dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogenetic analyses For the MLST tree, we manually aligned nucleotide sequences of the coxA, fbpA, ftsZ, gatB and hcpA genes  [72]  from wMel, wRi, wPip, wBol1-a, wBol1-b, wBol2 and wBm, obtained from Genbank, then concatenated the alignments. We inferred a phylogenetic tree using PHYML  [73] , using the HKY substitution matrix, a discrete gamma model with four rate classes and a gamma shape parameter estimated from the data. For the phylogenetic network, we used t_coffee  [74]  to align sequences of the 654 core genes from the five Wolbachia genomes. These single-gene alignments were concatenated to form an alignment 681,717 nt in length. A Neighbor-Net network  [33]  was inferred from this alignment using default parameter values in SplitsTree  [75]. For the phylogenetic analyses of secA and other putatively horizontally transferred genes, we aligned amino acid sequences using t_coffee  [74] , then edited and trimmed alignments by eye.
Sequencing of secA genes The presence and position of secA genes in wBol1-b was confirmed by sequencing PCR products obtained using primers spanning the wBol1-b_1089-wBol1-b_1091 boundary (mutLSecA1-F + mutLSecA1-R) and the wBol1-b_1092-wBol1-b_1093 boundary (SecA2Tran-F + SecA2 Tran-R). The wBol1-b_1092 ortholog in wHa was amplified using various combinations of primers 1092-2, -3, -4, -5, -6, which were designed based on the wHa sequence fragments present in the NCBI WGS database. The wBol1-b_1091 wHa ortholog was amplified using primers 1091-F and 1091-R, also based on wHa sequence. Primer sequences are listed in Additional file 2: Table  S2. PCR cycling conditions were as follows: 94°C 3 min, (94°C 30 s, 52°C 30 s, 68°C 150 s) × 35 cycles, then 68°C 10 min. The reaction mixture contained 625 nM of each primer, 125 μM dNTPs, 1.
Acknowledgements We would like to thank  B. Cass ,  J. Howie ,  M. Sidhu  and  Y-S. Leong  for their help in the lab. We are also extremely grateful to  A. Men  and  D. Miller  from  AGRF  and to  A. Darby  for advice on wBol1-b DNA preparation prior to whole genome sequencing. We thank  T. Walker  and  L. Klasson  for constructive discussions, and  K. Bourtzis  and  D. Cordeaux  for examining and commenting on a previous draft of this study. Financial Support was provided by the  Australian Research Council  ( DP0772992 ) grant awarded to SLO, the  Genetics Society (Heredity Fieldwork Grants)  and  Sigma Xi  ( Grant in Aid of Research ) grants awarded to AD, and  NSF  (grant- 0416268 ) and  NERC  (grant- NE/ B503292/1 ) grants awarded to SC and GDDH.
Introduction Bio-medical imaging (BMI) domain is currently confronted to similar issues than those of manufacturing industries twenty years ago. The inherent complexity of biological data has always challenged researchers, as they have to deal with many data sources, natures and types of processing  [6]. However, with the recent technological improvements in imaging devices, but also networking and computing, the data handled by researchers has remarkably evolved. The size and amount of data have increased significantly, as huge cohorts of patients are required to draw strong inferences. In addition, more and more crossing analyses (eg. imaging, behavioral and genetics data together) are performed. Thus, there is a need of a unique, efficient and mature data management system. The imaging technologies have reach a plateau which guarantee BMI data consistency at least for several years; it allows the data to be reused for longitudinal studies or any other purpose.
However, costs, time and difficulty of leading BMI studies are not decreasing. So researchers would be rewarded to move toward a more synthesis-oriented research strategy: some types of studies should focus on the synthesis of previous findings  [19]. Capitalizing the data among laboratories and institutions is an evident solution to get sufficient data, but it raises traceability, collaborative and confidentiality issues. Product Lifecycle Management (PLM) software solve these issues, and permits manufacturing industry to stay efficient and competitive. Consequently, PLM systems promise to be adequate tools for the management of BMI data coming from large-scale studies that handle heterogeneous data. The data of a neuroimaging laboratory, the GIN 1  , is chosen as an application; the neuroscience field is particularly active concerning imaging data management and constitutes a fertile ground for the work presented in this paper.
