dataesr/scientific-paragraphs-categorization huggingface dataset:
6%). The average read count per transcript was 30. 9 and 29. 6 for A-type and B-type cells respectively. The average Pearson correlation computed from read counts between replicates of the same cell type was at least 97% (Figure  6A ), a value slightly above the correlations found in previous reports using NuGEN technology  (Clément-Ziza et al. , 2009; Tariq et al. , 2011; Adiconis et al. , 2013). As expected, within cell-type replicates correlated better than between cell type comparisons with the exception of A-type replicate 1, which showed a similar correlation with other A-type and with B-type transcriptomes (Figure  6A ). Sample clustering analysis carried out using the DESeq R package  (Anders and Huber, 2010)  showed that within cell-type replicates clustered together (Figure  6B ), thus confirming the Pearson correlations.
Differential gene expression between the A-and B-type cells was analyzed using the EdgeR package  (Robinson et al. , 2010)  with a maximum False Discovery Rate of 10 -5 and represented as a MA-plot (Figure  6C ). A total of 114 transcripts were differentially expressed: 36 transcripts were detected at higher levels (fold-change of 3. 7-293) in A-type cells than B-type cells, and 78 transcripts were detected at higher levels (fold-change of 3. 6-764) in B-type cells than A-type cells. EF1α and TUA transcript levels were quantified in A-type and B-type cells by counting the number of reads that matched (with at most 1 substitution) to the respective mRNA sequence, and then calculating the ratio between the two (Figure  6D  and see Supplementary Material). The two genes showed expression ratios close to the values reported in qPCR.
METHOD DETAILS Mito-spores were obtained from E. siliculosus strain Ec32 (accession CCAP 1310/4) routinely cultured in a growth chamber in natural sea water supplemented with Provasoli medium (NSWp)  (Starr and Zeikus, 1993, as described in Le Bail and Charrier, 2013)  with the exception that light conditions were set to a 12 h light:12 h dark cycle. Mito-spores were cultivated on glass slides in the same conditions. Nuclease-free 1. 0 polyethylene naphthalate (PEN) membrane slides were obtained from Carl Zeiss Microscopy (#415190-9081-000). Laser capture was performed using a Carl Zeiss PALM MicroBeam unit equipped with an AxioVert 200 microscope and a CryLas UV laser, and assisted by the PalmRobo 4. 5 software. Captures were collected in 500 μL tubes with adhesive tube caps from Carl Zeiss Microscopy (#415190-9201-000).
Acknowledgements. We thank the many individuals and organizations that assisted with this study. S. Godsey ,  A. Olsson ,  L. Koenig , and  P. Tobin  assisted with laboratory and field work. R. Cory  and  G. Kling  provided technical assistance and advice with DOC analysis. A. Balser  and  J. Stuckey  provided assistance with landscape classification and watershed characteristics and  J. Noguera  with the  Toolik Field Station GIS  and  Remote Sensing Facility  provided the map for this manuscript. We thank the staff of  Toolik Field Station  and of CH2M Hill Polar Services logistical services and support. Staff from the  Arctic Network of the National Park Service and Bureau of Land Management  facilitated research permits. This work was supported by the  National Science Foundation 's  Arctic Systems Science Program  under grant number  ARC-0806394.
Cost functions that have been proposed in the literature are mostly based on abstractions of the original system. An important class of abstraction-based cost functions is based on pattern databases (PDBs). PDBs have originally been proposed in the area of Artificial Intelligence  [17]  and also have successfully been applied to model checking discrete and timed systems  [29, 30, 37, 42]. Roughly speaking, a PDB is a data structure that contains abstract states together with abstract cost values based on an abstraction of the original system. During the concrete search, concrete states s are mapped to corresponding abstract states in the PDB, and the corresponding abstract cost values are used to estimate the costs of s. Overall, PDBs have demonstrated to be powerful for finding errors in different formalisms.
General framework of pattern databases For a given system S, a pattern database (PDB) in the classical sense (i. e. , in the sense PDBs have been considered for discrete and timed systems) is represented as a table-like data structure that contains abstract states together with abstract cost values. The PDB is used as a cost estimation function by mapping concrete states s to corresponding abstract states s # in the PDB, and using the abstract cost value of s # as an estimation of the cost value of s. The computation of a classical PDB is performed in three steps. First, a subset P of variables and automata of the original system S is selected. Such subsets P are called pattern. Second, based on P, an abstraction S # is computed that only keeps the variables occurring in P. Third, the entire state space of S # is computed and stored in the PDB.
