Multi-Agent Deep Reinforcement Learning for Multi-Object Tracker

Multi-object tracking has been a key research subject in many computer vision applications. We propose a novel approach based on multi-agent deep reinforcement learning (MADRL) for multi-object tracking to solve the problems in the existing tracking methods, such as a varying number of targets, non-causal, and non-realtime. At first, we choose YOLO V3 to detect the objects included in each frame. Unsuitable candidates were screened out and the rest of detection results are regarded as multiple agents and forming a multi-agent system. Independent Q-Learners (IQL) is used to learn the agents' policy, in which, each agent treats other agents as part of the environment. Then, we conducted offline learning in the training and online learning during the tracking. Our experiments demonstrate that the use of MADRL achieves better performance than the other state-of-art methods in precision, accuracy, and robustness

The work of Chinese chronic conditions: adaptation and validation of the Distribution of Co-Care Activities Scale

PurposeThe Distribution of Co-Care Activities Scale was adapted into Chinese for the purposes of this study, and then the psychometric characteristics of the Chinese version of the DoCCA scale were confirmed in chronic conditions.MethodsA total of 434 patients with chronic diseases were recruited from three Chinese cities. A cross-cultural adaptation procedure was used to translate the Distribution of Co-Care Activities Scale into Chinese. Cronbach's alpha coefficient, split-half reliability, and test-retest reliability were used to verify the scale's reliability. Content validity indices, exploratory factor analysis, and confirmatory factor analysis were used to confirm the scale's validity.ResultsThe Chinese DoCCA scale includes five domains: demands, unnecessary tasks, role clarity, needs support, and goal orientation. The S-CVI was 0.964. Exploratory factor analysis yielded a five-factor structure that explained 74.952% of the total variance. According to the confirmatory factor analysis results, the fit indices were within the range of the reference values. Convergent and discriminant validity both met the criteria. Also, the scale's Cronbach's alpha coefficient is 0.936, and the five dimensions' values range from 0.818 to 0.909. The split-half reliability was 0.848, and the test-retest reliability was 0.832.ConclusionsThe Chinese version of the Distribution of Co-Care Activities Scale had high levels of validity and reliability for chronic conditions. The scale can assess how patients with chronic diseases feel about their service of care and provide data to optimize their personalized chronic disease self-management strategies

Correlation Between Circulating Tumor Cell DNA Genomic Alterations and Mesenchymal CTCs or CTC-Associated White Blood Cell Clusters in Hepatocellular Carcinoma

PurposeLiquid biopsy is attracting attention as a method of real-time monitoring of patients with tumors. It can be used to understand the temporal and spatial heterogeneity of tumors and has good clinical application prospects. We explored a new type of circulating tumor cell (CTC) enrichment technology combined with next-generation sequencing (NGS) to analyze the correlation between genomic alterations in circulating tumor cells of hepatocellular carcinoma and the counts of mesenchymal CTCs and CTC-associated white blood cell (CTC-WBC) clusters.MethodsWe collected peripheral blood samples from 29 patients with hepatocellular carcinoma from January 2016 to December 2019. We then used the CanPatrol™ system to capture and analyze mesenchymal CTCs and CTC-WBC clusters for all the patients. A customized Illumina panel was used for DNA sequencing and the Mann–Whitney U test was used to test the correlation between mesenchymal CTCs, CTC-WBC cluster counts, and specific genomic changes.ResultsAt least one somatic hotspot mutation was detected in each of the 29 sequenced patients. A total of 42 somatic hot spot mutations were detected in tumor tissue DNA, and 39 mutations were detected in CTC-DNA, all of which included common changes in PTEN, MET, EGFR, RET, and FGFR3. The number of mesenchymal CTCs was positively correlated with the somatic genomic alterations in the PTEN and MET genes (PTEN, P = 0.021; MET, P  = 0.008, Mann–Whitney U test) and negatively correlated with the somatic genomic alterations in the EGFR gene (P = 0.006, Mann–Whitney U test). The number of CTC-WBC clusters was positively correlated with the somatic genomic alterations in RET genes (P  = 0.01, Mann–Whitney U test) and negatively correlated with the somatic genomic alterations in FGFR3 (P = 0.039, Mann–Whitney U test).ConclusionsWe report a novel method of a CTC enrichment platform combined with NGS technology to analyze genetic variation, which further demonstrates the potential clinical application of this method for spatiotemporal heterogeneity monitoring of hepatocellular carcinoma. We found that the number of peripheral blood mesenchymal CTCs and CTC-WBC clusters in patients with hepatocellular carcinoma was related to a specific genome profile

Visual Object Tracking Based on Cross-Modality Gaussian-Bernoulli Deep Boltzmann Machines with RGB-D Sensors

