bluuebunny/crossref_metadata_2025_split huggingface dataset:
CancerSiamese: one-shot learning for predicting primary and metastatic tumor types unseen during model training
Authors: Mostav Milad , Chiu Yu-Chiao , Chen Yidong , Huang Yufei 
Published: September 2020

AbstractWe consider cancer classification based on one single gene expression profile. We proposed CancerSiamese, a new one-shot learning model, to predict the cancer type of a query primary or metastatic tumor sample based on a support set that contains only one known sample for each cancer type. CancerSiamese receives pairs of gene expression profiles and learns a representation of similar or dissimilar cancer types through two parallel Convolutional Neural Networks joined by a similarity function. We trained CancerSiamese for both primary and metastatic cancer type predictions using samples from TCGA and MET500. Test results for differentN-way predictions yielded an average accuracy improvement of 8% and 4% over the benchmark 1-Nearest Neighbor (1-NN) classifier for primary and metastatic tumors, respectively. Moreover, we applied the guided gradient saliency map and feature selection to CancerSiamese to identify and analyze the marker-gene candidates for primary and metastatic cancers. Our work demonstrated, for the first time, the feasibility of applying one-shot learning for expression-based cancer type prediction when gene expression data of cancer types are limited and could inspire new and ingenious applications of one-shot and few-shot learning solutions for improving cancer diagnosis, treatment planning, and our understanding of cancer.
Inhibition of guanosine monophosphate synthetase (GMPS) blocks glutamine metabolism and prostate cancer growthin vitroandin vivo
Authors: Wang Qian , Guan Yi F. , Hancock Sarah E. , Wahi Kanu , van Geldermalsen Michelle , Zhang Blake K. , Pang Angel , Nagarajah Rajini , Mak Blossom , Horvath Lisa G. , Turner Nigel , Holst Jeff 
Published: September 2020

AbstractCancer cells increase their uptake of nutrients and metabolize them to provide the necessary building blocks for new cancer cells. Glutamine is a critical nutrient in cancer, however its contribution to purine metabolism in prostate cancer has not previously been determined. Guanosine monophosphate synthetase (GMPS) acts in thede novopurine biosynthesis pathway, utilizing a glutamine amide to synthesize the guanine nucleotide and replenish the purine pool in proliferative cancer cells. This study demonstrates that GMPS mRNA expression correlates with Gleason score in prostate cancer samples, while high GMPS expression was associated with decreased rates of overall and disease/progression-free survival. Pharmacological inhibition or knockdown of GMPS significantly decreased cell growth in both LNCaP and PC-3 prostate cancer cells. GMPS knockdown was rescued by addition of extracellular guanosine to the media, suggesting a direct effect on nucleotide synthesis. We utilized15N-(amide)-glutamine and U-13C5-glutamine metabolomics to dissect the pathways involved, and intriguingly, despite similar growth inhibition by GMPS knockdown, we show unique metabolic effects across each cell line. PC-3 cells showed a build-up of purine precursors, as well as activation of purine salvage pathways highlighted by significant increases in guanine, adenosine, inosine and cytosine. Both cell lines exhibited increased levels of pyrimidines and prioritized TCA cycle in distinct ways to produce increased aspartate, another important purine precursor. Using a PC-3 xenograft mouse model, tumor growth was also significantly decreased after GMPS knockdown. These data further highlight the importance of glutamine metabolism for prostate cancer cell growth and provide support for GMPS as a new therapeutic target in prostate cancer.
Massachusetts General Hospital Covid-19 Registry reveals two distinct populations of hospitalized patients by race and ethnicity
Authors: Bassett Ingrid V. , Triant Virginia A. , Bunda Bridget A. , Selvaggi Caitlin A. , Shinnick Daniel J. , He Wei , Lu Frances , Porneala Bianca C. , Cao Tingyi , Lubitz Steven A. , Meigs James B. , Hsu John , Foulkes Andrea S. 
Published: September 2020

