Synthesis and X-ray crystallography of (1R,3aR,7aR)-1-((S)-1-((2R,5S)-5-(3-hydroxypentan-3-yl)tetrahydrofuran-2-yl)ethyl)-7a-methyloctahydro-4H-inden-4-one

The crystal of the title compound, C21H36O3 contains an oxolane ring, and six defined stereocenters which are unambigously established by the crystallography study. A three dimensional supramolecular architecture is ensured by hydrogen bonds from the hydroxy group which is both engaged in inter (O-H···O2) and intramolecular C-H···O-H) hydrogen bonds. Weak C-H···O=C hydrogen bonds are involved also into the consolidation of the network

Egocentric image captioning for privacy-preserved passive dietary intake monitoring

Camera-based passive dietary intake monitoring is able to continuously capture the eating episodes of a subject, recording rich visual information, such as the type and volume of food being consumed, as well as the eating behaviors of the subject. However, there currently is no method that is able to incorporate these visual clues and provide a comprehensive context of dietary intake from passive recording (e.g., is the subject sharing food with others, what food the subject is eating, and how much food is left in the bowl). On the other hand, privacy is a major concern while egocentric wearable cameras are used for capturing. In this article, we propose a privacy-preserved secure solution (i.e., egocentric image captioning) for dietary assessment with passive monitoring, which unifies food recognition, volume estimation, and scene understanding. By converting images into rich text descriptions, nutritionists can assess individual dietary intake based on the captions instead of the original images, reducing the risk of privacy leakage from images. To this end, an egocentric dietary image captioning dataset has been built, which consists of in-the-wild images captured by head-worn and chest-worn cameras in field studies in Ghana. A novel transformer-based architecture is designed to caption egocentric dietary images. Comprehensive experiments have been conducted to evaluate the effectiveness and to justify the design of the proposed architecture for egocentric dietary image captioning. To the best of our knowledge, this is the first work that applies image captioning for dietary intake assessment in real-life settings

Egocentric Image Captioning for Privacy-Preserved Passive Dietary Intake Monitoring

Camera-based passive dietary intake monitoring is able to continuously capture the eating episodes of a subject, recording rich visual information, such as the type and volume of food being consumed, as well as the eating behaviours of the subject. However, there currently is no method that is able to incorporate these visual clues and provide a comprehensive context of dietary intake from passive recording (e.g., is the subject sharing food with others, what food the subject is eating, and how much food is left in the bowl). On the other hand, privacy is a major concern while egocentric wearable cameras are used for capturing. In this paper, we propose a privacy-preserved secure solution (i.e., egocentric image captioning) for dietary assessment with passive monitoring, which unifies food recognition, volume estimation, and scene understanding. By converting images into rich text descriptions, nutritionists can assess individual dietary intake based on the captions instead of the original images, reducing the risk of privacy leakage from images. To this end, an egocentric dietary image captioning dataset has been built, which consists of in-the-wild images captured by head-worn and chest-worn cameras in field studies in Ghana. A novel transformer-based architecture is designed to caption egocentric dietary images. Comprehensive experiments have been conducted to evaluate the effectiveness and to justify the design of the proposed architecture for egocentric dietary image captioning. To the best of our knowledge, this is the first work that applies image captioning to dietary intake assessment in real life settings

Effet de l’association du haricot mungo sur le rendement du mil dans le Bassin arachidier, Sénégal

