PhD/ Karine Aillerie
email: karine.aillerie@reseau-canope.fr

Sarah Mcnicol
email: s.mcnicol@mmu.ac.uk

Information literacy and social networking sites: challenges and stakes regarding teenagers' uses

Keywords: media sociaux, réseaux sociaux numériques, pratiques informationnelles, adolescents, information literacy, compétences du 21 ème siècle, capacitation social media, social networking sites, information uses, teenagers, information literacy, 21 st century skills, empowerment

Information literacy and social networking sites: challenges and stakes regarding teenagers' uses

Karine Aillerie, Sarah Mcnicol

Introduction

In this paper, we focus on the social networking sites (SNS) as defined by boyd & Ellison (2013). From a "uses & gratifications" point of view [START_REF] Ruggiero | Uses and Gratifications Theory in the 21st Century[END_REF], several kinds of motivations can explain the needs that SNS, Facebook, Twitter or Snapchat among others, satisfy for people, as [START_REF] Whiting | Why people use social media: a uses and gratifications approach[END_REF] described: social interaction, information seeking, passing time, entertainment, relaxation, communicatory utility, convenience utility, expression of opinion, information sharing, and surveillance/knowledge about others. We focus here especially on teenagers' uses. Teenagers are often great SNS users as many surveys show, but here we want to go beyond numbers and try to understand more deeply how teenagers really use SNS and what they use them for. More specifically we choose to pay attention to teenagers' information uses of SNS.

Digital practices of teenagers and young adults have been widely studied. The heterogeneity among experiences and skills has been one of the main findings for more than a decade [START_REF] Aillerie | Quelle compétence informationnelle pour les usages juvéniles du Web ? Terminal[END_REF][START_REF] Hargittai | Digital Inequality[END_REF][START_REF] Mercklé | La stratification sociale des pratiques numériques des adolescents[END_REF][START_REF] Robinson | Digital inequalities and why they matter[END_REF]. Regarding SNS, the majority of research has attempted to describe how social uses of SNS, as well as communication tools in general, play a major role during adolescence. Again, a number of authors highlight inequalities among young users regarding social and communication skills (boyd, 2014). While SNS are frequently dismissed by schools and perceived more as danger than an edu-cational opportunity, it seems crucial to understand better whether teenagers find out, seek or share information on SNS and for what purposes.

Research shows that an information motivation to use SNS exists and, in this paper, we firstly identify emerging results about information uses of SNS focusing on teenagers. Secondly, based on this literature review, we will explore issues these findings raise in terms of empowerment.

Youths' motivations to use SNS

Information uses of SNS

SNS form complex objects to study. It is difficult to extract a certain type of use, such as information, while the more visible motivation remains socialization. This is especially the case for young people for whom the social group of peers contributes to the construction of personality [START_REF] Pasquier | Cultures lycéennes : la tyrannie de la majorité[END_REF]. SNS are nowadays one of the main "places" where peers gather. Nevertheless, results from empirical studies show the reality of specific information motivations to use SNS. Several studies have addressed one particular information need that people satisfy by using SNS such as health (Azusa & Costa-i-Front 2013), travel information [START_REF] Amaro | Travelers' use of social media: A clustering approach[END_REF], purchase intention [START_REF] Wang | Social Media Peer Communication and Impacts on Purchase Intentions: A Consumer Socialization Framework[END_REF] or job search [START_REF] Nikolaou | Social Networking Web Sites in Job Search and Employee Recruitment[END_REF]. From a political point of view, it has been widely demonstrated that information retrieved and shared on SNS can foster commitment [START_REF] Amsidder | La mobilisation sociale à l'ère des réseaux sociaux : Cas du Maroc[END_REF]. More widely, SNS currently play an important role in press and breaking news information [START_REF] Newman | Reuters institute digital news report[END_REF]. Regarding young people and college students more specifically, emerging findings describe information purposes that could motivate them to use SNS. In Norway, [START_REF] Bae Brandtzaeg | Why People Use Social Networking Sites[END_REF] carried a qualitative survey on SNS users (aged from 16 to 29): 10% of the participants listed information need as a reason for using SNS. It is noticeable that younger participants cited academic homework as a motivation to seek information on SNS. However some of the most significant studies that demonstrate the use of SNS as direct information sources concern undergraduate students. Over several years, the research led by Kyung-Sun Kim investigated the kinds of social media used as information sources by college students and actions users take to evaluate the trustworthiness of information. This showed that a range of social media are used for everyday life information seeking while some specific sources (Wikipedia, blogs, YouTube, Q&A sites and forums) are used for both everyday life information and, here again, academic purposes [START_REF] Kim | Social Media as Information Source: Undergraduates' Use and Evaluation Behavior[END_REF][START_REF] Kim | Social Media as Information Sources: Use and Evaluation of Information from Social Media[END_REF].

Are teenagers concerned?

From these results, we may wonder whether students from high school could have similar information habits. Les stratégies d'information des jeunes Bretons is a recurring survey led by the Réseau Information Jeunesse Bretagne, a public 00 Karine AILLERIE, Sarah McNICOL Information literacy and social networking sites: challenges and stakes regarding teenagers' uses … service in France, since 2007. The last issue of this survey (n=3914, 72% 15-19 years old) underlined the importance of information provided by social interactions (relatives, friends, advisors…) (Centre Régional Information Jeunesse Bretagne, 2013). The Internet remains in the first place (88%) but friends (54%), parents and family (50%), followed by teachers, educators and leaders (26%) arrive in second, third and fifth places. The authors describe this result as a strong trend when compared to the findings of the previous surveys. They explain this kind of information seeking strategy by the need of youths for reliability as they are likely to be more confident in information provided face-to-face by a person. The importance of faceto-face contact was a central result about information habits of teenagers indicated earlier by [START_REF] Agosto | Toward a Model of the Everyday Life Information Needs of Urban Teenagers, Part 1: Theoretical Model[END_REF] when establishing their models of urban teens' everyday life information needs. Furthermore, another strong result of the Les stratégies d'information des jeunes Bretons survey is the growing place of SNS in these young people's information uses: when all ages are included, in 2013, SNS were used by 48% (28% in 2010) and search engines by 72% (93% in 2010). The 15-19 years old participants use mostly search engines (67%) and navigation (66%), closely followed by SNS (59%).

A recurring study from Switzerland, called JAMES (Jeunes Activités Médias) also sheds light on teenagers' information seeking on SNS [START_REF] Willemse | JAMES: Jeunes, activités, médias enquête Suisse[END_REF]. Since 2010, this representative study has examined the use of media by young people (aged from 12 to 19) every two years. In 2014, video-sharing websites such as YouTube were included in the survey as an information channel. The participants (n=1086) declared that this kind of website is not useful for leisure exclusively but for information seeking as well. While in the last JAMES investigations, search engines were still ranked top of the most frequently used online information channels, they now have to share the first places with SNS. For the 2014 survey, across all ages, video portals were used by 83% as web-based information sources, search engines by 78%, SNS by 78% and press portals by 41%. Regarding the SNS declared as information channels, they were used by 73% of 16-17 years olds (vs 83% for the search engines) and by 86% of 18-19 years olds (vs 89% for the search engines).

Academic uses of SNS

Findings from a web-based survey carried out in 2014-15 among students from the UK, France, Thailand and Denmark show that SNS are information sources for most teenagers [START_REF] Aillerie | Are Social Networking Sites information sources: Informational purposes of high-school students in using SNS?[END_REF]. A short online questionnaire (10 questions) was developed. One of the limitations of this method is that it may encourage responses from those students who are most comfortable online and have good access to the internet. However, we decided this potential issue was outweighed by the fact that an online questionnaire allowed us to gain wide internation-al coverage.After questions aimed to gather basic demographic information, participants were asked about their general use of SNS (number of accounts, type of SNS) and their information uses of SNS (type of SNS used to seek information, frequency, topics sought, content shared or published on SNS). Regarding the topics sought, we predefined 13 categories from the literature review and from two pretest interviews of the questionnaire. The responses were analyzed with SPSS. Descriptive statistics were generated for all the questions and chi-squared analysis conducted for investigated differences between gender categories of respondents. In total, 473 responses were received from people aged between 15 and 18. The findings suggest that, like their older peers, 15-19 year olds make use of a wide range of possible information sources, including those sources where social interaction plays a decisive role. The results showed that more than half the respondents (55.8%) used SNS to seek information regularly and almost a further quarter (23.7%) did so occasionally. Facebook was the site used most regularly to search for information (39.1%), followed by YouTube (30.2%) and Twitter (20.0%). A little over half the number of students who used Facebook regularly in general used it regularly specifically to search for information, but more than four-fifths of the number using Twitter and YouTube in general used them specifically to search for information. The fact that even SNS with a strong social element such as Facebook were regarded as information sources by a large proportion of those surveyed means that it is important to look deeper into the question of the multiple motivations for using SNS. This demonstrates that social motivations, such as maintaining links with friends and following conversations, are definitely not the only motivation for teenagers to make use of SNS or, more precisely, that social interaction plays a great role in their information uses. Unsurprisingly, information related to friends was the most common type of information respondents looked for on SNS (69.8%). But this was followed by information about cultural events (56.0%) and international news (49.3%). Regarding academic purposes, more than one-quarter of students (27.7%) said they used SNS to find information for a task at the direction of a teacher and one-fifth (20.3%) used SNS to find information for a school project independently (i.e. not explicitly directed teacher). Around the same percentage (20.1%) said they had used SNS to find additional information about topics taught in class. In addition, over one-fifth (22.4%) used SNS to search for information related to educational and vocational guidance. These findings also highlight the heterogeneity of young people's uses of SNS for information purposes. While some teenagers make use of a wide range of SNS for both academic and everyday purposes, others restrict their information seeking to non-academic purposes, or do not feel they use SNS for information seeking at all.

