Comparing Models for Early Warning Systems of Neglected Tropical Diseases

Early Warning Systems (EWS) are management tools to predict the occurrence of epidemics. They are based on the dependence of a given infectious disease on environmental variables. Although several neglected tropical diseases are sensitive to the effect of climate, our ability to predict their dynamics has been barely studied. In this paper, we use several models to determine if the relationship between cases and climatic variability is robust—that is, not simply an artifact of model choice. We propose that EWS should be based on results from several models that are to be compared in terms of their ability to predict future number of cases. We use a specific metric for this comparison known as the predictive R2, which measures the accuracy of the predictions. For example, an R2 of 1 indicates perfect accuracy for predictions that perfectly match observed cases. For cutaneous leishmaniasis, R2 values range from 72% to77%, well above predictions using mean seasonal values (64%). We emphasize that predictability should be evaluated with observations that have not been used to fit the model. Finally, we argue that EWS should incorporate climatic variables that are known to have a consistent relationship with the number of observed cases

Hypoglycemia in Non-Diabetic In-Patients: Clinical or Criminal?

BACKGROUND AND AIM: We wished to establish the frequency of unexpected hypoglycemia observed in non diabetic patients outside the intensive care unit and to determine if they have a plausible clinical explanation. METHODS: We analysed data for 2010 from three distinct sources to identify non diabetic hypoglycaemic patients: bedside and laboratory blood glucose measurements; medication records for those treatments (high-strength glucose solution and glucagon) commonly given to reverse hypoglycemia; and diagnostic codes for hypoglycemia. We excluded from the denominator admissions of patients with a diagnosis of diabetes or prescribed diabetic medication. Case notes of patients identified were reviewed. We used capture-recapture methods to establish the likely frequency of hypoglycemia in non-diabetic in-patients outside intensive care unit at different cut-off points for hypoglycemia. We also recorded co-morbidities that might have given rise to hypoglycemia. RESULTS: Among the 37,898 admissions, the triggers identified 71 hypoglycaemic episodes at a cut-off of 3.3 mmol/l. Estimated frequency at 3.3 mmol/l was 50(CI 33-93), at 3.0 mmol/l, 36(CI 24-64), at 2.7 mmol/l, 13(CI 11-19), at 2.5 mmol/l, 11(CI 9-15) and at 2.2 mmol/l, 8(CI 7-11) per 10,000 admissions. Admissions of patients aged above 65 years were approximately 50% more likely to have an episode of hypoglycemia. Most were associated with important co-morbidities. CONCLUSION: Significant non-diabetic hypoglycemia in hospital in-patients (at or below 2.7 mmol/l) outside critical care is rare. It is sufficiently rare for occurrences to merit case-note review and diagnostic blood tests, unless an obvious explanation is found

How Landscape Heterogeneity Frames Optimal Diffusivity in Searching Processes

Theoretical and empirical investigations of search strategies typically have failed to distinguish the distinct roles played by density versus patchiness of resources. It is well known that motility and diffusivity of organisms often increase in environments with low density of resources, but thus far there has been little progress in understanding the specific role of landscape heterogeneity and disorder on random, non-oriented motility. Here we address the general question of how the landscape heterogeneity affects the efficiency of encounter interactions under global constant density of scarce resources. We unveil the key mechanism coupling the landscape structure with optimal search diffusivity. In particular, our main result leads to an empirically testable prediction: enhanced diffusivity (including superdiffusive searches), with shift in the diffusion exponent, favors the success of target encounters in heterogeneous landscapes

