Influence of naphthalene acetic acid (NAA) and integrated nutrient management (INM) on yield and economics attributes of chilli (Capsicum annuum L.)

The present experiment was conducted to study the response of naphthalene acetic acid NAA and integrated nutrient management on yield and yield attributes as well as and economics of chilli (Capsicum annuum L.) with four levels of NAA (0 ppm, 25 ppm, 50 ppm and 75 ppm) and five levels of vermicompost (VC) along with RDF (V0-100% Recommended dose of fertilizer i.e. 120:60:60 Kg N P K /ha ), V1-100% N through Vermicompost, V2-75% N through VC + 25% (RDF), V3-50% N through VC + 50% (RDF) , V4-25% N through VC + 75% (RDF), having 20 treatment combinations. The results revealed that the combine application of 50 ppm NAA and 100% N through vermicomposti.e. P2V1 performed well in respect of fruit length (8.73 cm), fruit diameter (1.46 cm) and fruit weight (2.91 g) while the application of NAA 50 ppm + 50% N through vermicompost along with 50% RDF i.e. P2V3 gave significantly (P=0.05) maximum number of fruits/plant (73.86) and fruit yield per hectare (121.20 q) with higher gross income (Rs.2,66,640.00/ ha), net profit (Rs.1,98,946.00/ ha) and benefit - cost ratio (2.94). Therefore, it can be concluded that the combine effect of NAA 50 ppm along with 50 % N through vermicompost +50% inorganic fertilizers (RDF) gave better result regarding growth and yield attributes and also generated maximum gross income, net return and B:C ratio while the next best treatment was application of NAA 75 ppm along with 25 % N through vermicompost + 75 % inorganic fertilizers (RDF)

Assessment of genetic variability among different genotypes of Cape gooseberry (Physalis peruviana L.) in India

The field experiment was carried out at the research farm of Horticulture Garden, Bihar Agricultural College, Sabour, Bhagalpur during 2014-15 for assessment of genetic variability among different genotypes of cape gooseberry in India. The experiment was laid out in Randomized Block Design with three replications having twelve genotypes. Analysis of variance revealed significant differences among genotypes for all the traits studies which suggesting sufficient variability for yield and quality parameters. The overall values of phenotypic coefficient of variation (PCV) were higher than those of genetic coefficient of variation (GCV). Higher magnitude of GCV and PCV were recorded for fruit per plant percent (33.30 and 36.61) followed by fruit diameter, fruit weight and flowers per branch. The maximum GCV (33.30) and PCV (36.61) were recorded in fruits/plant respectively. The high values of GCV are the indication of excess variability among the genotypes and thus the scope for crop improvement depends on the selection of superior parents for crossing to get better parents for hybridization. In present study, the magnitude of heritability ranged from 37% to 98% indicating that these traits are controlled by additive gene action which is very useful in selection. The traits like plant girth, plant height, inter nodal length, appearance of 50% of flowering, bud break to full bloom, number of flowers per branch, number of fruits set per branch, duration of fruit set to maturity, fruit weight, fruit diameter and number of fruits per plant with high GCV, PCV, heritability and genetic advance as percentage of mean, indicating that these characters are under additive gene effects and more reliable for effective selection

