Deciphering complex patterns of class-I HLA-peptide cross-reactivity via hierarchical grouping

T-cell responses in humans are initiated by the binding of a peptide antigen to a human leukocyte antigen (HLA) molecule. The peptide-HLA complex then recruits an appropriate T cell, leading to cell-mediated immunity. More than 2000 HLA class-I alleles are known in humans, and they vary only in their peptide-binding grooves. The polymorphism they exhibit enables them to bind a wide range of peptide antigens from diverse sources. HLA molecules and peptides present a complex molecular recognition pattern, as many peptides bind to a given allele and a given peptide can be recognized by many alleles. A powerful grouping scheme that not only provides an insightful classification, but is also capable of dissecting the physicochemical basis of recognition specificity is necessary to address this complexity. We present a hierarchical classification of 2010 class-I alleles by using a systematic divisive clustering method. All-pair distances of alleles were obtained by comparing binding pockets in the structural models. By varying the similarity thresholds, a multilevel classification was obtained, with 7 supergroups, each further subclassifying to yield 72 groups. An independent clustering performed based only on similarities in their epitope pools correlated highly with pocket-based clustering. Physicochemical feature combinations that best explain the basis of clustering are identified. Mutual information calculated for the set of peptide ligands enables identification of binding site residues contributing to peptide specificity. The grouping of HLA molecules achieved here will be useful for rational vaccine design, understanding disease susceptibilities and predicting risk of organ transplants.

Self accomodation morphology of martensite variants in Zr---2.5wt%Nb alloy

The role of self accomodation of the different mertensite variants controlling the morphologies of the Zr---2.5wt%Nb alloy martensite has been examined. Three distinct types of grouping of martensite variants have been found to be predominantly present. Crystallographic descriptions of these groups have been provided and the degrees of self accomodation for these have been estimated and compared with those corresponding to other possible variant groupings around the symmetry axes of the parent phase. The frequently observed 3-variant group, which shows an “indentation mark” morphology when viewed along left angle bracket111right-pointing angle bracketβ directions in the transmission electron microscope, has been seen to have the highest degree of self accomodation amongst the cases considered. Based on the observations made, a growth sequence leading to the formation of the final martensitic structure has been proposed.

A blind indic script recognizer for multi-script documents

We report a hierarchical blind script identifier for 11 different Indian scripts. An initial grouping of the 11 scripts is accomplished at the first level of this hierarchy. At the subsequent level, we recognize the script in each group. The various nodes of this tree use different feature-classifier combinations. A database of 20,000 words of different font styles and sizes is collected and used for each script. Effectiveness of Gabor and Discrete Cosine Transform features has been independently, evaluated using nearest neighbor linear discriminant and support vector machine classifiers. The minimum and maximum accuracies obtained, using this hierarchical mechanism, are 92.2% and 97.6%, respectively.

Revised Cam-Clay Model

The Cam-clay models, or any other plasticity-based models, do not make distinction between the mode of stress transfer in coarse- and fine-grained soils. An examination of behavior at micro level in fine-grained soils, from the consideration of load transfer through physico-chemical interactions, suggests that the plastic compressions result from the grouping of particles into larger clusters and that elastic compressions result from the decrease in the spacing between particles. During shearing, these clusters gradually get dismembered, releasing the locked-in energy. The effect of such dismembering of clusters can be easily incorporated into the original Cam-clay model, and better predictions can be obtained with the associated flow rule, itself, for both normally and over consolidated states. The method essentially defines the hardening of yield surfaces with internal changes in the spacing between particles, instead of changes in externally observed plastic strains. The approach describes the behavior of over consolidated soils as yielding along successfively hardening Roscoe surfaces with gradually varying plastic properties.

Tropical Clays .1. Index Properties And Microstructural Aspects

Soils showing changes in plasticity characteristics upon driving form an important group in tropical soils. These changes are attributed to the grouping of particles into aggregates either due to mineralogy or presence of cementing agents and/or pore fluid characteristics. These changes are found to be permanent. In this paper, the effect of these changes leading to changes in index properties is discussed. The coefficient of permeability has been found to be comparable at liquid limit water content for different soils of varying liquid limit values. Permeability is an indirect reflection of microstructure and indicates the flow rate, which depends upon pore geometry. Other mechanical properties like compressibility and shear strength also depend upon pore geometry. These microstructural aspects of liquid limit as a reference state for the analysis of engineering behavior of tropical soils are examined in detail.

Variable Sparsity Kernel Learning

This paper(1) presents novel algorithms and applications for a particular class of mixed-norm regularization based Multiple Kernel Learning (MKL) formulations. The formulations assume that the given kernels are grouped and employ l(1) norm regularization for promoting sparsity within RKHS norms of each group and l(s), s >= 2 norm regularization for promoting non-sparse combinations across groups. Various sparsity levels in combining the kernels can be achieved by varying the grouping of kernels-hence we name the formulations as Variable Sparsity Kernel Learning (VSKL) formulations. While previous attempts have a non-convex formulation, here we present a convex formulation which admits efficient Mirror-Descent (MD) based solving techniques. The proposed MD based algorithm optimizes over product of simplices and has a computational complexity of O (m(2)n(tot) log n(max)/epsilon(2)) where m is no. training data points, n(max), n(tot) are the maximum no. kernels in any group, total no. kernels respectively and epsilon is the error in approximating the objective. A detailed proof of convergence of the algorithm is also presented. Experimental results show that the VSKL formulations are well-suited for multi-modal learning tasks like object categorization. Results also show that the MD based algorithm outperforms state-of-the-art MKL solvers in terms of computational efficiency.

