Caractérisation de filtrabilité par la filtration centrifuge – CEFU

Il existe une grande diversité des techniques pour la mesure de la filtrabilité de suspensions. Cependant, les techniques existantes ne sont pas adaptées à la caractérisation rapide d’un grand nombre de petits échantillons (surtout, des échantillons liquides).Depuis quelques années l’Université Technologique de Compiègne et la société LUM GmbH travaillent sur le développement de la centrifugation analytique pour la caractérisation accélérée de la filtrabilité des suspensions et dispersions. La méthode consiste à réaliser des essais de filtration à l’aide de la centrifugation analytique puis à analyser les courbes de la filtration centrifuge obtenues pour en extraire des propriétés des solutions.La comparaison simple des courbes de filtration obtenues permet de classifier les échantillons selon leur filtrabilité. De plus, l’analyse des courbes de filtration permet la caractérisation quantitative de la filtrabilité : détermination de la résistance de la membrane colmatée, de la résistance spécifique du gâteau et des propriétés de réversibilité de la compression du gâteau. La méthode d’analyse des données dépend de la nature des échantillons : deux méthodes adaptées pour des suspensions concentrées et des solutions des colloïdes sont validées actuellement .Le congrès MemPro6 sera l’occasion de présenter les résultats les plus récents sur l’ultra- et la microfiltration centrifuge pour la caractérisation de la filtrabilité des solution

Cooperative control of striated muscle mass and metabolism by MuRF1 and MuRF2

The muscle-specific RING finger proteins MuRF1 and MuRF2 have been proposed to regulate protein degradation and gene expression in muscle tissues. We have tested the in vivo roles of MuRF1 and MuRF2 for muscle metabolism by using knockout (KO) mouse models. Single MuRF1 and MuRF2 KO mice are healthy and have normal muscles. Double knockout (dKO) mice obtained by the inactivation of all four MuRF1 and MuRF2 alleles developed extreme cardiac and milder skeletal muscle hypertrophy. Muscle hypertrophy in dKO mice was maintained throughout the murine life span and was associated with chronically activated muscle protein synthesis. During ageing (months 4–18), skeletal muscle mass remained stable, whereas body fat content did not increase in dKO mice as compared with wild-type controls. Other catabolic factors such as MAFbox/atrogin1 were expressed at normal levels and did not respond to or prevent muscle hypertrophy in dKO mice. Thus, combined inhibition of MuRF1/MuRF2 could provide a potent strategy to stimulate striated muscles anabolically and to protect muscles from sarcopenia during ageing

Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts

In our article Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts accepted for publication in User Modeling and User-Adapted Interaction (UMUAI) we examined a classification-based approach to analyze what makes a recommendation successful. In the process we generated over 95 features for each single recommendation action in our data set provided by the online fashion retailer Zalando. Due to space issues we could only explain some of the most relevant features in the article itself. As an addition, the following table lists all investigated features in detail. Furthermore, in our article we reported the top ten feature weights regarding the label prediction calculated by the methods Gain ratio and Chi-squared to highlight the most important success signals. Here, we additionally reveal the weights for all features and also include the Information gain ratio and the Gini index

Item Familiarity Effects in User-Centric Evaluations of Recommender Systems

ABSTRACT Laboratory studies are a common way of comparing recommendation approaches with respect to different quality dimensions that might be relevant for real users. One typical experimental setup is to first present the participants with recommendation lists that were created with different algorithms and then ask the participants to assess these recommendations individually or to compare two item lists. The cognitive effort required by the participants for the evaluation of item recommendations in such settings depends on whether or not they already know the (features of the) recommended items. Furthermore, lists containing popular and broadly known items are correspondingly easier to evaluate. In this paper we report the results of a user study in which participants recruited on a crowdsourcing platform assessed system-provided recommendations in a between-subjects experimental design. The results surprisingly showed that users found non-personalized recommendations of popular items the best match for their preferences. An analysis revealed a measurable correlation between item familiarity and user acceptance. Overall, the observations indicate that item familiarity can be a potential confounding factor in such studies and should be considered in experimental designs

Item Familiarity Effects in User-Centric Evaluations of Recommender Systems

ABSTRACT Laboratory studies are a common way of comparing recommendation approaches with respect to different quality dimensions that might be relevant for real users. One typical experimental setup is to first present the participants with recommendation lists that were created with different algorithms and then ask the participants to assess these recommendations individually or to compare two item lists. The cognitive effort required by the participants for the evaluation of item recommendations in such settings depends on whether or not they already know the (features of the) recommended items. Furthermore, lists containing popular and broadly known items are correspondingly easier to evaluate. In this paper we report the results of a user study in which participants recruited on a crowdsourcing platform assessed system-provided recommendations in a between-subjects experimental design. The results surprisingly showed that users found non-personalized recommendations of popular items the best match for their preferences. An analysis revealed a measurable correlation between item familiarity and user acceptance. Overall, the observations indicate that item familiarity can be a potential confounding factor in such studies and should be considered in experimental designs

Personalized Next-Track Music Recommendation with Multi-dimensional Long-Term Preference Signals

ABSTRACT The automated generation of playlists given a user's most recent listening history is a common feature of modern music streaming platforms. In the research literature, a number of algorithmic proposals for this "next-track recommendation" problem have been made in recent years. However, nearly all of them are based on the user's most recent listening history, context, or location but do not consider the users' long-term listening preferences or social network. In this work, we explore the value of long-term preferences for personalizing the playlist generation process and evaluate different strategies of applying multi-dimensional user-specific preference signals. The results of an empirical evaluation on five different datasets show that although the short-term listening history should generally govern the next-track selection process, long-term preferences can measurably help to increase the personalization quality

Analytical Photo-Centrifugal Filtration (ACF): Membrane Resistance and Filterability

Filtration has broad applications in academic and industrial research as well as in product development. In general, filter-nutsch or filter-pressure approaches are applied. Recently, an analytical photo-centrifugal set up was introduced as a new laboratory technique to characterize membranes and filterability of samples (suspensions, protein solutions, extracts, etc.).Analytical centrifugal filtration (ACF) is based on continuous in-situ measurement of spatial resolved light transmission through a centrifugal filtration cell monitoring the volume decrease of the sample (top) or the filtrate increase (bottom) by STEP-Technology®). Centrifugal acceleration for dispersion separation can be simply set by programming rotor revolutions from 200 – 5000 rpm. Up to 12 samples (0,2 ml -1 ml) can be analyzed simultaneously. Different procedures can be applied to characterize membrane resistance and filterability of various sample types (diluted or concentrated macromolecule solutions or suspensions).In this paper the basic measuring technique of ACF, the newly designed centrifugal filtration cells are described and corresponding quantitative analysis discussed..