Abstract

Earlier works has explored that a foreign language can be learnt from pictures than merely using native language word translations. These efforts on language learning using pictures were limited to colors, animals, birds, vehicles and common noun categories. In this paper, we present our research that learning of verbs is also possible by using right set of pictures and gestures, and we show that this is effective in second language learning. We report the acquisition of words in tourist domain in a second language by working with subjects who are between 14 and 48 years of age. From pre-learning and post-learning evaluations, we show that acquisition of vocabulary like nouns and verbs in a new language is better with the fusion of pictures and voice. We also show the subjects are able to generalize their learning towards phrase-level vocabulary without any additional training and efforts.

Abstract

In the recent past, remotely sensed data with high spectral resolution has been made available and has been explored for various agricultural and geological applications. While these spectral signatures of the objects of interest provide important clues, the relatively poor spatial resolution of these hyperspectral images limits their utility and performance. In this context, hyperspectral image enhancement using multispectral data has been actively pursued to improve spatial resolution of such imageries and thus enhancing its use for classification and composition analysis in various applications. But, this also poses a challenge in terms of managing the trade-off between improved spatial detail and the distortion of spectral signatures in these fused outcomes. This paper proposes a strategy of using vector decomposition, as a model to transfer the spatial detail from relatively higher resolution data, in association with sensor simulation to generate a fused hyperspectral image while preserving the inter band spectral variability. The results of this approach demonstrates that the spectral separation between classes has been better captured and thus helped improve classification accuracies over mixed pixels of the original low resolution hyperspectral data. In addition, the quantitative analysis using a rank-correlation metric shows the appropriateness of the proposed method over the other known approaches with regard to preserving the spectral signatures.

Abstract

The land use changes in forested landscape are highly complex and dynamic, affected by the natural, socio-economic, cultural, political and other factors. The remote sensing (RS) and geographical information system (GIS) techniques coupled with multi-criteria evaluation functions such as Markov-cellular automata (CA–Markov) model helps in analysing intensity, extent and future forecasting of human activities affecting the terrestrial biosphere. Karwar taluk of Central Western Ghats in Karnataka state, India has seen rapid transitions in its forest cover due to various anthropogenic activities, primarily driven by major industrial activities. A study based on Landsat and IRS derived data along with CA–Markov method has helped in characterizing the patterns and trends of land use changes over a period of 2004–2013, expected transitions was predicted for a set of scenarios through 2013-2022. The analysis reveals the loss of pristine forest cover from 75.51% to 67.36%(1973 to 2013) and increase in agriculture land as well as built-up area of 8.65%(2013), causing impact on local flora and fauna. The other factors driving these changes are the aggregated level of demand for land, local and regional effects of land use activities such as deforestation, improper practices in expansion of agriculture and infrastructure development, deteriorating natural resources availability. The spatio temporal models helped in visualizing on-going changes apart from prediction of likely changes. The CA-Markov based analysis provides us insights into the localized changes impacting these regions and can be useful in developing appropriate mitigation management …

Abstract

Statistical morph analyzers have proved to be highly accurate while being comparatively easier to maintain than rule based approaches. Our morph analyzer (SMA++) is an improvement over the statistical morph analyzer (SMA) described in Malladi and Mannem (2013). SMA++ predicts the gender, number, person, case (GNPC) and the lemma (L) of a given token. We modified the SMA in Malladi and Mannem (2013), by adding some rich machine learning features. The feature set was chosen specifically to suit the characteristics of Indian Languages. In this paper we apply SMA++ to four Indian languages viz. Hindi, Urdu, Telugu and Tamil. Hindi and Urdu belong to the Indic1 language family. Telugu and Tamil belong to the Dravidian2 language family. We compare SMA++ with some state-of-art statistical morph analyzers viz. Morfette in Chrupała et al. (2008) and SMA in Malladi and Mannem (2013). In all four languages, our system performs better than the above mentioned state-of-art SMAs

Abstract

This paper proposes a novel approach to capture the concepts1 of an NL query. Given an NL query, the query is mapped to a tagset, which carries the concept information. The tagset was created by mapping every noun chunk to the attribute of a table (tableName.attributeName) and every verb chunk to a relation in the ER schema. The approach is discussed using the Courses Management domain of a University and can be extended to other domains. The tagset here was formed using the ER-schema of the Courses Management Portal of our university. We used the statistical approach to identify the concepts. We ourselves formed a tagged corpus with different types of NL queries. Conditional Random Field algorithm was used for the classification. The results are very promising and are compared to the rule based approach seen in Gupta et al. (2012) [1] .

Abstract

In this paper we explain the identification of karaka relations in an English sentence. We explain the genesis of the problem and present two different approaches, rule based and statistical. We briefly describe about rule based and focus more on statistical approach. We process a sentence through various stages and extract features at each stage. We train our data and identify Karaka relations using Support Vector Machines (SVM). We also explain the impact of our work on Natural Language Interfaces for Database systems.

