Laboratoire des Sciences de l'Ingénieur, de l'Informatique et de l'Imagerie

Neurologic Features in SARS-CoV-2 Infection In a consecutive series of 64 patients with Covid-19 and ARDS, 58 of whom underwent neurologic examination, severe agitation and corticospinal signs were...

Land surface temperature (LST) is one of the key parameters in the physics of land surface processes from local through global scales. The importance of LST is being increasingly recognized and there is a strong interest in developing methodologies to measure LST from space. However, retrieving LST is still a challenging task since the LST retrieval problem is ill-posed. This paper reviews the current status of selected remote sensing algorithms for estimating LST from thermal infrared (TIR) data. A brief theoretical background of the subject is presented along with a survey of the algorithms employed for obtaining LST from space-based TIR measurements. The discussion focuses on TIR data acquired from polar-orbiting satellites because of their widespread use, global applicability and higher spatial resolution compared to geostationary satellites. The theoretical framework and methodologies used to derive the LST from the data are reviewed followed by the methodologies for validating satellite-derived LST. Directions for future research to improve the accuracy of satellite-derived LST are then suggested.

Abstract The split window method is successfully being used to retrieve the temperature over sea surfaces from satellite radiances in clear sky and has the great advantage of simplicity. However, such a method does not work over land surfaces, mainly because the emissivity is not equal to 1 and depends on the channel. An extension of this method to apply to land surfaces requires one to take account of emissivity—such an extension is presented in this paper. First, using Lowtran 6, the accuracies of the various linearizations of the radiative transfer equation leading to the split window are checked. This implies that the retrieved surface temperature depends linearly on emissivities and brightness temperatures. Such behaviour has been checked on actual examples. Theoretical equations are then derived which show that the actual surface temperature can again be expressed as a linear combination of the brightness temperatures measured in two adjacent channels with coefficients depending on spectral emissivities but not on atmospheric conditions. Using Lowtran 6 these properties have been verified and the dependence of these coefficients has been explicitly computed leading to a local split window method for the NOAA-9 Advanced Very High Resolution Radiometer. Finally, we show that accurate surface temperatures can be retrieved using this local split window method once emissivities in two adjacent channels are known with sufficient accuracy.

In recent years improvements to existing programs and the introduction of new iterative algorithms have changed the state-of-the-art in protein sequence alignment. This paper presents the first systematic study of the most commonly used alignment programs using BAliBASE benchmark alignments as test cases. Even below the 'twilight zone' at 10-20% residue identity, the best programs were capable of correctly aligning on average 47% of the residues. We show that iterative algorithms often offer improved alignment accuracy though at the expense of computation time. A notable exception was the effect of introducing a single divergent sequence into a set of closely related sequences, causing the iteration to diverge away from the best alignment. Global alignment programs generally performed better than local methods, except in the presence of large N/C-terminal extensions and internal insertions. In these cases, a local algorithm was more successful in identifying the most conserved motifs. This study enables us to propose appropriate alignment strategies, depending on the nature of a particular set of sequences. The employment of more than one program based on different alignment techniques should significantly improve the quality of automatic protein sequence alignment methods. The results also indicate guidelines for improvement of alignment algorithms.

An overview of the commonly applied evapotranspiration (ET) models using remotely sensed data is given to provide insight into the estimation of ET on a regional scale from satellite data. Generally, these models vary greatly in inputs, main assumptions and accuracy of results, etc. Besides the generally used remotely sensed multi-spectral data from visible to thermal infrared bands, most remotely sensed ET models, from simplified equations models to the more complex physically based two-source energy balance models, must rely to a certain degree on ground-based auxiliary measurements in order to derive the turbulent heat fluxes on a regional scale. We discuss the main inputs, assumptions, theories, advantages and drawbacks of each model. Moreover, approaches to the extrapolation of instantaneous ET to the daily values are also briefly presented. In the final part, both associated problems and future trends regarding these remotely sensed ET models were analyzed to objectively show the limitations and promising aspects of the estimation of regional ET based on remotely sensed data and ground-based measurements.