AbstractObjectiveTo evaluate differences by race/ethnicity in clinical characteristics and outcomes among hospitalized patients with Covid-19 at Massachusetts General Hospital (MGH).MethodsThe MGH Covid-19 Registry includes confirmed SARS-CoV-2-infected patients hospitalized at MGH and is based on manual chart reviews and data extraction from electronic health records (EHRs). We evaluated differences between White/Non-Hispanic and Hispanic patients in demographics, complications and 14-day outcomes among the N=866 patients hospitalized with Covid-19 from March 11, 2020 – May 4, 2020.ResultsOverall, 43% of patients hospitalized with Covid-19 were women, median age was 60.4 [IQR=(48.2, 75)], 11.3% were Black/non-Hispanic and 35.2% were Hispanic. Hispanic patients, representing 35.2% of patients, were younger than White/non-Hispanic patients [median age 51y; IQR=(40.6, 61.6) versus 72y; (58.0, 81.7) (p< 0.001)]. Hispanic patients were symptomatic longer before presenting to care (median 5 vs 3d, p=0.039) but were more likely to be sent home with self-quarantine than be admitted to hospital (29% vs 16%, p< 0.001). Hispanic patients had fewer comorbidities yet comparable rates of ICU or death (34% vs 36%). Nonetheless, a greater proportion of Hispanic patients recovered by 14 days after presentation (62% vs 45%, p< 0.001; OR=1.99, p=0.011 in multivariable adjusted model) and fewer died (2% versus 18%, p< 0.001).ConclusionsHospitalized Hispanic patients were younger and had fewer comorbidities compared to White/non-Hispanic patients; despite comparable rates of ICU care or death, a greater proportion recovered. These results have implications for public health policy and the design and conduct of clinical trials.
Sleep Disturbances, Anxiety, and Burnout during the COVID-19 Pandemic: a nationwide cross-sectional study in Brazilian Healthcare Professionals
Authors: Drager Luciano F. , Pachito Daniela V. , Moreno Claudia R.C. , Tavares Almir R. , Conway Silvia G. , Assis Márcia , Sguillar Danilo A. , Moreira Gustavo A. , Bacelar Andrea , Genta Pedro R. 
Published: September 2020

ABSTRACTStudy objectivesTo evaluate the impact of COVID-19 pandemic on sleep, anxiety, and Burnout in healthcare professionals.MethodsA survey was distributed using social media and organizational emails to Brazilian active healthcare professionals during the COVID-19 outbreak. We explored potential associated factors including age, gender, occupation, workplace, work hours, income, previous infection with COVID-19, recent/current contact with COVID-19 patients, regional number of incident deaths due to COVID-19, anxiety, and burnout. We evaluated new-onset or previous insomnia worsening (primary outcome), sleep quality, and duration (secondary outcomes).ResultsA total of 4,384 health professionals from all regions of the country were included in the analysis (mean age: 44±12 years, 76% females, 53.8% physicians). Overall, 55.7% were assisting patients with COVID-19, and 9.2% had a previous COVID-19 infection. New-onset insomnia symptoms or previous insomnia worsening occurred in 41.4% of respondents in parallel to 13% (n=572) new pharmacological treatments for insomnia. Prevalent anxiety and burnout during the pandemic were observed in 44.2% and 21% of participants, respectively. Multivariate analyses showed that females (OR:1.756; 95% CI 1.487-2.075), weight change (decrease: OR:1.852; 95% CI 1.531-2.240; increase: OR:1.542; 95% CI 1.323-1.799), prevalent anxiety(OR:3.209; 95% CI 2.796-3.684), new-onset burnout (OR:1.986; 95% CI 1.677-2.352), family income reduction >30% (OR:1.366; 95% CI 1.140-1.636) and assisting patients with COVID-19 (OR:1.293; 95% CI 1.104-1.514) were independently associated with new-onset or worsening of previous insomnia.ConclusionsWe observed a huge burden of insomnia in healthcare professionals during the COVID-19 pandemic. In this scenario, dedicated approaches for sleep health are highly desirable.Statement of SignificanceConsidering the stressful routine and risk of infection by COVID-19 among healthcare professionals, it is conceivable that sleep disturbances are significantly impaired during the pandemic. This nationwide survey conducted in Brazil found that 41.4% developed new-onset or worsening of previous insomnia symptoms. Moreover, 572 (13%) of respondents initiated pharmacological treatments for insomnia. Females, weight change, anxiety, Burnout development, family income reduction >30%, and recent/current care of patients with COVID-19 were independently associated with the development of insomnia or exacerbated previous insomnia symptoms. Considering the potential impact of insomnia on work performance/healthcare decisions as well as the potential long-term dependence of pharmacological treatments for insomnia, this study underscores the need for dedicated sleep and mental health programs for healthcare professionals.
LongGeneDB: a data hub for long genes
Authors: Moon Sohyun , Kim Yura , Naghavi Mariam , Zhao Jerry Yingtao 
Published: September 2020

