A conditional random field-based downscaling method for assessment of climate change impact on multisite daily precipitation in the Mahanadi basin

Downscaling to station-scale hydrologic variables from large-scale atmospheric variables simulated by general circulation models (GCMs) is usually necessary to assess the hydrologic impact of climate change. This work presents CRF-downscaling, a new probabilistic downscaling method that represents the daily precipitation sequence as a conditional random field (CRF). The conditional distribution of the precipitation sequence at a site, given the daily atmospheric (large-scale) variable sequence, is modeled as a linear chain CRF. CRFs do not make assumptions on independence of observations, which gives them flexibility in using high-dimensional feature vectors. Maximum likelihood parameter estimation for the model is performed using limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimization. Maximum a posteriori estimation is used to determine the most likely precipitation sequence for a given set of atmospheric input variables using the Viterbi algorithm. Direct classification of dry/wet days as well as precipitation amount is achieved within a single modeling framework. The model is used to project the future cumulative distribution function of precipitation. Uncertainty in precipitation prediction is addressed through a modified Viterbi algorithm that predicts the n most likely sequences. The model is applied for downscaling monsoon (June-September) daily precipitation at eight sites in the Mahanadi basin in Orissa, India, using the MIROC3.2 medium-resolution GCM. The predicted distributions at all sites show an increase in the number of wet days, and also an increase in wet day precipitation amounts. A comparison of current and future predicted probability density functions for daily precipitation shows a change in shape of the density function with decreasing probability of lower precipitation and increasing probability of higher precipitation.

Occupational release of engineered nanoparticles: A review

Characterising the release of different types of Engineered Nanoparticles (ENPs) from various processes is of critical importance for the assessment of human exposure, as well as understanding the possible health effects of these particles. Therefore, the main aim of this chapter is to present a comprehensive review of studies which report on the release of airborne ENPs in different nanotechnology workplaces. The chapter will cover topics of relevance to the occupational characterisation of ENP emissions, ranging from the identification of different particle release sources and scenarios, to measurement methods and working towards a more uniform approach to characterisation. Furthermore, a brief review of ENP exposure control strategies, together with the application of mathematical modelling as an effective tool for the characterisation of emissions at nanotechnology workplaces is included.

Encapsulation and release of rifampicin using poly(vinyl pyrrolidone)-poly (methacrylic acid) polyelectrolyte capsules

Poly(vinyl pyrrolidone) and poly(methacrylic acid) multilayer capsules based on hydrogen bonding have been prepared by the layer-by-layer approach and used to encapsulate and release rifampicin, an antituberculosis drug. Removal of silica core using a buffer of ammonium fluoride and hydrofluoric acid at about pH 3 was found to produce better capsules than hydrofluoric acid alone. An eight-layered capsule had a wall thickness of 20 rim. Maximum encapsulation was found to be about 86 mu g at 40 degrees C with 1 +/- 0.2 x 10(6) capsules. Release studies showed a burst kind of release and maximum release was obtained above pH 7 where the capsules disintegrate rapidly thereby releasing the drug in a short period. Interactions studies with Mycobacterium smegmatis showed that the capsules were cytocompatible and the released drug functioned with the same efficacy as the free drug.

Influence of surface chemistry of mesoporous alumina with wide pore distribution on controlled drug release

The crucial role of the drug carrier surface chemical moeities on the uptake and in vitro release of drug is discussed here in a systematic manner. Mesoporous alumina with a wide pore size distribution (2-7 nm) functionalized with various hydrophilic and hydrophobic surface chemical groups was employed as the carrier for delivery of the model drug ibuprofen. Surface functionalization with hydrophobic groups resulted in low degree of drug loading (approximately 20%) and fast rate of release (85% over a period of 5 h) whereas hydrophilic groups resulted in a significantly higher drug payloads (21%-45%) and slower rate of release (12%-40% over a period of 5 h). Depending on the chemical moiety, the diffusion controlled (proportional to time(-0.5)) drug release was additionally observed to be dependent on the mode of arrangement of the functional groups on the alumina surface as well as on the pore characteristics of the matrix. For all mesoporous alumina systems the drug dosages were far lower than the maximum recommended therapeutic dosages (MRTD) for oral delivery. We envisage that the present study would aid in the design of delivery systems capable of sustained release of multiple drugs.

Cu2+-induced room temperature hydrogen release from ammonia borane

Mechanical stirring of ammonia borane with CuCl2 in the solid state resulted in the release of hydrogen at room temperature through the intermediacy of [NH4](+)[BCl4](-).

