dataesr/scientific-paragraphs-categorization huggingface dataset:
prolixus infects the same range of hosts, we cannot ruled out the hypothesis of independent HTs of the transposons from a common but unknown mammalian host. Taken together our data indicate the existence of frequent HTs of mariner transposons between R. prolixus and a large variety of organisms. In addition, keeping in mind the recent data supporting the existence of other transposon HTs with mammals  [2, 47]  and insects  [10, 48] , our analyses demonstrate the existence of a diverse horizontal flux of transposons in the genome of R. prolixus. By providing new invading elements, we can hypothesize that this flux balances the inevitable stochastic losses of mariner elements and thus participate to the strong preponderance of this super-families in the R. prolixus genome.
These lineages are usually well delimited and reflect the diversity of mariner within the whole metazoan clade, since lineages from most mariner subfamilies could be identified. iii) frequent occurrence of HT of mariner elements within various species including other insects in particular parasitoids, hematophagous nematodes, parasite worms and a South American bat. Finally, this huge dataset of copies has revealed some aspect of the biology of mariner elements, for example, the generation of shorter lineages that seems to be highly constrained by size, the fact that these shorter lineages are frequent within some subfamilies only, that rearranged lineages can also arise by 5' replacement with 3'part. We believe that the data and interpretations provided here will offer a basis to future study aiming to understand the role play by transposable elements during evolution and the adaptation to human of Triatomine bugs.
Survey strategy overview The VUDS survey is designed to study the galaxy population beyond redshift z ≃ 2 and up to the highest redshifts possible in the wavelength range up to λ = 9350Å accessible with the VIMOS spectrograph on the VLT (see below), corresponding to Lyα at a redshift up to z ≃ 6. 6. A total of one square degree is observed in three well separated fields, the COSMOS field, the extended Chandra Deep Field South (ECDFS) and the VVDS-02h field, in order to mitigate cosmic variance effects. This program was awarded Large Program status with 640h of VLT-VIMOS observing time. A key feature of the survey is the target selection. It is primarily based on photometric redshifts and their probability distribution function (PDF), complemented by color-color selections and analysis of the SED shape when not already in the photo-z selection.
g. Ilbert et al. 2010 , Cardamone et al. 2010 ) has opened new perspectives in pre-selecting galaxies. In essence, photometric redshift techniques are a generalisation of color selection techniques such as LBG or BzK to the complete spectral energy distribution (SED) which must verify a color distribution accross wavelengths compatible with reference galaxy templates. We elected to use the best possible photometric redshifts as our main selection criterion. Photometric redshifts have been computed with the code Le Phare  (Arnouts et al. 1999 , Ilbert et al. 2006 ) using the multi-band photometric catalogues covering from the u-band to the Spitzer 4. 5µm band. The techniques are described in  Ilbert et al. (2009) , and more recently in  Ilbert et al. (2013). The observed photometric data are correlated against 33 templates covering a range from early to late type galaxies of different ages, star formation histories and metallicities, leaving the E(B-V) extinction as a free parameter.
While the selection strategy described above could lead to an increase in the contamination from objects at lower redshifts, this contamination remains quite small as described below. For each of the targeted samples, slits are placed at random using the vmmps slit mask design software  (Bottini et al. 2005) , providing a fair and representative sample of the general population. These various selection criteria are summarized in Table  2. A large fraction ∼ 75% of the sample has been selected on the basis of the first peak of photometric redshifts, ∼ 15% on the second peak of photometric redshifts, and the rest ∼ 10% about equally split between the LBG color-color selection and purely magnitude selected samples.
The redshift given is the most probable given the observed continuum, it has a ∼ 80% probability to be correct. The probabilities associated to these reliability flags are remarkably stable because of the process involving several independant people, smoothing out individual biases  (Le Fèvre et al. 2013). VUDS enters a redshift domain which has never been probed by spectroscopic redshift surveys on this scale. The expertise of the VUDS team members grew steadily as more and more of the data was being processed. Upon examination of the first measurements we realised that redshifts were probably wrongly assigned for a small but sizeable (∼ 10%) fraction of the objects. Several standard cases for erroneous measurements were identified, including the possible confusion between early M stars (M0-M3) and z ∼ 5 absorption-line only galaxies (going both ways), the assignment of Lyα-1215Å instead of OII-3727Å (or vice-versa), the setting of a continuum break to the Balmer-D4000 break rather than the Lyα break (or vice-versa).
