dataesr/scientific-paragraphs-categorization huggingface dataset:
non-commuters -is anyway more pronounced likely because of the intrinsic assumption made on the original identification of home/work locations from the data. Different choices can be made that can improve the correct identification of home/work locations, leveraging on the availability of additional data. If timing of the call activity is provided, one possible refined definition would be to identify as home location the tower cell with the largest activity during nighttime, and the work location as the one with the largest activity constrained to daytime (with variations of the definition of these intervals)  [32, 33, 38, 56]. We tested this approach in the Portuguese dataset and found that the identification of the two locations was not substantially altered by the time-constrained definition chosen, and did not affect our results (see Text  S1 ).
Increasingly sophisticated approaches can also be envisioned, based on clustering methods applied to calling behavior  [57, 58]. In addition to the need for access to the metadata associated to the activity data, results from time-constrained or clustering methods may anyway be affected by biases induced by users' call plans (influencing the pattern of calls to given timeframes during the 24h or depending on the day of the week, e. g. weekday vs. weekend), job types (altering the expected timing pattern of call activity from work), and more generally the definition of normal business hours that may have a strong cultural component. Second, our basic normalization may be too simplistic, thus inducing strong overestimation because the population sampled through the mobile phone data is not representative of the general population, being characterized by specific different features affecting the resulting mobility behavior.
Biases may be induced by mobile phones ownership, with fluctuations strongly dependent on socio-economic status  [31, 58] , and by market share of the specific operator providing the data, In Spain, for example, In poorer countries these effects are expected to be of a larger magnitude, given that mobile phone users still represent a privileged minority of the population  [31]. Recent work has however showed that mobility estimates in Africa are very robust to biases in phone ownership  [59]. The introduction of a refined normalization to account for the non-representative nature of the mobile phone sample fixes the total number of commuters equally in the two datasets and leads to an improvement of the comparison of the commuting fluxes on a link-by-link basis. Discrepancies on traffic flows along links are however still observed that are responsible for differences in the resulting epidemic observables, even though the overall systematic overestimation obtained with the basic normalization has been discounted.
Increasingly sophisticated approaches can be developed that use iterative proportional fitting, fixing two marginal values that need to be assumed, i. e. the total numbers of incoming commuters and of outgoing commuters per location (or additional data, such as points of interest in the case of intra-city commuting)  [60]. Knowledge of these quantities may however not be largely accessible across different regions of the world. Third, there may be inconsistencies in the definition of commuting for both datasets, or differences in the year of collection of each dataset. We have no information on users' age in the mobile phone dataset, therefore movements for work or study are both tracked in users' trajectories. Commuting for study reasons is included in the Portuguese and French census data, whereas Spain reports about workflows only.
The impact of not considering students' commuting in the Spanish case is however estimated to be rather low. Spanish data is indeed collected at a high administrative level (provinces), where students' commuting flows may be very weak given that they are usually more localized than those of workers. Data from France shows that 95% of students (aged<15) travel on distances less than 10km  [4]. In order to estimate the impact of missing students in the Spanish dataset on the province scale, we examined the fraction of commuter movements of students in the French census commuting network aggregated at the level of regions, i. e. similar to the size of Spanish provinces. In France, students represent about 10% of the total commuting flows across regions. If we assume a similar statistics for Spain too, such ratio is not sufficient to explain the discrepancy observed between the normalized mobile phone commuting flows and the census commuting flows in Spain (Table  1 ).
In addition, the lack of a portion of individuals in the dataset (e. g. students) would have no impact when using the refined normalization because, in that case, the total number of commuters is set to be equal in both data sets by definition. Discrepancies in the year of data collection for the two sources range from two years for Spain (2005 is the year of collection of census data, 2007 the year of collection of phone data) to five years for Portugal  (2001, 2006 ). In the case of France, the two datasets belong to the same year (2007). To assess the possible changes in commuting flows with time, we analyzed French yearly data between 2006 and 2009, given their availability (see Table  S1 ). In three years, the total number of commuters in the country increased by about 3%, and every year, the total number of commuters increased by 1% or less.