ABSTRACTThe human genome contains more than 4000 genes that are longer than 100 kb. These long genes require more time and resources to make a transcript than shorter genes do. Long genes have also been linked to various human diseases. Specific mechanisms are utilized by long genes to facilitate their transcription and co-transcriptional processes. This results in unique features in their multi-omics profiles. Although these unique profiles are important to understand long genes, a database that provides an integrated view and easy access to the multi-omics profiles of long genes does not exist. We leveraged the publicly accessible multi-omics data and systematically analyzed the genomic conservation, histone modifications, chromatin organization, tissue-specific transcriptome, and single cell transcriptome of 992 protein-coding genes that are longer than 200 kb in the mouse genome. We also examined the evolution history of their gene lengths in 15 species that belong to six Classes and 11 Orders. To share the multi-omics profiles of long genes, we developed a user-friendly and easy-to-use database, LongGeneDB (https://longgenedb.org), for users to search, browse, and download these profiles. LongGeneDB will be a useful data hub for the biomedical research community to understand long genes.
Tumors widely express hundreds of embryonic germline genes
Authors: Bruggeman Jan Willem , Irie Naoko , Lodder Paul , van Pelt Ans M.M. , Koster Jan , Hamer Geert 
Published: September 2020

AbstractWe have recently described a class of 756 genes that are widely expressed in cancer, while normally restricted to adult germ cells, referred to as germ cell cancer genes (GC-genes). We hypothesized that carcinogenesis involves reactivation of biomolecular processes and regulatory mechanisms that, under normal circumstances, are restricted to germline development. This would imply that cancer cells share gene expression profiles with primordial germ cells (PGCs). We therefore compared the transcriptomes of human PGCs (hPGCs) and PGC-like cells (PGCLCs) with 17 382 samples from 54 healthy somatic tissues (GTEx) and 11 003 samples from 33 tumor types (TCGA), and identified 672 GC-genes, expanding the known GC-gene pool by 387 genes (51%). Because GC-genes specific to the embryonic germline are not expressed in any adult tissue, targeting these in cancer treatment may result in fewer side effects than targeting conventional cancer/testis (CT) or GC-genes and may preserve fertility. We anticipate that our extended GC-dataset enables improved understanding of tumor development and may provide multiple novel targets for cancer treatment development.
EEG Source Identification through Phase Space Reconstruction and Complex Networks
Authors: Soroush Morteza Zangeneh 
Published: September 2020

AbstractArtifact elimination has become an inseparable part while processing electroencephalogram (EEG) in most brain computer interface (BCI) applications. Scientists have tried to introduce effective and efficient methods which can remove artifacts and also reserve desire information pertaining to brain activity. Blind source separation (BSS) methods have been receiving a great deal of attention in recent decades since they are considered routine and standard signal processing tools and are commonly used to eliminate artifacts and noise. Most studies, mainly EEG-related ones, apply BSS methods in preprocessing sections to achieve better results. On the other hand, BSS methods should be followed by a classifier in order to identify artifactual sources and remove them in next steps. Therefore, artifact identification is always a challenging problem while employing BSS methods. Additionally, removing all detected artifactual components leads to loss of information since some desire information related to neural activity leaks to these sources. So, an approach should be employed to suppress the artifacts and reserve neural activity. In this study, a new hybrid method is proposed to automatically separate and identify electroencephalogram (EEG) sources with the aim of classifying and removing artifacts. Automated source identification is still a challenge. Researchers have always made efforts to propose precise, fast and automated source verification methods. Reliable source identification has always been of great importance. This paper addresses blind source separation based on second order blind identification (SOBI) as it is reportedly one of the most effective methods in EEG source separation problems. Then a new method for source verification is introduced which takes advantage of components phase spaces and their dynamics. A new state space called angle space (AS) is introduced and features are extracted based on the angle plot (AP) and Poincare planes. Identified artifactual sources are eliminated using stationary wavelet transform (SWT). Simulated, semi-simulated and real EEG signals are employed to evaluate the proposed method. Different simulations are performed and performance indices are reported. Results show that the proposed method outperforms most recent studies in this subject.
Comprehensive interrogation of the ADAR2 deaminase domain for engineering enhanced RNA base-editing activity, functionality and specificity
Authors: Katrekar Dhruva , Palmer Nathan , Xiang Yichen , Saha Anushka , Meluzzi Dario , Mali Prashant 
Published: September 2020

