dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors thank the curators of the following herbaria for sending their material on loan: A, BKF, E, GH, KEP and L; we thank the curators and Director,  Botanical Survey of India  and the curator of WRSL for providing type images, and especially  Avishek Bhattacharjee  for his help with the nomenclatural status of several names; the  ERASMUS programme  for financial support for the first author; the artists  Lucy Smith  and  Anita Barley  for their excellent illustrations; and to  Yayasan Pecinta Budaya Bebali  for assistance in the field in Indonesia. The comments and suggestions of two anonymous reviewers were extremely useful and we thank them for helping to improve the paper; we also thank  Heather Lindon ,  Helen Hartley  and the rest of the IPNI editorial team for nomenclatural advice; and  David Middleton  for useful information on the floristics of  Thailand and Indo-China.
FTH Counters Iron Heme-Driven Inhibition of Liver G6Pase To address whether FTH regulates the expression of genes controlling liver metabolic pathways that might contribute to disease tolerance to polymicrobial sepsis we performed a microarray analysis. Eight genes were differentially expressed in the liver of Mx1 Cre Fth D/D versus Fth lox/lox mice subjected to CLP (Figure  4A ; GEO: GSE92703). These included Fth1, epoxide hydrolase 1 (Ephx1), glutathione S-transferase Mu 1 (Gstm1) and 2 (Gstm2), lysine-specific demethylase hairless (Hr), natriuretic peptide receptor B (Npr2), and transferrin receptor protein 1 (Tfrc), as well as the G6Pase catalytic subunit (G6pc1) (Figure  4A ), encoding the final and ratelimiting enzyme of the gluconeogenic and glycogenolytic pathways  (Mutel et al. , 2011; van Schaftingen and Gerin, 2002).
These pathways can be targeted pharmacologically to promote the establishment of disease tolerance to systemic infections, possibly providing a novel therapeutic approach against sepsis. Cardiovascular Function Cardiovascular function was measured using pressure-volume conductance catheter technique  (Pacher et al. , 2008). Briefly, 48 hr after CLP mice were anesthetized with isoflurane, tracheotomized and artificially ventilated. Temperature was recorded continuously and kept stable. The apex of the left ventricle was punctured with a 27 G needle using the open chest approach and a FTS-1912B-8018; pressure-volume conductance catheter (Scisense, London, Canada) was inserted in the left ventricle. Mice were allowed to stabilize for 3-10 min. Baseline values, values with varying preload caused by inferior vena cava clamps using a blunt forceps and aortic pressures, were recorded with the Scisense pressure-volume control unit FV896B and analyzed using the Labscribe2 (Labscribe, iWorx Systems, USA) software.
Metabolic Model Analysis The metabolome data were analyzed in the context of an in silico metabolic model representation, using ratios between the 48 hr after CLP and 0 hr data points for every quantified metabolite. This was done independently for the metabolome datasets of both genotypes Fth lox/lox and Mx1 Cre Fth D/D. Ratios were integrated into the genome-scale metabolic model iMM1415 of Mus musculus  (Sigurdsson et al. , 2010). Metabolic network reconstructions are concise representations of all stoichiometrically accurate metabolic reactions in a given organism. Reaction specific lower and upper thermodynamically feasible flux bounds were integrated if such information is available, otherwise were set to infinite. Of note, the exchange reaction subset comprises reactions that interchange metabolites with the environment or between different compartments within the model.
In silico single gene and reaction deletion studies are realized by setting the respective feasible reaction flux range to zero. Of note, the former leads to one or multiple reaction knock outs, if a gene is essential for these reactions. Impact of gene and reaction deletions was studied by comparing FBA derived ATP yields for Fth lox/lox 48h/0h and Mx1 Cre Fth D/D 48h/0h constrained models. Model modification, FVA, FBA and single gene/reaction deletion studies have been performed with the MATLAB toolbox COBRA  (Schellenberger et al. , 2011).
