bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pollinator and host sharing lead to hybridization and introgression in Panamanian free-standing figs, but not in their pollinator wasps
Authors: Satler Jordan D. , Herre Edward Allen , Heath Tracy A. , Machado Carlos A. , Zúñiga Adalberto Gómez , Jandér K. Charlotte , Eaton Deren A. R. , Nason John D. 
Published: August 2022

AbstractObligate pollination mutualisms, in which plant and pollinator lineages depend on each other for reproduction, often exhibit high levels of species-specificity. However, cases in which two or more pollinator species share a single host species (host sharing), or two or more host species share a single pollinator species (pollinator sharing), are known to occur in current ecological time. Further, evidence for host switching in evolutionary time is increasingly being recognized in these systems. The degree to which departures from strict specificity differentially affect the potential for hybridization and introgression in the associated host or pollinator is unclear. We addressed this question using genome-wide sequence data from five sympatric Panamanian free-standing fig species (Ficus subgenus Pharmacosycea, section Pharmacosycea) and their six associated fig pollinator wasp species (Tetrapus). Two of the five fig species, F. glabrata and F. maxima, were found to regularly share pollinators. In these species, ongoing hybridization was demonstrated by the detection of several first-generation (F1) hybrid individuals and historical introgression was indicated by phylogenetic network analysis. In contrast, although two of the pollinator species regularly share hosts, all six species were genetically distinct and deeply divergent, with no evidence for either hybridization or introgression. This pattern is consistent with results from other obligate pollination mutualisms, suggesting that, in contrast to their host plants, pollinators appear to be reproductively isolated, even when different species of pollinators mate in shared hosts.
Identification and prediction of Parkinson’s disease subtypes and progression using machine learning in two cohorts
Authors: Dadu Anant , Satone Vipul K. , Kaur Rachneet , Hashemi Sayed Hadi , Leonard Hampton , Iwaki Hirotaka , Makarious Mary B. , Billingsley Kimberley , Bandres-Ciga Sara , Sargent Lana J. , Noyce Alastair J. , Daneshmand Ali , Blauwendraat Cornelis , Marek Ken , Scholz Sonja W. , Singleton Andrew B. , Nalls Mike A. , Campbell Roy H. , Faghri Faraz 
Published: August 2022

AbstractBackgroundThe clinical manifestations of Parkinson’s disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. The emergence of machine learning to detect hidden patterns in complex, multi-dimensional datasets provides unparalleled opportunities to address this critical need.Methods and FindingsWe used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinson’s Disease Progression Marker Initiative (PPMI) (n = 294 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinson’s Disease Biomarker Program (PDBP) (n = 263 cases). Our analysis distinguished three distinct disease subtypes with highly predictable progression rates, corresponding to slow, moderate, and fast disease progression. We achieved highly accurate projections of disease progression five years after initial diagnosis with an average area under the curve (AUC) of 0.92 (95% CI: 0.95 ± 0.01 for the slower progressing group (PDvec1), 0.87 ± 0.03 for moderate progressors, and 0.95 ± 0.02 for the fast progressing group (PDvec3). We identified serum neurofilament light (Nfl) as a significant indicator of fast disease progression among other key biomarkers of interest. We replicated these findings in an independent validation cohort, released the analytical code, and developed models in an open science manner.ConclusionsOur data-driven study provides insights to deconstruct PD heterogeneity. This approach could have immediate implications for clinical trials by improving the detection of significant clinical outcomes that might have been masked by cohort heterogeneity. We anticipate that machine learning models will improve patient counseling, clinical trial design, allocation of healthcare resources, and ultimately individualized patient care.
Tensor Decomposition Discriminates Tissues Using scATAC-seq
Authors: Taguchi Y-h. , Turki Turki 
Published: August 2022

