bluuebunny/crossref_metadata_2025_split huggingface dataset:
The kinetic of SARS-CoV-2 antibody (Ab) decline determines the threshold for Ab persistence up to one year
Authors: Garner-Spitzer Erika , Wagner Angelika , Kundi Michael , Stockinger Hannes , Repic Anna , Schoetta Anna-Margarita , Gudipati Venugopal , Huppa Johannes B. , Kunert Renate , Mayrhofer Patrick , Kreil Thomas R. , Farcet Maria R. , Hoeltl Eva , Wiedermann Ursula 
Published: September 2021

AbstractTwelve subjects with positive SARS-CoV-2 neutralization test (NT) titers (>1:10) identified in a seroprevalence study with 1655 working adults were followed up for one year. Here we report that 7 of these 12 individuals (58%) still had NT titers ≥1:50, S1-specific IgG concentrations ≥50 BAU/ml and ≥26% ACE2 receptor binding inhibition, measured with surrogate virus NT one year after mild COVID infection. Furthermore, NT_50 titers >1:10 and S1-specific IgG levels >60 BAU/ml present at three months post-infection persisted at detectable levels for 1 year and correlated with circulating S1-specific memory B-cells. Vaccine-induced SARS-CoV2 immune responses decline at similar rates as those after infection; thus the describes threshold of 60 BAU/ml at three months post infection might also be relevant for assessment of Ab persistence after vaccination.
Estimation of country-specific tuberculosis antibiograms using genomic data
Authors: Dixit Avika , Freschi Luca , Vargas Roger , Groeschel Matthias I. , Tahseen Sabira , Alam SM Masud , Kamal SM Mostofa , Skrahina Alena , Basilio Ramon P. , Lim Dodge R. , Ismail Nazir A , Farhat Maha R. 
Published: September 2021

AbstractBackgroundGlobal tuberculosis (TB) drug resistance (DR) surveillance is largely focused on the drug rifampicin. We leveraged public and surveillance M. tuberculosis (Mtb) whole genome sequencing (WGS) data, to generate more comprehensive country-level resistance prevalence estimates (antibiograms) using in silico resistance prediction.MethodsWe curated and quality-controlled Mtb WGS data. We used a validated random forest model to predict phenotypic resistance to twelve drugs and bias-corrected for model performance, outbreak sampling, and resistance oversampling. We validated our estimates using a national DR survey conducted in South Africa.ResultsMtb isolates from 29 countries (n=19,149) met sequence quality criteria. Marginal genotypic resistance estimates overlapped with the South African DR survey for all drugs except isoniazid and second-line injectables that were underestimated (n=3,134); among multi-drug resistant (MDR) TB, estimates overlapped for pyrazinamide and the fluoroquinolones. Globally, mono-resistance to isoniazid was estimated at 10.9% (95% CI: 10.2-11.7%, n = 14,012. Mono-levofloxacin resistance rates were highest in South Asia (Pakistan 3.4% [0.1-11%], n=111 and India 2.8% [0.08-9.4%], n=114). Rates of resistance discordance between isoniazid and ethionamide were high with 74.4% (IQR: 64.5-79.7%) of isoniazid resistant isolates predicted to be ethionamide susceptible. The global susceptibility rate to pyrazinamide and levofloxacin among MDR was 15.1% (95% CI: 10.2-19.9%, n=3,964).ConclusionsThis is the first attempt at global Mtb antibiogram estimation. DR prevalence in Mtb can be reliably estimated using public WGS and phenotypic resistance prediction for key antibiotics. Our results raise concerns about the empiric use of short-course fluoroquinolone regimens for drug susceptible TB in South Asia and suggest that ethionamide is an under-utilized drug in MDR treatment.
Dissecting the Epigenomic Differences between Smoking and Nicotine Dependence in a Veteran Cohort
Authors: Nagamatsu Sheila Tiemi , Pietrzak Robert H , Xu Ke , Krystal John H , Gelernter Joel , Montalvo-Ortiz Janitza Liz 
Published: September 2021

