bluuebunny/crossref_metadata_2025_split huggingface dataset:
Canonical features of human antibodies recognizing the influenza hemagglutinin trimer interface
Authors: Zost Seth J. , Dong Jinhui , Gilchuk Iuliia , Gilchuk Pavlo , Thornburg Natalie J. , Bangaru Sandhya , Kose Nurgun , Finn Jessica A. , Bombardi Robin , Soto Cinque , Nargi Rachel , Irving Ryan P. , Suryadevara Naveenchandra , Westover Jonna B. , Carnahan Robert H. , Turner Hannah L. , Li Sheng , Ward Andrew B. , Crowe James E. 
Published: January 2021

SUMMARYBroadly reactive antibodies targeting the influenza A hemagglutinin (HA) head domain are thought to be rare and to require extensive somatic mutations or unusual structural features to achieve breadth against divergent HA subtypes. Here we describe common genetic and structural features of diverse human antibodies from several individuals recognizing the trimer interface (TI) of the influenza HA head, a recently identified site of vulnerability1–3. We examined the sequence of TI-reactive antibodies, determined crystal structures for TI antibody-antigen complexes, and analyzed the contact residues of the antibodies on HA to discover common genetic and structural features of TI antibodies. Our data reveal that many TI antibodies are encoded by a light chain variable gene segment incorporating a shared somatic mutation. In addition, these antibodies have a shared acidic residue in the heavy chain despite originating from diverse heavy chain variable gene segments. These studies show that the TI region of influenza HA is a major antigenic site with conserved structural features that are recognized by a common human B cell public clonotype. The canonical nature of this antibody-antigen interaction suggests that the TI epitope might serve as an important new target for structure-based vaccine design.
Estimation of the Acetylcholinesterase activity of honey bees in Nepal
Authors: Pandey Shishir , Gotame Shankar , Sejuwal Sachin , Giri Basant , Giri Susma 
Published: January 2021

AbstractDecline in honey bee colonies possess a serious threat to biodiversity and agriculture. Prior detection of the stresses with the help of biomarkers and their management ensures honey bee’s survivability. Acetylcholinesterase (AChE) is a promising biomarker to monitor exposure of honey bees towards environmental pollutants. In this preliminary study, we measured AChE activity in forager honey bees collected from six districts of Nepal, Kathmandu, Bhaktapur, Lalitpur, Chitwan, Rupandehi and Pyuthan during autumn and winter seasons. We estimated AChE tissue and specific activities from bee’s heads using commercial kit based on Ellman assay and protein concentration using Lowry assay. In total, we collected 716 foragers belonging toA. cerana, A. melliferaandA. dorsata. A significant increase in all three parameters measured: AChE tissue activity, AChE specific activity and protein concentration was observed in winter samples. Both AChE tissue and specific activities were lower inA. melliferacompared to eitherA. ceranaorA. dorsata. Protein concentration was higher inA. melliferathan inA. dorsataand lower than inA. cerana. We show correlation between both AChE tissue and specific activities and protein concentration across season and species and discuss possible factors contributing to the observations. Our results clearly indicate the presence of stress in the winter which is manifested through overexpression of the AChE. We recommend a detailed study to determine the factors accountable for the stresses for better management of honey bees in Nepal.
Comprehensive profiling of genomic and transcriptomic differences between risk groups of lung adenocarcinoma and lung squamous cell carcinoma
Authors: Zengin Talip , Önal-Süzek Tuğba 
Published: January 2021