PROTECTIVE EFFECT OF CURCUMA LONGA ADMINISTRATION ON LUNG OF MICE EXPOSED TO CADMIUM

Objective: Cadmium (Cd) is a toxic heavy metal which is introduced into the environment by various anthropogenic and natural activities. It can cause various health problems even at low concentration by inducing oxidative damage in tissues of organisms. Nowadays, the focus has been raised toward the use of herbal treatment against the heavy metal toxicity. Hence, the present study was aimed to investigate the protective effect of curcumin (Cur) against Cd-induced toxicity in the lung of albino mice.Materials and Methods: Albino mice were divided into 4 groups and 5 mice were kept in each group. The experiment was carried out for 15 and 45 days. Group 1 mice were kept as control. Group 2 mice were given an oral dose of 1mg/kg body weight of Cd on alternate days. Group 3 mice were administered an oral dose of 1mg/kg body weight of Cd on alternate days and 100 mg/kg body weight of Cur daily. Group 4 mice were received an oral dose of 100 mg/kg body weight of Cur daily. Autopsies were done on 15 and 45 days post-treatment.Result: Biochemical observations showed an increased level of lipid peroxidation and decreased the activity of antioxidant enzymes, i.e., superoxide dismutase, catalase, and glutathione peroxidase. However, Cur administration improved the level of malondialdehyde and oxidative stress in lung tissue by its antioxidant activity. Furthermore, cotreatment of Cd and Cur ameliorated the antioxidant level.Conclusion: The results of the present experiment showed the protective action of Cur on the Cd-induced oxidative damage in the lung of mice

Synthesis of ABG (avidin-biotin-GlcNAc) glycocluster as an attractive bioprobe & its multivalent recognition with WGA by PL & SPR methods

