bluuebunny/crossref_metadata_2025_split huggingface dataset:
Risk Score Stratification of Alzheimer’s Disease and Mild Cognitive Impairment using Deep Learning
Authors: Nagaraj Sanjay , Duong Tim Q 
Published: November 2020

ABSTRACTAlzheimer Disease (AD) is a progressive neurodegenerative disease that can significantly impair cognition and memory. AD is the leading cause of dementia and affects one in ten people age 65 and older. Current diagnoses methods of AD heavily rely on the use of Magnetic Resonance Imaging (MRI) since non-imaging methods can vary widely leading to inaccurate diagnoses. Furthermore, recent research has revealed a substage of AD, Mild Cognitive Impairment (MCI), that is characterized by symptoms between normal cognition and dementia which makes it more prone to misdiagnosis.A large battery of clinical variables are currently used to detect cognitive impairment and classify early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD from cognitive normal (CN) patients. The goal of this study was to derive a simplified risk-stratification algorithm for diagnosis and identify a few significant clinical variables that can accurately classify these four groups using an empirical deep learning approach. Over 100 variables that included neuropsychological/neurocognitive tests, demographics, genetic factors, and blood biomarkers were collected from EMCI, LMCI, AD, and CN patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Feature engineering was performed with 5 different methods and a neural network was trained on 90% of the data and tested on 10% using 10-fold cross validation. Prediction performance used area under the curve (AUC) of the receiver operating characteristic analysis.The five different feature selection methods consistently yielded the top classifiers to be the Clinical Dementia Rating Scale - Sum of Boxes (CDRSB), Delayed total recall (LDELTOTAL), Modified Preclinical Alzheimer Cognitive Composite with Trails test (mPACCtrailsB), the Modified Preclinical Alzheimer Cognitive Composite with Digit test (mPACCdigit), and Mini-Mental State Examination (MMSE). The best classification model yielded an AUC of 0.984, and the simplified risk-stratification score yielded an AUC of 0.963 on the test dataset.Our results show that this deep-learning algorithm and simplified risk score derived from our deep-learning algorithm accurately diagnose EMCI, LMCI, AD and CN patients using a few commonly available neurocognitive tests. The project was successful in creating an accurate, clinically translatable risk-stratified scoring aid for primary care providers to diagnose AD in a fast and inexpensive manner.
Characterizing the genetic architecture of Parkinson’s disease in Latinos
Authors: Loesch Douglas , Horimoto Andrea R. V. R. , Heilbron Karl , Sarihan Elif Irem , Inca-Martinez Miguel , Mason Emily , Cornejo-Olivas Mario , Torres Luis , Mazzetti Pilar , Cosentino Carlos , Sarapura-Castro Elison , Rivera-Valdivia Andrea , Medina Angel C. , Dieguez Elena , Raggio Victor , Lescano Andres , Tumas Vitor , Borges Vanderci , Ferraz Henrique B. , Rieder Carlos R. , Schumacher-Schuh Artur , Santos-Lobato Bruno L. , Velez-Pardo Carlos , Jimenez-Del-Rio Marlene , Lopera Francisco , Moreno Sonia , Chana-Cuevas Pedro , Fernandez William , Arboleda Gonzalo , Arboleda Humberto , Arboleda-Bustos Carlos E. , Yearout Dora , Zabetian Cyrus P. ,   the 23andMe Research Team, Cannon Paul , Thornton Timothy A. , O’Connor Timothy D. , Mata Ignacio F. 
Published: November 2020

