dataesr/scientific-paragraphs-categorization huggingface dataset:
However, after the measurements, the intercorrelation between both signals was calculated in order to check the temporal synchronization. If needed, the time lag was corrected. The recorded SPL correspond to L Aeq,1s and the associated third octave frequency band levels. The sound level meters used complies with class 1 of the standard CEI 61672 (maximum accuracy in acoustic measurement). The processing of data associated with aircraft noise outside the dwelling is in accordance with the procedures of ISO 20906, and NF S  31-190. Bruitparif (the center for technical assessment of the noise environment in the Île-de-France region of France, around Paris) has developed an algorithm to deduce, from these measurements, the original SPL of aircraft noise inside the bedroom. This algorithm is composed of four steps.
The participants were instructed to sleep under normal conditions, and to place two ECG electrodes on their upper chest before going to bed. Data were then uploaded by means of a USB reader and Actiheart software (CamNtech, Cambridge, UK) version 4. 0. 16, then exported to a file which enabled calculation of the heart rate. The Actiheart measurements were then used to determine the number of heart beats per minute (HR) of each participant every 15 s during their sleep. Measurements from six subjects were judged unusable because of a hardware problem. In the end, Bpm measurements <35 and >130 were excluded from analysis  [9, 15] : at rest, bpm below 35 or above 130 is considered abnormal  [16]. The two sound level meters and the Actiheart monitor were synchronized at the beginning of the measurements to the nearest second.
The interaction between the background SPL 10 min before the onset of the event and L Amax,1s was included initially in the multivariate regression model, but did not contribute significantly to the model and had no impact on estimating the noise effect, and was therefore not included in the final model. Sensitivity analyses were carried out on the population sample who had resided longer than 5 years in their present dwelling. Analyses were performed using SAS software version 9. 4 (SAS Institute Inc. , Cary, NC, USA).
Acknowledgments: The Airport Pollution Control Authority (Acnusa) requested the  French Institute of Science and Technology for Transport, Development and Networks (Ifsttar)  to carry out this study. The authors would like to thank them for their confidence. The authors wish to express gratitude to all of the participants in the study. They also thank  Paris Airports  and the  French Civil Aviation Authority  for providing noise exposure maps. They are also grateful to  Inès Khati  for her help in implementing the study, to  Iain McGill  for the translation of the manuscript from  French into English , and to  Lise Giorgis-Allemand  for her skilful revision of the manuscript.
The Gravitrap is a black cylindrical contraption that contains aged hay infusion as a lure to attract female Aedes mosquitoes that are seeking water containers to lay their eggs. Gravitraps were placed in 2013 in 580 residential blocks within 34 sentinel locations to address the spatial dynamics of Aedes population  [26]. After 5 years, 50,000 datapoints were recorded for resource prioritization. The results show that Aedes mosquitoes were heterogeneously distributed among blocks and among floors within the block. The abundance of Ae. aegypti was positively associated with the age of the blocks. A before-after-control-impact (BACI) analysis to compare the dengue-case ratio between estates with and without Gravitraps indicated a 30% reduction in case burden in estates with Gravitraps. Beyond providing spatial and temporal data on vector risk, the direct removal of the adult females by Gravitraps deployed in public housing has shown to have an epidemiological impact.
PQT-VC currently works on label improvement to ensure that efficacy claims are supported by robust evidence and is evaluating 7 new submissions, including PBO LLINs. Dr Nick Hamon (IVCC, UK) presented the development of a toolbox of solutions that can be deployed as part of an integrated vector management programme with the aim to control or even eliminate vector-borne diseases. IVCC is a product development partnership (PDP) that works with funders, innovators, academic groups, testing facilities, and international organizations to develop new solutions for vector control (http://www. ivcc. com/). IVCC has supported the development and launch of several new tools for tackling malaria vectors and has major collaborative projects with leading companies for the development of novel insecticide active ingredients for use in public health and the adoption of new resistance-breaking products through the IVCC NgenIRS programme funded by Unitaid.
