Trends of hypercholesterolemia change in Shenzhen, China during 1997-2018

To demonstrate the trends of hypercholesterolemia change in Shenzhen, China from 1997 to 2018. Participants were residents aged 18 to 69 years in Shenzhen, China, and were recruited using multi-stage cluster sampling. All participants were surveyed about their socio-demographics, lifestyle, occupation, mental health, and social support. Physical measurements and blood samples for subsequent measurements were collected according to a standardized protocol. A total of 26,621 individuals participated in the three surveys with 8,266 in 1997, 8,599 in 2009, and 9,756 in 2018. In both women and men, there was a significant downward linear trend in age-adjusted mean high-density lipoprotein-cholesterol (HDL-C) from 1997 to 2018 (women: 0.17 ± 0.06, p = 0.008 vs. men: 0.21 ± 0.04, p < 0.001). In contrast, the age-adjusted total triglycerides and total cholesterol in both sexes have demonstrated an increasing trend in the past two decades. However, no significant changes in age-adjusted low-density lipoprotein-cholesterol (LDL-C) in both men and women between 2009 and 2018 were found (women: 0.00 ± 0.02, p = 0.85 vs. men 0.02 ± 0.03, p = 0.34). The age-adjusted prevalence of hypercholesterolemia observed a rapid rise from 1997 to 2009 and appeared to be stabilized in 2018, which was similar to the trend of the prevalence of high total triglycerides in women. Changes in trends were varied by different types of lipids traits. Over the observed decades, there was a clear increasing trend of prevalence of low HDL-C (<1.04 mmol/L) in both sexes (women: 8.8% in 1997 and doubled to reach 17.5% in 2018 vs. men was 22.1% in 1997 and increased to 39.1% in 2018), particularly among younger age groups. Hence, a bespoke public health strategy aligned with the characteristics of lipids epidemic considered by sex and age groups needs to be developed and implemented

Forecasting Carbon Emissions Related to Energy Consumption in Beijing-Tianjin-Hebei Region Based on Grey Prediction Theory and Extreme Learning Machine Optimized by Support Vector Machine Algorithm

Carbon emissions and environmental protection issues have brought pressure from the international community during Chinese economic development. Recently, Chinese Government announced that carbon emissions per unit of GDP would fall by 60&ndash;65% compared with 2005 and non-fossil fuel energy would account for 20% of primary energy consumption by 2030. The Beijing-Tianjin-Hebei region is an important regional energy consumption center in China, and its energy structure is typically coal-based which is similar to the whole country. Therefore, forecasting energy consumption related carbon emissions is of great significance to emissions reduction and upgrading of energy supply in the Beijing-Tianjin-Hebei region. Thus, this study thoroughly analyzed the main energy sources of carbon emissions including coal, petrol, natural gas, and coal power in this region. Secondly, the kernel function of the support vector machine was applied to the extreme learning machine algorithm to optimize the connection weight matrix between the original hidden layer and the output layer. Thirdly, the grey prediction theory was used to predict major energy consumption in the region from 2017 to 2030. Then, the energy consumption and carbon emissions data for 2000&ndash;2016 were used as the training and test sets for the SVM-ELM (Support Vector Machine-Extreme Learning Machine) model. The result of SVM-ELM model was compared with the forecasting results of SVM (Support Vector Machine Algorithm) and ELM (Extreme Learning Machine) algorithm. The accuracy of SVM-ELM was shown to be higher. Finally, we used forecasting output of GM (Grey Prediction Theory) (1, 1) as the input of the SVM-ELM model to predict carbon emissions in the region from 2017 to 2030. The results showed that the proportion of energy consumption seriously affects the amount of carbon emissions. We found that the energy consumption of electricity and natural gas will reach 45% by 2030 and carbon emissions in the region can be controlled below 96.9 million tons. Therefore, accelerating the upgradation of industrial structure will be the key task for the government in controlling the amount of carbon emissions in the next step

Toward Efficient Online Scheduling for Distributed Machine Learning Systems

Recent years have witnessed a rapid growth of distributed machine learning (ML) frameworks, which exploit the massive parallelism of computing clusters to expedite ML training. However, the proliferation of distributed ML frameworks also introduces many unique technical challenges in computing system design and optimization. In a networked computing cluster that supports a large number of training jobs, a key question is how to design efficient scheduling algorithms to allocate workers and parameter servers across different machines to minimize the overall training time. Toward this end, in this paper, we develop an online scheduling algorithm that jointly optimizes resource allocation and locality decisions. Our main contributions are three-fold: i) We develop a new analytical model that considers both resource allocation and locality; ii) Based on an equivalent reformulation and observations on the worker-parameter server locality configurations, we transform the problem into a mixed packing and covering integer program, which enables approximation algorithm design; iii) We propose a meticulously designed approximation algorithm based on randomized rounding and rigorously analyze its performance. Collectively, our results contribute to the state of the art of distributed ML system optimization and algorithm design.Comment: IEEE Transactions on Network Science and Engineering (TNSE), accepted in July 2021, to appea

Additional file 1: of ART manipulation after controlled ovarian stimulation may not increase the risk of abnormal expression and DNA methylation at some CpG sites of H19,IGF2 and SNRPN in foetuses: a pilot study

Sequences of primers used for pyrosequencing reactions and the sequence to analyse. (DOCX 18 kb