bluuebunny/crossref_metadata_2025_split huggingface dataset:
Faster growth enhances low carbon fuel and chemical production through gas fermentation
Authors: de Lima Lorena Azevedo , Ingelman Henri , Brahmbhatt Kush , Reinmets Kristina , Barry Craig , Harris Audrey , Marcellin Esteban , Köpke Michael , Valgepea Kaspar 
Published: February 2022

ABSTRACTGas fermentation offers both fossil carbon-free sustainable production of fuels and chemicals and recycling of gaseous and solid waste using gas-fermenting microbes. Bioprocess development, systems-level analysis of biocatalyst metabolism, and engineering of cell factories are advancing the widespread deployment of the commercialised technology. Acetogens are particularly attractive biocatalysts but effects of the key physiological parameter – specific growth rate (μ) – on acetogen metabolism and the gas fermentation bioprocess have not been established yet. Here, we investigate the μ-dependent bioprocess performance of the model-acetogenClostridium autoethanogenumin CO and syngas (CO+CO2+H2) grown chemostat cultures and assess systems-level metabolic responses using gas analysis, metabolomics, transcriptomics, and metabolic modelling. We were able to obtain steady-states up to μ ~2.8 day-1(~0.12 h-1) and show that faster growth supports both higher yields and productivities for reduced by-products ethanol and 2,3-butanediol. Transcriptomics data revealed differential expression of 1,337 genes with increasing μ and suggest thatC. autoethanogenumuses transcriptional regulation to a large extent for facilitating faster growth. Metabolic modelling showed significantly increased fluxes for faster growing cells that were, however, not accompanied by gene expression changes in key catabolic pathways for CO and H2metabolism. Cells thus seem to maintain sufficient “baseline” gene expression to rapidly respond to CO and H2availability without delays to kick-start metabolism. Our work advances understanding of transcriptional regulation in acetogens and shows that faster growth of the biocatalyst improves the gas fermentation bioprocess.
Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains
Authors: Demontis Ditte , Walters G. Bragi , Athanasiadis Georgios , Walters Raymond , Therrien Karen , Farajzadeh Leila , Voloudakis Georgios , Bendl Jaroslav , Zeng Biao , Zhang Wen , Grove Jakob , Als Thomas D. , Duan Jinjie , Satterstrom F. Kyle , Bybjerg-Grauholm Jonas , Bækved-Hansen Marie , Gudmundsson Olafur O. , Magnusson Sigurdur H. , Baldursson Gisli , Davidsdottir Katrin , Haraldsdottir Gyda S. , Nielsen Trine Tollerup , Agerbo Esben , Hoffman Gabriel E. , Dalsgaard Søren , Martin Joanna , Ribasés Marta , Boomsma Dorret I. , Artigas Maria Soler , Mota Nina Roth , Howrigan Daniel , Medland Sarah E. , Zayats Tetyana , Manikandan Veera ,   ADHD Working Group of the Psychiatric Genomics Consortium,   iPSYCH-Broad Consortium, Nordentoft Merete , Mors Ole , Hougaard David M. , Mortensen Preben Bo , Daly Mark J. , Faraone Stephen V. , Stefansson Hreinn , Roussos Panos , Franke Barbara , Werge Thomas , Neale Benjamin M. , Stefansson Kari , Børglum Anders D. 
Published: February 2022

AbstractAttention deficit hyperactivity disorder (ADHD) is a prevalent childhood psychiatric disorder, with a major genetic component. Here we present a GWAS meta-analysis of ADHD comprising 38,691 individuals with ADHD and 186,843 controls. We identified 27 genome-wide significant loci, which is more than twice the number previously reported. Fine-mapping risk loci highlighted 76 potential risk genes enriched in genes expressed in brain, particularly the frontal cortex, and in early brain development. Overall, ADHD was associated with several brain specific neuronal sub-types and especially midbrain dopaminergic neurons. In a subsample of 17,896 exome-sequenced individuals, we identified increased load of rare protein-truncating variants in cases for a set of risk genes enriched with likely causal common variants, suggesting implication of SORCS3 in ADHD by both common and rare variants. We found ADHD to be highly polygenic, with around seven thousand variants explaining 90% of the SNP heritability. Bivariate gaussian mixture modeling estimated that more than 90% of ADHD influencing variants are shared with other psychiatric disorders (autism, schizophrenia and depression) and phenotypes (e.g. educational attainment) when both concordant and discordant variants are considered. Additionally, we demonstrated that common variant ADHD risk was associated with impaired complex cognition such as verbal reasoning and a range of executive functions including attention.
COVID-19 mortality and excess mortality among working-age Californians, by occupational sector: March 2020 through November 2021
Authors: Chen Yea-Hung , Riley Alicia R , Duchowny Kate A , Aschmann Hélène E , Chen Ruijia , Kiang Mathew V , Mooney Alyssa , Stokes Andrew C , Glymour M Maria , Bibbins-Domingo Kirsten 
Published: February 2022