Hyponatremia revisited: Translating physiology to practice

The complexity of hyponatremia as a clinical problem is likely caused by the opposite scenarios that accompany this electrolyte disorder regarding pathophysiology (depletional versus dilutional hyponatremia, high versus low vasopressin levels) and therapy (rapid correction to treat cerebral edema versus slow correction to prevent osmotic demyelination, fluid restriction versus fluid resuscitation). For a balanced differentiation between these opposites, an understanding of the pathophysiology of hyponatremia is required. Therefore, in this review an attempt is made to translate the physiology of water balance regulation to strategies that improve the clinical management of hyponatremia. A physiology-based approach to the patient with hyponatremia is presented, first addressing the possibility of acute hyponatremia, and then asking if and if so why vasopressin is secreted non-osmotically. Additional diagnostic recommendations are not to rely too heavily of the assessment of the extracellular fluid volume, to regard the syndrome of inappropriate antidiuresis as a diagnosis of exclusion, and to rationally investigate the pathophysiology of hyponatremia rather than to rely on isolated laboratory values with arbitrary cutoff values. The features of the major hyponatremic disorders are discussed, including diuretic-induced hyponatremia, adrenal and pituitary insufficiency, the syndrome of inappropriate antidiuresis, cerebral salt wasting, and exercise-associated hyponatremia. The treatment of hyponatremia is reviewed from simple saline solutions to the recently introduced vasopressin receptor antagonists, including their promises and limitations. Given the persistently high rates of hospital-acquired hyponatremia, the importance of improving the management of hyponatremia seems both necessary and achievable. Copyrigh

Pitfalls of predicting complex traits from SNPs

The success of genome-wide association studies (GWASs) has led to increasing interest in making predictions of complex trait phenotypes, including disease, from genotype data. Rigorous assessment of the value of predictors is crucial before implementation. Here we discuss some of the limitations and pitfalls of prediction analysis and show how naive implementations can lead to severe bias and misinterpretation of results

Seasonal and Ontogenetic Changes in Movement Patterns of Sixgill Sharks

Understanding movement patterns is fundamental to population and conservation biology. The way an animal moves through its environment influences the dynamics of local populations and will determine how susceptible it is to natural or anthropogenic perturbations. It is of particular interest to understand the patterns of movement for species which are susceptible to human activities (e.g. fishing), or that exert a large influence on community structure, such as sharks.We monitored the patterns of movement of 34 sixgill sharks Hexanchus griseus using two large-scale acoustic arrays inside and outside Puget Sound, Washington, USA. Sixgill sharks were residents in Puget Sound for up to at least four years before making large movements out of the estuary. Within Puget Sound, sixgills inhabited sites for several weeks at a time and returned to the same sites annually. Across four years, sixgills had consistent seasonal movements in which they moved to the north from winter to spring and moved to the south from summer to fall. Just prior to leaving Puget Sound, sixgills altered their behavior and moved twice as fast among sites. Nineteen of the thirty-four sixgills were detected leaving Puget Sound for the outer coast. Three of these sharks returned to Puget Sound.For most large marine predators, we have a limited understanding of how they move through their environment, and this clouds our ability to successfully manage their populations and their communities. With detailed movement information, such as that being uncovered with acoustic monitoring, we can begin to quantify the spatial and temporal impacts of large predators within the framework of their ecosystems

The National Lung Matrix Trial: translating the biology of stratification in advanced non-small-cell lung cancer

© The Author 2015.Background: The management of NSCLC has been transformed by stratified medicine. The National Lung Matrix Trial (NLMT) is a UK-wide study exploring the activity of rationally selected biomarker/targeted therapy combinations. Patients and methods: The Cancer Research UK (CRUK) Stratified Medicine Programme 2 is undertaking the large volume national molecular pre-screening which integrates with the NLMT. At study initiation, there are eight drugs being used to target 18 molecular cohorts. The aim is to determine whether there is sufficient signal of activity in any drug-biomarker combination to warrant further investigation. A Bayesian adaptive design that gives a more realistic approach to decision making and flexibility to make conclusions without fixing the sample size was chosen. The screening platform is an adaptable 28-gene Nextera next-generation sequencing platform designed by Illumina, covering the range of molecular abnormalities being targeted. The adaptive design allows new biomarker-drug combination cohorts to be incorporated by substantial amendment. The pre-clinical justification for each biomarker-drug combination has been rigorously assessed creating molecular exclusion rules and a trumping strategy in patients harbouring concomitant actionable genetic abnormalities. Discrete routes of pathway activation or inactivation determined by cancer genome aberrations are treated as separate cohorts. Key translational analyses include the deep genomic analysis of pre- and post-treatment biopsies, the establishment of patient-derived xenograft models and longitudinal ctDNA collection, in order to define predictive biomarkers, mechanisms of resistance and early markers of response and relapse. Conclusion: The SMP2 platform will provide large scale genetic screening to inform entry into the NLMT, a trial explicitly aimed at discovering novel actionable cohorts in NSCLC

