bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation of the PREDIGT Score in Discriminating Parkinson Disease from Neurological Health
Authors: Li Juan , Mestre Tiago A. , Mollenhauer Brit , Frasier Mark , Tomlinson Julianna J. , Trenkwalder Claudia , Ramsay Tim , Manuel Douglas , Schlossmacher Michael G. 
Published: November 2020

AbstractBackgroundWe previously created the PREDIGT Score as an algorithm to predict the incidence of Parkinson disease. The model rests on a hypothesis-driven formula, [PR=(E+D+I)xGxT], that uses numerical values for five categories known to modulate Parkinson’s risk (PR): environmental exposure (E); DNA variants (D); evidence of gene-environment interactions (I); gender (G); and time (T). Notably, the formula does not rely on motor examination results.MethodsTo evaluate the PREDIGT Score, we tested it in two established case-control cohorts: ‘De Novo Parkinson Study’ (DeNoPa) and ‘Parkinson’s Progression Marker Initiative’ (PPMI). Using baseline data from 589 patients and 309 controls enrolled in the DeNoPa and PPMI cohorts, we evaluated the PREDIGT Score’s discriminative performance in distinguishing Parkinson’s patients from healthy controls by area-under-the-curve (AUC) analyses.FindingsWhen examining cohorts separately and using all available variables in each cohort to calculate the PREDIGT Score, AUCs were 0.83 (95% CI 0.77-0.89) for DeNoPa and 0.87 (95% CI 0.84-0.9) for PPMI, respectively, in distinguishing Parkinson disease patients from healthy individuals. When combining DeNoPa and PPMI data sets by using eleven variables that had been collected in both cohorts, the PREDIGT Score discriminated patients from controls with an AUC of 0.84 (95% CI 0.81-0.87). The mean score of Parkinson disease patients was significantly higher than that of control individuals at 108.48 (+52.08) and 47.33 (+34.1), respectively (p < 0.0001).InterpretationOur results demonstrate a robust performance of the original PREDIGT Score in distinguishing patients diagnosed with Parkinson disease from neurologically healthy subjects without reliance on motor examination data. In future efforts, the predictive performance of the algorithm will be studied in longitudinal cohorts of at-risk persons.FundingParkinson Canada, Michael J. Fox Foundation, Department of Medicine (The Ottawa Hospital), Uttra & Subash Bhargava Family, Paracelsus-Elena-Klinik Kassel, Parkinson Fond Deutschland, and Deutsche Parkinson Vereinigung.
Low blood pressure phenotype underpins the tendency to reflex syncope
Authors: Brignole Michele , Rivasi Giulia , Sutton Richard , Kenny Rose Anne , Morillo Carlos A , Sheldon Robert , Raj Satish R , Ungar Andrea , Furlan Raffaello , van Dijk Gert , Hamdan Mohamed , Hamrefors Viktor , Engström Gunnar , Park Chloe , Soranna Davide , Zambon Antonella , Parati Gianfranco , Fedorowski Artur 
Published: November 2020

AbstractBACKGROUNDWe hypothesized that cardiovascular physiology differs in reflex syncope patients compared with general population, predisposing such subjects to vasovagal reflex.METHODSIn this multicohort cross-sectional study, we compared resting systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP) and heart rate (HR), collected from 6 community-based cohort studies (64,968 observations) with those from 6 databases of reflex syncope patients (6516 observations), subdivided by age decades and sex.RESULTSOverall, in males with reflex syncope, SBP (−3.4 mmHg) and PP (−9.2 mmHg) were lower and DBP (+2.8 mmHg) and HR (+5.1 bpm) were higher than in the general population; the difference in SBP was higher at ages >60 years. In females, PP (−6.0 mmHg) was lower and DBP (+4.7 mmHg) and HR (+4.5 bpm) were higher than in the general population; differences in SBP were less pronounced, becoming evident only above 60 years. Compared with males, SBP in females exhibited slower increase until age 40, and then demonstrated steeper increase that continued throughout life.CONCLUSIONThe patients prone to reflex syncope demonstrate a different resting cardiovascular hemodynamic profile characterized by reduced venous return and stroke volume, evidenced by lower SBP and PP, and compensatory increase in HR and vascular resistance, the latter expressed by elevated DBP. The data presented here contribute to our understanding why some subjects with similar demographic characteristics develop reflex syncope and others not.
Predicting Trends of Coronavirus Disease (COVID-19) Using SIRD and Gaussian-SIRD Models
Authors: Sedaghat Ahmad , Mosavi Amir 
Published: November 2020

