bluuebunny/crossref_metadata_2025_split huggingface dataset:
Perioperative changes in oxygen consumption: estimations from minimal-invasive cardiac output and a-cvO2difference parallel to indirect calorimetry
Authors: Jakobsson Julia , Norén Carl , Hagel Eva , Backheden Magnus , Kalman Sigridur , Bartha Erzsébet 
Published: July 2022

AbstractFeasible estimations of perioperative changes in oxygen consumption could enable larger studies of its role in postoperative outcomes. Current methods, by pulmonary artery catheterisation or breathing gas analysis, are often regarded as either invasive or technically requiring. In this pilot study, we investigated the relationship between estimations of oxygen consumption, based on minimal-invasive cardiac output and arterial-central venous blood gas sampling, and indirect calorimetry in the perioperative period using the data collected during a clinical trial on perioperative oxygen transport.In 20 patients >65 years during epidural and general anaesthesia for open abdominal surgery, Fick-based estimations of oxygen consumption(EVO2), the product of cardiac output from LiDCO™plus(LiDCO Ltd, Cambridge, UK) and arterial-central venous oxygen content difference, were compared with indirect calorimetry(GVO2) using QuarkRMR(COSMED, srl. Italy). Eighty-five simultaneous intra- and postoperative measurements at different time-points were analysed for prediction, parallelity and by traditional agreement assessment. There was an overall association between GVO2and EVO2, 73(95% CI 62 to 83) + 0.45(95% CI 0.29 to 0.61) EVO2ml min-1m-2,P<0.0001. GVO2and EVO2changed in parallel intra- and postoperatively when normalised to their respective overall means. Unadjusted mean difference between GVO2and EVO2indexed for body surface area was 26(95% CI 20 to 32) with limits of agreement (1.96SD) of -32 to 85 ml min-1m-2and did not change over time. There was low correlation for absolute agreement, ICC(A,1) 0.37(95% CI 0.34 to 0.65) [F(84,10.2)=3.07,P=0.0266].Despite lack of absolute agreement, the estimated oxygen consumption changed in parallel to the metabolic measurements in the perioperative period. Prediction or trending of oxygen consumption by this or similar methods could be further evaluated in larger samples.
A Mixed Methods Approach Incorporating Text Analytics to Examine Persistence of Depression
Authors: Johnson Sarah A. , Cassells Andrea , Lin T.J. , Weiss Elisa S. , Tobin Jonathan N. 
Published: July 2022

ABSTRACTBackgroundSome patients’ depression persists despite evidence-based interventions; understanding factors associated with depression persistence could inform screening and treatment. We used a novel mixed-methods approach to examine demographic, clinical, and social factors affecting depression persistence among older, low-income women; we also assessed the utility of this approach for evaluating intervention fidelity.MethodsData used for this study were generated from a comparative effectiveness study comparing the impact of prevention care management (PCM) versus a collaborative care intervention (CCI) on depression among women overdue for cancer screening: We reviewed 700 care manager logs to identify themes among patients’ experiences and analyzed language use using NVivo®’s natural language processing (NLP) functionality. 757 women age 50-64 who screened positive for depression at baseline using the Patient Health Questionnaire (PHQ)-9 and were overdue for ≥1 cancer screening test (breast, cervical, and/or colorectal) participated. All received primary care in XXX Federally Qualified Health Centers. We used NLP to quantify differences in language use across intervention groups and explored how often themes appeared in logs of participants whose depression did not meaningfully improve based on PHQ-9 scores. Differences in demographic, clinical, and social factors were examined.ResultsParticipants with persistent depression were more likely to discuss pain, fear, and transportation. Asthma and anxiety were associated with lower likelihood of depression remission, while no differences were observed in depression remission rates among those with diabetes or hypertension. Patient-centered words, including “needs” and “feelings”, were more common in the CCI group, while procedure-related words, like “screening” and “mammography”, appeared more frequently in the PCM group.ConclusionsPatient-related factors and social barriers contributed to depression persistence. NLP identified patterns of language use in case logs, suggesting unmet needs among depressed patients. NLP is an efficient, effective method for identifying themes in unstructured text and monitoring intervention fidelity.
Contrasting and conserved roles of NPR pathways in diverged land plant lineages
Authors: Jeon Hyung-Woo , Iwakawa Hidekazu , Naramoto Satoshi , Herrfurth Cornelia , Gutsche Nora , Schlüter Titus , Kyozuka Junko , Miyauchi Shingo , Feussner Ivo , Zachgo Sabine , Nakagami Hirofumi 
Published: July 2022

