dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. S. C. C. B. acknowledges support by grants  98761  by  CNES  and the  Fundacão para a Ciência e a Tecnologia (FCT)  through the  Investigador FCT  Contract No. IF/01312/2014. A. S. is supported by the  European Union  under a  Marie Curie Intra-European Fellowship for Career Development  with reference  FP7-PEOPLE-2013-IEF , number  627202. Part of this work was supported by  Fundacão para a Ciência e a Tecnologia, FCT , (ref. UID/FIS/04434/2013  and  PTDC/FIS-AST/1526/2014 ) through national funds and by  FEDER  through  COMPETE2020 (ref. POCI-01-0145-FEDER-007672 and  POCI-01-0145-FEDER-016886 ). We thank  S. Csizmadia  and  N. Espinoza  for the fruitful discussions about limb darkening,  D. Kipping  for his help concerning the use of the macula code (Kipping 2012),  R.
1 µs duration) at 450 nm at a rate of 2 µs to achieve full saturation of PSII reaction centers followed by longer intervals of 100 µs to allow relaxation and re-oxidation of Q A. Profiles of the fluorescence emission were fitted within the FastPro8 software (v. 1. 0. 55; Chelsea Technologies) to the Kolber-Prasil-Falkowski model to yield the minimum (F O or F ′ ; "indicates light adapted samples") and maximum fluorescence (F M or F ′ M ), effective absorption cross section of PSII (σ or σ ′ ; nm 2 ) and re-oxidation kinetic of Q A (τ; µs). Values for the photochemical efficiency ( PSII or PSII ′ ; both dimensionless) were calculated from these parameters as (F M -F O /F M ) or (F M ′ -F ′ /F M ′ ) for light adapted samples. Sample filtrate (after filtration through 0. 2 µm Millipore syringe filters) was also measured in the FRRf to account for background fluorescence.
The data was visually inspected to ensure no fluorescence induction in the filtrate (indicating an absence of phytoplankton) and the mean F O and F M of the filtrate were averaged into a single value to be subtracted from all F O (or F ′ ) and F M (or F M ′ ) values. A second series of measurements was performed to parameterize the light-dependent response of electron transport kinetics and identify shifts in the light-saturated rates of electron transport and the light utilization efficiency. In the interest of minimizing diurnal impacts  (Schuback et al. , 2016) , we conducted rapid light curves on a single replicate for each temperature to reduce the time period of measurements. Measurements of the light dependent electron transport rate (ETR) were achieved with a rapid light curve protocol where samples were illuminated by the FASTAct instrument chamber that supplied actinic white light at increasing light intensities of  0, 10, 28, 47, 66, 85, 104, 123, 160, 251, 330, 402, 550, 756, 905, 1208  µmol photons m -2 s -1.
This process was repeated a total of 5000 times. From these distributions the 95% confidence intervals were calculated as the range between the 2. 5th and 97. 5th quantiles. These estimates of uncertainty were vital for subsequent analyses, as they allowed us to appropriately account for the inherent differences in uncertainty between different traits. Confidence bands were calculated in a similar fashion. For each synthetic dataset the function values were stored from 0 to 40 • C in 0. 5 • C increments. At each stored temperature the confidence intervals were calculated as above.
Geographic information system Geographical coordinates of study sites were collected with a GPS device (Garmin12) and recorded in a Geographic Information System built with MapInfo Professional (Version 8. 5. 1 © 2006 MapInfo Corporation) and Quantum GIS (version 1. 7. 4-Worclaw) [15]. The sites were characterized by their orthogonal distance to the main stream (median of the riverbed line), ordered as following: "1", <500 m, "2", 500-1500 m, "3" >1500 m; and by their relative situation along the river stream, beginning by the most upstream site at 0 m: upstream (<2500 m, "1") or downstream (>6000 m, "3") or at the city level (2500-6000 m, "2"). The dataset was built up with mosquito aquatic stages and absolute numbers for Anopheles and Culicinae by site and by month. The breeding sites were characterized by their typology and the estimated surface of the water body.
