Flow Level QoE of Video Streaming in Wireless Networks

The Quality of Experience (QoE) of streaming service is often degraded by frequent playback interruptions. To mitigate the interruptions, the media player prefetches streaming contents before starting playback, at a cost of delay. We study the QoE of streaming from the perspective of flow dynamics. First, a framework is developed for QoE when streaming users join the network randomly and leave after downloading completion. We compute the distribution of prefetching delay using partial differential equations (PDEs), and the probability generating function of playout buffer starvations using ordinary differential equations (ODEs) for CBR streaming. Second, we extend our framework to characterize the throughput variation caused by opportunistic scheduling at the base station, and the playback variation of VBR streaming. Our study reveals that the flow dynamics is the fundamental reason of playback starvation. The QoE of streaming service is dominated by the first moments such as the average throughput of opportunistic scheduling and the mean playback rate. While the variances of throughput and playback rate have very limited impact on starvation behavior.Comment: 14 page

Bone Marrow Adipocyte: An Intimate Partner With Tumor Cells in Bone Metastasis

The high incidences of bone metastasis in patients with breast cancer, prostate cancer and lung cancer still remains a puzzling issue. The “seeds and soil” hypothesis suggested that bone marrow (soil) may provide a favorable “niche” for tumor cells (seed). When seeking for effective ways to prevent and treat tumor bone metastasis, most researchers focus on tumor cells (seed) but not the bone marrow microenvironment (soil). In reality, only a fraction of circulating tumor cells (CTCs) could survive and colonize in bone. Thus, the bone marrow microenvironment could ultimately determine the fate of tumor cells that have migrated to bone. Bone marrow adipocytes (BMAs) are abundant in the bone marrow microenvironment. Mounting evidence suggests that BMAs may play a dominant role in bone metastasis. BMAs could directly provide energy for tumor cells, enhance the tumor cell proliferation, and resistance to chemotherapy and radiotherapy. BMAs are also known for releasing some inflammatory factors and adipocytokines to promote or inhibit bone metastasis. In this review, we made a comprehensive summary for the interaction between BMAs and bone metastasis. More importantly, we discussed the potentially promising methods for the prevention and treatment of bone metastasis. Genetic disruption and pharmaceutical inhibition may be effective in inhibiting the formation and pro-tumor functions of BMAs

Transcriptome analysis of differential sugar accumulation in the developing embryo of contrasting two Castanea mollissima cultivars

Chinese chestnut (Castanea mollissima) is an important nut tree species, and its embryo is rich in sugar. We combined metabolomic and transcriptomic data to analyze metabolites and genes related to sugar in two Chinese chestnut cultivars at 60, 70, 80, 90 and 100 days after flowering (DAF). The soluble sugar content of high-sugar cultivar at maturity is 1.5 times that of low-sugar cultivar. Thirty sugar metabolites were identified in embryo, with the most dominant being sucrose. Analysis of the gene expression patterns revealed that the high-sugar cultivar promoted the conversion of starch to sucrose by up-regulating genes related to starch degradation and sucrose synthesis at 90-100 DAF. It also strongly increased the enzyme activity of SUS-synthetic, which may promote sucrose synthesis. Gene co-expression network analysis showed that ABA and peroxide were related to starch decomposition during Chinese chestnut ripening. Our study analyzed the composition and molecular synthesis mechanism of sugar in Chinese chestnut embryos, and provided a new insight into the regulation pattern of high sugar accumulation in Chinese chestnut nuts

Trends in template/fragment-free protein structure prediction

Predicting the structure of a protein from its amino acid sequence is a long-standing unsolved problem in computational biology. Its solution would be of both fundamental and practical importance as the gap between the number of known sequences and the number of experimentally solved structures widens rapidly. Currently, the most successful approaches are based on fragment/template reassembly. Lacking progress in template-free structure prediction calls for novel ideas and approaches. This article reviews trends in the development of physical and specific knowledge-based energy functions as well as sampling techniques for fragment-free structure prediction. Recent physical- and knowledge-based studies demonstrated that it is possible to sample and predict highly accurate protein structures without borrowing native fragments from known protein structures. These emerging approaches with fully flexible sampling have the potential to move the field forward

