Physical Blocking Neural Tube Closure Affects Radial Intercalation and Neural Crest Midline-directed Migration in Xenopus Dorsal Explants

神经管闭合缺陷(NTDs)是一种严重的先天畸形疾病,在新生儿中有千分之一的发病率.神经管融合前后,多种组织参与形态发生运动.神经管一经融合,神经嵴细胞就会向背侧中线方向产生单极突出并向此方向迁移形成神经管的顶部.与此同时,神经管从腹侧开始发生辐射状切入以实现单层化.在此,我们在非洲爪蟾的移植体中机械阻断神经管的闭合以检测其细胞运动及随后的图式形成.结果显示神经管闭合缺陷的移植体不能形成单层化的神经管,并且神经嵴细胞滞留在侧面区域不能向背侧中线迁移,而对神经前体标记基因的检测显示神经管的背腹图式形成并未受到影响.以上结果表明神经管的融合对于辐射状切入和神经嵴细胞向背侧中线方向的迁移过程是必需的,而对于神经管的沿背腹轴方向的图式形成是非必需的.

Xenopus RCOR2 (REST corepressor 2) interacts with ZMYND8, which is involved in neural differentiation

Regulation of neuronal gene expression is critical to nervous system development. REST (RE1-silencing transcription factor) regulates neuronal gene expression through interacting with a group of corepressor proteins including REST corepressors (RCOR). Here we show that Xenopus RCOR2 is predominantly expressed in the developing nervous system. Through a yeast two-hybrid screen, we isolated Xenopus ZMYND8 (Zinc finger and MYND domain containing 8) as an XRCOR2 interacting factor. XRCOR2 and XZMYND8 bind each other in co-immunoprecipitation assays and both of them can function as transcriptional repressors. XZMYND8 is co-expressed with XRCOR2 in the nervous system and overexpression of XZMYND8 inhibits neural differentiation in Xenopus embryos. These data reveal a RCOR2/ZMYND8 complex which might be involved in the regulation of neural differentiation. (C) 2010 Elsevier Inc. All rights reserved.

Transplantable neural progenitor populations derived from Rhesus monkey embryonic stem cells

Cell-based therapies using embryonic stem cells (ESCs) in the treatment of neural disease will require the generation of homogenous donor neural progenitor (NP) populations. Here we describe an efficient culture system containing hepatocyte growth factor (HGF) and G5 supplement for the production of highly enriched (88.3% +/- 8.1%)populations of NPs from rhesus monkey ESCs. Additional purification resulted in NP preparations that were 98% nestin positive. Moreover, NPs, as monolayers or neurospheres, could be maintained for prolonged periods of time in media containing HGF+G5 or G5 alone. In vitro differentiation and in vivo transplantation assays showed that NPs could differentiate into neurons, astrocytes, and oligodendrocytes. The kinds and quantities of differentiated cells derived from NPs were closely correlated with their niches in vivo. Glial differentiation was predominant in periventricular areas, whereas cells migrating into the cortex were mostly neurons. Cell counts showed that 2 months after transplantation, approximately 25% of transplanted NPs survived and 65% - 80% of the surviving transplanted cells migrated along the ventricular wall or in a radial fashion. Subcloning demonstrated that several clonal lines derived from NPs expressed nestin and differentiated into three neural lineages in vitro and in rat brains in vivo. In contrast, some subcloned lines showed restricted differentiation both in vitro and in vivo in rat brains. These observations set the stage for obtaining highly enriched NPs and evaluating the efficacy of NP-based transplantation therapy in the nonhuman primate and will provide a platform for probing the molecular mechanisms that control neural induction.

Neural progenitors derived from monkey embryonic stem cells in a simple monoculture system

A simple monoculture system. combined with a chemically defined medium containing hepatocyte growth factor (HGF) and G5 supplement, was used to induce rhesus monkey embryonic stem cells (rESC) directly into neuroepithelial (NE) cells. Under these conditio

Aging Affects the Neural Representation of Speed in Macaque Area MT

Human perception of speed declines with age. Much of the decline is probably mediated by changes in the middle temporal (MT) area, an extrastriate area whose neural activity is linked to the perception of speed. In the present study, we used random-dot pa

Forecasting Daily Chlorophyll a Concentration during the Spring Phytoplankton Bloom Period in Xiangxi Bay of the Three-Gorges Reservoir by Means of a Recurrent Artificial Neural Network

A recurrent artificial neural network was used for 0-and 7-days-ahead forecasting of daily spring phytoplankton bloom dynamics in Xiangxi Bay of Three-Gorges Reservoir with meteorological, hydrological, and limnological parameters as input variables. Daily data from the depth of 0.5 m was used to train the model, and data from the depth of 2.0 m was used to validate the calibrated model. The trained model achieved reasonable accuracy in predicting the daily dynamics of chlorophyll a both in 0-and 7-days-ahead forecasting. In 0-day-ahead forecasting, the R-2 values of observed and predicted data were 0.85 for training and 0.89 for validating. In 7-days-ahead forecasting, the R-2 values of training and validating were 0.68 and 0.66, respectively. Sensitivity analysis indicated that most ecological relationships between chlorophyll a and input environmental variables in 0-and 7-days-ahead models were reasonable. In the 0-day model, Secchi depth, water temperature, and dissolved silicate were the most important factors influencing the daily dynamics of chlorophyll a. And in 7-days-ahead predicting model, chlorophyll a was sensitive to most environmental variables except water level, DO, and NH3N.