Assuming that similar trends apply to the other countries, we conclude that the total census commuting flows of Spain and Portugal may have increased by 2% and 5%, respectively, a difference being much smaller than the average discrepancy observed between census data and mobile phone data. Finally, the epidemic model considered adopts some approximations that we would like to discuss in the following. Even if countries under consideration belong to a contiguous area in continental Europe, numerical simulations for the epidemic spread were performed for each country in isolation. This choice is driven by the lack of mobile phone data for cross-border movements (given their national nature), and by the negligible fraction of commuting across countries with respect to national commuting (about 780,000 people in the EU, including EEA/EFTA, were cross-border commuters in the year 2006/2007  [61]  over a total of more than 100 million national commuters).
Simulations were performed assuming a continuous series of working days, given the purpose of the study and the knowledge that the inclusion of weekend movements has little or no effect in the resulting epidemic profile  [42]. To apply this framework to real case studies, more refined compartmental models, movements and interactions between individuals may need to be considered. Our study was performed on three European countries, and we expect that our conclusions are applicable to other developed countries in the world characterized by similar cultural, social, and economic profiles. Our approach for the extraction of commuting patterns from mobile phone data was based on minimal assumptions in order to facilitate its generalizability in other settings where data knowledge may be limited or completely absent.
Further work is necessary to extend this work to the analysis of the adequacy of mobile phone data as proxy for human mobility in underdeveloped countries where cultural and socio-economic factors may affect differently the biases here exposed. We also note that diseases other than ILI may be of higher interest for these regions, and in that case the relevant mobility mode and epidemic model would need to be updated in the approach we presented. For instance, the transmission of the disease under study may be strongly affected by seasonal forces, such as the variations in human density and contact rates due to agricultural cycles that drive the spatial spread of measles in Niger  [62] , or other factors, such as poor sanitation standards that were linked to the persistence of the poliovirus in India  [63].
Mobile phone data performs similarly to the radiation model once the corresponding epidemic models are seeded in a central location, except for the case of Lisbon, and performs better or similar when they are seeded in a peripheral location. If the epidemic starts from a mid-size populated region, the relative performance of the radiation model against mobile phone data in the epidemic outcomes depends on , with improvements observed as increases. To test for the role of overestimation of flows, we also performed the same analysis by considering the refined normalization of the mobile phone commuting data that keeps the same total number of commuters per administrative region as in the census dataset and explicitly discounts overestimation biases. The refined normalization allows the mobile phone data to better reproduce the invasion paths obtained from census commuting flows for central and medium-type locations for all , and to perform slightly worse in case the seed is located in a peripheral location (Figure 5 for the case of France).
the strong undersampling of the population in Galicia and Basque region, characterized by a strong political and cultural identity, may be due to the presence of local operators that account for a larger market share than what observed at national level. In Portugal, we observe larger fluctuations per region around the average national coverage than in other countries. Predictions for the invasion path obtained with mobile phones for epidemics starting in the capital of the country are not in good agreement with those obtained with census flows ( , Figure4). In this case, the central role of the capital, responsible for leading to higher similarity as discussed before, is reduced by the presence of larger (and overestimated) flows connecting less central regions in the mobile phone dataset.
Basic properties of the commuting networks. Number of nodes, of links, and of commuters for each commuting network under study, without considering self-loops. Rows correspond to different countries and geographical subdivisions within a country. Columns indicate values from the census dataset and the mobile phone dataset. Commuters for the mobile phone dataset correspond to the values obtained directly from the samples, prior to the normalization procedure, and after the basic normalization procedure. Values obtained with the refined normalization are not reported as they are equal to those of the census dataset, by definition.
