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Background This study aimed to identify the epidemiology, seasonality, aetiology and clinical characteristics of sporadic foodborne diseases in Zhejiang province during 2016–2020. Methods Descriptive statistical methods were used to analyze the data from surveillance network established by the Zhejiang Provincial Center for Disease Control and Prevention. There were 31 designated hospitals in all 11 cities which were selected using probability proportionate to size sampling method. Results During the study period, the surveillance system received 75,124 cases with 4826 (6.42%) hospitalizations from 31 hospitals. The most common cause was Norovirus, 6120 cases (42.56%), followed by Salmonella, 3351 cases (23.30%). A significant seasonal trend was observed for the V. parahaemolyticus, with the highest rates over the summer period, peaking in August, 1171 cases (38.75%), a similar trend was also observed with Salmonella and Diarrheagenic E. coli. Norovirus infections showed the highest rate in November (904, 14.77%) and March (660,10.78%), the lowest in August, 215 cases (3.51%). Patients between 19 ~ 40 years were more likely to infected by Norovirus, V. parahaemolyticus and Diarrheagenic E. coli, patients below 1 year were the highest among patients with Salmonella infection, 881 cases (26.3%). The Norovirus, V. parahaemolyticus and Diarrheagenic E. coli infection with the highest positive detection rates among the workers were observed. The largest number cases of food categories were from aquatic product infection. The private home was the most common exposure setting. Conclusion Our study highlighted the necessity for conducting an active, comprehensive surveillance for pathogens in all age groups, to monitor the changing dynamics in the epidemiology and aetiology of foodborne diseases to guide policies that would reduce related illnesses.

Particle suspensions, present in many natural and industrial settings, typically contain aggregates or other microstructures that can complicate macroscopic flow behaviors and damage processing equipment. Recent work found that applying uniform periodic shear near a critical transition can reduce fluctuations in the particle concentration across all length scales, leading to a hyperuniform state. However, this strategy for homogenization requires fine tuning of the strain amplitude. Here we show that in a model of sedimenting particles under periodic shear, there is a well-defined regime at low sedimentation speed where hyperuniform scaling automatically occurs. Our simulations and theoretical arguments show that the homogenization extends up to a finite length scale that diverges as the sedimentation speed approaches zero. Suspensions appear in a wide range of industrial settings, and dispersing particles in a uniform manner throughout a fluid remains challenging for applications. Wang et al. obtain hyperuniform mixtures without fine tuning by harnessing self-organized criticality due to slow sedimentation and shear.

Deep learning has made great advances in the field of image processing, which allows automotive devices to be more widely used in humans’ daily lives than ever before. Nowadays, the mobile robot navigation system is among the hottest topics that researchers are trying to develop by adopting deep learning methods. In this paper, we present a system that allows the mobile robot to localize and navigate autonomously in the accessible areas of an indoor environment. The proposed system exploits the Convolutional Neural Network (CNN) model’s advantage to extract data feature maps for image classification and visual localization, which attempts to precisely determine the location region of the mobile robot focusing on the topological maps of the real environment. The system attempts to precisely determine the location region of the mobile robot by integrating the CNN model and topological map of the robot workspace. A dataset with small numbers of images is acquired from the MYNT EYE camera. Furthermore, we introduce a new loss function to tackle the bounded generalization capability of the CNN model in small datasets. The proposed loss function not only considers the probability of the input data when it is allocated to its true class but also considers the probability of allocating the input data to other classes rather than its actual class. We investigate the capability of the proposed system by evaluating the empirical studies based on provided datasets. The results illustrate that the proposed system outperforms other state-of-the-art techniques in terms of accuracy and generalization capability.

Outer membrane vesicles (OMVs) are attractive for biomedical applications based on their intrinsic properties in relation to bacteria and vesicles. However, their widespread use is hampered by low yields and purities. In this study, EVscore47 multifunctional chromatography microspheres were synthesized and used to efficiently isolate functional OMVs from Escherichia coli. Through this technology, OMV loss can be kept to a minimum, and OMVs can be harvested using EVscore47 at 11-fold higher yields and ~13-fold higher purity than those achieved by means of ultracentrifugation. Based on the results presented here, we propose a novel EVscore47-based isolation of OMVs that is fast and scalable.

