Electrical Compartmentalization in Neurons

The dendritic tree of neurons plays an important role in information processing in the brain. While it is thought that dendrites require independent subunits to perform most of their computations, it is still not understood how they compartmentalize into functional subunits. Here, we show how these subunits can be deduced from the properties of dendrites. We devised a formalism that links the dendritic arborization to an impedance-based tree graph and show how the topology of this graph reveals independent subunits. This analysis reveals that cooperativity between synapses decreases slowly with increasing electrical separation and thus that few independent subunits coexist. We nevertheless find that balanced inputs or shunting inhibition can modify this topology and increase the number and size of the subunits in a context-dependent manner. We also find that this dynamic recompartmentalization can enable branch-specific learning of stimulus features. Analysis of dendritic patch-clamp recording experiments confirmed our theoretical predictions.Peer reviewe

The impact of rate heterogeneity on inference of phylogenetic models of trait evolution

Rates of trait evolution are known to vary across phylogenies; however, standard evolutionary models assume a homogeneous process of trait change. These simple methods are widely applied in small-scale phylogenetic studies, whereas models of rate heterogeneity are not, so the prevalence and patterns of potential rate variation in groups up to hundreds of species remain unclear. The extent to which trait evolution is modelled accurately on a given phylogeny is also largely unknown because studies typically lack absolute model fit tests. We investigated these issues by applying both rate-static and variable-rates methods on (i) body mass data for 88 avian clades of 10–318 species, and (ii) data simulated under a range of rate-heterogeneity scenarios. Our results show that rate heterogeneity is present across small-scaled avian clades, and consequently applying only standard single-process models prompts inaccurate inferences about the generating evolutionary process. Specifically, these approaches underestimate rate variation, and systematically mislabel temporal trends in trait evolution. Conversely, variable-rates approaches have superior relative fit (they are the best model) and absolute fit (they describe the data well). We show that rate changes such as single internal branch variations, rate decreases and early bursts are hard to detect, even by variable-rates models. We also use recently developed absolute adequacy tests to highlight misleading conclusions based on relative fit alone (e.g. a consistent preference for constrained evolution when isolated terminal branch rate increases are present). This work highlights the potential for robust inferences about trait evolution when fitting flexible models in conjunction with tests for absolute model fit

Goodness-of-fit tests of Gaussianity: constraints on the cumulants of the MAXIMA data

In this work, goodness-of-fit tests are adapted and applied to CMB maps to detect possible non-Gaussianity. We use Shapiro-Francia test and two Smooth goodness-of-fit tests: one developed by Rayner and Best and another one developed by Thomas and Pierce. The Smooth tests test small and smooth deviations of a prefixed probability function (in our case this is the univariate Gaussian). Also, the Rayner and Best test informs us of the kind of non-Gaussianity we have: excess of skewness, of kurtosis, and so on. These tests are optimal when the data are independent. We simulate and analyse non-Gaussian signals in order to study the power of these tests. These non-Gaussian simulations are constructed using the Edgeworth expansion, and assuming pixel-to-pixel independence. As an application, we test the Gaussianity of the MAXIMA data. Results indicate that the MAXIMA data are compatible with Gaussianity. Finally, the values of the skewness and kurtosis of MAXIMA data are constrained by |S| \le 0.035 and |K| \le 0.036 at the 99% confidence level.Comment: New Astronomy Reviews, in pres

Novel bi- and trifunctional inhibitors of tumor-associated proteolytic systems

Serine proteases, cysteine proteases, and matrix metalloproteinases (MMPs) are involved in cancer cell invasion and metastasis. Recently, a recombinant bifunctional inhibitor (chCysuPA(19-31)) directed against cysteine proteases and the urokinasetype plasminogen activator (uPA)/plasmin serine protease system was generated by introducing the uPA receptor (uPAR)binding site of uPA into chicken cystatin (chCysWT). In the present study, we designed and recombinantly produced multifunctional inhibitors also targeting MMPs. The inhibitors comprise the Nterminal inhibitory domain of human TIMP-1 (tissue inhibitor of matrix metalloproteinase-1) or TIMP-3, fused to chCysuPA(19-31) or chCysWT. As demonstrated by various techniques, these fusion proteins effectively interfere with all three targeted protease systems. In in vitro Matrigel invasion assays, the addition of recombinant inhibitors strongly reduced invasion of ovarian cancer cells (OVMZ-6\#8). Additionally, OVMZ 6\#8 cells were stably transfected with expression plasmids encoding the various inhibitors. Synthesis and secretion of the inhibitors was verified by a newly developed ELISA, which selectively detects the recombinant proteins. Invasive capacity of inhibitorproducing cells was significantly reduced compared to vectortransfected control cells. Thus, these novel, compact, and smallsize inhibitors directed against up to three different tumorassociated proteolytic systems may represent promising agents for prevention of tumor cell migration and metastasis

