Hyporheic Source and Sink of Nitrous Oxide

Nitrous oxide (N2O) is a potent greenhouse gas with an estimated 10% of anthropogenic N2O coming from the hyporheic zone of streams and rivers. However, difficulty in making accurate fine-scale field measurements has prevented detailed understanding of the processes of N2O production and emission at the bedform and flowline scales. Using large-scale, replicated flume experiments that employed high-density chemical concentration measurements, we have been able to refine the current conceptualization of N2O production, consumption, and emission from the hyporheic zone. We present a predictive model based on a Damköhler-type transformation (τ̃) in which the hyporheic residence times (τ) along the flowlines are multiplied by the dissolved oxygen consumption rate constants for those flowlines. This model can identify which bedforms have the potential to produce and emit N2O, as well as the portion and location from which those emissions may occur. Our results indicate that flowlines with τ̃up (τ̃ as the flowline returns to the surface flow) values between 0.54 and 4.4 are likely to produce and emit N2O. Flowlineswith τ̃up values of less than 0.54 will have the same N2O as the surface water and those with values greater than 4.4 will likely sink N2O (reference conditions: 17C, surface dissolved oxygen 8.5 mg/L). N2O production peaks approximately at τ̃ = 1.8. A cumulative density function of τ̃up values for all flowlines in a bedform (or multiple bedforms) can be used to estimate the portion of flowlines, and in turn the portion of the streambed, with the potential to emit N2O

Effect of fibre configurations on mechanical properties of flax/tannin composites.

Flax reinforced tannin-based composites have a potential to be used in vehicle applications due to the environmental advantages and good mechanical properties. In this paper, the effects of fibre configuration on mechanical properties of flax/tannin composites were investigated for nonwoven and woven fabric lay-up angles (UD, [0°, 90°]2 and [0°, +45°, 90°, -45°]2). The tannin/flax composites were prepared by compression moulding. The manufactured specimens were then characterized for quasi-static tensile properties, dynamic mechanical properties and low-energy impact performance. Failure mechanism was further investigated using microscopy and demonstrated the need for further adhesion improvements. The study shows that the UD fabric reinforced composite performs better in tensile strength and modulus whereas [0°, +45°, 90°, -45°]2 composite provides the best impact energy absorption performance

Defect-induced activation of symmetry forbidden infrared resonances in individual metallic nanorods

International audienceWe report on the observation of second-order infrared (IR) plasmon resonances in lithographically prepared gold nanorods investigated by means of far-field microscopic IR spectroscopy. In addition to the fundamental antennalike mode, even and odd higher order resonances are observed under normal incidence of light. The activation of even-order modes under normal incidence is surprising since even orders are dipole-forbidden because of their centrosymmetric charge density oscillation. Performing atomic force microscopy and calculations with the boundary element method, we determine that excitation of even modes is enabled by symmetry breaking by structural deviations of the rods from an ideal, straight shape. (C) 2010 American Institute of Physics. [doi: 10.1063/1.3437093

Fragment Flow and the Nuclear Equation of State

We use the Boltzmann-Uehling-Uhlenbeck model with a momentum-dependent nuclear mean field to simulate the dynamical evolution of heavy ion collisions. We re-examine the azimuthal anisotropy observable, proposed as sensitive to the equation of state of nuclear matter. We obtain that this sensitivity is maximal when the azimuthal anisotropy is calculated for nuclear composite fragments, in agreement with some previous calculations. As a test case we concentrate on semi-central $^{197}{\rm Au}\ +\ ^{197}{\rm Au}$ collisions at 400 $A$ MeV.Comment: 12 pages, ReVTeX 3.0. 12 Postscript figures, uuencoded and appende

Nonlinear partial differential equations and applications: Gene expression profiling of isogenic cells with different TP53 gene dosage reveals numerous genes that are affected by TP53 dosage and identifies CSPG2 as a direct target of p53

TP53 does not fully comply with the Knudson model [Knudson, A. G., Jr. (1971) Proc. Natl. Acad. Sci. USA 68, 820–823] in that a reduction of constitutional expression of p53 may be sufficient for tumor predisposition . This finding suggests a gene-dosage effect for p53 function. To determine whether TP53 gene dosage affects the transcriptional regulation of target genes, we performed oligonucleotide-array gene expression analysis by using human cells with wild-type p53 (p53 +/+), or with one (p53 +/−), or both (p53 −/−) TP53 alleles disrupted by homologous recombination. We identified 35 genes whose expression is significantly correlated to the dosage of TP53. These genes are involved in a variety of cellular processes including signal transduction, cell adhesion, and transcription regulation. Several of them are involved in neurogenesis and neural crest migration, developmental processes in which p53 is known to play a role. Motif search analysis revealed that of the genes highly expressed in p53 +/+ and +/− cells, several contain a putative p53 consensus binding site (bs), suggesting that they could be directly regulated by p53. Among those genes, we chose CSPG2 (which encodes versican) for further study because it contains a bona fide p53 bs in its first intron and its expression highly correlates with TP53 dosage. By using in vitro and in vivo assays, we showed CSPG2 to be directly transactivated by p53. In conclusion, we developed a strategy to demonstrate that many genes are affected by TP53 gene dosage for their expression. We report several candidate genes as potential downstream targets of p53 in nonstressed cells. Among them, CSPG2 is validated as being directly transactivated by p53. Our method provides a useful tool to elucidate additional mechanisms by which p53 exerts its functions

