Application of conjugated materials in sports training

In recent years, with the rapid development of the sports industry, the quality of sports training products on the market is uneven. Problems such as inaccurate detection of athletes’ physical indicators, low comfort of sportswear, and reduced satisfaction with sports equipment often occur. To this end, this article proposes to apply conjugated materials with excellent optical, electrical, thermal and other properties to sports training and sports products, by summarizing the properties of conjugated materials and their applications in sports training, explores the potential of conjugated materials in improving athletes’ training effects, monitoring sports status, and improving sports equipment. This article rates the application of conjugated materials in sports training products in terms of comfort, waterproofness, portability, lightness, aesthetics and breathability. The results showed that the average scores of the 20 sports participants on sportswear were 9.0475, 9.0075, 9.01, 9.025, 9.0325 and 9.04 respectively; the average scores on sports shoes were 9.035, 9.055, 9.02, 9.085, 9.0175 and 8.9975 respectively. Research shows that applying conjugated materials to sports training can improve athletes’ performance and contribute to the better development of sports

A Robust Deformable Linear Object Perception Pipeline in 3D: From Segmentation to Reconstruction

3D perception of deformable linear objects (DLOs) is crucial for DLO manipulation. However, perceiving DLOs in 3D from a single RGBD image is challenging. Previous DLO perception methods fail to extract a decent 3D DLO model due to different textures, occlusions, sparse and false depth information. To address these problems and provide a more robust DLO perception initialization for downstream tasks like tracking and manipulation in complex scenarios, this paper proposes a 3D DLO perception pipeline to first segment a DLO in 2D images and post-process masks to eliminate false positive segmentation, reconstruct the DLO in 3D space to predict the occluded part of the DLO, and physically smooth the reconstructed DLO. By testing on a synthetic DLO dataset and further validating on a real-world dataset with seven different DLOs, we demonstrate that the proposed method is an effective and robust 3D perception pipeline solution with better performance on 2D DLO segmentation and 3D DLO reconstruction compared to State-of-the-Art algorithms

Development and Characterization of EST-SSR Markers From RNA-Seq Data in Phyllostachys violascens

Bamboo are woody grass species containing important economic and ecological values. Lei bamboo (Phyllostachys violascens) is a kind of shoot-producing bamboo species with the highest economic yield per unit area. However, identifying different varieties of Lei bamboo based on morphological characteristics is difficult. Microsatellites play an important role in plant identification and genetic diversity analysis and are superior to other molecular markers. In this study, we identified 18,356 expressed sequence tag-simple sequence repeat (EST-SSR) loci in Lei bamboo transcriptome data. A total of 11,264 primer pairs were successfully designed from unigenes of all EST-SSR loci, and 96 primer pairs were randomly selected and synthesized. A total of 54 primer pairs were used for classifying 16 Lei bamboo varieties and 10 different Phyllostachys species. The number of polymorphism alleles among the 54 primer pairs ranged from 3 to 12 for P. violascens varieties and 3 to 20 for Phyllostachys. The phylogenetic tree based on polymorphism alleles successfully distinguished 16 P. violascens varieties and 10 Phyllostachys species. Our study provides abundant EST-SSR resources that are useful for genetic diversity analysis and molecular verification of bamboo and suggests that SSR markers developed from Lei bamboo are more efficient and reliable than ISSR, SRAP or AFLP markers

The spatiotemporal features of Greenhouse Gases Emissions from Biomass Burning in China from 2000-2012

Greenhouse gases emissions from biomass burning have been given a little attention, especially the spatiotemporal features of biomass burning sources and greenhouse gases emissions have not been comprehensively uncovered. This research undertook IPCC bottom-up inventory guideline to estimate Chinese greenhouse gases emissions from biomass burning and applied geographical information system to reveal biomass burning emissions spatiotemporal features. The purposes were to quantify greenhouse gases emissions from various biomass burning sources and to uncover the spatial and temporal emissions features so to deliver future policy implications in China. The results showed that the average annual biomass burning emissions in China from 2000-2012 were 880.66 Mt for CO2, 96.59 Mt CO2-eq for CH4, and 16.81 Mt CO2-eq for N2O. The spatial pattern of biomass greenhouse gases emissions showed about 50 % of national emission were in the east and south-central regions. The majority of biomass burning emissions were from firewood and crop residues, which accounted for more than 90 % of national biomass burning emissions. All types of biomass burning emissions exhibited similar temporal trends from 2000-2012, with strong inter-annual variability and fluctuant increase. The large grassland and forest fires induced the significant greenhouse gases emissions peaks in the years of 2001, 2003 and 2006. We found that biofuel burning, with low combustion efficiency, is the major emission source. Open burning of biomass was widespread in China, and east and south-central regions were the major distribution of biomass burning greenhouse gases emission. Optimized design for improving the efficiency of biomass utilization and making emission control policy combination with its spatiotemporal features will be the effective way to reduce the biomass burning emissions

