bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quasiprobability decompositions with reduced sampling overhead
Authors: Piveteau Christophe , Sutter David , Woerner Stefan 
Published: February 2022

AbstractQuantum error-mitigation techniques can reduce noise on current quantum hardware without the need for fault-tolerant quantum error correction. For instance, the quasiprobability method simulates a noise-free quantum computer using a noisy one, with the caveat of only producing the correct expected values of observables. The cost of this error mitigation technique manifests as a sampling overhead which scales exponentially in the number of corrected gates. In this work, we present an algorithm based on mathematical optimization that aims to choose the quasiprobability decomposition in a noise-aware manner. This directly leads to a significantly lower basis of the sampling overhead compared to existing approaches. A key element of the novel algorithm is a robust quasiprobability method that allows for a tradeoff between an approximation error and the sampling overhead via semidefinite programming.
Entanglement-based quantum communication complexity beyond Bell nonlocality
Authors: Ho Joseph , Moreno George , Brito Samuraí , Graffitti Francesco , Morrison Christopher L. , Nery Ranieri , Pickston Alexander , Proietti Massimiliano , Rabelo Rafael , Fedrizzi Alessandro , Chaves Rafael 
Published: February 2022

AbstractEfficient distributed computing offers a scalable strategy for solving resource-demanding tasks, such as parallel computation and circuit optimisation. Crucially, the communication overhead introduced by the allotment process should be minimised—a key motivation behind the communication complexity problem (CCP). Quantum resources are well-suited to this task, offering clear strategies that can outperform classical counterparts. Furthermore, the connection between quantum CCPs and non-locality provides an information-theoretic insight into fundamental quantum mechanics. Here we connect quantum CCPs with a generalised non-locality framework—beyond Bell’s paradigmatic theorem—by incorporating the underlying causal structure, which governs the distributed task, into a so-called non-local hidden-variable model. We prove that a new class of communication complexity tasks can be associated with Bell-like inequalities, whose violation is both necessary and sufficient for a quantum gain. We experimentally implement a multipartite CCP akin to the guess-your-neighbour-input scenario, and demonstrate a quantum advantage when multipartite Greenberger-Horne-Zeilinger (GHZ) states are shared among three users.
Influential nodes identification using network local structural properties
Authors: Wang Bin , Zhang Junkai , Dai Jinying , Sheng Jinfang 
Published: February 2022

AbstractWith the rapid development of information technology, the scale of complex networks is increasing, which makes the spread of diseases and rumors harder to control. Identifying the influential nodes effectively and accurately is critical to predict and control the network system pertinently. Some existing influential nodes detection algorithms do not consider the impact of edges, resulting in the algorithm effect deviating from the expected. Some consider the global structure of the network, resulting in high computational complexity. To solve the above problems, based on the information entropy theory, we propose an influential nodes evaluation algorithm based on the entropy and the weight distribution of the edges connecting it to calculate the difference of edge weights and the influence of edge weights on neighbor nodes. We select eight real-world networks to verify the effectiveness and accuracy of the algorithm. We verify the infection size of each node and top-10 nodes according to the ranking results by the SIR model. Otherwise, the Kendall $$\tau$$
τ
coefficient is used to examine the consistency of our algorithm with the SIR model. Based on the above experiments, the performance of the LENC algorithm is verified.
Associations and recovery dynamics of the nasopharyngeal microbiota during influenza-like illness in the aging population
Authors: Shetty Sudarshan A. , van Beek Josine , Bijvank Elske , Groot James , Kuiling Sjoerd , Bosch Thijs , van Baarle Debbie , Fuentes Susana 
Published: February 2022

