Data Set Construction Method for Intelligent Health Care and Its Application

The rapid development of Internet and computer technology makes it possible to improve smart health care services in today’s aging population. However, there are some data problems that seriously restrict the process of intelligence in the field of elderly care, such as the lack of real data, the interference of dirty data, and too few standard samples. To solve the problem of lacking data set, this paper proposes a three-stage data set construction method based on machine learning on the basis of small sample data which are collected from the community health care in a city. In the first stage, this paper designs a tree structure-based generation strategy to generate the basic attributes of the data set according to the distribution of the original data. In the second stage, this paper obtains the basic behavioral ability evaluation index of the samples with naive Bayesian algorithm. In the third stage, this paper constructs a variety of multiple linear regression equations to get high-order behavioral ability index and evaluation stage on the basis of the first two stages. In order to verify the effectiveness of the data set generated by the model for downstream tasks, this paper designs multiple rehabilitation training plan recommendation models based on the generated data with neural network, and achieves 5 multi-classification tasks and 2 multi-label classification tasks. This paper verifies the authenticity and validity of generated data through analysis of experimental results and expert knowledge

Collaborative multiple change detection methods for monitoring the spatio-temporal dynamics of mangroves in Beibu Gulf, China

Mangrove ecosystems are one of the most diverse and productive marine ecosystems around the world, although losses of global mangrove area have been occurring over the past decades. Therefore, tracking spatio-temporal changes and assessing the current state are essential for mangroves conservation. To solve the issues of inaccurate detection results of single algorithms and those limited to historical change detection, this study proposes the detect–monitor–predict (DMP) framework of mangroves for detecting time-series historical changes, monitoring abrupt near-real-time events, and predicting future trends in Beibu Gulf, China, through the synergetic use of multiple detection change algorithms. This study further developed a method for extracting mangroves using multi-source inter-annual time-series spectral indices images, and evaluated the performance of twenty-one spectral indices for capturing expansion events of mangroves. Finally, this study reveals the spatio-temporal dynamics of mangroves in Beibu Gulf from 1986 to 2021. In this study, we found that our method could extract mangrove growth regions from 1986 to 2021, and achieved 0.887 overall accuracy, which proved that this method is able to rapidly extract large-scale mangroves without field-based samples. We confirmed that the normalized difference vegetation index and tasseled cap angle outperform other spectral indexes in capturing mangrove expansion changes, while enhanced vegetation index and soil-adjusted vegetation index capture the change events with a time delay. This study revealed that mangrove changes displayed historical changes in the hierarchical gradient from land to sea with an average annual expansion of 239.822 ha in the Beibu Gulf during 1986–2021, detected slight improvements and deteriorations of some contemporary mangroves, and predicted 72.778% of mangroves with good growth conditions in the future

Combination of Hyperspectral and Quad-Polarization SAR Images to Classify Marsh Vegetation Using Stacking Ensemble Learning Algorithm

Combinations of multi-sensor remote sensing images and machine learning have attracted much attention in recent years due to the spectral similarity of wetland plant canopy. However, the integration of hyperspectral and quad-polarization synthetic aperture radar (SAR) images for classifying marsh vegetation has still been faced with the challenges of using machine learning algorithms. To resolve this issue, this study proposed an approach to classifying marsh vegetation in the Honghe National Nature Reserve, northeast China, by combining backscattering coefficient and polarimetric decomposition parameters of C-band and L-band quad-polarization SAR data with hyperspectral images. We further developed an ensemble learning model by stacking Random Forest (RF), CatBoost and XGBoost algorithms for marsh vegetation mapping and evaluated its classification performance of marsh vegetation between combinations of hyperspectral and full-polarization SAR data and any of the lone sensor images. Finally, this paper explored the effect of different polarimetric decomposition methods and wavelengths of radar on classifying wetland vegetation. We found that a combination of ZH-1 hyperspectral images, C-band GF-3, and L-band ALOS-2 quad-polarization SAR images achieved the highest overall classification accuracy (93.13%), which was 5.58–9.01% higher than that only using C-band or L-band quad-polarization SAR images. This study confirmed that stacking ensemble learning provided better performance than a single machine learning model using multi-source images in most of the classification schemes, with the overall accuracy ranging from 77.02% to 92.27%. The CatBoost algorithm was capable of identifying forests and deep-water marsh vegetation. We further found that L-band ALOS-2 SAR images achieved higher classification accuracy when compared to C-band GF-3 polarimetric SAR data. ALOS-2 was more sensitive to deep-water marsh vegetation classification, while GF-3 was more sensitive to shallow-water marsh vegetation mapping. Finally, scattering model-based decomposition provided important polarimetric parameters from ALOS-2 SAR images for marsh vegetation classification, while eigenvector/eigenvalue-based and two-component decompositions produced a great contribution when using GF-3 SAR images

