Masked Spatial-Spectral Autoencoders Are Excellent Hyperspectral Defenders

Deep learning methodology contributes a lot to the development of hyperspectral image (HSI) analysis community. However, it also makes HSI analysis systems vulnerable to adversarial attacks. To this end, we propose a masked spatial-spectral autoencoder (MSSA) in this paper under self-supervised learning theory, for enhancing the robustness of HSI analysis systems. First, a masked sequence attention learning module is conducted to promote the inherent robustness of HSI analysis systems along spectral channel. Then, we develop a graph convolutional network with learnable graph structure to establish global pixel-wise combinations.In this way, the attack effect would be dispersed by all the related pixels among each combination, and a better defense performance is achievable in spatial aspect.Finally, to improve the defense transferability and address the problem of limited labelled samples, MSSA employs spectra reconstruction as a pretext task and fits the datasets in a self-supervised manner.Comprehensive experiments over three benchmarks verify the effectiveness of MSSA in comparison with the state-of-the-art hyperspectral classification methods and representative adversarial defense strategies.Comment: 14 pages, 9 figure

Predicting progression of white matter hyperintensity using coronary artery calcium score based on coronary CT angiography—feasibility and accuracy

ObjectiveCoronary artery disease (CAD) usually coexists with subclinical cerebrovascular diseases given the systematic nature of atherosclerosis. In this study, our objective was to predict the progression of white matter hyperintensity (WMH) and find its risk factors in CAD patients using the coronary artery calcium (CAC) score. We also investigated the relationship between the CAC score and the WMH volume in different brain regions.MethodsWe evaluated 137 CAD patients with WMH who underwent coronary computed tomography angiography (CCTA) and two magnetic resonance imaging (MRI) scans from March 2018 to February 2023. Patients were categorized into progressive (n = 66) and nonprogressive groups (n = 71) by the change in WMH volume from the first to the second MRI. We collected demographic, clinical, and imaging data for analysis. Independent risk factors for WMH progression were identified using logistic regression. Three models predicting WMH progression were developed and assessed. Finally, patients were divided into groups based on their total CAC score (0 to <100, 100 to 400, and > 400) to compare their WMH changes in nine brain regions.ResultsAlcohol abuse, maximum pericoronary fat attenuation index (pFAI), CT-fractional flow reserve (CT-FFR), and CAC risk grade independently predicted WMH progression (p < 0.05). The logistic regression model with all four variables performed best (training: AUC = 0.878, 95% CI: 0.790, 0.938; validation: AUC = 0.845, 95% CI: 0.734, 0.953). An increased CAC risk grade came with significantly higher WMH volume in the total brain, corpus callosum, and frontal, parietal and occipital lobes (p < 0.05).ConclusionThis study demonstrated the application of the CCTA-derived CAC score to predict WMH progression in elderly people (≥60 years) with CAD

Temporal and Within-Sporophyte Variations in Triphenyltin Chloride (TPTCL) and Its Degradation Products in Cultivated <i>Undaria pinnatifida</i>

Undaria pinnatifida can effectively deal with organotin pollution through its excellent accumulation and degradation capabilities found under laboratory conditions. However, nothing is known regarding its accumulation, degradation performance, and related impact factors in the wild farming area. In this study, we monitored triphenyltin chloride (TPTCL) contents and degradation products in different algal parts (blades, stipes, sporophylls, and holdfasts) of cultivated U. pinnatifida from December 2018 to May 2019. Our results showed that sporophytes had an accumulation and degradation capacity for TPTCL. The TPTCL contents and degradation products varied with the algal growth stages and algal parts. TPTCL accumulated in the blades at the growth stage and the blades, stipes, sporophylls, and holdfasts at the mature stage. The TPTCL content among algal parts was blades (74.92 ± 2.52 μg kg−1) > holdfasts (62.59 ± 1.42 μg kg−1) > sporophylls (47.24 ± 1.41 μg kg−1) > stipes (35.53 ± 0.55 μg kg−1). The primary degradation product DPTCL accumulated only in the blades at any stage, with a concentration of 69.30 ± 3.89 μg kg−1. The secondary degradation product MPTCL accumulated in the blades at the growth stage and in the blades, stipe, and sporophyll at the mature stage. The MPTCL content among algal parts was blades (52.80 ± 3.48 μg kg−1) > sporophylls (31.08 ± 1.53 μg kg−1) > stipes (20.44 ± 0.85 μg kg−1). The accumulation pattern of TPTCL and its degradation products seems closely related to nutrient allocation in U. pinnatifida. These results provide the basis for applying cultivated U. pinnatifida in the bioremediation of organotin pollution and the food safety evaluation of edible algae

