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Influential nodes play a critical role in boosting or curbing spreading phenomena in complex networks. Numerous centrality measures have been proposed for identifying and ranking the nodes according to their importance. Classical centrality measures rely on various local or global properties of the nodes. They do not take into account the network community structure. Recently, a growing number of researches have shifted to community-aware centrality measures. Indeed, it is a ubiquitous feature in a vast majority of real-world networks. In the literature, the focus is on designing community-aware centrality measures. However, up to now, there is no systematic evaluation of their effectiveness. This study fills this gap. It allows answering which community-aware centrality measure should be used in practical situations. We investigate seven influential community-aware centrality measures in an epidemic spreading process scenario using the Susceptible-Infected-Recovered (SIR) model on a set of fifteen real-world networks. Results show that generally, the correlation between community-aware centrality measures is low. Furthermore, in a multiple-spreader problem, when resources are available, targeting distant hubs using Modularity Vitality is more effective. However, with limited resources, diffusion expands better through bridges, especially in networks with a medium or strong community structure., Comment: This version of the article has been accepted for publication, after peer review but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Final Version of Record is available online at: https://doi.org/10.1007/s11135-022-01416-7

It is of paramount importance to uncover influential nodes to control diffusion phenomena in a network. In recent works, there is a growing trend to investigate the role of the community structure to solve this issue. Up to now, the vast majority of the so-called community-aware centrality measures rely on non-overlapping community structure. However, in many real-world networks, such as social networks, the communities overlap. In other words, a node can belong to multiple communities. To overcome this drawback, we propose and investigate the "Overlapping Modularity Vitality" centrality measure. This extension of "Modularity Vitality" quantifies the community structure strength variation when removing a node. It allows identifying a node as a hub or a bridge based on its contribution to the overlapping modularity of a network. A comparative analysis with its non-overlapping version using the Susceptible-Infected-Recovered (SIR) epidemic diffusion model has been performed on a set of six real-world networks. Overall, Overlapping Modularity Vitality outperforms its alternative. These results illustrate the importance of incorporating knowledge about the overlapping community structure to identify influential nodes effectively. Moreover, one can use multiple ranking strategies as the two measures are signed. Results show that selecting the nodes with the top positive or the top absolute centrality values is more effective than choosing the ones with the maximum negative values to spread the epidemic., Conference: ASONAM '21: International Conference on Advances in Social Networks Analysis and Mining