AbstractLung cancer is the second frequently diagnosed cancer type and responsible for the highest number of cancer deaths worldwide. Lung adenocarcinoma and lung squamous cell carcinoma are subtypes of non-small cell lung cancer which has the highest frequency of lung cancer cases. We aimed to analyze genomic and transcriptomic variations including simple nucleotide variations (SNVs), copy number variations (CNVs) and differential expressed genes (DEGs) in order to find key genes and pathways for diagnostic and prognostic prediction for lung adenocarcinoma and lung squamous cell carcinoma. We performed univariate cox model and then lasso regularized cox model with leave-one-out cross-validation using TCGA gene expression data in tumor samples. We generated a 35-gene signature and a 33-gene signature for prognostic risk prediction based on the overall survival time of the patients with LUAD and LUSC, respectively. When we clustered patients into high-risk and low-risk groups, the survival analysis showed highly significant results with high prediction power for both training and test datasets. Then we characterized the differences including significant SNVs, CNVs, DEGs, active subnetworks, and the pathways. We described the results for the risk groups and cancer subtypes separately to identify specific genomic alterations between both high-risk groups and cancer subtypes. Both LUAD and LUSC high-risk groups have more down-regulated immune pathways and upregulated metabolic pathways. On the other hand, low-risk groups have both upregulated and downregulated genes on cancer-related pathways. Both LUAD and LUSC have important gene alterations such as CDKN2A and CDKN2B deletions with different frequencies. SOX2 amplification occurs in LUSC and PSMD4 amplification in LUAD. EGFR and KRAS mutations are mutually exclusive in LUAD samples. EGFR, MGA, SMARCA4, ATM, RBM10, and KDM5C genes are mutated only in LUAD but not in LUSC. CDKN2A, PTEN, and HRAS genes are mutated only in LUSC samples. Low-risk groups of both LUAD and LUSC, tend to have a higher number of SNVs, CNVs, and DEGs. The signature genes and altered genes have the potential to be used as diagnostic and prognostic biomarkers for personalized oncology.
Comparing instance segmentation methods for analyzing clonal growth of single cells in microfluidic chips
Authors: SoRelle Elliott D. , White Scott , Yellen Benjamin B. , Wood Kris C. , Luftig Micah A. , Chan Cliburn 
Published: January 2021

AbstractAppropriately tailored segmentation techniques can extract detailed quantitative information from biological image datasets to characterize and better understand sample distributions. Practically, high-resolution characterization of biological samples such as cell populations can provide insights into the sources of variance in biomarker expression, drug resistance, and other phenotypic aspects, but it is still unclear what is the best method for extracting this information from large image-based datasets. We present a software pipeline and comparison of multiple image segmentation methods to extract single-cell morphological and fluorescence quantitation from time lapse images of clonal growth rates using a recently reported microfluidic system. The inputs in all pipelines consist of thousands of unprocessed images and the outputs are the detection of cell counts, chamber identifiers, and individual morphological properties of each clone over time detected through multi-channel fluorescence and bright field imaging. Our conclusion is that unsupervised learning methods for cell segmentation substantially outperform supervised statistical methods with respect to accuracy and have key advantages including individual cell instance detection and flexibility through model training. We expect this system and software to have broad utility for researchers interested in high-throughput single-cell biology.
HOPMA: Boosting protein functional dynamics with colored contact maps
Authors: Laine Elodie , Grudinin Sergei 
Published: January 2021

In light of the recent very rapid progress in protein structure prediction, accessing the multitude of functional protein states is becoming more central than ever before. Indeed, proteins are flexible macromolecules, and they often perform their function by switching between different conformations. However, high-resolution experimental techniques such as X-ray crystallography and cryogenic electron microscopy can catch relatively few protein functional states. Many others are only accessible under physiological conditions in solution. Therefore, there is a pressing need to fill this gap with computational approaches.We present HOPMA, a novel method to predict protein functional states and transitions using a modified elastic network model. The method exploits patterns in a protein contact map, taking its 3D structure as input, and excludes some disconnected patches from the elastic network. Combined with nonlinear normal mode analysis, this strategy boosts the protein conformational space exploration, especially when the input structure is highly constrained, as we demonstrate on a set of more than 400 transitions. Our results let us envision the discovery of new functional conformations, which were unreachable previously, starting from the experimentally known protein structures.The method is computationally efficient and available athttps://github.com/elolaine/HOPMAandhttps://team.inria.fr/nano-d/software/nolb-normal-modes.
Distribution of Huntington’s disease Haplogroups in Indian population
Authors: Venkatesh Sowmya Devatha , Ratna Nikhil , Lakshmi.P Swathi , Murari Geetanjali , Kamble Nitish , Yadav Ravi , Viswanath Biju , Varghese Mathew , Pal Pramod Kumar , Jain Sanjeev , Purushottam Meera 
Published: January 2021

