Lessons learned from developing a national NCD programme in low- and middle-income countries (Seychelles)

This paper identifies selected issues and lessons learned from the implementation of a national program of prevention and control of non-communicable diseases (NCD) during the past 20 years in the Seychelles, a small island state in the African region. As early as in 1989, population-based surveys demonstrated high levels of several cardiovascular risk factors, which prompted an organized response by the government. The early creation of a NCD unit within the Ministry of Health, coupled with cooperation with international partners, enabled incremental capacity building and coherent development of NCD programs and policy. Information campaigns and screening for hypertension and diabetes in work/public places raised awareness and rallied increasingly broad awareness and support to NCD prevention and control. A variety of interventions were organized for tobacco control and comprehensive tobacco control legislation was enacted in 2009 (including total bans on tobacco advertising and on smoking in all enclosed public and work places). A recent School Nutrition Policy prohibits the sale of soft drinks in schools. At primary health care level, guidelines were developed for the management of hypertension and diabetes (these conditions are managed in all health centers within a national health system); regular interactive education sessions were organized for groups of high risk patients ("heart health club"); and specialized "NCD nurses" were trained. Decreasing prevalence of smoking is evidence of success, but the raising "diabesity epidemic" calls for strengthened health care to high-risk patients and broader multisectoral policy to mould an environment conducive to healthy behaviors. Key components of NCD prevention and control in Seychelles include effective surveillance mechanisms supplemented by focused research; generating broad interest and consensus on the need for prevention and control of NCD; mobilizing leadership and commitment at all levels; involving local and international expertise; building on existing efforts; and seeking integrated, multi-disciplinary and multisectoral approaches.

A Delphi study on National PICU nursing research priorities in Australia and New Zealand.

BACKGROUND: There is a lack of evidence to direct and support nursing practice in the specialty of paediatric intensive care (PIC). The development of national PIC nursing research priorities may facilitate the process of undertaking clinical research and translating evidence into practice. PURPOSE: To (a) identify research priorities for the care of patients and their family as well as for the professional needs of PIC nurses, (b) foster nursing research collaboration, (c) develop a research agenda for PIC nurses. METHODS: Over 13 months in 2007-2008, a three-round questionnaire, using the Delphi technique, was sent to all specialist level registered nurses working in Australian and New Zealand PICUs. This method was used to identify and prioritise nursing research topics. Content analysis was used to analyse Round I data and descriptive statistics for Round II and III data. RESULTS: In Round I, 132 research topics were identified, with 77 research priorities (mdn>6, mean MAD(median) 0.68±0.01) identified in subsequent rounds. The top nine priorities (mean>6 and median>6) included patient issues related to neurological care (n=2), pain/sedation/comfort (n=3), best practice at the end of life (n=1), and ventilation strategies (n=1), as well as two priorities related to professional issues about nurses' stress/burnout and professional development needs. CONCLUSION: The research priorities identified reflect important issues related to critically ill patients and their family as well as to the nurses caring for them. These priorities can be used for the development of a research agenda for PIC nursing in Australia and New Zealand.

Statistical learning theory for geospatial data. Case study: Aral sea

In recent years there has been an explosive growth in the development of adaptive and data driven methods. One of the efficient and data-driven approaches is based on statistical learning theory (Vapnik 1998). The theory is based on Structural Risk Minimisation (SRM) principle and has a solid statistical background. When applying SRM we are trying not only to reduce training error ? to fit the available data with a model, but also to reduce the complexity of the model and to reduce generalisation error. Many nonlinear learning procedures recently developed in neural networks and statistics can be understood and interpreted in terms of the structural risk minimisation inductive principle. A recent methodology based on SRM is called Support Vector Machines (SVM). At present SLT is still under intensive development and SVM find new areas of application (www.kernel-machines.org). SVM develop robust and non linear data models with excellent generalisation abilities that is very important both for monitoring and forecasting. SVM are extremely good when input space is high dimensional and training data set i not big enough to develop corresponding nonlinear model. Moreover, SVM use only support vectors to derive decision boundaries. It opens a way to sampling optimization, estimation of noise in data, quantification of data redundancy etc. Presentation of SVM for spatially distributed data is given in (Kanevski and Maignan 2004).

