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To investigate the impact of the amount and change of traffic loads on the performance reliability of network,a simple traffic model for computer networks was established based on the traffic dynamics.The ratio between the ex-pected and the practical delay and the packet loss rate of each demand were used to characterize the performance reliabil-ity of the network.The performance reliability of a special local network was simulated and analyzed using the proposed approach.It was shown that the performance reliability of the network exhibited an obvious phase transition,no matter whether traffic loads were the Poisson traffic model or the Burstiness Constraints traffic model.In addition,the value of transition point can be considered as a measurement of network communication capability under special performance constraints.

We aim to develop a deep-learning-based method for automatic proximal femur segmentation in quantitative computed tomography (QCT) images. We proposed a spatial transformation V-Net (ST-V-Net), which contains a V-Net and a spatial transform network (STN) to extract the proximal femur from QCT images. The STN incorporates a shape prior into the segmentation network as a constraint and guidance for model training, which improves model performance and accelerates model convergence. Meanwhile, a multi-stage training strategy is adopted to fine-tune the weights of the ST-V-Net. We performed experiments using a QCT dataset which included 397 QCT subjects. During the experiments for the entire cohort and then for male and female subjects separately, 90% of the subjects were used in ten-fold stratified cross-validation for training and the rest of the subjects were used to evaluate the performance of models. In the entire cohort, the proposed model achieved a Dice similarity coefficient (DSC) of 0.9888, a sensitivity of 0.9966 and a specificity of 0.9988. Compared with V-Net, the Hausdorff distance was reduced from 9.144 to 5.917 mm, and the average surface distance was reduced from 0.012 to 0.009 mm using the proposed ST-V-Net. Quantitative evaluation demonstrated excellent performance of the proposed ST-V-Net for automatic proximal femur segmentation in QCT images. In addition, the proposed ST-V-Net sheds light on incorporating shape prior to segmentation to further improve the model performance.

Abstract Nasopharyngeal carcinoma (NPC) is a complex disease caused by a combination of Epstein-Barr virus chronic infection, the environment and host genes in a multi-step process of carcinogenesis. The identity of genetic factors involved in the development of chronic Epstein-Barr virus infection and NPC remains elusive, however. Here, we describe a two-phase, population-based, case-control study of Han Chinese from Guangxi province, where the NPC incidence rate rises to a high of 25-50 per 100,000 individuals. Phase I, powered to detect single gene associations, enrolled 984 subjects to determine feasibility, to develop infrastructure and logistics and to determine error rates in sample handling. A microsatellite screen of Phase I study participants, genotyped for 319 alleles from 34 microsatellites spanning an 18-megabase region of chromosome 4 (4p15.1-q12), previously implicated by a linkage analysis of familial NPC, found 14 alleles marginally associated with developing NPC or chronic immunoglobulin A production (p = 0.001-0.03). These associations lost significance after applying a correction for multiple tests. Although the present results await confirmation, the Phase II study population has tripled patient enrolment and has included environmental covariates, offering the potential to validate this and other genomic regions that influence the onset of NPC.

BackgroundHip fracture occurs when an applied force exceeds the force that the proximal femur can support (the fracture load or "strength") and can have devastating consequences with poor functional outcomes. Proximal femoral strengths for specific loading conditions can be computed by subject-specific finite element analysis (FEA) using quantitative computerized tomography (QCT) images. However, the radiation and availability of QCT limit its clinical usability. Alternative low-dose and widely available measurements, such as dual energy X-ray absorptiometry (DXA) and genetic factors, would be preferable for bone strength assessment. The aim of this paper is to design a deep learning-based model to predict proximal femoral strength using multi-view information fusion.ResultsWe developed new models using multi-view variational autoencoder (MVAE) for feature representation learning and a product of expert (PoE) model for multi-view information fusion. We applied the proposed models to an in-house Louisiana Osteoporosis Study (LOS) cohort with 931 male subjects, including 345 African Americans and 586 Caucasians. We performed genome-wide association studies (GWAS) to select 256 genetic variants with the lowest p-values for each proximal femoral strength and integrated whole genome sequence (WGS) features and DXA-derived imaging features to predict proximal femoral strength. The best prediction model for fall fracture load was acquired by integrating WGS features and DXA-derived imaging features. The designed models achieved the mean absolute percentage error of 18.04%, 6.84% and 7.95% for predicting proximal femoral fracture loads using linear models of fall loading, nonlinear models of fall loading, and nonlinear models of stance loading, respectively.ConclusionThe proposed models are capable of predicting proximal femoral strength using WGS features and DXA-derived imaging features. Though this tool is not a substitute for predicting FEA using QCT images, it would make improved assessment of hip fracture risk more widely available while avoiding the increased radiation exposure from QCT.