Summary: Structural Variations (SV) are a major source of variability in the human genome that shaped its actual structure during evolution. Moreover, many human diseases are caused by SV, highlighting the need to accurately detect those genomic events but also to annotate them and assist their biological interpretation. Therefore, we developed AnnotSV that compiles functionally, regulatory and clinically relevant information and aims at providing annotations useful to (i) interpret SV potential pathogenicity and (ii) filter out SV potential false positive. In particular, AnnotSV reports heterozygous and homozygous counts of single nucleotide variations (SNVs) and small insertions/deletions called within each SV for the analyzed patients, this genomic information being extremely useful to support or question the existence of an SV. We also report the computed allelic frequency relative to overlapping variants from DGV (MacDonald et al., 2014), that is especially powerful to filter out common SV. To delineate the strength of AnnotSV, we annotated the 4751 SV from one sample of the 1000 Genomes Project, integrating the sample information of four million of SNV/indel, in less than 60 s. Availability and implementation: AnnotSV is implemented in Tcl and runs in command line on all platforms. The source code is available under the GNU GPL license. Source code, README and Supplementary data are available at http://lbgi.fr/AnnotSV/. Supplementary information: Supplementary data are available at Bioinformatics online.

As an intrinsic property of natural materials, land surface emissivity (LSE) is an important surface parameter and can be derived from the emitted radiance measured from space. Besides radiometric calibration and cloud detection, two main problems need to be resolved to obtain LSE values from space measurements. These problems are often referred to as land surface temperature (LST) and emissivity separation from radiance at ground level and as atmospheric corrections in the literature. To date, many LSE retrieval methods have been proposed with the same goal but different application conditions, advantages, and limitations. The aim of this article is to review these LSE retrieval methods and to provide technical assistance for estimating LSE from space. This article first gives a description of the theoretical basis of LSE measurements and then reviews the published methods. For clarity, we categorize these methods into (1) (semi-)empirical or theoretical methods, (2) multi-channel temperature emissivity separation (TES) methods, and (3) physically based methods (PBMs). This article also discusses the validation methods, which are of importance in verifying the uncertainty and accuracy of retrieved emissivity. Finally, the prospects for further developments are given.

This paper introduces the FIT IoT-LAB testbed, an open testbed composed of 2728 low-power wireless nodes and 117 mobile robots available for experimenting with large-scale wireless IoT technologies, ranging from low-level protocols to advanced Internet services. IoT-LAB is built to accelerate the development of tomorrow's IoT technology by offering an accurate open-access and open-source multi-user scientific tool. The IoT-LAB testbed is deployed in 6 sites across France. Each site features different node and hardware capabilities, but all sites are interconnected and available through the same web portal, common REST interfaces and consistent CLI tools. The result is a heterogeneous testing environment, which covers a large spectrum of IoT use cases and applications. IoT-LAB is a one-of-a-kind facility, allowing anyone to test their solution at scale, experiment and fine-tune new networking concept.

BACKGROUND: Neurotropism of SARS-CoV-2 and its neurological manifestations have now been confirmed. We aimed at describing delirium and neurological symptoms of COVID-19 in ICU patients. METHODS: We conducted a bicentric cohort study in two French ICUs of Strasbourg University Hospital. All the 150 patients referred for acute respiratory distress syndrome due to SARS-CoV-2 between March 3 and May 5, 2020, were included at their admission. Ten patients (6.7%) were excluded because they remained under neuromuscular blockers during their entire ICU stay. Neurological examination, including CAM-ICU, and cerebrospinal fluid analysis, electroencephalography, and magnetic resonance imaging (MRI) were performed in some of the patients with delirium and/or abnormal neurological examination. The primary endpoint was to describe the incidence of delirium and/or abnormal neurological examination. The secondary endpoints were to describe the characteristics of delirium, to compare the duration of invasive mechanical ventilation and ICU length of stay in patients with and without delirium and/or abnormal neurological symptoms. RESULTS: The 140 patients were aged in median of 62 [IQR 52; 70] years old, with a median SAPSII of 49 [IQR 37; 64] points. Neurological examination was normal in 22 patients (15.7%). One hundred eighteen patients (84.3%) developed a delirium with a combination of acute attention, awareness, and cognition disturbances. Eighty-eight patients (69.3%) presented an unexpected state of agitation despite high infusion rates of sedative treatments and neuroleptics, and 89 (63.6%) patients had corticospinal tract signs. Brain MRI performed in 28 patients demonstrated enhancement of subarachnoid spaces in 17/28 patients (60.7%), intraparenchymal, predominantly white matter abnormalities in 8 patients, and perfusion abnormalities in 17/26 patients (65.4%). The 42 electroencephalograms mostly revealed unspecific abnormalities or diffuse, especially bifrontal, slow activity. Cerebrospinal fluid examination revealed inflammatory disturbances in 18/28 patients, including oligoclonal bands with mirror pattern and elevated IL-6. The CSF RT-PCR SARS-CoV-2 was positive in one patient. The delirium/neurological symptoms in COVID-19 patients were responsible for longer mechanical ventilation compared to the patients without delirium/neurological symptoms. Delirium/neurological symptoms could be secondary to systemic inflammatory reaction to SARS-CoV-2. CONCLUSIONS AND RELEVANCE: Delirium/neurological symptoms in COVID-19 patients are a major issue in ICUs, especially in the context of insufficient human and material resources. TRIAL REGISTRATION: NA.

