feature selection is a critical step for improving the accuracy and interpretability of single-cell clustering. Existing feature selection methods underutilize the discriminatory potential of genes across distinct cell types. We hypothesize that incorporating such information could further boost the performance of single cell clustering.

Results
We develop CellBRF, a feature selection method that considers genes’ relevance to cell types for single-cell clustering. The key idea is to identify genes that are most important for discriminating cell types through random forests guided by predicted cell labels. Moreover, it proposes a class balancing strategy to mitigate the impact of unbalanced cell type distributions on feature importance evaluation. We benchmark CellBRF on 33 scRNA-seq datasets representing diverse biological scenarios and demonstrate that it substantially outperforms state-of-the-art feature selection methods in terms of clustering accuracy and cell neighborhood consistency. Furthermore, we demonstrate the outstanding performance of our selected features through three case studies on cell differentiation stage identification, non-malignant cell subtype identification, and rare cell identification. CellBRF provides a new and effective tool to boost single-cell clustering accuracy.

Availability and implementation
All source codes of CellBRF are freely available at https://github.com/xuyp-csu/CellBRF.
ChromDL: a next-generation regulatory DNA classifier
Authors: Hill Christopher , Hudaiberdiev Sanjarbek , Ovcharenko Ivan 
Published: June 2023

Abstract

Motivation
Predicting the regulatory function of non-coding DNA using only the DNA sequence continues to be a major challenge in genomics. With the advent of improved optimization algorithms, faster GPU speeds, and more intricate machine-learning libraries, hybrid convolutional and recurrent neural network architectures can be constructed and applied to extract crucial information from non-coding DNA.

Results
Using a comparative analysis of the performance of thousands of Deep Learning architectures, we developed ChromDL, a neural network architecture combining bidirectional gated recurrent units, convolutional neural networks, and bidirectional long short-term memory units, which significantly improves upon a range of prediction metrics compared to its predecessors in transcription factor binding site, histone modification, and DNase-I hyper-sensitive site detection. Combined with a secondary model, it can be utilized for accurate classification of gene regulatory elements. The model can also detect weak transcription factor binding as compared to previously developed methods and has the potential to help delineate transcription factor binding motif specificities.

Availability and implementation
The ChromDL source code can be found at https://github.com/chrishil1/ChromDL.
Seeding with minimized subsequence
Authors: Li Xiang , Shi Qian , Chen Ke , Shao Mingfu 
Published: June 2023

Abstract

Motivation
Modern methods for computation-intensive tasks in sequence analysis (e.g. read mapping, sequence alignment, genome assembly, etc.) often first transform each sequence into a list of short, regular-length seeds so that compact data structures and efficient algorithms can be employed to handle the ever-growing large-scale data. Seeding methods using kmers (substrings of length k) have gained tremendous success in processing sequencing data with low mutation/error rates. However, they are much less effective for sequencing data with high error rates as kmers cannot tolerate errors.

Results
We propose SubseqHash, a strategy that uses subsequences, rather than substrings, as seeds. Formally, SubseqHash maps a string of length n to its smallest subsequence of length k, k &lt; n, according to a given order overall length-k strings. Finding the smallest subsequence of a string by enumeration is impractical as the number of subsequences grows exponentially. To overcome this barrier, we propose a novel algorithmic framework that consists of a specifically designed order (termed ABC order) and an algorithm that computes the minimized subsequence under an ABC order in polynomial time. We first show that the ABC order exhibits the desired property and the probability of hash collision using the ABC order is close to the Jaccard index. We then show that SubseqHash overwhelmingly outperforms the substring-based seeding methods in producing high-quality seed-matches for three critical applications: read mapping, sequence alignment, and overlap detection. SubseqHash presents a major algorithmic breakthrough for tackling the high error rates and we expect it to be widely adapted for long-reads analysis.

