OPTIMIZATION OF BIOMASS CULTURE YIELD AND L-DOPA COMPOUND IN THE CALLUS CULTURE FROM COTYLEDONARY LEAVES OF MUCUNA PRURIENS

ABSTRACTObjective:  The objective of the present study was to evaluate the optimization of callus biomass culture yield and high-performance liquid chromatography (HPLC) analysis of L-DOPA compound in the callus culture from cotyledonary leaves of Mucuna pruriensMethods : M. pruriens seed explants were inoculated onto Murashige and Skoog medium supplemented with different concentrations of 2-isopentenyl adenine (2iP) and Gibberellic acid (GA3)  for germination of plants. The in vitro cotyledonary leaf and hypocotyl explants were cultured on MS basal media containing various concentrations of 2,4-dichlorophenoxyacetic acid (2,4-D), 1- Naphthaleneacetic acid (NAA), 6-Benzylaminopurine (BA) and 2iP for callus induction. A standard approach of Latin square method was followed for screening of media to establish optimum culturing of callus by manipulating the concentration of auxins (2, 4-D, Indole-3-acetic acid (IAA) and NAA) and cytokinins (BA and 2iP) alone and in combinations. The harvested callus biomass was screened for a major metabolite namely L- Dopa compound using HPLC.Results: Cotyledonary leaf explants showed better callus initiation than hypocotyl explants. Maximum callus induction was observed in Murashige and Skoog (MS) medium containing 4.92µM 2iP. Further screening of callus culture was carried out on MS medium supplemented with different concentrations and combinations of 2, 4-D, NAA,  IAA, (BA)  and 2iP individually and in combinations. Optimum callus biomass of 21.63 g/L dry weight (246.31 g/L fresh weight) was developed on MS media containing 2.26µM 2, 4-D, 2.22µM BA and 4.92µM 2iP. The harvested callus biomass was subjected to extraction and purification of L- Dopa compound.Conclusion: The present study concludes that  HPLC analysis of cell biomass extracts in comparison  with extracts from seeds of mother plants of Mucuna prurienss showed main component of L- Dopa was present in sufficiently large amounts in the undifferentiated cultured cells.Keywords: Mucuna pruriens, Callus biomass, L-Dopa, HPLC analysi

A Reusable Interaction Management Module: Use case for Empathic Robotic Tutoring

We demonstrate the workings of a stochastic Interaction Management and showcase this working as part of a learning environment that includes a robotic tutor who interacts with students, helping them through a pedagogical task

WoZ Pilot Experiment for Empathic Robotic Tutors: Opportunities and Challenges

We discuss the challenges and opportunities in building empathic robotic tutors based on a preliminary Wizard-of-Oz (WoZ) pilot study. From the data collected in this study, we identify situations where empathy in a robotic tutor could have helped the conversation between the learner and the tutor. The video presented with this paper captures these situations where two children participants are interacting with a map application and a robot tutor operated by a wizard

How Expressiveness of a Robotic Tutor is Perceived by Children in a Learning Environment

We present a study investigating the expressiveness of two different types of robots in a tutoring task. The robots used were i) the EMYS robot, with facial expression capabilities, and ii) the NAO robot, without facial expressions but able to perform expressive gestures. Preliminary results show that the NAO robot was perceived to be more friendly, pleasant and empathic than the EMYS robot as a tutor in a learning environment

STUDIES ON PHYTOCHEMICAL SCREENING, TANNINS CONTENT AND ANTIBACTERIAL ACTIVITY FROM LEAF AND CALLUS EXTRACTS OF MEMECYLON UMBELLATUM.

Objective: This study aims to determine the phytochemical screening, tannins content, and antibacterial activity from leaf and callus extracts of Memecylon umbellatum.Methods: Preliminary screening involved the qualitative methods to detect the presence of terpenoids, flavonoids, phenols, tannins, steroids, quinones, saponins, cardiac glycosides, and alkaloids. Total tannins contents were quantitatively estimated with tannic acid as standard. Different concentrations of ethanolic leaf and callus extracts were tested using the agar disc diffusion technique for the antibacterial activity against Bacillus subtilis, Bacillus cereus, Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli.Results: The phytochemical analysis of leaf and callus extract of M. umbellatum revealed the presence of significant secondary metabolites such as tannins, saponins, quinones, cardiac glycosides, phenols, flavonoids, terpenoids, steroids, and alkaloids. The total tannins content in callus and leaf extract were found to be 11.37 mg tannic acid equivalents (TAE)/g and 7.1 mg TAE/g, respectively. The antibacterial activity of ethanolic leaf and callus extracts of M. umbellatum shown more active against B. subtilis. Both the callus and leaf extract of M. umbellatum was found to be inactiveagainst E. coli.Conclusion: It was concluded that the powerful antibacterial effect is attributed to the greater amount of tannin compounds in the ethanolic callus extracts of M. umbellatum.Â

