dataesr/scientific-paragraphs-categorization huggingface dataset:
The sensor hardware represents a module of a mobile polygraph system, which has already been applied in space applications (HealthLab, Koralewski Industrie Elektronik, Hambühren, Germany). This new module was tested for data reliability and validity including the actigraphy measurement, prior to its system-synchronized combination with the other (physiological, voice, and environmental) measurements  [49] [50] [51]  for future studies. The aim was to register the presence of any other sensor in a certain distance range with a sample rate, sufficient for comparisons to physiological reactions. To the author's knowledge this has never been published and might enhance the possibilities in group research. These feasibility, reliability, and validity tests were performed in the Mars520 project, simulating a manned space flight to Mars and back (in Moscow, IBMP [52-53]; described briefly below).
Study design This study was part of the Mars500-project, a 520-day simulated return mission to Mars performed in the terrestrial experimental complex (NEK) at the Institute of Biomedical Problems (IBMP) in Moscow, Russia  (2009) (2010) (2011)  and was supported by the European Space Agency (ESA) and the German aerospace Center (DLR). A 105-day pilot study preceded the 520-day study. Six international crew members participated in each study. A detailed report on the design of the Mars500-project has been published by IBMP and ESA  [53] [54]. The data reported in this manuscript were gathered in the 520-day study.
5 V, 660 mA), and a standard USB-2 connector for data transfer. The size of the sensor box was 85mm x 46mm x 16mm, and it weighed 70g. There were two different sensor modules. The crew member module could be attached to clothes with a clip at the module's back. The habitat modules were attached to their designated positions in the isolation chamber with adhesive tape. All sensor modules worked within a 2. 4 MHz frequency band. Modules recorded (a) signal strength (in dB) of the received signals from other transceivers, (b) their identity, and (c) a time-stamp. A 3-axial acceleration sensor registered the orientation of the sensor in space. Changes in orientation were operationalized as activity of the subject. The maximal operating time of the sensor modules in use was 20 hours. Recharging the integrated Lithium-Polymer battery (capacity 660 mAh) took approximately 2 hours at a rate of 400 mA.
Mars500 experiments of other PI's used for comparison In another Mars500 experiment, Basner et al. [56, 57]  continuously registered actigraphy. However, the internal system time of the WLGS-system and the ActiGraph-System shifted differently over the whole 520-days study phase. Therefore the comparison of the internal actigraphy data of the WLGS system with external actigraphy data required a synchronization of both systems data on a daily basis. The maximum of their cross correlation function was corrected for each day separately towards the zero-lag position by adding a day specific time constant to the WLGS data. For the comparison of the WLGS-data with classical sociometric data (choices) data from the Vinokhodova et al. Mars500 experiment  [58]  were used. The sociometric questionnaire asked for personal preferences with whom of the crew members to spend the next mission (professional cohesion) and the next vacation (social cohesion).
The pairwise registered time spent together was sorted and recoded in analogy to choices. The largest time was recoded as a "2" (respective first choice answer in the questionnaire), the second largest time as "1" (second choice). All other times were recoded as "0". Correlations were calculated for both sociometric questions over the 30 possible pairings. For safety reasons the crew was constantly surveyed by several video cameras. For technical reasons the system overwrote these sequences after a few weeks, so at the end only the last weeks were still available. The organizers (IBMP) as well as the crew members gave their consent to use the video data of one whole day (mission day 475) to validate the WLGS data. A late day of the study was sufficient assuming that the WLGS measurements before had the same quality.
By means of the INTERACT video analysis software (version 9, Mangold International, Arnstorf, Germany) the WLGS data files were read for comparison with video files. The raw data of day 475 for all sensors were exported separately in one-hour segments. The video films, captured from the Mars500 survey system via a screen capture program (VLC), were edited with regard to the same time intervals and stored as standard video files. Data files and video-files were individually synchronized for each one-hour segment. Tafforin's Mars500 experiment  [59]  provided ethological video analyses of the crew's interindividual behavior (whole visual interactions, body interactions, and object interactions) during a collective activity (breakfast) monitored every other week. Analyses were performed using quantitative descriptions with the OBSERVER XT software.
