A new graph-theoretic approach to determine the similarity of genome sequences based on nucleotide triplets.

Methods of finding sequence similarity play a significant role in computational biology. Owing to the rapid increase of genome sequences in public databases, the evolutionary relationship of species becomes more challenging. But traditional alignment-based methods are found inappropriate due to their time-consuming nature. Therefore, it is necessary to find a faster method, which applies to species phylogeny. In this paper, a new graph-theory based alignment-free sequence comparison method is proposed. A complete-bipartite graph is used to represent each genome sequence based on its nucleotide triplets. Subsequently, with the help of the weights of edges of the graph, a vector descriptor is formed. Finally, the phylogenetic tree is drawn using the UPGMA algorithm. In the present case, the datasets for comparison are related to mammals, viruses, and bacteria. In most of the cases, the phylogeny in the present case is found to be more satisfactory as compared to earlier methods.

Genome sequence comparison under a new form of tri-nucleotide representation based on bio-chemical properties of nucleotides.

Genetic sequence analysis, classification of genome sequence and evolutionary relationship between species using their biological sequences, are the emerging research domain in Bioinformatics. Several methods have already been applied to DNA sequence comparison under tri-nucleotide representation. In this paper, a new form of tri-nucleotide representation is proposed for sequence comparison. The comparison does not depend on the alignment of the sequences. In this representation, the bio-chemical properties of the nucleotides are considered. The novelty of this method is that the sequences of unequal lengths are represented by vectors of the same length and each of the tri-nucleotide formed out of the given sequence has its unique representation. To validate the proposed method, it is verified on several data sets related to mammalians, viruses and bacteria. The results of this method are further compared with those obtained by methods such as probabilistic method, natural vector method, Fourier power spectrum method, multiple encoding vector method, and feature frequency profiles method. Moreover, this method produces accurate phylogeny in all the cases. It is also proved that the time complexity of the present method is less.

Optimal choice of k-mer in composition vector method for genome sequence comparison.

Several proteins and genes are members of families that share a public evolutionary. In order to outline the evolutionary relationships and to recognize conserved patterns, sequence comparison becomes an emerging process. The current work investigates critically the k-mer role in composition vector method for comparing genome sequences. Generally, composition vector methods using k-mer are applied under choice of different value of k to compare genome sequences. For some values of k, results are satisfactory, but for other values of k, results are unsatisfactory. Standard composition vector method is carried out in the proposed work using 3-mer string length. In addition, special type of information based similarity index is used as a distance measure. It establishes that use of 3-mer and information based similarity index provide satisfactory results especially for comparison of whole genome sequences in all cases. These selections provide a sort of unified approach towards comparison of genome sequences.

Analyzing text recognition from tactually evoked EEG.

Tactual exploration of objects produce specific patterns in the human brain and hence objects can be recognized by analyzing brain signals during tactile exploration. The present work aims at analyzing EEG signals online for recognition of embossed texts by tactual exploration. EEG signals are acquired from the parietal region over the somatosensory cortex of blindfolded healthy subjects while they tactually explored embossed texts, including symbols, numbers, and alphabets. Classifiers based on the principle of supervised learning are trained on the extracted EEG feature space, comprising three features, namely, adaptive autoregressive parameters, Hurst exponents, and power spectral density, to recognize the respective texts. The pre-trained classifiers are used to classify the EEG data to identify the texts online and the recognized text is displayed on the computer screen for communication. Online classifications of two, four, and six classes of embossed texts are achieved with overall average recognition rates of 76.62, 72.31, and 67.62% respectively and the computational time is less than 2 s in each case. The maximum information transfer rate and utility of the system performance over all experiments are 0.7187 and 2.0529 bits/s respectively. This work presents a study that shows the possibility to classify 3D letters using tactually evoked EEG. In future, it will help the BCI community to design stimuli for better tactile augmentation n also opens new directions of research to facilitate 3D letters for visually impaired persons. Further, 3D maps can be generated for aiding tactual BCI in teleoperation.

A Generic Transferable EEG Decoder for Online Detection of Error Potential in Target Selection.