AbstractEruption of COVID-19 patients in 215 countries worldwide have urged for robust predictive methods that can detect as early as possible size and duration of the contagious disease and also providing precision predictions. In many recent literatures reported on COVID-19, one or more essential parts of such investigation were missed. One of crucial elements for any predictive method is that such methods should fit simultaneously as many data as possible; these data could be total infected cases, daily hospitalized cases, cumulative recovered cases and deceased cases and so on. Other crucial elements include sensitivity and precision of such predictive methods on amount of data as the contagious disease evolved day by day. To show importance of these aspects, we have evaluated the standard SIRD model and a newly introduced Gaussian-SIRD model on development of COVID-19 in Kuwait. It is observed that SIRD model quickly pick up main trends of COVID-19 development; but Gaussian-SIRD model provides precise prediction at longer period of time.
Performance comparison: exome-sequencing as a single test replacing Sanger-sequencing
Authors: Fridman Hila , Bormans Concetta , Einhorn Moshe , Au Daniel , Bormans Arjan , Porat Yuval , Sanchez Luisa Fernanda , Manning Brent , Levy-Lahad Ephrat , Behar Doron M. 
Published: November 2020

ABSTRACTSystematic performance comparing the results of exome-sequencing as a single test replacing Sanger-sequencing of targeted gene(s) is still lacking. In this study we compared Sanger-sequencing results of 258 genes to those obtained from next generation sequencing (NGS) using two exome-sequencing enrichment kits: Agilent-SureSelectQXT and Illumina-Nextera. Sequencing was performed on leukocytes and buccal-derived DNA from a single individual, and all 258 genes were sequenced a total of 11 times (using different sequencing methods and DNA sources). Sanger-sequencing was completed for all exons, including flanking ±8bp regions. For the 258 genes, NGS mean coverage was >20x for >98% and >91% of the regions targeted by SureSelect and Nextera, respectively. Overall, 449 variants were identified in at least one experiment, and 407/449 (90.6%) were detected by all. Of the 42 discordant variants, 23 were determined as true calls, summing-up to a truth set of 430 variants. Sensitivity of true-variant detection was 99% for Sanger-sequencing and 97%-100% for the NGS experiments. Mean false-positive rates were 3.7E-6 for Sanger-sequencing, 2.5E-6 for SureSelect-NGS and 5.2E-6 for Nextera-NGS. Our findings suggest a high overall concordance between Sanger-sequencing and NGS. Both methods demonstrated false positive and false negative calls and similar performances. Consequently, high clinical suspicion for a specific diagnosis should override negative results of either Sanger-sequencing or NGS.
An Evolving-Dynamic Network Activity Approach to Epileptic Seizure Prediction using Machine Learning
Authors: Liu Christine Joy , Sorokin Jordan , Ganguli Surya , Huguenard John 
Published: November 2020

AbstractAbsence epilepsy is a neurological condition characterized by abnormally synchronous electrical activity within two mutually connected brain regions, the thalamus and cortex, that results in seizures and affects more than 6.5 million people. Epilepsy is commonly studied through the use of the electroencephalogram (EEG), a device that monitors brain waves over time. In this study, we introduced machine learning models to predict epileptic seizures in two ways, one to train logistic regression models to provide an accurate decision boundary to predict based off frequency features, and second to train convolutional neural networks to predict based off spectral power images from EEG. This pipeline employed a two model approach, using logistic regression and convolutional neural networks to predict seizures. The evaluation, performed on data from 9 mice, achieved prediction accuracies of 98%. The proposed methodology introduces a novel aspect of looking at predicting absence seizures, which are known to be short events, in addition to the comparison between a time-dependent and time-agnostic seizure prediction classifier. The overall goal of these experiments were to build a model that can accurately predict whether or not a seizure will occur.
Microfluidic Device Facilitates NovelIn VitroModeling of Human Neonatal Necrotizing Enterocolitis-on-a-Chip
Authors: Lanik Wyatt E. , Luke Cliff J. , Nolan Lila S. , Gong Qingqing , Rimer Jamie M. , Gale Sarah E. , Luc Raymond , Bidani Shay S. , Sibbald Carrie A. , Lewis Angela N. , Mihi Belgacem , Agrawal Pranjal , Goree Martin , Maestas Marlie , Hu Elise , Peters David G. , Good Misty 
Published: November 2020

