Adaptive developmental plasticity: what is it,how can we recognize it and when can it evolve?

Developmental plasticity describes situations where a specific input during an individual''s development produces a lasting alteration in phenotype. Some instances of developmental plasticity may be adaptive, meaning that the tendency to produce the phenotype conditional on having experienced the developmental input has been under positive selection. We discuss the necessary assumptions and predictions of hypotheses concerning adaptive developmental plasticity (ADP) and develop guidelines for how to test empirically whether a particular example is adaptive. Central to our analysis is the distinction between two kinds of ADP: informational, where the developmental input provides information about the future environment, and somatic state-based, where the developmental input enduringly alters some aspect of the individual''s somatic state. Both types are likely to exist in nature, but evolve under different conditions. In all cases of ADP, the expected fitness of individuals who experience the input and develop the phenotype should be higher than that of those who experience the input and do not develop the phenotype, while the expected fitness of those who do not experience the input and do not develop the phenotype should be higher than those who do not experience the input and do develop the phenotype. We describe ancillary predictions that are specific to just one of the two types of ADP and thus distinguish between them.     

Models to predict lake annual mean total phosphorus

A lake is a product of processes in its watershed, and these relationships should be empirically quantifiable. Yet few studies have made that attempt. This study quantifies and ranks variables of significance to predict annual mean values of total phosphorus (TP) in small glacial lakes. Several new empirical models based on water chemistry variables, on map parameters of the lake and its catchment, and combinations of such variables are presented. Each variable provides only a limited (statistical) explanation of the variation in annual mean values of TP among lakes. The models are markedly improved by accounting for the distribution of the characteristics (e.g., the mires) in the watershed. The most important map parameters were the proportion of the watershed lying close to the lake covered by rocks and open land (as determined with the drainage area zonation method), relief of the drainage area, lake area and mean depth. These empirical models can be used to predict annual mean TP but only for lakes of the same type. The model based on map parameters (r ²=0.56) appears stable. The effects of other factors/variables not accounted for in the model (like redox-induced internal loading and anthropogenic sources) on the variation in annual mean TP may then be estimated quantitatively by residual analysis. A new mixed model (which combines a dynamic mass-balance approach with empirical knowledge) was also developed. The basic objective was to put the empirical results into a dynamic framework, thereby increasing predictive accuracy. Sensitivity tests of the mixed model indicate that it works as intended. However, comparisons against independent data for annual mean TP show that the predictive power of the mixed model is low, likely because crucial model variables, like sedimentation rate, runoff rate, diffusion rate and precipitation factor, cannot be accurately predicted. These model variables vary among lakes, but this mixed model, like most dynamic models, assumed that they are constants.     

Pleural Fluid Adenosine Deaminase and Lymphocyte Proportion: Clinical Usefulness In the Diagnosis of Tuberculosis

Adenosine deaminase (ADA) and lymphocyte proportion are known to be independently elevated in tuberculous effusions, but are non-specific, and false positive results are frequent. to overcome this problem the combined use of both parameters was prospectively studied in 276 patients with pleural effusion seen at Porto Alegre, Brazil. Using a cut-off level of 40 U/l at 37°C (method of Giusti¹⁹) for ADA activity and lymphocyte proportion of more than 50%, the correct diagnosis of tuberculosis (sensitivity) was made in 90.7% (CI 87.3–94.1%) of 54 patients. A specificity of 97.7% (CI 95.9–99.5%) was recorded. Five false positive diagnoses of tuberculous effusion were made. Five false negative diagnoses were made: three cases with haematogenous tuberculous dissemination with low ADA levels, and two other patients with low lymphocyte proportion. the combined use of ADA activity determination and lymphocyte proportion is a highly efficient diagnostic strategy of low cost, that merits wider use.     

