bluuebunny/crossref_metadata_2025_split huggingface dataset:
Phenotyping in the era of genomics:MaTrics– a digital character matrix to document mammalian phenotypic traits coded numerically
Authors: Stefen Clara , Wagner Franziska , Asztalos Marika , Giere Peter , Grobe Peter , Hiller Michael , Hofmann Rebecca , Jähde Maria , Lächele Ulla , Lehmann Thomas , Ortmann Sylvia , Peters Benjamin , Ruf Irina , Schiffmann Christian , Thier Nadja , Unterhitzenberger Gabi , Vogt Lars , Rudolf Matthias , Wehner Peggy , Stuckas Heiko 
Published: January 2021

AbstractA new and uniquely structured matrix of mammalian phenotypes,MaTrics(MammalianTraits for Comparative Genomics) is presented in a digital form. By focussing on mammalian species for which genome assemblies are available,MaTricsprovides an interface between mammalogy and comparative genomics.MaTricswas developed as part of a project to link phenotypic differences between mammals to differences in their genomes usingForward Genomics.Apart from genomes this approach requires information on homologous phenotypes that are numerically encoded (presence-absence; multistate character coding*) in a matrix.MaTricsprovides these data, links them to at least one reference (e.g., literature, photographs, histological sections, CT-scans, or museum specimens) and makes them available in a machine actionable NEXUS-format. By making the data computer readable,MatTricsopens a new way for digitizing collections. Currently,MaTricscovers 147 mammalian species and includes 207 characters referring to structure, morphology, physiology, ecology and ethology. Researching these traits revealed substantial knowledge gaps, highlighting the need for substantial phenotyping efforts in the genomic era. Using the trait information documented inMaTrics, previous Forward Genomics screens identified changes in genes that are associated with various phenotypes, ranging from fully-aquatic lifestyle to dietary specializations. These results motivate the continuous expansion of phenotype information, both by filling research gaps or by adding additional taxa and traits.MaTricsis digitally available online within the data repository Morph·D·Base (www.morphdbase.de).
Function-structure Coupling: White matter fMRI hyper-activation associates with structural integrity reductions in schizophrenia
Authors: Jiang Yuchao , Luo Cheng , Li Xiangkui , Huang Huan , Zhao Guocheng , Li Xuan , Li Shicai , Song Xufeng , Yao Dezhong , Duan Mingjun 
Published: January 2021

AbstractBackgroundWhite matter (WM) microstructure deficit may be an underlying factor in the brain dysconnectivity hypothesis of schizophrenia using diffusion tensor imaging (DTI). However, WM dysfunction is unclear in schizophrenia. This study aimed to investigate the association between structural deficits and functional disturbances in major WM tracts in schizophrenia.MethodsUsing functional magnetic resonance imaging (fMRI) and DTI, we developed the skeleton-based white matter functional analysis, which could achieve voxel-wise function–structure coupling by projecting the fMRI signals onto a skeleton in WM. We measured the fractional anisotropy (FA) and WM low-frequency oscillation activation and their couplings in ninety-three schizophrenia patients and 122 healthy controls (HCs). An independent open database (62 schizophrenia patients and 71 HCs) was used to test the reproducibility. Finally, associations between WM activations and five behaviour assessment categories (cognition, emotion, motor, personality and sensory) were examined.ResultsThis study revealed a reversed pattern of structure and function in frontotemporal tracts, as follows. (1) WM hyper-activation was associated with reduced FA in schizophrenia. (2) The function–structure association was positive in healthy controls but negative in schizophrenia patients. Furthermore, function–structure dissociation was exacerbated by long illness duration and severe negative symptoms. (3) WM activations were significantly related to cognition and emotion.ConclusionsThis study indicated function–structure dys-coupling, with higher functional activation and reduced structural integration in frontotemporal WM, which may reflect a potential mechanism in WM neuropathologic processing of schizophrenia.
Differential investment in visual and olfactory brain regions mirrors the sensory needs of a paper wasp social parasite and its host
Authors: Rozanski Allison N. , Cini Alessandro , Lopreto Taylor E. , Gandia Kristine M. , Hauber Mark E. , Cervo Rita , Uy Floria M. K. 
Published: January 2021