Based on H # , the PDB is represented by a data structure that contains abstract states together with corresponding cost values. The abstract states and cost values are obtained by a region space exploration of H #. The abstract cost value of an abstract state s # is defined as the length of a shortest found trajectory in H # from s # to an abstract error state. The PDB computes the cost function cost P (s) := cost # (s # ), where s is a symbolic state, s # is a corresponding abstract state to s in the PDB, and cost # is the length of the corresponding trajectory from s # to an abstract error state as defined above.
Pattern databases for hybrid systems In Sect. 2. 4, we have described the general approach for computing and using a PDB for guiding the search. However, for hybrid systems, there are several challenges using the classical PDB approach. First, it is not clear how to effectively design and compute suitable abstractions H # for hybrid systems H with complex variable dependencies. Second, in Sect. 3. 2, we address the general problem that the precomputation of a PDB is often quite expensive, where in many cases, only a small fraction of the PDB is actually needed for the search  [24]. This is undesirable in general, and specifically becomes problematic in the context of hybrid systems because the reachability analysis in hybrid systems is typically much more expensive than, e. g. , for discrete systems.
4  and 5 , where Fig. 4  shows a coarsegrained space abstraction based on increased sampling time of the regions in Fig. 5. In the following, we apply coarse-grained space abstractions to obtain abstractions as the basis for pattern databases. This is a significant difference compared to classical PDB approaches (see Sect. 2. 4): Instead of computing an explicit (projection) abstraction H # based on a subset of all variables, we keep all variables (and hence, the original system H), and instead choose a coarser exploration of the abstract region space of H to obtain the abstraction used for the PDB. (In practice, we apply unguided search provided by SpaceEx to compute this coarser abstraction. ) As an additional difference to classical PDBs, we will apply a variant of partial PDBs, which are introduced in the next section.
Partial pattern databases As already outlined, a general drawback of classical PDBs is the fact that their precomputation might become quite expensive. Even worse, in many cases, most of this precomputation time is often unnecessary because only a small fraction of the PDB is actually needed during the symbolic search in the region space  [24]. One way that has been proposed in the literature to overcome this problem is to compute the PDB on demand: The so-called switchback search maintains a family of abstractions with increasing granularity; these abstractions are used to compute the PDB to guide the search in the next finer level  [32]. In the following, we apply a variant of partial PDBs  [7]  based on coarse-grained space abstractions to address this problem: Instead of computing the whole abstract region space for a given abstraction, we restrict the abstract search to explore only a fraction of the abstract region space while focusing on those abstract states that are likely to be sufficient for the concrete search.
In the following definition, we call an abstract state s # corresponding to state s if s and s # agree on their discrete part, the region of s is included in region of s # , and s # is an abstract state with minimal abstract costs that satisfies these requirements. Definition 2 (Partial Pattern Database) Let H be a hybrid system. A partial pattern database for H is a pattern database for H that contains only abstract state/cost value pairs for abstract states that are part of some trajectory of shortest length (in terms of number of location switches) from an initial state to some abstract error state. The partial pattern database computes the function cost PP (s) := cost # (s # ) if ex. corresponding s # to s +∞ otherwise where s, s # , and cost # are defined as above, and +∞ is a default value indicating that no corresponding abstract state to s exists.
For example, this is the case when only abstract states are excluded from which no abstract error state is reachable anyway. More generally, under the idealized assumption that the abstraction is fine enough such that no spurious behavior occurs on shortest possible error trajectories, the partial PDB already delivers the same search behavior as a perfect search algorithm that finds an error trajectory without backtracking. Proposition 1 Let H be a hybrid system. Let n ∈ N 0 be the length of a shortest concrete error trajectory. If all shortest abstract error trajectories in H (obtained by a coarse-grained space abstraction to build a pattern database) correspond to concrete error trajectories of the same length, then guided search with Algorithm 1 and cost PP finds an error trajectory after n steps.