The growing interest in soft robots comes from the new possibilities offered by these systems to cope with problems that cannot be addressed by robots built from rigid bodies. Many innovative solutions have been developed in recent years to design soft components and systems. They all demonstrate how soft robotics development is closely dependent on advanced manufacturing processes. This review aims at giving an insight on the current state of the art in soft robotics manufacturing. It first puts in light the elementary components that can be used to develop soft actuators, whether they use fluids, shape memory alloys, electro-active polymers or stimuli-responsive materials. Other types of elementary components, such as soft smart structures or soft-rigid hybrid systems, are then presented. The second part of this review deals with the manufacturing methods used to build complete soft structures. It includes molding, with possibly reinforcements and inclusions, additive manufacturing, thin-film manufacturing, shape deposition manufacturing, and bonding. The paper conclusions sums up the pros and cons of the presented techniques, and open to developing topics such as design methods for soft robotics and sensing technologies.

Satellite Image Time Series are becoming increasingly available and will continue to do so in the coming years thanks to the launch of space missions which aim at providing a coverage of the Earth every few days with high spatial resolution. In the case of optical imagery, it will be possible to produce land use and cover change maps with detailed nomenclatures. However, due to meteorological phenomena, such as clouds, these time series will become irregular in terms of temporal sampling, and one will need to compare time series with different lengths. In this paper, we present an approach to image time series analysis which is able to deal with irregularly sampled series and which also allows the comparison of pairs of time series where each element of the pair has a different number of samples. We present the dynamic time warping from a theoretical point of view and illustrate its capabilities with two applications to real-time series.

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.

Human brain networks have topological properties in common with many other complex systems, prompting the following question: what aspects of brain network organization are critical for distinctive functional properties of the brain, such as consciousness? To address this question, we used graph theoretical methods to explore brain network topology in resting state functional MRI data acquired from 17 patients with severely impaired consciousness and 20 healthy volunteers. We found that many global network properties were conserved in comatose patients. Specifically, there was no significant abnormality of global efficiency, clustering, small-worldness, modularity, or degree distribution in the patient group. However, in every patient, we found evidence for a radical reorganization of high degree or highly efficient "hub" nodes. Cortical regions that were hubs of healthy brain networks had typically become nonhubs of comatose brain networks and vice versa. These results indicate that global topological properties of complex brain networks may be homeostatically conserved under extremely different clinical conditions and that consciousness likely depends on the anatomical location of hub nodes in human brain networks.

Abstract A study has been carried out using LOWTRAN-7 simulations of the Along-Track Scanning Radiometer (ATSR) data at 11 and 12 μm wavelengths to compare the merits of the multi-angle technique with those of the currently used multi-channel technique (split-window method) to retrieve both sea surface temperature (SST) and land surface temperature (LST). To this end a simple single-channel double-angle viewing model is presented, which relates actual surface temperature to the two brightness temperatures measured from space in the two views of interest (ATSR nadir and forward). Subsequently, statistical retrieval coefficients for the double angle and split-window techniques are derived via a regression analysis of the synthetic measurements. The results show that the double angle technique is capable of producing SSTs with a standard deviation of 0.23 deg K if the satellite data are error free and, furthermore, confirm the advantage of the double-viewing angle technique in comparison with the split-window technique for LST determination in homogeneous surfaces if the emissivity's spectral variation and the emissivity's angular variation, are of the same order of magnitude. Finally we present the preliminary results obtained using the proposed model from ATSR data over a semi-arid region of New South Wales, Australia provided by Prata, and over the Pacific Ocean provided by Barton.