Other research has indicated that college students resort to SNS, mostly Facebook, to organize their academic work. [START_REF] Selwyn | Faceworking: Exploring students' education-related use of Facebook[END_REF] conducted ethnographic analysis of the interactions of 909 students (18-25 years old) on Facebook. In response to the representation that the SNS uses are likely to be completely separated from academic concerns, the objectives of this study were specifically designed to 00 Karine AILLERIE, Sarah McNICOL Information literacy and social networking sites: challenges and stakes regarding teenagers' uses … question the reality of the activities of these students on Facebook. The author highlighted several types of interactions related to academic work and university life: exchanging information about courses or even about teachers themselves; sharing practical information (e.g. timetable) or information directly related to academic work (e.g. bibliographic requirements, coming assessments or homework assignments); and passing on teachers' advice or reading recommendations. Even if they are not specifically designed as such, SNS can be used by students to contact peers more easily, as well as to understand academic requirements or to manage collaboration. As demonstrated by the works of [START_REF] Lampe | Student use of Facebook for organizing collaborative classroom activities[END_REF] in the United States or [START_REF] Roland | Facebook au service de l'apprentissage: Regards sur quelques pratiques d'étudiants universitaires[END_REF] in Belgium, SNS are indeed known to, and localized by, most of the college students surveyed, in preference to the multiple platforms or virtual learning environments provided by universities, allowing different interaction modes and gathering of information in a single place.

A study led by [START_REF] Khan | Actual friends matter: An internet skills perspective on teens' informal academic collaboration on Facebook[END_REF] (n=690) aiming to address the lack of literature on SNS uses for academic purposes by high-school students found that more advanced students in the curriculum are also more likely to collaborate on SNS for school. The number of "active friends", rather than the total number of relationships, plays a role in this possible collaboration, as well as the positive perception of these "friends" as a possible help source. These results can be put into perspective with the French USATICE survey's conclusions. For 18 months, Cottier and Burban (2014) interviewed about 80 education professionals and 200 high school students (face to face interviews, online questionnaires and in situ observations). The authors found out that if the students have obviously personal digital practices, just a few of them acquire academic proficiencies on this basis, skillfulness in information seeking and collaboration especially. The analysis highlighted a gap between the most studious students who make their daily Internet uses scholastically profitable and students in difficulty who have personal practices sometimes competing demands of school. The most studious students cooperate for their homework assignments and know how to use non-institutional artefacts (e.g. Facebook chat) that allow them to complete or catch-up with courses, to exchange on teachers' instructions, to share search results, to cooperate, etc. The authors describe this finding as a deep pattern: generally speaking high school students use the same kind of tools but their uses are highly differentiated according to their relation to the school world more than to the technique itself. In this way USATICE study showed that students use multiple resources and tools to perform their academic tasks, resources and tools that compose a "transitional space" ("espace intermédiaire"): this space does not refer only to school practices, nor solely personal practices, is not confined to school grounds nor private life; it is composite, combining uses of academic and non-academic artefacts: academic websites for subjects, personal email accounts, Twitter, Facebook and so forth. The authors view this as a complex composition that always comes under the competency of the individual actor.

Issues and stakes for Information Literacy (IL)

Validating information as a core skill

The possibility of learning from SNS is often limited within schools due to filtering restrictions. However, there is an increasing expectation that the role of schools should be to emancipate students with what have been termed '21st century skills' [START_REF] Binkley | Defining Twenty-First Century Skills[END_REF]. 21st century skills are the competencies required to be successful in learning, in the workplace and to live effectively in the 21st century; they include collaboration, communication, ICT (or digital) literacy, creativity, critical thinking and problem solving [START_REF] Voogt | A comparative analysis of international frameworks for 21st century competences: Implications for national curriculum policies[END_REF]. Attempting to teach these skills without access to collaborative, creative digital communication tools such as SNS, presents a significant challenge. Moreover the restrictions mentioned above deny students experience of the real uses of SNS, which are definitely not limited to youth purposes and are noticeably heterogeneous. As pointed out above, information uses of SNS could be tightly linked with academic goals, occurring at school and beyond. This is not a totally new situation, as many academic activities have never taken place solely with the formal school environment. However, this phenomenon is greatly accentuated in the landscape of the digital era and this raises crucial learning stakes in terms of IL.

The first, and maybe the more significant, point is related to the validation of information, one of the core competencies of IL (ALA, 1989). Validating information found and shared on SNS requires new skills, or at least a wider dimension of capability to assess information, as the authors of the Metaliteracy framework, point out: "our understanding of information in this complex environment has been radically altered by a revolution in authority, challenging the notion of expert, and replacing it with the new distributed authorities of networked participants" (Mackey & Jacobson, 2014, p.46). Using information on SNS involves being able to assess the information that comes from both social interactions and algorithms based on individual profiles or subscriptions, across a complex range of sources from personal opinions to press releases by news agencies. Furthermore, not only information and facts, but also misinformation or rumors, can spread "virally" through SNS [START_REF] Nahon | Going Viral[END_REF]. This means it is important to be able to characterize the information available on SNS as such but not necessarily reject it straightaway for what it is. Critical thinking and analyzing skills are then crucial to identify the source, to compare information, and possibly share or publish it. This is not specific to SNS, but SNS increase the necessity of these kinds of learnings.

Implicit learnings: collaboration and organization

The second point is that collaboration is one of the skills most strongly demanded by the 21st century workplace [START_REF] Dede | Comparing Frameworks for 21st Century skills[END_REF]. While the importance of collaboration has been recognized as a feature of the changing educational landscape, IL models Information literacy and social networking sites: challenges and stakes regarding teenagers' uses … remain focused on individual skills and endeavors for the most part. Models often refer to the "information literate individual" or "information literate person" (ACRL, 2000, SCONUL, 2011), but make little or no reference to appropriate methods of sharing information or collective strategies to validate information. It would therefore appear that the growing emphasis placed on teamwork in relation to the use of new technologies and social constructivist approaches to learning has not been fully reflected in the development of information literacy models which fail to frame information seeking as a social process. While this may not be a major problem when dealing with more traditional information sources, it is problematic when dealing with SNS. Students are increasingly searching for information on SNS and using SNS to share the work they create. They also need to reflect on and assess the information they obtain via SNS, both at school and in their daily lives. Because, as indicated above, SNS are not widely available (or encouraged) in school, students are denied opportunities to develop these scholarly skills. Moreover, these uses highlight the organizational and productive dimension of the information research process that is crucial but often left on the sidelines. Students also invent "on the job" collective organizing strategies: division of tasks, validation of done work and planning of future work, adjusting to the academic requirements and end products that are requested. These organizational strategies frequently take place outside of the school, via Facebook messenger or Google hangouts for instance, and could remain at best implicit, at worse not taught at all.

Porous contexts and inequalities

From an educational and ethical point of view, it is not conceivable to let these experiences grow only in the personal sphere, family or household, without risking aggravating inequalities in terms of digital skills where inequalities already glaring [START_REF] Ali | Lecture sur support numérique en fin de collège : un peu plus d'un élève sur deux est capable de développer des stratégies d'appropriation de l'information. Note d'information Direction de l'évaluation, de la prospective et de la performance[END_REF]Hatlevik & Christophersen, 2013). In an illustration of the porosity of students' lives within and outside school, a study showed that gaps exist between what academic librarians and undergraduate students each deem useful when assessing the quality of information available through social media (Kim Sin, 2016). Librarians (n=189) appreciate strategies reminiscent of those used for evaluating a typical scholarly source, such as a journal or a book while students (n=1355) often operate strategies that rely on others' reactions and physical characteristics of the postings (e.g. length, images and sounds posted). Similar findings were demonstrated by the works of [START_REF] Cordier | Les collégiens et la recherche d'information sur Internet: Entre imaginaires, pratiques et prescriptions[END_REF] focusing on representations developed by middle school students on finding information on the Internet then comparing teaching representations and practices implemented by school librarians. The risk of such differences in approach is a total mismatch between librarians' or teachers' conceptions and their students' conceptions or behaviors. This means that advice and training offered by librarians might not make sense for students. In summary, these results about teenagers' uses of SNS call for a wider dimension for IL to take into account students' strategies in obtaining, evaluating, producing information or collaborating via SNS and the different contexts where these uses take place.

