SCFusion: Infrared and Visible Fusion Based on Salient Compensation.

The aim of infrared and visible image fusion is to integrate the complementary information of the two modalities for high-quality fused images. However, many deep learning fusion algorithms have not considered the characteristics of infrared images in low-light scenes, leading to the problems of weak texture details, low contrast of infrared targets and poor visual perception in the existing methods. Therefore, in this paper, we propose a salient compensation-based fusion method that makes sufficient use of the characteristics of infrared and visible images to generate high-quality fused images under low-light conditions. First, we design a multi-scale edge gradient module (MEGB) in the texture mainstream to adequately extract the texture information of the dual input of infrared and visible images; on the other hand, the salient tributary is pre-trained by salient loss to obtain the saliency map based on the salient dense residual module (SRDB) to extract salient features, which is supplemented in the process of overall network training. We propose the spatial bias module (SBM) to fuse global information with local information. Finally, extensive comparison experiments with existing methods show that our method has significant advantages in describing target features and global scenes, the effectiveness of the proposed module is demonstrated by ablation experiments. In addition, we also verify the facilitation of this paper's method for high-level vision on a semantic segmentation task.

Does the medical insurance system play a real role in reducing catastrophic economic burden in elderly patients with cardiovascular disease in China? Implication for accurately targeting vulnerable characteristics.

BACKGROUND: The vulnerability of cardiovascular disease (CVD) patients' health abilities, combined with the severity of the disease and the overlapping risk factors, leads such people to bear the economic burden of the disease due to the medical services. We estimated the economic burden of CVD and identified the weak link in the design of the medical insurance. METHODS: Data from 5610 middle-aged and elderly with CVD were drawn from the 2015 wave of "China Health and Retirement Longitudinal Study" (CHARLS). The recommended method of the "World Health Organization" (WHO) was adopted to calculate "catastrophic health expenditure" (CHE), "impoverishment by medical expenses" (IME), and applied the treatment-effect model to analyze the determinants of CHE. RESULTS: The incidence of CHE was 19.9% for the elderly families with CVD members, which was 3.6% higher than for uninsured families (16.3%). Families with CVD combined with > 3 other chronic diseases (38.88%) were the riskiest factor for the high CHE in the new rural cooperative medical system (NCMS). Moreover, families with members > 75 years old (33.33%), having two chronic disease (30.74%), and families having disabled members (33.33%), hospitalization members (32.41%) were identified as the high risky determinants for the high CHE in NCMS. CONCLUSIONS: Elderly with physical vulnerabilities were more prone to CHE. The medical insurance only reduced barriers to accessing health resources for elderly with CVD; however it lacked the policy inclination for high-utilization populations, and had poorly accurate identification of the vulnerable characteristics of CVD, which in turn affects the economic protection ability of the medical insurance. The dispersion between the multiple medical security schemes leads to the existence of blind spots in the economic risk protection of individuals and families.

Targeting vulnerable groups of health poverty alleviation in rural China- what is the role of the New Rural Cooperative Medical Scheme for the middle age and elderly population?

BACKGROUND: In light of the health poverty alleviation policy, we explore whether the New Rural Cooperative Medical System (NRCMS) has effectively reduced the economic burden of medical expenses on rural middle-aged and elderly people and other impoverished vulnerable groups. The study aims to provide evidence that can be used to improve the medical insurance system. METHODS: Data were obtained from the 2015 China Health and Retirement Longitudinal Study (CHARLS). The method of calculating the catastrophic health expenditure (CHE) and impoverishment by medical expense (IME) was adopted from the World Health Organization (WHO). The treatment effect model was used to identify the determinants of CHE for rural middle-aged and elderly people. RESULTS: The incidence of CHE in rural China for middle-aged and elderly people is 21.8%, and the IME is 8.0%. The households that had enrolled in the NRCMS suffered higher CHE (21.9%) and IME (8.0%), than those that had not enrolled (CHE: 20.6% and IME: 7.7%). The NRCMS did not provide sufficient economic protection from CHE for households with three or more chronic diseases, inpatients, or households with members aged over 65 years. Key risk factors for the CHE included education levels, households with inpatients, households with members aged over 65 years, and households with disabilities. CONCLUSIONS: Although the NRCMS has reduced barriers to the usage of household health services by reducing people's out-of-pocket payments, it has not effectively reduced the risk of these households falling into poverty. Our research identifies the characteristics of vulnerable groups that the NRCMS does not provide enough support for, and which puts them at a greater risk of falling into poverty due to health impoverishment.

