dataesr/scientific-paragraphs-categorization huggingface dataset:
The statistical module that MOSAIC offers for SSDs (MOSAIC SSD ) enables the selection of a log-normal and a log-logistic model. We always selected the log-normal model. The MOSAIC SSD is able to calculate HC5 or HP5 values even if the input data are censored, that is, entered as intervals. In using MOSAIC SSD , we thus constructed the SSDs with the lower and upper bounds of the 95% confidence intervals of the EC50 or EP50 estimates for each species. If for a specific species the 95% confidence interval of the 96-h EC50 was not provided in the data set of  Rubach et al. 2011  (see Table  1 ), the point estimates were used instead for both the lower and upper bounds of the confidence interval. A Tier-3 experimental ditch study on the effects of a single application of CPF on the invertebrate community and on invertebrate populations  (Van den Brink et al.
Acknowledgment-The authors wish to thank the Panel on Plant Protection Products and their residues of  EFSA  ( 2016EFSA ( -2018 ) ) for their constructive support provided to the  EFSA Scientific Opinion , on which this paper is based. For the writing of this paper,  T Brock  and  A Focks  were financially supported by the  Dutch Ministry of Agriculture, Nature and Food Quality  (project  BO-20-0002-001 ). The contribution to the writing of this paper by  S Charles  was performed within the framework of the  EUR H2O ' Lyon  ( ANR-17-EURE-0018 ) of  Universit é de Lyon (UdL) , within the program " Investissements d'Avenir " operated by the  French National Research Agency (ANR). Disclaimer-The authors declare no conflicts of interest. The views expressed in this publication are those from the authors and do not necessarily represent the official position of EFSA.
, 2020). The hybrid power system sizing and optimization tools are very well developed and have been used successfully globally. Among these software and modelling tools, HOMER is largely utilized for simulating renewable based hybrid technologies accurately  (Shezan et al. , 2016). It is mainly exploited for pre-feasibility studies and has the capability of handling several technologies including hydroelectricity, PV, wind, biomass with a large meteorological database permitting its usage in various zones around the world. Currently it is widely used by more than 120,000 users in more than 193 countries. Further, among several approaches for techno-economic analysis relating with acquiring, operating, maintaining and disposing cost of a system, HOMER is useful for techno-economic optimization of the hybrid systems based on least net present cost (NPC),  (Kousksou et al.
Load assessment According to the data collected from the Moroccan National Office of Electricity (ONE), the electricity consumption of Tazouta was approximately 18 GWh in 2016. The electrical consumption covers several end-uses (Fig. 2 ). It is obvious that the household takes the majority of the load by 72%, public lightning in second position with 13%, commercial use by 8%, agriculture with 4% and the administrative state by 2% and industrial consumption with 1%, respectively. The proposed system seems to be a promising option to supply households with electricity need for domestic purposes. The studied zone consists of 10 separated traditional houses which are already connected to the electricity grid. However, the main purpose of this manuscript is to analyse the possibility of implementing centralized stand-alone HRES in similar rural regions without access to electricity network.
Solar irradiation data Solar energy data for the region under examination is collected from NASA (surface meteorology and solar energy database). The geographical coordinates of the village are 33°40'12'' N latitude, 4°38'2'' W longitude and has an elevation of 1146 m. Fig. 5  displays the daily irradiation and clearness index throughout the year. The annual average solar global horizontal irradiance is 5. 07 kWh/m 2 /day with maximum of 7. 18 kWh/m 2 /day observed in June and a minimum of 2. 74 kWh/m 2 /day in December. Fig. 5  indicates an average clearness index of 0. 597 at the site under study.
The purity of all standards was higher than 98%. Individual stock solutions were prepared at concentrations of 1 mg mL - for the next 6 min then increasing to 100% (B) for 1 min. Then the initial conditions (5% B, 95% A) were restored within 0. 1 min to re-equilibrate the column for the next injection. MS detection was carried out in the multiple reaction monitoring (MRM) mode. Two MRM transitions were monitored for quantitation and confirmation purposes (see Table  S1 ). No pharmaceuticals or pesticides were detected in solvent and procedural blanks. Method performance data are given in Table  S2.
