Unsupervised Change Detection in Hyperspectral Images using Principal Components Space Data Clustering

Change detection of hyperspectral images is a very important subject in the field of remote sensing application. Due to the large number of bands and the high correlation between adjacent bands in the hyperspectral image cube, information redundancy is a big problem, which increases the computational complexity and brings negative factor to detection performance. To address this problem, the principal component analysis (PCA) has been widely used for dimension reduction. It has the capability of projecting the original multi-dimensional hyperspectral data into new eigenvector space which allows it to extract light but representative information. The difference image of the PCA components is obtained by subtracting the two dimensionality-reduced images, on which the change detection is considered as a binary classification problem. The first several principal components of each pixel are taken as a feature vector for data classification using k-means clustering with k=2, where the two classes are changed pixels and unchanged pixels, respectively. The centroids of two clusters are determined by iteratively finding the minimum Euclidean distance between pixel's eigenvectors. Experiments on two publicly available datasets have been carried out and evaluated by overall accuracy. The results have validated the efficacy and efficiency of the proposed approach.</p

PCA-Domain Fused Singular Spectral Analysis for Fast and Noise-Robust Spectral-Spatial Feature Mining in Hyperspectral Classification

The principal component analysis (PCA) and 2-D singular spectral analysis (2DSSA) are widely used for spectral- and spatial-domain feature extraction in hyperspectral images (HSIs). However, PCA itself suffers from low efficacy if no spatial information is combined, while 2DSSA can extract the spatial information yet has a high computing complexity. As a result, we propose in this letter a PCA domain 2DSSA approach for spectral-spatial feature mining in HSI. Specifically, PCA and its variation, folded PCA (FPCA) are fused with the 2DSSA, as FPCA can extract both global and local spectral features. By applying 2DSSA only on a small number of PCA components, the overall computational cost can be significantly reduced while preserving the discrimination ability of the features. In addition, with the effective fusion of spectral and spatial features, our approach can work well on the uncorrected dataset without removing the noisy and water absorption bands, even under a small number of training samples. Experiments on two publicly available datasets have fully validated the superiority of the proposed approach, in comparison to several state-of-the-art methods and deep learning models.</p

CBANet: an end-to-end cross band 2-D attention network for hyperspectral change detection in remote sensing.

As a fundamental task in remote sensing observation of the earth, change detection using hyperspectral images (HSI) features high accuracy due to the combination of the rich spectral and spatial information, especially for identifying land-cover variations in bi-temporal HSIs. Relying on the image difference, existing HSI change detection methods fail to preserve the spectral characteristics and suffer from high data dimensionality, making them extremely challenging to deal with changing areas of various sizes. To tackle these challenges, we propose a cross-band 2-D self-attention Network (CBANet) for end-to-end HSI change detection. By embedding a cross-band feature extraction module into a 2-D spatial-spectral self-attention module, CBANet is highly capable of extracting the spectral difference of matching pixels by considering the correlation between adjacent pixels. The CBANet has shown three key advantages: 1) less parameters and high efficiency; 2) high efficacy of extracting representative spectral information from bi-temporal images; and 3) high stability and accuracy for identifying both sparse sporadic changing pixels and large changing areas whilst preserving the edges. Comprehensive experiments on three publicly available datasets have fully validated the efficacy and efficiency of the proposed methodology

MLM-LSTM: multi-layer memory learning framework based on LSTM for hyperspectral change detection.

Hyperspectral change detection plays a critical role in remote sensing by leveraging spectral and spatial information for accurate land cover variation identification. Long short-term memory (LSTM) has demonstrated its effectiveness in capturing dependencies and handling long sequences in hyperspectral data. Building on these strengths, a multilayer memory learning model based on LSTM for hyperspectral change detection is proposed, called MLM-LSTM for hyperspectral change detection is proposed. It incorporates shallow memory learning and deep memory learning. The deep memory learning module performs deep feature extraction of long-term and short-term memory separately. Then fully connected layers will be used to fuse the features followed by binary classification for change detection. Notably, our model has higher detection accuracy compared to other state-of-the-art deep learning-based models. Through comprehensive experiments on publicly available datasets, we have successfully validated the effectiveness and efficiency of the proposed MLM-LSTM approach

Status of the Science and Technology Award of the Chinese Nursing Association and analysis of the development trend of nursing research

