Search results for: Prasanna Gopinath

Authors: B. M. Vishal, Monamie Basu, Gopinath M., Rose Havilah Pulla

Abstract:

Though there are several methods of synthesizing silver nano particles, Green synthesis always has its own dignity. Ranging from the cost-effectiveness to the ease of synthesis, the process is simplified in the best possible way and is one of the most explored topics. This study of extracting secondary metabolites from Perilla frutescens and using them for therapeutic additives has its own significance. Unlike the other researches that have been done so far, this study aims to synthesize Silver nano particles from Perilla frutescens using three available forms of the plant: leaves, seed, and commercial leaf extract powder. Perilla frutescens, commonly known as 'Beefsteak Plant', is a perennial plant and belongs to the mint family. The plant has two varieties classed within itself. They are frutescens crispa and frutescens frutescens. The species, frutescens crispa (commonly known as 'Shisho' in Japanese), is generally used for edible purposes. Its leaves occur in two forms, varying on the colors. It is found in two different colors of red with purple streaks and green with crinkly pattern on it. This species is aromatic due to the presence of two major compounds: polyphenols and perillaldehyde. The red (purple streak) variety of this plant is due to the presence of a pigment, Perilla anthocyanin. The species, frutescens frutescens (commonly known as 'Egoma' in Japanese), is the main source for perilla oil. This species is also aromatic, but in this case, the major compound which gives the aroma is Perilla ketone or egoma ketone. Shisho grows short as compared with Wild Sesame and both produce seeds. The seeds of Wild Sesame are large and soft whereas that of Shisho is small and hard. The seeds have a large proportion of lipids, ranging about 38-45 percent. Excluding those, the seeds have a large quantity of Omega-3 fatty acids, linoleic acid, and an Omega-6 fatty acid. Other than these, Perilla leaf extract has gold and silver nano particles in it. The yield comparison in all the cases have been done, and the process’ optimal conditions were modified, keeping in mind the efficiencies. The characterization of secondary metabolites includes GC-MS and FTIR which can be used to identify the components of purpose that actually helps in synthesizing silver nano particles. The analysis of silver was done through a series of characterization tests that include XRD, UV-Vis, EDAX, and SEM. After the synthesis, for being used as therapeutic additives, the toxin analysis was done, and the results were tabulated. The synthesis of silver nano particles was done in a series of multiple cycles of extraction from leaves, seeds and commercially purchased leaf extract. The yield and efficiency comparison were done to bring out the best and the cheapest possible way of synthesizing silver nano particles using Perilla frutescens. The synthesized nano particles can be used in therapeutic drugs, which has a wide range of application from burn treatment to cancer treatment. This will, in turn, replace the traditional processes of synthesizing nano particles, as this method will prove effective in terms of cost and the environmental implications.

Keywords: nanoparticles, green synthesis, Perilla frutescens, characterisation, toxin analysis

Procedia PDF Downloads 201

Authors: Yogesh Kumar Singh, T. S. Murugesh Prabhu, Upasana Dutta, Girishchandra Yendargaye, Rahul Yadav, Rohini Gopinath Kale, Binay Kumar, Manoj Khare

Abstract:

The Indian subcontinent is severely affected by floods that cause intense irreversible devastation to crops and livelihoods. With increased incidences of floods and their related catastrophes, an Early Warning System for Flood Prediction and an efficient Flood Management System for the river basins of India is a must. Accurately modeled hydrological conditions and a web-based early warning system may significantly reduce economic losses incurred due to floods and enable end users to issue advisories with better lead time. This study describes the design and development of an EWS-FP using advanced computational tools/methods, viz. High-Performance Computing (HPC), Remote Sensing, GIS technologies, and open-source tools for the Mahanadi River Basin of India. The flood prediction is based on a robust 2D hydrodynamic model, which solves shallow water equations using the finite volume method. Considering the complexity of the hydrological modeling and the size of the basins in India, it is always a tug of war between better forecast lead time and optimal resolution at which the simulations are to be run. High-performance computing technology provides a good computational means to overcome this issue for the construction of national-level or basin-level flash flood warning systems having a high resolution at local-level warning analysis with a better lead time. High-performance computers with capacities at the order of teraflops and petaflops prove useful while running simulations on such big areas at optimum resolutions. In this study, a free and open-source, HPC-based 2-D hydrodynamic model, with the capability to simulate rainfall run-off, river routing, and tidal forcing, is used. The model was tested for a part of the Mahanadi River Basin (Mahanadi Delta) with actual and predicted discharge, rainfall, and tide data. The simulation time was reduced from 8 hrs to 3 hrs by increasing CPU nodes from 45 to 135, which shows good scalability and performance enhancement. The simulated flood inundation spread and stage were compared with SAR data and CWC Observed Gauge data, respectively. The system shows good accuracy and better lead time suitable for flood forecasting in near-real-time. To disseminate warning to the end user, a network-enabled solution is developed using open-source software. The system has query-based flood damage assessment modules with outputs in the form of spatial maps and statistical databases. System effectively facilitates the management of post-disaster activities caused due to floods, like displaying spatial maps of the area affected, inundated roads, etc., and maintains a steady flow of information at all levels with different access rights depending upon the criticality of the information. It is designed to facilitate users in managing information related to flooding during critical flood seasons and analyzing the extent of the damage.

