Healthy child, healthy future - Speech and language therapy: Factsheet for parents 5. Does my child have a voice problem?

This factsheet describes voice disorders such as 'hoarseness' in children and what parents can do to help their child with a voice problem.

The problem of Antimicrobial Resistance in the food chain

Antimicrobial resistance (AMR) associated with the food chain is currently a subject of major interest to many food chain stakeholders. In response safefood commissioned this report to update our knowledge of this area and to raise awareness of the issue. Its primary focus is on the food chain where it impacts consumer health. This review will inform and underpin any future action to be taken by safefood with regard to AMR.

The Road to Recovery: A New Approach to Tackling Scotland's Drug Problem

The Scottish National Drugs Strategy requires the 22 regional Drug Action Teams to prepare and submit to the Scottish Executive annual action plans for tackling drug misuse in their areas. These plans should address national and local priorities, including their contribution to the achievement of national targets. These comprise three parts: Part A provides an overview of the DAT structures and working; Part B provides detailed information on current local services and Part C reports plans for 2003/04.This resource was contributed by The National Documentation Centre on Drug Use.

The Size of the Problem: NAO Report - Tackling Obesity in England

Powerpoint presentation by Rob Pridequx from the National Audit office

Healthy child, healthy future - Speech and language therapy: Factsheet for parents 5. Does my child have a voice problem?

This factsheet describes voice disorders such as 'hoarseness' in children and what parents can do to help their child with a voice problem.

Thyroid hormone enhances transected axonal regeneration and muscle reinnervation following rat sciatic nerve injury.

Improvement of nerve regeneration and functional recovery following nerve injury is a challenging problem in clinical research. We have already shown that following rat sciatic nerve transection, the local administration of triiodothyronine (T3) significantly increased the number and the myelination of regenerated axons. Functional recovery is a sum of the number of regenerated axons and reinnervation of denervated peripheral targets. In the present study, we investigated whether the increased number of regenerated axons by T3-treatment is linked to improved reinnervation of hind limb muscles. After transection of rat sciatic nerves, silicone or biodegradable nerve guides were implanted and filled with either T3 or phosphate buffer solution (PBS). Neuromuscular junctions (NMJs) were analyzed on gastrocnemius and plantar muscle sections stained with rhodamine alpha-bungarotoxin and neurofilament antibody. Four weeks after surgery, most end-plates (EPs) of operated limbs were still denervated and no effect of T3 on muscle reinnervation was detected at this stage of nerve repair. In contrast, after 14 weeks of nerve regeneration, T3 clearly enhanced the reinnervation of gastrocnemius and plantar EPs, demonstrated by significantly higher recovery of size and shape complexity of reinnervated EPs and also by increased acetylcholine receptor (AChRs) density on post synaptic membranes compared to PBS-treated EPs. The stimulating effect of T3 on EP reinnervation is confirmed by a higher index of compound muscle action potentials recorded in gastrocnemius muscles. In conclusion, our results provide for the first time strong evidence that T3 enhances the restoration of NMJ structure and improves synaptic transmission.

Using problem-based learning for introducing producer theory and market structure in intermediate microeconomics

This paper shows how instructors can use the problem‐based learning method to introduce producer theory and market structure in intermediate microeconomics courses. The paper proposes a framework where different decision problems are presented to students, who are asked to imagine that they are the managers of a firm who need to solve a problem in a particular business setting. In this setting, the instructors’ role isto provide both guidance to facilitate student learning and content knowledge on a just‐in‐time basis

Use of high-resolution geophysical data to characterize heterogeneous aquifers: influence of data integration method on hydrological predictions

The integration of geophysical data into the subsurface characterization problem has been shown in many cases to significantly improve hydrological knowledge by providing information at spatial scales and locations that is unattainable using conventional hydrological measurement techniques. The investigation of exactly how much benefit can be brought by geophysical data in terms of its effect on hydrological predictions, however, has received considerably less attention in the literature. Here, we examine the potential hydrological benefits brought by a recently introduced simulated annealing (SA) conditional stochastic simulation method designed for the assimilation of diverse hydrogeophysical data sets. We consider the specific case of integrating crosshole ground-penetrating radar (GPR) and borehole porosity log data to characterize the porosity distribution in saturated heterogeneous aquifers. In many cases, porosity is linked to hydraulic conductivity and thus to flow and transport behavior. To perform our evaluation, we first generate a number of synthetic porosity fields exhibiting varying degrees of spatial continuity and structural complexity. Next, we simulate the collection of crosshole GPR data between several boreholes in these fields, and the collection of porosity log data at the borehole locations. The inverted GPR data, together with the porosity logs, are then used to reconstruct the porosity field using the SA-based method, along with a number of other more elementary approaches. Assuming that the grid-cell-scale relationship between porosity and hydraulic conductivity is unique and known, the porosity realizations are then used in groundwater flow and contaminant transport simulations to assess the benefits and limitations of the different approaches.

