Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 7

Search results for: Vineetha Cherian

7 Effects of Plant Growth Promoting Rhizobacteria on the Yield and Nutritive Quality of Tomato Fruits

Authors: Narjes Dashti, Nida Ali, Magdy Montasser, Vineetha Cherian

Abstract:

The influence of two PGPR strains, Pseudomonas aeruginosa and Stenotrophomonas rhizophilia, on fruit yields, pomological traits and chemical contents of tomato (Solanum lycopersicum) fruits were studied. The study was conducted separately on two different cultivar varieties of tomato, namely Supermarmande and UC82B. The results indicated that the presence of the PGPR almost doubled the average yield per plant. There was a significant improvement in the pomological qualities of the PGPR treated tomato fruits compared to the corresponding healthy treatments especially in traits such as the average fruit weight, height, and fruit volume. The chemical analysis of tomato fruits revealed that the presence of the PGPRs increased the total protein, lycopene, alkalinity and phenol content of the tomato fruits compared to the healthy controls. They had no influence on the reduced sugar, total soluble solids or the titerable acid content of fruits. However their presence reduced the amount of ascorbic acid in tomato fruits compared to the healthy controls.

Keywords: PGPR, tomato, fruit quality

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6 Hierarchical Piecewise Linear Representation of Time Series Data

Authors: Vineetha Bettaiah, Heggere S. Ranganath

Abstract:

This paper presents a Hierarchical Piecewise Linear Approximation (HPLA) for the representation of time series data in which the time series is treated as a curve in the time-amplitude image space. The curve is partitioned into segments by choosing perceptually important points as break points. Each segment between adjacent break points is recursively partitioned into two segments at the best point or midpoint until the error between the approximating line and the original curve becomes less than a pre-specified threshold. The HPLA representation achieves dimensionality reduction while preserving prominent local features and general shape of time series. The representation permits course-fine processing at different levels of details, allows flexible definition of similarity based on mathematical measures or general time series shape, and supports time series data mining operations including query by content, clustering and classification based on whole or subsequence similarity.

Keywords: data mining, dimensionality reduction, piecewise linear representation, time series representation

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5 Increased Expression Levels of Soluble Epoxide Hydrolase in Obese and Its Modulation by Physical Exercise

Authors: Abdelkrim Khadir, Sina Kavalakatt, Preethi Cherian, Ali Tiss

Abstract:

Soluble epoxide hydrolase (sEH) is an emerging therapeutic target in several chronic states that have inflammation as a common underlying cause such as immunometabolic diseases. Indeed, sEH is known to play a pro-inflammatory role by metabolizing anti-inflammatory, epoxyeicosatrienoic acids (EETs) to pro-inflammatory diols. Recently, it was shown sEH to be linked to diet and microbiota interaction in rat models of obesity. Nevertheless, the functional contribution of sEH and its anti-inflammatory substrates EETs in obesity remain poorly understood. In the current study, we compared the expression pattern of sEH between lean and obese nondiabetic human subjects using subcutaneous adipose tissue (SAT) and peripheral blood mononuclear cells (PBMCs). Using RT-PCR, western blot and immunofluorescence confocal microscopy, we show here that the level of sEH mRNA and protein to be significantly increased in obese subjects with concomitant increase in endoplasmic reticulum (ER) stress components (GRP78 and ATF6α) and inflammatory markers (TNF-α, IL-6) when compared to lean controls. The observation that sEH was overexpressed in obese subjects’ prompt us to investigate whether physical exercise could reduce its expression. In this study, we report here 3-months supervised physical exercise significantly attenuated the expression of sEH in both the SAT and PBMCs, with a parallel decrease in the expression of ER stress markers along with attenuated inflammatory response. On the other hand, homocysteine, a sulfur containing amino acid deriving from the essential amino acid methionine was shown to be directly associated with insulin resistance. When 3T3-L1 preadipocytes cells were treated with homocysteine our results show increased sEH levels along with ER stress markers. Collectively, our data suggest that sEH upregulation is strongly linked to ER stress in adiposity and that physical exercise modulates its expression. This gives further evidence that exercise might be useful as a strategy for managing obesity and preventing its associated complications.

