Delivering universal health coverage for an aging population : an analysis of the Chinese rural health insurance program

There is now high level international commitment to the goal of universal health coverage. But how can countries make this a reality in the face of a limited budget and an aging population? Since 2008, China has been rolling out an ambitious reform program, which aims to achieve affordable health insurance coverage for all Chinese citizens. Under this reform program, Chinese living in rural areas are eligible to enroll in a subsidized scheme called the New Cooperative Medical System (NCMS). Using a three stage game model involving a government, a private fund manager and population, we explore the impact of population aging on NCMS. Our model highlights the role of government regulation and subsidy in ensuring operation efficiency of the system. We show that at optimality the government sets the operating framework for the fund manager to constrain the potential for monopoly profits. The Government subsidizes the scheme to prevent an adverse selection death spiral. However, the effectiveness of the subsidy in achieving this goal is moderated by the age structure of the population. Our model gives insights into the strengths of the NCMS framework and also can be used to support decisions about resource allocation and understand how scheme dynamics may unfold as the Chinese population ages further

Diffusion-Model-Assisted Supervised Learning of Generative Models for Density Estimation

We present a supervised learning framework of training generative models for density estimation. Generative models, including generative adversarial networks, normalizing flows, variational auto-encoders, are usually considered as unsupervised learning models, because labeled data are usually unavailable for training. Despite the success of the generative models, there are several issues with the unsupervised training, e.g., requirement of reversible architectures, vanishing gradients, and training instability. To enable supervised learning in generative models, we utilize the score-based diffusion model to generate labeled data. Unlike existing diffusion models that train neural networks to learn the score function, we develop a training-free score estimation method. This approach uses mini-batch-based Monte Carlo estimators to directly approximate the score function at any spatial-temporal location in solving an ordinary differential equation (ODE), corresponding to the reverse-time stochastic differential equation (SDE). This approach can offer both high accuracy and substantial time savings in neural network training. Once the labeled data are generated, we can train a simple fully connected neural network to learn the generative model in the supervised manner. Compared with existing normalizing flow models, our method does not require to use reversible neural networks and avoids the computation of the Jacobian matrix. Compared with existing diffusion models, our method does not need to solve the reverse-time SDE to generate new samples. As a result, the sampling efficiency is significantly improved. We demonstrate the performance of our method by applying it to a set of 2D datasets as well as real data from the UCI repository

Blood-brain barrier dysfunction and Alzheimer’s disease: associations, pathogenic mechanisms, and therapeutic potential

Alzheimer’s disease (AD) is a common neurodegenerative disorder characterized by the accumulation of amyloid-beta (Aβ), hyperphosphorylation of tau, and neuroinflammation in the brain. The blood–brain barrier (BBB) limits solutes from circulating blood from entering the brain, which is essential for neuronal functioning. Focusing on BBB function is important for the early detection of AD and in-depth study of AD pathogenic mechanisms. However, the mechanism of BBB alteration in AD is still unclear, which hinders further research on therapeutics that target the BBB to delay the progression of AD. The exact timing of the vascular abnormalities in AD and the complex cause-and-effect relationships remain uncertain. Thus, it is necessary to summarize and emphasize this process. First, in this review, the current evidence for BBB dysfunction in AD is summarized. Then, the interrelationships and pathogenic mechanisms between BBB dysfunction and the risk factors for AD, such as Aβ, tau, neuroinflammation, apolipoprotein E (ApoE) genotype and aging, were analyzed. Finally, we discuss the current status and future directions of therapeutic AD strategies targeting the BBB. We hope that these summaries or reviews will allow readers to better understand the relationship between the BBB and AD

A Parameter-Free Hybrid Clustering algorithm used for Malware Categorization

Nowadays, numerous attacks made by the malware, such as viruses, backdoors, spyware, trojans and worms, have presented a major security threat to computer users. The most significant line of defense against malware is anti-virus products which detects, removes, and characterizes these threats. The ability of these AV products to successfully characterize these threats greatly depends on the method for categorizing these profiles of malware into groups. Therefore, clustering malware into different families is one of the computer security topics that are of great interest. In this paper, resting on the analysis of the extracted instruction of malware samples, we propose a novel parameter-free hybrid clustering algorithm (PFHC) which combines the merits of hierarchical clustering and K-means algorithms for malware clustering. It can not only generate stable initial division, but also give the best K. PFHC first utilizes agglomerative hierarchical clustering algorithm as the frame, starting with N singleton clusters, each of which exactly includes one sample, then reuses the centroids of upper level in every level and merges the two nearest clusters, finally adopts K-means algorithm for iteration to achieve an approximate global optimal division. PFHC evaluates clustering validity of each iteration procedure and generates the best K by comparing the values. The promising studies on real daily data collection illustrate that, compared with popular existing K-means and hierarchical clustering approaches, our proposed PFHC algorithm always generates much higher quality clusters and it can be well used for malware categorization

A clinically relevant online patient QA solution with daily CT scans and EPID-based in vivo dosimetry: A feasible study on rectal cancer

