Effects of Exercise on Cardiopulmonary Function in Patients with Obstructive Sleep Apnea: A Systematic Review and Meta-Analysis

Obstructive sleep apnea (OSA) is a common sleep disorder characterized by repeated episodes of apnea and hypopnea during sleep. Snoring and daytime sleepiness are the most common manifestations of OSA. Patients with OSA are considered to have poor cardiopulmonary function. Exercise has been proposed as a treatment for OSA that could lower apnea hypopnea indices (AHI) and improve sleep quality. Study shows that constantly aerobic exercise improved cardiopulmonary function in patients with chronic heart failure. However, whether exercise training will benefit cardiopulmonary functioning in patients with OSA is still in doubt. The purpose of this review is to investigate the effect of exercise on cardiopulmonary functioning in adults with OSA by summarizing the results of clinical trials. A systematic review of the PubMed, Web of science, Wan Fang and CNKI databases was conducted for randomized controlled trials (up to October, 2021; language in English or Chinese) comparing exercise treatments to no exercise treatments for patients with OSA. Focused outcomes included AHI, VO2peak, mean oxygen saturations (SaO2mean%), lowest oxygen saturations (SaO2min%), sleep quality (PSQI scale), and quality of life (ESS scale). Pooled data were assessed by using random-effects. This study adhered to the PRISMA guidelines. Of 262 identified studies, 12 were eligible and included in final analysis (N = 530 adult participants). Compared to no exercise treatment, exercise yielded an improved mean reduction in AHI of 6.69 [95%CI: -8.50 to -4.87], an improved mean increases in VO2peak of 0.98 [95%CI: 0.39, 1.57], besides, PSQI and ESS scores decreased by 2.1 [95%CI: -3.95 to 0.24] and 6.69 [95%CI: -8.50 to 4.87], respectively. Although SaO2min% and SaO2mean% were improved by exercise, the improvement was clinically small. Exercise can improve multiple aspects of functioning in patients with OSA, including AHI, sleep quality, quality of life, and cardiopulmonary functions. Exercise is thus recommended as a potential therapeutic strategy to improve conditions of patients with OSA

A consensus linkage map of the grass carp (Ctenopharyngodon idella) based on microsatellites and SNPs

<p>Abstract</p> <p>Background</p> <p>Grass carp (<it>Ctenopharyngodon idella</it>) belongs to the family Cyprinidae which includes more than 2000 fish species. It is one of the most important freshwater food fish species in world aquaculture. A linkage map is an essential framework for mapping traits of interest and is often the first step towards understanding genome evolution. The aim of this study is to construct a first generation genetic map of grass carp using microsatellites and SNPs to generate a new resource for mapping QTL for economically important traits and to conduct a comparative mapping analysis to shed new insights into the evolution of fish genomes.</p> <p>Results</p> <p>We constructed a first generation linkage map of grass carp with a mapping panel containing two F1 families including 192 progenies. Sixteen SNPs in genes and 263 microsatellite markers were mapped to twenty-four linkage groups (LGs). The number of LGs was corresponding to the haploid chromosome number of grass carp. The sex-specific map was 1149.4 and 888.8 cM long in females and males respectively whereas the sex-averaged map spanned 1176.1 cM. The average resolution of the map was 4.2 cM/locus. BLAST searches of sequences of mapped markers of grass carp against the whole genome sequence of zebrafish revealed substantial macrosynteny relationship and extensive colinearity of markers between grass carp and zebrafish.</p> <p>Conclusions</p> <p>The linkage map of grass carp presented here is the first linkage map of a food fish species based on co-dominant markers in the family Cyprinidae. This map provides a valuable resource for mapping phenotypic variations and serves as a reference to approach comparative genomics and understand the evolution of fish genomes and could be complementary to grass carp genome sequencing project.</p

Quantum Neuronal Sensing of Quantum Many-Body States on a 61-Qubit Programmable Superconducting Processor

Classifying many-body quantum states with distinct properties and phases of matter is one of the most fundamental tasks in quantum many-body physics. However, due to the exponential complexity that emerges from the enormous numbers of interacting particles, classifying large-scale quantum states has been extremely challenging for classical approaches. Here, we propose a new approach called quantum neuronal sensing. Utilizing a 61 qubit superconducting quantum processor, we show that our scheme can efficiently classify two different types of many-body phenomena: namely the ergodic and localized phases of matter. Our quantum neuronal sensing process allows us to extract the necessary information coming from the statistical characteristics of the eigenspectrum to distinguish these phases of matter by measuring only one qubit. Our work demonstrates the feasibility and scalability of quantum neuronal sensing for near-term quantum processors and opens new avenues for exploring quantum many-body phenomena in larger-scale systems.Comment: 7 pages, 3 figures in the main text, and 13 pages, 13 figures, and 1 table in supplementary material

Experimental quantum computational chemistry with optimised unitary coupled cluster ansatz