Simple Summary Probiotics are increasingly documented to confer health and performance benefits across farmed animals. The study assessed the effect of dietary supplementation with the single-strain probiotic Pedicococcus acidilactici MA18/5M on the growth, nutritional indices, and metabolic status of the adult western blue shrimp, Litopenaeus stylirostris. The aim was to estimate its potential at optimizing the performance of the penaeid feed and shrimp farming industry. Supplementation with P. acidilactici MA18/5M improved the feed conversion efficiency and daily growth rate across fixed ration sizes; and decreased both the maintenance and optimal ration size for growth. This appeared linked to a better use of dietary carbohydrates as shown by a higher α-amylase activity, free-glucose and glycogen concentration in the digestive gland. Interestingly, P. acidilactici intake was also associated with a higher antioxidant status which may be linked to enhanced carbohydrates utilization. Using a fixed ration size approach under controlled laboratory conditions, the study documented a clear potential for P. acidilactici MA18/5M to enhance the growth, feed efficiency and metabolic health of adult penaeid shrimp during on-growing. These findings raise interesting prospects to optimize penaeid feed formulation and the performance of the shrimp-farming industry. Abstract Probiotics are increasingly documented to confer health and performance benefits across farmed animals. The aim of this study was to assess the effect of a constant daily intake of the single-strain probiotic Pedicococcus acidilactici MA18/5M (4 × 108 CFU.day−1.kg−1 shrimp) fed over fixed, restricted ration sizes (1% to 6% biomass.day−1) on the nutritional performance and metabolism of adult penaeid shrimp Litopenaeus stylirostris (initial body-weight, BWi = 10.9 ± 1.8 g). The probiotic significantly increased the relative daily growth rate (RGR) across all ration size s tested (Mean-RGR of 0.45 ± 0.08 and 0.61 ± 0.07% BWi.day−1 for the control and probiotic groups, respectively) and decreased the maintenance ration (Rm) and the optimal ration (Ropt) by 18.6% and 11.3%, respectively. Accordingly, the probiotic group exhibited a significantly higher gross (K1) and net (K2) feed conversion efficiency with average improvement of 35% and 30%, respectively. Enhanced nutritional performances in shrimps that were fed the probiotic P. acidilactici was associated with, in particular, significantly higher α-amylase specific activity (+24.8 ± 5.5% across ration sizes) and a concentration of free-glucose and glycogen in the digestive gland at fixed ration sizes of 3% and below. This suggests that the probiotic effect might reside in a better use of dietary carbohydrates. Interestingly, P. acidilactici intake was also associated with a statistically enhanced total antioxidant status of the digestive gland and haemolymph (+24.4 ± 7.8% and +21.9 ± 8.5%, respectively; p < 0.05). As supported by knowledge in other species, enhanced carbohydrate utilization as a result of P. acidilactici intake may fuel the pentose-phosphate pathway, generating NADPH or directly enhancing OH-radicals scavenging by free glucose, in turn resulting in a decreased level of cellular oxidative stress. In conclusion, the growth-ration method documented a clear contribution of P. acidilactici MA18/5M on growth and feed efficiency of on-growing L. stylirostris that were fed fixed restricted rations under ideal laboratory conditions. The effect of the probiotic on α-amylase activity and carbohydrate metabolism and its link to the shrimp’s antioxidant status raises interesting prospects to optimize dietary formulations and helping to sustain the biological and economic efficiency of the penaeid shrimp-farming industry.

Tensors are used in a wide range of analytics tools and as intermediary data structures in machine learning pipelines. Implementations of tensor decompositions at large-scale often select only a specific type of optimization, and neglect the possibility of combining different types of optimizations. Therefore, they do not include all the improvements available, and are less effective than what they could be. We propose an algorithm that uses both dense and sparse data structures and that leverages coarse and fine grained optimizations in addition to incremental computations in order to achieve large scale CP (CANDECOMP/PARAFAC) tensor decomposition. We also provide an implementation in Scala using Spark, MuLOT, that outperforms the baseline of large-scale CP decomposition libraries by several orders of magnitude, and run experiments to show its large-scale capability. We also study a typical use case of CP decomposition on social network data.

Unlike classical centrality measures, recently developed community-aware centrality measures use a network’s community structure to identify influential nodes in complex networks. This paper investigates their relationship on a set of fifty real-world networks originating from various domains. Results show that classical and community-aware centrality measures generally exhibit low to medium correlation values. These results are consistent across networks. Transitivity and efficiency are the most influential macroscopic network features driving the correlation variation between classical and community-aware centrality measures. Additionally, the mixing parameter, the modularity, and the Max-ODF are the main mesoscopic topological properties exerting the most substantial effect.

