Miracle-dz/UniFAR-FTUs huggingface dataset:
Spec-seq unveils transcriptional subpopulations of antibody-secreting cells following influenza vaccination

Vaccines are among the most effective public health tools for combating certain infectious diseases such as influenza. The role of the humoral immune system in vaccine-induced protection is widely appreciated; however, our understanding of how antibody specificities relate to B cell function remains limited due to the complexity of polyclonal antibody responses. To address this, we developed the Spec-seq framework, which allows for simultaneous monoclonal antibody (mAb) characterization and transcriptional profiling from the same single cell. Here, we present the first application of the Spec-seq framework, which we applied to human plasmablasts after influenza vaccination in order to characterize transcriptional differences governed by B cell receptor (BCR) isotype and vaccine reactivity. Our analysis did not find evidence of long-term transcriptional specialization between plasmablasts of different isotypes. However, we did find enhanced transcriptional similarity between clonally related B cells, as well as distinct transcriptional signatures ascribed by BCR vaccine recognition. These data suggest IgG and IgA vaccine–positive plasmablasts are largely similar, whereas IgA vaccine–negative cells appear to be transcriptionally distinct from conventional, terminally differentiated, antigen-induced peripheral blood plasmablasts.
The relationship between computed tomography‐derived body composition, systemic inflammatory response, and survival in patients undergoing surgery for colorectal cancer

Colorectal cancer is the fourth leading cause of cancer mortality in developed countries. There is evidence supporting a disproportionate loss of skeletal muscle as an independent prognostic factor. The importance of the systemic inflammatory response as a unifying mechanism for specific loss of skeletal muscle mass in patients with cancer is increasingly recognized. The aim of the present study was to delineate the relationship between the systemic inflammatory response, skeletal muscle index (SMI), skeletal muscle density (SMD), and overall survival in patients with colorectal cancer.
The many faces of asthma-chronic obstructive pulmonary disease overlap

Purpose of review Asthma and chronic obstructive pulmonary disease (COPD) are common diseases that often overlap. The term asthma-COPD overlap (ACO) has been used to define this entity but there remain several speculations on its exact definition, impact, pathophysiology, clinical features, and management. We reviewed recent publications on ACO to obtain more insight of current knowledge and outline future needs. Recent findings Criteria for ACO vary from one publication to another and the many variable features of these patients underline the need to reconsider the evaluation and approach of patients with overlapping features based on clinical traits and underlying biological mechanisms. Epidemiological studies reveal that ACO patients have generally an increased burden of illness and healthcare use in addition to poorer quality of life (QoL) compared with asthma and higher or equal to COPD. However, their long-term outcome seems better than patients with COPD alone. Various methods have been proposed to evaluate these patients but their usefulness compared to ‘classical’ investigation of obstructive lung diseases remains speculative and needs further evaluation. Furthermore, there are no formal studies that examined and compared the different treatment strategies of well-characterized patients with ACO as such patients are usually excluded from clinical trials. Summary ACO is a common condition with variable features and a high burden of disease. There is no consensus on its definition, diagnostic, and clinical features and more research should be done on its optimal management and long-term outcomes.
Evaluation of a Systems-Based Tobacco Cessation Program Using Bedside Volunteers.

Introduction Hospitalization and post-discharge provides an opportune time for tobacco cessation. This study tested the feasibility, uptake, and cessation outcomes of a hospital-based tobacco cessation program, delivered by volunteers to the bedside with post-discharge referral to Quitline services. Patient characteristics associated with Quitline uptake and cessation were assessed. Methods Between February and November 2016, trained hospital volunteers approached inpatient tobacco users on six pilot units. Volunteers shared a cessation brochure and used the ASK-ADVISE-CONNECT model to connect ready-to-quit patients to the Delaware Quitline via fax-referral. Volunteers administered a follow-up survey to all admitted tobacco users via telephone or email at 3-months post-discharge. Results Of the 743 admitted tobacco users, 531 (72%) were visited by a volunteer, and 97% (531/547) of those approached, accepted the visit. Over one third (201/531; 38%) were ready-to-quit and fax-referred to the Quitline, and 36% of those referred accepted Quitline services. At 3-months post discharge, 37% (135/368) reported not using tobacco in the last 30 days; intent-to-treat cessation rate was 18% (135/743). In a multivariable regression model of Quitline fax-referral completion, receiving NRT during hospitalization was the strongest predictor (OR: 1.97; 95% CI: 1.34-2.90). In a model of 3-month cessation, receiving Quitline services (OR: 3.21, 95%CI: 1.35-7.68) and having coronary artery disease (OR: 2.28; 95% CI: 1.11-4.68) were associated with tobacco cessation, but a volunteer visit was not. Conclusions An "opt-out" tobacco cessation service using trained volunteers is feasible for connecting patients to Quitline services.
Patterns of Fluoroquinolone Resistance in Enterobacteriaceae Isolated from the Assiut University Hospitals, Egypt: A Comparative Study.

