Induced chlorophyll mutations in bell pepper (Capsicum annuum L. var. grossum)

An investigation entitled “Induced chlorophyll mutations in bell pepper (Capsicum annuum L. var. grossum)” was conducted during kharif (summer-rainy season) 2012 and 2013 at Experimental farm of the Department of Vegetable Science and Floriculture, CSK Himachal Pradesh Krishi Vishvavidyalaya, Palampur, H.P., India. Healthy seeds of California Wonder of bell pepper were exposed to physical mutagen Gamma rays using 60CO as a source of radiation at Mutation Breeding Centre, Department of Biotechnology, BARC Trombay, Mumbai and chemical mutagen EMS (Ethyl Methane Sulphonate) to obtain the spectrum and frequency of chlorophyll mutations in M2 generation. The M1 generation was produced from these mutagen treated seeds. Several unique and interesting chlorophyll and viable mutants were obtained in M2 generation. In M2 generation, gamma rays induced higher proportion of chlorophyll mutants then EMS. A progressive increase in mutation frequency of chlorophyll mutations was observed with increasing doses/concentrations. Four different types of chlorophyll mutants namely xantha, yellow xantha, chlorina and viridis were induced. Out of these mutants, chlorina and viridis were most frequent and were produced even in lower doses/concentrations while yellow xantha was least frequent and produced only in higher doses. The highest frequency of chlorophyll mutations (18.8 %) was reported in the 22 kR of gamma dose, while the lowest (0.80 %) frequency of chlorophyll mutations was found in the treatment of 1.0 % EMS. There was a dose dependent increase in the spectrum and frequency of chlorophyll mutations. These chlorophyll mutants induced by gamma radiation and EMS could be used in mutation breeding programme for inducing viable mutations for improvement of bell pepper varieties

On Retention of Eventual Stability of Perturbed Impulsive Differential Systems

In this paper, a system of non nonlinear differential equations withimpulse effect at fixed time moments is considered and criteria for retention ofuniform eventual stability of its perturbed impulsive differential systems undervanishing perturbations are established. Sufficient conditions are obtained byusing piecewise continuous Lyapunov functions. An example is also worked outto illustrate the results.AMS Subject Classification: 34CXX, 34DXX, 34A37, 34K45

Epidemiology of ovarian malignancies

Background: The location of the ovaries makes it difficult to diagnose ovarian cancer at an earlier more curable stage. This study was done with intend of developing insight into risk factors, clinical presentation and prognostic factors for the same.Methods: An observational prospective study was undertaken over a period of 2 years of 77 cases in a tertiary care hospital with cancer unit. Case records of cases during study period were evaluted and epidemiology studied.Results: Most of the malignant tumors i.e. 35.1% were in the age group of 40- 50 yrs and 42 % of tumors seen in the age group of 41-60 years. 9.1% were post hysterectomy, 46.8% were premenopausal and 44.2% were postmenstrual. Majority cases were parous women and none had used oral contraceptive pills. In the present study, blood group A was found more commonly more commonly associated with ovarian tumors in comparison to the general population. the most common complaint of the patient was pain in abdomen (70.1%) followed by distension of abdomen (45.5%) and menstrual irregularities (16.9 %).Conclusions: The epidemiology and presentation of ovarian tumours has remained unchanged since last 3 decades. Nulliparity is not as significant a factor in the aetiology of ovarian malignancy however contraception leading to anovulatory cycles (OC pills) may have a protective role. Infertility can be suggested as a risk factor but the treatment for infertility & its role in oncogenesis remains controversial

Analysis of Magneto-inductive System for Rocket Sled Velocity Measurement Beyond Mach 1.5

The rail track rocket sled (RTRS) national test facility at Terminal Ballistics Research Laboratory (TBRL) has been established to provide simulated flight environment for carrying out aero dynamic studies, terminal studies and kinematic studies of variety of test articles. The sled velocity is a critical parameter in evaluation trials.  This velocity is also used to ensure that the maximum speed and allowable g loading does not exceed the value which the test article will experience under free flight in air1. Overseas, the facilities have been set up to attain velocities ranging from sub-sonic to hypersonic2. The rocket sled at TBRL can be presently accelerated to travel along the rail track at velocities up to 500 m/s and capability is being built to increase velocity beyond 500 m/s. Signals acquired from existing magneto-inductive arrangement have been analysed in the present work. The experiments indicate that with increase in velocity the rate of change of flux increases, the amplitude of induced emf also increases but terminal voltage decreases and shape of the acquired pulse gets distorted. The parameters of magneto-inductive pick up have been modified in such a way that there is improvement in amplitude and shape of the received pulse with increase in velocity. The improved signals have been analysed and simulation results validated with feasible experiments. This paper also discusses issues, challenges and proposes recommendations in improving the sensor for measurement of velocity beyond Mach 1.5. It has been found that it is prudent to reduce the inductance by reducing the number of turns and changing the core from soft iron core to air core which will improve the response of inductive pick up coil at high velocity.Defence Science Journal, 2014, 64(2), pp. 143-151. DOI: http://dx.doi.org/10.14429/dsj.64.503

On Stability of Differential Systems with Noninstantaneous Impulses

A new class of impulsive differential equations with noninstantaneous fixed time impulses is considered. Uniform stability and uniform asymptotic stability of solutions of the system have been established by employing piecewise Lyapunov functions. An example is also given to illustrate the theoretical results

