dataesr/scientific-paragraphs-categorization huggingface dataset:
nal. usda. gov/content/rulesweb-apollo-annotation-i5k-pilot-project). After the curation period, the manually curated models were exported in gff3 format and quality checked for formatting and curation errors, and then integrated with the MAKER-predicted gene models to generate a non-redundant official gene set OGSv1. 1. A subsequent quality-control check using two separate gff3-checking pipelines (https://github. com/hotdogee/gff3py/releases/tag/0. 3. 0; https://github. com/chienyuehlee/gff-cmp-cat) and custom scripts resulted in an updated OGSv1. 2. Orthology/phylogeny analyses. The OrthoDB (http://orthodb. org/) resource was used to find shared orthologues among C. lectularius (v1. 0) and seven other arthropods: Daphnia pulex, Pediculus humanus humanus ( ¼ Pediculus humanus corporis), Acyrthosiphon pisum, Apis mellifera, Tribolium castaneum, Danaus plexippus and Drosophila melanogaster.
Custom Perl scripts were used to find the number of genes in each category (Fig. 1 , single copy, present in all species and so on). For the phylogenetic analysis, only the single-copy orthologues were used to build a concatenated phylogenetic tree using RAxML (http://sco. h-its. org/exelixis/ software. html). Briefly, a multiple sequence alignment was performed using MUSCLE (http://www. drive5. com/muscle/) for each orthologous cluster, separately. Then, the resulting alignments were trimmed using trimAI (http://trimal. cgenomics. org/) and these alignments were concatenated using the 'seqret' program from the EMBOSS suite (http://emboss. sourceforge. net/). This concatenated alignment was used to build the phylogeny using RAxML 7. 6. 6 with 100 bootstraps. BUSCO-based quality assessment.
The completeness of genome assemblies can be measured by searching for the presence of conserved genes. Absence of such genes means that the assembly is incomplete to a greater or lesser degree depending on the fraction of missing genes. Moreover, if these conserved genes are also single copy, the assembly can also be tested for unexpected duplications, which is a sign of erroneous haplotype assembly. To this end, we used the BUSCO (http://busco. ezlab. org/)  18 , to measure the completeness of the bed bug genome as well as its set of predicted genes (Supplementary Data 3). We used the Arthropoda gene set, which consists of 2,675 single-copy genes that are present in at least 90% of Arthropoda. Lateral gene transfer identification. Putative LGT events in the assembled bed bug genome were computationally identified using two different python-based computational pipelines.
The bed bug assembly was first analysed using the homology-based pipeline described in Wheeler et al. 43  Because this pipeline outputs data on the best putative LGT candidate on each scaffold (best on e-value), along with the number and range of putative LGTs on the scaffold, it was most helpful for identifying scaffolds that appear to be of bacterial, not bed bug, origin (Supplementary Data 21). In addition, if the best putative LGT on each scaffold had a higher bacterial score than animal score, it was manually annotated using Blastn and Blastx analysis to the NCBI nr/nt database. In addition, 12 scaffolds with a high number of potential LGTS were manually searched for additional LGT candidates using Blastn similarity (e-value 1e À 5 cutoff) on the NCBI nr/nt database. Due to the vast number of putative LGT candidates, a second python script was used to break long scaffolds into 1,000-bp intervals and search each of them against the bacterial database.
Any positive hits of the 1,000-bp regions were then searched against the animal database. The bacterial database contained B1,000 bacterial species and was masked for low-complexity regions using the NCBI Dustmasker function. The animal database contained transcripts from a representative from each of the following animal genera: Anopheles, Apis, Drosophila, Xenopus, Tribolium, Nasonia, Daphnia, Strongylocentrotus, Mus, Homo sapiens, Aplysia, Caenorhabditis, Hydra, Monosiga and Acanthamoeba. The significance e-value cutoff used was 1e À 5 for both the animal and bacterial hits. Regions of bacterial similarity that fell from the end of one 1,000-bp interval to the adjacent interval were joined if they were o50-bp apart. Putative LGT regions that were Z100 bp were used in the final analysis.
