Memory-Augmented 3D Point Cloud Semantic Segmentation Network for Intelligent Mining Shovels.

The semantic segmentation of the 3D operating environment represents the key to intelligent mining shovels' autonomous digging and loading operation. However, the complexity of the operating environment of intelligent mining shovels presents challenges, including the variety of scene targets and the uneven number of samples. This results in low accuracy of 3D semantic segmentation and reduces the autonomous operation accuracy of the intelligent mine shovels. To solve these issues, this paper proposes a 3D point cloud semantic segmentation network based on memory enhancement and lightweight attention mechanisms. This model addresses the challenges of an uneven number of sampled scene targets, insufficient extraction of key features to reduce the semantic segmentation accuracy, and an insufficient lightweight level of the model to reduce deployment capability. Firstly, we investigate the memory enhancement learning mechanism, establishing a memory module for key semantic features of the targets. Furthermore, we address the issue of forgetting non-dominant target point cloud features caused by the unbalanced number of samples and enhance the semantic segmentation accuracy. Subsequently, the channel attention mechanism is studied. An attention module based on the statistical characteristics of the channel is established. The adequacy of the expression of the key features is improved by adjusting the weights of the features. This is done in order to improve the accuracy of semantic segmentation further. Finally, the lightweight mechanism is studied by adopting the deep separable convolution instead of conventional convolution to reduce the number of model parameters. Experiments demonstrate that the proposed method can improve the accuracy of semantic segmentation in the 3D scene and reduce the model's complexity. Semantic segmentation accuracy is improved by 7.15% on average compared with the experimental control methods, which contributes to the improvement of autonomous operation accuracy and safety of intelligent mining shovels.

The role of integrins in brain health and neurodegenerative diseases.

Integrins are heterodimeric membrane proteins expressed on the surface of most cells. They mediate adhesion and signaling processes relevant for a wealth of physiological processes, including nervous system development and function. Interestingly, integrins are also recognized therapeutic targets for inflammatory diseases, such as multiple sclerosis. Here, we discuss the role of integrins in brain development and function, as well as in neurodegenerative diseases affecting the brain (Alzheimer's disease, multiple sclerosis, stroke). Furthermore, we discuss therapeutic targeting of these adhesion receptors in inflammatory diseases of the brain.

ß2-Integrins Regulate Microglial Responses and the Functional Outcome of Hemorrhagic Stroke In Vivo.

Stroke is one of the leading causes of death and long-term disabilities worldwide. In addition to interruption of blood flow, inflammation is widely recognized as an important factor mediating tissue destruction in stroke. Depending on their phenotype, microglia, the main leukocytes in the CNS, are capable of either causing further tissue damage or promoting brain restoration after stroke. ß2-integrins are cell adhesion molecules that are constitutively expressed on microglia. The function of ß2-integrins has been investigated extensively in animal models of ischemic stroke, but their role in hemorrhagic stroke is currently poorly understood. We show in this study that dysfunction of ß2-integrins is associated with improved functional outcome and decreased inflammatory cytokine expression in the brain in a mouse model of hemorrhagic stroke. Furthermore, ß2-integrins affect microglial phenotype and cytokine responses in vivo. Therefore, our findings suggest that targeting ß2-integrins in hemorrhagic stroke may be beneficial.

Deep learning model fusion improves lung tumor segmentation accuracy across variable training-to-test dataset ratios.

This study aimed to investigate the robustness of a deep learning (DL) fusion model for low training-to-test ratio (TTR) datasets in the segmentation of gross tumor volumes (GTVs) in three-dimensional planning computed tomography (CT) images for lung cancer stereotactic body radiotherapy (SBRT). A total of 192 patients with lung cancer (solid tumor, 118; part-solid tumor, 53; ground-glass opacity, 21) who underwent SBRT were included in this study. Regions of interest in the GTVs were cropped based on GTV centroids from planning CT images. Three DL models, 3D U-Net, V-Net, and dense V-Net, were trained to segment the GTV regions. Nine fusion models were constructed with logical AND, logical OR, and voting of the two or three outputs of the three DL models. TTR was defined as the ratio of the number of cases in a training dataset to that in a test dataset. The Dice similarity coefficients (DSCs) and Hausdorff distance (HD) of the 12 models were assessed with TTRs of 1.00 (training data: validation data: test data = 40:20:40), 0.791 (35:20:45), 0.531 (31:10:59), 0.291 (20:10:70), and 0.116 (10:5:85). The voting fusion model achieved the highest DSCs of 0.829 to 0.798 for all TTRs among the 12 models, whereas the other models showed DSCs of 0.818 to 0.804 for a TTR of 1.00 and 0.788 to 0.742 for a TTR of 0.116, and an HD of 5.40 ± 3.00 to 6.07 ± 3.26 mm better than any single DL models. The findings suggest that the proposed voting fusion model is a robust approach for low TTR datasets in segmenting GTVs in planning CT images of lung cancer SBRT.

CT Image-Based Biopsy to Aid Prediction of HOPX Expression Status and Prognosis for Non-Small Cell Lung Cancer Patients.

