Efficient and Direct Inference of Heart Rate Variability using Both Signal Processing and Machine Learning

Heart Rate Variability (HRV) measures the variation of the time between consecutive heartbeats and is a major indicator of physical and mental health. Recent research has demonstrated that photoplethysmography (PPG) sensors can be used to infer HRV. However, many prior studies had high errors because they only employed signal processing or machine learning (ML), or because they indirectly inferred HRV, or because there lacks large training datasets. Many prior studies may also require large ML models. The low accuracy and large model sizes limit their applications to small embedded devices and potential future use in healthcare. To address the above issues, we first collected a large dataset of PPG signals and HRV ground truth. With this dataset, we developed HRV models that combine signal processing and ML to directly infer HRV. Evaluation results show that our method had errors between 3.5% to 25.7% and outperformed signal-processing-only and ML-only methods. We also explored different ML models, which showed that Decision Trees and Multi-level Perceptrons have 13.0% and 9.1% errors on average with models at most hundreds of KB and inference time less than 1ms. Hence, they are more suitable for small embedded devices and potentially enable the future use of PPG-based HRV monitoring in healthcare

Exploring the impact of “double carbon target” on environmental efficiency of coal cities in China

Chinese government proposed the “Double Carbon Target” (DCT) in 2020 to deal with the increasing global warming crisis. In this regard, the study identifies temporal and spatial evolution characteristics of environmental efficiency through the DEA-SBM model and further explores the impact of DCT on the environmental efficiency of coal cities using scenario analysis method. Empirical results show that: 1) Both economic efficiency and environmental efficiency of China’s coal cities are first rising and then falling during the period 2003–2022, and the gap between coal cities and non-coal cities was very small before 2011, but it begins to be enlarged after 2011. The main reason is environmental regulation has exerted a significant impact on coal cities; 2) the difference in environmental efficiency among coal cities is huge due to their policies for supporting renewable energy. Some cities have broken carbon lock-in by the favorite policy for renewable energy, while others have been trapped into path dependence on the coal-related industry; 3) generally, the more amount of emission reduction required, the lower the environmental efficiency of coal cities in the carbon neutralization scenario. Furthermore, some cities rich of renewable energy resources, such as Erdos, and Xuzhou, still have better environmental performance under different carbon neutralization scenarios, while others will encounter many transformation barriers and even may cause a social crisis. Therefore, it is suggested that some coal cities in northwest China can vigorously develop solar energy to improve environmental efficiency

PPG-based Heart Rate Estimation with Efficient Sensor Sampling and Learning Models

Recent studies showed that Photoplethysmography (PPG) sensors embedded in wearable devices can estimate heart rate (HR) with high accuracy. However, despite of prior research efforts, applying PPG sensor based HR estimation to embedded devices still faces challenges due to the energy-intensive high-frequency PPG sampling and the resource-intensive machine-learning models. In this work, we aim to explore HR estimation techniques that are more suitable for lower-power and resource-constrained embedded devices. More specifically, we seek to design techniques that could provide high-accuracy HR estimation with low-frequency PPG sampling, small model size, and fast inference time. First, we show that by combining signal processing and ML, it is possible to reduce the PPG sampling frequency from 125 Hz to only 25 Hz while providing higher HR estimation accuracy. This combination also helps to reduce the ML model feature size, leading to smaller models. Additionally, we present a comprehensive analysis on different ML models and feature sizes to compare their accuracy, model size, and inference time. The models explored include Decision Tree (DT), Random Forest (RF), K-nearest neighbor (KNN), Support vector machines (SVM), and Multi-layer perceptron (MLP). Experiments were conducted using both a widely-utilized dataset and our self-collected dataset. The experimental results show that our method by combining signal processing and ML had only 5% error for HR estimation using low-frequency PPG data. Moreover, our analysis showed that DT models with 10 to 20 input features usually have good accuracy, while are several magnitude smaller in model sizes and faster in inference time

Efficacy and safety of acupuncture-point stimulation combined with opioids for the treatment of moderate to severe cancer pain: a network meta-analysis of randomized controlled trials

