Evaluating the influence of spatial uncertainty in locality points for species distributional modeling

1. Species distribution modelling is used increasingly in both applied and theoretical research to predict how species are distributed and to understand attributes of species' environmental requirements. In species distribution modelling, various statistical methods are used that combine species occurrence data with environmental spatial data layers to predict the suitability of any site for that species. While the number of data sharing initiatives involving species' occurrences in the scientific community has increased dramatically over the past few years, various data quality and methodological concerns related to using these data for species distribution modelling have not been addressed adequately. 2. We evaluated how uncertainty in georeferences and associated locational error in occurrences influence species distribution modelling using two treatments: (1) a control treatment where models were calibrated with original, accurate data and (2) an error treatment where data were first degraded spatially to simulate locational error. To incorporate error into the coordinates, we moved each coordinate with a random number drawn from the normal distribution with a mean of zero and a standard deviation of 5 km. We evaluated the influence of error on the performance of 10 commonly used distributional modelling techniques applied to 40 species in four distinct geographical regions. 3. Locational error in occurrences reduced model performance in three of these regions; relatively accurate predictions of species distributions were possible for most species, even with degraded occurrences. Two species distribution modelling techniques, boosted regression trees and maximum entropy, were the best performing models in the face of locational errors. The results obtained with boosted regression trees were only slightly degraded by errors in location, and the results obtained with the maximum entropy approach were not affected by such errors. 4. Synthesis and applications. To use the vast array of occurrence data that exists currently for research and management relating to the geographical ranges of species, modellers need to know the influence of locational error on model quality and whether some modelling techniques are particularly robust to error. We show that certain modelling techniques are particularly robust to a moderate level of locational error and that useful predictions of species distributions can be made even when occurrence data include some error.

Dynamical modeling and analysis of large cellular regulatory networks.

The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.

Development of sporogonic cycle of Plasmodium vivax in experimentally infected Anopheles albimanus mosquitoes

The sporogonic cycle of Plasmodium vivax was established and maintained under laboratory conditions in two different strains of Anopheles albimanus mosquitoes using as a parasite source blood from human patients or from Aotus monkeys infected with the VCC-2 P.vivax colombian isolate. Both the Tecojate strain isolate from Guatemala and the Cartagena strain from the colombian Pacific coast were susceptible to infections with P.vivax. A higher percentage of Cartagena mosquitoes was infected per trial, however the Tecojate strain developed higher sporozoite loads. Intravenous inoculation of Aotus monkeys with sporozoites obtained from both anopheline strains resulted in successful blood infections. Animals infected with sporozoites from the Tecojate strain presented a patent period of 21-32 days whereas parasitemia appeared between days 19-53 in monkeys infected with sporozites from Cartagena strain.

In vivo detection of brain Krebs cycle intermediate by hyperpolarized magnetic resonance.

The Krebs (or tricarboxylic acid (TCA)) cycle has a central role in the regulation of brain energy regulation and metabolism, yet brain TCA cycle intermediates have never been directly detected in vivo. This study reports the first direct in vivo observation of a TCA cycle intermediate in intact brain, namely, 2-oxoglutarate, a key biomolecule connecting metabolism to neuronal activity. Our observation reveals important information about in vivo biochemical processes hitherto considered undetectable. In particular, it provides direct evidence that transport across the inner mitochondria membrane is rate limiting in the brain. The hyperpolarized magnetic resonance protocol designed for this study opens the way to direct and real-time studies of TCA cycle kinetics.

Thermal effect on the life-cycle parameters of the medically important freshwater snail species Lymnaea (Radix) luteola (Lamarck)

The snails Lymnaea (Radix) luteola exhibited marked variations in growth, longevity, and attaining sexual maturity at different temperatures and diets. At 10°C, irrespective of foods, pH and salinity of water, the snails had minimum life span, maximum death rate and lowest growth rate. At 15°C, the growth rate was comparatively higher and the snails survived for a few more days. But at these temperatures they failed to attain sexual maturity. Snails exposed to pH 5 and 9 at 20°, 25°, 30°, 35°C and room temperatures (19.6°-29.6°C); to 0.5, 1.5 and 2.5 NaCl ‰ at 20° and 35ºC; to 2.5 NaCl ‰ at 25°C and room temperatures failed to attain sexual maturity. The snails exposed to pH 7 and different salinity grades at 20°, 25°, 30°, 35°C and room temperatures became sexually mature between 25-93 days depending upon the type of foods used in the culture.

