NIPD: A Federated Learning Person Detection Benchmark Based on Real-World Non-IID Data

Federated learning (FL), a privacy-preserving distributed machine learning, has been rapidly applied in wireless communication networks. FL enables Internet of Things (IoT) clients to obtain well-trained models while preventing privacy leakage. Person detection can be deployed on edge devices with limited computing power if combined with FL to process the video data directly at the edge. However, due to the different hardware and deployment scenarios of different cameras, the data collected by the camera present non-independent and identically distributed (non-IID), and the global model derived from FL aggregation is less effective. Meanwhile, existing research lacks public data set for real-world FL object detection, which is not conducive to studying the non-IID problem on IoT cameras. Therefore, we open source a non-IID IoT person detection (NIPD) data set, which is collected from five different cameras. To our knowledge, this is the first true device-based non-IID person detection data set. Based on this data set, we explain how to establish a FL experimental platform and provide a benchmark for non-IID person detection. NIPD is expected to promote the application of FL and the security of smart city.Comment: 8 pages, 5 figures, 3 tables, FL-IJCAI 23 conferenc

3D printing high interfacial bonding polyether ether ketone components via pyrolysis reactions

Recently, 3D-printed polyether-ether-ketone (PEEK) components have been shown to offer many applications in state-of-the-art electronics, 5G wireless communications, medical implantations, and aerospace components. Nevertheless, a critical barrier that limits the application of 3D printed PEEK components is their weak interfacial bonding strength. Herein, we propose a novel method to improve this unsatisfied situation via the interface plasticizing effect of benzene derivatives obtained from the thermal pyrolysis of trisilanolphenyl polyhedral oligomeric silsequioxane (POSS). Based on this method, the bonding strength of the filaments and interlayers of 3D-printed POSS/PEEK components can reach 82.9 MPa and 59.8 MPa, respectively. Moreover, the enhancing mechanism of the pyrolysis products derived from the POSS is characterized using pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS), Fourier transform infrared spectroscopy (FTIR), and X-ray computed tomography (X-CT). Our proposed strategy broadens the novel design space for developing additional 3D-printed materials with satisfactory interfacial bonding strength

Reliability and Validity of Simplified Chinese Version of Roland-Morris Questionnaire in Evaluating Rural and Urban Patients with Low Back Pain

OBJECTIVE: The causes of low back pain in China and Western countries are extremely different. We attempted to analyze the risk factors of low back pain in urban and rural patients under the dual economy with the simplified Chinese version of Roland-Morris disability questionnaire (SC-RMDQ) to demonstrate that SC-RMDQ could evaluate patients with low back pain arising from different causes. METHODS: Roland-Morris disability questionnaire was translated into SCRMDQ according to international guidelines for questionnaire adaptation. In this study, causes of low back pain of 187 outpatients and inpatients (99 urban patients and 88 rural patients) were analyzed. All patients underwent simplified Chinese version of Roland-Morris disability questionnaire (SC-RMDQ), simplified Chinese Oswestry disability index (SCODI) and visual analogue scale (VAS). Reliability was tested using reproducibility (intraclass coefficient of correlation--ICC) and internal consistency (Cronbach's alpha). Validity was tested using Pearson correlation analysis. RESULTS: The leading causes for low back pain were sedentariness (38.4%) and vibration (18.1%) in urban patients and waist bending (48.9%) and spraining (25%) in rural patients. Although causes of low back pain in the two groups of population were completely different, SCRMDQ had high internal consistency (Cronbach's α value of 0.874 in urban patients and 0.883 in rural patients) and good reproducibility (ICC value of .952 in urban patients and 0.949 in rural patients, P<0.01). SCRMDQ also showed significant correlation with Simplified Chinese version of Oswestry disability index (SCODI) and visual analogue scale (VAS) in rural areas (SCRMDQ-SCODI r = 0.841; SCRMDQ-VAS: r = 0.685, P<0.01) and in urban areas (SCRMDQ-SCODI: r = 0.818, P<0.01; SCRMDQ-VAS: r = 0.666, P<0.01). CONCLUSIONS: Although causes of low back pain are completely different in rural and urban patients, SCRMDQ has a good reliability and validity, which is a reliable clinical method to evaluate disability of rural and urban patients

A Hybrid Residential Short-Term Load Forecasting Method Using Attention Mechanism and Deep Learning

