dataesr/scientific-paragraphs-categorization huggingface dataset:
Model optimization. Model outputs from the sensitivity analysis were compared to those obtained from field data using least-square optimization  [40]  between simulated and observed FST. The optimization procedure generated 300 FST values (10 migration rates values x 5 dispersal distances values x 6 domestic habitat attraction values), repeated 30 times. Based on this data, we determined the values of migration rate, maximum dispersal distance and attraction by domestic habitat that minimized the difference between simulated and observed FST data. Additionally, we plotted a ternary plot using an Akima interpolation  [41]  on the mean values of the 30 repetitions to visualize the topography of the least-square optimization. To evaluate the goodness of fit of our simulation, we compared simulated and observed FST values using one-sample Student tests (see  [42, 43]  for examples of FST comparisons).
Moreover, additional field studies, such as those conducted for other Triatomine species (e. g. , studies describing and quantifying the influence on vectors of public street lights;  [51, 52] ; or describing the vectors active and passive dispersal  [53] ) would help refining our results for T. brasiliensis. Additionally, the use of a more complex population dynamic model might have permitted to analyze the impact of other demographic parameters on population structuring, like the density of the population or the lifespan. Consequently, prospective scenarios, grounded on actual knowledge and limited dataset, should be regarded as theoretical insights. They remain pertinent tools with implications in term of vectors surveillance and control. For example, our T. brasiliensis prospective scenarios in Northeastern Brazil revealed a significant influence of land-use on vector spatial genetic structure.
[22]  SAMtools  [23]  was used to pile up aligned reads and call variants with quality filters. The resulting data were then subjected to quality control (QC) procedures, including variantlevel and sample-level QC. Detailed QC methods are described in the accompanying CHARGE Targeted Sequencing Study Design manuscript. Variants were categorized as known or novel by comparison with the dbSNP database and the 1000 Genomes Project. Functional annotations were produced using a combination of ANNOVAR,  [24]  dbNSFP,  [25]  and custom internal tools.
To explore the possibility that rare variants within a gene did not have the same direction or magnitude of association, we also implemented the Sequence Kernel Association Test (SKAT)  [28] , which approximates the score test that would be obtained fitting a model that includes all the variants, using customized R scripts for meta-analyses. (Lumley T, Brody J, Dupuis J, Cupples LA http:// stattech. wordpress. fos. auckland. ac. nz/files/2012/11/skat-meta-paper. pdf).
N01-HC-25195 ), and its contract with  Affymetrix, Inc. , for genome-wide genotyping services (Contract No. N02-HL-6-4278 ), for quality control by  Framingham Heart Study  investigators using genotypes in the  SNP Health Association Resource (SHARe)  project. A portion of this research was conducted using the Linux Cluster for Genetic Analysis (LinGA-II) funded by the  Robert Dawson Evans Endowment  of the  Department of Medicine at Boston University School of Medicine and Boston Medical Center. This CHS research was supported by  NHLBI  contracts  HHSN268201200036C ,  HHSN268200800007C ,  N01HC55222 ,  N01HC85079 ,  N01HC85080 ,  N01HC85081 ,  N01HC85082 ,  N01HC85083 ,  N01HC85086 ; and  NHLBI  grants  HL080295 ,  HL087652 ,  HL105756 ,  HL103612 , and  HL120393  with additional contribution from the  National Institute of Neurological Disorders and Stroke (NINDS).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Bruce M. Psaty  serves on the DSMB of a clinical trial of a device funded by  Zoll LifeCor. The  Framingham Heart Study  is conducted and supported by the  NHLBI  in collaboration with  Boston University  (Contract No. N01-HC-25195 ), and its contract with  Affymetrix, Inc. , for genome-wide genotyping services (Contract No. N02-HL-6-4278 ). This study did not receive support from  Affymetrix. There are no patents, products in development or marketed products to declare.
