Analysis of the Dilemma and Strategies of Elderly Patients Access to Outpatient Services - Based on the Examples from three Grade A Tertiary Hospitals in Jiangxi Province

Objective: To identify the dilemma of elderly patients' access to outpatient services, develop strategies to improve the environment and functions of the outpatient department, and encourage the elderly to access medical services independently. Methods: By observing and interviewing, this paper studies the environment, behavior, and experiences of elderly patients when accessing medical services, identifies and classifies the key issues, and provides corresponding suggestions. Results: Existing signs and voice prompt systems fail to guide elderly patients to access to medical services; Elderly patients have difficulty in finding places to transit and rest when accessing to outpatient services; Elderly patients have problems in using AI (artificial intelligence) technologies when they access to outpatient services; There are communication barriers between elderly patients and medical staffs. Conclusion: Optimizing the guiding signs and voice prompt systems according to the characteristics of elderly patients; Designing the areas of transition and rest reasonably; Enhancing the ability of elderly patients to use self-service equipment; Promoting the medical treatment process to the elderly in a humanized way

A One Stop 3D Target Reconstruction and multilevel Segmentation Method

3D object reconstruction and multilevel segmentation are fundamental to computer vision research. Existing algorithms usually perform 3D scene reconstruction and target objects segmentation independently, and the performance is not fully guaranteed due to the challenge of the 3D segmentation. Here we propose an open-source one stop 3D target reconstruction and multilevel segmentation framework (OSTRA), which performs segmentation on 2D images, tracks multiple instances with segmentation labels in the image sequence, and then reconstructs labelled 3D objects or multiple parts with Multi-View Stereo (MVS) or RGBD-based 3D reconstruction methods. We extend object tracking and 3D reconstruction algorithms to support continuous segmentation labels to leverage the advances in the 2D image segmentation, especially the Segment-Anything Model (SAM) which uses the pretrained neural network without additional training for new scenes, for 3D object segmentation. OSTRA supports most popular 3D object models including point cloud, mesh and voxel, and achieves high performance for semantic segmentation, instance segmentation and part segmentation on several 3D datasets. It even surpasses the manual segmentation in scenes with complex structures and occlusions. Our method opens up a new avenue for reconstructing 3D targets embedded with rich multi-scale segmentation information in complex scenes. OSTRA is available from https://github.com/ganlab/OSTRA

Learning Two-Stream CNN for Multi-Modal Age-related Macular Degeneration Categorization

This paper tackles automated categorization of Age-related Macular Degeneration (AMD), a common macular disease among people over 50. Previous research efforts mainly focus on AMD categorization with a single-modal input, let it be a color fundus image or an OCT image. By contrast, we consider AMD categorization given a multi-modal input, a direction that is clinically meaningful yet mostly unexplored. Contrary to the prior art that takes a traditional approach of feature extraction plus classifier training that cannot be jointly optimized, we opt for end-to-end multi-modal Convolutional Neural Networks (MM-CNN). Our MM-CNN is instantiated by a two-stream CNN, with spatially-invariant fusion to combine information from the fundus and OCT streams. In order to visually interpret the contribution of the individual modalities to the final prediction, we extend the class activation mapping (CAM) technique to the multi-modal scenario. For effective training of MM-CNN, we develop two data augmentation methods. One is GAN-based fundus / OCT image synthesis, with our novel use of CAMs as conditional input of a high-resolution image-to-image translation GAN. The other method is Loose Pairing, which pairs a fundus image and an OCT image on the basis of their classes instead of eye identities. Experiments on a clinical dataset consisting of 1,099 color fundus images and 1,290 OCT images acquired from 1,099 distinct eyes verify the effectiveness of the proposed solution for multi-modal AMD categorization

Pedestal looseness extent recognition method for rotating machinery based on vibration sensitive time-frequency feature and manifold learning