General properties of the VUDS sample In this section we report on the main properties of the VUDS sample, giving an overview of the parameter space probed by the survey. As of this writing, the VUDS sample contains 6 250 objects with a measured redshift, including 6 003 galaxies, 20 AGNs and 227 stars, and no redshift measurement could be obtained for 750 objects. These numbers will increase by 15-20% when data processing will be completed. The projected sky distribution of the VUDS sample follows the layout of the VIMOS pointings, as identified in Figures  1 to 3. With 8 pointings on the COSMOS field, ∼ 4150 objects previously without measurements have been observed and ∼ 3700 have now spectroscopic redshift measurements (these numbers are being revised following the last data processing), covering a total area of 1800 arcmin 2.
4 < z < 2. 5 redshift range would be outside this range at z < 1. 4 or z > 2. 5 (right panel of Figure  20 ); at K AB = 22 we find this contamination to be lower, at the ∼ 30% level. This trend is similar to that found by Le Fèvre et al. (  2014 ), who identified a magnitude-dependent contamination level. As the VUDS data is selected with z phot > 2. 4 the contamination level is further enhanced compared to the pure iband magnitude selection of the VVDS. Going to higher redshifts, we show the distribution of VUDS galaxies with 2. 5 < z < 3. 5 in the ugr color-color diagram in Figure  21. Here again a large fraction ∼ 80% of galaxies appear in the expected locus of the color-color diagram. The contamination by galaxies at z < 2. 5 or z > 3. 5 is quite high at about 40%. We note that we did not attempt to optimize the redshift range on the basis of the exact shape of the photometric bands used for the CFHTLS photometry used in this plot, but we rather elected to show the distribution of a large population.
Absolute magnitudes, stellar masses and star formation rates The distribution of apparent magnitudes with redshift is shown in Figure  22. In all ∼ 90% of the distribution is within 23 ≤ i AB ≤ 25, while ∼ 10% have 25 ≤ i AB ≤ 27. The VUDS sample covers a large range of galaxy physical properties. Using the VUDS spectroscopic redshifts we perform SED fitting on the multi-wavelength photometry using the code Le Phare  (Ilbert et al. 2006) , as described in  Ilbert et al. (2013). We summarize here a few key points, but we refer to the recent description of the fitting process in  Ilbert et al. (2013)  for a detailed account and associated limitations. Galaxy luminosities are transformed into stellar mass using the best fit synthetic template, from a list of templates built from  Bruzual & Charlot (2003)  stellar population synthesis models with 3 metallicities (Z = 0.
We report on the general properties of the sample based on ∼ 80% of the data which has already been processed. The redshift distribution of the current sample at z ≥ 2 peaks at a mean z = 3, and extends beyond z = 6. A secondary sample at z < 2 is the result of the selection function and provides interesting very low intrinsic luminosity galaxies. The average spectral properties of galaxies with z > 2 are discussed based on high S/N stacks of VUDS spectra in several increasing redshift bins. Galaxies with and without Lyα in emission are found at any redshift, but it is found that the fraction of galaxies with Lyα in emission increases with redshift. This is quantified in the accompaning paper by  Cassata et al. (submitted). Using stacked spectra we find that there is observed flux below the 912Å Lyman limit in all the redshift ranges explored, the origin of this is being investigated and will be the subject of future papers.