Acknowledgements This work was supported the  ANR   FROG-ANR-16-CE32-0006  project. The authors thank  Simon Navel  for assisting with the field observatory in urban water management (OTHU -www. othu. org/-FED 4161) work. We also acknowledge the  Greater Lyon Water Agency , the  Rhone Mediterranean Corsica Water Agency  and the  Rhône Alpes Auvergne Region  for logistic support during the various field trials.
Next, 25 cycles of 30s at 94 °C, 30s at 55 °C and 75s at 72 °C, followed a final extension 5 min at 72 °C. Negative controls without template DNA were used at all amplification steps. Fragments of the expected size were used for Sanger sequencing. The new 18S rRNA gene sequences were aligned with a collection of sequences of available aphelids and several Chytridiomycota species as outgroup using Mafft  (Katoh et al. , 2002)  with default parameters. The multiple alignment was then manually trimmed to eliminate gaps and ambiguously aligned sites. 1,351 unambiguously aligned sites were retained to reconstruct a phylogenetic tree by Maximum Likelihood (ML) using IQ-TREE 2  (Minh et al. , 2020)  with the GTR+G+I sequence evolution model. The new 18S rRNA gene sequences have been deposited in GenBank with accession numbers MW186929.
Essential actions included the setup of AMS teams in all veterinary settings, building government-supported AMS programmes and following specific requirements on the production, collection and communication of AMU and AMR data. Activities of AMS teams should be tailored to the local situation and capacities, and be linked to local or national surveillance systems and infection control programmes. Several research priorities were also identified, such as the need to develop more clinical breakpoints in veterinary medicine. Conclusions: This White Paper offers a practical tool to veterinary practitioners and policy makers to improve AMS in the One Health approach, thanks to surveillance data generated in the veterinary setting. This work may also be useful to medical doctors wishing to better understand the specificities of the veterinary setting and facilitate cross-sectoral collaborations.
38  Of note, commensal bacteria are a useful indicator for the comparison of AMR rates over time and between different animal species, countries or farms. Despite a general call to develop monitoring systems that are able to provide relevant, accurate and complete data to drive AMS programmes in all settings, including the animal sector, many gaps still remain, along with many harmonization challenges. For example, only fragmentary data on AMU and AMR are available for companion and aquatic animals. [39] [40] [41] [42]  Regarding AMR data, most of the available information comes from healthy terrestrial food-producing animals and relatively few multi-annual programmes are in place for the monitoring of AMR in animal pathogens. [43] [44] [45]  The complexity of the veterinary setting (including its interconnections with the human sector, the varying purposes of surveillance systems  26, 46, 47  and the non-homogeneous availability of surveillance data and resources around the world  48, 49  ) make many activities difficult to standardize.
Thus, practical details are not available for many AMS activities, and indications on how surveillance data should intertwine with AMS interventions are lacking. The JPIAMR ARCH  50  and COMBACTE-MAGNET EPI-Net 51 networks have come together for the shared goal of implementing a framework of actions to facilitate antibiotic policy interventions and foster use of surveillance data on AMR and consumption and implementation of AMS activities in human and animal health. In this regard, the ARCH and COMBACTE-MAGNET EPI-Net international expert panel is focusing on four settings: hospital, outpatient, long-term care facility, and veterinary. The efforts involve three areas: (i) AMS leadership and accountability; (ii) AMU and AMS; and (iii) AMR and AMS, all considering the feasibility of the actions and the One Health approach.
This White Paper focuses on the veterinary sector and targets veterinarians, policymakers and other actors in animal and human health who are involved in AMS activities. The proposed guidance is intended as a practical tool regarding the production and use of AMU and AMR data to plan and implement AMS programmes in line with the One Health approach and applicable in different veterinary contexts, including resource-limited countries. Dissemination to the intended audience will be ensured by the networks involved in the JPIAMR ARCH project as listed in Table  1  of the first paper in this series. 52  Checklist formats of the target actions are downloadable from the ARCH website. 50  These checklists can be used by health professionals and policymakers to establish and/or monitor stewardship activities.
