Drug Discovery for Schistosomiasis: Hit and Lead Compounds Identified in a Library of Known Drugs by Medium-Throughput Phenotypic Screening

The flatworm disease schistosomiasis infects over 200 million people with just one drug (praziquantel) available—a concern should drug resistance develop. Present drug discovery approaches for schistosomiasis are slow and not conducive to automation in a high-throughput format. Therefore, we designed a three-component screen workflow that positions the larval (schistosomulum) stage of S. mansoni at its apex followed by screens of adults in culture and, finally, efficacy tests in infected mice. Schistosomula are small enough and available in sufficient numbers to interface with automated liquid handling systems and prosecute thousands of compounds in short time frames. We inaugurated the workflow with a 2,160 compound library that includes known drugs in order to cost effectively ‘re-position’ drugs as new therapies for schistosomiasis and/or identify compounds that could be modified to that end. We identify a variety of ‘hit’ compounds (antibiotics, psychoactives, antiparasitics, etc.) that produce behavioral responses (phenotypes) in schistosomula and adults. Tests in infected mice of the most promising hits identified a number of ‘leads,’ one of which compares reasonably well with praziquantel in killing worms, decreasing egg production by the parasite, and ameliorating disease pathology. Efforts continue to more fully automate the workflow. All screen data are posted online as a drug discovery resource

Evaluation and mangement of fungal risk in cystic fibrosis: first results of a national French study

Date du colloque&nbsp;: 06/2009</p

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Etude des défauts profonds dans l'arséniure de gallium implantés en oxygène et co-implantés en silicium par la méthode FTDLTS

We describe the setting up of our isotherm FTDLTS (Fourier Transform Deep Level Transient Spectroscopy) method which has a better time constant resolution than that of classic DLTS methods. This method has been used to characterize deep levels in O-implanted and (O + Si) co-implanted GaAs. Several deep centers with closely-spaced levels of the U band and the EL2 family levels have been characterized. Discret levels have been obtained. The signatures of these defects are closed, in the first case, to that of the EL3 center, in the second case, to that of the EL0 and EL2 centers. The (E$_{\rm a}$= 0.56 eV) level seems to be the Ga-O-Ga complex. The (O + Si) co-implantation favors the ED2 defect formation in 650 $^{\circ}$C and 900 $^{\circ}$C annealed samples.On rappelle d'abord la mise en oeuvre d'une nouvelle méthode isotherme appelée FTDLTS (Fourier Transform Deep Level Transient Spectroscopy) dont le pouvoir séparateur en constante de temps est nettement supérieur à celui des méthodes DLTS classiques. On l'applique à la caractérisation des défauts profonds, dans le domaine de températures 200-450 K, des échantillons de GaAs implantés en oxygène et en silicium. Plusieurs défauts à niveau d'énergie discret appartenant à la bande U et dont les signatures, toutes proches de celle de EL3, sont voisines ont été mis en évidence. L'un de ces défauts (E$_{\rm a}$ = 0,56 eV) semble être le défaut complexe Ga-O-Ga. Trois défauts EDI, ED2 et ED3 de la famille EL2 ont été également mis en évidence ; deux d'entre eux, ED2 et ED3 ont une signature proche de celle de EL0 et EL2 respectivement. La co-implantation de silicium avec l'oxygène favorise, dans nos échantillons recuits à 650 $^{\circ}$C et à 900 $^{\circ}$C, la formation de ED2 par rapport à celle de ED1