Detecting pulsatile hormone secretions using nonlinear mixed effects partial spline model

Neuroendocrine ensembles communicate with their remote and proximal target cells via an intermittent pattern of chemical signaling. The identification of episodic releases of hormonal pulse signals constitutes a major emphasis of endocrine investigation. Estimating the number, temporal locations, secretion rate and elimination rate from hormone concentration measurements is of critical importance in endocrinology. In this paper, we propose a new flexible statistical method for pulse detection based on nonlinear mixed effects partial spline models. We model pulsatile secretions using biophysical models and investigate biological variation between pulses using random effects. Pooling information from different pulses provides more efficient and stable estimation for parameters of interest. We combine all nuisance parameters including a non-constant basal secretion rate and biological variations into a baseline function which is modeled nonparametrically using smoothing splines. We develop model selection and parameter estimation methods for the general nonlinear mixed effects partial spline models and a R package for pulse detection and estimation. We evaluate performance and the benefit of shrinkage by simulations and apply our methods to data from a medical experiment

A New Method for Fatigue Evaluation of Titanium Alloy Welded Structures

In this paper, a new fatigue life evaluation method, namely augmented-reverse notch equivalent stress method, is proposed for titanium alloy welded structures with stress singularities. First, a new three-parameter power function model is proposed in this paper, and the notch stress equivalent value method with correction factor is deduced. Combining the two, the theoretical framework of the augmented-reverse notch equivalent stress method is obtained. Within this framework, the fatigue test data of four titanium alloy welded joints with the same grade were used for analysis, and a new three-parameter power function model of titanium alloy welded structure was established. According to the calculation method of the notch equivalent stress correction factor, the correction factor suitable for the titanium alloy welded structure was obtained. Finally, the fatigue test data, different from the above titanium alloy welded structure, are used to verify the augmented-reverse notch equivalent stress method. The verification results show that the augmented-reverse notch equivalent stress method improves the effectiveness and accuracy of fatigue life evaluation and establishes a new life evaluation method for titanium alloy welded structures with stress singularities

Rural Population Aging and the Hospital Utilization in Cities: The Rise of Medical Tourism in China

The disparity of rural and urban hospital utilization has aroused much concern. With the improvement of their living standards, patients in rural areas have an emerging need for traveling across borders for better medical treatment in China. This paper reveals the medical tourism of rural residents towards urban hospitals driven by hospital needs and points out that such disparities may be caused by medical tourism. The ratio of people aged 65 and above in total rural populations was used to identify the potential target customers for medical tourism. Based on rural and urban datasets ranging from 2007–2017 on the provincial level, this paper presents a mobile treatment model and market concentration model with an ecological foundation. The feasible generalized least squared approach was used in the estimation of the fixed-effect regressions. The study found that there was a positive and significant relationship between rural old-age ratios and urban inpatient visits from different income groups. On average, a one percent rise in rural old-age ratio would increase the inpatient visits of urban hospitals by 138 thousand persons. There was also a positive and significant relationship between the rural old-age ratio and the market concentration of urban inpatient visits. It was found that the rural old-age ratio significantly influenced the market concentration of urban inpatient visits in the middle-high income regions. The research showed that each income group from the rural aged population had participated in medical tourism, traveled to urbanized regions and made inpatient visits to urbanized medical facilities. It was also indicated that the rural aged population, especially from the middle-high income groups had a positive and significant influence on the market concentration of urban inpatient visits in the province

Sparse Positive-Definite Estimation for Large Covariance Matrices with Repeated Measurements

In many fields of biomedical sciences, it is common that random variables are measured repeatedly across different subjects. In such a repeated measurement setting, dependence structures among random variables that are between subjects and within a subject may differ and should be estimated differently. Ignoring this fact may lead to questionable or even erroneous scientific conclusions. In this paper, we study the problem of sparse and positive-definite estimation of between-subject and within-subject covariance matrices for high-dimensional repeated measurements. Our estimators are defined as solutions to convex optimization problems that can be solved efficiently. We establish estimation error rates for our proposed estimators of the two target matrices, and demonstrate their favorable performance through theoretical analysis and comprehensive simulation studies. We further apply our methods to recover two covariance graphs of clinical variables from hemodialysis patients