bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sexual satisfaction of middle-aged and older adults: longitudinal findings from a nationally representative sample
Authors: Buczak-Stec Elżbieta , König Hans-Helmut , Hajek André 
Published: July 2020

Abstract

Background
whilst previous studies have investigated the determinants of sexual satisfaction (i) using longitudinal data or (ii) among older adults, only a few studies have done both at the sametime.

Objective
the purpose of this study was to investigate the determinants of sexual satisfaction longitudinally among middle-aged and older adults.

Design
nationally representative longitudinal study (German Ageing Survey—DEAS).

Setting
community-dwelling individuals in Germany. Data drawn from three waves (2002, 2008, 2011).

Subjects
individuals aged 40–95 (36.9% age group 65+). At wave 2 in the year 2002, n = 3,843 individuals took part.

Methods
well-established and widely used scales were used to quantify the independent variables. We included variables such as sociodemographic factors, self-rated health, physical functioning, depression and loneliness in our analysis. Sexual satisfaction was our outcome measure. Results were stratified by age (40–64, 65+). To take into account the multilevel data structure, we used random coefficient models.

Results
random-effects regressions showed that increased sexual satisfaction was consistently associated with the following variables in both age groups: lower number of physical illnesses, β = −0.03, P &lt; 0.001 (betas coefficients given for individuals 65 years and over); better self-rated health, β = −0.06, P &lt; 0.001; absence of depression, β = −0.16, P &lt; 0.01; and higher importance of sexuality and intimacy, β = 0.08, P &lt; 0.001. Moreover, sexual satisfaction was associated with having a partner: β = 0.16, P &lt; 0.001; living with a partner in the same household, β = 0.26, P &lt; 0.001; and a lower score of loneliness, β = −.28, P &lt; 0.001. In contrast, sexual satisfaction was, for example, not associated with cognitive functioning.

Conclusions
the most surprising findings were that among both middle-aged and older adults, almost the same determinants (with exception of sociodemographic factors) were associated with satisfaction with sexlife.
TERL: classification of transposable elements by convolutional neural networks
Authors: da Cruz Murilo Horacio Pereira , Domingues Douglas Silva , Saito Priscila Tiemi Maeda , Paschoal Alexandre Rossi , Bugatti Pedro Henrique 
Published: July 2020

Abstract
Transposable elements (TEs) are the most represented sequences occurring in eukaryotic genomes. Few methods provide the classification of these sequences into deeper levels, such as superfamily level, which could provide useful and detailed information about these sequences. Most methods that classify TE sequences use handcrafted features such as k-mers and homology-based search, which could be inefficient for classifying non-homologous sequences. Here we propose an approach, called transposable elements pepresentation learner (TERL), that preprocesses and transforms one-dimensional sequences into two-dimensional space data (i.e., image-like data of the sequences) and apply it to deep convolutional neural networks. This classification method tries to learn the best representation of the input data to classify it correctly. We have conducted six experiments to test the performance of TERL against other methods. Our approach obtained macro mean accuracies and F1-score of 96.4% and 85.8% for superfamilies and 95.7% and 91.5% for the order sequences from RepBase, respectively. We have also obtained macro mean accuracies and F1-score of 95.0% and 70.6% for sequences from seven databases into superfamily level and 89.3% and 73.9% for the order level, respectively. We surpassed accuracy, recall and specificity obtained by other methods on the experiment with the classification of order level sequences from seven databases and surpassed by far the time elapsed of any other method for all experiments. Therefore, TERL can learn how to predict any hierarchical level of the TEs classification system and is about 20 times and three orders of magnitude faster than TEclass and PASTEC, respectively https://github.com/muriloHoracio/TERL. Contact:murilocruz@alunos.utfpr.edu.br
PAMOGK: a pathway graph kernel-based multiomics approach for patient clustering
Authors: Tepeli Yasin Ilkagan , Ünal Ali Burak , Akdemir Furkan Mustafa , Tastan Oznur 
Published: July 2020

