Significant efforts in wet and dry laboratories are devoted to resolving molecular structures. In particular, computational methods can now compute thousands of tertiary structures that populate the structure space of a protein molecule of interest. These advances are now allowing us to turn our attention to analysis methodologies that are able to organize the computed structures in order to highlight functionally relevant structural states. In this paper, we propose a methodology that leverages community detection methods, designed originally to detect communities in social networks, to organize computationally probed protein structure spaces. We report a principled comparison of such methods along several metrics on proteins of diverse folds and lengths. We present a rigorous evaluation in the context of decoy selection in template-free protein structure prediction. The results make the case that network-based community detection methods warrant further investigation to advance analysis of protein structure spaces for automated selection of functionally relevant structures.

Radio Frequency Identification (RFID) technology utilizes the electromagnetic fields for data transfer in order to perform automatic detection and tracking of tags or tags of objects. It can provide ways to design and implement relatively inexpensive systems particularly for security aspects. In this paper, we have proposed a digital access control system that can be employed to a protected area where none but people with authenticated credentials can enter. In fact, we have implemented the system in the server room of an educational Institute to test its efficiency as well as expenditure. The implemented system comprises of digital door lock which is unlock able in real time to ensure secured access specifying activation, authentication and validation of users prior to bringing the RFID card close to the reader. The entire system is associated with a central client-server sub-system to ensure and maintain the overall system integrity. Associated sub-system also generates a log report to maintain check-in and check-out status of visitors in accordance with the primary credentials of each. This system will certainly provide an idea about the design and installation of a relatively inexpensive security system which is suitable in the perspective of a developing country like Bangladesh.

This paper scrutinizes Mongol Barota - a fully functional, stand-alone mobile platform rover which is capable to act as a human assistant to perform various scientific tasks in extreme adversities. The control system of the rover is designed in such a way that it can be commanded from a blind station within 1 kilometer range. It has successfully taken part in 8th annual University Rover Challenge organized by the Mars Society at the Mars Desert Research Station (MDRS) in the remote, barren desert of southern Utah, USA in late May, 2014. It has been traced out as the first entrance in this competition from Bangladesh and occupied 12th position out of 31 registered teams from 6 countries of 4 continents. The rover architecture maps the associated components to make it capable to perform the assigned tasks namely - Sample Return Task, Astronaut Assistance Task, Equipment Servicing Task and Terrain Traversing Task. Among these, the first task refers to search for the evidence to identify the existence of life after detailed analysis of collected soil sample from a selected site. In Equipment servicing task, rover has to perform a sequence of operations that mainly includes switching on a compressor and working with a series of pipes, hoses, valves and other such equipment. Astronaut assistance task intends the rover to collect tools from some given GPS locations and then delivery of each of them to the corresponding locations with provided GPS coordinates. Rover has to traverse an adverse terrain in order to pass through a set of target gates for completion of the terrain traversing task. This paper provides a detailed demonstration of the Mongol Barota rover, ins and outs of its architecture, facts and features, system components, logic, logistics and techniques adopted to implement several tasks representing its overall capabilities.

The energy landscape that organizes microstates of a molecular system and governs the underlying molecular dynamics exposes the relationship between molecular form/structure, changes to form, and biological activity or function in the cell. However, several challenges stand in the way of leveraging energy landscapes for relating structure and structural dynamics to function. Energy landscapes are high-dimensional, multi-modal, and often overly-rugged. Deep wells or basins in them do not always correspond to stable structural states but are instead the result of inherent inaccuracies in semi-empirical molecular energy functions. Due to these challenges, energetics is typically ignored in computational approaches addressing long-standing central questions in computational biology, such as protein decoy selection. In the latter, the goal is to determine over a possibly large number of computationally-generated three-dimensional structures of a protein those structures that are biologically-active/native. In recent work, we have recast our attention on the protein energy landscape and its role in helping us to advance decoy selection. Here, we summarize some of our successes so far in this direction via unsupervised learning. More importantly, we further advance the argument that the energy landscape holds valuable information to aid and advance the state of protein decoy selection via novel machine learning methodologies that leverage supervised learning. Our focus in this article is on decoy selection for the purpose of a rigorous, quantitative evaluation of how leveraging protein energy landscapes advances an important problem in protein modeling. However, the ideas and concepts presented here are generally useful to make discoveries in studies aiming to relate molecular structure and structural dynamics to function.

Over the past decade, Markov State Models (MSM) have emerged as powerful methodologies to build discrete models of dynamics over structures obtained from Molecular Dynamics trajectories. The identification of macrostates for the MSM is a central decision that impacts the quality of the MSM but depends on both the selected representation of a structure and the clustering algorithm utilized over the featurized structures. Motivated by a large molecular system in its free and bound state, this paper investigates two directions of research, further reducing the representation dimensionality in a non-parametric, data-driven manner and including more structures in the computation. Rigorous evaluation of the quality of obtained MSMs via various statistical tests in a comparative setting firmly shows that fewer dimensions and more structures result in a better MSM. Many interesting findings emerge from the best MSM, advancing our understanding of the relationship between antibody dynamics and antibody–antigen recognition.

In spite of wide use of projection-based features in handwritten character recognition of several languages, its implementation was somewhat scanty in Bangla handwritten character recognition. This paper introduces the usage of projection profile features in recognizing handwritten Bangla basic characters. Alongside it also demonstrates a qualitative and quantitative analysis to visualize the effect of using projection based features on accuracy of recognition of Bangla handwritten characters through a number of approaches. In fact, this particular effort comprises of five different approaches where first one used longest-run, quad-tree and octant centroid features, second one adopted additional shadow features in association with the features of first approach, third one used longest run, quad-tree, shadow and chain code histogram features, next approach used longest-run, quadratic center of mass, shadow and left projection profile features and finally fifth approach with additional right projection profile features along with other features involved in the fourth approach. Throughout this analysis, neural network (trained via back-propagation algorithm) acted as classifier to observe the change in accuracy of recognition. It is seen that, with the increase in number of projection-based features, percentage of accuracy enhances at a greater rate than in case of inclusion of other features. This effective analysis can certainly assist a researcher to choose the optimal feature vector (consisting of several feature sets) for handwritten Bangla basic characters recognition.