Certain issues of teaching the German language as the Second Foreign Language in Technical Higher Educational Institution

Formulation of the problem. The article considers various approaches and methods for teaching German as the second foreign language offered by domestic and foreign scientists testifying to the need for additional comprehension and refinement of the main principles for teaching German as the second foreign language after learning English. The linguistic characteristics of English and German are analyzed at the phonetic, grammatical and lexical levels.Results. The theoretical analysis of the factors for teaching and learning German as the second language is given. It allowed us to reveal the following principles as fundamental ones: the principle of support and comparative connection with the first foreign and native languages, the principle of individualization of teaching, as well as the cognitive principle. It was found out how to approach the organization of the content of teaching, forms and methods of teaching so that students acquire explicit and implicit experience in the formation and improvement of teaching strategies. The study proved that parallel learning of the English and German languages has special advantages for learners based on the linguistic similarity of these two languages.Conclusions. The theoretical and practical analysis of the factors for teaching and learning German as the second language allowed us to reveal the following principles as fundamental ones: the principle of support and comparative connection with the first foreign and native languages, the principle of individualization of teaching, as well as the cognitive principle. The analysis allowed us to understand how to approach the organization of the content of training, forms and methods of teaching, so that students acquired explicit and implicit experience in the formation and improvement of teaching strategies. The study also clearly demonstrated that the parallel learning of the English and German languages had special advantages for learners, based on the linguistic similarity of these two languages

Emotive-expressive potential of phraseology of linguistic identity: Vladimir Putin

Deals with the linguistic identity of the politician on the material of statements of V.V. Putin. The politician's discourse is studied based on the interpretative analysis of phraseolog

Transcriptome Profiling of Lotus japonicus Roots During Arbuscular Mycorrhiza Development and Comparison with that of Nodulation

To better understand the molecular responses of plants to arbuscular mycorrhizal (AM) fungi, we analyzed the differential gene expression patterns of Lotus japonicus, a model legume, with the aid of a large-scale cDNA macroarray. Experiments were carried out considering the effects of contaminating microorganisms in the soil inoculants. When the colonization by AM fungi, i.e. Glomus mosseae and Gigaspora margarita, was well established, four cysteine protease genes were induced. In situ hybridization revealed that these cysteine protease genes were specifically expressed in arbuscule-containing inner cortical cells of AM roots. On the other hand, phenylpropanoid biosynthesis-related genes for phenylalanine ammonia-lyase (PAL), chalcone synthase, etc. were repressed in the later stage, although they were moderately up-regulated on the initial association with the AM fungus. Real-time RT–PCR experiments supported the array experiments. To further confirm the characteristic expression, a PAL promoter was fused with a reporter gene and introduced into L. japonicus, and then the transformants were grown with a commercial inoculum of G. mosseae. The reporter activity was augmented throughout the roots due to the presence of contaminating microorganisms in the inoculum. Interestingly, G. mosseae only colonized where the reporter activity was low. Comparison of the transcriptome profiles of AM roots and nitrogen-fixing root nodules formed with Mesorhizobium loti indicated that the PAL genes and other phenylpropanoid biosynthesis-related genes were similarly repressed in the two organs

Carbon nanomaterials for targeted cancer therapy drugs: a critical review.

Cancer represents one of the main causes of human death in developed countries. Most current therapies, unfortunately, carry a number of side effects, such as toxicity and damage to healthy cells, as well as the risk of resistance and recurrence. Therefore, cancer research is trying to develop therapeutic procedures with minimal negative consequences. The use of nanomaterial-based systems appears to be one of them. In recent years, great progress has been made in the field of possible use of nanomaterials with high potential in biomedical applications. Carbon nanomaterials, thanks to their unique physicochemical properties, are gaining more and more popularity in cancer therapy. They are valued especially for their ability to deliver drugs or small therapeutic molecules to these cells. Through surface functionalization, they can specifically target tumor tissues, increasing the therapeutic potential and significantly reducing the adverse effects of therapy. Their potential future use could, therefore, as vehicles for drug delivery. This review presents the latest findings of research studies using carbon nanomaterials in the treatment of various types of cancer. To carry out this study, different databases such as Web of Science, PubMed, MEDLINE and Google Scholar were employed. The findings of research studies chosen from more than 2000 viewed scientific publications from the last 15 years were compared

Recognition of CpG oligodeoxynucleotides by human Toll-like receptor 9 and subsequent cytokine induction