AbstractTo date, over 90 Parkinson’s disease (PD) risk variants have been reported from genome-wide association studies (GWAS). However, these GWAS efforts have been limited to individuals of European and East Asian ancestry. We performed the first GWAS of Latino PD patients from South America, comparing 807 cases against 690 controls followed by association testing of suggestive loci in a replication cohort of 1,234 cases and 439,522 controls. We demonstrated that SNCA plays a significant role in PD etiology in a Latino cohort and identified a suggestive locus near NRROS on chromosome 3 that appeared to be driven by Peruvian subjects. We also characterized the overlap of PD genetic architecture between Europeans and Latinos with a replication of significant variants identified by Nalls et al. in their 2019 GWAS1, finding 80% concordance in direction of effect. We then leveraged the population history of Latinos via admixture mapping, identifying a significant locus on chromosome 14 in a joint test of ancestries, driven by the Native American ancestral background, and a significant locus on chromosome 6 in our test of African ancestry, containing the genes STXBP6 and RPS6KA2, respectively. Ultimately, our work reflects the most comprehensive characterization of PD genetic architecture in Latinos to date.
Prevalence and Relationship of Rest Tremor and Action Tremor in Parkinson’s Disease
Authors: Gupta Deepak K. , Marano Massimo , Zweber Cole , Boyd James T. , Kuo Sheng-Han 
Published: November 2020

AbstractBackgroundDespite the significance of tremor in Parkinson’s disease (PD) diagnosis, classification, and patient’s quality of life, there is a relative lack of data on prevalence and relationship of different tremor types in PD.MethodsThe presence of rest tremor (RT) and action tremor (AT; defined as combination of both postural and kinetic tremor) was determined and RT severity was defined using the Movement Disorders Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) at baseline in the Progression Marker Initiative (PPMI, n=423), the Fox Investigation for New Discovery of Biomarkers (BioFIND, n=118) and the Parkinson’s Disease Biomarkers Program (PDBP, n=873) cohorts.ResultsAcross baseline data of all three cohorts, RT prevalence (58.2%) was higher than AT prevalence (39.0%). Patients with RT had significantly higher (Chi-square test, p<0.05) prevalence of AT compared to patients without RT in the PPMI (40.0% versus 30.1%), BioFIND (48.0% versus 40.0%) and PDBP (49.9% versus 21.0%) cohorts. Furthermore, patients with AT had significantly (Student t-test, p<0.05) higher RT severity that those without AT in PPMI (5.7 ± 5.4 versus 3.9 ± 3.3), BioFIND, 6.4 ± 6.3 versus 3.8 ± 4.4) and PDBP (6.4 ± 6.6 versus 3.7 ± 4.4) cohorts.DiscussionThe RT is the most frequent tremor type and present in more than half of the PD patients. However, AT is also present in nearly one-third of the PD patients. Our results also indicate that RT and AT may have cross-interactions in PD.
Strengthening government’s response to COVID-19 in Indonesia: a modified Delphi study of medical and health academics
Authors: Mahendradhata Yodi , Lestari Trisasi , Djalante Riyanti 
Published: November 2020

AbstractThe Indonesian government has issued various policies to control COVID-19. However, COVID-19 new cases continued to increase and there remains uncertainties as to the future trajectory. We aimed to investigate how do medical and health academics view the Indonesian government’s handling of the COVID-19 and which area of health systems that need to be prioritized to improve government’s response to COVID-19. We conducted a modified Delphi study adapting the COVID-19 assessment score card (COVID-SCORE) as the measurement criteria. We invited medical and health academics from ten universities across Indonesia to take part in the Delphi study. In the first round, participants were presented with 20 statements of COVID-SCORE and asked to rate their agreement with each statement using five-point Likert scale. All participants who have completed the first cycle were invited to participate in the second cycle in which they had the opportunity to revise their answer based on results of previous cycle and to rank a priority of actions to improve government response. We achieved consensus for 5 statements, majority agreements for 13 statements and no consensus for 2 statements. The prioritization suggested that top priorities for improving government’s response to COVID-19 in Indonesia, according to medical and health academics, encompass: (1) The authorities communicate clearly and consistently about COVID-19 and provide public health grounds for their decisions; (2) Everyone can get a free, reliable COVID-19 test quickly and receive the results promptly; (3) Contact tracing is implemented for positive cases; (4) Public health experts, government officials, and academic researchers agree on COVID-19 nomenclature and clearly explain the reasons for public health measures; and (5) Government communications target the entire diverse population. Ultimately, our study highlights the importance of strengthening health system functions during the pandemic and to improve health system resilience for dealing with future public health emergencies.
Versatile and flexible microfluidic qPCR test for high-throughput SARS-CoV-2 and cellular response detection in nasopharyngeal swab samples
Authors: Fassy Julien , Lacoux Caroline , Leroy Sylvie , Noussair Latifa , Hubac Sylvain , Degoutte Aurélien , Vassaux Georges , Leclercq Vianney , Rouquié David , Marquette Charles-Hugo , Rottman Martin , Touron Patrick , Corbel Antoinette Lemoine , Herrmann Jean-Louis , Barbry Pascal , Nahon Jean-Louis , Zaragosi Laure-Emmanuelle , Mari Bernard 
Published: November 2020