Following an early measurement of the merger rate at z ∼ 3  (Tasca et al. 2013) , several papers presenting results from the VUDS are submitted together with this paper  (Cucciati et al. submitted, Cassata et al. submitted, Lemaux et al. submitted, Amorin et al. submitted). A number of other analyses are in progress. VUDS is the first deep spectroscopic survey covering such a large redshift range with such a large sample of galaxies with confirmed spectroscopic redshifts. It is ideally suited for detailed studies of the galaxy population at early times 2 < z < 6. When the full data set will be completed, it is foreseen to make future VUDS data releases publicly available. 3 ), while the imprint of the 4 quadrants is in magenta. The size of the image is 1 × 1 deg 2. All of the VIMOS pointings are covered by CFHTLS visible photometry and WIRDS near infrared photometry.
Acknowledgements. We thank  ESO  staff for their continuous support for the VUDS survey, particularly the Paranal staff conducting the observations and  Marina Rejkuba  and the  ESO  user support group in Garching. This work is supported by funding from the  European Research Council Advanced  Grant  ERC-2010-AdG-268107-EARLY  and by  INAF  Grants  PRIN 2010 ,  PRIN 2012  and  PICS 2013. AC, OC, MT and VS acknowledge the grant  MIUR PRIN 2010-2011. DM gratefully acknowledges LAM hospitality during the initial phases of the project. This work is based on data products made available at the  CESAM data center, Laboratoire d'Astrophysique de Marseille. This work partly uses observations obtained with  MegaPrime/MegaCam , a joint project of CFHT and CEA/DAPNIA, at the  Canada-France-Hawaii Telescope (CFHT)  which is operated by the  National Research Council (NRC)  of  Canada , the  Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France , and the  University of Hawaii.
Methods First, the model description is provided, following the ODD (Overview, Design concepts, Details) protocol  [11, 39]. Then, verification and sensitivity analysis procedures are described. This model was built using NetLogo software  [40] , Version 5. 2. Model code is available via website repository, "Open ABM CoMSES Computational Model Library", to interested users (https://www. openabm. org/model/4710/version/1/view). Empirical data for parameterizing landowner characteristics was analyzed from the Penobscot County Survey dataset in the state of Maine, U. S. [41].
Therefore, the higher the trust value, the more likely they are to be influenced by the forester to harvest sustainably. However, there is still a chance that a landowner with 90% trust in foresters won't change their sustainability value. Variables are initialized based on empirical data when available, personal communication, and a pseudo-random number generator, bounded by known distributions or ranges (Table  2 ). This model uses data from the Penobscot County Survey of Maine landowners who own forestland in Penobscot county  [41]. These data are used to initialize landowner demographic characteristics and parameterize the logistic regression equation. Forester and peer leader visits were initialized using data from personal communication with foresters (stand and public) and state forestry data in Maine.
The landowner could have a positive or negative experience with the forester and there is no empirical data to support the probability of negative vs. positive interactions. The amount that trust increases or decreases is a random number between 0-0. 75, and 0-0. 5 respectively, but cannot increase the trust probability above 1. 0 or below 0 (Table  3 ). It is assumed that while gaining a landowner's trust can occur in large increments due to the cognitive desire for confirming evidence once an opinion forms, losing a landowner's trust occurs slowly until it is lost completely. 4. Peer Leader Interaction: The number of peer leader-landowner interactions is based on averages from the Keystone Peer leader program in Massachusetts (David Kittredge, personal communication, 2015), which surveyed peer leaders as to how many referrals they made in their conversations with community members about conservation options.
0. 5. Community Interaction: Based on existing NetLogo Rumor Mill code  [40] , landowner agents either share an opinion in a random interaction across the landscape or in an adjacent neighbor interaction (but not both). In this step, community members share an opinion. If a landowner is influenced by that opinion, they will update their Forester-trust and Peer leader- trust according to whether or not the opinion is positive, negative, or neutral. This does not change the harvest decision. The amount that trust increases or decreases is a random number between 0-1. 0, but cannot increase the trust probability above 1. 0. Originally, the model user could define whether or not the opinion would be spread globally or locally. A sensitivity analysis demonstrated little effect on model output, so this was changed to a stochastic operator.
