In vitro micropropagation of elite rosewood (Dalbergia latifolia Roxb

Induction of single and multiple shoots was obtained from nodal expiants of 60–80 year-old elite trees of rosewood on Murashige and Skoog's basal medium supplemented with 6-benzylaminopurine (1.0 mg 1-1) and delta -Naphthalene acetic acid (0.05 mg 1-1) or indole acetic acid (0.5 mg 1-1). Multiplication of shoots was obtained on MS (reduced major elements) or Woody Plant Medium supplemented with 6-benzylaminopurine (1.0 mg 1-1) and kinetin (0.5–1.0 mg 1-1). Excised shoots were rooted on half-strength MS with IBA (2.0 mg 1-1) to obtain complete plantlets. The regenerated plantlets have been acclimatized and successfully transferred to the soil.

Biodiversity and India's Degraded Lands

''Ecosystem people'' of the world subsist by producing or gathering a diversity of biological resources from their immediate vicinity. Their quality of life is intimately linked to the maintenance of modest levels of biodiversity in their own circumscribed resource catchments. Their resource base has been extensively degraded by pressures created by ''biosphere people''; i.e. the Third World elite and citizens of industrial countries, who can draw resources from all over the world and are thus, indifferent to environmental degradation in the Third World. Because ''ecosystem people'' have a genuine stake in biodiversity maintenance in their immediate surrounding, it is important that conservation efforts include maintenance and restoration of at least modest levels of biodiversity throughout the Third World. In the case of India this may be achieved by (a) dedicating the bulk of reserve forests to production of nontimber forest produce (NTFP), to support rural economy; (b) organizing effective community-based management systems to fulfill subsistence biomass requirements of peasants and tribals; (c) encouraging a switchover from shifting cultivation to horticulture; (d) supporting traditional practices of growing a variety of plant species, including keystone resources like Ficus spp, in rural habitats and on roadsides, farm and canal bunds; and (e) promoting tree farming on private lands to fulfill commercial needs.

Comparison of North American universities with and without medical schools and their technology transfer initiatives

Empirical research available on technology transfer initiatives is either North American or European. Literature over the last two decades shows various research objectives such as identifying the variables to be measured and statistical methods to be used in the context of studying university based technology transfer initiatives. AUTM survey data from years 1996 to 2008 provides insightful patterns about the North American technology transfer initiatives, we use this data in our paper. This paper has three sections namely, a comparison of North American Universities with (n=1129) and without Medical Schools (n=786), an analysis of the top 75th percentile of these samples and a DEA analysis of these samples. We use 20 variables. Researchers have attempted to classify university based technology transfer initiative variables into multi-stages, namely, disclosures, patents and license agreements. Using the same approach, however with minor variations, three stages are defined in this paper. The first stage is to do with inputs from R&D expenditure and outputs namely, invention disclosures. The second stage is to do with invention disclosures being the input and patents issued being the output. The third stage is to do with patents issued as an input and technology transfers as outcomes.

Differentially private stochastic gradient descent algorithm for multiparty classification

We consider the problem of developing privacy-preserving machine learning algorithms in a dis-tributed multiparty setting. Here different parties own different parts of a data set, and the goal is to learn a classifier from the entire data set with-out any party revealing any information about the individual data points it owns. Pathak et al [7]recently proposed a solution to this problem in which each party learns a local classifier from its own data, and a third party then aggregates these classifiers in a privacy-preserving manner using a cryptographic scheme. The generaliza-tion performance of their algorithm is sensitive to the number of parties and the relative frac-tions of data owned by the different parties. In this paper, we describe a new differentially pri-vate algorithm for the multiparty setting that uses a stochastic gradient descent based procedure to directly optimize the overall multiparty ob-jective rather than combining classifiers learned from optimizing local objectives. The algorithm achieves a slightly weaker form of differential privacy than that of [7], but provides improved generalization guarantees that do not depend on the number of parties or the relative sizes of the individual data sets. Experimental results corrob-orate our theoretical findings.