bluuebunny/crossref_metadata_2025_split huggingface dataset:
The ETKA study - Impact of early therapy termination on treated phonemes in conversational speech tasks in children aged 5 to 6 years with phonologically delayed development: protocol for a multicenter randomized controlled trial
Authors: Detter-Biesl Doris , Schwartz Bernhard 
Published: April 2022

AbstractBackgroundChildren with speech and language disorders frequently display speech sound disorders, the treatment of which improves children’s ability to communicate. However, there is a significant gap between those therapy capacities needed and those provided in Austria. In addition, as the effect of early therapy termination has not yet been systematically evaluated, the aim of this project is to investigate the influence of an early therapy termination on phonological processes in children aged 5 to 6 years with phonologically delayed development.Methods/DesignForty 5- to 6-year-old children, currently participating in speech therapies in the Upper Austrian region will participate in this multicenter randomized controlled trial. The participants will be randomly allocated to either the intervention group (early therapy termination) or the control group (uninterrupted ongoing therapy) by envelop randomization. Phoneme accuracy in spontaneous speech determined during 15 minutes of conversational sampling and phoneme accuracy for single-word production determined with the PLAKKS assessment will be used to assess therapy efficacy. The results from both groups will be compared regarding the correct use of the target phoneme/consonant cluster in conversational speech situation.DiscussionThis study will explore the effect of early therapy termination on speech sound disorders in 5- to 6-yearold children. If an early termination exhibits non-inferiority to the current ongoing therapy, therapy resources could be used more effectively. Subsequently, earlier therapy starts for more children could be facilitated and negative consequences of delayed therapies could be counteracted.Ethics and disseminationThe study was approved by an institutional review board (ID: A-2019-035). Dissemination will be achieved through peer-review conferences and journals.Trial registrationThe study is registered at ClinicalTrials.gov (Identifier: NCT03972891, Registered 4 June 2019, https://www.clinicaltrials.gov/ct2/show/NCT03972891).
COVID-19 vaccine effectiveness against severe disease from the Omicron BA.1 and BA.2 subvariants – surveillance results from southern Sweden, December 2021 to March 2022
Authors: Björk Jonas , Bonander Carl , Moghaddassi Mahnaz , Rasmussen Magnus , Malmqvist Ulf , Inghammar Malin , Kahn Fredrik 
Published: April 2022

AbstractWe compared vaccine effectiveness (VE) against severe COVID-19 during calendar periods from December 2021 to March 2022 when Omicron BA.1 and BA.2, respectively, were the dominating virus variants in Scania county, Sweden. We used continuous density case-control sampling matched for sex and age, and with further adjustment for differences in comorbidities and prior infection. VE remained relatively stable after the transition from BA.1 to BA.2 among people with at least three doses but decreased markedly among those with only two doses. Protection from prior infection was also lower after the transition to BA.2. These findings suggest that booster vaccination is needed to maintain sufficient protection against severe COVID-19.
EHR Foundation Models Improve Robustness in the Presence of Temporal Distribution Shift
Authors: Guo Lin Lawrence , Steinberg Ethan , Fleming Scott Lanyon , Posada Jose , Lemmon Joshua , Pfohl Stephen R , Shah Nigam , Fries Jason , Sung Lillian 
Published: April 2022