Analysis and statistics of the aquatic stage ecology In a first step, exploratory analysis of the association between abundances of mosquito aquatic stages and environmental factors was performed with the ade4  [24]  package for R-CRAN. A principal component analysis (PCA) was drawn out for both Culicinae and Anopheles followed by a between-groups analysis with environmental factors  [25]. The statistical significance was assessed with permutation tests (Monte-Carlo procedure with 1000 permutations). Then bilateral associations were tested by a Kruskall-Wallis non-parametric test with the Rcmdr  [26]  package on each of the Culicinae and Anopheles datasets for each variable. Two variables were temporal: Season (Rainy: June to October, Cold Dry: November to February and Hot Dry: March to May), and Month of collection (=12).
The variable Sites including 84 modalities, was stored as information as supplementary variable in the dataset and used in the PCA but not for model analysis. In addition to the variables describing the typology and the surface of the water bodies, two other variables addressed the spatial dimension of the data set, both with three ordered modalities: the distance to the river bed DistFlFAC (<500 m, 500-1500 m, and >1500 m) and the situation upstream or downstream to the city UpDown ('upstream' , 'city' , and 'downstream'). To assess the hierarchy of the effects of the variables and to estimate the interaction between variables, two models were built: logistic regression (LG) with binomial distribution and the response variable (mosquito) set as presence/absence (0/1), and generalized linear model (GLM) with Poisson distribution for the response variable set as counts.
Interaction between variables was tested by the effect of the 'Season' on the typology of sites and the distance to the river. The data included high number of zeros (dry breeding sites, no access). To minimize this effect, a dataset excluding the sites which were either dry or not accessible (coded '0' or '8' in the surface size variable) was built up and used in modeling. The goodness of model was visually assessed by the distribution of the residuals which should follow a Gaussian or near-Gaussian distribution. The residual deviance was compared to the null deviance. The best model within each model family was chosen after the Akaike Information Criterion (AIC).
Ecological study: anopheles ecological preferences The PCA performed on the Anopheles and Culicinae datasets with the three last larval and pupal stages by collection site and by month showed a different distribution of the two groups of taxa according to two main axes (Fig. 3a ). These two first axes held for 61 % of the total variance of the data. They were comparable (respective Eigen values of 2. 61 and 2. 26) and supported the variance of Culicinae (x-axis) and Anopheles specimens (y-axis) (Fig. 3a ). To assess the effect of each environmental factor on the mosquito distribution, the 'between-classes analysis' option was performed on this PCA against the variables: 'typology' (Fig. 3b ), 'site' (Fig. 3c ), 'season' , 'surface' (Fig. 3d ) of breeding sites, the orthogonal distance to the Niger River 'distf ' (Fig.
As for Anopheles, the LR model improved the data and decreased the residual deviance with also a very small difference between the AIC with or without interactions between variables (Table  5   interactions of the Season upon Typology and Distance to the river (Table  5 ) (see Additional file 1). The histogram of distribution of residuals was better than for Anopheles but still deviant from normality (see Additional file 2). As for Anopheles the plot of residuals vs fitted deviated in the higher values (see Additional file 3). All the predictors were kept: The 'Pond' (p < 0. 0045) and 'River' (p < 0. 025) modalities of the Typology are negatively associated with Culicinae in the two models. The 'Kori' modality is very significantly and positively associated with the abundances of Culicinae in GLM, but not with their presence in case of LR model (Table  6 ).
However, most of the changes in urban structure have occurred in the Northern and Eastern boundaries of the city, far from the heart of the city and the river, which was the main focus of the study. The ecological factors identified here are not outdated and provide important features of the ecology of malaria vectors in Niamey. These data represent the first steps for the potential larval source management  [38]  as control method. The Goudel Pond, and the Gountou Yena kori and its proximity are identified as permanent or semi-permanent sources of Anopheles. If models could be considered as relatively accurate, the proximity of the river is confirmed as a risk factor for Anopheles. The upstream location of the breeding sites is a newly identified risk factor for Anopheles in Niamey. However many sites are transitory and may make larval control difficult  [36].
Acknowledgements We would like to thank the populations of Boukoki 4 and  Goudel  for participating in the project, and the medical team (Health Director of Niamey, manager of the  CSI for Goudel and Boukoki 4 ) for performing some of the field work. We would also like to thank  Dr Jocelyne Rocourt ,  Dr Odile Ouwe Missi Oukem ,  Dr François Rivière  and  Sani Ousmane  for critical reading of the manuscript, and the  laboratory staff of the "Unité de Parasitologie du CERMES ", including  Aboubacar Mahamadou ,  Mahamadou Izamné ,  Sani Haladou  and  Saadou Kadri  in particular, for technical assistance.