AbstractInfluenza-like illness (ILI), a disease caused by respiratory pathogens including influenza virus, is a major health concern in older adults. There is little information on changes and recovery dynamics of the nasopharyngeal (NP) microbiota of older adults associated with an ILI. Here, we compared the NP microbiota in older adults reporting (n = 240) or not (n = 157) ILI during the 2014–2015 influenza season at different times of the ILI event. A small but significant effect of the ILI was observed on the microbiota community composition and structure when compared to controls and samples collected at recovery.Corynebacteriumwas negatively associated with ILI and its abundance increased after recovery. Potential pathobionts such asHaemophilus,PorphyromonasandGemellahad higher abundances during acute-ILI. Stability and changes in the NP microbial community showed individual dynamics. Key core genera,Corynebacterium, MoraxellaandDolosigranulumexhibited higher inter-individual variability in acute-ILI, but showed comparable variability to controls after recovery. Participants in the ILI group with higher core microbiota abundances at the acute phase showed higher microbiota stability after recovery. Our findings demonstrate that acute-ILI is associated with alterations in the phylogenetic structure of the NP microbiota in older adults. The variation in the core microbiota suggests imbalances in the ecosystem, which could potentially play a role in the susceptibility and recovery of the NP microbiota after an ILI event.
Classification of regular and chaotic motions in Hamiltonian systems with deep learning
Authors: Celletti Alessandra , Gales Catalin , Rodriguez-Fernandez Victor , Vasile Massimiliano 
Published: February 2022

AbstractThis paper demonstrates the capabilities of convolutional neural networks (CNNs) at classifying types of motion starting from time series, without any prior knowledge of the underlying dynamics. The paper applies different forms of deep learning to problems of increasing complexity with the goal of testing the ability of different deep learning architectures at predicting the character of the dynamics by simply observing a time-ordered set of data. We will demonstrate that a properly trained CNN can correctly classify the types of motion on a given data set. We also demonstrate effective generalisation capabilities by using a CNN trained on one dynamic model to predict the character of the motion governed by another dynamic model. The ability to predict types of motion from observations is then verified on a model problem known as the forced pendulum and on a relevant problem in Celestial Mechanics where observational data can be used to predict the long-term evolution of the system.
APOE genetics influence murine gut microbiome
Authors: Zajac Diana J. , Green Stefan J. , Johnson Lance A. , Estus Steven 
Published: February 2022

AbstractApolipoprotein E (APOE) alleles impact pathogenesis and risk for multiple human diseases, making them primary targets for disease treatment and prevention. Previously, we and others reported an association between APOE alleles and the gut microbiome. Here, we evaluated effects of APOE heterozygosity and tested whether these overall results extended to mice maintained under ideal conditions for microbiome analyses. To model human APOE alleles, this study used APOE targeted replacement (TR) mice on a C57Bl/6 background. To minimize genetic drift, homozygous APOE3 mice were crossed to homozygous APOE2 or homozygous APOE4 mice prior to the study, and the resulting heterozygous progeny crossed further to generate the study mice. To maximize environmental homogeneity, mice with mixed genotypes were housed together and used bedding from the cages was mixed and added back as a portion of new bedding. Fecal samples were obtained from mice at 3-, 5- and 7-months of age, and microbiota analyzed by 16S ribosomal RNA gene amplicon sequencing. Linear discriminant analysis of effect size (LefSe) identified taxa associated with APOE status, depicted as cladograms to show phylogenetic relatedness. The influence of APOE status was tested on alpha-diversity (Shannon H index) and beta-diversity (principal coordinate analyses and PERMANOVA). Individual taxa associated with APOE status were identified by classical univariate analysis. Whether findings in the APOE mice were replicated in humans was evaluated by using published microbiome genome wide association data. Cladograms revealed robust differences with APOE in male mice and limited differences in female mice. The richness and evenness (alpha-diversity) and microbial community composition (beta-diversity) of the fecal microbiome was robustly associated with APOE status in male but not female mice. Classical univariate analysis revealed individual taxa that were significantly increased or decreased with APOE, illustrating a stepwise APOE2-APOE3–APOE4 pattern of association with heterozygous animals trending as intermediate in the stepwise pattern. The relative abundance of bacteria from the class Clostridia, order Clostridiales, family Ruminococacceae and related genera increased with APOE2 status. The relative abundance of Erysipelotrichia increased with APOE4 status, a finding that extended to humans. In this study, wherein mice were maintained in an ideal fashion for microbiome studies, gut microbiome profiles were strongly and significantly associated with APOE status in male APOE-TR mice. Erysipelotrichia are increased with APOE4 in both mice and humans. APOE allelic effects appeared generally intermediate in heterozygous animals. Further evaluation of these findings in humans, as well as studies evaluating the impact of the APOE-associated microbiota on disease-relevant phenotypes, will be necessary to determine if alterations in the gut microbiome represent a novel mechanism whereby APOE alleles impact disease.
Endogenous controls and microRNA profile in female patients with obstructive sleep apnea
Authors: Zapater Andrea , Benítez Iván D. , Santamaria-Martos Fernando , Pinilla Lucía , Targa Adriano , De Gonzalo-Calvo David , Torres Gerard , Mínguez Olga , Cortijo Anunciación , Dalmases Mireia , Barbé Ferrán , Sánchez-de-la-Torre Manuel 
Published: February 2022