The Monitoring Atmospheric Composition and Climate Interim Implementation (MACC-II) delayed-mode (DM) system has been producing an atmospheric methane (CH 4 ) analysis 6 months behind real time since June 2009. This analysis used to rely on the assimilation of the CH 4 product from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument onboard Envisat. Recently the Laboratoire de Météorologie Dynamique (LMD) CH 4 products from the Infrared Atmospheric Sounding Interferometer (IASI) and the SRON Netherlands Institute for Space Research CH 4 products from the Thermal And Near-infrared Sensor for carbon Observation (TANSO) were added to the DM system. With the loss of Envisat in April 2012, the DM system now has to rely on the assimilation of methane data from TANSO and IASI.
This paper documents the impact of this change in the observing system on the methane tropospheric analysis. It is based on four experiments: one free run and three analyses from respectively the assimilation of SCIAMACHY, TANSO and a combination of TANSO and IASI CH 4 products in the MACC-II system. The period between December 2010 and April 2012 is studied. The SCIAMACHY experiment globally underestimates the tropospheric methane by 35 part per billion (ppb) compared to the HIAPER Pole-to-Pole Observations (HIPPO) data and by 28 ppb compared the Total Carbon Column Observing Network (TCCON) data, while the free run presents an underestimation of 5 ppb and 1 ppb against the same HIPPO and TCCON data, respectively. The assimilated TANSO product changed in October 2011 from version v. 1 to version v.
Methane fluxes are prescribed in IFS using inventory and climatological data sets. These fluxes are consistent with those used as prior information in the CH 4 flux inversions from  Bergamaschi et al. (2009). The anthropogenic fluxes are from the EDGAR 4. 2 database  (Janssens-Maenhout et al. , 2012)  for the year 2008, i. e. the last available year. All the anthropogenic categories are based on annual mean values, except for rice, which has been modulated with the seasonal cycle from Matthews' monthly inventory for rice  (Matthews et al. , 1991). The wetland fluxes are from the Kaplan climatology described in  Bergamaschi et al. (2007). The biomass burning emissions are from the MACC-II GFAS data set  (Kaiser et al. , 2012). The other sources/sinks include wild animals  (Houweling et al. , 1999) , termites  (Sanderson, 1996) , oceans  (Houweling et al.
The a priori and the averaging kernel have 6 vertical levels for version v. 1 and 4 vertical levels for version v. 2. 0 (Fig. 3 ). Note that the TANSO averaging kernels have a similar shape as the SCIAMACHY ones as they both measure in the near infrared. That also makes TANSO sensitive to the lower troposphere. The given values of the observation errors are lower than the ones from SCIAMACHY. Their averaged value is 12. 6 ppb, which means that the assimilation algorithm will give more confidence to the TANSO data than to the SCIAMACHY ones. The coverage of both versions of the product is similar over land even when the solar zenith angle limit was changed from 70 • to 75 • (Fig. 2 ). As for SCIAMACHY, the coverage of the Northern Hemisphere is larger during the summer, reaching 70 • N or 75 • N (depending on the version of the product), and smaller during the winter, reaching 50 • N.
Contrary to SCIAMACHY, the coverage of the Southern Hemisphere does not vary much with the season. The measurements are mainly located north of 45 • S, with few points over Antarctica. Version v. 2. 0 of the product provides data over the oceans (sunglint instrument pointing) where no data were retrieved in version v. 1. These observations over oceans are located in a tropical band that varies with the season, with more measurements in the Northern Hemisphere during June-July-August (JJA) and more measurements in the Southern Hemisphere during December-January-February (DJF). These observations allow one to increase the number of assimilated data per 12 h assimilation window from about 200 observations (for version v. 1) to about 360 observations (for version v. 2. 0). This nevertheless remains more than 3 times less than the number of assimilated SCIAMACHY observations.
