SARS-CoV-2 incidence, seroprevalence, and COVID-19 vaccination coverage in the homeless population: a systematic review and meta-analysis

ObjectivesSARS-CoV-2 infection and COVID-19 vaccination of homeless people are a serious public health concern during COVID-19 pandemic. We aimed to systematically assess SARS-CoV-2 incidence, seroprevalence, and COVID-19 vaccination coverage in homeless people, which are important to inform resource allocation and policy adjustment for the prevention and control of COVID-19.MethodsWe searched PubMed, Web of Science, and the World Health Organization COVID-19 database for the studies of SARS-CoV-2 incidence, seroprevalence, and COVID-19 vaccination coverage in the homeless population. Subgroup analyses were conducted to pool SARS-CoV-2 incidence and seroprevalence in sheltered homeless, unsheltered homeless, and mixed population, respectively. Potential sources of heterogeneity in the estimates were explored by meta-regression analysis.ResultsForty-nine eligible studies with a total of 75,402 homeless individuals and 5,000 shelter staff were included in the meta-analysis. The pooled incidence of SARS-CoV-2 infection was 10% (95% CI: 7 to 12%) in the homeless population and 8% (5 to 12%) for shelter staff. In addition, the overall estimated SARS-CoV-2 specific seroprevalence was 19% (8 to 33%) for homeless populations and 22% (3 to 52%) for shelter staff, respectively. Moreover, for the homeless subjects, the pooled incidence was 10% (4 to 23%) for asymptomatic SARS-CoV-2 infections, 6% (1 to 12%) for symptomatic SARS-CoV-2 infections, 3% (1 to 4%) for hospitalization for COVID-19, and 1% (0 to 2%) for severe COVID-19 cases, respectively while no COVID-19-related death was reported. Furthermore, the data derived from 12 included studies involving 225,448 homeless individuals revealed that the pooled proportion of one dose COVID-19 vaccination was 41% (35 to 47%), which was significantly lower than those in the general population.ConclusionOur study results indicate that the homeless people remain highly susceptible to SARS-CoV-2 infection, but COVID-19 vaccination coverage was lower than the general population, underscoring the need for prioritizing vaccine deployment and implementing enhanced preventive measures targeting this vulnerable group

Objective comparison of particle tracking methods

Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers

Coal and gangue recognition research based on improved YOLOv5

The existing deep learning-based coal and gangue recognition methods are prone to false detection and missed detection when applied to underground complex environments. The recognition precision of small target coal and gangue is low. In order to solve this problem, an improved YOLOv5 model is proposed, and coal and gangue recognition is realized based on that model. Data enhancement is carried out on the collected coal and gangue data to enrich the data set and improve the data utilization rate. The atrous convolution and residual block are introduced into the spatial pyramid pooling (SPP) module to obtain the residual ASPP module. On the premise of not losing image information, the convolution output receptive field can be increased to enhance the extraction of deep features from the model. The AdaBelief optimization algorithm is used to replace the original Adam optimization algorithm of YOLOv5 to improve the convergence speed and recognition precision of the model. The experimental results show that the AdaBelief optimization algorithm and residual ASPP module can effectively improve the precision, recall rate and mean average precision (mAP) of the YOLOv5 model. The mAP of the improved YOLOv5 model reaches 94.43%, which is 2.27% higher than that of original YOLOv5 model. The frame rate is reduced by 0.03 frames/s. The performance of the improved YOLOv5 model is superior to SSD, Faster R-CNN, YOLOv3, YOLOv4 and other mainstream target detection models. In extremely dark environments, the improved YOLOv5 model can also accurately delineate the target boundary, and the recognition effect is better than other improved YOLOv5 models

Anaphylaxis induced by intra‐articular injection of chitosan: A case report and literature review

Abstract Generally, we consider chitosan being a safe, nontoxic natural polymer with wide clinical applications. However, allergic reactions caused by chitosan have been reported on rare occasions. We report here a case of allergy and perform a literature review

Research on key technologies of intelligent gangue sorting robot

This paper introduces the application and research status of the intelligent gangue sorting robot. This paper points out that the intelligent gangue sorting robot is mainly based on the principle of X-ray and image identification. And the high-pressure pneumatic sorting and truss robot grasping sorting are used to separate coal and gangue. The sorting actuators are mainly truss type, parallel type and series type of intelligent gangue sorting robot. The sorting actuators have fast response speed and often separate the gangue in the form of 'pulling' and 'grasping'. In the process of belt transportation, the compatibility of different gangue sizes and the optimization of movement path need to be considered in the 'pulling' of the intelligent gangue sorting robot. And the working space of the manipulator and the bearing capacity of the robot need to be considered in the 'grasping'. This paper analyzes the key technologies such as deep learning-based coal and gangue identification, unstructured multi-constraint environment-oriented motion planning of gangue sorting manipulator, force feedback-based active compliance control of manipulator and multi-arm cooperative sorting task allocation strategy and control. These technologies are used for intelligent gangue sorting robot to effectively realize gangue sorting in complex on-site environment. This paper points out that coal and gangue identification technology based on deep learning is one of the key technologies of gangue sorting robot. It still needs further research on the efficient construction method of coal gangue data set, improving the generalization of coal gangue identification algorithm, and the real-time optimization of coal gangue identification algorithm. Combined with the demand of field application and intelligent robot development, the future research directions of intelligent gangue sorting robot are pointed out. In the complex environment on site, it is suggested to improve the robustness and adaptability of the coal gangue identification algorithm. It is suggested to develop intelligent sensing and control technology for complex environment and high-precision three-dimensional pose estimation technology for gangue. It is suggested to develop intelligent gangue picking technology of gangue picking robot based on force position hybrid control. It is suggested to research intelligent gangue sorting robot underground gangue sorting technology

