From microsatellites to single nucleotide polymorphisms for the genetic monitoring of a critically endangered sturgeon

International audienceThe use of genetic information is crucial in conservation programs for the establishment of breeding plans and for the evaluation of restocking success. Short tandem repeats (STRs) have been the most widely used molecular markers in such programs, but next‐generation sequencing approaches have prompted the transition to genome‐wide markers such as single nucleotide polymorphisms (SNPs). Until now, most sturgeon species have been monitored using STRs. The low diversity found in the critically endangered European sturgeon (Acipenser sturio), however, makes its future genetic monitoring challenging, and the current resolution needs to be increased. Here, we describe the discovery of a highly informative set of 79 SNPs using double‐digest restriction‐associated DNA (ddRAD) sequencing and its validation by genotyping using the MassARRAY system. Comparing with STRs, the SNP panel proved to be highly efficient and reproducible, allowing for more accurate parentage and kinship assignments' on 192 juveniles of known pedigree and 40 wild‐born adults. We explore the effectiveness of both markers to estimated relatedness and inbreeding, using simulated and empirical datasets. Interestingly, we found significant correlations between STRs and SNPs at individual heterozygosity and inbreeding that give support to a reasonable representation of whole genome diversity for both markers. These results are useful for the conservation program of A. sturio in building a comprehensive studbook, which will optimize conservation strategies. This approach also proves suitable for other case studies in which highly discriminatory genetic markers are needed to assess parentage and kinship

Removing noise from pyrosequenced amplicons

Background In many environmental genomics applications a homologous region of DNA from a diverse sample is first amplified by PCR and then sequenced. The next generation sequencing technology, 454 pyrosequencing, has allowed much larger read numbers from PCR amplicons than ever before. This has revolutionised the study of microbial diversity as it is now possible to sequence a substantial fraction of the 16S rRNA genes in a community. However, there is a growing realisation that because of the large read numbers and the lack of consensus sequences it is vital to distinguish noise from true sequence diversity in this data. Otherwise this leads to inflated estimates of the number of types or operational taxonomic units (OTUs) present. Three sources of error are important: sequencing error, PCR single base substitutions and PCR chimeras. We present AmpliconNoise, a development of the PyroNoise algorithm that is capable of separately removing 454 sequencing errors and PCR single base errors. We also introduce a novel chimera removal program, Perseus, that exploits the sequence abundances associated with pyrosequencing data. We use data sets where samples of known diversity have been amplified and sequenced to quantify the effect of each of the sources of error on OTU inflation and to validate these algorithms

A distance-limited sample of massive star-forming cores from the RMS survey

We analyse C18O (J = 3−2) data from a sample of 99 infrared (IR)-bright massive young stellar objects (MYSOs) and compact H ii regions that were identified as potential molecular-outflow sources in the Red MSX Source survey. We extract a distance-limited (D < 6 kpc) sample shown to be representative of star formation covering the transition between the source types. At the spatial resolution probed, Larson-like relationships are found for these cores, though the alternative explanation, that Larson's relations arise where surface-density-limited samples are considered, is also consistent with our data. There are no significant differences found between source properties for the MYSOs and H ii regions, suggesting that the core properties are established prior to the formation of massive stars, which subsequently have little impact at the later evolutionary stages investigated. There is a strong correlation between dust-continuum and C18O-gas masses, supporting the interpretation that both trace the same material in these IR-bright sources. A clear linear relationship is seen between the independently established core masses and luminosities. The position of MYSOs and compact H ii regions in the mass–luminosity plane is consistent with the luminosity expected from a cluster of protostars when using an ∼40 per cent star formation efficiency and indicates that they are at a similar evolutionary stage, near the end of the accretion phase

Ensemble analyses improve signatures of tumour hypoxia and reveal inter-platform differences

BACKGROUND: The reproducibility of transcriptomic biomarkers across datasets remains poor, limiting clinical application. We and others have suggested that this is in-part caused by differential error-structure between datasets, and their incomplete removal by pre-processing algorithms. METHODS: To test this hypothesis, we systematically assessed the effects of pre-processing on biomarker classification using 24 different pre-processing methods and 15 distinct signatures of tumour hypoxia in 10 datasets (2,143 patients). RESULTS: We confirm strong pre-processing effects for all datasets and signatures, and find that these differ between microarray versions. Importantly, exploiting different pre-processing techniques in an ensemble technique improved classification for a majority of signatures. CONCLUSIONS: Assessing biomarkers using an ensemble of pre-processing techniques shows clear value across multiple diseases, datasets and biomarkers. Importantly, ensemble classification improves biomarkers with initially good results but does not result in spuriously improved performance for poor biomarkers. While further research is required, this approach has the potential to become a standard for transcriptomic biomarkers

An injectable bone marrow-like scaffold enhances T cell immunity after hematopoietic stem cell transplantation.

