Nef from SIVmac239 down-modulates cell surface CXCR4 in tumor cells and inhibits proliferation, migration and angiogenesis

Aim: To evaluate if the lentiviral accessory protein Nef can down-regulate CXCR4 in tumor cells and affect tumor cell proliferation, migration and angiogenesis. Materials and Methods: HeLa-ACC cells were transfected with Nef from SIVmac239 and expression levels of cell surface CXCR4 were monitored by FACS analysis. Real-time proliferation and migration of cells was measured with the xCELLigence system or in vitro scratch assay. In vitro tube formation was deployed to assess the effect of Nef on angiogenesis. Results: Cell surface down-modulation of CXCR4 could be observed in HeLa-ACC cells after Nef-transfection as well as in COS-7 cells after co-transfection of CXCR4 and Nef. Proliferation as well as migration of Nef-transfected HeLa-ACC cells appeared significantly reduced. In vitro tube formation was markedly lowered after Nef-transfection or CXCR4 knockdown with siRNA. Conclusion: SIV-Nef could serve as an interesting tool to study the biologic behavior of CXCR4-expressing tumor cells and could be helpful in the discovery of new therapeutic approaches for the treatment of CXCR4-positive tumors

QCD with zero, two and four flavors of light quarks - results from QCDSP

We present the results from full QCD simulations with four flavors of light stag gered dynamical quarks on {\it {\it QCDSP}} supercomputer. Previous results are reproduced and the simulation reported here yields new results consistent with o ur previous runs. The hadron spectrum obtained with Wilson valence fermions reported here will allow us to determine if our earlier conclusions are independent of lattice form alism

ShenZhen transportation system (SZTS): a novel big data benchmark suite

Data analytics is at the core of the supply chain for both products and services in modern economies and societies. Big data workloads, however, are placing unprecedented demands on computing technologies, calling for a deep understanding and characterization of these emerging workloads. In this paper, we propose ShenZhen Transportation System (SZTS), a novel big data Hadoop benchmark suite comprised of real-life transportation analysis applications with real-life input data sets from Shenzhen in China. SZTS uniquely focuses on a specific and real-life application domain whereas other existing Hadoop benchmark suites, such as HiBench and CloudRank-D, consist of generic algorithms with synthetic inputs. We perform a cross-layer workload characterization at the microarchitecture level, the operating system (OS) level, and the job level, revealing unique characteristics of SZTS compared to existing Hadoop benchmarks as well as general-purpose multi-core PARSEC benchmarks. We also study the sensitivity of workload behavior with respect to input data size, and we propose a methodology for identifying representative input data sets

Parameter identification of JONSWAP spectrum acquired by airborne LIDAR

International audienceIn this study, we developed the first linear Joint North Sea Wave Project (JONSWAP) spectrum (JS), which involves a transformation from the JS solution to the natural logarithmic scale. This transformation is convenient for defining the least squares function in terms of the scale and shape parameters. We identified these two wind-dependent parameters to better understand the wind effect on surface waves. Due to its efficiency and high-resolution, we employed the airborne Light Detection and Ranging (LIDAR) system for our measurements. Due to the lack of actual data, we simulated ocean waves in the MATLAB environment, which can be easily translated into industrial programming language. We utilized the Longuet-Higgin (LH) random-phase method to generate the time series of wave records and used the fast Fourier transform (FFT) technique to compute the power spectra density. After validating these procedures, we identified the JS parameters by minimizing the mean-square error of the target spectrum to that of the estimated spectrum obtained by FFT. We determined that the estimation error is relative to the amount of available wave record data. Finally, we found the inverse computation of wind factors (wind speed and wind fetch length) to be robust and sufficiently precise for wave forecasting

Clinical Characteristics and Survival Analysis of Two Groups of Patients with Colon Cancer with Different Social Support

Colon cancer is the third largest cancer in the world at present[1], which is very common in developed countries, and the incidence rate in developing countries is also increasing year by year. The latest epidemiological report shows that 376000 new colon cancer patients and 191000 deaths have occurred in China. In the past ten years, our understanding of cancer has made new progress [2]. However, in the current research, there has been no progress in the research on the occurrence, development and prevention of colon cancer related to physical and mental diseases. In the latest research, there are studies on the influence of psychological factors in the molecular field from the perspective of psychology, which is of great help to the research on the occurrence, development and prognosis of colon cancer. In order to explore the influence of social psychological factors on the occurrence, development and prognosis of colon cancer, the experiment collected clinical data, social support scores, and followed up disease-free survival period and total survival period of colon cancer patients

Toward Efficient Automated Feature Engineering

Automated Feature Engineering (AFE) refers to automatically generate and select optimal feature sets for downstream tasks, which has achieved great success in real-world applications. Current AFE methods mainly focus on improving the effectiveness of the produced features, but ignoring the low-efficiency issue for large-scale deployment. Therefore, in this work, we propose a generic framework to improve the efficiency of AFE. Specifically, we construct the AFE pipeline based on reinforcement learning setting, where each feature is assigned an agent to perform feature transformation \com{and} selection, and the evaluation score of the produced features in downstream tasks serve as the reward to update the policy. We improve the efficiency of AFE in two perspectives. On the one hand, we develop a Feature Pre-Evaluation (FPE) Model to reduce the sample size and feature size that are two main factors on undermining the efficiency of feature evaluation. On the other hand, we devise a two-stage policy training strategy by running FPE on the pre-evaluation task as the initialization of the policy to avoid training policy from scratch. We conduct comprehensive experiments on 36 datasets in terms of both classification and regression tasks. The results show $2.9\%$ higher performance in average and 2x higher computational efficiency comparing to state-of-the-art AFE methods