To realize automation and high accuracy of pedestal looseness extent recognition for rotating machinery, a novel pedestal looseness extent recognition method for rotating machinery based on vibration sensitive time-frequency feature and manifold learning dimension reduction is proposed. Firstly, the pedestal looseness extent of rotating machinery is characterized by vibration signal of rotating machinery and its spectrum, then the time-frequency features are extracted from vibration signal to construct the origin looseness extent feature set. Secondly, the algorithm of looseness sensitivity index is designed to filter out the non-sensitive feature and poor sensitivity feature from the origin looseness extent feature set, avoiding the interference of non-sensitive and poor sensitivity feature. The sensitive features are selected to construct the looseness extent sensitive feature set, which has stronger characterization capabilities than the origin looseness extent feature set. Moreover, an effective manifold learning method called linear local tangent space alignment (LLTSA) is introduced to compress the looseness extent sensitive feature set into the low-dimensional looseness extent sensitive feature set. Finally, the low-dimensional looseness extent sensitive feature set is inputted into weight K nearest neighbor classifier (WKNNC) to recognize the different pedestal looseness extents of rotating machinery, the WKNNC’s recognition accuracy is more stable compared with that of a k nearest neighbor classification (KNNC). At the same time, the pedestal looseness extent recognition of rotating machinery is realized. The feasibility and validity of the present method are verified by successful pedestal looseness extent recognition application in a rotating machinery

Construction and characterization of mice with conditional knockout of Stat3 gene in microglia

Objective·To construct mice with conditional knockout of Stat3 gene in microglia based on the Cre-Loxp system and validate their knockout efficiency.Methods·Cx3cr1creERT2 and Stat3fl/fl genotypic mice were bred for conditional knockout mice (CKO) and Wild Type mice (WT). The mouse genotypes were determined by extracting DNA from mouse tissues through Polymerase Chain Reaction (PCR) combined with the amplification results of cre and flox primers. Stat3 knockdown was induced by intraperitoneal injection of tamoxifen in the CKO and WT mice at 6 weeks of age. The CKO mice (n=4) and WT mice (n=4) were randomly selected for the detection. After two weeks of observation, microglia cells were sorted out by Magnetic Activated Cell Sorting (MACS). Real-time PCR (RT-PCR) was used to detect gene knockout efficiency at the gene level. The expression of STAT3 in microglia was observed by brain immunofluorescence staining. The expression rate of STAT3 in microglia was detected by flow cytometry. The expression rate of STAT3 in macrophages of the spleen was detected by flow cytometry. The condition of neuronal cells was examined by Nissl staining. The condition of the microglia in the cortex and hippocampus was observed by brain immunofluorescence staining. The phenotype of the microglia was detected by flow cytometry.Results·The CKO mice and WT mice were successfully bred. MACS boosted the proportion of microglia in brain cells from 10% to 85%. RT-PCR results showed that mRNA levels of Stat3 were down-regulated in microglia of CKO mice, compared with the WT mice (P=0.001). The relative mRNA expression of Stat3 in microglia of the CKO mice was 0.331 7±0.041 4. Immunofluorescence staining of brain tissues showed that the fluorescence intensity of STAT3 in microglia of the CKO mice was weaker than that of the WT mice. Flow cytometry of brain tissues showed that the STAT3-positive cells in microglia of the WT mice was (85.30±5.69)% and the CKO mice was (39.70±3.88)%. STAT3 expression was decreased in microglia of the CKO mice (P=0.001). Flow cytometry of spleen tissues showed that there was no statistical difference in the percentage of STAT3-positive cells in splenic macrophages between the CKO and WT mice (P>0.05). Nissl staining showed that there were no significant differences between the neuronal cells of the CKO mice and WT mice. Immunofluorescencestaining of brain tissues showed that there was no significant difference in the shape of microglia between the CKO mice and WT mice. Flow cytometry showed that the phenotype of microglia in the CKO mice was not remarkably different from that of the WT mice.Conclusion·We successfully construct the STAT3 gene conditional knockout mice from microglia, which provides the foundation for subsequent related studies

