Hierarchical sampling for metastable conformers determines biomolecular recognition: the case of malectin and diglucosylated N-glycan interactions

Structural information over the entire course of binding interactions based on the analyses of energy landscapes is described, which provides a framework to understand the events involved during biomolecular recognition. Conformational dynamics of malectin's exquisite selectivity for diglucosylated N-glycan (Dig-N-glycan), a highly flexible oligosaccharide comprising of numerous dihedral torsion angles, are described as an example. For this purpose, a novel approach based on hierarchical sampling for acquiring metastable molecular conformations constituting low-energy minima for understanding the structural features involved in a biologic recognition is proposed. For this purpose, four variants of principal component analysis were employed recursively in both Cartesian space and dihedral angles space that are characterized by free energy landscapes to select the most stable conformational substates. Subsequently, k-means clustering algorithm was implemented for geometric separation of the major native state to acquire a final ensemble of metastable conformers. A comparison of malectin complexes was then performed to characterize their conformational properties. Analyses of stereochemical metrics and other concerted binding events revealed surface complementarity, cooperative and bidentate hydrogen bonds, water-mediated hydrogen bonds, carbohydrate-aromatic interactions including CH-pi and stacking interactions involved in this recognition. Additionally, a striking structural transition from loop to beta-strands in malectin CRD upon specific binding to Dig-N-glycan is observed. The interplay of the above-mentioned binding events in malectin and Dig-N-glycan supports an extended conformational selection model as the underlying binding mechanism.

Thermal Response on the Microstructure and Texture of ECAP and Cold-Rolled Pure Magnesium

This paper deals with dynamic recrystallization (DRX), static recrystallization, and grain growth phenomena of pure magnesium after equal channel angular pressing (ECAP) by route A and B-C at 523 K (250 A degrees C) followed by 80 pct cold rolling. The ECAP-deformed and the subsequently rolled samples were annealed at 373 K and 773 K (100 A degrees C and 500 A degrees C). The associated changes in the microstructure and texture were studied using electron back-scattered diffraction. ECAP produced an average grain size of 12 to 18 A mu m with B and C-2 fiber textures. Subsequent rolling led to an average grain size 8 to 10 A mu m with basal texture fiber parallel to ND. There was no noticeable increase in the average grain size on annealing at 373 K (100 A degrees C). However, significant increase in the average grain size occurred at 773 K (500 A degrees C). The occurrence of different DRX mechanisms was detected: discontinuous dynamic recrystallization was attributed to basal slip activity and continuous dynamic recovery and recrystallization to prismatic/pyramidal slip systems. Only continuous static recrystallization could be observed on annealing.

Microstructure and Crystallographic Texture Evolution During the Friction-Stir Processing of a Precipitation-Hardenable Aluminum Alloy

Friction-stir processing (FSP) has been proven as a successful method for the grain refinement of high-strength aluminum alloys. The most important attributes of this process are the fine-grain microstructure and characteristic texture, which impart suitable properties in the as-processed material. In the current work, FSP of the precipitation-hardenable aluminum alloy 2219 has been carried out and the consequent evolution of microstructure and texture has been studied. The as-processed materials were characterized using electron back-scattered diffraction, x-ray diffraction, and electron probe microanalysis. Onion-ring formation was observed in the nugget zone, which has been found to be related to the precipitation response and crystallographic texture of the alloy. Texture development in the alloy has been attributed to the combined effect of shear deformation and dynamic recrystallization. The texture was found heterogeneous even within the nugget zone. A microtexture analysis revealed the dominance of shear texture components, with C component at the top of nugget zone and the B and A(2)* components in the middle and bottom. The bulk texture measurement in the nugget zone revealed a dominant C component. The development of a weaker texture along with the presence of some large particles in the nugget zone indicates particle-stimulated nucleation as the dominant nucleation mechanism during FSP. Grain growth follows the Burke and Turnbull mechanism and geometrical coalescence.

Analysis of Microstructure and Texture Evolution in Mg-3Al-1Zn Alloy Processed Through Groove Rolling

The mechanism of grain refinement in a AZ31 Mg alloy subjected to hot groove rolling is investigated up to large strain (epsilon(t) similar to 2.5). The alloy shows enhanced yield strength without compromising ductility. The change in strain path during rolling has resulted in significant weakening of basal texture. The microstructure analyses show that dynamic recrystallization (DRX) contributed significantly to grain refinement and hence to the observed mechanical properties. The combined effects of DRX and texture evolution on mechanical properties have been addressed.

