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There are many way to by which human being communicate with each other verbal, sign language or facial expression are one of them. Here we propose artificial intelligence based facial expression detection system by which we can detect behaviour of human being by computer system. Facial expression detection have lot of real life applications including computer to human interaction, Human behaviour reorganisation, cognitive study, emotion analysis, personality development etc. Here we set a study which uses a new technique for facial expressions using single frame uses combination of vector and geometrical feature based classification method Generally to study human face the image of face is divided into small grid of pixels . In this Study we have divided whole face into sub region of local regions. This technique acquire most important sub regions of the domain and start collecting most important sub regions incrementally which enhance accuracy and speed. The consequences of looks acknowledgment utilizing highlights from space explicit locations , likewise contrasted and the outcomes acquired utilizing all encompassing portrayal. The presentation of the proposed look acknowledgment framework has been approved on openly accessible expanded Cohn-Kanade (CK+) look informational collections.

Food is consumed to provide nutritional support to an organism. The primary source of food and feed are field crops such as wheat, rice, maize, barley, sorghum, soybean and sugarcane etc. These field crops are a rich source of carbohydrates and cellulosic biomass which help in ethanol production. With the passage of time, agriculture facing the major issue of improving the productivity of field crops in an environmentally sustainable manner. The Productivity of field crops is mainly affected by the vulnerability of pests and diseases and also through annual climate variables such as temperature extremes, droughts and floods. Crop improvement through conventional breeding methods is very expensive, tedious and time-consuming. Therefore, these cannot be utilized to meet progressive demands and living standard of the present world. Genetic and epigenetic modifications support the improvement of plant varieties concerning for nutritional quality and yield by creating novel epialleles, transgenic RNAi. Present review article describes the genetic and epigenetic improvement of crops to various biotic and abiotic stress resistance.

Food is indispensable for fulfilling the nutritional requirements of living beings. The principal source of food, nutrition and feed are horticultural commodities. Grains, pulses, fruits, vegetables, flowers, ornamental plants and medicinal plants are just a few of the important horticulture crops that are widely grown as cash crops by farmers and to meet these needs. However, eventually, horticulture crops are facing major challenges to enhance the productivity of field crops due to changes in environmental conditions. Crops are affected by many environmental stresses including both biotic as well as abiotic. Environmental stresses affect the overall growth, development, physiology and metabolism of plants which leads to the loss of crop quality produce and yield. Old approaches to decipher the mechanism of stress management and overall improvement in plants are involved with tedious experiments, long breeding programs and different cultivation environments. With the advancement in the field of bioinformatics and next-generation sequencing technologies, it has become possible to study the molecular mechanisms of plants coping with stresses. It is the need of the hour to investigate the mechanism of stress tolerance in economically important horticultural crops. This area has sought the attention of horticulturists as many regions are under the spell of environmental alterations and climate change. Thus, this review takes up the genetic and epigenetic aspects of stress management in high-value horticultural fruit and vegetable plants.

Secondary metabolites obtained from natural sources have played an important role in health care in modern as well as ancient times. Around 60% of recently approved drugs are prepared using various natural sources. For example, the various traditional medicinal systems like Ayurveda, Yunani and Traditional Chinese Medicines etc., make use of various plant species and their extracts for treatment of numerous human disorders. Picrorhiza kurroa Royle ex Benth. is one of the most employed plant species in traditional medicinal practices of the Himalayan region, specifically, for treatment of various immune related disorders. Furthermore, P. kurroa is widely known for its diverse pharmacological properties: anticancer, hepatoprotective, immunomodulatory, antimicrobial, antioxidant, antiallergic, anti-asthmatic etc. Further, iridoids, terpenoids, flavonoids, phenolic acids, steroids and their glycosides are the key classes of secondary metabolites reported in P. kurroa. However, recent overexploitation of this plant, owing to its wide range of therapeutic potential places it among the list of the endangered medicinal herbs of the Himalayas. Consequently, this review presents up to date data on conservation, agro-techniques, biotechnology, sustainable harvesting, traditional uses, phytochemistry and pharmacological potential of P. kurroa, an important medicinal herb of the western Himalayas.