Model selection in historical research using approximate Bayesian computation

Formal Models and History Computational models are increasingly being used to study historical dynamics. This new trend, which could be named Model-Based History, makes use of recently published datasets and innovative quantitative methods to improve our understanding of past societies based on their written sources. The extensive use of formal models allows historians to reevaluate hypotheses formulated decades ago and still subject to debate due to the lack of an adequate quantitative framework. The initiative has the potential to transform the discipline if it solves the challenges posed by the study of historical dynamics. These difficulties are based on the complexities of modelling social interaction, and the methodological issues raised by the evaluation of formal models against data with low sample size, high variance and strong fragmentation. This work examines an alternate approach to this evaluation based on a Bayesian-inspired model selection method. The validity of the classical Lanchester's laws of combat is examined against a dataset comprising over a thousand battles spanning 300 years. Four variations of the basic equations are discussed, including the three most common formulations (linear, squared, and logarithmic) and a new variant introducing fatigue. Approximate Bayesian Computation is then used to infer both parameter values and model selection via Bayes Factors. Results indicate decisive evidence favouring the new fatigue model. The interpretation of both parameter estimations and model selection provides new insights into the factors guiding the evolution of warfare. At a methodological level, the case study shows how model selection methods can be used to guide historical research through the comparison between existing hypotheses and empirical evidence.Funding for this work was provided by the SimulPast Consolider Ingenio project (CSD2010-00034) of the former Ministry for Science and Innovation of the Spanish Government and the European Research Council Advanced Grant EPNet (340828).Peer ReviewedPostprint (published version

The spatial structure of lithic landscapes : the late holocene record of east-central Argentina as a case study

Fil: Barrientos, Gustavo. División Antropología. Facultad de Ciencias Naturales y Museo. Universidad Nacional de La Plata; ArgentinaFil: Catella, Luciana. División Arqueología. Facultad de Ciencias Naturales y Museo. Universidad Nacional de La Plata; ArgentinaFil: Oliva, Fernando. Centro Estudios Arqueológicos Regionales. Facultad de Humanidades y Artes. Universidad Nacional de Rosario; Argentin

Breaking Functional Connectivity into Components: A Novel Approach Using an Individual-Based Model, and First Outcomes

Landscape connectivity is a key factor determining the viability of populations in fragmented landscapes. Predicting ‘functional connectivity’, namely whether a patch or a landscape functions as connected from the perspective of a focal species, poses various challenges. First, empirical data on the movement behaviour of species is often scarce. Second, animal-landscape interactions are bound to yield complex patterns. Lastly, functional connectivity involves various components that are rarely assessed separately. We introduce the spatially explicit, individual-based model FunCon as means to distinguish between components of functional connectivity and to assess how each of them affects the sensitivity of species and communities to landscape structures. We then present the results of exploratory simulations over six landscapes of different fragmentation levels and across a range of hypothetical bird species that differ in their response to habitat edges. i) Our results demonstrate that estimations of functional connectivity depend not only on the response of species to edges (avoidance versus penetration into the matrix), the movement mode investigated (home range movements versus dispersal), and the way in which the matrix is being crossed (random walk versus gap crossing), but also on the choice of connectivity measure (in this case, the model output examined). ii) We further show a strong effect of the mortality scenario applied, indicating that movement decisions that do not fully match the mortality risks are likely to reduce connectivity and enhance sensitivity to fragmentation. iii) Despite these complexities, some consistent patterns emerged. For instance, the ranking order of landscapes in terms of functional connectivity was mostly consistent across the entire range of hypothetical species, indicating that simple landscape indices can potentially serve as valuable surrogates for functional connectivity. Yet such simplifications must be carefully evaluated in terms of the components of functional connectivity they actually predict