dataesr/scientific-paragraphs-categorization huggingface dataset:
With our complete database, biologists could test various probes or primers to select the most adapted to answer their biological questions. To reduce the complexity of probes and primers selection, we indicate in the database degenerate signatures from which specific probes and primers are deduced. The problem of probes accessibility is particularly relevant for the FISH approach. However, it is possible to resolve this difficulty using helper oligonucleotides, as previously described  (30). Recently, a promising strategy for reducing the biocomplexity of environmental samples by enriching the desired genomic target using probes before massive sequencing is being adapted for microbial ecology  (3). Most efficient methods rely on the complementary hybridization of oligonucleotide capture probes to the targeted DNA sequences; these methods use capture arrays  (31) (32) (33) , or solution phase, also known as solution hybridization selection  (3, 34, 35).
Furthermore, the use of explorative probes, as provided by PhylOPDb, in sequence-capture methods allows the full identification and characterization of new taxa (3). Despite its comprehensiveness, the probeset described in PhylOPDb, however, suffers from a lack of homogeneity that is the third important criterion of a probe design after sensitivity and specificity. Nevertheless, it has been demonstrated that in silico approaches for predicting the hybridization behaviour of microarray probes are limited: the only solution is to perform an extensive empirical testing of the probes  (36). Moreover, in silico assessment of specificity is not sufficient and only an experimental validation can ensure a complete specificity. Prediction of crosshybridizations strongly relies on both the database and the algorithm.
Until now, no clear consensus has been obtained to classify 16S rRNA genes in a unique database. Furthermore, specificity tests suffer from the same complexity without a unique 'universal' tool. Moreover, for some experiments, low melting temperature probes could be used  (37). Consequently, we preferred to provide a comprehensive probeset to let the users be able to select the best probes for their own experiments. Therefore, we recommend the PhylOPDb users to combine multiple tools (e. g. BLAST, SILVA-TestProbe, RDP-ProbeMatch) to confirm the specificity results of their selected probes. Future work will be directed towards the development of specialized sets of phylogenetic oligonucleotides for specific ecosystems. Specificity tests are usually not performed against a suitable subset of sequences, primarily because of the lack of databases for microbial ecology.
Depending on the environment studied, it would be more relevant to perform these tests against reduced databanks dedicated to specific ecosystems (e. g. soil, marine, freshwater and gut). Thus, additional probes specific to the targeted environments could be defined. Furthermore, specificity tests will be performed against reduced database limiting computational resources needs. Probes provided through the PhylOPDb web interface were designed at the genus level but it would be interesting for some purposes such as PCR-based analyses to define probes at different phylogenetic levels. In addition, the current probe collection of the PhylOPDb will be extended to include 18S rRNA gene-targeted oligonucleotide for investigating fungal species within complex environments, where they may play crucial role.
Moreover, IHE (Integrating Healthcare Enterprise) initiative has defined an integration profile to medical imaging scenario, aiming to promote trans-institutional sharing of data. Dicoogle is a PACS archive that innovates in the information system and services provided  [8]. It is an open source solution that replaced the traditional relational database by an indexing engine, allowing advanced searches over DICOM objects metadata. The application scenarios are vast like, for instance, research associated to dose surveillance and quality assurance  [8]. To support proprietary services in distributed environments, it was necessary to develop a private network protocol. This article presents an integrationframework to medical imaging that provides interface with three main protocols: DICOM standard (Storage and Query and Retrieve) to be interoperable with traditional equipment's of imaging laboratories, a XDS-I to support normalized communications with other institutional domains, and a private communication protocol to support Dicoogle functionalities in distributed environments.
Dicoogle Dicoogle PACS (http://www. dicoogle. com) is a free and open source solution that supports medical imaging storage and DICOM data mining. It allows the extraction of information from the PACS image archives and performs flexible queries on DICOM metadata  [3]. The software installation requirements are minimal and their flexibility to extract different level of data is high. It can run as operating system service but also as a portable application. So, the utilization of this software, as a knowledge extraction tool, only needs to have a view over repository file-system, with read-only permission. The data produced by digital modalities generate information on performed exams that can be used in different scenarios, like, for example, to monitor patient radiation doses, radiographic procedures and image quality.
Some important parameters such as image processing parameters, exposure index, patient dose and geometric information are generated by the modality and transferred to the PACS repository. Despite of this information being stored on the repository imaging objects, the information systems are not able to access them. The flexibility to index different DICOM objects with different data elements is due to the replacement of the traditional database by an indexing engine. With this approach, it is possible to index all existent DICOM data elements (text-based) without the need to create new fields, new tables, and new relations that would be necessary in the database supported approach. Dicoogle index engine is based on Apache Lucene  [18] , a Java index and search library that is used in a large variety of applications, including Wikipedia.