International audience; Identifying influential nodes in social networks is a fundamental issue. Indeed, it has many applications, such as inhibiting epidemic spreading, accelerating information diffusion, preventing terrorist attacks, and much more. Classically, centrality measures quantify the node's importance based on various topological properties of the network, such as Degree and Betweenness. Nonetheless, these measures are agnostic of the community structure, although it is a ubiquitous characteristic encountered in many real-world networks. To overcome this drawback, there is a growing trend to design so-called community-aware centrality measures. Although several works investigate the relationship between various classical centrality measures [1-3], the interplay between classical and community-aware centrality measures is still unexplored. This work presents an extensive investigation aimed at a better understanding of the relationship between community-aware centrality measures, classical centrality measures, and network topology. Artificial and real-world networks are used in the experiments. The Kendall's Tau correlation quantifies the interaction between ten classical and twenty-eight community-aware centrality measures. The community-aware centrality measures are divided into three groups. The first group's ten measures are based on the intra-community links of a node (local measures). The second group's twelve measures are based on the inter-community links of a node (global measures). Finally, the six measures of the third group consider both types of links (mixed measures). The LFR algorithm generates artificial networks with controlled properties. Indeed, the community structure strength (µ), the exponent of the degree distribution (γ), and the community size distribution (θ) can be specified. The experiments show that the community structure strength is the main feature governing the correlation between classical and community-aware centrality measures. The heatmap on the left of Figure 1 represents the correlation in an artificial network with a strong community structure. The global community-aware centrality measures exhibit a low correlation with classical centrality measures, while local communityaware centrality measures show a high correlation. The results are inverted when the network has a weak community structure. Differences are more subtle for mixed community-aware measures. One can also notice that results are relatively insensitive to variation of the degree and community size distributions' exponents. Fifty real-world networks originating from various domains are also investigated. Linear regression is performed considering six macroscopic (

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Community structure is a ubiquitous feature in natural and artificial systems. Identifying key nodes is a fundamental task to speed up or mitigate any diffusive processes in these systems. Centrality measures aim to do so by selecting a small set of critical nodes. Classical centrality measures are agnostic to community structure, while community-aware centrality measures exploit this property. Several works study the relationship between classical centrality measures, but the relationship between classical and community-aware centrality measures is almost unexplored. In this work [1], we answer two questions: (1) How do classical and community-aware centrality measures relate? (2) What is the influence of the network topology on their relationship? We perform an analysis involving a set of popular classical and community-aware centrality measures on synthetic and real-world networks. Communityaware centrality measures can be classified into two groups. The local ones are relying mainly on the intra-community links. The global ones exploit the connections bridging the communities. First, we calculate the Kendall's Tau correlation between all pair of classical and community-aware centrality measures on a set of synthetic LFR networks with controllable parameters (community structure strength (), degree distribution exponent (), and community size distribution exponent ()). Results show that the main feature driving the correlation variation between classical and community-aware centrality measures is the community structure strength. Furthermore, local community-aware centrality measures correlate with classical ones in networks with a strong community structure, while correlation is weak for global community-aware centrality measures. One observes the opposite behavior in networks with a weak community structure. Second, we calculate the Kendall's Tau correlation using a set of 50 real-world networks from various domains. We perform a linear regression analysis to assess the relationship between the networks' macroscopic and mesoscopic features and the mean correlation value. Results show that the community structure strength and transitivity are the most significant features driving the correlation between classical and community-aware centrality measures. These findings open a new perspective on designing community-aware centrality measures tailored to the network topology.

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This study was conducted to evaluate the mucosal immune responses of rainbow trout when supplementing an experimental formulated feed with multi-strain yeast fraction product (Saccharomyces cerevisiae and Cyberlindnera jardinii). In total, 360 fish (initial BW 23.1 ± 0.2 g) were randomly allotted into three dietary treatments in an 8-week feeding trial. The dietary treatments included basal diet (control) and control + 1.5 g/kg multi-strain yeast fraction product (MsYF) fed continuously and pulsed every two weeks between control and MsYF diet. No negative effects on growth performance of feeding the MsYF supplemented diet were observed. SGR and FCR averaged 2.30 ± 0.03%/day and 1.03 ± 0.03, respectively, across experimental groups. Muscularis thickness in the anterior intestine after 8 weeks of feeding was significantly elevated by 44.3% in fish fed the MsYF continuously, and by 14.4% in fish fed the MsYF pulsed (P < 0.02). Significant elevations in goblet cell density in the anterior and posterior (>50% increase) intestine were observed after 8 weeks of feeding the MsYF supplemented diet (P< 0.03). In contrast, lamina propria width was significantly lower in fish fed the experimental diets (>10% reduction). The gene expression analysis of the intestine revealed significant elevations in expression of tlr2, il1r1, irak4, and tollip2 after 4 weeks of feeding the MsYF. Significant elevations in effector cytokines tnfα, il10 and tgfβ were observed after 4 weeks of feeding the MsYF regime. After 8 weeks significant elevations in the gene expression levels of il1β, ifnγ, and il12 were observed in fish fed the MsYF. Likewise, the expression of the transcription factor gata3 was significantly elevated (P

The increasing availability of data from online social networks attracts researchers’ interest, who seek to build algorithms and machine learning models to analyze users’ interactions and behaviors. Different methods have been developed to detect remarkable precursors preceding events, using text mining and Machine Learning techniques on documents, or using network topology with graph patterns.