Visual object tracking technology is one of the key issues in computer vision. In this paper, we propose a visual object tracking algorithm based on cross-modality featuredeep learning using Gaussian-Bernoulli deep Boltzmann machines (DBM) with RGB-D sensors. First, a cross-modality featurelearning network based on aGaussian-Bernoulli DBM is constructed, which can extract cross-modality features of the samples in RGB-D video data. Second, the cross-modality features of the samples are input into the logistic regression classifier, andthe observation likelihood model is established according to the confidence score of the classifier. Finally, the object tracking results over RGB-D data are obtained using aBayesian maximum a posteriori (MAP) probability estimation algorithm. The experimental results show that the proposed method has strong robustness to abnormal changes (e.g., occlusion, rotation, illumination change, etc.). The algorithm can steadily track multiple targets and has higher accuracy

Optimizing Livers for Transplantation Using Machine Perfusion versus Cold Storage in Large Animal Studies and Human Studies: A Systematic Review and Meta-Analysis

Background. Liver allograft preservation frequently involves static cold storage (CS) and machine perfusion (MP). With its increasing popularity, we investigated whether MP was superior to CS in terms of beneficial outcomes. Methods. Human studies and large animal studies that optimized livers for transplantation using MP versus CS were assessed (PubMed/Medline/EMBASE). Meta-analyses were conducted for comparisons. Study quality was assessed according to the Newcastle-Ottawa quality assessment scale and SYRCLE’s risk of bias tool. Results. Nineteen studies were included. Among the large animal studies, lower levels of lactate dehydrogenase (SMD -3.16, 95% CI -5.14 to -1.18), alanine transferase (SMD -2.46, 95% CI -4.03 to -0.90), and hyaluronic acid (SMD -2.48, 95% CI -4.21 to -0.74) were observed in SNMP-preserved compared to CS-preserved livers. NMP-preserved livers showing lower level of hyaluronic acid (SMD -3.97, 95% CI -5.46 to -2.47) compared to CS-preserved livers. Biliary complications (RR 0.45, 95% CI 0.28 to 0.73) and early graft dysfunction (RR 0.56, 95% CI 0.34 to 0.92) also significantly reduced with HMP preservation in human studies. No evidence of publication bias was found. Conclusions. MP preservation could improve short-term outcomes after transplantation compared to CS preservation. Additional randomized controlled trials (RCTs) are needed to develop clinical applications of MP preservation

Flame-Retarded Rigid Polyurethane Foam Composites with the Incorporation of Steel Slag/Dimelamine Pyrophosphate System: A New Strategy for Utilizing Metallurgical Solid Waste

Rigid polyurethane (RPUF) was widely used in external wall insulation materials due to its good thermal insulation performance. In this study, a series of RPUF and RPUF-R composites were prepared using steel slag (SS) and dimelamine pyrophosphate (DMPY) as flame retardants. The RPUF composites were characterized by thermogravimetric (TG), limiting oxygen index (LOI), cone calorimetry (CCT), and thermogravimetric infrared coupling (TG-FTIR). The results showed that the LOI of the RPUF-R composites with DMPY/SS loading all reached the combustible material level (22.0 vol%~27.0 vol%) and passed UL-94 V0. RPUF-3 with DMPY/SS system loading exhibited the lowest pHRR and THR values of 134.9 kW/m2 and 16.16 MJ/m2, which were 54.5% and 42.7% lower than those of unmodified RPUF, respectively. Additionally, PO· and PO2· free radicals produced by pyrolysis of DMPY could capture high energy free radicals, such as H·, O·, and OH·, produced by degradation of RPUF matrix, effectively blocking the free radical chain reaction of composite materials. The metal oxides in SS reacted with the polymetaphosphoric acid produced by the pyrolysis of DMPY in combustion. It covered the surface of the carbon layer, significantly insulating heat and mass transport in the combustion area, endowing RPUF composites with excellent fire performance. This work not only provides a novel strategy for the fabrication of high-performance RPUF composites, but also elucidates a method of utilizing metallurgical solid waste

Design and simulation of RF MEMS switches with the supporting columns

This paper introduces the principle of RF MEMS switch and performs modeling analysis. By analyzing the low control voltage and microwave power of the switch, phenomena such as long response time and susceptibility to mechanical failure appear. Therefore, this paper analyzes the current research on RF MEMS switches, improves the internal structure of the switch by adding support columns, fixes the double ends of the beam, and increases the spring coefficient K to slow down the falling speed of the internal cantilever of the switch. The impact force between the cantilever and the dielectric layer is reduced, which greatly reduces the loss of the switch and the problems of mechanical fatigue failure, thus improving the service life of the switch. The simulation shows that the stability of the model is relatively high when the length of the cantilever is 300 um. After adding a 0.7 um support column at a distance of 25 um from the dielectric layer, the turn-on voltage is increased from 42.5 V to 61 V, and the switching capacitance remains unchanged. Finally, by calculating the percentage error between the simulation value and the model theory of 1.6%, the validity and accuracy of the model are checked