Les microARNs

MiRNAs are small non-coding RNAs ensuring the post-transcriptional regulation of gene expression. Their expression is tissue-specific and some miRNAs have diagnostic and / or prognostic value for tumor classes. MiRNAs are involved in tumorigenesis by two mechanisms: amplification or deletion of chromosomal regions containing clusters of genes encoding miRNAs (quantitative effect) or modification of the effects of miRNAs on their target genes by mutation in the region of interaction with the mRNA (qualitative effect). Their specificity, the possibility for miRNA measurement in blood, must now lead to consider miRNAs as markers for therapeutic management. A better understanding of the different regulatory mechanisms involving miRNAs will also consider new therapeutic approaches

A study on the use of the PACS bolometer arrays for submillimeter ground-based telescopes

A new kind of bolometric architecture has been successfully developed for the PACS photometer onboard the Herschel submillimeter observatory. These new generation CCD-like arrays are buttable and enable the conception of large fully sampled focal planes. We present a feasibility study of the adaptation of these bolometer arrays to ground-based submillimeter telescopes. We have developed an electro-thermal numerical model to simulate the performances of the bolometers under specific ground-based conditions (different wavelengths and background powers for example). This simulation permits to determine the optimal parameters for each condition and shows that the bolometers can be background limited in each transmission window between 200 and 450 microns. We also present a new optical system that enables to have a maximum absorption of the bolometer in each atmospheric windows. The description of this system and measurements are showed.Comment: 9 pages, 12 figures, to be published in the Proceedings of SPIE Vol. 6275, "Astronomical Telescopes and Instrumentation, Millimeter and Submillimeter Detectors and Instrumentation for Astronomy III

Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination

OBJECTIVE: Prenatal diagnosis of a rare disease on ultrasound relies on a physician's ability to remember an intractable amount of knowledge. We developed a real-time decision support system (DSS) that suggests, at each step of the examination, the next phenotypic feature to assess, optimizing the diagnostic pathway to the smallest number of possible diagnoses. The objective of this study was to evaluate the performance of this real-time DSS using clinical data. METHODS: This validation study was conducted on a database of 549 perinatal phenotypes collected from two referral centers (one in France and one in the UK). Inclusion criteria were: at least one anomaly was visible on fetal ultrasound after 11 weeks' gestation; the anomaly was confirmed postnatally; an associated rare disease was confirmed or ruled out based on postnatal/postmortem investigation, including physical examination, genetic testing and imaging; and, when confirmed, the syndrome was known by the DSS software. The cases were assessed retrospectively by the software, using either the full phenotype as a single input, or a stepwise input of phenotypic features, as prompted by the software, mimicking its use in a real-life clinical setting. Adjudication of discordant cases, in which there was disagreement between the DSS output and the postnatally confirmed (‘ascertained’) diagnosis, was performed by a panel of external experts. The proportion of ascertained diagnoses within the software's top-10 differential diagnoses output was evaluated, as well as the sensitivity and specificity of the software to select correctly as its best guess a syndromic or isolated condition. RESULTS: The dataset covered 110/408 (27%) diagnoses within the software's database, yielding a cumulative prevalence of 83%. For syndromic cases, the ascertained diagnosis was within the top-10 list in 93% and 83% of cases using the full-phenotype and stepwise input, respectively, after adjudication. The full-phenotype and stepwise approaches were associated, respectively, with a specificity of 94% and 96% and a sensitivity of 99% and 84%. The stepwise approach required an average of 13 queries to reach the final set of diagnoses. CONCLUSIONS: The DSS showed high performance when applied to real-world data. This validation study suggests that such software can improve perinatal care, efficiently providing complex and otherwise overlooked knowledge to care-providers involved in ultrasound-based prenatal diagnosis. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology

Unveiling Far-Infrared Counterparts of Bright Submillimeter Galaxies Using PACS Imaging

We present a search for Herschel-PACS counterparts of dust-obscured, high-redshift objects previously selected at submillimeter and millimeter wavelengths in the Great Observatories Origins Deep Survey North field. We detect 22 of 56 submillimeter galaxies (39%) with a SNR of >=3 at 100 micron down to 3.0 mJy, and/or at 160 micron down to 5.7 mJy. The fraction of SMGs seen at 160 micron is higher than that at 100 micron. About 50% of radio-identified SMGs are associated with PACS sources. We find a trend between the SCUBA/PACS flux ratio and redshift, suggesting that these flux ratios could be used as a coarse redshift indicator. PACS undetected submm/mm selected sources tend to lie at higher redshifts than the PACS detected ones. A total of 12 sources (21% of our SMG sample) remain unidentified and the fact that they are blank fields at Herschel-PACS and VLA 20 cm wavelength may imply higher redshifts for them than for the average SMG population (e.g., z>3-4). The Herschel-PACS imaging of these dust-obscured starbursts at high-redshifts suggests that their far-infrared spectral energy distributions have significantly different shapes than template libraries of local infrared galaxies.Comment: 6 pages, 4 figures. ApJ Letters in pres

Aquatic community response to volcanic eruptions on the Ecuadorian Andean flank: evidence from the palaeoecological record

Aquatic ecosystems in the tropical Andes are under increasing pressure from human modification of the landscape (deforestation and dams) and climatic change (increase of extreme events and 1.5 °C on average temperatures are projected for AD 2100). However, the resilience of these ecosystems to perturbations is poorly understood. Here we use a multi-proxy palaeoecological approach to assess the response of aquatic ecosystems to a major mechanism for natural disturbance, volcanic ash deposition. Specifically, we present data from two Neotropical lakes located on the eastern Andean flank of Ecuador. Laguna Pindo (1°27.132′S–78°04.847′W) is a tectonically formed closed basin surrounded by a dense mid-elevation forest, whereas Laguna Baños (0°19.328′S–78°09.175′W) is a glacially formed lake with an inflow and outflow in high Andean Páramo grasslands. In each lake we examined the dynamics of chironomids and other aquatic and semi-aquatic organisms to explore the effect of thick (> 5 cm) volcanic deposits on the aquatic communities in these two systems with different catchment features. In both lakes past volcanic ash deposition was evident from four large tephras dated to c.850 cal year BP (Pindo), and 4600, 3600 and 1500 cal year BP (Baños). Examination of the chironomid and aquatic assemblages before and after the ash depositions revealed no shift in composition at Pindo, but a major change at Baños occurred after the last event around 1500 cal year BP. Chironomids at Baños changed from an assemblage dominated by Pseudochironomus and Polypedilum nubifer-type to Cricotopus/Paratrichocladius type-II, and such a dominance lasted for approximately 380 years. We suggest that, despite potential changes in the water chemistry, the major effect on the chironomid community resulted from the thickness of the tephra being deposited, which acted to shallow the water body beyond a depth threshold. Changes in the aquatic flora and fauna at the base of the trophic chain can promote cascade effects that may deteriorate the ecosystem, especially when already influenced by human activities, such as deforestation and dams, which is frequent in the high Andes

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

Grain Dynamics in a Two-dimensional Granular Flow

We have used particle tracking methods to study the dynamics of individual balls comprising a granular flow in a small-angle two-dimensional funnel. We statistically analyze many ball trajectories to examine the mechanisms of shock propagation. In particular, we study the creation of, and interactions between, shock waves. We also investigate the role of granular temperature and draw parallels to traffic flow dynamics.Comment: 17 pages, 24 figures. To appear in Phys.Rev.E. High res./color figures etc. on http://www.nbi.dk/CATS/Granular/GrainDyn.htm

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