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.

Energy expenditure by doubly labeled water: validation in humans and proposed calculation.

To further validate the doubly labeled water method for measurement of CO2 production and energy expenditure in humans, we compared it with near-continuous respiratory gas exchange in nine healthy young adult males. Subjects were housed in a respiratory chamber for 4 days. Each received 2H2(18)O at either a low (n = 6) or a moderate (n = 3) isotope dose. Low and moderate doses produced initial 2H enrichments of 5 and 10 X 10(-3) atom percent excess, respectively, and initial 18O enrichments of 2 and 2.5 X 10(-2) atom percent excess, respectively. Total body water was calculated from isotope dilution in saliva collected at 4 and 5 h after the dose. CO2 production was calculated by the two-point method using the isotopic enrichments of urines collected just before each subject entered and left the chamber. Isotope enrichments relative to predose samples were measured by isotope ratio mass spectrometry. At low isotope dose, doubly labeled water overestimated average daily energy expenditure by 8 +/- 9% (SD) (range -7 to 22%). At moderate dose the difference was reduced to +4 +/- 5% (range 0-9%). The isotope elimination curves for 2H and 18O from serial urines collected from one of the subjects showed expected diurnal variations but were otherwise quite smooth. The overestimate may be due to approximations in the corrections for isotope fractionation and isotope dilution. An alternative approach to the corrections is presented that reduces the overestimate to 1%.

Spectral analysis of random sparse matrices

"Vegeu el resum a l'inici del document del fitxer adjunt."

Level-wise node size distribution of randomly generated regular trees

"Vegeu el resum a l'inici del document del fitxer adjunt."

Propagation connectivity of random hypergraphs

We study the concept of propagation connectivity on random 3-uniform hypergraphs. This concept is inspired by a simple linear time algorithm for solving instances of certain constraint satisfaction problems. We derive upper and lower bounds for the propagation connectivity threshold, and point out some algorithmic implications.

The use of classification and regression trees to predict the likelihood of seasonal influenza.

Background Individual signs and symptoms are of limited value for the diagnosis of influenza. Objective To develop a decision tree for the diagnosis of influenza based on a classification and regression tree (CART) analysis. Methods Data from two previous similar cohort studies were assembled into a single dataset. The data were randomly divided into a development set (70%) and a validation set (30%). We used CART analysis to develop three models that maximize the number of patients who do not require diagnostic testing prior to treatment decisions. The validation set was used to evaluate overfitting of the model to the training set. Results Model 1 has seven terminal nodes based on temperature, the onset of symptoms and the presence of chills, cough and myalgia. Model 2 was a simpler tree with only two splits based on temperature and the presence of chills. Model 3 was developed with temperature as a dichotomous variable (≥38°C) and had only two splits based on the presence of fever and myalgia. The area under the receiver operating characteristic curves (AUROCC) for the development and validation sets, respectively, were 0.82 and 0.80 for Model 1, 0.75 and 0.76 for Model 2 and 0.76 and 0.77 for Model 3. Model 2 classified 67% of patients in the validation group into a high- or low-risk group compared with only 38% for Model 1 and 54% for Model 3. Conclusions A simple decision tree (Model 2) classified two-thirds of patients as low or high risk and had an AUROCC of 0.76. After further validation in an independent population, this CART model could support clinical decision making regarding influenza, with low-risk patients requiring no further evaluation for influenza and high-risk patients being candidates for empiric symptomatic or drug therapy.

Large multi-gene phylogenetic trees of the grasses (Poaceae): progress towards complete tribal and generic level sampling.

In this paper we included a very broad representation of grass family diversity (84% of tribes and 42% of genera). Phylogenetic inference was based on three plastid DNA regions rbcL, matK and trnL-F, using maximum parsimony and Bayesian methods. Our results resolved most of the subfamily relationships within the major clades (BEP and PACCMAD), which had previously been unclear, such as, among others the: (i) BEP and PACCMAD sister relationship, (ii) composition of clades and the sister-relationship of Ehrhartoideae and Bambusoideae + Pooideae, (iii) paraphyly of tribe Bambuseae, (iv) position of Gynerium as sister to Panicoideae, (v) phylogenetic position of Micrairoideae. With the presence of a relatively large amount of missing data, we were able to increase taxon sampling substantially in our analyses from 107 to 295 taxa. However, bootstrap support and to a lesser extent Bayesian inference posterior probabilities were generally lower in analyses involving missing data than those not including them. We produced a fully resolved phylogenetic summary tree for the grass family at subfamily level and indicated the most likely relationships of all included tribes in our analysis.

DNA polymorphism of schistosomes and their snail hosts

Analysis of the genomes of schistosomes and one of their intermediate hosts, Biomphalaria glabrata, using Random Amplified Polymorphic DNA (RAPD) demonstrated that intraspecific genetic polymorphism in the parasite is limited but in the snail is highly pronounced. This suggests an important role for the snail in the determination of the epidemiology of the disease. In addition to their intraspecific stability, schistosome derived RAPDs exhibit a high level of interspecific polymorphism and are thus ideal for the construction of phylogenetic trees. For the detection of intraspecific polymorphisms extensive variation in the mitochondrial DNA is being exploited for the development of a PCR based test for Schistosoma mansoni. Gene level polymorphisms are being analyzed by Low Stringency Single Specific Primer PCR.

