ShRNA-Targeted Centromere Protein A Inhibits Hepatocellular Carcinoma Growth

Centromere protein A (CENP-A) plays important roles in cell-cycle regulation and genetic stability. Herein, we aimed to investigate its expression pattern, clinical significance, and biological function in hepatocellular carcinoma (HCC).. Conversely, CENP-A overexpression promoted HCC cell growth and reduced apoptosis. Furthermore, many genes implicated in cell-cycle regulation and apoptosis, including CHK2, P21waf1, P27 Kip1, SKP2, cyclin G1, MDM2, Bcl-2, and Bax, were deregulated by manipulating CENP-A.Overexpression of CENP-A is frequently observed in HCC. Targeting CENP-A can inhibit HCC growth, likely through the regulation of a large number genes involved in cell-cycle progression and apoptosis, and thereby represents a potential therapeutic strategy for this malignancy

A Retrospective Survey of Research Design and Statistical Analyses in Selected Chinese Medical Journals in 1998 and 2008

BACKGROUND: High quality clinical research not only requires advanced professional knowledge, but also needs sound study design and correct statistical analyses. The number of clinical research articles published in Chinese medical journals has increased immensely in the past decade, but study design quality and statistical analyses have remained suboptimal. The aim of this investigation was to gather evidence on the quality of study design and statistical analyses in clinical researches conducted in China for the first decade of the new millennium. METHODOLOGY/PRINCIPAL FINDINGS: Ten (10) leading Chinese medical journals were selected and all original articles published in 1998 (N = 1,335) and 2008 (N = 1,578) were thoroughly categorized and reviewed. A well-defined and validated checklist on study design, statistical analyses, results presentation, and interpretation was used for review and evaluation. Main outcomes were the frequencies of different types of study design, error/defect proportion in design and statistical analyses, and implementation of CONSORT in randomized clinical trials. From 1998 to 2008: The error/defect proportion in statistical analyses decreased significantly ( = 12.03, p<0.001), 59.8% (545/1,335) in 1998 compared to 52.2% (664/1,578) in 2008. The overall error/defect proportion of study design also decreased ( = 21.22, p<0.001), 50.9% (680/1,335) compared to 42.40% (669/1,578). In 2008, design with randomized clinical trials remained low in single digit (3.8%, 60/1,578) with two-third showed poor results reporting (defects in 44 papers, 73.3%). Nearly half of the published studies were retrospective in nature, 49.3% (658/1,335) in 1998 compared to 48.2% (761/1,578) in 2008. Decreases in defect proportions were observed in both results presentation ( = 93.26, p<0.001), 92.7% (945/1,019) compared to 78.2% (1023/1,309) and interpretation ( = 27.26, p<0.001), 9.7% (99/1,019) compared to 4.3% (56/1,309), some serious ones persisted. CONCLUSIONS/SIGNIFICANCE: Chinese medical research seems to have made significant progress regarding statistical analyses, but there remains ample room for improvement regarding study designs. Retrospective clinical studies are the most often used design, whereas randomized clinical trials are rare and often show methodological weaknesses. Urgent implementation of the CONSORT statement is imperative

Efficient Resource-Aware Convolutional Neural Architecture Search for Edge Computing with Pareto-Bayesian Optimization

With the development of deep learning technologies and edge computing, the combination of them can make artificial intelligence ubiquitous. Due to the constrained computation resources of the edge device, the research in the field of on-device deep learning not only focuses on the model accuracy but also on the model efficiency, for example, inference latency. There are many attempts to optimize the existing deep learning models for the purpose of deploying them on the edge devices that meet specific application requirements while maintaining high accuracy. Such work not only requires professional knowledge but also needs a lot of experiments, which limits the customization of neural networks for varied devices and application scenarios. In order to reduce the human intervention in designing and optimizing the neural network structure, multi-objective neural architecture search methods that can automatically search for neural networks featured with high accuracy and can satisfy certain hardware performance requirements are proposed. However, the current methods commonly set accuracy and inference latency as the performance indicator during the search process, and sample numerous network structures to obtain the required neural network. Lacking regulation to the search direction with the search objectives will generate a large number of useless networks during the search process, which influences the search efficiency to a great extent. Therefore, in this paper, an efficient resource-aware search method is proposed. Firstly, the network inference consumption profiling model for any specific device is established, and it can help us directly obtain the resource consumption of each operation in the network structure and the inference latency of the entire sampled network. Next, on the basis of the Bayesian search, a resource-aware Pareto Bayesian search is proposed. Accuracy and inference latency are set as the constraints to regulate the search direction. With a clearer search direction, the overall search efficiency will be improved. Furthermore, cell-based structure and lightweight operation are applied to optimize the search space for further enhancing the search efficiency. The experimental results demonstrate that with our method, the inference latency of the searched network structure reduced 94.71% without scarifying the accuracy. At the same time, the search efficiency increased by 18.18%

Interfacial properties of nMOSFETs with different Al2O3 capping layer thickness and TiN gate stacks

In this article, the interfacial properties of nMOSFETs with different thickness high- κ Al 2 O 3 capping layer on an 8-nm SiO 2 and TiN gate stacks have been investigated using electrical measurement and low-frequency noise at room temperature. It is shown that the predominant 1/ f noise is governed by the number fluctuations mechanism. It is indicated that: 1) presence of an Al 2 O 3 capping layer increases the noise power spectral density and, hence, the oxide trap density has an influence on the low-field mobility further and 2) effective work-function and the threshold voltage of nMOSFET should be modulated using the high- κ Al 2 O 3 capping layers

