Microbiota, a key player in alcoholic liver disease

Alcoholic liver disease (ALD) is a major cause of morbidity and mortality worldwide. Only 20% of heavy alcohol consumers develop alcoholic liver cirrhosis. The intestinal microbiota (IM) has been recently identified as a key player in the severity of liver injury in ALD. Common features of ALD include a decrease of gut epithelial tight junction protein expression, mucin production, and antimicrobial peptide levels. This disruption of the gut barrier, which is a prerequisite for ALD, leads to the passage of bacterial products into the blood stream (endotoxemia). Moreover, metabolites produced by bacteria, such as short chain fatty acids, volatile organic compounds (VOS), and bile acids (BA), are involved in ALD pathology. Probiotic treatment, IM transplantation, or the consumption of dietary fiber, such as pectin, which all alter the ratio of bacterial species, have been shown to improve liver injury in animal models of ALD and to be associated with an improvement in gut barrier function. Although the connections between the microbiota and the host in ALD are well established, the underlying mechanisms are still an active area of research. Targeting the microbiome through the use of prebiotic, probiotic, and postbiotic modalities could be an attractive new approach to manage ALD

High dimensional RM

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, 2014.Cataloged from PDF version of thesis.Includes bibliographical references (pages 149-153).We present potential solutions to several problems that arise in making revenue management (RM) practical for online advertising and related modern applications. Principally, RM solutions for these problems must contend with (i) highly volatile demand processes that are hard to forecast, and (ii) massive scale that makes even basic optimization problems challenging. Our solutions to these problems are interesting in their own right in the areas of stochastic optimization, high dimensional learning and distributed optimization. In the first part of the thesis, we propose a model predictive control approach to combat volatile demand. This approach is conceptually simple, uses available demand data in a natural way, and, most importantly, can be shown to generate significant revenue advantages on real-world data from ad networks. Under mild restrictions, we prove that our algorithm achieves uniform relative performance guarantees vis-a-vis a clairvoyant in the face of arbitrary volatility, while simultaneously being optimal in the event that volatility is negligible. This is the first result of its kind for model predictive control. While our approach above is effective at hedging demand shocks that occur over "large" time horizons, it relies on the ability to estimate snapshots of the prevailing demand distribution over "short" time horizons. The second part of the thesis deals with learning the extremely high dimensional demand distributions that are typical in display advertising applications. This work exploits the special structure of the display advertising version of the NRM problem to achieve a sample complexity that scales gracefully in the dimensions of the problem. The third part of the thesis focuses on the problem of solving terabyte sized LPs on an hourly basis given a distributed computational infrastructure; solving these massive LPs is the computational primitive required to make our model predictive control approach practical. Here we design a linear optimization algorithm that fits a paradigm for distributed computation referred to as 'Map-Reduce'. An implementation of our solver in a shared memory environment where we can benchmark against solvers such as CPLEX shows that the algorithm outperforms those solvers on the types of LPs that an ad network would have to solve in practice.by Dragos Florin Ciocan.Ph. D

Des bactéries intestinales responsables des maladies nutritionnelles du foie éradiquées par phagothérapie

International audienc

Interactive and Noninteractive Zero Knowledge Coincide in the Help Model

We show that a problem in AM has a interactive zero-knowledge proof system if and only if it has a noninteractive zero knowledge proof system in the ‘help model’ of Ben-Or and Gutfreund (J. Cryptology, 2003). In this model, the shared reference string is generated by a probabilistic polynomial-time dealer who is given access to the statement to be proven. Our result holds for both computational zero knowledge and statistical zero knowledge, and does not rely on any unproven complexity assumptions. We also show that help does not add power to interactive computational zero-knowledge proofs, paralleling a result of Ben-Or and Gutfreund for the case of statistical zero knowledge

Pectin in Metabolic Liver Disease

Alterations in the composition of the gut microbiota (dysbiosis) are observed in nutritional liver diseases, including non-alcoholic fatty liver disease (NAFLD) and alcoholic liver disease (ALD) and have been shown to be associated with the severity of both. Editing the composition of the microbiota by fecal microbiota transfer or by application of probiotics or prebiotics/fiber in rodent models and human proof-of-concept trials of NAFLD and ALD have demonstrated its possible contribution to reducing the progression of liver damage. In this review, we address the role of a soluble fiber, pectin, in reducing the development of liver injury in NAFLD and ALD through its impact on gut bacteria

Interactive and Noninteractive Zero Knowledge are Equivalent in the Help Model

We show that interactive and noninteractive zero-knowledge are equivalent in the ‘help model’ of Ben-Or and Gutfreund (J. Cryptology, 2003). In this model, the shared reference string is generated by a probabilistic polynomial-time dealer who is given access to the statement to be proven. Our results do not rely on any unproven complexity assumptions and hold for statistical zero knowledge, for computational zero knowledge restricted to AM, and for quantum zero knowledge when the help is a pure quantum state