Antimicrobial usage and antimicrobial stewardship -Which antibiotics should be monitored? -Which metrics should be employed for monitoring antimicrobial usage? -Should the surveillance specify the purpose of antimicrobial usage? -Which criteria for reporting and time interval should be used? -Who should be the end user of the report? -Should a restrictive policy be adopted? -Should a ranking for antibiotic use be adopted? 3. Antimicrobial resistance and antimicrobial stewardship -Which criteria are needed to target resistant pathogens? -How should resistance be monitored? -Which prevalence data and selective monitoring screening should be considered? -Which criteria for reporting and time interval should be used? -Which criteria should be applied to stratify the results? -Who should be the end user of the report? -Should the report set specific thresholds for establishing empirical therapy and surgical prophylaxis? -Which criteria should be used to drive selective reporting of antibiograms? Compri et al.
If usage of all antimicrobials cannot be monitored, as would be desirable,  64  targeting specific animal species and antimicrobial classes is preferred. A major issue is that AMU data can be collected in different ways. 26, 28, 40, 92  When possible, prescriptions and, even better, animal administration data should be collected from veterinarians and farmers as these provide the most accurate data on AMU (when properly recorded) and allow benchmarking. 27, 36, 62, 112  If this is not possible, sales data may be collected from pharmaceutical companies. 26, 65, 66  Table  2. Leadership commitment, accountability and antimicrobial stewardship team Antimicrobial stewardship programme and team 1. 1. Essential Antimicrobial stewardship programmes should be in place in every setting where antimicrobials are used to treat food-producing or companion animals, with targets and interventions tailored to the local situation and linked to local and national surveillance systems and infection control programmes.
Desirable Quantitative data (MICs or inhibition zone diameters) should be collected rather than interpreted data (susceptible/intermediate/resistance or wild type/non-wild type). 3. 12. Desirable Specific monitoring schemes may be performed in healthy animals and food thereof using selective media, e. g. to detect the presence ESBL/AmpC, carbapenemase-producing, colistin-resistant Enterobacterales, MRSA or vancomycin-resistant enterococci to assess public health risk.
35, 36, 55, 68, 97  This leads to very different sampling schemes: passive monitoring in diseased animals for animal pathogens and active monitoring in healthy animals for commensal and zoonotic bacteria. Monitoring systems may rely on phenotypic identification of resistance, but molecular identification of AMR determinants brings more accurate insights into the epidemiology of resistance genes,  36, 48, 58, 69  especially between the animal and human sectors, and can generate better riskmanagement measures. A specific challenge that applies to the translation of AMR surveillance data into AMS actions is the lack of clinical breakpoints to interpret antimicrobial susceptibility testing (AST) results for many combinations of animal species, bacterial species and clinical form. The large majority of existing breakpoints were produced by the CLSI.
Because interpretative criteria, either clinical breakpoints or ECOFFs, can change over time, it is important to collect and report on quantitative data (inhibition diameters or MICs) to enable the recalculation of resistance rates in historical data and still be able to analyse trends. 33, 36, 46, 69, 115  To ensure that AMR and AMU monitoring systems serve AMS in the One Health approach, it is key to report data jointly with those of AMU and AMR in other sectors, especially the human one. 36, 48, 60  This prevents the provision of a fragmented picture of the AMR situation and facilitates a reciprocal understanding between actors that need to collaborate to improve AMS. However, cross-sectoral data analysis of AMU and AMR data is complex and requires prudent interpretation. This is why experts should always seek to summarize the results in a format that is understandable by all stakeholders in animal, public and environmental health, as well as by the general public.