A two years study on vectors of cutaneous leishmaniasis: Evidence for sylvatic transmission cycle in the State of Campeche, Mexico

Vectors of cutaneous leishmaniasis in the State of Campeche were studied in relation to the transmission cycle of Leishmania (Le.) mexicana. To determine how transmission of leishmaniasis occurs, we collected phlebotomine sand flies for two years. In the first year (October 1990 to November 1991) the collections were made with CDC light traps, Shannon traps and direct captures at natural shelters around the village (<200 m) of La Libertad. In the second year (February 1993 to January 1994) the catches were performed at 8 km southeast of La Libertad in the forest. Female sand flies were examined for Leishmania. During the first year, 347 sand flies of nine species were collected, most of which were Lutzomyia deleoni (61.3%). When all nine species were considered, more females than males were captured. Low densities of anthropophillic species of sand flies around the village indicated that sylvatic transmission was taking place. For the second year, 1484 sand flies of 16 species were caught. The most common were L. olmeca olmeca (21.7%), L. cruciata (19.2%) and L. ovallesi (14.1%). Similarly, more females were caught than males. Thirty-five females of five species were found infected with flagellates believed to be Leishmania sp. The highest infection rate was found in L. olmeca olmeca (7.1%) followed by L. cruciata (4.5%) and L. ovallesi (1.1%). These data plus other evidence on the epidemiology of human cases and results from reservoir studies are discussed in relation to the sylvatic transmission cycle.

Life cycle of a biologist.

Review of the book : "Lives of a biologist: Adventures in a century of extraordinary science", by J.T. Bonner, Harvard University Press, Cambridge, USA

Multimodel inference and multimodel averaging in empirical modeling of occupational exposure levels.

Empirical modeling of exposure levels has been popular for identifying exposure determinants in occupational hygiene. Traditional data-driven methods used to choose a model on which to base inferences have typically not accounted for the uncertainty linked to the process of selecting the final model. Several new approaches propose making statistical inferences from a set of plausible models rather than from a single model regarded as 'best'. This paper introduces the multimodel averaging approach described in the monograph by Burnham and Anderson. In their approach, a set of plausible models are defined a priori by taking into account the sample size and previous knowledge of variables influent on exposure levels. The Akaike information criterion is then calculated to evaluate the relative support of the data for each model, expressed as Akaike weight, to be interpreted as the probability of the model being the best approximating model given the model set. The model weights can then be used to rank models, quantify the evidence favoring one over another, perform multimodel prediction, estimate the relative influence of the potential predictors and estimate multimodel-averaged effects of determinants. The whole approach is illustrated with the analysis of a data set of 1500 volatile organic compound exposure levels collected by the Institute for work and health (Lausanne, Switzerland) over 20 years, each concentration having been divided by the relevant Swiss occupational exposure limit and log-transformed before analysis. Multimodel inference represents a promising procedure for modeling exposure levels that incorporates the notion that several models can be supported by the data and permits to evaluate to a certain extent model selection uncertainty, which is seldom mentioned in current practice.

Complementarities between mathematical and computational modeling: the case of the repeated Prisoners' Dilemma

We study the properties of the well known Replicator Dynamics when applied to a finitely repeated version of the Prisoners' Dilemma game. We characterize the behavior of such dynamics under strongly simplifying assumptions (i.e. only 3 strategies are available) and show that the basin of attraction of defection shrinks as the number of repetitions increases. After discussing the difficulties involved in trying to relax the 'strongly simplifying assumptions' above, we approach the same model by means of simulations based on genetic algorithms. The resulting simulations describe a behavior of the system very close to the one predicted by the replicator dynamics without imposing any of the assumptions of the mathematical model. Our main conclusion is that mathematical and computational models are good complements for research in social sciences. Indeed, while computational models are extremely useful to extend the scope of the analysis to complex scenarios hard to analyze mathematically, formal models can be useful to verify and to explain the outcomes of computational models.