AbstractRecent studies have evaluated the potential of circulating microRNAs (miRNAs) as valuable biomarkers for characterizing obstructive sleep apnea (OSA) in males. The potential use of miRNAs as clinical indicators in females is unknown. The objective is to identify a set of miRNAs to be used as endogenous controls (ECs) in female patients with OSA. Then, to analyze differences in the miRNA expression profile between patients with and without OSA. This observational, longitudinal study included 85 females with suspected OSA who underwent a polysomnography. OSA was defined as an apnea hypopnea index ≥ 15 events/h. The study population was stratified into 50 OSA patients and 38 non-OSA patients. Exploratory expression profiling of 188 miRNAs consistent and reliable in plasma was performed in a discovery cohort of 21 patients by TaqMan-Low-Density-Array (TLDA). The best ECs were identified by mean centre + standard deviation normalization and concordance correlation restricted normalization. Differentially expressed candidate miRNAs were selected for RT-qPCR validation in a validation cohort of 64 patients. Three circulating miRNAs (miR-30a-5p, miR-93-3p and miR-532-5p) were identified as most stable for use as ECs. Twenty-seven miRNA candidates were identified as potential biomarkers for OSA screening (p value < 0.025) in the TLDA cohort. However, validation cohort showed no differences in the circulating miRNA profile in female patients with and without OSA. We identified a set of ECs in females with OSA that may contribute to result homogeneity in determining circulating miRNAs. Exploratory analysis did not identify a significantly miRNA profile between female patients with and without OSA.
Single bout of exercise triggers the increase of vitamin D blood concentration in adolescent trained boys: a pilot study
Authors: Dzik Katarzyna Patrycja , Grzywacz Tomasz , Łuszczyk Marcin , Kujach Sylwester , Flis Damian Józef , Kaczor Jan Jacek 
Published: February 2022

AbstractVitamin D is necessary for musculoskeletal health, however, the supplementation of vitamin D above the sufficiency level does not bring additional bone mass density (BMD), unlike physical exercise which enhances the bone formatting process. Regular physical activity has been shown to upregulate VDR expression in muscles and to increase circulating vitamin D. Here we investigate whether a single bout of exercise might change 25(OH)D3 blood concentration and how it affects metabolic response to exercise. Twenty-six boys, 13.8 years old (SD ± 0.7) soccer players, participated in the study. The participants performed one of two types of exercise: the first group performed the VO2max test until exhaustion, and the second performed three times the repeated 30 s Wingate Anaerobic Test (WAnT). Blood was collected before, 15 min and one hour after the exercise. The concentration of 25(OH)D3, parathyroid hormone (PTH), interleukin-6 (IL-6), lactate, non-esterified fatty acids (NEFA) and glycerol were determined. 25(OH)D3 concentration significantly increased after the exercise in all boys. The most prominent changes in 25(OH)D3, observed after WAnT, were associated with the rise of PTH. The dimensions of response to the exercises observed through the changes in the concentration of 25(OH)D3, PTH, NEFA and glycerol were associated with the significant increases of IL-6 level. A single bout of exercise may increase the serum’s 25(OH)D3 concentration in young trained boys. The intensive interval exercise brings a more potent stimulus to vitamin D fluctuations in young organisms. Our results support the hypothesis that muscles may both store and release 25(OH)D3.
Effects of congested fixture and matches’ participation on internal and external workload indices in professional soccer players
Authors: Garcia Gabriel Rodrigues , Gonçalves Luiz Guilherme C. , Clemente Filipe Manuel , Nakamura Fábio Yuzo , Nobari Hadi , Bedo Bruno Luiz Souza , Azevedo Angelo Melim , Guerra Mauro Antonio , Aquino Rodrigo 
Published: February 2022

