PI3K/AKT/mTOR pathway activation in actinic cheilitis and lip squamous cell carcinomas

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156448/2/jdv16420_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156448/1/jdv16420.pd

Overcoming adaptive resistance in mucoepidermoid carcinoma through inhibition of the IKK-β/IκBα/NFκB axis

Patients with mucoepidermoid carcinoma (MEC) experience low survival rates and high morbidity following treatment, yet the intrinsic resistance of MEC cells to ionizing radiation (IR) and the mechanisms underlying acquired resistance remain unexplored. Herein, we demonstrated that low doses of IR intrinsically activated NFκB in resistant MEC cell lines. Moreover, resistance was significantly enhanced in IR-sensitive cell lines when NFκB pathway was stimulated. Pharmacological inhibition of the IKK-β/IκBα/ NFκB axis, using a single dose of FDA-approved Emetine, led to a striking sensitization of MEC cells to IR and a reduction in cancer stem cells. We achieved a major step towards better understanding the basic mechanisms involved in IR-adaptive resistance in MEC cell lines and how to efficiently overcome this critical problem

Combining Information Theoretic Kernels with Generative Embeddings for Classification

Classical approaches to learn classifiers for structured objects (e.g., images, sequences) use generative models in a standard Bayesian framework. To exploit the state-of-the-art performance of discriminativelearning, while also taking advantage of generative models of the data, generative embeddings havebeen recently proposed as a way of building hybrid discriminative/generative approaches. A generativeembedding is a mapping, induced by a generative model (usually learned from data), from the objectspace into a fixed dimensional space, adequate for discriminative classifier learning. Generative embeddings have been shown to often outperform the classifiers obtained directly from the generative models upon which they are built. Using a generative embedding for classification involves two main steps: (i) defining and learning a generative model and using it to build the embedding; (ii) discriminatively learning a (maybe kernel) classifier with the embedded data. The literature on generative embeddings is essentially focused on step (i), usually taking some standard off-the-shelf tool for step (ii). Here, we adopt a different approach, by focusing also on the discriminative learning step. In particular, we exploit the probabilistic nature of generative embeddings, by using kernels defined on probability measures; in particular we investigate the use of a recent family of non-extensive information theoretic kernels on the top of different generative embeddings. We show, in different medical applications that the approach yields state-of-the-art performance

Calibration and test of the cropgro-dry bean model for edaphoclimatic conditions in the savanas of Central Brazil Calibração e teste do modelo "cropgro-dry bean" para as condições edafoclimáticas do Brasil Central

Simulation models are important tools for the analysis of cultivated systems to estimate the performance of crops in different environments. The CROPGRO- model (DSSAT) was calibrated and validated using Carioca bean (Phaseolus vulgaris L.) to estimate yield and the development of the crop, sown in three row spacings (0.4, 0.5, and 0.6 m) and two fertilization rates (300 and 500 kg ha-1 of 4-30-16 N-P-K), in Santo Antônio de Goiás, GO, Brazil. To calibrate the model a combination of the genetic coefficients that characterize the phenology and morphology of the dry bean crop was used to obtain the best possible fit between predicted and observed anthesis and physiological maturity dates, leaf area index (LAI), total dry matter (TDM), yield components, and grain yield for the 0.6 m row spacing. To test the model the experimental records of the 0.4 and 0.5 m row spacings were used. In both, calibration and test, the performance of the model was evaluated plotting observed and predicted values of LAI and TDM versus time, using the r², and the agreement index (d) as statistical criteria. In relation to yield and yield components the percent difference between the observed and predicted data was calculated. The model appeared to be adequate to simulate phenology, grain yield and yield components for the Carioca bean cultivar, related to different levels of fertilization and row spacing, either during calibration or the testing phase. During the test, the grain yield was overestimated by less than 15.4%, indicating a potential use for the calibrated model in assessing climatic risks in this region.<br>Modelos de simulação são importantes ferramentas na análise de sistemas cultivados para estimar a performance da cultura em diferentes ambientes. O modelo CROPGRO- foi calibrado e testado, utilizando-se o cultivar Carioca para estimar a produtividade e o desenvolvimento do feijoeiro (Phaseolus vulgaris L.) sob três espaçamentos (0,4, 0,5 e 0,6 m) e duas doses de adubação (300 e 500 kg ha-1 de 4-30-16 de N-P-K), em Santo Antônio de Goiás, GO. A calibração consistiu na modificação dos coeficientes genéticos característicos da fenologia e morfologia do feijoeiro, visando obter os melhores ajustes possíveis entre os dados simulados e os observados a campo das datas de antese e maturação fisiológica, índice de área foliar (IAF), massa de matéria seca total (MMST), componentes de produção e produtividade de grãos para o espaçamento de 0,6 m. Para o teste do modelo foram utilizados os dados experimentais correspondentes aos espaçamentos de 0,4 e 0,5 m. Em ambos, calibração e teste, a aferição da performance do modelo foi efetuada plotando-se os valores observados e simulados do IAF e MMST ao longo do tempo (dias após semeadura), e utilizando-se o r² e o índice de concordância (d) como critério estatístico. Para produtividade de grãos e componentes de produção determinou-se a diferença percentual entre os valores observados e simulados. O modelo simulou satisfatoriamente a fenologia, a produtividade de grãos e os componentes de produção, para as diferentes doses de adubação e espaçamentos, tanto na fase de calibração como na de teste. Durante o teste, a produtividade de grãos foi superestimada, no máximo, em 15,4%, indicando o potencial do modelo calibrado em futura análise de riscos climáticos