For all countries, at all scales considered, all administrative units are included in the datasets (i. e. they have at least one incoming or outgoing commuting flux to another administrative unit in the country). A summary of the basic statistics of the networks extracted from census data is reported in 1,643,938commuters traveling between the 278 municipalities through 25,634 weighted directed connections, and (ii) 469,089 commuters traveling between the 18 districts on a fully connected network. In Spain we consider the provinces' geographical scale only, as constrained by the information available in the census survey. The.
Conversely, co-activation of MBON cell types with opposing effects resulted in intermediate responses. Together, these data are consistent with a simple combinatorial model of valence integration. The following figure supplements are available for figure  3 :   How do MBONs bias behavior to cause approach or avoidance? A conditioned response to a learned odor is unlikely to be achieved by eliciting a predetermined motor pattern. To carry out appropriate changes in speed and direction, a fly needs to evaluate both the valence of the learned odor and its own trajectory relative to the location of the odor source. To assess which aspects of locomotion are modified by MBON activation to generate a bias between approach and avoidance, we analyzed the behavior of flies in a 10 mm-wide graded lighting choice zone centered on the border between dark and illuminated quadrants.
, 2012)  or Ctrax  (Branson et al. , 2009). Statistical comparisons were performed using Prism (Graphpad Inc, La Jolla, CA 92037); Kruskal Wallis One way ANOVA followed by Dunn's posttest for comparison between control and experimental genotype in Figure  2C  and Figure  5 -figure supplement 1; One way ANOVA followed by Bonferroni's multiple comparison test for Figures  3G  and 4 ; In Figure  5D , p-values for the exit direction were computed using the test of equal proportions from R (http://stat. ethz. ch/R-manual/R-patched/library/stats/html/prop. test. html) followed by multiple comparisons with the Dunn-Sidak correction. Only data obtained with 20XUAS-CsChrimson-mVenus (attP18) are shown in this study. Our preliminary results with 5XUAS-CsChrimson-mVenus (attP18) or 10XUAS-CsChrimson-mVenus (attP18) indicate that either too weak or too strong expression may result in a failure to observe a phenotype.
Statistical analyses were performed using the statistical software JMP 10. 0. 0 (SAS Institute, Inc. , Cary, NC 27513-2414). Each split-GAL4/UAS-Shi ts1 cross was run on two separate days and pooled for a total with n = 12/group. Statistical significance for any split-GAL4 line was determined by performing a Wilcoxon test for each split-GAL4/UAS-Shi ts cross against a pooled control. The pooled control included 12 randomly sampled means from the pBPDG4U/UAS-Shi ts daily control. A Benjamini-Hochberg False discovery rate (FDR) test  (Benjamini and Hochberg, 1995)  was performed on the p-values for each Wilcoxon comparison. Lines showing p < 0. 05 following the FDR test were considered statistically significant. GAL4/+ controls were performed for these significant hits, and Kruskal-Wallis comparisons were made comparing each split-GAL4/UAS-shi ts , split-GAL4/+ and +/UAS-Shi ts.
Sleep Split-GAL4 flies were crossed to either 10X UAS-dTrpA1 (attP16)  (Hamada et al. , 2008)  or pJFRC124-20XUAS-IVS-dTrpA1 (attP18) and maintained at 21-22°C. Virgin female progeny, 3-7 days posteclosion, (n = 24-35) were placed in 65 mm × 5 mm transparent plastic tubes with standard cornmeal dextrose agar media, placed in a Drosophila Activity Monitoring system (Trikinetics) and locomotor activity data were collected in 1-min bins for 7 days. Activity monitors were maintained with a 12 hr:12 hr light-dark cycle at 65% relative humidity. Total 24-hr sleep amounts (daytime plus nighttime sleep) were extracted from the locomotor data as described by  (Donelson et al. , 2012) ; sleep was defined as 5 min or more of inactivity  (Hendricks et al. , 2000; Shaw et al. , 2000). Sleep profiles were generated representing average (n = 24-32) sleep (min/30 min) for day 3 (baseline), days 4 and 5 (activation), and day 6 (recovery).