ABSTRACTBackgroundTemporal distribution shift negatively impacts the performance of clinical prediction models over time. Pretraining foundation models using self-supervised learning on electronic health records (EHR) may be effective in acquiring informative global patterns that can improve the robustness of task-specific models.ObjectiveTo evaluate the utility of EHR foundation models in improving the in-distribution (ID) and out-of-distribution (OOD) performance of clinical prediction models.MethodsThe cohort consisted of adult inpatients admitted between 2009-2021. Gated recurrent unit (GRU)- and transformer (TRANS)-based foundation models were pretrained on EHR of patients admitted between 2009-2012 and were subsequently used to construct patient representations (CLMBR). These representations were used to learn logistic regression models (CLMBRGRU and CLMBRTRANS) to predict hospital mortality, long length of stay, 30-day readmission, and ICU admission. We compared CLMBRGRU and CLMBRTRANS with baseline logistic regression models learned on count-based representations (count-LR) and end-to-end (ETE) GRU and transformer models in ID (2009-2012) and OOD (2013-2021) year groups. Performance was measured using area-under-the-receiver-operating-characteristic curve, area- under-the-precision-recall curve, and absolute calibration error.ResultsModels trained on CLMBR generally showed better discrimination relative to count-LR in both ID and OOD year groups. In addition, they often matched or were better than their ETE counterparts. Finally, foundation models’ performance in the self-supervised learning task tracked closely with the ID and OOD performance of the downstream models.ConclusionsThese results suggest that pretraining foundation models on electronic health records is a useful approach for developing clinical prediction models that perform well in the presence of temporal distribution shift.
Effect of SARS-CoV-2 digital droplet RT-PCR assay sensitivity on COVID-19 wastewater based epidemiology
Authors: Kim Sooyeol , Wolfe Marlene K. , Criddle Craig S. , Duong Dorothea H. , Chan-Herur Vikram , White Bradley J. , Boehm Alexandria B. 
Published: April 2022

AbstractWe developed and implemented a framework for examining how molecular assay sensitivity for a viral RNA genome target affects its utility for wastewater-based epidemiology. We applied this framework to digital droplet RT-PCR measurements of SARS-CoV-2 and Pepper Mild Mottle Virus genes made using 10 replicate wells, and determined how using fewer wells affected assay sensitivity and its performance for wastewater-based epidemiology applications. We used a computational, downsampling approach. When percent of positive droplets was between 0.024% and 0.5% (as was the case for SARS-CoV-2 genes during the Delta surge), measurements obtained with 3 or more wells were similar to those obtained using 10. When percent of positive droplets was less than 0.024%, then 6 or more wells were needed to obtain similar results as those obtained using 10 wells. When COVID-19 incidence is low, as it was before the Delta surge and SARS-CoV-2 gene concentrations are <104 cp/g, using 6 wells will yield a detectable concentration 90% of the time. Overall, results support an adaptive approach where assay sensitivity is increased by running 6 or more wells during periods of low SARS-CoV-2 gene concentrations, and 3 or more wells during periods of high SARS-CoV-2 gene concentrations.SynopsisAdaptive approaches developed with assay sensitivity in consideration may reduce cost and increase sensitivity for wastewater-based epidemiology.Abstract Art
A Model to Predict In-hospital Mortality in Elderly Patients with Community-acquired Pneumonia: a Retrospective Study
Authors: Hao Yanting , Zhang Hua , Yan Yingying , Zhu Yun , Zhang Fuchun 
Published: April 2022