Allogeneic hematopoietic stem cell transplantation (HSCT) is a curative treatment for multiple disorders, but deficiency and dysregulation of T cells limit its utility. Here we report a biomaterial-based scaffold that mimics features of T cell lymphopoiesis in the bone marrow. The bone marrow cryogel (BMC) releases bone morphogenetic protein-2 to recruit stromal cells and presents the Notch ligand Delta-like ligand-4 to facilitate T cell lineage specification of mouse and human hematopoietic progenitor cells. BMCs subcutaneously injected in mice at the time of HSCT enhanced T cell progenitor seeding of the thymus, T cell neogenesis and diversification of the T cell receptor repertoire. Peripheral T cell reconstitution increased ~6-fold in mouse HSCT and ~2-fold in human xenogeneic HSCT. Furthermore, BMCs promoted donor CD4+ regulatory T cell generation and improved survival after allogeneic HSCT. In comparison to adoptive transfer of T cell progenitors, BMCs increased donor chimerism, T cell generation and antigen-specific T cell responses to vaccination. BMCs may provide an off-the-shelf approach for enhancing T cell regeneration and mitigating graft-versus-host disease in HSCT

Diameter dependence of the optoelectronic properties of single walled carbon nanotubes determined by ellipsometry

International audienceWe report ellipsometric measurement on single walled carbon nanotube (SWCNT) films performed in a large spectral range from 0.07eV to 4.97eV. The complex dielectric functions of SWCNTs are correlated to their diameter distribution extracted from transmission electron microscopy. Here we show that the transition energies between Van Hove singularities are directly related to the strong one dimensional confinement. In the infrared spectral range, the real part of the dielectric function becomes negative. The electronic properties of SWCNTs are extracted from ellipsometry by using a Drude model. The mobility and the mean free path of charge carriers are limited by the high number of SWCNT contacts. In accordance with tight binding simulation, the conductivity and the charge carrier concentration increase with the SWCNT diameter. Finally, we demonstrate that the S-plasmon energy depends on the charge carrier concentration.

Pasting and gelation of faba bean starch-protein mixtures

Starch and protein are major components in many foods, contributing to nutritional and textural properties. Understanding how the behaviour and interactions of these components contribute to different textures is important. In this study, mixed gel systems were created with different ratios of starch to protein (constant solid content 12%) extracted from faba bean, a promising crop for locally produced plant-based foods in cold climate regions. The mixed starch-protein gels were characterised in terms of pasting, texture and microstructure. Starch-rich mixtures showed higher water binding and water absorption than samples with higher protein content. A tendency for more efficient hydration in starch-rich samples was confirmed by NMR. Iodine affinity appeared to be lower for high-protein samples, particularly at higher temperatures. Mixtures with high starch content also showed higher viscosity during pasting, higher storage modulus throughout gelation, lower tan delta and lower frequency dependence of the final gel. Characterisation by compression tests showed stronger and more elastic gels with increasing starch content. Light microscopy revealed that starch granules were tightly packed, espe-cially at higher starch content, with protein filling the spaces between starch granules. SEM micrographs revealed a network structure with larger pores and thicker strands in samples with higher starch content. Overall, increasing protein content reduced viscosity during pasting and caused softer gels, likely owing to different gelation and hydration properties of starch and protein

Shape Deformation driven Structural Transitions in Quantum Hall Skyrmions

The Quantum Hall ground state away from $\nu = 1$ can be described by a collection of interacting skyrmions. We show within the context of a nonlinear sigma model, that the classical ground state away from $\nu = 1$ is a skyrmion crystal with a generalized N\'eel order. We show that as a function of filling $\nu$, the skyrmion crystal undergoes a triangle to square to triangle transition at zero temperature. We argue that this structural transition, driven by a change in the shape of the individual skyrmions, is stable to thermal and quantum fluctuations and may be probed experimentally.Comment: 4 pages (REVTEX) and 4 .eps figure

Predicting Deep Hypnotic State From Sleep Brain Rhythms Using Deep Learning:A Data-Repurposing Approach

BACKGROUND: Brain monitors tracking quantitative brain activities from electroencephalogram (EEG) to predict hypnotic levels have been proposed as a labor-saving alternative to behavioral assessments. Expensive clinical trials are required to validate any newly developed processed EEG monitor for every drug and combinations of drugs due to drug-specific EEG patterns. There is a need for an alternative, efficient, and economical method. METHODS: Using deep learning algorithms, we developed a novel data-repurposing framework to predict hypnotic levels from sleep brain rhythms. We used an online large sleep data set (5723 clinical EEGs) for training the deep learning algorithm and a clinical trial hypnotic data set (30 EEGs) for testing during dexmedetomidine infusion. Model performance was evaluated using accuracy and the area under the receiver operator characteristic curve (AUC). RESULTS: The deep learning model (a combination of a convolutional neural network and long short-term memory units) trained on sleep EEG predicted deep hypnotic level with an accuracy (95% confidence interval [CI]) = 81 (79.2-88.3)%, AUC (95% CI) = 0.89 (0.82-0.94) using dexmedetomidine as a prototype drug. We also demonstrate that EEG patterns during dexmedetomidine-induced deep hypnotic level are homologous to nonrapid eye movement stage 3 EEG sleep. CONCLUSIONS: We propose a novel method to develop hypnotic level monitors using large sleep EEG data, deep learning, and a data-repurposing approach, and for optimizing such a system for monitoring any given individual. We provide a novel data-repurposing framework to predict hypnosis levels using sleep EEG, eliminating the need for new clinical trials to develop hypnosis level monitors