Data processing and statistical analyses The primary WLGS data were electronically stored on the sensors' memory cards and then downloaded to the host's hard disc. The data were transferred to a server of the Mars500 Experiment Control Center, and then electronically forwarded to the PI on a regular basis. For statistical analyses the data were exported with the application software. Quick graphs (sociogram graphs) were directly provided by the software. Further analyses were mainly performed with IBM SPSS Statistics, version 20 (IBM Corporation, New York, USA). Some analyses and graphs were carried out in the "R" statistical environment (version 2. 9. 2, www. r-project. org). Latent space models with non-binary response were calculated with the ergmm procedure of the R-package latentnet. This approach is based on Bayesian inference statistics.
Data were read and merged per day in separate files. The measured signal amplitudes of other sensors were recoded into "1" (presence) and "0" (absence). The result was a data matrix for each sensor containing presence and absence of other sensors in a five second interval (sample rate = 0. 2 Hz). The sum of the "presence" values (multiplied by 5 seconds) provided the measure of the time spent together. In parallel the movements of the sensors were registered by an internal three-dimensional accelerometer. WLGS data, ethological data of Tafforin's Mars500 experiment and sociometric data  [58]  were sampled twice a week, twice a month and monthly respectively. Resampling of the three time series data was required to yield equidistant measurement points, which then were subjected to correlation analysis.
Results WLGS data were obtained twice a week starting with mission day 15. Out of 132 expected data sets 130 (98. 5%) were obtained. However, only 89 (67. 4%) were complete during day time for the analysis. The remaining 42 data sets were missing primarily due to insufficient battery recharging. The WLGS data matrix can be found in the S1 Data. Graphs similar to  Moreno  The reference distribution was chosen as recommended by Krivitsky in a short e-mail communication. All crew members were assigned to an "agency", the "hosts" or the "guests". The structures look similar but the estimate of the covariate coefficient of the agency effect provided opposite significance results for the F-value (MCMC sample size = 4000): highly significant (p < 2. 2e -16 ) vs. not significant (p =. 405). The lower Bayesian Information Criterion (BIC) 187.
The systems of WLGS and ActiWatch used the different system times of their host computers. The different system times and their time shifts over 520 days were compensated by means of crosscorrelation. In external reliability verification for mission day 475, the crew localization was verified by video and compared to the registrations of the habitat sensors. Eleven hours of WLGS data recording from ca. 9:30-20:30 were analyzed. Table  1  shows the percentage of time that a crew member was registered by the respective habitat sensor if he appeared on the survey video. Private locations such as bedrooms or restrooms were not filmed and excluded from this analysis. The overall percentage of congruent localization was about 95%. For sociometric analyses the interaction of subjects is most relevant.
This amount of significances is highly above the likelihood of occasion (5%) in multiple comparisons. For a comparison of the CRTI with results of the latent position analysis five series of ergmm models were calculated for 49 randomly selected mission days. The intercept value of the ergmm models was taken as analogue for the CRTI. Significant correlations were found among the ergmm time series, but there was no correlation with the CRTI scores. The WLGS data were also validated by comparing integral cohesion measures over the study period. The integrated index for crew relations (the Crew Relation Time Index, CRTI), based on the WLGS measurements across Mars-520 is depicted in Fig 5. As can be seen from the figure, CRTI fluctuated considerably, and moderately decreased throughout the study (regression over all analyzed mission days: slope Beta = -0.