Aims and objective: This study focuses on the status quo and development trend of nursing research in our country to provide a reference for nursing research workers and improve the development of nursing research in China. Methods: The official website of the Chinese Nursing Association was searched to obtain the total number, category, geographical distribution, and job of project leaders of winning projects of the Chinese Nursing Association Technology Award. Findings were analyzed using webometrics and content analysis approach to understand the “study hotspots,” “study level,” and “development trend” of nursing research in China. Results: The total number of winning projects was 144. Among them, the number of first, second, and third prizes were 8, 36, and 100, respectively. Beijing is the area with the largest number of winners (21), followed by Shanghai (17) and Jiangsu (13). Among the winning projects, the top 3 research areas were specialty nursing, nursing management, and nursing education, with percentages of 50%, 27%, and 18%, respectively. Among the 144 project leaders, 96% were academic or hospital directors and only 4% were ordinary staff. Conclusions: The geographical distribution of winning projects presented a regional concentration trend. The categories of winning projects are diverse, and our country has made great achievements in every category, particularly in specialty nursing. Most of the project leaders were academic or hospital directors. The stumbling block to nursing research is the lack of high-quality research projects with international influence. Future research should learn from the experiences of developed countries, focus on the in-depth refinement of specialty nursing, and highlight the importance of collaboration to coordinate and promote nursing development in all regions

PCA-domain fused singular spectral analysis for fast and noise-robust spectral-spatial feature mining in hyperspectral classification

The principal component analysis (PCA) and 2-D singular spectral analysis (2DSSA) are widely used for spectral domain and spatial domain feature extraction in hyperspectral images (HSI). However, PCA itself suffers from low efficacy if no spatial information is combined, whilst 2DSSA can extract the spatial information yet has a high computing complexity. As a result, we propose in this paper a PCA domain 2DSSA approach for spectral-spatial feature mining in HSI. Specifically, PCA and its variation, folded-PCA are utilized to fuse with the 2DSSA, as folded-PCA can extract both global and local spectral features. By applying 2DSSA only on a small number of PCA components, the overall computational complexity has been significantly reduced whilst preserving the discrimination ability of the features. In addition, with the effective fusion of spectral and spatial features, the proposed approach can work well on the uncorrected dataset without removing the noisy and water absorption bands, even under a small number of training samples. Experiments on two publicly available datasets have fully demonstrated the superiority of the proposed approach, in comparison to several state-of-the-art HSI classification methods and deep-learning models

Notch Signaling Regulates the Function and Phenotype of Dendritic Cells in <i>Helicobacter pylori</i> Infection

Notch signaling manipulates the function and phenotype of dendritic cells (DCs), as well as the interaction between DCs and CD4+ T cells. However, the role of Notch signaling in Helicobacter pylori (H. pylori) infection remains elusive. Murine bone marrow-derived dendritic cells (BMDCs) were pretreated in the absence or presence of Notch signaling inhibitor DAPT prior to H. pylori stimulation and the levels of Notch components, cytokines and surface markers as well as the differentiation of CD4+ T cells in co-culture were measured using quantitative real-time PCR (qRT-PCR), Western blot, enzyme-linked immunosorbent assay (ELISA) and flow cytometry. Compared with the control, the mRNA expression of all Notch receptors and Notch ligands Dll4 and Jagged1 was up-regulated in H. pylori-stimulated BMDCs. The blockade of Notch signaling by DAPT influenced the production of IL-1β and IL-10 in H. pylori-pulsed BMDCs, and reduced the expression of Notch1, Notch3, Notch4, Dll1, Dll3 and Jagged2. In addition, DAPT pretreatment decreased the expression of maturation markers CD80, CD83, CD86, and major histocompatibility complex class II (MHC-II) of BMDCs, and further skewed Th17/Treg balance toward Treg. Notch signaling regulates the function and phenotype of DCs, thus mediating the differentiation of CD4+ T cells during H. pylori infection

A Quasi-Randomized Controlled Trial of an Integrated Healthcare Model for Patients with Coronary Heart Disease

