Studies on Desi and Kabuli Chickpea (Cicer arietinum L.) Cultivars.The Levels of Amylase Inhibitots, Levels of Oligosaccharides and In Vitro Starch Digestibility

Amylase inhibitor activity (AIA) of chickpea extracts was investigated usmg pancreatic and salivary amylases. The extracts showed higher inhibitor activity towards pancreatic amylase than salivary amylase..Mean values indicated slightly higher inhibitory activity in desi than kabuli cultivars, though clear-cut differences were..not observed among- the cultivars. While in vitro starch digestibility of meal samples indicated no large differences among desi and kabuli types of chickpea, the mean values of digestibility of- isolated starches of kabuli -types wasp higher than those -of desi types: The mean values of stachyose were higher in desi cultivars. When desi and kabuli types were considered together, stachyose- and raffmose contents were not found significantly related to the concentrations of total soluble sugars while stachyose showed a significant correlation with raftinose

Appraisal of laboratory culture experiments on benthic foraminifera to assess/develop paleoceanographic proxies

301-321The laboratory culture studies, carried out on benthic foraminifera, with the aim to refine paleoceanographic/paleoclimatic or environmental application of benthic foraminifera, have been reviewed. The review includes studies, which refined the understanding of factors that bring out changes in benthic foraminiferal abundance, morphology and chemical composition (of the test). Additionally, studies dealing with taxonomic aspects of benthic foraminifera have also been discussed, since such studies have significantly improved application of benthic foraminifera for stratigraphic correlation. Most of the laboratory culture studies on benthic foraminifera in the early days were carried out to monitor the complete life-cycle of selected species. Such studies revealed presence of morphologically different stages in the life-cycle of single species. Thus the forms that were earlier recognized as different species were later on clubbed as developmental or ontogenetic stages of single species. Interesting relationship between mode of reproduction and coiling direction were also observed. Later on, with the growing application of foraminiferal characteristics for past climatic and oceanographic reconstruction, benthic foraminifera were maintained under controlled physico-chemical conditions in the laboratory. Such studies helped to refine the differences in the foraminiferal characteristics from physico-chemically different environments, as observed in the field. As it was proposed that the amount and type of food material is the major factor that controls the benthic foraminiferal population, numerous studies were carried out to assess the response of benthic foraminifera to different type and amount of food and oxygen concentration. Surprisingly limited laboratory culture studies have been carried out to understand the factors that govern the chemical composition of the benthic foraminiferal tests. It probably reflects the difficulties in simulating the conditions under which physico-chemical parameters can be kept constant throughout the experiment. Towards the end of 20th century application of molecular systematic analysis techniques on foraminifera started and such studies refined the evolutionary history and taxonomic position of foraminifera as well as helped recognize cryptic species. However, despite a large number of culture studies being carried out on benthic foraminifera with their paleoceanographic/paleoclimatic application in focus, still much more efforts are needed to understand the parameters affecting the benthic foraminiferal abundance, morphology and chemical composition

Learning to separate text content and style for classification

Many text documents naturally have two kinds of labels. For example, we may label web pages from universities according to their categories, such as “student” or “faculty”, or according the source universities, such as “Cornell” or “Texas”. We call one kind of labels the content and the other kind the style. Given a set of documents, each with both content and style labels, we seek to effectively learn to classify a set of documents in a new style with no content labels into its content classes. Assuming that every document is generated using words drawn from a mixture of two multinomial component models, one content model and one style model, we propose a method named Cartesian EM that constructs content models and style models through Expectation Maximization and performs classification of the unknown content classes transductively. Our experiments on real-world datasets show the proposed method to be effective for style independent text content classification

A PAC-style model for learning from labeled and unlabeled data

There has recently been substantial interest in practice in using unlabeled data together with labeled data in machine learning, and a number of di erent approaches have been developed. However, the assumptions these methods are based on are often quite distinct and not captured by standard theoretical models. In this paper we describe a PAC-style model that captures many of these assumptions, and analyze sample-complexity issues in this setting: that is, how much ofeachtype of data one should expect to need in order to learn well, and what are the basic quantities that these numbers depend on. Our model can be viewed as an extension of the standard PAC model, where in addition to a concept class C, one also proposes a type of compatibility that one believes the target concept should have with the underlying distribution. In this view, unlabeled data can be helpful because it allows one to estimate compatibility over the space of hypotheses, and reduce the size of the search space to those that are, in a sense, a-priori reasonable with respect to the distribution. We discuss a number of technical issues that arise in this context, and provide sample-complexity bounds both for uniform convergence and speci c-cover based algorithms. We also consider algorithmic issues, and give an algorithm for a natural problem of learning a linear separator from example-pairs, motivated by co-training.