Galanin Signaling in the Brain Regulates Color Pattern Formation in Zebrafish

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Neurofilament Proteins Are Distributed in a Diminishing Proximodistal Gradient Along Rat Sciatic Nerve

Neurofilament (NF) proteins are distributed in a diminishing proximodistal gradient along rat sciatic nerve when compared with total noncollagen or other proteins in nerve. About a twofold decline of NF proteins can be detected by quantitating nerve proteins that have been separated by gel electrophoresis. A similar decrease of immunoreactivity to each NF subunit is seen in distal nerve segments when noncollagen nerve proteins are immunoblotted. Parallel decreases occur in all three NF proteins, thereby maintaining neurofilament subunit stoichiometry along the neuraxis. The same NF gradient can be detected when the NF contents in nerve branches to the gluteus and gastrocnemius muscles are compared with each other and with those in nerve segments taken from the same proximodistal levels of the parent sciatic nerve. The gradient of NF proteins increases during postnatal development and is readily detected by postnatal day 16. During the same period of development, the heavy NF subunit appears for the first time and is rapidly incorporated throughout the sciatic nerve. Hence, the NF gradient becomes manifest during the development and maturation of the adult form of the axonal cytoskeleton. The basis for the proximodistal gradient of NF proteins in peripheral nerve is presently unknown. The extent of the gradient cannot be accounted for on the basis of diminishing numbers of nerve fibers or increasing amounts of other nerve proteins, e.g., collagen, in distal nerve. An alternative interpretation is that the gradient reflects a low level of NF protein turnover during axonal transport.     

An annotated checklist of the fish fauna of the river systems draining the Kahuzi-Biega National Park (Upper Congo: Eastern DR Congo)

The Kahuzi-Biega National Park (KBNP), situated mainly in the Eastern Highlands Ecoregion of the Upper Congo basin, is drained by the Lowa and Ulindi rivers, and some western affluents of Lake Kivu. In this study, the first list of the fish diversity of these systems is provided based on museum collections and complemented, for the Lowa River system and the western Lake Kivu affluents, with recently collected specimens (2013–2017). A total of 118 species are reported from the Lowa basin, 22 from the Ulindi basin and seven from these Lake Kivu affluents. Within the Lowa and Ulindi, respectively, five and one species, all cichlids, have been introduced. Currently, 51 species are reported from within the park, only two of which have been reported from the highlands, i.e., Amphilius kivuensis from the Luha, the source of the Luka River, and Clarias liocephalus from the headwaters of the Lake Kivu’ affluents. With a total of 30 species, Cyprinidae is by far the largest family, representing 25% of the total species diversity of the Lowa basin. It is followed by Mormyridae with 13 species (11%), Alestidae and Mochokidae with 10 species each (8%), Clariidae and Amphiliidae with eight species (7%), and Distichodontidae with six species (5%). Seven new species for science were discovered and 11 species were found to be endemic to the Lowa system. Although further exploration is needed, this underscores the importance of the KBNP in protecting the fish fauna of the Lowa basin but also highlights the park's limited coverage of the fish fauna of the Lowa basin.     

Testing prediction accuracy in short-term ecological studies

Applied ecology is based on an assumption that a management action will result in a predicted outcome. Testing the prediction accuracy of ecological models is the most powerful way of evaluating the knowledge implicit in this cause-effect relationship, however, the prevalence of predictive modeling and prediction testing are spreading slowly in ecology. The challenge of prediction testing is particularly acute for small-scale studies, because withholding data for prediction testing (e.g., via k-fold cross validation) can reduce model precision. However, by necessity small-scale studies are common. We use one such study that explored small mammal abundance along an elevational gradient to test prediction accuracy of models with varying degrees of information content. For each of three small mammal species, we conducted 5000 iterations of the following process: (1) randomly selected 75 % of the data to develop generalized linear models of species abundance that used detailed site measurements as covariates, (2) used an information theoretic approach to compare the top model with detailed covariates to habitat type-only and null models constructed with the same data, (3) tested those models’ ability to predict the 25 % of the randomly withheld data, and (4) evaluated prediction accuracy with a quadratic loss function. Detailed models fit the model-evaluation data best but had greater expected prediction error when predicting out-of-sample data relative to the habitat type models. Relationships between species and detailed site variables may be evident only within the framework of explicitly hierarchical analyses. We show that even with a small but relatively typical dataset (n = 28 sampling locations across 125 km over two years), researchers can effectively compare models with different information content and measure models’ predictive power, thus evaluating their own ecological understanding and defining the limits of their inferences. Identifying the appropriate scope of inference through prediction testing is ecologically valuable and is attainable even with small datasets.     

Temperature gradient gel electrophoresis: detection of a single base substitution in the cattle β-lactoglobulin gene

An A in equilibrium with G transition in exon III is known to differentiate alleles A and B of the cattle beta-lactoglobulin (BLG) gene. A BLG exon III fragment containing the transition site was amplified by the polymerase chain reaction. Temperature gradient gel electrophoresis (TGGE) was then used to detect this transition and hence to genotype cattle: the AT base-pair in allele A was readily distinguished from the GC base-pair of allele B. TGGE can be used to detect any single base-pair substitution, and thus is a powerful method of detecting genetic variability.     

Control of mammalian locomotion by ventral spinocerebellar tract neurons

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