Effective Light-weight Masonry Mortars with Dispersed Reinforcement

AbstractThe article is devoted to the development of masonry mortars with hollow ceramic microspheres and reinforcing fibers with improved properties. The mortars with ceramic microspheres and light-weight masonry mortars based on them have low average density, low thermal conductivity, high compressive strength, high specific strength, but insufficient crack resistance, frost resistance and mortar durability. One way of improvement of the properties of such mortars is the introduction of dispersed reinforcing fibers into their structure. In this study, the following types of fibers were used: basalt ones, glass ones, chrysotile ones and two types of polymer fibers – polyacrylic and polypropylene ones. The basic properties of mortar mixtures and mortar have been determined; the dependences of the influence of consumption of various fiber types on the basic properties of mortars have been studied, primarily, on the average density, compressive strength and tensile bending strength. The optimum composition of the masonry mortars with hollow ceramic microspheres and fibers were obtained, the results of microstructural analysis of the samples are given

Using DEDICOM for completely unsupervised part-of-speech tagging.

A standard and widespread approach to part-of-speech tagging is based on Hidden Markov Models (HMMs). An alternative approach, pioneered by Schuetze (1993), induces parts of speech from scratch using singular value decomposition (SVD). We introduce DEDICOM as an alternative to SVD for part-of-speech induction. DEDICOM retains the advantages of SVD in that it is completely unsupervised: no prior knowledge is required to induce either the tagset or the associations of terms with tags. However, unlike SVD, it is also fully compatible with the HMM framework, in that it can be used to estimate emission- and transition-probability matrices which can then be used as the input for an HMM. We apply the DEDICOM method to the CONLL corpus (CONLL 2000) and compare the output of DEDICOM to the part-of-speech tags given in the corpus, and find that the correlation (almost 0.5) is quite high. Using DEDICOM, we also estimate part-of-speech ambiguity for each term, and find that these estimates correlate highly with part-of-speech ambiguity as measured in the original corpus (around 0.88). Finally, we show how the output of DEDICOM can be evaluated and compared against the more familiar output of supervised HMM-based tagging

Utilization of a deoxynucleoside diphosphate substrate by HIV reverse transcriptase

Background: Deoxynucleoside triphosphates (dNTPs) are the normal substrates for DNA sysnthesis is catalyzed by polymerases such as HIV-1 reverse transcriptase (RT). However, substantial amounts of deoxynucleoside diphosphates (dNDPs) are also present in the cell. Use of dNDPs in HIV-1 DNA sysnthesis could have significant implications for the efficacy of nucleoside RT inhibitors such as AZT which are first line therapeutics fro treatment of HIV infection. Our earlier work on HIV-1 reverse transcriptase (RT) suggested that the interaction between the γ phosphate of the incoming dNTP and RT residue K65 in the active site is not essential for dNTP insertion, implying that this polymerase may be able to insert dNPs in addition to dNTPs. Methodology/Principal Findings: We examined the ability of recombinant wild type (wt) and mutant RTs with substitutions at residue K65 to utilize a dNDP substrate in primer extension reactions. We found that wild type HIV-1 RT indeed catalyzes incorporation of dNDP substrates whereas RT with mutations of residue K645 were unable to catalyze this reaction. Wild type HIV-1 RT also catalyzed the reverse reaction, inorganic phosphate-dependent phosphorolysis. Nucleotide-mediated phosphorolytic removal of chain-terminating 3′-terminal nucleoside inhibitors such as AZT forms the basis of HIV-1 resistance to such drugs, and this removal is enhanced by thymidine analog mutations (TAMs). We found that both wt and TAM-containing RTs were able to catalyze Pi-mediated phosphorolysis of 3′-terminal AZT at physiological levels of Pi with an efficacy similar to that for ATP-dependent AZT-excision. Conclusion: We have identified two new catalytic function of HIV-1 RT, the use of dNDPs as substrates for DNA synthesis, and the use of Pi as substrate for phosphorolytic removal of primer 3′-terminal nucleotides. The ability to insert dNDPs has been documented for only one other DNA polymerase The RB69 DNA polymerase and the reverse reaction employing inorganic phosphate has not been documented for any DNA polymerase. Importantly, our results show that Pi-mediated phosphorolysis can contribute to AZT resistance and indicates that factors that influence HIV resistance to AZT are more complex than previously appreciated. © 2008 Garforth et al