ABSTRACTBackgroundSmoking is a serious public health issue linked to more than 8 million deaths per year worldwide. It also may lead to nicotine dependence (ND). Smoking can induce long-lasting epigenetic changes. Although epigenetic alterations related to tobacco smoke have been largely studied, few works have investigated ND and its interaction with smoking status (SS).ObjectiveWe investigated the peripheral epigenomic profile of SS and ND in a U.S. male veteran cohort.MethodsDNA from saliva was collected from 1,135 European American (EA) male U.S. military veterans. DNAm was assessed using the Illumina Infinium Human MethylationEPIC BeadChip array. SS was evaluated as: current smokers (n=137; 12.1%) and non-current smokers (never and former smokers; n=998; 87.9%). ND was assessed using the Fagerström Test for Nicotine Dependence (FTND). EWAS and co-methylation analyses were conducted for SS and ND.ResultsA total of 450 and 22 genome-wide significant differentially methylated sites (DMS) were associated with SS and ND, respectively (fifteen overlapped sites). We identified 97 DMS (43 genes) in SS-EWAS previously reported in the literature, including AHRR, and F2RL3 genes (p-value range: 1.95×10−83 to 4.5×10−33). ND novel DMS mapped to NEUROG1, ANPEP, and SLC29A1. Co-methylation analysis identified 386 modules (11 SS-related and 19 ND-related). SS-related modules showed enrichment for alcoholism, chemokine signaling pathway, and neurogenesis; while ND-related modules were enriched for cellular adhesion, and nicotine addiction.ConclusionsThis study confirms previous findings and identifies novel and -potentially specific - epigenetic signatures for SS and ND in a sample of EA male veterans.
Geographic partitioning of dengue virus transmission risk in Florida
Authors: Stephenson Caroline J. , Coatsworth Heather , Waits Christy M. , Nazario-Maldonado Nicole M. , Mathias Derrick K. , Dinglasan Rhoel R. , Lednicky John A. 
Published: September 2021

AbstractDengue viruses (DENVs) cause the greatest public health burden globally among the arthropod-borne viruses. DENV transmission risk has also expanded from tropical to subtropical regions due to the increasing range of its principal mosquito vector, Aedes aegypti. Focal out-breaks of dengue fever (dengue) in the state of Florida (FL) in the USA have increased since 2009. However, little is known about the competence of Ae. aegypti populations across different regions of FL to transmit DENVs. To understand the effects of DENV genotype and serotype variations on vector susceptibility and transmission potential in FL, we orally infected a colony of Ae. aegypti (Orlando/ORL) with laboratory and wild type DENV-1 through -4. Wild type DENVs were more infectious to and transmissible by ORL mosquitoes. We used these same DENVs to examine natural Ae. aegypti populations to determine whether spatial distributions correlated with differential vector competence. Vector competence across all DENV serotypes was greater for mosquitoes from areas with the highest dengue incidence in south FL compared to north FL. Vector competence for wild type DENVs was significantly higher, revealing that transmission risk is influenced by virus/vector combinations. These data support a targeted mosquito-plus-pathogen screening approach to more accurately estimate DENV transmission risk.
Children Living with Disabilities Are Absent From Severe Malnutrition Guidelines
Authors: Engl Magdalena , Binns Paul , Trehan Indi , Lelijveld Natasha , Angood Chloe , McGrath Marie , Groce Nora , Kerac Marko 
Published: September 2021

ABSTRACTPurposeChildren living with disabilities (CLWD) are at high risk of malnutrition but have long been marginalised in malnutrition treatment programmes and research. The 2013 World Health Organization (WHO) guidelines for Severe Acute Malnutrition (SAM) mention disability but do not contain specific details for treatment or support. This study assesses inclusion of CLWD in national and international SAM guidelines.MethodsNational and international SAM guidelines were sourced online and via direct enquiries. Eight scoping key informant interviews were conducted with experts involved in guideline development to help understand possible barriers to formalising malnutrition guidance for CLWD.Results71 malnutrition guidelines were reviewed (63 national, 8 international). Only 4% (3/71) had a specific section for CLWD, while the remaining lacked guidance on consistently including CLWD in programmes or practice. Only one guideline mentioned strategies to include CLWD during a nutritional emergency. Most (99%,70/71) did not link to other disability-specific guidelines. Of the guidelines that included CLWD, most only discussed disability in general terms despite the fact that different interventions are often needed for children with different conditions. Interviews pointed towards barriers related to medical complexity, resource constraints, epidemiology (e.g., unrecognised burden), lack of evidence, and difficulty of integration into existing guidelines.ConclusionChildren living with disability are not recognised in most SAM guidelines. Where they are, recommendations are very limited. Better evidence is urgently needed to identify and manage CLWD in malnutrition programmes. More inclusion in the 2022 update of the WHO malnutrition guidelines could support this vulnerable group.
Antibody responses against SARS-CoV-2 variants induced by four different SARS-CoV-2 vaccines
Authors: van Gils Marit J. , Ayesha Lavell A. H. , van der Straten Karlijn , Appelman Brent , Bontjer Ilja , Poniman Meliawati , Burger Judith A. , Oomen Melissa , Bouhuijs Joey H. , van Vught Lonneke A. , Slim Marleen A. , Schinkel Michiel , Wynberg Elke , van Willigen Hugo D.G. , Grobben Marloes , Tejjani Khadija , van Rijswijk Jacqueline , Snitselaar Jonne L. , Caniels Tom G. ,   Amsterdam UMC COVID-19 S3/HCW study group, Vlaar Alexander P. J. , Prins Maria , de Jong Menno D. , de Bree Godelieve J. , Sikkens Jonne J. , Bomers Marije K. , Sanders Rogier W. 
Published: September 2021