To a certain extent a mistrust still exists within the academic world towards students' personal digital habits. They are often seen as unsafe, differentiated from serious work that is done at school and from best practices taught. Although teachers are regularly encouraged to do more to help develop students' 21st century skills, as Rutowski et al (2011) acknowledge, "significant challenge remains in integrating ICT into the pedagogical practices aimed at developing 21st-century skills" (p. 190). One issue, as [START_REF] Voogt | Challenges to learning and schooling in the digital networked world of the 21st century[END_REF] argue in line with the results exposed above, is that the development of 21st century skills can (and often does) take place outside formal education and may therefore go unacknowledged. The skills students develop through their use of social media outside school could prove useful if teachers are able to capitalize on this. One of the key precepts of social constructivism (Vvgotsky, 1978) is that learning extends to the home and other out-of-school environments and activities and all learning situations should be related. As Lewin and McNicol (2014) point out, "the increasing use of social media is another way in which the learning environments are changing to facilitate the development of 21st century skills...this can impact on digital literacy, collaboration and communication skills, creativity and critical thinking as students are presented with new ways to work together, create new types of learning output " (p.194).

Conclusion

As we have argued above, current IL models and approaches are often not well-suited to the needs of SNS. We would suggest that IL models need to be concerned with the needs of learners in practice, not merely as theoretical ideals. At its most basic level, this means not ignoring SNS, but acknowledging that they are used by learners for informational purposes, possibly in creative and sophisticated ways. Filtering SNS is not an appropriate way of dealing with such information sources, rather they need to be harnessed and exploited to the full. This is likely to mean an increased focus on collaboration. It is important that any IL education takes account of, and capitalizes on, the pre-existing knowledge and practices of learners, but this is especially relevant in the case of SNS which are so widely used outside of formal education settings. As well as the differing experiences of students, IL education also needs to take account of differences between the types of information on SNS and between different users. Information about friends and information about international affairs, for example, are judged by different criteria; there is no 'one size fits all' approach to evaluating information from SNS. Information uses of SNS by teenagers present us with a concrete situation where digital literacy should be directly and critically implemented to inform the personal and social life of students as future citizens. SNS are indeed used for information, or even decision-making, in many

Information literacy and social networking sites: challenges and stakes regarding teenagers' uses … areas of people's personal and social lives, such as health, educational and vocational guidance or politics and learnings developed at school are crucial here.

The empowerment of everyone by using SNS (for socialization, information and participation) is related directly to the consideration of the educational issues they raise. Technology is not an empowering resource in its own right, but SNS and other technologies can empower learners when they are combined with education about the ways in which they can best be used.

The research field of digital education has made few links between institutional settings and informal learnings; those which do exist are focused mostly on formal settings of school/classroom, in particular to measure impact of ICT on learning gains [START_REF] Selwyn | Looking beyond learning : Notes towards the critical study of educational technology[END_REF]. Teenagers' uses of SNS raise educational major issues, but also epistemological and methodological points, we would expose within a coming paper.

Information


Yubing Pu

Bertrand Laratte

Robert S Marks

Rodica E Ionescu
email: elenarodica.ionescu@utt.fr

Impact of copper nanoparticles on porcine neutrophils: ultrasensitive characterization factor combining chemiluminescence information and USEtox assessment model

Keywords:

There is a need by the European Commission (EC) regulations, as well as others, to reduce the use of in vivo toxicity tests made on animals. Conventional in vitro tests were designed for non-nanoparticle sized entities, and therefore do not necessarily assess the adverse impacts of engineered nanoparticles (ENPs) on humans. Life cycle assessment (LCA) is a widely used methodology for evaluating the environmental performance of products. Nevertheless, the application of LCA on ENPs is difficult because the characterization factors (CFs) of ENPs, as significant input parameters in LCA, remain a major unknown. It is a premise of this study to monitor the chemiluminescence (CL) spectra resulting from the reactive oxygen species (ROS) trigger made from the presence of copper nanoparticles (CuNPs) to porcine neutrophils in vitro, thereby enabling to calculate the porcine and human toxicity CFs. The framework of a scientific consensus model, USEtox model, is selected and the midpoint of CF is set as the inflammation of pig or human. Finally, the present study recommends human and porcine inflammation CFs of CuNPs in Europe to be 1.07 and 2.90 CTU (comparative toxic units) respectively.

Introduction

With the rapid development of newly engineered nanoparticles (ENPs) [1][2][3], and their putative introduction to the environment, there are serious safety concerns towards consumers and production staff and therefore should be considered since the ENPs are likely to enter the bloodstream of mammals via skin, inhalation and ingestion [4][5][6]. Neutrophils are an abundant type of white blood cell in mammals and are considered to be one of the earliest and most prominent immune defense responders, acting through various mechanisms including phagocytosis, degranulation, etc [7]. Once ENPs invade a blood system, neutrophils will find a role to play as an essential part of the innate immune system [8]. Many experiments have been performed to investigate the mechanisms of interactions between ENPs and human neutrophils [5,[9][10][11]. However, to the best of our knowledge, using neutrophils to assess the toxicity of ENPs has never been reported and is the premise of our present study. In addition, when evaluating the potential impact of ENPs-containing products (especially the under-design ones) to humans, the experimentations fall short of expectation being either insufficient or impractical. Therefore, many assessment methods for human toxicity have been developed to help relieve the pressure of animal experimentation and provide the end-user with a decision-making tool [12][13][14]. One such method is the life cycle assessment (LCA) method that thrives to be an internationally standardized tool and has been recommended by the European Commission (EC) [15,[START_REF]ILCD Handbook: Recommendations for Life Cycle Impact Assessment in the European Context[END_REF]. Characterization factors (CFs) are essential parameters for LCA and reflect the potential environmental impacts and relative importance of materials [START_REF] Laratte | [END_REF]. Nevertheless, there is a complete lack of human toxicity CFs towards various ENPs [18].

The authors have selected copper nanoparticles (CuNPs) as the representative ENPs, to in vitro investigate the chemiluminescence of freshly isolated neutrophils from pigs. The pig is usually considered as an excellent animal biomedical model for human, because of the many similarities between human and pig [19,20]. The present work has two purposes. The first aim concerns the investigation of the impact of CuNPs on life-status of porcine neutrophils while observing their impact via chemiluminescent (CL) kinetic imprinting and scanning electron microscopy (SEM) characterization. The second aim uses the CL results to estimate porcine and human inflammation CFs of CuNPs under the framework of a scientific consensus model.

Materials and methods

Materials and instrumentation

Heparin sodium (H3393), histopaque (10771), dextran (D4876), trypan (T8154), dimethyl sulfoxide (DMSO, D4540), glutaraldehyde (G7651), copper NPs (CuNPs, <100 nm, 634220) were purchased from Sigma-Aldrich. Ethanol (02860) was provided by Fluka (Lyon, France). Luminol/Enhancer (Bio-Rad HRP, CA) was used to amplify chemiluminescence activity. Phosphate buffered saline (PBS buffer, pH 7.4) was freshly prepared in our lab using sodium chloride (S7653), sodium phosphate dibasic (94046) and sodium phosphate monobasic (71505) purchased from Sigma (France). Zymosan A (Z4250, sigma) suspension, used as a stimulating agent, in previous prepared Krebs-Ringer phosphate (KRP) medium was opsonized for 30 min at 38 • C in a water bath (Grant Instruments Type VF, Cambridge Ltd, UK) before being used.

Prior to preparing "a" stock analyte solution, an autoclave-steam sterilizer (2540 ML-Tuttnauer, Netherlands) was used to sterilize deionized water produced by Millipore water purification system (Molsheim, France). A conventional optical microscope was used to count cells by a hemocytometer (Hausser scientific, Horsham, USA). Different dilutions of nano copper solutions were well dispersed by an ultrasonic water bath (Elmasonic S30H) and a vortex mixer (G560E, Scientific Industries Inc., USA). A centrifuge (Universal 320R, Andreas Hettich GmbH, Germany) was employed. The morphology of cells was characterized by a field emission scanning electron microscopy (Hitachi SU8030, Japan) with an accelerating voltage of 10 kV.

Experimentation methodologies

The experiments in this study were first based on a representative group of four pigs, and once similar tendencies were observed, it was sufficient to rely on experimental results from a single pig with replicas made thrice. It should be noted, however, that the calculation of characterization factors used the results from all four pigs.

Neutrophilisolation

For each experiment, porcine blood was collected from the external jugular vein of a clinically healthy domestic pig (≤6 month, 100-110 kg) in the morning between 4:30 to 5:10 in an slaughter housenear Troyes (Sicaba, Pont-Sainte-Marie, France). The use of pigs in this study was approved by the Service of Animal Health Protection and Environment in the Prefecture of Aube (approval no. 10-387-901). In the day of the experiment, 50 mL porcine blood was collected and anticoagulated with 7 mg Heparin sodium dissolved in 1 mL KRP medium. According to the gradient density centrifugation method as previously described [11,21,22], a slightly modified isolation protocol of porcine neutrophils was used and described in detail in the Supplementary material (Section S1).