AbstractThe emergence and quick spread of SARS-CoV-2 has pointed at a low capacity response for testing large populations in many countries, in line of material, technical and staff limitations. The traditional RT-qPCR diagnostic test remains the reference method and is by far the most widely used test. These assays are limited to a couple of probe sets, require large sample PCR reaction volumes, along with an expensive and time-consuming RNA extraction steps. Here we describe a quantitative nanofluidic assay that overcomes some of these shortcomings, based on the Biomark instrument from Fluidigm. This system offers the possibility of performing 4608 qPCR end-points in a single run, equivalent to 192 clinical samples combined with 12 pairs of primers/probe sets in duplicate, thus allowing the monitoring in addition to SARS-CoV-2 probes of other pathogens and/or host cellular responses (virus receptors, response markers, microRNAs). Its 10 nL range volume is compatible with sensitive and reproducible reactions that can be easily and cost-effectively adapted to various RT-qPCR configurations and sets of primers/probe. Finally, we also evaluated the use of inactivating lysis buffers composed of various detergents in the presence or absence of proteinase K to assess the compatibility of these buffers with a direct reverse transcription enzymatic step and we propose several procedures, bypassing the need for RNA purification. We advocate that the combined utilization of an optimized processing buffer and a high-throughput real-time PCR device would contribute to improve the turn-around-time to deliver the test results to patients and increase the SARS-CoV-2 testing capacities.
Google Searches for Taste and Smell Loss Anticipate Covid-19 Epidemiology
Authors: Lippi Giuseppe , Henry Brandon M. , Mattiuzzi Camilla , Sanchis-Gomar Fabian 
Published: November 2020

AbstractBackgroundAs evidence emerged that loss of taste and/or loss of smell is frequently triggered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, we investigated whether Google searches volume for these two disease-specific symptoms could be associated with disease epidemiology in United States (US).Materials and MethodsWe performed an electronic search in Google Trends using the keywords “taste loss” and “smell loss” within the US. The Google searches volume was correlated with the number of new weekly cases of coronavirus disease 2019 (COVID-19) in the country.ResultsThe weekly Google searches for taste and smell loss exhibited a trend similar to that of new weekly SARS-CoV-2 infections in the US. A nearly perfect correlation was found between Google Trends scores of taste and smell loss (r=0.98; 95% CI, 0.97-0.99; p<0.001). Although a significant association was found between Google searches for the two symptoms and the concomitant number of new weekly SARS-CoV-2 infections reported during the same week, the correlation improved over time. The highest correlation was found comparing Google Trends scores for taste or smell loss and the number of new weekly SARS-CoV-2 infections two weeks later. The correlation coefficient of summing Google Trends scores for the two symptoms and the number of new weekly SARS-CoV-2 infections two weeks later was 0.82 (95% CI, 0.68-0.90; p<0.001), and was associated 0.89 diagnostic accuracy.ConclusionsThese findings suggest that Google searches numbers for olfactory and gustatory dysfunctions may help predicting the epidemiological trajectory of COVID-19 early before official reporting.
Movement disorders ontology for clinically oriented and clinicians-driven data mining of multi-center cohorts in Parkinson’s disease
Authors: Gupta Deepak K. , Marano Massimo , Aurora Raj , Boyd James , Sahoo Satya S. 
Published: November 2020