AbstractBackgroundEmerging and future SARS-CoV-2 variants may jeopardize the effectiveness of vaccination campaigns. Therefore, it is important to know how the different vaccines perform against diverse SARS-CoV-2 variants.MethodsIn a prospective cohort of 165 SARS-CoV-2 naive health care workers, vaccinated with either one of four vaccines (BNT162b2, mRNA-1273, AZD1222 or Ad26.COV2.S), we performed a head-to-head comparison of the ability of sera to recognize and neutralize SARS-CoV-2 variants of concern (VOCs; Alpha, Beta, Gamma, Delta and Omicron). Repeated serum sampling was performed 5 times during a year (from January 2021 till January 2022), including before and after booster vaccination with BNT162b2.FindingsFour weeks after completing the initial vaccination series, SARS-CoV-2 wild-type neutralizing antibody titers were highest in recipients of BNT162b2 and mRNA-1273 (geometric mean titers (GMT) of 197 [95% CI 149-260] and 313 [95% CI 218-448], respectively), and substantially lower in those vaccinated with the adenovirus vector-based vaccines AZD1222 and Ad26.COV2.S (GMT of 26 [95% CI 18-37] and 14 [95% CI 8-25] IU/ml, respectively). These findings were robust for adjustment to age and sex. VOCs neutralization was reduced in all vaccine groups, with the largest (9- to 80-fold) reduction in neutralization observed against the Omicron variant. The booster BNT162b2 vaccination increased neutralizing antibody titers for all groups with substantial improvement against the VOCs including the Omicron variant. Study limitations include the lack of cellular immunity data.ConclusionsOverall, this study shows that the mRNA vaccines appear superior to adenovirus vector-based vaccines in inducing neutralizing antibodies against VOCs four weeks after initial vaccination and after booster vaccination.
The activities of LRRK2 and GCase are positively correlated in clinical biospecimens and experimental models of Parkinson’s disease
Authors: Kedariti Maria , Frattini Emanuele , Baden Pascale , Cogo Susanna , Civiero Laura , Ziviani Elena , Aureli Massimo , Kaganovich Alice , Cookson Mark R , Stefanis Leonidas , Surface Matthew , Deleidi Michela , Di Fonzo Alessio , Alcalay Roy N. , Rideout Hardy , Greggio Elisa , Plotegher Nicoletta 
Published: September 2021

AbstractLRRK2 is a kinase involved in different cellular functions, including autophagy, endolysosomal pathways and vesicle trafficking. Mutations inLRRK2cause autosomal dominant forms of Parkinson’s disease (PD). Heterozygous mutations inGBA1, the gene encoding the lysosomal enzyme glucocerebrosidase (GCase), are the most common genetic risk factors for PD. Moreover, GCase function is altered in idiopathic PD and in other genetic forms of the disease. Recent work suggests that LRRK2 kinase activity can regulate GCase function. However, both a positive and a negative correlation have been described. To gain insights into the impact of LRRK2 on GCase, we investigated GCase levels and activity inLRRK2G2019S knockin mice, in clinical biospecimens from PD patients carrying this mutation and in patient-derived cellular models. In these models we found a positive correlation between the activities of LRRK2 and GCase, which was further confirmed in cell lines with genetic and pharmacological manipulation of LRRK2 kinase activity. Overall, our study indicates that LRRK2 kinase activity affects both the levels and the catalytic activity of GCase.
Generative Adversarial Networks (GAN) for the simulation of central-place foraging trajectories
Authors: Roy Amédée , Bertrand Sophie Lanco , Fablet Ronan 
Published: September 2021