AbstractBackgroundDuring the first year of the pandemic, essential workers faced higher rates of SARS-CoV-2 infection and COVID-19 mortality than non-essential workers. It is unknown whether disparities in pandemic-related mortality across occupational sectors have continued to occur, amidst SARS-CoV-2 variants and vaccine availability.MethodsWe obtained data on all deaths occurring in the state of California from 2016 through 2021. We restricted our analysis to California residents who were working age (18–65 years at time of death) and died of natural causes. Occupational sector was classified into 9 essential sectors; non-essential; or not in the labor market. We calculated the number of COVID-19 deaths in total and per capita that occurred in each occupational sector. Separately, using autoregressive integrated moving average models, we estimated total, per-capita, and relative excess natural-cause mortality by week between March 1, 2020, and November 30, 2021, stratifying by occupational sector. We additionally stratified analyses of occupational risk into regions with high versus low vaccine uptake, categorizing high-uptake regions as counties where at least 50% of the population completed a vaccination series by August 1, 2021.FindingsFrom March 2020 through November 2021, essential work was associated with higher COVID-19 and excess mortality compared with non-essential work, with the highest per-capita COVID-19 mortality in agriculture (131.8 per 100,000), transportation/logistics (107.1), manufacturing (103.3), and facilities (101.1). Essential workers continued to face higher COVID-19 and excess mortality during the period of widely available vaccines (March through November 2021). Between July and November 2021, emergency workers experienced higher per-capita COVID-19 mortality (113.7) than workers from any other sector. Essential workers faced the highest COVID-19 mortality in counties with low vaccination rates, a difference that was more pronounced during the period of the Delta surge in Summer 2021.InterpretationEssential workers have continued to bear the brunt of high COVID-19 and excess mortality throughout the pandemic, particularly in the agriculture, emergency, manufacturing, facilities, and transportation/logistics sectors. This high death toll has continued during periods of vaccine availability and the delta surge. In an ongoing pandemic without widespread vaccine coverage and anticipated threats of new variants, the US must actively adopt policies to more adequately protect essential workers.
Contribution of deep soil layers to the transpiration of a temperate deciduous forest: quantification and implications for the modelling of productivity
Authors: Maysonnave Jean , Delpierre Nicolas , François Christophe , Jourdan Marion , Cornut Ivan , Bazot Stéphane , Vincent Gaёlle , Morfin Alexandre , Berveiller Daniel 
Published: February 2022

AbstractClimate change is imposing drier atmospheric and edaphic conditions on temperate forests. Here, we investigated how deep soil (down to 300 cm) water extraction contributed to the provision of water in the Fontainebleau-Barbeau temperate oak forest over two years, including the 2018 record drought. Deep water provision was key to sustain canopy transpiration during drought, with layers below 150 cm contributing up to 60% of the transpired water in August 2018, despite their very low density of fine roots. We further showed that soil databases used to parameterize ecosystem models largely underestimated the amount of water extractable from the soil by trees, due to a considerable underestimation of the tree rooting depth. The consensus database established for France gave an estimate of 207 mm for the soil water holding capacity (SWHC) at Fontainebleau-Barbeau, when our estimate based on the analysis of soil water content measurements was 1.9 times as high, reaching 390±17 mm. Running the CASTANEA forest model with the database-derived SWHC yielded a 350 gC m−2 y−1 average underestimation of annual gross primary productivity under current climate, reaching up to 700 gC m−2 y−1 under climate change scenario RCP8.5. It is likely that the strong underestimation of SWHC that we show at our site is not a special case, and concerns a large number of forest sites. Thus, we argue for a generalisation of deep soil water content measurements in forests, in order to improve the estimation of SWHC and the simulation of the forest carbon cycle in the current context of climate change.HighlightsForest-atmosphere carbon exchanges remained insensitive to record drought.Deep soil (150-300 cm) provisioned up to 60% of the water transpired by the forest during drought.Soil databases were underestimating soil water holding capacity by a factor of two.Simulated forest productivity is strongly sensitive to soil water holding capacity parameter.Deep soil water content measurements are urgently needed to correctly estimate the soil water holding capacity.
Sequentially-reduced representation of artificial neural network to determine cardiovascular intrinsic frequencies
Authors: Alavi Rashid , Wang Qian , Gorji Hossein , Pahlevan Niema M. 
Published: February 2022

