dataesr/scientific-paragraphs-categorization huggingface dataset:
Our results confirm our hypothesis that this should exert strong selection pressure on small group size because the 14 groups were ranging between one and 25 adults. This is in contrast to opportunistically eavesdropping species that do not emerge in coordinated flight and maintain group cohesion throughout their foraging period  [76, 77]. While our expectations regarding small group size were confirmed, we did not find that individual lifespan was influenced by variation of group size. In both datasets (capture and BaTLi data), group size had no effect on lifespan or monthly survival despite group size being the most explanatory variable in the multistate mark recapture analysis. We postulate that selection on group size is so strong that the resulting variation, mainly caused by the brief appearance of pre-dispersing juveniles, is too small to have an effect on the adult females in the group.
The apparent absence of relationship between group size and survival in M. molossus could have technical and/or biological explanations. Our estimates of monthly survival for the three categories of group size were associated with narrow confidence intervals (respectively 0. 85-0. 97, 0. 91-0. 97 and 0. 93-0. 98). A dataset including more groups and individuals may lower confidence intervals and reveal survival differences that remained hidden so far. Alternatively, our results correspond to the reality and selection on ideal group size is so strong that the remaining low variation does not affect survival. In the framework of life history, the relationship between group size and reproduction remains to be investigated in future studies. Bats are well known for roosting in large or even gigantic colonies, e.
BaTLis We monitored the roost entrance of four of the 14 groups with the BaTLis between April 2013 and June 2014 and followed the presence of each marked bat. Each BaTLi contained two light beams to determine direction of individuals passing as well as a balance. These two latter datasets allowed us to follow and estimate the number of unmarked bats using the roost entrance, e. g. , immigrants into the group as well as freshly fledged juveniles. Over the 15 recorded months, these four roosts were recaptured 4-5 times each to mark new individuals with a maximum interval of 6 months between recaptures. Capture and handling of animals were carried out with permission from the Autoridad Nacional del Ambiente in Panama with approval from the Institutional Animal Care and Use Committee of the Smithsonian Tropical Research Institute (2012-0505-2015,  [92] ).
Estimates of female lifespan Our capture data showed that M. molossus lives in harems with regular replacement of the harem male, but stable female social groups (see "Results"). Based on the switch from pregnant to lactating females as well as calculating back roughly 1 month from the time we first caught freshly fledged offspring, we determined that the major birth peak occurs in May. From the disappearance of marked female subadults from groups, we were able to tell that all offspring disperse within 1-8 months of fledging. We then estimated lifespan by filtering the capture data in the following way: for each unmarked bat we captured, we determined if there had been a previous capture of the same roost where this individual had not been present. This meant that it had dispersed from its natal group and immigrated since the last capture.
Cox proportional hazard model using capture data These survival analyses are less robust because they do not take into account detection probability and changes in group size but we included more females over a longer time period in this dataset (n = 70 over a maximum of 2. 5 years after filtering) and we could estimate lifespan. We used the estimates derived from the capture data: time between estimated birth and last capture as described above, i. e. lifespan (see Additional file 2 for raw data). Survival data were right-censored when the female was still alive at the last capture event. We used the Cox PH model  [93] , based on continuous time and the assumption of perfect detection (100 % probability of capture) and available in the R package survival  [94]. We built two models combining the lifespan estimates and two predictor variables: total group size and adult group size.
In these models, the individual survival probability at the recapture event [t] was based on the group size (total or adult) of the individual's group during the previous recapture event [t -1]. We tested the two models for the proportional hazard assumption of the predictor variables based on the scaled Schoenfeld residuals also using the R package survival. We also split the survival range into yearly categories (0-1, 1-2, 2-3 and 3-4) and determined the number and proportion of females for each of them.
