Modeling and numerical analysis of beam matrix plasma display system

This research investigates a new display device - Beam Matrix Plasma Display Panel (BM PDP). The scan of a PDP in such a system is accomplished through two electrical-beam guns instead of semiconductor switches. The BM-PDP eliminates the expensive semiconductor switches in the current plasma display device systems. Its drive circuit has only three parts: electron beam guns, resistors and capacitors. During the operation of BM PDP, first a switch cell is turned on by a selection gun (X gun). Then another electron gun (Y gun) emits electrons onto column electrodes and capacitors. When the voltage over the corresponding luminous cell reaches its breakdown point, gas discharges and generates light. Drive circuit design and analysis for BM-PDP is an important research topic. This work derives the formulae describing the operation of the drive circuit. With these formulae all the cases in which the drive circuit may work are discussed theoretically and numerically. Two equations are also given to determine the time of cell breakdown in these cases. The results of numerical simulation show that the current of an electron beam gun can be employed to carry the signal of image, the capacitance of a display cell is not sensitive to the initial current of gas discharge. The later property can be used to reduce the difficulties of manufacturing. The process of gas discharge in a display cell is also discussed and a multi-particle physical model is given to simulate the plasma cell

The Impact of Genomic Profiling for Novel Cancer Therapy–Recent Progress in Non-Small Cell Lung Cancer

There is high expectation for significant improvements in cancer patient care after completion of the human genome project in 2003. Through pains-taking analyses of genomic profiles in cancer patients, a number of targetable gene alterations have been discovered, with some leading to novel therapies, such as activating mutations of EGFR, BRAF and ALK gene fusions. As a result, clinical management of cancer through targeted therapy has finally become a reality for a subset of cancers, such as lung adenocarcinomas and melanomas. In this review, we summarize how gene mutation discovery leads to new treatment strategies using non-small cell lung cancer (NSCLC) as an example. We also discuss possible future implications of cancer genome analyses

A YBCO RF-SQUID magnetometer and its applications

An applicable RF-superconducting quantum interference detector (SQUID) magnetometer was made using a bulk sintered yttrium barium copper oxide (YBCO). The temperature range of the magnetometer is 77 to 300 K and the field range 0 to 0.1T. At 77 K, the equivalent flux noise of the SQUID is 5 x 10 to minus 4 power theta sub o/square root of Hz at the frequency range of 20 to 200 Hz. The experiments show that the SQUID noise at low-frequency end is mainly from 1/f noise. A coil test shows that the magnetic moment sensitivity delta m is 10 to the minus 6th power emu. The RF-SQUID is shielded in a YBCO cylinder with a shielding ability B sub in/B sub ex of about 10 to the minus 6th power when external dc magnetic field is about a few Oe. The magnetometer is successfully used in characterizing superconducting thin films

Biophysical Environmental Chemistry: A New Frontier for Chemistry

The paper discusses the position and role of environmental chemistry among the other environmental disciplines. It discusses the various aspects of environmental chemistry and emphasizes the need for developing fundamental studies in biophysical environmental chemistry in order to better understand the functioning of environmental systems. These systems include a large number of various structures in the nanometer to meter range which play key roles on compound fluxes and consequently on the homeostasis of ecosystems and on their disturbance by anthropogenic activities. Both structures and fluxes are presently ill-known and new concepts and methods must be developed in this field. For chemistry, this is a challenging area where supramolecular structures and processes play dominant roles. It is also a challenging field for the development of environmental sciences since detailed and sound physico-chemical processes are needed in macroscopic modeling of compound circulation in ecosystems. In addition, teaching this discipline to chemistry students would allow them to confront complex, structured real systems. This paper also discusses the relationship between biophysical environmental chemistry and the other environmental disciplines within integrated multidisciplinary studies. The structure used at the Faculty of Sciences of the University of Geneva to favour a flexible but efficient integration is briefly described

Effects of tributyrin supplemented in a high-soybean meal diet on the growth performance and intestinal histopathology of juvenile <em>Scophthal musmaximus</em> L.

This study was conducted to evaluate the influence of tributyrin (TB) supplemented in diet on the growth, feed utilization and intestinal health of turbot (Scophthalmus maximus L.). Four experimental diets were formulated with TB at 0 mg/kg, 100 mg/kg, 200 mg/kg and 300 mg/kg levels respectively. A total of 2016 turbot (initial body weight: 31.22±0.05g) were randomly distributed into 12 tanks and fed the corresponding diets for 70d. Results showed that fish in 100 mg/kg group had significantly higher weight gain rate (WGR) and specific growth rate (SGR) than that in other groups (P0.05). There was no significant difference in intestinal superoxide dismutase (SOD) activities and intestinal malonaldehyde (MDA) contents among all treatments (P>0.05), while the highest SOD activity and the lowest MDA content were found in 100 mg/kg group. The height of intestinal fold in 100 mg/kg treatment was significantly higher than that in other groups (P0.05). The present study suggested that 100 mg/kg TB inclusion level was recommended in the diets of turbot to promote the growth and improve the intestinal morphology structure and health in turbot

