Arrhythmia Classifier Based on Ultra-Lightweight Binary Neural Network

Reasonably and effectively monitoring arrhythmias through ECG signals has significant implications for human health. With the development of deep learning, numerous ECG classification algorithms based on deep learning have emerged. However, most existing algorithms trade off high accuracy for complex models, resulting in high storage usage and power consumption. This also inevitably increases the difficulty of implementation on wearable Artificial Intelligence-of-Things (AIoT) devices with limited resources. In this study, we proposed a universally applicable ultra-lightweight binary neural network(BNN) that is capable of 5-class and 17-class arrhythmia classification based on ECG signals. Our BNN achieves 96.90% (full precision 97.09%) and 97.50% (full precision 98.00%) accuracy for 5-class and 17-class classification, respectively, with state-of-the-art storage usage (3.76 KB and 4.45 KB). Compared to other binarization works, our approach excels in supporting two multi-classification modes while achieving the smallest known storage space. Moreover, our model achieves optimal accuracy in 17-class classification and boasts an elegantly simple network architecture. The algorithm we use is optimized specifically for hardware implementation. Our research showcases the potential of lightweight deep learning models in the healthcare industry, specifically in wearable medical devices, which hold great promise for improving patient outcomes and quality of life. Code is available on: https://github.com/xpww/ECG_BNN_NetComment: 6 pages, 3 figure

BMAD: Benchmarks for Medical Anomaly Detection

Anomaly detection (AD) is a fundamental research problem in machine learning and computer vision, with practical applications in industrial inspection, video surveillance, and medical diagnosis. In medical imaging, AD is especially vital for detecting and diagnosing anomalies that may indicate rare diseases or conditions. However, there is a lack of a universal and fair benchmark for evaluating AD methods on medical images, which hinders the development of more generalized and robust AD methods in this specific domain. To bridge this gap, we introduce a comprehensive evaluation benchmark for assessing anomaly detection methods on medical images. This benchmark encompasses six reorganized datasets from five medical domains (i.e. brain MRI, liver CT, retinal OCT, chest X-ray, and digital histopathology) and three key evaluation metrics, and includes a total of fourteen state-of-the-art AD algorithms. This standardized and well-curated medical benchmark with the well-structured codebase enables comprehensive comparisons among recently proposed anomaly detection methods. It will facilitate the community to conduct a fair comparison and advance the field of AD on medical imaging. More information on BMAD is available in our GitHub repository: https://github.com/DorisBao/BMA

Discoidin Receptor 2 Controls Bone Formation and Marrow Adipogenesis

Cell–extracellular matrix (ECM) interactions play major roles in controlling progenitor cell fate and differentiation. The receptor tyrosine kinase, discoidin domain receptor 2 (DDR2), is an important mediator of interactions between cells and fibrillar collagens. DDR2 signals through both ERK1/2 and p38 MAP kinase, which stimulate osteoblast differentiation and bone formation. Here we show that DDR2 is critical for skeletal development and differentiation of marrow progenitor cells to osteoblasts while suppressing marrow adipogenesis. Smallie mice (Ddr2slie/slie), which contain a nonfunctional Ddr2 allele, have multiple skeletal defects. A progressive decrease in tibial trabecular bone volume/total volume (BV/TV) was observed when wild‐type (WT), Ddr2wt/slie, and Ddr2slie/slie mice were compared. These changes were associated with reduced trabecular number (Tb.N) and trabecular thickness (Tb.Th) and increased trabecular spacing (Tb.Sp) in both males and females, but reduced cortical thickness only in Ddr2slie/slie females. Bone changes were attributed to decreased bone formation rather than increased osteoclast activity. Significantly, marrow fat and adipocyte‐specific mRNA expression were significantly elevated in Ddr2slie/slie animals. Additional skeletal defects include widened calvarial sutures and reduced vertebral trabecular bone. To examine the role of DDR2 signaling in cell differentiation, bone marrow stromal cells (BMSCs) were grown under osteogenic and adipogenic conditions. Ddr2slie/slie cells exhibited defective osteoblast differentiation and accelerated adipogenesis. Changes in differentiation were related to activity of runt‐related transcription factor 2 (RUNX2) and PPARγ, transcription factors that are both controlled by MAPK‐dependent phosphorylation. Specifically, the defective osteoblast differentiation in calvarial cells from Ddr2slie/slie mice was associated with reduced ERK/MAP kinase and RUNX2‐S319 phosphorylation and could be rescued with a constitutively active phosphomimetic RUNX2 mutant. Also, DDR2 was shown to increase RUNX2‐S319 phosphorylation and transcriptional activity while also increasing PPARγ‐S112 phosphorylation, but reducing its activity. DDR2 is, therefore, important for maintenance of osteoblast activity and suppression of marrow adipogenesis in vivo and these actions are related to changes in MAPK‐dependent RUNX2 and PPARγ phosphorylation. © 2016 American Society for Bone and Mineral Research.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135235/1/jbmr2893_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135235/2/jbmr2893.pd

Regulatory capacity building and the governance of clinical stem cell research in China

While other works have explained difficulties in applying ‘international’ guidelines in the field of regenerative medicine in so-called low- and middle-income countries (LMICs) in terms of ‘international hegemony’, ‘political and ethical governance’ and ‘cosmopolitisation’, this article on stem cell regulation in China emphasises the particular complexities faced by large LMICs: the emergence of alternative regulatory arrangements made by stakeholders at a provincial level at home. On the basis of ethnographic and archival research of clinical stem cell research hubs, we have characterized six types of entrepreneurial ‘bionetworks’, each of which embodies a regulatory orientation that developed in interaction with China’s regulatory dilemmas. Rather than adopting guidelines from other countries, we argue that regulatory capacity building is more appropriately viewed as a relational concept, referring to the ability to develop regulatory requirements that can cater for different regulatory research needs on an international level and at home

