Browsing by Author "Alisha"
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ThesisItem Restricted Identification of animal species through reminiscent biological material(LUVAS, 2010) Alisha; MinakshiPoaching and illegal trade in wild animals and their products are highly detrimental for wildlife biodiversity and ecological balance. Therefore, it is important to effectively implement the existing laws pertaining to wildlife protection. For efficient implementation of such laws, wildlife protection agencies must have validated quick, easy and reliable tools for the identification of wild animals from different type of samples such as skin, feather follicles, faeces, tissue and bone. As forensic experts many a times face such situation in which samples are presented in degraded, old condition and scant quantity. So, there is also a need to develop suitable and reliable protocols for identification of species from such samples. Keeping in view, the depleting populations of wild animals especially important animals of food chain like Tiger (top predators), black buck and symbols of our cultural heritage such as peacock and all other perspectives, a study for developing protocols for identification of species from different kinds of samples and further differentiation of wild species from closely related species has been carried out. Several quicker and non–hazardous protocols such as chelating resin extraction method and CF11 extraction method were standardized for DNA extraction from different kinds of old, degraded and other samples collected from field. The genomic DNA extracted was quantified using high fidelity Picodrop™ UV spectrophotometer. Several old samples collected in 2006, from peacock, blackbuck and deer were used in the study to extract DNA and extracted DNA was successfully amplified by PCR/ Real time PCR. The samples yielding amplifiable amount of DNA were amplified using different sets of universal and species specific primers. Universal primers targeting 12S rRNA were designed during the study to amplify DNA extracted from Avian spp. samples. Species specific primers targeting D-loop mitochondrial gene of tiger were used to amplify the DNA extracted from tiger blood samples. DNA extracted from different samples were also amplified using cyt b gene targeting universal primers. A 12S rRNA gene based PCR-RFLP method was standardized to differentiate between peacock and chicken. The differentiating enzymes such as Alu I and Mbo II were used. On digestion of PCR product, AluI yielded two products in case of peacock while three in chicken. However, Mbo II was yielding a clear difference between the two species, giving two products in case of peacock while it had no cut site in chicken. These results were confirmed using in silico RFLP analysis of the sequences obtained after nucleotide sequencing. Hence, PCR-RFLP was found to be of value in differentiating between such closely related species. The amplified products obtained by using different sets of primers such as cyt b gene products of neelgai, black buck, D-loop gene species specific products of male and female tiger and 12S rRNA gene products of peacock and chicken were cloned and sequenced. In order to determine that there is no cross amplification and to know the Forensically Informative Nucleotide Sequence (FINS), the sequence analysis was done. The sequence analysis revealed several facts that were found to be supported with the already documented data. From the sequence analysis of black buck sequence used in the study (cyt b gene) it was found to be closer to blackbuck from Japan and France. Sequence analysis of D-loop gene specific sequence of female tiger revealed that it does not belong to Panthera tigris tigris (Bengal tiger). However, this fact was confirmed and further made clear when the sequencing of cyt b gene PCR product of female tiger was done. The sequence analysis revealed that the female tiger belonged to Panthera tigris jacksoni (Malayan tiger), thus confirming it to be of different breed. Sequence analysis of sequence obtained from peacock was also done and the peacock in the study was found to be closer to peacock of India and was grouped with other peafowls such as Pavo cristatus of USA and Pavo muticus on phylogenetic tree analysis. Sequence analysis of neelgai cyt b gene sequence revealed it to be closer to Neelgai of India and species Tetracerus quadricornis of deer family. Thus, the origin of samples was identified/confirmed using various confirmatory tools such as sequencing and PCR-RFLP. The samples in which end point PCR was not able to yield any appreciable result was further amplified and quantified using real time PCR and the assay was able to detect DNA amount upto 21.4 pg/μl. The study conducted paves