Keywords: flood, modeling, HPC, FOSS

Procedia PDF Downloads 63

Authors: Vijaya Kumar, Vidayasagar Pagilla, Abraham Joshua, Rakshith Kedambadi, Prasanna Mithra

Abstract:

Background: Post stroke rehabilitation are aimed to accelerate for optimal sensorimotor recovery, functional gain and to reduce long-term dependency. Intensive physical therapy interventions can enhance this recovery as experience-dependent neural plastic changes either directly act at cortical neural networks or at distal peripheral level (muscular components). Neuromuscular Electrical Stimulation (NMES), a traditional bottom-up approach, mirror therapy (MT), a relatively new top down approach have found to be an effective adjuvant treatment methods for lower extremity motor and functional recovery in stroke rehabilitation. However there is a scarcity of evidence to compare their therapeutic gain in stroke recovery.Aim: To compare the efficacy of neuromuscular electrical stimulation (NMES) and mirror therapy (MT) in very early phase of post stroke rehabilitation addressed to lower extremity motor recovery and balance. Design: observer blinded Randomized Clinical Trial. Setting: Neurorehabilitation Unit, Department of Physical Therapy, Tertiary Care Hospitals. Subjects: 32 acute stroke subjects with first episode of unilateral stroke with hemiparesis, referred for rehabilitation (onset < 3 weeks), Brunnstorm lower extremity recovery stages ≥3 and MMSE score more than 24 were randomized into two group [Group A-NMES and Group B-MT]. Interventions: Both the groups received eclectic approach to remediate lower extremity recovery which includes treatment components of Roods, Bobath and Motor learning approaches for 30 minutes a day for 6 days. Following which Group A (N=16) received 30 minutes of surface NMES training for six major paretic muscle groups (gluteus maximus and medius,quadriceps, hamstrings, tibialis anterior and gastrocnemius). Group B (N=16) was administered with 30 minutes of mirror therapy sessions to facilitate lower extremity motor recovery. Outcome measures: Lower extremity motor recovery, balance and activities of daily life (ADLs) were measured by Fugyl Meyer Assessment (FMA-LE), Berg Balance Scale (BBS), Barthel Index (BI) before and after intervention. Results: Pre Post analysis of either group across the time revealed statistically significant improvement (p < 0.001) for all the outcome variables for the either group. All parameters of NMES had greater change scores compared to MT group as follows: FMA-LE (25.12±3.01 vs. 23.31±2.38), BBS (35.12±4.61 vs. 34.68±5.42) and BI (40.00±10.32 vs. 37.18±7.73). Between the groups comparison of pre post values showed no significance with FMA-LE (p=0.09), BBS (p=0.80) and BI (p=0.39) respectively. Conclusion: Though either groups had significant improvement (pre to post intervention), none of them were superior to other in lower extremity motor recovery and balance among acute stroke subjects. We conclude that eclectic approach is an effective treatment irrespective of NMES or MT as an adjunct.

Keywords: balance, motor recovery, mirror therapy, neuromuscular electrical stimulation, stroke

Procedia PDF Downloads 256

Authors: Elena Kokkoni, Prasanna Kannappan, Ashkan Zehfroosh, Effrosyni Mavroudi, Kristina Strother-Garcia, James C. Galloway, Jeffrey Heinz, Rene Vidal, Herbert G. Tanner

Abstract:

Early immobility affects the motor, cognitive, and social development. Current pediatric rehabilitation lacks the technology that will provide the dosage needed to promote mobility for young children at risk. The addition of socially assistive robots in early interventions may help increase the mobility dosage. The aim of this study is to examine the feasibility of an early intervention paradigm where non-walking infants experience independent mobility while socially interacting with robots. A dynamic environment is developed where both the child and the robot interact and learn from each other. The environment involves: 1) a range of physical activities that are goal-oriented, age-appropriate, and ability-matched for the child to perform, 2) the automatic functions that perceive the child’s actions through novel activity recognition algorithms, and decide appropriate actions for the robot, and 3) a networked visual data acquisition system that enables real-time assessment and provides the means to connect child behavior with robot decision-making in real-time. The environment was tested by bringing a two-year old boy with Down syndrome for eight sessions. The child presented delays throughout his motor development with the current being on the acquisition of walking. During the sessions, the child performed physical activities that required complex motor actions (e.g. climbing an inclined platform and/or staircase). During these activities, a (wheeled or humanoid) robot was either performing the action or was at its end point 'signaling' for interaction. From these sessions, information was gathered to develop algorithms to automate the perception of activities which the robot bases its actions on. A Markov Decision Process (MDP) is used to model the intentions of the child. A 'smoothing' technique is used to help identify the model’s parameters which are a critical step when dealing with small data sets such in this paradigm. The child engaged in all activities and socially interacted with the robot across sessions. With time, the child’s mobility was increased, and the frequency and duration of complex and independent motor actions were also increased (e.g. taking independent steps). Simulation results on the combination of the MDP and smoothing support the use of this model in human-robot interaction. Smoothing facilitates learning MDP parameters from small data sets. This paradigm is feasible and provides an insight on how social interaction may elicit mobility actions suggesting a new early intervention paradigm for very young children with motor disabilities. Acknowledgment: This work has been supported by NIH under grant #5R01HD87133.

Keywords: activity recognition, human-robot interaction, machine learning, pediatric rehabilitation

Procedia PDF Downloads 269