Do habitat suitability models reliably predict the recovery areas of threatened species?

1. Identifying those areas suitable for recolonization by threatened species is essential to support efficient conservation policies. Habitat suitability models (HSM) predict species' potential distributions, but the quality of their predictions should be carefully assessed when the species-environment equilibrium assumption is violated.2. We studied the Eurasian otter Lutra lutra, whose numbers are recovering in southern Italy. To produce widely applicable results, we chose standard HSM procedures and looked for the models' capacities in predicting the suitability of a recolonization area. We used two fieldwork datasets: presence-only data, used in the Ecological Niche Factor Analyses (ENFA), and presence-absence data, used in a Generalized Linear Model (GLM). In addition to cross-validation, we independently evaluated the models with data from a recolonization event, providing presences on a previously unoccupied river.3. Three of the models successfully predicted the suitability of the recolonization area, but the GLM built with data before the recolonization disagreed with these predictions, missing the recolonized river's suitability and badly describing the otter's niche. Our results highlighted three points of relevance to modelling practices: (1) absences may prevent the models from correctly identifying areas suitable for a species spread; (2) the selection of variables may lead to randomness in the predictions; and (3) the Area Under Curve (AUC), a commonly used validation index, was not well suited to the evaluation of model quality, whereas the Boyce Index (CBI), based on presence data only, better highlighted the models' fit to the recolonization observations.4. For species with unstable spatial distributions, presence-only models may work better than presence-absence methods in making reliable predictions of suitable areas for expansion. An iterative modelling process, using new occurrences from each step of the species spread, may also help in progressively reducing errors.5. Synthesis and applications. Conservation plans depend on reliable models of the species' suitable habitats. In non-equilibrium situations, such as the case for threatened or invasive species, models could be affected negatively by the inclusion of absence data when predicting the areas of potential expansion. Presence-only methods will here provide a better basis for productive conservation management practices.

Automatic Mapping and Classification of Spatial Environmental Data

The paper deals with the development and application of the generic methodology for automatic processing (mapping and classification) of environmental data. General Regression Neural Network (GRNN) is considered in detail and is proposed as an efficient tool to solve the problem of spatial data mapping (regression). The Probabilistic Neural Network (PNN) is considered as an automatic tool for spatial classifications. The automatic tuning of isotropic and anisotropic GRNN/PNN models using cross-validation procedure is presented. Results are compared with the k-Nearest-Neighbours (k-NN) interpolation algorithm using independent validation data set. Real case studies are based on decision-oriented mapping and classification of radioactively contaminated territories.

Building predictive models of soil particle-size distribution

Is it possible to build predictive models (PMs) of soil particle-size distribution (psd) in a region with complex geology and a young and unstable land-surface? The main objective of this study was to answer this question. A set of 339 soil samples from a small slope catchment in Southern Brazil was used to build PMs of psd in the surface soil layer. Multiple linear regression models were constructed using terrain attributes (elevation, slope, catchment area, convergence index, and topographic wetness index). The PMs explained more than half of the data variance. This performance is similar to (or even better than) that of the conventional soil mapping approach. For some size fractions, the PM performance can reach 70 %. Largest uncertainties were observed in geologically more complex areas. Therefore, significant improvements in the predictions can only be achieved if accurate geological data is made available. Meanwhile, PMs built on terrain attributes are efficient in predicting the particle-size distribution (psd) of soils in regions of complex geology.

Prevalence and factors of intensive care unit conflicts: the conflicus study.

RATIONALE: Many sources of conflict exist in intensive care units (ICUs). Few studies recorded the prevalence, characteristics, and risk factors for conflicts in ICUs. OBJECTIVES: To record the prevalence, characteristics, and risk factors for conflicts in ICUs. METHODS: One-day cross-sectional survey of ICU clinicians. Data on perceived conflicts in the week before the survey day were obtained from 7,498 ICU staff members (323 ICUs in 24 countries). MEASUREMENTS AND MAIN RESULTS: Conflicts were perceived by 5,268 (71.6%) respondents. Nurse-physician conflicts were the most common (32.6%), followed by conflicts among nurses (27.3%) and staff-relative conflicts (26.6%). The most common conflict-causing behaviors were personal animosity, mistrust, and communication gaps. During end-of-life care, the main sources of perceived conflict were lack of psychological support, absence of staff meetings, and problems with the decision-making process. Conflicts perceived as severe were reported by 3,974 (53%) respondents. Job strain was significantly associated with perceiving conflicts and with greater severity of perceived conflicts. Multivariate analysis identified 15 factors associated with perceived conflicts, of which 6 were potential targets for future intervention: staff working more than 40 h/wk, more than 15 ICU beds, caring for dying patients or providing pre- and postmortem care within the last week, symptom control not ensured jointly by physicians and nurses, and no routine unit-level meetings. CONCLUSIONS: Over 70% of ICU workers reported perceived conflicts, which were often considered severe and were significantly associated with job strain. Workload, inadequate communication, and end-of-life care emerged as important potential targets for improvement.