Discussion Our pattern database approach for finding error states exploits abstractions in a different way than in common approaches for verification (see Sect. 4 for a discussion on related work). Most notably, the main focus of our abstraction is to provide the basis for the cost function to guide the search, rather than to prove correctness (although, under certain circumstances, it can be efficiently used for verification as well-we will come back to this point in the experiments section). As a short summary of the overall approach, we first compute a symbolic abstract region space (as described in Sect. 3. 1), where the encountered symbolic abstract states s # are stored in a table together with the corresponding abstract cost values of s #. To avoid the (possibly costly) computation of an entire PDB, we only compute the PDB partially (as described in Sect.
In addition, Tiwari et al. [40]  introduce a method based on the quantifier elimination decision procedure for real closed fields. Furthermore, Tiwari  [39]  investigates Lie derivatives and their application to the abstraction generation. Jha et al. [25]  computes abstractions by removing some of the continuous variables. Finally, Bogomolov et al. [13]  abstract hybrid systems by merging locations. The abstract dynamics is computed by eliminating the state variables and computing a convex hull. Our pattern database approach belongs to the first group outlined above as we exploit the parametrization of the symbolic region representation. A prominent model checking approach for hybrid systems is based on counterexample-guided abstraction refinement (CEGAR)  [3, 4]. In a nutshell, CEGAR iteratively refines the considered abstraction until the abstraction is fine enough to prove or refute the property.
Acknowledgments This work is based on the SPIN 2013 paper " Abstraction-Based Guided Search for Hybrid Systems " [12]. We thank  Jerome Meinke  and  Daniel Saier  for their help with the benchmark evaluation. This work was partly supported by the  German Research Foundation (DFG)  as part of the  Transregional Collaborative Research Center "Automatic Verification and Analysis of Complex Systems " (SFB/TR 14 AVACS, http://www. avacs. org/),by the  European Research Council (ERC)  under grant  267989  (QUAREM) and by the  Austrian Science Fund (FWF)  under grants  S11402-N23  (RiSE) and  Z211-N23  ( Wittgenstein Award ), by the  Swiss National Science Foundation (SNSF)  as part of the project " Abstraction Heuristics for Planning and Combinatorial Search " (AHPACS) and by  STARnet, a Semiconductor Research Corporation program  sponsored by  MARCO  and  DARPA.
Simulated data showed that cirrus clouds could lead to a maximum land surface temperature (LST) retrieval error of 11. 0 K when using the generalized split-window (GSW) algorithm with a cirrus optical depth (COD) at 0. 55 μm of 0. 4 and in nadir view. A correction term in the COD linear function was added to the GSW algorithm to extend the GSW algorithm to cirrus cloudy conditions. The COD was acquired by a look up table of the isolated cirrus bidirectional reflectance at 0. 55 μm. Additionally, the slope k of the linear function was expressed as a multiple linear model of the top of the atmospheric brightness temperatures of MODIS channels 31-34 and as the difference between split-window channel emissivities. The simulated data showed that the LST error could be reduced from 11. 0 to 2. 2 K.
Simulated Datasets The atmospheric radiative transfer code MODerate resolution atmospheric TRANsmission (MODTRAN) is used to simulate Top of the Atmosphere (TOA) radiances for methodology development. First, 60 clear-sky atmospheric profiles, 54 main land cover types  [14]  and cirrus optical properties of a general habit mixture (GHM) selected from radiosonde observation databases-Thermodynamic Initial Guess Retrieval (TIGR) database  [15] , Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) spectral library  [16] , and Ice Cloud Bulk Scattering Models  [17, 18] , respectively, are used in the simulations. The specific procedures for the selection of atmospheric profiles and land cover types from TIGR and ASTER, respectively, and a brief introduction of TIGR, ASTER and GHM are provided by  [10].
Two simulated datasets, Data-cirrus and Data-clearsky, are produced to extend the GSW algorithm in cirrus skies, where the differences in the two dataset-estimated LSTs are considered as the influences of cirrus on LST retrieval. Data-cirrus contains the TOA radiances of MODIS channels 31-34 (centered at 11. 0, 12. 0, 13. 4, and 13. 7 μm) with considering the influences of cirrus clouds, and Data-clearsky contains the TOA radiances of split-window channels without considering such influences. As the detection limitation of the MODIS cloud mask products is approximately 0. 4 for COD for the visible bands  [19] , namely the conventional clear-sky-based LST retrieval algorithms are commonly misused to retrieve LST under cirrus skies when the COD is less than 0. 4, the slant path COD at 0. 55 μm varies from 0.