the way for quick and easy identification of species from various kinds of forensic samples obtained from fieldThesisItem Restricted Metabolic changes in roots and nodules of lentil genotypes with imazethapyr treatment(Punjab Agricultural University, Ludhiana, 2021) Alisha; Grewal, Satvir KaurLentil (Lens culinaris) is an important pulse crop. Weed infestation affects the yield of lentil as it is a poor competitor of weeds due to their short stature and sluggish growth rate. The present study was to screen lentil genotypes of imazethapyr treatment. Twenty lentil genotypes were sown under three treatments: weed check (T1), weed-free (T2) and sprayed with imazethapyr (T3). Four contrasting genotypes comprising of two imazethapyr tolerant (LL1397 and LL1612) and two susceptible (Flip 2004-7L and PL07) genotypes were selected based on their morpho-physiological traits, number of pods/plant, 100-seed weight and yield. Activity of acetolactate synthase (ALS), carbon (alkaline/acid invertase, sucrose synthase and malate dehydrogenase) and nitrogen (glutamate dehydrogenase, glutamine synthetase and glutamate synthase) metabolizing enzymes were analyzed in four contrasting genotypes in both roots and nodules under three treatments. Imazethapyr inhibits the synthesis of acetolactate synthase (ALS), an enzyme responsible for the synthesis of branched-chain amino acids in plants. Higher activity of ALS in roots and nodules of LL1397 and LL1612 might be responsible for their better plant growth as compared to Flip 2004-7L and PL07. Higher activity of alkaline invertase, sucrose synthase, malate dehydrogenase (MDH), glutamate dehydrogenase (GDH), glutamine synthetase (GOGAT) and glutamate synthase (GS) in both roots and nodules might be helping their growth and nodule metabolism in LL1612 and LL1397 as compared to Flip 2004-7L and PL07 lentil genotypes.ThesisItem Open Access Random walk and ARIMAX modeling for cotton yield in western zone of Haryana(CCSHAU, 2018) Alisha; Verma, UrmilCrop yield models are abstract presentation of interaction of the crop with its environment and can range from simple correlation of yield with a finite number of variables to the complex statistical models with predictive end. The pre-harvest forecasts are useful to farmers to decide in advance their future prospects and course of action. The study has been categorized into three parts i.e. the fitting of Random Walk, ARIMA and ARIMAX models for cotton yield forecasting in Hisar, Fatehabad, Sirsa and Bhiwani districts of Haryana. The Random Walk and ARIMA models have been fitted using the time-series cotton yield data for the period 1980-81 to 2010-11 of Hisar and Sirsa districts and 1997-98 to 2010-11 of Fatehabad district. The fortnightly weather data have been utilized as input series from 1980-81 to 2016-17 for fitting/testing the Random walk/ARIMA with weather input i.e. ARIMAX models. Models have been validated using the data on subsequent years i.e. 2011-12 to 2016- 17, not included in the development of the models.The multiple linear regression models with crop condition term as dummy regressor were fitted for Bhiwani district as the cotton yield data being stationary in nature and showing non-significant autocorrelations was not suitable for ARIMA modeling. Though, the MA models were tried but the yield forecasts were beyond acceptable limits. Random Walk i.e. I(1) and ARIMA(0,1,1) for Hisar, Fatehabad and Sirsa districts have been fitted for pre-harvest cotton yield forecasting. Alternatively, the Random Walk models with exogenous input were tried by utilizing the fortnightly weather variables (viz., TMIN1, RF11, SSH3 and SSH4 over the crop growth period). Lastly, the ARIMA models with alternative combinations of weather variables were tried for fitting the ARIMAX models. Following the steps required in SPSS; ARIMA(2,1,0) for Hisar and Fatehabad and ARIMA(0,1,1) for Sirsa districts along with fortnightly weather variables (viz., TMAX5, RF7, SSH4 and RH4 over the crop growth period) as input were finalized as ARIMAX models for district-level cotton yield forecasting. The predictive performance(s) of the contending models i.e. Random Walk, ARIMA and ARIMAX models were observed in terms of the percent deviations of cotton yield forecasts in relation to the observed yield(s) and root mean square error(s) as well. The level of accuracy achieved by ARIMA model(s) with weather input was considered adequate for estimating the cotton yield(s) i.e. the ARIMAX models consistently showed the superiority over Random Walk and ARIMA models in capturing the percent relative deviations pertaining to cotton yield forecasts. The ARIMAX models performed well with lower error metrics as compared to the Random Walk and ARIMA models in all time regimes.