Multiple introductions, polyploidy and mixed reproductive strategies are linked to genetic diversity and structure in the most widespread invasive plant across Southern Ocean archipelagos

This work received support from the Swiss Polar Institute and Ferring Pharmaceuticals through the Antarctic Circumnavigation Expedition (“ACE”). Additional fi- nancial and logistical support was provided by the South African National Research Foundation (NRF) and by the South African National Antarctic Programme (SANAP). MM and CH were also supported by the National Research Foundation (grant 89967). We thank Tasmanian Parks and Wildlife Service for granting ac- cess and collection permits for Macquarie Island and the Australian Antarctic Program for logistical support. We thank Institut polaire français Paul- Émile Victor for logistical support for sampling on Iles Kerguelen and Ile de la Possession. We thank wintering staff for sam- ple collection on Ile de la Possession. We thank the Department of Conservation for granting access and collection permits for the New Zealand islands. We thank Heritage Expeditions for providing logis- tics and voyage support. Collections were undertaken on the ACE expedition under permits granted to the expedition and its research- ers. Collections at the Prince Edward Islands, and at the Tristan da Cunha and Gough islands were permitted through the South African National Antarctic Program, notably via the Prince Edward Islands Management Committee for the former and the Tristan da Cunha Conservation Department for the latter.Biological invasions in remote areas that experience low human activity provide unique opportunities to elucidate processes responsible for invasion success. Here we study the most widespread invasive plant species across the isolated islands of the Southern Ocean, the annual bluegrass, Poa annua. To analyse geographical variation in genome size, genetic diversity and reproductive strategies, we sampled all major sub-Antarctic archipelagos in this region and generated microsatellite data for 470 individual plants representing 31 populations. We also estimated genome sizes for a subset of individuals using flow cytometry. Occasional events of island colonization are expected to result in high genetic structure among islands, overall low genetic diversity and increased self-fertilization, but we show that this is not the case for P. annua. Microsatellite data indicated low population genetic structure and lack of isolation by distance among the sub-Antarctic archipelagos we sampled, but high population structure within each archipelago. We identified high levels of genetic diversity, low clonality and low selfing rates in sub-Antarctic P. annua populations (contrary to rates typical of continental populations). In turn, estimates of selfing declined in populations as genetic diversity increased. Additionally, we found that most P. annua individuals are probably tetraploid and that only slight variation exists in genome size across the Southern Ocean. Our findings suggest multiple independent introductions of P. annua into the sub-Antarctic, which promoted the establishment of genetically diverse populations. Despite multiple introductions, the adoption of convergent reproductive strategies (outcrossing) happened independently in each major archipelago. The combination of polyploidy and a mixed reproductive strategy probably benefited P. annua in the Southern Ocean by increasing genetic diversity and its ability to cope with the novel environmental conditions.National Research Foundation 89967Antarctic Circumnavigation Expedition “ACE”South African National Research Foundation (NRF)South African National Antarctic Programme (SANAP

Generative discriminative models for multivariate inference and statistical mapping in medical imaging

This paper presents a general framework for obtaining interpretable multivariate discriminative models that allow efficient statistical inference for neuroimage analysis. The framework, termed generative discriminative machine (GDM), augments discriminative models with a generative regularization term. We demonstrate that the proposed formulation can be optimized in closed form and in dual space, allowing efficient computation for high dimensional neuroimaging datasets. Furthermore, we provide an analytic estimation of the null distribution of the model parameters, which enables efficient statistical inference and p-value computation without the need for permutation testing. We compared the proposed method with both purely generative and discriminative learning methods in two large structural magnetic resonance imaging (sMRI) datasets of Alzheimer's disease (AD) (n=415) and Schizophrenia (n=853). Using the AD dataset, we demonstrated the ability of GDM to robustly handle confounding variations. Using Schizophrenia dataset, we demonstrated the ability of GDM to handle multi-site studies. Taken together, the results underline the potential of the proposed approach for neuroimaging analyses.Comment: To appear in MICCAI 2018 proceeding

Audio Source Separation with Discriminative Scattering Networks

In this report we describe an ongoing line of research for solving single-channel source separation problems. Many monaural signal decomposition techniques proposed in the literature operate on a feature space consisting of a time-frequency representation of the input data. A challenge faced by these approaches is to effectively exploit the temporal dependencies of the signals at scales larger than the duration of a time-frame. In this work we propose to tackle this problem by modeling the signals using a time-frequency representation with multiple temporal resolutions. The proposed representation consists of a pyramid of wavelet scattering operators, which generalizes Constant Q Transforms (CQT) with extra layers of convolution and complex modulus. We first show that learning standard models with this multi-resolution setting improves source separation results over fixed-resolution methods. As study case, we use Non-Negative Matrix Factorizations (NMF) that has been widely considered in many audio application. Then, we investigate the inclusion of the proposed multi-resolution setting into a discriminative training regime. We discuss several alternatives using different deep neural network architectures

