bluuebunny/crossref_metadata_2025_split huggingface dataset:
A statistical 3D model of the human cortical vasculature to compute the hemodynamic fingerprint of the BOLD fMRI signal
Authors: Báez-Yáñez Mario Gilberto , Siero Jeroen C.W. , Petridou Natalia 
Published: October 2020

ABSTRACTBOLD fMRI is a commonly used technique to map brain activity; nevertheless, BOLD fMRI is an indirect measurement of brain function triggered by neurometabolic and neurovascular coupling. Hence, the origin of the BOLD fMRI signal is quite complex, and the signal formation depends, among others, on the geometry of the cortical vasculature and the associated hemodynamic behavior. To characterize and quantify the hemodynamic contributions to the BOLD signal response in humans, it is necessary to adopt a computational model that resembles the human cortical vascular architecture and mimics realistic hemodynamic changes. To this end, we have developed a statistically defined 3D vascular model that resembles the human cortical vasculature. Using this model, we simulated hemodynamic changes triggered by a neuronal activation and local magnetic field disturbances created by the vascular topology and the blood oxygenation changes. The proposed model considers also the biophysical interactions and the intrinsic magnetic properties of the nearby tissue in order to compute adynamicBOLD fMRI signal response. This computational pipeline results in an integrated biophysical model that can provide a better insight on the understanding and quantification of the hemodynamic fingerprint of the BOLD fMRI signal evolution.
LABRAT reveals association of alternative polyadenylation with transcript localization, RNA binding protein expression, transcription speed, and cancer survival
Authors: Goering Raeann , Engel Krysta L. , Gillen Austin E. , Fong Nova , Bentley David L. , Taliaferro J. Matthew 
Published: October 2020

ABSTRACTThe sequence content of the 3′ UTRs of many mRNA transcripts is regulated through alternative polyadenylation (APA). The study of this process using RNAseq data, though, has been historically challenging. To combat this problem, we developed LABRAT, an APA quantification method. LABRAT takes advantage of newly developed transcriptome quantification techniques to accurately determine relative APA site usage and how it varies across conditions. Using LABRAT, we found consistent relationships between gene-distal APA and subcellular RNA localization in multiple cell types. We also observed connections between transcription speed and APA site choice as well as tumor-specific transcriptome-wide shifts in APA in hundreds of patient-derived tumor samples that were associated with patient prognosis. We investigated the effects of APA on transcript expression and found a weak overall relationship, although many individual genes showed strong correlations between APA and expression. We interrogated the roles of 191 RNA-binding proteins in the regulation of APA, finding that dozens promote broad, directional shifts in relative APA isoform abundance bothin vitroand in patient-derived samples. Finally, we find that APA site shifts in the two classes of APA, tandem UTRs and alternative last exons, are strongly correlated across many contexts, suggesting that they are coregulated.
Single-Cell Analysis of Hyperthermic Intraperitoneal Chemotherapy Treated Tumors Reveals Distinct Cellular and Molecular Responses
Authors: Horowitz Max P. , Alali Zahraa , Alban Tyler , Hong Changjin , Esakov Emily L. , Hwang Tae Hyun , Lathia Justin D. , Michener Chad M. , DeBernardo Robert , Reizes Ofer 
Published: October 2020

SummaryHyperthermic intraperitoneal chemotherapy (HIPEC) has emerged as a clinical regimen that prolongs overall survival for patients with advanced Epithelial Ovarian Cancer (EOC). However, the mechanism of action of HIPEC remains poorly understood. To provide insights into the rapid changes that accompany HIPEC, tumors from five patients with high grade serous ovarian cancer were harvested from the omentum at time of debulking and after 90 minutes of HIPEC treatment. Specimens were rapidly dissociated into single cells and processed for single cell RNA-seq. Unbiased clustering identified 19 cell clusters that were annotated based on cellular transcriptome signatures to identify the epithelial, stromal, T and B immune cells, macrophages, and natural killer cell populations. Hallmark pathway analysis revealed heat shock, metabolic reprogramming, inflammatory, and EMT pathway enrichment in distinct cell populations upon HIPEC treatment. Collectively, our findings provide the foundation for mechanistic studies focused on how HIPEC orchestrates the ovarian cancer tissue response.
PlasForest: a homology-based random forest classifier for plasmid detection in genomic datasets
Authors: Pradier Léa , Tissot Tazzio , Fiston-Lavier Anna-Sophie , Bedhomme Stéphanie 
Published: October 2020