Macroeconomic shocks and the business cycle: evidence from a structural factor model

We use a dynamic factor model to provide a semi-structural representation for 101 quarterly US macroeconomic series. We find that (i) the US economy is well described by a number of structural shocks between two and six. Focusing on the four-shock specification, we identify, using sign restrictions, two non-policy shocks, demand and supply, and two policy shocks, monetary and fiscal. We obtain the following results. (ii) Both supply and demand shocks are important sources of fluctuations; supply prevails for GDP, while demand prevails for employment and inflation. (ii) Policy matters, Both monetary and fiscal policy shocks have sizeable effects on output and prices, with little evidence of crowding out; both monetary and fiscal authorities implement important systematic countercyclical policies reacting to demand shocks. (iii) Negative demand shocks have a large long-run positive effect on productivity, consistently with the Schumpeterian "cleansing" view of recessions.

Life Cycle and Fecundity Analysis of Lutzomyia shannoni (Dyar) (Diptera: Psychodidae)

The life cycle of Lutzomyia shannoni (Dyar), was described for laboratory conditions with maximum daily temperatures of 27-30°C, minimum daily temperatures of 22-27°C and relative humidity between 87-99 %. Life cycle in each stage was as follows: egg 6-12 days (ave. 8.5 days); first stage larva 5-13 days (ave. 9.6 days); second stage larva 4-13 days (ave. 9.2 days ); third stage larva 5-19 days (ave. 11.8 days); fourth stage larva 7-37 days (ave. 19.9 days); pupa 7-32 days (ave. 15.2 days). The life expectancy of adults ranged from 4 to 15 days (ave. 8.6 days). The entire egg to adult period ranged from 36 to 74 days (ave. 54.6 days). On average, each female oviposited 22.7 eggs; the average egg retention per female was 24.3 eggs.

Life Cycle and Reproductive Patterns of Triatoma rubrofasciata (De Geer, 1773) (Hemiptera: Reduviidae), under Laboratory Conditions

The life cycle and reproductive patterns of Triatoma rubrofasciata were studied along with laboratory conditions for the establishment of a prolific colony. The insects were divided into four groups: two of them were maintained at room temperature (20.5°C to 33°C and 85% ± 5% of relative humidity), the other two in a climatic chamber (CC) (temperature: 29°C, humidity: 80% ± 5%). The groups were fed weekly or fortnightly on Swiss mice. The females from the group kept in the CC and fed weekly had longer life span, as well as a higher number of eggs, fertile eggs and hatchings; the group kept in the CC and fed fortnightly had a shorter life span for the 1st, 2nd and 3rd instars and a lower mortality rate for all instars. It was concluded that a constant high temperature (CC at 29°C) is the most suitable condition for the maintenance of a colony of T. rubrofasciata regardless of the interval between repasts.

Copeptin Levels Remain Unchanged during the Menstrual Cycle.

BACKGROUND: Copeptin, a surrogate marker for arginin vasopressin production, is evaluated as an osmo-dependent stress and inflammatory biomarker in different diseases. We investigated copeptin during the menstrual cycle and its relationship to sex hormones, markers of subclinical inflammation and estimates of body fluid. METHODS: In 15 healthy women with regular menstrual cycles, blood was drawn on fifteen defined days of their menstrual cycle and was assayed for copeptin, progesterone, estradiol, luteinizing hormone, high-sensitive C-reactive protein, tumor necrosis factor-alpha and procalcitonin. Symptoms of fluid retention were assessed on each visit, and bio impedance analysis was measured thrice to estimate body fluid changes. Mixed linear model analysis was performed to assess the changes of copeptin across the menstrual cycle and the relationship of sex hormones, markers of subclinical inflammation and estimates of body fluid with copeptin. RESULTS: Copeptin levels did not significantly change during the menstrual cycle (p = 0.16). Throughout the menstrual cycle, changes in estradiol (p = 0.002) and in the physical premenstrual symptom score (p = 0.01) were positively related to copeptin, but changes in other sex hormones, in markers of subclinical inflammation or in bio impedance analysis-estimated body fluid were not (all p = ns). CONCLUSION: Although changes in estradiol and the physical premenstrual symptom score appear to be related to copeptin changes, copeptin does not significantly change during the menstrual cycle.