Designing software architectures for Big Data is a complex task that has to take into consideration multiple parameters, such as the expected functionalities, the properties that are untradeable, or the suitable technologies. Patterns are abstractions that guide the design of architectures to reach the requirements. One of the famous patterns is the Lambda Architecture, which proposes real-time computations with correctness and fault-tolerance guarantees. But the Lambda has also been highly criticized, mostly because of its complexity and because the real-time and correctness properties are each effective in a different layer but not in the overall architecture. Furthermore, its use cases are limited, whereas Big Data need an adaptive and flexible environment to fully reveal the value of data. Nevertheless, it proposes some interesting mechanisms. We present a renewal of the Lambda Architecture: the Lambda+ Architecture, supporting both exploratory and real-time analyzes on data. We propose to study the conservation of properties in composition of components in an architecture using the category theory. We relate a real implementation of our approach to architecture a social network observatory platform.

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Centrality measures are crucial in quantifying the influence of the members of a social network. Although there has been a great deal of work dealing with this issue, the vast majority of classical centrality measures are agnostic of the community structure characterizing many social networks. Recent works have developed community-aware centrality measures that exploit features of the community structure information encountered in most real-world complex networks. In this paper, we investigate the interactions between 5 popular classical centrality measures and 5 community-aware centrality measures using 8 real-world online networks. Correlation as well as similarity measures between both types of centrality measures are computed. Results show that community-aware centrality measures can be divided into two groups. The first group, which includes Bridging centrality, Community Hub-Bridge, and Participation Coefficient, provides distinctive node information as compared to classical centrality. This behavior is consistent across the networks. The second group which includes Community-based Mediator and Number of Neighboring Communities is characterized by more mixed results that vary across networks.

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A passive-acoustic telemetry system was used for the first time for the fine-scale, three-dimensional tracking of individual cleaner fish in commercial Scottish salmon sea-pens in full commercial operation. The aim was to test the system performance and provide baseline data on the comparative distribution and swimming activity of individual ballan wrasse and lumpfish under standard farm practices with the long-term aim of informing stocking and husbandry strategies. In March 2015, wild ballan wrasse (Labrus bergylta) (115 ± 20 g; n = 13) and farmed lumpfish (Cyclopterus lumpus) (281 ± 42 g; n = 13) previously deployed in June–October 2014 were recaptured, implanted with acoustic tags pinging every 6–12 s and released into their original sea cage holding Atlantic salmon (Q2 2014; 2059 ± 35 g mean-weight). Control tags were deployed in cleaner fish hides to validate the system performance. Positional data from nine specimens per species were analysed from March 24th to June 1st 2015, during which time water temperature rose from 7.2 to 9.1 °C and water salinity averaged 26.8 ± 1.5 ppt at 4 m depth. The accuracy of the acoustic positions averaged 0.6 m across the three dimensions of all control tags and was

The trial investigated the effects of dietary supplementation with the commercial prebiotic galactooligosaccharide (1% GOS; GOS diet), the commercial probiotic Pediococcus acidilactici MA18/5 M (6.3 log CFU g− 1 PA; PA diet) and their combination as synbiotic (1% GOS and 6.3 log CFU g− 1 PA; SYN diet) on the growth, innate immunity and disease resistance of juvenile rockfish (Sebastes schlegeli). Triplicate groups of fish (18.2 ± 0.3 g) were fed one of the four diets for 8 weeks and then subjected to a standard challenge with Edwardsiella tarda by intraperitoneal injection. The results showed significant (P

This paper presents a Tensor Data Model to carry out logical data independence in polystore systems. TDM is an expressive model that can link different data models of different data stores and simplifies data transformations by expressing them by means of operators whose semantics are clearly defined. Our contribution is the definition of a data model based on tensors for which we add the notions of typed schema using associative arrays. We describe a set of operators and we show how the model constructs take place in a mediator/wrapper like architecture.