AbstractObjectivesTo develop and validate a predictive model for evaluating in-hospital mortality risk in elderly patients with community-acquired pneumonia.SettingTwo tertiary care hospitals with 2000 beds and 1000 beds respectively in Beijing, China.ParticipantsElderly patients (age ≥ 65 years) with community-acquired pneumonia admitted to the Department of Internal Medicine of the two hospitals from January 2010 to December 2019 or from January 2019 to December 2019 respectively.DesignIt was a retrospective study. After dividing the data set into training and validation sets, we created a mortality model that included patient demographics, hospitalization time, hospital outcome, and various clinical conditions associated with hospitalization. We then applied the model to the validation set.Main outcome measuresIn-hospital mortality.ResultsThe training cohort included 2,466 patients admitted to the one hospital, while the validation cohort included 424 patients at the other hospital. The overall in-hospital mortality rate was 15.6%. In the multivariable model, age, respiratory failure, heart failure, and malignant tumors were associated with mortality. The model had excellent discrimination with AUC values of 0.877 and 0.930 in the training and validation cohorts, respectively.ConclusionsThe predictive model to evaluate in-hospital mortality in elderly patients with community-acquired pneumonia, which was established based on administrative data, provides a simple tool for physicians to assess the prognosis of elderly patients with community-acquired pneumonia in Beijing.Strengths and limitations of this studyThe clinical data collected were obtained from a large retrospective cohort over a 10-year period, resulting in good reliability.The model based on administrative data can help healthcare workers determine the prognosis and outcomes of elderly patients of CAP, especially in resource limited regions in China.Our main outcome was in-hospital mortality, not 30-day mortality or longer.All included cases were collected from inpatient, outpatient and emergency patients were not included.The model was only verified in two hospitals in Beijing and further verification should be conducted in other areas and different levels of hospitals.
A boost with SARS-CoV-2 BNT162b2 mRNA vaccine elicits strong humoral responses independently of the interval between the first two doses
Authors: Tauzin Alexandra , Gong Shang Yu , Painter Mark M. , Goel Rishi R. , Chatterjee Debashree , Beaudoin-Bussières Guillaume , Marchitto Lorie , Boutin Marianne , Laumaea Annemarie , Okeny James , Gendron-Lepage Gabrielle , Bourassa Catherine , Medjahed Halima , Goyette Guillaume , Williams Justine C. , Bo Yuxia , Gokool Laurie , Morrisseau Chantal , Arlotto Pascale , Bazin Renée , Fafard Judith , Tremblay Cécile , Kaufmann Daniel E. , De Serres Gaston , Côté Marceline , Duerr Ralf , Martel-Laferrière Valérie , Greenplate Allison R. , Wherry E. John , Finzi Andrés 
Published: April 2022

SUMMARYDue to the recrudescence of SARS-CoV-2 infections worldwide, mainly caused by Omicron BA.1 and BA.2 variants of concern, several jurisdictions are administering a mRNA vaccine boost. Here, we analyzed humoral responses induced after the second and third doses of mRNA vaccine in naïve and previously-infected donors who received their second dose with an extended 16-week interval. We observed that the extended interval elicited robust humoral responses against VOCs, but this response was significantly diminished 4 months after the second dose. Administering a boost to these individuals brought back the humoral responses to the same levels obtained after the extended second dose. Interestingly, we observed that administering a boost to individuals that initially received a short 3-4 weeks regimen elicited humoral responses similar to those elicited in the long interval regimen. Nevertheless, humoral responses elicited by the boost in naïve individuals did not reach those present in previously-infected vaccinated individuals.
WFA-GPU: Gap-affine pairwise alignment using GPUs
Authors: Aguado-Puig Quim , Doblas Max , Matzoros Christos , Espinosa Antonio , Moure Juan Carlos , Marco-Sola Santiago , Moreto Miquel 
Published: April 2022

AbstractMotivationAdvances in genomics and sequencing technologies demand faster and more scalable analysis methods that can process longer sequences with higher accuracy. However, classical pairwise alignment methods, based on dynamic programming (DP), impose impractical computational requirements to align long and noisy sequences like those produced by PacBio, and Nanopore technologies. The recently proposed WFA algorithm paves the way for more efficient alignment tools, improving time and memory complexity over previous methods. However, high-performance computing (HPC) platforms require efficient parallel algorithms and tools to exploit the computing resources available on modern accelerator-based architectures.ResultsThis paper presents the WFA-GPU, a GPU (Graphics Processing Unit)-accelerated tool to compute exact gap-affine alignments based on the WFA algorithm. We present the algorithmic adaptations and performance optimizations that allow exploiting the massively parallel capabilities of modern GPU devices to accelerate the alignment computations. In particular, we propose a CPU-GPU co-design capable of performing inter-sequence and intra-sequence parallel sequence alignment, combining a succinct WFA-data representation with an efficient GPU implementation. As a result, we demonstrate that our implementation outperforms the original multi-threaded WFA implementation between 1.5-7.7× and up to 17× when using heuristic methods on long and noisy sequences. Compared to other state-of-the-art tools and libraries, the WFA-GPU is up to 29× faster than other GPU implementations and up to four orders of magnitude faster than other CPU implementations.AvailabilityWFA-GPU code and documentation are publicly available athttps://github.com/quim0/WFA-GPU.Contactquim.aguado@uab.cat
Comparative analysis of temperature preference behavior and effects of temperature on daily behavior in eleven Drosophila species
Authors: Ito Fumihiro , Awasaki Takeshi 
Published: April 2022