Satellite Data Four MODIS products are used in the study:  (1)   The observed radiances in channels 31-34 of the MOD021KM/MYD021KM dataset at a 1-km spatial resolution are used to acquire the corresponding TOA brightness temperatures for extending the GSW algorithm in cirrus cloudy conditions. The MOD03/MYD03 dataset contains latitude and longitude, solar zenith and azimuth angles, and satellite viewing zenith and azimuth angles for every pixel at a 1-km spatial resolution. The latitude and longitude data are used to perform geometric corrections for three other 1-km resolution MODIS products used in this study. The solar and satellite viewing geometries are applied to convert cirrus reflectance into COD, and the satellite viewing zenith angles (VZAs) are used to determine the coefficients of the proposed extension algorithm.
Determination of Slope k The determination of slope k in Equation (  3 ) is necessary to improve the retrieval accuracy of the GSW algorithm under cirrus skies. Because the difference between LST and the cirrus temperature (Tc) is a dominant factor for the reduction in the split-window channel TOA brightness temperatures for a given COD, the temperature difference between LST and the cirrus temperature (Ts -Tc) is used to obtain k. Additionally, because the cirrus effective radius controls the differences of cirrus transmittances in split-window channels  [26] , R is also employed to calculate k. Furthermore, the emissivity difference in split-window channels is also considered to determine k. The coefficients in Equation (  4 ) are determined from the previously simulated dataset in nadir view with the Levenberg-Marquardt statistical regression method.
Conclusions Conventional LST retrieval algorithms from TIR data are developed and applied for clear-sky conditions, and the influences of cirrus clouds are not considered in their development. To study the influences of cirrus clouds on LST retrieval, two different simulated datasets (with and without including the influences of cirrus clouds) were used to retrieve LSTs using the GSW algorithm with the same coefficients obtained from the later dataset. The differences between the two groups of retrieved LSTs indicated that the maximum RMSE was 11. 0 K when the COD at 0. 55 μm was 0. 4 and in nadir view. An extension algorithm is proposed by adding a correction term in function of COD in the GSW algorithm to improve the retrieval accuracy in cirrus cloudy conditions. A LUT of the ICBR at 0.
Calibrated Radiances 5-Min L1B Swath 1-km dataset (MOD021KM/MYD021KM) in collection 6; (2) Geolocation Fields 5-Min L1A Swath 1-km dataset (MOD03/MYD03) in collection 6; (3) Clouds 5-Min L2 Swath 1-km dataset (MOD06_L2/MYD06_L2) in collections 5. 1 and 6, respectively; and (4) Land Surface Temperature and Emissivity 5-Min L2 Swath 1-km dataset (MOD11_L2/MYD11_L2) in collection 5. Those products are saved in Hierarchical Data Format (HDF) and are available from NASA's Goddard Space Flight Center (GSFC) Level 1 and Atmosphere Archive and Distribution System (LAADS).
Models geographical coverage Ecological models can be applied to many different areas with adequate customization  (Henry et al. , 2012; Mateus et al. , 2012). The models in the catalogue have not been applied with the same spatial scale in all European regional seas (Fig. 2 ). The majority of reported indicators related to the Mediterranean Sea, representing more than half of the indicators entered in the catalogue (137), followed by the North-East Atlantic Ocean (78), Black Sea (29), Baltic Sea (18), non-EU regional seas (11) and EU scale (2). The EwE software was the most widely used model and has been applied in each EU regional sea area and most sub-regions; the second most commonly used model was ECOSMO, which has been implemented for the Baltic Sea, the North-East Atlantic Ocean and one non-EU regional sea (Barents Sea).
In this sense, the large majority of the state indicators in the catalogue (S1) were reported as able to reflect the impact of these regime-shifts with strong ecological implications throughout the food web. Only 19 are pressure indicators, essentially physical-chemical indicators derived from coupled models with physical (hydrodynamic)-biogeochemical modules. The EwE food web and the BS-PAR bio-optical (remote sensing) were the other type of models providing two of these pressures indicators (respectively, '1/(landings/biomass) -Inverse fishing pressure' and 'Habitat condition -water transparency'). The pressures 'Contamination by synthetic compounds', 'Contamination by non-synthetic substances & compounds' and 'Acute pollution' (represented as 'Sum of contamination Pressures' in Fig. 4 ) were addressed by a total of 17 models of different types (multispecies, meta-community, SDM/habitat suitability and coupled models).
