Renaissance in Fisheries: Outlook and Strategies - Book of Abstracts 9th Indian Fisheries Forum, December 19-23, 2011, Chennai, India

The Asian Fisheries Society – Indian Branch (AFSIB) since its inception in 1986, has been providing a platform for discussion at the national level on issues related to research, development, education and policies by organizing Indian Fisheries Forum (IFF) every three years in different parts of the country. The 9th Indian Fisheries Forum (9th iff) will be hosted by the Central Marine Fisheries Research Institute (CMFRI), at Chennai during 19-23 December 2011. The main theme of the 9th iff is “Renaissance in Fisheries: Outlook & Strategies”. It would have a comprehensive look for the fisheries and aquaculture sectors, for achieving greater synergy among the stakeholders and planning strategies for capture fisheries and aquafarming to build higher levels of sustainability and profitability. The forum would also address the issues of impact of climate change and its mitigation, resource constraint and species diversification for the expansion of fish production activity; and encourage young scientists to undertake need-based and resource specific research. An international symposium sponsored by the Bay of Bengal Large Marine Ecosystem (BoBLME) is scheduled to be held during the forum on 21 December, 2011 with theme: Bay of Bengal–Ecosystem Approach to Fisheries Management

Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease

Background: Experimental and clinical data suggest that reducing inflammation without affecting lipid levels may reduce the risk of cardiovascular disease. Yet, the inflammatory hypothesis of atherothrombosis has remained unproved. Methods: We conducted a randomized, double-blind trial of canakinumab, a therapeutic monoclonal antibody targeting interleukin-1β, involving 10,061 patients with previous myocardial infarction and a high-sensitivity C-reactive protein level of 2 mg or more per liter. The trial compared three doses of canakinumab (50 mg, 150 mg, and 300 mg, administered subcutaneously every 3 months) with placebo. The primary efficacy end point was nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death. RESULTS: At 48 months, the median reduction from baseline in the high-sensitivity C-reactive protein level was 26 percentage points greater in the group that received the 50-mg dose of canakinumab, 37 percentage points greater in the 150-mg group, and 41 percentage points greater in the 300-mg group than in the placebo group. Canakinumab did not reduce lipid levels from baseline. At a median follow-up of 3.7 years, the incidence rate for the primary end point was 4.50 events per 100 person-years in the placebo group, 4.11 events per 100 person-years in the 50-mg group, 3.86 events per 100 person-years in the 150-mg group, and 3.90 events per 100 person-years in the 300-mg group. The hazard ratios as compared with placebo were as follows: in the 50-mg group, 0.93 (95% confidence interval [CI], 0.80 to 1.07; P = 0.30); in the 150-mg group, 0.85 (95% CI, 0.74 to 0.98; P = 0.021); and in the 300-mg group, 0.86 (95% CI, 0.75 to 0.99; P = 0.031). The 150-mg dose, but not the other doses, met the prespecified multiplicity-adjusted threshold for statistical significance for the primary end point and the secondary end point that additionally included hospitalization for unstable angina that led to urgent revascularization (hazard ratio vs. placebo, 0.83; 95% CI, 0.73 to 0.95; P = 0.005). Canakinumab was associated with a higher incidence of fatal infection than was placebo. There was no significant difference in all-cause mortality (hazard ratio for all canakinumab doses vs. placebo, 0.94; 95% CI, 0.83 to 1.06; P = 0.31). Conclusions: Antiinflammatory therapy targeting the interleukin-1β innate immunity pathway with canakinumab at a dose of 150 mg every 3 months led to a significantly lower rate of recurrent cardiovascular events than placebo, independent of lipid-level lowering. (Funded by Novartis; CANTOS ClinicalTrials.gov number, NCT01327846.

A convergence rate estimate for the SVM decomposition method

The training of Support Vector Machines using the decomposition method has one drawback; namely the selection of working sets such that convergence is as fast as possible. It has been shown by Lin that the rate is linear in the worse case under the assumption that all bounded Support Vectors have been determined. The analysis was done based on the change in the objective function and under a SVMlight selection rule. However, the rate estimate given is independent of time and hence gives little indication as to how the linear convergence speed varies during the iteration. In this initial analysis, we provide a treatment of the convergence from a gradient contraction perspective. We propose a necessary and sufficient condition which when satisfied provides strict linear convergence of the algorithm. The condition can also be interpreted as a basic requirement for a sequence of working sets in order to achieve such a convergence rate. Based on this condition, a time dependant rate estimate is then further derived. This estimate is shown to monotonically approach unity from below.August 1-4, 200

Differential Induction of Chitinase and β-1,3-Glucanase in Rice in Response to Inoculation with a Pathogen (Rhizoctonia solani) and a Non-Pathogen (Pestalotia palmarum)

Rice leaf sheaths were inoculated with R. solani (pathogen) and P. palmarum (non-pathogen) and were analyzed for the accumulation of pathogenesis-related (PR) proteins. Inoculation of rice plants with R. solani and P. palmarum resulted in a marked increase in activities of chitinase and b-1,3-glucanase. The levels of both enzymes were higher in incompatible interactions than in compatible interactions. Western blot analysis indicated that two proteins with molecular weights of 33 and 35 kDa cross-reacting with barley chitinase antibody were induced in rice in response to inoculation with R. solani. The appearance of these chitinases was correlated with increase in enzyme activity

Machine learning using support vector machines

Machine learning invokes the imagination of many scientific minds due to its potential to solve complex and difficult real world problems. This paper gives methods of constructing machine learning tools using Support Vector Machines (SVMs). We first give a simple example to illustrate the basic concept and then demonstrate further with a practical problem. The practical problem is concerned with electronic monitoring of fishways for automatic counting of different fish species for the purpose of environmental management in Australian rivers. The results illustrate the power of the SVM approaches on the sample problem and their computational attractiveness for practical implementations.December 200

Incremental training of support vector machines

© 2005 IEEE. This is a publishers version of an article published in IEEE Transactions on Neural Networks 2005 published by IEEE. This version is reproduced under the journals author licence agreement. http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?puNumber=72We propose a new algorithm for the incremental training of support vector machines (SVMs) that is suitable for problems of sequentially arriving data and fast constraint parameter variation. Our method involves using a "warm-start" algorithm for the training of SVMs, which allows us to take advantage of the natural incremental properties of the standard active set approach to linearly constrained optimization problems. Incremental training involves quickly retraining a support vector machine after adding a small number of additional training vectors to the training set of an existing (trained) support vector machine. Similarly, the problem of fast constraint parameter variation involves quickly retraining an existing support vector machine using the same training set but different constraint parameters. In both cases, we demonstrate the computational superiority of incremental training over the usual batch retraining method