Iron bioavailability to phytoplankton: an empirical approach

Phytoplankton are often limited by iron in aquatic environments. Here we examine Fe bioavailability to phytoplankton by analyzing iron uptake from various Fe substrates by several species of phytoplankton grown under conditions of Fe limitation and comparing the measured uptake rate constants (Fe uptake rate/ substrate concentration). When unchelated iron, Fe′, buffered by an excess of the chelating agent EDTA is used as the Fe substrate, the uptake rate constants of all the eukaryotic phytoplankton species are tightly correlated and proportional to their respective surface areas (S.A.). The same is true when FeDFB is the substrate, but the corresponding uptake constants are one thousand times smaller than for Fe′. The uptake rate constants for the other substrates we examined fall mostly between the values for Fe′ and FeDFB for the same S.A. These two model substrates thus empirically define a bioavailability envelope with Fe′ at the upper and FeDFB at the lower limit of iron bioavailability. This envelope provides a convenient framework to compare the relative bioavailabilities of various Fe substrates to eukaryotic phytoplankton and the Fe uptake abilities of different phytoplankton species. Compared with eukaryotic species, cyanobacteria have similar uptake constants for Fe′ but lower ones for FeDFB. The unique relationship between the uptake rate constants and the S.A. of phytoplankton species suggests that the uptake rate constant of Fe-limited phytoplankton has reached a universal upper limit and provides insight into the underlying uptake mechanism.     

Predicting the impacts of an introduced species from its invasion history: an empirical approach applied to zebra mussel invasions

1. Quantitative models of impact are lacking for the vast majority of known invasive species, particularly in aquatic ecosystems. Consequently, managers lack predictive tools to help them prioritise invasion threats and decide where they can most effectively allocate limited resources. Predictive tools would also enhance the accuracy of water quality assessments, so that impacts caused by an invader are not erroneously attributed to other anthropogenic stressors. 2. The invasion history of a species is a valuable guide for predicting the consequences of its introduction into a new environment. Regression analysis of data from multiple invaded sites can generate empirical models of impact, as is shown here for the zebra mussel Dreissena polymorpha. Dreissena's impacts on benthic invertebrate abundance and diversity follow predictable patterns that are robust across a range of habitat types and geographic regions. Similar empirical models could be developed for other invaders with a documented invasion history. 3. Because an invader's impact is correlated with its abundance, a surrogate model may be generated (when impact data are unavailable) by relating the invader's abundance to environmental variables. Such a model could help anticipate which habitats will be most affected by invasion. Lack of precision should not be a deterrent to developing predictive models where none exist. Crude predictions can be refined as additional data become available. Empirical modelling is a highly informative and inexpensive, but underused, approach in the management of aquatic invasive species.     

DNA-based patents: an empirical analysis

The perception of rising litigation rates is driving the push for patent reform.     

An empirical approach to the analysis of forest stratification

Natural forest communities consist of different overlapping elementary subpopulations. Using the results of forest stratification in the preceding study, the properties of mean tree height for subpopulation in a stratified forest stand were examined. Mean tree height decreased as tree density per subpopulation increased. This relation was described by a simple mathematical model consisting of a power equation of tree density and two coefficients. The first coefficient or exponent of tree density was close to −1/2 in its expectation, while the other coefficient depended on life forms, especially in tropical forests. For tropical deciduous forests which suffered from seasonal forest fires, the latter coefficient was smaller than those for tropical evergreen and Japanese forests. This difference of the coefficient was not clear between tropical evergreen and Japanese forests and between deciduous and evergreen forests in Japan. In conclusion, the proposed model is similar to the 1/2 power law of tree height in man-made forests with simple architecture, and is designated the quasi-1/2 power law of tree height.     

Fisheries for small pelagic species: an empirical approach to management targets

Summary Long-term management targets based on MSY, F_max or F_0.1 are inappropriate for small pelagic fish because of the possibility of stock collapse owing to a stock-recruit relationship at low biomasses. Better reference points such as F_med and F_high that take account of stock and recruit data cannot be used in developing fishery situations because they are too demanding of data. A simple model was fitted to medium-term (about 10 year) periods in exploited small pelagic fisheries, relating change in stock biomass to exploitation rate. Data from 28 stocks and 11 species were used. The fitted model was used to estimate likelihood of stock decrease at different exploitation rates. The pelagic stocks included in the model appeared to be in equilibrium for an exploitation rate F/Z=0.4, which may be used as a guideline for the appropriate exploitation of pelagic stocks.     

