bluuebunny/crossref_metadata_2025_split huggingface dataset:
tRNAstudio: facilitating the study of human mature tRNAs from deep sequencing datasets
Authors: Murillo-Recio Marina , Martínez de Lejarza Samper Ignacio Miguel , Tuñí i Domínguez Cristina , Ribas de Pouplana Lluís , Torres Adrian Gabriel 
Published: April 2022

Abstract

Summary
High-throughput sequencing of transfer RNAs (tRNA-Seq) is a powerful approach to characterize the cellular tRNA pool. Currently, however, analyzing tRNA-Seq datasets requires strong bioinformatics and programming skills. tRNAstudio facilitates the analysis of tRNA-Seq datasets and extracts information on tRNA gene expression, post-transcriptional tRNA modification levels, and tRNA processing steps. Users need only running a few simple bash commands to activate a graphical user interface that allows the easy processing of tRNA-Seq datasets in local mode. Output files include extensive graphical representations and associated numerical tables, and an interactive html summary report to help interpret the data. We have validated tRNAstudio using datasets generated by different experimental methods and derived from human cell lines and tissues that present distinct patterns of tRNA expression, modification and processing.

Availability and implementation
Freely available at https://github.com/GeneTranslationLab-IRB/tRNAstudio under an open-source GNU GPL v3.0 license.

Supplementary information
Supplementary data are available at Bioinformatics online.
TPpred-ATMV: therapeutic peptide prediction by adaptive multi-view tensor learning model
Authors: Yan Ke , Lv Hongwu , Guo Yichen , Chen Yongyong , Wu Hao , Liu Bin 
Published: April 2022

Abstract

Motivation
Therapeutic peptide prediction is important for the discovery of efficient therapeutic peptides and drug development. Researchers have developed several computational methods to identify different therapeutic peptide types. However, these computational methods focus on identifying some specific types of therapeutic peptides, failing to predict the comprehensive types of therapeutic peptides. Moreover, it is still challenging to utilize different properties to predict the therapeutic peptides.

Results
In this study, an adaptive multi-view based on the tensor learning framework TPpred-ATMV is proposed for predicting different types of therapeutic peptides. TPpred-ATMV constructs the class and probability information based on various sequence features. We constructed the latent subspace among the multi-view features and constructed an auto-weighted multi-view tensor learning model to utilize the high correlation based on the multi-view features. Experimental results showed that the TPpred-ATMV is better than or highly comparable with the other state-of-the-art methods for predicting eight types of therapeutic peptides.

Availability and implementation
The code of TPpred-ATMV is accessed at: https://github.com/cokeyk/TPpred-ATMV.

Supplementary information
Supplementary data are available at Bioinformatics online.
Driver gene detection through Bayesian network integration of mutation and expression profiles
Authors: Chen Zhong , Lu You , Cao Bo , Zhang Wensheng , Edwards Andrea , Zhang Kun 
Published: April 2022

AbstractMotivationThe identification of mutated driver genes and the corresponding pathways is one of the primary goals in understanding tumorigenesis at the patient level. Integration of multi-dimensional genomic data from existing repositories, e.g., The Cancer Genome Atlas (TCGA), offers an effective way to tackle this issue. In this study, we aimed to leverage the complementary genomic information of individuals and create an integrative framework to identify cancer-related driver genes. Specifically, based on pinpointed differentially expressed genes, variants in somatic mutations and a gene interaction network, we proposed an unsupervised Bayesian network integration (BNI) method to detect driver genes and estimate the disease propagation at the patient and/or cohort levels. This new method first captures inherent structural information to construct a functional gene mutation network and then extracts the driver genes and their controlled downstream modules using the minimum cover subset method.ResultsUsing other credible sources (e.g. Cancer Gene Census and Network of Cancer Genes), we validated the driver genes predicted by the BNI method in three TCGA pan-cancer cohorts. The proposed method provides an effective approach to address tumor heterogeneity faced by personalized medicine. The pinpointed drivers warrant further wet laboratory validation.Availability and implementationThe supplementary tables and source code can be obtained from https://xavieruniversityoflouisiana.sharefile.com/d-se6df2c8d0ebe4800a3030311efddafe5.Supplementary informationSupplementary data are available at Bioinformatics online.
Relational graph convolutional networks for predicting blood–brain barrier penetration of drug molecules
Authors: Ding Yan , Jiang Xiaoqian , Kim Yejin 
Published: April 2022