Effects of Multi-Growth Periods UAV Images on Classifying Karst Wetland Vegetation Communities Using Object-Based Optimization Stacking Algorithm

Combining machine learning algorithms with multi-temporal remote sensing data for fine classification of wetland vegetation has received wide attention from researchers. However, wetland vegetation has different physiological characteristics and phenological information in different growth periods, so it is worth exploring how to use different growth period characteristics to achieve fine classification of vegetation communities. To resolve these issues, we developed an ensemble learning model by stacking Random Forest (RF), CatBoost, and XGBoost algorithms for karst wetland vegetation community mapping and evaluated its classification performance using three growth periods of UAV images. We constructed six classification scenarios to quantitatively evaluate the effects of combining multi-growth periods UAV images on identifying vegetation communities in the Huixian Karst Wetland of International Importance. Finally, we clarified the influence and contribution of different feature bands on vegetation communities’ classification from local and global perspectives based on the SHAP (Shapley Additive explanations) method. The results indicated that (1) the overall accuracies of the four algorithms ranged from 82.03% to 93.37%, and the classification performance was Stacking > CatBoost > RF > XGBoost in order. (2) The Stacking algorithm significantly improved the classification results of vegetation communities, especially Huakolasa, Reed-Imperate, Linden-Camphora, and Cephalanthus tetrandrus-Paliurus ramosissimus. Stacking had better classification performance and generalization ability than the other three machine learning algorithms. (3) Our study confirmed that the combination of spring, summer, and autumn growth periods of UAV images produced the highest classification accuracy (OA, 93.37%). In three growth periods, summer-based UAVs achieved the highest classification accuracy (OA, 85.94%), followed by spring (OA, 85.32%) and autumn (OA, 84.47%) growth period images. (4) The interpretation of black-box stacking model outputs found that vegetation indexes and texture features provided more significant contributions to classifying karst wetland vegetation communities than the original spectral bands, geometry features, and position features. The vegetation indexes (COM and NGBDI) and texture features (Homogeneity and Standard Deviation) were very sensitive when distinguishing Bermudagrass, Bamboo, and Linden-Camphora. These research findings provide a scientific basis for the protection, restoration, and sustainable development of karst wetlands

Combination of Hyperspectral and Quad-Polarization SAR Images to Classify Marsh Vegetation Using Stacking Ensemble Learning Algorithm

Combinations of multi-sensor remote sensing images and machine learning have attracted much attention in recent years due to the spectral similarity of wetland plant canopy. However, the integration of hyperspectral and quad-polarization synthetic aperture radar (SAR) images for classifying marsh vegetation has still been faced with the challenges of using machine learning algorithms. To resolve this issue, this study proposed an approach to classifying marsh vegetation in the Honghe National Nature Reserve, northeast China, by combining backscattering coefficient and polarimetric decomposition parameters of C-band and L-band quad-polarization SAR data with hyperspectral images. We further developed an ensemble learning model by stacking Random Forest (RF), CatBoost and XGBoost algorithms for marsh vegetation mapping and evaluated its classification performance of marsh vegetation between combinations of hyperspectral and full-polarization SAR data and any of the lone sensor images. Finally, this paper explored the effect of different polarimetric decomposition methods and wavelengths of radar on classifying wetland vegetation. We found that a combination of ZH-1 hyperspectral images, C-band GF-3, and L-band ALOS-2 quad-polarization SAR images achieved the highest overall classification accuracy (93.13%), which was 5.58–9.01% higher than that only using C-band or L-band quad-polarization SAR images. This study confirmed that stacking ensemble learning provided better performance than a single machine learning model using multi-source images in most of the classification schemes, with the overall accuracy ranging from 77.02% to 92.27%. The CatBoost algorithm was capable of identifying forests and deep-water marsh vegetation. We further found that L-band ALOS-2 SAR images achieved higher classification accuracy when compared to C-band GF-3 polarimetric SAR data. ALOS-2 was more sensitive to deep-water marsh vegetation classification, while GF-3 was more sensitive to shallow-water marsh vegetation mapping. Finally, scattering model-based decomposition provided important polarimetric parameters from ALOS-2 SAR images for marsh vegetation classification, while eigenvector/eigenvalue-based and two-component decompositions produced a great contribution when using GF-3 SAR images

HJH-1, a Broad-Spectrum Antimicrobial Activity and Low Cytotoxicity Antimicrobial Peptide