376, p =. 001, R 2 =. 141). The CRTI data were compared with ethological analyses of videos during breakfast. Tafforin  [59] ) assessed the number of interactions among crew members and the duration of interactions during breakfast. However, the timing of the different methods (WLGS and breakfast video analysis) differed between the methods and was not equidistant. Therefore, the oversampling and resampling method was used to create time series with congruent values for each day. Significant correlations of day-by-day resampled indexes of crew cohesion in WLGS (CRTI) and ethological breakfast video analysis  [59] ) were found. The correlation between CRTI and the amount of crew interactions during breakfast was. 320 (p =. 000). Also a correlation of. 230 (p =. 000) was found between CRTI and the duration of these crew interactions during the breakfast.
No such correlations were found during the 520-day period when comparing sociometric results directly with unselected WLGS data. Significant correlations appeared only when limiting WLGS analysis to time periods in the afternoon and relating them to the "ACTIVE" state of actigraphy. The main reason for the lack of correlation in the whole-day data is thought to be due to the large portion of "private time" during the morning hours with a remaining stochastic variability in dependency on the work schedule. The agreement between WLGS registrations and the video analysis, instead, was highest during the morning phase. The participants stayed longer in a certain location for their work. During the spare time there was much more quick movement within the chamber. Subjects, e. g. , were crossing the kitchen only (visible in the video) but the sample rate of the WLGS was too low for an assessment.
Validating the WLGS approach in comparison to other measures of cohesion was difficult. In view of the very small sample size, it was unreasonable to expect highly consistent results across individuals and measures. The study design was far from ideal for validation analyses as none of the correlates were synchronized with the WLGS data during the preparation phase and data sharing started only after the study. Unfortunately, there was a noticeable lack of any objective performance measures or team-based tasks allowing the crew to behaviorally demonstrate cohesion. The fact that any significant relationships were found is noteworthy, but even the statistically significant correlations still account for a modest proportion of variance. However, future research is needed to verify this assumption.
SNV Calling Reads were mapped on the draft genome of the rhesus macaque (rheMac2), the draft Y chromosome sequence  (Hughes et al. 2012) , and the mitochondrial genome (DDBJ/GenBank/ EMBL accession number: AY612638) using the BWA aln/ sampe algorithm with default parameter settings, except for quality trimming score of À15  (Li and Durbin 2009). Among the samples, the average mapping rate of reads was 93. 3%. Potential polymerase chain reaction duplicates were marked using Picard software (http://picard. sourceforge. net, last accessed March 5, 2015). SNVs were jointly called on all six samples using the Best Practice pipeline of the Genome Analysis Toolkit software package  (Mckenna et al. 2010) , which includes base quality score recalibration, insertion/deletion (indel) realignment, SNV calling, and variant quality score recalibration  (Van Der Auwera et al.
2002; Depristo et al. 2011). After calling the initial set of variants, further application of the following hard filters was employed: FS > 60. 0, HaplotypeScore > 13. 0, MQ < 40. 0, MQRankSum < À12. 5, QD < 2. 0, ReadPosRankSum < À8. 0. SNVs on fragmented scaffolds (chrUr) were not included in analysis. Heterozygosity within individuals and nucleotide diversity () were estimated using only high coverage sites (!10-folds). All raw read sequences and initial sets of variants are deposited into the public database (EMBL-EBI accession number: PRJEB7871).
Principal Component Analysis and Population Tree Principal component analysis (PCA) was conducted using the smartpca program in the EIGENSOFT software package  (Patterson et al. 2006). Extraction, filtering, and processing of data were performed using custom-made perl scripts. A population tree was constructed using three additional genome sequences of macaques  (Yan et al. 2011; Higashino et al. 2012). PHYLIP software  (Felsenstein 1989 ) was used to generate the distance matrix for the macaque individuals by Nei's genetic distance  (Nei 1972). For the tree construction, the allele frequency data of SNV sites (coverage ! 10) found in all nine individuals were used. The phylogenetic tree was constructed using the neighbor-joining method  (Saitou and Nei 1987)  implemented in MEGA6  (Tamura et al.