A behavioral ecology of shermen: hidden stories from trajectory data in the Northern Humboldt Current System

This work proposes an original contribution to the understanding of shermen spatial behavior, based on the behavioral ecology and movement ecology paradigms. Through the analysis of Vessel Monitoring System (VMS) data, we characterized the spatial behavior of Peruvian anchovy shermen at di erent scales: (1) the behavioral modes within shing trips (i.e., searching, shing and cruising); (2) the behavioral patterns among shing trips; (3) the behavioral patterns by shing season conditioned by ecosystem scenarios; and (4) the computation of maps of anchovy presence proxy from the spatial patterns of behavioral mode positions. At the rst scale considered, we compared several Markovian (hidden Markov and semi-Markov models) and discriminative models (random forests, support vector machines and arti cial neural networks) for inferring the behavioral modes associated with VMS tracks. The models were trained under a supervised setting and validated using tracks for which behavioral modes were known (from on-board observers records). Hidden semi-Markov models performed better, and were retained for inferring the behavioral modes on the entire VMS dataset. At the second scale considered, each shing trip was characterized by several features, including the time spent within each behavioral mode. Using a clustering analysis, shing trip patterns were classi ed into groups associated to management zones, eet segments and skippers' personalities. At the third scale considered, we analyzed how ecological conditions shaped shermen behavior. By means of co-inertia analyses, we found signi cant associations between shermen, anchovy and environmental spatial dynamics, and shermen behavioral responses were characterized according to contrasted environmental scenarios. At the fourth scale considered, we investigated whether the spatial behavior of shermen re ected to some extent the spatial distribution of anchovy. Finally, this work provides a wider view of shermen behavior: shermen are not only economic agents, but they are also foragers, constrained by ecosystem variability. To conclude, we discuss how these ndings may be of importance for sheries management, collective behavior analyses and end-to-end models.

Active learning methods for remote sensing image classification

In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated. Starting with a small and nonoptimal training set, the model itself builds the optimal set of samples which minimizes the classification error. We have applied the proposed algorithms to a variety of remote sensing data, including very high resolution and hyperspectral images, using support vector machines. Experimental results confirm the consistency of the methods. The required number of training samples can be reduced to 10% using the methods proposed, reaching the same level of accuracy as larger data sets. A comparison with a state-of-the-art active learning method, margin sampling, is provided, highlighting advantages of the methods proposed. The effect of spatial resolution and separability of the classes on the quality of the selection of pixels is also discussed.

Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation.

BACKGROUND: With the large amount of biological data that is currently publicly available, many investigators combine multiple data sets to increase the sample size and potentially also the power of their analyses. However, technical differences ("batch effects") as well as differences in sample composition between the data sets may significantly affect the ability to draw generalizable conclusions from such studies. FOCUS: The current study focuses on the construction of classifiers, and the use of cross-validation to estimate their performance. In particular, we investigate the impact of batch effects and differences in sample composition between batches on the accuracy of the classification performance estimate obtained via cross-validation. The focus on estimation bias is a main difference compared to previous studies, which have mostly focused on the predictive performance and how it relates to the presence of batch effects. DATA: We work on simulated data sets. To have realistic intensity distributions, we use real gene expression data as the basis for our simulation. Random samples from this expression matrix are selected and assigned to group 1 (e.g., 'control') or group 2 (e.g., 'treated'). We introduce batch effects and select some features to be differentially expressed between the two groups. We consider several scenarios for our study, most importantly different levels of confounding between groups and batch effects. METHODS: We focus on well-known classifiers: logistic regression, Support Vector Machines (SVM), k-nearest neighbors (kNN) and Random Forests (RF). Feature selection is performed with the Wilcoxon test or the lasso. Parameter tuning and feature selection, as well as the estimation of the prediction performance of each classifier, is performed within a nested cross-validation scheme. The estimated classification performance is then compared to what is obtained when applying the classifier to independent data.