AbstractThe NPR proteins function as salicylic acid (SA) receptors inArabidopsis thaliana. AtNPR1 plays a central role in SA-induced transcriptional reprogramming whereby positively regulates SA-mediated defense. NPRs are found in the genomes of nearly all land plants. However, we know little about the molecular functions and physiological roles of NPRs in most plant species. Our phylogenetic and alignment analyses show that Brassicaceae NPR1-like proteins have characteristically gained or lost functional residues or motifs identified in AtNPRs, pointing to the possibility of a unique evolutionary trajectory for the Brassicaceae NPR1-like proteins that has resulted in peculiar functions. In line with this observation, we find that the only NPR inMarchantia polymorpha, MpNPR, is not the master regulator of SA-induced transcriptional reprogramming and negatively regulates bacterial resistance in this species. Interspecies complementation analysis indicated that the molecular properties of AtNPR1 and MpNPR are partially conserved, implying the diversification of NPR-associated pathways contributed to distinct roles of NPR in different species. The Mpnprtranscriptome suggested potential roles of MpNPR in heat and far-red light responses. We identify both Mpnprand Atnpr1-1display enhanced thermomorphogenesis. NPRs and NPR-associated pathways clearly have evolved distinctively in diverged land plant lineages to cope with different terrestrial environments.
Leaf trichome diversity, acylsugar concentration, and their relationships to leaf area inSolanum galapagense
Authors: Henzler Ilan , Khazaei Hamid 
Published: July 2022

AbstractGlandular trichomes are physical and chemical barriers used by some tomato wild relatives to confer resistance against insect pests and diseases transmitted by them.Solanum galapagensehas been identified as one of the potential sources of insect pest resistance. The present study aimed to examine the trichome diversity and acylsugar concentration of 26 accessions ofS. galapagensealong with one cultivated tomato (S. lycopersicum) and one cherry tomato (S. l. cerasiforme) cultivar. The results revealed large genetic variation amongS. galapagenseaccessions for all studied traits. TheS. galapagenseaccessions had significantly higher trichome types IV on the adaxial and abaxial surfaces of the leaf and greater acylsugar concentration but smaller leaflet area than cultivated tomato. The selected cherry tomato line represents greater trichome type IV and acylsugar than other groups. The acylsugar concentration was positively associated with trichome type IV but negatively associated with trichome type V on both leaf surfaces. Leaflet area was negatively associated with trichome IV density and acylsugar concentration. Analysis of DNA markers revealed the presence of two previously identified whitefly-resistance alleles inS. galapagenseaccessions. This study will support breeding programs aiming to improve insect pest resistance in tomato cultivars using crop wild relatives.
Inference of selective sweep parameters through supervised learning
Authors: Caldas Ian V. , Clark Andrew G. , Messer Philipp W. 
Published: July 2022