, 2016). Despite recent advances, additional enzymes that are required to determine the final structures of the polysaccharides detected in mucilage remain to be identified  (Voiniciuc et al. , 2015c). In the past 15 years, several strategies have been successfully employed to discover genes involved in seed coat cell wall biogenesis  (North et al. , 2014). Forward genetic screens of mucilage-defective seeds in chemically mutagenized populations  (Western et al. , 2001 (Western et al. , , 2004;; Dean et al. , 2007; Arsovski et al. , 2009; Huang et al. , 2011; Voiniciuc et al. , 2013) , and natural Arabidopsis variants  (Macquet et al. , 2007; Saez-Aguayo et al. , 2013)  have yielded some of the key regulators of mucilage production and modification. Nevertheless, these screens likely have not been saturated since reverse genetic approaches based on seed coat transcriptional datasets have recently identified multiple glycosyltransferases directly involved in cell wall polysaccharide biosynthesis  (Kong et al.
RR Staining and Area Measurements Using 24-well plates, 20-30 seeds were mixed with 500 µL of water for 5 min. After removing the water, mucilage was stained with 300 µL of 0. 01% (w/v) RR (VWR International, A3488. 0001) for 5 min. The dye solution was then replaced with 300 µL of water, and an image of each well was captured with a Leica MZ12 stereomicroscope equipped with a Leica DFC 295 camera. Seed and mucilage areas were quantified using the Fiji image processing software  (Schindelin et al. , 2012) , as previously described  (Voiniciuc et al. , 2015b). Mucilage plus seed regions were segmented using the following color threshold (minimum, maximum) parameters: red (0, 255), green (0, 115), and blue (0, 255), while seeds were segmented using red (0, 120), green (0, 255), and blue (0, 255).
Analyses of Genome Sequences Only 175 of the Arabidopsis natural accessions screened for defects in RR-stained mucilage had available genotyping data  (Cao et al. , 2011; Horton et al. , 2012). Each accession was assigned a qualitative score for its RR-stained mucilage area relative to Col-0 capsules: larger (1. 2), similar (1. 0), partly smaller (0. 8), moderately smaller (0. 6), or very small (0. 4). The complete phenotypic dataset is compiled in Supplemental Tables  3A, B. We analyzed these semi-quantitative values together with the 250 k single-nucleotide polymorphism (SNP) chip data of these lines  (Horton et al. , 2012). First, SNPs with a minor allele frequency of less than 0. 05 were removed and the resulting data were analyzed with Factored Spectrally Transformed Linear Mixed Models (FaST-LMM) version 2.
, 2011; Gan et al. , 2011). Nucleotide sequences (Chr2: 9743900-9748899) were exported from the browser, and converted to FASTA format using A Plasmid Editor (ApE; http://biologylabs. utah. edu/jorgensen/wayned/ape). Phylogenetic analysis was performed in MEGA6. 0 (http://www. megasoftware. net; Tamura et al. , 2013) , according to a published guide  (Hall, 2013). A Maximum Likelihood tree  (Tamura 3-parameter model; Tamura, 1992)  was built for 24 DNA sequences aligned with the MUSCLE method. Branches with less than 50% reliability (bootstrap method, 500 replicates) were condensed. Transcription factor binding sites upstream of MUCI10 were identified using the Plant Promoter Analysis Navigator (PlantPAN; http://PlantPAN2. itps. ncku. edu. tw; Chow et al. , 2016) , and cross-referenced with the peaks of the GWAS analysis and the sequence polymorphisms of the examined accessions in the Arabidopsis 1001 Genome Browser.
Arabidopsis Accessions Show a Wide Range of Mucilage Staining Defects To discover how mucilage structure varies in Arabidopsis thaliana natural populations, we screened the seeds of around 280 accessions for defects in RR staining (full dataset in Supplemental Table  3 ). A surprisingly large number of lines (around 50 variants) released mucilage capsules noticeably different from the Col-0 reference accession. We further characterized only a subset of 25 variants (Table  1 ), which displayed smaller RR-stained capsules. These particular lines were selected since (in March 2014) they had been or were scheduled to be sequenced as part of the Arabidopsis 1001 Genomes Project (http://1001genomes. org;  Weigel and Mott, 2009). The newly identified accessions with modified mucilage properties originate predominantly from Western and Central Europe, but also include two North American varieties: Ri-0 and Knox-18 (Knox, Indiana, USA).