Mortality rates calculation and chemiluminescence measurement

The freshly isolated porcine neutrophils were exposed to CuNPsat ten different concentrations in ten eppendorf tubes (1 mL) at 20 • C for about 11 h. Here, a specific concentration unit was introduced to better understand the relationship between CuNPs and neutrophils: g/mln, which means the mass of CuNPs per million neutrophils (The conversion method and an example were shown in Supplementary Fig. S1). By subjecting the tube for a few seconds of hand-shaking we assume that both neutrophils and CuNPs were well dispersed. The 10 concentrations of CuNPs were 0, 0.1, 0.4, 1, 1.5, 2, 4, 10, 20, 40 g/mln, respectively. During exposure, the mortality rates of neutrophils were calculated at six selected time points (0.875, 2.925, 5.025, 7.175, 9.025 and 11.025 h) with Trypan blue and Hemacytometer (Supplementary Fig. S2 illustrating the cell counting procedures). In addition, during the exposure, chemiluminescence (CL) spectra were also recorded for each concentration over six selected periods of time (0.5, 2.5, 4.6, 6.8, 8.7 and 10.7 h, respectively). CL measurements were performed within standard 96 wells Costar microtiter-plates (Corning, USA) and a luminometer (Luminoskan Ascent, Thermo Fisher Scientific, USA). The relative areas under each CL spectrum were respectively calculated (an example of an original control CL spectrum is shown in Supplementary Fig. S3).

Morphology characterization of CuNPs and neutrophils

Three different sample preparation procedures were applied for the surface morphology characterization of CuNPs. For apristine sample, CuNPs powder without any treatment was spread onto a carbon conductive tape; for CuNPs in water, 200 g/mL CuNPs solution with ddH 2 O was ultrasonicated for 5 min, followed by deposition of one drop of solution onto the gold-palladium covered glass substrate then dried for SEM imaging; furthermore, for CuNPs in KRP buffer solution, 200 g/mL CuNPs in KRP solution was ultrasonicated for 5 min then followed the same procedures as for neutrophils to prepare the SEM samples. The preparation of goldpalladium covered glass substrate was described in Supplementary material (Section S5).

The neutrophils exposed to selected concentrations of CuNPs were all fixed in 2.5 wt% glutaraldehyde aqueous solutions overnight after certain time intervals. Then, the neutrophil suspension was centrifuged (4000 rpm, 5 min), followed by a stepwise dehydration using a series of graded ethanol aqueous solutions of 30%, 50%, 70%, 100% and 100% respectively. Each step was conducted for 10 min at room temperature followed by centrifugation (4000 rpm, 5 min). Hereafter, the neutrophils were well dispersed in absolute ethanol, and deposited as 2.5-5 L solution onto the surface of prepared glass substrates. Prior to SEM characterization, the samples were dried freely in the hood and covered with a thin layer (2-3 nm) of gold-palladium to suppress the charging effects.

Characterization factor

Life cycle impact assessment (LCIA) is one of the conventional steps when conducting life cycle assessment (LCA), which could translate the emissions into indicators that reflect the environment and its health impacts [START_REF]Environmental Management, Life Cycle Assessment, Requirements and Guidelines[END_REF]. The typical calculation in LCIA is based on the following equation:

Ecosystemorhumanimpact = M × CF (1)
where CF is the substance-specific characterization factor that expresses the potential impact of each single elementary material flow (M) contributing to the total impacts [START_REF] Hauschild | [END_REF]. Characterization factor (CF) thus facilitates the comparisons between the different substances in terms of their ability of contribution to the impacts on ecosystem or human.

Selection of characterization model

Many characterization methods are available to calculate the CFs for the human toxicity impact category, such as IMPACT 2002+ [25], ReCiPe [START_REF] Goedkoop | A life cycle impact assessment method which comprises harmonised category indicators at the midpoint and the endpoint level[END_REF], EDIP2003 [START_REF] Potting | Background for Spatial Differentiation in Life Cycle Impact Assessment: The EDIP2003 Methodology[END_REF], MEEuP [START_REF] Kemna | MEEUP Methodology Report[END_REF], USEtox [START_REF] Rosenbaum | [END_REF], etc. A comprehensive comparison between these models were performed by Joint Research Centre of European Commission [START_REF]ILCD Handbook: Recommendations for Life Cycle Impact Assessment in the European Context[END_REF]. The compared models were rated under the criteria defined in ILCD Handbook: LCIA -Framework and Requirements [START_REF]ILCD Handbook: Framework and Requirements for Life Cycle Impact Assessment Models and Indicatiors[END_REF]. Several features make the USEtox model stand out compared to others [START_REF]ILCD Handbook: Recommendations for Life Cycle Impact Assessment in the European Context[END_REF]. For example, the USEtox model accounts for a chemical's fate in the ecosystem, human exposure and differences in toxicological response, while the MEEup model is not [START_REF]ILCD Handbook: Recommendations for Life Cycle Impact Assessment in the European Context[END_REF]. In addition, the USEtox model includes all vital model elements in a scientifically sound way for most substances, which makes it have the best compliance with the science-based criteria [START_REF]ILCD Handbook: Recommendations for Life Cycle Impact Assessment in the European Context[END_REF]. Therefore, the USEtox has been recommended as a scientific consensus method for the calculation of CFs by many international organizations such as the European Commission (EC), Business Council for Sustainable Development (WBCSD), etc [START_REF]ILCD Handbook: Recommendations for Life Cycle Impact Assessment in the European Context[END_REF][START_REF] Hischier | [END_REF][START_REF] Fantke | USEtox ® 2.0 Documentation (Version 1), USEtox ® Team[END_REF]. In this study, the framework of the USEtox model has been selected for calculating the porcine and human toxicity CFs.

Model concept

In USEtox, a characterization factor (CF) describes a potential impact on human health related to the emission of a given substance. It can be expressed by [START_REF] Rosenbaum | [END_REF]:

CF = FF × XF × EF (2)
The human toxicity CF has a unit of comparative toxic units (CTU), which represents the estimated increase in morbidity in the total human population, per unit mass of a substance emitted (cases/kg emission ). The fate factor, FF (unit: day), represents the residence time of a substance in a particular environment (such as freshwater). The exposure factor, XF (unit: day -1 ), reflects the rate at which a pollutant is able to transfer from a receiving compartment into the human population through a series of exposure pathways. The EF is the human effect factor (unit: cases/kg intake ) and reflects the change in the life time disease probability, due to the change in life time intake of a pollutant.

It should be highlighted that the chemiluminescence of exposed porcine neutrophils provides the essential data for calculating the effect factors (EFs) of CuNPs for both pigs and humans. The humanequivalent toxicological data could be obtained by applying an extrapolation factor for interspecies (pig to human) differences, which makes it possible to use toxicological data on laboratory animals to calculate the human toxicity CFs.

Fate factor and exposure factor calculation

The fate factor (FF) depends on the properties of the substances and the interaction compartments (e.g. from urban air to fresh water). Compared to materials in bulk phases, the ENPs show different fate behaviors after entering the environment. Several previously published studies proposed nano-specific fate models based on the colloidal science and the parameters of ENPs [15,[START_REF] Meesters | [END_REF][34][35][36]. Main differences between these models are the different compartments (air, water and soil) and removal processes considered. A recently published model by our group took water and sediment account and proposed a FF of 1.803 day of CuNPs in Europe freshwater for eco-toxicity assessment [37]. In terms of the USEtox model, the FFs of a substance in a given compartment are the same for ecotoxicity and human toxicity [START_REF] Fantke | USEtox ® 2.0 Documentation (Version 1), USEtox ® Team[END_REF]. Thus, in this study, the FF of 1.803 day of CuNPs in European freshwater was applied to calculate the characterization factors of CuNPs. The further details about the fate model could be found in the related reference [37].

In this study, the exposure factor (XF) was set as 1 conservatively due to the lack of bioaccumulation factors (BAF) of ENPs. The experimental data are unavailable for BAF and the estimation of BAF is also impracticable due to the invalid partition coefficients for ENPs [38]. Nevertheless, as one important factor in CF calculation, XF may have a big influence on the final CF values. Therefore, further investigations of XF are necessary in the future.

Effect factor calculation

The EF (cases/kg intake ) in this paper reflects the change of the inflammation's probability due to the change of the CuNPs intake in the whole life time of either pig or human. Under the assumption of linearity in dose-response (up to where the life time disease probability is 0.5), EF equals [START_REF] Rosenbaum | [END_REF]:

EF = 0.5 ED50 (3)
ED50 (kg lifetime -1 ) is the dose amount of ingested substance for either animal (ED50 a , e.g. pig) or human (ED50 h ) during whole its life time that causes an inflammation with a probability of 50%. The ED50 h of a human is calculated based on the following equation [START_REF] Rosenbaum | [END_REF]:

ED50 h = ED50 a,t • BW h • LT h • N AF a • AF t • 10 6 (4)
ED50 a,t (mg kg -1 day -1 ) is the dose for animal during exposure time t (day). AF a is the extrapolation factor for interspecies differences (pig to human is 1.1) [START_REF] Rosenbaum | [END_REF], because the ED50 h is derived from the toxic data of pigs. BW h and LT h respectively represent average body weight (70 kg, data from USEtox) and average lifetime of human (70 years, data from USEtox). N is the number of days per year (365 days•year -1 ). Since chronic toxicity values have priority in USEtox, thus an extrapolation factor AF t of 5 for acute to chronic exposure is applied.