ABSTRACTAdenosine deaminases acting on RNA (ADARs) can be repurposed to enable programmable RNA editing, however their exogenous delivery leads to transcriptome-wide off-targeting, and additionally, enzymatic activity on certain RNA motifs, especially those flanked by a 5’ guanosine is very low thus limiting their utility as a transcriptome engineering toolset. To address this, we explored comprehensive ADAR2 protein engineering via three approaches: First, we performed a novel deep mutational scan of the deaminase domain that enabled direct coupling of variants to corresponding RNA editing activity. Experimentally measuring the impact of every amino acid substitution across 261 residues, i.e. ~5000 variants, on RNA editing, revealed intrinsic domain properties, and also several mutations that greatly enhanced RNA editing. Second, we performed a domain-wide mutagenesis screen to identify variants that increased activity at5’-GA-3’ motifs, and discovered novel mutants that enabled robust RNA editing. Third, we engineered the domain at the fragment level to create split deaminases. Notably, compared to full-length deaminase overexpression, split-deaminases resulted in >1000 fold more specific RNA editing. Taken together, we anticipate this comprehensive deaminase engineering will enable broader utility of the ADAR toolset for RNA biotechnology and therapeutic applications.
Regarding theF-word: the effects of dataFilteringon inferred genotype-environment associations
Authors: Ahrens Collin W , Jordan Rebecca , Bragg Jason , Harrison Peter A , Hopley Tara , Bothwell Helen , Murray Kevin , Steane Dorothy A , Whale John W , Byrne Margaret , Andrew Rose , Rymer Paul D. 
Published: September 2020

AbstractGenotype-environment association (GEA) methods have become part of the standard landscape genomics toolkit, yet, we know little about how to filter genotype-by-sequencing data to provide robust inferences for environmental adaptation. In many cases, default filtering thresholds for minor allele frequency and missing data are applied regardless of sample size, having unknown impacts on the results. These effects could be amplified in downstream predictions, including management strategies. Here, we investigate the effects of filtering on GEA results and the potential implications for adaptation to environment. Using empirical and simulated datasets derived from two widespread tree species to assess the effects of filtering on GEA outputs. Critically, we find that the level of filtering of missing data and minor allele frequency affect the identification of true positives. Even slight adjustments to these thresholds can change the rate of true positive detection. Using conservative thresholds for missing data and minor allele frequency substantially reduces the size of the dataset, lessening the power to detect adaptive variants (i.e. simulated true positives) with strong and weak strength of selections. Regardless, strength of selection was a good predictor for GEA detection, but even SNPs under strong selection went undetected. We further show that filtering can significantly impact the predictions of adaptive capacity of species in downstream analyses. We make several recommendations regarding filtering for GEA methods. Ultimately, there is no filtering panacea, but some choices are better than others, depending largely on the study system, availability of genomic resources, and desired objectives of the study.
Quantifying proximity, confinement, and interventions in disease outbreaks: a decision support framework for air-transported pathogens
Authors: Bond Tami C. , Bosco-Lauth Angela , Farmer Delphine K. , Francisco Paul W. , Pierce Jeffrey R. , Fedak Kristen M. , Ham Jay M. , Jathar Shantanu H. , VandeWoude Sue 
Published: September 2020

AbstractThe inability to communicate how infectious diseases are transmitted in human environments has triggered avoidance of interactions during the COVID-19 pandemic. We define a metric, Effective ReBreathed Volume (ERBV), that encapsulates how infectious pathogens transport in air. This measure distinguishes environmental transport from other factors in the chain of infection, thus allowing quantitative comparisons of the riskiness of different situations for any pathogens transported in air, including SARS-CoV-2. Particle size is a key factor in transport, removal onto surfaces, and elimination by mitigation measures, so ERBV is presented for a range of exhaled particle diameters: 1 μm, 10 μm, and 100 μm. Pathogen transport is enhanced by two separate but interacting effects: proximity and confinement. Confinement in enclosed spaces overwhelms proximity after 10–15 minutes for all but the largest particles. Therefore, we review plausible strategies to reduce the confinement effect. Changes in standard ventilation and filtration can reduce person-to-person transport of 1-μm particles (ERBV1) by 13-85% in residential and commercial situations. Deposition to surfaces competes with intentional removal for 10-μm and 100-μm particles, so the same interventions reduce ERBV10 by only 3-50%, and ERBV100 is unaffected. Determining transmission modes is critical to identify intervention effectiveness, and would be accelerated with prior knowledge of ERBV. When judiciously selected, the interventions examined can provide substantial reduction in risk, and the conditions for selection are identified. The framework of size-dependent ERBV supports analysis and mitigation decisions in an emerging situation, even before other infectious parameters are well known.
Protecting synapses from amyloid β-associated degeneration by manipulations of Wnt/planar cell polarity signaling
Authors: Feng Bo , Freitas Andiara E. , Tian Runyi , Lee Yeo Rang , Grewal Akumbir S. , Wang Jingyi , Zou Yimin 
Published: September 2020