AbstractHuntington’s disease (HD), a rare neurodegenerative disorder, is inherited in an autosomal dominant manner, and caused by a pathological trinucleotide expansion at exon1 of theHTTlocus. Previous studies have described the haplogroups at theHTTlocus that can explain the differences in prevalence of HD. We have selected three informative SNPs (rs762855, rs3856973 and rs4690073) to study these haplogroups in an Indian sample. Our results show that the genotype frequencies are significantly different between cases and controls for these SNPs. More than 90% of both cases and controls belong to Haplogroup A which is the predominant European haplogroup.
metaGEM: reconstruction of genome scale metabolic models directly from metagenomes
Authors: Zorrilla Francisco , Patil Kiran R. , Zelezniak Aleksej 
Published: January 2021

AbstractAdvances in genome-resolved metagenomic analysis of complex microbial communities have revealed a large degree of interspecies and intraspecies genetic diversity through the reconstruction of metagenome assembled genomes (MAGs). Yet, metabolic modeling efforts still tend to rely on reference genomes as the starting point for reconstruction and simulation of genome scale metabolic models (GEMs), neglecting the immense intra- and inter-species diversity present in microbial communities. Here we present metaGEM (https://github.com/franciscozorrilla/metaGEM), an end-to-end highly scalable pipeline enabling metabolic modeling of multi-species communities directly from metagenomic samples. The pipeline automates all steps from the extraction of context-specific prokaryotic GEMs from metagenome assembled genomes to community level flux balance simulations. To demonstrate the capabilities of the metaGEM pipeline, we analyzed 483 samples spanning lab culture, human gut, plant associated, soil, and ocean metagenomes, to reconstruct over 14 000 prokaryotic GEMs. We show that GEMs reconstructed from metagenomes have fully represented metabolism comparable to the GEMs reconstructed from reference genomes. We further demonstrate that metagenomic GEMs capture intraspecies metabolic diversity by identifying the differences between pathogenicity levels of type 2 diabetes at the level of gut bacterial metabolic exchanges. Overall, our pipeline enables simulation-ready metabolic model reconstruction directly from individual metagenomes, provides a resource of all reconstructed metabolic models, and showcases community-level modeling of microbiomes associated with disease conditions allowing generation of mechanistic hypotheses.
A Practical Guide to Sparse K-Means Clustering for Studying Molecular Development of the Human Brain
Authors: Balsor Justin L. , Arbabi Keon , Singh Desmond , Kwan Rachel , Zaslavsky Jonathan , Jeyanesan Ewalina , Murphy Kathryn M. 
Published: January 2021

AbstractStudying the molecular development of the human brain presents unique challenges for selecting a data analysis approach. The rare and valuable nature of human postmortem brain tissue, especially for developmental studies, means the sample sizes are small (n), but the use of high throughput genomic and proteomic methods measure the expression levels for hundreds or thousands of variables (e.g. genes or proteins (p)) for each sample. This leads to a data structure that is high dimensional (p >> n) and introduces thecurse of dimensionality,which poses a challenge for traditional statistical approaches. In contrast, high dimensional analyses, especially cluster analyses developed for sparse data, have worked well for analyzing genomic datasets wherep >> n.Here we explore applying a lasso-based clustering method developed for high dimensional genomic data with small sample sizes. Using protein and gene data from the developing human visual cortex, we compared clustering methods. We identified an application of sparseK-means clustering (Robust SparseK-means Clustering (RSKC)) that partitioned samples into age-related clusters that reflect lifespan stages from birth to aging. RSKC adaptively selects a subset of the genes or proteins contributing to partitioning samples into age-related clusters that progress across the lifespan. This approach addresses a problem in current studies that could not identify multiple postnatal clusters. Moreover, clusters encompassed a range of ages like a series of overlapping waves illustrating that chronological- and brain-age have a complex relationship. In addition, a recently developed workflow to create plasticity phenotypes (Balsor et al., 2020) was applied to the clusters and revealed neurobiologically relevant features that identified how the human visual cortex changes across the lifespan. These methods can help address the growing demand for multimodal integration, from molecular machinery to brain imaging signals, to understand the human brain’s development.
Survival analysis of all critically ill patients with COVID-19 admitted to the main hospital in Mogadishu, Somalia, 30 March–12 June 2020: what interventions are proving effective?
Authors: Ali Mohamed M. , Rahman Malik Sk Md Mamunur , Ahmed Abdulrazaq Yusuf , Bashir Ahmed Mohamed , Mohamed Abdulmunim , Abdi Abdulkadir , Obtel Majdouline 
Published: January 2021