Effect of deletion of Ghrelin-O-Acyltransferase on the pulsatile release of growth hormone in mice

Ghrelin, a gut hormone originating from the post-translational cleavage of preproghrelin, is the endogenous ligand of growth hormone secretagogue receptor 1a (GHS-R1a). Within the growth hormone (GH) axis, the biological activity of ghrelin requires octanoylation by ghrelin-O-acyltransferase (GOAT), conferring selective binding to the GHS-R1a receptor via acylated ghrelin. Complete loss of preproghrelin-derived signalling (through deletion of the Ghrl gene) contributes to a decline in peak GH release; however, the selective contribution of endogenous acyl-ghrelin to pulsatile GH release remains to be established. We assessed the pulsatile release of GH in ad lib. fed male germline goat−/− mice, extending measures to include mRNA for key hypothalamic regulators of GH release, and peripheral factors that are modulated relative to GH release. The amount of GH released was reduced in young goat−/− mice compared to age-matched wild-type mice, whereas pulse frequency and irregularity increased. Altered GH release did not coincide with alterations in hypothalamic Ghrh, Srif, Npy or Ghsr mRNA expression, or pituitary GH content, suggesting that loss of Goat does not compromise canonical mechanisms that contribute to pituitary GH production and release. Although loss of Goat resulted in an irregular pattern of GH release (characterised by an increase in the number of GH pulses observed during extended secretory events), this did not contribute to a change in the expression of sexually dimorphic GH-dependent liver genes. Of interest, circulating levels of insulin-like growth factor (IGF)-1 were elevated in goat−/− mice. This rise in circulating levels of IGF-1 was correlated with an increase in GH pulse frequency, suggesting that sustained or increased IGF-1 release in goat−/− mice may occur in response to altered GH release patterning. Our observations demonstrate that germline loss of Goat alters GH release and patterning. Although the biological relevance of altered GH secretory patterning remains unclear, we propose that this may contribute to sustained IGF-1 release and growth in goat−/− mice.

Effects of compressibility and heat release on entrainment processes in mixing layers

Characteristics of the process of entrainment in plane mixing layers, and the changes with compressibility and heat release, were studied using temporal DNS with simultaneous fluid packet tracking. Convective Mach numbers of the simulations are 0.15, 0.7 and 1.1. The Reynolds number is quite high (between 11 000 and 37 000 based on layer width and velocity difference), and is above the mixing transition. The study agrees with recent findings in round jets: first, engulfed fluid volume and its growth rate are both very small compared with the volume of the turbulent region and its growth rate, respectively. Secondly, most often, the process occurs close to the turbulent-nonturbulent boundaries. A new finding is that both compressibility and heat release retard the entrainment process so that it takes an O(1) time for vorticity or scalar levels to grow even after growth has been initiated. This delay is manifested as the fall in mixing layer growth rates as compressibility and heat release levels increase.

Conditional Probability-Based Significance Tests for Sequential Patterns in Multineuronal Spike Trains

We consider the problem of detecting statistically significant sequential patterns in multineuronal spike trains. These patterns are characterized by ordered sequences of spikes from different neurons with specific delays between spikes. We have previously proposed a data-mining scheme to efficiently discover such patterns, which occur often enough in the data. Here we propose a method to determine the statistical significance of such repeating patterns. The novelty of our approach is that we use a compound null hypothesis that not only includes models of independent neurons but also models where neurons have weak dependencies. The strength of interaction among the neurons is represented in terms of certain pair-wise conditional probabilities. We specify our null hypothesis by putting an upper bound on all such conditional probabilities. We construct a probabilistic model that captures the counting process and use this to derive a test of significance for rejecting such a compound null hypothesis. The structure of our null hypothesis also allows us to rank-order different significant patterns. We illustrate the effectiveness of our approach using spike trains generated with a simulator.

Deeper and wider fully convolutional network coupled with conditional random fields for scene labeling

Deep convolutional neural networks (DCNNs) have been employed in many computer vision tasks with great success due to their robustness in feature learning. One of the advantages of DCNNs is their representation robustness to object locations, which is useful for object recognition tasks. However, this also discards spatial information, which is useful when dealing with topological information of the image (e.g. scene labeling, face recognition). In this paper, we propose a deeper and wider network architecture to tackle the scene labeling task. The depth is achieved by incorporating predictions from multiple early layers of the DCNN. The width is achieved by combining multiple outputs of the network. We then further refine the parsing task by adopting graphical models (GMs) as a post-processing step to incorporate spatial and contextual information into the network. The new strategy for a deeper, wider convolutional network coupled with graphical models has shown promising results on the PASCAL-Context dataset.

A real coded genetic algorithm for data partitioning and scheduling in networks with arbitrary processor release time

The problem of scheduling divisible loads in distributed computing systems, in presence of processor release time is considered. The objective is to find the optimal sequence of load distribution and the optimal load fractions assigned to each processor in the system such that the processing time of the entire processing load is a minimum. This is a difficult combinatorial optimization problem and hence genetic algorithms approach is presented for its solution.