The amount of data available and information over the past few decades has grown manifold and will only increase exponentially. The ability to harvest and manipulate information from this data has become a crucial activity for effective and faster development. Multiple algorithms and approaches have been developed in order to harvest information from this data. These algorithms have different approaches and therefore result in varied outputs in terms of performance and interpretation. Due to their functionality, different algorithms perform differently on different datasets. In order to compare the effectiveness of these algorithms, they are run on different datasets under a given set of fixed restrictions (e.g., hardware platform, etc.). This paper is an in-depth analysis of different algorithms based on trivial classifier algorithm, kNN, and the newly developed ARSkNN. The algorithms were executed on three different datasets, and analysis was done by evaluating their performance taking into consideration the accuracy percentage and execution time as performance measures.

Premature leaf fall caused by Marssonina coronaria is one of the most destructive diseases of apple in India. In this study, host induced gene silencing approach was exploited to develop resistance to this disease in an apple cultivar ‘Red Chief’. Calmodulin gene (CaM) having its role in fungal differentiation, development and pathogenicity was selected as target. hpRNAi construct was prepared from the conserved off target free partial gene sequence of CaM and used for transformation trials. Upto 6% kanamycin resistant shoots were obtained on selective medium having 5–6 mg/l kan after 7 weeks of coculture. In PCR analysis of 13 RNAi putative transformants, 10 lines were found positive with CaM and nptII gene specific primers and six lines showed hybridization signal. Semi qRT-PCR revealed variable levels of transgene expression among RNAi lines which seems to be related to copy number of integrated gene. In vitro detached leaf assay revealed lesion development and disease progression in wild type after 5 dpi but not visible in five CaM RNAi lines. Microscopic examination of infected control leaves showed fully developed, septate mycelium, and conidia along with necrosis of whole tissue while three transformants showed reduced growth and differentiation of fungus and in rest three, hyphal development and necrosis were strongly restricted. We conclude that trafficking of dsRNA/ siRNA from apple plants to pathogen might have triggered the down regulation of fungal CaM gene which confirms that deciphering the role of CaM through HIGS lead to resistance to Marssonina blotch in apple.

Premature leaf fall of apple caused by Marssonina coronaria is economically very important apple disease and all the commercially available apple cultivars are susceptible to this disease. The non-availability of an efficient transformation system for this fungus hinders the functional genomics research. Herein, we report for the first time, the successful Agrobacterium-mediated transformation in apple leaf blotch fungus M. coronaria by transferring T-DNA harbouring the genes for hygromycin phosphotransferase (hpt), β-glucuronidase (uidA) and green fluorescent protein (gfp) under the control of CaMV 35S promoter. The key factors that affect the transformation efficiency including type of recipient fungal material, acetosyringone concentration, the conditions for co-cultivation, Agrobacterium concentration, Agrobacterium strains and membrane types as support were investigated. The present results have recommended that 250 μM concentration of acetosyringone, 24 °C temperature and 48 h time, 0.5 OD600 of A. tumefaciens, EHA105 Agrobacterium strain and Whatman filter paper were the optimal co-cultivation conditions for the transformation of M. coronaria by using fragmented mycelia suspension and mycelial plugs. We observed that conidia were tedious to transform as compared to the fragmented mycelia and mycelial plugs of this slow growing fungus. These optimized parameters yielded 54 and 70 average transformants per 60 mycelial plugs and 104 fragmented mycelia, respectively. Fungal transformants were analysed for T-DNA integration, gus gene expression and gfp gene expression. Strong gus histochemical staining and green fluorescence expression indicated that the CaMV 35S promoter can drive gene expression in M. croronaria. Some mutants showed difference in the morphology of the colony as compared to the wild type control. This report will be very useful to inspect molecular basis of apple-M. coronaria interactions by deciphering the functional roles of various genes in this pathogenic fungus.