ABSTRACTATAC-seq is a powerful tool for measuring the landscape structure of a chromosome. scATAC-seq is a recently updated version of ATAC-seq performed in a single cell. The problem with scATAC-seq is data sparsity and most of the genomic sites are inaccessible. Here, tensor decomposition (TD) was used to fill in missing values. In this study, TD was applied to massive scATAC-seq datasets generated by approximately 200 bp intervals, and this number can reach 13,627,618. Currently, no other methods can deal with large sparse matrices. The proposed method could not only provide UMAP embedding that coincides with tissue specificity, but also select genes associated with various biological enrichment terms and transcription factor targeting. This suggests that TD is a useful tool to process a large sparse matrix generated from scATAC-seq.
IFNγ and antibody synergize to enhance protective immunity againstChlamydiadissemination and female reproductive tract reinfections
Authors: Gann Jessica A. , Malaviarachchi Priyangi A. , Du Wuying , Mercado Miguel A.B. , Li Lin-Xi 
Published: August 2022

AbstractCD4 T cell-dependent IFNγ production and antibody are the two best known effectors for protective immunity againstChlamydiafemale reproductive tract (FRT) infection. Nevertheless, mice lacking either IFNγ or B cells are capable of clearing vast majority ofChlamydiafrom the female reproductive tract (FRT), while suffering from varying degrees of disseminated infection. In this study, we investigated whether IFNγ and B cells play complimentary roles in host defense againstChlamydiaand evaluated their relative contributions in systemic and mucosal tissues. Using mice deficient in both IFNγ and B cells (IFNγ-/-x µMT), we showed that mice lacking both effectors are highly susceptible to lethal systemicChlamydiadissemination. Passive transfer of immune convalescent serum, but not recombinant IFNγ, reduced bacterial burden in both systemic and mucosal tissues in IFNγ-/-x µMT mice. Moreover, we observed a reduction of bacterial shedding of more than two orders of magnitude in IFNγ-/-x µMT mice following bothChlamydia muridarumandChlamydia trachomatisinfections. Lastly, protective immunity againstC. muridarumreinfection was completely abrogated in the absence of IFNγ and B cells. Our results suggest that IFNγ and B cells synergize to combat bacterial dissemination, while an IFNγ and B cell-independent mechanism exists for host resistance toChlamydiain the FRT.
The Association Between Cognitive Ability and Body Mass Index: A Sibling-Comparison Analysis in Four Longitudinal Studies
Authors: Wright Liam , Davies Neil M , Bann David 
Published: August 2022

AbstractBackgroundBody mass index (BMI) and obesity rates have increased sharply since the 1980s. While multiple epidemiologic studies have found higher adolescent cognitive ability is associated with lower adult BMI, residual and unobserved confounding due to family background may explain these associations. We used a sibling design to test this association accounting for confounding factors shared within households.MethodsWe used data from four cohort studies: the National Longitudinal Study of Youth 1979 (NLSY-79), the NLSY-79 Children and Young Adult, the NLSY 1997 (NLSY-97) and the Wisconsin Longitudinal Study (WLS); a total of 12,250 siblings from 5,602 households. We used random effects within-between (REWB) and residualized quantile regression (RQR) models to compare between- and within-family estimates of the association between adolescent cognitive ability and adult BMI (20-64 years).ResultsIn REWB models, moving from 0th to 100th percentile of adolescent cognitive ability was associated with −1.89 kg/m2 (95% CI = −2.41, −1.37) lower BMI between families. Adjusting for family socioeconomic position reduced the association to −1.23 (−1.79, −0.66) points. However, within families the association was just −0.13 (−0.70, 0.45) points. This pattern of results was found across multiple specifications, including analyses conducted in separate cohorts, models examining age-differences in association, and in RQR models examining the association across the distribution of BMI.ConclusionThe association between high adolescent cognitive ability and low adult BMI was substantially smaller in within-family compared with between-family analysis. The well-replicated associations between cognitive ability and subsequent BMI may largely reflect confounding by family background factors.
Clinical and neurobiologic predictors of long-term outcome in schizophrenia
Authors: Nickl-Jockschat Thomas , Ho Beng-Choon , Andreasen Nancy 
Published: August 2022