Multistate mark recapture models using BaTLi data In this second analysis, fewer females were analysed over a shorter time period (n = 63 over 15 months) but the models implemented detection probability and transitions in group size. We investigated predictors of monthly female survival using BaTLi data from the four groups (n = 63 adult females, see Additional file 3) and multistate mark recapture models (MSMR,  [95] ). These models are Markovian (conditional on the present state of the system, its past and future are independent) and rely on discrete time categories (e. g. calendar month) that we used to model temporal change in individual state (e. g. group size). With the MSMR models, we simultaneously estimated initial state (i. e. group size whenever first captured), survival probability, changes in group size, as well as detection probability for one or several predictor variables (e.
g. social group, observation month). Detection probability (P) is a crucial parameter, often smaller than one and highly variable in natural populations, which can lead to flawed biological conclusions when not considered in mark-recapture analyses  [96]. Our dataset consisted of 63 rows (one for each adult female) and 15 columns (one for each month of the study period). Each cell of the matrix contained either a "0" when the focal individual was absent or recorded the adult group size, when the focal individual was present. We categorized group size into "small" (3-6 individuals), "medium" (7-9) and "large" (10-13) to obtain higher confidence in the survival estimates. We assigned an age (i. e. marking year) and social group to each female. Although we observed occasional brief visits of adult females into neighbouring roosts (n = 9 events), no adult female was ever observed to permanently change groups and we therefore assumed stable group identity.
We performed multisite goodness-of-fit tests on the adult females dataset  [97]  using the software U-Care v. 2. 3. 2. [98]. We modelled monthly survival based on this survival matrix and five MSMR models. Each model comprised the following parameters (see also Table  2 ): IS[gs] or initial state for adult group size (the percentage of individuals initially observed in "small", "medium" and "large" groups, the same in all models), Ф or survival probability (i. e. from one month to the next), ѱ[. ] or group size transition probability (implemented in one of the five models, see below) and a constant detection probability P  [. ]. Preliminary investigation showed that the use of more than one predictor variable caused high uncertainty in the estimates. Consequently, we only estimated survival (Ф) using a single predictor variable per model: (1) adult group size (gs), (2) social group identity, (3) month of the study, (4) marking year, and (5) a null model without predictor variable.
Acknowledgements We thank  Rachel Page  and the members of the bat lab, as well as  Julia Cramer ,  Matthieu Ménage ,  Marion Muturi ,  Sebastian Rikker  and  Sebastian Stockmaier. We are grateful to the workshop of the  University of Konstanz  for developing the automated transponder readers, especially  Georg Heine ,  Karl Hoerenberg ,  Harald Kautz  and  Harald Richter. We also thank  Martin Wikelski  and  Kamran Safi  for scientific advice as well as  Brian Cusak ,  Marina Lehmann ,  Kim G. Mortega ,  Ireneusz Ruczyński ,  Sebastian Stockmaier  and two anonymous reviewers for valuable comments on the manuscript. This work was funded by the  German Research Foundation  ( DFG-GZ DE 1807/3-1  to DKND) and the  International Max Planck Research School for Organismal Biology.
then a difficult problem  [3]. This problem is also called SPL configuration as it consists in configuring products from the FMs. It is even more challenging as this problem is typically a multi-objective one: software designers and architects make their decisions based on various perspectives  [4, 8] , such as, cost, technical feasibility or reliability. Another related problem that has not been studied yet is the feature selection in a multi-objective context when the FMs evolve. It is not a surprise to say that software requirements and artefacts evolve constantly. For instance, stakeholders and customers often change their opinions about how applications should work, or new coding paradigms are introduced. FMs reflect that, and for instance, we have seen in our study that a large FM (such as the one behind the Linux kernel) evolves regularly and substantially (every few months a new FM is released with up to 7% difference from the previous one).
In this context, it seems odd to generate random bootstrapping populations for the stateof-the-art genetic algorithms, such as, SATIBEA. It is tempting on the contrary to use the fact that FMs have evolved and that the SPL configurations generated previously, while not totally applicable, are close and can be adapted. Our contributions in this paper are the following: (i) We propose a benchmark  4  for the analysis of evolving SPL; this data set has been generated following a study of the demographics and evolution of a large SPL (Linux kernel). This data set is important to provide a good evaluation of the different algorithms under different evolution scenarios; (ii) We propose eSATIBEA which is a modification of the state-of-the-art SATIBEA  [4]  for evolving SPL. eSATIBEA adapts previous solutions to new FMs to improve and speed-up the results of SATIBEA; (iii) We evaluate SATIBEA and eSATI-BEA on the evolving SPL problem and show that eSATIBEA converges nearly 10 times faster and gets a 113% improvement after one generation of genetic algorithm.