Keywords: obesity, adipose tissue, epoxide hydrolase, ER stress

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4 Variation in N₂ Fixation and N Contribution by 30 Groundnut (Arachis hypogaea L.) Varieties Grown in Blesbokfontein Mpumalanga Province, South Africa

Authors: Titus Y. Ngmenzuma, Cherian. Mathews, Feilx D. Dakora

Abstract:

In Africa, poor nutrient availability, particularly N and P, coupled with low soil moisture due to erratic rainfall, constitutes the major crop production constraints. Although inorganic fertilizers are an option for meeting crop nutrient requirements for increased grain yield, the high cost and scarcity of inorganic inputs make them inaccessible to resource-poor farmers in Africa. Because crops grown on such nutrient-poor soils are micronutrient deficient, incorporating N₂-fixing legumes into cropping systems can sustainably improve crop yield and nutrient accumulation in the grain. In Africa, groundnut can easily form an effective symbiosis with native soil rhizobia, leading to marked N contribution in cropping systems. In this study, field experiments were conducted at Blesbokfontein in Mpumalanga Province to assess N₂ fixation and N contribution by 30 groundnut varieties during the 2018/2019 planting season using the ¹⁵N natural abundance technique. The results revealed marked differences in shoot dry matter yield, symbiotic N contribution, soil N uptake and grain yield among the groundnut varieties. The percent N derived from fixation ranged from 37 to 44% for varieties ICGV131051 and ICGV13984. The amount of N-fixed ranged from 21 to 58 kg/ha for varieties Chinese and IS-07273, soil N uptake from 31 to 80 kg/ha for varieties IS-07947 and IS-07273, and grain yield from 193 to 393 kg/ha for varieties ICGV15033 and ICGV131096, respectively. Compared to earlier studies on groundnut in South Africa, this study has shown low N₂ fixation and N contribution to the cropping systems, possibly due to environmental factors such as low soil moisture. Because the groundnut varieties differed in their growth, symbiotic performance and grain yield, more field testing is required over a range of differing agro-ecologies to identify genotypes suitable for different cropping environments

Keywords: ¹⁵N natural abundance, percent N derived from fixation, amount of N-fixed, grain yield

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3 Developing Sustainable Rammed Earth Material Using Pulp Mill Fly Ash as Cement Replacement

Authors: Amin Ajabi, Chinchu Cherian, Sumi Siddiqua

Abstract:

Rammed earth (RE) is a traditional soil-based building material made by compressing a mixture of natural earth and binder ingredients such as chalk or lime, in temporary formworks. However, the modern RE uses 5 to 10% cement as a binder in order to meet the strength and durability requirements as per the standard specifications and guidelines. RE construction is considered to be an energy-efficient and environmental-friendly approach when compared to conventional concrete systems, which use 20 to 30% cement. The present study aimed to develop RE mix designs by utilizing non-hazardous wood-based fly ash generated by pulp and paper mills as a partial replacement for cement. The pulp mill fly ash (PPFA)-stabilized RE is considered to be a sustainable approach keeping in view of the massive carbon footprints associated with cement production as well as the adverse environmental impacts due to disposal of PPFA in landfills. For the experimental study, as-received PPFA, as well as PPFA-based geopolymer (synthesized by alkaline activation method), were incorporated as cement substitutes in the RE mixtures. Initially, local soil was collected and characterized by index and engineering properties. The PPFA was procured from a pulp manufacturing mill, and its physicochemical, mineralogical and morphological characterization, as well as environmental impact assessment, was conducted. Further, the various mix designs of RE material incorporating local soil and different proportions of cement, PPFA, and alkaline activator (a mixture of sodium silicate and sodium hydroxide solutions) were developed. The compacted RE specimens were cured and tested for 7-day and 28-day unconfined compressive strength (UCS) variations. Based on UCS results, the optimum mix design was identified corresponding to maximum strength improvement. Further, the cured RE specimens were subjected to freeze-thaw cycle testing for evaluating its performance and durability as a sustainable construction technique under extreme climatic conditions.