AbstractNecrotizing enterocolitis (NEC) is a deadly gastrointestinal disease of premature infants characterized by an exaggerated inflammatory response, dysbiosis of the gut microbiome, decreased epithelial cell proliferation, and gut barrier disruption. Here, we describe a novelin vitromodel of human neonatal small intestinal epithelium (Neonatal-Intestine-on-a-Chip) that mimics key features of intestinal physiology by utilizing a combination of premature infant intestinal enteroids co-cultured with human intestinal microvascular endothelial cells within a microfluidic device. We used our Neonatal-Intestine-on-a-Chip to recapitulate NEC pathophysiology in anin vitromodel system of the premature gut inoculated with infant-derived microbiota. This model, also known as NEC-on-a-Chip, emulates the prominent features of NEC, demonstrating significant upregulation of pro-inflammatory cytokines, decreased intestinal epithelial cell markers, reduced epithelial proliferation and disrupted epithelial barrier integrity. NEC-on-a-Chip provides a novel preclinical model of NEC, which may be used as a personalized medicine approach to test new therapeutics for this devastating disease.
Mapping the Cellular Origin and Early Evolution of Leukemia in Down Syndrome
Authors: Wagenblast Elvin , Araújo Joana , Gan Olga I. , Cutting Sarah K. , Murison Alex , Krivdova Gabriela , Azkanaz Maria , McLeod Jessica L. , Smith Sabrina A. , Marhon Sajid A , Gabra Martino , Chan-Seng-Yue Michelle , Garcia-Prat Laura , Salmena Leonardo , De Carvalho Daniel D , Chong Karen , Roifman Maian , Shannon Patrick , Wang Jean C Y , Hitzler Johann K. , Chitayat David , Dick John E. , Lechman Eric R. 
Published: November 2020

AbstractChildren with Down syndrome have a 150-fold increased risk of developing myeloid leukemia, but the mechanism of predisposition is unclear. As Down syndrome leukemogenesis initiates during fetal development, we characterized the cellular context of preleukemic initiation and leukemic progression using gene editing in human disomic and trisomic fetal liver hematopoietic cells and xenotransplantation.GATA1mutations caused transient preleukemia only when introduced into trisomy 21 long-term hematopoietic stem cells, where a subset of chromosome 21 miRNAs triggers predisposition to preleukemia. By contrast, progression to leukemia was independent of trisomy 21 and originated in various stem and progenitor cells through additional mutations in cohesin genes. CD117+/KIT cells mediated the propagation of preleukemia and leukemia, and functional KIT inhibition targeted preleukemic stem cells, blocking progression to leukemia.
Quantitatively Monitoringin situMitochondrial Thermal Dynamics by Upconversion Nanoparticles
Authors: Di Xiangjun , Wang Dejiang , Zhou Jiajia , Zhang Lin , Stenzel Martina , Su Qian Peter , Jin Dayong 
Published: November 2020