A Bayesian network approach to the database serch problem in criminal proceedings

Background The 'database search problem', that is, the strengthening of a case - in terms of probative value - against an individual who is found as a result of a database search, has been approached during the last two decades with substantial mathematical analyses, accompanied by lively debate and centrally opposing conclusions. This represents a challenging obstacle in teaching but also hinders a balanced and coherent discussion of the topic within the wider scientific and legal community. This paper revisits and tracks the associated mathematical analyses in terms of Bayesian networks. Their derivation and discussion for capturing probabilistic arguments that explain the database search problem are outlined in detail. The resulting Bayesian networks offer a distinct view on the main debated issues, along with further clarity. Methods As a general framework for representing and analyzing formal arguments in probabilistic reasoning about uncertain target propositions (that is, whether or not a given individual is the source of a crime stain), this paper relies on graphical probability models, in particular, Bayesian networks. This graphical probability modeling approach is used to capture, within a single model, a series of key variables, such as the number of individuals in a database, the size of the population of potential crime stain sources, and the rarity of the corresponding analytical characteristics in a relevant population. Results This paper demonstrates the feasibility of deriving Bayesian network structures for analyzing, representing, and tracking the database search problem. The output of the proposed models can be shown to agree with existing but exclusively formulaic approaches. Conclusions The proposed Bayesian networks allow one to capture and analyze the currently most well-supported but reputedly counter-intuitive and difficult solution to the database search problem in a way that goes beyond the traditional, purely formulaic expressions. The method's graphical environment, along with its computational and probabilistic architectures, represents a rich package that offers analysts and discussants with additional modes of interaction, concise representation, and coherent communication.

Autonomous underwater vehicle control using reinforcement learning policy search methods

Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task

Systematic survey of clonal complexity in tuberculosis at a populational level and detailed characterization of the isolates involved.

Clonally complex infections by Mycobacterium tuberculosis are progressively more accepted. Studies of their dimension in epidemiological scenarios where the infective pressure is not high are scarce. Our study systematically searched for clonally complex infections (mixed infections by more than one strain and simultaneous presence of clonal variants) by applying mycobacterial interspersed repetitive-unit (MIRU)-variable-number tandem-repeat (VNTR) analysis to M. tuberculosis isolates from two population-based samples of respiratory (703 cases) and respiratory-extrapulmonary (R+E) tuberculosis (TB) cases (71 cases) in a context of moderate TB incidence. Clonally complex infections were found in 11 (1.6%) of the respiratory TB cases and in 10 (14.1%) of those with R+E TB. Among the 21 cases with clonally complex TB, 9 were infected by 2 independent strains and the remaining 12 showed the simultaneous presence of 2 to 3 clonal variants. For the 10 R+E TB cases with clonally complex infections, compartmentalization (different compositions of strains/clonal variants in independent infected sites) was found in 9 of them. All the strains/clonal variants were also genotyped by IS6110-based restriction fragment length polymorphism analysis, which split two MIRU-defined clonal variants, although in general, it showed a lower discriminatory power to identify the clonal heterogeneity revealed by MIRU-VNTR analysis. The comparative analysis of IS6110 insertion sites between coinfecting clonal variants showed differences in the genes coding for a cutinase, a PPE family protein, and two conserved hypothetical proteins. Diagnostic delay, existence of previous TB, risk for overexposure, and clustered/orphan status of the involved strains were analyzed to propose possible explanations for the cases with clonally complex infections. Our study characterizes in detail all the clonally complex infections by M. tuberculosis found in a systematic survey and contributes to the characterization that these phenomena can be found to an extent higher than expected, even in an unselected population-based sample lacking high infective pressure.

Proposal of a parallel architecture for a motion detection algorithm

This paper proposes a parallel architecture for estimation of the motion of an underwater robot. It is well known that image processing requires a huge amount of computation, mainly at low-level processing where the algorithms are dealing with a great number of data. In a motion estimation algorithm, correspondences between two images have to be solved at the low level. In the underwater imaging, normalised correlation can be a solution in the presence of non-uniform illumination. Due to its regular processing scheme, parallel implementation of the correspondence problem can be an adequate approach to reduce the computation time. Taking into consideration the complexity of the normalised correlation criteria, a new approach using parallel organisation of every processor from the architecture is proposed