Texture evolution during hot deformation of Moly-TZM

Moly-TZM was deformed at constant strain rate of 1.0 s(-1) to investigate the high strain rate deformation behaviour by microstructural and stress response change within a temperature range of 1400-1700 degrees C. To correlate the deformation behaviour with orientational change, recrystallization and recovery of the material, the microstructural investigation was undertaken using scanning electron microscopy (SEM) of electron back scattered diffraction (EBSD). Depending on the grain size and orientation spread recrystallized grains were identified and texture was calculated. Change in grain boundary characteristics with increasing temperature was determined by the misorientation angle distribution for the deformed and recrystallized grains. Subgrain coalescence and increase in subgrain size with increasing temperature was observed, indicating recrystallization not only occurred from the nucleation of the dislocation free grains in grain boundaries but also from the subgrain rotation and merging of the subgrains by annihilation of the low angle grain boundaries. Detailed studies on the evolution of texture of recrystallized grains showed continuous increase in <001> fiber texture in recrystallised grains, in contrast to a mixed fiber <001> +<111> for the deformed grains.

Role of stacking fault energy on texture evolution revisited

Three materials, pure aluminium, Al-4 wt.% Mg, alpha-brass have been chosen to understand the evolution of texture and microstructure during rolling. Pure Al develops a strong copper-type rolling texture and the deformation is entirely slip dominated. In Al-4Mg alloy, texture is copper-type throughout the deformation. The advent of Cu-type shear bands in the later stages of deformation has a negligible effect on the final texture. alpha-brass shows a characteristic brass-type texture from the early stages of rolling. Extensive twinning in the intermediate stages of deformation (epsilon(t) similar to 0.5) causes significant texture reorientation towards alpha-fiber. Beyond 40% reduction, deformation is dominated by Bs-type shear bands, and the banding coincides with the evolution of <111>parallel to ND components. The crystallites within the bands preferentially show <110>parallel to ND components. The absence of the Cu component throughout the deformation process indicates that, for the evolution of brass-type texture, the presence of Cu component is not a necessary condition. The final rolling texture is a synergistic effect of deformation twinning and shear banding.

Evolution of texture and its influence on the failure of components in some aluminium alloys

This paper describes the evolution of crystallographic texture in three of the most important high strength aluminium alloys, viz., AA2219, AA7075 and AFNOR7020 in the cold rolled and artificially aged condition. Bulk texture results were obtained by plotting pole figures from X-ray diffraction results followed by Orientation Distribution Function (ODF) analysis and micro-textures were measured using EBSD. The results indicate that the deformation texture components Cu, Bs and S, which were also present in the starting materials, strengthen with increase in amount of deformation. On the other hand, recrystallization texture components Goss and Cube weaken. The Bs component is stronger in the deformation texture. This is attributed to the shear banding. In-service applications indicate that the as-processed AFNOR7020 alloy fails more frequently compared to the other high strength Al alloys used in the aerospace industry. Detailed study of deformation texture revealed that strong Brass (Bs) component could be associated to shear banding, which in turn could explain the frequent failures in AFNOR7020 alloy. The alloying elements in this alloy that could possibly influence the stacking fault energy of the material could be accounted for the strong Bs component in the texture.

Sensory cues employed for the acquisition of familiarity-dependent recognition of a shoal of conspecifics by climbing perch (Anabas testudineus Bloch)

In this study we showed that a freshwater fish, the climbing perch (Anabas testudineus) is incapable of using chemical communication but employs visual cues to acquire familiarity and distinguish a familiar group of conspecifics from an unfamiliar one. Moreover, the isolation of olfactory signals from visual cues did not affect the recognition and preference for a familiar shoal in this species.

Role of deformation temperature on the evolution and heterogeneity of texture during equal channel angular pressing of magnesium

Investigations on texture evolution and through-thickness texture heterogeneity during equal channel angular pressing (ECAP) of pure magnesium at 200 degrees C, 150 degrees C and room temperature (RT) was carried out by neutron, high energy synchrotron X-ray and electron back-scatter diffraction. Irrespective of the ECAP temperature, a distinctive basal (B) and pyramidal (C-2) II type of fibers forms. The texture differs in the bottom 1 mm portion, where the B-fiber is shifted similar to 55 degrees due to negative shear attributed to friction. (C) 2015 Elsevier Inc. All rights reserved.

Evolution of annealing texture in cryo-rolled copper

The present study addresses the evolution of texture and microstructure during annealing in a cryorolled copper. Transition from copper to brass texture during the cryo-rolling has been illustrated. Twinning and interaction between twins and shear bands have been found to play the important role in grain refinement and strengthening. The low temperature vacancy clustering and its effect on the recrystallization have been experimentally demonstrated. Fine scale twinning, and grain refinement have been attributed to the higher yield strength found in the case of samples subjected to cryo-rolling. (C) 2015 Elsevier B.V. All rights reserved.