Abstract

Improving thermostability of industrial enzymes is an important protein engineering challenge. Point mutations, induced to increase thermostability, affect the structure and dynamics of the target protein in several ways and thus can also affect its activity. There appears to be no general rules for improving thermostabilty of enzymes, without adversely affecting their enzymatic activity. We report MD simulations, of wild type Bacillus subtilis lipase (WT) and its six progressively thermostable mutants (2M, 3M, 4M, 6M, 9M and 12M), performed at different temperatures, to address this issue. Less thermostable mutants (LTMs), 2M - 6M, show WT-like dynamics at all simulation temperatures. However, the two more thermostable mutants (MTMs), show required flexibility at appropriate temperature ranges and maintain conformational stability at high temperature. They show deep and rugged free-energy landscape, confining them within a near-native conformational space by conserving non-covalent interactions, and thus protecting them from possible aggregation. In contrast, the LTMs having marginally higher thermostabilities than WT show greater probabilities of accessing non-native conformations, which, due to aggregation, have reduced possibilities of reverting to their respective native states under refolding conditions. Our analysis indicates the possibility of non-additive effects of point mutations on the conformational stability of LTMs.

Abstract

Hydrogen bonded non-canonical base pairs have been identified as the key player behind the structural complexity and functional diversity of the RNA molecule. Enormous research have been performed to understand the correlation between different base pairing geometries and their contributions in the overall structural stabilities and functionalities. Towards this goal, calculation of intrinsic interaction energy of different base pairing geometries using ab-initio and DFT based Quantum Mechanical methods provide a significant insight in the context of RNA. Analysis of deviation in geometry and intrinsic interaction energy between the crystal and ground state optimized structures reveal the chemical behaviours of a particular base pair and the contextual effects on its stability. Fueled by the advancement of post Hartree-Fock QM theories and more accurate basis sets, the present knowledge base is flooded with a large amount of QM analysis data which lacks the systematic organization and hence fails to serve the scientific community in the best possible way. We, therefore, have attempted to build a relational database, “RNA Base Pair Count Geometry and Stability (RNABP COGEST)”, which relates the occurrence frequencies of different base pairs in RNA with their crystal and ground state optimized geometries (calculated at different levels of QM theory) and corresponding intrinsic interaction energies. To the best of our knowledge, this is the first attempt to organize energetics information for different RNA base pairs along with their occurrences and geometries. Our database provides an interactive interface which enables the user to extract data by constructing diverse geometry, frequency and energy based search queries for the purpose of better understanding of the complex relation between the geometry and stability of RNA base pairs. RNABP COGEST is a relational database developed in MYSQL with PHP and HTML based web interface. Availability of consolidated QM analysis data regarding interaction energies, structural parameters, and occurrences of all putative base pairing geometries at a glance, in RNABP COGEST, along with multiple comparative searching options will definitely be useful to bridge the gap in the understanding of complex RNA structures and their functionalities

Abstract

Protonated nucleobases have significant roles in facilitating catalytic functions of RNA, and in stabilizing different structural motifs. Reported pKa values of nucleobase protonation suggest that the population of neutral nucleobases is 103 – 104 times higher than that of protonated nucleobases under physiological conditions (pH  7.4). Therefore, a molecular level understanding of various putative roles of protonated nucleobases cannot be achieved without addressing the question of how their occurrence propensities and stabilities are related to the free energy costs associated with the process of protonation under physiological conditions. With water as proton donor, we use advanced QM methods to evaluate the site specific protonation propensities of nucleobases in terms of their associated free energy changes (
Gprot). Quantitative follow up on the energetics of base pair formation and database search for evaluating their occurrence frequencies, reveal a lack of correlation between base pair stability and occurrence propensities on the one hand, and ease of protonation on the other. For example, although N7 protonated Adenine (
Gprot = 40.0 kcal/mol) is found to participate in stable base pairing, base pairs involving N7 protonated Guanine (
Gprot = 36.8 kcal/mol), on geometry optimization, converge to a minima where Guanine transfers its extra proton to its partner base. Such observations, along with examples of weak base pairs involving N3 protonation of Cytosine (
Gprot = 37.0 kcal/mol) are rationalized by analysing the protonation induced charge redistributions which are found to significantly influence, both positively and negatively, the hydrogen bonding potentials of different functional sites of individual nucloebases. Protonation induced charge redistribution is also found to strongly influence (i) the aromatic character of the rings of the participating bases and (ii) hydrogen bonding potential of the free edges of the protonated base pair. Comprehensive analysis of a non-redundant RNA crystal structure dataset further reveals that, while availability of stabilization possibilities determine the feasibility of occurrence of protonated bases, their occurrence context and specific functional roles are important factors determining their occurrence propensities.

Abstract

Efficiency of 6-ethynylpyridone (E), a potential Thymine (T) analogue, which forms high-fidelity base pairs with Adenine (A) and gives rise to stabler DNA duplexes, with stability comparable to those containing canonical Cytosine(C):Guanine(G) base pairs, has been reported recently. Estimates of the interaction energies, involving geometry optimization at the DFT level (including middle range dispersion interactions) followed by single point energy calculation at MP2 level, in excellent correlation with the experimentally observed trends, show that E binds more strongly and more discriminatively with A, than T does. Detailed analysis reveals that the increase in base-base interaction arises out of conjugation of acetylenic  electrons with the ring  system of E, which results in not only an extra stabilizing C–H· · ·  interaction in the EA pair, it also leads to a strengthening of the conventional hydrogen bonds. However, the computed base-base interaction energy for the EA pair was found to be much less than that of the canonical CG pair implying that the difference in the TA versus EA base pairing interaction alone cannot explain the large experimentally observed increase in the thermostability of DNA duplexes, where a TA pair is replaced with an EA pair. Our computations show that the conjugation of acetylenic  electrons with the ring  system also possibly plays a role in increasing the stacking potential of the EA pair, which in turn, can explain its marked influence in the enhancement of duplex stability.