ABSTRACTObligate social parasites evolve traits to effectively locate and then exploit their hosts, whereas hosts have complex social behavioral repertoires, which include sensory recognition to reject potential conspecific intruders and heterospecific parasites. While social parasite and host behaviors have been studied extensively, less is known about how their sensory systems function to meet their specific selective pressures. Here, we compare investment in visual and olfactory brain regions in the paper waspPolistes dominula, and its obligate social parasiteP. sulcifer, to explore the link between sensory systems and brain plasticity. Our results show opposite and significant differences, consistent with their very different life-histories, in the sensory investments between these two closely-related species. Social parasites initially invest in the optic lobes to likely locate their hosts. After host colony usurpation, the parasite increases its brain volume, with specific investment in antennal lobes, which mirrors the behavioral switch from a usurping parasite to an integrated parasitic queen of the host colony. Contrastingly, hosts initially invest in the antennal lobes and sensory processing compared to social parasites, as predicted by their need to maintain social cohesion, allocate colony tasks, and recognize con- and heterospecific intruders. Host queens show a trend of higher investment in all sensory brain regions compared to workers, paralleling differences in task allocations. Our work provides novel insights into how intraspecific brain plasticity can facilitate the unique sensory adaptations needed to perform specific tasks by the host or to transition from searching to successful host exploitation by the social parasite.
Defining the cell surface proteomic landscape of multiple myeloma reveals immunotherapeutic strategies and biomarkers of drug resistance
Authors: Ferguson Ian D. , Escobar Bonell Patiño , Tuomivaara Sami T. , Lin Yu-Hsiu T. , Nix Matthew A. , Leung Kevin K. , Hale Martina , Choudhry Priya , Lopez-Girona Antonia , Ramos Emilio , Wong Sandy W. , Wolf Jeffrey L. , Martin Thomas G. , Shah Nina , Vandenberg Scott , Prakash Sonam , Besse Lenka , Driessen Christoph , Wells James A. , Wiita Arun P. 
Published: January 2021

ABSTRACTThe myeloma cell surface proteome (“surfaceome”) not only determines tumor interaction with the microenvironment but serves as an emerging arena for therapeutic development. Here, we use glycoprotein capture proteomics to first define surface markers most-enriched on myeloma when compared to B-cell malignancy models, revealing unexpected biological signatures unique to malignant plasma cells. We next integrate our proteomic dataset with existing transcriptome databases, nominating CCR10 and TXNDC11 as possible monotherapeutic targets and CD48 as a promising co-target for increasing avidity of BCMA-directed cellular therapies. We further identify potential biomarkers of resistance to both proteasome inhibitors and lenalidomide including changes in CD53, EVI2B, CD10, and CD33. Comparison of short-term treatment with chronic resistance delineates large differences in surface proteome profile under each type of drug exposure. Finally, we develop a miniaturized version of the surface proteomics protocol and present the first surface proteomic profile of a primary myeloma patient plasma cell sample. Our dataset provides a unique resource to advance the biological, therapeutic, and diagnostic understanding of myeloma.
Seroepidemiological Study of Novel Corona Virus (CoVID-19) in Tehran, Iran
Authors: Tabanejad Zeinab , Darvish Sorena , Boroujeni Zeinab Borjian , Asadi Seyed Saeed , Mesri Morteza , Raiesi Omid , Getso Muhammad Ibrahim , Zareei Mahdi 
Published: January 2021

