The Clinical Signifcance of Expression of ERCC1 and PKCalpha in Non-small Cell Lung Cancer

Background and objective Excision repair cross-complementing 1 (Excision-Repair Cross-Complementing 1, ERCC1), an important member of the DNA repair gene family, plays a key role in nucleotide excision repair and apoptosis of tumor cells. Protein kinase C-α (Protein kinase C, PKCα), an isozyme in protein kinase C family, is an important signaling molecule in signal transduction pathways of tumors, which has been implicated in malignant transformation and proliferation. The aim of this study was to explore the clinical significance of ERCC1 and PKCα in non-small cell lung cancer (NSCLC). Methods The expression of ERCC1 and PKCα were examined by immunohistochemistry (IHC) in the specimens of 51 cases of NSCLC patients tissue and 21 cases of paracancerous tissue. The relationship between detected data and patients′ clinical parameters was analyzed by SPSS 13.0 software. Results The positive expression rate of ERCC1 and PKCα in NSCLC tissues was significantly higher than paracancerous tissues (Ρ<0.05). Expression of ERCC1 was closely related to clinical stage and N stage. The positive rate of ERCC1 was higher in III+IV or N1+N2 stage patients compared with I+II or N0 stage (Ρ=0.011, P=0.015). We also found that 5-year survival of negative group of ERCC1 was remarkably higher than that of positive group by χ2 test (Ρ<0.05). Expression of ERCC1 was positively correlative to PKCα by Spearman′s correlation analysis (r=0.425, P=0.002) in NSCLC. Conclusion The results suggest ERCC1 and PKCα might be correlated with the development of NSCLC. ERCC1 might be related to prognosis of NSCLC. There might be existed a mechanism of coordination or regulation between ERCC1 and PKCα

Preparation of Low-loss Ge15Ga10Te75 chalcogenide glass for far-IR optics applications

International audienceGe15Ga10Te75 (GGT) glass shows good transparency between 2 and 25 μm wavelengths, good chemical and thermal stability to be drawn into fiber, which appears to be a good candidate for developing far-IR fiber-optics devices, although there are strong absorption peaks caused by impurities in the glass. With the aim of decreasing the content of impurities and micro-crystal particles in prepared \GGT\ glass samples, a rapid heating furnace and the fast distillation method based on vapor evaporation plus deposition under vacuum condition was adopted. Properties measurements including Differential Scanning Calorimeter (DSC), Vis-NIR and \IR\ transmitting spectra were performed on the prepared glass samples. Dependence of optical loss on the types of oxygenic getters and their contents and glass quenching temperature was also studied. All these results show that the average optical losses of distilled glass samples were greatly improved by the designated purification processes. Besides, the quality of the glass samples can be improved with the optimized quenching temperature. In all, the optical loss of the glass can be reduced effectively. Minimum optical losses of 0.042 dB/mm at 9 μm and 0.037 dB/mm at 12 μm are obtained after a right purification process, which are the lowest loss of the \GGT\ chalcogenide glass nowadays

Tuning ice nucleation with counterions on polyelectrolyte brush surfaces

Heterogeneous ice nucleation (HIN) on ionic surfaces is ubiquitous in a wide range of atmospheric aerosols and at biological interfaces. Despite its great importance in cirrus cloud formation and cryopreservation of cells, organs, and tissues, it remains unclear whether the ion-specific effect on ice nucleation exists. Benefiting from the fact that ions at the polyelectrolyte brush (PB)/water interface can be reversibly exchanged, we report the effect of ions on HIN on the PB surface, and we discover that the distinct efficiency of ions in tuning HIN follows the Hofmeister series. Moreover, a large HIN temperature window of up to 7.8°C is demonstrated. By establishing a correlation between the fraction of ice-like water molecules and the kinetics of structural transformation from liquid- to ice-like water molecules at the PB/water interface with different counterions, we show that our molecular dynamics simulation analysis is consistent with the experimental observation of the ion-specific effect on HIN

Average Convergence Rate of Evolutionary Algorithms

In evolutionary optimization, it is important to understand how fast evolutionary algorithms converge to the optimum per generation, or their convergence rate. This paper proposes a new measure of the convergence rate, called average convergence rate. It is a normalised geometric mean of the reduction ratio of the fitness difference per generation. The calculation of the average convergence rate is very simple and it is applicable for most evolutionary algorithms on both continuous and discrete optimization. A theoretical study of the average convergence rate is conducted for discrete optimization. Lower bounds on the average convergence rate are derived. The limit of the average convergence rate is analysed and then the asymptotic average convergence rate is proposed

Linking stoichiometric homeostasis with ecosystem structure, functioning, and stability