Availability and implementation
SubseqHash is freely available at https://github.com/Shao-Group/subseqhash.
Deep statistical modelling of nanopore sequencing translocation times reveals latent non-B DNA structures
Authors: Hosseini Marjan , Palmer Aaron , Manka William , Grady Patrick G S , Patchigolla Venkata , Bi Jinbo , O’Neill Rachel J , Chi Zhiyi , Aguiar Derek 
Published: June 2023

AbstractMotivationNon-canonical (or non-B) DNA are genomic regions whose three-dimensional conformation deviates from the canonical double helix. Non-B DNA play an important role in basic cellular processes and are associated with genomic instability, gene regulation, and oncogenesis. Experimental methods are low-throughput and can detect only a limited set of non-B DNA structures, while computational methods rely on non-B DNA base motifs, which are necessary but not sufficient indicators of non-B structures. Oxford Nanopore sequencing is an efficient and low-cost platform, but it is currently unknown whether nanopore reads can be used for identifying non-B structures.ResultsWe build the first computational pipeline to predict non-B DNA structures from nanopore sequencing. We formalize non-B detection as a novelty detection problem and develop the GoFAE-DND, an autoencoder that uses goodness-of-fit (GoF) tests as a regularizer. A discriminative loss encourages non-B DNA to be poorly reconstructed and optimizing Gaussian GoF tests allows for the computation of P-values that indicate non-B structures. Based on whole genome nanopore sequencing of NA12878, we show that there exist significant differences between the timing of DNA translocation for non-B DNA bases compared with B-DNA. We demonstrate the efficacy of our approach through comparisons with novelty detection methods using experimental data and data synthesized from a new translocation time simulator. Experimental validations suggest that reliable detection of non-B DNA from nanopore sequencing is achievable.Availability and implementationSource code is available at https://github.com/bayesomicslab/ONT-nonb-GoFAE-DND.
Phylogenomic branch length estimation using quartets
Authors: Tabatabaee Yasamin , Zhang Chao , Warnow Tandy , Mirarab Siavash 
Published: June 2023

Abstract

Motivation
Branch lengths and topology of a species tree are essential in most downstream analyses, including estimation of diversification dates, characterization of selection, understanding adaptation, and comparative genomics. Modern phylogenomic analyses often use methods that account for the heterogeneity of evolutionary histories across the genome due to processes such as incomplete lineage sorting. However, these methods typically do not generate branch lengths in units that are usable by downstream applications, forcing phylogenomic analyses to resort to alternative shortcuts such as estimating branch lengths by concatenating gene alignments into a supermatrix. Yet, concatenation and other available approaches for estimating branch lengths fail to address heterogeneity across the genome.

Results
In this article, we derive expected values of gene tree branch lengths in substitution units under an extension of the multispecies coalescent (MSC) model that allows substitutions with varying rates across the species tree. We present CASTLES, a new technique for estimating branch lengths on the species tree from estimated gene trees that uses these expected values, and our study shows that CASTLES improves on the most accurate prior methods with respect to both speed and accuracy.

Availability and implementation
CASTLES is available at https://github.com/ytabatabaee/CASTLES.
CProMG: controllable protein-oriented molecule generation with desired binding affinity and drug-like properties
Authors: Li Jia-Ning , Yang Guang , Zhao Peng-Cheng , Wei Xue-Xin , Shi Jian-Yu 
Published: June 2023

Abstract

Motivation
Deep learning-based molecule generation becomes a new paradigm of de novo molecule design since it enables fast and directional exploration in the vast chemical space. However, it is still an open issue to generate molecules, which bind to specific proteins with high-binding affinities while owning desired drug-like physicochemical properties.

Results
To address these issues, we elaborate a novel framework for controllable protein-oriented molecule generation, named CProMG, which contains a 3D protein embedding module, a dual-view protein encoder, a molecule embedding module, and a novel drug-like molecule decoder. Based on fusing the hierarchical views of proteins, it enhances the representation of protein binding pockets significantly by associating amino acid residues with their comprising atoms. Through jointly embedding molecule sequences, their drug-like properties, and binding affinities w.r.t. proteins, it autoregressively generates novel molecules having specific properties in a controllable manner by measuring the proximity of molecule tokens to protein residues and atoms. The comparison with state-of-the-art deep generative methods demonstrates the superiority of our CProMG. Furthermore, the progressive control of properties demonstrates the effectiveness of CProMG when controlling binding affinity and drug-like properties. After that, the ablation studies reveal how its crucial components contribute to the model respectively, including hierarchical protein views, Laplacian position encoding as well as property control. Last, a case study w.r.t. protein illustrates the novelty of CProMG and the ability to capture crucial interactions between protein pockets and molecules. It’s anticipated that this work can boost de novo molecule design.