Pairwise Sequentially Markovian Coalescent Method The analysis was performed using Pairwise Sequentially Markovian Coalescent (PSMC) software  (Li and Durbin 2011). Consensus genome sequences for PSMC input were constructed using samtools and vcf2fq utility  (Li et al. 2009 ). The time interval parameter of 4 + 25*2 + 4 + 6 and the number of iterations of 25 were used for the parameters of PSMC.
Prediction of Disease Causing Mutations To infer the disease causality of nonsynonymous mutations in the Mauritian cynomolgus macaques, we identified respective nonsynonymous mutations in human orthologs and predicted the functional effect using PolyPhen-2  (Adzhubei et al. 2010). Gene annotation of the rhesus macaque followed the annotation in a previous study  (Higashino et al. 2012). The humanmacaque ortholog information was retrieved from the Ensembl database  (Flicek et al. 2014). Human and macaque protein sequences were aligned using ClustalW  (Thompson et al. 1994 ) and only the sites that have the same amino acid residues between human and macaque reference proteins in the alignment were analyzed through the PolyPhen-2 website (http://genetics. bwh. harvard. edu/pph2/, last accessed  March 5, 2015).
Genetic Relationship between and within Populations In addition to the Malaysian cynomolgus macaque genome, we retrieved the two more previously published macaque genomes (Vietnamese cynomolgus macaque and Chinese rhesus macaque). Genetic relationships among the six Mauritian cynomolgus macaque individuals were examined using PCA plot (fig. 1 ). We confirmed that no individuals were closely overlapped in the plot. A plot including all nine macaque genomes is presented in the supplementary figure  S1 , Supplementary Material online. We further examined whether the Mauritian cynomolgus macaques are genetically closer to the Malaysian or to the Indochina cynomolgus macaques. Figure  2  shows the phylogenetic relationship of the four macaque populations. Consistent with the results from mitochondrial data  (Smith et al.
Acknowledgments This work was supported by the funds from  University Toulouse III  ( EA3034 ,  UniversitePaul Sabatier)  and  French Ministry of Research  and by the  Grant-in-Aid for Scientific Research  (A), Grant number  26251040 , to N. S. and N. O. They thank  Dr Tomas Marques-Bonet  for the assistance of data analysis and the two anonymous reviewers for helpful comments on the manuscript.
7%  (2). Pyrosequencing was performed on a Roche 454 GS-FLX Titanium system by Shanghai Hanyu Bio-Tech Company. The contigs were ordered using the Newbler assembler (Roche Diagnostics, Basel, Switzerland), and gap filling and closure was achieved by combinatorial multiplex PCR and sequencing. The genome of Pa. pacificus DY20341 T is a single circular 1,859,370-bp chromosome without an extrachromosomal element, with a GϩC content of 43. 04%. A total of 2,001 protein-coding sequences were predicted by Glimmer version 3. 02. Of the genes, 78. 11% (1,563/2,001) were assigned to specific Clusters of Orthologous Groups (COG) Database functional gene groups, and 49. 83% (997/2,001) were assigned an enzyme classification number. The average gene size is 845 bp, comprising CDSs ranging from 38 to 1,749 amino acids.
These genes are also absent in Thermococcus species except Thermococcus sibiricus  (5). Analysis of the repeated sequences and a search against the IS database revealed that a remarkable feature of the Pa. pacificus genome is the absence of mobile genetic elements including transposons, transposases, integrases and virus-related region. It indicates that Pa. pacificus genome has not been subjected to frequent or recent genomic rearrangements during its evolution. Further comparative genomic studies of several Thermococcales species will provide a foundation to identify the biochemical mechanisms responsible for its adaptation to a hydrothermal vent environment and to understand the systematic evolution of members in this order. Nucleotide sequence accession number. This whole-genome shotgun project has been deposited in DDBJ/ENA/GenBank under the accession number CP006019.