'Toxic' and 'Nontoxic': confirming critical terminology concepts and context for clear communication

If 'the dose makes the poison', and if the context of an exposure to a hazard shapes the risk as much as the innate character of the hazard itself, then what is 'toxic' and what is 'nontoxic'? This article is intended to help readers and communicators: anticipate that concepts such as 'toxic' and 'nontoxic' may have different meanings to different stakeholders in different contexts of general use, commerce, science, and the law; recognize specific situations in which terms and related information could potentially be misperceived or misinterpreted; evaluate the relevance, reliability, and other attributes of information for a given situation; control actions, assumptions, interpretations, conclusions, and decisions to avoid flaws and achieve a desired outcome; and confirm that the desired outcome has been achieved. To meet those objectives, we provide some examples of differing toxicology terminology concepts and contexts; a comprehensive decision-making framework for understanding and managing risk; along with a communication and education message and audience-planning matrix to support the involvement of all relevant stakeholders; a set of CLEAR-communication assessment criteria for use by both readers and communicators; example flaws in decision-making; a suite of three tools to assign relevance vs reliability, align know vs show, and refine perception vs reality aspects of information; and four steps to foster effective community involvement and support. The framework and supporting process are generally applicable to meeting any objective.

Discordant increases in CD4+ T cells in human immunodeficiency virus-infected patients experiencing virologic treatment failure: role of changes in thymic output and T cell death.

Some patients infected with human immunodeficiency virus (HIV) who are experiencing antiretroviral treatment failure have persistent improvement in CD4+ T cell counts despite high plasma viremia. To explore the mechanisms responsible for this phenomenon, 2 parameters influencing the dynamics of CD4+ T cells were evaluated: death of mature CD4+ T cells and replenishment of the CD4+ T cell pool by the thymus. The improvement in CD4+ T cells observed in patients with treatment failure was not correlated with spontaneous, Fas ligand-induced, or activation-induced T cell death. In contrast, a significant correlation between the improvement in CD4+ T cell counts and thymic output, as assessed by measurement of T cell receptor excision circles, was observed. These observations suggest that increased thymic output contributes to the dissociation between CD4+ T cell counts and viremia in patients failing antiretroviral therapy and support a model in which drug-resistant HIV strains may have reduced replication rates and pathogenicity in the thymus.

Drug adherence in chronic kidney diseases and dialysis.

Poor long-term adherence and persistence to drug therapy is universally recognized as one of the major clinical issues in the management of chronic diseases, and patients with renal diseases are also concerned by this important phenomenon. Chronic kidney disease (CKD) patients belong to the group of subjects with one of the highest burdens of daily pill intake with up to >20 pills per day depending on the severity of their disease. The purpose of the present review is to discuss the difficulties encountered by nephrologists in diagnosing and managing poor adherence and persistence in CKD patients including in patients receiving maintenance dialysis. Our review will also attempt to provide some clues and new perspectives on how drug adherence could actually be addressed and possibly improved. Working on drug adherence may look like a long and tedious path, but physicians and healthcare providers should always be aware that drug adherence is in general much lower than what they may think and that there are many ways to improve and support drug adherence and persistence so that renal patients obtain the full benefits of their treatments.

Vertical Syndication-Proof Competitive Prices in Multilateral Markets

[eng] A multi-sided Böhm-Bawerk assignment game (Tejada, to appear) is a model for a multilateral market with a finite number of perfectly complementary indivisible commodities owned by different sellers, and inflexible demand and support functions. We show that for each such market game there is a unique vector of competitive prices for the commodities that is vertical syndication-proof, in the sense that, at those prices, syndication of sellers each owning a different commodity is neither beneficial nor detrimental for the buyers. Since, moreover, the benefits obtained by the agents at those prices correspond to the nucleolus of the market game, we provide a syndication-based foundation for the nucleolus as an appropriate solution concept for market games. For different solution concepts a syndicate can be disadvantageous and there is no escape to Aumman’s paradox (Aumann, 1973). We further show that vertical syndicationproofness and horizontal syndication-proofness – in which sellers of the same commodity collude – are incompatible requirements under some mild assumptions. Our results build on a self-interesting link between multi-sided Böhm-Bawerk assignment games and bankruptcy games (O’Neill, 1982). We identify a particular subset of Böhm-Bawerk assignment games and we show that it is isomorphic to the whole class of bankruptcy games. This isomorphism enables us to show the uniqueness of the vector of vertical syndication-proof prices for the whole class of Böhm-Bawerk assignment market using well-known results of bankruptcy problems.