INTEGRATION FRAMEWORK -PROPOSAL As expressed, Dicoogle will be used as proof-of-concept of proposed integration framework. So, the proposed will be presented using this challenging context. The main architectural advantage of proposed integration framework is its extensibility supported by a plugin mechanism that promotes the development of new features, in an easy and simple way (Fig. 2 ). This flexibility will be used to support distinct medical imaging network interfaces (i. e. services). The network interfacing is fundamental to permit interoperability with third systems. Moreover, Dicoogle was special requirements, namely the support of enhanced queries over distributed medical imaging environments. So, our architecture must contemplate two distinct layers of communications, described in detail in the next sub-sections: -Standard: It is an indispensable communications module that ensures standard interactions with other equipment's through DICOM and XDS-I.
Reliable Multicast Communications Concerning the Dicoogle peers communication in Intranet, it was chosen a fully decentralized network topology, minimizing the infrastructure requirements and the points of failure. Another advantage of this topology is the stability of the network when composed by a small number of peers. The proposed LAN module is based on Jgroups  [36]  and uses reliable multicast communications. The Jgroups concept of group was extended to support Dicoogle affinity domains and a new network message protocol was created to support medical imaging services. To join a group, i. e. an affinity domain, a peer sends a multicast message with the name of the group it wants to join. If the group already exists, one group member will return a list containing the active peers. If not, a new group is created with that single peer.
Relay Service Healthcare institution networks are usually not prepared to support communications from the outside to the inside of institutions and, even from the inside to the outside, communications are limited to a reduced set of protocols and ports. So, the implementation of new services obligates, many times, to adjust network configurations or to establish dedicated communication channels (e. g. VPN), consuming time and resources. To avoid the previously mentioned constraints, we decided to implement an inter-institutional communications module fully compliant with Web 2. 0. Taking into consideration that a peer located inside a network institution can only send requests and receive responses, we decided to use a Relay service to bypass this limitation. This bridge unit forwards messages to their destinations, transforming requests into responses.
Moreover, the system uses HTTPS protocol to support communications between Dicoogle peers and Relay server. This Relay is a key element in communications and must be stable, resilient and scalable. The option was to design a software module of easy instantiation at Cloud, namely by institutions using Dicoogle. After analysing the services offered by Cloud providers, we decided to use a Cloud PaaS (Platform as a Service) solution and develop the service relay as a web application. The Dicoogle Relay service was developed to run in Google App Engine (GAE)  [37]. This choice does not require the acquisition and setup of a virtual machine (i. e. an operating system) and has several advantages like, for instance, the automatic scaling, the service resiliency and the geo-distribution of application platform.
The automatic scaling is a very important feature to our scenario, because the state of Dicoogle peers can change very quickly from idle to very active, generating significant amounts of traffic. Finally, a software utility (i. e. Relay Installer -Fig. 5 ) was developed to facilitate the setup of new services. The administrator of Dicoogle only needs to create an account in the GAE platform and use the Installer to setup the relay. The new service has an address that must be configured in every Dicoogle peer associated to this new domain.
One issue in sending medical imaging data through Internet is data privacy. For that reason, to assure data privacy, the system uses HTTPS between peers and GAE. Moreover, affinity groups are identified by a farmID, a crypto token generated by the relay administrator. It is a 256 bits key used to assure communication's privacy between peers, through Advanced Encryption Standard (AES) ciphering. All Dicoogle network messages are ciphered with a farmID key. Those ciphered messages are encapsulated in HTTPS messages. The HTTP message header is also used to transmit some important data to forward medical imaging message, such as the name of the destination, the source peers and the message purpose.
Application The proposed framework was integrated in Dicoogle Open Source project and is available for medical and scientific community at www. dicoogle. com, including binaries and source code. Consequently, the article results are focused on Dicoogle application that, implicitly, uses the proposed framework to support network processes. Fig. (7)  presents the GUI of Dicoogle Desktop version. With this interface, users can select the range of searches performed (Fig. 7-1 ). For instance, it can search on all interfaces at the same time. The results are presented following the DIM structure and organization (Patient/Studies/Series/Images), identifying the objects data sources (Fig. 7-2 ). If the object is in an external data source (LAN or WAN), the user can download it (Fig. 7-3 ). It is also possible to see the number of patients and objects returned (Fig.
7-4 ) from query. Finally, it is possible to see the list of active peers (and users) in affinity group (Fig. 7-5 ). This medical imaging platform has been used in several distributed environments. For instance, Dicoogle is used to support a regional PACS archive, integrating two diagnostic centres, with Radiologists reporting from several places  [38]. The load over the repository and network is considerable because the system manages more than 106k DICOM object per month. On other use case, Dicoogle is being used has DICOM data mining over several distributed repositories of hospitals from Aveiro region  [8]. There is a Dicoogle installation in every hospital and it is possible to perform distributed queries over those federated indexes, representing more than 8 million of DICOM objects.