Neural Networks for Constitutive Modeling -- From Universal Function Approximators to Advanced Models and the Integration of Physics

Analyzing and modeling the constitutive behavior of materials is a core area in materials sciences and a prerequisite for conducting numerical simulations in which the material behavior plays a central role. Constitutive models have been developed since the beginning of the 19th century and are still under constant development. Besides physics-motivated and phenomenological models, during the last decades, the field of constitutive modeling was enriched by the development of machine learning-based constitutive models, especially by using neural networks. The latter is the focus of the present review, which aims to give an overview of neural networks-based constitutive models from a methodical perspective. The review summarizes and compares numerous conceptually different neural networks-based approaches for constitutive modeling including neural networks used as universal function approximators, advanced neural network models and neural network approaches with integrated physical knowledge. The upcoming of these methods is in-turn closely related to advances in the area of computer sciences, what further adds a chronological aspect to this review. We conclude this review paper with important challenges in the field of learning constitutive relations that need to be tackled in the near future

Surgical Lift for Dr. Muraszko

Two previous ME 450 student teams have successfully designed and built a surgical lift for Dr. Karin Muraszko, Chair of the University of Michigan Neurosurgery Department, who overcame Spina Bifida to become a top neurosurgeon in the world. Dr. Muraszko would now like a new seat for the surgical lift that she can use during her surgeries. This project is aimed reproducing the old lift, while choosing new medical grade casters and fabricating a novel, improved model of the seat for Dr. Muraszko. Neurosurgeons are typically involved in long operations that usually last for 12 hours. During surgery, the stability of the surgeon’s body is critical. A seat on the lift can help Dr. Muraszko perform her surgeries in greater comfort. If successful, this seat can be used by other surgeons in the future.Albert Shih (Mechanical Engineering) and Karin Muraszko (Department of Neurosurgery)http://deepblue.lib.umich.edu/bitstream/2027.42/91778/1/me450w10project15_report.pd

Learning user modelling strategies for adaptive referring expression generation in spoken dialogue systems

We address the problem of dynamic user modelling for referring expression generation in spoken dialogue systems, i.e how a spoken dialogue system should choose referring expressions to refer to domain entities to users with different levels of domain expertise, whose domain knowledge is initially unknown to the system. We approach this problem using a statistical planning framework: Reinforcement Learning techniques in Markov Decision Processes (MDP). We present a new reinforcement learning framework to learn user modelling strategies for adaptive referring expression generation (REG) in resource scarce domains (i.e. where no large corpus exists for learning). As a part of the framework, we present novel user simulation models that are sensitive to the referring expressions used by the system and are able to simulate users with different levels of domain knowledge. Such models are shown to simulate real user behaviour more closely than baseline user simulation models. In contrast to previous approaches to user adaptive systems, we do not assume that the user’s domain knowledge is available to the system before the conversation starts. We show that using a small corpus of non-adaptive dialogues it is possible to learn an adaptive user modelling policy in resource scarce domains using our framework. We also show that the learned user modelling strategies performed better in terms of adaptation than hand-coded baselines policies on both simulated and real users. With real users, the learned policy produced around 20% increase in adaptation in comparison to the best performing hand-coded adaptive baseline. We also show that adaptation to user’s domain knowledge results in improving task success (99.47% for learned policy vs 84.7% for hand-coded baseline) and reducing dialogue time of the conversation (11% relative difference). This is because users found it easier to identify domain objects when the system used adaptive referring expressions during the conversations

Learning Adaptive Referring Expression Generation Policies for Spoken Dialogue Systems using Reinforcement Learning

Abstract Adaptive generation of referring expressions in dialogues is beneficial in terms of grounding between the dialogue partners. However, handcoding adaptive REG policies is hard. We present a reinforcement learning framework to automatically learn an adaptive referring expression generation policy for spoken dialogue systems