AbstractParkinsonian disorders, including Parkinson’s disease (PD) and atypical parkinsonian disorders (APD), are characterized by shared clinical features of parkinsonism and although there are distinct clinical and pathological diagnostic criterion for PD and APD, patients present in the clinic with overlapping clinical features, which evolve with a great deal of variability and complexity over time. This leads to high level of uncertainty in the prediction of diagnosis and progression for an individual patient on clinical grounds. There have been recent initiatives to make available large-scale datasets from multiple research studies such as Accelerating Medicines Partnership for Parkinson’s Disease (AMP PD). However, these is a clear lack of a common terminological system or ontology that can support query analysis of datasets in AMP PD and map across multiple instruments used in the assessment of PD as well as APD. To address these challenges, we developed the Movement Disorder Ontology (MDO) that used a systematic analysis of movement disorder instruments, extensive review of literature led by a movement disorder specialists, and the AMP PD knowledge portal with an iterative ontology engineering process. The current version of MDO is focused on parkinsonian disorders with 203 concepts modeled in three broad categories of: (1) neurological findings, (2) treatment plans, and (3) instruments used to evaluate various traits of PD. MDO holds potential for use in clinical research especially in the context of large-scale phenotypic data available in public repositories such AMP PD with support for concept-based data analysis and potentially correlating with genotypic data.
Diverse chemotypes drive biased signaling by cannabinoid receptors
Authors: Miljuš Tamara , Heydenreich Franziska M. , Gazzi Thais , Kimbara Atsushi , Rogers-Evans Mark , Nettekoven Matthias , Zirwes Elisabeth , Osterwald Anja , Rufer Arne C. , Ullmer Christoph , Guba Wolfgang , Le Gouill Christian , Fingerle Jürgen , Nazaré Marc , Grether Uwe , Bouvier Michel , Veprintsev Dmitry B. 
Published: November 2020

AbstractCannabinoid CB1 and CB2 receptors are members of the G protein-coupled receptor family, which is the largest class of membrane proteins in the human genome. As part of the endocannabinoid system, they have many regulatory functions in the human body. Their malfunction therefore triggers a diverse set of undesired conditions, such as pain, neuropathy, nephropathy, pruritus, osteoporosis, cachexia and Alzheimer’s disease. Although drugs targeting the system exist, the molecular and functional mechanisms involved are still poorly understood, preventing the development of better therapeutics with fewer undesired effects. One path toward the development of better and safer medicines targeting cannabinoid receptors relies on the ability of some compounds to activate a subset of pathways engaged by the receptor while sparing or even inhibiting the others, a phenomenon known as biased signaling. To take advantage of this phenomenon for drug development, a better profiling of the pathways engaged by the receptors is required. Using a BRET-based signaling detection platform, we systematically analyzed the primary signaling cascades activated by CB1 and CB2 receptors, including 9 G protein and 2 β-arrestin subtypes. Given that biased signaling is driven by ligand-specific distinct active conformations of the receptor, establishing a link between the signaling profiles elicited by different drugs and their chemotypes may help designing compounds that selectively activate beneficial pathways while avoiding those leading to undesired effects. We screened a selection of 35 structurally diverse ligands, including endocannabinoids, phytocannabinoids and synthetic compounds structurally similar or significantly different from natural cannabinoids. Our data show that biased signaling is a prominent feature of the cannabinoid receptor system and that, as predicted, ligands with different chemotypes have distinct signaling profiles. The study therefore allows for better understanding of cannabinoid receptors signaling and provides the information about tool compounds that can now be used to link signaling pathways to biological outcomes, aiding the design of improved therapeutics.
Ultraconserved elements help resolve the phylogeny of an ancient radiation of venomous flies (Diptera: Asilidae)
Authors: Cohen Chris M. , Noble Katherine , Cole T. Jeffrey , Brewer Michael S. 
Published: November 2020