AbstractA selective sweep occurs when positive selection drives an initially rare allele to high population frequency. In nature, the precise parameters of a sweep are seldom known: How strong was positive selection? Did the sweep involve only a single adaptive allele (hard sweep) or were multiple adaptive alleles at the locus sweeping at the same time (soft sweep)? If the sweep was soft, did these alleles originate from recurrent new mutations (RNM) or from standing genetic variation (SGV)? Here, we present a method based on supervised machine learning to infer such parameters from the patterns of genetic variation observed around a given sweep locus. Our method is trained on sweep data simulated with SLiM, a fast and flexible framework that allows us to generate training data across a wide spectrum of evolutionary scenarios and can be tailored towards the specific population of interest. Inferences are based on summary statistics describing patterns of nucleotide diversity, haplotype structure, and linkage disequilibrium, which are estimated across systematically varying genomic window sizes to capture sweeps across a wide range of selection strengths. We show that our method can accurately infer selection coefficients in the range 0.01 < s < 100 and classify sweep types between hard sweeps, RNM soft sweeps, and SGV soft sweeps with accuracy 69 % to 95 % depending on sweep strength. We also show that the method infers the correct sweep types at three empirical loci known to be associated with the recent evolution of pesticide resistance in Drosophila melanogaster. Our study demonstrates the power of machine learning for inferring sweep parameters from present-day genotyping samples, opening the door to a better understanding of the modes of adaptive evolution in nature.Author summaryAdaptation often involves the rapid spread of a beneficial genetic variant through the population in a process called a selective sweep. Here, we develop a method based on machine learning that can infer the strength of selection driving such a sweep, and distinguish whether it involved only a single adaptive variant (a so-called hard sweep) or several adaptive variants of independent origin that were simultaneously rising in frequency at the same genomic position (a so-called soft selective sweep). Our machine learning method is trained on simulated data and only requires data sampled from a single population at a single point in time. To address the challenge of simulating realistic datasets for training, we explore the behavior of the method under a variety of testing scenarios, including scenarios where the history of the population of interest was misspecified. Finally, to illustrate the accuracy of our method, we apply it to three known sweep loci that have contributed to the evolution of pesticide resistance in Drosophila melanogaster.
Cell-type-specific aberrant R-loop accumulation regulates target gene and confers cell-specificity
Authors: Pan Xingxin , Huang L. Frank 
Published: July 2022

AbstractAberrant R-loops have been found associated with diverse biological dysfunction, including cancers and neurological disorders. However, there isn’t any systematic research to characterize aberrant R-loops at the whole genome level at a large scale. Here, we identified aberrant R-loops, including proliferative and suppressive R-loops of 5’ end, body, 3’ end respectively for the first time, which are found prevalent and vary across diverse physiological conditions. After that, we proposed a deep neural network-based framework, named Deep R-looper Discriminant to identify aberrant R-loops against housekeeping R-loops. To evaluate the predictive performance of the deep learning framework, we constructed multiple prediction models as benchmarks and it showed our framework achieves robust performance for identifying aberrant R-loops against those normal R-loops. Furthermore, we found the customized Deep R-looper Discriminant was capable of distinguishing between proliferative and suppressive R-loops at 5’ end, body, 3’ end respectively, outperforming baselines. When inspecting the contribution of epigenetic marks to aberrant R-loops of each class, we inferred landmark epigenetic modifications which play a crucial role in the differentiated formation of those aberrant R-loops, and cell line specificity of epigenetic marks map was found as well. To explore the characteristics of these aberrant R-loops, we depicted the histone landscapes for aberrant R-loops. Finally, we integrated omics and identified target genes regulated directly by aberrant R-loops and found key transcription factors involved in R-loop regulation, which may be implicated in conferring cell-specificity and cancer development and progression.
Monitoring Non-Specific Adsorption at Solid-Liquid Interfaces by Supercritical Angle Fluorescence Microscopy
Authors: Au Aaron , Ho Man , Wheeler Aaron R. , Yip Christopher M. 
Published: July 2022

ABSTRACTSupercritical angle fluorescence (SAF) microscopy is a novel imaging tool based on the use of distance-dependent fluorophore emission patterns to provide accurate locations of fluorophores relative to a surface. This technique has been used extensively to construct accurate cellular images and to detect surface phenomena in a static environment. However, the capability of SAF microscopy in monitoring dynamic surface phenomena and changes in millisecond intervals is underexplored. Here we report on a hardware add-on for a conventional inverted microscope coupled with a post-processing Python module that extends the capability of SAF microscopy to monitor dynamic surface phenomena thereby greatly expanding the range of potential applications of this tool. We first assessed the performance of the system by probing the specific binding of biotin-fluorescein conjugates to a neutravidin-coated cover glass in the presence of non-binding fluorescein. The SAF emission was observed to increase with the quantity of bound fluorophore on the cover glass. However, high concentration of unbound fluorophore also contributed to overall SAF emission, leading to over-estimation in surface-bound fluorescence. To expand the applications of SAF in monitoring surface phenomena, we monitored the non-specific surface adsorption of BSA and non-ionic surfactants on a Teflon-AF surface. Solution mixtures of BSA and nine Pluronic/Tetronic surfactants were exposed to a Teflon-AF surface. No significant BSA adsorption was observed in all BSA-surfactant solution mixture with negligible SAF intensity. Finally, we monitored the adsorption dynamics of BSA onto the Teflon-AF surface and observed rapid BSA adsorption on Teflon-AF surface within 10 seconds of addition. The adsorption rate constant (ka) and half-life of BSA adsorption on Teflon-AF were determined to be 0.2093±0.002 s−1 and 3.312±0.032 s respectively using a pseudo-first-order adsorption equation.
Deep learning enabled analysis of cardiac sphericity
Authors: Vukadinovic Milos , Kwan Alan C. , Yuan Victoria , Salerno Michael , Lee Daniel C. , Albert Christine M. , Cheng Susan , Li Debiao , Ouyang David , Clarke Shoa L. 
Published: July 2022

