Optimizing the Refractive Index Sensitivity of Plasmonically Coupled Gold Nanoparticles

Plasmonics - The possibility to enhance the local refractive index sensitivity using plasmonic coupling between spherical gold nanoparticles (Au-NPs) has been investigated. A strong and distinct...     

A goal function approach to remodeling of arteries uncovers mechanisms for growth instability

A novel, goal function-based formulation for the growth dynamics of arteries is introduced and used for investigating the development of growth instability in blood vessels. Such instabilities would lead to abnormal growth of the vessel, reminiscent of an aneurysm. The blood vessel is modeled as a thin-walled cylindrical tube, and the constituents that form the vessel wall are assumed to deform together as a constrained mixture. The growth dynamics of the composite material of the vessel wall are described by an evolution equation, where the effective area of each constituent changes in the direction of steepest descent of a goal function. This goal function is formulated in such way that the constituents grow toward a target potential energy and a target composition. The convergence of the simulated response of the evolution equation toward a target homeostatic state is investigated for a range of isotropic and orthotropic material models. These simulations suggest that elastin-deficient vessels are more prone to growth instability. Increased stiffness of the vessel wall, on the other hand, gives a more stable growth process. Another important finding is that an increased rate of degradation of materials impairs growth stability.     

High-throughput profiling of point mutations across the HIV-1 genome

Background

The HIV-1 pandemic is not the result of a static pathogen but a large genetically diverse and dynamic viral population. The virus is characterized by a highly mutable genome rendering efforts to design a universal vaccine a significant challenge and drives the emergence of drug resistant variants upon antiviral pressure. Gaining a comprehensive understanding of the mutational tolerance of each HIV-1 genomic position is therefore of critical importance.

Results

Here we combine high-density mutagenesis with the power of next-generation sequencing to gauge the replication capacity and therefore mutational tolerability of single point mutations across the entire HIV-1 genome. We were able to achieve the evaluation of point mutational effects on viral replicative capacity for 5,553 individual HIV-1 nucleotide positions – representing 57% of the viral genome. Replicative capacity was assessed at 3,943 nucleotide positions for a single alternate base change, 1,459 nucleotide positions for two alternate base changes, and 151 nucleotide positions for all three possible alternate base changes. This resulted in the study of how a total of 7,314 individual point mutations impact HIV-1 replication on a single experimental platform. We further utilize the dataset for a focused structural analysis on a capsid inhibitor binding pocket.

Conclusion

The approach presented here can be applied to any pathogen that can be genetically manipulated in a laboratory setting. Furthermore, the methodology can be utilized under externally applied selection conditions, such as drug or immune pressure, to identify genetic elements that contribute to drug or host interactions, and therefore mutational routes of pathogen resistance and escape.

     

Unimodal response of fish yield to dissolved organic carbon

Here, we demonstrate a contrasting effect of terrestrial coloured dissolved organic material on the secondary production of boreal nutrient poor lakes. Using fish yield from standardised brown trout gill‐net catches as a proxy, we show a unimodal response of lake secondary productivity to dissolved organic carbon (DOC). This suggests a trade‐off between positive and negative effects, where the initial increase may hinge upon several factors such as energy subsidising, screening of UV‐radiation or P and N load being associated with organic carbon. The subsequent decline in production with further increase in DOC is likely associated with light limitations of primary production. We also show that shallow lakes switch from positive to negative effects at higher carbon loads than deeper lakes. These results underpin the major role of organic carbon for structuring productivity of boreal lake ecosystems.     

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.     

Structure-revealing data fusion

Background

Analysis of data from multiple sources has the potential to enhance knowledge discovery by capturing underlying structures, which are, otherwise, difficult to extract. Fusing data from multiple sources has already proved useful in many applications in social network analysis, signal processing and bioinformatics. However, data fusion is challenging since data from multiple sources are often (i) heterogeneous (i.e., in the form of higher-order tensors and matrices), (ii) incomplete, and (iii) have both shared and unshared components. In order to address these challenges, in this paper, we introduce a novel unsupervised data fusion model based on joint factorization of matrices and higher-order tensors.

Results

While the traditional formulation of coupled matrix and tensor factorizations modeling only shared factors fails to capture the underlying structures in the presence of both shared and unshared factors, the proposed data fusion model has the potential to automatically reveal shared and unshared components through modeling constraints. Using numerical experiments, we demonstrate the effectiveness of the proposed approach in terms of identifying shared and unshared components. Furthermore, we measure a set of mixtures with known chemical composition using both LC-MS (Liquid Chromatography - Mass Spectrometry) and NMR (Nuclear Magnetic Resonance) and demonstrate that the structure-revealing data fusion model can (i) successfully capture the chemicals in the mixtures and extract the relative concentrations of the chemicals accurately, (ii) provide promising results in terms of identifying shared and unshared chemicals, and (iii) reveal the relevant patterns in LC-MS by coupling with the diffusion NMR data.

Conclusions

We have proposed a structure-revealing data fusion model that can jointly analyze heterogeneous, incomplete data sets with shared and unshared components and demonstrated its promising performance as well as potential limitations on both simulated and real data.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-239) contains supplementary material, which is available to authorized users.     

Identification of metabolites from 2D 1H-13C HSQC NMR using peak correlation plots

Background

Identification of individual components in complex mixtures is an important and sometimes daunting task in several research areas like metabolomics and natural product studies. NMR spectroscopy is an excellent technique for analysis of mixtures of organic compounds and gives a detailed chemical fingerprint of most individual components above the detection limit. For the identification of individual metabolites in metabolomics, correlation or covariance between peaks in ¹H NMR spectra has previously been successfully employed. Similar correlation of 2D ¹H-¹³C Heteronuclear Single Quantum Correlation spectra was recently applied to investigate the structure of heparine. In this paper, we demonstrate how a similar approach can be used to identify metabolites in human biofluids (post-prostatic palpation urine).

Results

From 50 ¹H-¹³C Heteronuclear Single Quantum Correlation spectra, 23 correlation plots resembling pure metabolites were constructed. The identities of these metabolites were confirmed by comparing the correlation plots with reported NMR data, mostly from the Human Metabolome Database.

Conclusions

Correlation plots prepared by statistically correlating ¹H-¹³C Heteronuclear Single Quantum Correlation spectra from human biofluids provide unambiguous identification of metabolites. The correlation plots highlight cross-peaks belonging to each individual compound, not limited by long-range magnetization transfer as conventional NMR experiments.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0413-z) contains supplementary material, which is available to authorized users.     

Direct action of endocrine disrupting chemicals on human sperm

Synthetic endocrine disrupting chemicals (EDCs), omnipresent in food, household, and personal care products, have been implicated in adverse trends in human reproduction, including infertility and increasing demand for assisted reproduction. Here, we study the action of 96 ubiquitous EDCs on human sperm. We show that structurally diverse EDCs activate the sperm‐specific CatSper channel and, thereby, evoke an intracellular Ca²⁺ increase, a motility response, and acrosomal exocytosis. Moreover, EDCs desensitize sperm for physiological CatSper ligands and cooperate in low‐dose mixtures to elevate Ca²⁺ levels in sperm. We conclude that EDCs interfere with various sperm functions and, thereby, might impair human fertilization.