EST analysis of gene expression in the tentacle of Cyanea capillata

AbstractJellyfish, Cyanea capillata, has an important position in head patterning and ion channel evolution, in addition to containing a rich source of toxins. In the present study, 2153 expressed sequence tags (ESTs) from the tentacle cDNA library of C. capillata were analyzed. The initial ESTs consisted of 198 clusters and 818 singletons, which revealed approximately 1016 unique genes in the data set. Among these sequences, we identified several genes related to head and foot patterning, voltage-dependent anion channel gene and genes related to biological activities of venom. Five kinds of proteinase inhibitor genes were found in jellyfish for the first time, and some of them were highly expressed with unknown functions

Estimation of the Age and Amount of Brown Rice Plant Hoppers Based on Bionic Electronic Nose Use

The brown rice plant hopper (BRPH), Nilaparvata lugens (Stal), is one of the most important insect pests affecting rice and causes serious damage to the yield and quality of rice plants in Asia. This study used bionic electronic nose technology to sample BRPH volatiles, which vary in age and amount. Principal component analysis (PCA), linear discrimination analysis (LDA), probabilistic neural network (PNN), BP neural network (BPNN) and loading analysis (Loadings) techniques were used to analyze the sampling data. The results indicate that the PCA and LDA classification ability is poor, but the LDA classification displays superior performance relative to PCA. When a PNN was used to evaluate the BRPH age and amount, the classification rates of the training set were 100% and 96.67%, respectively, and the classification rates of the test set were 90.67% and 64.67%, respectively. When BPNN was used for the evaluation of the BRPH age and amount, the classification accuracies of the training set were 100% and 48.93%, respectively, and the classification accuracies of the test set were 96.67% and 47.33%, respectively. Loadings for BRPH volatiles indicate that the main elements of BRPHs’ volatiles are sulfur-containing organics, aromatics, sulfur-and chlorine-containing organics and nitrogen oxides, which provide a reference for sensors chosen when exploited in specialized BRPH identification devices. This research proves the feasibility and broad application prospects of bionic electronic noses for BRPH recognition

From cropland to cropped field: A robust algorithm for national-scale mapping by fusing time series of Sentinel-1 and Sentinel-2

Detailed and updated maps of actively cropped fields on a national scale are vital for global food security. Unfortunately, this information is not provided in existing land cover datasets, especially lacking in smallholder farmer systems. Mapping national-scale cropped fields remains challenging due to the spectral confusion with abandoned vegetated land, and their high heterogeneity over large areas. This study proposed a large-area mapping framework for automatically identifying actively cropped fields by fusing Vegetation-Soil-Pigment indices and Synthetic-aperture radar (SAR) time-series images (VSPS). Three temporal indicators were proposed and highlighted cropped fields by consistently higher values due to cropping activities. The proposed VSPS algorithm was exploited for national-scale mapping in China without regional adjustments using Sentinel-2 and Sentinel-1 images. Agriculture in China illustrated great heterogeneity and has experienced tremendous changes such as non-grain orientation and cropland abandonment. Yet, little is known about the locations and extents of cropped fields cultivated with field crops on a national scale. Here, we produced the first national-scale 20 m updated map of cropped and fallow/abandoned land in China and found that 77 % of national cropland (151.23 million hectares) was actively cropped in 2020. We found that fallow/abandoned cropland in mountainous and hilly regions were far more than we expected, which was significantly underestimated by the commonly applied VImax-based approach based on the MODIS images. The VSPS method illustrates robust generalization capabilities, which obtained an overall accuracy of 94 % based on 4,934 widely spread reference sites. The proposed mapping framework is capable of detecting cropped fields with a full consideration of a high diversity of cropping systems and complexity of fallow/abandoned cropland. The processing codes on Google Earth Engine were provided and hoped to stimulate operational agricultural mapping on cropped fields with finer resolution from the national to the global scale