AbstractThis study aimed to verify the effects of congested fixture and matches’ participation on internal and external workload indexes in professional Brazilian soccer players. Rate of perceived exertion-based training load (sRPE), distance- and accelerometry-based measures were daily monitored over 119 training sessions and 33 matches performed by 29 male outfield players. Weeks were classified as congested (n = 11, two or more matches within a 7-day period) and regular (n = 10, one match or less within a 7-day period). The players were divided based on the matches’ participation: (1) players who played ≥ 60 min (G1); (2) players who played < 60 min (G2); (3) players who did not participate of the match (G3). The findings showed that independent of the levels of participation during the matches, regular weeks presented greater acute, monotony, and strain indices for internal and external workload measures than congested weeks. The G1 presented the highest values for most of the workload indices in both regular and congested weeks, except for monotony indices (internal and external load) that G2 showed greater values than G1 and G3. Coaches and practitioners should plan the training “doses” to reduce disparities of these different match’s participation and congested schedule weeks.
Thermo-mechanical behavior measurement of polymer-bonded sugar under shock compression using in-situ time-resolved Raman spectroscopy
Authors: Dhiman Abhijeet , Lewis Nolan S. , Olokun Ayotomi , Dlott Dana D. , Tomar Vikas 
Published: February 2022

AbstractQuantitative information regarding the local behavior of interfaces in an inhomogeneous material during shock loading is limited due to challenges associated with time and spatial resolution. This paper reports the development of a novel method for in-situ measurement of the thermo-mechanical response of polymer bonded sugar composite where measurements are performed during propagagtion of shock wave in sucrose crystal through polydimethylsiloxane binder. The time-resolved measurements were performed with 5 ns resolution providing an estimation on local pressure, temperature, strain rate, and local shock viscosity. The experiments were performed at two different impact velocities to induce shock pressure of 4.26 GPa and 2.22 GPa and strain rate greater than 106/s. The results show the solid to the liquid phase transition of sucrose under shock compression. The results are discussed with the help of fractography analyses of sucrose crystal in order to obtain insights into the underlying heat generation mechanism.
Detecting major introgressions in wheat and their putative origins using coverage analysis
Authors: Keilwagen Jens , Lehnert Heike , Berner Thomas , Badaeva Ekaterina , Himmelbach Axel , Börner Andreas , Kilian Benjamin 
Published: February 2022

AbstractIntrogressions from crop wild relatives (CWRs) have been used to introduce beneficial traits into cultivated plants. Introgressions have traditionally been detected using cytological methods. Recently, single nucleotide polymorphism (SNP)-based methods have been proposed to detect introgressions in crosses for which both parents are known. However, for unknown material, no method was available to detect introgressions and predict the putative donor species. Here, we present a method to detect introgressions and the putative donor species. We demonstrate the utility of this method using 10 publicly available wheat genome sequences and identify nine major introgressions. We show that the method can distinguish different introgressions at the same locus. We trace introgressions to early wheat cultivars and show that natural introgressions were utilised in early breeding history and still influence elite lines today. Finally, we provide evidence that these introgressions harbour resistance genes.
Automated meta-analysis of the event-related potential (ERP) literature
Authors: Donoghue Thomas , Voytek Bradley 
Published: February 2022