Ecosystem structure, functioning, and stability have been a focus of ecological and environmental sciences during the past two decades. The mechanisms underlying their relationship, however, are not well understood. Based on comprehensive studies in Inner Mongolia grassland, here we show that species-level stoichiometric homeostasis was consistently positively correlated with dominance and stability on both 2-year and 27-year temporal scales and across a 1200-km spatial transect. At the community level, stoichiometric homeostasis was also positively correlated with ecosystem function and stability in most cases. Thus, homeostatic species tend to have high and stable biomass; and ecosystems dominated by more homeostatic species have higher productivity and greater stability. By modulating organism responses to key environmental drivers, stoichiometric homeostasis appears to be a major mechanism responsible for the structure, functioning, and stability of grassland ecosystems

Balancing human energy needs and conservation of panda habitat

Thesis (Ph. D.)--Michigan State University. Fisheries and Wildlife, 2008Includes bibliographical references (pages 141-158

Register-Aware Optimizations for Parallel Sparse Matrix-Matrix Multiplication

General sparse matrix–matrix multiplication (SpGEMM) is a fundamental building block of a number of high-level algorithms and real-world applications. In recent years, several efficient SpGEMM algorithms have been proposed for many-core processors such as GPUs. However, their implementations of sparse accumulators, the core component of SpGEMM, mostly use low speed on-chip shared memory and global memory, and high speed registers are seriously underutilised. In this paper, we propose three novel register-aware SpGEMM algorithms for three representative sparse accumulators, i.e., sort, merge and hash, respectively. We fully utilise the GPU registers to fetch data, finish computations and store results out. In the experiments, our algorithms deliver excellent performance on a benchmark suite including 205 sparse matrices from the SuiteSparse Matrix Collection. Specifically, on an Nvidia Pascal P100 GPU, our three register-aware sparse accumulators achieve on average 2.0 × (up to 5.4 × ), 2.6 × (up to 10.5 × ) and 1.7 × (up to 5.2 × ) speedups over their original implementations in libraries bhSPARSE, RMerge and NSPARSE, respectively.acceptedVersionThis is a post-peer-review, pre-copyedit version of an article published in [International journal of parallel programming] Locked until 1.1.2020 due to copyright restrictions. The final authenticated version is available online at: https://doi.org/10.1007/s10766-018-0604-

Numerical Simulation on Forced Convection Cooling of Horizontal Ionic Wind with Multi-electrodes

Enhancement ofheat transfer plays an important role in the cooling of electronic or refrigeration systems, and its characteristics could strongly affect the stability and performance of such systems. To enhance heat transfer, air cooling of forced convection remains one of the main solutions. For example, conventional rotary-fan air cooling is still dominant in many areas. However, with the increasing of heat generation in these systems, the limitation of the conventional rotary-fan air cooling is become more obvious. So, demands in novel air cooling technology become necessary, e.g., silent and high efficient air cooling. Recently, ionic wind, which has no moving part and is easily miniaturized, shows great potential in heat dissipation and attracts widespread attentions. In this work, ionic wind, which is produced by wire to plate configuration for forced convection enhancement of horizontal flow along the plate, is numerically investigated. Firstly, a multi-physic model, which accounts for electric field, charge distribution, fluid dynamics, and heat transfer phenomenon, is presented. Comparisons between the simulation and literature data are conducted. Results show that better agreements are achieved by the developed model. Secondly, influences of the emitting electrodes numbers are analyzed. Results show that multiple electrodes configuration has higher performance in terms of heat transfer coefficient than that of the single electrode. Investigations are also carried out on the influences of the distances between the emitting electrodes. Thirdly, effects of the main parameters of ionic wind, such as the inlet velocity, and voltage applied on the electrodes etc., are investigated. Finally, by using the multi-physic model of ionic wind, characteristics of the heat transfer are predicted. It is found that the maximum enhancement of average heat transfer coefficient could reach around 150 %

The two-way knowledge interaction interface between humans and neural networks

Despite neural networks (NN) have been widely applied in various fields and generally outperforms humans, they still lack interpretability to a certain extent, and humans are unable to intuitively understand the decision logic of NN. This also hinders the knowledge interaction between humans and NN, preventing humans from getting involved to give direct guidance when NN's decisions go wrong. While recent research in explainable AI has achieved interpretability of NN from various perspectives, it has not yet provided effective methods for knowledge exchange between humans and NN. To address this problem, we constructed a two-way interaction interface that uses structured representations of visual concepts and their relationships as the "language" for knowledge exchange between humans and NN. Specifically, NN provide intuitive reasoning explanations to humans based on the class-specific structural concepts graph (C-SCG). On the other hand, humans can modify the biases present in the C-SCG through their prior knowledge and reasoning ability, and thus provide direct knowledge guidance to NN through this interface. Through experimental validation, based on this interaction interface, NN can provide humans with easily understandable explanations of the reasoning process. Furthermore, human involvement and prior knowledge can directly and effectively contribute to enhancing the performance of NN