Micropropagation of fruit crops: a review

In vitro culture has the greatest commercial value in the extremely quick creation of clonal plants compared to conventional techniques of propagation. It has also proven to be of tremendous practical utility as assistance to the development and maintenance of disease-free planting material, germplasm conservation, and supplements to the regular methods of plant progress, such as plant genetic engineering. The discovery and implementation of current tissue culture techniques are thought to be paving the way for a second green revolution. Here, the standardization of techniques for the in vitro clonal propagation of fruit crops is highlighted. In addition, we summarize the effects of several surface sterilizing agents, plant growth regulators (PGRs), and anti-phenolic chemicals on culture establishment, shoot proliferation, in vitro rooting, hardening and economics of in vitro generation of true-to-type plants

Impact of safety-related dose reductions or discontinuations on sustained virologic response in HCV-infected patients: Results from the GUARD-C Cohort

BACKGROUND: Despite the introduction of direct-acting antiviral agents for chronic hepatitis C virus (HCV) infection, peginterferon alfa/ribavirin remains relevant in many resource-constrained settings. The non-randomized GUARD-C cohort investigated baseline predictors of safety-related dose reductions or discontinuations (sr-RD) and their impact on sustained virologic response (SVR) in patients receiving peginterferon alfa/ribavirin in routine practice. METHODS: A total of 3181 HCV-mono-infected treatment-naive patients were assigned to 24 or 48 weeks of peginterferon alfa/ribavirin by their physician. Patients were categorized by time-to-first sr-RD (Week 4/12). Detailed analyses of the impact of sr-RD on SVR24 (HCV RNA <50 IU/mL) were conducted in 951 Caucasian, noncirrhotic genotype (G)1 patients assigned to peginterferon alfa-2a/ribavirin for 48 weeks. The probability of SVR24 was identified by a baseline scoring system (range: 0-9 points) on which scores of 5 to 9 and <5 represent high and low probability of SVR24, respectively. RESULTS: SVR24 rates were 46.1% (754/1634), 77.1% (279/362), 68.0% (514/756), and 51.3% (203/396), respectively, in G1, 2, 3, and 4 patients. Overall, 16.9% and 21.8% patients experienced 651 sr-RD for peginterferon alfa and ribavirin, respectively. Among Caucasian noncirrhotic G1 patients: female sex, lower body mass index, pre-existing cardiovascular/pulmonary disease, and low hematological indices were prognostic factors of sr-RD; SVR24 was lower in patients with 651 vs. no sr-RD by Week 4 (37.9% vs. 54.4%; P = 0.0046) and Week 12 (41.7% vs. 55.3%; P = 0.0016); sr-RD by Week 4/12 significantly reduced SVR24 in patients with scores <5 but not 655. CONCLUSIONS: In conclusion, sr-RD to peginterferon alfa-2a/ribavirin significantly impacts on SVR24 rates in treatment-naive G1 noncirrhotic Caucasian patients. Baseline characteristics can help select patients with a high probability of SVR24 and a low probability of sr-RD with peginterferon alfa-2a/ribavirin

Moving towards vertically integrated artificial intelligence development

Substantial interest and investment in clinical artificial intelligence (AI) research has not resulted in widespread translation to deployed AI solutions. Current attention has focused on bias and explainability in AI algorithm development, external validity and model generalisability, and lack of equity and representation in existing data. While of great importance, these considerations also reflect a model-centric approach seen in published clinical AI research, which focuses on optimising architecture and performance of an AI model on best available datasets. However, even robustly built models using state-of-the-art algorithms may fail once tested in realistic environments due to unpredictability of real-world conditions, out-of-dataset scenarios, characteristics of deployment infrastructure, and lack of added value to clinical workflows relative to cost and potential clinical risks. In this perspective, we define a vertically integrated approach to AI development that incorporates early, cross-disciplinary, consideration of impact evaluation, data lifecycles, and AI production, and explore its implementation in two contrasting AI development pipelines: a scalable “AI factory” (Mayo Clinic, Rochester, United States), and an end-to-end cervical cancer screening platform for resource poor settings (Paps AI, Mbarara, Uganda). We provide practical recommendations for implementers, and discuss future challenges and novel approaches (including a decentralised federated architecture being developed in the NHS (AI4VBH, London, UK)). Growth in global clinical AI research continues unabated, and introduction of vertically integrated teams and development practices can increase the translational potential of future clinical AI projects

Moving towards vertically integrated artificial intelligence development

Substantial interest and investment in clinical artificial intelligence (AI) research has not resulted in widespread translation to deployed AI solutions. Current attention has focused on bias and explainability in AI algorithm development, external validity and model generalisability, and lack of equity and representation in existing data. While of great importance, these considerations also reflect a model-centric approach seen in published clinical AI research, which focuses on optimising architecture and performance of an AI model on best available datasets. However, even robustly built models using state-of-the-art algorithms may fail once tested in realistic environments due to unpredictability of real-world conditions, out-of-dataset scenarios, characteristics of deployment infrastructure, and lack of added value to clinical workflows relative to cost and potential clinical risks. In this perspective, we define a vertically integrated approach to AI development that incorporates early, cross-disciplinary, consideration of impact evaluation, data lifecycles, and AI production, and explore its implementation in two contrasting AI development pipelines: a scalable "AI factory" (Mayo Clinic, Rochester, United States), and an end-to-end cervical cancer screening platform for resource poor settings (Paps AI, Mbarara, Uganda). We provide practical recommendations for implementers, and discuss future challenges and novel approaches (including a decentralised federated architecture being developed in the NHS (AI4VBH, London, UK)). Growth in global clinical AI research continues unabated, and introduction of vertically integrated teams and development practices can increase the translational potential of future clinical AI projects