Consequently, there is a strong need for a global and harmonized legislative framework for vector-related activities. Dr Samuel Rund (Notre Dame University, USA) described the PopBioMap by VectorBase. org, an online resource for insecticide resistance data that was developed by Notre Dame University, Imperial College London and EMBL-EBI with financial support from NIH (https ://www. vecto rbase. org/popbi o/map/). The Pop-BioMap module has been created to respond to the emergence and spread of insecticide resistance in mosquitoes of public health importance. The PopBioMap is a graphical, map based, online tool for visualizing available information on the prevalence and mechanisms of insecticide resistance in vectors as well as surveillance data such as species, abundance, pathogen infections, etc.
Data are submitted directly by researchers or extracted from publications by the VectorBase curators. The module contains significant amounts of genotypic and phenotypic data for major mosquito taxa (i. e. Anopheles, Culex and Aedes) helping national programmes to implement more effective, locally adapted vector control. Dr Vincent Corbel (Institut de Recherche pour le Développement, France) closed this session by presenting the missions of the WIN. WIN is supported by the WHO Special Programme for Research and Training in Tropical Diseases (TDR) and the Department of Neglected Tropical Diseases (NTDs) since 2016  [7]. It brings together 19 internationally recognized institutions to track and combat insecticide resistance in arbovirus vectors worldwide (http://win-netwo rk. ird. fr/). Since its creation, the WIN has published 6 review papers to fill knowledge gaps on topics related to vector resistance and IVM and has organized biannual international conferences to foster innovation and strengthening mosquito control and surveillance efforts worldwide  [8].
Acknowledgements The authors gratefully acknowledge the support of the  Worldwide Insecticide Resistance Network  ( WIN ) as well as the contributions of all of the conference speakers, rapporteurs and participants. The authors thank the  WHO-TDR  ( Dr Florence Fouque ),  WHO-NTD  ( Dr Raman Velayudhan ),  IRD ,  Montpellier University of Excellence (MUSE) ,  CNRS  and other private sponsors for their participation and contribution to the conference.
The city of Santiago experiences extreme pollution events during winter due to particulate matter and the associated health impact depends on the exposure to this pollutant, particularly to PM 2. 5. We present and apply a method that estimates the exposure of users of the public transport system of Santiago by combining smart card mobility data with measured surface concentrations from the monitoring network of Santiago and simulated concentrations by the CHIMERE model. The method was applied between July 20th and 24th of 2015 to 105,588 users corresponding to 12% of the frequent users of the public transport system and approximately 2% of the total population of Santiago. During those five days, estimated exposure based on measured concentrations varied between 44 and 75 μg/m 3 while exposure based on simulated concentrations varied between 45 and 89 μg/m 3.
, 2013)  Location of the population and activity patterns may be estimated from sources providing geographic position as a function of time. Previous studies have analyzed this with surveys where participants declare where and when they are doing an activity during a specific day (e. g. Leech et al. , 1996; Klepeis et al. , 2001; Muñoz et al. , 2016; Olguín et al. , 2009). Other studies have considered mobile phone data (e. g. Phithakkitnukoon et al. , 2010; Alexander et al. , 2015) , smart card data used in public transport systems (e. g. Devillaine et al. , 2012; Ma et al. , 2013)  and social media data (e. g. Hasan et al. , 2013)  where a device sends a GPS pulse at regular time intervals. Various methods have been proposed to estimate human exposure in different environments. Most studies have estimated the exposure to air pollutants using portable instruments during commuting (e.
g. Rivas et al. , 2017; Salvatierra, 2016; Suárez et al. , 2014; Cortese and Spengler, 1976; Violante et al. , 2006; Karanasiou et al. , 2014). Previous studies have also proposed analyzing exposure by combining mobility patterns estimated with mobile phone data with pollution monitoring from stationary sites (e. g. Liu et al. , 2013a,b; Nyhan et al. , 2016)  and air quality models (e. g. Dewulf et al. , 2016; Yu et al. , 2018). In this paper, we present a method to estimate population exposure to PM 2. 5 using public transport data from frequent users to obtain population mobility and air pollutant data from a chemical transport model and observations from stationary monitoring stations. To the best of our knowledge, passive public transport data has not been used for this purpose. Due to the lack indoor concentration measurements, outdoor concentration will be used in this study to estimate exposure.