AbstractA novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has now spread to all countries of the world, including Iran. Although new anti-coronavirus antibodies in patients may be identified by immunological methods with sufficient sensitivity and specificity, the conclusive diagnosis of the disease is by the molecular RT-PCR process. We used a population-based seroepidemiological survey to quantify the proportion of the exposed population with SARS-CoV-2 antibodies and evaluated whether the antibodies are a marker of total or partial immunity to the proportion of the population that remains susceptible to the virus. This cross-sectional study was conducted to investigate the seroprevalence of CoVID-19 in Tehran, the capital of Iran, between April and end of October 2020. Specimens of clotted and heparinized blood (2ml) were collected from the patients. The serum and plasma were separated and stored at − 80LJ°C until use. We examined serum anti-SARS-CoV-2 IgG and IgM antibodies from 1375 in-patients admitted to our hospitals using ELISA kits. In total, 1375 participants were enrolled in this study, and SARS-CoV-2 antibodies were detected using IgM-IgG antibody assay in 291 patients. Among the seropositive patients studied, 187 were men (64.3%), and 104 were women (35.7%) (P<0.05). The mean age of the patients was 49±8.4 years; the majority (27%) were in age group 31-40 years. Also, the lowest frequency of cases was reported in the age group of 1-10 years (P <0.05). We determined the seroprevalence of SARS-CoV-2 for IgM or IgG antibodies to be 21.2%. Diabetes mellitus was the most common underlying disease among SARS-CoV-2 patients [P=0.05; Odd Ratio=1.61(0.90-2.91)]. Conventional serological assays in SARS-CoV-2 cases, such as the enzyme-linked immunoassay (ELISA) for specific IgM and IgG antibodies, have a high-throughput advantage and minimize false-negative rates that occur with the RT-PCR method. This study determined the seroprevalence of SARS-CoV-2 antibodies to be 21%. Control of diabetes among other cases factors shall play important role in management and control of COVID-19.
Improved screening of COVID-19 cases through a Bayesian network symptoms model and psychophysical olfactory test
Authors: Eyheramendy Susana , Saa Pedro A. , Undurraga Eduardo A. , Valencia Carlos , López Carolina , Méndez Luis , Pizarro-Berdichevsky Javier , Finkelstein-Kulka Andrés , Solari Sandra , Salas Nicolás , Bahamondes Pedro , Ugarte Martín , Barceló Pablo , Arenas Marcelo , Agosin Eduardo 
Published: January 2021

AbstractThe infectiousness and presymptomatic transmission of SARS-CoV-2 hinder pandemic control efforts worldwide. Therefore, the frequency of testing, accessibility, and immediate results are critical for reopening societies until an effective vaccine becomes available for a substantial proportion of the population. The loss of sense of smell is among the earliest, most discriminant, and prevalent symptoms of COVID-19, with 75-98% prevalence when clinical olfactory tests are used. Frequent screening for olfactory dysfunction could substantially reduce viral spread. However, olfactory dysfunction is generally self-reported, which is problematic as partial olfactory impairment is broadly unrecognized. To address this limitation, we developed a rapid psychophysical olfactory test (KOR) deployed on a web platform for automated reporting and traceability based on a low-cost (about USD 0.50/test), six-odor olfactory identification kit. Based on test results, we defined an anosmia score –a classifier for olfactory impairment–, and a Bayesian Network (BN) model that incorporates other symptoms for detecting COVID-19. We trained and validated the BN model on two samples: suspected COVID-19 cases in five healthcare centers (n = 926; 32% COVID-19 prevalence) and healthy (asymptomatic) mining workers (n = 1, 365; 1.1% COVID-19 prevalence). All participants had COVID-19 assessment by RT-PCR assay. Using the BN model, we predicted COVID-19 status with 76% accuracy (AUC=0.79 [0.75 − 0.82]) in the healthcare sample and 84% accuracy (AUC=0.71 [0.63 − 0.79]) among miners. The KOR test and BN model enabled the detection of COVID-19 cases that otherwise appeared asymptomatic. Our results confirmed that olfactory dysfunction is the most discriminant symptom to predict COVID-19 status when based on olfactory function measurements. Overall, this work highlights the potential for low-cost, frequent, accessible, routine testing for COVID-19 surveillance to aid society’s reopening.
Ranking videolaryngoscopes by orotracheal intubation performance: protocol of a systematic review and network meta-analysis of clinical trials at patient level
Authors: de Carvalho Clístenes Crístian , da Silva Danielle Melo , Lemos Victor Macedo , dos Santos Thiago Gadelha Batista , Agra Ikaro Cavalcante , Miná Pinto Gustavo , Ramos Isabella Beserra , da Cunha Costa Yuri Soares , dos Santos Neto Jayme Marques 
Published: January 2021