ABSTRACTOBJECTIVESTo determine risk factors for death in patients with COVID-19 admitted to the main public sector hospital in Somalia and identify interventions contributing to improved clinical outcome in a low-resource and fragile setting.SETTINGMain public sector tertiary hospital in Mogadishu, Somalia.PARTICIPANTSAll 131 laboratory-confirmed COVID-19 patients admitted to the main public tertiary hospital in Somalia between 30 March and 12 June 2020.MAIN OUTCOME MEASURESWe extracted demographic and clinical data from hospital records of all 131 COVID-19 patients admitted to hospital until their death or recovery. We used Kaplan–Meier statistics to estimate survival probabilities and the log-rank test to assess significant differences in survival between groups. We used the Cox proportional hazard model to estimate likelihood of death and assess the effect of risk factors on survival.RESULTSOf the 131 patients, 52 (40%) died in the hospital and 79 (60%) survived to discharge. The factors independently associated with increased risk of in-hospital death were: age ≥ 60 years – survival probability on day 21 in patients < 60 years was 0.789 (95% confidence interval (CI): 0.658–0.874) compared with 0.339 (95% CI: 0.205–0.478) in patients ≥ 60 years; cardiovascular disease (survival probability 0.478 (95% CI: 0.332–0.610) in patients with cardiovascular disease compared with 0.719 (95% CI: 0.601–0.807) in patients without cardiovascular disease); and non-invasive ventilation on admission – patients who were not ventilated were significantly more likely to survive than those who were (P < 0.001).CONCLUSIONOur study, which includes the largest cohort of COVID-19 patients admitted to a single hospital in a sub-Saharan African country, confirms that underlying conditions and age are associated with increased risk of in-hospital death in patients with COVID-19. Our results show the advantage of medical oxygen over non-invasive ventilation in the treatment of patients with severe COVID-19 symptoms.
Accuracy of the Veterans Health Administration COVID-19 (VACO) Index for predicting short-term mortality among 1,307 Yale New Haven Hospital inpatients and 427,224 Medicare patients
Authors: King Joseph T. , Yoon James S. , Bredl Zachary M. , Habboushe Joseph P. , Walker Graham A. , Rentsch Christopher T. , Tate Janet P. , Kashyap Nitu M. , Hintz Richard C. , Chopra Aneesh P. , Justice Amy C. 
Published: January 2021