The tool made possible to collect, identify and characterize DICOM metadata that can be related with different quality assurance vectors. The characterization of data inconsistencies and the identification of missing data, along with the collection of important data for practice improvement, are contributing for a better dose surveillance and patient safety. Other innovator scenarios were potentiated with Dicoogle networking. As an example, Dicoogle Mobile  [39]  is an application developed for Android mobile devices. It uses the relay service to communicate with normal Dicoogle instances. In this way, this application is able to search and display medical images stored in other Dicoogle instances.
Performance Trials To evaluate the performance of data search and results integration, especially in inter-institutional processes supported by Dicoogle private protocol, sometests were performed. For what concerns WAN testing, the bandwidth of Internet access from inside an institution depends on numerous factors like, for instance, the traffic generated by the users in a specific instant. Thus, tests were done to assess the average Internet access bandwidth of trials region, the results pointed to 17 megabits per second. For that reason, in the system's tests, the peers had an Internet access bandwidth of 17 megabits per second of upstream and downstream. The LAN scenario was tested with 4 machines linked through a connection with a bandwidth of 100 megabits per second and with several machines generating some daily traffic.
CONCLUSION Medical imaging repositories are often looked on as "inert bags" of DICOM objects, accessible only though the DICOM query and retrieve service. In this paper, we have presented a framework capable of retrieving data from distinct sources through three communication protocols: XDS-I, DICOM and a private protocol based on P2P paradigm. As a proof-of-concept, we used Dicoogle that is an open source PACS solution with an extensible platform. As a result, we have obtained a solution that can retrieve data from medical imaging repositories through distinct ways. Due to the variety of protocols used, the solution combines the advantages of the distinct paradigms, being easy to deploy and maintain, transparent and scalable. The data source bus endows the system with the ability to give access to repositories that are not directly accessible, using a member of the network with direct access to the repository as an intermediary.
Only the local database is consulted (Local); (2) The peers consulted are in the same LAN, via DICOM (LAN 2 Peers, LAN 3 Peers, LAN 4 Peers); (3) The peers consulted are in the WAN, connected via Dicoogle GAE relay (WAN 2 Peers, WAN 3 Peers, WAN 4 Peers); (4) The peer that starts the search sends the query via WAN, this query is propagated also via DICOM to other peers not directly connected via WAN as depicted in Fig. (9), (a) is named as WAN2+LAN3, while (b) is named as WAN3+LAN2. In Fig. (9), the peer that starts the search is the one signalled as client and all the other peers collaborate in the search.
( 1 ) 1 Only the local database is consulted (Local); (2) The peers consulted are in the same LAN, via DICOM (LAN 2 Peers, LAN 3 Peers, LAN 4 Peers); (3) The peers consulted are in the WAN, connected via Dicoogle GAE relay (WAN 2 Peers, WAN 3 Peers, WAN 4 Peers); (4) The peer that starts the search sends the query via WAN, this query is propagated also via DICOM to other peers not directly connected via WAN as depicted in Fig. (9), (a) is named as WAN2+LAN3, while (b) is named as WAN3+LAN2. In Fig. (9), the peer that starts the search is the one signalled as client and all the other peers collaborate in the search.
Then each hole is filled by two-dimensional (2D) extrapolation starting from the smallest hole. Web 7   [52]. A corresponding library function 'bwboundaries' is part of MATLAB  [53]. For our evaluation the algorithm has been implemented in PV-WAVE  [54]. In figure  2  the box 'Manual Align' indicates a misorientation angle φ or. φ corr = -φ or is the angle by which fiber axis of the pattern must be rotated to make it point in vertical direction. Only if φ or is constant for all the patterns from a scan it may be meaningful to apply computed tomography. In the favorable case φ or only corrects the tilt between the sample orientation and the detector orientation. Working with a tilted detector helps with the filling of blind areas. Although the multidimensional chord distribution function (CDF)  [55] , z (r), has been computed from each of the patterns I (s) = I (s 12 , s 3 ), it is not discussed here.
In equation (1) r is the radius of the molded rod and φ max = 14. 7 • is the maximum misorientation angle found in a p-scan. From the geometrical point of view equation (  1 ) describes the orientation of those MMT platelets for which the layer-normal lies in the plane that is defined by the machined radius and by the axis of the injection-molded rod. Equation (  1 ) represents a quantitative description of the 'numerical orientation prediction' reviewed by Altan  [42]  as sketched in his figure  6. Our data show that in the center the inclination of the MMT-platelets is inverted in a rather narrow zone. This core region coincides with the zone in which oriented MMT flakes are detected in the n-scans (cf Figure  6 ). In the n-scans a complex variation of the inclination angles is recorded.