This is why the problem of SPL configuration has been described as multi-objective. Here, following other classical approaches  [4, 8]  we use 5 objectives: (i) number of selected features, (ii) number of selected features that were not used in the past, (iii) sum of known defects in the selected features, (iv) number of compatibility violations, and (v) cost of the selected features. Evolution of the SPLs and FMs is known to be an important challenge for the domain, as they both represent long term investments  [6]. For instance, in the next section we present a study of a large scale FM, the Linux kernel, and we show that every few months a new FM is released with up to 7% modifications among the features (features added or removed). The FM/SPL evolution perspective has not been addressed in the multi-objective feature selection literature -as far as we know.
The problem we address concerns adapting the solutions found previously for F M 1 to F M 2. 3 Towards a Benchmark for Feature-Model Evolution We studied the largest open source FM we found: the Linux kernel  [9]  containing 6,888 features and 343,944 constraints (in its version 2. 6. 28). We evaluated the demographics (features, constraints) and evolution of 21 versions of the kernel: from version 2. 6. 12 to version 2. 6. 32. We observed that on average there was only 4. 6% difference in terms of features between a version and the next: 21. 22% of removed features and 78. 78% of new (added) features. We also evaluated the size of the clauses/constraints in the problem, as we need to know how the constraints we add in the problem should look like. We found that a large proportion of the FMs' constraints have 6 features (39%), 5 features (16%), 18 features (14%) or 19 features (14%).
From this study, we generated a synthetic benchmark of FM evolution based on the real evolution of the Linux kernelhence a realistic benchmark but with more variability than in a real one, allowing us also to get several synthetic data sets for each evolution values. Our FM generator uses two parameters representing the percentage of feature modifications (added/removed) and the percentage of constraint modifications (added/removed). The higher those percentages are, the more different the new FM will be from the original one. Our FM generator uses the proportions we observed in the 20 FMs to generate new features/remove old ones, and to generate new constraints of a particular length. Values we use can be seen in our benchmark in Figure  2 : from 5% of modified features and 1% of modified constraints (FM 5 1) to 20% of modified features and 10% of modified constraints (FM 20 10).
We propose eSATI-BEA, which aims at taking advantage of previous SPL configurations (when they exist) to feed in SATIBEA with previous solutions. Let's assume a FM F M 1 that evolves into another FM F M 2 over time (e. g. , features and constraints added or removed). An SPL configuration was performed on F M 1 (e. g. , using SATIBEA) and a set of solutions (S 1 ) Fig. 2 : Hypervolume of the solutions given by SATIBEA and eSATIBEA on evolved Linux kernels. We gave a label x y to each evolution, 'x' representing the percentage of modified features and 'y' the percentage of modified constraints. We generated 10 evolved FMs for each combination and only show the average here. was found. Now instead of randomly generating individuals for SATIBEA, we decide in eSATIBEA to adapt the set of solutions S 1 to the feature Model F M 2 and to give these solutions to SATIBEA.
Conclusion This paper has presented a new problem: the configuration of Software Product Lines when the Feature Models they are based on evolves. To study this problem, we have proposed a benchmark using a survey of the evolution of a large (nearly 7,000 features and 350,000 constraints) FM. We have compared SATIBEA, the leading algorithm in the literature, and our contribution eSATIBEA (which takes an adaptation of the previous solutions as initial population) in this evolving context. We have shown that eSATIBEA outperforms SATIBEA, in particular it converges nearly 10 times faster and achieves an improvement of 113% after the first generation of genetic algorithm ( 100s). The two directions we plan to follow in the future are: an adaptation of the seed given to SATIBEA in eSATIBEA, and an improvement of eSATIBEA to overcome the problem of the plateau phase.