In recent years, Simultaneous Localization and Mapping (SLAM) systems have shown significant performance, accuracy, and efficiency gains, especially when Neural Radiance Fields (NeRFs) are implemented. NeRF-based SLAM in mapping aims to implicitly understand irregular environmental information using large-scale parameters of deep learning networks in a data-driven manner so that specific environmental information can be predicted from a given perspective. NeRF-based SLAM in tracking jointly optimizes camera pose and implicit scene network parameters through inverse rendering or combines VO and NeRF mapping to achieve real-time positioning and mapping. This paper firstly analyzes the current situation of NeRF and SLAM systems and then introduces the state-of-the-art in NeRF-based SLAM. In addition, datasets and system evaluation methods used by NeRF-based SLAM are introduced. In the end, current issues and future work are analyzed. Based on an investigation of 30 related research articles, this paper provides in-depth insight into the innovation of SLAM and NeRF methods and provides a useful reference for future research.

In this paper, a novel and complete navigation system is proposed for mobile robots in a park environment, which can achieve safe and stable navigation as well as robust dynamic obstacle avoidance. The navigation system includes a global planning layer and a local planning layer. The global planner plans a series of way-points using the A* algorithm based on an offline stored occupancy grid map and sends them to the local planner. The local planner incorporates a dynamic obstacle avoidance mechanism. In contrast to existing dynamic obstacle avoidance algorithms based on trajectory tracking, we innovatively construct a two-dimensional Difference ESDF (Euclidean Signed Distance Field) map to represent obstacle motion information. The local planner outputs control actions by scoring candidate paths. A series of simulation experiments and real-world tests are conducted to verify that the navigation system can safely and robustly accomplish navigation tasks. The safety distance of the simulation experiment group with the dynamic obstacle avoidance scoring item added increased by 1.223 compared to the group without the dynamic obstacle avoidance scoring item.

In recent years, electric vehicles have achieved rapid development. Intelligence is one of the important trends to promote the development of electric vehicles. As a result, autonomous driving system is becoming one of the core systems of electric vehicles. Considering that environmental perception is the basis of intelligent planning and safe decision-making for intelligent vehicles, this paper presents a survey of the existing perceptual methods in vehicles, especially 3D object detection, which guarantees the reliability and safety of vehicles. In this review, we first introduce the role of perceptual module in autonomous driving system and a relationship with other modules. Then, we classify and analyze the corresponding perception methods based on the different sensors. Finally, we compare the performance of the surveyed works on public datasets and discuss the possible future research interests.

Subarachnoid hemorrhage (SAH) is a life-threatening cerebrovascular disease that usually has a poor prognosis. Heat shock proteins (HSPs) have been implicated in the mechanisms of SAH-associated damage, including increased inflammation and reduced neurogenesis. The aim of this study was to investigate the effects of HSP90 inhibition on inflammation and neurogenesis in a mouse model of experimental SAH induced by endovascular surgery. Western blotting showed HSP90 levels to be decreased, while neurogenesis, evaluated by 5-bromo-2'-deoxyuridine (BrdU) immunohistochemistry, was decreased in the hippocampuses of SAH mice. SAH also induced pro-inflammatory factors such as interleukin-1β (IL-1β), capase-1 and the NLRP3 inflammasome. However, intraperitoneal administration of the specific HSP90 inhibitor 17-allylamino-17-demethoxygeldanamycin (17-AAG) reduced the levels of HSP90, NLRP3, ASC, caspase-1 and IL-1β, while increasing the levels of brain-derived neurotrophic factor and doublecortin (DCX), as well as the number of BrdU-positive cells in SAH mice. In addition, 17-AGG improved short- and long-term neurobehavioral outcomes. The neuroprotective and anti-inflammatory effects of 17-AGG were reversed by recombinant HSP90 (rHSP90); this detrimental effect of HSP90 was inhibited by the specific P2X7 receptor (P2X7R) inhibitor A438079, indicating that SAH-induced inflammation and inhibition of neurogenesis were likely mediated by HSP90 and the P2X7R/NLRP3 inflammasome pathway. HSP90 inhibition by 17-AAG may be a promising therapeutic strategy for the treatment of SAH.