ACKNOWLEDGMENTS This work was supported by the  National Program on Key Basic Research Project  ( 973 Program , no. 2012CB417300 ),  National Natural Science Foundation of China  (grant no. 41006100 ). This work was also partially crossmark Genome Announcements September/October 2015 Volume 3 Issue 5 e01080-15 genomea. asm. org 1 supported by the  Agence Nationale de la Recherche  ( ANR-10-BLAN-1752-01 Living Deep ), by the  EU   FP7 program  MaCuMBA (grant agreement no. 311975 ), the  PICS-CNRS-INEE Phypress , and  PHC Cai Yuan-Pei   30412WG.
showed that it was possible to define a measure of city attractiveness by exploring big data from photograph sharing websites  [25]. They analyzed two types of digital footprints generated by mobile phones that were in physical proximity to the New York City Waterfalls: cellular network activity from AT&T and photographic activity from Flickr. They distinguished between attractiveness and popularity. Regarding attractiveness they defined the Comparative Relative Strength indicator to compare the activity in one area of interest with respect to the overall activity of the city. They measured the attractiveness of a particular event in New York City. In this study we consider the attractiveness in the overall area of the city during three years, comparing the attractiveness and spatial distribution of activities in different cities.
In this paper we define city global attractiveness as the absolute number of photographs taken in a city by tourists, while local attractiveness of hotspots within a city is defined by the spatial distribution of photographs taken by all users (either local residents or tourists). In our analysis we are using a dataset that consist of more than 100 million publicly shared geotagged photographs took during a period of 10 years. The dataset is divided into 8,910 files denoting 3,015 different locations (e. g. , cities or certain areas of interest such as Niagara Falls) where for almost every location three different labels are given: resident, tourist and unknown, to denote people who are living in the area, visiting the area or are uncategorized.
Data For each photograph in the dataset, which is publicly available on the website sfgeo. org, the following data is given: user id, timestamp, geo coordinates and link from which the photograph can be downloaded. From the given dataset we omitted duplicates (9. 33% of the dataset in total) together with the photographs with incorrect timestamps (0. 01% of the dataset in total). In the end we were left with more than 90 million photographs. Figure  1  shows how the number of taken photographs and users changed over the period of 10 years. We can see that almost 75% of the photographs were taken from 2007 until 2010. In the further analysis we are thus considering only photographs taken during those three years (about 70 million of photographs in total).
Definition of user home cities and countries In order to determinate if there is any difference between how residents vs. tourists are attracted to a certain location, for each user in the dataset we have to determinate his/her home city and country. Even though the dataset has tags for: resident, tourist and unknown, the given categorization is not comprehensive and in some cases is not consistent. For instance, for more than 85% of users in the dataset their home city is not defined (i. e. , their photographs are always in unknown files). In addition, for almost 25% of users for who their home city is defined at least two or more cities are listed as their home cities making the proposed categorization inconsistent. Due to the aforementioned reasons, we used our own criteria to determinate if a person is living in the area where he/she took a photograph or not.
We are considering that a person is a resident if at that location he/she took the highest number of the photographs (at least 10 of them) over the longest period of time (at least longer than 180 days) calculated as the time between the first and last photograph taken at the location. Once when we determinate a user home city, he/she is automatically becoming a tourist in all other cities in the dataset. A category "tourist" in this sense denotes many different kinds of visitors including business visitors. However, most of people taking photographs at locations other than their home cities in fact act like tourists during this particular instance of time. From almost 1 million users that took photographs between 2007 and 2010, for only 11% of them we were able to determinate their home city and country using our criteria.
However, these users took more than 40% of all photographs (i. e. , almost 30 million). Our classification was not consistent with the initial categorization (i. e. , users whose photographs were listed in only one resident file) for only less than 2% of users. Moreover, for every city in the dataset we identified its country code allowing us to classify tourists as domestic or foreign ones, where domestic tourists are coming from the same country as the considered city, while foreign tourists are all the othersvisitors from different countries. Finally, for every city all the observed activity is assigned to residents, domestic tourists or foreign tourists and for each of these categories, we keep the following data: user id, geo coordinates and for tourists their home city id together with their country code and continent id.