Variability between laboratories performing coagulation tests with identical platforms: a nationwide evaluation study.

BACKGROUND: While the assessment of analytical precision within medical laboratories has received much attention in scientific enquiry, the degree of as well as the sources causing variation between them remains incompletely understood. In this study, we quantified the variance components when performing coagulation tests with identical analytical platforms in different laboratories and computed intraclass correlations coefficients (ICC) for each coagulation test. METHODS: Data from eight laboratories measuring fibrinogen twice in twenty healthy subjects with one out of 3 different platforms and single measurements of prothrombin time (PT), and coagulation factors II, V, VII, VIII, IX, X, XI and XIII were analysed. By platform, the variance components of (i) the subjects, (ii) the laboratory and the technician and (iii) the total variance were obtained for fibrinogen as well as (i) and (iii) for the remaining factors using ANOVA. RESULTS: The variability for fibrinogen measurements within a laboratory ranged from 0.02 to 0.04, the variability between laboratories ranged from 0.006 to 0.097. The ICC for fibrinogen ranged from 0.37 to 0.66 and from 0.19 to 0.80 for PT between the platforms. For the remaining factors the ICC's ranged from 0.04 (FII) to 0.93 (FVIII). CONCLUSIONS: Variance components that could be attributed to technicians or laboratory procedures were substantial, led to disappointingly low intraclass correlation coefficients for several factors and were pronounced for some of the platforms. Our findings call for sustained efforts to raise the level of standardization of structures and procedures involved in the quantification of coagulation factors.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

An integer-based score to predict functional outcome in acute ischemic stroke: The ASTRAL score.

OBJECTIVE: To develop and validate a simple, integer-based score to predict functional outcome in acute ischemic stroke (AIS) using variables readily available after emergency room admission. METHODS: Logistic regression was performed in the derivation cohort of previously independent patients with AIS (Acute Stroke Registry and Analysis of Lausanne [ASTRAL]) to identify predictors of unfavorable outcome (3-month modified Rankin Scale score >2). An integer-based point-scoring system for each covariate of the fitted multivariate model was generated by their β-coefficients; the overall score was calculated as the sum of the weighted scores. The model was validated internally using a 2-fold cross-validation technique and externally in 2 independent cohorts (Athens and Vienna Stroke Registries). RESULTS: Age (A), severity of stroke (S) measured by admission NIH Stroke Scale score, stroke onset to admission time (T), range of visual fields (R), acute glucose (A), and level of consciousness (L) were identified as independent predictors of unfavorable outcome in 1,645 patients in ASTRAL. Their β-coefficients were multiplied by 4 and rounded to the closest integer to generate the score. The area under the receiver operating characteristic curve (AUC) of the score in the ASTRAL cohort was 0.850. The score was well calibrated in the derivation (p = 0.43) and validation cohorts (0.22 [Athens, n = 1,659] and 0.49 [Vienna, n = 653]). AUCs were 0.937 (Athens), 0.771 (Vienna), and 0.902 (when pooled). An ASTRAL score of 31 indicates a 50% likelihood of unfavorable outcome. CONCLUSIONS: The ASTRAL score is a simple integer-based score to predict functional outcome using 6 readily available items at hospital admission. It performed well in double external validation and may be a useful tool for clinical practice and stroke research.

Renoncement aux soins: comment appréhender cette réalité en médecine de premier recours [Patients forgoing health care for economic reasons: how to identify this in a primary care setting?].

Although the performance of the Swiss health system is high, one out of ten patients in general practitioner's (GP) office declares having foregone care in the previous twelve months for economic reasons. Reasons for foregoing care are several and include a lack of knowledge of existing social aids in getting health insurance, unavailability of GPs and long waiting lists for various types of care. Although long term knowledge of patients or a psychosocial history of deprivation or poverty may help identify individuals at risk of foregoing care, many may remain undetected. We propose then a few instruments to help GPs to identify, in a simple and structured approach, patients at risk of forgoing care for economic reasons; these patients are frequently deprived and sometimes poor.