DATA AND SOFTWARE AVAILABILITY The accession number for the raw data files of the RNA Illumina screen reported in this paper are deposited in the GEO database (GEO: GSE92703), https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE92703. 2  (a) Food intake in Fth lox/lox and Mx1 Cre Fth D/D mice before and after CLP. Data are shown as mean and SD from 3 mice per genotype. (b) Blood glucose levels in Fth lox/lox (n = 3) and Mx1 Cre Fth D/D (n = 3) mice in the first 12 hr after CLP. Mean ± SD, data from 1 experiment. (c) Blood glucose levels in C57BL/6 mice in the first 12 hr after CLP (n = 7) or control sham operation (n = 12). Mean ± SD, data from 3-4 independent experiments. (d) Total heme levels in the peritoneal cavity in C57BL/6 mice before (n = 4) and 3 hr after CLP (n = 3). (e) Blood glucose levels in C57BL/6 mice in the first 8 hr after heme (n = 15) or protoporphyrin IX (PPIX) (n = 10) administration (i.
ACKNOWLEDGMENTS We thank members of the  Inflammation Group (IGC)  for insightful discussions,  Tania Carvalho  and  Jasmin Tischer  for histopathological examination,  Amandine Gautier-Stein  ( UniversiteClaude Bernard Lyon, France ) for luciferase reporter constructs and advise,  Luis F. Moita  ( IGC ) and  Rui Martins  ( Medical University, Vienna, Austria ) for critical appraisal of the manuscript as well as  Marta Monteiro  ( IGC, Flow Cytometry ). Support was provided by  Fundaca ˜o para a Ciencia e Tecnologia  ( PTDC/SAU-TOX/116627/2010  and  HMSP-ICT/ 0018/2011  to M. P. S. and  SFRH/BPD/101608/2014  to A. R. C. ), the European Community 7th Framework (ERC-2011-AdG 294709-DAMAGECONTROL to M. P. S. ), the  Deutsche Forschungsgemeinschaft (DFG ;  WE 4971/3  to S. W. ), the  German Federal Ministry of Education and Research (BMBF ;  01EO1002 ,  Center for Sepsis Control and Care  to M.
B. ),  Meta-ZIK  ( 03Z2J52  to M. B. ), the  German Ministry for Research and Education e:Med initiative GlioPATH  ( FKZ 01ZX1402C  to  S. Scha  ¨uble), the  French National Research Agency  ( ANR-11-BSV1-009  and  ANR-15-CE14-0026-03  to F. R. ), and the  Medical Research Foundation  ( DRM20101220448  to G. M. ). Received: January 9, 2017 Revised: April 10, 2017 Accepted: May 17, 2017 Published: June 15, 2017.
3C ). Harsh denaturing conditions such as boiling in 2. 5% ␤-mercaptoethanol, 8 M urea, or diethylenetriaminepentaacetic acid did not result in any decrease in the intensity of the slow-migrating species, suggesting that a highly stable molecular complex containing ERK forms in the presence of OspF (supplemental Fig. S7 ). We next sought to determine the molecular identity of the 170-kDa complex. ERK2 immunoprecipitates were separated in a SDS gel, and the 170-kDa band was excised and submitted to mass spectrometry analysis. The results confirmed the presence of ERK peptides in the complex upon infection with the WT strain, whereas no peptide from other proteins species was significantly detected (Table  5 ). Overall, our data are the first demonstration that emergence of Dhb residues rapidly results in covalent protein cross-linking in live cells.
Briefly, the instrument settings were as follows: resolution was set to 70,000 for MS scans and 17,500 for the data-dependent MS/MS scans to increase speed. The MS AGC target was set to 3 ϫ 10 6 counts, whereas MS/MS AGC target was set to 1 ϫ 10 5. The MS scan range was from 400 to 2000 m/z. MS and MS/MS scans were recorded in profile mode. Dynamic exclusion was set to 30-s duration. Raw files corresponding to Flag-ERK bands were processed using the Proteome Discoverer 1. 4 software (Thermo Scientific, San Jose, CA) and searched against the Human Uniprot KB/Swiss-Prot database 2014-11 release integrated with sequence of FLAG-ERK. Search parameters included fixed modification: carbamidomethyl (Cys), and variable modification: oxidation (Met), dehydratation (Ser, Thr, and Tyr), phosphorylation (Ser, Thr, and Tyr), and two missed cleavage.