Background: An increasing pattern of fluoroquinolone resistance (FQR) among bacterial pathogens has been described worldwide. In this study, we compared the patterns of genetic mechanisms that confer FQR for Escherichia coli and Klebsiella pneumoniae isolated from the Assiut University Hospitals in Egypt. Methods: Eighty-seven clinical E. coli and K. pneumoniae isolates were tested for mutations in gyrA, gyrB, parC, and parE genes by polymerase chain reaction (PCR) amplification and DNA sequencing. The presence of plasmid-mediated quinolone resistance (PMQR) genes qnrA, qnrB, qnrS, aac(6')-Ib, qepA was screened by PCR and characterized by conjugation. Correlations between different FQR mechanisms and ciprofloxacin minimal inhibitory concentration (MIC) levels were determined. Results: A higher number of quinolone resistance-determining region (QRDR) mutations was detected in E. coli, while the number of PMQR determinants was significantly higher in K. pneumoniae. However, K. pneumoniae showed stronger correlations than E. coli between MIC levels and number of mutations in the QRDR per isolate (rs = 0.8, p < 0.0001 and rs = 0.7, p < 0.0001, respectively) as well as between MIC levels and number of plasmids (rs = 0.4, p = 0.005 and rs = 0.3, p = 0.02, respectively). Conclusions: Although we observed a prevalence of chromosomal mutations for E. coli and the presence of plasmid-encoded genes for K. pneumoniae that resulted in FQR phenotype, high levels of FQR appeared to occur as a result of gradual accumulation of mutations in QRDR for both bacteria. To our best of knowledge, this is the first study to report and compare the correlation between FQ MIC levels and different genetic mechanisms for FQR in Enterobacteriaceae.
Balancing Prediction and Sensory Input in Speech Comprehension: The Spatiotemporal Dynamics of Word Recognition in Context

Spoken word recognition in context is remarkably fast and accurate, with recognition times of ∼200 ms, typically well before the end of the word. The neurocomputational mechanisms underlying these contextual effects are still poorly understood. This study combines source-localized electroencephalographic and magnetoencephalographic (EMEG) measures of real-time brain activity with multivariate representational similarity analysis to determine directly the timing and computational content of the processes evoked as spoken words are heard in context, and to evaluate the respective roles of bottom-up and predictive processing mechanisms in the integration of sensory and contextual constraints. Male and female human participants heard simple (modifier-noun) English phrases that varied in the degree of semantic constraint that the modifier (W1) exerted on the noun (W2), as in pairs, such as “yellow banana. ” We used gating tasks to generate estimates of the probabilistic predictions generated by these constraints as well as measures of their interaction with the bottom-up perceptual input for W2. Representation similarity analysis models of these measures were tested against electroencephalographic and magnetoencephalographic brain data across a bilateral fronto-temporo-parietal language network. Consistent with probabilistic predictive processing accounts, we found early activation of semantic constraints in frontal cortex (LBA45) as W1 was heard. The effects of these constraints (at 100 ms after W2 onset in left middle temporal gyrus and at 140 ms in left Heschl's gyrus) were only detectable, however, after the initial phonemes of W2 had been heard. Within an overall predictive processing framework, bottom-up sensory inputs are still required to achieve early and robust spoken word recognition in context. SIGNIFICANCE STATEMENT Human listeners recognize spoken words in natural speech contexts with remarkable speed and accuracy, often identifying a word well before all of it has been heard. In this study, we investigate the brain systems that support this important capacity, using neuroimaging techniques that can track real-time brain activity during speech comprehension. This makes it possible to locate the brain areas that generate predictions about upcoming words and to show how these expectations are integrated with the evidence provided by the speech being heard. We use the timing and localization of these effects to provide the most specific account to date of how the brain achieves an optimal balance between prediction and sensory input in the interpretation of spoken language.
LSTM Accelerator for Convolutional Object Identification

Deep Learning has dramatically advanced the state of the art in vision, speech and many other areas. Recently, numerous deep learning algorithms have been proposed to solve traditional artificial intelligence problems. In this paper, in order to detect the version that can provide the best trade-off in terms of time and accuracy, convolutional networks of various depths have been implemented. Batch normalization is also considered since it acts as a regularizer and achieves the same accuracy with fewer training steps. For maximizing the yield of the complexity by diminishing, as well as minimizing the loss of accuracy, LSTM neural net layers are utilized in the process. The image sequences are proven to be classified by the LSTM in a more accelerated manner, while managing better precision. Concretely, the more complex the CNN, the higher the percentages of exactitude; in addition, but for the high-rank increase in accuracy, the time was significantly decreased, which eventually rendered the trade-off optimal. The average improvement of performance for all models regarding both datasets used amounted to 42 % .
Screening, isolation and evaluation of a nematicidal compound from actinomycetes against the pine wood nematode, Bursaphelenchus xylophilus.