Adaptive radiation therapy (ART) could protect organs at risk (OARs) while maintain high dose coverage to targets. However, there still lack efficient online patient QA methods. We aim to develop a clinically relevant online patient quality assurance (QA) solution for ART using daily CT scans and electronic portal imaging device (EPID)-based in vivo dosimetry. Ten patients with rectal cancer at our center were included. Patients' daily CT scans and portal images were collected to generate reconstructed 3D dose distributions. Contours of targets and OARs were recontoured on these daily CT scans by a clinician or an auto-segmentation algorithm, then dose-volume indices were calculated, and the percent deviation of these indices to their original plans were determined. This deviation was regarded as the metric for clinically relevant patient QA. The tolerance level was obtained using a 95% interval of the QA metric distribution. These deviations could be further divided into anatomically relevant or delivery relevant indicators for error source analysis. Finally, our QA solution was validated on an additional six clinical patients. In rectal cancer, the lower and upper tolerance of the QA metric for PTV {\Delta}D95 (%) were [-3.11%, 2.35%], and for PTV {\Delta}D2 (%) were [-0.78%, 3.23%]. In validation, the 68% for PTV {\Delta}D95 (%) and the 79% for PTV {\Delta}D2 ({%)of the 28 fractions are within tolerances of the QA metrics. By using four or more out-of-tolerance QA metrics as an action level, there were 5 fractions (18%) have four or more out-of-tolerance QA metrics in validation patient dataset. The online patient QA solution using daily CT scans and EPID-based in vivo dosimetry is clinically feasible. Source of error analysis has the potential for distinguishing sources of error and guiding ART for future treatments

PKC-Dependent Phosphorylation of eNOS at T495 Regulates eNOS Coupling and Endothelial Barrier Function in Response to G(+) -Toxins

Gram positive (G(+)) infections make up similar to 50% of all acute lung injury cases which are characterized by extensive permeability edema secondary to disruption of endothelial cell (EC) barrier integrity. A primary cause of increased permeability are cholesterol-dependent cytolysins (CDCs) of G(+)-bacteria, such as pneumolysin (PLY) and listeriolysin-O (LLO) which create plasma membrane pores, promoting Ca2+-influx and activation of PKC alpha. In human lung microvascular endothelial cells (HLMVEC), pretreatment with the nitric oxide synthase (NOS) inhibitor, ETU reduced the ability of LLO to increase microvascular cell permeability suggesting an endothelial nitric oxide synthase (eNOS)-dependent mechanism. LLO stimulated superoxide production from HLMVEC and this was prevented by silencing PKC alpha or NOS inhibition suggesting a link between these pathways. Both LLO and PLY stimulated eNOS T495 phosphorylation in a PKC-dependent manner. Expression of a phosphomimetic T495D eNOS (human isoform) resulted in increased superoxide and diminished nitric oxide (NO) production. Transduction of HLMVEC with an active form of PKC alpha resulted in the robust phosphorylation of T495 and increased peroxynitrite production, indicative of eNOS uncoupling. To determine the mechanisms underlying eNOS uncoupling, HLMVEC were stimulated with LLO and the amount of hsp90 and caveolin-1 bound to eNOS determined. LLO stimulated the dissociation of hsp90, and in particular, caveolin-1 from eNOS. Both hsp90 and caveolin-1 have been shown to influence eNOS uncoupling and a peptide mimicking the scaffolding domain of caveolin-1 blocked the ability of PKC alpha to stimulate eNOS-derived superoxide. Collectively, these results suggest that the G(+) pore-forming toxins promote increased EC permeability via activation of PKC alpha, phosphorylation of eNOS-T495, loss of hsp90 and caveolin-1 binding which collectively promote eNOS uncoupling and the production of barrier disruptive superoxide

Chromosome-level genome assembly of a high-altitude-adapted frog (Rana kukunoris) from the Tibetan plateau provides insight into amphibian genome evolution and adaptation

Background The high-altitude-adapted frog Rana kukunoris, occurring on the Tibetan plateau, is an excellent model to study life history evolution and adaptation to harsh high-altitude environments. However, genomic resources for this species are still underdeveloped constraining attempts to investigate the underpinnings of adaptation. Results The R. kukunoris genome was assembled to a size of 4.83 Gb and the contig N50 was 1.80 Mb. The 6555 contigs were clustered and ordered into 12 pseudo-chromosomes covering similar to 93.07% of the assembled genome. In total, 32,304 genes were functionally annotated. Synteny analysis between the genomes of R. kukunoris and a low latitude species Rana temporaria showed a high degree of chromosome level synteny with one fusion event between chr11 and chr13 forming pseudo-chromosome 11 in R. kukunoris. Characterization of features of the R. kukunoris genome identified that 61.5% consisted of transposable elements and expansions of gene families related to cell nucleus structure and taste sense were identified. Ninety-five single-copy orthologous genes were identified as being under positive selection and had functions associated with the positive regulation of proteins in the catabolic process and negative regulation of developmental growth. These gene family expansions and positively selected genes indicate regions for further interrogation to understand adaptation to high altitude. Conclusions Here, we reported a high-quality chromosome-level genome assembly of a high-altitude amphibian species using a combination of Illumina, PacBio and Hi-C sequencing technologies. This genome assembly provides a valuable resource for subsequent research on R. kukunoris genomics and amphibian genome evolution in general.Peer reviewe