Reliable detection of error from electroencephalography (EEG) signals as feedback while performing a discrete target selection task across sessions and subjects has a huge scope in real-time rehabilitative application of Brain-computer Interfacing (BCI). Error Related Potentials (ErrP) are EEG signals which occur when the participant observes an erroneous feedback from the system. ErrP holds significance in such closed-loop system, as BCI is prone to error and we need an effective method of systematic error detection as feedback for correction. In this paper, we have proposed a novel scheme for online detection of error feedback directly from the EEG signal in a transferable environment (i.e., across sessions and across subjects). For this purpose, we have used a P300-speller dataset available on a BCI competition website. The task involves the subject to select a letter of a word which is followed by a feedback period. The feedback period displays the letter selected and, if the selection is wrong, the subject perceives it by the generation of ErrP signal. Our proposed system is designed to detect ErrP present in the EEG from new independent datasets, not involved in its training. Thus, the decoder is trained using EEG features of 16 subjects for single-trial classification and tested on 10 independent subjects. The decoder designed for this task is an ensemble of linear discriminant analysis, quadratic discriminant analysis, and logistic regression classifier. The performance of the decoder is evaluated using accuracy, F1-score, and Area Under the Curve metric and the results obtained is 73.97, 83.53, and 73.18%, respectively.

Bypassing the Natural Visual-Motor Pathway to Execute Complex Movement Related Tasks Using Interval Type-2 Fuzzy Sets.

In visual-motor coordination, the human brain processes visual stimuli representative of complex motion-related tasks at the occipital lobe to generate the necessary neuronal signals for the parietal and pre-frontal lobes, which in turn generates movement related plans to excite the motor cortex to execute the actual tasks. The paper introduces a novel approach to provide rehabilitative support to patients suffering from neurological damage in their pre-frontal, parietal and/or motor cortex regions. An attempt to bypass the natural visual-motor pathway is undertaken using interval type-2 fuzzy sets to generate the approximate EEG response of the damaged pre-frontal/parietal/motor cortex from the occipital EEG signals. The approximate EEG response is used to trigger a pre-trained joint coordinate generator to obtain the desired joint coordinates of the link end-points of a robot imitating the human subject. The robot arm is here employed as a rehabilitative aid in order to move each link end-points to the desired locations in the reference coordinate system by appropriately activating its links using the well-known inverse kinematics approach. The mean-square positional errors obtained for each link end-points is found within acceptable limits for all experimental subjects including subjects with partial parietal damage, indicating a possible impact of the proposed approach in rehabilitative robotics. Subjective variation in EEG features over different sessions of experimental trials is modeled here using interval type-2 fuzzy sets for its inherent power to handle uncertainty. Experiments undertaken confirm that interval type-2 fuzzy realization outperforms its classical type-1 counterpart and back-propagation neural approaches in all experimental cases, considering link positional error as a metric. The proposed research offers a new opening for the development of possible rehabilitative aids for people with partial impairment in visual-motor coordination.

Vibrotactile feedback for conveying object shape information as perceived by artificial sensing of robotic arm.

This work is a preliminary study towards developing an alternative communication channel for conveying shape information to aid in recognition of items when tactile perception is hindered. Tactile data, acquired during object exploration by sensor fitted robot arm, are processed to recognize four basic geometric shapes. Patterns representing each shape, classified from tactile data, are generated using micro-controller-driven vibration motors which vibrotactually stimulate users to convey the particular shape information. These motors are attached on the subject's arm and their psychological (verbal) responses are recorded to assess the competence of the system to convey shape information to the user in form of vibrotactile stimulations. Object shapes are classified from tactile data with an average accuracy of 95.21 %. Three successive sessions of shape recognition from vibrotactile pattern depicted learning of the stimulus from subjects' psychological response which increased from 75 to 95 %. This observation substantiates the learning of vibrotactile stimulation in user over the sessions which in turn increase the system efficacy. The tactile sensing module and vibrotactile pattern generating module are integrated to complete the system whose operation is analysed in real-time. Thus, the work demonstrates a successful implementation of the complete schema of artificial tactile sensing system for object-shape recognition through vibrotactile stimulations.

Texture- and deformability-based surface recognition by tactile image analysis.

