Design of laser uniform illumination system based on aspheric lens and compound ellipsoidal cavity

In order to achieve uniform laser illumination with small aperture diameter and large field Angle,study laser active illumination system.An aspheric mirror combined with a composite ellipsoidal cavity is designed to achieve uniform illumination in this paper.Through an aspheric mirror,the fundamental mode of Gaussian beam is shaped into double Gaussian radiation and Flat-top radiation.The double Gaussian radiation rays are reflected again by the complex ellipsoidal cavity and decomposed into equal radiation flux,which is superimposed with the through Flat-top radiation rays to form a uniform distribution.The parameters of the complex ellipsoidal cavity are obtained by mapping equalization algorithm.After the superposition of the aspherical transmission Flat-top shaping and the composite ellipsoidal cavity secondary reflection shaping,the aperture is 29.7mm,whose aperture angle is 84.0 degrees,and the uniformity is 92.7% with 2m distance and 3.6m diameter.The optimization of uniformity is influenced by three factors:RMS,transmission and reflection power density ratio MT/R and transmission and reflection overlap degree.RMS and MT/R determine the design effect of the composite ellipsoidal cavity, which depends on the maximum reflection Angle and transmission Angle.MT/R is negatively correlated with the maximum reflection of Angle,and RMS is positively correlated with the transmission Angle.When the maximum reflection Angle is set to 32.0 degrees and the transmission Angle to 8.0 degrees,the minimum root-mean-square focusing radius is 108.6um,and the minimum effective transmission reflection power density ratio is 1.07.The degree overlap of transmission and reflection directly affects the uniformity of the target plane.The degree of transmission and reflection is adjusted by setting an adjustment factor.When the adjustment factor is 0.9,the uniformity of the target plane reaches the maximum.Comment: 7 pages, 13 figure

Hepatitis E virus infection in swine workers: A meta‐analysis

Hepatitis E virus (HEV) infects both humans and animals. Swine has been confirmed to be the principal natural reservoir, which raises a concern that HEV infection would be substantially increasing among swine workers. The present study calculated the pooled prevalence of IgG antibodies against HEV among swine workers and the general population in previous cross‐sectional studies. We conducted a meta‐analysis comparing the prevalence of HEV infection between swine workers and the general population, including local residents, blood donors and non‐swine workers. Through searches in three databases (PubMed and OVID in English, and CNKI in Chinese) and after study selection, a total of 32 studies from 16 countries (from 1999 through 2018) were included in the meta‐analysis. A random‐effect model was employed in the study; an I 2 statistic assessed heterogeneity, and the Egger’s test detected publication bias. The comparative prevalence of anti‐HEV IgG was pooled from the studies. Compared to the general population, the prevalence ratio (PR) for swine workers was estimated to be 1.52 (95% CI 1.38–1.76) with the I 2 being 71%. No publication bias was detected (p = 0.40). A subgroup analysis further indicated increased prevalence of anti‐HEV IgG in the swine workers in Asia (PR = 1.49, 95% CI: 1.35–1.64), in Europe (PR = 1.93, 95% CI: 1.49–2.50) and in all five swine‐related occupations, including swine farmers, butchers, meat processors, pork retailers and veterinarians (PR ranged between 1.19 and 1.75). In summary, swine workers have a relatively higher prevalence of past HEV infection, and this finding is true across swine‐related occupations, which confirms zoonotic transmission between swine and swine workers.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147857/1/zph12548_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147857/2/zph12548.pd

SCTNet: Single-Branch CNN with Transformer Semantic Information for Real-Time Segmentation