BACKGROUND Bursaphelenchus xylophilus is a migratory endoparasitic nematode known to cause severe environmental damage and economic losses in pine forest ecosystems. This present study investigated the nematicidal metabolites of actinomycetes in vitro and evaluated the disease control efficacy of the active compound and metabolites under greenhouse and field conditions. RESULTS Five thousand types of actinobacteria from Korean forest soil samples were screened to identify novel nematicidal agents against the pine wood nematode. Streptomyces sp. AN091965 showed the strongest nematicidal activity. One active compound, spectinabilin, was obtained by nematicidal asssy-directed fractionation, and it showed significant nematicidal activity against B. xylophilus, with an LC50 value of 0.84 µg mL-1 . Spectinabilin effectively suppressed the development of pine wilt disease in 5-year-old Pinus densiflora trees, even at 0.9 mg per tree under greenhouse conditions. Moreover, the acetone extract of the active strain's mycelia efficiently suppressed the development of pine wilt disease under field conditions. CONCLUSION To the best of our knowledge, this the first report to describe the nematicidal activity of spectinabilin against B. xylophilus. The cell extracts described herein merit further field studies as potential nematicides against the pine wood nematode. © 2018 Society of Chemical Industry.
A domestication-associated reduction in K+ -preferring HKT transporter activity underlies maize shoot K+ accumulation and salt tolerance.

Maize was domesticated from Balsas teosinte c. 10 000 yr ago. Previous studies have suggested that increased tolerance to environmental stress occurred during maize domestication. However, the underlying genetic basis remains largely unknown. We used a maize (W22)-teosinte recombinant inbred line (RIL) to investigate the salt wild-type tolerance aspects of maize domestication. We revealed that ZmHKT2 is a major QTL that regulates K+ homeostasis in saline soils. ZmHKT2 encodes a K+ -preferring HKT family transporter and probably reduces shoot K+ content by removing K+ ions from root-to-shoot flowing xylem sap, ZmHKT2 deficiency increases xylem sap and shoot K+ concentrations, and increases salt tolerance. A coding sequence polymorphism in the ZmHKT2W22 allele (SNP389-G) confers an amino acid variant ZmHKT2 that increases xylem sap K+ concentration, thereby increasing shoot K+ content and salt tolerance. Additional analyses showed that SNP389-G first existed in teosinte (allele frequency 56% in assayed accessions), then swept through the maize population (allele frequency 98%), and that SNP389-G probably underwent positive selection during maize domestication. We conclude that a domestication-associated reduction in K+ transport activity in ZmHKT2 underlies maize shoot K+ content and salt tolerance, and propose that CRISPR-based editing of ZmHKT2 might provide a feasible strategy for improving maize salt tolerance.
Peptide-based PET quantifies target engagement of PD-L1 therapeutics

Immune checkpoint therapies have shown tremendous promise in cancer therapy. However, tools to assess their target engagement, and hence the ability to predict their efficacy, have been lacking. Here, we show that target engagement and tumor-residence kinetics of antibody therapeutics targeting programmed death ligand-1 (PD-L1) can be quantified noninvasively. In computational docking studies, we observed that PD-L1–targeted monoclonal antibodies (atezolizumab, avelumab, and durvalumab) and a high-affinity PD-L1–binding peptide, WL12, have common interaction sites on PD-L1. Using the peptide radiotracer [64Cu]WL12 in vivo, we employed positron emission tomography (PET) imaging and biodistribution studies in multiple xenograft models and demonstrated that variable PD-L1 expression and its saturation by atezolizumab, avelumab, and durvalumab can be quantified independently of biophysical properties and pharmacokinetics of antibodies. Next, we used [64Cu]WL12 to evaluate the impact of time and dose on the unoccupied fraction of tumor PD-L1 during treatment. These quantitative measures enabled, by mathematical modeling, prediction of antibody doses needed to achieve therapeutically effective occupancy (defined as >90%). Thus, we show that peptide-based PET is a promising tool for optimizing dose and therapeutic regimens employing PD-L1 checkpoint antibodies, and can be used for improving therapeutic efficacy.
NINJA-associated ERF19 negatively regulates Arabidopsis pattern-triggered immunity