AbstractPlasmids are mobile genetic elements that often carry accessory genes, and are vectors for horizontal transfer between bacterial genomes. The detection of plasmids in large sets of genomes is crucial to analyze their spread and quantify their role in bacteria adaptation and particularly in antibiotic resistance genes propagation. Several bioinformatics methods have been developed to detect plasmids. However, they suffer from low sensitivity (i.e., most plasmids remain undetected) or low precision (i.e., these methods identify chromosomes as plasmids), and are overall not adapted to identify plasmids in whole genomes that are not fully assembled (contigs and scaffolds). Here, we present PlasForest, a homology-based random forest classifier identifying bacterial plasmid sequences in unassembled genomes. This tool is based on the determination of homologies against a database of plasmid sequences, which allow a random forest classifier to discriminate plasmid contigs. Without knowing the taxonomical origin of the samples, PlasForest identifies contigs as plasmids or chromosomes with an accuracy of 98%. Notably, it can detect 96% of plasmid contigs over 50kb with 3.3% of false positives. PlasForest outperforms other currently available tools on test datasets by being both sensitive and precise. We implemented this tool in a user-friendly pipeline that can identify plasmids in large datasets in a reasonable amount of time.
Deep transcriptome profiling of multiple myeloma with quantitative measures using the SPECTRA approach
Authors: Waller Rosalie Griffin , Hanson Heidi A. , Avery Brian J. , Madsen Michael J. , Sborov Douglas W. , Camp Nicola J. 
Published: October 2020

ABSTRACTSPECTRA is a new data framework to describe variation in a transcriptome as a set of unsupervised quantitative variables. Spectra variables provide a deep dive into the transcriptome, representing both large and small sources of variance, and are ideal for modeling alongside other variables for any outcome of interest. Each spectrum can also be considered a phenotypic trait, providing new avenues for disease characterization or to explore disease risk. We applied the SPECTRA approach to multiple myeloma (MM), the second most common blood cancer. Using RNA sequencing from malignant CD138+ cells, we derived 39 spectra in 767 patients from the MMRF CoMMpass study. We included spectra in prediction models for clinical endpoints, compared to established expression-based risk scores, and used descriptive modeling to identify associations with patient characteristics. Spectra-based risk scores added predictive value beyond established clinical risk factors and other expression-based risk scores for overall survival, progression-free survival, and time to first-line treatment failure. Significant spectra in models may provide mechanistic insight via gene set enrichment based on their gene weights. Gene set enrichment in CD138+ spectrum S5, which was significant for all prognostic endpoints, indicated enrichment for genes in the unfolded protein response, a mechanism targeted by proteasome inhibitors, common first line agents in MM treatment. We also identified significant associations between CD138+ spectra and tumor cytogenetics, race, gender, and age at diagnosis. The SPECTRA approach provides measures of transcriptome variation to deeply profile tumors with greater flexibility to model clinical outcomes and characteristics.AUTHOR SUMMARYComplex diseases, including cancer, are highly heterogeneous, and large molecular datasets are increasingly part of describing an individual’s unique experience. Gene expression is particularly attractive because it captures genetic, epigenetic, and environmental consequences. Transcriptome studies are gaining momentum in genomic epidemiology, and the need to incorporate these data in multivariable models alongside other risk factors brings demands for new approaches. The SPECTRA approach is a new intrinsic quantitative data framework for transcriptomes. A tissue is described by a set of quantitative measures (or ‘spectra’ variables) to deeply profile gene expression in a tissue. Spectra variables are independent and offer flexibility for use in predictive or descriptive modeling. We applied the SPECTRA approach to multiple myeloma, the second most common blood cancer. A set of 39 spectra variables were derived to represent the myeloma tumors. Outcome modeling provided SPECTRA-based risk scores that added predictive value for clinical outcomes beyond established risk factors.
A Real-life Study of the Positive Response to DAA-based Therapies for Hepatitis C in Brazil
Authors: Vivaldini Simone Monzani , Abraão Ribeiro Rachel , Júnior Gláucio Mosimann , Tonini Karen Cristine , Pereira Gerson Fernando Mendes , de Araújo Wildo Navegantes 
Published: October 2020

