bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Joint Impact of COVID-19 Vaccination and Non-Pharmaceutical Interventions on Infections, Hospitalizations, and Mortality: An Agent-Based Simulation
Authors: Patel Mehul D. , Rosenstrom Erik , Ivy Julie S. , Mayorga Maria E. , Keskinocak Pinar , Boyce Ross M. , Lich Kristen Hassmiller , Smith Raymond L. , Johnson Karl T. , Swann Julie L. 
Published: January 2021

AbstractBackgroundVaccination against SARS-CoV-2 has the potential to significantly reduce transmission and morbidity and mortality due to COVID-19. This modeling study simulated the comparative and joint impact of COVID-19 vaccine efficacy and coverage with and without non-pharmaceutical interventions (NPIs) on total infections, hospitalizations, and deaths.MethodsAn agent-based simulation model was employed to estimate incident SARS-CoV-2 infections and COVID-19-associated hospitalizations and deaths over 18 months for the State of North Carolina, a population of roughly 10.5 million. Vaccine efficacy of 50% and 90% and vaccine coverage of 25%, 50%, and 75% (at the end of a 6-month distribution period) were evaluated. Six vaccination scenarios were simulated with NPIs (i.e., reduced mobility, school closings, face mask usage) maintained and removed during the period of vaccine distribution.ResultsIn the worst-case vaccination scenario (50% efficacy and 25% coverage), 2,231,134 new SARS-CoV-2 infections occurred with NPIs removed and 799,949 infections with NPIs maintained. In contrast, in the best-case scenario (90% efficacy and 75% coverage), there were 450,575 new infections with NPIs maintained and 527,409 with NPIs removed. When NPIs were removed, lower efficacy (50%) and higher coverage (75%) reduced infection risk by a greater magnitude than higher efficacy (90%) and lower coverage (25%) compared to the worst-case scenario (absolute risk reduction 13% and 8%, respectively).ConclusionSimulation results suggest that premature lifting of NPIs while vaccines are distributed may result in substantial increases in infections, hospitalizations, and deaths. Furthermore, as NPIs are removed, higher vaccination coverage with less efficacious vaccines can contribute to a larger reduction in risk of SARS-CoV-2 infection compared to more efficacious vaccines at lower coverage. Our findings highlight the need for well-resourced and coordinated efforts to achieve high vaccine coverage and continued adherence to NPIs before many pre-pandemic activities can be resumed.
An analysis of patients’ perspectives on qualitative olfactory dysfunction using social media
Authors: Parker Jane K. , Kelly Christine E. , Smith Barry , Hopkins Claire , Gane Simon B. 
Published: January 2021

SUMMARYBackgroundThe impact of qualitative olfactory disorders is underestimated. Parosmia is the triggered perception of distorted odours whereas phantosmia is the perception of odours in the absence of a trigger. Both can arise from post-infectious anosmia and have increased substantially since the outbreak of COVID-19.Methodology/PrincipalThematic analysis of a social media support group for parosmia and phantosmia was used to articulate the perspectives and concerns of those affected by these debilitating olfactory disorders.ResultsA novel symptom (olfactory perseveration) was identified where a triggered, identifiable, and usually unpleasant olfactory percept persisted in the absence of an ongoing stimulus. Fluctuations in intensity and duration of perseveration, parosmia and phantosmia were observed. Coffee, meat, onion, and toothpaste were identified as common triggers of these disorders, but people struggled to describe the distortions, using words associated with disgust and revulsion. Common strategies to avoid triggers may result in a diet lacking in both nutrition and reward. The emotional aspect of living with qualitative olfactory dysfunction was evident and highlighted the detrimental impact on mental health.ConclusionsThe data acquired can inform rehabilitation strategies and drive our ongoing research into understanding the molecular triggers associated with parosmia, and research into patient benefit.
Patient-Reported Outcomes of Ugandans Living with Autoimmune Rheumatic Diseases
Authors: Bongomin Felix , Sekimpi Maria , Natukunda Barbra , Makhoba Anthony , Kaddumukasa Mark 
Published: January 2021