AbstractRobber flies or assassin flies (Diptera: Asilidae) are a diverse family of venomous predators. The most recent classification organizes Asilidae into 14 subfamilies based on a comprehensive morphological phylogeny, but many of these have not been supported in a subsequent molecular study using traditional molecular markers. To address questions of monophyly in Asilidae, we leveraged the recently developed Diptera-wide UCE baitset to compile seven datasets comprising 151 robber flies and 146 - 2,508 loci, varying in the extent of missing data. We also studied the behavior of different nodal support metrics, as the non-parametric bootstrap is known to perform poorly with large genomic datasets. Our ML phylogeny was fully resolved and well-supported, but partially incongruent with the coalescent phylogeny. Further examination of the datasets suggested the possibility that GC bias had influenced gene tree inference and subsequent species tree analysis. The subfamilies Brachyrhopalinae, Dasypogoninae, Dioctriinae, Stenopogoninae, Tillobromatinae, Trigonomiminae, and Willistonininae were not recovered as monophyletic in either analysis, consistent with a previous molecular study. The inter-subfamily relationships are summarized as follows: Laphriinae and Dioctriinae (in part) are successively sister to the remaining subfamilies, which form two clades; the first consists of a grade of Stenopogoninae (in part), Willistonininae (in part), Bathypogoninae+Phellinae, Stichopogoninae, Leptogastrinae, Ommatiinae, and Asilinae; the second clade consists of a thoroughly paraphyletic assemblage of genera from Dioctriinae (in part), Trigonomiminae, Stenopogoninae (in part), Tillobromatinae, Brachyrhopalinae, and Dasypogoninae. We find that nodal support does not significantly vary with missing data. Furthermore, the bootstrap appears to overestimate nodal support, as has been reported from many recent studies. Gene concordance and site concordance factors seem to perform better, but may actually underestimate support. We instead recommend quartet concordance as a more appropriate estimator of nodal support. Our comprehensive phylogeny demonstrates that the higher classification of Asilidae is far from settled, and it will provide a much-needed foundation for a thorough revision of the subfamily classification.
iSMNN: Batch Effect Correction for Single-cell RNA-seq data via Iterative Supervised Mutual Nearest Neighbor Refinement
Authors: Yang Yuchen , Li Gang , Xie Yifang , Wang Li , Yang Yingxi , Liu Jiandong , Qian Li , Li Yun 
Published: November 2020

ABSTRACTBatch effect correction is an essential step in the integrative analysis of multiple single cell RNA-seq (scRNA-seq) data. One state-of-the-art strategy for batch effect correction is via unsupervised or supervised detection of mutual nearest neighbors (MNNs). However, both two kinds of methods only detect MNNs across batches on the top of uncorrected data, where the large batch effect may affect the MNN search. To address this issue, we presented iSMNN, a batch effect correction approach via iterative supervised MNN refinement across data after correction. Our benchmarking on both simulation and real datasets showed the advantages of the iterative refinement of MNNs on the performance of correction. Compared to popular alternative methods, our iSMNN is able to better mix the cells of the same cell type across batches. In addition, iSMNN can also facilitate the identification of differentially expression genes (DEGs) relevant to the biological function of certain cell types. These results indicated that iSMNN will be a valuable method for integrating multiple scRNA-seq datasets that can facilitate biological and medical studies at single-cell level.
CoSIR: Optimal control of SIR epidemic dynamics by mapping to Lotka-Volterra System
Authors: Maheshwari Harsh , Shetty Shreyas , Bannur Nayana , Merugu Srujana 
Published: November 2020

AbstractMultiple macro-phenomena such as disease epidemics, online information propagation, and economic activity can be well-approximated using simple dynamical systems. Shaping these phenomena with adaptive control of key levers has long been the holy grail of policymakers. In this paper, we focus on optimal control of transmission rate in epidemic systems following the widely applicable SIR dynamics. We first demonstrate that the SIR model with infectious patients and susceptible contacts (i.e., product of transmission rate and susceptible population) interpreted as predators and prey respectively reduces to a Lotka-Volterra (LV) predator-prey model. The modified SIR system (LVSIR) has a stable equilibrium point, an “energy” conservation property, and exhibits bounded cyclic behavior. We exploit this mapping using a control-Lyapunov approach to design a novel adaptive control policy (CoSIR) that nudges the SIR model to the desired equilibrium. Combining CoSIR policy with data-driven estimation of parameters and adjustments for discrete transmission levels yields a control strategy with practical utility. Empirical comparison with periodic lockdowns on simulated and real COVID-19 data demonstrates the efficacy and adaptability of our approach.