埼玉大学博士(学術)xiii, 126 p.Interactions between carbohydrates and proteins are progressively accelerating to a greater extent and being recognized as crucial in many biological processes, such as cellular adhesion, cell signaling, immune responses, fertilization, cancer metastasis and communication. The concernment of these natural events at the cell membrane surface provided the motivation for their study in a biomimetic environment. In order to investigate the interactions of carbohydrates and proteins, the development of efficient analytic technologies, as well as novel strategies for the synthesis of carbohydrates, have to be explored. In this research a tetravalent GlcNAc pendant glycocluster was fruitfully constructed with a linker of C6 length consisting of biotin. Carbohydrate-protein interactions are especially well suited for multivalency, therefore in this research we are using such reaction system to synthesize a glycopolymer of tetrameric structure using N-acetyl-D-glucosamine (GlcNAc) as a target carbohydrate conjugated with biotin via DMT-MM as coupling reagent, followed by biotin-avidin interaction leading to the formation of glycocluster of avidin-biotin-GlcNAc conjugate (ABG complex). To check the binding affinity of GlcNAc conjugate with a lectin WGA (Wheat Germ Agglutinin) we used fluorometric assay by means of specific excitation of tryptophan at λex 295 nm and it was found to be very high in case of ABG complex as compared to GlcNAc only with the phenomenon proven to be due to glycocluster effect. Another analytical biosensing method Surface plasmon resonance (SPR) was implied to give the detailed information of binding kinetics between ABG and WGA compared to three linear-type GlcNAc polymers binding with WGA. WGA used as a ligand and immobilized on sensor surface via amine coupling method. Artificial glycopolymers of GlcNAc with polystyrene based polymeric backbone of acrylamide (linear-type polymers) was used as control against ABG complex. Kinetic analysis was performed by separated numerical integration of the association and dissociation phases by using a Langmuir (1:1) model. The efficiency of the method was exhibited in the analysis of the interactions that covered a high affinity range; namely the strong binding (KA ～ 107 M-1) for ABG compared with the control polymers, which show the binding of (KA ～ 105 M-1). Both the techniques PL and SPR shown to be very rapid and powerful techniques, which supervise the mechanistic studies of carbohydrate-protein interactions at interfaces in real time and quantitative manner. These methodologies have been used to probe carbohydrate-lectin-interactions for a cereal lectin named WGA and the usefulness of these synthetic glycoconjugates as tools in the study of sugar-lectin interactions has been proved due to glycocluster effect. SPR and PL analysis can afford data with desirable reproducibility and therefore offers the possibility of a detailed computational analysis.List of Figures, Schemes, Tables and Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii CHAPTER-1 1. INTRODUCTION 1.1 Carbohydrates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 N-acetyl-D-glucosamine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Lectins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.1 Animal lectins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7 1.2.2 C-type lectins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.2.3 Plant lectins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.2.4 L-type lectin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.2.5 Wheat germ agglutinin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13 1.3 Carbohydrate-Lectin interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15 1.4 Bioprobes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17 1.5 Glycocluster effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20 1.6 Methods for measuring protein-carbohydrate interactions . . . . . . . . . . . . . . . 22 1.7 Photoluminescence spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 1.7.1 PL Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 1.7.2 Hill`s Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 1.7.3 Tryptophan fluorescence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24 1.8 Surface Plasmon Resonance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 1.8.1 SPR Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 1.8.2 SPR instrumentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 1.8.3 Sensor chip . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .31 1.9 Aim of the present study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 CHAPTER-2 2. CARBOHYDRATE SYNTHESIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.1 Synthesis of GlcNAc derivatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.1.1 acetamido-tetra-O-acetyl-α-D-glucosamine(2) . . . . . . . . . . . . . . . . . . . . . . . . .37 2.1.2 Oxazoline derivative(3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 2.1.3 6-Chloro-2-Acetamido-3,4,6-tri-O-acetyl-2-deoxy-β-D-glucopyranoside(4) . .38 2.1.4 6-Azidohexyl-2-Acetamido-3,4,6-tri-O-acetyl-2-deoxy-β-D-glucopyranoside(5)40 2.1.5 6-Azidohexyl-2-Acetamido -2-deoxy-β-D- glucopyranoside (6) . . . . . . . . . . . . 41 2.1.6 6-Aminohexyl-2-Acetamido -2-deoxy-β-D- glucopyranoside (7) . . . . . . . . . . . . 42 2.2 Synthesis of Biotin-GlcNAc conjugate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 2.2.1 DMAP-DIC Coupling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 2.2.2 DMT-MM Coupling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .43 2.2.3 6-(Biotynylamido)hexyl-2-Acetamido-3,4,6-tri-O-acetyl-2-deoxy-β-D-glucopyranoside (9) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .45 2.2.4 6-(Biotynylamido)hexyl-2-Acetamido-2-deoxy-β-D-glucopyranoside (10) . . . . .46 2.3 Physical & Chemical properties of compound 9 & 10 . . . . . . . . . . . . . . . . . . . . . . .48 CHAPTER-3 3. ANALYTICAL TOOLS TO EXPLAIN CARBOHYDRATE STRUCTURES . . . . . 49 3.1 Nuclear magnetic resonance (NMR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .49 3.2 Infrared spectroscopy (IR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .52 3.3 Mass spectroscopy (MS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 CHAPTER-4 4. SYNTHESIS OF ABG COMPLEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .56 4.1 Avidin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.2 Biotin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.3 Avidin-Biotin system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.4 ABG tetrameric complex synthesis & purification . . . . . . . . . . . . . . . . . . . . . . . . .58 4.5 Absorbance concentration analysis of ABG complex . . . . . . . . . . . . . . . . . . . . . . .60 CHAPTER-5 5. BIOLOGICAL EVALUATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 5.1 Fluorometric assay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 5.1.1 Preparation of WGA solution & sampling to measure PL . . . . . . . . . . . . . . . . . .62 5.1.2 PL analysis for GlcNAc only . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.1.3 PL analysis for compound 10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .65 5.1.4 PL analysis for ABG complex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .67 5.1.5 Kinetic affinity results for PL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .69 CHAPTER-6 6. BIOSENSOR ASSAY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6.1 Surface Plasmon Resonance (SPR) experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 6.1.1 pH Scouting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 6.1.2 Immobilization of WGA to CM5 chip . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 6.1.3 Analytes used for kinetic analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 6.1.4 Kinetic analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 6.1.4.1 Regeneration of the sensor chip surface . . . . . . . . . . . . . . . . . . . . . . . . 80 6.1.4.2 Kinetic analysis for ABG complex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 6.1.4.3 Kinetic analysis for glycopolymer 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 6.1.4.4 Kinetic analysis for glycopolymer 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 6.1.4.5 Kinetic analysis for glycopolymer 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 6.2 Kinetic parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .90 CHAPTER-7 7. COMPARATIVE STUDY BETWEEN SPR & PL . . . . . . . . . . . . . . . . . . . . . . . . . . . .91 CHAPTER-8 8. CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .92 CHAPTER-9 9. GENERAL METHODS & MATERIALS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .94 CHAPTER-10 10. SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 List of conferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 List of publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 Awards and Honors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 Declaration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 Thesis dedicated to . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126主指導教員 : 松岡浩司textapplication/pdfdoctoral thesi