Enzyme was trypsin, monoisotopic peptide mass tolerance was Ϯ 3 ppm, and fragment mass tolerance was Ϯ 0. 02 Da. The false discovery rate (FDR) was set to 1%. Annotated MS/MS spectra were extracted using Proteome Discoverer 1. 4 software. The extracted ion chromatograms and the histogram reporting the intensities of the unmodified and Dhb-modified peptide were obtained using Skyline v2. 6. 0. Raw files corresponding to the proteins immunoprecipitated using anti-Dhb-ERK antibody were analyzed using MaxQuant 1. 5. 3. 30 software against the human Uniprot KB/Swiss-Prot database 2016-01  (21). To search parent mass and fragment ions, we set a mass deviation of 3 and 20 ppm, respectively, with no match between runs allowed. Carbamidomethylation (Cys) was set as fixed modification, whereas oxidation (Met), N-terminal acetylation, dehydratation (Ser and Thr), and phosphorylation (Ser, Thr, Tyr) were set as variable modifications.
The FDRs at the protein and peptide level were set to 1%. The scores were calculated in MaxQuant as described previously  (21). The peptides were quantified according to the MaxQuant MS1 signal intensities  (21, 22). Statistical and bioinformatic analysis, including volcano plot, were performed with Perseus software, version 1. 5. 0. 31. For statistical comparison, we set four groups, each containing three biological replicates: antibody control (preimmune serum), non-stimulated (NS), WT Shigella, and ospF deficient Shigella (⌬ospF) strains. We filtered out all proteins identified in the preimmune serum condition. We then retained only proteins that were quantified three times in at least one group. Next, the data were imputed to fill missing data points by creating a Gaussian distribution of random numbers with a standard deviation of 33% relative to the standard deviation of the measured values and 6 S.
Life table analysis. The developmental time and daily fecundity of all individuals were analyzed according to an age-stage, two-sex life table  43, 44  using the program TWOSEXMS Chart  45. The intrinsic rate of increase (r) was estimated using an interactive bisection method and the following Euler-Lotka equation (equation 1), with the age indexed from 0  46  : The net reproductive rate (R 0 ) was calculated as follows (equation 2): ∑ = = ∞ -+ e l m 1 (1) ∑ = = ∞ R l m (2) x x x 0 0 The finite rate of increase λ (equation 3) and the mean generation time T (equation 4) were calculated as follows: λ = e (3) r = T R r ln (4) 0 Population projection. Based on the results of the age-stage, two-sex life table, a TIMING-MS Chart was used to predict the population growth of the studied S. avenae clones.
For this prediction, we assumed unlimited growth with an initial population of 10 pairs of newly emerged S. avenae nymphs  29, 47, 48. Experimental design and data analysis. A 2 × 3 factorial randomized in complete blocks with 30 replications was deployed to test R. insecticola on Sitobion avenae clones. All data were analyzed using IBM SPSS version 20. The differences in life history traits produced from the life table were separately subjected to a linear two-way nested analysis of variance (ANOVA) to analyze the effects of 'R. insecticola' and clone (i. e. , 'Linyi' and 'Neixiang') nested in 'R. insecticola'. We used Tukey's procedure  49  to compare the differences among treatments, following the description of Sokal & Rohlf  50. All mean separation tests were performed at P < 0. 05. Mean values (± standard errors of the mean) were calculated and used in the above analyses.
Acknowledgements The authors are grateful to  Marylène Poirié  for her critical comments on the logic and sentences of this manuscript and to the anonymous reviewers for their critical comments on earlier versions of this manuscript. This work was supported by the  National Natural Science Foundation of China  ( 31401735 ), the  China Postdoctoral Science Foundation  funded project ( 2015T81057 ,  2014M560807 ), and project funding by the  Postdoctoral Science Foundation of Shaanxi Province.