Background Osteoporosis is a major global health issue, weakening bones and increasing fracture risk. Dual-energy X-ray absorptiometry (DXA) is the standard for measuring bone mineral density (BMD) and diagnosing osteoporosis, but its costliness and complexity impede widespread screening adoption. Predictive modeling using genetic and clinical data offers a cost-effective alternative for assessing osteoporosis and fracture risk. This study aims to develop BMD prediction models using data from the UK Biobank (UKBB) and test their performance across different ethnic and geographical populations. Methods and findings We developed BMD prediction models for the femoral neck (FNK) and lumbar spine (SPN) using both genetic variants and clinical factors (such as sex, age, height, and weight), within 17,964 British white individuals from UKBB. Models based on regression with least absolute shrinkage and selection operator (LASSO), selected based on the coefficient of determination (R.sup.2) from a model selection subset of 5,973 individuals from British white population. These models were tested on 5 UKBB test sets and 12 independent cohorts of diverse ancestries, totaling over 15,000 individuals. Furthermore, we assessed the correlation of predicted BMDs with fragility fractures risk in 10 years in a case-control set of 287,183 European white participants without DXA-BMDs in the UKBB. With single-nucleotide polymorphism (SNP) inclusion thresholds at 5x10.sup.-6 and 5x10.sup.-7, the prediction models for FNK-BMD and SPN-BMD achieved the highest R.sup.2 of 27.70% with a 95% confidence interval (CI) of [27.56%, 27.84%] and 48.28% (95% CI [48.23%, 48.34%]), respectively. Adding genetic factors improved predictions slightly, explaining an additional 2.3% variation for FNK-BMD and 3% for SPN-BMD over clinical factors alone. Survival analysis revealed that the predicted FNK-BMD and SPN-BMD were significantly associated with fragility fracture risk in the European white population (P < 0.001). The hazard ratios (HRs) of the predicted FNK-BMD and SPN-BMD were 0.83 (95% CI [0.79, 0.88], corresponding to a 1.44% difference in 10-year absolute risk) and 0.72 (95% CI [0.68, 0.76], corresponding to a 1.64% difference in 10-year absolute risk), respectively, indicating that for every increase of one standard deviation in BMD, the fracture risk will decrease by 17% and 28%, respectively. However, the model's performance declined in other ethnic groups and independent cohorts. The limitations of this study include differences in clinical factors distribution and the use of only SNPs as genetic factors. Conclusions In this study, we observed that combining genetic and clinical factors improves BMD prediction compared to clinical factors alone. Adjusting inclusion thresholds for genetic variants (e.g., 5x10.sup.-6 or 5x10.sup.-7) rather than solely considering genome-wide association study (GWAS)-significant variants can enhance the model's explanatory power. The study highlights the need for training models on diverse populations to improve predictive performance across various ethnic and geographical groups., Author(s): Yong Liu 1, Xiang-He Meng 2, Chong Wu 3, Kuan-Jui Su 4, Anqi Liu 4, Qing Tian 4, Lan-Juan Zhao 4, Chuan Qiu 4, Zhe Luo 4, Martha I [...]

Deng and Lynch (1, 2) proposed to characterize deleterious genomic mutations from changes in the mean and genetic variance of fitness traits upon selfing in outcrossing populations. Such observations can be readily acquired in cyclical parthenogens. Selfing and life-table experiments were performed for two such Daphnia populations. A significant inbreeding depression and an increase of genetic variance for all traits analyzed were observed. Deng and Lynch's (2) procedures were employed to estimate the genomic mutation rate (U), mean dominance coefficient ( ), mean selection coefficient ( ), and scaled genomic mutational variance ( ). On average, , , and (^ indicates an estimate) are 0.84, 0.30, 0.14 and 4.6E-4 respectively. For the true values, the and are lower bounds, and and upper bounds.

With the economic and social development, material life in the era of abundance is not only in meeting the basic needs of life, but also modern consumers become forced to pursue the spiritual and cultural needs. On the other side, clothing will not just fulfill the basic functions of beauty and suitability, thus, more consumers begin to pay closer attentions to apparel textile’s individuation expression and technological elements, or to some other deeper emotional requirements etc. Smart textiles originally belongs to the cutting-edge scientific field of fashion industry, however, with the booming development of internet industry and smart phone devices, acute apparel manufacturers must have to take a ride on advanced tech-trends and launch a wide expansion of smart textile fibres’s applications into the clothing industry. This thesis would present a basic introduction on the concept and classifications of the smart textile fibres, and then like to deploy a profound analysis of smart textiles applied in the brand fashion design.