Application of Machine Learning Algorithms to Predict Central Lymph Node Metastasis in T1-T2, Non-invasive, and Clinically Node Negative Papillary Thyroid Carcinoma

Purpose: While there are no clear indications of whether central lymph node dissection is necessary in patients with T1-T2, non-invasive, clinically uninvolved central neck lymph nodes papillary thyroid carcinoma (PTC), this study seeks to develop and validate models for predicting the risk of central lymph node metastasis (CLNM) in these patients based on machine learning algorithms. Methods: This is a retrospective study comprising 1,271 patients with T1-T2 stage, non-invasive, and clinically node negative (cN0) PTC who underwent surgery at the Department of Endocrine and Breast Surgery of The First Affiliated Hospital of Chongqing Medical University from February 1, 2016, to December 31, 2018. We applied six machine learning (ML) algorithms, including Logistic Regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), and Neural Network (NNET), coupled with preoperative clinical characteristics and intraoperative information to develop prediction models for CLNM. Among all the samples, 70% were randomly selected to train the models while the remaining 30% were used for validation. Indices like the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and accuracy were calculated to test the models' performance. Results: The results showed that ~51.3% (652 out of 1,271) of the patients had pN1 disease. In multivariate logistic regression analyses, gender, tumor size and location, multifocality, age, and Delphian lymph node status were all independent predictors of CLNM. In predicting CLNM, six ML algorithms posted AUROC of 0.70–0.75, with the extreme gradient boosting (XGBoost) model standing out, registering 0.75. Thus, we employed the best-performing ML algorithm model and uploaded the results to a self-made online risk calculator to estimate an individual's probability of CLNM (https://jin63.shinyapps.io/ML_CLNM/). Conclusions: With the incorporation of preoperative and intraoperative risk factors, ML algorithms can achieve acceptable prediction of CLNM with Xgboost model performing the best. Our online risk calculator based on ML algorithm may help determine the optimal extent of initial surgical treatment for patients with T1-T2 stage, non-invasive, and clinically node negative PTC

Research progress in cardiotoxicity of organophosphate esters

Organophosphate esters (OPEs) have been extensively utilized worldwide as a substitution for brominated flame retardants. With an increased awareness of the need for environmental protection, the potential health risks and ecological hazards of OPEs have attracted widespread attention. As the dynamic organ of the circulatory system, the heart plays a significant role in maintaining normal life activities. Currently, there is a lack of systematic appraisal of the cardiotoxicity of OPEs. This article summarized the effects of OPEs on the morphological structure and physiological functions of the heart. It is found that these chemicals can lead to pericardial edema, abnormal looping, and thinning of atrioventricular walls in the heart, accompanied by alterations in heart rate, with toxic effects varying by the OPE type. These effects are primarily associated with the activation of endoplasmic reticulum stress response, the perturbation of cytoplasmic and intranuclear signal transduction pathways in cardiomyocytes. This paper provides a theoretical basis for further understanding of the toxic effects of OPEs and contributes to environmental protection and OPEs’ ecological risk assessment

Enhancing Event Sequence Modeling with Contrastive Relational Inference

Neural temporal point processes(TPPs) have shown promise for modeling continuous-time event sequences. However, capturing the interactions between events is challenging yet critical for performing inference tasks like forecasting on event sequence data. Existing TPP models have focused on parameterizing the conditional distribution of future events but struggle to model event interactions. In this paper, we propose a novel approach that leverages Neural Relational Inference (NRI) to learn a relation graph that infers interactions while simultaneously learning the dynamics patterns from observational data. Our approach, the Contrastive Relational Inference-based Hawkes Process (CRIHP), reasons about event interactions under a variational inference framework. It utilizes intensity-based learning to search for prototype paths to contrast relationship constraints. Extensive experiments on three real-world datasets demonstrate the effectiveness of our model in capturing event interactions for event sequence modeling tasks.Comment: 6 pages, 2 figure