AbstractTemperature dynamics reflect the physiological conditions of cells and organisms. Mitochondria regulates temperature dynamics in living cells, as they oxidize the respiratory substrates and synthesize ATP, with heat being released as a by-product of active metabolism. Here, we report an upconversion nanoparticles based thermometer that allowsin situthermal dynamics monitoring of mitochondria in living cells. We demonstrate that the upconversion nanothermometers can efficiently target mitochondria and the temperature responsive feature is independent of probe concentration and medium conditions. The relative sensing sensitivity of 3.2% K−1in HeLa cells allows us to measure the mitochondrial temperature difference through the stimulations of high glucose, lipid, Ca2+shock and the inhibitor of oxidative phosphorylation. Moreover, cells display distinct response time and thermal dynamic profiles under different stimulations, which highlights the potential applications of this thermometer to studyin situvital processes related to mitochondrial metabolism pathways and interactions between organelles.
Group-Specific Carbon Fixation Activity Measurements Reveal Distinct Responses to Oxygen Among Hydrothermal ventCampylobacteria
Authors: McNichol Jesse , Dyksma Stefan , Mußmann Marc , Seewald Jeffrey S. , Sylva Sean P. , Sievert Stefan M. 
Published: November 2020

AbstractMolecular surveys of low temperature deep-sea hydrothermal vent fluids have shown thatCampylobacteria(prev.Epsilonproteobacteria) often dominate the microbial community and that three subgroups -Arcobacter,SulfurimonasandSulfurovum -frequently coexist. In this study, we used replicated radiocarbon incubations of deep-sea hydrothermal fluids to investigate the activities of each group under three distinct incubation conditions. In order to quantify group-specific radiocarbon incorporation, we used newly designed oligonucleotide probes forArcobacter, Sulfurimonas,andSulfurovumto quantify their activity using catalyzed-reporter deposition fluorescence in-situ hybridization (CARD-FISH) combined with fluorescence-activated cell sorting. All three groups actively fixed CO2in short-term (~ 20 h) incubations with either nitrate, oxygen, or no additions (control) at similar per-cell carbon fixation rates. Oxygen additions had the largest effect on community composition and overall cell numbers, and caused a pronounced shift in community composition at the amplicon sequence variant (ASV) level after only 20 h of incubation for all three groups. Interestingly, the effect of oxygen on carbon fixation rates appeared to depend on the initial starting community. Higher carbon fixation rates in oxygen-amended treatments were noted for all three taxa after an unintended disturbance to the sample site that may have selected for more oxygen-tolerant phylotypes. When viewed from a coarse taxonomic level, our data support assertions that these chemoautotrophic groups are functionally redundant in terms of their core metabolic capabilities since they were simultaneously active under all incubation conditions. In contrast, the higher resolution of amplicon sequencing allowed us to reveal finer-scale differences in growth that likely reflect adaptation of physiologically-distinct subtypes to varying oxygen concentrationsin situ. Despite this progress, we still know remarkably little about the factors that maintain genomic diversity and allow for stable co-existence among these three campylobacterial groups. Moving forward, we suggest that more subtle biological factors such as enzyme substrate specificity, motility, cell morphology, and tolerance to environmental stress should be more thoroughly investigated to better understand ecological niche differentiation at deep-sea hydrothermal vents.
Cwl0971, a novel peptidoglycan hydrolase, plays pleiotropic roles inClostridioides difficileR20291
Authors: Zhu Duolong , Patabendige Hiran Malinda Lamabadu Warnakulasur , Tomlinson Brooke Rene , Wang Shaohui , Hussain Syed , Flores Domenica , He Yongqun , Shaw Lindsey N , Sun Xingmin 
Published: November 2020

SummaryClostridioides difficileis a Gram-positive, spore-forming, toxin-producing anaerobe that can cause nosocomial antibiotic-associated intestinal disease. Although the production of toxin A (TcdA) and toxin B (TcdB) contribute to the main pathogenesis ofC. difficile, the mechanism of TcdA and TcdB release from cell remains unclear. In this study, we identified and characterized a new cell wall hydrolase Cwl0971 (CDR20291_0971) fromC. difficileR20291, which is involved in bacterial autolysis. The gene 0971 deletion mutant (R20291Δ0971) generated with CRISPR-AsCpfI exhibited significantly delayed cell autolysis and increased cell viability compared to R20291, and the purified Cwl0971 exhibited hydrolase activity forBacillus subtiliscell wall. Meanwhile, 0971 gene deletion impaired TcdA and TcdB release due to the decreased cell autolysis in the stationary / late phase of cell growth. Moreover, sporulation of the mutant strain decreased significantly compared to the wild type strain.In vivo, the defect of Cwl0971 decreased fitness over the parent strain in a mouse infection model. Collectively, Cwl0971 is involved in cell wall lysis and cell viability, which affects toxin release, sporulation, germination, and pathogenicity of R20291, indicating that Cwl0971 could be an attractive target forC. difficileinfection therapeutics and prophylactics.
The structure-fitness landscape of pairwise relations in generative sequence models
Authors: Marshall Dylan , Wang Haobo , Stiffler Michael , Dauparas Justas , Koo Peter , Ovchinnikov Sergey 
Published: November 2020