Flow experiments Flow experiments were performed as previously described (see  [31]  and Online Supplement). For the perfusion with nanoparticles, two different concentrations were used (100 μg/ml and 400 μg/ml). Subsequently, HUVECs were stained with Alexa488phalloidin (PromoKine, Heidelberg, Germany). Cell nuclei were counterstained with DAPI (Molecular Probes, Darmstadt, Germany). Images were obtained using fluorescence microscope Zeiss Axio Observer. Z1 (Carl Zeiss AG, Oberkochen, Germany). The confluence was determined on ×10 objective magnification images using ImageJ software.
LP-NP1 and zymosan were injected in the animals in bolus (<10 s) via the left external jugular vein. The amount of test material injected is given as mg phospholipids/kg, unless otherwise indicated. Hemodynamic changes were continuously monitored at 1000 Hz sampling rate, using an AD Instruments PowerLab System with LabChart Pro v6 software. From the mean PAP, SAP and heart rate (HR) data about 20 s intervals were averaged and evaluated by AD Instruments LabChart Pro v6 software modules. The usual evaluated periods were: before the test material injection, then 20 s in every minute for 10 min, and every 5 min until the end of the reaction. Plasma levels of thromboxane B2 (TXB2, the stable metabolite of TXA2) were measured with a commercially available ELISA kit (Cayman Chemi-cals, MI, USA).
As nanoparticles may interfere with the available photometric methods for testing cell viability and/or metabolic activity [26,  [43] [44] , it is important to use in parallel at least two different methods for toxicity testing, to enable the verification of the results. In our studies, the biological effects of nanoparticles on endothelial cells were analysed in vitro using two real-time cell-monitoring methods. The obtained data underscore the importance of applying different methods to assess the toxicity of nanoparticles, as one single method may increase the risk of bias. For example, the impedance measurements indicated a negative effect of LD-NP1 at 100 μg/ml on HUVECs (cell-index significantly lower in comparison to control), hinting to a reduced endothelial cell numbers, whereas the results of live-cell microscopy indicated a change in cell morphology, possibly resulting in weaker adherence of the cells, that is responsible for the measured impedance differences.
disclosure This work was supported by the EU ('NanoAthero' project FP7-NMP-2012-LARGE-6-309820), the DFG (CI 162/2-1), and received the financial support from ANR-13-LAB1-0005-01 'Fuc-oChem. ' LETI/DTBS is part of the Arcane Labex program, funded by the French National Research Agency (ARCANE project n° ANR-12-LABX-003). The authors thank Prof. M. Beckmann (Department of Gynaecology, University Hospital Erlangen, Germany) for providing umbilical cords, N Jaziri for help with HUVEC isolation and P Dörfler for help with flow experiments. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
Financial & competing interests disclosure This work was supported by the EU ('NanoAthero' project FP7-NMP-2012-LARGE-6-309820), the DFG (CI 162/2-1), and received the financial support from ANR-13-LAB1-0005-01 'Fuc-oChem. ' LETI/DTBS is part of the Arcane Labex program, funded by the French National Research Agency (ARCANE project n° ANR-12-LABX-003). The authors thank Prof. M. Beckmann (Department of Gynaecology, University Hospital Erlangen, Germany) for providing umbilical cords, N Jaziri for help with HUVEC isolation and P Dörfler for help with flow experiments. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
Results and Discussion Two ZIKV strains with well-known laboratory passage histories were selected. The Asian strain PF is a clinical isolate derived from the serum of a patient infected in 2013 in French Polynesia and sequenced in our laboratory. The African strain DAK is a low-passaged strain isolated from an Aedes taylori mosquito in 1984 in Dakar, Senegal. For the latter virus, we used the GenBank sequence number KU955592 to design the reverse genetics system. We used the previously described ISA procedure to implement both reverse genetics systems  22. The schematic representation of the procedure used to recover infectious ZIKVs is presented in Fig. 1. Both complete ZIKV genomes flanked respectively at 5′ and 3′ termini by the human cytomegalovirus immediate early enhancer/promoter (pCMV) and the hepatitis delta ribozyme followed by the simian virus 40 polyadenylation signal (HDR/ SV40pA) were de novo synthesized in three double-stranded DNA fragments of approximately 4.