Monitoring station data Surface concentrations of several air pollutants in Santiago are measured by an air-monitoring network (http://sinca. mma. gob. cl) controlled by the MMA. The network is composed of eleven stations distributed in different zones of the city (Fig. 1 ). Out of the eleven monitoring stations, four have monitored PM 2. 5 since January 1st, 2000 (La Florida, Las Condes, Pudahuel, Parque O'Higgins) and the rest since May 2008. These stations are located in an urban area and at each station PM 2. 5 is measured using a beta attenuation monitor (Met One BAM-1020)  (Liu et al. , 2013a,b; SINCA, 2010). Each station provides hourly concentrations of sulfur dioxide (SO 2 ), nitrogen oxides (NO and NO 2 ), carbon monoxide (CO), ozone (O 3 ) and particulate matter PM 10 and PM 2. 5.
In this study, we assume that each activity is executed in the vicinity of the alighting bus stop until the user travels again. Raw smart card data gathered during the days of interest contains around 22 million trips and 2 million different smartcards. In this study, we focus only on frequent transport users, defined as those that use the public transport system five days within a week (45% of the total smart cards used in a week), which reduces the database to around 900,000 cards. Of these users, the alighting stops, activities and residence could be estimated for 105,588 frequent transport users (1,7% of the population in Santiago and 12% of the frequent transport users during this week) with a total of 1. 3 million trips in a week (5,7% of the total trips). These 105,588 constitute the sample used in this study.
As an example, the data from a typical working day (July 20th), is used to examine the distribution of the five activity types defined above throughout the day. As expected, we observe that home based activities are mainly performed during night time hours contrary to work related activities which start at 9:00 a. m. and finish around 7:00 p. m. (Fig. 4 ). Study activities are concentrated between 10:00 a. m. and 5:00 p. m. Also, morning and evening travel peaks can be observed at 8:00 a. m. and at 7:00 p. m. when most users travel from home to work in the morning and return in the afternoon, respectively. Time assigned to each activity generally remains unchanged throughout the examined period (see Figure  A1  in the appendix).
User's destination is located at S 3 where the purpose of the trip is performed. The duration of this trip (D) is from the boarding time at bus stop S 1 to the alighting time at the bus stop S 3. The duration of the activity conducted at S 3 corresponds to the time from arrival at S 3 to the time the user starts a new trip. The exposure to PM2. 5 is estimated using two different datasets; measurements from the monitoring stations (MS) and simulated concentrations from a chemical transport model (CHIMERE). This is done to analyze impact on the exposure estimate from a larger information grouping with the model and compare it to observations from a limited number of stations. The impact of the model bias with respect to observations in estimated exposure is examined in section 5. The concentration at each bus stop is taken either from the nearest monitoring station (Fig.
The average daily exposure is now examined based on the thresholds of pollution levels established by the Chilean AQ legislation (Table  1 ). According to exposure estimates based on MS observations, the population considered in this study was only exposed to Preemergency pollution levels (between 110 and 169 [μg/m 3 ] on a 24-hr average) on July 21st (2% of the users) whereas based on CHIMERE outputs the population was exposed to this pollution level on the 23rd and 24th of July (5% and 1%, respectively) (Table  3 ). In addition, the percentage of users exposed to Alert levels (daily average concentration between 80 and 109 [μg/m 3 ]) never exceeded 31% based on MS observations, while the exposure calculated with CHIMERE reached up to 73% of the users on July 23rd. However, while the percentage of users exposed to Alert concentration levels is larger on the 21st and 22nd for MS estimates, the 23rd and 24th estimates based on CHIMERE data suggest a larger number of users exposed to this level of pollution.