AbstractPurposeThis study aimed to assess the patient–reported outcomes (PROs) in rheumatic patients attending two tertiary rheumatology clinics in Uganda.MethodsA cross-sectional, clinical audit of patients aged 16 years or older with a confirmed diagnosis of rheumatic disease and receiving disease modifying anti-rheumatic drugs (DMARDs) was conducted between September and December 2020. Health index and overall self-rated health status were assessed using the ED-5D-5L tool. Comparisons for variables was performed using Student’s t-test or Mann-Whitney U for continuous numerical data while categorical data was compared using either Χ2 tests or Fisher’s exact tests as appropriate.ResultsWe enrolled 74 eligible patients: 48 (64.9%) had rheumatoid arthritis (RA), 14 (18.9%) had systemic lupus erythematosus (SLE), and 12 (16.2%) had other autoimmune rheumatic disorders. Majority (n=69, 93.2%) were female with a mean ±SD age of 45 ± 17 years. Fourteen (18.9%) patients were on concomitant herbal medication while using DMARDs and 26 (35.1%) self-reported at least 1 adverse drug reactions to the DMARDS. Any level of problem was reported by 54 (72.5%) participants for mobility, 47 (63.5%) for self-care, 56 (75.6%) for usual activity, 66 (89.1%) for pain and discomfort, and 56 (75.6%) for anxiety/depression. Patients with SLE had higher median health index compared to those other autoimmune rheumatic disorders (p<0.0001). Overall self-rated health status was comparable across clinical diagnoses (p=0.2), but better for patients who received care from private (Nsambya Hospital) compared to public hospital (Mulago Hospital) (65 vs. 50, p=0.009).ConclusionThere is a substantial negative impact of autoimmune rheumatic diseases on quality of life of patients, especially those receiving care from a public facility in Uganda.Clinical SignificanceAdverse drug reactions to DMARDs was reported by more than one-third of the patientsSLE patients have better quality of life compared to patients with other autoimmune rheumatic disease.Concomitant use of herbal medication is common and associated with lower health index and lower overall self-rated health status.Autoimmune rheumatic diseases impose a heavy financial burden on affected patients, over 70% of the study patients required financial support for management of their disease and a high proportion of these patients were not on their DMARD therapy the week prior to their scheduled clinic appoints.
Simple quantitative assessment of the outdoor versus indoor airborne transmission of viruses and covid-19
Authors: Rowe B.R. , Canosa A. , Drouffe J.M. , Mitchell J.B.A. 
Published: January 2021

AbstractIn this paper we develop a simple model of the inhaled flow rate of aerosol particles of respiratory origin i.e. that have been exhaled by other humans. A connection is made between the exposure dose and the probability of developing an airborne disease. This allows a simple assessment of the outdoor versus indoor risk of contamination to be made in a variety of meteorological situations. It is shown quantitatively that for most cases, the outdoor risk is orders of magnitude less than the indoor risk and that it can become comparable only for extremely specific meteorological and geographical situations. It sheds a light on various observations of Covid-19 spreading in mountain valleys with temperature inversions while at the same time other areas are much less impacted.HighlightsRisk of covid-19 airborne transmission.Quantitative assessment of outdoor versus indoor airborne risk of transmission.Meteorological and geographical influence on covid-19 airborne transmission.
Need of care in interpreting Google Trends-based COVID-19 infodemiological study results: potential risk of false-positivity
Authors: Sato Kenichiro , Mano Tatsuo , Iwata Atsushi , Toda Tatsushi 
Published: January 2021