Second, the amount of time that elapses between interactions should be recorded for each agent and moderate the extent to which interactions influence behavior. Future research could add explicit social network data such as a network topology to the model or empirical interaction patterns  [52] [53] [54] [55] , add a utility or objective function that determines whether or not a harvest maximizes their ownership objectives allows PWOs to thin or partially cut a stand, and test other decision making models besides logistic regression in the pre-interaction harvest decision. Finally, it is highly recommended that future research quantifies the effect of psychological distance on PWOs and uses the model as an experimental framework for testing the effect of it on forest management decisions. Although several scholars and practitioners were solicited for advice on model parameterization, stakeholder knowledge could be more rigorously incorporated into model construction and improvement  [56].
Introduction Time-lapse two photon microscopy provides stunning video of the 3-dimensional movement and interaction of immune cells within living tissue  [1] [2] [3] [4]. This technology affords us an unprecedented view into the behavior of immune cells in vivo. An on-going question is how best to analyze quantitative data extracted from such videos in order to more accurately describe T cell motility and determine differences in behavior across different T cell populations. There has been increasing interest in precise quantitative analysis of T cell motility to parameterize computational models of immune responses  [5] [6] [7] [8]. The movement of thousands of T cells can be captured in videos of two-photon images, and the speeds and turning angles of each T cell can be calculated. However, there are difficulties in obtaining unbiased analyses of in vivo motility data.
WT:PKCθ-/-and WT:CCR7-/-imaging experiments were previously published. WT: PKCθ-/-experiments are described in  Cannon et al. , 2013 ; WT:CCR7-/-raw data (microscopy acquisitions) were taken from a published study  [14]  and reanalyzed. These experiments used cells dyed with 1 μM CMFDA or 2 μM fura-2 AM from Invitrogen (Carlsbad, CA) and imaging is described in  [14]. The structure of the data is presented in WT:CARMA1-/-experiments were conducted with a 2-photon microscope in the Fluorescence Microscopy Facility in the UNM Cancer Center with a mode locked Ti:Sapphire infrared laser (Coherent Ultra II; tunable from 680-1080 nm; avg. power 3. 5 W) for multiphoton fluorescence excitation. The microscope stand is a Zeiss Axiovert 200 with motorized XY stage and IR-corrected long working distance objectives (25X:multi-immersion and 40X:water immersion) and image acquisition via a Zeiss LSM510 scanhead.
Ex vivo tissue and organs are maintained during microscopic observation in a stage microincubator system (LCI-Live Cell Imaging) equipped with heating, humidity, CO 2 atmosphere and perfusion. Explanted lymph nodes were placed on a glass cover-slip in the chamber. The sample is perfused with a 37°C solution of DMEM (phenol red free, Gibco) bubbled with 95% O 2 and 5% CO 2. T cell behavior within a lymph node was monitored in the T cell area at a minimum of 50μm below the surface of the node. For 4D analysis of T cell motility, multiple stacks in the z axis (z step = 3μm) were acquired every 15-20sec (depending on the number of z stacks acquired) for 15-50 min, with an overall field thickness of 40-60μm. Cell motility was analyzed with Imaris software (version 6; Bitplane). Tracks that lasted fewer than 3 time steps (corresponding to tracks lasting less than 100 seconds using the duration filter in Imaris) were not taken into account in the analysis.
We enter mouse IDs nested in date; lymph nodes nested in mouse and date; fields nested in lymph node, mouse, and date; and finally cells nested in field, lymph node, mouse and date. (See S5 Fig sfor model specification in JMP. ) It is important to ensure that all the IDs at each level are unique (similar to our treatment of the cell IDs, our unique mouse IDs are concatenated strings of the date of observation and the mouse ID). Note that if there is perfect overlap between any of the nested levels, such as only one mouse used on each date of observation, the lower level variable will take up all the variance explained by the other level. This was the case in our CARMA1 dataset, for example, and we therefore excluded date from our nested analysis of these data. We repeated this analysis using rank-transformed speeds and migration angles in order to obtain non-parametric estimates.