Efficient Resource-Aware Convolutional Neural Architecture Search for Edge Computing with Pareto-Bayesian Optimization

With the development of deep learning technologies and edge computing, the combination of them can make artificial intelligence ubiquitous. Due to the constrained computation resources of the edge device, the research in the field of on-device deep learning not only focuses on the model accuracy but also on the model efficiency, for example, inference latency. There are many attempts to optimize the existing deep learning models for the purpose of deploying them on the edge devices that meet specific application requirements while maintaining high accuracy. Such work not only requires professional knowledge but also needs a lot of experiments, which limits the customization of neural networks for varied devices and application scenarios. In order to reduce the human intervention in designing and optimizing the neural network structure, multi-objective neural architecture search methods that can automatically search for neural networks featured with high accuracy and can satisfy certain hardware performance requirements are proposed. However, the current methods commonly set accuracy and inference latency as the performance indicator during the search process, and sample numerous network structures to obtain the required neural network. Lacking regulation to the search direction with the search objectives will generate a large number of useless networks during the search process, which influences the search efficiency to a great extent. Therefore, in this paper, an efficient resource-aware search method is proposed. Firstly, the network inference consumption profiling model for any specific device is established, and it can help us directly obtain the resource consumption of each operation in the network structure and the inference latency of the entire sampled network. Next, on the basis of the Bayesian search, a resource-aware Pareto Bayesian search is proposed. Accuracy and inference latency are set as the constraints to regulate the search direction. With a clearer search direction, the overall search efficiency will be improved. Furthermore, cell-based structure and lightweight operation are applied to optimize the search space for further enhancing the search efficiency. The experimental results demonstrate that with our method, the inference latency of the searched network structure reduced 94.71% without scarifying the accuracy. At the same time, the search efficiency increased by 18.18%

Effects of Heavy Metal Stress on Physiology, Hydraulics, and Anatomy of Three Desert Plants in the Jinchang Mining Area, China

The physiological mechanisms and phytoremediation effects of three kinds of native quinoa in a desert mining area were studied. We used two different types of local soils (native soil and tailing soil) to analyze the changes in the heavy metal content, leaf physiology, photosynthetic parameters, stem hydraulics, and anatomical characteristics of potted quinoa. The results show that the chlorophyll content, photosynthetic rate, stomatal conductance, and transpiration rate of Kochia scoparia were decreased, but intercellular CO2 concentration (Ci) was increased under heavy metal stress, and the net photosynthetic rate (Pn) was decreased due to non-stomatal limitation. The gas exchange of Chenopodium glaucum and Atriplex centralasiatica showed a decrease in Pn, stomatal conductance (Gs), and transpiration rate (E) due to stomatal limitation. The three species showed a similar change in heavy metal content; they all showed elevated hydraulic parameters, decreased vessel density, and significantly thickened vessel walls under heavy metal stress. Physiological indicators such as proline content and activity of superoxide dismutase (SOD) and peroxidase (POD) increased, but the content of malondialdehyde (MDA) and glutathione (GSH), as well as catalase (CAT) activity, decreased in these three plants. Therefore, it can be concluded that these three species of quinoa, possibly the most dominant 30 desert plants in the region, showed a good adaptability and accumulation capacity under the pressure of heavy metal stress, and these plants can be good candidates for tailings remediation in the Jinchang desert mining area

Consistent responses of ecosystem CO2 exchange to grassland degradation in alpine meadow of the Qinghai-Tibetan Plateau

The effect of grassland degeneration on the emissions and sinks of carbon dioxide (CO2) received extensive attention because of the increase of degraded area and the degeneration level in alpine meadow. To quantify its effect, we investigated net ecosystem exchange (NEE), gross primary productivity (GPP), ecosystem respiration (Reco), plant respiration (Rplant) and heterotrophic respiration (Rh), as well as environmental variability (soil water content (SWC), soil total carbon (STC), soil total nitrogen content (STN) and aboveground biomass (AGB)) from different degraded grasslands and non-degraded (ND) grassland in alpine meadow, Qinghai-Tibetan Plateau in growing season. The results indicated that compared with ND grassland, land degradation significantly decreased net CO2 uptake (-NEE), GPP, Reco, Rplant and Rh by 60.61%, 63.22%, 67.53% 78.82% and 43.56%, respectively in extremely degraded (ED) grassland. These consistent responses suggested that the ecosystem CO2 fluxes were very sensitive to grassland degradation. Degradation also decreased Rplant/Reco and Rplant/GPP by 16.8% and 8.1%, respectively, while increased Rh/Reco, Rh/Rplant and Rh/GPP by 16.8%, 41.4% and 3.8%, respectively, suggested that grassland degradation could eventually shifted Reco from autotrophic dominated to heterotrophic dominated. Four structural equation models (SEM) indicated that decline in NEE, GPP and Reco directly related to AGB and STC and decline in Rplant directly related to AGB, and indirectly related to SWC and STN. Our study highlighted that the consistent responses of CO2 fluxes to grassland degradation could alter the ecosystem’s carbon balance, and further would be influence carbon-climate feedbacks under deterioration of ecological environment