AbstractMiniature electronic device such as GPS have enabled ecologists to document relatively large amount of animal trajectories. Modeling such trajectories may attempt (1) to explain mechanisms underlying observed behaviors and (2) to elucidate ecological processes at the population scale by simulating multiple trajectories. Existing approaches to animal movement modeling mainly addressed the first objective and they are yet soon limited when used for simulation. Individual-based models based on ad-hoc formulation and empirical parametrization lack of generability, while state-space models and stochastic differential equations models, based on rigorous statistical inference, consist in 1st order Markovian models calibrated at the local scale which can lead to overly simplistic description of trajectories.We introduce a ‘state-of-the-art’ tool from artificial intelligence - Generative Adversarial Networks (GAN) - for the simulation of animal trajectories. GAN consist in a pair of deep neural networks that aim at capturing the data distribution of some experimental dataset, and that enable the generation of new instances of data that share statistical similarity. In this study, we aim on one hand to identify relevant deep networks architecture for simulating central-place foraging trajectories and on the second hand to evaluate GAN benefits over classical methods such as state-switching Hidden Markov Models (HMM).We demonstrate the outstanding ability of GAN to simulate ‘realistic’ seabirds foraging trajectories. In particular, we show that deep convolutional networks are more efficient than LSTM networks and that GAN-derived synthetic trajectories reproduce better the Fourier spectral density of observed trajectories than those simulated using HMM. Therefore, unlike HMM, GAN capture the variability of large-scale descriptive statistics such as foraging trips distance, duration and tortuosity.GAN offer a relevant alternative to existing approaches to modeling animal movement since it is calibrated to reproduce multiple scales at the same time, thus freeing ecologists from the assumption of first-order markovianity. GAN also provide an ultra-flexible and robust framework that could further take environmental conditions, social interactions or even bio-energetics model into account and tackle a wide range of key challenges in movement ecology.
Response of Endozoicomonas montiporae to heat stress and coral host lysates
Authors: Chan Ya-fan , Chen Chia-Yu , Lu Chih-Ying , Tu Yung-Chi , Tandon Kshitij , Shikina Shinya , Tang Sen-Lin 
Published: September 2021

AbstractEndozoicomonas, a core bacterial group in corals, may also be a coral symbiont. Endozoicomonas communities often decrease rapidly in corals under heat stress. However, how the bacteria respond to changes in temperature and coral host during heat stress is unknown. Here, we employed the cultivable, dominant species E. montiporae as a working organism to explore how Endozoicomonas responds to heat stress. We designed two experiments to clarify the extent to which E. montiporae is influenced by temperature and coral host. We detected differentially expressed protein (DEP) profiles in this bacterium at 31°C and 33°C compared to 25°C by tandem mass tags-based quantitative proteome analysis. Fifty DEPs, including many heat shock proteins, were detected when the temperature changed. The expression of antioxidant defense proteins and key pyruvate synthase proteins decreased, suggesting that E. montiporae were in a physiology of stress at 33°C. Furthermore, some proteins were differentially expressed because of the heat-stress-treated coral lysate specifically, suggesting that not only heat but also heat-induced host factors can affect the protein expression of the bacterium. This study provides an in-depth analysis of how the molecular mechanisms of Endozoicomonas are affected by heat stress and coral host.
Protein-protein interaction-Gaussian accelerated molecular dynamics (PPI-GaMD): Characterization of protein binding thermodynamics and kinetics
Authors: Wang Jinan , Miao Yinglong 
Published: September 2021