The Cherenkov Telescope Array (CTA), an array of many tens of Imaging Atmospheric Cherenkov Telescopes deployed on an unprecedented scale, is the next-generation instrument in the field of very high energy gamma-ray astronomy. An average data stream of about 0. 9 GB/s for about 1300 hours of observation per year is expected, therefore resulting in 4 PB of raw data per year and a total of 27 PB/year, including archive and data processing. The start of CTA operation is foreseen in 2018 and it will last about 30 years. The installation of the first telescopes in the two selected locations (Paranal, Chile and La Palma, Spain) will start in 2017. In order to select the best site candidate to host CTA telescopes (in the Northern and in the Southern hemispheres), massive Monte Carlo simulations have been performed since 2012.
Introduction The CTA  [1]  computing model is based on the use of a distributed computing infrastructure, where a few first-class computing centers will share the global storage and computing loads. The current baseline foresees 4 computing centers, with one of them playing the role of 'Science Data Management Center' (SDMC), in charge of the orchestration of the activities of all the contributing computing centers. In particular, the SDMC will run both the Computing Resource Management (CRMS) and the Archive systems. The CRMS is responsible for the workload management, i. e. the management of the whole job life cycle, starting from the job submission by the user, passing through the job brokering, the execution on the computing infrastructure and ending with the result retrieval. At a higher level, the CRMS also manages the different steps of the data processing (workflow management), where each step is composed of a large number of jobs, processing a given dataset.
Finally, the Archive system is in charge of all data management operations, i. e. data ingestion, retrieval, transfer and replication, also providing catalog functionalities. A production setup prototype, based on the DIRAC framework  [2] [3], has been deployed in 2011 to perform CTA Monte Carlo (MC) studies on the EGI (European Grid Infrastructure) grid. Indeed, during the past CTA preparatory phase (2010-2014) and the current pre-construction phase, massive Monte Carlo (MC) simulations have been required to guide various choices in the CTA design. The first big MC campaign, called 'prod2', was run in 2012-2013, with the goal of selecting the best site candidate in each hemisphere, to host the CTA telescopes. Once the two sites have been selected, i. e. Paranal, Chile and La Palma, Spain, a new MC campaign, called 'prod3', was started to determine the optimal array layout at each site.
For the South site at Paranal, 892 telescope positions were considered, as well as different types of telescopes and cameras, resulting in 3092 simulated telescopes. In order to make sure that the conclusions were robust, two fully independent analysis chains, Eventdisplay [4] and MAGIC Reconstruction Software (MARS)  [5]  (in use respectively within the VERITAS [6] and MAGIC [7] collaborations), have been used to process the full prod3 dataset for a large number of telescope configurations. In these proceedings, we describe the production setup, called 'CTA-DIRAC', used to handle these MC simulations, with particular reference to the computing and operational challenges represented by the prod3. The current production setup covers both the CRMS and archive functionalities, but in this paper we will focus only on the two aspects of the CRMS, i.
CTA-DIRAC setup The developed production setup fully relies on the DIRAC interware, which is designed to handle the computing activities in a distributed infrastructure. In particular, the DIRAC Workload Management System (WMS) and the Transformation System (TS), cover the functionalities required by the CTA CRMS. The TS is in charge of creating the jobs associated to a given high level workflow description (see section 3). Once the jobs are created, these are submitted to the WMS, which in turn submits them to the computing infrastructure. In order to store the production data on the grid storage elements, we have been using the DIRAC Data Management System. All production files have been registered in the DIRAC File Catalog, together with some useful meta-data information. As an example, in order to characterize the datasets of the prod3, we have defined about 10 meta-data, covering both physical information (simulation conditions) and provenance information (e.