(2011)  and  Borja et al. (2013). For the last point, it can be argued that there is not enough information at this stage for model developers to set meaningful targets for MSFD purpose. Therefore, threshold setting should be guided by clear objectives and end goals as achievable targets and these are not known at present. In this context, several initiatives have been created to support and address, at least partly, most of the issues arise above; for example FP7 projects such as MEECE (completed) and DEVOTES (in progress) have been developed to explore the use of ecological models in assessing ecosystem status and in support of decision making and EU policy. More recently, MIDAS, a modelling inventory database with models currently in use by the European Commission, allows the assessment of how models are used and/or support impact assessments at EU level.
Acknowledgements This manuscript has resulted from the  DEVOTES (DEVelopment Of innovative Tools for understanding marine biodiversity and assessing good Environmental Status ) project funded by the  European Union  under the 7th  Framework Programme, 'The Ocean of Tomorrow' Theme  (Grant Agreement No. 308392 ), http://www. devotes-project. eu. Special thanks go to  Ana Queiros  and  Christian Wilson  who kindly revised and made constructive comments on the manuscript.
Subsequently, in order to compare the correct cortical index value at each vertex, we adopted the Closest Point Search (CPS) technique, essential to establish the correct topographical match of the same morphological points obtained with 3D GVF. For each point, CPS returned the mutual match between Civet's and Freesurfer's cortical thickness array. The entire process enabling the head-to-head comparison is illustrated in Fig. 1. The procedure was implemented using Matlab (v2009b). The data generated in this study are made publicly available to promote the evaluation of cortical thickness tool (https://neugrid4you. eu/datasets).
Future works should focus on further validating both pipelines against a database of cortical thickness derived from a population of normal and abnormal cadaveric brains, such as those recently defined in the BigBrain initiative (https://bigbrain. loris. ca/). Some limitations should be considered in the interpretation of the present results. First, the tools here described need to be further compared with other recent available techniques, such as: Toads-Cruise  [35] , ARCTIC  [36] , MILXCTE  [37] , DiReCT  [38] , or CLADA  [39]. Second, as expert manual rater in neuroimaging represents the gold standard, independent evaluators should compare the performance and accuracy of each automatic pipeline. Third, each tool should be validated against harmonized MR datasets, such as: standardized ADNI analysis dataset  [40] , WW-ADNI  [41] , AddNeuroMed  [42]  and OASIS  [43].
Acknowledgments The authors thank all the partners in  FP7  neuGRID (i. e. ,  Provincia Lombardo Veneta Ordine Ospedaliero di San Giovanni di Dio Fatebenefratelli :  Alberto Redolfi ,  Gabriele Corbetta ;  Paolo Bosco ;  Cristina Bagnoli ; Libera Virginia Cavaliere; Gnúbila France:  David Manset ;  Jerome Revillard ;  Baptiste Grenier ;  University of the West of England: Richard McClatchey ;  Andrew Branson ;  Kamran Munir ;  Jetendr Shamdasani ;  Saad Liaquat ;  VU University Medical Center Amsterdam: Frederik Barkhof ;  Keith S Cover; Bob van Dijk ;  Ronald van Schijndel ;  Adriaan Versteeg ;  Karolinska Institutet :  Wahlund Lars-Olof ;  Gabriela Spulber ;  Örndahl Eva ; CF consulting: Carla Finocchiaro; NeuroSpin-CEA:  Jean-François Mangin ;  Jean-Baptiste Poline ;  Edouard Duchesnay ;  Vincent Frouin ;  Yann Cointepas ;  University of Southern California: Arthur Toga; McGill University: Alan Evans ;  Marc-Etienne Rousseau ; CNRS: Vincent Breton;  Genevieve Romier ;  Hôpitaux Universitaires de Genève :  Luigi Antelmi , and Principal Investigator:  Giovanni B Frisoni , (gfrisoni@fatebenefratelli.