Does the medical insurance system really achieved the effect of poverty alleviation for the middle-aged and elderly people in China? Characteristics of vulnerable groups and failure links.

BACKGROUND: We examined the physiological, household, and spatial agglomeration characteristics of the health poverty population in China. We identified weak links that affect the implementation of the medical insurance and further improve its effectiveness for health poverty alleviation. METHODS: A national representative sample from the China Health and Retirement Longitudinal Study (CHARLS) was analyzed. The WHO recommended method was adopted to calculate catastrophic health expenditure (CHE) and impoverishment by medical expenses (IME). We created a binary indicator for IME as the outcome variable and applied the treatment-effect model to analyze the determinants of IME. RESULTS: The incidence of IME was 7.2% of the overall population, compared to 20.3% of the sample households trapped in CHE. The incidence of IME enrolled in insurance schemes was 7.4% higher than that of uninsured families (4.8%). Economic level, living area, family size, age of household head, having hospitalized members, and participating in insurance were statistically significant for the occurrence of IME. CONCLUSIONS: The original poverty-promoting policies has not reached the maximum point of convergence with China's current demand for health. The overlapped health vulnerabilities exacerbated the risk of poverty among the elderly and households with high health needs and utilization. In addition, the medical insurance schemes have proven to be insufficient for protection against economic burden of poor households. So, special health needs, age, and household capacity to pay should be comprehensively considered while strengthening the connection between the disease insurance scheme with supplementary insurance.

Endogenous sterol intermediates of the mevalonate pathway regulate HMGCR degradation and SREBP-2 processing.

Sterol-regulated HMG-CoA reductase (HMGCR) degradation and SREBP-2 cleavage are two major feedback regulatory mechanisms governing cholesterol biosynthesis. Reportedly, lanosterol selectively stimulates HMGCR degradation, and cholesterol is a specific regulator of SREBP-2 cleavage. However, it is unclear whether other endogenously generated sterols regulate these events. Here, we investigated the sterol intermediates from the mevalonate pathway of cholesterol biosynthesis using a CRISPR/Cas9-mediated genetic engineering approach. With a constructed HeLa cell line expressing the mevalonate transporter, we individually deleted genes encoding major enzymes in the mevalonate pathway, used lipidomics to measure sterol intermediates, and examined HMGCR and SREBP-2 statuses. We found that the C4-dimethylated sterol intermediates, including lanosterol, 24,25-dihydrolanosterol, follicular fluid meiosis activating sterol, testis meiosis activating sterol, and dihydro-testis meiosis activating sterol, were significantly upregulated upon mevalonate loading. These intermediates augmented both degradation of HMGCR and inhibition of SREBP-2 cleavage. The accumulated lanosterol induced rapid degradation of HMGCR, but did not inhibit SREBP-2 cleavage. The newly synthesized cholesterol from the mevalonate pathway is dispensable for inhibiting SREBP-2 cleavage. Together, these results suggest that lanosterol is a bona fide endogenous regulator that specifically promotes HMGCR degradation, and that other C4-dimethylated sterol intermediates may regulate both HMGCR degradation and SREBP-2 cleavage.

Determination of the Absolute Configurations of Microtermolides A and B.

Absolute configurations of the three consecutive chiral centers in the cyclic depsipeptide microtermolide A have been tentatively assigned as 2‴R, 3‴R, and 4‴R. However, on the basis of a structural comparison with vinylamycin, another depsipeptide with a unique 4-amino-2,4-pentadienolate structure, the chiral centers could also be assigned as 2‴R, 3‴R, and 4‴S. Here, the first total synthesis of microtermolide A is reported and the configurations of the three consecutive chiral centers were confirmed to be 2‴R, 3‴R, and 4‴S. A similar approach was used to determine the analogous centers in microtermolide B as 2‴R, 3‴R, and 4‴S.

