dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016). The quantile splits the dataset into the q smallest and the 1-q largest values, where q is a proportion chosen by the experimenter. All measures of RMR were considered for each individual, hence 68 measures over 17 hr, and q was fixed at 0. 12 such that 12% of values fell below true SMR  (McKenzie et al. , 2015; Chabot et al. , 2016).
| Series I: group observations Immediately following measurements of oxygen uptake, the four fish were moved to a behavioural arena for measurements of group airbreathing behaviour. This was done on 11 cohorts of fish, overall providing data on individual oxygen uptake and group behaviour for 44 fish, split into 11 groups of four fish each. Group observations were performed in a 250-L circular tank equipped with a 25-L overflow reservoir. A recirculation pump was placed in the overflow reservoir, which returned water to the primary observation tank. Aeration was performed in the overflow tank to prevent any disturbance to the animals and video quality. Oxygen levels were monitored using a galvanic oxygen electrode (OxyGuard mini probe, www. oxyguard. dk) placed in-line from the recirculation reservoir.
| Statistical analysis All analyses were conducted using r v. 3. 4. 0 (R Development Core Team, 2017) using the function lmer in package lme4  (Bates et al. , 2016)  and MuMIn 1. 9. 13  (Barton, 2015)  (http://CRAN. R-project. org/ package=MuMIn). The effect of social context (isolation vs. group) on air-breathing frequency was assessed using a linear mixed effects model (LME) with breaths as the dependent variable, social treatment and oxygen availability (normoxia and 20% air saturation) as fixed factors, and fish ID as a random factor. Individual variation in behaviour while in groups was further analysed using LMEs with breaths as the dependent variable, and log mass, log SMR, %MO 2AIR , activity (log-transformed total distance moved), attacks (log-transformed) and oxygen concentration (categorical variable) as explanatory variables, along with all possible interactions with oxygen concentration.
Darker points represent data collected at higher levels of aquatic oxygen; each individual and group was tested at each oxygen level. Black lines represent linear regression lines through all individual data points for significant effects shown in Table  1 ; grey shaded area is the 95% confidence interval of the regression [Colour figure can be viewed at wileyonlinelibrary. com] (a) (b) (c) (d) effect of social treatment, F 1, 126. 09 = 86. 28, p <. 0001). The large influence of social environment on individual air-breathing was highlighted by the observation that fish in groups in normoxia took, on average, 8. 2-fold more air breaths than solitary fish exposed to severe hypoxia-a condition that is normally expected to stimulate air-breathing. Indeed, air breathing increased in hypoxia in both isolation and in groups (Figure  1 ; LME, effect of oxygen, F 1, 115.
The category "attack" refers to a breath being taken after that fish performed an attack; "avoid" refers to a breath after that fish performed an avoid; "interaction" includes attacks, pushes and avoids (see Section 2 for description of each). Each data point equals the mean for one group of four fish (n = 11 groups). All groups were measured at each level of oxygen availability. Boxplot lower and upper hinges represent the 25th and 75th percentiles, respectively; the horizontal line within the box represents the median; the length of whiskers represents the range data points between each hinge and 1. 5× the difference between the 25th and 75th percentiles. Data beyond these limits are outliers [Colour figure can be viewed at wileyonlinelibrary. com] availability in determining individual behaviour.
Indeed, the social influences drove air-breathing to the extent of superseding any direct physiological drive stemming from exposure to environmental hypoxia. This illustrates how social context can strongly F I G U R E 6 The effect of intraspecific aggression on air-breathing in groups of Clarias gariepinus. Each data point represents data for one group of four fish (n = 11 groups). Panel a shows the relationship between the total attacks and total air breaths performed by all fish within each group. Panel b only shows the attacks performed by the most aggressive individual within each group (i. e. the dominant) and the breaths performed by all other fish but excluding the most aggressive individual. Darker points represent data collected at higher levels of aquatic oxygen; each individual and group was tested at each oxygen level.
ACKNOWLEDGEMENTS The authors thank  Nathan Diaz Martins  and  Cleo Leite  for their assistance. This work was funded by an  International Exchanges Grant  from the  Royal Society of London. We also thank  Jolle Jolles ,  Damien Farine  and an anonymous reviewer for constructive feedback on the manuscript. S. S. K. was also supported by  NERC  Advanced Fellowship NE/J019100/1 and  European Research Council   Starting Grant  no. 640004. D. J. M. was supported by the  CNRS  and the  Centre for Marine Biodiversity Exploitation and Conservation.
