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As part of the Large Scale Biosphere-Atmosphere Experiment in Amazonia – Smoke, Aerosols, Clouds, Rainfall and Climate (LBA-SMOCC) campaign, detailed surface and airborne aerosol measurements were performed over the Amazon Basin during the dry to wet season from 16 September to 14 November 2002. Optical and physical properties of aerosols at the surface, and in the boundary layer (BL) and free troposphere (FT) during the dry season are discussed in this article. Carbon monoxide (CO) is used as a tracer for biomass burning emissions. At the surface, good correlation among the light scattering coefficient (σs at 545 nm), PM2.5, and CO indicates that biomass burning is the main source of aerosols. Accumulation of haze during some of the large-scale biomass burning events led to high PM2.5 (225 μg m−3), σs (1435 Mm−1), aerosol optical depth at 500 nm (3.0), and CO (3000 ppb). A few rainy episodes reduced the PM2.5, number concentration (CN) and CO concentration by two orders of magnitude. The correlation analysis between σs and aerosol optical thickness shows that most of the optically active aerosols are confined to a layer with a scale height of 1617 m during the burning season. This is confirmed by aircraft profiles. The average mass scattering and absorption efficiencies (545 nm) for small particles (diameter Dp2 g−1, respectively, when relating the aerosol optical properties to PM2.5 aerosols. The observed mean single scattering albedo (ωo at 545 nm) for submicron aerosols at the surface is 0.92±0.02. The light scattering by particles (Δσs/Δ CN) increase 2–10 times from the surface to the FT, most probably due to the combined affects of coagulation and condensation.

Fine aerosol particles were collected separately during daytime and nighttime at a tropical pasture site in Rondônia, Brazil, during the burning and dry-to-wet transition period in 2002. Total carbon (TC) and water-soluble organic carbon (WSOC) were measured by evolved gas analysis (EGA). Based on the thermochemical properties of the fine aerosol, the relative amounts of the volatile and refractory compounds were estimated. It was found that the thermally refractory (possibly higher molecular weight) compounds dominated the TC composition. Their contribution to TC was higher in the daytime than in the nighttime samples. The relative share of WSOC also showed a statistically significant diel variation as did its refractory fraction. Anhydrosugars and phenolic acids were determined by GC-MS and their diel variation was studied. Based on the decrease of their relative concentrations between the biomass burning and transition periods and their distinctly different diel variations, we suggest that the phenolic acids may undergo chemical transformations in the aerosol phase, possibly towards more refractory compounds (humic-like substances, HULIS), as has been suggested previously. These conclusions are supported by the results of the thermally assisted hydrolysis and methylation gas chromatography-mass spectrometry of the same filter samples.

We present here the optical properties of humic-like substances (HULIS) isolated from the fine fraction of biomass-burning aerosol collected in the Amazon basin during the LBA-SMOCC (Large scale Biosphere atmosphere experiment in Amazonia – SMOke aerosols, Clouds, rainfall and Climate) experiment in September 2002. From the isolated HULIS, aerosol particles were generated and their scattering and absorption coefficients measured. The size distribution and mass of the particles were also recorded. The value of the index of refraction was derived from "closure" calculations based on particle size, scattering and absorption measurements. On average, the complex index of refraction at 532 nm of HULIS collected during day and nighttime was 1.65–0.0019i and 1.69–0.0016i, respectively. In addition, the imaginary part of the complex index of refraction was calculated using the measured absorption coefficient of the bulk HULIS. The mass absorption coefficient of the HULIS at 532 nm was found to be quite low (0.031 and 0.029 m2 g−1 for the day and night samples, respectively). However, due to the high absorption Ångström exponent (6–7) of HULIS, the specific absorption increases substantially towards shorter wavelengths (~2–3 m2 g−1 at 300 nm), causing a relatively high (up to 50%) contribution to the light absorption of our Amazonian aerosol at 300 nm. For the relative contribution of HULIS to light absorption in the entire solar spectrum, lower values (6.4–8.6%) are obtained, but those are still not negligible.

