bluuebunny/crossref_metadata_2025_split huggingface dataset:
Proteomic analysis of liver tissue revealsAeromonas hydrophilainfection mediated modulation of host metabolic pathways inLabeo rohita
Authors: Nissa Mehar Un , Pinto Nevil , Ghosh Biplab , Singh Urvi , Goswami Mukunda , Srivastava Sanjeeva 
Published: November 2021

ABSTRACTAeromonas hydrophila (Ah)is an opportunistic Gram-negative bacterium and a serious global pathogen causing Motile Aeromonas Septicaemia (MAS) in fish and many other vertebrates. The pathogenesis of aeromonas septicaemia is complex and involves multiple perturbed pathways. Molecular analysis of host tissues could be a powerful approach to identify mechanistic and diagnostic immune signatures of disease. We performed a deep proteomic analysis ofLabeo rohitaliver tissue to examine changes in the host proteome duringAhinfection. A total of 2525 proteins were identified of which 158 were found differentially expressed duringAhinfection. Functional analysis of significant proteins identified the dysregulation of several metabolic enzymes, antioxidative proteins, cytoskeletal proteins and immune related proteins. Proteomic analysis revealed the alterations in the cellular defence mechanisms including phagolysosomal killing and apoptosis duringAhinfection. Our systemic approach revealed the protein dynamics in the host cells to explore the putative biological processes underlying the metabolic reprogramming of the host cells duringAhinfection. Our findings paved the way for future research into the role of Toll-like receptors (Tlr3), C-type lectins (Clec4e) and metabolic enzymes inAhpathogenesis leading towards host directed immunotherapies to tackle theAhinfection in fish.IMPORTANCEBacterial disease is one of the most serious problems in aquaculture industry.Aeromonas hydrophila(Ah), a Gram-negative bacterium causes motile aeromonas septicaemia (MAS) in fish. Small molecules that target the metabolism of the host have recently emerged as potential treatment possibilities in infectious diseases. However, the ability to develop new therapies is hampered due to lack of knowledge about pathogenesis mechanisms and host-pathogen interactions. Molecular level analysis of host tissues could be helpful in finding mechanistic immunological markers of diseases. We examined alterations in the host proteome duringAhinfection inLabeo rohitaliver tissue to find cellular proteins and processes affected byAhinfection. Our systemic approach revealed protein dynamics underlying the host cells’ metabolic reprogramming duringAhinfection. Our work is an important step towards leveraging host metabolism in targeting the disease by providing a bigger picture on proteome pathology correlation duringAhinfection.
Anti-SARS-CoV-2 antibodies seroprevalence among patients submitted to treatment for tuberculosis in Rio de Janeiro, Brazil: a cross-sectional study
Authors: Gomes Karen Machado , Leite Suzanne Pereira , Moutinho Maria Helena Vieira , de Souza Thatiana Alfena , de Cássia Batista Rita , de Oliveira Luiz Claudio Motta , Redner Paulo , Ramos Jesus Pais , da Rocha Fatima Maria Gomes , de Oliveira Gisele Pinto , Teva Antônio , do Couto Motta Fernando , de Siqueira Marilda Agudo Mendonça Teixeira , Duarte Rafael Silva , Bastos Francisco Inácio Pinkusfeld Monteir , de Sousa Viana Paulo Victor 
Published: November 2021

ABSTRACTDue to tuberculosis (TB) patients’ pulmonary damages, some authors believe that a SARS-CoV-2 coinfection may result in unfavorable outcomes. A cross-sectional anti-SARS-CoV-2 antibodies seroprevalence study was conducted at a TB treatment tertiary referral unit in Rio de Janeiro, Brazil, to estimate the proportion (in %) of TB patients exposed to the new coronavirus and their main outcomes. Of 83 patients undergoing TB treatment, 26.5% have already been infected by the new coronavirus. Most patients were asymptomatic (69%) or had mild COVID-19 cases (31%). Only one patient required hospitalization. Among the symptoms and signs presented, the most frequently reported were: fever, headache, and myalgia. People with less education and less purchasing power seemed to had been more exposed to SARS-CoV-2.
A territory-wide study of arrhythmogenic right ventricular cardiomyopathy patients from Hong Kong
Authors: Lakhani Ishan , Zhou Jiandong , Lee Sharen , Christien Li Ka Hou , Kit Leung Keith Sai , Li Guoliang , Liu Tong , Wong Wing Tak , Kei Wong Ian Chi , Mok Ngai Shing , Mak Chloe , Zhang Qingpeng , Tse Gary 
Published: November 2021