The accessions were regrown in the conditions previously used to screen mucilage-related mutants  (Voiniciuc et al. , 2015b) , and showed heritable defects in RR staining. Compared to the Col-0 reference, the selected variants had 35-79% smaller RRstained capsules (Figure  1 ). In contrast to their altered mucilage, these genotypes did not have any significant changes in seed area relative to Col-0 (t-test, P < 0. 05), except for the 24% larger seeds of Lc-0 (Loch Ness, UK; Supplemental Table  1 ). In addition to impaired RR staining of pectic polymers, we used light microscopy to observe two other mucilage properties that typically reflect the structure of cellulose. We developed custom macros in the Fiji image processing software for high-throughput quantification of the intensity of S4B fluorescence and the area of birefringent regions (containing crystalline polymers) around seeds.
Glc represented only 1% of Col-0 mucilage extracts, but was at least 5 times more abundant in 23 of the 137 mucilage samples analyzed for Supplemental Table  2. These dramatic increases in Glc did not strongly correlate with the genotype and were not detected in other growth batches, while the changes in mucilage Gal and/or Man levels were stable for most accessions (data not shown). Known mucilage mutants that disrupt the synthesis of different polysaccharides were grown and analyzed alongside the natural accessions. Consistent with previous results  (Voiniciuc et al. , 2015b) , the GGM-deficient muci10-1 and csla2-3 lines had similar reductions in Gal (around 50%) compared to wildtype Col-0 mucilage (Figure  1 ), but contained distinct amounts of Man (45 vs. 18%). Most of the mucilage of Gal-deficient accessions more closely resembled the Man content of the muci10-1 mutant, rather than csla2-3 (Figure  1 ).
In addition to these candidates, close examination of the highest peak on Chr2 (arrow in Figure  3A ) revealed an association with MUCI10 (Figure  3B ), which directly affects GGM synthesis and mucilage structure  (Voiniciuc et al. , 2015b). In contrast to MUCI10, no CSLA or MANNAN SYNTHESIS-RELATED genes  (Wang et al. , 2012)  were found within 1 million bases (representing 100 times the average linkage disequilibrium decay in Arabidopsis;  Kim et al. , 2007)  of the GWAS peaks in Table  2. Although, MUCI10 is the only cell wall-related gene near the Chr2 peak, two other genes are expressed in seeds according to the public microarray data  (Winter et al. , 2007; Belmonte et al. , 2013). While At2g22870 (EMBRYO DEFECTIVE 2001, EMB2001) is primarily expressed in the embryo, At2g22910 (NAGS1), which is predicted to facilitate amino acid synthesis  (Kalamaki et al.
Although, both changes result in amino acids with distinct chemical properties  (Betts and Russell, 2007) , these SNPs occur between the MUCI10 transmembrane and galactosyltransferase domains annotated in the ARAMMEMNON database (http://aramemnon. botanik. uni-koeln. de; Schwacke et al. , 2003). While the SNPs in the MUCI10 coding sequence do not have obvious deleterious effects, the preliminary 1001 Genome data suggests that many of the mucilage-modified accessions have large gaps (see pink bars in Figure  3C ) and other polymorphisms in the large intergenic region upstream of the MUCI10 start codon. We examined if the MUCI10 polymorphisms correlate with the mucilage chemotypes reported in Figure  1 , but did not identify sets of mutations that were consistent with the phenotypes. Indeed, the Man-rich accessions (Dr-0, Le-0, Mz-0, and Sei-0), which were collected from distinct parts of Europe (Figure  4A ), clustered with Mandeficient accessions in a phylogenetic tree of MUCI10 coding and upstream sequences (Figure  4B ).
MUCI10 May Contribute to Natural Variation in Seed Mucilage Staining The most significant SNP detected on Chr2 (the second highest overall; Table  2 ), in our GWAS analysis was located near MUCI10 (Figures  3A, B ). Polymorphisms in this gene could explain the class B mucilage phenotype (Figure  1 ), which includes 15 accessions identified in this study. Our analysis of preliminary sequencing data from the Arabidopsis 1001 Genome project did not identify SNPs likely to severely disrupt protein function (Figure  3C ), but there were several large sequencing gaps upstream of the MUCI10 start codon. Since we hypothesized that altered expression of MUCI10 may contribute to natural variation in mucilage structure, we identified many putative regulators of this gene that are also near the GWAS peaks.