Design considerations for two-stage enrichment clinical trials

When there is a predictive biomarker, enrichment can focus the clinical trial on a benefiting subpopulation. We describe a two-stage enrichment design, in which the first stage is designed to efficiently estimate a threshold and the second stage is a “phase III-like” trial on the enriched population. The goal of this paper is to explore design issues: sample size in Stages 1 and 2, and re-estimation of the Stage 2 sample size following Stage 1. By treating these as separate trials, we can gain insight into how the predictive nature of the biomarker specifically impacts the sample size. We also show that failure to adequately estimate the threshold can have disastrous consequences in the second stage. While any bivariate model could be used, we assume a continuous outcome and continuous biomarker, described by a bivariate normal model. The correlation coefficient between the outcome and biomarker is the key to understanding the behavior of the design, both for predictive and prognostic biomarkers. Through a series of simulations we illustrate the impact of model misspecification, consequences of poor threshold estimation, and requisite sample sizes that depend on the predictive nature of the biomarker. Such insight should be helpful in understanding and designing enrichment trials.     

TTLL11 gene is associated with sustained attention performance and brain networks: A genome-wide association study of a healthy Chinese sample

Genetic studies on attention have mainly focused on children with attention-deficit/hyperactivity disorder (ADHD), so little systematic research has been conducted on genetic correlates of attention performance and their potential brain mechanisms among healthy individuals. The current study included a genome-wide association study (GWAS, N = 1145 healthy young adults) aimed to identify genes associated with sustained attention and an imaging genetics study (an independent sample of 483 healthy young adults) to examine any identified genes' influences on brain function. The GWAS found that TTLL11 showed genome-wide significant associations with sustained attention, with rs13298112 as the most significant SNP and the GG homozygotes showing more impulsive but also more focused responses than the A allele carriers. A retrospective examination of previously published ADHD GWAS results confirmed an un-reported, small but statistically significant effect of TTLL11 on ADHD. The imaging genetics study replicated this association and showed that the TTLL11 gene was associated with resting state activity and connectivity of the somatomoter network, and can be predicted by dorsal attention network connectivity. Specifically, the GG homozygotes showed lower brain activity, weaker brain network connectivity, and non-significant brain-attention association compared to the A allele carriers. Expression database showed that expression of this gene is enriched in the brain and that the G allele is associated with lower expression level than the A allele. These results suggest that TTLL11 may play a major role in healthy individuals' attention performance and may also contribute to the etiology of ADHD.     

Summer Epidemics of Apple Scab: The Relationship between Measurements and their Implications for the Development of Predictive Models and Threshold Levels under Different Disease Control Regimes

A 2‐year study on epidemic progress of apple scab was conducted at Randwijk, the Netherlands, in 1998 and 1999. The summer epidemic caused by conidia was studied instead of the well‐described spring season epidemic originating from ascospores. The aim was to investigate relationships between disease measurements, i.e. disease incidence and severity measures of apple scab, and their implications for the development of predictive models and threshold levels. The study characterized good relationships between the measurements on cultivar Jonagold using regression analyses in three disease control regimes (untreated, organic and integrated). For fruit quality prediction, the relationship between fruit incidence (I_f) and leaf incidence (I_l) in the organic control regime was given by I_f = 1.966 + 0.402 × (I_l) (R² = 0.92). As a result of low level of disease in the integrated control regime, shoot incidence (I_s), with higher values than leaf incidence, was better suited for prediction. The relationship was given by I_f = ?0.162 + 0.028 × (I_s) (R² = 0.91). For the integrated control regime, disease threshold levels were constructed for timing of the final fungicide application. If an apple grower wants to keep fruit infection under 1% incidence (harvest scab threshold), the timing of the final fungicide application (action threshold) should correspond to 4% shoot scab incidence at the beginning of August. The results are compared with similar studies and their biological interpretation is discussed.     

Evaluation of Fourier-transform infrared spectroscopy for data capture in predictive microbiology

Chemical changes in the medium, induced by the fermentative species Lactobacillus plantarum and Lactobacillus brevis and by the enzymatic action of a proteolytic, spoilage species, Yarrowia lipolytica, were analysed using Fourier-transform i.r. spectroscopy (FTIR). Changes in the absorbance data over time could be modelled using one of the more current predictive, mathematical models of microbial growth, such as the Gompertz equation. Moreover, a linear correlation between FTIR data (expressed as absorbance of some selected peaks) and viability data (expressed as log₁₀ c.f.u./g or ml) was observed during the fermentation process, both for L. plantarum and L. brevis.