From the first passage, a clear CPE was systematically observed in all experiments between days 4-7 post-inoculation except for chimeric PF/MART viruses and average amounts of viral RNA as well as infectious titres were comparable with those observed with wild-type viruses (Table  1 ). In conclusion, using the ISA procedure we have generated two reverse genetics systems for low-passage isolates of ZIKV belonging to both the epidemic Asian lineage and to the African lineage. Both reverse genetics systems are available for the scientific community from the EVAg website under reference no. 001N-01891 and 001N-01890 for the PF and the DAK strains respectively (http://www. european-virus-archive. com/). This work was initiated for the PF strain in December 2015. At that time, very few entire genome sequences of ZIKV with complete 5′ and 3′ noncoding regions were available in the Genbank database.
Starting from these sequences, we designed and de novo synthesized three DNA fragments. Following many unsuccessful attempts to produce replicative virus, we sequenced the 5′ and 3′ noncoding regions of the PF ZIKV strain and identified 4 and 8 nt differences respectively within the 5′ and 3′ noncoding region sequences available in GenBank. Corrected DNA fragments were then designed and de novo synthesized, allowing the successful generation of infectious ZIKV. Subsequently, new sequences deposited in Genbank confirmed our findings. Since it was designed later, we did not encounter any of these problems with the reverse genetics for the DAK strain. This highlights the importance of the integrity of terminal non-coding flaviviral sequences for producing effective reverse genetics systems, as well as the benefit of a rapid sharing of genomic information during emergence situations.
Viruses. ZIKV strain H/PF/2013, called 'PF' in the present study, was isolated in November 2013 from the serum of a 51-year-old female patient who was hospitalized in French Polynesia (available on http://www. european-virus-archive. com/ under reference no. 001v-EVA1545; GenBank accession number KJ776791)  27. Before sequencing, PF ZIKV strain had been passaged three times in Vero cells. ZIKV strain A. taylori-tc/ SEN/1984/41662-DAK, called 'DAK' in the present study, was isolated in December 1984 from Aedes taylori mosquito in Dakar, Senegal (GenBank accession number KU955592). Prior to deposition of the sequence of the DAK ZIKV strain it had been passaged once in AP61 Aedes pseudoscutellaris cells, once in C6/36 Aedes albopictus cells and three times in Vero cells. ZIKV strain MRS_OPY_Martinique_PaRi_2015, called 'MART' in the present study, was isolated in December 2015 from the serum of a 54-year-old woman who was hospitalized in Martinique (available on http://www.
european-virus-archive. com/ under reference no. 001v-EVAg1589; GenBank accession number KU647676)  28. Before sequencing, MART ZIKV strain had been passaged once in Vero cells. All EVAg products were provided in accordance with relevant guidelines and regulations. PCR amplification of overlapping DNA fragments. Primer and DNA fragment sequences are detailed in Supplementary Table  S1  and Supplementary Note S1 respectively. Amplicons of PF and DAK ZIKV strains were produced using the Platinum PCR SuperMix High Fidelity kit (Life Technologies). The mixture (final volume, 50 μ l) contained 45 μ L of SuperMix, 2 μ l of DNA template at 1 ng/μ l (De novo synthesized DNA fragment; Synthetic genes were supplied in a standard pUC57 vector) and 1. 5 μ L of each primer (10 μ M working solution were used).
Nucleic acid extracts from cell supernatants (extracted as described above) were used to amplify (Superscript III One-Step RT-PCR Platinum TaqHifi kit; Life Technologies) the complete viral genome in three fragments using a set of specific primers. Pooled amplicons were analyzed using the Ion PGM Sequencer (Life technologies) according to the manufacturer's instructions. Using the CLC Genomics Workbench 6 software, read sequences were trimmed using quality score, by removing the primers used to amplify the complete genome by RT-PCR, by systematically removing 6 nt at the 5′ and 3′ termini and finally by discarding those with a length below 30 nt. Remaining reads were mapped using the expected sequence of the virus and mutation frequency at each position was calculated (number of reads with a mutation compared to the reference divided by the total number of reads; only mutations with frequency ≥ 5% were recorded).