Arsenic in Rice Grown in Low-Arsenic Environments in Bangladesh

It has previously been reported that rice grown in regions of Bangladesh with low-arsenic (As) concentrations in irrigation water can have relatively high concentrations of As within their grains. This study aims to determine how widespread this issue is, and determine the seasonal variation in grain As in these regions. Levels of As were measured in shallow tube well (STW) water, soils, and rice grains collected during the Boro (dry) and Aman (wet) seasons from six Upazilas (sub-districts) of Bangladesh where As levels in groundwater were known to be low. In all the Upazilas, the As concentrations in STW water were <50 mu g L-1. The As levels in soil samples collected from the Upazilas ranged between 0.2-4.0 mgkg(-1) in the sam-ples collected during the Boro season, and 0.4-5.7 mg kg(-1) in the samples collected in the Aman season. Levels of As in both Boro and Aman rice grain varied widely: in Boro 0.02-0.45 mg kg(-1), and in Aman 0.01-0.29 mg kg(-1). Additionally, a household survey of dietary habits was also conducted in one Upazila by estimating As ingestion by 15 head female members. On average, the women consumed 3.1 L of water, 1.1 kg of cooked rice, and 42 g dry weight of curry per day. The total As ingestion rates ranged from 31.1-129.3 mu g day(-1) (mean 63.5 mu g kg(-1)). These findings indicate that the major route of As ingestion in low groundwater As areas of Bangladesh is rice, followed by curry and then water.

Enhancing Linear Programming with Motion Modeling for Multi-target Tracking

In this paper we extend the minimum-cost network flow approach to multi-target tracking, by incorporating a motion model, allowing the tracker to better cope with longterm occlusions and missed detections. In our new method, the tracking problem is solved iteratively: Firstly, an initial tracking solution is found without the help of motion information. Given this initial set of tracklets, the motion at each detection is estimated, and used to refine the tracking solution.
Finally, special edges are added to the tracking graph, allowing a further revised tracking solution to be found, where distant tracklets may be linked based on motion similarity. Our system has been tested on the PETS S2.L1 and Oxford town-center sequences, outperforming the baseline system, and achieving results comparable with the current state of the art.

A Programming Model and Runtime System for Significance-Aware Energy-Efficient Computing.

We introduce a task-based programming model and runtime system that exploit the observation that not all parts of a program are equally significant for the accuracy of the end-result, in order to trade off the quality of program outputs for increased energy-efficiency. This is done in a structured and flexible way, allowing for easy exploitation of different points in the quality/energy space, without adversely affecting application performance. The runtime system can apply a number of different policies to decide whether it will execute less-significant tasks accurately or approximately.

The experimental evaluation indicates that our system can achieve an energy reduction of up to 83% compared with a fully accurate execution and up to 35% compared with an approximate version employing loop perforation. At the same time, our approach always results in graceful quality degradation.

Approximate credal network updating by linear programming with applications to decision making

Credal nets are probabilistic graphical models which extend Bayesian nets to cope with sets of distributions. An algorithm for approximate credal network updating is presented. The problem in its general formulation is a multilinear optimization task, which can be linearized by an appropriate rule for fixing all the local models apart from those of a single variable. This simple idea can be iterated and quickly leads to accurate inferences. A transformation is also derived to reduce decision making in credal networks based on the maximality criterion to updating. The decision task is proved to have the same complexity of standard inference, being NPPP-complete for general credal nets and NP-complete for polytrees. Similar results are derived for the E-admissibility criterion. Numerical experiments confirm a good performance of the method.

Inference in Credal Networks Through Integer Programming

A credal network associates a directed acyclic graph with a collection of sets of probability measures; it offers a compact representation for sets of multivariate distributions. In this paper we present a new algorithm for inference in credal networks based on an integer programming reformulation. We are concerned with computation of lower/upper probabilities for a variable in a given credal network. Experiments reported in this paper indicate that this new algorithm has better performance than existing ones for some important classes of networks.

Inference in credal networks using multilinear programming

A credal network is a graphical tool for representation and manipulation of uncertainty, where probability values may be imprecise or indeterminate. A credal network associates a directed acyclic graph with a collection of sets of probability measures; in this context, inference is the computation of tight lower and upper bounds for conditional probabilities. In this paper we present new algorithms for inference in credal networks based on multilinear programming techniques. Experiments indicate that these new algorithms have better performance than existing ones, in the sense that they can produce more accurate results in larger networks.