Non-invasive detection of coronary endothelial response to sequential handgrip exercise in coronary artery disease patients and healthy adults.

OBJECTIVES: Our objective is to test the hypothesis that coronary endothelial function (CorEndoFx) does not change with repeated isometric handgrip (IHG) stress in CAD patients or healthy subjects. BACKGROUND: Coronary responses to endothelial-dependent stressors are important measures of vascular risk that can change in response to environmental stimuli or pharmacologic interventions. The evaluation of the effect of an acute intervention on endothelial response is only valid if the measurement does not change significantly in the short term under normal conditions. Using 3.0 Tesla (T) MRI, we non-invasively compared two coronary artery endothelial function measurements separated by a ten minute interval in healthy subjects and patients with coronary artery disease (CAD). METHODS: Twenty healthy adult subjects and 12 CAD patients were studied on a commercial 3.0 T whole-body MR imaging system. Coronary cross-sectional area (CSA), peak diastolic coronary flow velocity (PDFV) and blood-flow were quantified before and during continuous IHG stress, an endothelial-dependent stressor. The IHG exercise with imaging was repeated after a 10 minute recovery period. RESULTS: In healthy adults, coronary artery CSA changes and blood-flow increases did not differ between the first and second stresses (mean % change ±SEM, first vs. second stress CSA: 14.8%±3.3% vs. 17.8%±3.6%, p = 0.24; PDFV: 27.5%±4.9% vs. 24.2%±4.5%, p = 0.54; blood-flow: 44.3%±8.3 vs. 44.8%±8.1, p = 0.84). The coronary vasoreactive responses in the CAD patients also did not differ between the first and second stresses (mean % change ±SEM, first stress vs. second stress: CSA: -6.4%±2.0% vs. -5.0%±2.4%, p = 0.22; PDFV: -4.0%±4.6% vs. -4.2%±5.3%, p = 0.83; blood-flow: -9.7%±5.1% vs. -8.7%±6.3%, p = 0.38). CONCLUSION: MRI measures of CorEndoFx are unchanged during repeated isometric handgrip exercise tests in CAD patients and healthy adults. These findings demonstrate the repeatability of noninvasive 3T MRI assessment of CorEndoFx and support its use in future studies designed to determine the effects of acute interventions on coronary vasoreactivity.

Gauge transformations in the Lagrangian and Hamilton formalisms of generally covariant theories

We study spacetime diffeomorphisms in the Hamiltonian and Lagrangian formalisms of generally covariant systems. We show that the gauge group for such a system is characterized by having generators which are projectable under the Legendre map. The gauge group is found to be much larger than the original group of spacetime diffeomorphisms, since its generators must depend on the lapse function and shift vector of the spacetime metric in a given coordinate patch. Our results are generalizations of earlier results by Salisbury and Sundermeyer. They arise in a natural way from using the requirement of equivalence between Lagrangian and Hamiltonian formulations of the system, and they are new in that the symmetries are realized on the full set of phase space variables. The generators are displayed explicitly and are applied to the relativistic string and to general relativity.