Sequencing and identification of expressed Schistosoma mansoni genes by random selection of cDNA clones from a directional library

We have initiated a gene discovery program in Schistosoma mansoni based on the technique of Expressed Sequence Tags (ESTs), i.e. partial sequences of cDNAs obtained from single passes in automatic DNA sequencers. ESTs can be used to identify genese onf the basis of their homology whith sequences from other species deposited in DNA or protein databases. Trasncripts with sequences without matches in teh databases may represent novel parasite-specific genes. This approach has shown to be very efficient and in less than two years a broad range of novel genes has already been ascertained, more than doubling the number of known S. mansoni genes.

Identification of species related to Anopheles (Nyssorhynchus) albitarsis by random amplified polymorphic DNA-Polymerase chain reaction (Diptera: Culicidae)

Species-specific Random Amplified Polymorphic DNA-Polymerase chain Reaction (RAPD-PCR) markers were used to identify four species related to Anopheles (Nyssorhynchus) albitarsis Lynch-Arribàlzaga from 12 sites in Brazil and 4 in Venezuela. In a previous study (Wilkerson et al. 1995), which included sites in Paraguay and Argentina, these four species were designated "A", "B", "C" and "D". It was hypothesized that species A is An. (Nys.) albitarsis, species B is undescribed, species C is An. (Nys) marajoara Galvão and Damasceno and species D is An. (Nys.) deaneorum Rosa-Freitas. Species D, previously characterized by RAPD-PCR from a small sample from northern Argentina and southern Brazil, is reported here from the type locality of An. (Nys.) deaneorum, Guajará-Mirim, state of Rondônia, Brazil. Species C and D were found by RAPD-PCR to be sympatric at Costa Marques, state of Rondônia, Brazil. Species A and C have yet to be encountered at the same locality. The RAPD markers for species C were found to be conserved over 4,620 km; from Iguape, state of São Paulo, Brazil to rio Socuavo, state of Zulia, Venezuela. RAPD-PCR was determined to be an effective means for the identification of unknown species within this species complex.

A protocol for preparing GFP-labeled neurons previously imaged in vivo and in slice preparations for light and electron microscopic analysis.

In vivo imaging of green fluorescent protein (GFP)-labeled neurons in the intact brain is being used increasingly to study neuronal plasticity. However, interpreting the observed changes as modifications in neuronal connectivity needs information about synapses. We show here that axons and dendrites of GFP-labeled neurons imaged previously in the live mouse or in slice preparations using 2-photon laser microscopy can be analyzed using light and electron microscopy, allowing morphological reconstruction of the synapses both on the imaged neurons, as well as those in the surrounding neuropil. We describe how, over a 2-day period, the imaged tissue is fixed, sliced and immuno-labeled to localize the neurons of interest. Once embedded in epoxy resin, the entire neuron can then be drawn in three dimensions (3D) for detailed morphological analysis using light microscopy. Specific dendrites and axons can be further serially thin sectioned, imaged in the electron microscope (EM) and then the ultrastructure analyzed on the serial images.

Pseudomarkets with priorities in large random assignment economies

I study large random assignment economies with a continuum of agents and a finite number of object types. I consider the existence of weak priorities discriminating among agents with respect to their rights concerning the final assignment. The respect for priorities ex ante (ex-ante stability) usually precludes ex-ante envy-freeness. Therefore I define a new concept of fairness, called no unjustified lower chances: priorities with respect to one object type cannot justify different achievable chances regarding another object type. This concept, which applies to the assignment mechanism rather than to the assignment itself, implies ex-ante envy-freeness among agents of the same priority type. I propose a variation of Hylland and Zeckhauser' (1979) pseudomarket that meets ex-ante stability, no unjustified lower chances and ex-ante efficiency among agents of the same priority type. Assuming enough richness in preferences and priorities, the converse is also true: any random assignment with these properties could be achieved through an equilibrium in a pseudomarket with priorities. If priorities are acyclical (the ordering of agents is the same for each object type), this pseudomarket achieves ex-ante efficient random assignments.

A linear optimization technique for graph pebbling

Graph pebbling is a network model for studying whether or not a given supply of discrete pebbles can satisfy a given demand via pebbling moves. A pebbling move across an edge of a graph takes two pebbles from one endpoint and places one pebble at the other endpoint; the other pebble is lost in transit as a toll. It has been shown that deciding whether a supply can meet a demand on a graph is NP-complete. The pebbling number of a graph is the smallest t such that every supply of t pebbles can satisfy every demand of one pebble. Deciding if the pebbling number is at most k is NP 2 -complete. In this paper we develop a tool, called theWeight Function Lemma, for computing upper bounds and sometimes exact values for pebbling numbers with the assistance of linear optimization. With this tool we are able to calculate the pebbling numbers of much larger graphs than in previous algorithms, and much more quickly as well. We also obtain results for many families of graphs, in many cases by hand, with much simpler and remarkably shorter proofs than given in previously existing arguments (certificates typically of size at most the number of vertices times the maximum degree), especially for highly symmetric graphs. Here we apply theWeight Function Lemma to several specific graphs, including the Petersen, Lemke, 4th weak Bruhat, Lemke squared, and two random graphs, as well as to a number of infinite families of graphs, such as trees, cycles, graph powers of cycles, cubes, and some generalized Petersen and Coxeter graphs. This partly answers a question of Pachter, et al., by computing the pebbling exponent of cycles to within an asymptotically small range. It is conceivable that this method yields an approximation algorithm for graph pebbling.