dataesr/scientific-paragraphs-categorization huggingface dataset:
This produced 15 genome bins, which contained between 60% and 93% of their respective core genes. Further gene copy number analysis was carried out on the genes that were present only in single copy in all reference genomes of the respective phylogenetic order. These analyses suggested that near-complete draft genomes were successfully assembled. Genome sequence data from individual uncultured microbial cells isolated directly from the same complex rumen community were obtained using single-cell isolation techniques. Following whole-genome amplification, their DNAs were screened for rrs sequences and one of the single cells analysed matched to genome bin APb, one of the largest bins assembled from metagenomic data. The rrs sequence indicated that the organism was related to Butyrivibrio fibrisolvens.
The quest for functions through metagenomics Metagenomics is the culture-independent genomic analysis of microbial communities and comprises the functional and sequence-based analysis of the collective microbial genomes contained in an environmental sample. Its application started using the functional screening approach and was mainly used to discover novel biological activities in complex ecosystems  (Handelsman et al. , 1998). With the development of high-throughput DNA sequencing technologies, this functional metagenomic approach has been complemented by sequence-based metagenomics, where metabolic activities are identified by comparison with various databases. As for any molecular biology technique, care in the steps preceding the application of metagenomics protocols, for example sampling procedure, sample handling and nucleic acid extraction, is crucial for obtaining good and reliable data.
The metagenomic profiles appear more similar to those shown by hemicellulose-and pectin-degrading organisms (Butyrivibrio and Prevotella). Overall the main GH activities found (GH43, GH3, GH2, GH13) correlate with those detected in metagenomes from a range of other microbial environments  (Li et al. , 2009). It would be helpful if the sequence-based metagenomic data could be placed in a taxonomic context. Analysis of metagenomic data would be facilitated by a reference set of rumen microbial genome sequences, analogous to the catalogue of reference genomes from the human microbiome (The Human Microbiome Jumpstart Reference Strains, 2010), being used to underpin analysis of human gut metagenomic datasets. The value of reference genomes for this type of analysis is seen in the rapid progress that is being made in this research area  (Arumugam et al.
Linking genomics and metagenomics data to nutrition and other animal production features Cataloguing the genes and functions of the microbiome is the step necessary for modelling and linking the rumen microbial metabolism to that of the host  (Raes and Bork, 2008; Karlsson et al. , 2011). Traditionally, nutrition and production traits in ruminants were assessed without consideration of the rumen microbiome. At present, the inclusion of this 'component' of the superorganism is a great opportunity to improve our understanding of the digestion and biological characteristics of the host. As stated in previous sections, the rumen microbiome is complex, hugely diverse and its gene catalogue is incomplete. On the basis of ecological concepts, the taxonomical and functional diversity of the microbiome should provide high levels of redundancy and, consequently, stability  (Konopka, 2009).
, 2011) , Prevotella ruminicola 23  (Purushe et al. , 2010)  and Ruminococcus albus 7. Information on their GH profiles was obtained from the CAZy database (http://www. cazy. org/). Analysis of GH families not included in the original publications was carried out as described by  Warnecke et al. (2007). of strains differing in their use of formate and the presence of adhesin-like proteins, which hypothetically allow these variants to interact with different bacteria and occupy different niches  (Hansen et al. , 2011). Considering the complexity and selection pressure prevailing in the rumen, it is certain that multiple ecotypes of methanogens, fibrolytic microbes and other microbes exist and play a functional role in the microbiome. The rumen microbiome is rich in microbial interactions between members, such as metabolic cooperation, synergism, predation, cell-cell signalling and structural organisation such as biofilms  (McAllister et al.
Future prospects It is clear from the preceding discussion that a catalogue of rumen microbial genes and assignment of functions to these genes are required to link the rumen microbiome to nutritional and production practices in ruminants. What is needed is an initiative within the rumen microbial community, similar to the Human Microbiome Project  (Turnbaugh et al. , 2007) , to produce a reference set of rumen microbial genomes that would support the analysis and comprehension of large metagenomic datasets. It would also help form testable hypotheses that could direct experimentation and lead to a better understanding of rumen biology. This could be achieved by sequencing the genomes of the available cultivated rumen bacteria and methanogenic archaea ( ,1000 cultures), together with representative cultures of rumen anaerobic fungi and protozoa.