AbstractBackgroundArrhythmogenic right ventricular cardiomyopathy/dysplasia (ARVC/D) is a hereditary disease characterized by fibrofatty infiltration of the right ventricular myocardium that predisposes affected patients to malignant ventricular arrhythmias, dual-chamber cardiac failure and sudden cardiac death (SCD). The present study aims to investigate the risk of detrimental cardiovascular events in an Asian population of ARVC/D patients, including the incidence of malignant ventricular arrhythmias, new-onset heart failure with reduced ejection fraction (HFrEF), as well as long-term mortality.Methods and ResultsThis was a territory-wide retrospective cohort study of patients diagnosed with ARVC/D between 1997 and 2019 in Hong Kong. This study consisted of 109 ARVC/D patients (median age: 61 [46-71] years; 58% male). Of these, 51 and 24 patients developed incident VT/VF and new-onset HFrEF, respectively. Five patients underwent cardiac transplantation, and 14 died during follow-up. Multivariable Cox regression identified prolonged QRS duration as a predictor of VT/VF (p < 0.05). Female gender, prolonged QTc duration, the presence of epsilon waves and T-wave inversion (TWI) in any lead except aVR/V1 predicted new-onset HFrEF (P < 0.05. The presence of epsilon waves, in addition to the parameters of prolonged QRS duration and worsening ejection fraction predicted all-cause mortality (p<0.05). Clinical scores were developed to predict incident VT/VF, new-onset HFrEF and all-cause mortality, and all were significantly improved by machine learning techniques.ConclusionClinical and electrocardiographic parameters are important for assessing prognosis in ARVC/D patients and should in turn be used in tandem to aid risk stratification in the hospital setting.
Impact of dexamethasone on persistent symptoms of COVID-19: an observational study
Authors: Milne A , Maskell S , Sharp C , Hamilton FW , Arnold DT 
Published: November 2021

AbstractIntroductionDexamethasone has been shown to reduce mortality for patients hospitalised with acute COVID-19 pneumonia. However, a significant proportion of patients suffer persistent symptoms following COVID-19 and little is known about the longer-term impact of this intervention on symptom burden.MethodsPatients initially hospitalised with COVID-19 were prospectively recruited to an observational study (April-August 2020) with follow-up at 8 months (Dec 2020-April 2021) post-admission. A review of ongoing symptoms using a standardised systems-based proforma was performed alongside health-related quality of life assessment. In the UK, patients with COVID-19 (requiring oxygen) only received dexamethasone following the pre-print of the RECOVERY trial (June 2020), or as part of randomisation to that trial, allowing for a comparison between patients treated and not treated with dexamethasone.ResultsBetween April to August 2020, 198 patients were recruited to this observational study. 87 required oxygen and were followed up at 8-months, so were eligible for this analysis. Of these 39 received an inpatient course of dexamethasone (cases) and 48 did not (controls). The groups were well matched at baseline in terms of age, comorbidity and frailty score. Over two-thirds of patients reported at least 1 ongoing symptom at 8-month follow-up. Patients in the dexamethasone group reported fewer symptoms (n=73, 1.9 per patient) than the non-dexamethasone group (n=152, 3.2 per patient) (p = 0.01).ConclusionsIn conclusion, in this case-control observational study, patients who received oral dexamethasone for hospitalised COVID-19 were less likely to experience persistent symptoms at 8-month follow-up. These are reassuring results for physicians administering dexamethasone to this patient group.
Kilohertz Transcranial Magnetic Perturbation (kTMP): A New Non-invasive Method to Modulate Cortical Excitability
Authors: Labruna Ludovica , Merrick Christina , Peterchev Angel V , Inglis Ben , Ivry Richard B , Sheltraw Daniel 
Published: November 2021