AbstractObjectiveGoogle Trends (GT) is being used as an epidemiological tool to study coronavirus disease (COVID-19) by identifying keywords in search trends that are predictive for the COVID-19 epidemiological burden. However, many of the earlier GT-based studies include potential statistical fallacies by measuring the correlation between non-stationary time sequences without adjusting for multiple comparisons or the confounding of media coverage, leading to concerns about the increased risk of obtaining false-positive results. In this study, we aimed to apply statistically more favorable methods to validate the earlier GT-based COVID-19 study results.MethodsWe extracted the relative GT search volume for keywords associated with COVID-19 symptoms, and evaluated their Granger-causality to weekly COVID-19 positivity in eight English-speaking countries and Japan. In addition, the impact of media coverage on keywords with significant Granger-causality was further evaluated using Japanese regional data.ResultsOur Granger causality-based approach largely decreased (by up to approximately one-third) the number of keywords identified as having a significant temporal relationship with the COVID-19 trend when compared to those identified by the Pearson correlation-based approach. “Sense of smell” and “loss of smell” were the most reliable GT keywords across all the evaluated countries; however, when adjusted with their media coverage, these keyword trends did not Granger-cause the COVID-19 positivity trends (in Japan).ConclusionOur results suggest that some of the search keywords reported as candidate predictive measures in earlier GT-based COVID-19 studies may potentially be unreliable; therefore, caution is necessary when interpreting published GT-based study results.
Divergences on expected pneumonia cases during the COVID-19 epidemic in Catalonia: A time-series analysis of primary care electronic health records covering about 6 million people
Authors: Coma Ermengol , Méndez-Boo Leonardo , Mora Núria , Guiriguet Carolina , Benítez Mència , Fina Francesc , Fàbregas Mireia , Balló Elisabet , Ramos Francisa , Medina Manuel , Argimon Josep M. 
Published: January 2021

AbstractBackgroundPneumonia is one of the complications of COVID-19. Primary care electronic health records (EHR) have shown the utility as a surveillance system.AimTo analyze the trends of pneumonia during two waves of COVID-19 pandemic in order to use it as a clinical surveillance system and an early indicator of severity.MethodsTime series analysis of pneumonia cases, January 2014-December 2020. We collected pneumonia diagnoses from primary care EHR, covering >6 million people in Catalonia (Spain). We compared the trend of pneumonia in the season 2019-2020 with that in the previous years. We estimated the expected pneumonia cases with data from 2014 to 2018 using a time series regression adjusted by seasonality and influenza epidemics.ResultsBetween 4 March and 5 May 2020, 11,704 excess pneumonia cases (95% CI: 9,909 to 13,498) were identified. We observed a second excess pneumonia period from 22 october to 15 november of 1,377 excess cases (95% CI: 665 to 2,089). In contrast, we observed two great periods with reductions of pneumonia cases in children, accounting for 131 days and 3,534 less pneumonia cases (95% CI: 1,005 to 6,064) from March to July; and 54 days and 1,960 less pneumonia cases (95% CI 917 to 3,002) from October to December.ConclusionsDiagnoses of pneumonia from the EHR could be used as an early and low cost surveillance system to monitor the spread of COVID-19.
Bayesian forecasting for intravenous tobramycin dosing in adults with Cystic Fibrosis using one versus two serum concentrations in a dosing interval
Authors: Drennan Philip G. , Thoma Yann , Barry Lucinda , Matthey Johan , Sivam Sheila , van Hal Sebastiaan J. 
Published: January 2021

AbstractBackgroundIntravenous tobramycin requires therapeutic drug monitoring (TDM) to ensure safety and efficacy when used for prolonged treatment, as in infective exacerbations of Cystic Fibrosis (CF). The 24 hour area under the concentration time curve (AUC24) is widely used to guide dosing, however there remains variability in practice around methods for its estimation.ObjectivesTo determine the potential for a sparse sampling strategy using a single post-infusion tobramycin concentration and Bayesian forecasting, to assess the AUC24 in routine practice.MethodsAdults with CF receiving once daily tobramycin had paired concentrations measured 2 hours (c1) and 6 hours (c2) following end of infusion as routine monitoring. We estimated AUC24 exposures using Tucuxi, a Bayesian forecasting application incorporating a validated population pharmacokinetic model. We performed simulations to estimate AUC24 using the full dataset using c1 and c2, compared to estimates using depleted datasets (c1 or c2 only), with and without concentration data from earlier in the course. We assessed agreement between each simulation condition and the reference graphically, and numerically using median difference (Δ) AUC24, and (relative) root mean square error (rRMSE) as measures of bias and accuracy respectively.Results55 patients contributed 512 concentrations from 95 tobramycin courses and 256 TDM episodes. Single concentration methods performed well, with median ΔAUC24 <2 mg.h.l-1 and rRMSE of <15% for sequential c1 and c2 conditions.ConclusionsBayesian forecasting, using single post-infusion concentrations taken 2-6 hours following tobramycin administration can adequately estimate true exposure in this patient group and are suitable for routine TDM practice.Key Points-In stable adult patients with Cystic fibrosis without significant renal impairment, Bayesian forecasting allows accurate estimation of tobramycin AUC24 using a single blood sample taken 2-6 hours post-infusion with acceptable accuracy, especially when including prior measured concentrations.-A single sample approach with Bayesian forecasting is logistically less complicated than a two-sample approach, and could facilitate best-practice TDM in the outpatient setting.-A more intensive sampling strategy with Bayesian forecasting using two tobramycin concentrations in a dosing interval should be considered in unstable patients, or where observed concentrations deviate significantly from model predictions.
LAVA: An integrated framework for local genetic correlation analysis
Authors: Werme J. , van der Sluis S. , Posthuma D. , de Leeuw C. A. 
Published: January 2021