Acknowledgments Farhad Jokari Sheshdeh  is acknowledged for his help with the data analysis. The authors thank the  Hamburg Synchrotron Radiation Laboratory (HASYLAB)  for beam time granted in the frame of project  I-2011-0087. Preparation of the materials had been supported by the seventh framework program of the  European Union  (Project  NANOTOUGH FP7-NMP-2007-LARGE ).
Historical, i. e. pre-1957, upper -air data are a valuable source of information on the state of the atmosphere, in some parts of the world dating back to the early 20th century. However, to date, reanalyses have only partially made use of these data, and only of observations made after 1948. Even for the period between 1948 (the starting year of the NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis) and the International Geophysical Year in 1957 (the starting year of the ERA-40 reanalysis), when the global upper-air coverage reached more or less its current status, many observations have not yet been digitised. The Comprehensive Historical Upper-Air Network (CHUAN) already compiled a large collection of pre-1957 upper-air data. In the framework of the European project ERA-CLIM (European Reanalysis of Global Climate Observations), significant amounts of additional upper-air data have been catalogued (> 1.
Even for the period 1948-1957, new upper-air data are expected to improve the quality of future reanalyses, particularly in the tropics and in the Southern Hemisphere. This is the approach pursued by the European FP7 (7th Framework Programme) project ERA-CLIM (European Reanalysis of Global Climate Observations; http://www. era-clim. eu). The project partners in work package 1 (WP1) -dealing with the recovery, imaging and digitisation of historical upper-air observations and metadata -were the Climatology Group at the Institute of Geography of the University of Bern, Switzerland (UBERN, WP1 lead), Météo-France in Toulouse, France (METFR), the Russian Research Institute for Hydrometeorological Information (RIHMI) in Obninsk, Russia, and the Fundação da Faculdade de Ciências da Universidade de Lisboa at the Dom Luiz Institute of the University of Lisbon  (FFCUL, Portugal).
In the framework of the project, more than 1. 3 million station days of upper-air data have been inventoried, more than 200 000 images of the sources taken, and ca. 750 000 station days digitised. Data were recovered for large parts of the globe, but with a focus on, so far, less well covered regions such as the tropics, the polar regions and the oceans, and on very early 20th century upper-air data from Europe and the US. The data rescue activities of ERA-CLIM were organised in close arrangement with the broader Atmospheric Circulation Reconstructions over the Earth initiative (ACRE; http://www. met-acre. org), and, in the case of surface pressure and temperature data, in cooperation with the International Surface Pressure Databank (ISPD; http://reanalyses. org/observations/ international-surface-pressure-databank) and the International Surface Temperature Initiative (ISTI, http://www.
surfacetemperatures. org). The new upper-air data complement the already available Comprehensive Historical Upper-Air Network (CHUAN,  Stickler et al. , 2010) , which already compiled large amounts of pre-1957 upper-air data, and which is also planned to be assimilated into the new ECMWF full reanalyses. The new ERA-CLIM data are available from the PANGAEA data repository (doi:10. 1594/PANGAEA. 821222). In this paper, we describe the ERA-CLIM upper-air data that result from the ERA-CLIM data rescue activities, including the search for the sources and the metadatabase (http://www. oeschger-data. unibe. ch/metads) which contains the complete metainformation on all inventoried records (Sect. 2), the imaging and digitisation process (Sect. 3), and the unit conversions, reformatting and quality checking procedures applied to the data (Sect.
Cataloguing of historical data and metadatabase The development of the ERA-CLIM historical, observational data set started with the search for and systematic cataloguing of potential data sources. While some project partners were more or less aware of the data sources they planned to digitise or at least knew in which archive to search for the respective metadata (RIHMI, FFCUL), a detailed inventory of available archives or potential sources first needed to be established in the case of others (UBERN, METFR). The METFR, RIHMI and FFCUL upper-air data rescue activities focussed on their home institutional archives (even though METFR has meanwhile also started with the inventorying of sources in the French National Archives), whereas UBERN   1 ). Observatory publications encompass reports from the Tegel/Lindenberg  (Assmann at al.
The preparatory work resulted in a catalogue of historical, meteorological data that are available in hard copy, digitally imaged or on micro-film in a large number of archives or libraries worldwide. To facilitate the coordination between the different project partners and to prevent unnecessary, duplicate efforts during ongoing or future projects within the data rescue community, the collected metadata have been made publicly available in the form of a centralised, web-based metadatabase (http://www. oeschger-data. unibe. ch/metads) that contains all inventoried records that could be obtained for imaging, whether or not they have been imaged or digitised, including the entire relevant information on the data. In total, more than 450 000 digital images have been taken (including surface and atmospheric transmission data 1  , more than 200 000 for upper-air data alone).