AbstractTreating hepatitis C virus (HCV) infections reduces overall mortality and the risk of multiple extrahepatic complications. Direct-acting antivirals (DAAs) are molecules that target specific non-structural proteins of the virus, resulting in the disruption of viral replication and infection. This report describes the activities undertaken to assess the cure of HCV in patients of different age groups, supported by the Viral Hepatitis National Program of the Unified Health System in Brazil.Around 11,000 patients wereevaluated in an electronic retrospective cohort, which used therapeutic schemes with Sofosbuvir (SOF), Daclatasvir (DCV), Simeprevir (SMV), and an association of Ombitasvir, Veruprevir/Ritonavir and Dasabuvir (3D) with or without Ribavirin (RBV), with sustained virologic response (SVR) or viral cure after a 12-week treatment. We conducted logistic regressions to identify factors independently associated with positive response to DAA-based therapies.Among evaluated patients, 57.1% were male; 48.3% identified as white; 78.3% were over 50 years old; 44.1% were from the Southeast region; 47.7% had genotype 1b; and 84.5% were treated for 12 weeks. The SVR rates with DAAs ranged from 87% to 100%. Genotypes 1 and 4 had a better response (96.3 to 100% of SVR) to therapy with DAAs, and genotypes 2 and 3 had SVR of 90.6% to 92.2%. Different treatment periods showed SVR with an average of 95.0% and 95.9% for 12 and 24 weeks, respectively. In the odds ratio assessment, we verified that females were half as likely (OR 0.5; CI 95% 0.4-0.6) to have a negative response to therapy, when compared to males, and that genotypes 2 and 3 were around twice more likely (OR 1.5-2.2; CI 95% 0.7-2.9; 1.2-3.6 and OR 2.7-2.8; CI 95% 2.0-3.8, respectively) to not have SVR after therapy when compared to genotype 1. Patients aged between 50 and 69 years old were 1.2 times (OR 1,2; CI95% 0,7-1,9) more likely to not have SVR when treated with DAAs, when compared to other age groups.This study is the first of this magnitude to be held in a Latin-American country with high SVR results, supported by a free-of-charge universal and public health system. We confirm the high performance of DAA-based therapies which support the Brazilian public health policy decision of adopting them as an important part of the country’s strategy to eliminate HCV by 2030.
Plasma ACE2 activity is persistently elevated following SARS-CoV-2 infection: implications for COVID-19 pathogenesis and consequences
Authors: Patel Sheila K , Juno Jennifer A , Lee Wen Shi , Wragg Kathleen M. , Hogarth P. Mark , Kent Stephen J , Burrell Louise M 
Published: October 2020