Background Methylation of RNA is involved in many pathophysiological processes. The roles of N6-methyladenosine (m6A) and N7-methylguanosine (m7G) in heart failure (HF) have been established. However, the impact of 5-methylcytosine (m5C) on HF and its relationship with the immune microenvironment (IME) remains elusive. Methods GSE141910 (200 HF, 166 NFDs) was used as training cohort. Focusing on 9 identified m5C differently expressed genes (DEGs), random forests (RF), LASSO logistic regression, and SVM-RFE were employed to identify hub genes. ROC curves were plotted to confirm the predictive value in diagnostic model. ScRNA-seq revealed cell-type-specific m5C regulator expression patterns and HF IME. Hub genes were validated using HF rat models after myocardial infarction (MI) through quantitative reverse-transcription PCR (qRT-PCR) and western blot (WB). Consensus clustering algorithms identified two m5C-related HF subtypes. Single-sample gene-set enrichment analysis (ssGSEA) and CIBERSORT deconvolution algorithm analyzed the IME in HF. Finally, we employed WGCNA and PPI network to find m5C associated key genes and their clinical significance in HF subgroups. Results In HF samples, four m5C regulators (NSUN6, DNMT3A, DNMT3B and ALYREF) were greatly upregulated, while five (NOP2, NSUN3, NSUN7, DNMT1 and TRDMT1) were downregulated compared to NFDs in the training set. ALYREF positively correlated with activated NK cells and monocytes, whereas TRDMT1 and NSUN3 showed inverse correlations with these cells. Four hub genes were identified by machine-learning algorithms and all verified by validation model. Single-cell RNA-seq dataset GSE183852 examined the levels of 13 m5C regulators across 11 different cell types in HF. In vivo experiments including qRT-PCR and WB finally identified NSUN6 as the most remarkable regulator. The diagnostic model demonstrated excellent performance in distinguishing between HF and NFDs (AUC 0.869, 95%CI 0.832–0.906). The two m5C subtypes exhibited distinct modification patterns, immune cell infiltration, immune checkpoints, and HLA gene expression. Additionally, 138 differentially expressed genes were uncovered based on m5C subtypes, and GSEA revealed associations with key pathophysiological mechanisms of HF. By using WGCNA and PPI network, three m5C associated key genes (RPS21, RPL36 and RPS19) were identified significantly influencing cardiac function in clinical practice. Conclusion HF diagnostic model is developed based on 4 robust m5C RNA modification biomarkers (DNMT3B, NOP2, NSUN6 and DNMT1). Two distinct m5C RNA modification patterns in HF are identified, illustrating different IME characteristics. Our findings underline the significance of m5C regulators in HF, offering new perspectives on HF mechanisms and potential diagnostic and therapeutic strategies.

In this paper, a novel LiDAR–inertial-based Simultaneous Localization and Mesh Reconstruction (LI-SLAMesh) system is proposed, which can achieve fast and robust pose tracking and online mesh reconstruction in an outdoor environment. The LI-SLAMesh system consists of two components, including LiDAR–inertial odometry and a Truncated Signed Distance Field (TSDF) free online reconstruction module. Firstly, to reduce the odometry drift errors we use scan-to-map matching, and inter-frame inertial information is used to generate prior relative pose estimation for later LiDAR-dominated optimization. Then, based on the motivation that the unevenly distributed residual terms tend to degrade the nonlinear optimizer, a novel residual density-driven Gauss–Newton method is proposed to obtain the optimal pose estimation. Secondly, to achieve fast and accurate 3D reconstruction, compared with TSDF-based mapping mechanism, a more compact map representation is proposed, which only maintains the occupied voxels and computes the vertices’ SDF values of each occupied voxels using an iterative Implicit Moving Least Squares (IMLS) algorithm. Then, marching cube is performed on the voxels and a dense mesh map is generated online. Extensive experiments are conducted on public datasets. The experimental results demonstrate that our method can achieve significant localization and online reconstruction performance improvements. The source code will be made public for the benefit of the robotic community.