As a further computational confirmation, junctions between candidate LGTs and flanking eukaryotic sequences were confirmed using mate-pair data from the different insert size libraries. Note added in proof: Since this manuscript was submitted the genome of the closely related hematophagous hemipteran, Rhodnius prolixus, was published (Mesquita et al. Genome of Rhodnius prolixus, an insect vector of Chagas disease, reveals unique adaptations to hematophagy and parasite infection. Proc. Natl Acad. Sci. USA (early edition) doi/10. 1073/pnas. 1506226112) and has not been included in analyses associated with this study.
Acknowledgements Funding for genome sequencing, assembly and automated annotation was provided by  NHGRI  grant  U54 HG003273  to  R. A. G. Funding  for bed bug lines development was provided by  Blanton J. Whitmire  endowment, Housing and  Urban Development  ( NCHHU-00017-13 ),  National Science Foundation  ( IOS-1052238 ),  Alfred P. Sloan Foundation  ( 2013-5-35 MBE ) to Cob. S. Funding for preparation of genomic DNA and RNA for sequencing and for manual curation of some of the genes was provided by  NIH  ( GM070559-9 ) to S. R. P. Funding for manual curation and other analyses were provided by the  Royal Society of New Zealand Marsden Fast Start Grant  ( 11-UOO-124 ) to  E. J. D. ,  Fralin Life Sciences Institutes and Virginia Agriculture Experimental Station  to Z. N. A. ,  European Research Council  ( ERC-CoG 616346 ) to A.
K. ,  Deutsche Forschungsgemeinschaft (DFG) :  Pe1798/2-1  to N. P. ,  Deutsche Forschungsgemeinschaft (DFG) :  PA2044/1-1  to K. A. P. ,  Deutsche Forschungsgemeinschaft (DFG) : collaborative research center SFB 1047 'Insect timing,' Project  A1  to P. M. , The Zukunftskonzept at  TU Dresden  funded by the  Exzellenzinitiative of the Deutsche Forschungsgemeinschaft to K. R. , Deutsche Forschungsgemeinschaft (DFG) :  BL469/7-1  to Wo. B. ,  Deutsche Forschungsgemeinschaft (DFG) :  766/11-1  to R. P. ,  European Research Council  grant  260986  to  P. J. ,  Biotechnology and Biological Sciences Research Council  to J. -J. Z. , University of  Cincinnati Faculty Development Research Grant  and  Ohio Supercomputer Center Research Allocation to J. B. B. ,  Marie Curie International Outgoing Fellowship   PIOF-GA-2011-303312  to  R.
To evaluate temporal trends at regional to global scales and to study their relationship to change in regional to global climate and human activities, studies of extensive data sets (monitoring networks) of such "point-data" are required. Hydroclimatically similar regions can be explored using a global database of historical climate data. Similarity between historical climates in different regions is a necessary starting point but may not be sufficient to constitute analogous climate change scenarios. Most hydrogeophysical methods have the advantage that they allow detection of change over larger volumes of the subsurface, but at the expense of detail, notably regarding water chemistry. Remote sensing of systematic change in the recent past and future across the globe has limited ability to "see" watershed-scale groundwater.
Future research will undoubtedly focus on the further integration of these multi-platform and multi-parameter observations, including GRACE data, in extensive hydrological models. Recent dedicated hydrological missions for improved monitoring of soil moisture (2009: SMOS/ESA; 2011: SMAP/NASA) and precipitation (2012: GPM/NASA) enhance RS capabilities of groundwater resources assessment. The monthly temporal resolution of GRACE is an issue for many applications, but it should be sufficient for regional groundwater assessments. To address such scale issues,  Zaitchik et al. (2008)  used an advanced data assimilation approach to incorporate GRACE data into a land surface model, and hence merge them with other datasets and our knowledge of physical processes as represented in the model. In simulations over the Mississippi River basin, the GRACE-assimilation groundwater storage output fit observations better than output from the open loop, and they were of much higher spatial and temporal resolution than GRACE alone.
