Direct modulation of electroluminescence from silicon nanocrystals beyond radiative recombination rates

We propose a light emitting transistor based on silicon nanocrystals provided with 200 Mbits/ s built-in modulation. Suppression of electroluminescence from silicon nanocrystals embedded into the gate oxide of a field effect transistor is achieved by fast Auger quenching. In this process, a modulating drain signal causes heating of carriers in the channel and facilitates the charge injection into the nanocrystals. This excess of charge enables fast nonradiative processes that are used to obtain 100% modulation depths at modulating voltages of 1 V.

Molecular basis of selective PPARgamma modulation for the treatment of Type 2 diabetes.

Peroxisome proliferator-activated receptors (PPARs) (alpha, beta/delta and gamma) are lipid sensors capable of adapting gene expression to integrate various lipid signals. As such, PPARs are also very important pharmaceutical targets, and specific synthetic ligands exist for the different isotypes and are either currently used or hold promises in the treatment of major metabolic disorders. In particular, compounds of the class of the thiazolinediones (TZDs) are PPARgamma agonists and potent insulin-sensitizers. The specific but still broad expression patterns of PPARgamma, as well as its implication in numerous pathways, constitutes also a disadvantage regarding drug administration, since this potentially increases the chance to generate side-effects through the activation of the receptor in tissues or cells not affected by the disease. Actually, numerous side effects associated with the administration of TZDs have been reported. Today, a new generation of PPARgamma modulators is being actively developed to activate the receptor more specifically, in a cell and time-dependent manner, in order to induce a specific subset of target genes only and modulate a restricted number of metabolic pathways. We will discuss here why and how the development of such selective PPARgamma modulators is possible, and summarize the results obtained with the published molecules.

Selection methods regulate evolution of cooperation in digital evolution.

A key, yet often neglected, component of digital evolution and evolutionary models is the 'selection method' which assigns fitness (number of offspring) to individuals based on their performance scores (efficiency in performing tasks). Here, we study with formal analysis and numerical experiments the evolution of cooperation under the five most common selection methods (proportionate, rank, truncation-proportionate, truncation-uniform and tournament). We consider related individuals engaging in a Prisoner's Dilemma game where individuals can either cooperate or defect. A cooperator pays a cost, whereas its partner receives a benefit, which affect their performance scores. These performance scores are translated into fitness by one of the five selection methods. We show that cooperation is positively associated with the relatedness between individuals under all selection methods. By contrast, the change in the performance benefit of cooperation affects the populations' average level of cooperation only under the proportionate methods. We also demonstrate that the truncation and tournament methods may introduce negative frequency-dependence and lead to the evolution of polymorphic populations. Using the example of the evolution of cooperation, we show that the choice of selection method, though it is often marginalized, can considerably affect the evolutionary dynamics.

Modulation of proteasomal activity required for the generation of a cytotoxic T lymphocyte-defined peptide derived from the tumor antigen MAGE-3.

We have analyzed the presentation of human histocompatability leukocyte antigen-A*0201-associated tumor peptide antigen MAGE-3271-279 by melanoma cells. We show that specific cytotoxic T lymphocyte (CTL)-recognizing cells transfected with a minigene encoding the preprocessed fragment MAGE-3271-279 failed to recognize cells expressing the full length MAGE-3 protein. Digestion of synthetic peptides extended at the NH2 or COOH terminus of MAGE-3271-279 with purified human proteasome revealed that the generation of the COOH terminus of the antigenic peptide was impaired. Surprisingly, addition of lactacystin to purified proteasome, though partially inhibitory, resulted in the generation of the antigenic peptide. Furthermore, treatment of melanoma cells expressing the MAGE-3 protein with lactacystin resulted in efficient lysis by MAGE-3271-279-specific CTL. We therefore postulate that the generation of antigenic peptides by the proteasome in cells can be modulated by the selective inhibition of certain of its enzymaticactivities.

Integração de dados do quickbird e atributos do terreno no mapeamento digital de solos por redes neurais artificiais