BMI data management tools According to the authors of  [18] , the BMI scientific community must be provided with methods to query, analyze and crosslink the complex, heterogeneous and large-scale image data resources. Several sizeable database, with more or less success, show the commitment of the BMI research field. For 15 years, the neuroimaging community has been aware of the need of neuroinformatics to advance its understanding of the brain (human and nonhuman): because of the growing amount of data to handle and the trend for collaborative studies, proper management and sharing has become crucial. Several solutions have been developed by networks of laboratories (BIRN, MRN, INCF, NIDAG 2  ), aiming at designing adequate tools. In addition, projects develop their own tools, combining design of a database and large-scale imaging acquisitions: the Human Connectome Project (HCP,  [16] ) and the Human Brain Project (HBP,  [14] ) are such projects.
All databases were not designed with the same purposes in mind, thus do not manage the data at the same stage of a study. A proposition of four phases of data analysis are presented in figure  1:  1 study specifications (everything that define a research study and its proceedings), 2 raw data (or acquired data), 3 derived data (both refined and final results), and 4 published data (peerreviewed data recognized by the community). Neuroimaging databases can be divided in two categories; although many handle raw and processed data (phases 2 and 3 ) accompanied by associated metadata for study management, others aim to share published results (phase 4 ) for future reuse: -The first category of database permits researchers to manage locally and to share between sites and laboratories during large-scale studies.
The types of handled data are demographic data in addition to imaging data. The most often used database tools by individual and collaborative projects 3  are XNAT  4  [9], LORIS  [2] , COINS  [13]  and IDA  [17]. -The second category focuses on results coming from peer-reviewed published papers. Activation coordinates with associated metadata are the most frequent managed data in these databases. In some ways, published results databases, in particular BrainMap  [4] , supplement the literature by providing a unusual consistency of description, required for reuse: it appears that the information provided by the authors of papers to meet the requirements of such databases are sometimes more complete and precise than what is written in the original papers  [3]. Nevertheless, no data management tool enables heterogeneous data management from study specifications 1 to published results 4.
Currently no database handles phase 1 ; even LORIS, which seems to be the most completed, doesn't manage it, neither published data 4. The figure  1  shows the distribution of the existing neuroimaging database tools: they all stay focused on no more than two phases. Though, neuroimaging field has now a global understanding of its acquisition and processing workflows  [5] ,  [9] ,  [12]. Few data management tools now integrate the management of workflows (XNAT, IDA, Connectome mapping toolkit  [5] ) between phases 2 and 3. In the figure  1 , these workflows are represented by the arrow "processing".
The BIL&GIN dataset and GINdb data management system The researchers of the GIN laboratory study brain maps of anatomical and functional cognitive activations of hundred-subject cohorts, acquired with MRI 8. Therefore they want to manage the whole process of their research studies, from raw data to analysis results. In 2010, the laboratory designed its own data management system, called GINdb, and which is a relational database that manage metadata and paths to related files  [7]. The GIN first Brain Imaging Laterality dataset (BIL&GIN1) includes 300 subjects, balanced by gender and handedness. This dataset was acquired between 2009 and 2011 and managed using GINdb  [11]. However, the laboratory wants to improve its data management system, to get a more integrated environment that handle data and metadata during the whole life of a study, and that enables the data to be repurposed, e.
PLM Neuroimaging datamodel Based on the GINdb data management tool, the litterature and interviews of key-users of the GIN, a first PLM object-oriented neuroimaging datamodel is defined. Globally, the business objects (BOs) are divided in two categories which constitute a basis for data property rights and PHI preservation : -The BOs that can be created at any time and used for any study: unique subject in database, imaging and non-imaging exam definition, processing definition, computing tool, acquisition device, imaging template and bibliographical reference. All of these BOs are concepts and definitions that are used to justify all the data that is produced during the whole lifecycle of a study. The information resulting from the association of objects, via relations, is called provenance of data.
-The BOs that can be created only inside a study: these objects contain subject's individual context data (subject demographic and clinical information) and result data (non-imaging and imaging exam, processing) of subjects, as well as result data of groups of subjects (processing). The BO study gathers data such as ethical research comity reports and Protected Health Information (PHI) policy. Raw data is stored in a hierarchical structure of three BOs: exam (temporally uninterrupted row of acquisition), acquisition (indivisible period of data collection), and data unit (single data). A visual summary of our proposal of a general neuroimaging data organization is given in figure  3 ; it highlights the category of BOs outside study (red), as well as subject's individual BOs (green) and cross subjects BOs (blue).