Marssonina coronaria causes apple blotch disease resulting in severe premature defoliation, and is distributed in many leading apple-growing areas in the world. Effective, reliable and high-quality RNA extraction is an indispensable procedure in any molecular biology study. No method currently exists for RNA extraction from M. coronaria that produces a high quantity of melanin-free RNA. Therefore, we evaluated eight RNA extraction methods including manual and commercial kits, to yield a sufficient quantity of high-quality and melanin-free RNA. Manual methods used here resulted in low quality and black colored RNA pellets showing the presence of melanin, despite all the modifications employed to original procedures. However, these methods when coupled with clean up resulted in melanin-free RNA. On the other hand, all commercial kits used were able to yield high-quality melanin-free RNA having variable yields. TRIzol™ Reagent + RNA Clean & Concentrator™-5 and Ambion-PureLink® RNA Mini Kit were found to be the best methods as the RNA extracted with these methods from 15 day old fungal culture grown on solid medium were free of melanin with good yield. RNA extracted by this improved methodology was applied for RT-PCR, subsequent PCR amplification, and isolation of calmodulin gene sequences from M. coronaria and infected apple leaf pieces. These methods are more time effective than traditional methods and take only an hour to complete. To our knowledge, this is the first report on the method of isolation of high-quality RNA for cDNA synthesis as well as isolation of the calmodulin gene sequence from this fungus.

River width is an essential parameter for studying the river’s hydrological process and has been widely used to estimate the river discharge. The existing approaches to measuring river width are based on remotely sensed imagery such as MODIS, Landsat to identify the river, and then estimate the river width. In this work, an alternate approach for river width estimation is proposed using the under-explored modality Synthetic Aperture Radar (SAR) images. SAR, unlike the traditional electro-optical sensors, can penetrate the clouds and can be used to collect the data in all weather conditions and even during the night. In this work, the river identification process is manifested as a binary semantic segmentation task in SAR images. For this, two state of the art deep learning algorithms (U-Net, DeepLabV3+) are utilized for river identification and subsequent width measurement. The proposed approach (DeepRivWidth) is used to estimate the width of the river of the Mangalore-Udupi region of Coastal Karnataka (India). These rivers originate or pass through Western Ghats (UNESCO world heritage site), and the proposed river width measurement approach could provide critical input for ecologists besides assisting efficient water management of the region. The estimated width is compared with the manually measured width, and significant improvement in the accuracy was obtained compared to existing river width measurement approaches. Besides, the performance evaluation of semantic segmentation approaches for river identification on a publicly available dataset provides valuable insights into segmenting rivers in SAR images.

Data mining is a process of evaluating practice of examining large preexisting databases in order to generate new information. The amount of data has been growing at an enormous rate ever since the development of computers and information technology. Many methods and algorithms have been developed in the last half-century to evaluate data and extract useful information to help develop faster. Due to the wide variety of algorithms and different approaches to evaluate data, several algorithms are compared. The performance of any algorithm on a particular dataset cannot be predicted without evaluating it with the same constraints and parameters. The following paper is a comparison between the trivial kNN algorithm and the newly proposed ARSkNN algorithm on classifying two datasets and subsequently evaluating their performance on average accuracy percentage and average runtime parameters.

Lactic acid fermentation of radish was conducted using various additive and growth stimulators such as salt (2 %-3 %), lactose, MgSO4 + MnSO4 and Mustard (1 %, 1.5 % and 2 %) to optimize the process. Response surface methodology (Design expert, Trial version 8.0.5.2) was applied to the experimental data for the optimization of process variables in lactic acid fermentation of radish. Out of various treatments studied, only the treatments having ground mustard had an appreciable effect on lactic acid fermentation. Both linear and quadratic terms of the variables studied had a significant effect on the responses studied. The interactions between the variables were found to contribute to the response at a significant level. The best results were obtained in the treatment with 2.5 % salt, 1.5 % lactose, 1.5 % (MgSO4 + MnSO4) and 1.5 % mustard. These optimized concentrations increased titrable acidity and LAB count, but lowered pH. The second-order polynomial regression model determined that the highest titrable acidity (1.69), lowest pH (2.49) and maximum LAB count (10 × 10(8) cfu/ml) would be obtained at these concentrations of additives. Among 30 runs conducted, run 2 has got the optimum concentration of salt- 2.5 %, lactose- 1.5 %, MgSO4 + MnSO4- 1.5 % and mustard- 1.5 % for lactic acid fermentation of radish. The values for different additives and growth stimulators optimized in this study could successfully be employed for the lactic acid fermentation of radish as a postharvest reduction tool and for product development.