g. software program, release, etc. ). The choice of DIRAC was driven by several considerations, related to its advanced functionalities, modularity, flexibility and scalability. In particular, the DIRAC WMS implements the pilot mechanism  [8] , which allows to optimize the resource usage. Moreover, the DIRAC capability to target different types of computing resources (grids, clouds, batch systems, etc. ), is of particular importance for a long life project like CTA. The DIRAC architecture is based on a Service Oriented paradigm, where each System is composed of services, agents and databases. The current CTA-DIRAC server setup is the result of a few hardware upgrades done during the last 4 years. Today, it is composed of 5 core servers, hosted at CC-IN2P3 1  , PIC 2  and DESY 3  computing centers, with 16 to 32 cores and 8 to 32 GB of RAM.
Workflow management For the management of the multiple steps composing the prod3 campaign, i. e. production and analysis, we have been fully relying on the DIRAC TS. From the computing point of view, the production part is relatively simple, since it doesn't require any input file and it consists of two applications that can be executed sequentially within the same job. On the other hand, the analysis workflow is more complex, since it's composed of several steps running different applications and requiring input files. In order to illustrate how we have been using the TS to manage the analysis workflow, we briefly describe here one of the two analysis chains run during the prod3, i. e. the MARS workflow. Even if this workflow is not the final one that will be used for the CTA data processing, the main functional blocks will very likely remain unchanged.
The Fig. 1  shows the main steps composing the MARS workflow in terms of software applications (e. g. chimp, stereo, flux, sensitivity) and input/output datasets. Multivariate event classification algorithms (e. g. random forest  [10] ) are used for the energy estimation and background suppression, so that some steps are dedicated to the training while others to the analysis. The initial input dataset is split into several samples according to the primary particle type (e. g. proton, gamma, gamma-diffuse, electron). Each sample is then subdivided into subsamples to be used either for the training or for the analysis. The building unit of the TS is the 'transformation', which is a collection of many identical jobs with a varying parameter. A transformation is essentially defined by a sequence of applications that must be executed within the same job (transformation body) and by a varying parameter.
Moreover, several plugins can be defined to group input files and to select destination sites according to different criteria. The simplest example of transformations are those associated to MC production jobs. Indeed, in this case, jobs don't require any input file and they simply differ by a 'run number'. On the other hand, analysis jobs consist in executing a given sequence of applications on an input dataset, which is typically defined by a meta-data query condition (provided files are registered in a meta-data catalog). In this case the varying parameter is represented by the input file. In order to execute the whole MARS workflow, about 10 transformations are required. As an example, the fourth block in the Fig. 1  requires 3 transformations, running chimp and stereo on 3 different datasets (80 % of proton, 80 % of gamma and the full electron dataset), while the fifth block requires a transformation running the flux application on the datasets resulting from the previous step.
Moreover, we have also successfully tested the flexibility of the DIRAC WMS regarding the integration of new types of computing resources. Indeed, after an initial usage of exclusively CREAM [12] resources, we have been able to easily integrate new types of resources that became available to CTA, like ARC resources as well as a Torque cluster. In future, we also plan to integrate cloud resources. Concerning the TS performances, an improvement was obtained, by developing a new functionality, that we have successfully adopted in the last phase of the prod3. This functionality introduces the use of meta-data filters, allowing to reduce the load on the File Catalog. The details of this development are explained in  [13]. Finally, even if we have been able to manage different types of workflows, we still need to further automatize the whole workflow execution.
Conclusions In order to manage the CTA production activities over a distributed infrastructure, we have developed a prototype based on the DIRAC interware. This prototype has been extensively exploited during the past four years, in the context of the MC simulations devoted to the CTA site selection and to the array layout optimization. Even if the obtained results are satisfying, both in terms of performances and functionalities, some improvements are still needed in view of the upcoming real data processing. Indeed, CTA will start collecting the first telescope data already in 2018, while science operations are expected to start in 2020. These improvements mainly consist in further automatizing the whole workflow management, as described in section 5. In particular, we aim to develop a high level system, on the top of the TS, allowing to easily describe and manage complex workflows, as those that will be used for the CTA data-processing.