Investigation into Detection Efficiency Deviations in Aviation Soot and Calibration Particles Based on Condensation Particle Counting

Aviation soot constitutes a significant threat to human well-being, underscoring the critical importance of accurate measurements. The condensation particle counter (CPC) is the primary instrument for quantifying aviation soot, with detection efficiency being a crucial parameter. The properties of small particles and the symmetry of their growth pathways are closely related to the detection efficiency of the CPC. In laboratory environments, sodium chloride is conventionally utilized to calibrate the CPC’s detection efficiency. However, aviation soot exhibits distinctive morphological characteristics compared to the calibration particles, leading to detection efficiencies obtained from calibration particles that may not be applicable to aviation soot. To address this issue, a quantitative study was performed to explore the detection efficiency deviations between aviation soot and calibration particles. The experiment initially utilized a differential mobility analyzer to size select the two types of polydisperse particles into monodisperse particles. Subsequently, measurements of the separated particles were performed using the TSI Corporation’s aerosol electrometer and a rigorously validated CPC (BH-CPC). These allowed for determining the detection efficiency deviation in the BH-CPC for the two types of particles at different particle sizes. Furthermore, the influence of the operating temperature of the BH-CPC on this detection efficiency deviation was investigated. The experimental results indicate a significant detection efficiency deviation between aviation soot and sodium chloride. In the range of 10–40 nm, the absolute detection efficiency deviation can reach a maximum of 0.15, and the relative deviation can reach a maximum of 0.75. And this detection efficiency deviation can be reduced by establishing a relevant relationship between the detection efficiency of the operating temperature and the calibration temperature. Compared to the saturated segment calibration temperature of 50 °C, the aviation soot detection efficiency is closer to the sodium chloride detection efficiency at the calibration temperature of 50 °C when the saturated segment operates at a temperature of 45 °C. These studies provide crucial theoretical guidance for enhancing the precision of aviation soot emission detection and establish a foundation for future research in monitoring and controlling soot emissions within the aviation sector

Remaining Useful Life Prediction for Lithium-Ion Batteries Based on the Partial Voltage and Temperature

Remaining useful life (RUL) prediction is vital to provide accurate decision support for a safe power system. In order to solve capacity measurement difficulties and provide a precise and credible RUL prediction for lithium-ion batteries, two health indicators (HIs), the discharging voltage difference of an equal time interval (DVDETI) and the discharging temperature difference of an equal time interval (DTDETI), are extracted from the partial discharging voltage and temperature. Box-Cox transformation, which is data processing, is used to improve the relation grade of HIs. In addition, the Pearson correlation is employed to evaluate the relationship degree between HIs and capacity. On this basis, a local Gaussian function and a global sigmoid function are utilized to improve the multi-kernel relevance vector machine (MKRVM), whose weights are optimized by applying a whale optimization algorithm (WOA). The availability of the extracted HIs as well as the accuracy of the RUL prediction are verified with the battery data from NASA

Disruption of PTPS Gene Causing Pale Body Color and Lethal Phenotype in the Silkworm, Bombyx mori

Phenylketonuria (PKU) is an inborn error of metabolism caused by mutations in the phenylalanine hydroxylase (PAH) gene or by defects in the tetrahydrobiopterin (BH4) synthesis pathway. Here, by positional cloning, we report that the 6-pyruvoyl-tetrahydropterin synthase (PTPS) gene, encoding a key enzyme of BH4 biosynthesis, is responsible for the alc (albino C) mutation that displays pale body color, head shaking, and eventually lethality after the first molting in silkworm. Compared to wild type, the alc mutant produced more substrates (phenylalanine (Phe) and tyrosine (Tyr)) and generated less DOPA and dopamine. Application of 2,4-diamino-6-hydroxypyrimidine (DAHP) to block BH4 synthesis in the wild type effectively produced the alc-like phenotype, while BH4 supplementation rescued the defective body color and lethal phenotype in both alc and DAHP-treated individuals. The detection of gene expressions and metabolic substances after drugs treatments in alc and normal individuals imply that silkworms and humans have a high similarity in the drugs metabolic features and the gene pathway related to BH4 and the dopamine biosynthesis. We propose that the alc mutant could be used as an animal model for drug evaluation for BH4-deficient PKU