Historically, the co-softhyphen;produced hot water has been an inconvenience and a disposal issue for oilfield operators. This paper focuses on brine or coproduced fluids (hot aqueous fluids produced during oil and gas production) as a potential source for electricity generation, which could be produced from the thermal energy available in the produced fluid. Oil and Gas (O&G) industry today is in possession of thousands of established wells with known temperatures and flows which can be used for producing emissions-free and cost-competitive electricity using binary cycle units. Power generation from coproduced fluids using a binary-cycle power plant is underway at the Rocky Mountain Oilfield Testing Center in Wyoming and being considered in locations in Texas, Louisiana, Florida, and Arkansas. Although currently there is no electricity generated from coproduced fluids in India, various studies, suggest that the oil and gas fields in the Cambay Basin basin have a promising geothermal gradient of 35-40 °C/km, while (Chakravorty et.al, 2008) reports around 88% water cut for production from Jhalora Oilfield in Mehsana Asset, North Cambay Basin. This combination of favorable geothermal gradient and large volume of water produced is promising for electricity generation from these coproduced fluids. In this paper, we studied and collated data of Jhalora Field in Mehsana asset of Cambay Basin for coproduction electricity generation and attempt to provide an estimate of the coproduced-water-electricity-generation potential of that field using two different approaches. The most significant parameters to economic viability for such a project include reservoir temperature as well as total fluid production rate. The reservoir temperature of Jhalora Field is 90-100°C and with produced water flow rate of 5096m3/day, it is a suitable candidate for application of this technology.

Different antibiotics were evaluated for their effects on in vitro callusing of leaf explants and suppressing the growth of Agrobacterium tumefaciens strain LBA 4404 in suspension cultures as well as during co-cultivation in apple rootstock MM 111. Maximum callusing on leaf explant (85–93%) was observed in 300 mg/l carbenicillin, 200 mg/l augmentin and 100–200 mg/l timentin. Low conc. of timentin (100–200 mg/l) effectively inhibited the bacterial growth in suspensions and completely controlled the bacterial overgrowth during co-cultivation after 1st and 2nd blot without leaving any deleterious effect on the leaf explants of MM 111.

Twenty phenotypically different apple cultivars from the collection of Department of Fruit Science, YS Parmar University of Horticulture and Forestry, Solan, India, were analysed using 28 RAPD and 5 SSR primers to establish their genetic diversity, relatedness and verify parentage. The primer study revealed higher level of genetic diversity among the cultivars. The level of polymorphism across twenty cultivars was 65.6% and 71.4% from RAPD and SSR markers respectively. Primers OPA-16(RAPD), IISRS-3 and IISRS-18(SSR) were more informative due to the highest Polymorphism Information Content (PIC) value. Three RAPD primers were able to distinguish the cultivars. RAPD and SSR revealed different genetic similarities among apple cultivars. The highest similarity was detected between ‘Fuji’ and ‘Fuji Rekaki’ (0.859) followed by ‘Thanedar early flowering’ and ‘Fuji’ by RAPD while between ‘Galaxy’ and ‘Jonadel’ (1.000) followed by ‘Neomi’ with ‘Arlet’, ‘Fuji’ and ‘Royal Gala’; ‘Fuji Rekaki’ with ‘Thanedar early flowering’ and ‘Reinette-Du-Canada’ by SSR. The dendrograms from RAPD and SSR data were generated using UPGMA method which revealed four and five clusters respectively and showed that most of the cultivars clustered in accordance with the recorded pedigree information and polymorphism found with SSR primers was higher as compared to RAPD.