AbstractMotivationAccurate classification of patients into molecular subgroups is critical for the development of effective therapeutics and for deciphering what drives these subgroups to cancer. The availability of multiomics data catalogs for large cohorts of cancer patients provides multiple views into the molecular biology of the tumors with unprecedented resolution.ResultsWe develop Pathway-based MultiOmic Graph Kernel clustering (PAMOGK) that integrates multiomics patient data with existing biological knowledge on pathways. We develop a novel graph kernel that evaluates patient similarities based on a single molecular alteration type in the context of a pathway. To corroborate multiple views of patients evaluated by hundreds of pathways and molecular alteration combinations, we use multiview kernel clustering. Applying PAMOGK to kidney renal clear cell carcinoma (KIRC) patients results in four clusters with significantly different survival times (P-value =1.24e−11). When we compare PAMOGK to eight other state-of-the-art multiomics clustering methods, PAMOGK consistently outperforms these in terms of its ability to partition KIRC patients into groups with different survival distributions. The discovered patient subgroups also differ with respect to other clinical parameters such as tumor stage and grade, and primary tumor and metastasis tumor spreads. The pathways identified as important are highly relevant to KIRC.Availability and implementationgithub.com/tastanlab/pamogk.Supplementary informationSupplementary data are available at Bioinformatics online.
FANCY: fast estimation of privacy risk in functional genomics data
Authors: Gürsoy Gamze , Brannon Charlotte M , Navarro Fabio C P , Gerstein Mark 
Published: July 2020

Abstract

Motivation
Functional genomics data are becoming clinically actionable, raising privacy concerns. However, quantifying privacy leakage via genotyping is difficult due to the heterogeneous nature of sequencing techniques. Thus, we present FANCY, a tool that rapidly estimates the number of leaking variants from raw RNA-Seq, ATAC-Seq and ChIP-Seq reads, without explicit genotyping. FANCY employs supervised regression using overall sequencing statistics as features and provides an estimate of the overall privacy risk before data release.

Results
FANCY can predict the cumulative number of leaking SNVs with an average 0.95 R2 for all independent test sets. We realize the importance of accurate prediction when the number of leaked variants is low. Thus, we develop a special version of the model, which can make predictions with higher accuracy when the number of leaking variants is low.

Availability and implementation
A python and MATLAB implementation of FANCY, as well as custom scripts to generate the features can be found at https://github.com/gersteinlab/FANCY. We also provide jupyter notebooks so that users can optimize the parameters in the regression model based on their own data. An easy-to-use webserver that takes inputs and displays results can be found at fancy.gersteinlab.org.

Supplementary information
Supplementary data are available at Bioinformatics online.
Few shot domain adaptation forin situmacromolecule structural classification in cryoelectron tomograms
Authors: Yu Liangyong , Li Ran , Zeng Xiangrui , Wang Hongyi , Jin Jie , Ge Yang , Jiang Rui , Xu Min 
Published: July 2020

AbstractMotivationCryoelectron tomography (cryo-ET) visualizes structure and spatial organization of macromolecules and their interactions with other subcellular components inside single cells in the close-to-native state at submolecular resolution. Such information is critical for the accurate understanding of cellular processes. However, subtomogram classification remains one of the major challenges for the systematic recognition and recovery of the macromolecule structures in cryo-ET because of imaging limits and data quantity. Recently, deep learning has significantly improved the throughput and accuracy of large-scale subtomogram classification. However, often it is difficult to get enough high-quality annotated subtomogram data for supervised training due to the enormous expense of labeling. To tackle this problem, it is beneficial to utilize another already annotated dataset to assist the training process. However, due to the discrepancy of image intensity distribution between source domain and target domain, the model trained on subtomograms in source domain may perform poorly in predicting subtomogram classes in the target domain.ResultsIn this article, we adapt a few shot domain adaptation method for deep learning-based cross-domain subtomogram classification. The essential idea of our method consists of two parts: (i) take full advantage of the distribution of plentiful unlabeled target domain data, and (ii) exploit the correlation between the whole source domain dataset and few labeled target domain data. Experiments conducted on simulated and real datasets show that our method achieves significant improvement on cross domain subtomogram classification compared with baseline methods.Availability and implementationSoftware is available online https://github.com/xulabs/aitom.Supplementary informationSupplementary data are available at Bioinformatics online.
A statistical approach for tracking clonal dynamics in cancer using longitudinal next-generation sequencing data
Authors: Vavoulis Dimitrios V , Cutts Anthony , Taylor Jenny C , Schuh Anna 
Published: July 2020