Acknowledgements The authors gratefully acknowledge the financial support for the methanogen genome sequencing and metagenomics projects from the following New Zealand funding agencies: the  NZ Pastoral Greenhouse Gas Research Consortium, NZ Ministry for Agriculture and Forestry Sustainable Land Management Mitigation and Adaptation to Climate Change (SLMACC) ,  New Economy Research Fund of the NZ Foundation for Research, Science and Technology  and the  NZ Agricultural Greenhouse Gas Research Centre. INRA 's work in rumen metagenomics is funded by the  French Ministry of Higher Education and Research  and the  French Ministry of Food, Agriculture and Fisheries.
Background: HIV-2 is endemic in West Africa. There is a lack of evidence-based guidelines on the diagnosis, management and antiretroviral therapy (ART) for HIV-2 or HIV-1/HIV-2 dual infections. Because of these issues, we designed a West African collaborative cohort for HIV-2 infection within the framework of the International epidemiological Databases to Evaluate AIDS (IeDEA). Methods: We collected data on all HIV-2 and HIV-1/HIV-2 dually seropositive patients (both ARV-naive and starting ART) and followed-up in clinical centres in the IeDEA-WA network including a total of 13 clinics in five countries: Benin, Burkina-Faso Cote d'Ivoire, Mali, and Senegal, in the West Africa region. Results: Data was merged for 1,754 patients (56% female), including 1,021 HIV-2 infected patients (551 on ART) and 733 dually seropositive for both HIV-1 and HIV 2 (463 on ART).
Because of these issues, we designed and organized a West African collaborative cohort of HIV-2 infection within the framework of the International epidemiological Database to evaluate AIDS (IeDEA)  [39]. We collected data on all HIV-2 and HIV-1/HIV-2 dually seropositive patients both ARV-naı ¨ve and ARV-treated and followed-up in clinical centres in the IeDEA-WA network in the West Africa region. Here, we report the characteristics of HIV-2 and HIV-1/HIV-2 dually seropositive adults in West Africa presenting for care and on ART.
Data Collection Standardized questionnaires capturing the relevant information on HIV-2 care have been developed with an electronic database implemented at the site level. All sites completed retrospectively and then prospectively the specific questionnaires and have entered the data in the unique IeDEA-WA HIV-2 database. The databases from each site are sent every six months to the Regional Centre in Abidjan, Cote d'Ivoire, and Bordeaux, France using compression/encryption software. Data collected include: 1) Baseline demographics: birth date, gender, HIV clinical stage (WHO or CDC stage), ART initiated, clinical assessment, medical history, 2) Follow-up: Clinical assessment (tuberculosis, other diseases/infection, HIV clinical stage, weight, height, medications such as antiretroviral drugs and cotrimoxazole), 3) Biological data: including CD4, haemoglobin, ALAT, ASAT, plasma HIV RNA viral load (when available, and 4) Outcomes: death, loss to followup, and transferred out.
Data Management Clinical and biological questionnaires are collected and centralized at the IeDEA West Africa, Regional office in the Programme PACCI in Abidjan where they are checked for accuracy and completeness and transfer to the INSERM U897 Epidemiology centre in Bordeaux, France, for statistical analysis and cohort description. The database used for the current manuscript included information recorded at enrolment as well as during follow-up up to December 2011.
Discussion The IeDEA-WA-HIV-2 cohort is a multi-centre, multi-country collaboration on HIV-2 and dually seropositive patients in West Africa, the only region of the world where the two viruses are endemic (HIV-2) and epidemic (HIV-1). This is one of the largest datasets in the world on HIV-2 and provides the opportunity to study HIV-2 in resource-limited settings (RLS). The only other large HIV-2 database is the ACHIEV 2 E HIV-2 Collaboration in Europe and North America  [35]. The IeDEA-WA HIV-2 cohort aims, first, to build and strengthen an operational and clinical research network that describes HIV-2 case management in RLS. Treatment outcomes and their determinants are being investigated by pooling individual patient data, and should thus appropriately inform policies and programmes by examining various models of care, clinical and operational outcomes.
