Humid Evolution of Haze in the Atmosphere of Super-Earths in the Habitable Zone

Photochemical hazes are expected to form and significantly contribute to the chemical and radiative balance of exoplanets with relatively moderate temperatures, possibly in the habitable zone of their host star. In the presence of humidity, haze particles might thus serve as cloud condensation nuclei and trigger the formation of water droplets. In the present work, we are interested in the chemical impact of such a close interaction between photochemical hazes and humidity on the organic content composing the hazes and on the capacity to generate organic molecules with high prebiotic potential. For this purpose, we explore experimentally the sweet spot by combining N-dominated super-Earth exoplanets in agreement with Titan's rich organic photochemistry and humid conditions expected for exoplanets in habitable zones. A logarithmic increase with time is observed for the relative abundance of oxygenated species, with O-containing molecules dominating after 1 month only. The rapidity of the process suggests that the humid evolution of N-rich organic haze provides an efficient source of molecules with high prebiotic potential

Genome Expression Dynamics Reveal the Parasitism Regulatory Landscape of the Root-Knot Nematode Meloidogyne incognita and a Promoter Motif Associated with Effector Genes.

Root-knot nematodes (genus Meloidogyne) are the major contributor to crop losses caused by nematodes. These nematodes secrete effector proteins into the plant, derived from two sets of pharyngeal gland cells, to manipulate host physiology and immunity. Successful completion of the life cycle, involving successive molts from egg to adult, covers morphologically and functionally distinct stages and will require precise control of gene expression, including effector genes. The details of how root-knot nematodes regulate transcription remain sparse. Here, we report a life stage-specific transcriptome of Meloidogyne incognita. Combined with an available annotated genome, we explore the spatio-temporal regulation of gene expression. We reveal gene expression clusters and predicted functions that accompany the major developmental transitions. Focusing on effectors, we identify a putative cis-regulatory motif associated with expression in the dorsal glands, providing an insight into effector regulation. We combine the presence of this motif with several other criteria to predict a novel set of putative dorsal gland effectors. Finally, we show this motif, and thereby its utility, is broadly conserved across the Meloidogyne genus, and we name it Mel-DOG. Taken together, we provide the first genome-wide analysis of spatio-temporal gene expression in a root-knot nematode and identify a new set of candidate effector genes that will guide future functional analyses

Deciphering the complex role of thrombospondin-1 in glioblastoma development

We undertook a systematic study focused on the matricellular protein Thrombospondin-1 (THBS1) to uncover molecular mechanisms underlying the role of THBS1 in glioblastoma (GBM) development. THBS1 was found to be increased with glioma grades. Mechanistically, we show that the TGFβ canonical pathway transcriptionally regulates THBS1, through SMAD3 binding to the THBS1 gene promoter. THBS1 silencing inhibits tumour cell invasion and growth, alone and in combination with anti-angiogenic therapy. Specific inhibition of the THBS1/CD47 interaction using an antagonist peptide decreases cell invasion. This is confirmed by CD47 knock-down experiments. RNA sequencing of patient-derived xenograft tissue from laser capture micro-dissected peripheral and central tumour areas demonstrates that THBS1 is one of the gene with the highest connectivity at the tumour borders. All in all, these data show that TGFβ1 induces THBS1 expression via Smad3 which contributes to the invasive behaviour during GBM expansion. Furthermore, tumour cell-bound CD47 is implicated in this process

The influence of molecular weight distribution of industrial polystyrene on its melt extensional and ultimate properties

We analyze the linear viscoelastic behavior and the strainrate dependence of nonlinear viscoelastic as well as the ultimate extensional properties of industrially relevant linear polystyrene mixtures (PS). The studied materials comprise different miscible binary mixtures of a well entangled matrix and unentangled diluent resulting in bimodal molar mass distribution (MWD). We also analyze the effect of the diluent weight average molar mass (Mw) by comparison with a mixture having broad but monomodal MWD. We show that the dilution effect on linear rheological properties is in agreement with the theoretical value of unity for the dilution exponent. We further show that the processing window, expressed as the ability of the material to withstand a given load without loss of homogeneity during elongation or ultimate loss of cohesion, is affected differently depending on the diluent Mw and concentration. Finally, we conclude that the existence of strain hardening is not sufficient for complete characterization of extension dominated operations. Our results demonstrate that significant enhancement of strain hardening achieved by adding small-Mw diluents is often accompanied by trade-off with respect to failure behavior of these mixtures

Classification of MODIS Time Series with Dense Bag-of-Temporal-SIFT-Words: Application to Cropland Mapping in the Brazilian Amazon

Mapping croplands is a challenging problem in a context of climate change and evolving agricultural calendars. Classification based on MODIS vegetation index time series is performed in order to map crop types in the Brazilian state of Mato Grosso. We used the recently developed Dense Bag-of-Temporal-SIFT-Words algorithm, which is able to capture temporal locality of the data. It allows the accurate detection of around 70% of the agricultural areas. It leads to better classification rates than a baseline algorithm, discriminating more accurately classes with similar profiles