Each instantaneous field of view has a ground resolution of 12 km at nadir. This provides about 4000 observations to assimilate per 12 h window. The CH 4 IASI retrievals used in this study are provided by a non linear inference scheme based on Multilayer Perceptron neural networks  (Crevoisier et al. , 2009 (Crevoisier et al. , , 2013)). This method relies on the simultaneous use of synchronised observations from IASI and the Advanced Microwave Sounding Unit (AMSU) to disentangle CH 4 from atmospheric temperature in the IASI radiances. IASI provides sub-column of CH 4 in the middle to upper troposphere, with associated weighting functions having their maximum at 250 hPa (Fig. 3 ). In this study we restricted the data to a large tropical belt (30 • N-30 • S) where higher-quality data are expected compared to higher latitudes because of the low variability of the temperature profiles, which facilitates the decorrelation between CH 4 and temperature signals.
The impact was found mainly during the winter and led to a better agreement with the TCCON data over the tropics with an averaged difference of 1. 5 ppb, while the difference was -11 ppb for the TANSO experiment. During the summer, the middle tropospheric increase was smaller and did not change the methane profile much on average. On the other hand, the variability was increased from 8 ppb (TANSO experiment) to 12 ppb (IASI+TANSO experiment) in the tropics when compared to the HIPPO data. The increase is not as pronounced when compared to the TCCON measurement (1 ppb). After the change in the version of the assimilated TANSO data, the impact of the IASI data on the methane profile changed considerably. Unfortunately we do not have profiles of independent measurements to evaluate the change in the profile of the analysis.
In terms of total column the impact of the IASI data was positive during the whole period, providing a low biased analysis with an error between 4 and 7 ppb depending on the region. The combination of the IASI data and version v. 2. 0 of the TANSO data has been shown to be a good candidate for continuing the monitoring of the tropospheric methane in the MACC-II system. Despite residual biases present in the products, the main concern is the deficiency of the data to constrain the system south of 45 • S and north of 45 • N in winter or 80 • N in summer, this last region also being not observed by SCIAMACHY. Extending the coverage of the IASI CH 4 data to the extra-tropical regions could help by increasing the number of data points in these regions.
Acknowledgements. This study was funded by the  European Commission  under the  EU Seventh Research Framework Programme  (grant agreement no. 283576 , MACC II). TCCON data were obtained from the  TCCON Data Archive , operated by the  California Institute of Technology  from the website at http://tccon. ipac. caltech. edu/. The HIPPO program was supported by  NSF  grants  ATM-0628575 ,  ATM-0628519 , and  ATM-0628388  to  Harvard University, University of California (San Diego ), and by  University Corporation for Atmospheric Research, University of Colorado/CIRES , by the  NCAR  and by the  NOAA Earth System Research Laboratory. A. Butz  is supported by  Deutsche Forschungsgemeinschaft (DFG)  through the  Emmy-Noether programme , grant  BU2599/1-1 (RemoteC ). The authors are grateful to  Marijana Crepulja  and  Martin Suttie  for the processing of the satellite data used in this study.
Phylogenetic Oligonucleotide Arrays (POAs) were recently adapted for studying the huge microbial communities in a flexible and easy-to-use way. POA coupled with the use of explorative probes to detect the unknown part is now one of the most powerful approaches for a better understanding of microbial community functioning. However, the selection of probes remains a very difficult task. The rapid growth of environmental databases has led to an exponential increase of data to be managed for an efficient design. Consequently, the use of high performance computing facilities is mandatory. In this paper, we present an efficient parallelization method to select known and explorative oligonucleotide probes at large scale using computing grids. We implemented a software that generates and monitors thousands of jobs over the European Computing Grid Infrastructure (EGI).
Introduction The total number of species on our planet is of about 9 million, according to the latest biodiversity estimate. However, the vast majority of these species are not yet discovered and only over 1. 2 million species have been already catalogued in a central database  [1]. Most nondescribed species are microorganisms. Microbial communities represent the most important and diverse group of organisms living on earth. They play an important role in the functioning of ecosystems  [2]. The comprehension of the role of microorganisms is then a major challenge of microbial ecology. Because of the huge microbial biocomplexity, high-throughput molecular tools allowing simultaneous analyses of existing populations are well adapted to survey microorganisms in complex environments  [3]. Phylogenetic Oligonucleotide Arrays (POAs) are currently widely used and are one of the most promising approaches for studying microbial communities.
