Models for Trypanosoma evansi (surra), its control and economic impact on small-hold livestock owners in the Philippines

Simple demographic and infectious disease models of buffaloes and other domestic hosts for animal trypanosomosis (surra) caused by Trypanosoma evansi were developed. The animal models contained deterministic and stochastic elements and were linked to simulate the benefit of control regimes for surra in village domestic animal populations in Mindanao, Philippines. The impact of the disease on host fertility and mortality were key factors in determining the economic losses and net-benefit from the control regimes. If using a high (99%) efficacy drug in surra-moderate to high risk areas, then treating all animals twice each year yielded low prevalence in 2 years; targeted treatment of clinically sick animals, constantly monitored (monthly), required 75% fewer treatments but took longer to reach a low prevalence than treating all animals twice each year. At high drug efficacy both of these treatment strategies increased the benefit over untreated animals by 81%. If drug efficacy declined then the benefit obtained from twice yearly treatment of all animals declined rapidly compared with regular monitoring and targeting treatment to clinically sick animals. The current control regimen applied in the Philippines of annual sero-testing for surra and only treating sero-positive animals provided the lowest net-benefit of all the control options simulated and would not be regarded as effective control. The total net-benefit from effective surra control for a typical village in a moderate/high risk area was 7.9 million pesos per annum (US $158,000). The value added to buffaloes, cattle, horses, goats/sheep and pigs as a result of this control was US $88, $84, $151, $7, $114 per animal/year, respectively.     

Genetic structure of a wide-spectrum chicken gene pool

The genetic structure of 65 chicken populations was studied using 29 simple sequence repeat loci. Six main clusters which corresponded to geographical origins and histories were identified: Brown Egg Layers; predominantly Broilers; native Chinese breeds or breeds with recent Asian origin; predominantly breeds of European derivation; a small cluster containing populations with no common history and populations that had breeding history with White Leghorn. Another group of populations that shared their genome with several clusters was defined as 'Multi-clusters'. Gallus gallus gallus (Multi-clusters), one of the subspecies of the Red Jungle Fowl, which was previously suggested to be one of the ancestors of the domesticated chicken, has almost no shared loci with European and White Egg layer populations. In a further sub-clustering of the populations, discrimination between all the 65 populations was possible, and relationships between each were suggested. The genetic variation between populations was found to account for about 34% of the total genetic variation, 11% of the variation being between clusters and 23% being between populations within clusters. The suggested clusters may assist in future studies of genetic aspects of the chicken gene pool.     

Do foliar endophytes affect grass litter decomposition? A microcosm approach using Lolium multiflorum

Symbiotic infection with fungal endophytes has been shown to decrease herbivory in several temperate grasses. We tested the hypothesis that foliar endophytes of grasses may also affect below-ground processes upon their host death, by altering the litter quality for detritivores or the microenvironment for decomposition. Microcosm–litterbag experiments were used to assess decay rates for litter produced by endophyte ( Neotyphodium sp.) infected vs uninfected Lolium multiflorum plants, and to examine endophyte-mediated effects of prior site occupants on current litter decomposition. We found that litter from endophyte-infected L. multiflorum decomposed more slowly than litter from endophyte-free conspecifics and from a naturally uninfected grass, Bromus unioloides . Furthermore, the endophyte–grass association modified the decomposition environment, so that B. unioloides litter decomposed faster when placed underneath a thick layer of endophyte-free L. multiflorum litter. Litter decay rates increased with the amount of root debris remaining in situ from the previous season, but were not affected by the infection status of prior site occupants. The lower decomposability of litter from infected L. multiflorum plants persisted across a range of microenvironments, as determined by different amounts of above-ground litter and soil moisture conditions. Endophyte infection tended to reduce the N content of decaying litter; however, litter N and C/N ratio mainly accounted for interspecific differences in decomposition. Our results imply that fungal endophytes not only can affect herbivory food chains, but also soil organisms and the ecosystem processes they regulate. This study suggests a novel role for symbiotic foliar endophytes in linking above-ground and below-ground sub-systems.     

MiR-101 and miR-144 Regulate the Expression of the CFTR Chloride Channel in the Lung

The Cystic Fibrosis Transmembrane conductance Regulator (CFTR) is a chloride channel that plays a critical role in the lung by maintaining fluid homeostasis. Absence or malfunction of CFTR leads to Cystic Fibrosis, a disease characterized by chronic infection and inflammation. We recently reported that air pollutants such as cigarette smoke and cadmium negatively regulate the expression of CFTR by affecting several steps in the biogenesis of CFTR protein. MicroRNAs (miRNAs) have recently received a great deal of attention as both biomarkers and therapeutics due to their ability to regulate multiple genes. Here, we show that cigarette smoke and cadmium up-regulate the expression of two miRNAs (miR-101 and miR-144) that are predicted to target CFTR in human bronchial epithelial cells. When premature miR-101 and miR-144 were transfected in human airway epithelial cells, they directly targeted the CFTR 3′UTR and suppressed the expression of the CFTR protein. Since miR-101 was highly up-regulated by cigarette smoke in vitro, we investigated whether such increase also occurred in vivo. Mice exposed to cigarette smoke for 4 weeks demonstrated an up-regulation of miR-101 and suppression of CFTR protein in their lungs. Finally, we show that miR-101 is highly expressed in lung samples from patients with severe chronic obstructive pulmonary disease (COPD) when compared to control patients. Taken together, these results suggest that chronic cigarette smoking up-regulates miR-101 and that this miRNA could contribute to suppression of CFTR in the lungs of COPD patients.     