This study confirmed that observations of blue-green colouration in plasma fractions of the ballan wrasse Labrus bergylta were caused by the linear tetra-pyrrole biliverdin and that the molecule was of the physiologically relevant IXα isomer. Accumulation appears driven by chromogenic association with an unknown protein moiety which precludes enzymatic reduction and would suggest active management. It was demonstrated that the pigment did not fluctuate relative to ontogeny, or indeed binary gender in the species of interest, but mobilisation and depletion in the subset of individuals undergoing sex change at the time of study supports a potential association with gender inversion processes. It is of note that although biliverdin does have some effect on external colouration, the evidence is indicative that crypsis is a supplementary function thus other factors must be considered.

Commercial production of ballan wrasse (Labrus bergylta) as a cleaner fish for the removal of sea lice (Lepeophtheirus salmonis) from farmed salmonids (Salmo salar) has increased due to its proven efficiency. One bottleneck in commercial hatchery production is working with the benthic adhesive eggs, which makes disinfection and incubation of eggs challenging; therefore, this study aimed to find a chemical or enzymatic treatment and process to remove the adhesive gum layer. Naturally spawned eggs were collected from artificial spawning substrates up to 24 h post spawning from wild caught broodstock kept in captivity at the Marine Harvest, Machrihanish facility. Four treatments were tested: tannic acid (0.2, 0.1, and 0.05%), sodium sulfite (2, 1, and 0.5%), l -cysteine (2, and 1%), and enzyme alcalase® (4.0, 3.0, 2.0, 1.0, and 0.5%) in vitro. Eggs were exposed for 25 min while being continually agitated, and the proportion of “degummed” eggs was counted at the end of each time period. Enzyme alcalase® was the only treatment that proved successful in degumming eggs, with the time to complete degumming (≥ 96%) inversely related to enzyme concentration. Complete degumming occurred between 15 and 30 min for all enzyme alcalase® dose rates. Mean hatch rates for eggs treated with enzyme alcalase ® were not compromised by the treatment and in the highest dose tested were actually found to be higher in treated eggs (78.9 ± 2.4%) than controls (71.3 ± 3.3%). The use of enzyme alcalase® has proven effective in degumming ballan wrasse eggs without affecting hatch rates. However, translation of this method to in situ degumming and thus removal of eggs from spawning substrate on farm remains to be standardised. Statement of relevance Increasing efficiency for commercial production of ballan wrasse.