Plugging space into predator-prey models: an empirical approach

Extrapolating ecological processes from small-scale experimental systems to scales of natural populations usually entails a considerable increase in spatial heterogeneity, which may affect process rates and, ultimately, population dynamics. We demonstrate how information on the heterogeneity of natural populations can be taken into account when scaling up laboratory-derived process functions, using the technique of moment approximation. We apply moment approximation to a benthic crustacean predator-prey system, where a laboratory-derived functional response is made spatial by including correction terms for the variance in prey density and the covariance between prey and predator densities observed in the field. We also show how moment approximation may be used to incorporate spatial information into a dynamic model of the system. While the nonspatial model predicts stable dynamics, its spatial equivalent also produces bounded fluctuations, in agreement with observed dynamics. A detailed analysis shows that predator-prey covariance, but not prey variance, destabilizes the dynamics. We conclude that second-order moment approximation may provide a useful technique for including spatial information in population models. The main advantage of the method is its conceptual value: by providing explicit estimates of variance and covariance effects, it offers the possibility of understanding how heterogeneity affects ecological processes.     

'Fingerprinting' snakes: a digital system applied to a population of Natrix maura

An identification system for individual snakes, Natrix maura , was developed, based on photographs of the pattern on the ventral scales. The patterns appeared to be constant, and contained sufficient information (allowing for non-randomness within the pattern) to render duplication unlikely, even in extremely large populations. After digitization, the patterns were compared for overall similarity by computer, and those which matched were checked visually. Using this system on a third of the scales, only 1% of known recaptures (those in which one of the compared photographs was of poor quality) were missed, and coding was reduced to 2.2 min. This sifting makes recognition by natural marks practicable in very large samples, and is robust enough to absorb considerable error in coding.     

Post-high-throughput screening analysis: an empirical compound prioritization scheme

An empirical scheme to evaluate and prioritize screening hits from high-throughput screening (HTS) is proposed. Negative scores are given when chemotypes found in the HTS hits are present in annotated databases such as MDDR and WOMBAT or for testing positive in toxicity-related experiments reported in TOXNET. Positive scores were given for higher measured biological activities, for testing negative in toxicity-related literature, and for good overlap when profiled against drug-related properties. Particular emphasis is placed on estimating aqueous solubility to prioritize in vivo experiments. This empirical scheme is given as an illustration to assist the decision-making process in selecting chemotypes and individual compounds for further experimentation, when confronted with multiple hits from high-throughput experiments. The decision-making process is discussed for a set of G-protein coupled receptor antagonists and validated on a literature example for dihydrofolate reductase inhibition.     

Along signal paths: an empirical gene set approach exploiting pathway topology

Gene set analysis using biological pathways has become a widely used statistical approach for gene expression analysis. A biological pathway can be represented through a graph where genes and their interactions are, respectively, nodes and edges of the graph. From a biological point of view only some portions of a pathway are expected to be altered; however, few methods using pathway topology have been proposed and none of them tries to identify the signal paths, within a pathway, mostly involved in the biological problem. Here, we present a novel algorithm for pathway analysis clipper, that tries to fill in this gap. clipper implements a two-step empirical approach based on the exploitation of graph decomposition into a junction tree to reconstruct the most relevant signal path. In the first step clipper selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it identifies within these pathways the signal paths having the greatest association with a specific phenotype. We test our approach on simulated and two real expression datasets. Our results demonstrate the efficacy of clipper in the identification of signal transduction paths totally coherent with the biological problem.     

EmPLiCS: an empirical approach for structure-based design of natural peptide drugs

The computer implementation of a peptide drug-design strategy has been developed. The system is named EmPLiCS (Empirical Peptide Ligand Construction System) according to the strategy of the system, which searches for peptide-ligand structures by referring to empirical rules that are derived from known protein 3D structures. The system was tested on several known peptide-protein complexes. The results demonstrated the ability of this system to detect key residues of peptides that are crucial for interaction with their specific proteins. The system also showed the ability to detect the main chain trace of these peptides. Some of the main chain atoms were detected even though the complete primary structures were not reproduced, suggesting that main chain structure is important in peptide-protein recognition. The results of the present study demonstrated that the empirical rules-based system can generate significant information for use in the design of natural peptide drugs.     