The network is reflecting on a similar successful experience of adult treatment centres in lower income countries  [39]  and aims to rapidly bridge the gap in clinical and programmatic information for people living with HIV-2. Furthermore, this collaboration includes not only patients on ART but also patient ''in care'' but ARV-naive. In addition, we have also implemented an HIV-2 and HIV-1/HIV-2 Drug Resistance Database that will allow us to conduct surveys of genotypic resistance and virological response in the future. Moreover, we are in the process of implementing and standardizing plasma HIV-2 RNA viral load quantification across sites. It is important to note that several centres identified as possible sites of enrolment and follow-up of HIV-2 patients in the West African region did not participate in this collaboration because they do not perform routinely HIV serologic testing that discriminates HIV-1 from HIV-2, (e.
Acknowledgments Special thanks to  Gerald Allou ,  Jean-Claude Azani  and  Severin Lenaud  who are in charge of the monitoring of the IeDEA cohort. The authors want to thank the many professionals dedicated to care in the participating centres and the patients for their participation. *The  IeDEA West Africa Collaboration Study Group  (as of January, 2013): Participating sites (*members of the Steering Committee):.
The International epidemiological Database to Evaluate AIDS in  West Africa  ( IeDEA West Africa ) is supported by the  National Institute of Allergy and Infectious Diseases (NIAID) of the U. S. National Institutes of Health (NIH)  as part of the  International Epidemiologic Databases to Evaluate AIDS (IeDEA)  (grant no. 5U01AI069919 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
0) 45 (9. 7). 37 months (%) 141 (30. 0) 231 (41. 9) 49 (18. 2) 219 (47. 3) Status (%) n (%) n (%) n (%) n (%) Alive 355 (75. 5) 449 (81. 5) 190 (70. 4) 387 (83. 6) Deceased 7 (1. 5) 32 (5. 8) 10 (3. 7) 21 (4. 6) LTFU* 96 (20. 4) 56 (10. 2) 69 (25. 6) 46 (9. 9) Dropped out 5 (1. 1) 4 (0. 7) 0(0. 0) 1 (0. 2) Transferred -out 7 (1. 5) 10 (1. 8) 1 (0. 3) 8 (1. 7) IQR: Interquartile range, ART: antiretroviral treatment initiated. *Number of patients lost to follow-up reported and recorded in the HIV-2 database. $ patients without any follow-up are included. doi:10. 1371/journal. pone. 0066135. t003 blots. 3).
The retrieval of the tropospheric ozone column from satellite data is very important for the characterization of tropospheric chemical and physical properties. However, the task of retrieving tropospheric ozone from space has to face with one fundamental difficulty: the contribution of the tropospheric ozone to the measured radiances is overwhelmed by a much stronger stratospheric signal, which has to be reliably filtered. The Tor Vergata University Earth Observation Laboratory has recently addressed this issue by developing a neural network (NN) algorithm for tropospheric ozone retrieval from NASA-Aura Ozone Monitoring Instrument (OMI) data. The performances of this algorithm were proven comparable to those of more consolidated algorithms, such as Tropospheric Ozone Residual and Optimal Estimation.
6 nm in the VIS channel. The OMI Global measurement mode provides almost global coverage in one day. In principle, a complete daily global coverage is possible at midlatitudes. However, a complex instrumental effect, called row anomalywhich started to appear in the Level 1B data on June 25th of 2007-creates some gaps in the instrumental coverage. More informations on this effect are available from the Royal Dutch Meteorological Institute (Koninklijk Nederlands Meteorologisch Instituut (KNMI)) website  [30]. In addition to the Global mode, two so-called "zoom-in" observation modes are available. In both modes the nadir pixel size is reduced to 13 × 12 km 2. In the Spatial zoomin mode the pixel size is reduced at the expense of the swath width, which decreases to 725 km; in the Spectral zoom-in mode the reduction comes at the expense of the wavelength range, which is limited to 306-432 nm  [29].
The OMI-TOC NN algorithm Recently, a NN algorithm for tropospheric ozone column retrieval from OMI reflectance measurements has been proposed  [26]. From now on, this algorithm will be referred to as OMI-TOC NN. The design and optimization stages of the algorithm are thoroughly discussed in  [26]. The OMI-TOC NN was trained and tested with an extended set of ozonesonde measurements taken at the northern midlatitudes between 2004 and 2008. The ozonesonde stations whose data were used in the training set are listed in Table  1. The OMI-TOC NN performances were found to be comparable, and in some cases slightly better, with respect to those of the trajectory enhanced tropospheric ozone residual (TTOR)  [12]  and OE  [17]  algorithms over a set of co-located ozonesonde measurements  [26]. These results suggest that the OMI-TOC NN is a valuable alternative method for tropospheric ozone retrievals from OMI data.