Acknowledgements We thank  Sung Soo Kim ,  TJ Florence ,  Michael Reiser ,  Igor Negrashov ,  Steven Sawtelle ,  Jinyang Liu  for help in establishing the optogenetics apparatus. The  Janelia Scientific Computing Software group  generated software tools,  Brandi Sharp  and the  Janelia Fly Facility  helped in fly husbandry,  Susana Tae ,  Rebecca Vorimo  and the FlyLight Project Team performed brain dissections, histological preparations and confocal imaging and  Heather Dionne  made several molecular constructs. We thank  Fabian Stamp  and  Megan Atkins  for assistance in visual learning and optogenetics assays, respectively. We thank  Brett Mensh ,  Joshua T Dudman ,  Vivek Jayaraman ,  Larry F Abbott ,  Daisuke Hattori ,  Toshide Hige  and  Glenn Turner  for stimulating discussions and for comments on earlier drafts of the manuscript.
10 5 conidia/mL (2 days old at 37°C) was used for flood. The inoculated dishes were incubated at 25°C for 7 days for filamentous strains and 48 h at 37°C for yeast. Three replicates for each concentration and microorganism were carried out and the data were analysed by the STATGRAPHIC 5. 0 software. The antifungal activity of the essential oil was evaluated according to the method of  Singh et al. (1993)  and  Nyegue (2006)  by calculating the percentage of inhibition (I%) from the diameter values of the colony in the control plate (dC) and from that obtained in the plates added with the essayed essential oil (dEO): I%= 100(dC-dEO)/dC. The efficiency of the essential oil was evaluated according to the strongest activity they exhibit: the essential oil has strong activity when > 50%, low activity when <50% or are not active when no growth was detected.
We also recorded starting distance (the distance between the observer and the individual bird being observed when the approach to the bird was initiation) between an observer and focal individual birds, although we did not report starting distances here for simplicity. We did not consider starting distance here because it may arise as an artefact of choice of study method. Observers wore neutral colored clothes and behaved as normal pedestrians. FIDs were recorded in a representative range of habitats by searching systematically for birds in all available habitats. FID was measured by a number of trained observers and therefore data were pooled for analysis  [14]. The FID estimates were initially reported in Blumstein  [13] , Møller  [6] , and Weston et al. [15]. No specific permissions were required for these locations/ activities.
Population Trends We obtained population trends for the years 1980-2012 for breeding birds relying on the European Bird Census Council (http://www. ebcc. info/index. php?ID=509). We used the US Audubon Society's Christmas Bird Count data to calculate trends for North American species for the years 1990-2012. We regressed year and number of sites reporting, on N birds counted, and interpreted the coefficient estimates of year as the population trend. The vast majority of FID data were collected in California; California and continental trends were positively correlated (Pearson r = 0. 45), so we used the California trend for 2000-2010 for the analyses. Assessing population trends in Australia is difficult, although two continental scale Atlases exist  [16]. A formal comparison between the reporting rates (i.
e. assuming a = 1)  [27] ; and (4) transforming the branch length of the phylogenetic tree (optimizing the a parameter by maximum likelihood) assuming an OU model of evolution so that it reflects the strength of the phylogenetic signal in the trait  [27]. These different evolutionary scenarios were simulated by transforming the variance/covariance matrix of the data given the specific phylogeny  [25]  using the R package ''ape''  [28]  and then including the correlation structure of the model using the ''gls'' function of the R package ''nlme''  [29]. In all cases (following our Ordinary Least Squares (OLS) methods), all PGLS models were weighted by sample size to account for differences in sample size among species  [30, 31]. To do so, we used the inverse of the sample size as a proxy of variance to be used in the variation function structure (argument ''weights'' of the ''gls'' function;  [28] ).