AbstractQuantification of chamber size and systolic function is a fundamental component of cardiac imaging, as these measurements provide a basis for establishing both diagnosis and appropriate treatment for a spectrum of cardiomyopathies. However, the human heart is a complex structure with significant uncharacterized phenotypic variation beyond traditional metrics of size and function. Characterizing variation in cardiac shape and morphology can add to our ability to understand and classify cardiovascular risk and pathophysiology. We describe deep learning enabled measurement of left ventricle (LV) sphericity using cardiac magnetic resonance imaging data from the UK Biobank and show that among adults with normal LV volumes and systolic function, increased sphericity is associated with increased risk for incident atrial fibrillation (HR 1.31 per SD, 95% CI 1.23-1.38), cardiomyopathy (HR 1.62 per SD, 95% CI 1.29-2.02), and heart failure (HR 1.24, 95% CI 1.11-1.39), independent of traditional risk factors including age, sex, hypertension, and body mass index. Using genome-wide association studies, we identify four loci associated with sphericity at genome-wide significance. These loci harbor known and suspected cardiomyopathy genes. Through genetic correlation and Mendelian randomization, we provide evidence that sphericity may represent a subclinical manifestation of non-ischemic cardiomyopathy.
Integrative analysis of clinical health records, imaging and pathogen genomics identifies personalized predictors of disease prognosis in tuberculosis
Authors: Sambarey Awanti , Smith Kirk , Chung Carolina , Arora Harkirat Singh , Yang Zhenhua , Agarwal Prachi , Chandrasekaran Sriram 
Published: July 2022

AbstractTuberculosis (TB) afflicts over 10 million people every year and its global burden is projected to increase dramatically due to multidrug-resistant TB (MDR-TB). The Covid-19 pandemic has resulted in reduced access to TB diagnosis and treatment, reversing decades of progress in disease management globally. It is thus crucial to analyze real-world multi-domain information from patient health records to determine personalized predictors of TB treatment outcome and drug resistance. We conduct a retrospective analysis on electronic health records of 5060 TB patients spanning 10 countries with high burden of MDR-TB including Ukraine, Moldova, Belarus and India available on the NIAID-TB portals database. We analyze over 200 features across multiple host and pathogen modalities representing patient social demographics, disease presentations as seen in cChest X rays and CT scans, and genomic records with drug susceptibility features of the pathogen strain from each patient. Our machine learning model, built with diverse data modalities outperforms models built using each modality alone in predicting treatment outcomes, with an accuracy of 81% and AUC of 0.768. We determine robust predictors across countries that are associated with unsuccessful treatmentclinical outcomes, and validate our predictions on new patient data from TB Portals. Our analysis of drug regimens and drug interactions suggests that synergistic drug combinations and those containing the drugs Bedaquiline, Levofloxacin, Clofazimine and Amoxicillin see more success in treating MDR and XDR TB. Features identified via chest imaging such as percentage of abnormal volume, size of lung cavitation and bronchial obstruction are associated significantly with pathogen genomic attributes of drug resistance. Increased disease severity was also observed in patients with lower BMI and with comorbidities. Our integrated multi-modal analysis thus revealed significant associations between radiological, microbiological, therapeutic, and demographic data modalities, providing a deeper understanding of personalized responses to aid in the clinical management of TB.
Accelerating genomic workflows using NVIDIA Parabricks
Authors: O’Connell Kyle A. , Yosufzai Zelaikha B. , Campbell Ross A. , Lobb Collin J. , Engelken Haley T. , Gorrell Laura M. , Carlson Thad B. , Catana Josh J. , Mikdadi Dina , Bonazzi Vivien R. , Klenk Juergen A. 
Published: July 2022