AbstractAnalysis of cardiovascular waveforms provides valuable clinical information about the state of health and disease. The intrinsic frequency (IF) method is a recently introduced framework that uses a single arterial pressure waveform to extract physiologically relevant information about the cardiovascular system. The clinical usefulness and physiological accuracy of the IF method have been well-established via several preclinical and clinical studies. However, the computational complexity of the current L2 optimization solver for IF calculations remains a bottleneck for practical deployment of the IF method in real-time settings. In this paper, we propose a machine learning (ML)-based methodology for determination of IF parameters from a single carotid waveform. We use a sequentially-reduced Feedforward Neural Network (FNN) model for mapping carotid waveforms to the output parameters of the IF method, thereby avoiding the non-convex L2 minimization problem arising from the conventional IF approach. Our methodology also includes procedures for data pre-processing, model training, and model evaluation. In our model development, we used both clinical and synthetic waveforms. Our clinical database is composed of carotid waveforms from two different sources: the Huntington Medical Research Institutes (HMRI) iPhone Heart Study and the Framingham Heart Study (FHS). In the HMRI and FHS clinical studies, various device platforms such as piezoelectric tonometry, optical tonometry (Vivio), and an iPhone camera were used to measure arterial waveforms. Our blind clinical test shows very strong correlations between IF parameters computed from the FNN-based method and those computed from the standard L2 optimization-based method (i.e., R≥0.93 and P-value ≤0.005 for each IF parameter). Our results also demonstrate that the performance of the FNN-based IF model introduced in this work is independent of measurement apparatus and of device sampling rate.
BinSPreader: refine binning results for fuller MAG reconstruction
Authors: Tolstoganov Ivan , Kamenev Yuri , Kruglikov Roman , Ochkalova Sofia , Korobeynikov Anton 
Published: February 2022

AbstractDespite the recent advances in high-throughput sequencing, analysis of the metagenome of the whole microbial population still remains a challenge. In particular, the metagenome-assembled genomes (MAGs) are often fragmented due to interspecies repeats, uneven coverage and vastly different strain abundance. MAGs are usually constructed via a dedicated binning process that uses different features of input data in order to cluster contigs that might belong to the same species. This process has some limitations and therefore binners usually discard input contigs that are shorter than several kilobases. Therefore, binning of even simple metagenome assemblies can miss a decent fraction of contigs and resulting MAGs oftentimes do not contain important conservative sequences that might be of great interest of researcher.In this work we present BinSPreader — a novel binning refiner tool that exploits the assembly graph topology and other connectivity information to refine the existing binning, correct binning errors, propagate binning from longer contigs to shorter contigs and infer contigs belonging to multiple bins. Furthermore, BinSPreader can split input reads in accordance with the resulting binning, predicting reads potentially belonging to multiple MAGs. We show that BinSPreader could effectively complete the binning, increasing the completeness of the bins without sacrificing the purity and could predict contigs belonging to several MAGs.
Modeling of protein conformational changes with Rosetta guided by limited experimental data
Authors: Sala Davide , del Alamo Diego , Mchaourab Hassane S. , Meiler Jens 
Published: February 2022