Keywords: sustainability, rammed earth, stabilization, pulp mill fly ash, geopolymer, alkaline activation, strength, durability

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2 Comparative Analysis of Reinforcement Learning Algorithms for Autonomous Driving

Authors: Migena Mana, Ahmed Khalid Syed, Abdul Malik, Nikhil Cherian

Abstract:

In recent years, advancements in deep learning enabled researchers to tackle the problem of self-driving cars. Car companies use huge datasets to train their deep learning models to make autonomous cars a reality. However, this approach has certain drawbacks in that the state space of possible actions for a car is so huge that there cannot be a dataset for every possible road scenario. To overcome this problem, the concept of reinforcement learning (RL) is being investigated in this research. Since the problem of autonomous driving can be modeled in a simulation, it lends itself naturally to the domain of reinforcement learning. The advantage of this approach is that we can model different and complex road scenarios in a simulation without having to deploy in the real world. The autonomous agent can learn to drive by finding the optimal policy. This learned model can then be easily deployed in a real-world setting. In this project, we focus on three RL algorithms: Q-learning, Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO). To model the environment, we have used TORCS (The Open Racing Car Simulator), which provides us with a strong foundation to test our model. The inputs to the algorithms are the sensor data provided by the simulator such as velocity, distance from side pavement, etc. The outcome of this research project is a comparative analysis of these algorithms. Based on the comparison, the PPO algorithm gives the best results. When using PPO algorithm, the reward is greater, and the acceleration, steering angle and braking are more stable compared to the other algorithms, which means that the agent learns to drive in a better and more efficient way in this case. Additionally, we have come up with a dataset taken from the training of the agent with DDPG and PPO algorithms. It contains all the steps of the agent during one full training in the form: (all input values, acceleration, steering angle, break, loss, reward). This study can serve as a base for further complex road scenarios. Furthermore, it can be enlarged in the field of computer vision, using the images to find the best policy.

Keywords: autonomous driving, DDPG (deep deterministic policy gradient), PPO (proximal policy optimization), reinforcement learning

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1 Constitutive Androstane Receptor (CAR) Inhibitor CINPA1 as a Tool to Understand CAR Structure and Function

Authors: Milu T. Cherian, Sergio C. Chai, Morgan A. Casal, Taosheng Chen

Abstract:

This study aims to use CINPA1, a recently discovered small-molecule inhibitor of the xenobiotic receptor CAR (constitutive androstane receptor) for understanding the binding modes of CAR and to guide CAR-mediated gene expression profiling studies in human primary hepatocytes. CAR and PXR are xenobiotic sensors that respond to drugs and endobiotics by modulating the expression of metabolic genes that enhance detoxification and elimination. Elevated levels of drug metabolizing enzymes and efflux transporters resulting from CAR activation promote the elimination of chemotherapeutic agents leading to reduced therapeutic effectiveness. Multidrug resistance in tumors after chemotherapy could be associated with errant CAR activity, as shown in the case of neuroblastoma. CAR inhibitors used in combination with existing chemotherapeutics could be utilized to attenuate multidrug resistance and resensitize chemo-resistant cancer cells. CAR and PXR have many overlapping modulating ligands as well as many overlapping target genes which confounded attempts to understand and regulate receptor-specific activity. Through a directed screening approach we previously identified a new CAR inhibitor, CINPA1, which is novel in its ability to inhibit CAR function without activating PXR. The cellular mechanisms by which CINPA1 inhibits CAR function were also extensively examined along with its pharmacokinetic properties. CINPA1 binding was shown to change CAR-coregulator interactions as well as modify CAR recruitment at DNA response elements of regulated genes. CINPA1 was shown to be broken down in the liver to form two, mostly inactive, metabolites. The structure-activity differences of CINPA1 and its metabolites were used to guide computational modeling using the CAR-LBD structure. To rationalize how ligand binding may lead to different CAR pharmacology, an analysis of the docked poses of human CAR bound to CITCO (a CAR activator) vs. CINPA1 or the metabolites was conducted. From our modeling, strong hydrogen bonding of CINPA1 with N165 and H203 in the CAR-LBD was predicted. These residues were validated to be important for CINPA1 binding using single amino-acid CAR mutants in a CAR-mediated functional reporter assay. Also predicted were residues making key hydrophobic interactions with CINPA1 but not the inactive metabolites. Some of these hydrophobic amino acids were also identified and additionally, the differential coregulator interactions of these mutants were determined in mammalian two-hybrid systems. CINPA1 represents an excellent starting point for future optimization into highly relevant probe molecules to study the function of the CAR receptor in normal- and pathophysiology, and possible development of therapeutics (for e.g. use for resensitizing chemoresistant neuroblastoma cells).

Keywords: antagonist, chemoresistance, constitutive androstane receptor (CAR), multi-drug resistance, structure activity relationship (SAR), xenobiotic resistance

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