ABSTRACTBackgroundVideolaryngoscopes (VLs) are regarded to improve glottic visualization as compared to Macintosh laryngoscope (ML). However, we currently do not know which one would be the best choice. We then designed this systematic review and network meta-analysis to rank the different VLs as compared to ML.MethodsWe will conduct a search in PubMed, LILACS, Scielo, Embase, Web of Science, and Cochrane Central Register of Controlled Trials (CENTRAL; 2020, Issue 6) on 11/01/2021. We will include randomized clinical trials fully reported with patients aged ≥ 16 years, comparing VLs with ML for failed intubation with the device, failed first intubation attempts, number of intubation attempts, time for intubation, difficulty of intubation, and improved visualization of the larynx. Pooled effects will be estimated by both fixed and random-effects models and presented according to qualitative and quantitative heterogeneity assessment. Sensitivity analyses will be performed as well as a priori subgroup, meta-regression and multiple meta-regression analyses. Additionally, network meta-analyses will be applied to rank the different VLs as compared to ML. We will also assess the risk of selective publication by funnel plot asymmetry.DiscussionThis systematic review and network meta-analysis aim at helping health services and clinicians involved in airway manipulation choose the best VLs for orotracheal intubation.Systematic review registrationThe current protocol was submitted to PROSPERO on 07/01/2021.
An Evaluation of Methods for Monitoring Annual Quality Measures by Month to Predict Year-End Values
Authors: Johnson Todd R , Kookal Krishna Kumar , Applegate R Joseph , Bangar Suhasini , Yansane Alfa , White Joel M , Brandon Ryan , Simmons Kristen , Mullins Joanna , Neumann Ana , Walji Muhammad F , Kalenderian Elsbeth 
Published: January 2021

AbstractBackgroundAn increasing number of healthcare quality measures are designed for annual reporting. These measures require an entire year of data to accurately report the percentage of patients who met the measure. Annual measures give providers latitude to prioritize clinical workload and patient needs; however, they do not provide a direct means to monitor performance throughout the reporting year. Although there are many possible methods for measuring annual measures at finer-grained timescales, our applied work showed that the most obvious methods could give a misleading and inaccurate view of progress throughout the year. Neither the definitions of the annual measures, nor the research literature, provided any guidance on the best methods for interim monitoring of annual measures.ObjectiveOur objective was to evaluate four different methods for monitoring annually reported quality measures monthly to best predict year-end performance throughout the reporting year.MethodsWe developed four methods for monitoring annual measures by month: 1) Monthly Proportion: The proportion of patients with one or more encounters in the month who still needed to meet the measure at their first encounter of the month and met the measure by the end of the month; (2) Monthly Lookback Proportion: The proportion of patients seen in the month who met the measure by the end of the month, regardless of whether it was met in that month or previously in the reporting year; (3) Rolling 12 Month: The annual measure reported as if each month was the twelfth month of a twelve-month reporting period; and (4) YTD (Year-to-Date) Cumulative: The proportion of patients with one or more visits from the start of the reporting year through the month who satisfy the measure. We applied each method to two annual dental quality measures using data from two reporting years, and four different dental sites. We used mean squared error (MSE) to evaluate year-end predictive performance.ResultsMethod 3 (Rolling 12 Month) had the lowest MSE in 11 out of 16 cases (2 measures X 2 years X 4 sites) and lowest total MSE (262.39) across all 16 cases. In 5 of the 16 cases, YTD Cumulative had the lowest MSE.ConclusionsThe Rolling 12 Month method was best for predicting the year-end value across both measures and all four sites.
Polymeric assembly of endogenous Tuberous Sclerosis Protein Complex
Authors: Dai David L. , Hasan S. M. Naimul , Woollard Geoffrey , Bueler Stephanie A. , Julien Jean-Philippe , Rubinstein John L , Mazhab-Jafari Mohammad T. 
Published: January 2021

AbstractTuberous Sclerosis protein complex (pTSC) nucleates a proteinaceous signaling hub that integrates information about the internal and external energy status of the cell in regulation of growth and energy consumption. Biochemical and electron cryomicroscopy (cryoEM) studies of recombinant pTSC have revealed the structure and stoichiometry of the pTSC and have hinted at the possibility that the complex form large oligomers. Here, we have partially purified endogenous pTSC from fasted mammalian brains of rat and pig by leveraging a recombinant antigen binding fragment (Fab) specific for the TSC2 subunit of pTSC. We demonstrate Fabdependent purification of pTSC from membrane solubilized fractions of the brain homogenates. Negative stain electron microscopy of the samples purified from pig brain demonstrates rod-shaped protein particles with a width of 10 nm, a variable length as small as 40 nm and a high degree of conformational flexibility. Larger filaments are evident with a similar 10 nm width and up to 1 μm in length in linear and web-like organizations prepared from pig brain. These observations suggest polymerization of endogenous pTSC into filamentous super-structures.
STING suppresses cancer pain via immune and neuronal modulation
Authors: Wang Kaiyuan , Donnelly Christopher R. , Jiang Changyu , Liao Yihan , Tao Xueshu , Bang Sangsu , Lee Michael , Hilton Matthew J. , Ji Ru-Rong 
Published: January 2021