We used the most recent avian super-tree  [32] , http://birdtree. org/) to reconstruct the evolutionary history of the species included in our data set. We used two phylogenetic trees in our analyses to test if our conclusions were sensitive to the choice of phylogeny: the Ericson backbone and the Hackett backbone phylogenies (File S2, S3). In addition to the main effects, we tested for significant interactions between continent and the other five predictor variables, while maintaining the main effects aiming to test if variables predict population trends in a different way depending on the continent where the bird species live. We also tested for the interaction between FID and body mass because FID may vary with body mass. Our candidate models comprise all possible combination among these terms.
Discussion The main findings of this intercontinental study of population trends of birds were that species with declining populations were mainly farmland migrants (though there were few farmland species in our final, comprehensive, data set that lived in Australia and North America). Moreover, we found a significant interaction between continent and flight initiation distances. There was a large effect of FID and the direction of the effect varied significantly among continents as shown by the interaction. While bird populations are declining with increasing FID in Australia and Europe, the opposite was the case in North America. There were also significant interactions between body mass and continent and between body mass and FID. These correlations are open to interpretation, and they potentially have important implications.
The relationships between population trends and FID differed among continents with North America being an exception for the pattern found in Australia and Europe, hence American exceptionalism. The continental variation was somewhat unexpected and we do not have any a posteriori explanation for it. Pocock  [9]  analyzed data of population trends of birds from eight regions in two continents, finding that population trend relationships from one region are poor predictors of population trend relationships in another. Our findings on heterogeneity in the relationship between FID and population trends fit well into this scenario, cautioning against using spatially unreplicated findings as a basis for decisionmaking such as conservation policy. It is important to note that effect sizes in biological sciences generally are small to intermediate only explaining 5-10% of the variance  [34].
The frequency distributions of FID and body mass were relatively similar with differences among continents reflecting small effect sizes. FID can vary with the degree of exposure to humans, but North American human population density fell within the bounds of the other two continents; 72. 6 inhabitants km 22 , Europe, over 31. 7 inhabitants km 22 in the USA and 3. 04 inhabitants km 22 in Australia, even though our FID data collection took place in parts of the continents with a higher than average population density. Each continent has its complement of aerial and terrestrial predators (flight is an anti-predator response  [3]. Irrespective of the reason for these differences in the relationship between FID and population trends among continents, we can infer that FID as a behavioral measure of susceptibility to human disturbance has different meaning or different information content in different continents.
Performance evaluation This section focuses on the performance assessment of the proposed handover mechanism (Hand4MAC) in comparison with RSSI-based handover algorithm  [17] [18] [19]. This study was performed by simulation using the OMNeT++  [34]. The proposed protocol was developed over IEEE 802. 15. 4 implementation from MiXiM package  [35]. MiXiM is a simulator of wireless and mobile networks for OMNeT++. In this work, the implementation of CSMA-CA transmission mode with non-beacon from the standard IEEE 802. 15. 4 available in MiXiM package was used. As above described, the proposed protocol was developed at MAC layer. At this layer, a new module to control and process all the messages involved in handover operations was included. The network settings considered in the scenario described at Sect.
These nodes move randomly with a constant speed of 2 m/s to simulate patients walking around the infirmary area. These nodes follow the Hand4MAC algorithm presented in Sect. 3 (Fig. 1 ). The algorithm was developed for the MiXiM IEEE 802. 15. 4. The standard IEEE 802. 15. 4 implementation used in the study follows the proposal presented in  [36]  with the same settings. All the nodes are equipped with a CC2420 radio transceiver  [32]  and the simulation parameters used for the operation of this module also are presented in  [36]. As above-mentioned, a network performance comparison study between the Hand4MAC algorithm and the handover method used in  [17] [18] [19]  was realized. From now on, to get an easier reference of the method proposed in those works will be referred to as RSSI threshold-based method (RTHM).