The role of GLI2-ABCG2 signaling axis for 5Fu resistance in gastric cancer

Gastric cancer is a leading cause of cancer-related mortality worldwide, and options to treat gastric cancer are limited. Fluorouracil (5Fu)-based chemotherapy is frequently used as a neoadjuvant or an adjuvant agent for gastric cancer therapy. Most patients with advanced gastric cancer eventually succumb to the disease despite the fact that some patients respond initially to chemotherapy. Thus, identifying molecular mechanisms responsible for chemotherapy resistance will help design novel strategies to treat gastric cancer. In this study, we discovered that residual cancer cells following 5Fu treatment have elevated expression of hedgehog (Hg) target genes GLI1 and GLI2, suggestive of Hh signaling activation. Hh signaling, a pathway essential for embryonic development, is an important regulator for putative cancer stem cells/residual cancer cells. We found that high GLI1/GLI2 expression is associated with some features of putative cancer stem cells, such as increased side population. We demonstrated that GLI2 knockdown sensitized gastric cancer cells to 5Fu treatment, decreased ABCG2 expression, and reduced side population. Elevated GLI2 expression is also associated with an increase in tumor sphere size, another marker for putative cancer stem cells. We believe that GLI2 regulates putative cancer stem cells through direct regulation of ABCG2. ABCG2 can rescue the GLI2 shRNA effects in 5Fu response, tumor sphere formation and side population changes, suggesting that ABCG2 is an important mediator for GLI2-associated 5Fu resistance. The relevance of our studies to gastric cancer patient care is reflected by our discovery that high GLI1/GLI2/ABCG2 expression is associated with a high incidence of cancer relapse in two cohorts of gastric cancer patients who underwent chemotherapy (containing 5Fu). Taken together, we have identified a molecular mechanism by which gastric cancer cells gain 5Fu resistance

Water Quality Prediction Method Based on OVMD and Spatio-Temporal Dependence

Water quality changes at one monitoring spot are not only related to local historical data but also spatially to the water quality of the adjacent spots. Additionally, the non-linear and non-stationary nature of water quality data has a significant impact on prediction results. To improve the accuracy of water quality prediction models, a comprehensive water quality prediction model has been initially established that takes into account both data complexity and spatio-temporal dependencies. The Optimal Variational Mode Decomposition (OVMD) technology is used to effectively decompose water quality data into several simple and stable time series, highlighting short-term and long-term features and enhancing the model\u27s learning ability. The component sequence and spot adjacency matrix are used as the input of Graph Convolutional Network (GCN) to extract the spatial characteristics of the data, and the spatio-temporal dependencies of water quality data at different spots are obtained by combining GCN into the neurons of Gated Recurrent Unit (GRU). The attention model is added to automatically adjust the importance of each time node to further improve the accuracy of the training model and obtain a multi-step prediction output that more closely aligns with the characteristics of water quality change. The proposed model has been validated with real monitoring data for ammonia nitrogen (NH3-N) and total phosphorus (TP), and the results show that the proposed model is better than ARIMA, GRU and GCN+GRU models in terms of prediction results and it shows obvious advantages in the benchmark comparison experiment, which can provide reliable evidence for water pollution source traceability or early warning

Water Quality Prediction Method Based on OVMD and Spatio-Temporal Dependence

Water quality changes at one monitoring spot are not only related to local historical data but also spatially to the water quality of the adjacent spots. Additionally, the non-linear and non-stationary nature of water quality data has a significant impact on prediction results. To improve the accuracy of water quality prediction models, a comprehensive water quality prediction model has been initially established that takes into account both data complexity and spatio-temporal dependencies. The Optimal Variational Mode Decomposition (OVMD) technology is used to effectively decompose water quality data into several simple and stable time series, highlighting short-term and long-term features and enhancing the model\u27s learning ability. The component sequence and spot adjacency matrix are used as the input of Graph Convolutional Network (GCN) to extract the spatial characteristics of the data, and the spatio-temporal dependencies of water quality data at different spots are obtained by combining GCN into the neurons of Gated Recurrent Unit (GRU). The attention model is added to automatically adjust the importance of each time node to further improve the accuracy of the training model and obtain a multi-step prediction output that more closely aligns with the characteristics of water quality change. The proposed model has been validated with real monitoring data for ammonia nitrogen (NH3-N) and total phosphorus (TP), and the results show that the proposed model is better than ARIMA, GRU and GCN+GRU models in terms of prediction results and it shows obvious advantages in the benchmark comparison experiment, which can provide reliable evidence for water pollution source traceability or early warning

GLI1-mediated regulation of side population is responsible for drug resistance in gastric cancer

Gastric cancer is the third leading cause of cancer-related mortality worldwide. Chemotherapy is frequently used for gastric cancer treatment. Most patients with advanced gastric cancer eventually succumb to the disease despite some patients responded initially to chemotherapy. Thus, identifying molecular mechanisms responsible for cancer relapse following chemotherapy will help design new ways to treat gastric cancer. In this study, we revealed that the residual cancer cells following treatment with chemotherapeutic reagent cisplatin have elevated expression of hedgehog target genes GLI1, GLI2 and PTCH1, suggestive of hedgehog signaling activation. We showed that GLI1 knockdown sensitized gastric cancer cells to CDDP whereas ectopic GLI1 expression decreased the sensitivity. Further analyses indicate elevated GLI1 expression is associated with an increase in tumor sphere formation, side population and cell surface markers for putative cancer stem cells. We have evidence to support that GLI1 is critical for maintenance of putative cancer stem cells through direct regulation of ABCG2. In fact, GLI1 protein was shown to be associated with the promoter fragment of ABCG2 through a Gli-binding consensus site in gastric cancer cells. Disruption of ABCG2 function, through ectopic expression of an ABCG2 dominant negative construct or a specific ABCG2 inhibitor, increased drug sensitivity of cancer cells both in culture and in mice. The relevance of our studies to gastric cancer patient care is reflected by our discovery that high ABCG2 expression was associated with poor survival in the gastric cancer patients who underwent chemotherapy. Taken together, we have identified a molecular mechanism by which gastric cancer cells gain chemotherapy resistance