This study aimed to elucidate a computed tomography (CT) image-based biopsy with a radiogenomic signature to predict homeodomain-only protein homeobox (HOPX) gene expression status and prognosis in patients with non-small cell lung cancer (NSCLC). Patients were labeled as HOPX-negative or positive based on HOPX expression and were separated into training (n = 92) and testing (n = 24) datasets. In correlation analysis between genes and image features extracted by Pyradiomics for 116 patients, eight significant features associated with HOPX expression were selected as radiogenomic signature candidates from the 1218 image features. The final signature was constructed from eight candidates using the least absolute shrinkage and selection operator. An imaging biopsy model with radiogenomic signature was built by a stacking ensemble learning model to predict HOPX expression status and prognosis. The model exhibited predictive power for HOPX expression with an area under the receiver operating characteristic curve of 0.873 and prognostic power in Kaplan-Meier curves (p = 0.0066) in the test dataset. This study's findings implied that the CT image-based biopsy with a radiogenomic signature could aid physicians in predicting HOPX expression status and prognosis in NSCLC.

Probabilistic-fuzzy risk assessment and source analysis of heavy metals in soil considering uncertainty: A case study of Jinling Reservoir in China.

Considering the uncertainty caused by the random error of the sample measurement, the heterogeneity of spatial and temporal distribution of pollutants, and the traditional method of selecting a single parameter for evaluation, based on fuzzy theory, Hakanson potential ecological risk index (considering heavy metal enrichment, ecotoxicity and bioavailability) and United States Environmental Protection Agency health risk assessment system, the fuzzy ecological risk and health risk assessment methods for of heavy metals in soil were established. In the soil of the Jinling Reservoir area, Cd, which has high bioavailability, had the highest average contribution rate to RI, and thus was, regarded as a priority metal for ecological risk. Sites JL9 and JL11 were the priority areas. The heavy metals did not pose a clear hazard to human health, but children had a higher health risk. Pb and As were regarded as the priority metals for health risk. Fuzzy evaluation provided the risk interval and membership degree, contained more parameter information, quantified and reduced the uncertainty of parameters, provided more comprehensive results, and compensated for the deficiency of deterministic evaluation. As the main source of heavy metals, the intensity of agricultural activities in the study area must be controlled to avoid excessive input and accumulation of heavy metals, which may damage the ecological environment and endanger human health.

Automated approach for segmenting gross tumor volumes for lung cancer stereotactic body radiation therapy using CT-based dense V-networks.

The aim of this study was to develop an automated segmentation approach for small gross tumor volumes (GTVs) in 3D planning computed tomography (CT) images using dense V-networks (DVNs) that offer more advantages in segmenting smaller structures than conventional V-networks. Regions of interest (ROI) with dimensions of 50 × 50 × 6-72 pixels in the planning CT images were cropped based on the GTV centroids when applying stereotactic body radiotherapy (SBRT) to patients. Segmentation accuracy of GTV contours for 192 lung cancer patients [with the following tumor types: 118 solid, 53 part-solid types and 21 pure ground-glass opacity (pure GGO)], who underwent SBRT, were evaluated based on a 10-fold cross-validation test using Dice's similarity coefficient (DSC) and Hausdorff distance (HD). For each case, 11 segmented GTVs consisting of three single outputs, four logical AND outputs, and four logical OR outputs from combinations of two or three outputs from DVNs were obtained by three runs with different initial weights. The AND output (combination of three outputs) achieved the highest values of average 3D-DSC (0.832 ± 0.074) and HD (4.57 ± 2.44 mm). The average 3D DSCs from the AND output for solid, part-solid and pure GGO types were 0.838 ± 0.074, 0.822 ± 0.078 and 0.819 ± 0.059, respectively. This study suggests that the proposed approach could be useful in segmenting GTVs for planning lung cancer SBRT.

Ecological risk, health risk assessment, and pollution source analysis of Xinli Lake wetland based on triangular fuzzy number.

Wetland environmental pollution has become a global problem involving the ecological environment and human health. This study measured the concentration of seven potentially toxic elements (PTEs Hg, Cd, Zn, Cu, Cr, Pb, and As) in the soil upstream of the Xinli Lake wetland in China. Based on the fuzzy theory, the sources, spatial distribution, ecological risks, and health risks of pollutants are studied. The result shows that the concentrations of the seven potentially toxic elements are close to or exceed the background value, and their spatial distribution showed irregular changes. The soil upstream of the wetland has not been seriously polluted, and Cd, which has higher bioavailability, is the priority element for ecological risk. Pollutants do not harm human health; children face higher health risks; Pb and As have the highest carcinogenic and non-carcinogenic risks, respectively. Zn, Cu, Cr, Pb, and As in the study area are derived from agricultural activities, while Hg and Cd are mainly affected by soil-forming parent materials. Attention should be paid to controlling the intensity of agricultural activities to avoid excessive input and accumulation of pollutants that would harm the ecological environment and human health.