The initial step of the literature review was useful in guiding group discussions and finding agreement. However, the subjective nature of the experts' opinions and the small number of participants remain limitations of the approach. A major strength of this document is that it provides practical and concise indications for AMS teams in the field, but also for institutions wishing to support AMS from a higher level, especially when it relates to the production of AMR and AMU data that are relevant for prescribers, sellers and users of antimicrobials in animals. These indications are also useful for medical doctors wishing to better understand the specificities of the veterinary sector in terms of surveillance and AMS, especially as some activities are designed with a public health perspective.
This work also enabled the identification of several important future research areas to facilitate the development of AMS programmes and the translation of AMR surveillance data into prudent use of antimicrobials in the veterinary setting. However, due to the diversity and complexity of the veterinary sector, along with the fact that diverse economic contexts were considered, many targets of the checklist are rather generic and need to be adapted to each specific context. In this scenario, the indications given herein represent a first step to better structure AMS activities in the animal sector.
veterinary backgrounds. The proposed indications, which consider the connection with human medicine and the heterogeneity of the veterinary setting, aim to facilitate the development of AMS programmes linked to AMU and AMR surveillance data in the animal sector, but with benefits for both animal and human health. This White Paper, together with those produced in the hospital, outpatient and long-term care facility settings, provides the opportunity to compare practices and learn from other areas. This series of White Papers represents a practical example of how the One Health approach can be useful in understanding priorities, capacities and needs among sectors. This overall initiative facilitates collaboration between all actors in animal and human health who are involved in AMS activities.
Rationale A specific challenge that applies to the translation of AMR surveillance data into AMS activities in the veterinary setting is the lack of clinical breakpoints to interpret AST results for many combinations of animal species/bacterial species/clinical condition. When no breakpoint is available, epidemiological cut-offs (ECOFFs) can be used as an alternative. However, ECOFFs detect decreased susceptibilities and, as such, are not proper indicators of clinical resistance. When using ECOFFs instead of clinical breakpoints, resistance rates can be overestimated, so one should be cautious when providing treatment recommendations based on them. • Provide indications for the selective reporting of AST results to veterinarians.
Funding This research project has received funding from the  Innovative Medicines Initiative Joint Undertaking  under grant agreement No  115737 , resources of which are composed of financial contribution from the  European Union   Seventh Framework Programme  ( FP7/2007-2013 ) and  European Federation of Pharmaceutical Industries and Federations (EFPIA)  companies in-kind contribution, and the  German Federal Ministry of Education and Research (BMBF)  [Project  ID 01KI1830 ] under the  Joint Programming Initiative on Antimicrobial Resistance (JPIAMR)  2018 call. The contribution of  Rodolphe Mader , as a member of the  European Antimicrobial Resistance Surveillance network  in Veterinary medicine (EARS-Vet), has been co-funded by the  3rd Health Programme of the European Union  ( 2014-2020 ) under the  European Union Joint Action   on Antimicrobial Resistance and Healthcare Associated Infections (EU-JAMRAI)  [grant agreement No  761296 ].
For the analysis, we combined the replicates involving the same lures at both sites and in both years of experiments. Consequently, we obtained 21 replicates to compare the attractiveness of Lures 1+a, 2 and 3+a, 20 replicates to compare Lures 3 and 3+a, and 7 replicates to compare the attractiveness of the four lures. Response variables were: (1) number of cerambycid individuals per trap per collection period; (2) number of cerambycid species per trap per collection period; and (3) numbers of individuals per trap per collection period of cerambycid species for which at least 100 specimens were captured. Because the data violated normality assumption, differences between trap captures were tested using the nonparametric Friedman's Q test (Statistix 10®, Analytical Software, Tallahassee, FL, USA).
testaceus in the Russian Far East and France, respectively, was achieved when ethanol was combined with 3-hydroxyhexan-2-one  (Sweeney et al. 2014; Fan et al. 2019). It is of interest to compare the species composition in the traps with faunistic data to evaluate the detection efficacy of the method. Combining both years, our study detected a total of 28 cerambycid species in Vienna (port and adjacent forest). The updated list of Cerambycidae for the 2 nd District of Vienna, where our study site is located, contains 57 species (W. Howorka personal communication). Hence, multi-lure trapping recorded more than half of the cerambycid fauna in this district in just two trapping seasons and a rather limited area. This illustrates that the method could also be very useful for faunistic studies. Species richness and abundance was higher in the adjacent forest than on the port premises, which likely corresponds to the availability of suitable habitats for cerambycids.