AbstractIf disentangled properly, patterns distilled from evolutionarily related sequences of a given protein family can inform their traits - such as their structure and function. Recent years have seen an increase in the complexity of generative models towards capturing these patterns; from sitewise to pairwise to deep and variational. In this study we evaluate the degree of structure and fitness patterns learned by a suite of progressively complex models. We introduce pairwise saliency, a novel method for evaluating the degree of captured structural information. We also quantify the fitness information learned by these models by using them to predict the fitness of mutant sequences and then correlate these predictions against their measured fitness values. We observe that models that inform structure do not necessarily inform fitness and vice versa, contrasting recent claims in this field. Our work highlights a dearth of consistency across fitness assays as well as divergently provides a general approach for understanding the pairwise decomposable relations learned by a given generative sequence model.
What is the uncertainty in efficacy of COVID-19 vaccines? A Bayesian analysis
Authors: Wibbens Phebo D. 
Published: November 2020

AbstractThis short paper reports a Bayesian analysis of the publicly available COVID-19 trial results. The analysis casts some doubts on whether the half+full dose regime of the AstraZeneca COVID-19 vaccine is truly more effective than the 2x full dose regime. The 95% posterior interval for the efficacy of the half+full dose regime is 66.6-96.3%, while for the 2x full dose regime it is 39.0-74.8%. Hence, it is possible that in both dosage regimes the vaccine has similar efficacy, around 70%. The estimated efficacy for the Pfizer vaccine is 89.9-97.4% and for Moderna 86.3-97.5%. These results should be interpreted with care though, since this analysis does not account for differences in for instance trial population, COVID-19 testing, and storage requirements for the various vaccines.
Increasing efficacy of contact-tracing applications by user referrals and stricter quarantining
Authors: Goldberg Leslie Ann , Jorritsma Joost , Komjáthy Júlia , Lapinskas John 
Published: November 2020

AbstractWe study the effects of two mechanisms which increase the efficacy of contact-tracing applications (CTAs) such as the mobile phone contact-tracing applications that have been used during the COVID-19 epidemic. The first mechanism is the introduction of user referrals. We compare four scenarios for the uptake of CTAs — (1) thep% of individuals that use the CTA are chosen randomly, (2) a smaller initial set of randomly-chosen users each refer a contact to use the CTA, achievingp% in total, (3) a small initial set of randomly-chosen users each refer around half of their contacts to use the CTA, achievingp% in total, and (4) for comparison, an idealised scenario in which thep% of the population that uses the CTA is thep% with the most contacts. Using agent-based epidemiological models incorporating a geometric space, we find that, even when the uptake percentagep% is small, CTAs are an effective tool for mitigating the spread of the epidemic in all scenarios. Moreover, user referrals significantly improve efficacy. In addition, it turns out that user referrals reduce the quarantine load. The second mechanism for increasing the efficacy of CTAs is tuning the severity of quarantine measures. Our modelling shows that using CTAs with mild quarantine measures is effective in reducing the maximum hospital load and the number of people who become ill, but leads to a relatively high quarantine load, which may cause economic disruption. Fortunately, under stricter quarantine measures, the advantages are maintained but the quarantine load is reduced. Our models incorporate geometric inhomogeneous random graphs to study the effects of the presence of super-spreaders and of the absence of long-distant contacts (e.g., through travel restrictions) on our conclusions.