As part of the LBA-SMOCC (Large-Scale Biosphere-Atmosphere Experiment in Amazonia - Smoke, Aerosols, Clouds, Rainfall, and Climate) 2002 campaign, we studied the emission of carbon monoxide (CO), carbon dioxide (CO2), and aerosol particles from Amazonian deforestation fires using an instrumented aircraft. Emission ratios for aerosol number (CN) relative to CO (ERCN/CO) fell in the range 14-32 cm-3 ppb-1 in most of the investigated smoke plumes. Particle number emission ratios have to our knowledge not been previously measured in tropical deforestation fires, but our results are in agreement with values usually found from tropical savanna fires. The number of particles emitted per amount biomass burned was found to be dependent on the fire conditions (combustion efficiency). Variability in ERCN/CO between fires was similar to the variability caused by variations in combustion behavior within each individual fire. This was confirmed by observations of CO-to-CO2 emission ratios (ERCO/CO2), which stretched across the same wide range of values for individual fires as for all the fires observed during the sampling campaign, reflecting the fact that flaming and smoldering phases are present simultaneously in deforestation fires. Emission factors (EF) for CO and aerosol particles were computed and a correction was applied for the residual smoldering combustion (RSC) fraction of emissions that are not sampled by the aircraft, which increased the EF by a factor of 1.5-2.1. Vertical transport of smoke from the boundary layer (BL) to the cloud detrainment layer (CDL) and the free troposphere (FT) was found to be a very common phenomenon. We observed a 20% loss in particle number as a result of this vertical transport and subsequent cloud processing, attributable to in-cloud coagulation. This small loss fraction suggests that this mode of transport is very efficient in terms of particle numbers and occurs mostly via non-precipitating clouds. The detrained aerosol particles released in the CDL and FT were larger than in the unprocessed smoke, mostly due to coagulation and secondary growth, and therefore more efficient at scattering radiation and nucleating cloud droplets. This process may have significant atmospheric implications on a regional and larger scale.

We investigated the size distribution, scattering and absorption properties of Amazonian aerosols and the optical thickness of the aerosol layer under the pristine background conditions typical of the wet season, as well as during the biomass-burning-influenced dry season. The measurements were made during two campaigns in 1999 as part of the European contribution to the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA-EUSTACH). In moving from the wet to the dry season, median particle numbers were observed to increase from values comparable to those of the remote marine boundary layer (~400 cm-3) to values more commonly associated with urban smog (~4000 cm-3), due to a massive injection of submicron smoke particles. Aerosol optical depths at 500 nm increased from 0.05 to 0.8 on average, reaching a value of 2 during the dry season. Scattering and absorption coefficients, measured at 550 nm, showed a concomitant increase from average values of 6.8 and 0.4 Mm-1 to values of 91 and 10 Mm-1, respectively, corresponding to an estimated decrease in single-scattering albedo from ca. 0.97 to 0.91. The roughly tenfold increase in many of the measured parameters attests to the dramatic effect that extensive seasonal biomass burning (deforestation, pasture cleaning) is having on the composition and properties of aerosols over Amazonia. The potential exists for these changes to impact on regional and global climate through changes to the extinction of solar radiation as well as the alteration of cloud properties.

Guidelines for management of Melody transcatheter pulmonary valve (TPV) infective endocarditis (IE) are lacking. We aimed to identify factors associated with surgical valve removal versus antimicrobial therapy in Melody TPV IE. Multicenter retrospective analysis of all patients receiving Melody TPV from 10/2010 to 3/2019 was performed to identify cases of IE. Surgical explants versus non-surgical cases were compared. Of the 663 Melody TPV implants, there were 66 cases of IE in 59 patients (59/663, 8.8%). 39/66 (59%) were treated with IV antimicrobials and 27/66(41%) underwent valve explantation. 26/59 patients (44%) were treated medically without explantation or recurrence with average follow-up time of 3.5 years (range:1-9). 32% of Streptococcus cases, 53% of MSSA, and all MRSA cases were explanted. 2 of the 4 deaths had MSSA. CART analysis demonstrated two important parameters associated with explantation: a peak echo gradient ≥ 47 mmHg at IE diagnosis(OR 10.6, p  24 mmHg compared to baseline (OR 6.7, p = 0.01). Rates of explantation varied by institution (27 to 64%). In our multicenter experience, 44% of patients with Melody IE were successfully medically treated without valve explantation or recurrence. The degree of valve stenosis at time of IE diagnosis was strongly associated with explantation. Rates of explantation varied significantly among the institutions.