In this study, in order to compare the impacts of CuNPs on human and pig, both porcine and human CFs were calculated. The ED50 a of pig can be calculated by:

ED50 a = ED50 a,t • BW a • LT a • N AF t • 10 6 (5)
where BW a represents average body weight (110 kg, data from the abattoir). The lifetime of pig (LT a ) is between 10-20 years [20]. Thus, here, an average LT a value of 15 years is used.

In this study, it was assumed that there are no individual differences among pigs or humans and when the amount of ROS produced by neutrophils decreases to the 50% amount of ROS of a control sample, the inflammation probability is 50%. Thereby, the ED50 a,t values were calculated based on the 50% calculated relative area under CL spectrum of the control samples. The detailed calculation method of ED50 a,t is described in Supplementary Fig. S4.

Results and discussion

Effects of CuNPs on neutrophils: mortality rates

Fig. 1a shows that the exposure durations and concentrations of CuNPs have both positive correlations on the mortality rates of neutrophils. For instance, after exposure for about 11 h, the mortality rate of neutrophils with 40 g/mln CuNPs was found to be 92.04%, while that of no-CuNPs control neutrophils (unexposed cells to CuNPs) was merely 34.63%. Fig. 1b displays the mortality induced specifically by CuNPs by subtracting the mortality rates of the no-CuNPs control neutrophils. Within about 3 h, all the nine curves showed obvious upward trends. After the initial increase, the mortality rates induced by CuNPs remain steady, which means that the CuNPs may no longer influence the mortality of neutrophils after an exposure of 3-7 h. Fig. 1c shows the average mortality rate speed (AMRS) of neutrophils exposed to CuNPs. The detailed calculations were described by equation S4 and S5 in Supplementary material. In this study, the mortality rates of neutrophils were tested at six time points (0.875, 2.925, 5.025, 7.175, 9.025 and 11.025 h), which divided the whole test period into five stages (I, II, III, IV, V in Fig. 1c). When the concentration of CuNPs was below 0.1 g/mln, the AMRS of neutrophils is observed to increase at the onset (stage I and first half of stage II) then decreases (second half of stage II and stage III). In contrast, when the concentration of CuNPs is above 0.4 g/mln, the AMRS of neutrophils continues a rapid decrease until 7 h (stage I, II and III). Nevertheless, for all the neutrophils exposed to the studied concentrations, the AMRS becomes stable in stage IV and V.

Effects of CuNPs on neutrophils: chemiluminescence kinetics

Inflammatory mediators such as nanomaterials can trigger neutrophils to produce reactive oxygen species (ROS) [39]. Since the intensity of light emitted by stimulated neutrophils is proportional to the production of reactive oxygen species (ROS) [40,41], neutrophils' capacity to generate ROS was indirectly estimated by conducting chemiluminescence (CL) measurement. The relative area under the CL spectrum represents the ability of neutrophils to generate ROS and has been found to be useful for detection of inflammatory diseases [41]. Fig. 2a shows the relative areas under CL curves of neutrophils exposed to nine concentrations of CuNPs at six selected time. All the curves in Fig. 2a have a trend of rise initially then fall to 0 at different CuNPs concentrations (see insert in Fig. 2a). It reveals that low concentrations of CuNPs could stimulate neutrophils, while high concentration CuNPs could inhibit the oxidative burst of neutrophils.

The mortality rates of neutrophils with time ∼50 min are smaller than with ∼3 h (Fig. 1a). However, the amounts of ROS for 0.5 h are lower than for 2.5 h when the concentration of CuNPs less than 5 g/mln (Fig. 2a). It means that the lower concentration of CuNPs (<5 g/mln) stimulated neutrophils slower than higher ones.

Fig. 2b presents the maximum values of the calculated relative area under CL spectra at six selected time points. Each circle indicates the given time point, at which concentration of CuNPs stimulate the neutrophils most. In Fig. 2b, the maximum relative area rises fast from 0.5 to 2.5 h, then declines continuously. The porcine neutrophils generate the most ROS at about 2.5 h. Such cellular behavior indicates that neutrophils would not be activated immediately when exposed to CuNPs. Furthermore, when the neutrophils are fully activated, the concentrations of CuNPs that most stimulate the neutrophils decrease along with time. We may infer that the neutrophils become increasingly sensitive along with their exposure to CuNPs.

Scanning electron microscope (SEM) characterization of CuNPs

The surface morphology of CuNPs is shown in Fig. 3. It reveals that the pristine CuNPs and the CuNPs dispersed in ddH 2 O are granulated and around 50-200 nm in diameter (Fig. 3a andb). After being dispersed in a salt-based KRP buffer solution, these same CuNPs were observed to form Cu nano-sheets (Fig. 3c andc1) due to some unknown mechanism. The plan dimension of highly anisotropic nanosheets can reach over 15 m (Fig. 3c) while the thickness is estimated around 25 nm (Fig. 3c1 inset).

Rearrangement of copper nano-sheets initiated by porcine neutrophils

Fig. 4 shows the morphology of neutrophils before and after being exposed to 40 g/mln CuNPs for different durations via scanning electron microscope (SEM) imaging. Moreover, similar characterization on neutrophils exposed to 20 g/mln CuNPs was also reported in Supplementary Fig. S4. The size of unexposed porcine neutrophils is about 3 m and the cells are rounded with membrane folds, as similar as the results in previous reporting [42,19]. It is obvious that CuNPs already changed to nano-sheets at 0.4 h incubation with neutrophils and the size of nano-sheets is around 3-5 m. The morphology of neutrophils changed after exposure to 40 g/mln CuNPs (Fig. 4b-d). After incubation with CuNPs for 1.1 h, the phagocytosis phenomenon of neutrophils was observed as shown in Fig. 4c. Interestingly, the neutrophils were surrounded by copper nano-sheet particles, at a considerably larger size (Fig. 4d). The thickness of a single copper nano-sheet covering the neutrophils, ranges from 20 to 50 nm (estimated from Fig. 4d1 ′ ) similar to the original nano-sheets in Fig. 3c1. Nevertheless, the plan dimension of nanosheets is about 2-5 m, which is much smaller than the nanosheets without neutrophils. In addition, it should be noted that after neutrophils were exposed to 40 g/mln CuNPs for 8 h, the CL signal decreased to 0, despite the fact that there were still about 10% live neutrophils (see Fig. 1a). Here two reasons are proposed. On one hand, neutrophils may 'exhaust' themselves during their interactions with the copper particles, thus reducing their light signal output drastically, though they are observed to be alive; on the other hand, the layers of copper nano-sheets may scatter and quench some of the light produced by the CL reaction.

Moreover, it was found when the CuNPs concentration was lower than 2 g/mln, there was no coverage of copper nanosheets on neutrophils, while the coverage started to be observed from 2 g/mln CuNPs onwards (Supplementary Fig. S5). Thus, even though the same pristine CuNPs (Fig. 3a) were used in all the experiments, the morphology of CuNPs during the experiment became complex, which may be influenced by the CuNPs concentrations and exposure time with neutrophils.

Characterization factor calculation

The EFs for pig and human over time (values are listed in Supplementary Table S1) were respectively calculated based on the average values of ED50 a,t which were derived from the chemiluminescence results. Fig. 5 shows the human and porcine inflammation CFs based on different exposure periods. To the best of our knowledge, this is the first time that the porcine toxicity CFs are reported. To simplify the comparison, the porcine toxicity CF keeps the same unit (CTU) as human toxicity CF but has a slightly different meaning. The CF for porcine toxicity impact represents the potential increase in morbidity in pigs per unit mass of a chemical emitted. Since the human inflammation CFs are based on the same porcine toxicological data, both human and porcine inflammation CFs display similar upward trends over time. No matter in which exposure period, the inflammation's cases of pig are 2.7 times higher than that of human. Except the employed extrapolation factor for interspecies differences (pig to human: 1.1), the different body weight and life-time between human and pig are also reasons causing the CFs variation between human and pig. The body weight of pig is about 1.6 times heavier than human, while the lifespan of pig is ∼4.7 times shorter than human.

The CFs values for 24 h and 8 h are respectively about 3000 and 20 times of CFs values for 2 h. It indicates that exposure time is a significant parameter when calculating CFs. Here, we give the recommendations for using CFs values of 8 h in the future life cycle assessment (LCA) of CuNPs containing products, because the human neutrophils are considered short-lived cells with a half-life in the circulation of approximately 8 h in humans [43]. Therefore, the recommended human and porcine inflammation CFs are 1.07 and 2.90 CTU respectively. Nevertheless, it should be noted that the recruitment of neutrophils is not considered, which may overestimate the CFs values.

Conclusion

In summary, a rapid and promising method to evaluate the impacts of cooper nanoparticles on pig and human via a combination of in vitro experiments and assessment model is reported. The results by further toxicity evaluation with USEtox model were considered representative, despite the fact that some assumptions and simplifications were made in the calculation of CFs. In the case of CuNPs, it was found that both the exposure time and concentrations of CuNPs have a positive correlation on neutrophil mortality rates. The CL kinetic results indicated that the neutrophils exposed 2.5 h to CuNPs showed the highest activity ability. In addition, an estimated rearrangement of copper nano-sheets initiated by porcine neutrophils was also presented. Furthermore, a porcine and human inflammation characterization factor of 1.07 and 2.90 CTU for CuNPs was recommended respectively. These CF values for CuNPs could be used in the future for assessing the risks of CuNPs containing products by LCA method.