In figure 4, a simplified UML schema of the relationships between the BO that are used in figure  3  is presented with their cardinalities. This data model supports BMI data management during the four phases of the BMI lifecycle; all needed metadata and information for a potential reuse can be stored in the BOs. The concepts associated to each BO are general enough to enable any type of subject's data (e. g. imaging, genetics, behavioral. ) to be managed. In this paper, PLM was shown to fulfill most of the need of the neuroscience field in BMI data management. The proposed neuroimaging data model allows heterogeneous data of BMI studies to be stored from the specifications of the studies to the resulting publications. The model has been implemented into a PLM software tool, and test sessions of the resulting database by the researchers of the GIN laboratory with a real dataset are planned.
A limitation of our actual works is the sharing of the data to an extended community: even if a PLM light web client exists, licenses to connect to the database and users training are required, which prevents from occasional use. So in the future, a simplified and more adequate non-licensed user interface has to be developed. Besides, traditional PLM systems are not as flexible as required by research practices, so as a following work we will propose a semantic enrichment, based on ontology, and that will handle the management of relationships between objects  [1]. As the understanding of these relationships is complex, a second point in our upcoming works will be to visualize them by graphs, in order to improve navigation as well as the visualization of data provenance in the resulting data management tool.
PFS and OS were measured from date of randomization until PD or death respectively. Toxicity was assessed at scheduled visits every 4 weeks while on therapy, and data were collected until the first follow-up visit 4 weeks after therapy discontinuation after which patients were followed every 3 months for 3 years for survival. Adverse events (AE) were defined by National Cancer Institute Common Terminology Criteria for Adverse Events (NCI-CTCAE) version 3. 0.
Although several of the SSS genes were related to important functions in cancer, there were no correlations of specific genes or groups of genes with sorafenib sensitivity. Sorafenib is a multitargeted kinase with antitumor effects on tumor angiogenesis via VEGFR and PDGFR  (33) , therefore, high levels of pro-angiogenic factors including fibroblast growth factor-1 (34), the decay accelerating factor CD55  (35) , PPAR-gamma  (36) , IGFBP-7, and gastrin-releasing peptide (37) are associated with sorafenib resistance. Together this suggests that tumors upregulating alternative pathways for promoting angiogenesis or protecting endothelial cells may be relatively resistant to sorafenib, due to their ability overcome blockade of the VEGFR and PDGFR pathways. The availability of two unique datasets comprising a large set of NSCLC cell lines and baseline core biopsies collected in a clinical trial allowed us to develop a SSS in-vitro and to test it in patient samples with associated outcome data.
Several pieces of middleware have emerged to meet these RFID issues. WinRFID is a flexible and extensible RFID middleware developed by WINMEC RFID Lab  [10] [11]capable of processing large amounts of data with real-time error fixing and recovering from faults and exceptions. RF²ID for "Reliable Framework for Radio Frequency IDentification"takes into consideration the unreliability of the RFID technology  [12]   [13]. RF²IDconsiders a mapping of the system (i. e. , the location of each reader) and manipulates some lists such as "observed Tag List" of the current reader, "expected Tag List" of the next reader, "missing Tag List", etc. to detect abnormal behaviors of the systemsuch as the disappearance of a tag. Fosstrak for "Free and Open Source Software for TRAce and tracK" implements the EPCglobalInc.
Related works Our implementation relies on a single, efficient micro-kernel able to run two specific execution models: one in hard real-time, the other in best-effort. This micro-kernel implements a sparing scheduling policy, that fully uses the "idle" CPU time left by real-time tasks to run the stream tasks. To the best of our knowledge, this is an original approach, and the first one to target many-core embedded chips. Our work is essentially inspired from separate research topics, from the Real-Time and from the Embedded Operating Systems communities. The idea of allocating a subset of the processors in a SMP system in order to increase responsiveness was proposed by Brosky & Rotolo in  [3] ; they called this technique "shielding", and implemented it in a Linux kernel to reduce the latency for soft real-time applications.