Toll-like receptor 9 (TLR9) recognizes a synthetic ligand, oligodeoxynucleotide (ODN) containing cytosine-phosphate-guanine (CpG). Induction of TLR9 by CpG ODN activates a signal transduction cascade that plays a pivotal role in first-line immune defense in the human body. The three-dimensional structure of TLR9 has not yet been reported, and the ligand-binding mechanism of TLR9 is still poorly understood; therefore, the mechanism of human TLR9 ligand binding needs to be elucidated. In functional studies of TLR9, phosphorothioate (PTO)-modified CpG ODNs have been utilized because "natural" CpG ODNs consist entirely of a phosphodiester (PD) backbone that is easily degraded by nucleases. However, PTO ODNs do not faithfully recapitulate natural DNA-mediated TLR9 activation. In this study, we constructed several human TLR9 mutants, including predicted truncated mutants and single mutants in the predicted CpG ODN-binding site. We used these mutants to analyze the role of potential important regions of TLR9 in receptor signaling induced by stable PD-ODNs that we developed. We clarified that both the C- and N-termini of the extracellular domain (ECD) are necessary for the function of TLR9 in human cells, even if only the C-terminal region of mouse TLR9-ECD was activated by CpG ODNs. Next, we identified residues in the C-terminus of TLR9-ECD (H505 in leucine-rich repeat (LRR)-16, H530 in LRR-17, and Y554 in LRR-18) that are essential for hTLR9 activation. Furthermore, we utilized PD-ODN to analyze the function of TLR9 in peripheral blood mononuclear cells and B cells. PD-ODNs showed perfect sequence-dependent TLR9 activation, whereas both CpG and non-CpG PTO-ODN activated TLR9. Hence, our study revealed the specific use of natural PD-ODN to explore the function of TLR9, which is required for its development as a potential therapeutic adjuvant

Efficient Principled Learning of Thin Junction Trees

We present the first truly polynomial algorithm for PAC-learning the structure of bounded-treewidth junction trees – an attractive subclass of probabilistic graphical models that permits both the compact representation of probability distributions and efficient exact inference. For a constant treewidth, our algorithm has polynomial time and sample complexity. If a junction tree with sufficiently strong intraclique dependencies exists, we provide strong theoretical guarantees in terms of KL divergence of the result from the true distribution. We also present a lazy extension of our approach that leads to very significant speed ups in practice, and demonstrate the viability of our method empirically, on several real world datasets. One of our key new theoretical insights is a method for bounding the conditional mutual information of arbitrarily large sets of variables with only polynomially many mutual information computations on fixed-size subsets of variables, if the underlying distribution can be approximated by a bounded-treewidth junction tree.

Query-Specific Learning and Inference for Probabilistic Graphical Models

In numerous real world applications, from sensor networks to computer vision to natural text processing, one needs to reason about the system in question in the face of uncertainty. A key problem in all those settings is to compute the probability distribution over the variables of interest (the query) given the observed values of other random variables (the evidence). Probabilistic graphical models (PGMs) have become the approach of choice for representing and reasoning with high-dimensional probability distributions. However, for most models capable of accurately representing real-life distributions, inference is fundamentally intractable. As a result, optimally balancing the expressive power and inference complexity of the models, as well as designing better approximate inference algorithms, remain important open problems with potential to significantly improve the quality of answers to probabilistic queries. This thesis contributes algorithms for learning and approximate inference in probabilistic graphical models that improve on the state of the art by emphasizing the computational aspects of inference over the representational properties of the models. Our contributions fall into two categories: learning accurate models where exact inference is tractable and speeding up approximate inference by focusing computation on the query variables and only spending as much effort on the remaining parts of the model as needed to answer the query accurately. First, for a case when the set of evidence variables is not known in advance and a single model is needed that can be used to answer any query well, we propose a polynomial time algorithm for learning the structure of tractable graphical models with quality guarantees, including PAC learnability and graceful degradation guarantees. Ours is the first efficient algorithm to provide this type of guarantees. A key theoretical insight of our approach is a tractable upper bound on the mutual information of arbitrarily large sets of random variables that yields exponential speedups over the exact computation. Second, for a setting where the set of evidence variables is known in advance, we propose an approach for learning tractable models that tailors the structure of the model for the particular value of evidence that become known at test time. By avoiding a commitment to a single tractable structure during learning, we are able to expand the representation power of the model without sacrificing efficient exact inference and parameter learning. We provide a general framework that allows one to leverage existing structure learning algorithms for discovering high-quality evidence-specific structures. Empirically, we demonstrate state of the art accuracy on real-life datasets and an order of magnitude speedup. Finally, for applications where the intractable model structure is a given and approximate inference is needed, we propose a principled way to speed up convergence of belief propagation by focusing the computation on the query variables and away from the variables that are of no direct interest to the user. We demonstrate significant speedups over the state of the art on large-scale relational models. Unlike existing approaches, ours does not involve model simplification, and thus has an advantage of converging to the fixed point of the full model. More generally, we argue that the common approach of concentrating on the structure of representation provided by PGMs, and only structuring the computation as representation allows, is suboptimal because of the fundamental computational problems. It is the computation that eventually yields answers to the queries, so directly focusing on structure of computation is a natural direction for improving the quality of the answers. The results of this thesis are a step towards adapting the structure of computation as a foundation of graphical models.</p

ABSTRACT

A problem of planning for cooperative teams under uncertainty is a crucial one in multiagent systems. Decentralized partially observable Markov decision processes (DEC-POMDPs) provide a convenient, but intractable model for specifying planning problems in cooperative teams. Compared to the single-agent case, an additional challenge is posed by the lack of free communication between the teammates. We argue, that acting close to optimally in a team involves a tradeoff between opportunistically taking advantage of agent’s local observations and being predictable for the teammates. We present a more opportunistic version of an existing approximate algorithm for DEC-POMDPs and investigate the tradeoff. Preliminary evaluation shows that in certain settings oportunistic modification provides significantly better performance

Distributed Constraint Optimization problems (DCOP). The

We present a new polynomial-space algorithm for solvin