CURCUMIN PROTECTION AGAINST CADMIUM CHLORIDE-INDUCED BIOCHEMICAL ALTERATIONS IN LUNGS OF SWISS ALBINO MICE

Objective: The aim of the present study was to investigate the protective effect of curcumin against cadmium chloride (CdCl2)-induced toxicity in lungs of albino mice. Methods: Albino mice were divided into eight groups and five mice were kept in each group. The experiment was carried out for 15 and 45 days. Group 1 mice were kept as control. Group 2 mice were given an oral dose of 1 mg/kg body weight of cadmium chloride on alternate days. Group 3 mice were administered an oral dose of 1 mg/kg body weight of cadmium chloride on alternate days and 100 mg/kg body weight of curcumin daily. Group 4 mice were received an oral dose of 100 mg/kg body weight of curcumin daily. Autopsies were done on 15 and 45 days post-treatment. Results: The results of the present study showed a significant decrease in organ weight at both the intervals. Biochemical analysis showed decline in total glycogen, cholesterol, and protein concentration in lung of cadmium chloride-treated mice. Furthermore, the cadmium chloride concentration in cadmium chloride-treated group was increased in comparison to the control group. However, the treatment with curcumin ameliorated cadmium chloride-induced changes in lung tissue as it instigated the antioxidant enzymes remarkably. However, cotreatment of cadmium chloride with curcumin boosted the changes due to cadmium chloride. Conclusion: Hence, we concluded that curcumin has protective efficacy in the lungs against the cadmium chloride generated toxicity in albino mice

Sirenomelia with Potter syndrome: a case report and review of literature

Sirenomelia or mermaid syndrome is a rare congenital anomaly characterized by variable degree of fusion of lower extremities. Awareness to this rare condition is important for prenatal diagnosis and prognosticating the fetus. The exact etiopathogenesis is still an area of research. Two pathogenic hypotheses are the vascular steal hypothesis and the defective blastogenesis hypothesis with exceptions reported in literature

Evaluation of risk of malignancy index as a diagnostic tool in cases with adnexal mass

Background: Ovarian tumour usually presents as adnexal mass but often it is difficult to differentiate between benign and malignant tumour. Several diagnostic modalities such as sonography and tumours markers have been evaluated in the past, but none have been established as an ultimate diagnostic tool individually. The development of a mathematical formula using a logistic model, incorporating menopausal status, the serum level of a glycoprotein called CA-125 and USG score has been described in the form of different malignancy indices. The purpose of this study was to evaluate the various risks of malignancy indices (RMI 1, 2, 3, and 4) in the pre-operative evaluation of adnexal masses especially to differentiate between benign and malignant masses. Another objective of the present study was to compare the four RMI with each other in terms of various statistical parameters like specificity and sensitivity.Methods: Women with adnexal masses who underwent surgical treatment were included in this study as histopathological examination was taken as gold standard to calculate the accuracy of RMI. The sensitivity, specificity and positive predictive value and negative predictive value of all the four RMI were calculated and data analyzed.Results: A total of 65 patients were included in the study. RMI 1,2,3,4 was calculated according to their formula. Sensitivity of RMI- 1, 2, 3 and 4 was calculated to be 63.63%, 77.27%, 63.63% and 77.27% respectively. Specificity of RMI- 1, 2, 3 and 4 was calculated to be 69.04%, 64.28%, 64.28% and 62.79% respectively.Conclusions: Risk of malignancy index is a good diagnostic tool to differentiate between benign and malignant pelvic masses. RMI- 2 and RMI-4 had maximum sensitivity while RMI-1 had maximum specificity. Overall RMI-2 appears to be the most accurate of all the four RMI