CRAC: an integrated approach to the analysis of RNA-seq reads

A large number of RNA-sequencing studies set out to predict mutations, splice junctions or fusion RNAs. We propose a method, CRAC, that integrates genomic locations and local coverage to enable such predictions to be made directly from RNA-seq read analysis. A k-mer profiling approach detects candidate mutations, indels and splice or chimeric junctions in each single read. CRAC increases precision compared with existing tools, reaching 99:5% for splice junctions, without losing sensitivity. Importantly, CRAC predictions improve with read length. In cancer libraries, CRAC recovered 74% of validated fusion RNAs and predicted novel recurrent chimeric junctions. CRAC is available at http://crac.gforge.inria.fr.     

A multivariate diagnosis approach applied to celery

Celery (Apium graveolens L. var Dulce) is a high value crop affected at different growth stages by a variety of nutrient disorders. Each nutrient concentration can be corrected for its dependence on concentrations of other nutrients by recognizing plant composition as a closed system whose components add up to one. New variables z _i are computed as logratioed values of individual nutrients, where each nutrient concentration is corrected for the geometric mean of all nutrient concentrations. The z _i are used together with principal component analysis (PCA) to relate celery composition to yield, deficiency symptoms and quality parameters. A survey of commercial celery fields suggested that (1) celery growth is most often limited by P and N deficiencies associated with Fe toxicity; (2) K uptake is most likely to become limiting when the crop reaches 15 cm in height; (3) blackheart incidence can be traced to low levels of K and Mg in external petioles, and (4) cracked stem incidence is related to low B when the crop is 30 cm in height.     

Transitive inference in non-human animals: an empirical and theoretical analysis

Transitive inference has long been considered one of the hallmarks of human deductive reasoning. Recent reports of transitive-like behaviors in non-human animals have prompted a flourishing empirical and theoretical search for the mechanism(s) that may mediate this ability in non-humans. In this paper, I begin by describing the transitive inference tasks customarily used with non-human animals and then review the empirical findings. Transitive inference has been demonstrated in a wide variety of species, and the signature effects that usually accompany transitive inference in humans (the serial position effect and the symbolic distance effect) have also been found in non-humans. I then critically analyze the most prominent models of this ability in non-human animals. Some models are cognitive, proposing for instance that animals use the rules of formal logic or form mental representations of the premises to solve the task, others are based on associative mechanisms such as value transfer and reinforcement and non-reinforcement. Overall, I argue that the reinforcement-based models are in a much better empirical and theoretical position. Hence, transitive inference in non-human animals should be considered a property of reinforcement history rather than of inferential processes. I finalize by shedding some light on some promising lines of research.     

GeneCluster 2.0: an advanced toolset for bioarray analysis

SUMMARY: GeneCluster 2.0 is a software package for analyzing gene expression and other bioarray data, giving users a variety of methods to build and evaluate class predictors, visualize marker lists, cluster data and validate results. GeneCluster 2.0 greatly expands the data analysis capabilities of GeneCluster 1.0 by adding classification, class discovery and permutation test methods. It includes algorithms for building and testing supervised models using weighted voting and k-nearest neighbor algorithms, a module for systematically finding and evaluating clustering via self-organizing maps, and modules for marker gene selection and heat map visualization that allow users to view and sort samples and genes by many criteria. GeneCluster 2.0 is a stand-alone Java application and runs on any platform that supports the Java Runtime Environment version 1.3.1 or greater. AVAILABILITY: http://www.broad.mit.edu/cancer/software     

Life-history evolution in Australian snakes: a path analysis

I recently attempted to investigate interspecific patterns in ecological traits of Australian snakes using univariate statistical techniques (Shine 1994), but high intercorrelations among variables (especially with mean adult body size) made it difficult to interpret the observed patterns. In the present paper, I attempt to tease apart causal factors using multivariate (path) analysis on the same data set (103 species, based on dissection of >22000 museum specimens). Two separate path analyses were conducted: one that treated each species as an independent unit (and thus, ignored phylogeny) and the other based on independent phylogenetic contrasts. Path coefficients from the two types of analyses were similar in magnitude, and highly correlated with each other, suggesting that most interspecific patterns among traits may reflect functional association rather than phylogenetic conservatism. Path analysis showed that indirect effects of one variable upon another (i.e., mediated via other traits) were often stronger than direct effects. Thus, even when two variables appeared to be uncorrelated in the univariate analysis, this apparent lack of relationship sometimes masked strong but conflicting indirect effects. For example, a tradeoff between clutch size and offspring size tends to mask the direct effect of mean adult body size on clutch size. Path analysis may also suggest original causal hypotheses. For example, interspecific allometry of sexual size dimorphism (as seen in Australian snakes, and many other animal groups) may result from a strong effect of another allometrically-tied trait (offspring size) on growth trajectories of females.