ABSTRACTGenetic correlation (rg) analysis is commonly used to identify traits that may have a shared genetic basis. Traditionally,rgis studied on a global scale, considering only the average of the shared signal across the genome; though this approach may fail to detect scenarios where thergis confined to particular genomic regions, or show opposing directions at different loci. Tools dedicated to localrganalysis have started to emerge, but are currently restricted to analysis of two phenotypes. For this reason, we have developed LAVA, an integrated framework for localrganalysis which, in addition to testing the standard bivariate localrg’s between two traits, can evaluate the local heritability for all traits of interest, and analyse conditional genetic relations between several traits using partial correlation or multiple regression. Applied to 20 behavioural and health phenotypes, we show considerable heterogeneity in the bivariate localrg’s across the genome, which is often masked by the globalrgpatterns, and demonstrate how our conditional approaches can elucidate more complex, multivariate genetic relations between traits.
Disrupted dynamic functional network connectivity among cognitive control networks in the progression of Alzheimer’s disease
Authors: Sendi Mohammad S. E. , Zendehrouh Elaheh , Fu Zening , Liu Jingyu , Du Yuhui , Mormino Elizabeth , Salat David H. , Calhoun Vince D. , Miller Robyn. L. 
Published: January 2021

AbstractBackgroundAlzheimer’s disease (AD) is the most common age-related dementia that promotes a decline in memory, thinking, and social skills. The initial stages of dementia can be associated with mild symptoms, and symptom progression to a more severe state is heterogeneous across patients. Recent work has demonstrated the potential for functional network mapping to assist in the prediction of symptomatic progression. However, this work has primarily used static functional connectivity (sFC) from rs-fMRI. Recently, dynamic functional connectivity (dFC) has been recognized as a powerful advance in functional connectivity methodology to differentiate brain network dynamics between healthy and diseased populations.MethodsGroup independent component analysis was applied to extract 17 components within the cognitive control network (CCN) from 1385 individuals across varying stages of AD symptomology. We estimated dFC among 17 components within the CCN, followed by clustering the dFCs into 3 recurring brain states and then estimated a hidden Markov model and the occupancy rate for each subject. Finally, we investigated the link between CCN dFC connectivity features with AD progression.ResultsProgression of AD symptoms were associated with increases in connectivity within the middle frontal gyrus. Also, the AD with mild and severer symptoms showed less connectivity within the inferior parietal lobule and between this region with the rest of CCN. Finally, comparing with mild dementia, we found that the normal brain spends significantly more time in a state with lower within middle frontal gyrus connectivity and higher connectivity between the hippocampus and the rest of CCN, highlighting the importance of assessing the dynamics of brain connectivity in this disease.ConclusionOur results suggest that AD progress not only alters the CCN connectivity strength but also changes the temporal properties in this brain network. This suggests the temporal and spatial pattern of CCN as a biomarker that differentiates different stages of AD.Impact StatementBy assuming that functional connectivity is static over time, many of previous studies have ignored the brain dynamic in Alzheimer’s disease progression. Here, a longitudinal resting-state functional magnetic resonance imaging data are used to explore the temporal changes of functional connectivity in the cognitive control network in Alzheimer’s disease progression. The result of this study would increase our understanding about the underlying mechanisms of Alzheimer’s Disease and help in finding future treatment of this neurological disorder.
scJoint: transfer learning for data integration of atlas-scale single-cell RNA-seq and ATAC-seq
Authors: Lin Yingxin , Wu Tung-Yu , Wan Sheng , Yang Jean Y.H. , Wong Wing H. , Wang Y. X. Rachel 
Published: January 2021