We also used PTX to inhibit T cell motility and used a non-active subunit B alone (suB) as the WT control. To formally assess whether parametric or non-parametric statistical tests should be used, we analyzed speed and turning angle distributions of WT T cells moving within LNs. T cell motility data-both migration speeds and angles, and particularly in step-based data-are not normally distributed, thus parametric tests like the t-test would be incorrect, requiring use of nonparametric statistical tests. Fig 2 and [1, 26]  show non normal distribution of instantaneous speed and turning angles; Fig  2  and  [10]  show non-normal distribution of instantaneous speeds. Thus, we performed the Mann-Whitney U test (for non-parametric data) for comparison to the Student's t-test (parametric data) on each of our four datasets: WT:PKCθ-/-; WT: CARMA1-/-; WT:CCR7-/-, and SuB:PTX.
To resolve the step-based and cell-based conundrum, we again use the factorial ANOVA to enter unique cell IDs as a predictor variable in our model. This allows our model to take into account the fact the multiple observations were made of each cell, and the fact that cells may show individual variation. We analyzed cell speed and migration angle data using mixedmodel ANOVA, including the dyes and the dye X cell type interaction we described previously, and also cell IDs to control the dependence in our data owing to repeated observations of the same cells. After including control for cell IDs as a random effect, estimates of cell speed (Table  1 ) are generally higher and migration angle (Table  2 ) generally lower than without the cell ID as control. This results from correcting for the over-sampling of cells with slow speeds or high migration angles that linger in the frame longer than cells that move quickly.
Agreement between the results of analysis of un-transformed and rank-transformed data gives confidence that these results are not due to violation of the assumption of normality. The median values provided by analysis of rank-transformed data may be regarded as more meaningful as mean values are of questionable usefulness in skewed data such as these. Interestingly, we also find the surprising result that CARMA1-/-T cells move faster than WT cells that we observed after controlling for dye effects persists when including controls for all nested experimental blocks. Nested analysis of rank-transformed data estimates median speed of 7. 8μm/min for WT T cells and 8. 4μm/min for KO T cells, with p = 0. 0304 (Table  2 ). Our new statistical analysis approach shows that inappropriate statistics may lead to the incorrect conclusion that CARMA1 deficiency leads to slower motility (if t-test is used).
Acknowledgments The authors would like to acknowledge  Francois Asperti-Boursin  for collection of data,  Rob J. de Boer  for helpful conversation and input in the development of this project and manuscript. We would like to acknowledge  Rebecca Lee ,  Genevieve Phillips , and  Angela Wandinger-Ness  of the fluorescence microscopy facility at the  University of New Mexico Health Sciences Center  for their assistance. We would also like to acknowledge  John Connor  and  Denis Bragin  of the  BRaIN Center Optical Core at the University of New Mexico Health Sciences Center.
While some of them extract mosquito distribution data from zoological databases established in other contexts, community-based approaches built upon the recognition, reporting, collection and submission of mosquito specimens by citizens are becoming more and more popular and increasingly support scientific research. Based on such reports and submissions, new populations, extended or new distribution areas and temporal activity patterns of invasive and native mosquito species were found. In all cases, extensive media work and communication with the participating individuals or groups was fundamental for success. The presented projects demonstrate that passive approaches are powerful tools to survey the mosquito fauna in order to supplement active mosquito surveillance strategies and render them more focused.
However, managing a network of traps that covers a whole country is not only expensive but also extremely time-and labourconsuming. In addition, not all mosquito species are attracted to the commonly used trap systems and some may even remain unnoticed. To assist active mosquito monitoring by trapping, some EU countries have launched passive surveillance activities, thereby using other data sources, such as existing databases, or addressing the general public. Such approaches provide plenty of additional data with minimum effort and high cost/benefit efficiency. Incorporating the observations by the interested public in data collection, also known as citizen science, has become increasingly popular [e. g. 26, 27]. Citizen science projects are of special relevance in mosquito research, as the presence of a nuisance species (native or invasive) is usually perceived for the first time by local inhabitants [e.