Modified particle swarm optimization for pattern clustering

Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning and data mining. Clustering is grouping of a data set or more precisely, the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait according to some defined distance measure. In this paper we present the genetically improved version of particle swarm optimization algorithm which is a population based heuristic search technique derived from the analysis of the particle swarm intelligence and the concepts of genetic algorithms (GA). The algorithm combines the concepts of PSO such as velocity and position update rules together with the concepts of GA such as selection, crossover and mutation. The performance of the above proposed algorithm is evaluated using some benchmark datasets from Machine Learning Repository. The performance of our method is better than k-means and PSO algorithm.

GPU-based normalized cuts for road extraction using satellite imagery

This paper presents a GPU implementation of normalized cuts for road extraction problem using panchromatic satellite imagery. The roads have been extracted in three stages namely pre-processing, image segmentation and post-processing. Initially, the image is pre-processed to improve the tolerance by reducing the clutter (that mostly represents the buildings, vegetation,. and fallow regions). The road regions are then extracted using the normalized cuts algorithm. Normalized cuts algorithm is a graph-based partitioning `approach whose focus lies in extracting the global impression (perceptual grouping) of an image rather than local features. For the segmented image, post-processing is carried out using morphological operations - erosion and dilation. Finally, the road extracted image is overlaid on the original image. Here, a GPGPU (General Purpose Graphical Processing Unit) approach has been adopted to implement the same algorithm on the GPU for fast processing. A performance comparison of this proposed GPU implementation of normalized cuts algorithm with the earlier algorithm (CPU implementation) is presented. From the results, we conclude that the computational improvement in terms of time as the size of image increases for the proposed GPU implementation of normalized cuts. Also, a qualitative and quantitative assessment of the segmentation results has been projected.

Robustness of the partitions in grouping multi-item inventories

A recent work obtained closed-form solutions to the.problem of optimally grouping a multi-item inventory into subgroups with a common order cycle per group, when the distribution by value of the inventory could be described by a Pareto function. This paper studies the sensitivity of the optimal subgroup boundaries so obtained. Closed-form expressions have been developed to find intervals for the subgroup boundaries for any given level of suboptimality. Graphs have been provided to aid the user in selecting a cost-effective level of aggregation and choosing appropriate subgroup boundaries for a whole range of inventory distributions. The results of sensitivity analyses demonstrate the availability of flexibility in the partition boundaries and the cost-effectiveness of any stock control system through three groups, and thus also provide a theoretical support to the intuitive ABC system of classifying the items.

The process of technological innovations in small enterprises: the Indian way

This paper probes how two small foundries in Belgaum, Karnataka State, India, have achieved technological innovations successfully based on their technological capability and customer needs, enabling them to sail through the competitive environment. This study brought out that technically qualified entrepreneurs of both the foundries have carried out technological innovations, mainly due to their self-motivation and self-efforts. Changing product designs, as desired or directed by the customers, cost reduction, quality improvement and import substitution through reverse engineering are the characteristics of these technological innovations. These incremental innovations have enabled the entrepreneurs of the two foundries to enhance competitiveness, grow in the domestic market and penetrate the international market and grow in size over time.

The process of technological innovations in small enterprises: the Indian way

This paper probes how two small foundries in Belgaum, Karnataka State, India, have achieved technological innovations successfully based on their technological capability and customer needs, enabling them to sail through the competitive environment. This study brought out that technically qualified entrepreneurs of both the foundries have carried out technological innovations, mainly due to their self-motivation and self-efforts. Changing product designs, as desired or directed by the customers, cost reduction, quality improvement and import substitution through reverse engineering are the characteristics of these technological innovations. These incremental innovations have enabled the entrepreneurs of the two foundries to enhance competitiveness, grow in the domestic market and penetrate the international market and grow in size over time.

Corporate social responsibilities of Indian public sector enterprises-a case study of Bharat Heavy Electricals Limited (BHEL)

The high level of public accountability attached to Public Sector Enterprises as a result of public ownership makes them socially responsible. The Committee of Public Undertakings in 1992 examined the issue relating to social obligations of Central Public Sector Enterprises and observed that ``being part of the `State', every Public Sector enterprise has a moral responsibility to play an active role in discharging the social obligations endowed on a welfare state, subject to the financial health of the enterprise''. It issued the Corporate Social Responsibility Guidelines in 2010 where all Central Public Enterprises, through a Board Resolution, are mandated to create a CSR budget as a specified percentage of net profit of the previous year. This paper examines the CSR activities of the biggest engineering public sector organization in India, Bharath Heavy Electricals Limited. The objectives are twofold, one, to develop a case study of the organization about the funds allocated and utilized for various CSR activities, and two, to examine its status with regard to other organizations, the 2010 guidelines, and the local socio-economic development. Secondary data analysis results show three interesting trends. One, it reveals increasing organizational social orientation with the formal guidelines in place. Two, Firms can no longer continue to exploit environmental resources and escape from their responsibilities by acting separate entities regardless of the interest of the society and Three the thrust of CSR in public sector is on inclusive growth, sustainable development and capacity building with due attention to the socio-economic needs of the neglected and marginalized sections of the society.