ABSTRACTSynapse loss is an early event in Alzheimer’s disease and is thought to be associated with amyloid pathology and caused by Amyloid β (Aβ) oligomers. Whether and how Aβ oligomers directly target signaling pathways for glutamatergic synapse maintenance is unknown. Glutamatergic synapse development is controlled by the opposing functions of Celsr3 and Vangl2, core components of the Wnt/planar cell polarity (PCP) signaling pathway, functioning directly in the synapses. Celsr3 promotes synapse formation, whereas Vangl2 inhibits synapse formation. Here we show that oligomeric Aβ binds to Celsr3 and assists Vangl2 in disassembling synapses by disrupting the intercellular Celsr3/Frizzled3-Celsr3 complex, essential for PCP signaling. Together with Vangl2, a Wnt receptor, Ryk, is also required for Aβ oligomer-induced synapse loss in a mouse model of Alzheimer’s disease, 5XFAD, where conditionalRykknockout protected synapses and preserved cognitive function. Our study reveals a fine balance of Wnt/PCP signaling components in glutamatergic synapse maintenance and suggests that overproduced Aβ oligomers may lead to excessive synapse loss by tipping this balance. Together with previous reports that an inhibitor of Wnt/Ryk signaling, WIF1, is found reduced in Alzheimer’s disease patients, our results suggest that the imbalance of PCP signaling in these patients may contribute to synapse loss in Alzheimer’s disease and manipulating Wnt/PCP signaling may preserve synapses and cognitive function.
Near-Physiological-Temperature Serial Femtosecond X-ray Crystallography Reveals Novel Conformations of SARS-CoV-2 Main Protease Active Site for Improved Drug Repurposing
Authors: Durdagi Serdar , Dag Cagdas , Dogan Berna , Yigin Merve , Avsar Timucin , Buyukdag Cengizhan , Erol Ismail , Ertem Betul , Calis Seyma , Yildirim Gunseli , Orhan Muge D. , Guven Omur , Aksoydan Busecan , Destan Ebru , Sahin Kader , Besler Sabri O. , Oktay Lalehan , Shafiei Alaleh , Tolu Ilayda , Ayan Esra , Yuksel Busra , Peksen Ayse B. , Gocenler Oktay , Yucel Ali D. , Can Ozgur , Ozabrahamyan Serena , Olkan Alpsu , Erdemoglu Ece , Aksit Fulya , Tanisali Gokhan , Yefanov Oleksandr M. , Barty Anton , Tolstikova Alexandra , Ketawala Gihan K. , Botha Sabine , Dao E. Han , Hayes Brandon , Liang Mengning , Seaberg Matthew H. , Hunter Mark S. , Batyuk Alex , Mariani Valerio , Su Zhen , Poitevin Frederic , Yoon Chun Hong , Kupitz Christopher , Sierra Raymond G. , Snell Edward , DeMirci Hasan 
Published: September 2020

AbstractThe COVID19 pandemic has resulted in 25+ million reported infections and nearly 850.000 deaths. Research to identify effective therapies for COVID19 includes: i) designing a vaccine as future protection; ii) structure-based drug design; and iii) identifying existing drugs to repurpose them as effective and immediate treatments. To assist in drug repurposing and design, we determined two apo structures of Severe Acute Respiratory Syndrome CoronaVirus-2 main protease at ambienttemperature by Serial Femtosecond X-ray crystallography. We employed detailed molecular simulations of selected known main protease inhibitors with the structures and compared binding modes and energies. The combined structural biology and molecular modeling studies not only reveal the dynamics of small molecules targeting main protease but will also provide invaluable opportunities for drug repurposing and structure-based drug design studies against SARS-CoV-2.One Sentence SummaryRadiation-damage-free high-resolution SARS-CoV-2 main protease SFX structures obtained at near-physiological-temperature offer invaluable information for immediate drug-repurposing studies for the treatment of COVID19.