Fig. 1 .

 1 Fig. 1. (a) Mortality rates of neutrophils exposed to CuNPs of different concentrations over time (the conversion of unit g/mL to g/mln can be found in Supplementary Fig. S1. For instance, 40 g/mln corresponding to 200 g/mL, 0.1 g/mln corresponding to 0.5 g/mL); (b) Neutrophil mortality induced by CuNPs after subtracting the control (the horizontal error bars indicate the ranges of time needed for counting the neutrophils numbers); (c) Average mortality rate speed (AMRS) of neutrophils exposed to CuNPs of different concentrations in five stages (divided by the red dash-dot line). Stage I from 0.878 to 2.925 h, stage II from 2.925 to 5.025 h, stage III from 5.025 to 7.175 h, stage IV from 7.175 to 9.025 h and stage V from 9.025 to 11.025 h. The x-axis values of the points in Fig. 1c are 1.9, 3.98, 6.1, 8.1 and 10.03 h respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2 .

 2 Fig. 2. (a) Calculated relative areas under CL spectra of neutrophils exposed to different concentrations of CuNPs for different durations; Error bars represent the standard deviation based on three replicas in one experiments. (b) Maximum values of calculated relative area under CL spectrums extracted from Fig. 2a. The size of the circle represents the relative area value.

Fig. 3 .

 3 Fig. 3. Scanning electron microscope (SEM) images of CuNPs: (a) Pristine CuNPs powders; (b) CuNPs in ddH2O; Cu nano-sheets originate from CuNPs in KRP buffer solution with (c) 2500 magnification times and (c1) 50000 magnification times. The inset in c1 is a SEM image showing that the thickness of the nano-sheet is around 25 nm.

Fig. 4 .

 4 Fig. 4. SEM images of: (a) fresh neutrophils after isolated 0.4 h; neutrophils exposed to 40 ug/mln CuNPs for (b) 0.4 h; (c) 1.1 h; (d) 8 h; (d1) and (d1 ′ ) with different magnification times are SEM images zoomed in from (d).

Fig. 5 .

 5 Fig. 5. Human and porcine inflammation CFs for different exposure periods. Error bars represent the standard deviation based on four individual pigs. The CFs values of 24 h are derived from the exponential extension of the original toxic data (details can be found in Supplementary Fig. S6).

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Acknowledgments

The authors kindly thank the Abattoir SICABA located at Pont-Sainte-Marie for providing the fresh porcine blood. Yubing PU kindly thanks the China Scholarship Council (CSC) for his PhD scholarship in France (September 2013-March 2017).

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.mtcomm.2017. 02.008.


Yubing Pu
email: yubing.pu@utt.fr

Bertrand Laratte

Rodica Elena Ionescu

Freshwater Sediment Characterization Factors of Copper Oxide Nanoparticles

Wide use of engineered nanoparticles (ENPs) is likely to result in the eventually accumulation of ENPs in sediment. The benthic organisms living in sediments may suffer relatively high toxic effects of ENPs. This study has selected copper oxide nanoparticles (nano-CuO) as a research object. To consider the impacts of spatial heterogeneity on ENPs toxicity, the characterization factor (CF) derived from life cycle assessment (LCA) methodology is used as an indicator in this study. A nano-specific fate model has been used to calculate the freshwater sediment fate factor (FF) of nano-CuO. A literature survey of the nano-CuO toxicology values has been performed to calculate the effect factor (EF). Seventeen freshwater sediment CFs of nano-CuO are proposed as recommended values for subcontinental regions. The region most likely to be affected by nano-CuO is northern Australia (CF of 21.01• 10 3 CTU e , comparative toxic units) and the least likely is northern Europe and northern Canada (CF of 8.55• 10 3 CTU e ). These sediment CFs for nano-CuO could be used in the future when evaluating the ecosystem impacts of products containing nano-CuO by LCA method.

Introduction

The volume of production of nanomaterials (NMs) have grown extensively over the last decade. [1] NMs are widely used in many household products including cosmetics, sunscreens, textiles etc. [2] Although people benefit a lot in using NMs, the potential negative impacts of NMs are also raised concerns. Many researches demonstrated the toxicity of NMs to environment. [3,4] As a principal member of NMs, the engineered nanoparticles (ENPs) are widely used and likely to be released into ecosystem. [1] It was reported that the ENPs are usually released into the freshwater through the wastewater treatment plant. [5] Freshwater sediment is one of the main fate of suspended ENPs in freshwater. [6] Thus, the benthic organisms living in sediments may suffer relatively high toxic effects of ENPs. [7] Generally, the bio-experiment is one effective way to investigate the toxicity of ENPs on organisms. However, most experiments have difficulty in assessing the impacts of spatial heterogeneity on ENPs toxicity. A recently published article indicated that the hydrogeology has significant influences on the impacts of ENPs because of the different residence time of ENPs in different freshwaters. [6] In order to assess the impacts of hydrogeology on nanotoxicity to sediment, characterization factor (CF) is used as an indicator in this study. The CF is derived from life cycle assessment (LCA) and reflects the relative importance of each component in life-cycle inventories. [START_REF]Environmental Management, Life Cycle Assessment, Principles and Framework[END_REF] It can be calculated under the framework of a scientific consensus environment model, USEtox model, which is recommended by many international organizations (e.g. European Commission, etc.). [START_REF] Fantke | 0 Documentation[END_REF] This study has selected copper oxide nanoparticles (nano-CuO) as a research object due to their relatively high toxicity than other metal oxide nanoparticles (e.g. nano-ZnO, nano-TiO 2 , etc.) [10] The aim of this study is to propose the freshwater sediment CFs of nano-CuO for different regions. A nano-specific fate model with 17 regional-specific input parameters has been applied to calculate the sediment fate factors (FFs) of nano-CuO. [6] In addtion, a literature survey of nano-CuO toxicology values has been performed to calculate the effect factor (EF). Finally, seventeen freshwater sediment CFs of nano-CuO are proposed as recommended values for subcontinental regions.

Methods

General framework

The USEtox model proposed a method to calculate the ecotoxicological characterization factor (CF) of a substance for freshwater ecosystems. In this study, to calculate the CF for freshwater sediment, a small modification of the method is needed. The research object should be changed to the sediment compartment. The adapted characterization factor (CF, unit: PAF• m 3 • day• kg -1 ) of a substance for freshwater sediment is the product of three factors: fate factor (FF, unit: day), exposure factor (XF, unit: dimensionless) and effect factor (EF, unit:

PAF• m 3 • kg -1 ):[9] 𝐶𝐹 = 𝐹𝐹 • 𝑋𝐹 • 𝐸𝐹 (1)
The FF represents the residence time of the substance in sediment and the XF is the bioavailability of a substance. The EF reflects the change in the potentially affected fraction (PAF) of species because of the change in substance concentration in sediment. In practice, the unit of CF is defined as comparative toxic units (CTU e ) for ecosystem.

Calculation of fate factor

2.2.1. Fate model concept A nano-specific fate model with 17 regional-specific input parameters has been used to calculate the sediment fate factors (FFs) of nano-CuO. [6] Figure 1 shows the simplified fate model of nano-CuO in freshwater-sediment ecosystem. It was assumed that there is no direct ENPs emissions to sediment. [6] The following behavior as shown in figure 1 is considered as the removal processes for sediment: (1) resuspension from sediment to water; (2) burial into the deep sediment; (3) horizontal bed load transfer to other sediment compartment.

The total removal rate of nano-CuO in sediment (k sed,sed , s -1 ) is expressed as a sum of the rate constant for horizontal bed load transfer (k sed,transfer , s -1 ), burial (k burial , s -1 ) and resuspension (k resusp , s -1 ):

𝑘 sed,sed = 𝑘 sed,transfer + 𝑘 burial + 𝑘 resusp

The detailed calculation methods and the input values for the above elements could be found in the study of Pu, et al. [6] The nano-CuO density of 6320 kg• m -3 was used in the calculation. If not specified, the FF refers to FF sed,sed in this study.

Calculation of exposure factor

In USEtox, the freshwater exposure factor (XF) is defined as the fraction of a chemical dissolved in freshwater. [START_REF] Fantke | 0 Documentation[END_REF] In this study, it is assumed that the CuO nanoparticles are all bioavailable and dispersed homogeneous in the freshwater sediment. Thus, here XF is set to be 1 to avoid significant errors in nano-CuO CF predictions.

Calculation of effect factor

The aquatic ecotoxicological effect factor (EF) is calculated based on geometric means of single species EC50 value in USEtox. [START_REF] Fantke | 0 Documentation[END_REF] EC50 (kg• m -3 ) is the effective concentration of a substance at which 50% of a population (e.g. fish) displays a phenomenon (e.g. mortality). In this study, due to the research object is sediment, the EC50 ref from reference has a unit of kg nano-CuO per kg sediment. Therefore, a unit conversion is necessary to obtain the EC50 in unit of kg nano-CuO per m 3 sediment. In addition, when chronic EC50 value is unavailable, an acute to chronic ratio (ACR, 15 in this study) will be used to derive the chronic-equivalent EC50.