In shared groundwater basins in the western US, which lack the complexity of highly connected surface water, "divided administration is the status quo" (Davenport 2008). Major interstate aquifers, like the High Plains Aquifer System (which includes the Ogallala Aquifer) underlying parts of South Dakota, Nebraska, Wyoming, Colorado, Kansas, Oklahoma, New Mexico, and Texas, are administered by each state under separate arrangements. There is no formal coordination of the sort found in interstate river basin commissions or compact arrangements  (Sophocleous 2010; Hathaway 2011) , and no Supreme Court litigation to apportion the groundwater  (Leshy 2008a). Rather, a situation of "de facto groundwater allocation" through "a combination of unilateral actions and lack of action" occurs in many basins, for example the Hueco Bolson Basin underlying New Mexico, Texas and Mexico; in others, some mechanisms like data sharing exist, but cooperation is notably lacking  (Hathaway 2011, p.
Analytical Support for Operational Management: Main Challenges Many analytical tools have become available to support operational management. With respect to groundwater, efforts are still required to harmonize monitoring and analysis methods used by different organizations, especially in the case of international basins. A second challenge is to make the information available to anybody involved or interested. The development in database technology, often in combination with internet applications, can provide powerful tools for data retrieval and map visualization. A more advanced type of operational support is to combine on-line monitoring with computer models in order to predict future conditions of the system. Examples are early warning systems, both for water-quantity issues (floods, droughts) and for water-quality issues (accidental spills).
An index flow is calculated from online data obtained from 147 established stream gages. Index flow is defined as the median flow for the summer month with lowest flow at a site. Summer months (usually August or September) were used because they commonly have the lowest flows and warmest temperatures, which result in the greatest stress to fisheries. A subsequent analytical withdrawal model estimates how much a proposed groundwater withdrawal will reduce streamflow in streams near the proposed pumping location. This model takes into account the amount and duration of pumping, well depth distance of well from stream, and aquifer properties  (Reeves et al. 2009). The withdrawal assessment tool can also account for direct surface water withdrawal by subtracting it from the amount of available water.
GDE Mapping and Database Challenges Information management constitutes a serious challenge for understanding and managing GDEs. Accurately georeferencing and archiving data on the distribution and ecohydrology of springs and other GDEs first involves developing a suitable database framework (Springs Stewardship Institute 2012). Some or many of the above methods for determining GDE distribution allows development of a geographic information system georeferenced map of springs within landscapes. However, a common problem in such mapping efforts is resolution of duplication error. We have repeatedly found that: (a) no single source of information (usually GIS layers or survey reports) provides a complete list of springs or other GDEs within a large landscape; (b) that each information source contains unique springs not found elsewhere; and (c) that the same GDEs may be mapped in multiple places with different names.
Stevens and Ledbetter (2012)  used 10 sources of information to identify 150 springs on the North Kaibab Forest District of northern Arizona, 50 % more springs than had been documented by the managing agency, and field surveys increased the number of known springs in that landscape to more than 200. Development of an adequate map and database on the springs of large landscapes provides an essential tool for monitoring, modelling and further research on the status of the underlying aquifers.
The primary geochemical reactions include dissolution of salts; ion exchange with clay minerals and surface sorption and desorption reactions. These geochemical reactions proceed towards chemical equilibrium and can therefore be assessed using models based on thermodynamic chemical databases. A simple approach utilises a water mixing diagram that can be developed prior to geochemical modelling based on conservative ion (e. g. chloride) concentrations of end members such as saline and fresh groundwater. Figure 15. 7, for example, illustrates that at high mixing ratios, based on conservative ion mixing, HCO 3 and SO 4 concentrations are non-linear indicating processes other than mixing controlling concentration. A relative increase of Na in fresh water samples is evident, indicating ion-exchange is an important geochemical process that occurs during mixing.