Mutations in RNA Polymerase Bridge Helix and Switch Regions Affect Active-Site Networks and Transcript-Assisted Hydrolysis

In bacterial RNA polymerase (RNAP), the bridge helix and switch regions form an intricate network with the catalytic active centre and the main channel. These interactions are important for catalysis, hydrolysis and clamp domain movement. By targeting conserved residues in Escherichia coli RNAP, we are able to show that functions of these regions are differentially required during σ70-dependent and the contrasting σ54-dependent transcription activations and thus potentially underlie the key mechanistic differences between the two transcription paradigms. We further demonstrate that the transcription factor DksA directly regulates σ54-dependent activation both positively and negatively. This finding is consistent with the observed impacts of DksA on σ70-dependent promoters. DksA does not seem to significantly affect RNAP binding to a pre-melted promoter DNA but affects extensively activity at the stage of initial RNA synthesis on σ54-regulated promoters. Strikingly, removal of the σ54 Region I is sufficient to invert the action of DksA (from stimulation to inhibition or vice versa) at two test promoters. The RNAP mutants we generated also show a strong propensity to backtrack. These mutants increase the rate of transcript-hydrolysis cleavage to a level comparable to that seen in the Thermus aquaticus RNAP even in the absence of a non-complementary nucleotide. These novel phenotypes imply an important function of the bridge helix and switch regions as an anti-backtracking ratchet and an RNA hydrolysis regulator

Automated methods for correcting errors in grammar and usage

Over the last several decades, the number of electronic documents has increased dramatically. With the growing availability of computers, more and more people are using text editors. However, the development of automated methods for correcting mistakes in text has not progressed as far. Text editors usually employ basic spell checking techniques and address very few mistakes of other types. In this thesis, we propose two methods for correcting errors in grammar and usage. First, we propose a novel approach to the problem of training classifiers to detect and correct errors in text by selectively introducing mistakes into the training data and show that this method is superior to the traditional method of training using clean data. Second, we define high-level features and propose a method of correcting mistakes using these features. We combine the two methods and build a system for correcting mistakes in article usage made by non-native speakers of English

Automated methods for text correction

Development of automatic text correction systems has a long history in natural language processing research. This thesis considers the problem of correcting writing mistakes made by non-native English speakers. We address several types of errors commonly exhibited by non-native English writers – misuse of articles, prepositions, noun number, and verb properties – and build a robust, state-of-the-art system that combines machine learning methods and linguistic knowledge. The proposed approach is distinguished from other related work in several respects. First, several machine learning methods are compared to determine which methods are most effective for this problem. Earlier evaluations, because they are based on incomparable data sets, have questionable conclusions. Our results reverse these conclusions and pave the way for the next contribution. Using the important observation that mistakes made by non-native writers are systematic, we develop models that utilize knowledge about error regularities with minimal annotation costs. Our approach differs from earlier ones that either built models that had no knowledge about error regularities or required a lot of annotated data. Next, we develop special strategies for correcting errors on open-class words. These errors, while being very prevalent among non-native English speakers, are the least studied and are not well-understood linguistically. The challenges that these mistakes present are addressed in a linguistically-informed approach. Finally, a novel global approach to error correction is proposed that considers grammatical dependencies among error types and addresses these via joint learning and joint inference. The systems and techniques described in this thesis are evaluated empirically and competitively in the context of several shared tasks, where they have demonstrated superior performance. In particular, our system ranked first in the most prestigious competition in the natural language processing field, the CoNLL-2013 shared task on text correction. Based on the analysis of this system, four design principles that are crucial for building a state-of-the-art error correction system are identified