AbstractCOVID-19 causes persistent endothelial inflammation, lung and cardiovascular complications. SARS-CoV-2 utilises the catalytic site of full-length membrane-bound angiotensin converting enzyme 2 (ACE2) for cell entry causing downregulation of tissue ACE2. We reported downregulation of cardiac ACE2 is associated with increased plasma ACE2 activity. In this prospective observational study in recovered COVID-19 patients, we hypothesised that SARS-CoV-2 infection would be associated with shedding of ACE2 from cell membranes and increased plasma ACE2 activity.MethodsWe measured plasma ACE2 catalytic activity using a validated, sensitive quenched fluorescent substrate-based assay in a cohort of Australians aged ≥18 years (n=66) who had recovered from mild, moderate or severe SARS-CoV-2 infection (positive result by PCR testing) and age and gender matched uninfected controls (n=70). Serial samples were available in 23 recovered SARS-CoV-2 patients.ResultsPlasma ACE2 activity at a median of 35 days post-infection [interquartile range 30-38 days] was 97-fold higher in recovered SARS-CoV-2 patients compared to controls (5.8 [2-11.3] vs. 0.06 [0.02-2.2] pmol/min/ml, p<0.0001). There was a significant difference in plasma ACE2 activity according to disease severity (p=0.033), with severe COVID-19 associated with higher ACE2 activity compared to mild disease (p=0.027). Men (n=39) who were SARS-CoV-2 positive had higher median plasma ACE2 levels compared to women (n=27) (p<0.0001). We next analysed whether an elevated plasma ACE2 activity level persisted following SARS-CoV-2 infection in subjects with blood samples at 63 [56-65] and 114 [111-125] days post infection. Plasma ACE2 activity remained persistently elevated in almost all subjects, with no significant differences between timepoints in post-hoc comparisons (p>0.05).DiscussionThis is the first description that plasma ACE2 activity is elevated after COVID-19 infection, and the first with longitudinal data indicating plasma ACE2 activity remains elevated out to a median of 114 days post-infection. Larger studies are now needed to determine if persistent elevated plasma ACE2 activity identifies people at risk of prolonged illness following COVID-19.
Applying Machine Learning to Increase Efficiency and Accuracy of Meta-Analytic Review
Authors: Gorelik Aaron J. , Gorelik Mark G. , Ridout Kathryn K. , Nimarko Akua F. , Peisch Virginia , Kuramkote Shamanth R. , Low Michelle , Pan Tracy , Singh Simirthi , Nrusimha Ananya , Singh Manpreet K. 
Published: October 2020

SummaryThe rapidly burgeoning quantity and complexity of publications makes curating and synthesizing information for meta-analyses ever more challenging. Meta-analyses require manual review of abstracts for study inclusion, which is time consuming, and variation among reviewer interpretation of inclusion/exclusion criteria for selecting a paper to be included in a review can impact a study’s outcome. To address these challenges in efficiency and accuracy, we propose and evaluate a machine learning approach to capture the definition of inclusion/exclusion criteria using a machine learning model to automate the selection process. We trained machine learning models on a manually reviewed dataset from a meta-analysis of resilience factors influencing psychopathology development. Then, the trained models were applied to an oncology dataset and evaluated for efficiency and accuracy against trained human reviewers. The results suggest that machine learning models can be used to automate the paper selection process and reduce the abstract review time while maintaining accuracy comparable to trained human reviewers. We propose a novel approach which uses model confidence to propose a subset of abstracts for manual review, thereby increasing the accuracy of the automated review while reducing the total number of abstracts requiring manual review. Furthermore, we delineate how leveraging these models more broadly may facilitate the sharing and synthesis of research expertise across disciplines.
jFuzzyMachine – An Open–source Fuzzy Logic–based Regulatory Inference Engine for High–throughput Biological Data
Authors: Aiyetan Paul 
Published: October 2020

AbstractElucidating mechanistic relationships between and among intracellular macromolecules is fundamental to understanding the molecular basis of normal and diseased processes. Here, we introducejFuzzyMachine– a fuzzy logic-based regulatory network inference engine for high-throughput biological data. We describe its design and implementation. We demonstrate its functions on a sampled expression profile of the vorinostat-resistant HCT116 cell line. We comparedjFuzzyMachine’s inferred regulatory network to that inferred by theARACNe(an Algorithm for the Reconstruction of Gene Regulatory Networks) tool. Potentially more sensitive,jFuzzyMachineshowed a slight increase in identified regulatory edges compared toARACNe. A significant overlap was also observed in the identified edges between the two inference methods. Over 70 percent of edges identified byARACNewere identified byjFuzzyMachine. Beyond identifying edges,jFuzzyMachineshows direction of interactions, including bidirectional interactions – specifying regulatory inputs and outputs of inferred relationships.jFuzzyMachineaddresses an apparent lack of freely available community tool implementing a fuzzy logic regulatory network inference method – mitigating a limitation to applying and extending benefits of the fuzzy inference system to understanding biological data.jFuzzyMachine’s source codes and precompiled binaries are freely available at the Github repository locations:https://github.com/paiyetan/jfuzzymachineandhttps://github.com/paiyetan/jfuzzymachine/releases/tag/v1.7.21.
CancerVar: an Artificial Intelligence empowered platform for clinical interpretation of somatic mutations in cancer
Authors: Li Quan , Ren Zilin , Cao Kajia , Li Marilyn M. , Wang Kai , Zhou Yunyun 
Published: October 2020