AbstractEvent-related potentials (ERPs) are a common approach for investigating the neural basis of cognition and disease. There exists a vast and growing literature of ERP-related articles, the scale of which motivates the need for efficient and systematic meta-analytic approaches for characterizing this research. Here we present an automated text-mining approach as a form of meta-analysis to examine the relationships between ERP terms, cognitive domains and clinical disorders. We curated dictionaries of terms, collected articles of interest, and measured co-occurrence probabilities in published articles between ERP components and cognitive and disorder terms. Collectively, this literature dataset allows for creating data-driven profiles for each ERP, examining key associations of each component, and comparing the similarity across components, ultimately allowing for characterizing patterns and associations between topics and components. Additionally, by examining large literature collections, novel analyses can be done, such as examining how ERPs of different latencies relate to different cognitive associations. This openly available dataset and project can be used both as a pedagogical tool, and as a method of inquiry into the previously hidden structure of the existing literature. This project also motivates the need for consistency in naming, and for developing a clear ontology of electrophysiological components.
Galectin-3 as a potential prognostic biomarker of severe COVID-19 in SARS-CoV-2 infected patients
Authors: Cervantes-Alvarez Eduardo , la Rosa Nathaly Limon-de , la Mora Moises Salgado-de , Valdez-Sandoval Paola , Palacios-Jimenez Mildred , Rodriguez-Alvarez Fatima , Vera-Maldonado Brenda I. , Aguirre-Aguilar Eduardo , Escobar-Valderrama Juan Manuel , Alanis-Mendizabal Jorge , Méndez-Guerrero Osvely , Tejeda-Dominguez Farid , Torres-Ruíz Jiram , Gómez-Martín Diana , Colborn Kathryn L. , Kershenobich David , Huang Christene A. , Navarro-Alvarez Nalu 
Published: February 2022

AbstractSevere COVID-19 is associated with a systemic hyperinflammatory response leading to acute respiratory distress syndrome (ARDS), multi-organ failure, and death. Galectin-3 is a ß-galactoside binding lectin known to drive neutrophil infiltration and the release of pro-inflammatory cytokines contributing to airway inflammation. Thus, we aimed to investigate the potential of galectin-3 as a biomarker of severe COVID-19 outcomes. We prospectively included 156 patients with RT-PCR confirmed COVID-19. A severe outcome was defined as the requirement of invasive mechanical ventilation (IMV) and/or in-hospital death. A non-severe outcome was defined as discharge without IMV requirement. We used receiver operating characteristic (ROC) and multivariable logistic regression analysis to determine the prognostic ability of serum galectin-3 for a severe outcome. Galectin-3 levels discriminated well between severe and non-severe outcomes and correlated with markers of COVID-19 severity, (CRP, NLR, D-dimer, and neutrophil count). Using a forward-stepwise logistic regression analysis we identified galectin-3 [odds ratio (OR) 3.68 (95% CI 1.47–9.20),p < 0.01] to be an independent predictor of severe outcome. Furthermore, galectin-3 in combination with CRP, albumin and CT pulmonary affection > 50%, had significantly improved ability to predict severe outcomes [AUC 0.85 (95% CI 0.79–0.91,p < 0.0001)]. Based on the evidence presented here, we recommend clinicians measure galectin-3 levels upon admission to facilitate allocation of appropriate resources in a timely manner to COVID-19 patients at highest risk of severe outcome.
Clinical data classification with noisy intermediate scale quantum computers
Authors: Moradi S. , Brandner C. , Spielvogel C. , Krajnc D. , Hillmich S. , Wille R. , Drexler W. , Papp L. 
Published: February 2022

AbstractQuantum machine learning has experienced significant progress in both software and hardware development in the recent years and has emerged as an applicable area of near-term quantum computers. In this work, we investigate the feasibility of utilizing quantum machine learning (QML) on real clinical datasets. We propose two QML algorithms for data classification on IBM quantum hardware: a quantum distance classifier (qDS) and a simplified quantum-kernel support vector machine (sqKSVM). We utilize these different methods using the linear time quantum data encoding technique ($${\mathrm{log}}_{2}N$$

log
2

N

) for embedding classical data into quantum states and estimating the inner product on the 15-qubit IBMQ Melbourne quantum computer. We match the predictive performance of our QML approaches with prior QML methods and with their classical counterpart algorithms for three open-access clinical datasets. Our results imply that the qDS in small sample and feature count datasets outperforms kernel-based methods. In contrast, quantum kernel approaches outperform qDS in high sample and feature count datasets. We demonstrate that the $${\mathrm{log}}_{2}N$$

log
2

N

encoding increases predictive performance with up to + 2% area under the receiver operator characteristics curve across all quantum machine learning approaches, thus, making it ideal for machine learning tasks executed in Noisy Intermediate Scale Quantum computers.