Mult-class differentiation of cannabis seedlings in a forensic context

This article presents an experimental study about the classification ability of several classifiers for multi-classclassification of cannabis seedlings. As the cultivation of drug type cannabis is forbidden in Switzerland lawenforcement authorities regularly ask forensic laboratories to determinate the chemotype of a seized cannabisplant and then to conclude if the plantation is legal or not. This classification is mainly performed when theplant is mature as required by the EU official protocol and then the classification of cannabis seedlings is a timeconsuming and costly procedure. A previous study made by the authors has investigated this problematic [1]and showed that it is possible to differentiate between drug type (illegal) and fibre type (legal) cannabis at anearly stage of growth using gas chromatography interfaced with mass spectrometry (GC-MS) based on therelative proportions of eight major leaf compounds. The aims of the present work are on one hand to continueformer work and to optimize the methodology for the discrimination of drug- and fibre type cannabisdeveloped in the previous study and on the other hand to investigate the possibility to predict illegal cannabisvarieties. Seven classifiers for differentiating between cannabis seedlings are evaluated in this paper, namelyLinear Discriminant Analysis (LDA), Partial Least Squares Discriminant Analysis (PLS-DA), Nearest NeighbourClassification (NNC), Learning Vector Quantization (LVQ), Radial Basis Function Support Vector Machines(RBF SVMs), Random Forest (RF) and Artificial Neural Networks (ANN). The performance of each method wasassessed using the same analytical dataset that consists of 861 samples split into drug- and fibre type cannabiswith drug type cannabis being made up of 12 varieties (i.e. 12 classes). The results show that linear classifiersare not able to manage the distribution of classes in which some overlap areas exist for both classificationproblems. Unlike linear classifiers, NNC and RBF SVMs best differentiate cannabis samples both for 2-class and12-class classifications with average classification results up to 99% and 98%, respectively. Furthermore, RBFSVMs correctly classified into drug type cannabis the independent validation set, which consists of cannabisplants coming from police seizures. In forensic case work this study shows that the discrimination betweencannabis samples at an early stage of growth is possible with fairly high classification performance fordiscriminating between cannabis chemotypes or between drug type cannabis varieties.

Major environmental change and bonebed genesis prior to the Triassic-Jurassic mass extinction

We present new geochemical and sedimentological data from marginal marine strata of Penarth Bay, south Wales (UK) to elucidate the origin of widespread but enigmatic concentrations of vertebrate hard parts (bonebeds) in marine successions of Rhaetian age (late Triassic). Sedimentological evidence shows that the phosphatic constituents of the bonebeds were subjected to intense phosphatization in shallow current-dominated settings and subsequently reworked and transported basinward by storms. Interbedded organic-rich strata deposited under quiescent and poorly oxygenated conditions record enhanced phosphorus regeneration from sedimentary organic matter into the water column and probably provided the main source of phosphate required for heavy bonebed clast phosphatization. The stratigraphically limited interval showing evidence for oxygen depletion and accelerated P-cycling coincides with a negative 4% organic carbon isotope excursion, which possibly reflects supra-regional changes in carbon cycling and clearly predates the 'initial isotope excursion' characterizing many Triassic-Jurassic boundary strata. our data indicate that Rhaetian bonebeds are the lithological signature of profound, climatically driven changes in carbon cycling and redox conditions and support the idea of a multi-pulsed environmental crisis at the end of the Triassic, possibly linked to successive episodes of igneous activity in the central Atlantic Magmatic Province.

Oral and intravenous methadone use: some clinical and pharmacokinetic aspects.

A sample of 15 patients participating in an injectable methadone trial and of 15 patients in an oral methadone maintenance treatment, who admitted injecting part or all of their methadone take-home doses, were compared to 20 patients in maintenance treatment who use methadone exclusively by mouth. The present study confirms the poorer general health, the higher levels of emotional, psychological or psychiatric problems, the higher use of illicit drugs, and the higher number of problems related to employment and support associated with the use of the intravenous mode of administration of methadone. As expected, due to the shunt of metabolism in the gut wall and of the liver first-pass effect, higher concentration to dose ratios of (R)-methadone, which is the active enantiomer, were measured in the intravenous group (23% increase). This difference reached an almost statistically significant value (P = 0.054). This raises the question whether the effect of a higher methadone dose could be unconsciously sought by some of the intravenous methadone users.