FECFusion: Infrared and visible image fusion network based on fast edge convolution.

The purpose of infrared and visible image fusion is to integrate the complementary information from heterogeneous images in order to enhance their detailed scene information. However, existing deep learning fusion methods suffer from an imbalance between fusion performance and computational resource consumption. Additionally, fusion layers or fusion rules fail to effectively combine heteromodal feature information. To address these challenges, this paper presents a novel algorithm called infrared and visible image fusion network base on fast edge convolution (FECFusion). During the training phase, the proposed algorithm enhances the extraction of texture features in the source image through the utilization of structural re-parameterization edge convolution (RECB) with embedded edge operators. Subsequently, the attention fusion module (AFM) is employed to sufficiently fuze both unique and public information from the heteromodal features. In the inference stage, we further optimize the training network using the structural reparameterization technique, resulting in a VGG-like network architecture. This optimization improves the fusion speed while maintaining the fusion performance. To evaluate the performance of the proposed FECFusion algorithm, qualitative and quantitative experiments are conducted. Seven advanced fusion algorithms are compared using MSRS, TNO, and M3FD datasets. The results demonstrate that the fusion algorithm presented in this paper achieves superior performance in multiple evaluation metrics, while consuming fewer computational resources. Consequently, the proposed algorithm yields better visual results and provides richer scene detail information.

Activation extending based on long-range dependencies for weakly supervised semantic segmentation.

Weakly supervised semantic segmentation (WSSS) principally obtains pseudo-labels based on the class activation maps (CAM) to handle expensive annotation resources. However, CAM easily involves false and local activation due to the the lack of annotation information. This paper suggests weakly supervised learning as semantic information mining to extend object mask. We proposes a novel architecture to mining semantic information by modeling through long-range dependencies from in-sample and inter-sample. Considering the confusion caused by the long-range dependencies, the images are divided into blocks and carried out self-attention operation on the premise of fewer classes to obtain long-range dependencies, to reduce false predictions. Moreover, we perform global to local weighted self-supervised contrastive learning among image blocks, and the local activation of CAM is transferred to different foreground area. Experiments verified that superior semantic details and more reliable pseudo-labels are captured through these suggested modules. Experiments on PASCAL VOC 2012 demonstrated the proposed model achieves 76.6% and 77.4% mIoU in val and test sets, which is superior to the comparison baselines.

The medical insurance system's weakness to provide economic protection for vulnerable citizens in China: A five-year longitudinal study.

OBJECTIVES: Little is known about the magnitude of catastrophic health expenditure (CHE) attributable to critical disease, especially in the middle-aged and elderly population. This research aimed to exploring the key aspects of how the health insurance fails to protect the middle-aged and elderly against CHE in the past five years. And propose corresponding measures to improve. METHODS: Data were obtained from the 2011 to 2015 China Health and Retirement Longitudinal Study. The method was adapted from WHO to calculate the catastrophic health expenditure (CHE) and impoverishment by medical expense (IME), and use Generalized Linear Mixed Models (GLMMs) to comprehensively analyze the risk factors that cause middle-aged and elderly people to fall into CHE. RESULTS: The incidence of CHE of China's middle-aged and elderly population has been rose in the five years from 2011 (10.5 %) to 2013 (17.5 %) to 2015 (19.7 %). The CHE of richest families was almost 6 times from 2011 to 2015. Urban Employee Medical Insurance Scheme, the incidence of CHE was up 10 percentage from 2011 to 2015. According to the GLMMs, families have inpatient cares as the most important factor to CHE. The incidence of CHE increased by 2.25 times compared with those who did not use inpatient services. CONCLUSIONS: The health system needs to control the irrational growth of health expenses and reduce residents' overuse of health services. Government should take supplementary measures to comprehensively strengthen the advantages of health insurance. Raise residents' awareness of health care, enhance citizens' physical fitness, and avoid unnecessary waste of health resources.

Total Synthesis and Determination of the Absolute Configuration of Vinylamycin.

The absolute configurations of the three unknown chiral centers in vinylamycin were predicted according to the structural comparison with microtermolide A and rakicidin A, and then total syntheses of vinylamycin were applied to determine the three unknown chiral centers as 14R, 15R, and 16S.