Evidence of triggered star formation in G327.3-0.6. Dust-continuum mapping of an infrared dark cloud with P-ArT\'eMiS

Aims. Expanding HII regions and propagating shocks are common in the environment of young high-mass star-forming complexes. They can compress a pre-existing molecular cloud and trigger the formation of dense cores. We investigate whether these phenomena can explain the formation of high-mass protostars within an infrared dark cloud located at the position of G327.3-0.6 in the Galactic plane, in between two large infrared bubbles and two HII regions. Methods: The region of G327.3-0.6 was imaged at 450 ? m with the CEA P-ArT\'eMiS bolometer array on the Atacama Pathfinder EXperiment telescope in Chile. APEX/LABOCA and APEX-2A, and Spitzer/IRAC and MIPS archives data were used in this study. Results: Ten massive cores were detected in the P-ArT\'eMiS image, embedded within the infrared dark cloud seen in absorption at both 8 and 24 ?m. Their luminosities and masses indicate that they form high-mass stars. The kinematical study of the region suggests that the infrared bubbles expand toward the infrared dark cloud. Conclusions: Under the influence of expanding bubbles, star formation occurs in the infrared dark areas at the border of HII regions and infrared bubbles.Comment: 4 page

Radiotherapy modification based on artificial intelligence and radiomics applied to (18F)-fluorodeoxyglucose positron emission tomography/computed tomography.

peer reviewedOver the last decades, the refinement of radiation therapy techniques has been associated with an increasing interest for individualized radiation therapy with the aim of increasing or maintaining tumor control and reducing radiation toxicity. Developments in artificial intelligence (AI), particularly machine learning and deep learning, in imaging sciences, including nuclear medecine, have led to significant enthusiasm for the concept of "rapid learning health system". AI combined with radiomics applied to (18F)-fluorodeoxyglucose positron emission tomography/computed tomography ([18F]-FDG PET/CT) offers a unique opportunity for the development of predictive models that can help stratify each patient's risk and guide treatment decisions for optimal outcomes and quality of life of patients treated with radiation therapy. Here we present an overview of the current contribution of AI and radiomics-based machine learning models applied to (18F)-FDG PET/CT in the management of cancer treated by radiation therapy

Statistical Computing on Non-Linear Spaces for Computational Anatomy

International audienceComputational anatomy is an emerging discipline that aims at analyzing and modeling the individual anatomy of organs and their biological variability across a population. However, understanding and modeling the shape of organs is made difficult by the absence of physical models for comparing different subjects, the complexity of shapes, and the high number of degrees of freedom implied. Moreover, the geometric nature of the anatomical features usually extracted raises the need for statistics on objects like curves, surfaces and deformations that do not belong to standard Euclidean spaces. We explain in this chapter how the Riemannian structure can provide a powerful framework to build generic statistical computing tools. We show that few computational tools derive for each Riemannian metric can be used in practice as the basic atoms to build more complex generic algorithms such as interpolation, filtering and anisotropic diffusion on fields of geometric features. This computational framework is illustrated with the analysis of the shape of the scoliotic spine and the modeling of the brain variability from sulcal lines where the results suggest new anatomical findings

Probing the structure of a massive filament: ArTéMiS 350 and 450 μm mapping of the integral-shaped filament in Orion A

