Selective toxicity of hydroxyl-rich carbon nanodots for cancer research

Nano Research - The toxicity of nanoparticles in a biological system is an integration of effects arising from surface functionality, particle size, ionic dissolution, etc. This complexity suggests...     

Autoimmunity, immunodeficiency and mucosal infections: chronic intestinal inflammation as a sensitive indicator of immunoregulatory defects in response to normal luminal microflora

Despite the fact that target antigens and the genetic basis of several autoimmune diseases are now better understood, the initial events leading to a loss of tolerance towards self-components remain unknown. One of the most attractive explanations for autoimmune phenomena involves various infections as possible natural events capable of initiating the process in genetically predisposed individuals. The most accepted explanation of how infection causes autoimmunity is based on the concept of "molecular mimicry" (similarity between the epitopes of an autoantigen and the epitopes in the environmental antigen). Infectious stimuli may also participate in the development of autoimmunity by inducing an increased expression of stress proteins (hsp), chaperones and transplantation antigens, which leads to abnormal processing and presentation of self antigens. Superantigens are considered to be one of the most effective bacterial components to induce inflammatory reactions and to take part in the development and course of autoimmune mechanisms. It has long been known that defects in the host defense mechanism render the individual susceptible to infections caused by certain microorganisms. Impaired exclusion of microbial antigens can lead to chronic immunological activation which can affect the tolerance to self components. Defects in certain components of the immune system are associated with a higher risk of a development of autoimmune disease. The use of animal models for the studies of human diseases with immunological pathogenesis has provided new insights into the influence of immunoregulatory factors and the lymphocyte subsets involved in the development of disease. One of the most striking conclusion arising from work with genetically engineered immunodeficient mouse models is the existence of a high level of redundancy of the components of the immune system. However, when genes encoding molecules involved in T cell immunoregulatory functions are deleted, spontaneous chronic inflammation of the gut mucosa (similar to human inflammatory bowel disease) develops. Surprisingly, when such immunocompromised animals were placed into germfree environment, intestinal inflammation did not develop. Impairment of the mucosal immune response to the normal bacterial flora has been proposed to play a crucial role in the pathogenesis of chronic intestinal inflammation. The use of immunodeficient models colonized with defined microflora for the analysis of immune reactivity will shed light on the mode of action of different immunologically important molecules responsible for the delicate balance between luminal commensals, nonspecific and specific components of the mucosal immune system.     

Atopic dermatitis and food allergy in infancy and childhood

Food allergies are causal factors for atopic dermatitis (AD) in 50% in infancy, in 20 to 30% in childhood, and only in 10 to 15% after puberty and in adulthood. Cow's milk, egg, fish, wheat, soy, nuts and citrus-fruits are the most proven allergens. Pseudoallergens, especially food-additiva, have to be regarded too. For the proof of the clinical relevance that food allergy is causing AD a positive result of elimination and provocation has to be required. When by these diagnostic procedure a special food is found as causing the AD it has to be eliminated in the diet of this patient. In severe cases of AD semi-elementary respectively few foods diets may be necessary. However in most cases of AD the "diet of choice" is an age related normal nutrition. To delay respectively to avoid the manifestation of atopy special recommendations for the nutrition of high risk newborns and infants (especially long breast feeding, late solid feeding) should be considered.     

Liquidus temperatures for primary crystallization of cryolite in molten salt systems of interest for aluminum electrolysis

Temperatures for primary crystallization of Na₃AlF₆ in multicomponent electrolyte systems of interest for the aluminum electrolysis process were determined by thermal analysis. The results are presented as binary and quasibinary diagrams and discussed in view of the literature data. An empirical equation describing liquidus temperatures for primary crystallization of Na₃AlF₆ was derived: $$\begin{gathered} t/(^\circ C) = 1011 + 0.50[AlF_3 ] - 0.13[AIF_3 ] - \frac{{3.45[CaF_2 ]}}{{1 + 0.0173[CaF_2 ]}} \hfill \\ + 0.124[CaF_2 ] \cdot [AlF_3 ] - 0.00542([CaF_2 ] \cdot [AlF_3 ])^{1.5} \hfill \\ - \frac{{7.93[Al_2 O_3 ]}}{{1 + 0.0936[Al_2 O_3 ] - 0.0017[Al_2 O_3 ]^2 - 0.0023[AlF_3 ] \cdot [Al_2 O_3 ]}} \hfill \\ - \frac{{8.90[LiF]}}{{1 + 0.0047[LiF] + 0.0010[AlF3]^2 }} - 3.95[MgF_2 ] - 3.95 \hfill \\ \end{gathered} $$ wheret is the temperature in degree Celsius and the square brackets denote the weight percent of components in the system Na₃AlF₆-AlF₃-CaF₂-Al₂O₃-LiF-MgF₂-KF. The composition limitations are [AlF₃] ≈ [CaF₂] ≈ [LiF] < 20 wt pct, [MgF₂] ≈ [KF] < 5 wt pct, and [A1₂O₃] up to saturation.     

Surrogate modeling approximation using a mixture of experts based on EM joint estimation

An automatic method to combine several local surrogate models is presented. This method is intended to build accurate and smooth approximation of discontinuous functions that are to be used in structural optimization problems. It strongly relies on the Expectation−Maximization (EM) algorithm for Gaussian mixture models (GMM). To the end of regression, the inputs are clustered together with their output values by means of parameter estimation of the joint distribution. A local expert is then built (linear, quadratic, artificial neural network, moving least squares) on each cluster. Lastly, the local experts are combined using the Gaussian mixture model parameters found by the EM algorithm to obtain a global model. This method is tested over both mathematical test cases and an engineering optimization problem from aeronautics and is found to improve the accuracy of the approximation.     

浅议以太网基本性能测试指标及相应的测试方法

本文就目前国际上对以太网基本性能测量的指标进行整理和分析并给出了相应的测试方法。     

Complexity issues in color-preserving graph embeddings

In the context of comparative analysis of protein–protein interaction graphs, we use a graph-based formalism to detect the preservation of a given protein complex (pattern graph) in the protein–protein interaction graph (target graph) of another species with respect to (w.r.t.) orthologous proteins. We give an efficient exponential-time randomized algorithm in case the occurrence of the pattern graph in the target graph is required to be exact. For approximate occurrences, we prove a tight inapproximability result and give four approximation algorithms that deal with bounded degree graphs, small ortholog numbers, linear forests and very simple yet hard instances, respectively.     

Solving subgraph isomorphism problems with constraint programming

The subgraph isomorphism problem consists in deciding if there exists a copy of a pattern graph in a target graph. We introduce in this paper a global constraint and an associated filtering algorithm to solve this problem within the context of constraint programming. The main idea of the filtering algorithm is to label every node with respect to its relationships with other nodes of the graph, and to define a partial order on these labels in order to express compatibility of labels for subgraph isomorphism. This partial order over labels is used to filter domains. Labelings can also be strengthened by adding information from the labels of neighbors. Such a strengthening can be applied iteratively until a fixpoint is reached. Practical experiments illustrate that our new filtering approach is more effective on difficult instances of scale free graphs than state-of-the-art algorithms and other constraint programming approaches.     

Optimal Sparse Matrix Dense Vector Multiplication in the I/O-Model

We study the problem of sparse-matrix dense-vector multiplication (SpMV) in external memory. The task of SpMV is to compute y:=Ax, where A is a sparse N×N matrix and x is a vector. We express sparsity by a parameter k, and for each choice of k consider the class of matrices where the number of nonzero entries is kN, i.e., where the average number of nonzero entries per column is k.