Abstract

Motivation
Tumours are composed of distinct cancer cell populations (clones), which continuously adapt to their local micro-environment. Standard methods for clonal deconvolution seek to identify groups of mutations and estimate the prevalence of each group in the tumour, while considering its purity and copy number profile. These methods have been applied on cross-sectional data and on longitudinal data after discarding information on the timing of sample collection. Two key questions are how can we incorporate such information in our analyses and is there any benefit in doing so?

Results
We developed a clonal deconvolution method, which incorporates explicitly the temporal spacing of longitudinally sampled tumours. By merging a Dirichlet Process Mixture Model with Gaussian Process priors and using as input a sequence of several sparsely collected samples, our method can reconstruct the temporal profile of the abundance of any mutation cluster supported by the data as a continuous function of time. We benchmarked our method on whole genome, whole exome and targeted sequencing data from patients with chronic lymphocytic leukaemia, on liquid biopsy data from a patient with melanoma and on synthetic data and we found that incorporating information on the timing of tissue collection improves model performance, as long as data of sufficient volume and complexity are available for estimating free model parameters. Thus, our approach is particularly useful when collecting a relatively long sequence of tumour samples is feasible, as in liquid cancers (e.g. leukaemia) and liquid biopsies.

Availability and implementation
The statistical methodology presented in this paper is freely available at github.com/dvav/clonosGP.

Supplementary information
Supplementary data are available at Bioinformatics online.
Triplet-based similarity score for fully multilabeled trees with poly-occurring labels
Authors: Ciccolella Simone , Bernardini Giulia , Denti Luca , Bonizzoni Paola , Previtali Marco , Della Vedova Gianluca 
Published: July 2020

Abstract

Motivation
The latest advances in cancer sequencing, and the availability of a wide range of methods to infer the evolutionary history of tumors, have made it important to evaluate, reconcile and cluster different tumor phylogenies. Recently, several notions of distance or similarities have been proposed in the literature, but none of them has emerged as the golden standard. Moreover, none of the known similarity measures is able to manage mutations occurring multiple times in the tree, a circumstance often occurring in real cases.

Results
To overcome these limitations, in this article, we propose MP3, the first similarity measure for tumor phylogenies able to effectively manage cases where multiple mutations can occur at the same time and mutations can occur multiple times. Moreover, a comparison of MP3 with other measures shows that it is able to classify correctly similar and dissimilar trees, both on simulated and on real data.

Availability and implementation
An open source implementation of MP3 is publicly available at https://github.com/AlgoLab/mp3treesim.

Supplementary information
Supplementary data are available at Bioinformatics online.
Co-phosphorylation networks reveal subtype-specific signaling modules in breast cancer
Authors: Ayati Marzieh , Chance Mark R , Koyutürk Mehmet 
Published: July 2020

Abstract

Motivation
Protein phosphorylation is a ubiquitous mechanism of post-translational modification that plays a central role in cellular signaling. Phosphorylation is particularly important in the context of cancer, as downregulation of tumor suppressors and upregulation of oncogenes by the dysregulation of associated kinase and phosphatase networks are shown to have key roles in tumor growth and progression. Despite recent advances that enable large-scale monitoring of protein phosphorylation, these data are not fully incorporated into such computational tasks as phenotyping and subtyping of cancers.