Observing a Furtive Groundwater Economy Official statistics on groundwater-based systems, and more particularly those pertaining to individual pump irrigation, are generally difficult to obtain. The data are fragmented and diffuse, mirroring the image of the furtive groundwater economy itself. The information is in the hands of different administrations, who commission private consultants to conduct surveys whose results are not always made public. The FAO Aquastat database, for instance, which we accessed on July 15th 2013, provided figures for groundwater-irrigated areas that date back to 2000 (Morocco, Tunisia), and 2001 (Algeria)! A scholar who wishes to understand the magnitude of the groundwater economy faces a number of problems, which we discuss further below: (1) the groundwater economy is often considered to be a private business by its protagonists, and the legitimacy of the State to even collect data is frequently challenged; (2) the diffuse, and often informal nature of the rapidly developing groundwater economy makes it an extremely complex system to observe; and (3) aquifer dynamics are complex, even more so when they are intertwined with human practices.
Farmers are accessing deeper (confined) aquifers, where the supply -in the short term -is better, but where the water resources are even less renewable. Finally, some of the most rapidly growing irrigated areas were based on the mining of non-renewable groundwater resources  (Margat 2008). Thirdly, when comparing these data with earlier official data, (available on the FAO database) it appears that the groundwater economy has expanded at a remarkable pace. Nowhere is this clearer than in Algeria, where the irrigated area increased from 228,000 ha in 1985 to slightly over a million ha in 2011. While the latter figure may be optimistic, there is no doubt about the galloping development of Algeria's groundwater economy. Algeria's irrigation map was redrawn after the exploitation of huge groundwater reserves in the south, ranking the country second only to Libya in terms of available groundwater resources in Africa  (MacDonald et al.
2013). Integrated methods that incorporate considerations beyond hydrogeologic analyses using a strict disciplinary focus can be employed to assess the factors of aquifer management or policy defined by both science and consensus conditions  (Pierce et al. 2013). The continuum view of aquifer yields  (Pierce et al. 2013)  fits within an integrated water resources management approach to groundwater science and lends itself to decision support applications. It also requires an adjustment to the underlying framework hydrogeologists use to describe and categorize types of yields. Every DSS is built using datasets and models that represent the problem domain and key elements of interest to decision makers and stakeholders. Building on the concept of interrelated knowledge processes, Fig. 25. 1 highlights the relationship between decision support and the knowledge processes of aquifer performance and groundwater governance.
Data and Modeled Attributes for Aquifer Performance Information and knowledge management challenges abound in groundwater sciences. Every DSS is built using datasets and models that represent the problem domain and key elements of interest to decision makers and stakeholders. The domain of hydrogeology is comprised of significant data collections that span spatial and temporal scales across many orders of magnitude with variable resolutions  (Narasimhan 2005). While the scales and extent of groundwater information are vast, the datasets often are sparse considering the complexity in the systems. The resultant uncertainty, paired with inherent variability in groundwater systems limit the predictive value of groundwater models that form the core of decision support systems (Chap. 28). Regardless, offsets of parameters are derived from direct measurements and field observations to quantify and describe groundwater system behavior.
Data Collection The next process involves collection of all the data for the integrated study. Data Processing In this step, the data is preprocessed into appropriate resolutions and formats such that it is suitable for the integrated models. Typically at this stage, a number of quality assurance and checks are undertaken. Data Archiving Next, the data is archived in preparation for further distribution and use. Data Distribution Prior to the study being undertaken, the data are made available through a distribution mechanism. This is very consistent with enterprise data management models where centralised data storage is used, either by way of databases or fileservers. These data stores are then accessed for the study by way of a data discovery process. More contemporary methods of data distribution using web services are now gaining favor.