Background: An increasing number of coronary heart disease (CHD) patients with an aging population are demanding available and effective out-of-hospital continuous healthcare services. However, great efforts still need to be made to promote out-of-hospital healthcare services for better CHD secondary prevention. This study aims to evaluate the effectiveness of a hospital-community-family (HCF)-based integrated healthcare model on treatment outcomes, treatment compliance, and quality of life (QoL) in CHD patients. Methods: A quasi-randomized controlled trial was conducted at the Department of Cardiology, a tertiary A-level hospital, Wuhan, China from January 2018 to January 2020 in accordance with the Consolidated Standards of Reporting Trials guidelines. CHD patients were enrolled from the hospital and quasi-randomly assigned to either HCF-based integrated healthcare model services or conventional healthcare services. The treatment outcomes and QoL were observed at the 12-month follow-up. Treatment compliance was observed at the 1-month and 12-month follow-ups. Results: A total of 364 CHD patients were quasi-randomly assigned to either integrated healthcare model services (n = 190) or conventional healthcare services (n = 174). Treatment outcomes including relapse and readmission rate (22.6% vs 41.9%; relative risk [RR] = 0.54; 95% confidence interval [CI], 0.40–0.74; p = 0.0031), the occurrence of major cardiovascular events (19.5% vs 45.4%; RR = 0.43; 95% CI, 0.30–0.59; p = 0.0023), complication rate (19.5% vs 35.0%; RR = 0.56; 95% CI, 0.39–0.79; p = 0.0042), and the control rate of CHD risk factors (p 0.05, average p = 0.872). Treatment compliance at the 12-month follow-up in the intervention group, including correct medication, reasonable diet, adherence to exercise, emotional control, self-monitoring, and regular re-examination, was higher than that of the control group (p < 0.05, average p = 0.007). No difference was found in the compliance with smoking cessation and alcohol restriction at the 12-month follow-up between groups (p = 0.043). QoL at the 12-month follow-up in the intervention group was better than that of the control group (86.31 ± 9.39 vs 73.02 ± 10.70, p = 0.0048). Conclusions: The integrated healthcare model effectively improves treatment outcomes, long-term treatment compliance, and QoL of patients, and could be implemented as a feasible strategy for CHD secondary prevention

Petrogênese e Geocronologia (U-Pb e Sm-Nd) do Granito Taquaral: Registro de um Arco Magmático Continental Orosiriano na Região de Corumbá - MS

The Taquaral Granite is located on southern Amazon Craton in the region of Corumbá, westernmost part of the Brazilian state of Mato Grosso do Sul (MS), near Brazil-Bolivia frontier. This intrusion of batholitic dimensions is partially covered by sedimentary rocks of the Urucum, Tamengo Bocaina and Pantanal formations and Alluvial Deposits. The rock types are classified as quartz-monzodiorites, granodiorites, quartz-monzonites, monzo and syenogranites. There are two groups of enclaves genetically and compositionally different: one corresponds to mafic xenoliths and the second is identified as felsic microgranular enclave. Two deformation phases are observed: one ductile (F1) and the other brittle (F2). Geochemical data indicate intermediate to acidic composition for these rocks and a medium to high-K, metaluminous to peraluminous calk-alkaline magmatism, suggesting also their emplacement into magmatic arc settings. SHRIMP zircon U-Pb geochronological data of these granites reveals a crystallization age of 1861 ± 5.3 Ma. Whole rock Sm-Nd analyses provided εNd(1,86 Ga) values of -1.48 and -1.28 and TDM model ages of 2.32 and 2.25 Ga, likely indicating a Ryacian crustal source. Here we conclude that Taquaral Granite represents a magmatic episode generated at the end of the Orosirian, as a part of the Amoguija Magmatic Arc.O Granito Taquaral situa-se no sul do Cráton Amazônico, na região de Corumbá, extremo ocidente do estado de Mato Grosso do Sul (MS), próximo à fronteira Brasil-Bolívia. Ocorre como um batólito, sendo parcialmente recoberto pelas rochas sedimentares das formações Urucum, Tamengo, Bocaina e Pantanal e pelas aluviões atuais. Seus litotipos são classificados como quartzo monzodioritos, granodioritos, quartzo-monzonitos, monzogranitos e sienogranitos. Dois tipos de enclaves de natureza e origens diferentes são encontrados, um de composição máfica correspondente a xenólito e outro como enclave microgranular félsico. Observam-se duas fases deformacionais, uma de natureza dúctil (F1) e outra rúptil (F2). Os dados geoquímicos indicam composição intermediária a ácida para essas rochas e um magmatismo cálcio-alcalino de médio a alto-K, metaluminoso a peraluminoso e sugerem uma colocação em ambiente de arco. A análise geocronológica pelo método U-Pb (SHRIMP) em zircão foi realizada em um granodiorito aponta para uma idade de 1861 ± 5,3 Ma para sua cristalização. Análises Sm-Nd em rocha total fornecem valores de εNd(1,86 Ga) de -1,48 e -1,28 e TDM de 2,32 e 2,25 Ga indicando uma provável fonte crustal riaciana. Admite-se que o Granito Taquaral corresponda a um magmatismo desenvolvido no final do Orosiriano, constituinte do Arco Magmático Amoguijá