Results
We develop a network-based algorithm, CoPPNet, to enable unsupervised subtyping of cancers using phosphorylation data. For this purpose, we integrate prior knowledge on evolutionary, structural and functional association of phosphosites, kinase–substrate associations and protein–protein interactions with the correlation of phosphorylation of phosphosites across different tumor samples (a.k.a co-phosphorylation) to construct a context-specific-weighted network of phosphosites. We then mine these networks to identify subnetworks with correlated phosphorylation patterns. We apply the proposed framework to two mass-spectrometry-based phosphorylation datasets for breast cancer (BC), and observe that (i) the phosphorylation pattern of the identified subnetworks are highly correlated with clinically identified subtypes, and (ii) the identified subnetworks are highly reproducible across datasets that are derived from different studies. Our results show that integration of quantitative phosphorylation data with network frameworks can provide mechanistic insights into the differences between the signaling mechanisms that drive BC subtypes. Furthermore, the reproducibility of the identified subnetworks suggests that phosphorylation can provide robust classification of disease response and markers.

Availability and implementation
CoPPNet is available at http://compbio.case.edu/coppnet/.

Supplementary information
Supplementary data are available at Bioinformatics online.
Rtpca: an R package for differential thermal proximity coaggregation analysis
Authors: Kurzawa Nils , Mateus André , Savitski Mikhail M 
Published: July 2020

Abstract

Summary
Rtpca is an R package implementing methods for inferring protein–protein interactions (PPIs) based on thermal proteome profiling experiments of a single condition or in a differential setting via an approach called thermal proximity coaggregation. It offers user-friendly tools to explore datasets for their PPI predictive performance and easily integrates with available R packages.

Availability and implementation
Rtpca is available from Bioconductor (https://bioconductor.org/packages/Rtpca).

Supplementary information
Supplementary data are available at Bioinformatics online.
Hive Panel Explorer: an interactive network visualization tool
Authors: Perez Sarah E I , Hahn Aria S , Krzywinski Martin , Hallam Steven J 
Published: July 2020

Abstract

Motivation
Networks are used to relate topological structure to system dynamics and function, particularly in ecology systems biology. Network analysis is often guided or complemented by data-driven visualization. Hive one of many network visualizations, distinguish themselves as providing a general, consistent and coherent rule-based representation to motivate hypothesis development and testing.

Results
Here, we present HyPE, Hive Panel Explorer, a software application that creates a panel of interactive hive plots. HyPE enables network exploration based on user-driven layout rules and parameter combinations for simultaneous of multiple network views. We demonstrate HyPE’s features by exploring a microbial co-occurrence network constructed from forest soil microbiomes.

Availability and implementation
HyPE is available under the GNU license: https://github.com/hallamlab/HivePanelExplorer.

Supplementary information
Supplementary data are available at Bioinformatics online.
Transcriptor: a comprehensive platform for annotation of the enzymatic functions of transcripts
Authors: Ren Ailin , Zhang Dachuan , Tian Yu , Cai Pengli , Zhang Tong , Hu Qian-Nan 
Published: July 2020

Abstract

Motivation
Rapid advances in sequencing technology have resulted huge increases in the accessibility of sequencing data. Moreover, researchers are focusing more on organisms that lack a reference genome. However, few easy-to-use web servers focusing on annotations of enzymatic functions are available. Accordingly, in this study, we describe Transcriptor, a novel platform for annotating transcripts encoding enzymes.

Results
The transcripts were evaluated using more than 300 000 in-house enzymatic reactions through bridges of Enzyme Commission numbers. Transcriptor also enabled ontology term identification and along with associated enzymes, visualization and prediction of domains and annotation of regulatory structure, such as long noncoding RNAs, which could facilitate the discovery of new functions in model or nonmodel species. Transcriptor may have applications in elucidation of the roles of organs transcriptomes and secondary metabolite biosynthesis in organisms lacking a reference genome.