Made available in DSpace on 2020-12-12T02:45:24Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-12-15 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) This study evaluated the effects of SOD-rich melon pulp concentrate (MPC) premix on growth performance, hematological profile and the antioxidant capacity of Nile tilapia (Oreochromis niloticus) subjected to heat/dissolved oxygen-induced stress (HDOIS). A group of 462 male Nile tilapia (8.87 ± 0.12 g) was randomly distributed in 42,250-L tank (11 fish/tank) and fed seven practical diets supplemented with graded levels of MPC (0.0%, 0.1%, 0.2%, 0.3%, 0.4%, 0.5% and 1.0% MPC premix of diet; PM Melofeed 2.5, SOD activity min. 65,000 IU kg−1, Lallemand SAS, Blagnac, France) with six replicate tank/diet. The diets were formulated to contain 30% crude protein and 18 MJ kg−1 crude energy. After 60 days of feeding, growth performance was evaluated and six fish per treatment were sampled for hematological profile and analysis of the hepatic activity of key antioxidant enzymes. Then, remaining fish were subjected to HDOIS (34 °C/1.87 mg L−1 dissolved oxygen) for 2 days and the same hematological profile and antioxidant enzyme activities were determined. MPC supplementation was associated with the recruitment of hepatic antioxidant defense in the form of increased SOD activity prior and after HDOIS as well as of GR activity before HDOIS at 0.4% MPC and above. These levels of MPC supplementation were weakly associated with reduced level of muscle lipid oxidation. This trial indicates that MPC supplementation at or above 0.4% contributes to a better maintenance of antioxidant defense in Nile tilapia when exposed to HDOIS environmental stress. This is expected to yield biological benefits in the longer-term when Nile tilapia are chronically or repetitively exposed to similar environmental stressors as may be the case under intensive aquaculture conditions. Department of Breeding and Animal Nutrition UNESP Universidade Estadual Paulista “Júlio de Mesquita Filho” Lallemand SAS, 19 rue des Briquetiers Department of Production and Plant Breeding UNESP Universidade Estadual Paulista “Júlio de Mesquita Filho” Department of Breeding and Animal Nutrition UNESP Universidade Estadual Paulista “Júlio de Mesquita Filho” Department of Production and Plant Breeding UNESP Universidade Estadual Paulista “Júlio de Mesquita Filho” FAPESP: #2017/13217-9

Ballan wrasse (Labrus bergylta) are used as cleaner fish in commercial Atlantic salmon farming to remove ectoparasitic sea lice. While the delousing performance of wild wrasse is usually good, that of farmed wrasse is variable, possibly because of different conditions in hatcheries and sea cages. In this study, three passive-acoustic telemetry (PAT) trials were conducted at a salmon farm to compare the behaviour of wild and farmed wrasse and test the effect of acclimatising farmed wrasse to sea-cage conditions before deployment. Up to 40 fish were monitored simultaneously for 60–124 days by triangulating tag positions within a hydrophone array every 6–10 s. This data was used to assess fish depth and preferred cage locations and calculate activity, orientation and home ranges. Wild wrasse occupied shallower depths (13.3 ± 2.4 m) than farmed wrasse, which remained near the bottom of the cages (18.2 ± 1.7 m). Swimming activity was higher in wild wrasse with significant diurnal variations due to nocturnal quiescence on 68% of observed days. Wild wrasse rapidly developed large home ranges (616.8 ± 110.1 m2), preferring cage corners. Hatchery-and-sea-cage acclimatisation improved the behaviour of farmed wrasse; they rapidly moved up the water column (9.08 ± 2.05 m after 1 week) and established home ranges (514.3 ± 146.6 m2), and they developed diurnal activity patterns, which may indicate a positive response to acclimatisation. Acclimatising farmed ballan wrasse to sea-cage conditions positively improved and encouraged behaviours similar to those seen in wild wrasse, including diurnal rhythms and the establishment of home ranges, and is recommended for all farmed wrasse prior to deployment to improve delousing performance.

This trial tested the use of lactic acid bacteria (LAB) on pikeperch (Sander lucioperca) larvae during their first feeding. The trial included the use of two probiotic treatments and one control (no probiotics). Pikeperch larvae were exposed to LAB as follows: (1) the live feed (Treatment 1, live feed) or (2) via the live feed and the larval rearing water (Treatment 2, probiotic). Significant differences were found between the treatments in terms of total length (TL), myomere height (MH), overall survival, and the tolerance to a high salinity challenge. Larvae exposed to LAB via both the live feed and the rearing water had a significantly higher overall survival rate (85%) than the other two treatments at 21 dph. When both treatments were subjected to high salinity rates (18 parts per thousand (ppt)), both treatments exposed to LAB demonstrated higher survival rates than the control treatment (28% and 40% survival rate at 180 min for the live feed and probiotic treatments, respectively, as compared with a 100% mortality rate at 150 min for the control). At the same time, larvae exposed to the probiotic treatment had a significantly higher TL as compared to the control after 12 and 21 days post hatch (dph) (probiotic 7.13 ± 0.21 and 11.71 ± 1.1 mm, control 5.86 and 10.79 mm at 12 and 21 dph, respectively). The results suggest that the use of LAB in both the live feed and the rearing water has a positive effect on pikeperch larval quality by strengthening their resilience to stress conditions, as well as improving the growth and survival rates.

