What explains aid project success in post-conflict situations ?

This paper investigates the effectiveness of post-conflict aid at the project level and aims to identify post-conflict situations as a window of opportunity for project success. The Independent Evaluation Group dataset provides extensive information on the characteristics of World Bank projects including an independent rating of their success, supervision and evaluationquality. The paper estimates the probability of success of aid projects depending on the characteristics of the intervention and looks for possible special patterns in post civil war situations. The results suggest that the probability of success of World Bank projects increases as peace lasts. Supervision appears to be a crucial determinant of the success of projects, especially during the first years of peace. Although the results of the sector-level analysis need to be taken with caution, the authors find that projects in the transport sector and in the urban development sector appear more successful in post-conflict environments. On the contrary, education projects seem less successful and therefore need to be highly supervised. Projects in the private sector should wait as they face a higher probability of failure in the first years of peace.Post Conflict Reconstruction,Post Conflict Reintegration,Social Conflict and Violence,Peace&Peacekeeping,Housing&Human Habitats

Louignac – La Reynie

code Insee commune : 19120Lien Atlas (MCC) :http://atlas.patrimoines.culture.fr/atlas/trunk/index.php?ap_theme=DOM_2.01.02&ap_bbox=1.228;45.188;1.311;45.243 En 1992, le site de la Reynie, sur la commune de Louignac, a été signalé au SRA Limousin par J-L. Couchard. Le potentiel archéologique du site a été rapidement décelé et une première étude a été réalisée (Couchard, 2004). En 2013, la Communauté de Communes du Pays de l’Yssandonnais ayant acquis la parcelle, une nouvelle étude est envisag..

Robust classification of spectroscopic data in agri-food: First analysis on the stability of results

We investigate here the stability of the obtained results of a variable selection method recently introduced in the literature, and embedded into a modelbased classification framework. It is applied to chemometric data, with the purpose of selecting a few wavenumbers (of the order of tens) among the thousands measured ones, to build a (robust) decision rule for classification. The robust nature of the method safeguards it from potential label noise and outliers, which are particularly dangerous in the field of food-authenticity studies. As a by-product of the learning process, samples are grouped into similar classes, and anomalous samples are also singled out. Our first results show that there is some variability around a common pattern in the obtained selection

Virophages and retrotransposons colonize the genomes of a heterotrophic flagellate

Virophages can parasitize giant DNA viruses and may provide adaptive anti-giant virus defense in unicellular eukaryotes. Under laboratory conditions, the virophage mavirus integrates into the nuclear genome of the marine flagellate Cafeteria burkhardae and reactivates upon superinfection with the giant virus CroV. In natural systems, however, the prevalence and diversity of host-virophage associations has not been systematically explored. Here, we report dozens of integrated virophages in four globally sampled C. burkhardae strains that constitute up to 2% of their host genomes. These endogenous mavirus-like elements (EMALEs) separated into eight types based on GC-content, nucleotide similarity, and coding potential and carried diverse promoter motifs implicating interactions with different giant viruses. Between host strains, some EMALE insertion loci were conserved indicating ancient integration events, whereas the majority of insertion sites were unique to a given host strain suggesting that EMALEs are active and mobile. Furthermore, we uncovered a unique association between EMALEs and a group of tyrosine recombinase retrotransposons, revealing yet another layer of parasitism in this nested microbial system. Our findings show that virophages are widespread and dynamic in wild Cafeteria populations, supporting their potential role in antiviral defense in protists

Exploratory analysis of hyperspectral imaging data

Characterizing sample composition and visualizing the distribution of its chemical compounds is a prominent topic in various research and applied fields. Integrating spatial and spectral information, hyperspectral imaging (HSI) plays a pivotal role in this pursuit. While self-modelling curve resolution techniques, like multivariate curve resolution - alternating least squares (MCR-ALS), and clustering methods, such as K-means, are widely used for HSI data analysis, their effectiveness in complex scenarios, where the structure of the data deviates from the models’ assumptions, deserves further investigation. The choice of a data analysis method is most often driven by research question at hand and prior knowledge of the sample. However, overlooking the structure of the investigated data, i.e. linearity, geometry, homogeneity, might lead to erroneous or biased results. Here, we propose an exploratory data analysis approach, based on the geometry of the data points cloud, to investigate the structure of HSI datasets and extract their main characteristics, providing insight into the results obtained by the above-mentioned methods. We employ the principle of essential information to extract archetype (most linearly dissimilar) spectra and archetype single-wavelength images. These spectra and images are then discussed and contrasted with MCR-ALS and K-means clustering results. Two datasets with varying characteristics and complexities were investigated: a powder mixture analyzed with Raman spectroscopy and a mineral sample analyzed with Laser Induced Breakdown Spectroscopy (LIBS). We show that the proposed approach enables to summarize the main characteristics of hyperspectral imaging data and provides a more accurate understanding of the results obtained by traditional data modelling methods, driving the choice of the most suitable one

Metal-induced malformations in early Palaeozoic plankton are harbingers of mass extinction

Glacial episodes have been linked to Ordovician–Silurian extinction events, but cooling itself may not be solely responsible for these extinctions. Teratological (malformed) assemblages of fossil plankton that correlate precisely with the extinction events can help identify alternate drivers of extinction. Here we show that metal poisoning may have caused these aberrant morphologies during a late Silurian (Pridoli) event. Malformations coincide with a dramatic increase of metals (Fe, Mo, Pb, Mn and As) in the fossils and their host rocks. Metallic toxins are known to cause a teratological response in modern organisms, which is now routinely used as a proxy to assess oceanic metal contamination. Similarly, our study identifies metal-induced teratology as a deep-time, palaeobiological monitor of palaeo-ocean chemistry. The redox-sensitive character of enriched metals supports emerging ‘oceanic anoxic event’ models. Our data suggest that spreading anoxia and redox cycling of harmful metals was a contributing kill mechanism during these devastating Ordovician–Silurian palaeobiological events