Mean 3D Dispersion for Automatic General Movement Assessment of Preterm Infants.

The General Movement assessment (GMA) is a validated assessment of brain maturation primarily based on the qualitative analysis of the complexity and the variation of spontaneous motor activity. The GMA can identify preterm infants presenting an early abnormal developmental trajectory before term-equivalent age, which permits a personalized early developmental intervention. However, GMA is time-consuming and relies on a qualitative analysis; these limitations restrict the implementation of GMA in clinical practice. In this study based on a validated dataset of 183 videos from 92 premature infants (54 males, 38 females) born <33 weeks of gestational age (GA) and acquired between 32 and 40 weeks of GA, we introduce the mean 3D dispersion (M3D) for objective quantification and classification of normal and abnormal GMA. Moreover, we have created a new 3D representation of skeleton joints which allows an objective comparison of spontaneous movements of infants of different ages and sizes. Preterm infants with normal versus abnormal GMA had a distinct M3D distribution (p <0.001). The M3D has shown a good classification performance for GMA (AUC=0.7723) and presented an accuracy of 74.1%, a sensitivity of 75.8%, and a specificity of 70.1% when using an M3D of 0.29 as a classification threshold.Clinical relevance- Our study paves the way for the development of quantitative analysis of GMA within the Neonatal Unit.

Deep learning detection of nanoparticles and multiple object tracking of their dynamic evolution during in situ ETEM studies.

In situ transmission electron microscopy (TEM) studies of dynamic events produce large quantities of data especially under the form of images. In the important case of heterogeneous catalysis, environmental TEM (ETEM) under gas and temperature allows to follow a large population of supported nanoparticles (NPs) evolving under reactive conditions. Interpreting properly large image sequences gives precious information on the catalytic properties of the active phase by identifying causes for its deactivation. To perform a quantitative, objective and robust treatment, we propose an automatic procedure to track nanoparticles observed in Scanning ETEM (STEM in ETEM). Our approach involves deep learning and computer vision developments in multiple object tracking. At first, a registration step corrects the image displacements and misalignment inherent to the in situ acquisition. Then, a deep learning approach detects the nanoparticles on all frames of video sequences. Finally, an iterative tracking algorithm reconstructs their trajectories. This treatment allows to deduce quantitative and statistical features about their evolution or motion, such as a Brownian behavior and merging or crossing events. We treat the case of in situ calcination of palladium (oxide) / delta-alumina, where the present approach allows a discussion of operating processes such as Ostwald ripening or NP aggregative coalescence.

Direct extraction of the mean particle size from a digital hologram.

Digital holography, which consists of both acquiring the hologram image in a digital camera and numerically reconstructing the information, offers new and faster ways to make the most of a hologram. We describe a new method to determine the rough size of particles in an in-line hologram. This method relies on a property that is specific to interference patterns in Fresnel holograms: Self-correlation of a hologram provides access to size information. The proposed method is both simple and fast and gives results with acceptable precision. It suppresses all the problems related to the numerical depth of focus when large depth volumes are analyzed.

Fluctuations of the solid discharge of gravity-driven particle flows in a turbulent stream.

Substantial variations in the particle flux are commonly observed in field measurements on gravel-bed rivers and in laboratory experiments mimicking river behavior on a smaller scale. These fluctuations can be explained by the natural variability of sediment supply and hydraulic conditions. We conducted laboratory experiments of particle transport down a two-dimensional inclined channel, for which the boundary conditions were properly controlled. Most flow variables and the features of particle trajectories were measured using a high-speed camera. The particles were 6-mm glass beads entrained by a rapid, turbulent, supercritical water flow. Even under these well-controlled experimental conditions and despite steady supply, solid discharge exhibited significant variations with time. The objective of this paper was to pinpoint the origins of these fluctuations by investigating different flow conditions. Two experiments were done with a fixed (smooth or corrugated) channel bottom and two others were run with a mobile bed (involving layers of closely packed particles lying along the channel base, which could be entrained by the stream); in the latter case, two particle arrangements were tested. It was found that, to a large extent, fluctuations reflected the finite size of the observation window. For fixed beds, the characteristic time scale of fluctuations and their probability distribution can be predetermined by evaluating the mean and fluctuating velocities of a single particle. Solid-discharge fluctuations were exacerbated when the bed was mobile because (i) the moving solid phase and the stationary bed exchanged particles and (ii) collective entrainment of particles occurred.