AbstractNon-invasive brain stimulation (NIBS) provides a method for safely perturbing brain activity, and has been employed in basic research to test hypotheses concerning brainbehavior relationships with increasing translational applications. We introduce and evaluate a novel subthreshold NIBS method: kilohertz transcranial magnetic perturbation (kTMP). kTMP is a magnetic induction method that delivers continuous kHz-frequency cortical electric fields (E-fields) which may be amplitude-modulated to potentially mimic electrical activity at endogenous frequencies. We used TMS to compare the amplitude of motor-evoked potentials (MEPs) in a hand muscle before and after kTMP. In Experiment 1, we applied kTMP for 10 min over motor cortex to induce an E-field amplitude of approximately 2.0 V/m, comparing the effects of waveforms at frequencies of 2.0, 3.5, or 5.0 kHz. In Experiments 2 and 3 we used two forms of amplitude-modulated kTMP with a carrier frequency at 3.5 kHz and modulation frequencies of either 20 or 140 Hz. The only percept associated with kTMP was an auditory tone, making kTMP amenable for doubleblind experimentation. Relative to sham stimulation, non-modulated kTMP at 2.0 and 3.5 kHz resulted in an increase in cortical excitability, with Experiments 2 and 3 providing a replication of this effect for the 3.5 kHz condition. Although amplitude-modulated kTMP increased MEP amplitude compared to sham, no enhancement was found compared to non-modulated kTMP. kTMP opens a new experimental NIBS space inducing relatively large amplitude subthreshold E-fields able to increase cortical excitability with minimal sensation.
Towards a catalog of pome tree architecture genes: the draft ‘d’Anjou’ genome (Pyrus communis L.)
Authors: Zhang Huiting , Wafula Eric K. , Eilers Jon , Harkess Alex E. , Ralph Paula E. , Timilsena Prakash Raj , dePamphilis Claude W. , Waite Jessica M. , Honaas Loren A. 
Published: November 2021

AbstractThe rapid development of sequencing technologies has led to a deeper understanding of horticultural plant genomes. However, experimental evidence connecting genes to important agronomic traits is still lacking in most non-model organisms. For instance, the genetic mechanisms underlying plant architecture are poorly understood in pome fruit trees, creating a major hurdle in developing new cultivars with desirable architecture, such as dwarfing rootstocks in European pear (Pyrus communis). Further, the quality and content of genomes vary widely. Therefore, it can be challenging to curate a list of genes with high-confidence gene models across reference genomes. This is often an important first step towards identifying key genetic factors for important traits. Here we present a draft genome of P. communis ‘d’Anjou’ and an improved assembly of the latest P. communis ‘Bartlett’ genome. To study gene families involved in tree architecture in European pear and other rosaceous species, we developed a workflow using a collection of bioinformatic tools towards curation of gene families of interest across genomes. This lays the groundwork for future functional studies in pear tree architecture. Importantly, our workflow can be easily adopted for other plant genomes and gene families of interest.
Evaluating the contribution of aestivation to the persistence of malaria mosquitoes through the Sahelian dry season using stable isotopes
Authors: Faiman Roy , Yaro Alpha S. , Dao Adama , Sanogo Zana L. , Diallo Moussa , Samake Djibril , Yossi Ousmane , Veru Laura R. , Graber Leland C. , Conte Abigail R. , Kouam Cedric , Krajacich Benjamin J. , Lehmann Tovi 
Published: November 2021