Probe design algorithms usually use specific algorithms such as suffix array method or BLAST  [6]  to check the specificity of probes by searching possible cross-hybridizations against datasets. However, the exponential increase of the number of sequences deposited in public databases induced an important increase in the computational capacity requirements of oligonucleotide probe design algorithms  [7]  and also a fundamental change in the way these algorithms are designed. It is true that we can find fast probe design software running on regular PCs because they allow selecting probes for few DNA sequences or/and do not check the specificity of the obtained probes. The probe specificity tests against the large and ever growing biological datasets require a particular attention to develop a new generation of probe design software able to deal with high performance computing.
In this context, parallel and distributed architectures such as computing clusters or computing grids  [8]  can provide interesting performances. Computing grids provide a promising approach to use distributed resources to meet the continuously evolving computational needs of bioinformatics tools  [9]. They are particularly suited when the parallelism can be based on data splitting providing true independent computing  [10]. They allow a transparent use of geographically dispersed resources for largescale distributed applications. They are adapted for time consuming algorithms that can be split into several independent jobs. In addition to the use of known probes in POAs that allow us to simultaneously study several thousand known organisms, it is also important to design explorative probes that can detect unknown sequences not yet available in public databases and explore the vast majority of microorganisms that are still nondiscovered  [3].
Here, we present a new parallelization method of a probe design algorithm to select known and explorative oligonucleotide probes using a computing grid. This software runs on the European Grid Infrastructure (EGI). EGI is a multidisciplinary grid infrastructure providing more than 250. 000 CPU cores and more than 100 petabytes over 51 countries (http://www. egi. eu/). We introduced an efficient parallelization method to take advantage of the computing power available in the EGI grid to perform largescale oligonucleotides selection. We present also a new algorithm for the construction of a personal high-quality phylogenetic database that can be used to select specific, sensitive, and explorative probes targeting any prokaryotic or fungal taxonomic group, for phylogenetic oligonucleotide microarrays.
Additionally, several tools were proposed to select probes for phylogenetic arrays; they are discussed hereafter and in Dugat-Bony et al. [3]. The PRIMROSE program  [18]  was proposed to select both oligonucleotide probes and PCR primers. The probe design mechanism of PRIMROSE consists in first producing a multiple alignment for a given group of sequences. Probes are then selected and subsequently tested against an input database, to search for potential cross-hybridizations and to verify the coverage of the targeted group of sequences. PRIMROSE has been mainly used in PCR-based and FISH (fluorescent in situ hybridization) approaches  [19, 20] , but only a few applications of POAs using PRIMROSE have been reported  [21]. The PRIMROSE tool does not allow selecting explorative probes. The ARB software package  [22]  proposed a probe design tool that allows selecting oligonucleotide probes with a length of 10 to 100 mers.
This tool consists in searching all possible signature sequences of a targeted group of organisms specified by the user. Probes are then selected and matched against a database using the ARB Probe Match software. The ARB probe design tool has been used to design low-density custom-made POAs, composed of only a few hundreds of probes  [23] [24] [25]. However, this probe design software is not well suited for large scale oligonucleotide probe design. Furthermore, it allows selecting only probes targeting known organisms and does not allow selecting explorative probes. The Scientific World Journal 3 ARB and PRIMROSE tools allow selecting promising probes or primers for a single organism or a group of related organisms. However, emerging experimental approaches seek to simultaneously detect numerous organisms of interest thereby requiring the identification of large numbers of compatible probes  [7, 26].
Oligonucleotide retrieving for molecular applications (ORMA)  [27]  is one of the most recent software proposed to select oligonucleotide probes. ORMA is composed of a set of scripts developed under Matlab and uses the BLAST program to check the specificity of the oligonucleotide probes selected. It allows designing probes for molecular application experiments on sets of highly similar sequences. ORMA was first applied to the design of probes targeting 16S rRNA genes, but it can also be used on any set of highly correlated sequences. This probe design tool has been used to design the HTF-Microbi-Array allowing high taxonomic level fingerprinting of the human intestinal microbial community  [28]. All of these programs allow selecting probes targeting only known microbial communities with available sequences in public environmental databases.