We report the detection of extended Ly emission around individual star-forming galaxies at redshifts z = 3-6 in an ultradeep exposure of the Hubble Deep Field South obtained with MUSE on the ESO-VLT. The data reach a limiting surface brightness (1) of 1 10 -19 erg s -1 cm -2 arcsec -2 in azimuthally averaged radial profiles, an order of magnitude improvement over previous narrowband imaging. Our sample consists of 26 spectroscopically confirmed Ly-emitting, but mostly continuum-faint (m AB > 27) galaxies. In most objects the Ly emission is considerably more extended than the UV continuum light. While five of the faintest galaxies in the sample show no significantly detected Ly haloes, the derived upper limits suggest that this is due to insufficient S/N. Ly haloes therefore appear to be ubiquitous even for low-mass (10 8 -10 9 M ) star-forming galaxies at z > 3. We decompose the Ly emission of each object into a compact component tracing the UV continuum and an extended halo component, and infer sizes and luminosities of the haloes. The extended Ly emission approximately follows an exponential surface brightness distribution with a scale length of a few kpc. While these haloes are thus quite modest in terms of their absolute sizes, they are larger by a factor of 5-15 than the corresponding rest-frame UV continuum sources as seen by HST. They are also much more extended, by a factor 5, than Ly haloes around low-redshift star-forming galaxies. Between 40% and > 90% of the observed Ly flux comes from the extended halo component, with no obvious correlation of this fraction with either the absolute or the relative size of the Ly halo. Our observations provide direct insights into the spatial distribution of at least partly neutral gas residing in the circumgalactic medium of low to intermediate mass galaxies at z > 3.

Abstract During the August 2002 Elbe river flood, different satellite sensor data were acquired, and especially Envisat Advanced Synthetic Aperture Radar (ASAR) data. The ASAR instrument was activated in Alternating Polarization (AP) and Image (IM) modes, providing high resolution datasets. Thus, the comparison with a quasi‐simultaneous ERS‐2 scene enables the evaluation of the contribution of polarization configurations to flood boundary delineation. This study highlights the increased capabilities of the Envisat ASAR instrument in flood mapping, especially the benefit of combining like‐ and cross‐polarizations for rapid mapping within a crisis context. Acknowledgements This work was realized within the framework of the Earth Observation Market Development of the European Space Agency.

We propose in this work a patch-based image labeling method relying on a label propagation framework. Based on image intensity similarities between the input image and an anatomy textbook, an original strategy which does not require any nonrigid registration is presented. Following recent developments in nonlocal image denoising, the similarity between images is represented by a weighted graph computed from an intensity-based distance between patches. Experiments on simulated and in vivo magnetic resonance images show that the proposed method is very successful in providing automated human brain labeling.

This work presents a predictive-control approach to active mechanical filtering of complex, periodic motions of organs induced by respiration or heart beating in robotized surgery. Two different predictive-control schemes are proposed for the compensation of respiratory motions or cardiac motions. For respiratory motions, the periodic property of the disturbance has been included into the input-output model of the controlled system so as to have the robotic system learn and anticipate perturbation motions. A new cost function is proposed for the unconstrained generalized predictive controller (GPC), where reference tracking is decoupled from the rejection of predictable periodic motions. Cardiac motions are more complex, since they are the combination of two periodic nonharmonic components. An adaptive disturbance predictor is proposed which outputs future predicted disturbance values. These predicted values are used to anticipate the disturbance by using the predictive feature of a regular GPC. Experimental results are presented on a laboratory testbed and in vivo on pigs. They demonstrate the effectiveness of the two proposed methods to compensate complex physiological motion.

Achieving high accuracy in orthology inference is essential for many comparative, evolutionary and functional genomic analyses, yet the true evolutionary history of genes is generally unknown and orthologs are used for very different applications across phyla, requiring different precision-recall trade-offs. As a result, it is difficult to assess the performance of orthology inference methods. Here, we present a community effort to establish standards and an automated web-based service to facilitate orthology benchmarking. Using this service, we characterize 15 well-established inference methods and resources on a battery of 20 different benchmarks. Standardized benchmarking provides a way for users to identify the most effective methods for the problem at hand, sets a minimum requirement for new tools and resources, and guides the development of more accurate orthology inference methods.

Abstract The Surface Water and Ocean Topography (SWOT) satellite mission planned for launch in 2020 will map river elevations and inundated area globally for rivers >100 m wide. In advance of this launch, we here evaluated the possibility of estimating discharge in ungauged rivers using synthetic, daily “remote sensing” measurements derived from hydraulic models corrupted with minimal observational errors. Five discharge algorithms were evaluated, as well as the median of the five, for 19 rivers spanning a range of hydraulic and geomorphic conditions. Reliance upon a priori information, and thus applicability to truly ungauged reaches, varied among algorithms: one algorithm employed only global limits on velocity and depth, while the other algorithms relied on globally available prior estimates of discharge. We found at least one algorithm able to estimate instantaneous discharge to within 35% relative root‐mean‐squared error (RRMSE) on 14/16 nonbraided rivers despite out‐of‐bank flows, multichannel planforms, and backwater effects. Moreover, we found RRMSE was often dominated by bias; the median standard deviation of relative residuals across the 16 nonbraided rivers was only 12.5%. SWOT discharge algorithm progress is therefore encouraging, yet future efforts should consider incorporating ancillary data or multialgorithm synergy to improve results.