In this article, we show how the mathematical object tensor can be used to build a multi-paradigm model for the storage of social data in data warehouses. From an architectural point of view, our approach allows to link different storage systems (polystore) and limits the impact of ETL tools performing model transformations required to feed different analysis algorithms. Therefore, systems can take advantage of multiple data models both in terms of query execution performance and the semantic expressiveness of data representation. The proposed model allows to reach the logical independence between data and programs implementing analysis algorithms. With a concrete case study on message virality on Twitter during the French presidential election of 2017, we highlight some of the contributions of our model.

Ballan wrasse (Labrus bergylta) aquaculture has emerged as a strategic development for the sustainable control of sea-lice (Lepeophtheirus salmonis) in the European Atlantic salmon (Salmo salar) industry. Farmed ballan wrasse juveniles were exposed to a standard acute stressor (1 min air exposure) and the patterns of change in blood haematocrit, plasma cortisol, glucose and lactate were described over a 24 h period using newly validated analytical methods in that species. Plasma cortisol concentrations were relatively high and rose from a resting basal level of 60.8 ± 5.5 ng·ml− 1 to a peak concentration of 284.3 ± 26.7 ng·ml− 1 (3.7-fold increase) 30 min post stress exposure. Cortisol was found to be the most sensitive indicator of stress followed by plasma glucose showing 83.9% increase from a resting basal level of 2.2 ± 0.1 mmol·l− 1 30 min post-stressor. The use of handheld metres for monitoring glucose and lactate levels was successfully validated against the reference spectrophotometric methods for on-site assessment. Validation of the methods and identification of the most sensitive stress indicators are expected to assist in the identification of adverse conditions and best rearing practices for this emerging new aquaculture species.

In wild ballan wrasse Labrus bergylta, mass-length relationships were not different between genders, and positive allometry was found in the mixed-gender population. Male-biased sexual size dimorphism was significant and the most effective morphometric method for sexing L. bergylta outside of the species spawning window used body mass (M(B) in g), total body length (L(T) in mm) and Fulton's condition factor (K) as discriminant variables to predict gender with 91% accuracy. The discriminant score (S(D)) of a specimen can be calculated as S(D) = 0.01 M(B)- 0.016 L(T)- 3.835 K + 6.252 to predict its gender as female or male if S(D) is 1.504, respectively. There was a potential trend towards earlier sexual inversion compared to previous studies at comparable latitudes. Sex change is a phenotypically plastic trait under social control in haremic fishes and should be monitored in increasingly exploited L. bergylta.

BACKGROUND:Cleaner-fish(wrasse,Labridae)areincreasinglydeployedwithintheAtlanticsalmon(Salmosalar L.)industryas a biological control against sea-lice (Lepeophtheirus salmonis Kroyer). Two tank-based trials were performed to test the effect of farmed ballan wrasse (Labrus bergylta Ascanius) body mass and supplementary feeding on the delousing of Atlantic salmon post-smolts with an initial infection level of ∼12 lice salmon −1 and a ∼5% wrasse:salmon ratio. RESULTS:Sea-louselevelsbelow0.5licesalmon −1 wereobtainedwithin84h,andpreferentialpreyinguponlargermotilestages wasfound.Thewrassebodymassandtheavailabilityoffresh,openedbluemussels(MytilusedulisL.)didnotsignificantlyaffect delousing efficiency. The functional predator response was linear, showing no minimum prey density threshold for sea-louse foragingandnosatiationplateau,inspiteofthehighconsumptionratesmeasured.Sea-louseinfectionlevelsdeclinedfollowing a one-phase exponential decay model, with a standardised decline time constant of 0.8‐1.3% h −1 for each wrasse stocked per 100 salmon. CONCLUSIONS: Farmed ballan wrasse are confirmed as highly effective therapeutic and preventive biological controls against sea-lice. The study supports the current minimum hatchery size target (10mm total length) and the use of supplementary feedingtosustainthewrassestocksinoperation.Thefunctionalpredatorresponseandthestandardiseddeclinetimeconstant of sea-louse abundance are proposed as useful indicators of delousing efficiency. c � 2013 Society of Chemical Industry