AbstractMotivationEvaluating the blood–brain barrier (BBB) permeability of drug molecules is a critical step in brain drug development. Traditional methods for the evaluation require complicated in vitro or in vivo testing. Alternatively, in silico predictions based on machine learning have proved to be a cost-efficient way to complement the in vitro and in vivo methods. However, the performance of the established models has been limited by their incapability of dealing with the interactions between drugs and proteins, which play an important role in the mechanism behind the BBB penetrating behaviors. To address this limitation, we employed the relational graph convolutional network (RGCN) to handle the drug–protein interactions as well as the properties of each individual drug.ResultsThe RGCN model achieved an overall accuracy of 0.872, an area under the receiver operating characteristic (AUROC) of 0.919 and an area under the precision-recall curve (AUPRC) of 0.838 for the testing dataset with the drug–protein interactions and the Mordred descriptors as the input. Introducing drug–drug similarity to connect structurally similar drugs in the data graph further improved the testing results, giving an overall accuracy of 0.876, an AUROC of 0.926 and an AUPRC of 0.865. In particular, the RGCN model was found to greatly outperform the LightGBM base model when evaluated with the drugs whose BBB penetration was dependent on drug–protein interactions. Our model is expected to provide high-confidence predictions of BBB permeability for drug prioritization in the experimental screening of BBB-penetrating drugs.Availability and implementationThe data and the codes are freely available at https://github.com/dingyan20/BBB-Penetration-Prediction.Supplementary informationSupplementary data are available at Bioinformatics online.
The ENDS of assumptions: an online tool for the epistemic non-parametric drug–response scoring
Authors: Amiryousefi Ali , Williams Bernardo , Jafari Mohieddin , Tang Jing 
Published: April 2022

AbstractMotivationThe drug sensitivity analysis is often elucidated from drug dose–response curves. These curves capture the degree of cell viability (or inhibition) over a range of induced drugs, often with parametric assumptions that are rarely validated.ResultsWe present a class of non-parametric models for the curve fitting and scoring of drug dose–responses. To allow a more objective representation of the drug sensitivity, these epistemic models devoid of any parametric assumptions attached to the linear fit, allow the parallel indexing such as half-maximal inhibitory concentration and area under curve. Specifically, three non-parametric models including spline (npS), monotonic and Bayesian and the parametric logistic are implemented. Other indices including maximum effective dose and drug–response span gradient pertinent to the npS are also provided to facilitate the interpretation of the fit. The collection of these models is implemented in an online app, standing as useful resource for drug dose–response curve fitting and analysis.Availability and implementationThe ENDS is freely available online at https://irscope.shinyapps.io/ENDS/ and source codes can be obtained from https://github.com/AmiryousefiLab/ENDS.Supplementary informationSupplementary data are available at Bioinformatics online.
A transdiagnostic review of neuroimaging studies of apathy and disinhibition in dementia
Authors: Jenkins Lisanne M , Wang Lei , Rosen Howie , Weintraub Sandra 
Published: April 2022

Abstract
Apathy and disinhibition are common and highly distressing neuropsychiatric symptoms associated with negative outcomes in persons with dementia. This paper is a critical review of functional and structural neuroimaging studies of these symptoms transdiagnostically in dementia of the Alzheimer type, which is characterized by prominent amnesia early in the disease course, and behavioural variant frontotemporal dementia, characterized by early social-comportmental deficits. We describe the prevalence and clinical correlates of these symptoms and describe methodological issues, including difficulties with symptom definition and different measurement instruments. We highlight the heterogeneity of findings, noting however, a striking similarity of the set of brain regions implicated across clinical diagnoses and symptoms. These regions involve several key nodes of the salience network, and we describe the functions and anatomical connectivity of these brain areas, as well as present a new theoretical account of disinhibition in dementia. Future avenues for research are discussed, including the importance of transdiagnostic studies, measuring subdomains of apathy and disinhibition, and examining different units of analysis for deepening our understanding of the networks and mechanisms underlying these extremely distressing symptoms.
Metaboloepigenetics in cancer, immunity, and cardiovascular disease
Authors: Keating Samuel T , El-Osta Assam 
Published: April 2022

