Fish condition in introduced tilapias of Ugandan crater lakes in relation to deforestation and fishing pressure

This study identifies environmental predictors of the condition of two introduced tilapia species (Oreochromis leucostictus and Tilapia zillii) that are known to have divergent trophic niches (planktivore and herbivore, respectively) in 17 crater lakes in western Uganda. We asked whether fish condition differs among lakes characterized by differences in fishing pressure and catchment deforestation; and we related relative condition factor to gradients of environmental variation across lakes. Lakes characterized by severe catchment deforestation tended to be lakes with high fishing pressure, so it was difficult to explore independent and interactive effects. However, mean relative condition factor was higher in populations with high fishing pressure compared to populations with low fishing pressure for both O. leucostictus and T. zillii. The condition of O. leucostictus populations was higher in lakes with severely deforested catchments; but mean relative condition factor of T. zillii did not differ between deforestation categories. Principal components analysis (PCA) was used to describe the major environmental gradients of variation among the lakes; and PCA factor scores were regressed against relative fish condition. The association between fish condition and environmental gradients was stronger for O. leucostictus than for T. zillii. For O. leucostictus, fish condition was related to PC1 (43% of the variance) and factors that loaded most heavily included Chl-a, water transparency, lake area and depth, suggesting higher condition in lakes characterized by higher primary productivity and smaller size. For T. zillii, PC3 (11%) was the only axis related to fish condition; and factors that loaded most heavily included lake area (positive), and conductivity and total nitrogen (negative). Some of the larger lakes are characterized by higher availability of macrophytes that may positively affect the food base for T. zillii.     

Resting Sinus Heart Rate and First Degree AV block: Modifiable Risk Predictors or Epiphenomena?

Simple and cost-effective tools that identify patients at increased risk for adverse cardiovascular events are actively sought. High resting sinus heart rate and first degree AV block are easily recognized and commonly encountered findings in a cardiology practice. A growing body of epidemiological and clinical evidence has been shown them to be independent predictors of all-cause and cardiovascular mortality, both in the general population and in patients with structural heart disease. This paper reviews the important role of heart rate and first degree AV block in predicting cardiovascular outcomes, examines the pathophysiological mechanisms underlying this increased risk, and discusses the effectiveness of available therapies to favorably modify these risk factors.     

Estimating and controlling for spatial structure in the study of ecological communities

Aim Variation partitioning based on canonical analysis is the most commonly used analysis to investigate community patterns according to environmental and spatial predictors. Ecologists use this method in order to understand the pure contribution of the environment independent of space, and vice versa, as well as to control for inflated type I error in assessing the environmental component under spatial autocorrelation. Our goal is to use numerical simulations to compare how different spatial predictors and model selection procedures perform in assessing the importance of the spatial component and in controlling for type I error while testing environmental predictors. Innovation We determine for the first time how the ability of commonly used (polynomial regressors) and novel methods based on eigenvector maps compare in the realm of spatial variation partitioning. We introduce a novel forward selection procedure to select spatial regressors for community analysis. Finally, we point out a number of issues that have not been previously considered about the joint explained variation between environment and space, which should be taken into account when reporting and testing the unique contributions of environment and space in patterning ecological communities. Main conclusions In tests of species‐environment relationships, spatial autocorrelation is known to inflate the level of type I error and make the tests of significance invalid. First, one must determine if the spatial component is significant using all spatial predictors (Moran's eigenvector maps). If it is, consider a model selection for the set of spatial predictors (an individual‐species forward selection procedure is to be preferred) and use the environmental and selected spatial predictors in a partial regression or partial canonical analysis scheme. This is an effective way of controlling for type I error in such tests. Polynomial regressors do not provide tests with a correct level of type I error.     

Identification of serum predictors of n-acetyl-l-cysteine and isoproterenol induced remodelling in cardiac hypertrophy

Cardiac hypertrophy (CH), leading to cardiac failure is due to chronic metabolic alterations occurring during cellular stress. Besides the already known relationship between oxidative stress and CH, there are implications of reductive stress leading to CH. This study attempted to develop reductive stress-based CH rat model using n-acetyl-L-cysteine (NAC), a glutathione agonist that was compared with typical isoproterenol (ISO) induced CH model. The main objective was to identify serum metabolites that can serve as potent predictors for seven routine clinical and diagnostic parameters in CH: 3-hydroxybutyrate (3-HB), lactic acid (LA), urea, and ECG-CH parameters (QRS complex, R-amplitude, R-R interval, heart rate) that were hypothesized to underlie metabolic remodelling in this study. CH was assessed using electrocardiography, hypertrophic index and histopathological analysis (H&E stain) in both ventricles after 2 weeks. Gas chromatography mass spectroscopy analysis (GC-MS) identified unique metabolite finger-prints. Correlation and pattern analysis revealed strong relationships between specific metabolites and parameters (Pearson’s score > 0.7) of this study. Multiple regression analysis (MRA) for the strongly related metabolites (independent variables) with each of the seven parameters (dependent variables) identified significant predictors for the latter namely fructose, valine, butanoic acid in NAC and cholesterol, erythrose, isoleucine in ISO models, with proline and succinic acid as common for both models. Metabolite set enrichment analysis (MSEA) of those significant predictors (p < 0.05) mapped butyrate metabolism as highly influential pathway in NAC, with arginine-proline metabolism and branched chain amino acid (BCAA) degradation as common pathways in both models, thus providing new insights towards initial metabolic remodeling in the pathogenesis of CH.     