Deformability and texture are two unique object characteristics which are essential for appropriate surface recognition by tactile exploration. Tactile sensation is required to be incorporated in artificial arms for rehabilitative and other human-computer interface applications to achieve efficient and human-like manoeuvring. To accomplish the same, surface recognition by tactile data analysis is one of the prerequisites. The aim of this work is to develop effective technique for identification of various surfaces based on deformability and texture by analysing tactile images which are obtained during dynamic exploration of the item by artificial arms whose gripper is fitted with tactile sensors. Tactile data have been acquired, while human beings as well as a robot hand fitted with tactile sensors explored the objects. The tactile images are pre-processed, and relevant features are extracted from the tactile images. These features are provided as input to the variants of support vector machine (SVM), linear discriminant analysis and k-nearest neighbour (kNN) for classification. Based on deformability, six household surfaces are recognized from their corresponding tactile images. Moreover, based on texture five surfaces of daily use are classified. The method adopted in the former two cases has also been applied for deformability- and texture-based recognition of four biomembranes, i.e. membranes prepared from biomaterials which can be used for various applications such as drug delivery and implants. Linear SVM performed best for recognizing surface deformability with an accuracy of 83 % in 82.60 ms, whereas kNN classifier recognizes surfaces of daily use having different textures with an accuracy of 89 % in 54.25 ms and SVM with radial basis function kernel recognizes biomembranes with an accuracy of 78 % in 53.35 ms. The classifiers are observed to generalize well on the unseen test datasets with very high performance to achieve efficient material recognition based on its deformability and texture.

Synthesis of gelatin nano/submicron particles by binary nonsolvent aided coacervation (BNAC) method.

A newly developed modified coacervation method is utilized to synthesize gelatin nano/submicron particles (GN/SPs) as a drug carrier. Binary nonsolvent aided coacervation (BNAC) method is a modified single step coacervation method, which has yielded approximately a threefold lower particle size and higher average yield in terms of weight percentage of around 94% in comparison to the conventional phase separation methods. In this study 0.5% (w/v) gelatin aqueous solution with a binary nonsolvent system of acetone and ethanol was used. Nanoparticle synthesis was optimized with respect to nonsolvent system type and pH. pH7 has resulted a minimum particle size of 55.67 (±43.74) nm in anhydrous medium along with a swollen particle size of 776nm (±38.57) in aqueous medium with a zeta potential of (-16.3±3.51) mV in aqueous medium. Swelling ratio of 13.95 confirms the crosslinked hydrogel nature of the particles. Furthermore, drug loading efficiency of the gelatin particles prepared at 7pH was observed with nitrofurazone as the model drug. Results of drug release study indicate the potential use of GN/SPs as drug loading matrix for wound management such as burn wound management.

Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose.

The paper proposes a novel approach toward EEG-driven position control of a robot arm by utilizing motor imagery, P300 and error-related potentials (ErRP) to align the robot arm with desired target position. In the proposed scheme, the users generate motor imagery signals to control the motion of the robot arm. The P300 waveforms are detected when the user intends to stop the motion of the robot on reaching the goal position. The error potentials are employed as feedback response by the user. On detection of error the control system performs the necessary corrections on the robot arm. Here, an AdaBoost-Support Vector Machine (SVM) classifier is used to decode the 4-class motor imagery and an SVM is used to decode the presence of P300 and ErRP waveforms. The average steady-state error, peak overshoot and settling time obtained for our proposed approach is 0.045, 2.8% and 44 s, respectively, and the average rate of reaching the target is 95%. The results obtained for the proposed control scheme make it suitable for designs of prosthetics in rehabilitative applications.

Object-shape recognition and 3D reconstruction from tactile sensor images.

This article presents a novel approach of edged and edgeless object-shape recognition and 3D reconstruction from gradient-based analysis of tactile images. We recognize an object's shape by visualizing a surface topology in our mind while grasping the object in our palm and also taking help from our past experience of exploring similar kind of objects. The proposed hybrid recognition strategy works in similar way in two stages. In the first stage, conventional object-shape recognition using linear support vector machine classifier is performed where regional descriptors features have been extracted from the tactile image. A 3D shape reconstruction is also performed depending upon the edged or edgeless objects classified from the tactile images. In the second stage, the hybrid recognition scheme utilizes the feature set comprising both the previously obtained regional descriptors features and some gradient-related information from the reconstructed object-shape image for the final recognition in corresponding four classes of objects viz. planar, one-edged object, two-edged object and cylindrical objects. The hybrid strategy achieves 97.62 % classification accuracy, while the conventional recognition scheme reaches only to 92.60 %. Moreover, the proposed algorithm has been proved to be less noise prone and more statistically robust.

Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata.

Brain-computer interfacing (BCI) has been the most researched technology in neuroprosthesis in the last two decades. Feature extractors and classifiers play an important role in BCI research for the generation of suitable control signals to drive an assistive device. Due to the high dimensionality of feature vectors in practical BCI systems, implantation of efficient feature selection algorithms has been an integral area of research in the past decade. This article proposes an efficient feature selection technique, realized by means of an evolutionary algorithm, which attempts to overcome some of the shortcomings of several state-of-the-art approaches in this field. The outlined scheme produces a subset of salient features which improves the classification accuracy while maintaining a trade-off with the computational speed of the complete scheme. For this purpose, an efficient memetic algorithm has also been proposed for the optimization purpose. Extensive experimental validations have been conducted on two real-world datasets to establish the efficacy of our approach. We have compared our approach to existing algorithms and have established the superiority of our algorithm to the rest.

Investigation of in vitro iontophoresis facilitated transdermal delivery of glycine.

The research presented in this paper discusses the potential of iontophoresis facilitated transdermal delivery of glycine. Iontophoresis has been widely investigated as a noninvasive transdermal drug delivery system. Iontophoresis is the use of a low electric current to carry ionized as well as unionized drug molecules across tissues (like skin) in a noninvasive technique. In the present paper, a custom made low cost, battery powered, portable transdermal iontophoretic system has been investigated for the various parameters of transdermal iontophoretic drug delivery namely drug density in donor formulation, current density, current profile, presence of competitive ions and type of electrode material. In vitro release studies of glycine, as a drug, were done using a modified Franz type glass diffusion cell through excised mouse skin. Factors affecting the delivery of glycine (a non-essential amino acid used to treat schizophrenia, anxiety, insomnia, hypoglycemia etc.) by iontophoresis include concentration of glycine in donor solution, ionic strength of donor solution, density of applied current & its aspect ratio. The change in frequency and electrode type (Ag/AgCl & stainless steel) did not have any significant effect on glycine delivery. In conclusion it could be stated that it is possible to achieve appreciable control over the transdermal delivery of glycine by the low-cost portable iontophoretic drug delivery system developed.

Pattern recognition in tachographic gait records of normal and lower extremity handicapped human subjects.

The electromechanical behaviour of the tachographic gait recording system developed by earlier research scientists was analysed. Some alterations were incorporated and the resulting modified system was used to record the gait of 25 normal, healthy, able-bodied adult males with sedentary habits, and 67 subjects having different types of lower extremity handicap. Some of the findings obtained from analysis of these gait records are reported in this paper. Basic differences in the pattern associated with different types of subjects have been explored through analysis of basic gait characteristics e.g. cadence, consistency etc., whereas the existence of specific patterns for each group of subjects has been recognised by applying the multi-variate statistical tool of discriminant analysis.

Static weight-bearing patterns of below-knee amputees using patellar-tendon-bearing prostheses.

While the weight-bearing patterns under human feet during dynamic conditions (i.e. walking or running) have been investigated by many scientists, only a few studies have been reported on such patterns during erect standing posture. This paper describes an investigation where a system composed of strain gauge load cells has been employed to study the static weight-bearing patterns of a group fo below-knee amputees using patellar-tendon-bearing prostheses, and of a matching group of normal persons. Experimental data were analysed to recognize the static weight-bearing patterns; it has been found that specific patterns exist for normal person as well as for healthy and affected sides of the amputees. It has been indicated how these findings could be used to define a performance index proportional to stance disability.

An integrated biomechanical-bioenergetic technique for evaluation of human locomotion.

The existing locomotion evaluation techniques are based either on bioenergetic measurements or on biomechanical principles and procedures. While the former basis has been preferred by a few, published literature in this field has contained more information on the latter. No significant work combining both the approaches in a single measurement system is known so far. This paper reports an investigation where an attempt was made to combine both the techniques in relation to a mixed handicapped population composed of 10 test subjects as well as a matching control group. It has been found from the present investigation that the combined application of the above two types of measurements might lead to more useful information about the performance level of the subjects in locomotion. Further, the statistical analysis of the experimental data revealed the existance of a linear correlationship among the parameters used for such performance evaluation. Finally, the study findings have been discussed to indicate how the biomechanical measurements alone can throw sufficient light on the subject's locomotor status.