Development in economics and social society has led to rapid growth in electricity demand. Accurate residential electricity load forecasting is helpful for the transformation of residential energy consumption structure and can also curb global climate warming. This paper proposes a hybrid residential short-term load forecasting framework (DCNN-LSTM-AE-AM) based on deep learning, which combines dilated convolutional neural network (DCNN), long short-term memory network (LSTM), autoencoder (AE), and attention mechanism (AM) to improve the prediction results. First, we design a T-nearest neighbors (TNN) algorithm to preprocess the original data. Further, a DCNN is introduced to extract the long-term feature. Secondly, we combine the LSTM with the AE (LSTM-AE) to learn the sequence features hidden in the extracted features and decode them into output features. Finally, the AM is further introduced to extract and fuse the high-level stage features to achieve the prediction results. Experiments on two real-world datasets show that the proposed method is good at capturing the oscillation characteristics of low-load data and outperforms other methods

Differential Regulation of TLR Signaling on the Induction of Antiviral Interferons in Human Intestinal Epithelial Cells Infected with Enterovirus 71

<div><p>Enterovirus 71 (EV71) causes hand-foot-and-mouth disease, which can lead to fatal neurological complications in young children and infants. Few gastrointestinal symptoms are observed clinically, suggesting the presence of a unique immunity to EV71 in the gut. We reported a robust induction of interferons (IFNs) in human intestinal epithelial cells (HT-29), which was suppressed in other types such as RD and HeLa cells. The underlying mechanism for the apparent difference remains obscure. In this study we report that in EV71-infected HT-29 cells, TLR/TRIF signaling was essential to IFN induction; viral replication increased and the induction of IFN-α, -β, -ω, -κ, and -ε decreased markedly in TRIF-silenced HT-29 cells. Importantly, TRIF was degraded by viral 3C^pro in RD cells, but resisted cleavage, and IRF3 was activated and translocated into the nucleus in HT-29 cells. Taken together, our data suggest that IFNs were induced differentially in human HT-29 cells through an intact TLR/TRIF signaling, which differs from other cell types and may be implicated in viral pathogenesis in EV71 infection.</p></div

Exploration of the mechanism by which Huangqi Guizhi Wuwu decoction inhibits Lps-induced inflammation by regulating macrophage polarization based on network pharmacology

Abstract Background Huangqi Guizhi Wuwu decoction (HQGZWWD) is a traditional Chinese herbal medicine formulation with significant anti-inflammatory activity. However, its underlying mechanism remains unknown. Through network pharmacology and experimental validation, this study aimed to examine the potential mechanism of HQGZWWD in regulating macrophage polarization and inflammation. Methods The active components were obtained from the Traditional Chinese Medicine Systems Pharmacology database and Analysis Platform (TCMSP), whereas the corresponding targets were obtained from the TCMSP and Swiss Target Prediction database. The GeneCards database identified targets associated with macrophage polarization and inflammation. Multiple networks were developed to identify the key compounds, principal biological processes, and pathways of HQGZWWD that regulate macrophage polarization and inflammation. Autodock Vina is utilized to assess the binding ability between targets and active compounds. Finally, confirm the experiment’s central hypothesis. Human histiocytic lymphoma (U-937) cells were transformed into M1 macrophages following stimulation with Lipopolysaccharide (LPS) to evaluate the effect of HQGZWWD drug-containing mouse serum (HQGZWWD serum) on regulating macrophage polarization and inflammation. Results A total of 54 active components and 859 HQGZWWD targets were obtained. There were 9972 targets associated with macrophage polarization and 11,109 targets associated with inflammation. After screening, 34 overlapping targets were identified, of which 5 were identified as central targets confirmed by experiments, including the α7 nicotinic acetylcholine receptor (α7 nAchR), interleukin 6 (IL-6), Interleukin-1 beta (IL-1β), interleukin 10 (IL-10) and growth factor beta (TGF-β1). Pathway enrichment analysis revealed that 34 overlapping targets were enriched in multiple pathways associated with macrophage polarization and inflammation, including the TGF beta signaling pathway, NF-kappa B signaling pathway, JAK-STAT signaling pathway, and TNF signaling pathway. Molecular docking confirmed that the majority of HQGZWWD’s compounds can bind to the target. In vitro experiments, HQGZWWD serum was shown to up-regulate the expression of α7 nAchR, reduce the number of M1 macrophages, stimulate the production of M2 macrophages, inhibit the expression of pro-inflammatory cytokines IL-6 and IL1-β, and increase the expression of anti-inflammatory cytokines IL-10 and TGF-β1. Conclusion HQGZWWD can regulate the number of M1/M2 macrophages and the level of inflammatory cytokines, and the underlying mechanism may be related to the up-regulation of α7 nAchR expression