EEG Cortical Source Feature based Hand Kinematics Decoding using Residual CNN-LSTM Neural Network

Motor kinematics decoding (MKD) using brain signal is essential to develop Brain-computer interface (BCI) system for rehabilitation or prosthesis devices. Surface electroencephalogram (EEG) signal has been widely utilized for MKD. However, kinematic decoding from cortical sources is sparsely explored. In this work, the feasibility of hand kinematics decoding using EEG cortical source signals has been explored for grasp and lift task. In particular, pre-movement EEG segment is utilized. A residual convolutional neural network (CNN) - long short-term memory (LSTM) based kinematics decoding model is proposed that utilizes motor neural information present in pre-movement brain activity. Various EEG windows at 50 ms prior to movement onset, are utilized for hand kinematics decoding. Correlation value (CV) between actual and predicted hand kinematics is utilized as performance metric for source and sensor domain. The performance of the proposed deep learning model is compared in sensor and source domain. The results demonstrate the viability of hand kinematics decoding using pre-movement EEG cortical source data

Molecular diversity analysis in selected fodder and dual purpose oat (Avena sativa L.) genotypes by using random amplified polymorphic DNA (RAPD)

Genetic variability among 15 oat genotypes comprising fodder and dual purpose oat varieties from different geographical regions was analyzed by random amplified polymorphic DNA (RAPD) marker method in Department of Genetics and Plant Breeding, College of Agriculture, Pant University of Agriculture and Technology (G.B.P.U.A. & T.), Pantnagar. The results show appreciably high genetic diversity among the oat genotypes studied. Fifteen (15) primers selected from 20 RAPD primers could amplify 259 clear and identifiable bands, of which 250 bands were polymorphic, accounting for 96.52% genetic polymorphism. All the oat genotypes studied could be distinctly divided into two major groups with the genetic distance level at 0.46 by cluster analysis based on the Jaccard’s coefficient of similarity. The cluster break indicated sufficient genetic variability among the genotypes. Clustering pattern of the varieties appeared such that it can be grouped in the genotypes suitable for the fodder purpose and the dual purpose varieties separately. Several polymorphic bands were also found in different genotypes which helped in molecular diversity analysis of these genotypes. The results found are encouraging and indicate that RAPD technique is an easy, quick and reliable technique used for molecular diversity analysis for preliminary selection.Keywords: Oats, RAPD, genetic diversity, polymorphism.African Journal of Biotechnology Vol. 12(22), pp. 3425-342

Comparative genetic diversity analysis of oat (Avena sativa L.) by microsatellite markers and morphological rainfed expressions

Equivalence was appraised between phenotypic and molecular markers (ISSR) to analyze the genetic diversity of 20 high yielding genotypes representing different geographical zones of the world. A moderate range of genetic similarity (0.84 to 0.20) was observed on the basis of 20 inter-simple sequence repeats (ISSR) markers, where it was found high (0.995 – 0.204) on the basis of 7 primary morphological rainfed expression. Genotypes in morphological character based dendogram were clustered into their respective geographic groups, while a random grouping was observed in dendogram based on the ISSR markers. A negative correlation (r = -0.186) was found among morphological and molecular marker systems, but the latter was found effective in distinguishing the genotypes using specific band positions for them. The genotypic classification agreed closely with the grouping observed in ISSR based 3D analysis.Keywords: Avena sativa, diversity, inter-simple sequence repeats (ISSR), morphology, oat, relationshipAfrican Journal of Biotechnology Vol. 12(22), pp. 3414-342

Brain Connectivity Features-based Age Group Classification using Temporal Asynchrony Audio-Visual Integration Task

The process of integration of inputs from several sensory modalities in the human brain is referred to as multisensory integration. Age-related cognitive decline leads to a loss in the ability of the brain to conceive multisensory inputs. There has been considerable work done in the study of such cognitive changes for the old age groups. However, in the case of middle age groups, such analysis is limited. Motivated by this, in the current work, EEG-based functional connectivity during audiovisual temporal asynchrony integration task for middle-aged groups is explored. Investigation has been carried out during different tasks such as: unimodal audio, unimodal visual, and variations of audio-visual stimulus. A correlation-based functional connectivity analysis is done, and the changes among different age groups including: young (18-25 years), transition from young to middle age (25-33 years), and medium (33-41 years), are observed. Furthermore, features extracted from the connectivity graphs have been used to classify among the different age groups. Classification accuracies of $89.4\%$ and $88.4\%$ are obtained for the Audio and Audio-50-Visual stimuli cases with a Random Forest based classifier, thereby validating the efficacy of the proposed method

SCLAiR : Supervised Contrastive Learning for User and Device Independent Airwriting Recognition

Airwriting Recognition is the problem of identifying letters written in free space with finger movement. It is essentially a specialized case of gesture recognition, wherein the vocabulary of gestures corresponds to letters as in a particular language. With the wide adoption of smart wearables in the general population, airwriting recognition using motion sensors from a smart-band can be used as a medium of user input for applications in Human-Computer Interaction. There has been limited work in the recognition of in-air trajectories using motion sensors, and the performance of the techniques in the case when the device used to record signals is changed has not been explored hitherto. Motivated by these, a new paradigm for device and user-independent airwriting recognition based on supervised contrastive learning is proposed. A two stage classification strategy is employed, the first of which involves training an encoder network with supervised contrastive loss. In the subsequent stage, a classification head is trained with the encoder weights kept frozen. The efficacy of the proposed method is demonstrated through experiments on a publicly available dataset and also with a dataset recorded in our lab using a different device. Experiments have been performed in both supervised and unsupervised settings and compared against several state-of-the-art domain adaptation techniques. Data and the code for our implementation will be made available at https://github.com/ayushayt/SCLAiR