AbstractThe influence of cellular metabolism on epigenetic pathways is well documented but misunderstood. Scientists have long known of the metabolic impact on epigenetic determinants. More often than not, that title role for DNA methylation was portrayed by the metabolite S-adenosylmethionine. Technically speaking, there are many other metabolites that drive epigenetic processes that instruct seemingly distant—yet highly connect pathways—and none more so than our understanding of the cancer epigenome. Recent studies have shown that available energy links the extracellular environment to influence cellular responses. This focused review examines the recent interest in epigenomics and casts cancer, metabolism, and immunity in unfamiliar roles—cooperating. There are not only language lessons from cancer research, we have come round to appreciate that reaching into areas previously thought of as too distinct are also object lessons in understanding health and disease. The Warburg effect is one such signature of how glycolysis influences metabolic shift during oncogenesis. That shift in metabolism—now recognized as central to proliferation in cancer biology—influences core enzymes that not only control gene expression but are also central to replication, condensation, and the repair of nucleic acid. These nuclear processes rely on metabolism, and with glucose at centre stage, the role of respiration and oxidative metabolism is now synonymous with the mitochondria as the powerhouses of metaboloepigenetics. The emerging evidence for metaboloepigenetics in trained innate immunity has revealed recognizable signalling pathways with antecedent extracellular stimulation. With due consideration to immunometabolism, we discuss the striking signalling similarities influencing these core pathways. The immunometabolic-epigenetic axis in cardiovascular disease has deeply etched connections with inflammation, and we examine the chromatin template as a carrier of epigenetic indices that determine the expression of genes influencing atherosclerosis and vascular complications of diabetes.
Outcomes of Children with Inflammatory Bowel Disease who Develop Anti-tumour Necrosis Factor-induced Skin Reactions
Authors: Dolinger Michael T , Rolfes Priya , Spencer Elizabeth , Stoffels Guillaume , Dunkin David , Dubinsky Marla C 
Published: April 2022

AbstractBackground and AimsAnti-tumour necrosis factor [anti-TNF] induced skin reactions are common adverse events in paediatric inflammatory bowel disease [IBD]. We aimed to report on outcomes of children with anti-TNF induced skin reactions who switched to ustekinumab [UST] vs. continued anti-TNF therapy.MethodsCharts were reviewed for paediatric IBD patients with anti-TNF induced skin reactions. Skin reactions, including psoriasiform dermatitis [PD], were classified as mild or severe based on a severity score. Primary outcome was frequency of skin resolution at 6 months. Secondary outcomes were combined clinical remission and skin resolution at 6 months and skin resolution at latest follow-up.ResultsA total of 111/638 [17%] children ([85, 21%] infliximab [IFX]; [26, 11%] adalimumab [ADA]) developed skin reactions. Eighty [72%] had PD, 25 [23%] infections, and four [4%] alopecia areata; 71 [64%] continued anti-TNF; and 40 [36%] switched to UST. In all, 73 [66%] had severe reactions and were more likely to switch to UST than if mild (37 [51%] vs. 3 [8%]; p &lt;0.0001). Switching to UST had a higher rate and odds of resolution (29 [73%] vs. 24 [34%]; p &lt;0.0001; odds ratio [OR] = 19.7, 95% confidence interval [CI]: 5.6, 69.5; p &lt;0.0001) and combined remission (21 [52%] vs. 22 [31%]; p = 0.03; OR = 8.5, 95% CI: 2.5, 28.4; p = 0.0005] vs. continuing anti-TNF at 6 months.ConclusionsChildren who switched to UST after anti-TNF induced skin reactions were more likely to have improved outcomes than those who continued anti-TNF therapy. Future studies are needed to determine immune mechanisms of anti-TNF induced skin reactions and treatment response.
Perioperative management of P2Y12 inhibitors in patients undergoing cardiac surgery within 1 year of PCI
Authors: Cao Davide , Swain Julie A , Sartori Samantha , Nardin Matteo , Zhang Zhongjie , Roumeliotis Anastasios , Nicolas Johny , Chiarito Mauro , Chandiramani Rishi , Pivato Carlo A , Spirito Alessandro , Giustino Gennaro , Stefanini Giulio G , Dangas George D , Baber Usman , Bhatt Deepak L , Adams David H , Sharma Samin K , Kini Annapoorna S , Mehran Roxana 
Published: April 2022

Abstract

Aims
To evaluate the impact of perioperative P2Y12 receptor inhibitor therapy among patients undergoing cardiac surgery within 1 year of percutaneous coronary intervention (PCI).