This paper reports on the second "Throughput" phase of the Tracking MachineLearning (TrackML) challenge on the Codalab platform. As in the first"Accuracy" phase, the participants had to solve a difficult experimentalproblem linked to tracking accurately the trajectory of particles as e.g.created at the Large Hadron Collider (LHC): given O($10^5$) points, theparticipants had to connect them into O($10^4$) individual groups thatrepresent the particle trajectories which are approximated helical. While inthe first phase only the accuracy mattered, the goal of this second phase was acompromise between the accuracy and the speed of inference. Both were measuredon the Codalab platform where the participants had to upload their software.The best three participants had solutions with good accuracy and speed an orderof magnitude faster than the state of the art when the challenge was designed.Although the core algorithms were less diverse than in the first phase, adiversity of techniques have been used and are described in this paper. Theperformance of the algorithms are analysed in depth and lessons derived.

This paper reports the results of an experiment in high energy physics: usingthe power of the "crowd" to solve difficult experimental problems linked totracking accurately the trajectory of particles in the Large Hadron Collider(LHC). This experiment took the form of a machine learning challenge organizedin 2018: the Tracking Machine Learning Challenge (TrackML). Its results werediscussed at the competition session at the Neural Information ProcessingSystems conference (NeurIPS 2018). Given 100.000 points, the participants hadto connect them into about 10.000 arcs of circles, following the trajectory ofparticles issued from very high energy proton collisions. The competition wasdifficult with a dozen front-runners well ahead of a pack. The singlecompetition score is shown to be accurate and effective in selecting the bestalgorithms from the domain point of view. The competition has exposed adiversity of approaches, with various roles for Machine Learning, a number ofwhich are discussed in the document

The success of ground-based, high contrast imaging for the detection of exoplanets in part depends on the ability to differentiate between quasi-static speckles caused by aberrations not corrected by adaptive optics (AO) systems, known as non-common path aberrations (NCPAs), and the planet intensity signal. Frazin (ApJ, 2013) introduced a post-processing algorithm demonstrating that simultaneous millisecond exposures in the science camera and wavefront sensor (WFS) can be used with a statistical inference procedure to determine both the series expanded NCPA coefficients and the planetary signal. We demonstrate, via simulation, that using this algorithm in a closed-loop AO system, real-time estimation and correction of the quasi-static NCPA is possible without separate deformable mirror (DM) probes. Thus the use of this technique allows for the removal of the quasi-static speckles that can be mistaken for planetary signals without the need for new optical hardware, improving the efficiency of ground-based exoplanet detection. In our simulations, we explore the behavior of the Frazin Algorithm (FA) and the dependence of its convergence to an accurate estimate on factors such as Strehl ratio, NCPA strength, and number of algorithm search basis functions. We then apply this knowledge to simulate running the algorithm in real-time in a nearly ideal setting. We then discuss adaptations that can be made to the algorithm to improve its real-time performance, and show their efficacy in simulation. A final simulation tests the technique's resilience against imperfect knowledge of the AO residual phase, motivating an analysis of the feasibility of using this technique in a real closed-loop Extreme AO system such as SCExAO or MagAO-X, in terms of computational complexity and the accuracy of the estimated quasi-static NCPA correction.

High-throughput RNA sequencing (RNA-seq) has recently become the method of choice to define and analyze transcriptomes. For the model moss Physcomitrella patens, although this method has been used to help analyze specific perturbations, no overall reference dataset has yet been established. In the framework of the Gene Atlas project, the Joint Genome Institute selected P. patens as a flagship genome, opening the way to generate the first comprehensive transcriptome dataset for this moss. The first round of sequencing described here is composed of 99 independent libraries spanning 34 different developmental stages and conditions. Upon dataset quality control and processing through read mapping, 28 509 of the 34 361 v3.3 gene models (83%) were detected to be expressed across the samples. Differentially expressed genes (DEGs) were calculated across the dataset to permit perturbation comparisons between conditions. The analysis of the three most distinct and abundant P. patens growth stages - protonema, gametophore and sporophyte - allowed us to define both general transcriptional patterns and stage-specific transcripts. As an example of variation of physico-chemical growth conditions, we detail here the impact of ammonium supplementation under standard growth conditions on the protonemal transcriptome. Finally, the cooperative nature of this project allowed us to analyze inter-laboratory variation, as 13 different laboratories around the world provided samples. We compare differences in the replication of experiments in a single laboratory and between different laboratories.