The input vector for the OMI-TOC NN consists of OMI spectral reflectances at 19 selected wavelengths, extracted from OMI Level 1b data; the solar zenith angle (SZA) and the total ozone column taken from the operational OMI Level 2 product. Only Global measurement mode data were used, because only this observation mode provides daily global coverage. The 19 wavelengths were selected according to an extended pruning (EP) technique  [31]. This technique aims at reducing the dimensionality of an input vector for a NN by retaining only the most informative inputs, i. e. , those who have the strongest influence on the NN output. Six of the selected wavelengths belong to the 305-307 nm range (covered by the OMI UV1 channel), while the remaining 13 wavelengths lie in the 322-325 nm range (covered by the OMI UV2 channel).
The spectroscopic relevance of these two spectral ranges in the context of ozone retrievals is discussed in  [27]. The dimensionality reduction of the reflectance spectra is useful for a number of reasons. First, using full spectra would lead to a very big input vector, which would in turn cause a need for a larger training dataset and longer training times. Second, there would be the risk of including irrelevant information in the input vector, which may compromise the learning capabilities of the NN (e. g. , by causing overfitting). In order to homogenize the spatial resolution of the input spectra, the UV2 reflectances were degraded to the spatial resolution of the OMI UV1 channel (see Section 2). The resolution degradation was performed through simple arithmetical averages between pairs of adjacent spatial pixels in the across-track direction.
Validation set and intercomparison methodology Six European ozonesonde stations were used in the validation of the OMI-TOC NN: Ankara (Turkey), Izaña (Canary Islands, Spain), Lerwick (Shetland Islands, United Kingdom), Valentia Island (Republic of Ireland), L' Aquila and San Pietro Capofiume (Italy). No data from such stations were used during the training of the OMI-TOC NN. Data acquired between October 2004 and December 2008 were considered in this validation exercise. This is the same period that is covered by the training dataset of the OMI-TOC NN. This choice was made in order to ensure that eventual problems in the algorithm are not caused by instrumental changes that may have occurred after the period covered by the training set. The data for Ankara, Lerwick and Valentia Observatory were taken from the World Ozone and Ultraviolet Data Center (WOUDC) archive.
The data for Izaña were taken from the public archive of the network for the detection of atmospheric composition change (NDACC). In addition to the data available from WOUDC and NDACC, data from two the two Italian ozonesonde stations of L' Aquila and San Pietro Capofiume were used. The L' Aquila ozone soundings were performed by the University of L' Aquila and the Centre of Excellence for the integration of remote sensing techniques and modeling for the forecast of severe weather (Centro di Eccellenza di Telerilevamento e Modellistica numerica per la Previsione di eventi Severi (CETEMPS)). The ozonesonde station is located at the CETEMPS atmospheric observatory, Casale Calore di San Vittorino (42. 3°N, 13. 31°E, 683 m a. s. l. ), near the town of L' Aquila. The ozonesondes are SPC-6A type electrochemical concentration cell  (ECC) sondes  [32, 33] , interfaced with Vaisala RS-92 PTH (Pressure, Temperature, Humidity) radiosondes.
The ozone sounding activity at L' Aquila is performed within the framework of a collaboration between CETEMPS, L' Aquila University and the Italian Ministry for the Environment and Territory. The first soundings were performed in 1994. Since 2004, about two soundings per month have been regularly carried out on average. In the past, L' Aquila ozonesonde data were used in the validation of ozone profiles retrieved by the Michelson interferometer for passive atmospheric sounding (MIPAS), onboard Envisat  [34, 35]. In the past, ozone soundings were regularly performed at San Pietro Capofiume from 1991 to 1995  [36, 37] , and a specific campaign was organized in 1997  [37]. In 2004 and 2005, a sporadic sounding activity was carried out. However, it was subsequently interrupted due to scarcity of research funds.