AbstractTemperature is one of the most critical environmental factors that influence various biological processes. Species distributed in different temperature regions are considered to have different optimal temperatures for daily life activities. However, how organisms have acquired various features to cope with particular temperature environments remains to be elucidated. In this study, we have systematically analyzed the temperature preference behavior and effects of temperatures on daily locomotor activity and sleep using 11 Drosophila species. We also investigated the function of antennae in the temperature preference behavior of these species. We found that, (1) an optimal temperature for daily locomotor activity and sleep of each species approximately matches with temperatures it frequently encounters in its habitat, (2) effects of temperature on locomotor activity and sleep are diverse among species, but each species maintains its daily activity and sleep pattern even at different temperatures, and (3) each species has a unique temperature preference behavior, and the contribution of antennae to this behavior is diverse among species. These results suggest that Drosophila species inhabiting different climatic environments have acquired species-specific temperature response systems according to their life strategies. This study provides fundamental information for understanding the mechanisms underlying their temperature adaptation and lifestyle diversification.
Antibody escape and cryptic cross-domain stabilization in the SARS-CoV-2 Omicron spike protein
Authors: Javanmardi Kamyab , Segall-Shapiro Thomas H. , Chou Chia-Wei , Boutz Daniel R. , Olsen Randall J. , Xie Xuping , Xia Hongjie , Shi Pei-Yong , Johnson Charlie D. , Annapareddy Ankur , Weaver Scott , Musser James M. , Ellington Andrew D. , Finkelstein Ilya J. , Gollihar Jimmy D. 
Published: April 2022

SummaryThe worldwide spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the repeated emergence of variants of concern. The Omicron variant has two dominant sub-lineages, BA.1 and BA.2, each with unprecedented numbers of nonsynonymous and indel spike protein mutations: 33 and 29, respectively. Some of these mutations individually increase transmissibility and enhance immune evasion, but their interactions within the Omicron mutational background is unknown. We characterize the molecular effects of all Omicron spike mutations on expression, human ACE2 receptor affinity, and neutralizing antibody recognition. We show that key mutations enable escape from neutralizing antibodies at a variety of epitopes. Stabilizing mutations in the N-terminal and S2 domains of the spike protein compensate for destabilizing mutations in the receptor binding domain, thereby enabling the record number of mutations in Omicron sub-lineages. Taken together, our results provide a comprehensive account of the mutational effects in the Omicron spike protein and illuminate previously unknown mechanisms of how the N-terminal domain can compensate for destabilizing mutations within the more evolutionarily constrained RBD.
gr Predictor: a Deep-Learning Model for Predicting the Hydration Structures around Proteins
Authors: Kawama Kosuke , Fukushima Yusaku , Ikeguchi Mitsunori , Ohta Masateru , Yoshidome Takashi 
Published: April 2022

ABSTRACTAmong the factors affecting biological processes such as protein folding and ligand binding, hydration, which is represented by a three-dimensional water-site-distribution-function around the protein, is crucial. The typical methods for computing the distribution functions, including molecular dynamics simulations and the three-dimensional reference interaction site model (3D-RISM) theory, require a long computation time from hours to tens of hours. Here, we propose a deep-learning model rapidly estimating the distribution functions around proteins obtained by the 3D-RISM theory from the protein 3D structure. The distribution functions predicted using our deep-learning model are in good agreement with those obtained by the 3D-RISM theory. Particularly, the coefficient of determination between the distribution function obtained by the deep-learning model and that obtained using the 3D-RISM theory is approximately 0.98. Furthermore, using a graphics processing unit (GPU), the calculation by the deep learning model is completed in less than one minute, more than 2 orders of magnitude faster than the calculation time of 3D-RISM theory. Therefore, our deep learning model provides a practical and efficient way to calculate the three-dimensional water-site-distribution-functions. The program called “gr Predictor” is available under the GNU General Public License from https://github.com/YoshidomeGroup-Hydration/gr-predictor.Table of Contents graphic
Population Dynamics of Immunological Synapse Formation Induced by Bispecific T-cell Engagers Predict Clinical Pharmacodynamics and Treatment Resistance
Authors: Liu Can , Zhou Jiawei , Kudlacek Stephan , Qi Timothy , Dunlap Tyler , Cao Yanguang 
Published: April 2022