A multivariate analysis of beta diversity across organisms and environments

We examined variability in hierarchical beta diversity across ecosystems, geographical gradients, and organism groups using multivariate spatial mixed modeling analysis of two independent data sets. The larger data set comprised reported ratios of regional species richness (RSR) to local species richness (LSR) and the second data set consisted of RSR: LSR ratios derived from nested species-area relationships. There was a negative, albeit relatively weak, relationship between beta diversity and latitude. We found only relatively subtle differences in beta diversity among the realms, yet beta diversity was lower in marine systems than in terrestrial or freshwater realms. Beta diversity varied significantly among organisms' major characteristics such as body mass, trophic position, and dispersal type in the larger data set. Organisms that disperse via seeds had highest beta diversity, and passively dispersed organisms showed the lowest beta diversity. Furthermore, autotrophs had lower beta diversity than organisms higher up the food web; omnivores and carnivores had consistently higher beta diversity. This is evidence that beta diversity is simultaneously controlled by extrinsic factors related to geography and environment, and by intrinsic factors related to organism characteristics.

Combining multi-band and frequency-filtering techniques for speech recognition in noisy environments

While current speech recognisers give acceptable performance in carefully controlled environments, their performance degrades rapidly when they are applied in more realistic situations. Generally, the environmental noise may be classified into two classes: the wide-band noise and narrow band noise. While the multi-band model has been shown to be capable of dealing with speech corrupted by narrow-band noise, it is ineffective for wide-band noise. In this paper, we suggest a combination of the frequency-filtering technique with the probabilistic union model in the multi-band approach. The new system has been tested on the TIDIGITS database, corrupted by white noise, noise collected from a railway station, and narrow-band noise, respectively. The results have shown that this approach is capable of dealing with noise of narrow-band or wide-band characteristics, assuming no knowledge about the noisy environment.

Diabetic retinopathy:morphometric analysis of basement membrane thickening of capillaries in different retinal layers within arterial and venous environments

AIMS: To assess quantitatively variations in the extent of capillary basement membrane (BM) thickening between different retinal layers and within arterial and venous environments during diabetes.

METHODS: One year after induction of experimental (streptozotocin) diabetes in rats, six diabetic animals together with six age-matched control animals were sacrificed and the retinas fixed for transmission electron microscopy (TEM). Blocks of retina straddling the major arteries and veins in the central retinal were dissected out, embedded in resin, and sectioned. Capillaries in close proximity to arteries or veins were designated as residing in either an arterial (AE) or a venous (VE) environment respectively, and the retinal layer in which each capillary was located was also noted. The thickness of the BM was then measured on an image analyser based two dimensional morphometric analysis system.

RESULTS: In both diabetics and controls the AE capillaries had consistently thicker BMs than the VE capillaries. The BMs of both AE and VE capillaries from diabetics were thicker than those of capillaries in the corresponding retinal layer from the normal rats (p < or = 0.005). Also, in normal AE and VE capillaries and diabetic AE capillaries the BM in the nerve fibre layer (NFL) was thicker than that in either the inner (IPL) or outer (OPL) plexiform layers (p < or = 0.001). However, in diabetic VE capillaries the BMs of capillaries in the NFL were thicker than those of capillaries in the IPL (p < or = 0.05) which, in turn, had thicker BMs than capillaries in the OPL (p < or = 0.005).

CONCLUSIONS: The variation in the extent of capillary BM thickening between different retinal layers within AE and VE environments may be related to differences in levels of oxygen tension and oxidative stress in the retina around arteries compared with that around veins.

Knowledge Exchange, Spatial Analysis & Healthy Urban Environments (KESUE): Project Report

The Knowledge Exchange, Spatial Analysis and Healthy Urban Environments (KESUE) project has extended work previously undertaken by a QUB team of inter-disciplinary researchers engaged with the Physical Activity in the Regeneration of Connswater (PARC) project (Tully et al, 2013). The PARC project focussed on parts of East Belfast to assess the health impact of the Connswater Community Greenway. The KESUE project has aimed to extend some of the tools used initially in East Belfast so that they have data coverage of all of Belfast and Derry-Londonderry. The purpose of this has been to enable the development of evidence and policy tools that link features of the built environment with physical activity in these two cities. The project has used this data to help shape policy decisions in areas such as physical activity, park management, public transport and planning.

Working with a range of local partners who part-funded the project (City Councils in Belfast and Derry-Londonderry, Public Health Agency, Belfast Healthy Cities and Department of Regional Development), this project has mapped all the footpaths in the two cities (covering 37% of the NI population) and employed this to develop evidence used in strategies related to healthy urban planning. Using Geographic Information Systems (GIS), the footpath network has been used as a basis for a wide range of policy-relevant analyses including pedestrian accessibility to public facilities, site options for new infrastructure and assessing how vulnerable groups can access services such as pharmacies. Key outputs have been Accessibility Atlases and maps showing how walkability of the built environment varies across the two cities.

In addition to generating this useful data, the project included intense engagement with potential users of the research, which has led to its continued uptake in a number of policies and strategies, creating a virtuous circle of research, implementation and feedback. The project has proved so valuable to Belfast City Council that they have now taken on one of the researchers to continue the work in-house.