Synthesis of ABG (avidin-biotin-GlcNAc) glycocluster as an attractive bioprobe & its multivalent recognition with WGA by PL & SPR methods

学位記号番号 : 博理工甲第1067号博士の専攻分野の名称 : 博士(学術) 学位授与年月日 : 平成29年3月22日textapplication/pdfthesi

Diabetic Type Classification using Supervised Machine Learning Approaches

Diabetic Retinopathy (DR) is the leading cause of blindness worldwide and a serious diabetic complication. To prevent vision loss, DR lesion diagnosis and categorization must be done early. DR early detection and treatment can significantly reduce the risk of vision loss. This paper focuses on classifying a sample into diabetic and non-diabetic using a variety of techniques, including Decision Tree, ANN, KNN, SVM, Random Forest, and Gradient Boosting Algorithms. The NCSU Diabetes the data set is pre-processed, and examples are trained and evaluated for accuracy; SVM and ANN achieve over 80% accuracy, demonstrating their potential in diabetes type classification. The PIMA Indians Dataset is used as a reference. The DR\u27s manual diagnosing procedure Ophthalmologists\u27 retina fundus scans take a lot of time, effort, money, and are prone to in contrast to computer-aided diagnosis systems, to misdiagnosis. Machine learning has recently been one of the most widely used methods that has improved performance in several categories, for example. The best classifier for diabetic retinopathy is determined by SVM, Decision. This compares ANN classifiers, Tree, Logistic Regression, and k-Nearest Neighbors paper. Additionally, a study of the existing DR datasets has been conducted. Numerous difficult also covered are topics that need further research. The results of comparing various machine learning algorithms with earlier studies are favourable. This research enhances the diagnosis of diabetic retinopathy by demonstrating the effectiveness of several machine learning classifiers and assisting in the creation of precise and effective computer-aided diagnostic tools for management and early detection

Assessment of Biomass and Carbon Stock of Trees within the Campus of IGNOU, New Delhi (India)

This study aims to assess the biomass and carbon stock of the trees within IGNOU campus situated at the Indian national capital, New Delhi for an enhanced understanding about the carbon sequestration potential of the university campuses in urban setting. The aim of the paper is centered on the need to assess terrestrial carbon pools within a campus situated in the semi-arid forests of India which is significant for building suitable action plans for the purpose of managing ecosystems amidst the threat of anthropogenic climate change occurring due to rapid urbanization. The assessment of the biomass and carbon stock of the trees of the selected species within the campus was done by non-destructive method using allometric equations used prominently in previous studies identifying a total of 20 species of the trees comprising 1260 individual trees belonging to 14 different families of the trees. Findings of this study on identified campus trees, which comprised 1,260 individual trees, demonstrated to have moderate maturity in terms of storing carbon in the form of their biomass with the average DBH 25.34 cm. The values of their estimated total biomass and carbon stock were 75.26446 t/tree  and 37.63223 tC/tree respectively. The maximum value of the total biomass 13.01 t/tree was of Ficus recemos, and of the carbon stock 6.50tC/tree was of Ficus recemosa. Azadirachta indica species were found to be the most dominant species and their sampled trees were found to be able to sequester 537.526 tons of carbon in their standing biomass. The Phyllanthus emblica had the lowest carbon sequestration potential with 10.9 tons. This paper offers valuable insight with respect to the carbon sequestration potential of university campus situated in urban settings of a semi-arid forest ecosystem of Delhi by assessing the above- and below ground carbon storage potential of the trees. The findings are of significance for different stakeholders including primarily future researchers, planners and decision-makers engaged in the process of urbanization. &nbsp