AbstractAgonists of the innate immune regulator stimulator of interferon genes (STING) have shown great efficacy in promoting antitumor immunity in preclinical models, leading to their exploration in cancer immunotherapy trials. Patients with advanced stage cancers frequently suffer from severe pain as a result of bone metastasis and bone destruction, for which there is no efficacious treatment. Here, using multiple mouse models of metastatic bone cancer, we report that STING agonists confer remarkable protection against cancer pain, bone destruction, and local tumor burden. Repeated systemic administration of STING agonists robustly attenuated bone cancer-induced pain symptoms and improved locomotor function. Interestingly, STING agonists provided acute pain relief through direct neuronal modulation, asex vivoincubation of STING agonists reduced excitability of pain-sensing nociceptive neurons from tumor-bearing mice. In addition, STING agonists protected local bone destruction and reduced local tumor burden through modulation of osteoclast and immune cell function in the tumor microenvironment, providing long-term cancer pain relief. Finally, thesein vivoeffects were dependent on host-intrinsic STING andIfnar1. Overall, STING activation provides unique advantages in controlling metastatic bone cancer pain through distinct and synergistic actions on nociceptors, immune cells, and osteoclasts.
scAdapt: Virtual adversarial domain adaptation network for single cell RNA-seq data classification across platforms and species
Authors: Zhou Xiang , Chai Hua , Zeng Yuansong , Zhao Huiying , Luo Ching-Hsing , Yang Yuedong 
Published: January 2021

AbstractMotivationIn single cell analyses, cell types are conventionally identified based on known marker gene expressions. Such approaches are time-consuming and irreproducible. Therefore, many new supervised methods have been developed to identify cell types for target datasets using the rapid accumulation of public datasets. However, these approaches are sensitive to batch effects or biological variations since the data distributions are different in cross-platforms or species predictions.ResultsWe developed scAdapt, a virtual adversarial domain adaptation network to transfer cell labels between datasets with batch effects. scAdapt used both the labeled source and unlabeled target data to train an enhanced classifier, and aligned the labeled source centroid and pseudo-labeled target centroid to generate a joint embedding. We demonstrate that scAdapt outperforms existing methods for classification in simulated, cross-platforms, cross-species, and spatial transcriptomic datasets. Further quantitative evaluations and visualizations for the aligned embeddings confirm the superiority in cell mixing and preserving discriminative cluster structure present in the original datasets.Availabilityhttps://github.com/zhoux85/scAdapt.Contactangyd25@mail.sysu.edu.cnorluojinx5@mail.sysu.edu.cn
ExpertRNA: A new framework for RNA structure prediction
Authors: Liu Menghan , Pedrielli Giulia , Poppleton Erik , Šulc Petr , Bertsekas Dimitri P. 
Published: January 2021

AbstractRibonucleic acid (RNA) is a fundamental biological molecule that is essential to all living organisms, performing a versatile array of cellular tasks. The function of many RNA molecules is strongly related to the structure it adopts. As a result, great effort is being dedicated to the design of efficient algorithms that solve the “folding problem”: given a sequence of nucleotides, return a probable list of base pairs, referred to as the secondary structure prediction. Early algorithms have largely relied on finding the structure with minimum free energy. However, the predictions rely on effective simplified free energy models that may not correctly identify the correct structure as the one with the lowest free energy. In light of this, new, data-driven approaches that not only consider free energy, but also use machine learning techniques to learn motifs have also been investigated, and have recently been shown to outperform free energy based algorithms on several experimental data sets.In this work, we introduce the new ExpertRNA algorithm that provides a modular framework which can easily incorporate an arbitrary number of rewards (free energy or non-parametric/data driven) and secondary structure prediction algorithms. We argue that this capability of ExpertRNA has the potential to balance out different strengths and weaknesses of state-of-the-art folding tools. We test the ExpertRNA on several RNA sequence-structure data sets, and we compare the performance of ExpertRNA against a state-of-the-art folding algorithm. We find that ExpertRNA produces, on average, more accurate predictions than the structure prediction algorithm used, thus validating the promise of the approach.