ABSTRACTBackgroundAs genome sequencing becomes a more integral part of scientific research, government policy, and personalized medicine, the primary challenge for researchers is shifting from generating raw data to analyzing these vast datasets. Although much work has been done to reduce compute times using various configurations of traditional CPU computing infrastructures, Graphics Processing Units (GPUs) offer the opportunity to accelerate genomic workflows by several orders of magnitude. Here we benchmark one GPU-accelerated software suite called NVIDIA Parabricks on Amazon Web Services (AWS), Google Cloud Platform (GCP), and an NVIDIA DGX cluster. We benchmarked six variant calling pipelines, including two germline callers (HaplotypeCaller and DeepVariant) and four somatic callers (Mutect2, Muse, LoFreq, SomaticSniper).ResultsFor germline callers, we achieved up to 65x acceleration, bringing HaplotypeCaller runtime down from 36 hours to 33 minutes on AWS, 35 minutes on GCP, and 24 minutes on the NVIDIA DGX. Somatic callers exhibited more variation between the number of GPUs and computing platforms. On cloud platforms, GPU-accelerated germline callers resulted in cost savings compared with CPU runs, whereas somatic callers were often more expensive than CPU runs because their GPU acceleration was not sufficient to overcome the increased GPU cost.ConclusionsGermline variant callers scaled with the number of GPUs across platforms, whereas somatic variant callers exhibited more variation in the number of GPUs with the fastest runtimes, suggesting that these workflows are less GPU optimized and require benchmarking on the platform of choice before being deployed at production scales. Our study demonstrates that GPUs can be used to greatly accelerate genomic workflows, thus bringing closer to grasp urgent societal advances in the areas of biosurveillance and personalized medicine.
On the Genes, Genealogies, and Geographies of Quebec
Authors: Anderson-Trocmé Luke , Nelson Dominic , Zabad Shadi , Diaz-Papkovich Alex , Baya Nikolas , Touvier Mathilde , Jeffery Ben , Dina Christian , Vézina Hélène , Kelleher Jerome , Gravel Simon 
Published: July 2022

AbstractPopulation genetic models only provide coarse representations of real-world ancestry. We use a pedigree compiled from four million parish records and genotype data from 2,276 French and 20,451 French Canadian (FC) individuals, to finely model and trace FC ancestry through space and time. The loss of ancestral French population structure and the appearance of spatial and regional structure highlights a wide range of population expansion models. Geographic features shaped migrations throughout, and we find enrichments for migration, genetic and genealogical relatedness patterns within river networks across Quebec regions. Finally, we provide a freely accessible simulated whole-genome sequence dataset with spatiotemporal metadata for 1,426,749 individuals reflecting intricate FC population structure. Such realistic populations-scale simulations provide new opportunities to investigate population genetics at an unprecedented resolution.Lay SummaryWe all share common ancestors ranging from a couple generations ago to hundreds of thousands of years ago. The genetic differences between individuals today mostly depends on how closely related they are. The only problem is that the actual genealogies that relate all of us are often forgotten over time. Some geneticists have tried to come up with simple models of our shared ancestry but they don’t really explain the full, rich history of humanity. Our study uses a multi-institutional project in Quebec that has digitized parish records into a single unified genealogical database that dates back to the arrival of the first French settlers four hundred years ago. This genealogy traces the ancestry of millions of French-Canadian and we have used it to build a very high resolution genetic map. We used this genetic map to study in detail how certain historical events, and landscapes have influenced the genomes of French-Canadians today.One-Sentence SummaryWe present an accurate and high resolution spatiotemporal model of genetic variation in a founder population.