A Three-Layered Multifactorial Evolutionary Algorithm with Parallelization for Large-Scale Engraving Path Planning

Today, although laser engraving technology is widely used in 2D image engraving, when the image is larger and more complicated, most existing algorithms for engraving path planning have a huge computational burden and reduced engraving efficiency. Accordingly, this article addresses the trajectory optimization problem in large-scale image engraving. First, we formulate the problem as an improved model based on the large-scale traveling salesman problem (TSP). Then, we propose a three-layered algorithm called 3L-MFEA-MP, structured as follows: an upper layer, the genetic algorithm (GA); a middle layer, the GA; and a bottom layer, the parallel multifactorial evolutionary algorithm. Experiments on four classic large-scale TSP datasets show that our algorithm exhibits superior performance in terms of the path length and engraving time compared with other algorithms. In particular, compared with the single-thread algorithm, the proposed parallel algorithm reduced the engraving time by 80%. Moreover, the engraving machine experiment demonstrated that the engraving time of our algorithm on mona-lisa 100K, vangogh 120K, and venus 140K was approximately one tenth that of the traditional dot engraving method. The results indicate that the proposed algorithm can reduce the computational burden and improve engraving efficiency in engraving path planning

Significant Increases in Water Vapor Pressure Correspond with Climate Warming Globally

Global warming has become indisputable in recent years; however, the mechanisms by which water vapor, radiation, and greenhouse gases such as carbon dioxide contribute to driving global warming remain unclear, and it is becoming increasingly important to clarify their respective effects on temperature warming. In this study, we used the Global Land Data Assimilation System (GLDAS) datasets and National Oceanic and the Atmospheric Administration (NOAA) Global Monitoring Laboratory (GML) data to investigate the spatiotemporal variation characteristics of global warming and its driving mechanisms. The effects of water vapor, radiation (net longwave radiation), and CO2 on temperature rise are quantified from the perspective of the coupled land–atmosphere system, and water vapor is characterized in terms of the vapor pressure deficit (difference between saturated and actual water vapor pressures) to explicitly characterize its impact on the global water–heat cycle. The results show the following: (1) Under significant global warming, the vapor pressure deficit (VPD) exhibits an increasing trend, which is attributed to the rate of increase in actual water vapor being relatively slower than saturated water vapor. (2) Compared with the significant positive contribution of water vapor to global warming, CO2 is not, as generally expected, the most critical greenhouse gas causing global warming. (3) Water vapor and net longwave radiation (NLR) have significant mutual feedbacks on global warming. (4) A remarkable complementary mechanism of global warming that involves water vapor and NLR was identified, whereby the increased saturated water vapor induced by the rising temperature dominates the decrease in NLR. The results from this study have important theoretical value by enabling a more complete understanding of the contribution of VPD to global climate change and shedding light on the large-scale water vapor–climate change mutual feedback mechanism through research

Characteristics of Hepatitis B virus integration and mechanism of inducing chromosome translocation

Abstract Hepatitis B virus (HBV) integration is closely associated with the onset and progression of tumors. This study utilized the DNA of 27 liver cancer samples for high-throughput Viral Integration Detection (HIVID), with the overarching goal of detecting HBV integration. KEGG pathway analysis of breakpoints was performed using the ClusterProfiler software. The breakpoints were annotated using the latest ANNOVAR software. We identified 775 integration sites and detected two new hotspot genes for virus integration, N4BP1 and WASHP, along with 331 new genes. Furthermore, we conducted a comprehensive analysis to determine the critical impact pathways of virus integration by combining our findings with the results of three major global studies on HBV integration. Meanwhile, we found common characteristics of virus integration hotspots among different ethnic groups. To specify the direct impact of virus integration on genomic instability, we explained the causes of inversion and the frequent occurrence of translocation due to HBV integration. This study detected a series of hotspot integration genes and specified common characteristics of critical hotspot integration genes. These hotspot genes are universal across different ethnic groups, providing an effective target for better research on the pathogenic mechanism. We also demonstrated more comprehensive key pathways affected by HBV integration and elucidated the mechanism for inversion and frequent translocation events due to virus integration. Apart from the great significance of the rule of HBV integration, the current study also provides valuable insights into the mechanism of virus integration

Arrhythmia Classifier using Binarized Convolutional Neural Network for Resource-Constrained Devices

Monitoring electrocardiogram signals is of great significance for the diagnosis of arrhythmias. In recent years, deep learning and convolutional neural networks have been widely used in the classification of cardiac arrhythmias. However, the existing neural network applied to ECG signal detection usually requires a lot of computing resources, which is not friendlyF to resource-constrained equipment, and it is difficult to realize real-time monitoring. In this paper, a binarized convolutional neural network suitable for ECG monitoring is proposed, which is hardware-friendly and more suitable for use in resource-constrained wearable devices. Targeting the MIT-BIH arrhythmia database, the classifier based on this network reached an accuracy of 95.67% in the five-class test. Compared with the proposed baseline full-precision network with an accuracy of 96.45%, it is only 0.78% lower. Importantly, it achieves 12.65 times the computing speedup, 24.8 times the storage compression ratio, and only requires a quarter of the memory overhead.Comment: IEEE-CISCE 202