ABSTRACTSeveral knowledgebases, such as CIViC and OncoKB, have been manually curated to support clinical interpretations of a limited number of “hotspot” somatic mutations in cancer, yet discrepancies or even conflicting interpretations have been observed among these knowledgebases. Additionally, while these knowledgebases have been extremely useful, they typically cannot interpret novel mutations, which may also have functional and clinical impacts in cancer. To address these challenges, we developed an automated interpretation tool called CancerVar (Cancer Variants interpretation) to score more than 12.9 million somatic mutations and classify them into four tiers: strong clinical significance, potential clinical significance, uncertain clinical significance, and benign/likely benign, based on the AMP/ASCO/CAP 2017 guideline. Considering that the AMP/ASCO/CAP rule-based scoring system may have inherent limitations, such as lack of a clear guidance on weighing different pieces of functional evidence or unclear definition for certain clinical evidence, it may cause misinterpretation for certain variants that have functional impacts but no proven clinical significance. To address this issue, we further introduced a deep learning-based scoring system to predict oncogenicity of mutations by semi-supervised generative adversarial network (SGAN) method using both functional and clinical evidence. We trained and validated the SGAN model on 5,234 somatic mutations from an in-house database of clinical reports on cancer patients, and achieved a good performance when testing on 6,226 variants that were curated by us through literature search. We also compared the prediction with several independent datasets and showed great utility in classifying variants with previously unknown interpretations. CancerVar is also incorporated into a web server that can generate automated texts with summarized descriptive interpretations, such as diagnostic, prognostic, targeted drug responses and clinical trial information for many hotspot mutations. In summary, CancerVar can facilitate clinical interpretation and hypothesis generation for somatic mutations, and greatly reduce manual workload for retrieving relevant evidence and implementing existing guidelines.
Targeting hypoxic habitats with hypoxia pro-drug evofosfamide in preclinical models of sarcoma
Authors: Jardim-Perassi Bruna V. , Mu Wei , Huang Suning , Tomaszewski Michal R. , Poleszczuk Jan , Abdalah Mahmoud A. , Budzevich Mikalai M. , Dominguez-Viqueira William , Reed Damon R. , Bui Marilyn M. , Johnson Joseph O. , Martinez Gary V. , Gillies Robert J. 
Published: October 2020

AbstractHypoxic regions (habitats) within tumors are heterogeneously distributed and can be widely variant. Hypoxic habitats are generally pan-therapy resistant. For this reason, hypoxia-activated prodrugs (HAPs) have been developed to target these resistant volumes. The HAP evofosfamide (TH-302) has shown promise in preclinical and early clinical trials of sarcoma. However, in a phase III clinical trial, TH-302 did not improve survival in combination with doxorubicin (dox), most likely due to a lack of patient stratification based on hypoxic status. Herein, our goal was to develop deep-learning (DL) models to identify hypoxic habitats, using multiparametric (mp) MRI and co-registered histology, and to non-invasively monitor response to TH-302 in a patient-derived xenograft (PDX) of rhabdomyosarcoma and a syngeneic model of fibrosarcoma (RIF-1). A DL convolutional neural network showed strong correlations (>0.81) between the true hypoxic portion in histology and the predicted hypoxic portion in multiparametric MRI. TH-302 monotherapy or in combination with Dox delayed tumor growth and increased survival in the hypoxic PDX model (p<0.05), but not in the RIF-1 model, which had lower volume of hypoxic habitats. Control studies showed that RIF-1 resistance was due to hypoxia and not to other causes. Notably, PDX tumors developed resistance to TH-302 under prolonged treatment. In conclusion, response to TH-302 can be attributed to differences in hypoxia status prior therapy. Development of non-invasive MR imaging to assess hypoxia is crucial in determining the effectiveness of TH-302 therapy and to follow response. In further studies, our approach can be used to better plan therapeutic schedules to avoid resistance.One Sentence SummaryDevelopment of non-invasive MR imaging to assess hypoxia is crucial in determining the effectiveness of TH-302 therapy and to follow response.