Simulation of quantum chemistry is one of the most promising applications of quantum computing. While recent experimental works have demonstrated the potential of solving electronic structures with variational quantum eigensolver (VQE), the implementations are either restricted to nonscalable (hardware efficient) or classically simulable (Hartree-Fock) ansatz, or limited to a few qubits with large errors for the more accurate unitary coupled cluster (UCC) ansatz. Here, integrating experimental and theoretical advancements of improved operations and dedicated algorithm optimisations, we demonstrate an implementation of VQE with UCC for H_2, LiH, F_2 from 4 to 12 qubits. Combining error mitigation, we produce high-precision results of the ground-state energy with error suppression by around two orders of magnitude. For the first time, we achieve chemical accuracy for H_2 at all bond distances and LiH at small bond distances in the experiment. Our work demonstrates a feasible path towards a scalable solution to electronic structure calculation, validating the key technological features and identifying future challenges for this goal.Comment: 8 pages, 4 figures in the main text, and 29 pages supplementary materials with 16 figure

14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon

Chemistry and materials science are complex. Recently, there have been great successes in addressing this complexity using data-driven or computational techniques. Yet, the necessity of input structured in very specific forms and the fact that there is an ever-growing number of tools creates usability and accessibility challenges. Coupled with the reality that much data in these disciplines is unstructured, the effectiveness of these tools is limited. Motivated by recent works that indicated that large language models (LLMs) might help address some of these issues, we organized a hackathon event on the applications of LLMs in chemistry, materials science, and beyond. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines

Clinical characteristics and oral care needs of perioperative patients with oral cancer

The treatment of oral cancer patients mainly involves surgery in combination with radiotherapy and che⁃ motherapy. This paper reviews the clinical features of perioperative patients with oral cancer, including oral flora imbal⁃ ance, oral complications after radiotherapy and chemotherapy, the presence of oral incisions (and flaps), special dietary needs, and airway management. In connection with the above characteristics, this article analyzes the necessity of three aspects of oral care to improve the patients comfort and prevent pulmonary and surgical site infections, with the goal of providing a reference for oral care research on patients undergoing oral cancer surgery and laying a foundation for the construction of comprehensive oral care programs during the perioperative period for patients with oral cancer

TT-M Finite Element Algorithm for the Coupled Schr&ouml;dinger&ndash;Boussinesq Equations

In this article, the coupled Schr&ouml;dinger&ndash;Boussinesq equations are solved numerically using the finite element method combined with the time two-mesh (TT-M) fast algorithm. The spatial direction is discretized by the standard Galerkin finite element method, the temporal direction is approximated by the TT-M Crank&ndash;Nicolson scheme, and then the numerical scheme of TT-M finite element (FE) system is formulated. The method includes three main steps: for the first step, the nonlinear system is solved on the coarse time mesh; for the second step, by an interpolation formula, the numerical solutions at the fine time mesh point are computed based on the numerical solutions on the coarse mesh system; for the last step, the linearized temporal fine mesh system is constructed based on Taylor&rsquo;s formula for two variables, and then the TT-M FE solutions can be obtained. Furthermore, theory analyses on the TT-M system including the stability and error estimations are conducted. Finally, a large number of numerical examples are provided to verify the accuracy of the algorithm, the correctness of theoretical results, and the computational efficiency with a comparison to the numerical results calculated by using the standard FE method

Data from: MoO2 nanosheets embedded in amorphous carbon matrix for sodium-ion batteries

MoO2 nanosheets embedded in the amorphous carbon matrix (MoO2/C) are successfully synthesized via a facile hydrothermal method and investigated as an anode for sodium-ion batteries. Because of the efficient ion transport channels and good volume change accommodation, MoO2/C delivers a discharge/charge capacity of 367.8/367.0 mAh g−1 with high coulombic efficiency (99.4%) after 100 cycles at a current density of 50 mA g−1

Improving Accuracy of Real-Time Positioning and Path Tracking by Using an Error Compensation Algorithm against Walking Modes

Wide-range application scenarios, such as industrial, medical, rescue, etc., are in various demand for human spatial positioning technology. However, the existing MEMS-based sensor positioning methods have many problems, such as large accuracy errors, poor real-time performance and a single scene. We focused on improving the accuracy of IMU-based both feet localization and path tracing, and analyzed three traditional methods. In this paper, a planar spatial human positioning method based on high-resolution pressure insoles and IMU sensors was improved, and a real-time position compensation method for walking modes was proposed. To validate the improved method, we added two high-resolution pressure insoles to our self-developed motion capture system with a wireless sensor network (WSN) system consisting of 12 IMUs. By multi-sensor data fusion, we implemented dynamic recognition and automatic matching of compensation values for five walking modes, with real-time spatial-position calculation of the touchdown foot, enhancing the 3D accuracy of its practical positioning. Finally, we compared the proposed algorithm with three old methods by statistical analysis of multiple sets of experimental data. The experimental results show that this method has higher positioning accuracy in real-time indoor positioning and path-tracking tasks. The methodology can have more extensive and effective applications in the future