Context. The Orion molecular cloud is the closest region of high-mass star formation. It is an ideal target for investigating the detailed structure of massive star-forming filaments at high resolution and the relevance of the filament paradigm for the earliest stages of intermediate- to high-mass star formation. Aims. Within the Orion A molecular cloud, the integral-shaped filament (ISF) is a prominent, degree-long structure of dense gas and dust with clear signs of recent and ongoing high-mass star formation. Our aim is to characterise the structure of this massive filament at moderately high angular resolution (8′′ or ~0.016 pc) in order to measure the intrinsic width of the main filament, down to scales well below 0.1 pc, which has been identified as the characteristic width of filaments. Methods. We used the ArTéMiS bolometer camera at APEX to map a ~0.6 × 0.2 deg2 region covering OMC-1, OMC-2, and OMC-3 at 350 and 450 μm. We combined these data with Herschel-SPIRE maps to recover extended emission. The combined Herschel-ArTéMiS maps provide details on the distribution of dense cold material, with a high spatial dynamic range, from our 8′′ resolution up to the transverse angular size of the map, ~10-15′. By combining Herschel and ArTéMiS data at 160, 250, 350, and 450 μm, we constructed high-resolution temperature and H2 column density maps. We extracted radial intensity profiles from the column density map in several representative portions of the ISF, which we fitted with Gaussian and Plummer models to derive their intrinsic widths. We also compared the distribution of material traced by ArTéMiS with that seen in the higher-density tracer N2H+(1-0) that was recently observed with the ALMA interferometer. Results. All the radial profiles that we extracted show a clear deviation from a Gaussian, with evidence for an inner plateau that had not previously been seen clearly using Herschel-only data. We measure intrinsic half-power widths in the range 0.06-0.11 pc. This is significantly larger than the Gaussian widths measured for fibres seen in N2H+, which probably only traces the dense innermost regions of the large-scale filament. These half-power widths are within a factor of two of the value of ~0.1 pc found for a large sample of nearby filaments in various low-mass star-forming regions, which tends to indicate that the physical conditions governing the fragmentation of pre-stellar cores within transcritical or supercritical filaments are the same over a large range of masses per unit length. © F. Schuller et al. 2021

Anisotropic smoothness classes : from finite element approximation to image models

We propose and study quantitative measures of smoothness which are adapted to anisotropic features such as edges in images or shocks in PDE's. These quantities govern the rate of approximation by adaptive finite elements, when no constraint is imposed on the aspect ratio of the triangles, the simplest examples of such quantities are based on the determinant of the hessian of the function to be approximated. Since they are not semi-norms, these quantities cannot be used to define linear function spaces. We show that they can be well defined by mollification when the function to be approximated has jump discontinuities along piecewise smooth curves. This motivates for using them in image processing as an alternative to the frequently used record variation semi-norm which does not account for the geometric smoothness of the edges.Comment: 24 pages, 2 figure

Proteomic identification, cDNA cloning and enzymatic activity of glutathione S-transferases from the generalist marine gastropod, Cyphoma gibbosum

Author Posting. © Elsevier B.V., 2008. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Archives of Biochemistry and Biophysics 478 (2008): 7-17, doi:10.1016/j.abb.2008.07.007.Glutathione S-transferases (GST) were characterized from the digestive gland of Cyphoma gibbosum (Mollusca; Gastropoda), to investigate the possible role of these detoxification enzymes in conferring resistance to allelochemicals present in its gorgonian coral diet. We identified the collection of expressed cytosolic Cyphoma GST classes using a proteomic approach involving affinity chromatography, HPLC and nanospray liquid chromatography-tandem mass spectrometry (LC-MS/MS). Two major GST subunits were identified as putative mu-class GSTs; while one minor GST subunit was identified as a putative theta-class GST, apparently the first theta-class GST identified from a mollusc. Two Cyphoma GST cDNAs (CgGSTM1 and CgGSTM2) were isolated by RT-PCR using primers derived from peptide sequences. Phylogenetic analyses established both cDNAs as mu-class GSTs and revealed a mollusc-specific subclass of the GST-mu clade. These results provide new insights into metazoan GST diversity and the biochemical mechanisms used by marine organisms to cope with their chemically defended prey.Support was provided by the WHOI-Cole Ocean Ventures Fund (KEW), the WHOI Ocean Life Institute (KEW and MEH), a grant from Walter A. and Hope Noyes Smith (MEH), the National Science Foundation Graduate Research Fellowship (KEW), and by the National Institutes of Health (P42-ES007381 and R01-ES015912 to JVG)

Characterizing filaments in regions of high-mass star formation: High-resolution submilimeter imaging of the massive star-forming complex NGC 6334 with ArTeMiS