Register-based predictors of violations of animal welfare legislation in dairy herds

The assessment of animal welfare can include resource-based or animal-based measures. Official animal welfare inspections in Denmark primarily control compliance with animal welfare legislation based on resource measures (e.g. housing system) and usually do not regard animal response parameters (e.g. clinical and behavioural observations). Herds selected for welfare inspections are sampled by a risk-based strategy based on existing register data. The aim of the present study was to evaluate register data variables as predictors of dairy herds with violations of the animal welfare legislation (VoAWL) defined as occurrence of at least one of the two most frequently violated measures found at recent inspections in Denmark, namely (a) presence of injured animals not separated from the rest of the group and/or (b) animals in a condition warranting euthanasia still being present in the herd. A total of 25 variables were extracted from the Danish Cattle Database and assessed as predictors using a multivariable logistic analysis of a data set including 73 Danish dairy herds, which all had more than 100 cows and cubicle loose-housing systems. Univariable screening was used to identify variables associated with VoAWL at a P-value<0.2 for the inclusion in a multivariable logistic regression analysis. Backward selection procedures identified the following variables for the final model predictive of VoAWL: increasing standard deviation of milk yield for first lactation cows, high bulk tank somatic cell count (⩾250 000 cells/ml) and suspiciously low number of recorded veterinary treatments (⩽25 treatments/100 cow years). The identified predictors may be explained by underlying management factors leading to impaired animal welfare in the herd, such as poor hygiene, feeding and management of dry or calving cows and sick animals. However, further investigations are required for causal inferences to be established.     

Phylogenetic measures of biodiversity uncover pteridophyte centres of diversity and hotspots in Tuscany

Biodiversity studies traditionally use species as their analysis unit. However, observed biodiversity is not necessarily an indicator of significant biodiversity and therefore, should not be used alone, particularly when the results will be employed for conservation planning. Here, we examine pteridophyte diversity in Tuscany using 4668 geo-referenced pteridophyte records stored in Wikiplantbase #Toscana and calculating standard richness measures and phylogenetic indices; in addition, we assessed the environmental predictors of each diversity metric. Finally, we identified which areas hosting significantly high diversity are not adequately covered by protected areas or intersected by human activities. Although considerable diversity is distributed along Tuscany within areas traditionally recognised as biodiversity hotspots, important areas of diversity are identified only after the application of randomisation tests and through the phylogenetic metrics. Topographic ruggedness resulted the single most important variable predicting high value of all indices; temperature seasonality and temperature continentality resulted the secondary explanatory variables with a significant negative effect on most diversity indices.     

Expression patterns of vascular-specific promoters RolC and Sh in transgenic potatoes and their use in engineering PLRV-resistant plants

The expression patterns of GUS fusion constructs driven by the Agrobacterium rhizogenes RolC and the maize Sh (Shrunken; sucrose synthase-1) promoters were examined in transgenic potatoes (cv. Atlantic). RolC drove high-level gene expression in phloem tissue, bundle sheath cells and vascular parenchyma, but not in xylem or non-vascular tissues. Sh expression was exclusively confined to phloem tissue. Potato leafroll luteovirus (PLRV) replicates only in phloem tissues, and we show that when RolC is used to drive expression of the PLRV coat protein gene, virus-resistant lines can be obtained. In contrast, no significant resistance was observed when the Sh promoter was used.     

Assessing fern diversity: relative species richness and its environmental correlates in Uganda

Techniques for the rapid quantification of tropical biodiversity are of critical importance in deciding where to invest scarce conservation resources. Here we describe a simple survey method for assessing species-level richness of a poorly known plant group, the pteridophytes. We then illustrate the use of a powerful, rarefaction- based technique of controlling for inevitable differences in sampling effort to calculate the relative species richness of our study sites. Lastly, we explore how closely observed patterns of relative species richness of Ugandan forests are correlated with a suite of simple environmental variables. We find that fully 75% of the variance in our estimate of fern diversity can be predicted from just two measures: soil fertility (scored as C/N ratio, itself related to rainfall); and distance from the nearest putative Pleistocene refugium.