ABSTRACTConformational changes are an essential component of functional cycles of many proteins such as transporters or receptors. Despite their importance, the characterization of functionally-relevant structural intermediates is often a hard task for both experimental and computational methods. This limitation can be at least partially relieved by integrating experimental data within computational approaches. Such integrative structural biology strategies can yield an ensemble of models consistent with experimental data. While the macromolecular modeling suite Rosetta has been extensively used to predict protein structures de novo using various types of experimental data, an all-purpose conformational change modeling approach has been absent from this suite. Here, we introduce and benchmark ConfChangeMover (CCM), a new Rosetta Mover tailored to model conformational changes in proteins using sparse experimental data. CCM is able to rotate and translate secondary structural elements and modify their backbone dihedral angles in regions of interest. We benchmarked CCM on soluble and membrane proteins with sparse experimental double electron-electron resonance (DEER) restraints and simulated Cα - Cα distance restraints, respectively. In both benchmarks, CCM outperformed state-of-the-art Rosetta methods, showing that it can model a diverse array of conformational changes. In addition, the Rosetta framework allows a wide variety of experimental data to be integrated with CCM, thus extending its capability beyond DEER restraints. Thus, our method will contribute to the biophysical characterization of protein dynamics.
Microbiota-derived aspartate drives pathogenic Enterobacteriaceae expansion in the inflamed gut
Authors: Yoo Woongjae , Zieba Jacob K. , Shealy Nicolas G. , Torres Teresa P. , Thomas Julia D. , Shelton Catherine D. , Foegeding Nora J. , Olsan Erin E. , Byndloss Mariana X. 
Published: February 2022

SUMMARYInflammation boosts the availability of electron acceptors in the intestinal lumen creating a favorable niche for pathogenic Enterobacteriaceae. However, the mechanisms linking intestinal inflammation-mediated changes in luminal metabolites and pathogen expansion remain unclear. Here, we show that mucosal inflammation induced by Salmonella enterica serovar Typhimurium (S. Tm) infection and chemical colitis results in increased intestinal levels of the amino acid aspartate. The S. Tm and E. coli genomes encode an aspartate ammonia-lyase (aspA) which converts aspartate into fumarate, an alternative electron acceptor. S. Tm and pathogenic E. coli used aspA-dependent fumarate respiration for growth in the murine gut only during inflammation. Such growth advantage was abolished in the gut of germ-free mice. However, mono-association of gnotobiotic mice with members of the classes Bacteroidia and Clostridia restored the benefit of aspartate utilization to the pathogens. Our findings demonstrate the role of microbiota-derived amino acids in driving respiration-dependent Enterobacteriaceae expansion during colitis.
In pursuit of the epileptogenic zone in focal epilepsy: A dynamical network biomarker approach
Authors: Runfola Claudio , Sheheitli Hiba , Bartolomei Fabrice , Wang Huifang , Jirsa Viktor 
Published: February 2022

AbstractThe success of resective surgery for drug-resistant epilepsy patients hinges on the correct identification of the epileptogenic zone (EZ) consisting of the subnetwork of brain regions that underlies seizure genesis in focal epilepsy. The dynamic network biomarker (DNB) method is a dynamical systems-based network analysis approach for identifying subnetworks that are the first to exhibit the transition as a complex system undergoes a bifurcation. The approach was devised and validated in the context of complex disease onset where the dynamics is known to be nonlinear and high-dimensional. We here adapt and implement the DNB approach for the identification of the EZ from the analysis of SEEG data. The method is first successfully tested on simulated data generated with a large-scale brain network model of epilepsy using The Virtual Brain neuroinformatic platform and then applied to clinical SEEG data from focal epilepsy patients. The results are compared with those obtained by expert clinicians that designate the EZ using the Epileptogenicity Index (EI) method. High average precision values are obtained and posit the presented approach as a promising candidate tool for the pursuit of EZ in focal epilepsy.Author SummaryWe present a novel SEEG signal analysis tool for the identification of EZ regions in patients with drug-resistant focal epilepsy. The proposed method adapts and implements the dynamic network biomarker approach which builds on dynamical systems theory for complex networked systems. The method is first successfully tested on synthetic seizure data generated with The Virtual Brain modeling framework and then applied to retrospective patients’ clinical SEEG data. High precision values are obtained when the DNB subnetwork is compared with that designated as EZ by expert clinicians using empirical signal analysis measures and indicate that the DNB approach is a promising tool for the identification of EZ regions through SEEG signal analysis.
Neutralization of SARS-CoV-2 Omicron BA.2 by Therapeutic Monoclonal Antibodies
Authors: Zhou Hao , Tada Takuya , Dcosta Belinda M. , Landau Nathaniel R. 
Published: February 2022