AbstractBackgroundSchizophrenia is a severe neuropsychiatric disorder accompanied by debilitating cognitive and psychosocial impairments over the course of the disease. As disease trajectories exhibit considerable inter-individual heterogeneity, early clinical and neurobiological predictors of long-term outcome are desirable for personalized treatment and care strategies.MethodsIn a naturalistic longitudinal approach, 381 schizophrenia patients from the Iowa Lon-gitudinal Study (ILS) cohort underwent an extensive characterization, including repeated magnetic resonance imaging (MRI) scans, over a mean surveillance period of 11.07 years. We explored whether pre-diagnostic markers, clinical markers at the first psychotic episode, or magnetic resonance imaging (MRI) measures at the onset of the disease were predictive of relapse or remission of specific symptom patterns later in life.ResultsWe identified a set of clinical parameters - namely premorbid adjustment during adolescence, symptom patterns, and neuropsychological profiles at disease onset – that were highly correlated with future disease trajectories. In general, brain measures at baseline did not correlate with outcome. Progressive regional brain volume losses over the observation period, however, were highly correlated with relapse patterns and symptom severity.ConclusionsOur findings provide clinicians with a set of highly robust, easily acquirable, and cost-effective predictors for long-term outcome in schizophrenia. These results can be directly translated to a clinical setting to improve prospective care and treatment planning for schizophrenia patients. (Funding sources: NIH MH68380, MH31593, MH40856, and MH43271).
Sleep and seizure risk in epilepsy: Bed and wake times are more important than sleep duration
Authors: Stirling Rachel E. , Hidajat Cindy M. , Grayden David B. , D’Souza Wendyl J , Dell Katrina L. , Boston Ray , Schneider Logan D. , Nurse Ewan , Freestone Dean , Cook Mark J. , Karoly Philippa J. 
Published: August 2022

AbstractSleep duration, sleep deprivation and the sleep-wake cycle are thought to play an important role in the generation of epileptic activity and may also influence seizure risk. Hence, people diagnosed with epilepsy are commonly asked to maintain consistent sleep routines. However, emerging evidence paints a more nuanced picture of the relationship between seizures and sleep, with bidirectional effects between changes in sleep and seizure risk in addition to modulation by sleep stages and transitions between stages. We conducted a longitudinal study investigating sleep parameters and self-reported seizure occurrence in an ambulatory at-home setting using mobile and wearable monitoring.Forty-four subjects wore a Fitbit smartwatch for at least 28 days while reporting their seizure activity in a mobile app. Multiple sleep features were investigated, including duration, oversleep and undersleep, and sleep onset and offset times. Sleep features in participants with epilepsy were compared to a large (n=37921) representative population of Fitbit users, each with 28 days of data. For participants with at least 10 seizure days (n=29), sleep features were analysed for significant changes prior to seizure days.A total of 3894 reported seizures (M = 88, SD = 130) and 17078 recorded sleep nights (M = 388, SD = 351) were included in the study. Participants with epilepsy slept an average of 2 hours longer than the average sleep duration within the general population. Just 1 of 29 participants showed a significant difference in sleep duration the night before seizure days compared to seizure-free days. However, 11 of 29 subjects showed significant differences between either their sleep onset (bed) or offset (wake) times prior to seizure occurrence. In contrast to previous studies, the current study found oversleeping was associated with a 20% increased seizure risk in the following 48h (p < 0.01), likely due to nocturnal seizures driving increased sleep durations.Nocturnal seizures were associated with both significantly longer sleep durations and increased risk of a seizure occurring in the following 48h. Oversleeping only significantly contributed to increased seizure risk when participants were already in a high-risk (rather than baseline- or low-risk) state, according to their endogenous cycles of seizure likelihood.Overall, the presented results demonstrated that day-to-day changes in sleep-duration had a minimal effect on reported seizures, while bed- and wake-times were more important for identifying seizure risk the following day. Oversleeping was linked to seizure occurrence, most likely due to nocturnal seizures driving oversleep. Wearables can be utilised to identify these sleep-seizure relationships and guide clinical recommendations or improve seizure forecasting algorithms.
Direct and indirect effects of the COVID-19 pandemic on mortality in Switzerland: A population-based study
Authors: Riou Julien , Hauser Anthony , Fesser Anna , Althaus Christian L. , Egger Matthias , Konstantinoudis Garyfallos 
Published: August 2022

