Toward Mesh-Invariant 3D Generative Deep Learning with Geometric Measures

3D generative modeling is accelerating as the technology allowing the capture of geometric data is developing. However, the acquired data is often inconsistent, resulting in unregistered meshes or point clouds. Many generative learning algorithms require correspondence between each point when comparing the predicted shape and the target shape. We propose an architecture able to cope with different parameterizations, even during the training phase. In particular, our loss function is built upon a kernel-based metric over a representation of meshes using geometric measures such as currents and varifolds. The latter allows to implement an efficient dissimilarity measure with many desirable properties such as robustness to resampling of the mesh or point cloud. We demonstrate the efficiency and resilience of our model with a generative learning task of human faces

Muscle activation during gait in children with Duchenne muscular dystrophy

The aim of this prospective study was to investigate changes in muscle activity during gait in children with Duchenne muscular Dystrophy (DMD). Dynamic surface electromyography recordings (EMGs) of 16 children with DMD and pathological gait were compared with those of 15 control children. The activity of the rectus femoris (RF), vastus lateralis (VL), medial hamstrings (HS), tibialis anterior (TA) and gastrocnemius soleus (GAS) muscles was recorded and analysed quantitatively and qualitatively. The overall muscle activity in the children with DMD was significantly different from that of the control group. Percentage activation amplitudes of RF, HS and TA were greater throughout the gait cycle in the children with DMD and the timing of GAS activity differed from the control children. Significantly greater muscle coactivation was found in the children with DMD. There were no significant differences between sides. Since the motor command is normal in DMD, the hyper-activity and co-contractions likely compensate for gait instability and muscle weakness, however may have negative consequences on the muscles and may increase the energy cost of gait. Simple rehabilitative strategies such as targeted physical therapies may improve stability and thus the pattern of muscle activity

Kajian potensi ekstrak bilberi sebagai penunjuk pH untuk memantau kesegaran makanan secara kromametri

Penunjuk pH sebagai suatu pendekatan untuk memantau kualiti atau kesegaran makanan semasa telah mendapat perhatian industri pembungkusan makanan. Penggunaan sumber semula jadi pigmen tumbuhan terutamanya daripada buah-buahan dan sayur-sayuran menjadi pilihan para pengguna untuk menggantikan pewarna sintetik dalam memastikan keselamatan makanan yang diambil setiap hari. Dalam kajian ini, ekstrak daripada bilberi telah digunakan sebagai pewarna sensitif pH. Perubahan warna sampel dikaji secara terperinci melalui kaedah kromametri dan juga kaedah spektrofotometri ultra-lembayung nampak. Warna merah terang terhasil dalam pH berasid, merah pudar pada neutral dan magenta ke kuning dalam pH beralkali. Keputusan kajian kromametri menunjukkan bahawa ekstrak bilberi berupaya mempamerkan perubahan warna yang jelas terhadap perubahan pH, iaitu terdapat perubahan nilai warna a* yang menyumbang kepada perubahan yang bererti dalam perbezaan warna keseluruhan (ΔE*). Nilai ΔE* juga ditentukan wujud hubungan linear dan kuantitatif terhadap julat pH tertentu. Oleh yang demikian, ekstrak bilberi didapati berpotensi sebagai bahan sensor untuk pH dalam membangunkan satu sensor pH bagi memantau kesegaran makanan terutamanya hasilan laut berbungkus memandangkan tahap kerosakan produk tersebut berkait rapat dengan perubahan pH ke arah alkali

Extracorporeal Membrane Oxygenation for Severe Acute Respiratory Distress Syndrome associated with COVID-19: An Emulated Target Trial Analysis.

RATIONALE: Whether COVID patients may benefit from extracorporeal membrane oxygenation (ECMO) compared with conventional invasive mechanical ventilation (IMV) remains unknown. OBJECTIVES: To estimate the effect of ECMO on 90-Day mortality vs IMV only Methods: Among 4,244 critically ill adult patients with COVID-19 included in a multicenter cohort study, we emulated a target trial comparing the treatment strategies of initiating ECMO vs. no ECMO within 7 days of IMV in patients with severe acute respiratory distress syndrome (PaO2/FiO2 <80 or PaCO2 ≥60 mmHg). We controlled for confounding using a multivariable Cox model based on predefined variables. MAIN RESULTS: 1,235 patients met the full eligibility criteria for the emulated trial, among whom 164 patients initiated ECMO. The ECMO strategy had a higher survival probability at Day-7 from the onset of eligibility criteria (87% vs 83%, risk difference: 4%, 95% CI 0;9%) which decreased during follow-up (survival at Day-90: 63% vs 65%, risk difference: -2%, 95% CI -10;5%). However, ECMO was associated with higher survival when performed in high-volume ECMO centers or in regions where a specific ECMO network organization was set up to handle high demand, and when initiated within the first 4 days of MV and in profoundly hypoxemic patients. CONCLUSIONS: In an emulated trial based on a nationwide COVID-19 cohort, we found differential survival over time of an ECMO compared with a no-ECMO strategy. However, ECMO was consistently associated with better outcomes when performed in high-volume centers and in regions with ECMO capacities specifically organized to handle high demand. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/)