With the overuse of antibiotics, multidrug-resistant bacteria pose a significant threat to human health. Antimicrobial peptides (AMPs) are a promising alternative to conventional antibiotics. This study examines the antimicrobial and membrane activity of HJH-1, a cationic peptide derived from the hemoglobin α-subunit of bovine erythrocytes P3. HJH-1 shows potent antimicrobial activity against different bacterial species associated with infection and causes weaker hemolysis of erythrocytes, at least five times the minimum inhibitory concentration (MIC). HJH-1 has good stability to tolerance temperature, pH value, and ionic strength. The anionic membrane potential probe bis-(1,3-dibutylbarbituric acid) trimethine oxonol [DiBAC4(3)] and propidium iodide are used as indicators of membrane integrity. In the presence of HJH-1 (1× MIC), Escherichia coli membranes rapidly depolarise, whereas red blood cells show gradual hyperpolarisation. Scanning electron microscopy and transmission electron micrographs show that HJH-1 (1× MIC) damaged the membranes of Escherichia coli, Staphylococcus aureus, and Candida albicans. In conclusion, HJH-1 damages the integrity of the bacterial membrane, preventing the growth of bacteria. HJH-1 has broad-spectrum antibacterial activity, and these activities are performed by changing the normal cell transmembrane potential and disrupting the integrity of the bacterial membrane

Evaluation of spatio-temporal variations of FVC and its relationship with climate change using GEE and Landsat images in Ganjiang River Basin

Fractional vegetation cover (FVC) is an important feature of the ecosystem, and continuous evaluation of vegetation status is one of the key issues in the ecological monitoring of river basins. This article divided the Ganjiang River Basin (GRB) into 73 subbasins, and quantitatively evaluated the spatio-temporal FVC changes at multiple scales from 2000 to 2019 using Google Earth Engine and Landsat series images. Theil-Sen slope estimator and Mann-Kendall algorithm were used to monitor the spatio-temporal change trend of the FVC in the basin and subbasins, respectively. Mann-Kendall test was executed to analyze the abrupt change of FVC. Hurst exponent and Theil-Sen Slope were integrated to evaluate the consistency of FVC change and predict its spatio-temporal evolution trend. Grey correlation analysis and Pearson correlation analysis were selected to quantify the relationship between FVC and terrain, climate factors in each subbasin, and further explore the effect of single factor and multiple-factor combination on FVC change. The results showed that: (1) FVC in the GRB was in good condition, and increased with the elevation and slope, but the FVC in the subbasins with elevation less than 200 m ranged from 25.95% to 72.86%. From 2000 to 2019, the area of high coverage increased significantly compared with other coverage grades. The FVC of most subbasins varied from 45% to 80%, and the change range fluctuated from 9.4% to 36.53%. (2) The FVC has presented a fluctuating growth trend in the past 20 years. The area with increasing FVC reached 68.93%, and the area of a significant improvement in FVC was 11.65% more than the area with the significant degradation. The FVC in the 64.38% of subbasins has abrupt changed, and 20.55% of subbasins has changed dramatically with 7 abrupt change points, which demonstrated that the subbasin-scale abrupt change analysis could accurately monitor the spatio-temporal change of FVC. (3) The 76% of basin appeared the weak consistency in the FVC, and the northern and southern of the basin present a trend of degradation in the future. (4) The spatio-temporal distribution of FVC in GRB was greatly affected by terrain. The effect of temperature and relative humidity on the FVC change was greater than that of precipitation. The synergistic effect of three climatic factors on FVC change was more significant than that of a single factor

Inhibitory Effects of Antimicrobial Peptide JH-3 on <i>Salmonella enterica</i> Serovar Typhimurium Strain CVCC541 Infection-Induced Inflammatory Cytokine Release and Apoptosis in RAW264.7 Cells

The antibiotic resistance of Salmonella has become increasingly serious due to the increased use of antibiotics, and antimicrobial peptides have been considered as an ideal antibiotic alternative. Salmonella can induce macrophage apoptosis and thus further damage the immune system. The antimicrobial peptide JH-3 has been shown to have a satisfactory anti-Salmonella effect in previous research, but its mechanism of action remains unknown. In this study, the effects of JH-3 on macrophages infected with Salmonella Typhimurium CVCC541 were evaluated at the cellular level. The results showed that JH-3 significantly alleviated the damage to macrophages caused by S. Typhi infection, reduced the release of lactic dehydrogenase (LDH), and killed the bacteria in macrophages. In addition, JH-3 decreased the phosphorylation level of p65 and the expression and secretion of interleukin 2 (IL-2), IL-6, and tumor necrosis factor-&#945; (TNF-&#945;) by inhibiting the activation of the mitogen-activated protein kinase (MAPK) (p38) signaling pathway and alleviating the cellular inflammatory response. From confocal laser scanning microscopy and flow cytometry assays, JH-3 was observed to inhibit the release of cytochrome c in the cytoplasm; the expression of TNF-&#945;R2, caspase-9, and caspase-8; to further weaken caspase-3 activation; and to reduce the S.-Typhi-induced apoptosis of macrophages. In summary, the mechanism by which JH-3 inhibits Salmonella infection was systematically explored at the cellular level, laying the foundation for the development and utilization of JH-3 as a therapeutic alternative to antibiotics