AbstractEffector T cells form immunological synapses (IS) with recognized target cells to elicit cytolytic effects. Facilitating IS formation is the principal pharmacological action of most T cell-based cancer immunotherapies. However, the dynamics of IS formation at the cell population level, the primary driver of the pharmacodynamics of many cancer immunotherapies, remains poorly defined. With classic immunotherapy CD3/CD19 bispecific T cell engager (BiTE) as a model system, we integrate experimental and theoretical approaches to investigate the population dynamics of IS formation and their relevance to clinical pharmacodynamics and treatment resistance. Our models produce experimentally consistent predictions when defining IS formation as a series of spatiotemporally coordinated events driven by molecular and cellular interactions. The models predict tumor-killing pharmacodynamics in patients and reveal trajectories of tumor evolution across anatomical sites under BiTE immunotherapy. Our model highlight the bone marrow may serve as a sanctuary site permitting tumor evolution and antigen escape. The models also suggest the optimal dosing regimens as a function of tumor growth and patient T cell abundance that confer adequate tumor control with minimal disease evolution. This work has implications for developing more effective T cell-based cancer immunotherapies.
NETSCOPE: Information-Theory based Network Discovery and Analysis
Authors: Bergmans Tido , Jamal Tousif , Celikel Tansu 
Published: April 2022

AbstractMolecular, cellular, structural, and functional networks in the brain each represent different levels of organization and complexity. These networks are interconnected, and their interactions underlie the brain’s abilities to process information, regulate bodily functions, and mediate behavior. While understanding the emergence of higher-level networks (structural and functional) from the interactions of lower-level (molecular and cellular) ones is crucial, a universal analytical method applicable to all brain network types has been lacking. Here we introduce an open-source toolbox (NETSCOPE) that identifies weighted network architectures using mutual information (MI) and variation of information (VI). We demonstrate the accuracy of the resulting networks using synthetic data and by recreating five molecular networks in S. cerevisiae. We finally deploy NETSCOPE to identify cell type specific transcriptional networks and reconstruct the brain-wide neural networks that encode touch in the mouse brain. The code is made available in Python, Google Colab, Jupyter Notebook, MATLAB, and Octave. Beyond its core functionality, this network discovery method holds potential for broader applications, including the development of bioinspired sparse artificial networks.
Phertilizer: Growing a Clonal Tree from Ultra-low Coverage Single-cell DNA Sequencing of Tumors
Authors: Weber Leah L. , Zhang Chuanyi , Ochoa Idoia , El-Kebir Mohammed 
Published: April 2022

AbstractEmerging ultra-low coverage single-cell DNA sequencing (scDNA-seq) technologies have enabled high resolution evolutionary studies of copy number aberrations (CNAs) within tumors. While these sequencing technologies are well suited for identifying CNAs due to the uniformity of sequencing coverage, the sparsity of coverage poses challenges for the study of single-nucleotide variants (SNVs). In order to maximize the utility of increasingly available ultra-low coverage scDNA-seq data and obtain a comprehensive understanding of tumor evolution, it is important to also analyze the evolution of SNVs from the same set of tumor cells.We present Phertilizer, a method to infer a clonal tree from ultra-low coverage scDNA-seq data of a tumor. Based on a probabilistic model, our method recursively partitions the data by identifying key evolutionary events in the history of the tumor. We demonstrate the performance of Phertilizeron simulated data as well as on two real datasets, finding that Phertilizereffectively utilizes the copynumber signal inherent in the data to more accurately uncover clonal structure and genotypes compared to previous methods.Availabilityhttps://github.com/elkebir-group/phertilizer