The success of ground-based, high contrast imaging for the detection of exoplanets in part depends on the ability to differentiate between quasi-static speckles caused by aberrations not corrected by adaptive optics (AO) systems, known as non-common path aberrations (NCPAs), and the planet intensity signal. Frazin (ApJ, 2013) introduced a post-processing algorithm demonstrating that simultaneous millisecond exposures in the science camera and wavefront sensor (WFS) can be used with a statistical inference procedure to determine both the series expanded NCPA coefficients and the planetary signal. We demonstrate, via simulation, that using this algorithm in a closed-loop AO system, real-time estimation and correction of the quasi-static NCPA is possible without separate deformable mirror (DM) probes. Thus the use of this technique allows for the removal of the quasi-static speckles that can be mistaken for planetary signals without the need for new optical hardware, improving the efficiency of ground-based exoplanet detection. In our simulations, we explore the behavior of the Frazin Algorithm (FA) and the dependence of its convergence to an accurate estimate on factors such as Strehl ratio, NCPA strength, and number of algorithm search basis functions. We then apply this knowledge to simulate running the algorithm in real-time in a nearly ideal setting. We then discuss adaptations that can be made to the algorithm to improve its real-time performance, and show their efficacy in simulation. A final simulation tests the technique's resilience against imperfect knowledge of the AO residual phase, motivating an analysis of the feasibility of using this technique in a real closed-loop Extreme AO system such as SCExAO or MagAO-X, in terms of computational complexity and the accuracy of the estimated quasi-static NCPA correction.

Cloud computing has become an attractive and easy-To-use solution for users who want to externalize the run of their applications. However, data centers hosting cloud systems consume enormous amounts of energy. Reducing this consumption becomes an urgent challenge with the rapid growth of cloud utilization. In this paper, we explore a way for energy-Aware HPC cloud users to reduce their footprint on cloud infrastructures by reducing the size of the virtual resources they are asking for. We study the influence of green users on the system energy consumption and compare it with the consumption of more aggressive users in terms of resource utilization. We found that larger resources are more energy demanding even if they are faster in executing the applications. But, reducing too much the resources' size is also not beneficial for the energy consumption. A tradeoff lies in between these two options.

Cloud computing has become an attractive and easy-To-use solution for users who want to externalize the run of their applications. However, data centers hosting cloud systems consume enormous amounts of energy. Reducing this consumption becomes an urgent challenge with the rapid growth of cloud utilization. In this paper, we explore a way for energy-Aware HPC cloud users to reduce their footprint on cloud infrastructures by reducing the size of the virtual resources they are asking for. We study the influence of green users on the system energy consumption and compare it with the consumption of more aggressive users in terms of resource utilization. We found that larger resources are more energy demanding even if they are faster in executing the applications. But, reducing too much the resources' size is also not beneficial for the energy consumption. A tradeoff lies in between these two options.

NMDA receptors, which regulate synaptic strength and are implicated in learning and memory, consist of several subtypes with distinct subunit compositions and functional properties. To enable spatiotemporally defined, rapid and reproducible manipulation of function of specific subtypes, we engineered a set of photoswitchable GluN subunits ('LiGluNs'). Photo-agonism of GluN2A or GluN2B elicits an excitatory drive to hippocampal neurons that can be shaped in time to mimic synaptic activation. Photo-agonism of GluN2A at single dendritic spines evokes spine-specific calcium elevation and expansion, the morphological correlate of LTP. Photo-antagonism of GluN2A alone, or in combination with photo-antagonism of GluN1a, reversibly blocks excitatory synaptic currents, prevents the induction of long-term potentiation and prevents spine expansion. In addition, photo-antagonism in vivo disrupts synaptic pruning of developing retino-tectal projections in larval zebrafish. By providing precise and rapidly reversible optical control of NMDA receptor subtypes, LiGluNs should help unravel the contribution of specific NMDA receptors to synaptic transmission, integration and plasticity.

A new coronagraphic instrument for the Large Binocular Telescope (LBT) has successfully passed the conceptual design phase. SHARK-NIR channel will be installed in one arm of the Large Binocular Telescope and it is designed to use different coronagraphic techniques, both to match as much as possible the different requirements of the different science cases and to explore the capabilities of such techniques for the next-generation of ELTs. By exploiting the combination with SHARK-VIS channel mounted at the other LBT arm, the instrument will offer simultaneous coronagraphic observations at different wavelengths, characterized by high contrast, even for relatively faint targets. This will be achievable thanks to the very efficient Adaptive Optics (AO) systems already operating at LBT (First Light AO, FLAO). Furthermore, the latter will be soon upgraded with new detectors, promising even better performance in terms of limiting magnitude. In this paper we present the status of the SHARK-NIR channel design.