Materials and Methods The sampling encompassed the Near East, South Asia, Europe and Central Asia (Supplementary Table  S2 ). For the purposes of this study, both Near East and Southwest Asia refer to the territory that includes the Levant, Anatolia, Caucasus, Iraq, Iran, Arabia, and Lower Egypt. U7 samples were selected from mtDNA databases of the research groups involved in this study. Blood specimens were collected from healthy unrelated adult individuals whose matrilineal ancestors for at least two generations belonged to the populations reported here. Informed consent was obtained from all participants in the study. All experimental procedures were carried out in accordance with the approved guidelines by the Research Ethics Committee of the University of Tartu and the Ethic Committee for Clinical Experimentation of the University of Pavia.
All experimental protocols were approved by the Research Ethics Committee of the University of Tartu (252/M-17) and the Ethic Committee for Clinical Experimentation of the University of Pavia (Board minutes of the October 5, 2010). The mitogenome sequencing was carried out following published protocols  83, 84. Mutations were scored relative to the Reconstructed Sapiens Reference Sequence (RSRS)  31  and the Revised Cambridge Reference Sequence (rCRS)  85, 86. For these tasks as well as for sequence alignments the following software packages were used -ChromasPro (Technelysium Pty Ltd, South Brisbane QLD 4101, Australia), mtDNACommunity  31  , and BioEdit 87. A maximum-parsimony tree was constructed using a total of 367 hg U7 mitogenomes (267 new from this study and 100 from the literature), guided by published principles  88  (Supplementary Figure  S1  and Supplementary Table  S2 ).
In addition, the control region and/or phylogenetically informative markers from the coding region were sequenced for 229 samples (Supplementary Table  S1 ). This, together with the re-constructed high-resolution phylogeny, has allowed us to assign into major branches almost 85% of all U7 samples available from the literature and our collection (Supplementary Table  S1 ). Spatial frequency maps were generated with Surfer program (version 8, Golden Software, Inc. , Golden, CO, USA), following the Kriging algorithm (input data is represented in Supplementary Table  S3 ). Coalescence times were calculated using the rho (ρ ) statistic  89  and standard deviations  90. Genetic distances were calculated with both the complete and synonymous clock models and converted into years using the published calculator  91.
Acknowledgements We thank all the DNA donors who participated in this study. This study was supported by  Estonian Institutional Research  grant  IUT24-1  (to H. S. , E. M. , M. R. , M. M. , T. K. , and R. V. );  ERC   Starting Investigator grant  ( FP7 -261213 ) (to T. K. );  EU European Regional Development Fund through the Centre of Excellence in Genomics to Estonian Biocentre; Estonian Research Council  grant  PUT1339  (to A. K. ),  PUT1217  (to Kr. T. ) and  PUT766  (to G. C. ); the  University of Pavia  strategic theme "Towards a governance model for international migration: an  interdisciplinary and diachronic perspective " (MIGRAT-IN-G); the  Italian Ministry of Education, University and Research: Futuro in Ricerca 2012  ( RBFR126B8I ) (to A. A. and A. O. ) and  Progetti Ricerca Interesse Nazionale 2012  (to A.
A. , O. S. , and A. T. ); the  Council of Scientific and Industrial Research, Government of India  (GENESIS:  BSC0121 ) and ( BSC 0118 ) (to Ku. T. ); S. S. and E. R. acknowledge the support of  National geographic Society  through  Genographic Project Research Grant  ( 6-13 ). R. T. and A. K. P. were supported by the  European Social Fund 's  Doctoral Studies and Internationalisation Programme DoRa. M. B. R. received support from the  Leverhulme Trust 's Doctoral Scholarship programme, and F. G. from the  University of Huddersfield's University Research Fund and Research Excellent Staff Scheme. P. S. was supported by the  FCT Investigator Programme  ( IF/01641/2013 ).
Overall the industryrelated OA contributed on average 7 % of the bulk OA mass  (El Haddad et al. , 2011 , 2013). However, these results were limited to 2 weeks of measurements during summer while the contribution of industrial emissions during the rest of the year remains unknown. There is a general paucity of AMS and aerosol chemical speciation monitor (ACSM) datasets in the Mediterranean region during winter. Exceptions include AMS campaigns  (Mohr et al. , 2012; Hildebrandt et al. , 2011)  covering a few weeks during late winter-early spring and studies with an ACSM (e. g. ,  Minguillón et al. , 2015). The measurement of organic markers and elements (e. g. ,  Salameh et al. , 2015; Reche et al. , 2012)  at different stations indicate a substantial contribution from biomass burning (BB).
The source apportionment results obtained from PMF applied to the OA mass spectra are corroborated using a comprehensive set of offline measurements including elemental and organic carbon (EC / OC) measurements, as well as measurements of elements by inductively coupled plasma mass spectrometry (ICP-MS), of molecular markers by gas chromatography mass spectrometry (GC-MS) and ultra-performance liquid chromatography mass spectrometry (UPLC-MS), and of major ions by ion chromatography (IC). We mainly focus on the sources and trends of winter OA and therefore we additionally analyzed an online-AMS dataset acquired at the same location during the winter of the previous year. The comparison of online-and offline-AMS data and organic marker concentrations enables an in-depth characterization of OA sources in Marseille and in particular the identification of the main processes by which biomass smoke is emitted and transformed in this region.
Between each sample, a measurement blank was recorded via nebulization of ultra-pure water to minimize and monitor the possible memory effects of the system. In total five mass spectra were collected per each measurement blank. Offline-AMS data were processed and analyzed using the HR-ToF-AMS analysis software SQUIRREL (Sequential Igor data Retrieval) v. 1. 52L and PIKA (Peak Integration by Key Analysis) v. 1. 11L for IGOR Pro software package (Wavemetrics, Inc. , Portland, OR, USA). HR analysis of the mass spectra was performed in the mass range 12-115 Da and in total 217 ion fragments were fitted. The interference of NH 4 NO 3 on the CO + 2 signal was corrected according to  Pieber et al. (2016)  as follows: CO 2,real = CO 2,meas -CO 2,meas NO 3,meas NH 4 NO 3 ,pure • NO 3,meas , (1) where the CO 2,meas NO 3,meas NH 4 NO 3 ,pure correction factor was 2.
Other offline measurements A complete list of the measurements performed can be found in Table  S1. To summarize, major ions (Ca 2+ , Mg 2+ , K + , Na + , NH + 4 , NO - 3 , SO 2- 4 , Cl -, oxalate, malate, succinate, and malonate) were measured by IC according to the methodology described by  Jaffrezo et al. (1998). A subset of the filters was selected for CO 2- 3 quantification following the method described by  Karanasiou et al. (2011). The method encompasses the fumigation of the filter samples with HCl. The CO 2 evolved by this acidification of the carbonates deposited on the filters is detected by thermal-optical transmittance determination. The CO 2- 3 measurements agreed fairly well with the CO 2- 3 estimate from ion balance calculations based on IC data (Fig. S1  in the Supplement).
76 × 10 -7 was applied to the dataset. The standard relative ionization (RIE) efficiency was assumed for organics (1. 4), SO 2- 4 (1. 2), NH + 4 (4), and Cl - (1. 3), while the collection efficiency (CE) was estimated using the composition-dependent collection efficiency model  (Middlebrook et al. , 2012). Total AMS-PAHs were estimated from AMS data according to  Dzepina et al. (2007). Similarly to offline-AMS, online-AMS data were also processed and analyzed using HR-ToF-AMS Analysis software SQUIRREL v. 1. 52L and PIKA v. 1. 11L for IGOR Pro software package (Wavemetrics, Inc. , Portland, OR, USA). HR analysis of the mass spectra was performed in the mass range 12-115 Da and in total 215 ion fragments were fitted. A NO x analyzer was run in parallel to the AMS to monitor the real-time NO x concentration.
Implementation The online-and offline-AMS source apportionment results discussed in this work were obtained from PMF analysis  (Paatero and Tapper, 1994)  of AMS spectra using the Multilinear Engine (ME-2;  Paatero, 1999). The Source Finder toolkit (SoFi;  Canonaco et al. , 2013, v. 5. 1)  for Igor Pro (Wavemetrics, Inc. , Portland, OR, USA) served as interface for data input and result evaluation. PMF is a multilinear statistical tool used to describe the variability of a multivari- ate dataset as the linear combination of static factor profiles times their corresponding time series, as described in Eq. (  2 ): x i,j = p z=1 g i,z • f z,j + e i,j. (2) Here x i,j , g i,z , f z,j , and e i,j represent, respectively, elements of the data matrix, factor time series matrix, factor profile matrix, and residual matrix, while subscripts i, j , and z denote time elements, variables (in our case AMS fragments), and discrete factor numbers, respectively.
The corresponding standard deviation represented the source apportionment uncertainty. Based on analysis of the PMF residuals, we selected a five-factor solution to explain the variability of our dataset (Fig. S11 ). Similar to online-AMS, we monitored the decrease in Q/Q exp when increasing the number of factors (z). In this study, a large Q/Q exp decrease was observed until five factors. We also observed a clear Q/Q exp structure removal until five factors, with higher-order solutions leading to additional factors that were not attributable to specific aerosol sources or processes. The five separated factors included HOA, COA, BBOA, OOA, and industry-related OA (INDOA). The complete validation of the PMF factors will be discussed in Sect. 3. 2. As already mentioned, the HOA and COA profiles were constrained using an a-value approach.
g. , BBOA and levoglucosan) and between COA and the same tracer (e. g. , levoglucosan), using a standard error on the l distribution of 1/ √ N -3  (Zar, 1999). In total, we retained 1. 5 % of the PMF runs. The criteria that discarded the largest number of solutions were the ones based on the COA (4-6) correlation with tracers of other sources. This suggests that for this dataset the COA separation from other sources was particularly difficult due to the absence of data with high temporal resolution, which aid the separation of a distinct COA diurnal cycle. Moreover, this separation is also complicated by the small COA contribution estimated by both online-and offline-AMS source apportionments (on average 0. 4 µg m -3 as discussed in the following sections). Furthermore, the relatively small COA factor recovery (R COA median 0.
SO 2-  correlated well with NO x (R = 0. 86), with peaks during rush hours (centered on 08:00 and 19:00) and higher concentrations during the first half of the campaign. The average HOA : NO x ratio (µg m -3 / µg m -3 ) was 0. 023, consistent with  Favez et al. (2010). The COA diurnal variation showed two peaks at lunch and dinner time (12:00 and 21:00), as observed in other cities  (Elser et al. , 2016; Mohr et al. , 2012). The BBOA factor profile showed the highest f C 2 H 4 O + 2 and f C 3 H 5 O + 2 contributions among the apportioned factors. Previous studies  (Alfarra et al. , 2007)   In this specific dataset they could partially derive from traffic, although from the AMS-PAHs multilinear regression we estimated that 79 % of the AMS-PAHs are related to BBOA and 21 % to HOA, indicating that BBOA dominates the PAH emissions.
Offline-AMS source apportionment validation PMF factors from the offline-AMS dataset were related to aerosol sources/processes based on mass spectral features (Fig. 5 ), seasonal trends, and correlation with tracers (Fig. 6 ). A comparison of the online-AMS and offline-AMS factor profiles is reported in the Supplement. In the following, for a generic k factor, we calculated the corresponding KOC i time series by dividing KOA i by the OM : OC ratio determined from the average HR-AMS factor profile. During summer, when biomass burning contributions to EC are low, HOA correlated well with EC (R = 0. 76) and yielded an HOC : EC (hydrocarbon-like OC = HOA / (OM : OC) HOA ) ratio of 0. 64, similar to other European studies  (El Haddad et al. , 2009, and references therein). Over the whole year, the retained PMF solutions showed an HOA correlation with NO x (R) spanning between 0.
The summer COA contribution was not resolved from HOA by El  Haddad et al. (2013) , possibly because the COA reference mass spectrum was not constrained and because of the lack of HR data which typically aid the separation of the two sources. Finally, the INDOA factor concentration estimated from offline-AMS was on average 2. 1 µg m -3 during winter and 0. 6 µg m -3 avg during summer, where this seasonal trend was driven by a strong episode that occurred during early February. The offline-AMS relative uncertainty was estimated as 17 %. As previously discussed (Sect. 3. 1), this factor was not separated by online-AMS analysis (February 2011) because of the absence of clear events, which in the offline-AMS dataset were observed only over a short period during January-February 2012. An industrial factor was instead resolved by El  Haddad et al.
05-0. 2; Zotter et al. , 2014; Herich et al. , 2014; Minguillón et al. , 2011) , while during late autumn and March (Fig. 11 ) it increased up to 0. 3, highlighting an evolution of the BBOA chemical composition. A similar seasonal trend was observed for the ratios of levoglucosan : vanillic acid, levoglucosan : syringic acid, and levoglucosan : non-sea-salt K + (nss-K + ; calculated according to  Seinfeld and Pandis, 2006 ) ratios (Fig. 11 ). Although the online dataset was limited to 1 month of measurements, the levoglucosan : vanillic acid ratio also showed a statistically significant increasing trend from early February to the beginning of March (confidence interval of 95 %, Mann-Kendall test). These results suggest the occurrence of different types of biomass combustions during low-temperature winter days compared to late autumn and early spring: levoglucosan derives from cellulose pyrolysis (> 300 • C), while vanillic and syringic acids result from lignin combustion  (Simoneit et al.
Online-and offline-AMS time-dependent levoglucosan : BBOC, levoglucosan : vanillic acid, levoglucosan : syringic acid, and levoglucosan : K + ratios. The plant wax concentrations were determined from GC-MS measurements of alkanes with an odd number of carbons  (Li et al. , 2010). As discussed in the main text the spike observed in late autumn could be related to incomplete green waste combustion. nigan et al. , 2010) and low BBOA emissions. Not surprisingly the levoglucosan : nss-K + ratio showed lower average values in summer (0. 23) than in winter (3. 14). During winter nss-K + is considered to be mostly emitted by BBOA, and consistently in our dataset it shows a good correlation with BBOA tracers (R = 0. 66 with syringic acid). Overall, the levoglucosan : nss-K + ratio during the cold season manifests a behavior that is opposite to the photochemical activity (with temperature considered as a proxy) as it shows higher values during March and late autumn (up to 7.
This hypothesis of at least two types of BB sources (one linked to domestic heating, another to agricultural activities) is also supported by the direct PMF analysis of the organic and inorganic markers measured for Batch 1  (Salameh et al. , 2017). 5 Conclusions PM 2. 5 filter samples were collected during an entire year (August 2011 to July 2012) at an urban site in Marseille, France. Filter samples were analyzed by water extraction followed by nebulization of the liquid extracts and subsequent measurement of the generated aerosol with an HR-ToF-AMS  (Daellenbach et al. , 2016). PMF analysis was conducted on the offline-AMS mass spectra and on online-AMS data collected at the same station during February 2011. Offline-AMS source apportionment results were also compared with a previous online-AMS source apportionment study of 2 weeks during July 2008 at the same location  (El Haddad et al.
Acknowledgements. Carlo Bozzetti  thanks the  Lithuanian-Swiss Cooperation Programme "Research and Development"  project  AEROLIT  (no. CH-3-. MM-01/08 ). Jay Gates Slowik acknowledges the support of the  Swiss National Science Foundation  (starting grant no. BSSGI0 155846 ). Imad El Haddad acknowledges the support of the  Swiss National Science Foundation  ( IZERZ0 142146 ). María Cruz Minguillón  acknowledges the  Ramón y Cajal Fellowship  awarded by the  Spanish Ministry of Economy, Industry and Competitiveness. This work has also been supported by the  MED program  (APICE, grant number  2G-MED09-026 : http://www. apice-project. eu/),the  French Environment and Energy Management Agency (ADEME) , and  Provence-Alpes-Côte d'Azur (PACA) region. Part of the OC / EC analysis carried out in MRS was supported by the  French national CARA program.
DATA 2. 1. Galactic Plane Survey Data We extracted multi-wavelength data covering the entire cloud from the GLIMPSE, MIPSGAL, Hi-GAL and AT-LASGAL surveys. Using the IRAC instrument on board the Spitzer Space Telescope  (Werner et al. 2004 ), the Galactic Legacy Infrared Mid-Plane Survey Extraordinaire (GLIMPSE,  Benjamin et al. 2003; Churchwell et al. 2009)  project surveyed the inner 130 • of the Galactic Plane at 3. 6, 4. 5, 5. 8, and 8. 0 µm with 5σ sensitivities of 0. 2, 0. 2, 0. 4, and 0. 4 mJy, respectively. The Galactic Plane Survey using the Multiband Infrared Photometer on Spitzer (MIPS-GAL,  Cyganowski et al. 2009 ) mapped an area comparable to GLIMPSE at longer infrared wavelengths. Version 3. 0 of the MIPSGAL data provides mosaics from only the 24 µm band with an angular resolution of 6 and a 5σ sensitivity of about 1.
SMA Observations Observations towards the central massive clump C1 using the Submillimeter Array (SMA) at 1. 3 mm were performed on 2010 August 10 th in the compact-north configuration with eight antennae used. The data are publicly available in the SMA data archive (PI: Claudia Cyganowski). The 2 × 4 GHz correlator was tuned to cover the frequency range 216. 8-220. 8 GHz in the lower sideband (LSB) and 218. 8-232. 8 GHz in the upper sideband (USB). The 4 GHz bandwidth in each sideband is allocated to 48 spectral windows, each of which consists of 128 channels with an uniform width of 812. 5 kHz (∼1. 1 km s -1 ). The projected baseline lengths ranged from 8 to 105 kλ, which indicates that the SMA observations are insensitive to smoothed structures larger than 20. 3c454. 3, J1733-130/J1911-201, and Callisto (Jupiter's moon) were observed as bandpass, gain and flux density calibrators.