𝐸𝐶50 chronic = 𝐸𝐶50 ref •𝜌 sed 𝐴𝐶𝑅 (4)
where ρ sed (kg sediment per m 3 sediment) is the bulk density of freshwater sediment. Here, the value of 1230 kg• m -3 has been applied. [6,[START_REF] Fantke | 0 Documentation[END_REF] The obtained EC50 chronic was used to calculate the sediment EF of nano-CuO. The detailed calculation methods of EF could be found in the study of Pu, et al. [6] 3. Results and discussions

Fate factors of nano-CuO

As shown in figure 2, a low persistence of nano-CuO in the sediment (~1218 day) occurs in northern Europe and northern Canada regions (W12), while high FF values (~2991 day) is observable in northern Australia (W3). In the following calculation of CFs, these values of FFs were used. The transport rate constants of nano-CuO between freshwater and sediment for 17 subcontinental regions are listed in Table 1 to explain the differences of FFs. For example, the transport rate of nano-CuO from freshwater to sediment (k w,sed ) for W3 region is more than 5 times faster than that for W12 region (2.07• 10 -5 versus 3.77• 10 -6 s -1 ), while the inverse transport rate (k sed,w ) for W3 region is less than one third of that for W12 region (3.32• 10 -9 versus 1.00• 10 -8 s -1 ). It's the apparent reason of longer residence time in sediment of W12 region than W3 region. The underlying cause is the the subcontinental difference of freshwater-sediment parameters.

Effect factor of nano-CuO

It is preferred to use toxicity data (EC50s in three trophic levels) reflecting the entire ecosystem in EF calculation. [START_REF] Hauschild | [END_REF] Nevertheless, the toxicity values of nano-CuO to the organisms living in sediment were rarely reported. In this study, one toxicity value (EC50 = 868 μg•g -1 ) of nano-CuO on an estuarine amphipod, Leptocheirus plumulosus, [7] was found and used in the calculation of sediment EF of nano-CuO. The sediment EF value of 7.025 PAF• m 3 • kg -1 for nano-CuO was calculated and applied to calculate the freshwater sediment characterization factors of nano-CuO. Based on the calculated EF and FFs of nano-CuO, the recommended world-wide nano-CuO sediment CFs were obtained and are depicted in figure 3. The deeper color on the map represents the larger CF of nano-CuO for the related region. The values reflect the potential influences of nano-CuO on organisms. The larger CF value of nano-CuO, the more sensitive is the ecosystem of the related region. These CFs can be used in the future LCA, when assessing products containing nano-CuO. This map can show a general view of nano-CuO sediment CFs in the world despite of the imprecision. The sediment most likely to be affected by nano-CuO is in northern Australia (CF of 21.01• 10 3 CTU e , comparative toxic units) and the least likely is in northern Europe and northern Canada (CF of 8.55• 10 3 CTU e ). A CF of 17.70• 10 3 CTU e is proposed for the DEFAULT region, which is a large value among all the studied regions. It means the impacts of nano-CuO to sediment would probably be overestimated for an unknown region. In addition, due to the relatively high uncertainty in the calculation of fate, exposure and effects of nano-CuO, the CFs proposed in this study should be classified as "indicative". [START_REF] Fantke | 0 Documentation[END_REF] 4. Perspectives This work proposed the freshwater sediment characterization factors of nano-CuO in 17 subcontinental regions. Many limitations may affect the absolute values of the output: CFs of nano-CuO. The immature fate model of ENPs used in this study may bring uncertainties to FFs of nano-CuO. Moreover, XF, as a significant factor in CF calculation, may have big influences on the CF values. Thus, further investigation of XF is necessary. Furthermore, since the toxicity values of nano-CuO to the organisms living in sediment were rarely reported, just one toxicity value from literature was used in this study. The sediement EF of nano-CuO can be updated once more toxicity values are available. However, despite having defects, to the best of our knowledge, it is the first reports focusing on the sediment CF of ENPs. The method proposed in this study for calculating the sediment CFs of nano-CuO is promising. The recommended CFs for nano-CuO could also be used in the future when evaluating the ecosystem impacts of products containing nano-CuO.

Characterization factors of nano-CuO

Figure 1 .

 1 Figure 1. The behavior and transport of nano-CuO in freshwater and sediment with the corresponding rate constants accounted (Modified from Pu, et al.).[6]

Figure 2 .

 2 Figure 2. The recommended values of sediment FFs for nano-CuO in 17 subcontinental regions. (Further details of the countries within each region can be found in the study of Shaked.[11])

Figure 3 .

 3 Figure 3. World map of freshwater sediment characterization factors (CFs) of nano-CuO, with histograms indicating the variation between the numerical values (units: 10 3 CTU e ) of 17 worldwideregions. The source of the underlying map was from Mapchart website.[START_REF]Create Custom Map -mapchart Website[END_REF]

  ).[6] 2.2.2. Sediment fate factor The residence time of nano-CuO in freshwater sediment (FF sed,sed , day) is calculated as equation 2:

	𝐹𝐹 sed,sed =	𝑘 w,w 𝑘 w,w• 𝑘 sed,sed -𝑘 w,sed •𝑘 sed,w

Table 1 .

 1 The transport rate constants of nano-CuO between freshwater and sediment for 17 subcontinental regions (Unit: s -1 ).

		k w,w	k w,sed	k sed,sed	k sed,w		k sed,w	k sed,w	k sed,w	k sed,w
	DEF 2.57• 10 -5 2.07• 10 -5 1.17• 10 -8 8.87• 10 -9	W9	8.14• 10 -6 3.11• 10 -6 1.33• 10 -8	9.97• 10 -9
	W1	9.99• 10 -6 4.97• 10 -6 9.43• 10 -9 6.30• 10 -9 W10 8.12• 10 -6 3.11• 10 -6 1.28• 10 -8	9.33• 10 -9
	W2	9.83• 10 -6 4.78• 10 -6 1.32• 10 -8 9.87• 10 -9 W12 8.78• 10 -6 3.77• 10 -6 1.38• 10 -8	1.00• 10 -8
	W3	2.60• 10 -5 2.07• 10 -5 6.51• 10 -9 3.32• 10 -9 W13 9.05• 10 -6 4.04• 10 -6 9.20• 10 -9	6.00• 10 -9
	W4	2.58• 10 -5 2.07• 10 -5 7.21• 10 -9 3.93• 10 -9 W14 2.58• 10 -5 2.07• 10 -5 1.23• 10 -8	9.00• 10 -9
	W5	6.36• 10 -6 1.35• 10 -6 1.10• 10 -8 7.80• 10 -9 IND 9.80• 10 -6 4.78• 10 -6 1.32• 10 -8	9.87• 10 -9
	W6	6.36• 10 -6 1.35• 10 -6 1.00• 10 -8 6.93• 10 -9 CHI 9.80• 10 -6 4.78• 10 -6 1.34• 10 -8	1.00• 10 -8
	W7	1.28• 10 -5 7.77• 10 -6 9.18• 10 -9 5.93• 10 -9 JAP 9.82• 10 -6 4.78• 10 -6 1.35• 10 -8	1.02• 10 -8
	W8	1.31• 10 -5 7.77• 10 -6 6.64• 10 -9 3.53• 10 -9

Acknowledgement

Yubing Pu kindly thanks the Chinese Scholarship Council (CSC) for funding his PhD scholarship in France.


Everton Cavalcante
email: evertonrsc@ppgsc.ufrn.br

Thais Batista

Frederico Lopes
email: fred.lopes@gmail.com

André Almeida
email: andre.almeida@ifrn.edu.br

Ana De Moura

Noemi Rodriguez

Gustavo Alves
email: gustavoalvescc@gmail.com

Flavia Delicato
email: fdelicato@gmail.com

Paulo Pires
email: paulo.f.pires@gmail.com

Ana Lúcia De Moura

Autonomous Adaptation of Cloud Applications

Keywords: Cloud Computing, Cloud services, Cloud Integrator, Semantic workflows, Autonomous adaptation

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The growing interest in the Cloud Computing paradigm is grounded on its utility model in which computing services are delivered through a pay-per-use model. By exploiting this model, applications can be composed of services provided by distinct third-party cloud providers. The selection of the proper cloud services that fit the application needs is based on non-functional information, i.e. properties of the services such as price, availability, response time, etc., and applications can rely on a middleware that abstracts away the burden of directly dealing with underlying mechanisms for service selection and communication with the cloud providers.

In this context, our previous work introduced Cloud Integrator [1-3], a serviceoriented middleware platform for composing, executing, and managing services provided by different Cloud Computing platforms, so that Cloud Integrator works as a mediator between the service providers and the applications (clients). Moreover, it provides an environment that facilitates the development and execution of fault-tolerant applications that use such services by composing semantic Web services [START_REF] Martin | Bringing semantics to Web services: The OWL-S approach[END_REF] in a semantic workflow [START_REF] Abbasi | A conceptual framework for smart workflow management[END_REF] composed of a sequence of abstract activities that must be performed by concrete cloud services in order to achieve the application's business goal. In order to execute such semantic workflow, it is necessary to create at least one execution plan containing a set of concrete Web services that perform each one of the activities specified in the workflow.