Features of mammalian microRNA promoters emerge from polymerase II chromatin immunoprecipitation data

Background: MicroRNAs (miRNAs) are short, non-coding RNA regulators of protein coding genes. miRNAs play a very important role in diverse biological processes and various diseases. Many algorithms are able to predict miRNA genes and their targets, but their transcription regulation is still under investigation. It is generally believed that intragenic miRNAs (located in introns or exons of protein coding genes) are co-transcribed with their host genes and most intergenic miRNAs transcribed from their own RNA polymerase II (Pol II) promoter. However, the length of the primary transcripts and promoter organization is currently unknown. Methodology: We performed Pol II chromatin immunoprecipitation (ChIP)-chip using a custom array surrounding regions of known miRNA genes. To identify the true core transcription start sites of the miRNA genes we developed a new tool (CPPP). We showed that miRNA genes can be transcribed from promoters located several kilobases away and that their promoters share the same general features as those of protein coding genes. Finally, we found evidence that as many as 26% of the intragenic miRNAs may be transcribed from their own unique promoters. Conclusion: miRNA promoters have similar features to those of protein coding genes, but miRNA transcript organization is more complex. © 2009 Corcoran et al

Profiling allele-specific gene expression in brains from individuals with autism spectrum disorder reveals preferential minor allele usage.

One fundamental but understudied mechanism of gene regulation in disease is allele-specific expression (ASE), the preferential expression of one allele. We leveraged RNA-sequencing data from human brain to assess ASE in autism spectrum disorder (ASD). When ASE is observed in ASD, the allele with lower population frequency (minor allele) is preferentially more highly expressed than the major allele, opposite to the canonical pattern. Importantly, genes showing ASE in ASD are enriched in those downregulated in ASD postmortem brains and in genes harboring de novo mutations in ASD. Two regions, 14q32 and 15q11, containing all known orphan C/D box small nucleolar RNAs (snoRNAs), are particularly enriched in shifts to higher minor allele expression. We demonstrate that this allele shifting enhances snoRNA-targeted splicing changes in ASD-related target genes in idiopathic ASD and 15q11-q13 duplication syndrome. Together, these results implicate allelic imbalance and dysregulation of orphan C/D box snoRNAs in ASD pathogenesis

Self-labeling techniques for semi-supervised time series classification: an empirical study

An increasing amount of unlabeled time series data available render the semi-supervised paradigm a suitable approach to tackle classification problems with a reduced quantity of labeled data. Self-labeled techniques stand out from semi-supervised classification methods due to their simplicity and the lack of strong assumptions about the distribution of the labeled and unlabeled data. This paper addresses the relevance of these techniques in the time series classification context by means of an empirical study that compares successful self-labeled methods in conjunction with various learning schemes and dissimilarity measures. Our experiments involve 35 time series datasets with different ratios of labeled data, aiming to measure the transductive and inductive classification capabilities of the self-labeled methods studied. The results show that the nearest-neighbor rule is a robust choice for the base classifier. In addition, the amending and multi-classifier self-labeled-based approaches reveal a promising attempt to perform semi-supervised classification in the time series context

Fusing R features and local features with context-aware kernels for action recognition

The performance of action recognition in video sequences depends significantly on the representation of actions and the similarity measurement between the representations. In this paper, we combine two kinds of features extracted from the spatio-temporal interest points with context-aware kernels for action recognition. For the action representation, local cuboid features extracted around interest points are very popular using a Bag of Visual Words (BOVW) model. Such representations, however, ignore potentially valuable information about the global spatio-temporal distribution of interest points. We propose a new global feature to capture the detailed geometrical distribution of interest points. It is calculated by using the 3D R transform which is defined as an extended 3D discrete Radon transform, followed by the application of a two-directional two-dimensional principal component analysis. For the similarity measurement, we model a video set as an optimized probabilistic hypergraph and propose a context-aware kernel to measure high order relationships among videos. The context-aware kernel is more robust to the noise and outliers in the data than the traditional context-free kernel which just considers the pairwise relationships between videos. The hyperedges of the hypergraph are constructed based on a learnt Mahalanobis distance metric. Any disturbing information from other classes is excluded from each hyperedge. Finally, a multiple kernel learning algorithm is designed by integrating the l2 norm regularization into a linear SVM classifier to fuse the R feature and the BOVW representation for action recognition. Experimental results on several datasets demonstrate the effectiveness of the proposed approach for action recognition