Recognition of microbe-associated molecular patterns (MAMPs) derived from invading pathogens by plant pattern recognition receptors (PRRs) initiates defense responses known as pattern-triggered immunity (PTI). Transcription factors (TFs) orchestrate the onset of PTI through complex signaling networks. Here, we characterize the function of ERF19, a member of the Arabidopsis thaliana ethylene response factor (ERF) family. ERF19 was found to act as a negative regulator of PTI against Botrytis cinerea and Pseudomonas syringae pv. tomato DC3000 (Pst). Notably, overexpression of ERF19 increased plant susceptibility to these pathogens and repressed MAMP-induced PTI outputs. In contrast, expression of the chimeric dominant repressor ERF19-SRDX boosted PTI activation, conferred increased resistance to B. cinerea, and enhanced elf18-triggered immunity against Pst. Consistent with a negative role of ERF19 in PTI, MAMP-mediated growth inhibition was respectively weakened or augmented in lines overexpressing ERF19 or expressing ERF19-SRDX. Moreover, we demonstrate that the transcriptional repressor Novel INteractor of JAZ (NINJA) associates with and represses the function of ERF19. Our work reveals ERF19 as a key player in a buffering mechanism to avoid defects imposed by over-activation of PTI and a potential role for NINJA in fine-tuning ERF19-mediated regulation.
Age- and sex-dependent changes in levels of circulating brain-enriched microRNAs during normal aging

Aging is a major risk factor for many common and life-threatening pathologies. The development of reliable biomarkers of aging should lead to a better understanding of aging-associated processes and facilitate the development of therapeutic regimens that delay aging. Levels of 38 brain-enriched microRNAs (miRNA) circulating in plasma were measured by quantitative RT-PCR in two age groups: 26-35 and 56-65 years old. An miRNA-pair approach was used for data normalization and determination of effective miRNA biomarker ratios. Nineteen miRNAs, comprising miRNA pairs and pair combinations (classifiers) that effectively differentiated the age and sex (individual pairs: 74-95% and 68-95%, respectively; classifiers: up to 100% accuracy) groups, were selected for further analysis of plasma samples from 5 donor age groups: 26-35, 36-45, 46-55, 56-65 and 66-75 years old. Dynamic changes in the plasma concentrations of certain miRNAs occurred at different ages in females and males, with peaks in the 46-55-year-old and 56-65-year-old groups, respectively. This finding suggests that the changes in miRNA levels can reflect centrally regulated processes, including changes in hormone levels during menopause. Certain miRNAs and miRNA pairs correlated with age in the sex-stratified groups at different ages and should be investigated further as potentially promising biomarkers of brain aging.
Fire Activity and Fuel Consumption Dynamics in Sub-Saharan Africa

African landscape fires are widespread, recurrent and temporally dynamic. They burn large areas of the continent, modifying land surface properties and significantly affect the atmosphere. Satellite Earth Observation (EO) data play a pivotal role in capturing the spatial and temporal variability of African biomass burning, and provide the key data required to develop fire emissions inventories. Active fire observations of fire radiative power (FRP, MW) have been shown to be linearly related to rates of biomass combustion (kg s−1). The Meteosat FRP-PIXEL product, delivered in near real-time by the EUMETSAT Land Surface Analysis Satellite Applications Facility (LSA SAF), maps FRP at 3 km resolution and 15-min intervals and these data extend back to 2004. Here we use this information to assess spatio-temporal variations in fire activity across sub-Saharan Africa, and identify an overall trend of decreasing annual fire activity and fuel consumption, agreeing with the widely-used Global Fire Emissions Database (GFEDv4) based on burned area measures. We provide the first comprehensive assessment of relationships between per-fire FRE-derived fuel consumption (Tg dry matter, DM) and temporally integrated Moderate Resolution Imaging Spectroradiometer (MODIS) net photosynthesis (PSN) (Tg, which can be converted into pre-fire fuel load estimates). We find very strong linear relationships over southern hemisphere Africa (mean r = 0.96) that are partly biome dependent, though the FRE-derived fuel consumptions are far lower than those derived from the accumulated PSN, with mean fuel consumptions per unit area calculated as 0.14 kg DM m−2. In the northern hemisphere, FRE-derived fuel consumption is also far lower and characterized by a weaker linear relationship (mean r = 0.76). Differences in the parameterization of the biome look up table (BLUT) used by the MOD17 product over Northern Africa may be responsible but further research is required to reconcile these differences. The strong relationship between fire FRE and pre-fire fuel load in southern hemisphere Africa is encouraging and highlights the value of geostationary FRP retrievals in providing a metric that relates very well to fuel consumption and fire emission variations. The fact that the estimated fuel consumed is only a small fraction of the fuel available suggests underestimation of FRE by Spinning Enhanced Visible and Infrared Imager (SEVIRI) and/or that the FRE-to-fuel consumption conversion factor of 0.37 MJ kg−1 needs to be adjusted for application to SEVIRI. Future geostationary imaging sensors, such as on the forthcoming Meteosat Third Generation (MTG), will reduce the impact of this underestimation through its ability to detect even smaller and shorter-lived fires than can the current second generation Meteosat.