No presente estudo, foi realizada uma avaliação de diferentes variáveis ambientais no mapeamento digital de solos em uma região no norte do Estado de Minas Gerais, utilizando redes neurais artificiais (RNA). Os atributos do terreno declividade e índice topográfico combinado (CTI), derivados de um modelo digital de elevação, três bandas do sensor Quickbird e um mapa de litologia foram combinados, e a importância de cada variável para discriminação das unidades de mapeamento foi avaliada. O simulador de redes neurais utilizado foi o "Java Neural Network Simulator", e o algoritmo de aprendizado, o "backpropagation". Para cada conjunto testado, foi selecionada uma RNA para a predição das unidades de mapeamento; os mapas gerados por esses conjuntos foram comparados com um mapa de solos produzido com o método convencional, para determinação da concordância entre as classificações. Essa comparação mostrou que o mapa produzido com o uso de todas as variáveis ambientais (declividade, índice CTI, bandas 1, 2 e 3 do Quickbird e litologia) obteve desempenho superior (67,4 % de concordância) ao dos mapas produzidos pelos demais conjuntos de variáveis. Das variáveis utilizadas, a declividade foi a que contribuiu com maior peso, pois, quando suprimida da análise, os resultados da concordância foram os mais baixos (33,7 %). Os resultados demonstraram que a abordagem utilizada pode contribuir para superar alguns dos problemas do mapeamento de solos no Brasil, especialmente em escalas maiores que 1:25.000, tornando sua execução mais rápida e mais barata, sobretudo se houver disponibilidade de dados de sensores remotos de alta resolução espacial a custos mais baixos e facilidade de obtenção dos atributos do terreno nos sistemas de informação geográfica (SIG).

Decay of unstable states in the presence of colored noise and random initial conditions. II. Analog experiments and digital simulations

The decay of an unstable state under the influence of external colored noise has been studied by means of analog experiments and digital simulations. For both fixed and random initial conditions, the time evolution of the second moment ¿x2(t)¿ of the system variable was determined and then used to evaluate the nonlinear relaxation time. The results obtained are found to be in excellent agreement with the theoretical predictions of the immediately preceding paper [Casademunt, Jiménez-Aquino, and Sancho, Phys. Rev. A 40, 5905 (1989)].

Digital soil mapping: strategy for data pre-processing

The region of greatest variability on soil maps is along the edge of their polygons, causing disagreement among pedologists about the appropriate description of soil classes at these locations. The objective of this work was to propose a strategy for data pre-processing applied to digital soil mapping (DSM). Soil polygons on a training map were shrunk by 100 and 160 m. This strategy prevented the use of covariates located near the edge of the soil classes for the Decision Tree (DT) models. Three DT models derived from eight predictive covariates, related to relief and organism factors sampled on the original polygons of a soil map and on polygons shrunk by 100 and 160 m were used to predict soil classes. The DT model derived from observations 160 m away from the edge of the polygons on the original map is less complex and has a better predictive performance.

Mapeamento digital de solos por redes neurais artificiais com base na relação solo-paisagem

Técnicas de mapeamento digital podem contribuir para agilizar a realização de levantamentos pedológicos detalhados. Objetivou-se com este trabalho obter um mapa digital de solos (MDS) com uso de redes neurais artificiais (RNA), utilizando correlações entre unidades de mapeamento (UM) e covariáveis ambientais. A área utilizada compreendeu aproximadamente 12.000 ha localizados no município de Barra Bonita, SP. A partir do resultado de uma análise de agrupamento das covariáveis ambientais, foram escolhidas cinco áreas de referência para realizar o mapeamento convencional. As UM identificadas subsidiaram a aplicação da técnica de RNA. Utilizaram-se o simulador de redes neurais JavaNNS e o algoritmo de aprendizado backpropagation. Pontos de referência foram coletados para avaliar o desempenho do mapa digital produzido. A posição na paisagem e o material de origem subjacente foram determinantes para o reconhecimento dos delineamentos das UM. Houve boa concordância entre as UM delineadas pelo MDS e pelo método convencional. A comparação entre os pontos de referência e o mapa de solos digital evidenciou exatidão de 72 %. O uso da abordagem MDS utilizada pode contribuir para diminuir a falta de informações semidetalhadas de solos em locais ainda não mapeados, tomando-se como base informações pedológicas obtidas de áreas de referência adjacentes.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

An appropriate data set size for digital soil mapping in Erechim, Rio Grande do Sul, Brazil

Digital information generates the possibility of a high degree of redundancy in the data available for fitting predictive models used for Digital Soil Mapping (DSM). Among these models, the Decision Tree (DT) technique has been increasingly applied due to its capacity of dealing with large datasets. The purpose of this study was to evaluate the impact of the data volume used to generate the DT models on the quality of soil maps. An area of 889.33 km² was chosen in the Northern region of the State of Rio Grande do Sul. The soil-landscape relationship was obtained from reambulation of the studied area and the alignment of the units in the 1:50,000 scale topographic mapping. Six predictive covariates linked to the factors soil formation, relief and organisms, together with data sets of 1, 3, 5, 10, 15, 20 and 25 % of the total data volume, were used to generate the predictive DT models in the data mining program Waikato Environment for Knowledge Analysis (WEKA). In this study, sample densities below 5 % resulted in models with lower power of capturing the complexity of the spatial distribution of the soil in the study area. The relation between the data volume to be handled and the predictive capacity of the models was best for samples between 5 and 15 %. For the models based on these sample densities, the collected field data indicated an accuracy of predictive mapping close to 70 %.