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.     

Isolation of polymorphic tetranucleotide microsatellite markers for the ornate nursery frog Cophixalus ornatus

We have isolated 18 polymorphic microsatellite loci for Cophixalus ornatus from genomic libraries enriched for (AAAG)_n, (AACC)_n and (AAGG)_n repetitive elements. The number of alleles ranges from five to 22 per locus with the observed heterozygosity ranging from 0.10 to 0.92. These markers will be useful for the analysis of population structure in C. ornatus and testing alternative models of speciation.     

Isolation and characterization of eight compound microsatellite markers in a mangrove tree Kandelia candel (L.) Druce

Kandelia candel is an important mangrove tree species of family Rhizophoraceae. Here we isolated eight codominant compound microsatellite simple sequence repeat (SSR) loci from K. candel. Our isolated loci provided compound SSR markers with polymorphism of three to 11 alleles per locus. The expected and observed heterozygosities ranged from 0.230 to 0.887 and from 0.083 to 1.00, respectively. These markers would be the useful tools for analysing questions concerning population genetic structure and mating system of K. candel.     

Cryptic species identification: a simple diagnostic tool for discriminating between two problematic bumblebee species

Distinguishing between cryptic species is a perennial problem for biologists. Bombus ruderatus and Bombus hortorum are two species of bumblebee, which can be indistinguishable from their morphology. The former species is in decline, whereas the latter is ubiquitous. In the UK, isolated records of B. ruderatus occur amongst many for B. hortorum. For ecological studies of B. ruderatus to be feasible, the two species need to be reliably distinguishable. We present a diagnostic tool for quick and reliable identification of problematic individuals based on a restriction enzyme digest of the cytochrome b region of mitochondrial DNA.     

Development of microsatellite markers in polyploid persimmon (Diospyros kaki Lf) from an enriched genomic library

The oriental persimmon (Diospyros kaki Lf) is believed to have originated in China with subsequent introduction into Japan and Korea in ancient times. The species was then brought to Europe, Brazil and the USA from Japan in the 19th century. Recent studies highlighted the poor state of identification of cultivars in these countries due to incorrect labelling and presence of synonyms among local varieties. Thus, molecular marker characterization of germplasm resources is of great value for genetic resource preservation and plant breeding of persimmon. Therefore, to identify accessions for further plant breeding and germplasm management, 37 microsatellite loci were developed from a CT/AG‐enriched persimmon genomic library.     

Exercise training attenuates increases in lumbar sympathetic nerve activity produced by stimulation of the rostral ventrolateral medulla.

Exercise training (ExTr) has been associated with blunted activation of the sympathetic nervous system in several animal models and in some human studies. Although these data are consistent with the hypothesis that ExTr reduces the incidence of cardiovascular diseases via reduced sympathoexcitation, the mechanisms are unknown. The rostral ventrolateral medulla (RVLM) is important in control of sympathetic nervous system activity in both physiological and pathophysiological states. The purpose of the present study was to test the hypothesis that ExTr results in reduced sympathoexcitation mediated at the level of the RVLM. Male Sprague-Dawley rats were treadmill trained or remained sedentary for 8-10 wk. RVLM microinjections were performed under Inactin anesthesia while mean arterial pressure, heart rate, and lumbar sympathetic nerve activity (LSNA) were recorded. Bilateral microinjections of the GABA(A) antagonist bicuculline (5 mM, 90 nl) into the RVLM increased LSNA in sedentary animals (169 +/- 33%), which was blunted in ExTr animals (100 +/- 22%, P < 0.05). Activation of the RVLM with unilateral microinjections of glutamate (10 mM, 30 nl) increased LSNA in sedentary animals (76 +/- 13%), which was also attenuated by training (26 +/- 2%, P < 0.05). Bilateral microinjections of the ionotropic glutamate receptor antagonist kynurenate (40 mM, 90 nl) produced small increases in mean arterial pressure and LSNA that were similar between groups. Results suggest that ExTr may reduce increases in LSNA due to reduced activation of the RVLM. Conversely, we speculate that the relatively enhanced activation of LSNA in sedentary animals may be related to the increased incidence of cardiovascular disease associated with a sedentary lifestyle.