Methods and results
Patients undergoing cardiac surgery in the year post-PCI at three tertiary care centres between 2011 and 2018 were stratified into those who had received at least one dose of P2Y12 inhibitor prior to surgery (within 5 days for clopidogrel or prasugrel, or within 3 days for ticagrelor) and those who had not. The outcomes of interest were major adverse cardiac and cerebrovascular events (MACCEs) and bleeding. Among 20 279 PCI patients, 359 (1.8%) underwent cardiac surgery in the ensuing year, 76.3% of whom received coronary artery bypass grafts. Overall, 33 (9.2%) MACCEs and 85 (23.7%) bleeding events occurred within 30 days post-cardiac surgery. Perioperative P2Y12 inhibition (N = 133, 37%) was not associated with the risk of MACCEs or bleeding, despite numerically lower rates of myocardial infarction or stent thrombosis (0.0% vs. 2.6%; P = 0.089). Patients who continued the P2Y12 inhibitor until the day of surgery (N = 60, 17%) had significantly higher bleeding risk [adjusted odds ratio 2.93, 95% confidence interval 1.53–5.59)]. Predictors of MACCEs included a time interval from PCI to cardiac surgery of ≤30 days and reduced ejection fraction, whereas urgent/emergent surgery predicted bleeding. Chronic kidney disease and myocardial infarction as indication for PCI predicted both MACCEs and bleeding.

Conclusion
Among patients undergoing cardiac surgery in the year after PCI, the perioperative risk of ischaemic and bleeding events might be influenced by P2Y12 inhibitor therapy in addition to other risk parameters, including the timing and urgency of the procedure.
Population genetics of polymorphism and divergence in rapidly evolving populations
Authors: Melissa Matthew J , Good Benjamin H , Fisher Daniel S , Desai Michael M 
Published: April 2022

AbstractIn rapidly evolving populations, numerous beneficial and deleterious mutations can arise and segregate within a population at the same time. In this regime, evolutionary dynamics cannot be analyzed using traditional population genetic approaches that assume that sites evolve independently. Instead, the dynamics of many loci must be analyzed simultaneously. Recent work has made progress by first analyzing the fitness variation within a population, and then studying how individual lineages interact with this traveling fitness wave. However, these “traveling wave” models have previously been restricted to extreme cases where selection on individual mutations is either much faster or much slower than the typical coalescent timescale Tc. In this work, we show how the traveling wave framework can be extended to intermediate regimes in which the scaled fitness effects of mutations (Tcs) are neither large nor small compared to one. This enables us to describe the dynamics of populations subject to a wide range of fitness effects, and in particular, in cases where it is not immediately clear which mutations are most important in shaping the dynamics and statistics of genetic diversity. We use this approach to derive new expressions for the fixation probabilities and site frequency spectra of mutations as a function of their scaled fitness effects, along with related results for the coalescent timescale Tc and the rate of adaptation or Muller’s ratchet. We find that competition between linked mutations can have a dramatic impact on the proportions of neutral and selected polymorphisms, which is not simply summarized by the scaled selection coefficient Tcs. We conclude by discussing the implications of these results for population genetic inferences.
Retinoids rescue ceruloplasmin secretion and alleviate oxidative stress in Wilson’s disease-specific hepatocytes
Authors: Song Dan , Takahashi Gou , Zheng Yun-Wen , Matsuo-Takasaki Mami , Li Jingyue , Takami Miho , An Yuri , Hemmi Yasuko , Miharada Natsumi , Fujioka Tsuyoshi , Noguchi Michiya , Nakajima Takashi , Saito Megumu K , Nakamura Yukio , Oda Tatsuya , Miyaoka Yuichiro , Hayashi Yohei 
Published: April 2022