BackgroundPain is one of the most common and troublesome symptoms of cancer. Although potential positive effects of acupuncture-point stimulation (APS) on cancer pain have been observed, knowledge regarding the selection of the optimal APS remains unclear because of a lack of evidence from head-to-head randomized controlled trials (RCTs).ObjectiveThis study aimed to carry out a network meta-analysis to compare the efficacy and safety of different APS combined with opioids in treating moderate to severe cancer pain and rank these methods for practical consideration.MethodsA comprehensive search of eight electronic databases was conducted to obtain RCTs involving different APS combined with opioids for moderate to severe cancer pain. Data were screened and extracted independently using predesigned forms. The quality of RCTs was appraised with the Cochrane Collaboration risk-of-bias tool. The primary outcome was the total pain relief rate. Secondary outcomes were the total incidence of adverse reactions, the incidence of nausea and vomiting, and the incidence of constipation. We applied a frequentist, fixed-effect network meta-analysis model to pool effect sizes across trials using rate ratios (RR) with their 95% confidence intervals (CI). Network meta-analysis was performed using Stata/SE 16.0.ResultsWe included 48 RCTs, which consisted of 4,026 patients, and investigated nine interventions. A network meta-analysis showed that a combination of APS and opioids was superior in relieving moderate to severe cancer pain and reducing the incidence of adverse reactions such as nausea, vomiting, and constipation compared to opioids alone. The ranking of total pain relief rates was as follows: fire needle (surface under the cumulative ranking curve (SUCRA) = 91.1%), body acupuncture (SUCRA = 85.0%), point embedding (SUCRA = 67.7%), auricular acupuncture (SUCRA = 53.8%), moxibustion (SUCRA = 41.9%), transcutaneous electrical acupoint stimulation (TEAS) (SUCRA = 39.0%), electroacupuncture (SUCRA = 37.4%), and wrist–ankle acupuncture (SUCRA = 34.1%). The ranking of total incidence of adverse reactions was as follows: auricular acupuncture (SUCRA = 23.3%), electroacupuncture (SUCRA = 25.1%), fire needle (SUCRA = 27.2%), point embedding (SUCRA = 42.6%), moxibustion (SUCRA = 48.2%), body acupuncture (SUCRA = 49.8%), wrist–ankle acupuncture (SUCRA = 57.8%), TEAS (SUCRA = 76.3%), and opioids alone (SUCRA = 99.7%).ConclusionsAPS seemed to be effective in relieving cancer pain and reducing opioid-related adverse reactions. Fire needle combined with opioids may be a promising intervention to reduce moderate to severe cancer pain as well as reduce opioid-related adverse reactions. However, the evidence was not conclusive. More high-quality trials investigating the stability of evidence levels of different interventions on cancer pain must be conducted.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/#searchadvanced, identifier CRD42022362054

Epidemiological investigation, determination of related factors, and spatial-temporal cluster analysis of wild type pseudorabies virus seroprevalence in China during 2022

IntroductionPseudorabies virus (PRV) is a linear DNA virus with a double-stranded structure, capable of infecting a diverse array of animal species, including humans. This study sought to ascertain the seroprevalence of Pseudorabies Virus (PRV) in China by conducting a comprehensive collection of blood samples from 16 provinces over the course of 2022.MethodsThe presence of PRV gE antibodies was detected through the utilization of an enzyme-linked immunosorbent assay (ELISA) technique. Logistic regression analysis was conducted to identify potential related factors associated with the serologic status of PRV gE at the animal level. Additionally, the SaTScan 10.1 software was used to analyze the spatial and temporal clusters of PRV gE seroprevalence.ResultsA comprehensive collection of 161,880 samples was conducted, encompassing 556 swine farms throughout the country. The analysis revealed that the seroprevalence of PRV gE antibodies was 12.36% (95% confidence interval [CI], 12.20% to 12.52%) at the individual animal level. However, at the swine farm level, the seroprevalence was considerably higher, reaching 46.22% (95% CI, 42.08% to 50.37%). Related factors for PRV infection at the farm level included the geographic distribution of farms and seasonal variables. Moreover, five distinct high seroprevalence clusters of PRV gE were identified across China, with the peak prevalence observed during the months of April through June 2022.ConclusionOur findings serve as a valuable addition to existing research on the seroprevalence, related factors, and temporal clustering of PRV gE in China. Furthermore, our study provides a reference point for the development of effective strategies for the prevention and control of pseudorabies and wild virus outbreaks

Genome-wide identification of nitrate-responsive microRNAs by small RNA sequencing in the rice restorer cultivar Nanhui 511

Rice productivity relies heavily on nitrogen fertilization, and improving nitrogen use efficiency (NUE) is important for hybrid rice breeding. Reducing nitrogen inputs is the key to achieving sustainable rice production and reducing environmental problems. Here, we analyzed the genome-wide transcriptomic changes in microRNAs (miRNAs) in the indica rice restorer cultivar Nanhui 511 (NH511) under high (HN) and low nitrogen (LN) conditions. The results showed that NH511 is sensitive to nitrogen supplies and HN conditions promoted the growth its lateral roots at the seedling stage. Furthermore, we identified 483 known miRNAs and 128 novel miRNAs by small RNA sequencing in response to nitrogen in NH511. We also detected 100 differentially expressed genes (DEGs), including 75 upregulated and 25 downregulated DEGs, under HN conditions. Among these DEGs, 43 miRNAs that exhibited a 2-fold change in their expression were identified in response to HN conditions, including 28 upregulated and 15 downregulated genes. Additionally, some differentially expressed miRNAs were further validated by qPCR analysis, which showed that miR443, miR1861b, and miR166k-3p were upregulated, whereas miR395v and miR444b.1 were downregulated under HN conditions. Moreover, the degradomes of possible target genes for miR166k-3p and miR444b.1 and expression variations were analyzed by qPCR at different time points under HN conditions. Our findings revealed comprehensive expression profiles of miRNAs responsive to HN treatments in an indica rice restorer cultivar, which advances our understanding of the regulation of nitrogen signaling mediated by miRNAs and provides novel data for high-NUE hybrid rice cultivation