A few tools such as PhylArray  [29]  were designed with the possibility of selecting explorative probes for phylogenetic microarrays. PhylArray was developed with the Perl language. It allows selecting probes for a group of SSU rRNA sequences to globally survey known and unknown bacterial communities. Probe selection using PhylArray can take several days for only one large group of sequences. In this work, we present a new parallel approach to select both known and explorative oligonucleotide probes on computing grids. The probe design strategy is based on the original algorithm PhylArray described in Militon et al. [29].
Material and Methods 3. 1. Implementation. Our method was implemented in a program called PhylGrid 2. 0. It was developed under Linux CentOS 5. 4 with C++ and Perl. It uses three other programs: BLAST  [6] , Clustalw-MPI  [30] , and Opal  [31]. Our approach hides the EGI grid to the user who just uses a regular computer which acts as a grid UI (User Interface: a grid component for user access to the grid). The first step was to implement the software on the User Interface (UI). This allows a direct connection to the EGI grid using a proxy authentication for the submission of multiple jobs. The main resources used by our grid application are the Workload Management System (WMS), a Berkeley Database Information Index (BDII), Computing Elements (CEs), and Storage Elements (SEs). We used the gLite middleware API commands.
SSU rRNA Database Building. Probe design requires building a SSU rRNA database used as input and also as a reference database to check the specificity of all possible probes. This database must be of high quality in order to obtain the right design and to avoid wrong cross-hybridization results caused by poor sequences quality and erroneous affiliation in public environmental databases. Here, we developed a new algorithm to revisit, for more precision, the initial database described in Militon et al. [29]. All SSU sequences of the taxonomic divisions Prokaryotes (PRO), fungi (FUN), and environmental samples (ENV) downloaded from the European Molecular Biology Laboratory (EMBL) nucleotide sequence database were used as a reference to build our database carefully crafted for our probe design software.
Several steps were needed. First, small subunit rRNA gene sequences (16S for prokaryotes and 18S for fungi) were extracted and filtered according to their quality and size. We kept only the sequences that met the following criteria. (i) The sequence length is greater than 1,200 bases. (ii) The sequence length is smaller than 1,600 bases for prokaryotic sequences and 1,800 bases for fungal sequences. (iii) The sequence is assigned to the genus level in EMBL database (taxonomic information is extracted from the (OC) organism classification EMBL field). (iv) The percentage of unknown nucleotides (not {A, C, G, T}) in the sequence is less than 1%. (v) The maximum number of consecutive unknown bases must not exceed 5. The last two criteria allow removing low quality sequences. These stringent parameters were chosen in order to allow an efficient probe design.
Then, extracted sequences were grouped at the genus taxonomic rank and each group was included in its specific kingdom (prokaryote or fungi) according to the NCBI taxonomy database. The next step consists in checking the orientation of the obtained sequences. We used BLASTN program and a reference sequence to check and correct the orientation of sequences that had been incorrectly oriented in the EMBL database. Subsequently, a BlastClust was made on each group to eliminate redundant sequences, using the following parameters allowing a single-linkage clustering at 100% identity cutoff: (i) -p F (nucleotide sequences); (ii) -S 100 (similarity threshold); (iii) -L 1 (minimum length coverage); (iv) -b F (required coverage as specified by -L and -S on only one sequence of a pair). Finally, for each group, we checked the homogeneity of its sequences.
The aim was to eliminate sequences badly annotated and to create a homogeneous group of sequences to allow selecting specific probes for this group. This step was done using a modified version of Clustalw  [32]  to compute distances between sequences and the K-means method  [33]  to clustering sequences. We used this algorithm to build a 16S rRNA database at the genus level. We obtained 2,069 prokaryotic genera; each is composed of a set of homogeneous sequences representing The Scientific World Journal  the whole diversity. Our algorithm can be easily adapted and used to build high-quality SSU rRNA databases for different taxonomic ranks (family, order, class, etc. ).