How Does Our Motor System Determine Its Learning Rate?

Motor learning is driven by movement errors. The speed of learning can be quantified by the learning rate, which is the proportion of an error that is corrected for in the planning of the next movement. Previous studies have shown that the learning rate depends on the reliability of the error signal and on the uncertainty of the motor system’s own state. These dependences are in agreement with the predictions of the Kalman filter, which is a state estimator that can be used to determine the optimal learning rate for each movement such that the expected movement error is minimized. Here we test whether not only the average behaviour is optimal, as the previous studies showed, but if the learning rate is chosen optimally in every individual movement. Subjects made repeated movements to visual targets with their unseen hand. They received visual feedback about their endpoint error immediately after each movement. The reliability of these error-signals was varied across three conditions. The results are inconsistent with the predictions of the Kalman filter because correction for large errors in the beginning of a series of movements to a fixed target was not as fast as predicted and the learning rates for the extent and the direction of the movements did not differ in the way predicted by the Kalman filter. Instead, a simpler model that uses the same learning rate for all movements with the same error-signal reliability can explain the data. We conclude that our brain does not apply state estimation to determine the optimal planning correction for every individual movement, but it employs a simpler strategy of using a fixed learning rate for all movements with the same level of error-signal reliability.     

Moving the Weber Fraction: The Perceptual Precision for Moment of Inertia Increases with Exploration Force

How does the magnitude of the exploration force influence the precision of haptic perceptual estimates? To address this question, we examined the perceptual precision for moment of inertia (i.e., an object''s “angular mass”) under different force conditions, using the Weber fraction to quantify perceptual precision. Participants rotated a rod around a fixed axis and judged its moment of inertia in a two-alternative forced-choice task. We instructed different levels of exploration force, thereby manipulating the magnitude of both the exploration force and the angular acceleration. These are the two signals that are needed by the nervous system to estimate moment of inertia. Importantly, one can assume that the absolute noise on both signals increases with an increase in the signals'' magnitudes, while the relative noise (i.e., noise/signal) decreases with an increase in signal magnitude. We examined how the perceptual precision for moment of inertia was affected by this neural noise. In a first experiment we found that a low exploration force caused a higher Weber fraction (22%) than a high exploration force (13%), which suggested that the perceptual precision was constrained by the relative noise. This hypothesis was supported by the result of a second experiment, in which we found that the relationship between exploration force and Weber fraction had a similar shape as the theoretical relationship between signal magnitude and relative noise. The present study thus demonstrated that the amount of force used to explore an object can profoundly influence the precision by which its properties are perceived.     

Modeling the Winter–to–Summer Transition of Prokaryotic and Viral Abundance in the Arctic Ocean

One of the challenges in oceanography is to understand the influence of environmental factors on the abundances of prokaryotes and viruses. Generally, conventional statistical methods resolve trends well, but more complex relationships are difficult to explore. In such cases, Artificial Neural Networks (ANNs) offer an alternative way for data analysis. Here, we developed ANN-based models of prokaryotic and viral abundances in the Arctic Ocean. The models were used to identify the best predictors for prokaryotic and viral abundances including cytometrically-distinguishable populations of prokaryotes (high and low nucleic acid cells) and viruses (high- and low-fluorescent viruses) among salinity, temperature, depth, day length, and the concentration of Chlorophyll-a. The best performing ANNs to model the abundances of high and low nucleic acid cells used temperature and Chl-a as input parameters, while the abundances of high- and low-fluorescent viruses used depth, Chl-a, and day length as input parameters. Decreasing viral abundance with increasing depth and decreasing system productivity was captured well by the ANNs. Despite identifying the same predictors for the two populations of prokaryotes and viruses, respectively, the structure of the best performing ANNs differed between high and low nucleic acid cells and between high- and low-fluorescent viruses. Also, the two prokaryotic and viral groups responded differently to changes in the predictor parameters; hence, the cytometric distinction between these populations is ecologically relevant. The models imply that temperature is the main factor explaining most of the variation in the abundances of high nucleic acid cells and total prokaryotes and that the mechanisms governing the reaction to changes in the environment are distinctly different among the prokaryotic and viral populations.