The Data management Challenge Data management is successful when data are discoverable, available, accessible, understandable, and usable  (Robbins 2012). This perspective comes from the ecological community and their long-term ecological research (LTER) program. It recognizes that successful studies depend on the development of integrated databases and data sets, many of which are collected by different teams over different timescales and are required to be brought together to tackle integrated scientific challenges (Costello 2009), such as integrated groundwater modeling studies. However, while management of data is a core part of the mission of large organizations such as USGS and Bureau of Meteorology in Australia, it is often the case that even within these organizations it is difficult to establish good data management practices in research projects.
Time Series Data Management There exist many commercial time-series data management systems, which specialize in the storage, dissemination and management of surface and groundwater data (e. g. WISKI,  3  Schlumberger 4  and Aquatic Informatics 5  ). These types of software packages typically allow ingestion of a variety of data sources including telemetry from automated gages, perform quality assurance, and usually are coupled to integrated analysis tools. They are also able to store a broad set of other hydrological, meteorological and climate data. Most of these systems use relational database technology as the persistence mechanism, which is then attached to a series of tools, as can be seen in the abstract model of a timeseries data management system in Fig. 26. 3 below. In this diagram, we map the functional elements described by WMO in Fig.
Examples of GIS Data models The widespread use of GIS systems as a data management and data integration tool has led to the development of domain specific geospatial databases, called GeoDatabases. These are optimized for the sorts of data commonly used in geospatial studies, in this case with integrated groundwater studies. These Geodatabase models  (Strassberg et al 2004; Jarar Oulidi et al 2009; Chesnaux et al 2011; Yang et al 2010b)  represent the features and properties of hydrogeological systems, in ways that allow storage, integration and manipulation of the spatial and time series data. In the hydrology domain, the two most widely used models are ARCHydro (Maidment 2002) for surface water studies, and ARCHydro-GW  (Strassberg et al. 2004 ) for groundwater studies. ARCHydro is a geographical data model for hydrological systems designed to support a cartographic representation of hydrological features.
It is designed to provides a unified model for geospatial and time series data in support of integrated hydrological modeling and analysis  (Strassberg et al. 2004 ). It allows different aspects of the water-resource systems, such as a drainage system, hydro-network and channel system, to be linked to time series flow observations and managed within the GIS system. ARCHydroGW provides a data model for hydrogeologic units, boreholes and other aspects of groundwater systems that can be used for integrated modelling. Argent (2003)  There are many studies which have successfully used these types of models  (Whiteaker et al. 2006 ) in conjunction with GIS toolsets. One issue that arises concerns unique identifiers in these types of systems (called HydroID in ARCHydro-GW), which identify features in the geospatial databases.
Usually these identifiers have local scope, meaning that they are assigned to be unique within a GeoDatabase, and are most usually non-unique when combining or integrating databases. As a result, it becomes difficult to automatically merge databases when conducting integrated studies, requiring significant effort to match or differentiate hydro-geological features based other information. Another issue concerns the assignment of a fixed geometry to a feature type. For example, a borehole might be represented by a point, in one particular GeoDatabase, and by a line in another GeoDatabase. Thus integrating the different representations between GeoDatabases becomes problematic. This has led to the development of the Hy-Features  (Atkinson et al. 2012)  conceptual model, in which the features are defined independently of representation.
Groundwater Data Networks Groundwater data networks are becoming an important source of data for groundwater studies, due to the increased breadth and depth of their data holdings  (Refsgaard et al. 2010). In data networks, autonomous data sources are federated into a composite entity, which behaves as a unified single enterprise. For example, regional groundwater monitoring networks, water well databases, aquifer maps, and other relevant data, are being variously integrated into larger networks in Australia, Canada, and the US  (Booth et al. 2011; Brodaric et al. 2011; Dahlhaus et al. 2012). Such networks are typically arranged in some form of distributed architecture, which dynamically retrieves data from original sources, thus ensuring access to current data. They also typically enable users to query and obtain data via a unified common view, shielding users from the heterogeneity of the original sources.