Recent real-time semantic segmentation methods usually adopt an additional semantic branch to pursue rich long-range context. However, the additional branch incurs undesirable computational overhead and slows inference speed. To eliminate this dilemma, we propose SCTNet, a single branch CNN with transformer semantic information for real-time segmentation. SCTNet enjoys the rich semantic representations of an inference-free semantic branch while retaining the high efficiency of lightweight single branch CNN. SCTNet utilizes a transformer as the training-only semantic branch considering its superb ability to extract long-range context. With the help of the proposed transformer-like CNN block CFBlock and the semantic information alignment module, SCTNet could capture the rich semantic information from the transformer branch in training. During the inference, only the single branch CNN needs to be deployed. We conduct extensive experiments on Cityscapes, ADE20K, and COCO-Stuff-10K, and the results show that our method achieves the new state-of-the-art performance. The code and model is available at https://github.com/xzz777/SCTNetComment: Accepted by AAAI 2024; typos corrected; code and models have been released at https://github.com/xzz777/SCTNe

The Research Summary on Logistics Safety in China

With the rise and rapid development of logistics industry, the safety problems which appear in the logistic operations constantly win wide attention in logistic industry. But seen from the domestic research, there are relatively few researches on logistic safety. In order to put forward constructive suggests for our country on the research of problems of logistic safety, this article analyses research literature selected from the relevant domestic logistic safety researches through five dimensions, which are the author, date of publication, published journals, research methods, and research scope, then summarize the relevant results.Key words: Logistic safety; Research method; Research summar

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, NLP based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely-used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.Comment: Accepted by Briefings in Bioinformatic

In vivo phenotypic and molecular characterization of retinal degeneration in mouse models of three ciliopathies

International audienceCilia are highly conserved and ubiquitously expressed organelles. Ciliary defects of genetic origins lead to ci-liopathies, in which retinal degeneration (RD) is one cardinal clinical feature. In order to efficiently find and design new therapeutic strategies the underlying mechanism of retinal degeneration of three murine model was compared. The rodent models correspond to three emblematic ciliopathies, namely: Bardet-Biedl Syndrome (BBS), Alström Syndrome (ALMS) and CEP290-mediated Leber Congenital Amaurosis (LCA). Scotopic rodent electroretinography (ERG) was used to test the retinal function of mice, Transmitted Electron microscopy (T.E.M) was performed to assess retinal structural defects and real-time PCR for targeted genes was used to monitor the expression levels of the major apoptotic Caspase-related pathways in retinal extracts to identify pathological pathways driving the RD in order to identify potential therapeutic targets. We found that BBS and CEP290-mediated LCA mouse models exhibit perinatal retinal degeneration associated with rhodopsin mis-localization in the photoreceptor and the induction of an Endoplasmic Reticulum (ER) stress. On the other hand, the tested ALMS mouse model, displayed a slower degeneration phenotype, with no Rhodopsin mislocalization nor ER-stress activity. Our data points out that behind the general phenotype of vision loss associated with these ciliopathies, the mechanisms and kinetics of disease progression are different

Effectiveness of Traditional Chinese Medicine Compound JieDuTongLuoShengJin Granules Treatment in Primary Sjögren’s Syndrome: A Randomized, Double-Blind, Placebo-Controlled Clinical Trial

Objective. To evaluate the clinical therapeutic efficacy and safety of JieDuTongLuoShengJin granules + HCQ in patients with pSS. Methods. 40 patients with low-activity-level pSS and without visceral involvement participated in this study and were randomized to receive either JieDuTongLuoShengJin granules with HCQ or placebo with HCQ. Patients and investigators were blinded to treatment allocation. The primary endpoint was week 12 ESSPRI score, while secondary endpoints included ESSDAI, salivary and lacrimal gland function, and some laboratory variables. Safety-related data were also assessed. Results. Comparing with the placebo group, the treatment group experienced statistically significant improvement in the mean change from baseline for the primary endpoint of ESSPRI score and also in PGA. Moreover, in comparison with baseline values, the treatment group had significantly improved ESSDAI score, unstimulated saliva flow rate, and several laboratory variables. However, upon comparison of the two groups, there were no significant differences for them. The incidence of AEs was 10.0%, one in treatment group and three in placebo group. Conclusion. Treatment with a combination of JieDuTongLuoShengJin granules with HCQ is effective in improving patients’ subjective symptoms and some objective indicators of pSS. These results indicate that JieDuTongLuoShengJin is promising as a safe and effective treatment of pSS