Empirical mode decomposition-based face recognition system

In this work we explore the multivariate empirical mode decomposition combined with a Neural Network classifier as technique for face recognition tasks. Images are simultaneously decomposed by means of EMD and then the distance between the modes of the image and the modes of the representative image of each class is calculated using three different distance measures. Then, a neural network is trained using 10- fold cross validation in order to derive a classifier. Preliminary results (over 98 % of classification rate) are satisfactory and will justify a deep investigation on how to apply mEMD for face recognition.

Prevalence and significance of anti-HILA antibodies after liver transplantation : P236

Background: The pathogenic role of anti-HLA antibodies (AHA) after kidney transplantation is well established. However, its significance after liver transplantation remains unclear. The aim of our study was to determine the prevalence and significance of AHA after liver transplantation. Methods: Between January 2007 and November 2007, all liver transplant recipients who were greater than 6 months posttransplantation and followed regularly at our transplant outpatient clinic (n = 95) were screened for AHA. All clinical and electronic records were reviewed. Serum samples were tested using multiplex technology (Luminex). A liver biopsy had been performed in 55 out of the 95 patients based on clinical grounds but no routine protocol biopsies were performed. Immunosuppression was calcineurin inhibitor-based in 90 patients, sirolimus-based in 4 patients and one patient had no anti-rejection therapy (operationally tolerant recipient). Results: The mean time from transplantation to study was 85 months (range 6-248 months). Overall, AHA were found in 23/95 (24.2%) of patients (5 had anti-class I alone, 13 anti-class II alone, and 4 had both anti-class I and II). However, only 4/95 patients (4.2%) had donor-specific antibodies (DSA) (one anti-class I and 3 anti-class II). Twenty-one out of 95 patients (22.1%) had a history of past or current biopsy-proven or radiological biliary complications (chronic rejection, ischemic cholangitis, ischemic type biliary lesions or biliary anastomosis stricture). Among patients with AHA, 4/23 (17,4%) had biliary complications, while it was 17/72 (23.6%) in patients without AHA (NS). Among patients with DSA, 3/4 (75%) had biliary complications (two with biopsy-proven chronic rejection in association with biliary strictures and one with ischemic cholangitis following hepatic artery thrombosis), versus 1/19 (5.3%) patients with AHA but no DSA (p = 0.009), versus 16/72 (22.2%) patients without AHA (p = 0.046). In patients with DSA, immunosuppression was not different than in patients without DSA. Conclusions: We found a 24% AHA prevalence. The presence of DSA, but not of AHA, was significantly associated with an increased incidence of biliary complications including chronic liver allograft rejection. The exact mechanisms and possible causal relationship linking DSA to biliary complications remain to be studied. Larger prospective trials are thus needed to further define the role of AHA and in particular of DSA after liver transplantation.

Effect of religion on suicide attempts in outpatients with schizophrenia or schizo-affective disorders compared with inpatients with non-psychotic disorders

Little is known of the relations between psychosis, religion and suicide. One hundred and fifteen outpatients with schizophrenia or schizo-affective disorder and 30 inpatients without psychotic symptoms were studied using a semi-structured interview assessing religiousness/spirituality. Their past suicide attempts were examined. Additionally, they were asked about the role (protective or incentive) of religion in their decision to commit suicide. Forty-three percent of the patients with psychosis had previously attempted suicide. Religiousness was not associated with the rate of patients who attempted suicide. Twenty-five percent of all subjects acknowledged a protective role of religion, mostly through ethical condemnation of suicide and religious coping. One out of ten patients reported an incentive role of religion, not only due to negatively connotated issues but also to the hope for something better after death. There were no differences between groups (i.e. psychotic vs. non-psychotic patients). Religion may play a specific role in the decisions patients make about suicide, both in psychotic and non-psychotic patients. This role may be protective, a finding particularly important for patients with psychosis who are known to be at high risk of severe suicide attempts. Interventions aiming to lower the number of suicide attempts in patients with schizophrenia should take these data into account.