Nonlinear Time-Series Adaptation for Land Cover Classification

International audienceAutomatic land cover classification from satellite image time series is of paramount relevance to assess vegetation and crop status, with important implications in agriculture, biofuels and food. However, due to the high cost and human resources needed to characterize and classify land cover through field campaigns, a recurrent limiting factor is the lack of available labeled data. On top of this, the bio-geo-physical variables exhibit particular temporal structures that need to be exploited. Land cover classification based on image time series is very complex because of the data manifold distortions through time. We propose the use of the kernel manifold alignment (KEMA) method for domain adaptation of remote sensing time series before classification. KEMA is nonlinear, semi-supervised, and reduces to solve a simple generalized eigenproblem. We give empirical evidence of performance through classification of biophysical (LAI, fAPAR, FVC, NDVI) time series at a global scale

Bag-of-Temporal-SIFT-Words for Time Series Classification

Abstract. Time series classification is an application of particular interest with the increase of data to monitor. Classical techniques for time series classification rely on point-to-point distances. Recently, Bag-ofWords approaches have been used in this context. Words are quantized versions of simple features extracted from sliding windows. The SIFT framework has proved efficient for image classification. In this paper, we design a time series classification scheme that builds on the SIFT framework adapted to time series to feed a Bag-of-Words. Experimental results show competitive performance with respect to classical techniques

Efficient Temporal Kernels between Feature Sets for Time Series Classification

International audienceIn the time-series classification context, the majority of the most accurate core methods are based on the Bag-of-Words framework, in which sets of local features are first extracted from time series. A dictionary of words is then learned and each time series is finally represented by a histogram of word occurrences. This representation induces a loss of information due to the quantization of features into words as all the time series are represented using the same fixed dictionary. In order to overcome this issue, we introduce in this paper a kernel operating directly on sets of features. Then, we extend it to a time-compliant kernel that allows one to take into account the temporal information. We apply this kernel in the time series classification context. Proposed kernel has a quadratic complexity with the size of input feature sets, which is problematic when dealing with long time series. However, we show that kernel approximation techniques can be used to define a good trade-off between accuracy and complexity. We experimentally demonstrate that the proposed kernel can significantly improve the performance of time series classification algorithms based on Bag-of-Words

Detecting Movement Changes in Children with Hemiparesis after Upper Limb Therapies: A Responsiveness Analysis of a 3D Bimanual Protocol

The “Be an Airplane Pilot” (BE API) protocol was developed to evaluate upper limb (UL) kinematics in children with unilateral cerebral palsy (uCP) during bimanual tasks. The aim of this study was to investigate the responsiveness of this protocol to changes in kinematics and movement quality after UL therapies, using individual and group analyses, and to analyse the relationships between kinematic and functional changes in these children. Twenty children with uCP (5–15 years old) either participated in bimanual intensive therapy or received UL botulinum toxin injections. All the children performed the BE API protocol and functional assessments (Assisting Hand Assessment [AHA]) before and after the interventions. The individual analyses found kinematic changes in 100% of the children after therapy. The group analysis found significantly higher trunk and shoulder deviations after the intensive therapy. No significant changes were found for smoothness or trajectory straightness. The changes in the kinematic deviations were moderately correlated with the changes in the AHA scores. This study confirmed the responsiveness of the BE API protocol to change after therapy; therefore, the protocol is now fully validated and can be implemented in clinical practice. Its use should help in the accurate identification of impairments so that individualized treatments can be proposed

Metalloenzyme‐Mediated Thiol‐Yne Addition Towards Photoisomerizable Fluorescent Dyes

International audienceProteins are able to irreversibly assemble biologically active ligands from building blocks bearing complementary reactive functions due their spatial proximity, through a kinetic target-guided synthetic process (also named in situ click chemistry). Although linkages thus formed are mostly passive, some of them have shown to significantly contribute to the protein binding through for instance hydrogen bonding and stacking interactions. Biocompatible reactions and click chemistry are a formidable source of inspiration for developing such new protein-directed ligations. This study reports a proximity-induced thiol-yne synthesis of carbonic anhydrase inhibitors. Not only this example widens the arsenal of kinetic target-guided synthesis (KTGS) eligible reactions, but the obtained product displayed unsuspected photophysical properties. The corresponding vinyl sulfide linkage conjugated to a coumarin core proved to be engaged in a monodirectional Z to E photoisomerization process. Further investigations guided by theoretical calculations showed that fine-tuning of the nature of the substituents on the coumarin moiety allows to obtain a bidirectional photochemical process, thus discovering a new photoswitching moiety, displaying moreover fluorescence properties. Due to the spectral tunability of coumarin derivatives, this work should open new opportunities for the design of vinyl sulfide-based photoswitch systems with modular photophysical properties