Fully Trainable and Interpretable Non-Local Sparse Models for Image Restoration

Non-local self-similarity and sparsity principles have proven to be powerful priors for natural image modeling. We propose a novel differentiable relaxation of joint sparsity that exploits both principles and leads to a general framework for image restoration which is (1) trainable end to end, (2) fully interpretable, and (3) much more compact than competing deep learning architectures. We apply this approach to denoising, jpeg deblocking, and demosaicking, and show that, with as few as 100K parameters, its performance on several standard benchmarks is on par or better than state-of-the-art methods that may have an order of magnitude or more parameters.Comment: ECCV 202

Development and validation of a computational model for steak double-sided pan cooking

The objective of this study was to develop and validate a numerical model to adequately simulate the double-sided pan cooking of beef in a domestic environment. The proposed model takes into account the heat flow from the pan to the meat and the moisture transfer, simultaneously with the meat deformation. The model considers the swelling pressure gradient caused by the shrinkage of the meat fibers and connective tissue due to the denaturation of proteins and the loss of the water holding capacity during cooking. The model results were successfully verified with experimental data of the central temperature and weight loss recorded during cooking for three degrees of doneness. The measured experimental temperatures at the center of the meat were 30 ± 3 °C (very rare), 44 ± 3 °C (rare) and 57 ± 2 °C (done) for a 19 mm steak thickness. Meanwhile, their water losses were 4 ± 2 %, 8 ± 1 % and 11 ± 2 %, respectively. The root mean squared errors of the model predictions were 2.16 °C (very rare), 3.56 °C (rare) and 4.57 °C (done) for the central temperature and 1.48 %, 2.08 % and 2.40 %, respectively for the water loss. The model also correctly predicts cooking times for steaks of different thicknesses, taking weight loss as a reference to set this time. The proposed model is postulated as a useful cooking assistance tool to estimate the optimal cooking time according to consumer preferences

Super-Resolved Enhancement of a Single Image and Its Application in Cardiac MRI

Super-resolved image enhancement is of great importance in medical imaging. Conventional methods often require multiple low resolution (LR) images from different views of the same object or learning from large amount of training datasets to achieve success. However, in real clinical environments, these prerequisites are rarely fulfilled. In this paper, we present a self-learning based method to perform superresolution (SR) from a single LR input. The mappings between the given LR image and its downsampled versions are modeled using support vector regression on features extracted from sparse coded dictionaries, coupled with dual-tree complex wavelet transform based denoising. We demonstrate the efficacy of our method in application of cardiac MRI enhancement. Both quantitative and qualitative results show that our SR method is able to preserve fine textural details that can be corrupted by noise, and therefore can maintain crucial diagnostic information

Towards domestic cooking efficiency: A case study on burger pan frying using experimental and computational results

It is well known that the use of efficient domestic cooking appliances and equipment can not only save energy, but also improve the quality of the food being prepared. This work raises the question of whether cooking procedures can also contribute to this energy efficiency. Focusing on burger pan frying, experimental data were used to develop a model able to predict cooking outcomes under different power levels supplied by an induction hob. The proposed model takes into account not only the heat consumed by water evaporation in the contact region but also the shrinkage process of the hamburger. A new formulation based on the multiplicative decomposition of the strain deformation gradient is proposed to describe the observed decoupling between weight and volume loss during the process. The model properly predicts temperature, moisture loss and shrinkage, and allows elucidation of the effects of supplying different amounts of energy on the final water content

The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures

Motivation: Biomarker discovery from high-dimensional data is a crucial problem with enormous applications in biology and medicine. It is also extremely challenging from a statistical viewpoint, but surprisingly few studies have investigated the relative strengths and weaknesses of the plethora of existing feature selection methods. Methods: We compare 32 feature selection methods on 4 public gene expression datasets for breast cancer prognosis, in terms of predictive performance, stability and functional interpretability of the signatures they produce. Results: We observe that the feature selection method has a significant influence on the accuracy, stability and interpretability of signatures. Simple filter methods generally outperform more complex embedded or wrapper methods, and ensemble feature selection has generally no positive effect. Overall a simple Student's t-test seems to provide the best results. Availability: Code and data are publicly available at http://cbio.ensmp.fr/~ahaury/

Single-Molecule Imaging of an in Vitro-Evolved RNA Aptamer Reveals Homogeneous Ligand Binding Kinetics

Many studies of RNA folding and catalysis have revealed conformational heterogeneity, metastable folding intermediates, and long-lived states with distinct catalytic activities. We have developed a single-molecule imaging approach for investigating the functional heterogeneity of in vitro-evolved RNA aptamers. Monitoring the association of fluorescently labeled ligands with individual RNA aptamer molecules has allowed us to record binding events over the course of multiple days, thus providing sufficient statistics to quantitatively define the kinetic properties at the single-molecule level. The ligand binding kinetics of the highly optimized RNA aptamer studied here displays a remarkable degree of uniformity and lack of memory. Such homogeneous behavior is quite different from the heterogeneity seen in previous single-molecule studies of naturally derived RNA and protein enzymes. The single-molecule methods we describe may be of use in analyzing the distribution of functional molecules in heterogeneous evolving populations or even in unselected samples of random sequences