Réduction de la consommation de sel: une mesure importante de santé publique en Suisse. [Reducing dietary salt intake: an important public health strategy in Switzerland]

La consommation actuelle de sel (chlorure de sodium) est très supérieure aux besoins physiologiques (1,5 g par jour, soit environ 550 mg par jour de sodium) dans la plupart des pays (> 8 g par jour). Les principales sources de sel sont les pains, les fromages, les produits dérivés de la viande et les plats précuisinés. En moyenne, une consommation élevée de sel est associée à une pression artérielle plus élevée. En Suisse, un adulte sur trois souffre d'hypertension artérielle. La moitié des accidents vasculaires cérébraux et des maladies cardiaques ischémiques sont attribuables à une pression artérielle trop élevée. L'Office fédéral de la santé publique conduit actuellement une stratégie visant à diminuer la consommation de sel dans la population suisse à moins de 5 g par jour sur le long terme (Salz Strategie 2008-2012). [Abstract] Current dietary salt (sodium chloride) intake largely exceeds physiological needs (about 1.5 g salt per day, or 550 mg sodium per day) in most countries (> 8 g salt per day). The main sources of dietar salt intake are breads, cheeses, products derived from meat and ready-to-eat meals. On average, a high-salt diet is associated with higher blood pressure levels. In Switzerland, one out of three adults suffers from arterial hypertension. Half of cerebrovascular events and ischaemic cardiac events are attributable to elevated blood pressure. The Swiss Federal Office of Public Health is currently running a strategy aiming at reducing dietary salt intake in the Swiss population to less than 5 g per day on the long run (Salz Strategie 2008-2012).

Use of niche models in invasive species risk assessments.

Risk maps summarizing landscape suitability of novel areas for invading species can be valuable tools for preventing species' invasions or controlling their spread, but methods employed for development of such maps remain variable and unstandardized. We discuss several considerations in development of such models, including types of distributional information that should be used, the nature of explanatory variables that should be incorporated, and caveats regarding model testing and evaluation. We highlight that, in the case of invasive species, such distributional predictions should aim to derive the best hypothesis of the potential distribution of the species by using (1) all distributional information available, including information from both the native range and other invaded regions; (2) predictors linked as directly as is feasible to the physiological requirements of the species; and (3) modelling procedures that carefully avoid overfitting to the training data. Finally, model testing and evaluation should focus on well-predicted presences, and less on efficient prediction of absences; a k-fold regional cross-validation test is discussed.

Impacts of climate change on Swiss biodiversity: An indicator taxa approach

We present a new indicator taxa approach to the prediction of climate change effects on biodiversity at the national level in Switzerland. As indicators, we select a set of the most widely distributed species that account for 95% of geographical variation in sampled species richness of birds, butterflies, and vascular plants. Species data come from a national program designed to monitor spatial and temporal trends in species richness. We examine some opportunities and limitations in using these data. We develop ecological niche models for the species as functions of both climate and land cover variables. We project these models to the future using climate predictions that correspond to two IPCC 3rd assessment scenarios for the development of 'greenhouse' gas emissions. We find that models that are calibrated with Swiss national monitoring data perform well in 10-fold cross-validation, but can fail to capture the hot-dry end of environmental gradients that constrain some species distributions. Models for indicator species in all three higher taxa predict that climate change will result in turnover in species composition even where there is little net change in predicted species richness. Indicator species from high elevations lose most areas of suitable climate even under the relatively mild B2 scenario. We project some areas to increase in the number of species for which climate conditions are suitable early in the current century, but these areas become less suitable for a majority of species by the end of the century. Selection of indicator species based on rank prevalence results in a set of models that predict observed species richness better than a similar set of species selected based on high rank of model AUC values. An indicator species approach based on selected species that are relatively common may facilitate the use of national monitoring data for predicting climate change effects on the distribution of biodiversity.

Are there control processes, and (if so) can they be studied?

Generally, so-called control processes are thought to be necessary when we must perform one out of several competing actions. Some examples include performance of a less well-practiced action instead of a well-practiced one (prepotency); learning a new action (novelty); and rapidly switching from one action to another (task-switching). While it certainly is difficult to perform the desired action in these circumstances, it is less clear that a separate set of processes (e.g., control processes) are necessary to explain the observed behavior. Another way to approach the study of control processes is to investigate physiological dependent measures (e.g., electrophysiological or neuroimaging measures). Although these offer another avenue of inquiry into control processes, they have yet to furnish unambiguous evidence that control processes exist. While this might suggest that there are no control processes, it is also possible that our methods are insufficiently sensitive to measure control processes. We have investigated this latter possibility using tasks that are neuroanatomically distinct, though within the same modality (vision). This approach did not yield evidence for a separable set of control processes. However, recent works using a task-switching paradigm in which subjects switch between a visual and an auditory task suggest that switching both task and modality may be importantly different than switching task within a given modality. This may represent a way forward in the study of control processes.