Both exposure estimates agree that over 80% of the population was exposed to concentrations lower than 50 [μg/m 3 ] on July 20th, the less polluted day of the week. For the analysis of hourly exposure, focus will be on the ranges with the largest concentration levels, i. e. between 110 and 169 [ug/m 3 ] and above 170 [ug/m 3 ]. These two levels have the largest health impact as well as a larger number of activity restrictions  (Mazzeo et al. , 2018). According to the exposure estimated with MS data, exposure to concentrations above 170 [ug/m 3 ] occurred only on the night of the 21st and 24th while according to CHIMERE data this occurred in the morning of the 24th and midnight of the 25th (Fig. 6 ). Furthermore, on the 21st and 22nd a larger percentage of the population was exposed to concentrations between 110 and 169 [ug/m 3 ] according to MS data, whereas on the 23rd and 24th more users were exposed to this level of pollution based on CHIMERE data.
Approximately 91% of the estimated average daily exposure was associated with work and home-based activities for both data sets (Fig. 7 ). Similar results are seen for students in both datasets (see figure A2 in appendix). Due to similar exposure between workers and students, we will focus the remainder of the paper on work related activity. Equivalent figures of student exposure compared to the ones presented hereafter are provided in the appendix. Although both methods estimate 91% combined exposure at work and home activities, the method using MS data estimates 38% and 53% exposure associated to work and home activities, respectively, whereas the one using simulated data estimates 48% and 43% exposure at work and home, respectively. The difference between both estimates reflects the different concentrations assigned by each method to the bus stop where the activity is conducted.
4-18 [μg/m 3 ]) with an average standard deviation of 12. 1 [μg/m 3 ]. Users from Lo Barnechea, La Reina, Peñalolén, Vitacura and Las Condes showed the largest variations (ranges between 14. 8 and 18 [μg/m 3 ]) while the rest of the municipalities showed exposure variations from 9. 4 to 13. 3 [μg/m 3 ]. The analysis of exposure and the average income in each one of the municipalities of residence suggests a correlation between these parameters for estimates using both datasets; MS and CHIMERE (Fig. 9 ). While exposure calculated with MS data showed an inverse relationship between these variables, i. e. poorer municipalities are exposed to larger PM2. 5 concentrations, the calculated exposure with CHIMERE data showed no such relation.
Discussion The presented research is an exploratory study to estimate exposure to PM 2,5 of frequent public transport users with two different pollution datasets from Santiago; measured concentrations from monitoring stations of the air quality network of Santiago and simulated concentrations from a chemical transport model (CHIMERE). The method is applied to selected public transport users during a week with deteriorated air quality from July 20th to July 24th, 2015. In this study, outdoor concentrations are used to estimate exposure during bus trips. No measurements of PM2. 5 concentrations were made inside buses during the analyzed period that would allow estimating the bias of this assumption. However,  Suárez et al. (2014)  measured  exposure for different transport modes (bicycle, bus, car and subway) from June 13th to October 13th, 2011 and from March 6th to May 15th, 2012.
Their results show that the average exposure inside buses was 60. 4 [ug/m 3 ] while on bicycles it was 50. 9 [ug/m 3 ]. As both trips were made on the same route, the difference in exposure between both transport modes suggests that using outside concentrations to estimate exposure could represent an underestimation of the real exposure inside buses. In order to disentangle the differences in exposure between the two datasets (MS and CHIMERE), the analysis was extended by calculating the exposure using CHIMERE simulated concentrations at each one of the eleven monitoring stations (CHIMERE-MS henceforth). The differences between the exposures calculated using MS and CHIMERE-MS (Table  4 , second and forth column, respectively) reveals the model bias. The model underestimates the exposure during the first three days while overestimating it during the last two days.
Exposure estimated with the synthetic observations (CHIMERE-MS) is lower than exposure based on data from the "larger network" (CHIMERE) throughout the five days with differences ranging between 1 and 3 [μg/m 3 ]. This range is comparable to the underestimation observed during the first three days in the case where exposure estimated from observations is compared to estimates based on synthetic observations (MS vs CHIMERE-MS). Although small, this difference suggests an impact on the estimate of the information density or size of the network. This impact is larger, however, when examining the exposure at the municipality level (Fig. 10  vs Fig. 8a  and b ). In general, a larger variability of exposure for residents of the different municipalities in Santiago is observed when estimating the exposure with a "larger network", in particular for the residents from municipalities with no measuring station.