Acknowledgements This article is a part of the conference "Detection and control of alien forest species in a changing world" organized by the project  LIFE ARTEMIS  ( LIFE15 GIE/SI/000770 ), co-funded by the  LIFE programme, Ministry of Environment  and Spatial planning of the Republic of Slovenia, the  Municipality of Ljubljana  and the  Slovenian Research Agency. The study was carried out in the framework of the  EUPHRESCO  project " Multi-lure and multi-trap surveillance for invasive tree pests " (MULTITRAP). We thank other partners in the project consortium, particularly  Dr. Neil Audsley  ( Fera Science Ltd. , UK ) and  Dr. Antoon Loomans  ( Nederlandse Voedsel-en Warenautoriteit NVWA, The Netherlands ) for the excellent cooperation. We are grateful to the  Port of Vienna  for their cooperation in trapping on their premises.
Significant main effects support hypothesis H1a, whereas significant interaction terms for both nutrients support H1b. In Supporting Information Table  A1 , we present an alternative statistical analysis which uses a 2 nd order polynomial regression of supplied N : P ratios instead of the single nutrient gradients, and again adds species (four monocultures + mix) as categorical factor. In case of a positive linear and negative quadratic term for N : P supply on algal biomass, RUE, and stoichiometry, this model allowed to test for the location of a significant hump (or pit) in the unimodal response to N : P. For this, we used the MOS test (Mitchell-Olds and Shaw 1987) as implemented in the vegan package in R  (Oksanen et al. 2015). Table  2. Statistical results for the analysis of algal stoichiometry (C : N, C : P, N : P), biomass, and RUE for N and P, separated for the exponential (EXP) and stationary (CAP) phases.
Acknowledgments The authors thank  Oliver Ferdinand ,  Silvia Heim ,  Heike Rickels , and  Ina Ulber  for technical support. Further we thank  Alexander Wacker  and  Thorsten Dittmar  for cooperation. This project was funded by the  Deutsche Forschungsgemeinschaft (DFG :  STR 1383/1-1 ,  HI 848/15-1 ,  WA2445/12-1 ,  SI 1668/1-1 ) within the  priority program DynaTrait  ( SPP 1704 ).
Somatic growth is a critical biological trait for organismal, population, and ecosystem-level processes. Due to its direct link with energetic demands, growth also represents an important parameter to estimate energy and nutrient fluxes. For marine fishes, growth rate information is most frequently derived from sagittal otoliths, and most of the available data stems from studies on temperate species that are targeted by commercial fisheries. Although the analysis of otoliths is a powerful tool to estimate individual growth, the time-consuming nature of otolith processing is one barrier for collection of comprehensive datasets across multiple species. This is especially true for coral reef fishes, which are extremely diverse. Here, we provide back-calculated size-at-age estimates (including measures of uncertainty) based on sagittal otoliths from 710 individuals belonging to 45 coral reef fish species from French Polynesia.
Such growth models can only be fitted based on a large number of individuals that cover the complete size range of the study species. However, due to the required sample sizes and the need for lethal sampling, obtaining such datasets is time consuming. Further, the raw data that permit size-at-age estimates are often unpublished, available only from technical reports, and/or available for a limited suite of commercial species. Multi-species growth curve comparisons are particularly rare, especially across a wide range of environmental conditions that may influence individual growth rates. Therefore, a back-calculation model that estimates fish size across previous ages based on otoliths represents an alternative to model growth  24. Here, we provide a comprehensive dataset of raw otolith reads (51 species, 855 individuals) for corals reef fishes, collected across six islands in French Polynesia.