Goodness-of-Fit Tests to study the Gaussianity of the MAXIMA data

Goodness-of-Fit tests, including Smooth ones, are introduced and applied to detect non-Gaussianity in Cosmic Microwave Background simulations. We study the power of three different tests: the Shapiro-Francia test (1972), the uncategorised smooth test developed by Rayner and Best(1990) and the Neyman's Smooth Goodness-of-fit test for composite hypotheses (Thomas and Pierce 1979). The Smooth Goodness-of-Fit tests are designed to be sensitive to the presence of ``smooth'' deviations from a given distribution. We study the power of these tests based on the discrimination between Gaussian and non-Gaussian simulations. Non-Gaussian cases are simulated using the Edgeworth expansion and assuming pixel-to-pixel independence. Results show these tests behave similarly and are more powerful than tests directly based on cumulants of order 3, 4, 5 and 6. We have applied these tests to the released MAXIMA data. The applied tests are built to be powerful against detecting deviations from univariate Gaussianity. The Cholesky matrix corresponding to signal (based on an assumed cosmological model) plus noise is used to decorrelate the observations previous to the analysis. Results indicate that the MAXIMA data are compatible with Gaussianity.Comment: MNRAS, in pres

Mapping adaptation of barley to droughted environments

Identifying barley genomic regions influencing the response of yield and its components to water deficits will aid in our understanding of the genetics of drought tolerance and the development of more drought tolerant cultivars. We assembled a population of 192 genotypes that represented landraces, old, and contemporary cultivars sampling key regions around the Mediterranean basin and the rest of Europe. The population was genotyped with a stratified set of 50 genomic and EST derived molecular markers, 49 of which were Simple Sequence Repeats (SSRs), which revealed an underlying population sub-structure that corresponded closely to the geographic regions in which the genotypes were grown. A more dense whole genome scan was generated by using Diversity Array Technology (DArT®) to generate 1130 biallelic markers for the population. The population was grown at two contrasting sites in each of seven Mediterranean countries for harvest 2004 and 2005 and grain yield data collected. Mean yield levels ranged from 0.3 to 6.2 t/ha, with highly significant genetic variation in low-yielding environments. Associations of yield with barley genomic regions were then detected by combining the DArT marker data with the yield data in mixed model analyses for the individual trials, followed by multiple regression of yield on markers to identify a multi-locus subset of significant markers/QTLs. QTLs exhibiting a pre-defined consistency across environments were detected in bins 4, 6, 6 and 7 on barley chromosomes 3H, 4H, 5H and 7H respectivel

Phosphorylation of the eIF4E-binding protein PHAS-I after exposure of PC12 cells to EGF and NGF

AbstractPHAS-I or the eIF4E-binding protein 1 regulates the cap-binding activity of eIF4E by sequestering eIF4E. Binding of eIF4E to PHAS-I is regulated by phosphorylation of PHAS-I. PC12 cells were used to study the signal transduction pathway leading to phosphorylation of PHAS-I. Both EGF and NGF induced phosphorylation of PHAS-I. Wortmannin, a PI-3 kinase inhibitor, staurosporine, a PKC inhibitor, and rapamycin, a FRAP inhibitor all blocked the phosphorylation of PHAS-I. Of the three inhibitors, only wortmannin was able to inhibit MAPK phosphorylation. This excludes a role for MAPK in NGF- and EGF-induced PHAS-I phosphorylation in PC12 cells. Apparently, PHAS-I was phosphorylated in a PI-3 kinase-, PKC-, and FRAP-dependent manner after EGF or NGF stimulation. Only PI-3 kinase and FRAP are involved in the regulation of the basal level of PHAS-I phosphorylation