An Unmixing-Based Network for Underwater Target Detection From Hyperspectral Imagery

Detecting underwater targets from hyperspectral imagery makes a profound impact on marine exploration. Available methods mainly tackle this problem by modifying the land-based detection algorithms with classical bathymetric models, which usually fail to remove the interference of background and ignore the effect of depth information, leading to a poor detection performance. To achieve a more precise result, in this work we propose a novel network based on hyperspectral unmixing (HU) methodology and bathymetric models to detect the desired underwater targets. The proposed network, called underwater target detection network (UTD-Net), first develops a novel joint anomaly detector with classical HU methods to separate out target-water mixed pixels, which is devoted to eliminate the adverse influence of background. Then, we explore a bathymetric model-based autoencoder to unmix the target-water mixed pixels for acquiring the target-associated abundance values and maps. One dimension convolutional neural network is exploited to construct the encoder part of above autoencoder for the sake of addressing spectral variability problem. Moreover, considering the physical meaningless endmembers issue, a particular spectral constraint is imposed on the objective function as a training guidance. In this way, the autoencoder would be capable of generating specific endmembers and their corresponding abundance maps. Finally, according to the physical essence of abundance maps, we figure out the detection result by fusing the outcomes of autoencoder with weight coefficients determined by abundance values. Qualitative and quantitative illustrations demonstrate the effectiveness and efficiency of UTD-Net in comparison with the state-of-the-art underwater target detection methods

Dynamic Change Characteristics of Litter and Nutrient Return in Subtropical Evergreen Broad-Leaved Forest in Different Extreme Weather Disturbance Years in Ailao Mountain, Yunnan Province

By studying the dynamic change characteristics of litter production, composition, nutrient content, and return amount of different components in different extreme weather interference years of Ailao Mountain evergreen broad-leaved forest, the paper provides theoretical support for the post-disaster nutrient cycle, ecological recovery, and sustainable development of the subtropical mid-mountain humid evergreen broad-leaved forest. Square litter collectors were randomly set up to collect litter. After drying to a constant mass, we calculated the seasonal and annual litter volume and the contents of organic carbon (C), total nitrogen (N), total phosphorus (P), total potassium (k), total sulfur (S), total calcium (Ca), and total magnesium (Mg). Finally, the nutrient return amount is comprehensively calculated according to the litter amount and element content. We tracked dynamic changes in litter quantity, nutrient composition, and nutrient components across different years. The results showed that the amount of litter from 2005 to 2015 was 7704&ndash;8818 kg&middot;hm&minus;2, and the order of magnitude was: 2005 (normal year) &gt; 2015 (extreme snow and ice weather interference) &gt; 2010 (extreme drought weather interference); the composition mainly included branches, leaves, fruit (flowers), and other components (bark, moss, lichen, etc.), of which the proportion of leaves was the largest, accounting for 41.70%&ndash;61.52%; The monthly changes and total amounts in different years exhibited single or double peak changes, and the monthly litter components in different years showed significant seasonality. In this study, the nutrient content of litter was higher than that of litter branches each year. The total amount of litter and the nutrient concentration of each component are C, Ca, N, K, Mg, S, and P, from large to small. The order of nutrient return in different years was the same as that of litter, and the returns of nutrients in litter leaves were greater than that of litter branches. The ratio of nutrient returns of litter and litter branches from 2005 to 2010 was 2.03, 1.23, and 3.69, respectively. The research shows that the litter decreased correspondingly under the extreme weather disturbance, and the impact of the extreme dry weather disturbance was greater than that of the extreme ice and snow weather disturbance. However, the evergreen broad-leaved forest in the study area recovers well after being disturbed. The annual litter amount and nutrient return amount is similar to that of evergreen broad-leaved forests in the same latitude and normal years in other subtropical regions. The decomposition rate and seasonal dynamics of litter nutrients are not greatly affected by extreme weather

Intelligent and safe design of recycling express packing boxes

In order to cope with the growing environmental pollution triggered by traditional disposable express packing box, the design and promotion of recycling express packing boxes (REPB) is of necessity and feasibility. However, relevant research is still lacking. To address the aforementioned issues, this paper attempted to propose a comprehensive method for designing REPB based on five intelligent functions of REPB, i.e., information processing, transportation environmental state perception, transportation environment interaction, human-machine interaction, and cloud interaction. Additionally, to ensure the reliability and safety of REFB, this paper discussed the material and structure of REPB based on shock absorption, as well as the intelligent functions to protect REFB from being stolen and lost. This paper can aid in the establishment of framework for REPB design pertaining to intelligence, safety, feasibility and reliability. Future research could further explore the standardization of REFB