3D Shape Sequence of Human Comparison and Classification using Current and Varifolds

International audienceIn this paper we address the task of the comparison and the classification of 3D shape sequences of human. The non-linear dynamics of the human motion and the changing of the surface parametrization over the time make this task very challenging. To tackle this issue, we propose to embed the 3D shape sequences in an infinite dimensional space, the space of varifolds, endowed with an inner product that comes from a given positive definite kernel. More specifically, our approach involves two steps: 1) the surfaces are represented as varifolds, this representation induces metrics equivariant to rigid motions and invariant to parametrization; 2) the sequences of 3D shapes are represented by Gram matrices derived from their infinite dimensional Hankel matrices. The problem of comparison of two 3D sequences of human is formulated as a comparison of two Gram-Hankel matrices. Extensive experiments on CVSSP3D and Dyna datasets show that our method is competitive with state-of-the-art in 3D human sequence motion retrieval. Code for the experiments is available at https://github.com/CRISTAL-3DSAM/ HumanComparisonVarifold

Milk molecular species of triacylglycerols characterized by lipidomic approach in cows and goats fed diets supplemented with various lipid sources

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Serotonin 5-HT 1A Receptor Biased Agonists Induce Different Cerebral Metabolic Responses: A [ 18 F]-Fluorodesoxyglucose Positron Emission Tomography Study in Conscious and Anesthetized Rats

International audienceSerotonin 5-HT1A receptors constitute an attractive therapeutic target for various psychiatric or neurodegenerative disorders. These receptors are expressed in multiple brain regions on different neuronal populations and can be coupled with distinct G-protein subtypes; such functional diversity complicates the use of 5-HT1A ligands in several pathologies where it would be desirable to stimulate the receptors in a precise region. Therefore, using “biased agonists” able to target specifically certain subpopulations of 5-HT1A receptors would enable achievement of better therapeutic benefit. Several 5-HT1A receptor biased agonists are currently in development, including NLX-101 (aka F15599) and NLX-112 (aka F13640, befiradol), with preclinical data suggesting that they preferentially target different populations of 5-HT1A receptors. However, most previous studies used invasive and regionally limited approaches. In this context, [18F]-fluorodesoxyglucose (FDG)-positron emission tomography (PET) imaging constitutes an interesting technique as it enables noninvasive mapping of the regional brain activity changes following a pharmacological challenge in conscious animals. We report here the evaluation of cerebral glucose metabolism following intraperitoneal injection of different doses of NLX-112 or NLX-101 in conscious or isoflurane-anesthetized rats. The biased agonists produced different metabolic “fingerprints” with distinct regional preferences, consistent with previous studies. At equal doses, the effect of NLX-101 was less marked than NLX-112 in the piriform cortex, in the striatum (in terms of inhibition), and in the pontine nuclei and the cerebellum (in terms of activation); furthermore, only NLX-112 increased the glucose metabolism in the parietal cortex, whereas only NLX-101 induced a clear activation in the colliculi and the frontal cortex, which may be related to its distinctive procognitive profile. Both agonist effects were almost completely unapparent in anesthetized animals, underlining the importance of studying serotonergic neurotransmission in the conscious state. In this regard, [18F]FDG-PET imaging seems very complementary with other functional imaging techniques such as pharmacological MRI

Assimilation of wide-swath altimetry water elevation anomalies to correct large-scale river routing model parameters