Abstract
Wilson’s disease (WD) is a copper metabolic disorder caused by a defective ATP7B function. Conventional therapies cause severe side effects and significant variation in efficacy, according to cohort studies. Thus, exploring new therapeutic approaches to prevent progression to liver failure is urgent. To study the physiology and pathology of WD, immortalized cell lines and rodent WD models have been used conventionally; however, a large gap remains among different species as well as in genetic backgrounds among individuals. We generated induced pluripotent stem cells (iPSCs) from four WD patients carrying compound heterozygous mutations in the ATP7B gene. ATP7B loss- and gain-of-functions were further manifested with ATP7B-deficient iPSCs and heterozygously corrected R778L WD patient-derived iPSCs using CRISPR-Cas9-based gene editing. Although the expression of ATP7B protein varied among WD-specific hepatocytes differentiated from these iPSCs, the expression and secretion of ceruloplasmin (Cp), a downstream copper carrier in plasma, were consistently decreased in WD patient-derived and ATP7B-deficient hepatocytes. A transcriptome analysis detected abnormalities in the retinoid signaling pathway and lipid metabolism in WD-specific hepatocytes. Drug screening using WD patient-derived hepatocytes identified retinoids as promising candidates for rescuing Cp secretion. All-trans retinoic acid also alleviates reactive oxygen species production induced by lipid accumulation in WD-specific hepatocytes treated with oleic acid. These patient-derived iPSC-based hepatic models function as effective platforms for the development of potential therapeutics for hepatic steatosis in WD and other fatty liver diseases.
Organizations Involved in Humanitarian Action: Introducing a New Dataset
Authors: Egger Clara , Schopper Doris 
Published: April 2022

AbstractAlthough the humanitarian sector has gained prominence in the management of contemporary conflicts, data on the diversity of organizations involved in humanitarian aid are scarce and do not rely on well-founded inclusion criteria. This lack of data limits not only our knowledge of humanitarian action but also our understanding of international security and of non-state actors’ role in foreign aid. The Humanitarian Organisations Dataset (HOD) seeks to fill these gaps. Based on clear and reproducible criteria, 2,505 organizations active in the humanitarian sector have been identified while information on their history, nature, activities, and geographical location has been collected. Our analyses depart from prevailing Western-centered accounts of humanitarian, show regional variations in types of organizations, and identify distinct historical patterns by region and by type of organization. They also document a large span of humanitarian activities, going beyond the current exclusive focus on in-kind assistance. We illustrate how the dataset can contribute to further research through its combination with other data and explore the relationship between NGOs and governments in humanitarian aid funding. Beyond its value for scholars, we anticipate that the HOD will also be of interest to policymakers and nonacademic users concerned with humanitarian action and crisis-management issues.
Quantitative Changes in Intratumoral Habitats on MRI Correlate With Pathologic Response in Early-stage ER/PR+ HER2− Breast Cancer Treated With Preoperative Stereotactic Ablative Body Radiotherapy
Authors: Jared Weinfurtner R , Abdalah Mahmoud , Stringfield Olya , Ataya Dana , Williams Angela , Mooney Blaise , Rosa Marilin , Lee Marie C , Khakpour Nazanin , Laronga Christine , Czerniecki Brian , Diaz Roberto , Ahmed Kamran , Washington Iman , Latifi Kujtim , Niell Bethany L , Montejo Michael , Raghunand Natarajan 
Published: April 2022

Abstract

Objective
To quantitatively evaluate intratumoral habitats on dynamic contrast-enhanced (DCE) breast MRI to predict pathologic breast cancer response to stereotactic ablative body radiotherapy (SABR).

Methods
Participants underwent SABR treatment (28.5 Gy x3), baseline and post-SABR MRI, and breast-conserving surgery for ER/PR+ HER2− breast cancer. MRI analysis was performed on DCE T1-weighted images. MRI voxels were assigned eight habitats based on high (H) or low (L) maximum enhancement and the sequentially numbered dynamic sequence of maximum enhancement (H1−4, L1−4). MRI response was analyzed by percent tumor volume remaining (%VR = volume post-SABR/volume pre-SABR), and percent habitat makeup (%HM of habitat X = habitat X voxels/total voxels in the segmented volume). These were correlated with percent tumor bed cellularity (%TC) for pathologic response.

Results
Sixteen patients completed the trial. The %TC ranged 20%–80%. MRI %VR demonstrated strong correlations with %TC (Pearson R = 0.7–0.89). Pre-SABR tumor %HMs differed significantly from whole breasts (P = 0.005 to &lt;0.00001). Post-SABR %HM of tumor habitat H4 demonstrated the largest change, increasing 13% (P = 0.039). Conversely, combined %HM for H1−3 decreased 17% (P = 0.006). This change correlated with %TC (P &lt; 0.00001) and distinguished pathologic partial responders (≤70 %TC) from nonresponders with 94% accuracy, 93% sensitivity, 100% specificity, 100% positive predictive value, and 67% negative predictive value.

Conclusion
In patients undergoing preoperative SABR treatment for ER/PR+ HER2− breast cancer, quantitative MRI habitat analysis of %VR and %HM change correlates with pathologic response.