AbstractSingle-cell multi-omics data continues to grow at an unprecedented pace, and effectively integrating different modalities holds the promise for better characterization of cell identities. Although a number of methods have demonstrated promising results in integrating multiple modalities from the same tissue, the complexity and scale of data compositions typically present in cell atlases still pose a significant challenge for existing methods. Here we present scJoint, a transfer learning method to integrate atlas-scale, heterogeneous collections of scRNA-seq and scATAC-seq data. scJoint leverages information from annotated scRNA-seq data in a semi-supervised framework and uses a neural network to simultaneously train labeled and unlabeled data, enabling label transfer and joint visualization in an integrative framework. Using multiple atlas data and a biologically varying multi-modal data, we demonstrate scJoint is computationally efficient and consistently achieves significantly higher cell type label accuracy than existing methods while providing meaningful joint visualizations. This suggests scJoint is effective in overcoming the heterogeneity in different modalities towards a more comprehensive understanding of cellular phenotypes.
Learning attention-controllable border-ownership for objectness inference and binding
Authors: Dedieu Antoine , Rikhye Rajeev V. , Lázaro-Gredilla Miguel , George Dileep 
Published: January 2021

AbstractHuman visual systems can parse a scene composed of novel objects and infer their surfaces and occlusion relationships without relying on object-specific shapes or textures. Perceptual grouping can bind together spatially disjoint entities to unite them as one object even when the object is entirely novel, and bind other perceptual properties like color and texture to that object using object-based attention. Border-ownership assignment, the assignment of perceived occlusion boundaries to specific perceived surfaces, is an intermediate representation in the mammalian visual system that facilitates this perceptual grouping. Since objects in a scene can be entirely novel, inferring border ownership requires integrating global figural information, while dynamically postulating what the figure is, a chicken-and egg process that is complicated further by missing or conflicting local evidence regarding the presence of boundaries. Based on neuroscience observations, we introduce a model – the cloned Markov random field (CMRF)– that can learn attention-controllable representations for border-ownership. Higher-order contour representations that distinguish border-ownerships emerge as part of learning in this model. When tested with a cluttered scene of novel 2D objects with noisy contour-only evidence, the CMRF model is able to perceptually group them, despite clutter and missing edges. Moreover, the CMRF is able to use occlusion cues to bind disconnected surface elements of novel objects into coherent objects, and able to use top-down attention to assign border ownership to overlapping objects. Our work is a step towards dynamic binding of surface elements into objects, a capability that is crucial for intelligent agents to interact with the world and to form entity-based abstractions.
Distinct cryo-EM Structure of α-synuclein Filaments derived by Tau
Authors: Hojjatian Alimohammad , Dasari Anvesh K. R. , Sengupta Urmi , Taylor Dianne , Daneshparvar Nadia , Yeganeh Fatemeh Abbasi , Dillard Lucas , Michael Brian , Griffin Robert G. , Borgnia Mario , Kayed Rakez , Taylor Kenneth A. , Lim Kwang Hun 
Published: January 2021

AbstractRecent structural studies of ex vivo amyloid filaments extracted from human patients demonstrated that the ex vivo filaments associated with different disease phenotypes adopt diverse molecular conformations distinct from those in vitro amyloid filaments. A very recent cryo-EM structural study also revealed that ex vivo α-synuclein filaments extracted from multiple system atrophy (MSA) patients adopt quite distinct molecular structures from those of in vitro α-synuclein filaments, suggesting the presence of co-factors for α-synuclein aggregation in vivo. Here, we report structural characterizations of α-synuclein filaments derived by a potential co-factor, tau, using cryo-EM and solid-state NMR. Our cryo-EM structure of the tau-promoted α-synuclein filament at 4.0 Å resolution is somewhat similar to one of the polymorphs of in vitro α-synuclein filaments. However, the N- and C-terminal regions of the tau-promoted α-synuclein filament have different molecular conformations. Our structural studies highlight the conformational plasticity of α-synuclein filaments, requiring additional structural investigation of not only more ex vivo α-synuclein filaments, but also in vitro α-synuclein filaments formed in the presence of diverse co-factors to better understand molecular basis of diverse molecular conformations of α-synuclein filaments.