ABSTRACTData suggests the malaria vector Anopheles coluzzii persists in the Sahel by dry-season aestivation though evidence is scant. We have marked Anopheles mosquitoes using deuterium (2H) to assess the contribution of aestivation to persistence of mosquitoes through the seven-month dry season. If local aestivation is the only way A. coluzzii persists, the frequency of marked mosquitoes should remain stable throughout, whereas finding no marked mosquitoes would be evidence against aestivation. Larval sites were spiked with 2H at the end of the 2017 wet season in two Sahelian villages in Mali. We monitored 2H-enriched populations until the onset of rains. By the end of the enrichment period, 33% of A. coluzzii mosquitoes were clearly marked. Expectedly, 2H levels in marked mosquitoes degraded over time, resulting in a partial overlap of the marked and non-marked 2H distributions. We utilized three methods to estimate the fraction of marked mosquitoes in the population. Seven months after enrichment, 7% of the population had 2H values above the highest pre-enrichment value. An excess of 21% exceeded the 3rd quartile of the pre-enrichment population. A finite mixed population model showed 2.5% represented a subpopulation of marked mosquitoes with elevated 2H, compatible with our predictions. We provide evidence that aestivation is a major persistence mechanism of A. coluzzii in the Sahel, contributing at least 20% of the adults at the onset of rains, suggesting A. coluzzii utilizes multiple persistence strategies enabling its populations rapid buildup, facilitating subsequent malaria resurgence. These may complicate vector control and malaria elimination campaigns.Significance statementHere we estimated the contribution of aestivation to the persistence of mosquitoes through the seven-month long dry season, by marking a known fraction of the adult population through larval site 2H-spiking by the end of the wet season and assessing the change in this fraction through the dry season, until after the onset of the first rain of the subsequent wet season. In a mark-release-recapture study using stable isotopes, we provide compelling evidence that the primary Sahelian malaria vector Anopheles coluzzii aestivates on a population-scale, contributing at least 20% of the adults which reestablish the population of the subsequent wet season. The capacity to use multiple strategies of persistence in time and space might complicate vector control and elimination campaigns.
Self-organization of in vitro neuronal assemblies drives to complex network topology
Authors: Antonello Priscila C. , Varley Thomas F. , Beggs John , Porcionatto Marimelia , Sporns Olaf , Faber Jean 
Published: November 2021

ABSTRACTActivity-dependent self-organization plays an important role in the formation of specific and stereotyped connectivity patterns in neural circuits. By combining neuronal cultures, tools with approaches from network neuroscience and information theory, we can study how complex network topology emerges from local neuronal interactions. We constructed effective connectivity networks using a transfer entropy analysis of electrophysiological signals recorded from rat embryo dissociated hippocampal neuron cultures between 6 and 35 days in vitro to investigate how the neuronal network topology evolves during maturation. The methodology for constructing the networks considered the synapse delay and addressed the influence of firing rate and population bursts as well as spurious effects on the inference of connections. We found that the number of links in the networks grew over the course of development, shifting from a segregated to a more integrated architecture. As part of this progression, three significant aspects of complex network topology emerged. In agreement with previous in silico and in vitro studies, a small-world architecture was detected, largely due to strong clustering among neurons. Additionally, the networks developed in a modular community topology, with most modules comprising nearby neurons. Finally, highly active neurons acquired topological characteristics that made them important nodes to the network and integrators of communities. These findings leverage new insights into how neuronal effective network topology relates to neuronal assembly self-organization mechanisms.
Modeling naturalistic face processing in humans with deep convolutional neural networks
Authors: Jiahui Guo , Feilong Ma , Visconti di Oleggio Castello Matteo , Nastase Samuel A. , Haxby James V. , Gobbini M. Ida 
Published: November 2021

AbstractDeep convolutional neural networks (DCNNs) trained for face identification can rival and even exceed human-level performance. The ways in which the internal face representations in DCNNs relate to human cognitive representations and brain activity are not well understood. Nearly all previous studies focused on static face image processing with rapid display times and ignored the processing of naturalistic, dynamic information. To address this gap, we developed the largest naturalistic dynamic face stimulus set in human neuroimaging research (700+ naturalistic video clips of unfamiliar faces). We used this novel naturalistic dataset to compare representational geometries estimated from DCNNs, behavioral responses, and brain responses. We found that DCNN representational geometries were consistent across architectures, cognitive representational geometries were consistent across raters in a behavioral arrangement task, and neural representational geometries in face areas were consistent across brains. Representational geometries in late, fully-connected DCNN layers, which are optimized for individuation, were much more weakly correlated with cognitive and neural geometries than were geometries in late-intermediate layers. The late-intermediate face-DCNN layers successfully matched cognitive representational geometries, as measured with a behavioral arrangement task that primarily reflected categorical attributes, and correlated with neural representational geometries in known face-selective topographies. Our study suggests that current DCNNs successfully capture neural cognitive processes for categorical attributes of faces, but less accurately capture individuation and dynamic features.
Analysis of the computational strategy of a detailed laminar cortical microcircuit model for solving the image-change-detection task
Authors: Scherr Franz , Maass Wolfgang 
Published: November 2021