By using Cloud Integrator, an application can specify the set of cloud services that it needs as well as the properties of each service, and the selection mechanism provided by the middleware platform is able to choose the cloud services provided by the integrated platforms that fulfill the application requirements. However, most cloud-related information (service properties) can change at any time during application execution, so that it is essential to support the adaptation of applications in such dynamic conditions to ensure that the cloud services currently provided to the applications adhere to the established requirements.

In this paper we present an adaptation process to coordinate the autonomous adaptation of cloud applications based on the replacement of services by alternative ones that fulfill the application requirements, in case of service failure or when any change in the properties of a cloud service (e.g. quality parameters) can potentially affect the running application(s). Section 2 details our adaptation process, which is evaluated in Section 3. Finally, Section 4 presents final remarks.

Adaptation of cloud applications

The service composition model adopted by Cloud Integrator is based both on the functionality of each service and on its metadata (such as QoS parameters and prices), thus enabling a better choice of the available services. Moreover, this composition mechanism enables Cloud Integrator to deal with situations in which a service that is available at composition time becomes unavailable at runtime or in case of quality degradation of any service that is included in the composition. If two or more services with similar functionality are available, then Cloud Integrator builds different execution plans for the current workflow, each of them using one of these alternative services. Thus, in case of service failure or quality degradation, another execution plan that contains the service with similar functionality can replace the current one in order to ensure the quality and availability of the running application. In this section we describe the adaptation process that supports this capability. Herein, an adaptation of an application means to replace a running execution plan by another one that performs the same activities. The adaptation process performed by Cloud Integrator currently addresses the replacement of application services, which may be SaaS and/or PaaS cloud services or even other traditional (non-cloud) services.

Factors that trigger an adaptation

There are three classes of changes in cloud environments that may trigger the adaptation process. The first one is the failure of one or more services due to loss of connection between Cloud Integrator and a service provider or internal service errors. In both cases, the service provider becomes unable to respond to requests and then the current execution plan that contains such service must be immediately replaced by an alternative execution plan that contains a service with similar functionality, when possible. For instance, suppose that the execution plan A → B → C 1 → D is being executed and the service C 1 becomes unavailable. In this case, the adaptation mechanism must select an alternative execution plan with the equivalent service C 2 (for instance, the execution plan A → B → C 2 → D), thus avoiding the failure of the whole application.

The second class of events that may trigger an adaptation is the quality degradation of one or more services. Cloud environments can present highly dynamic execution conditions in which fluctuations in the network and high service usage may affect the quality of executing services. In this case, an alternative execution plan containing a service that provides similar functionality to the degraded service can be adopted if its utility1 [START_REF] Cavalcante | Optimizing services selection in a cloud multiplatform scenario[END_REF] is significantly higher than the utility of the current execution plan. Finally, the third case is the arising of new services, which can be dynamically discovered. Since these new services may provide more advantageous alternative execution plans for running applications, it is necessary to analyze the need and convenience of replacing a current execution plan with an alternative one that contains one or more new services. Similarly to quality degradation, it is also important to consider the utility gains and the impact regarding the replacement of the current execution plan. In the current development state of Cloud Integrator, the adaptation process is only triggered in case of service failures. Adaptation triggered by quality degradation or discovery of new services will be addressed in future works.

Factors that affect the adaptation process

When the adaptation process replaces an execution plan regarding an application, its major concern is to ensure that the application requirements continue to be satisfied. Although the quality of alternative execution plans is an essential factor to be considered, it is not sufficient to ensure an efficient adaptation. Moreover, since replacing an execution plan may lead to the re-execution of services or other costly actions, the decision about such possible replacement must consider the adaptation cost, which is the overhead imposed by actions that must be performed in order to resume the application after replacing the current execution plan with an alternative one. As an example, when some of the services included in the current execution plan have already been executed, it may be advantageous to select the alternative execution plan that has more services in common with the current one. Therefore, such similar plan can offer a lower adaptation cost by reusing the outputs produced by the executed services, thus avoiding the need of actions that may have considerable impact.

The computation of the adaptation cost regarding alternative execution plans must consider the following factors: (i) reuse of executed services; (ii) the rollbacks required to return services that have already been executed to a previous execution state, and; (iii) compensatory actions taken in order to restore an previous execution state when a service needs to return to a previous state and it does not support rollbacking. In this perspective, the computation of the adaptation cost of an execution plan p starts with the computation of its absolute adaptation cost c abs (p) (Eq. 1), which is defined as the sum of the number of services to be executed after the replacement of the current execution plan (e), the number of services that require rollbacks (r), and the number of services that require compensatory actions (a):

c abs (p) = e + r + a (1)
In turn, the adaptation cost c(p) regarding an execution plan p is calculated through a vector normalization of its absolute adaptation cost c abs (p) (Eq. 2):

c(p) = 1 - 1 c abs(p) r∈EP 1 c abs (r) 2 (2)
in which c abs (r) is the absolute adaptation cost of each execution plan r in the set of available execution plans EP .

The adaptation process

The adaptation process performed by Cloud Integrator was inspired in Adapt-UbiFlow [START_REF] Lopes | AdaptUbiFlow: Selection and adaptation in workflows for Ubiquitous Computing[END_REF], an adaptation mechanism proposed in the context of ubiquitous applications. The selection of an alternative execution plan considers two essential parameters: (i) the initial utility (u) of the candidate execution plans, which expresses their properties in terms of price and quality parameters, and; (ii) the adaptation cost (c), which weights the actions that must be performed when replacing the application's execution plan. When the adaptation process is triggered, the initial utility of the alternative plans is computed by using the same criteria (QoS parameters and prices) that were originally used for selecting the application's current execution plan [START_REF] Cavalcante | Optimizing services selection in a cloud multiplatform scenario[END_REF]. Next, the adaptation cost is computed for each alternative plan, as shown in Equation 2. In the adaptation process, the selection of an execution plan is based on the computation of the adaptation utility µ(p) of each alternative execution plan p defined as a weighted sum of the initial utility u(p) and the adaptation cost c(p). Equation 3 shows how the adaptation process computes the adaptation utility µ(p) for each alternative execution plan p, so that the alternative plan with maximum adaptation utility is selected to replace the current execution plan. If two or more execution plans have maximum utility, the adaptation process selects one of them at random.

µ(p) = [u(p) * w EP ] + [c(p) * w AC ] (3)
The weights w EP and w AC in Equation 3 are respectively assigned by the user to the initial utility and the adaptation cost, with w EP , w AC ∈ [0, 1] and w EP + w AC = 1. The values assigned to these weights are defined in terms of five different configurations called adaptation profiles, as shown in Table 1. After selecting an alternative execution plan, the required rollbacks and compensatory actions are transparently performed in order to replace the current execution plan and resume the execution of the application.

Evaluation

A preliminary evaluation was performed aiming to assess the time spent by the adaptation process triggered in case of failure of a service involved in the execution plan that is being executed, so that such failure is detected when a timeout in which the service does not respond to the request is reached. In this perspective, services involved in the selected execution plan for executing the application were forced to fail in order to trigger the adaptation process. As shown in Table 2, the time spent in milliseconds to perform the adaptation process (and that covers the selection of an alternative execution plan to replace the current one) is significantly small, thus not significantly impacting on the application execution. More details about the performed evaluation can be found at http://consiste.dimap.ufrn.br/projects/cloudintegrator/dais2013.

Final remarks

In this work we discussed the events that can trigger the need for adaptation in a cloud-based environment, as well as the factors that should be taken into consideration when choosing the best way to react to changes in the runtime environment in order to ensure the quality and availability of the application. We described the adaptation support designed for Cloud Integrator in which service failures and quality degradation are addressed with an algorithm that takes the adaptation cost (incurred in the replacement of services) into account.

A preliminary evaluation of such adaptation process in case of service failures has shown that the adaptation process does not significantly impact in the application execution. In addition, the proposed adaptation process works with minimal user awareness, thus promoting the autonomy of the application in case of failures or other conditions that may trigger an adaptation.

Table 1 .

 1 Adaptation profiles for the definition of the weights assigned to initial utility and adaptation cost.

	Adaptation profile	Description	Assigned weights wEP wAC
	maximum initial utility	exclusive priority to the initial util-	1.00	0.00
		ity of the execution plan
	high initial utility	prioritizes the initial utility of the	0.75	0.25
		execution plan while considering
		the adaptation cost
	balanced	default configuration, with equal	0.50	0.50
		weights
	low adaptation cost	prioritizes the adaptation cost,	0.25	0.75
		while also considering the initial
		utility of the execution plan
	minimum adaptation cost exclusive priority to the adaptation	0.00	1.00
		cost

Table 2 .

 2 Minimum and maximum values, average and standard deviation regarding the time (in milliseconds) spent by the adaptation process.

	Execution plans Minimum Maximum Average Standard deviation
	2	0.2754	0.5950	0.4694	0.0708
	4	0.4598	0.9522	1.2352	0.1626
	8	0.7716	1.9416	1.7846	0.2574
	12	1.4680	3.7006	3.2596	0.5168
	18	4.0158	11.7134	7.2724	1.7644

As presented in our previous work[START_REF] Cavalcante | Optimizing services selection in a cloud multiplatform scenario[END_REF], the computation of the utility of an execution plan takes into account both QoS parameters and the monetary costs of the services that compose it.