AbstractThe direct and indirect impact of the COVID-19 pandemic on population-level mortality is of concern to public health but challenging to quantify. We modelled excess mortality and the direct and indirect effects of the pandemic on mortality in Switzerland. We analyzed yearly population data and weekly all-cause deaths by age, sex, and canton 2010-2019 and all-cause and laboratory-confirmed COVID-19 deaths from February 2020 to April 2022 (study period). Bayesian models predicted the expected number of deaths. A total of 13,130 laboratory-confirmed COVID-19 deaths were reported. The model estimated that COVID-19-related mortality was underestimated by a factor of 0.72 [95% Credible Interval: 0.46-0.78] resulting in 18,140 [15,962-20,174] excess deaths. After accounting for COVID-19 deaths, the observed mortality was 3% [-1-7] lower than expected, corresponding to a deficit of 4,406 deaths, with a wide credibility interval [-1,776-10,700]. Underestimation of COVID-19 deaths was greatest for ages 70 years and older; the mortality deficit was most pronounced in age groups 40 to 69 years. We conclude that shortcomings in testing caused underestimation of COVID-19-related deaths in Switzerland, particularly in older people. Although COVID-19 control measures may have negative effects (e.g., delays in seeking care or mental health impairments), after subtracting COVID-19 deaths, there were fewer deaths in Switzerland during the pandemic than expected, suggesting that any negative effects of control measures on mortality were offset by the positive effects. These results have important implications for the ongoing debate about the appropriateness of COVID-19 control measures.
An empirical investigation into the impact of winner’s curse on estimates from Mendelian randomization
Authors: Jiang Tao , Gill Dipender , Butterworth Adam S. , Burgess Stephen 
Published: August 2022

AbstractIntroductionGenetic associations for variants identified through genome-wide association studies (GWAS) tend to be overestimated in the original discovery dataset; as if the association was underestimated, the variant may not have been detected. This bias, known as winner’s curse, can affect Mendelian randomization estimates, but its severity and potential impact is unclear.MethodsWe performed an empirical investigation to assess the potential bias from winner’s curse in practice. We considered Mendelian randomization estimates for the effect of body mass index (BMI) on coronary artery disease risk. We randomly divided a UK Biobank dataset 100 times into three equal-sized subsets. The first subset was treated as the “discovery GWAS”. We compared genetic associations estimated in the discovery GWAS to those estimated in the other subsets for each of the 100 iterations.ResultsFor variants associated with BMI at p<5×10−8 in at least one iteration, genetic associations with BMI were up to five-fold greater in iterations where the variant was statistically significantly associated with BMI compared to its mean association across all iterations. If the minimum p-value for association with BMI was p=10−13 or lower, then this inflation was less than 25%. Mendelian randomization estimates were affected by winner’s curse bias. However, bias did not materially affect results; all analyses indicated a deleterious effect of BMI on CAD risk.ConclusionsWinner’s curse can bias Mendelian randomization estimates, although its practical impact may not be substantial. If avoiding sample overlap is infeasible, analysts should consider performing a sensitivity analysis based on variants strongly associated with the exposure.
Improving estimates of pertussis burden in Ontario, Canada 2010-2017 by combining validation and capture-recapture methodologies
Authors: McBurney Shilo H. , Kwong Jeffrey C. , Brown Kevin A. , Rudzicz Frank , Wilton Andrew , Crowcroft Natasha S. 
Published: August 2022