AbstractBackgroundThe Veterans Health Administration COVID-19 (VACO) Index incorporates age, sex, and pre-existing comorbidity diagnoses readily available in the electronic health record (EHR) to predict 30-day all-cause mortality in both inpatients and outpatients infected with SARS-CoV-2. We examined the performance of the Index using data from Yale New Haven Hospital (YNHH) and national Medicare data overall, over time, and within important patient subgroups.Methods and findingsWith measures and weights previously derived and validated in a national Veterans Healthcare Administration (VA) sample, we evaluated the accuracy of the VACO Index for estimating inpatient (YNHH) and both inpatient and outpatient mortality (Medicare) using area under the receiver operating characteristic curve (AUC) and comparisons of predicted versus observed mortality by decile (calibration plots). The VACO Index demonstrated similar discrimination and calibration in both settings, over time, and among important patient subgroups including women, Blacks, Hispanics, Asians, and Native Americans. In sensitivity analyses, we allowed component variables to be re-weighted in the validation datasets and found that weights were largely consistent with those determined in VA data. Supplementing the VACO Index with body mass index and race/ethnicity had no effect on discrimination.ConclusionAmong COVID-19 positive individuals, the VACO Index accurately estimates risk of short-term mortality among a wide variety of patients. While it modestly over-estimates risk in recent intervals, the Index consistently identifies those at greatest relative risk. The VACO Index could identify individuals who should continue practicing social distancing, help determine who should be prioritized for vaccination, and among outpatients who test positive for SARS-CoV-2, indicate who should receive greater clinical attention or monoclonal antibodies.
FLT3-ITD transduces autonomous growth signals during its biosynthetic trafficking in acute myelogenous leukemia cells
Authors: Yamawaki Kouhei , Shiina Isamu , Murata Takatsugu , Tateyama Satoru , Maekawa Yutarou , Niwa Mariko , Shimonaka Motoyuki , Okamoto Koji , Suzuki Toshihiro , Nishida Toshirou , Abe Ryo , Obata Yuuki 
Published: January 2021

AbstractFMS-like tyrosine kinase 3 (FLT3) in hematopoietic cells binds to its ligand at the plasma membrane (PM), then transduces growth signals.FLT3gene alterations that lead the kinase to assume its permanently active form, such asinternal tandem duplication (ITD)andD835Ysubstitution, are found in 30~40% of acute myelogenous leukemia (AML) patients. Thus, the drugs for molecular targeting of FLT3 mutants have been developed for the treatment of AML. Several groups have reported that compared with wild-type FLT3 (FLT3-wt), FLT3 mutants are retained in organelles, resulting in low levels of PM localization of the receptor. However, the precise subcellular localization of mutant FLT3 remains unclear, and the relationship between oncogenic signaling and the mislocalization is not completely understood. In this study, we show that in cell lines established from AML patients, endogenous FLT3-ITD but not FLT3-wt clearly accumulates in the perinuclear region. Our co-immunofluorescence assays demonstrate that Golgi markers are co-localized with the perinuclear region, indicating that FLT3-ITD mainly localizes to the Golgi region in AML cells. FLT3-ITD biosynthetically traffics to the Golgi apparatus and remains there in a manner dependent on its tyrosine kinase activity. A tyrosine kinase inhibitor midostaurin (PKC412) markedly decreases in FLT3-ITD retention and increases in the PM levels of the mutant. FLT3-ITD activates downstream in the endoplasmic reticulum (ER) and the Golgi apparatus during its biosynthetic trafficking. Results of our trafficking inhibitor treatment assays show that FLT3-ITD in the ER activates STAT5, whereas that in the Golgi can cause the activation of AKT and ERK. We provide evidence that FLT3-ITD signals from the early secretory compartments before reaching the PM in AML cells.
Characterizing and correcting camera noises in back-illuminated sCMOS cameras
Authors: Zhang Zhaoning , Wang Yujie , Piestun Rafael , Huang Zhen-Li 
Published: January 2021

AbstractWith promising properties of fast imaging speed, large field-of-view, relative low cost and many others, back-illuminated sCMOS cameras have been receiving intensive attentions for low-light imaging in the past several years. However, due to the pixel-to-pixel difference of camera noises (called noise non-uniformity) in sCMOS cameras, researchers may hesitate to use them in some application fields, and sometimes wonder whether they should optimize the noise non-uniformity of their sCMOS cameras before using them in a specific application scenario. In this paper, we systematically characterize the impact of different types of sCMOS noises on image quality and perform corrections to these sCMOS noises. We verify that it is possible to use appropriate correction methods to push the non-uniformity of major camera noises, including readout noise, offset, and photon response, to a satisfactory level for conventional microscopy and single molecule localization microscopy. We further find out that, after these corrections, global read noise becomes a major concern that limits the imaging performance of back-illuminated sCMOS cameras. We believe this study provides new insights into the understanding of camera noises in back-illuminated sCMOS cameras, and also provides useful information for future development of this promising camera technology.