Sea lice continue to be one of the largest issues for the salmon farming industry and the use of ballan wrasse (Labrus bergylta) as a biological control is considered to be one of the most sustainable solutions in development. Broodstock management has proved challenging in the initial phases due to the significant lack of understanding of basic reproductive physiology and behaviour in the species. The aim of the study was to monitor captive breeding populations throughout a spawning season to examine timing and duration of spawning, quantify egg production, and look at seasonal changes in egg quality parameters as well as investigate the parental contribution to spawning events. A clear spawning rhythm was shown with 3–5 spawning periods inclusive of spawning windows lasting 1–9 days followed by interspawning intervals of 8–12 days. Fertilization rate remained consistently high (> 87.5%) over the spawning season and did not differ significantly between spawning populations. Hatch rate was variable (0–97.5%), but peaked in the middle of the spawning season. Mean oocyte diameter and gum layer thickness decreased slightly over the spawning season with no significant differences between spawning populations. Fatty acid (FA) profile of eggs remained consistent throughout the season and with the exception of high levels of ARA (3.8 ± 0.5% of total FA) the FA profile was similar to that observed in other marine fish species. Parental contribution analysis showed 3 out of 6 spawning events to be single paired mating while the remaining 3 had contributions from multiple parents. Furthermore, the proposed multiple batch spawning nature of this species was confirmed with proof of a single female contributing to two separate spawning events. Overall this work represents the first comprehensive dataset of spawning activity of captive ballan wrasse, and as such and will be helpful in formulating sustainable broodstock management plans for the species. Statement of relevance First dataset of spawning activity in captive ballan wrasse.

International audience; There is an ongoing interest in examining users' experiences made available through social media. Unfortunately these experiences like reviews on products and/or services are sometimes conflicting and thus, do not help develop a concise opinion on these products and/or services. This paper presents a multi-stage approach that extracts and consolidates reviews after addressing specific issues such as user multi-identity and user limited credibility. A system along with a set of experiments demonstrate the feasibility of the approach.

International audience; Influence in Twitter has become recently a hot research topic, since this micro-blogging service is widely used to share and disseminate information. Some users are more able than others to influence and persuade peers. Thus, studying most influential users leads to reach a large-scale information diffusion area, something very useful in marketing or political campaigns. In this study, we propose a new approach for multi-level influence assessment on multi-relational networks, such as Twitter. We define a social graph to model the relationships between users as a multiplex graph where users are represented by nodes, and links model the different relations between them (e.g., retweets, mentions, and replies). We explore how relations between nodes in this graph could reveal about the influence degree and propose a generic computational model to assess influence degree of a certain node. This is based on the conjunctive combination rule from the belief functions theory to combine different types of relations. We experiment the proposed method on a large amount of data gathered from Twitter during the European Elections 2014 and deduce top influential candidates. The results show that our model is flexible enough to to consider multiple interactions combination according to social scientists needs or requirements and that the numerical results of the belief theory are accurate. We also evaluate the approach over the CLEF RepLab 2014 data set and show that our approach leads to quite interesting results.

International audience; In this article we show how a multi-paradigm framework can fulfil the requirements of tweets analysis and reduce the waiting time for researchers that use computational resources and storage systems to support large-scale data analysis. The originality of our approach is to combine concerns about data harvesting, data storage, data analysis and data visualisation into a framework that supports inductive reasoning in multidisciplinary scientific research. Our main contribution is a polyglot storage system with a generic data model to support logical data independence and a set of tools that can provide a suitable solution for mixing different types of algorithms in order to maximise the extraction of knowledge. We describe the software architecture of our framework, the generic model and we show how it has been used in major projects and what characteristics have been validated.