AbstractAn underestimation of pertussis burden has impeded understanding of transmission and disallows effective policy and prevention to be prioritized and enacted. Capture-recapture analyses can improve burden estimates; however, uncertainty remains around incorporating health administrative data due to accuracy limitations. The aim of this study is to explore the impact of pertussis case definitions and data accuracy on capture-recapture estimates. We used a dataset from March 7, 2010 to December 31, 2017 comprised of pertussis case report, laboratory, and health administrative data. We compared Chao capture-recapture abundance estimates using prevalence, incidence, and adjusted false positive case definitions. The latter was developed by removing the proportion of false positive physician billing code-only case episodes after validation. We calculated sensitivity by dividing the number of observed cases by abundance. Abundance estimates demonstrated that a high proportion of cases were missed by all sources. Under the primary analysis, the highest sensitivity of 78.5% (95% CI 76.2-80.9%) for those less than one year of age was obtained using all sources after adjusting for false positives, which dropped to 43.1% (95% CI 42.4-43.8%) for those one year of age or older. Most code-only episodes were false positives (91.0%), leading to considerably lower abundance estimates and improvements in laboratory testing and case report sensitivity using this definition. Accuracy limitations can be accounted for in capture-recapture analyses using different case definitions and adjustment. The latter enhanced the validity of estimates, furthering the utility of capture-recapture methods to epidemiological research. Findings demonstrated that all sources consistently fail to detect pertussis cases. This is differential by age, suggesting ascertainment and testing bias. Results demonstrate the value of incorporating real time health administrative data into public health surveillance if accuracy limitations can be addressed.
Rapid threat detection in SARS-CoV-2
Authors: Barrett Christopher L. , Huang Fenix W.D. , Li Thomas J.X. , Warren Andrew S. , Reidys Christian M. 
Published: August 2022

AbstractThis paper presents a novel virus surveillance framework, completely independent of phylogeny-based methods. The framework issues timely alerts with an accuracy exceeding 85% that are based on the co-evolutionary relations between sites of the viral multiple sequence array (MSA). This set of relations is formalized via a motif complex, whose dynamics contains key information about the emergence of viral threats without the referencing of strain prevalence. Our notion of threat is centered at the emergence of a certain type of critical cluster consisting of key co-evolving sites. We present three case studies, based on GISAID data from UK, US and New York, where we perform our surveillance. We alert on May 16, 2022, based on GISAID data from New York, to a critical cluster of co-evolving sites mapping to the Pango-designation, BA.5. The alert specifies a cluster of seven genomic sites, one of which exhibits D3N on the M (membrane) protein–the distinguishing mutation of BA.5, three encoding ORF6:D61L and the remaining three exhibiting the synonymous mutations C26858T, C27889T and A27259C. New insight is obtained: when projected onto sequences, this cluster splits into two, mutually exclusive blocks of co-evolving sites (m:D3N,nuc:C27889T) linked to the five reverse mutations (nuc:C26858T,nuc:A27259C,ORF6:D61L). We furthermore provide an in depth analysis of all major signaled threats, during which we discover a specific signature concerning linked reverse mutation in the critical cluster.
Two years of COVID-19 Pandemic: Framework of Health Interventions in a Brazilian City
Authors: Faiões Vanessa dos Santos , Póvoa Helvécio Cardoso Corrêa , Thurler Bruna Alves , Chianca Gabriela Ceccon , Assaf Andréa Videira , Iorio Natalia Lopes Pontes Póvoa 
Published: August 2022

AbstractThe COVID-19 pandemic and its effects on public health have urgently demanded effective health policies to avoid the spread of COVID-19. Thus, public administrators have implemented non-pharmacological and pharmacological interventions to mitigate the pandemic’s impacts and strengthen health services. The aim of this ecological study is to describe the scenario of COVID-19 pandemic in a Brazilian city, during two years. This ecological study was carried out in Nova Friburgo, a Brazilian city, for 105 weeks (two years), from March 29, 2020 (week 1) to April 02, 2022 (week 105). Data on COVID-19 cases and COVID-19 deaths, occupation of COVID-19 exclusive beds in hospitals, community mobility, vaccination, government regulation on the opening of city establishments and city risk assessment were collected from public datasets. Four waves of COVID-19 cases and deaths were observed during this period. The first case occurred in week 1 and first death in week 3 of this study. The highest peaks of cases and deaths were observed during the third wave with 1,131 cases (week 54) and 47 deaths (week 55) and where the highest occupation of COVID-19 exclusive beds in local hospitals occurred. Interventions from more restrictive to more flexible, were implemented throughout this study, including lockdown and gradual return in economic and social strata levels. Vaccination began on week 43 and at the end of this study 89.91% of the total population was vaccinated with at least one dose, being 83.22% fully vaccinated. A deep description of several interventions used to avoid COVID-19 spread in a Brazilian city during two years of this pandemic can help promote better decision-making in the future while it exposes the challenges of conducting public health policies in a pandemic scenario.