ImAiR: Airwriting Recognition framework using Image Representation of IMU Signals

The problem of Airwriting Recognition is focused on identifying letters written by movement of finger in free space. It is a type of gesture recognition where the dictionary corresponds to letters in a specific language. In particular, airwriting recognition using sensor data from wrist-worn devices can be used as a medium of user input for applications in Human-Computer Interaction (HCI). Recognition of in-air trajectories using such wrist-worn devices is limited in literature and forms the basis of the current work. In this paper, we propose an airwriting recognition framework by first encoding the time-series data obtained from a wearable Inertial Measurement Unit (IMU) on the wrist as images and then utilizing deep learning-based models for identifying the written alphabets. The signals recorded from 3-axis accelerometer and gyroscope in IMU are encoded as images using different techniques such as Self Similarity Matrix (SSM), Gramian Angular Field (GAF) and Markov Transition Field (MTF) to form two sets of 3-channel images. These are then fed to two separate classification models and letter prediction is made based on an average of the class conditional probabilities obtained from the two models. Several standard model architectures for image classification such as variants of ResNet, DenseNet, VGGNet, AlexNet and GoogleNet have been utilized. Experiments performed on two publicly available datasets demonstrate the efficacy of the proposed strategy. The code for our implementation will be made available at https://github.com/ayushayt/ImAiR

Impacts of coal mine water and Damodar River water irrigation on soil and maize (Zea mays L.) in a coalfield area of Damodar Valley, India

The present investigation was carried out to assess the environmental and biochemical impacts due to irrigation of coal mine water and Damodar River water on Kharif crop, maize (Zea mays L.) in a coalfield area of Damodar Valley, India. Coal mine water and Damodar River water samples were collected for the monitoring of its quality from a coalfield area of Damodar Valley. The samples were analyzed for various parameters and compared with prescribed standard, which revealed that the total suspended solids of coal mine water were higher as Damodar River water. A pot experiment with Z. mays was conducted to study the suitability of this coal mine water for irrigation. The plants of Z. mays in the pots were irrigated with coal mine water and Damodar River water in two concentrations (100% and 50% dilution with double distilled water) and pure double distilled water was used for control. There was 100% germination of Z. mays in all the treatments. The plant growth, chlorophyll content of Z. mays and soil quality parameters were significantly better in coal mine water and Damodar River water treated pots. However, the Damodar River water and coal mine water could be successfully used for irrigation. In general, coal mine water and Damodar River water can be used after mixing with good quality of water has shown better growth of Z. mays

Seed treatments for sustainable agriculture-A review

Seed treatment refers to the application of certain agents physical, chemical or biological to the seed prior to sowing in order to suppress, control or repel pathogens, insects and other pests that attack seeds, seedlings or plants and it ranges from a basic dressing to coating and pelleting. Introduction and ban of arsenic (used from 1740 until 1808) is the key milestones in the history of modern seed treatment till then a continuous research and advancement in this technology is going on. The technological advancement prepared a roadmap for refiningexisting seed treatment technologies and future work on technologies like fluid drilling as a way to sow germinated seeds where gel can also serve as a delivery system for other materials, seed priming advances the early phase of germination without redicle emergence. Another advanced technology, solid matrix priming (SMP) has been evaluated as a means to advances the germination of seeds and serve as a carrier for useful material too. Physical and biological seed treatments alone an alternative to chemicals or in combination with a chemical treatment are being used worldwide because of their environmental safety and socioeconomic aspects. Biological seed treatments are expected to be one of the fastest growing seed treatment sectors in the near future, in part because they are easier to register at Environment Protection Agency (EPA). Lack of awareness to seed treatments at farmer’s level is one of the limiting factors in disease management and hence, efforts should be made at farmer’s level to adopt the technology. Keeping the all above facts in mind, selected seed treatment technologies with their improvement and significance will be discussed in this review

Analyzing and Predicting Low-Listenership Trends in a Large-Scale Mobile Health Program: A Preliminary Investigation

Mobile health programs are becoming an increasingly popular medium for dissemination of health information among beneficiaries in less privileged communities. Kilkari is one of the world's largest mobile health programs which delivers time sensitive audio-messages to pregnant women and new mothers. We have been collaborating with ARMMAN, a non-profit in India which operates the Kilkari program, to identify bottlenecks to improve the efficiency of the program. In particular, we provide an initial analysis of the trajectories of beneficiaries' interaction with the mHealth program and examine elements of the program that can be potentially enhanced to boost its success. We cluster the cohort into different buckets based on listenership so as to analyze listenership patterns for each group that could help boost program success. We also demonstrate preliminary results on using historical data in a time-series prediction to identify beneficiary dropouts and enable NGOs in devising timely interventions to strengthen beneficiary retention.Comment: Accepted to Data Science for Social Good Workshop, KDD 202