The Cherenkov Telescope Array (CTA) is the next-generation atmospheric Cherenkov gammaray observatory. CTA will consist of two installations, one in the northern, and the other in the southern hemisphere, containing tens of telescopes of different sizes. The CTA performance requirements and the inherent complexity associated with the operation, control and monitoring of such a large distributed multi-telescope array leads to new challenges in the field of the gammaray astronomy. The ACTL (array control and data acquisition) system will consist of the hardware and software that is necessary to control and monitor the CTA arrays, as well as to time-stamp, read-out, filter and store -at aggregated rates of few GB/s-the scientific data. The ACTL system must be flexible enough to permit the simultaneous automatic operation of multiple sub-arrays of telescopes with a minimum personnel effort on site.
Introduction The Cherenkov Telescope Array (CTA; see  [1]  for a detailed description) is the next generation ground-based very-high-energy gamma-ray observatory, planning to deploy about 100 (20) telescopes on an area of roughly 4 km 2 (0. 4 km 2 ) on a southern (northern) site. In the southern array, at least 3 types (2 types in the northern array) of imaging atmospheric Cherenkov telescopes (IACTs), and potentially more, will be deployed. These telescopes correspond to different sizes (with typical reflector diameters of 23 m, 12 m, and 4 m) whose cameras will comprise 1,000-10,000 pixels, acquiring data in the kHz domain. The CTA performance requirements and the inherent complexity in operation, control and monitoring of such a large distributed multi-telescope array leads to new challenges in designing and developing the CTA control software and data acquisition (ACTL hereafter) system.
System Overview CTA operations will be carried out from on-site data centres (OSDC) at the CTA sites. Each OSDC comprises computing hardware for execution of ACTL software and for mass storage, and is connected to the local CTA array (for readout and control) and the outside world (for data export). The ACTL work-package will deliver to each CTA installation both the hardware and software components. The concept for the ACTL hardware emphasises the use of standard, off-the-shelf networking and computing elements and tries to minimize the amount of hardware that must be specifically developed for CTA. The main ACTL hardware components will comprise (see Figure  1 ): • A single-mode fibre Ethernet wide area network (several 10 Gbit/s) connecting the telescopes with the OSDC and facilitating data transfer, array-level triggering, and clock-distribution.
• Computers (so called camera servers) located in the OSDC and assigned to one telescope to receive the data after a camera trigger. • A central computing cluster (also located in the OSDC) for execution of ACTL software, event building and filtering, and operation of the data repository. • A White Rabbit (WR) network connecting a central GPS clock with each telescope and the software array trigger (SWAT) and WR interface cards attached to IACT cameras, for timestamping and array-level triggering, respectively. The design of the ACTL software accounts for the need to develop, maintain, and operate a more complex and more stable (when compared to existing IACT installations) software system with limited manpower. It prescribes the use of software frameworks, the application of widely accepted standards, tools and protocols, and follows basically an open-source approach.
The ACTL software is being developed on top of ACS (ALMA Common Software, see  [2] ), a software framework developed by the European Southern Observatory (ESO) for the implementation of distributed data acquisition and control systems. ACS has been successfully applied in a project of similar scale, ALMA  [3]. ACS is based on a container-component model and supports the programming languages C++, Java and Python, the three languages to be used in CTA, for different purposes. Indeed, C++ will be used for the high performance systems as the data acquisition (DAQ) system (see below), Java for the central and slow control system, and Python for the high level scripts implementing operation modes. The high-level ACTL software will be executed on the central computer cluster and will access most hardware devices via the industry standard Object Linking and Embedding for Process Control Unified Architecture (OPC UA, see section 7).
The main ACTL software components will comprise (see Figure  1 ): • A scheduler for CTA (SCTA) optimizing the array use for observations at any point in time given a list of selected targets, their CTA-assigned scientific priority, the available telescopes, and external conditions (e. g. weather). • A central control system, implementing the execution of observations using the scheduler and governing the control and coordination the whole array processes, under local supervision by human operators with a graphical user interface (GUI). • A slow control system commanding and monitoring each hardware assembly on board of telescopes and central facilities such as weather station. • A DAQ system for the telescope Cherenkov data, implementing the further filtering of data and its storage in the on-site data repository.