AbstractMonoclonal antibody therapy for the treatment of SARS-CoV-2 infection has been highly successful in decreasing disease severity; however, the recent emergence of the heavily mutated Omicron variant has posed a challenge to this treatment strategy. The Omicron variant BA.1 has been found to evade neutralization by several of the therapeutic monoclonal antibodies authorized for emergency use, while Vir-7831 and a cocktail consisting of monoclonal antibodies AZD8895+AZD1061 retain significant neutralizing activity. A newly emerged variant, Omicron BA.2, containing some of the BA.1 mutations plus an additional 6 mutations and 3 deletions, 3 of which lie in the receptor binding domain, has been found to be spreading with increased transmissibility. We report here, using spike protein-pseudotyped lentiviruses, decreased neutralization of BA.2 by several therapeutic monoclonal antibodies but that the mixture of AZD8895+AZD1061 retained substantial neutralizing activity against BA.2.
Non-productive exposure of PBMCs to SARS-CoV-2 induces cell-intrinsic innate immunity responses
Authors: Kazmierski Julia , Friedmann Kirstin , Postmus Dylan , Fischer Cornelius , Jansen Jenny , Richter Anja , de Jarcy Laure Bosquillon , Schüler Christiane , Sohn Madlen , Sauer Sascha , Drosten Christian , Saliba Antoine-Emmanuel , Sander Leif Erik , Niemeyer Daniela , Goffinet Christine 
Published: February 2022

AbstractCell-intrinsic responses mounted in vivo in PBMCs during mild and severe COVID-19 differ quantitatively and qualitatively. Whether they are triggered by signals emitted by productively infected cells of the respiratory tract or are, at least partially, resulting from physical interaction with virus particles, remains unclear. Here, we analyzed susceptibility and expression profiles of PBMCs from healthy donors upon ex vivo exposure to SARS-CoV and SARS-CoV-2. In line with the absence of detectable ACE2 receptor expression, human PBMCs were refractory to productive infection. Bulk and single cell RNA-sequencing revealed JAK/STAT-dependent induction of interferon-stimulated genes, but not pro-inflammatory cytokines. This SARS-CoV-2-specific response was most pronounced in monocytes. SARS-CoV-2-RNA-positive monocytes displayed a lower ISG signature as compared to bystander cells of the identical culture. This suggests a preferential invasion of cells with a low ISG base-line profile or delivery of a SARS-CoV-2-specific sensing antagonist upon efficient particle internalization. Together, non-productive physical interaction of PBMCs with SARS-CoV-2-but not SARS-CoV particles stimulates JAK/STAT-dependent, monocyte-accentuated innate immune responses that resemble those detected in vivo in patients with mild COVID-19.
Reduced antigenicity of Omicron lowers host serologic response
Authors: Tubiana Jérôme , Xiang Yufei , Fan Li , Wolfson Haim J. , Chen Kong , Schneidman-Duhovny Dina , Shi Yi 
Published: February 2022

AbstractSARS-CoV-2 Omicron variant of concern (VOC) contains fifteen mutations on the receptor binding domain (RBD), evading most neutralizing antibodies from vaccinated sera. Emerging evidence suggests that Omicron breakthrough cases are associated with substantially lower antibody titers than other VOC cases. However, the mechanism remains unclear. Here, using a novel geometric deep-learning model, we discovered that the antigenic profile of Omicron RBD is distinct from the prior VOCs, featuring reduced antigenicity in its remodeled receptor binding sites (RBS). To substantiate our deep-learning prediction, we immunized mice with different recombinant RBD variants and found that the Omicron’s extensive mutations can lead to a drastically attenuated serologic response with limited neutralizing activity in vivo, while the T cell response remains potent. Analyses of serum cross-reactivity and competitive ELISA with epitope-specific nanobodies revealed that the antibody response to Omicron was reduced across RBD epitopes, including both the variable RBS and epitopes without any known VOC mutations. Moreover, computational modeling confirmed that the RBS is highly versatile with a capacity to further decrease antigenicity while retaining efficient receptor binding. Longitudinal analysis showed that this evolutionary trend of decrease in antigenicity was also found in hCoV229E, a common cold coronavirus that has been circulating in humans for decades. Thus, our study provided unprecedented insights into the reduced antibody titers associated with Omicron infection, revealed a possible trajectory of future viral evolution and may inform the vaccine development against future outbreaks.