Ce numéro est constitué d’articles issus de la seconde édition des Rencontres Francophones sur les Légumineuses (RFL2) qui s’est tenu à Toulouse (France) les 17 et 18 octobre 2018.Agricultural production in Senegal is constrained by rainfall variability, persistent droughts, and low soilfertility. These constraints have decreased pearl millet yield in Senegal. Mungbean, a short-duration andrelatively drought-tolerant grain legume, is capable of improving soil fertility and increasing cropproduction. To investigate the potential of mungbean for increasing millet yield through intercropping,field experiments were conducted during the 2017 growing season in Bambey (Senegal). Intercroppingtreatments (sole millet (T1), sole mungbean (T2), and 23% (T3), 43% (T4), 47% (T5), 62% (T6), 125% (T7)et 164% (T8) of mungbean in sole millet) were laid out in a randomized complete block design andreplicated four times. Studied parameters included millet and mungbean yield parameters and combinedyields, normalized difference vegetation index (NDVI), canopy cover, and land equivalent ratio (LER).Intercropping (millet + mungbean) did not significantly increase millet biomass or grain yield comparedto millet alone. However, grain yield, number of pods and seeds, and plant height of mungbean plantswere significantly higher in T7 and T8 compared to other treatments. Combined yields were alsosignificantly (P ≤ 0.05) higher (up to 56%) under millet – mungbean intercropping. Similarly,intercropping significantly (P ≤ 0.05) increased LER and canopy cover (62%) over millet alone but notNDVI values. Results from this first-year suggest that intercropping with mungbean could sustain milletproduction through diversification of agricultural systems.La production agricole au Sénégal est limitée par la variabilité des précipitations, les sécheressespersistantes et la faible fertilité des sols. Ces contraintes ont contribué à la baisse des rendements dumil au Sénégal. Le haricot mungo, une légumineuse à graines à cycle court et relativement tolérante àla sécheresse, est capable d'améliorer la fertilité du sol et d’augmenter la production agricole. Pourétudier le potentiel du haricot mungo à augmenter le rendement du mil, des essais ont été menéspendant la saison culturale de 2017 à Bambey (Diourbel). Les systèmes d’association (mil seul (T1),haricot mungo seul (T2) et 23% (T3), 43% (T4), 47% (T5), 62% (T6), 125% (T7) et 164% (T8) de haricotmungo en association avec le mil) ont été disposés en blocs complets randomisés et répliqués quatrefois. Les données collectées comprennent les paramètres de rendement du mil et du haricot mungo etle rendement total en grains, l'indice de végétation par différence normalisée (NDVI), le taux decouverture du sol, et le land equivalent ratio (LER). L’association du mil avec le haricot mungo n'a passignificativement augmenté la biomasse et le rendement en grains du mil par rapport au mil seul.Cependant, le rendement en grains, le nombre de gousses et de graines, et la hauteur des plants deharicot mungo ont été significativement plus élevés pour les traitements T7 et T8 par rapport aux autrestraitements. Le rendement total en grains était aussi significativement (P ≤ 0,05) plus élevé (jusqu'à56%) en association. De même, l’association des cultures a entrainé des différences significatives pourle LER et le taux de couverture du sol (62%) mais pas pour les valeurs de NDVI. Les résultats de cettepremière année suggèrent que l'association avec le haricot mungo pourrait soutenir la production de milgrâce à la diversification des systèmes agricoles

Unexpected Rift Valley Fever Outbreak, Northern Mauritania

During September–October 2010, an unprecedented outbreak of Rift Valley fever was reported in the northern Sahelian region of Mauritania after exceptionally heavy rainfall. Camels probably played a central role in the local amplification of the virus. We describe the main clinical signs (hemorrhagic fever, icterus, and nervous symptoms) observed during the outbreak

Food/Non-Food Classification of Real-Life Egocentric Images in Low- and Middle-Income Countries Based on Image Tagging Features.

Malnutrition, including both undernutrition and obesity, is a significant problem in low- and middle-income countries (LMICs). In order to study malnutrition and develop effective intervention strategies, it is crucial to evaluate nutritional status in LMICs at the individual, household, and community levels. In a multinational research project supported by the Bill & Melinda Gates Foundation, we have been using a wearable technology to conduct objective dietary assessment in sub-Saharan Africa. Our assessment includes multiple diet-related activities in urban and rural families, including food sources (e.g., shopping, harvesting, and gathering), preservation/storage, preparation, cooking, and consumption (e.g., portion size and nutrition analysis). Our wearable device ("eButton" worn on the chest) acquires real-life images automatically during wake hours at preset time intervals. The recorded images, in amounts of tens of thousands per day, are post-processed to obtain the information of interest. Although we expect future Artificial Intelligence (AI) technology to extract the information automatically, at present we utilize AI to separate the acquired images into two binary classes: images with (Class 1) and without (Class 0) edible items. As a result, researchers need only to study Class-1 images, reducing their workload significantly. In this paper, we present a composite machine learning method to perform this classification, meeting the specific challenges of high complexity and diversity in the real-world LMIC data. Our method consists of a deep neural network (DNN) and a shallow learning network (SLN) connected by a novel probabilistic network interface layer. After presenting the details of our method, an image dataset acquired from Ghana is utilized to train and evaluate the machine learning system. Our comparative experiment indicates that the new composite method performs better than the conventional deep learning method assessed by integrated measures of sensitivity, specificity, and burden index, as indicated by the Receiver Operating Characteristic (ROC) curve