Availability and implementation
Transcriptor is available at http://design.rxnfinder.org/transcriptor/.

Supplementary information
Supplementary data are available at Bioinformatics online.
Persistent minimal sequences of SARS-CoV-2
Authors: Pratas Diogo , Silva Jorge M 
Published: July 2020

Abstract

Motivation
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused more than 14 million cases and more than half million deaths. Given the absence of implemented therapies, new analysis, diagnosis and therapeutics are of great importance.

Results
Analysis of SARS-CoV-2 genomes from the current outbreak reveals the presence of short persistent DNA/RNA sequences that are absent from the human genome and transcriptome (PmRAWs). For the PmRAWs with length 12, only four exist at the same location in all SARS-CoV-2. At the gene level, we found one PmRAW of size 13 at the Spike glycoprotein coding sequence. This protein is fundamental for binding in human ACE2 and further use as an entry receptor to invade target cells. Applying protein structural prediction, we localized this PmRAW at the surface of the Spike protein, providing a potential targeted vector for diagnostics and therapeutics. In addition, we show a new pattern of relative absent words (RAWs), characterized by the progressive increase of GC content (Guanine and Cytosine) according to the decrease of RAWs length, contrarily to the virus and host genome distributions. New analysis shows the same property during the Ebola virus outbreak. At a computational level, we improved the alignment-free method to identify pathogen-specific signatures in balance with GC measures and removed previous size limitations.

Availability and implementation
https://github.com/cobilab/eagle.

Supplementary information
Supplementary data are available at Bioinformatics online.
Bringing Botany into Focus: Addressing Plant Blindness in Undergraduates Through an Immersive Botanical Experience
Authors: Colon Jessica , Tiernan Nichole , Oliphant Simone , Shirajee Ateev , Flickinger Jonathan , Liu Hong , Francisco-Ortega Javier , McCartney Melissa 
Published: July 2020

Abstract
Undergraduate science, technology, engineering, and math students (STEM) are usually not attuned to the intricacies of plant life histories or to the dynamic role plants play in ecosystems and human society, a phenomenon termed plant blindness. Botany education has declined in the past decades, whereas career paths that need and benefit from a workforce with botanical knowledge have increased. Consequently, there is a need to reduce plant blindness among undergraduate students, regardless of their career trajectories. We provide evidence that participation in a botanical experience as part of a general biology course can positively shift undergraduates’ perception of botany, the study of plants. Students participating in the botanical experience showed significant positive shifts in their ability to connect botany to their college major and future careers. In addition, we show qualitative data indicating a new respect for plants and a new appreciation for the diversity among plants.
What prevents health policy being ‘evidence-based’? New ways to think about evidence, policy and interventions in health
Authors: Lancaster Kari , Rhodes Tim 
Published: July 2020

AbstractBackgroundEvidence-based policy decision-making is a dominant paradigm in health but realizing this ideal has proven challenging.Sources of dataThis paper conceptually maps health policy, policy studies and social science literature critically engaged with evidence and decision-making. No new data were generated or analysed in support of this review.Areas of agreementBarriers to evidence-based policy have been documented, with efforts made to increase the uptake of evidence.Areas of controversyEvident complexities have been regarded as a problem of translation. However, this assumes that policy-making is a process of authoritative choice, and that ‘evidence’ is inherently valuable policy knowledge, which has been critiqued.Growing pointsAlternative accounts urge consideration of how evidence comes to bear on decisions made within complex systems, and what counts as evidence.Areas timely for developing researchAn ‘evidence-making intervention’ approach offers a framework for conceptualizing how evidence and interventions are made relationally in practices, thus working with the politics and contingencies of implementation and policy-making.