International audienceLand surface models combined with river routing models are widely used to study the continental part of the water cycle. They give global estimates of water flows and storages, but they are not without non-negligible uncertainties , among which inexact input parameters play a significant part. The incoming Surface Water and Ocean Topography (SWOT) satellite mission, with a launch scheduled for 2021 and with a required lifetime of at least 3 years, will be dedicated to the measuring of water surface elevations, widths and surface slopes of rivers wider than 100 m, at a global scale. SWOT will provide a significant number of new observations for river hydrology and maybe combined, through data assimilation, with global-scale models in order to correct their input parameters and reduce their associated uncertainty. Comparing simulated water depths with measured water surface elevations remains however a challenge and can introduce within the system large bias. A promising alternative for assimilating water surface elevations consists of assimilating water surface elevation anomalies which do not depend on a reference surface. The objective of this study is to present a data assimilation platform based on the asyn-chronous ensemble Kalman filter (AEnKF) that can assimilate synthetic SWOT observations of water depths and water elevation anomalies to correct the input parameters of a large-scale hydrologic model over a 21 d time window. The study is applied to the ISBA-CTRIP model over the Amazon basin and focuses on correcting the spatial distribution of the river Manning coefficients. The data assimilation algorithm , tested through a set of observing system simulation experiments (OSSEs), is able to retrieve the true value of the Manning coefficients within one assimilation cycle much of the time (basin-averaged Manning coefficient root mean square error, RMSEn, is reduced from 33 % to [1 %-10 %] after one assimilation cycle) and shows promising perspectives with assimilating water anomalies (basin-averaged Manning coefficient RMSEn is reduced from 33 % to [1 %-2 %] when assimilating water surface elevation anomalies over 1 year), which allows us to overcome the issue of unknown bathymetry

Temporal Variance-Based Sensitivity Analysis of the River-Routing Component of the Large-Scale Hydrological Model ISBA–TRIP: Application on the Amazon Basin

International audienceThe continental part of the water cycle is commonly represented with hydrological models. Yet, there are limits in their capacity to accurately estimate water storage and dynamics because of their coarse spatial resolution, simplified physics, and an incomplete knowledge of atmospheric forcing and input parameters. These errors can be diminished using data assimilation techniques. The model's most sensitive parameters should be identified beforehand. The objective of the present study is to highlight key parameters impacting the river-routing scheme Total Runoff Integrating Pathways (TRIP) while simulating river water height and discharge as a function of time focusing on the annual cycle. Thus, a sensitivity analysis based on the decomposition of model output variance (using a method called ANOVA) is utilized and applied over the Amazon basin. Tested parameters are perturbed with correcting factors. First, parameter-correcting coefficients are considered uniform over the entire basin. The results are specific to the TRIP model and show that geomorphological parameters explain around 95% of the water height variance with purely additive contributions, all year long, with a dominating impact of the river Manning coefficient (40%), the riverbed slope (35%), and the river width (20%). The results also show that discharge is essentially sensitive to the groundwater time constant that makes up more than 90% of the variance. To a lesser extent, in rising/falling flow period, the discharge is also sensitive to geomorphological parameters. Next, the Amazon basin is divided into nine subregions and the sensitivity analysis is carried out for regionalized parameter-correcting coefficients. The results show that local-region parameters impact water height, while upstream-region parameters affect discharge

Enrichissement de la base de données spectrale peakforest en LC-MS

L’analyse métabolomique non ciblée est une approche puissante permettant la caractérisation du phénotype métabolique lié aux développements de maladies chroniques. L’identification des biomarqueurs qui y sont associés est devenue un enjeu majeur pour ce type d’approche. Il existe aujourd’hui un très large panel de banques de données en métabolomique, pouvant aider lors de cette étape d’identification, telles que MassBank, HMDB ou Lipidmaps, mais ces bases n’intègrent pas de données chromatographiques alors que le temps de rétention peut être un paramètre contribuant largement dans l’identification de molécules (Sumner et al, 2014). La base de données PeakForest est une banque de données de spectres de référence dédiée à l’annotation des données métabolomiques. Plus de 1000 composés standards (métabolites endogènes déjà décrits dans les biofluides) ont été analysés en LC-HRMS (Orbitrap, QTof) et selon des méthodes chromatographiques complémentaires au sein des quatre plateformes du consortium MetaboHUB. L’implémentation de PeakForest est réalisé via des fichiers ‘template’ qui intègrent les metadata et les peaklists annotées. L’originalité de cette base est qu’elle intègre également les conditions chromatographiques ainsi que les temps de rétention de chaque molécule, ce qui permet d’intégrer ce paramètre dans les requêtes. L’objectif sera de pouvoir utiliser cette ressource pour une annotation automatique des jeux de données. C’est pourquoi la base de données PeakForest est utilisable via des outils Galaxy, bientôt intégrés au sein de la plate-forme web Galaxy W4M (Workflow4Metabolomics ; Giacomoni et al, 2015). Une preuve de concept de l’utilisation de cet outil a été réalisée pour l’annotation d’un échantillon de plasma de référence du NIST. Au total plus de 70 métabolites ont été confirmés avec un score égal à 5 (Sumner et al, 2014). Ces résultats pourront à terme être intégrés au sein de PeakForest après curation effectuée par des experts dans le but de valider les résultats. Des données MS/MS viendront également complémenter la base. Cet outil dédié à l’annotation de métabolites en haut débit contribuera donc à terme à enrichir la caractérisation des métabolomes de différents systèmes biologique