Prediction and elucidation of the population dynamics of Microcystis spp. in Lake Dianchi (China) by means of artificial neural networks

Lake Dianchi is a shallow and turbid lake, located in Southwest China. Since 1985, Lake Dianchi has experienced severe cyanabacterial blooms (dominated by Microcystis spp.). In extreme cases, the algal cell densities have exceeded three billion cells per liter. To predict and elucidate the population dynamics ofMicrocystis spp. in Lake Dianchi, a neural network based model was developed. The correlation coefficient (R 2) between the predicted algal concentrations by the model and the observed values was 0.911. Sensitivity analysis was performed to clarify the algal dynamics to the changes of environmental factors. The results of a sensitivity analysis of the neural network model suggested that small increases in pH could cause significantly reduced algal abundance. Further investigations on raw data showed that the response of Microcystis spp. concentration to pH increase was dependent on algal biomass and pH level. When Microcystis spp. population and pH were moderate or low, the response of Microcystis spp. population would be more likely to be positive in Lake Dianchi; contrarily, Microcystis spp. population in Lake Dianchi would be more likely to show negative response to pH increase when Microcystis spp. population and pH were high. The paper concluded that the extremely high concentration of algal population and high pH could explain the distinctive response of Microcystis spp. population to +1 SD (standard deviation) pH increase in Lake Dianchi. And the paper also elucidated the algal dynamics to changes of other environmental factors. One SD increase of water temperature (WT) had strongest positive relationship with Microcystis spp. biomass. Chemical oxygen demand (COD) and total phosphorus (TP) had strong positive effect on Microcystis spp. abundance while total nitrogen (TN), biological oxygen demand in five days (BOD5), and dissolved oxygen had only weak relationship with Microcystis spp. concentration. And transparency (Tr) had moderate positive relationship with Microcystis spp. concentration.

Modelling algal blooms in Lake Dianchi, China using neural networks

Lake Dianchi is one of the most extensively impacted freshwater lakes by algal blooms. To investigate the response of dominant algal genera, neural networks were applied to model the relationship between water quality parameters and the biomass of four dominant genera (Microcystic spp., Anabaena sp., Quadricauda (Turp.) Breb, Pediastrum Mey) in Dianchi. Results showed that the timing and magnitude of algal blooms of Microcystic spp., nabaena sp., Quadricauda (Turp.) Breb, and Pediastrum Mey in Dianchi could be successfully predicted. The evaluation of environmental factors showed that pH had more significant impact on concentrations of all the four dominant algal genera than the nutrient factors, such as total phosphorus and total nitrogen.

Using the moving window incorporated neural network to forecast the population behavior of Nostocales spp. in the River Darling, Australia

The paper demonstrates the nonstationarity of algal population behaviors by analyzing the historical populations of Nostocales spp. in the River Darling, Australia. Freshwater ecosystems are more likely to be nonstationary, instead of stationary. Nonstationarity implies that only the near past behaviors could forecast the near future for the system. However, nonstionarity was not considered seriously in previous research efforts for modeling and predicting algal population behaviors. Therefore the moving window technique was incorporated with radial basis function neural network (RBFNN) approach to deal with nonstationarity when modeling and forecasting the population behaviors of Nostocales spp. in the River Darling. The results showed that the RBFNN model could predict the timing and magnitude of algal blooms of Nostocales spp. with high accuracy. Moreover, a combined model based on individual RBFNN models was implemented, which showed superiority over the individual RBFNN models. Hence, the combined model was recommended for the modeling and forecasting of the phytoplankton populations, especially for the forecasting.

Modeling phytoplankton dynamics in the River Darling (Australia) using the radial basis function neural network

A radial basis function neural network was employed to model the abundance of cyanobacteria. The trained network could predict the populations of two bloom forming algal taxa with high accuracy, Nostocales spp. and Anabaena spp., in the River Darling, Australia. To elucidate the population dynamics for both Nostocales spp. and Anabaena spp., sensitivity analysis was performed with the following results. Total Kjeldahl nitrogen had a very strong influence on the abundance of the two algal taxa, electrical conductivity had a very strong negative relationship with the population of the two algal species, and flow was identified as one dominant factor influencing algal blooms after a scatter plot revealed that high flow could significantly reduce the algal biomass for both Nostocales spp. and Anabaena spp. Other variables such as turbidity, color, and pH were less important in determining the abundance and succession of the algal blooms.

Correcting the systematic error of the density functional theory calculation: the alternate combination approach of genetic algorithm and neural network

The alternate combinational approach of genetic algorithm and neural network (AGANN) has been presented to correct the systematic error of the density functional theory (DFT) calculation. It treats the DFT as a black box and models the error through external statistical information. As a demonstration, the AGANN method has been applied in the correction of the lattice energies from the DFT calculation for 72 metal halides and hydrides. Through the AGANN correction, the mean absolute value of the relative errors of the calculated lattice energies to the experimental values decreases from 4.93% to 1.20% in the testing set. For comparison, the neural network approach reduces the mean value to 2.56%. And for the common combinational approach of genetic algorithm and neural network, the value drops to 2.15%. The multiple linear regression method almost has no correction effect here.

A low-cost CMOS programmable temperature

A novel uncalibrated CMOS programmable temperature switch with high temperature accuracy is presented. Its threshold temperature T-th can be programmed by adjusting the ratios of width and length of the transistors. The operating principles of the temperature switch circuit is theoretically explained. A floating gate neural MOS circuit is designed to compensate automatically the threshold temperature T-th variation that results form the process tolerance. The switch circuit is implemented in a standard 0.35 mu m CMOS process. The temperature switch can be programmed to perform the switch operation at 16 different threshold temperature T(th)s from 45-120 degrees C with a 5 degrees C increment. The measurement shows a good consistency in the threshold temperatures. The chip core area is 0.04 mm(2) and power consumption is 3.1 mu A at 3.3V power supply. The advantages of the temperature switch are low power consumption, the programmable threshold temperature and the controllable hysteresis.