Intelligent and safe design of recycling express packing boxes

In order to cope with the growing environmental pollution triggered by traditional disposable express packing box, the design and promotion of recycling express packing boxes (REPB) is of necessity and feasibility. However, relevant research is still lacking. To address the aforementioned issues, this paper attempted to propose a comprehensive method for designing REPB based on five intelligent functions of REPB, i.e., information processing, transportation environmental state perception, transportation environment interaction, human-machine interaction, and cloud interaction. Additionally, to ensure the reliability and safety of REFB, this paper discussed the material and structure of REPB based on shock absorption, as well as the intelligent functions to protect REFB from being stolen and lost. This paper can aid in the establishment of framework for REPB design pertaining to intelligence, safety, feasibility and reliability. Future research could further explore the standardization of REFB

Multi-Tiered Assessment of Gene Expression Provides Evidence for Mechanisms That Underlie Risk for Type 2 Diabetes

IntroductionIntegrated transcriptome and microRNA differential gene expression (DEG) analyses may help to explain type 2 diabetes (T2D) pathogenesis in at-risk populations. The purpose of this study was to characterize DEG in banked biospecimens from underactive adult participants who responded to a randomized clinical trial measuring the effects of lifestyle interventions on T2D risk factors. DEGs were further examined within the context of annotated biological pathways.MethodsParticipants (n = 52) in a previously completed clinical trial that assessed a 12-week behavioural intervention for T2D risk reduction were included. Participants who showed &gt;6mg/dL decrease in fasting blood glucose were identified as responders. Gene expression was measured by RNASeq, and overrepresentation analysis within KEGG pathways and weighted gene correlation network analysis (WGCNA) were performed.ResultsNo genes remained significantly differentially expressed after correction for multiple comparisons. One module derived by WGCNA related to body mass index was identified, which contained genes located in KEGG pathways related to known mechanisms underlying risk for T2D as well as pathways related to neurodegeneration and protein misfolding. A network analysis showed indirect connections between genes in this module and islet amyloid polypeptide (IAPP), which has previously been hypothesized as a mechanism for T2D.DiscussionWe validated prior studies that showed pathways related to metabolism, inflammation/immunity, and endocrine/hormone function are related to risk for T2D. We identified evidence for new potential mechanisms that include protein misfolding. Additional studies are needed to determine whether these are potential therapeutic targets to decrease risk for T2D

Additional file 4: Table S4. of Antimicrobial resistance in Neisseria gonorrhoeae in China: a meta-analysis

The Begg and Egger test of heterogeneity. (DOCX 14 kb

Turnover intention and related factors among general practitioners in Hubei, China: a cross-sectional study

Abstract Background High turnover among general practitioners (GPs) is a significant challenge in China’s efforts to build a sustainable, effective primary care system, but little data is available to help understand and address this issue. The study was aiming at assessing the intention to leave their posts among a sample of GPs and investigating associated factors. Methods A cross-sectional survey was conducted between December 12, 2014 and March 10, 2015 in Hubei Province, Central China. A total of 1016 GPs (response rate, 85.67%) were investigated by using a structured self-administered questionnaire. A generalized linear regression model was used to identify the associated factors with turnover intention among GPs. Results Based on a full score of 24, the average score for GPs’ turnover intention was 15.40 (SD = 3.43). 78.35% of the GPs had a moderate or higher level of turnover intention. Six hundred and thirty one (62.37%) GPs had ever been exposed to abuse of any kind (physical assault, 18.92%; verbal abuse, 54.38%; threat, 33.79%; verbal sexual harassment, 22.66%; and physical sexual harassment, 7.59%). Generalized linear regression analysis indicated that GPs who were male; who had a vocational school or higher; who had a temporary work contract; who were with lower level of job satisfaction; who reported higher scores on emotional exhaustion; who had been exposed to higher frequency of workplace violence were expressed higher intention to leave their present positions. Conclusion This study shows that GP’s intention to leave general practices is high in Hubei, China. In addition, the prevalence of workplace violence is high among GPs, particularly in the verbal abuse and threat. Measures such as offering permanent contract status, increasing overall job satisfaction, and improving doctor-patient relationship, are needed to moderate GP’s turnover intention in order to maintain the foundation of China’s three-tier health system