Context. Herschel observations of nearby molecular clouds suggest that interstellar filaments and prestellar cores represent two fundamental steps in the star formation process. The observations support a picture of low-mass star formation according to which filaments of ~0.1 pc width form first in the cold interstellar medium, probably as a result of large-scale compression of interstellar matter by supersonic turbulent flows, and then prestellar cores arise from gravitational fragmentation of the densest filaments. Whether this scenario also applies to regions of high-mass star formation is an open question, in part because the resolution of Herschel is insufficient to resolve the inner width of filaments in the nearest regions of massive star formation. Aims. In an effort to characterize the inner width of filaments in high-mass star-forming regions, we imaged the central part of the NGC 6334 complex at a resolution higher by a factor of >3 than Herschel at 350 μm. Methods. We used the large-format bolometer camera ArTéMiS on the APEX telescope and combined the high-resolution ArTéMiS data at 350 μm with Herschel/HOBYS data at 70–500 μm to ensure good sensitivity to a broad range of spatial scales. This allowed us to study the structure of the main narrow filament of the complex with a resolution of 8″ or <0.07 pc at d ~ 1.7 kpc. Results. Our study confirms that this filament is a very dense, massive linear structure with a line mass ranging from ~500 M⊙/pc to ~2000 M⊙/pc over nearly 10 pc. It also demonstrates for the first time that its inner width remains as narrow as W ~ 0.15 ± 0.05 pc all along the filament length, within a factor of <2 of the characteristic 0.1 pc value found with Herschel for lower-mass filaments in the Gould Belt. Conclusions. While it is not completely clear whether the NGC 6334 filament will form massive stars in the future, it is two to three orders of magnitude denser than the majority of filaments observed in Gould Belt clouds, and has a very similar inner width. This points to a common physical mechanism for setting the filament width and suggests that some important structural properties of nearby clouds also hold in high-mass star-forming regions

On the evolutionary ecology of symbioses between chemosynthetic bacteria and bivalves

Mutualistic associations between bacteria and eukaryotes occur ubiquitously in nature, forming the basis for key ecological and evolutionary innovations. Some of the most prominent examples of these symbioses are chemosynthetic bacteria and marine invertebrates living in the absence of sunlight at deep-sea hydrothermal vents and in sediments rich in reduced sulfur compounds. Here, chemosynthetic bacteria living in close association with their hosts convert CO2 or CH4 into organic compounds and provide the host with necessary nutrients. The dominant macrofauna of hydrothermal vent and cold seep ecosystems all depend on the metabolic activity of chemosynthetic bacteria, which accounts for almost all primary production in these complex ecosystems. Many of these enigmatic mutualistic associations are found within the molluscan class Bivalvia. Currently, chemosynthetic symbioses have been reported from five distinct bivalve families (Lucinidae, Mytilidae, Solemyidae, Thyasiridae, and Vesicomyidae). This brief review aims to provide an overview of the diverse physiological and genetic adaptations of symbiotic chemosynthetic bacteria and their bivalve hosts

Rate-invariant analysis of covariance trajectories

Statistical analysis of dynamic systems, such as videos and dynamic functional connectivity, is often translated into a problem of analyzing trajectories of relevant features, particularly covariance matrices. As an example, in video-based action recognition, a natural mathematical representation of activity videos is as parameterized trajectories on the set of symmetric, positive-definite matrices (SPDMs). The variable execution-rates of actions, implying arbitrary parameterizations of trajectories, complicates their analysis and classification. To handle this challenge, we represent covariance trajectories using transported square-root vector fields (TSRVFs), constructed by parallel translating scaled-velocity vectors of trajectories to their starting points. The space of such representations forms a vector bundle on the SPDM manifold. Using a natural Riemannian metric on this vector bundle, we approximate geodesic paths and geodesic distances between trajectories in the quotient space of this vector bundle. This metric is invariant to the action of the reparameterization group, and leads to a rate-invariant analysis of trajectories. In the process, we remove the parameterization variability and temporally register trajectories during analysis. We demonstrate this framework in multiple contexts, using both generative statistical models and discriminative data analysis. The latter is illustrated using several applications involving video-based action recognition and dynamic functional connectivity analysis