Dataset In order to generate the validation set, the same colocation criteria as those used in the development of the OMI-TOC NN algorithm  [26]  were followed. Specifically, an ozone sounding and an OMI pixel were considered as co-located if two criteria were met: (i) the nominal coordinates of the ozonesonde station and those of the pixel center were no more than ±1°apart; and (ii) no more than 12 hours had elapsed between the ozone sounding and the Aura overpass on the ozonesonde station. By using these criteria, a total of 808 input-output pairs for validation were created. The number of co-locations obtained for each station is given in Table  2. An exiguous number of co-locations was obtained for San Pietro Capofiume. However, such data have been included in the present study for sake of completeness.
Correction of tropopause related errors The results shown in Section 5 confirm the hypothesis that a relationship between the retrieval accuracy of the OMI-TOC NN and the tropopause pressure exists. Furthermore, they suggest that the use of tropopause information as an input for the algorithm has the potential to enhance the retrieval accuracy. For this reason, a first attempt was made to design a new NN algorithm receiving such information as an input. The OMI Level 1B data were co-located with the NCEP/NCAR tropopause pressure fields in order to generate training, testing and validation sets for the new NN. The same stations used in the OMI-TOC NN were used to train the new NN. A comparison between the two NNs in terms of training, test and validation RMSE is shown in Table  3. The standard deviations of the sonde TCOs in the three sets are also reported.
It can be observed that the new NN has a lower RMSE with respect to the previous one on all the three sets. The overall results for the set of ozonesonde stations considered in this article are shown in Figure  4. A significant reduction in the both RMSE and the bias is evident. Particularly significant is the reduction in the underestimation tendency for high values of TCO. Out of the 48 TCOs larger than 60 DU, 25 were found to be underestimated by the modified OMI-TOC NN, in contrast with the 42 underestimations found for the original NN (Section 5). In more formal terms, if TOC retr is the retrieved TOC and TOC sonde is the TOC measured by an ozonesonde, we can say that the conditional probability Prob(TOC retr < 60 DU|TOC sonde ≥ 60 DU) on the validation dataset can be estimated in about 88% for the original OMI-TOC NN described in  [26]  and about 52% for the modified NN proposed in this article.
Methods: We developed a patient-level (Monte Carlo) model of HIV progression among untreated children <5 years of age, using the Cost-Effectiveness of Preventing AIDS Complications model framework: the CEPAC-Pediatric model. We populated the model with data on opportunistic infection and mortality risks from the International Epidemiologic Database to Evaluate AIDS (IeDEA), with mean CD4% at birth (42%) and mean CD4% decline (1. 4%/ month) from the Women and Infants' Transmission Study (WITS). We internally validated the model by varying WITS-derived CD4% data, comparing the corresponding model-generated survival curves to empirical survival curves from IeDEA, and identifying best-fitting parameter sets as those with a root-mean square error (RMSE) <0. 01. We then calibrated the model to other African settings by systematically varying immunologic and HIV mortalityrelated input parameters.
Analytic overview We developed a microsimulation model of pediatric HIV disease progression, the CEPAC-Pediatric model. As in the adult CEPAC model, clinical events are first simulated and validated in the absence of ART (a "natural history" model), in order to describe disease progression in the absence of ART and to provide a framework against which to compare the impact of HIV treatment  [4, 5]. In collaboration with the International Epidemiologic Databases to Evaluate AIDS (IeDEA) consortium  [26, 27] , we derived model input parameters for the CEPAC-Pediatric model, reflecting outcomes in HIV-infected children prior to the initiation of ART. These model input data included rates of WHO Stage 3 and Stage 4 clinical events, tuberculosis (TB), and mortality  [28] , stratified by age and CD4%.
Calibration seeks to explicitly modify model input parameters, in order to make the model useful for predicting outcomes in cohorts or datasets distinct from the dataset used in internal validation  [19, [23] [24] [25] 29]. The IeDEA East African cohort represents a highly selected population of children with excellent access to HIV care. In order to produce analyses more generalizable to other African settings, we identified data parameter sets that allowed model output to match published survival curves from a pooled UNAIDS analysis of >1,300 untreated, perinatally HIV-infected children in eight sub-Saharan African countries  [30, [35] [36] [37] [38].