Agronomical and environmental performances of organic farming in the Seine watershed, France

This work suggests that Soil Surface Balance is a robust indicator to compare the performances of organic agriculture with those of conventional agriculture, even strictly following the rules of rational and optimised application of fertilisers. The results of long term nitrogen budget calculation brought us to seriously reconsider the relevance of the need to increase crop yields, and more broadly to reconsider cropping patterns and production systems. In terms of policy levers for mitigating nitrogen contamination of water resources, only the shift to organic farming provides a possible way to reconcile agricultural production and water quality. Further, this view points out the need for specific measures to encourage more mixed farming approach to organic farming on a territorial basis, thus reversing a 50 years trend to regional specialization into either crop or livestock farming

Improving music genre classification using automatically induced harmony rules

We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 × 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates

La notion d'involution dans le Brouillon Project de Girard Desargues

Nous tentons dans cet article de proposer une th\`ese coh\'erente concernant la formation de la notion d'involution dans le Brouillon Project de Desargues. Pour cela, nous donnons une analyse d\'etaill\'ee des dix premi\`eres pages dudit Brouillon, comprenant les d\'eveloppements de cas particuliers qui aident \`a comprendre l'intention de Desargues. Nous mettons cette analyse en regard de la lecture qu'en fait Jean de Beaugrand et que l'on trouve dans les Advis Charitables. The purpose of this article is to propose a coherent thesis on how Girard Desargues arrived at the notion of involution in his Brouillon Project of 1639. To this purpose we give a detailed analysis of the ten first pages of the Brouillon, including developments of particular cases which help to understand the goal of Desargues, as well as to clarify the links between the notion of involution and that of harmonic division. We compare the conclusions of this analysis with the very critical reading Jean de Beaugrand made of the Brouillon Project in the Advis Charitables of 1640.Comment: 50 pages, in French, submitted article, 22 figure

Visualizing Archival Collections

The proposed project will build on the research and prototype development work done in the creation of ArchivesZ. This project has two goals. The first is to design and evaluate interfaces for visualizing aggregated data harvested from EAD encoded archival finding aids. The second is to analyze and develop recommendations for handling issues related to the lack of subject term standardization in the description of archival collections. This will lay the foundation for future work to develop a tool for use in visualizing archival collections from institutions using EAD to encode their finding aids. A tool for visualizing this broad range of archival collections would support both experienced and amateur researchers in their efforts to locate new materials. Any set of archival collections could be evaluated an an aggregated manner. Visualization tools can support discovery of relationships among time periods and subjects that otherwise may never be detected

Sirtuins and proteolytic systems: implications for pathogenesis of synucleinopathies

Insoluble and fibrillar forms of a-synuclein are the major components of Lewy bodies, a hallmark of several sporadic and inherited neurodegenerative diseases known as synucleinopathies. a-Synuclein is a natural unfolded and aggregation-prone protein that can be degraded by the ubiquitin-proteasomal system and the lysosomal degradation pathways. a-Synuclein is a target of the main cellular proteolytic systems, but it is also able to alter their function further, contributing to the progression of neurodegeneration. Aging, a major risk for synucleinopathies, is associated with a decrease activity of the proteolytic systems, further aggravating this toxic looping cycle. Here, the current literature on the basic aspects of the routes for a-synuclein clearance, as well as the consequences of the proteolytic systems collapse, will be discussed. Finally, particular focus will be given to the sirtuins's role on proteostasis regulation, since their modulation emerged as a promising therapeutic strategy to rescue cells from a-synuclein toxicity. The controversial reports on the potential role of sirtuins in the degradation of a-synuclein will be discussed. Connection between sirtuins and proteolytic systems is definitely worth of further studies to increase the knowledge that will allow its proper exploration as new avenue to fight synucleinopathies.Belem Sampaio-Marques is supported by the fellowship SFRH/BPD/90533/2012 funded by the Fundacao para a Ciencia e Tecnologia (FCT, Portugal). The authors apologize for not citing the pioneering work of many colleagues, as this was not intended as an exhaustive review

Improving music genre classification using automatically induced harmony rules

We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 × 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates