dataesr/scientific-paragraphs-categorization huggingface dataset:
5% from VSDI [Fig. 1(b) and Table 1]. This relative lack of attention stands in contrast to the fact that more than 95% of interneuronal connections occur between neurons separated by less than 2 mm in cortex. 4 These under-explored techniques, with VSDI in particular because it remains unique in the field-of-view and temporal resolution it reaches, have, therefore, an enormous potential for unraveling new fundamental scientific discoveries. Two points, however, stand in the way of this potential: (1) insufficient understanding of the VSDI signal's origin and (2) the lack of standardized signal processing tools. Developing dedicated computational approaches may, therefore, be the key to improve our knowledge and know how in VSDI studies. Here, we review recent advances in that direction. First, we describe how generative models of VSDI data can help to better understand the origin of the signal and unravel possible underlying mechanisms.
In particular, biophysical models have proved very useful in describing the signal's multicomponent origin. Second, we strongly suggest a standardization of signal processing tools that are key for generalization and comparison of results obtained with VSDI. With this imaging technique, the raw signal is corrupted by many noise components arising from physiological, mechanical, or electronical sources. We review the signal processing tools that have been developed for VSDI signal analysis. Finally, we show that combining single-trial data analysis with computational models can reveal the origin of the observed signal's dynamics. Similar to the convergent interdisciplinary efforts that have been undertaken to optimize our understanding and use macroscopic brain imaging signals [i. e. , functional magnetic resonance imaging (fMRI), magneto-encephalogram, and electro-encephalogram], we believe that such computational approaches are needed for the development of VSDI and are vital to establishing this as a standard neuroscientific imaging technique.
This effect is logical since anesthetic specifically slows down inhibitory conductances and not excitatory. At response onset, inhibition will have the possibility to transiently override excitation, before being re-equilibrated by the strongly recurrent cortical network. VSDI generates complex experimental data that are difficult to interpret. Computational models can help explain the origin of this signal. These models are useful to understand the role of the major components of the VSDI signal and to generate experimentally testable predictions. However, the validation of such models cannot be achieved in isolation to real experiments, which are necessary to constrain them. In order to facilitate the confrontation of computational models and real data, it is, therefore, necessary to improve the signal processing algorithms available for VSDI data, and we now review the recent literature dedicated to this.
ICA has been mostly applied on VSDI recordings in anesthetized preparations at the single-trial level. A posteriori component identification also mostly relies on statistical criteria  54, 55  or with a "weak model" on intrinsic optical imaging data. 56 Again, these decomposition techniques focus on the temporal dimension and can easily be combined with complementary spatial routines such as spatial ICA  57  or local similarity minimization. 58 As noted above, the modes are not determined upon physiological criteria but upon signal statistics. Thus, nothing guarantees perfect source separation and parameters estimation is impossible. 59 Signal and noise modes can also be classified as user judgment after decomposition, for instance based on complementary recordings. 60 hese techniques can also be successfully applied on datasets obtained with specifically designed paradigms with periodic stimuli.
This is usually trivial for VSDI data as noise components often show a much faster dynamic and periodicity than response ones. Figure  4 (a) shows the model design used by Reynaud et al. 69  to denoise VSDI data acquired in the awake monkey. The noise and response components are modeled in the following steps. The first noise component represents the baseline level X 0 by a constant value. Electronic 74 and physiological artifacts X 1 (Ref. 1) are characterized by periodic oscillations and are thus modeled with Fourier series, allowing for phase changes. Finally, the dye bleaching can be defined with exponential functions X 2. 75 The response components X 3 are the FLOBS and describe the neuronal response. The decomposition of the temporal signal on the basis of all these regressors will lead to the identification of individual coefficients.
105 ] [108] [109] [110] [111]  To bridge this gap between network synaptic operations and the mesoscopic population response, computational models are crucial. More generally, theoretical considerations about the information processing and encoding capacities that reside specifically at the mesoscopic scale are clearly needed, both for improving our understanding of the experimental results, but also to generate tractable predictions. Advances in signal processing, on the other hand, are essential to generate standardized and comparable datasets among studies. It is also necessary to remove artifacts that could potentially contaminate the signal and hinder access to analysis of single trials. Since the signal is spatio-temporally inseparable (i. e. , cannot be described by two independent spatial and temporal functions), because of inherent propagations due to feedforward and feedback divergence, but also to intracortical connectivity, analyzing data at the singletrial level is essential to understand its dynamics, which are easily corrupted by averaging.
Acknowledgments This work was supported by the  CNRS , the  European Community  ( FET Grant  Nos. FACETS FP6-015879 ,  BrainScaleS FP7-269921 , and the  Human Brain  Project  H2020-720270  (to AD), the project " BalaV1 ," awarded by the  Agence Nationale de la Recherche  ( ANR-13-BSV4-0014-02 ), and the  FRQS Vision Health Research Network of Quebec networking  grant. The experimental protocol was approved beforehand by the local Ethical Committee for Animal Research and all procedures complied with the French and European regulations on Animal Research (Approval No. A12/01/13 ) as well as the guidelines from the  Society for Neuroscience.
pestis q3 phylogenetic subgroup within group I (node k). The matched genetic grouping between the 2 human samples is consistent with human-to-human transmission. The matched genetic grouping among all 3 samples is consistent with Y. pestis in the initial case-patient originating from the environment rather than another human. We compared these phylogenetic data with data from 4 archived isolates (human and rodent) obtained in 2000, 2002, and 2003 from the same area. The archived isolates were also assigned to group I (node k), but unlike the isolates from this study, they were assigned to the q2 subgroup  (15). This finding indicates that the recent outbreak did not arise from the same phylogenetic groups responsible for past outbreaks and illustrates how outbreaks in different years are probably conferred by Y.
Acknowledgments We are grateful to the staff of  Moramanga District Hospital  and the  Ministry of Health  for assistance in different aspects during the outbreak response, to the staff of the  Plague Central Laboratory-Institut Pasteur de Madagascar  for technical assistance, to  Dave Wagner  for editing this manuscript, and to  Anthonio Rakotoarison  for study site mapping. This study was supported by the  Institut Pasteur de Madagascar. Dr. Ramasindrazana  is an ecologist-mammalogist in the  Plague Unit at Institut Pasteur de Madagascar. He conducts ecological and epidemiological surveys on small mammals to investigate host-parasite relationships.
, 2012) , especially when integrated with molecular data  (James et al. , 2006). However, some studies have indicated that cryptic species might exist at both the genetic and ultrastructural levels  (Letcher et al. , 2008; Simmons, 2011). Ultrastructural analyses require concentrated suspensions of zoospores, but given the limited number of chytrids strains available in culture (especially obligate parasitic chytrids), relatively few taxa are currently available for study. However, the use of molecular tools for identification of sequences originating from environmental DNA by reference to sequence databases  (Hibbett et al. , 2016)  can overcome many limitations of traditional microscopic approaches, not only to discover, classify and name fungal species according to their phylogenetic relationships and taxonomy, but also to perform ecological studies.
In this context, two key considerations must be taken into account: (i) there does not appear to be a universal genetic marker able to discriminate among distant taxa, and simultaneously provide adequate resolution to identify organisms at the species level, and (ii) current representation of Chytridiomycota, and especially parasitic chytrids, in sequence databanks is limited. The nuclear rRNA gene region, consisting of three genic markers evolving at different rates, has been instrumental for fungal identification by molecular barcoding. First, the small ribosomal subunit (SSU), which can be aligned across the breadth of the phylum level due to its conservative nature, allows the placement and identification of a broad and divergent range of taxa. Such analyses can result in phylogenies with strongly supported lineages, but may suffer a poorly supported backbone due to many polytomies with little or no indication of relative relationships among clades  (Letcher et al.
, 2015). Additionally, phylogenetic analyses could be complemented by the use of other novel fungal markers such as the elongation factor TEF1a and the single-copy protein-coding gene RPB2  (Stielow et al. , 2015; V etrovsk y et al. , 2016). Another promising approach could involve the development of a large number of new candidate loci from sequencing different chytrid genomes from divergent lineages (e. g. through single cell genomics) and the development of specific primers for these regions  (Gawad et al. , 2016; Rutschmann et al. , 2016). The second major constraint for the taxonomy of this group is a general lack of representatives, especially parasitic species (or those described as such), in sequence databases. A survey of the most important databases for fungal taxonomic assignment reveals that Chytridiomycota represent between 0.
1 and 4% of the fungal sequences, where the number of those that are parasitic species is difficult to estimate, but not larger than a few dozen (Table  2 ). It is therefore not surprising that some species of parasitic chytrids were recently found to be related to sequences of novel lineages only characterized by environmental sequences  (Karpov et al. , 2014; Seto et al. , 2017). The use of cultureindependent molecular methods, e. g. single cell/colony/ spore PCR  (Ishida et al. , 2015) , as well as sequencing of bulk phytoplankton samples, will likely improve chytrid representation in future sequence databases.
, 2011; Kagami et al. , 2014) , however, they still show limitations. Inverse models provide only a snapshot of the natural complexity, illustrating steadystate webs for a chosen time period, but do not integrate temporal evolution nor allow describing complex dynamics like host-parasite interactions  (Miki et al. , 2011). In turn, empirical dynamic models require good quality long-term datasets with a time resolution matching the relevant rates of the biological process in question, i. e. grazing or infection. Hence, they are still limited by the current lack of datasets showcasing long-term dynamics of chytrid infections. Moreover, the interpretation of results from linear models (e. g. MAR models) is not always straightforward. Regression approaches carry a risk of yielding spurious relationships.
Acknowledgements The authors want to thank all participants of the 1st Plankton Chytridiomycosis Workshop (PCW) in Berlin, Germany (2015) and the 2nd PCW in Skagastr€ ond, Iceland (2016) for valuable discussions prior to the writing of this paper. The  Leibniz Association  is acknowledged for supporting the 1st  PCW in Berlin (Germany)  and the  University of Akureyri  and the S oknar aaetlun  Nordurlands Vestra  for their financial support to the 2nd PCW in Skagastr€ ond (Iceland). HPG and  ECB  were supported by the  Leibniz Pakt/SAW -project ' MycoLink ' (Pakt/ SAW-2014-IGB-1). ASG was funded by the  NWO  ( 016. Veni. 171. 063 ). SVdW and MG were each funded by the  IGB  fellowship programme. RA was supported by the  Alexander von Humboldt Foundation.
The values retrieved from satellite data enable us to derive the spatial distribution of methane (CH 4 ), identifying August as the month with the highest levels over the EM. The outcomes of a recent extensive examination of the distribution of methane over the tropospheric Mediterranean Basin, as part of the Chemistry-Aerosol Mediterranean Experiment (ChArMEx) program, using model simulations and satellite measurements, are coherent with other previous studies. Moreover, this methane study provides some insight into the role of the Asian monsoon anticyclone in controlling the variability of CH 4 pollu-.
s. l. ) for July-August. The relative vorticity vector is generally perpendicular to the ground, positive when the vector points upward, and negative when it points downward. Note the negative relative vorticity region located over the southeastern Mediterranean as a result from both shear and curvature negative relative vorticity. Relative vorticity units are 10 -5 s -1. NCEP reanalysis data are provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA; http://www. esrl. noaa. gov/psd/. horizontal advection, at lower tropospheric levels, featuring the EM during PT synoptic conditions, drive the wind flow out of the thermal wind balance inducing a vertical downward motion (Figs. 2  and 7 ). Ziv et al. (2004)  found that the cool advection associated with the PT (Fig. 2 ) and the subsidence related to both descending branches of the African and Asian monsoons (Fig.
Regions explained by the varying distance from the polluted coastline. The aerosol optical depth (AOD), the vertical integral over an atmospheric column of the incident light scattered and absorbed by aerosols, is often used to estimate the aerosol loading in the atmosphere. Particulate SO 4 is among the numerous aerosol types. Nabat et al. (2013)  compared AOD from several model data to satellite-derived data for the period 2003-2010 over the Mediterranean region. They found that the AOD seasonal cycle obtained from the Monitoring Atmospheric Composition and Climate (MACC) reanalysis model, which includes Moderate-Resolution Imaging Spectroradiometer (MODIS) AOD assimilation at 550 nm, much resembles the satellite-derived AOD variability and has the best spatiotemporal correlation compared to AErosol RObotic NETwork (AERONET) stations.
Based on these models and satellite-derived data,  Nabat et al. (2013)  have clearly shown that particulate SO 4 has a maximum during spring and summer over the EM (Fig. 16 ). Matvev et al. (2002)  performed airborne measurements along a 150 km line west of the Israeli coast. They derived an annual flux of the order of 0. 06 Tg yr -1 of (dry) S across the corresponding surface. Given the observed ratio of SO 4 to total S of 40-90 % in the region  (Matvev et al. , 2002; Sciare et al. , 2003) , the annual flux of SO 4 based on field measurements is 0. 024-0. 054 Tg yr -1. Rudich et al. (2008)  used satellite data to estimate the pollution transport toward the EM. MODIS Terraand Aqua-derived estimates of the annual SO 4 flux along the same transect are 0. 038 and 0. 040 Tg yr -1 , respectively, in the middle of the range obtained from field observations.
The diurnal cycles of these two tracers based on 3. 5 years of measurements point at a maximum value of ∼ 70 pptv for NO and up to ∼ 1. 55 ppbv for NO z. These maxima were observed 1-2 h after the minimal O 3 concentration was measured at about 06:30 UTC. Ambient concentrations of NO, NO 2 , and NO x have been also reported over the northwestern parts of Turkey. An NO 2 concentration of 8. 5 ± 4. 8 ppbv was obtained for the summer of 2005 by collecting weekly average data in a sampling site in the city Eskişehir, located 230 km to the west to the capital of Turkey, by use of passive samplers  (Özden et al. , 2008). Im et al. (2008)  studied O 3 pollution and its relationship with NO x species based on hourly concentration levels of O 3 , NO, and hydrocarbon measured between 2001 and 2005 in Kadıköy, an urban district in the Anatolian side of Istanbul.
23 ) from 400 to 200 hPa. Consequently, the comparisons between measurements and model outputs of CH 4 are mainly concentrated on the layer around 300 hPa for AIRS and GOSAT, or considering the total column for IASI. In summer, the horizontal distribution of CH 4 in the upper troposphere shows a clear longitudinal gradient between the east and the west of the Mediterranean Basin, both in the space-borne measurements and in the model calculations (Fig. 24 ). There is a maximum of CH 4 in the eastern MB compared to the western MB, both considering the upper tropospheric layer and the total column information. The difference between the east and the west of the MB has been calculated within all the datasets, and the seasonal variations have been investigated (Fig. 25 ). This clearly shows that the east-west difference peaks in summer, mainly in August.
Acknowledgements. We would like to thank two anonymous reviewers for their valuable and insightful comments, and  Eric Hamonou  for his efficient assistance in taking care of the copyright permission process. The first author is grateful for the partial funding from  CNRS, France , for carrying out this study in the framework of the  Chemistry-Aerosol Mediterranean Experiment (ChArMEx) program  and for the hosting granted by  Météo-France  in the initiation of the program. Edited by: Nikolaos Mihalopoulos Reviewed by: three anonymous referees.
Data availability. NCEP reanalysis data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, for the period 1948-2016 were used to calculate the composite long-term mean of sea-level pressure, temperature at 850 hPa, wind vectors, relative humidity, relative vorticity at 200 hPa, and omega at the 500 hPa and 700 hPa levels; these data are available at http://www. esrl. noaa. gov/psd/.
Acknowledgements We thank  S. Aeby  for technical help. We also thank  A. L'Huillier ,  A. Hagerman ,  C. Salomon ,  S. Meier  and  S. Heiniger  for their invaluable help in collecting water and biofilm samples. Supported in part by  Suez-Environnement  and the  Association Nationale de la Recherche Technologique (ANRT)  ( CIFRE 680/ 2008 ).
42], as detailed in Ref. [23]. With the recent and present data on the 87 Fr ground FIG. 2. Spectroscopic quadrupole moments of even-N 87 Fr isotopes below the N = 126 shell closure. This work is shown in blue squares, literature values are shown in red circles  [12, 13]. A linear fit of the data between N = 118-126 is shown as a dashed line. states  [12, 13]  probing a rather pure πh 5  9/2 configuration, the proposed parabolic behavior can be further tested. A similar plot is presented in Fig. 3 , displaying only the 8 + states in 84 Po, and the 9/2 -states in 83 Bi and 87 Fr, for clarity. Note that, because the 83 Bi and 87 Fr measurements were obtained through laser spectroscopy, the ratio of the quadrupole moments is equal to the ratio of the measured hyperfine B factors and is thus independent of any electric-field-gradient calibration.
ACKNOWLEDGMENTS We acknowledge the support of the ISOLDE collaboration and technical teams. This work was supported by  ERC  Consolidator Grant No. 648381 ; the  Science and Technology Facilities Council  Consolidated Grant No. ST/F012071/1 , Continuation Grant No. ST/J000159/1 , and  Ernest Rutherford Grant  No. ST/L002868/1 ; the  IUAP-Belgian State Belgian Science Policy (BRIX network   P7/12 ),  FWO-Vlaanderen (Belgium)  and  GOA 15/010  from  KU Leuven ; and the  European Union Seventh Framework Programme for Research and Technological Development  under Grant Agreements No. 262010  (ENSAR), No. 267194  (COFUND), and No. 289191  ( LA3NET ). We acknowledge financial aid from the  Ed Schneiderman Fund at New York University.
Quantitative morphological data By confocal microscopy, quantitative depth profiles were obtained. For example, Figure  9  shows a 2D depth map of the surface of the Reference ST sample. The graph corresponds to the vertically-averaged depth profile along the horizontal abscissa in the area highlighted by the dotted rectangle. Other profiles are given as supplementary material. From them, it is concluded that the surface of the horizontal bundles are lying some 8 µm above the vertical ones, themselves lying less than 10 µm above the octets, this last value being only approximate because of pores in the octets. These results are surprising because the vertical fiber bundles have been described as the most resistive phase  [9]. Actually, measurements in vertical bundles are poorly accurate too, because only fiber tips can be seen by the microscope.
Acknowledgements The authors wish to acknowledge  CEA  for financial support to  C. Levet  under the form of a Ph. D. grant. The Plasmatron facility was operated by  P. Collin. VKI ICP calculations were performed by  Z. Alsalihi. Confocal microscopy data was obtained at PACEA lab with the kind assistance of  A. Queffélec  and  3D  digital microscopy was achieved at  CEA Le Ripault  with the help of  T. Le Bihan.
, 2011). Such studies enable clear differences to be seen between real-life escape panic and the mimicry displayed during simulated exercises. Observational data on individual evacuation behaviour remain rare, however. It is for this reason that a number of recent initiatives have sought to create video databases. Analysing images enables behavioural models to be defined and people's movements in such situations to be more accurately quantified  (Bernardini et al. , 2016b). After an earthquake such as that in Lorca, people have to adapt immediately to more or less altered surroundings. Awareness of the new situation and subsequent decisionmaking processes are linked to individual and collective assessments of the new environment  (Weiss et al. , 2011). Nevertheless, in a troubled situation (especially with distur-bances to electrical and telephone networks), this is mainly done when people physically go to see what has happened, thus increasing individual mobility.
Methodology for analysing dynamic exposure This section describes the details of the present paper data collection methodology (Sect. 3. 1). Two different analytical methods will also be described: a spatial analysis of exposure (Sect. 3. 2) and a temporal analysis of exposure using actograms (Sect. 3. 3). In both cases, the spatio-temporal window retained for the analysis included the seismic crisis period as it occurred in Lorca city centre. Our focus will be on a sample of individuals who were inside the city when the earthquake struck until the time they were evacuated outside the city. When respondents were outside the affected area (Fig. 1 ), they were considered to have no longer been in a seismic crisis period and no further data on them were collected. Nevertheless, as shown below, respondents were exposed more or less constantly while inside the city, which for the purposes of this study corresponded to the crisis period.