AbstractProtein-protein interactions (PPIs) play key roles in many fundamental biological processes such as cellular signaling and immune responses. However, it has proven challenging to simulate repetitive protein association and dissociation in order to calculate binding free energies and kinetics of PPIs, due to long biological timescales and complex protein dynamics. To address this challenge, we have developed a new computational approach to all-atom simulations of PPIs based on a robust Gaussian accelerated molecular dynamics (GaMD) technique. The method, termed “PPI-GaMD”, selectively boosts interaction potential energy between protein partners to facilitate their slow dissociation. Meanwhile, another boost potential is applied to the remaining potential energy of the entire system to effectively model the protein’s flexibility and rebinding. PPI-GaMD has been demonstrated on a model system of the ribonuclease barnase interactions with its inhibitor barstar. Six independent 2 μs PPI-GaMD simulations have captured repetitive barstar dissociation and rebinding events, which enable calculations of the protein binding thermodynamics and kinetics simultaneously. The calculated binding free energies and kinetic rate constants agree well with the experimental data. Furthermore, PPI-GaMD simulations have provided mechanistic insights into barstar binding to barnase, which involve long-range electrostatic interactions and multiple binding pathways, being consistent with previous experimental and computational findings of this model system. In summary, PPI-GaMD provides a highly efficient and easy-to-use approach for binding free energy and kinetics calculations of PPIs.
Deep Learning Enables Design of Multifunctional Synthetic Human Gut Microbiome Dynamics
Authors: Baranwal Mayank , Clark Ryan L. , Thompson Jaron , Sun Zeyu , Hero Alfred O. , Venturelli Ophelia 
Published: September 2021

AbstractPredicting the dynamics and functions of microbiomes constructed from the bottom-up is a key challenge in exploiting them to our benefit. Current ordinary differential equation-based models fail to capture complex behaviors that fall outside of a predetermined ecological theory and do not scale well with increasing community complexity and in considering multiple functions. We develop and apply a long short-term memory (LSTM) framework to advance our understanding of community assembly and health-relevant metabolite production using a synthetic human gut community. A mainstay of deep learning, the LSTM learns a high dimensional data-driven non-linear dynamical system model used to design communities with desired metabolite profiles. We show that the LSTM model can outperform the widely used generalized Lotka-Volterra model. We build methods decipher microbe-microbe and microbe-metabolite interactions from an otherwise black-box model. These methods highlight that Actinobacteria, Firmicutes and Proteobacteria are significant drivers of metabolite production whereas Bacteroides shape community dynamics. We use the LSTM model to navigate a large multidimensional functional landscape to identify communities with unique health-relevant metabolite profiles and temporal behaviors. In sum, the accuracy of the LSTM model can be exploited for experimental planning and to guide the design of synthetic microbiomes with target dynamic functions.
Rapid parameter estimation for selective inversion recovery myelin imaging using an open-source Julia toolkit
Authors: Sisco Nicholas J. , Wang Ping , Stokes Ashley , Dortch Richard 
Published: September 2021

AbstractBackgroundMagnetic resonance imaging (MRI) is used extensively to quantify myelin content, however computational bottlenecks remain challenging for advanced imaging techniques in clinical settings. We present a fast, open-source toolkit for processing quantitative magnetization transfer derived from selective inversion recovery (SIR) acquisitions that allows parameter map estimation, including the myelin-sensitive macromolecular pool size ratio (PSR). Significant progress has been made in reducing SIR acquisition times to improve clinically feasibility. However, parameter map estimation from the resulting data remains computationally expensive. To overcome this computational limitation, we developed a computationally efficient, open-source toolkit implemented in the Julia language.MethodsTo test the accuracy of this toolkit, we simulated SIR images with varyingPSRand spin-lattice relaxation time of the free water pool (R1f) over a physiologically meaningful scale from 5 to 20% and 0.5 to 1.5 s-1, respectively. Rician noise was then added, and the parameter maps were estimated using our Julia toolkit. Probability density histogram plots and Lin’s concordance correlation coefficients (LCCC) were used to assess accuracy and precision of the fits to our known simulation data. To further mimic biological tissue, we generated five cross-linked bovine serum albumin (BSA) phantoms with concentrations that ranged from 1.25 to 20%. The phantoms were imaged at 3T using SIR, and data were fit to estimatePSRandR1f. Similarly, a healthy volunteer was imaged at 3T, and SIR parameter maps were estimated to demonstrate the reduced computational time for a real-world clinical example.ResultsEstimated SIR parameter maps from our Julia toolkit agreed with simulated values (LCCC> 0.98). This toolkit was further validated using BSA phantoms and a whole brain scan at 3T. In both cases, SIR parameter estimates were consistent with published values using MATLAB. However, compared to earlier work using MATLAB, our Julia toolkit provided an approximate 20-fold reduction in computational time.ConclusionsPresented here, we developed a fast, open-source, toolkit for rapid and accurate SIR MRI using Julia. The reduction in computational cost should allow SIR parameters to be accessible in clinical settings.