AbstractThe neocortex can be viewed as a tapestry consisting of variations of rather stereotypical local cortical microcircuits. Hence understanding how these microcircuits compute holds the key to understanding brain function. Intense research efforts over several decades have culminated in a detailed model of a generic cortical microcircuit in the primary visual cortex from the Allen Institute. We are presenting here methods and first results for understanding computational properties of this largescale data-based model. We show that it can solve a standard image-change-detection task almost as well as the living brain. Furthermore, we unravel the computational strategy of the model and elucidate the computational role of diverse subtypes of neurons. Altogether this work demonstrates the feasibility and scientific potential of a methodology based on close interaction of detailed data and large-scale computer modelling for understanding brain function.
Awake ripples enhance emotional memory encoding in the human brain
Authors: Zhang Haoxin , Skelin Ivan , Ma Shiting , Paff Michelle , Yassa Michael A. , Knight Robert T. , Lin Jack J. 
Published: November 2021

AbstractIntracranial recordings from the human amygdala and the hippocampus during an emotional memory encoding and discrimination task reveal increased awake sharp-wave/ripples (aSWR) after encoding of emotional compared to neutral stimuli. Further, post-encoding aSWR-locked memory reinstatement in the amygdala and the hippocampus was predictive of later memory discrimination. These findings provide electrophysiological evidence that post-encoding aSWRs enhance memory for emotional events.
In silicodrug discovery strategies identified ADMET properties of decoquinate RMB041 and its potential drug targets againstMycobacterium Tuberculosis
Authors: Knoll Kirsten E. , van der Walta Mietha M. , Loots Du Toit 
Published: November 2021

AbstractThe highly adaptive cellular response ofMycobacterium tuberculosisto various antibiotics and the high costs for clinical trials, hampers the development of novel antimicrobial agents with improved efficacy and safety. Subsequently,in silicodrug screening methods are more commonly being used for the discovery and development of drugs, and have been proven useful for predicting the pharmacokinetics, toxicities, and targets, of prospective new antimicrobial agents. In this investigation we used a reversed target fishing approach to determine potential hit targets and their possible interactions betweenM. tuberculosisand decoquinate RMB041, a propitious new antituberculosis compound. Two of the thirteen identified targets, Cyp130 and BlaI, were strongly proposed as optimal drug-targets for dormantM. tuberculosis, of which the first showed the highest comparative binding affinity to decoquinate RMB041. The metabolic pathways associated to the selected target proteins were compared to previously published molecular mechanisms of decoquinate RMB041 againstM. tuberculosis, whereby we confirmed disrupted metabolism of proteins, cell wall components, and DNA. We also described the steps within these pathways that are inhibited and elaborated on decoquinate RMB041’s activity against dormantM. tuberculosis. This compound has previously showed promisingin vitrosafety and good oral bioavailability, which were both supported by thisin silicostudy. The pharmacokinetic properties and toxicity of this compound were predicted and investigated using the online tools pkCSM and SwissADME, and Discovery Studio software, which furthermore supports previous safety and bioavailability characteristics of decoquinate RMB041 for use as an antimycobacterial medication.
Trajectories: a framework for detecting temporal clinical event sequences from health data standardized to the OMOP Common Data Model
Authors: Künnapuu Kadri , Ioannou Solomon , Ligi Kadri , Kolde Raivo , Laur Sven , Vilo Jaak , Rijnbeek Peter R. , Reisberg Sulev 
Published: November 2021

ABSTRACTObjectiveTo develop a framework for identifying prominent clinical event trajectories from OMOP-formatted observational healthcare data.MethodsA four-step framework based on significant temporal event pair detection is described and implemented as an open-source R package. It is used on a population-based Estonian dataset to first replicate a large Danish population-based study and second, to conduct a disease trajectory detection study for Type 2 Diabetes patients in the Estonian and Dutch databases as an example.ResultsAs a proof of concept, we apply the methods in the Estonian database and provide a detailed breakdown of our findings. All Estonian population-based event pairs are shown. We compare the event pairs identified from Estonia to Danish and Dutch data and discuss the causes of the differences.ConclusionsFor the first time, there is a complete software package for detecting disease trajectories in health data.