On the Heterogeneity of Dowry Motives

Dowries have been modeled as pre-mortem bequests to daughters or as groom-prices paid to in-laws. These two classes of models yield mutually exclusive predictions, but empirical tests of these predictions have been mixed. We argue that the heterogeneity of findings can be explained by a heterogeneous world--some households use dowries as a bequest and others use dowries as a price. We estimate a model with heterogeneous dowry motives and use the predictions from the competing theories in an exogenous switching regression to place households in the price or bequest regime. Our empirical strategy generates multiple, independent checks on the validity of regime assignment. Using retrospective marriage data from rural Bangladesh, we find robust evidence of heterogeneity in dowry motives in the population; that bequest dowries have declined in prevalence and amount over time; and that bequest households are better off compared to price households on a variety of welfare measures.

Developing procedures for assessment of ecological status of Indian River basins in the context of environmental water requirements

River basins / Ecology / Indicators / Environmental flows / Environmental management / Habitats / Biota / Fish / Ecosystems / India / Krishna River Basin / Chauvery River Basin / Narmada River Basin / Periyar River Basin / Ganga River Basin

Random matrix techniques in quantum information theory

The purpose of this review article is to present some of the latest developments using random techniques, and in particular, random matrix techniques in quantum information theory. Our review is a blend of a rather exhaustive review, combined with more detailed examples -- coming from research projects in which the authors were involved. We focus on two main topics, random quantum states and random quantum channels. We present results related to entropic quantities, entanglement of typical states, entanglement thresholds, the output set of quantum channels, and violations of the minimum output entropy of random channels

Parallel symbolic state-space exploration is difficult, but what is the alternative?

State-space exploration is an essential step in many modeling and analysis problems. Its goal is to find the states reachable from the initial state of a discrete-state model described. The state space can used to answer important questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a starting point for sophisticated investigations expressed in temporal logic. Unfortunately, the state space is often so large that ordinary explicit data structures and sequential algorithms cannot cope, prompting the exploration of (1) parallel approaches using multiple processors, from simple workstation networks to shared-memory supercomputers, to satisfy large memory and runtime requirements and (2) symbolic approaches using decision diagrams to encode the large structured sets and relations manipulated during state-space generation. Both approaches have merits and limitations. Parallel explicit state-space generation is challenging, but almost linear speedup can be achieved; however, the analysis is ultimately limited by the memory and processors available. Symbolic methods are a heuristic that can efficiently encode many, but not all, functions over a structured and exponentially large domain; here the pitfalls are subtler: their performance varies widely depending on the class of decision diagram chosen, the state variable order, and obscure algorithmic parameters. As symbolic approaches are often much more efficient than explicit ones for many practical models, we argue for the need to parallelize symbolic state-space generation algorithms, so that we can realize the advantage of both approaches. This is a challenging endeavor, as the most efficient symbolic algorithm, Saturation, is inherently sequential. We conclude by discussing challenges, efforts, and promising directions toward this goal

Memecylon edule leaf extract mediated green synthesis of silver and gold nanoparticles

We used an aqueous leaf extract of Memecylon edule (Melastomataceae) to synthesize silver and gold nanoparticles. To our knowledge, this is the first report where M. edule leaf broth was found to be a suitable plant source for the green synthesis of silver and gold nanoparticles. On treatment of aqueous solutions of silver nitrate and chloroauric acid with M. edule leaf extract, stable silver and gold nanoparticles were rapidly formed. The gold nanoparticles were characterized by UV-visible spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersive X-ray analysis (EDAX) and Fourier transform infra-red spectroscopy (FTIR). The kinetics of reduction of aqueous silver and gold ions during reaction with the M. edule leaf broth were easily analyzed by UV-visible spectroscopy. SEM analysis showed that aqueous gold ions, when exposed to M. edule leaf broth, were reduced and resulted in the biosynthesis of gold nanoparticles in the size range 20–50 nm. TEM analysis of gold nanoparticles showed formation of triangular, circular, and hexagonal shapes in the size range 10–45 nm. The resulting silver nanoparticles were predominantly square with uniform size range 50–90 nm. EDAX results confirmed the presence of triangular nanoparticles in the adsorption peak of 2.30 keV. Further FTIR analysis was also done to identify the functional groups in silver and gold nanoparticles. The characterized nanoparticles of M. edule have potential for various medical and industrial applications. Saponin presence in aqueous extract of M. edule is responsible for the mass production of silver and gold nanoparticles

Global distribution of a key trophic guild contrasts with common latitudinal diversity patterns

Most hypotheses explaining the general gradient of higher diversity toward the equator are implicit or explicit about greater species packing in the tropics. However, global patterns of diversity within guilds, including trophic guilds (i.e., groups of organisms that use similar food resources), are poorly known. We explored global diversity patterns of a key trophic guild in stream ecosystems, the detritivore shredders. This was motivated by the fundamental ecological role of shredders as decomposers of leaf litter and by some records pointing to low shredder diversity and abundance in the tropics, which contrasts with diversity patterns of most major taxa for which broad-scale latitudinal patterns haven been examined. Given this evidence, we hypothesized that shredders are more abundant and diverse in temperate than in tropical streams, and that this pattern is related to the higher temperatures and lower availability of high-quality leaf litter in the tropics. Our comprehensive global survey (129 stream sites from 14 regions on six continents) corroborated the expectedlatitudinal pattern and showed that shredder distribution (abundance, diversity and assemblage composition) was explained by a combination of factors, including water temperature (some taxa were restricted to cool waters) and biogeography (some taxa were more diverse in particular biogeographic realms). In contrast to our hypothesis, shredder diversity was unrelated to leaf toughness, but it was inversely related to litter diversity. Our findings markedly contrast with global trends of diversity for most taxa, and with the general rule of higher consumer diversity at higher levels of resource diversity. Moreover, they highlight the emerging role of temperature in understanding global patterns of diversity, which is of great relevance in the face of projected global warming. © 2011 by the Ecological Society of America.Peer Reviewe

Assessing microscope image focus quality with deep learning

Background Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image analysis with high accuracy is important for obtaining a clean, unbiased image dataset. Complicating this task is the fact that image focus quality is only well-defined in foreground regions of images, and as a result, most previous approaches only enable a computation of the relative difference in quality between two or more images, rather than an absolute measure of quality. Results We present a deep neural network model capable of predicting an absolute measure of image focus on a single image in isolation, without any user-specified parameters. The model operates at the image-patch level, and also outputs a measure of prediction certainty, enabling interpretable predictions. The model was trained on only 384 in-focus Hoechst (nuclei) stain images of U2OS cells, which were synthetically defocused to one of 11 absolute defocus levels during training. The trained model can generalize on previously unseen real Hoechst stain images, identifying the absolute image focus to within one defocus level (approximately 3 pixel blur diameter difference) with 95% accuracy. On a simpler binary in/out-of-focus classification task, the trained model outperforms previous approaches on both Hoechst and Phalloidin (actin) stain images (F-scores of 0.89 and 0.86, respectively over 0.84 and 0.83), despite only having been presented Hoechst stain images during training. Lastly, we observe qualitatively that the model generalizes to two additional stains, Hoechst and Tubulin, of an unseen cell type (Human MCF-7) acquired on a different instrument. Conclusions Our deep neural network enables classification of out-of-focus microscope images with both higher accuracy and greater precision than previous approaches via interpretable patch-level focus and certainty predictions. The use of synthetically defocused images precludes the need for a manually annotated training dataset. The model also generalizes to different image and cell types. The framework for model training and image prediction is available as a free software library and the pre-trained model is available for immediate use in Fiji (ImageJ) and CellProfiler