Consistent with the above and regardless of the dataset used, the impact of including the I/O ratios increases the average exposure for low-income municipalities by approximately 6 μg/m 3 and decreases it for high-income ones up to 0. 6 μg/m 3 (Fig. 9 ). We note that when applying the I/O ratios to the exposure computed based on CHIMERE data, a dependency of exposure to household income appears which was not apparent in the ratios without I/O. Similar results were found when applying the same I/O ratios as used for residence (not shown) to work and study.
Conclusions In this study we estimate the PM 2,5 exposure of 105,588 frequent public transport users in Santiago by combining smart card mobility data with surface concentration data from 11 monitoring stations and simulated concentrations from the CHIMERE model. This method was applied five working days from July 20th until the 24th, 2015, and the activities identified were home, work, study, travel and other. For the five days, the average exposure estimated with observations ranged between 44 and 75 [μg/m 3 ] while exposure based on simulated  concentrations varied between 45 and 89 [μg/m 3 ]. Furthermore, the highest average daily exposure estimated with measured concentration was reported on the 21st of July with 2% of the users exposed to concentrations between 110 and 179 [μg/m 3 ].
In the case of modeled data, highest average daily exposure to PM 2,5 occurred on the 23rd and 24th of July with, respectively 5% and 1% of the users exposed to the same concentration levels. Nearly 70% of the total workplaces are in Santiago, Providencia and Las Condes municipalities, thus the larger exposure estimated with CHIMERE at work is mostly due to higher modeled concentrations in these locations. Households, on the contrary, are distributed more evenly than workplaces and are mostly on the periphery. The larger calculated exposure with MS data suggests that the simulated concentrations underestimate the concentrations on the periphery during night hours. The relationship between user's socioeconomic background and exposure to PM 2,5 varied depending on the dataset used. An inverse relationship between exposure and income is observed for concentrations of the AQ network: i.
Acknowledgements The presented research work is partially funded by  Fondecyt  ( 1150873 ,  1181139  and  1161589 ), Fondap (15110009) and  Complex Engineering Systems Institute  ( CONICYT FB0816 ). The research leading to these results has received funding from the  European Union   H2020  programme  PAPILA  ( GA 777544 ). Powered@NLHPC: This research was partially supported by the supercomputing infrastructure of the NLHPC ( ECM-02 ).
Because of their large conformational heterogeneity, structural characterization of intrinsically disordered proteins (IDPs) is very challenging using classical experimental methods alone. In this study, we use NMR and small-angle x-ray scattering (SAXS) data with multiple molecular dynamics (MD) simulations to describe the conformational ensemble of the fully disordered verprolin homology domain of the neural Aldrich syndrome protein involved in the regulation of actin polymerization. First, we studied several back-calculation software of SAXS scattering intensity and optimized the adjustable parameters to accurately calculate the SAXS intensity from an atomic structure. We also identified the most appropriate force fields for MD simulations of this IDP. Then, we analyzed four conformational ensembles of neural Aldrich syndrome protein verprolin homology domain, two generated with the program flexible-meccano with or without NMR-derived information as input and two others generated by MD simulations with two different force fields.
The human N-WASP sequence (505 residues) can be decomposed into seven domains: a primary WASP homology domain (WH1) (segment 1-150), a basic domain (186-200), a GTPase-binding domain (203-274), a proline-rich domain (277-392), domain V (405-450), a cofilin homology domain (C) (451-485), and an acidic domain (486-505). It should be noted that domain V is composed of two secondary WASP homology domains or motifs (WH2), each of them being able to bind a monomer of actin  (33). Indeed, Gaucher et al. demonstrated that, in presence of actins, peptides constructed from N-WASP domain V or VC form 1:2 V-actin or VC-actin complexes  (34). Data reported by Gaucher et al. indicate as well that the second WH2 motif of N-WASP domain V has a higher binding affinity for actin than the first one. This is also suggested by the crystallographic structure of N-WASP segment VC (392-484) in complex with actin (Protein Data Bank [PDB]: 2VCP)  (34) , which only shows the second WH2 motif bound to an actin monomer.