Human Action Recognition in Compressed Domain using PBL-McRBFN Approach

Large variations in human actions lead to major challenges in computer vision research. Several algorithms are designed to solve the challenges. Algorithms that stand apart, help in solving the challenge in addition to performing faster and efficient manner. In this paper, we propose a human cognition inspired projection based learning for person-independent human action recognition in the H.264/AVC compressed domain and demonstrate a PBL-McRBEN based approach to help take the machine learning algorithms to the next level. Here, we use gradient image based feature extraction process where the motion vectors and quantization parameters are extracted and these are studied temporally to form several Group of Pictures (GoP). The GoP is then considered individually for two different bench mark data sets and the results are classified using person independent human action recognition. The functional relationship is studied using Projection Based Learning algorithm of the Meta-cognitive Radial Basis Function Network (PBL-McRBFN) which has a cognitive and meta-cognitive component. The cognitive component is a radial basis function network while the Meta-Cognitive Component(MCC) employs self regulation. The McC emulates human cognition like learning to achieve better performance. Performance of the proposed approach can handle sparse information in compressed video domain and provides more accuracy than other pixel domain counterparts. Performance of the feature extraction process achieved more than 90% accuracy using the PTIL-McRBFN which catalyzes the speed of the proposed high speed action recognition algorithm. We have conducted twenty random trials to find the performance in GoP. The results are also compared with other well known classifiers in machine learning literature.

An Optical How Feature and McFIS Based Approach for 3-Dimensional Human Action Recognition

We propose to develop a 3-D optical flow features based human action recognition system. Optical flow based features are employed here since they can capture the apparent movement in object, by design. Moreover, they can represent information hierarchically from local pixel level to global object level. In this work, 3-D optical flow based features a re extracted by combining the 2-1) optical flow based features with the depth flow features obtained from depth camera. In order to develop an action recognition system, we employ a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). The m of McFIS is to find the decision boundary separating different classes based on their respective optical flow based features. McFIS consists of a neuro-fuzzy inference system (cognitive component) and a self-regulatory learning mechanism (meta-cognitive component). During the supervised learning, self-regulatory learning mechanism monitors the knowledge of the current sample with respect to the existing knowledge in the network and controls the learning by deciding on sample deletion, sample learning or sample reserve strategies. The performance of the proposed action recognition system was evaluated on a proprietary data set consisting of eight subjects. The performance evaluation with standard support vector machine classifier and extreme learning machine indicates improved performance of McFIS is recognizing actions based of 3-D optical flow based features.

Database Independent Human Emotion Recognition with Meta-Cognitive Neuro-Fuzzy Inference System

Facial emotions are the most expressive way to display emotions. Many algorithms have been proposed which employ a particular set of people (usually a database) to both train and test their model. This paper focuses on the challenging task of database independent emotion recognition, which is a generalized case of subject-independent emotion recognition. The emotion recognition system employed in this work is a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). McFIS has two components, a neuro-fuzzy inference system, which is the cognitive component and a self-regulatory learning mechanism, which is the meta-cognitive component. The meta-cognitive component, monitors the knowledge in the neuro-fuzzy inference system and decides on what-to-learn, when-to-learn and how-to-learn the training samples, efficiently. For each sample, the McFIS decides whether to delete the sample without being learnt, use it to add/prune or update the network parameter or reserve it for future use. This helps the network avoid over-training and as a result improve its generalization performance over untrained databases. In this study, we extract pixel based emotion features from well-known (Japanese Female Facial Expression) JAFFE and (Taiwanese Female Expression Image) TFEID database. Two sets of experiment are conducted. First, we study the individual performance of both databases on McFIS based on 5-fold cross validation study. Next, in order to study the generalization performance, McFIS trained on JAFFE database is tested on TFEID and vice-versa. The performance The performance comparison in both experiments against SVNI classifier gives promising results.

Action Recognition from Motion Capture Data using Meta-cognitive RBF Network Classifier

Action recognition plays an important role in various applications, including smart homes and personal assistive robotics. In this paper, we propose an algorithm for recognizing human actions using motion capture action data. Motion capture data provides accurate three dimensional positions of joints which constitute the human skeleton. We model the movement of the skeletal joints temporally in order to classify the action. The skeleton in each frame of an action sequence is represented as a 129 dimensional vector, of which each component is a 31) angle made by each joint with a fixed point on the skeleton. Finally, the video is represented as a histogram over a codebook obtained from all action sequences. Along with this, the temporal variance of the skeletal joints is used as additional feature. The actions are classified using Meta-Cognitive Radial Basis Function Network (McRBFN) and its Projection Based Learning (PBL) algorithm. We achieve over 97% recognition accuracy on the widely used Berkeley Multimodal Human Action Database (MHAD).