Availability and implementation
The code and data underlying this article are freely available at https://github.com/lijianing0902/CProMG.
A weighted distance-based approach for deriving consensus tumor evolutionary trees
Authors: Guang Ziyun , Smith-Erb Matthew , Oesper Layla 
Published: June 2023

Abstract

Motivation
The acquisition of somatic mutations by a tumor can be modeled by a type of evolutionary tree. However, it is impossible to observe this tree directly. Instead, numerous algorithms have been developed to infer such a tree from different types of sequencing data. But such methods can produce conflicting trees for the same patient, making it desirable to have approaches that can combine several such tumor trees into a consensus or summary tree. We introduce The Weighted m-Tumor Tree Consensus Problem (W-m-TTCP) to find a consensus tree among multiple plausible tumor evolutionary histories, each assigned a confidence weight, given a specific distance measure between tumor trees. We present an algorithm called TuELiP that is based on integer linear programming which solves the W-m-TTCP, and unlike other existing consensus methods, allows the input trees to be weighted differently.

Results
On simulated data we show that TuELiP outperforms two existing methods at correctly identifying the true underlying tree used to create the simulations. We also show that the incorporation of weights can lead to more accurate tree inference. On a Triple-Negative Breast Cancer dataset, we show that including confidence weights can have important impacts on the consensus tree identified.

Availability
An implementation of TuELiP and simulated datasets are available at https://bitbucket.org/oesperlab/consensus-ilp/src/main/.
Deep Local Analysis deconstructs protein–protein interfaces and accurately estimates binding affinity changes upon mutation
Authors: Mohseni Behbahani Yasser , Laine Elodie , Carbone Alessandra 
Published: June 2023

Abstract

Motivation
The spectacular recent advances in protein and protein complex structure prediction hold promise for reconstructing interactomes at large-scale and residue resolution. Beyond determining the 3D arrangement of interacting partners, modeling approaches should be able to unravel the impact of sequence variations on the strength of the association.

Results
In this work, we report on Deep Local Analysis, a novel and efficient deep learning framework that relies on a strikingly simple deconstruction of protein interfaces into small locally oriented residue-centered cubes and on 3D convolutions recognizing patterns within cubes. Merely based on the two cubes associated with the wild-type and the mutant residues, DLA accurately estimates the binding affinity change for the associated complexes. It achieves a Pearson correlation coefficient of 0.735 on about 400 mutations on unseen complexes. Its generalization capability on blind datasets of complexes is higher than the state-of-the-art methods. We show that taking into account the evolutionary constraints on residues contributes to predictions. We also discuss the influence of conformational variability on performance. Beyond the predictive power on the effects of mutations, DLA is a general framework for transferring the knowledge gained from the available non-redundant set of complex protein structures to various tasks. For instance, given a single partially masked cube, it recovers the identity and physicochemical class of the central residue. Given an ensemble of cubes representing an interface, it predicts the function of the complex.

Availability and implementation
Source code and models are available at http://gitlab.lcqb.upmc.fr/DLA/DLA.git.
AttOmics: attention-based architecture for diagnosis and prognosis from omics data
Authors: Beaude Aurélien , Rafiee Vahid Milad , Augé Franck , Zehraoui Farida , Hanczar Blaise 
Published: June 2023

AbstractMotivationThe increasing availability of high-throughput omics data allows for considering a new medicine centered on individual patients. Precision medicine relies on exploiting these high-throughput data with machine-learning models, especially the ones based on deep-learning approaches, to improve diagnosis. Due to the high-dimensional small-sample nature of omics data, current deep-learning models end up with many parameters and have to be fitted with a limited training set. Furthermore, interactions between molecular entities inside an omics profile are not patient specific but are the same for all patients.ResultsIn this article, we propose AttOmics, a new deep-learning architecture based on the self-attention mechanism. First, we decompose each omics profile into a set of groups, where each group contains related features. Then, by applying the self-attention mechanism to the set of groups, we can capture the different interactions specific to a patient. The results of different experiments carried out in this article show that our model can accurately predict the phenotype of a patient with fewer parameters than deep neural networks. Visualizing the attention maps can provide new insights into the essential groups for a particular phenotype.Availability and implementationThe code and data are available at https://forge.ibisc.univ-evry.fr/abeaude/AttOmics. TCGA data can be downloaded from the Genomic Data Commons Data Portal.