Notably, at most one hard real-time task can run on a shielded core, without migration nor preemption, and the scheduling policy is restricted to fixed-priority. Besides, the programming model is based on usual POSIX and Linux APIs, which clearly shows advantages regarding legacy softwares, but also brings an inherent unpredictability. Vestal  [5]  was one of the first to propose a formal approach for using spare CPU time when running tasks with mixed-criticalities, regardless of the underlying platform. For that purpose, compositional or hierarchical scheduling are now active research topics in the Real-Time community  [6, 7, 8] , and aim at providing safe policies for running heterogeneous tasks on a single system, e. g. hard real-time, soft real-time, and best effort. Note that these work usually attempt to comply with the safety requirements of avionics systems, and most of them come with an implementation prototype as a Linux kernel module or patch, such as LITMUS RT  [9].
These works however differ from ours both in their approach and their implementation. Our approach is different because the scheduling policy and the kernel are tightly bounded to dedicated programming models and languages. As for our prototype implementation, we believe that the seek of performances and restrained latency requires a simple, micro-kernel design, whereas the complex monolithic architecture of a Linux kernel makes it unsuitable for hard real-time applications. A more radical (but non exclusive) way to conceive mixed criticalities is through virtualization: a hypervisor manages the hardware resources, and provides real-time guarantees to one or several of its Virtual Machines (VMs). Real-time extensions were brought to the major hypervisors of the Linux world, namely KVM  [10, 11]  and Xen  [12] , but they only support soft real-time tasks.
In  [13]  however, Lee et al. use compositional scheduling in RT-Xen to run some classes of hard real-time tasks. Outside the Linux world,  [14]  proposes a dedicated realtime micro-kernel with strict resource sharing policies, able to run a para-virtualized Linux host next to hard real time tasks. Also, several proprietary solutions exist such as the real-time hypervisors of National Instruments or of WindRiver; unfortunately only "commercial" documentation is available. Both allow running para-or fullyvirtualized general purposes OSes such as Windows or Linux, next to hard real-time bare applications or RTOSes. However the core shielding appears to be strict: no best-effort VM may run on RT-cores. In a general way, virtualization is more flexible but clearly less efficient than our micro-kernel solution, as it supposes managing multiple OSes on a single system.
A model of aluminium has been developed and implemented in an Ocean General Circulation Model (NEMO-PISCES). In the model, aluminium enters the ocean by means of dust deposition. The internal oceanic processes are described by advection, mixing and reversible scavenging. The model has been evaluated against a number of selected high-quality datasets covering much of the world ocean, especially those from the West Atlantic Geotraces cruises of 2010 and 2011. Generally, the model results are in fair agreement with the observations. However, the model does not describe well the vertical distribution of dissolved Al in the North Atlantic Ocean. The model may require changes in the physical forcing and the vertical dependence of the sinking velocity of biogenic silica to account for other discrepancies.
, 2003)  when the dust deposition field of  Mahowald et al. (1999)  was used. The main goal of  Han et al. (2008)  was to better constrain the dust deposition field. For this purpose they used the Biogeochemical Elemental Cycling (BEC) model improved by  Moore et al. (2008)  as a starting point. They used all dissolved Al datasets used by  Gehlen et al. (2003)  and added more datasets. Except for scavenging  Han et al. (2008)  added a biological Al uptake module where the Al : Si uptake ratio is a function of the ambient Al and Si concentrations  (Han et al. , 2008). The surface residence time of Al for both modelling studies varies strongly between different locations (from less than one year to almost 80 years). Overall, there are a number of questions regarding the oceanic Al cycle that remain to be fully addressed.
] = κ([Al eq ads ] -[Al ads ]) - ∂ ∂z (w s • [Al ads ]) + (A∇ 2 H + ν E ∂ 2 ∂z 2 )[Al ads ] , (6) d dt [Al diss ] = -κ([Al eq ads ] -[Al ads ]) + (A∇ 2 H + ν E ∂ 2 ∂z 2 )[Al diss ] , (7) where the general advection (v • ∇) of the tracers is implicit in the full time derivatives. A and ν E are the horizontal and vertical eddy diffusivity coefficients, respectively. The code of the NEMO model, as well as PISCES and the aluminium model, is free software: software that can be used, studied, and modified without restriction (except that published modifications must fall under the same license). It is available at http://www. nemo-ocean. eu/. Version 3. 1 of the NEMO model is used, specifically svn revision 1183. This can be accessed through http: //forge. ipsl. jussieu. fr/igcmg/svn/modipsl/. The aluminium specific code is available under the same conditions as an electronic supplement to this article: trcal sed.
(2006)  the model that is used to create this dust deposition field is validated against high-quality observational datasets, but there are a number of uncertainties, because there are not enough observations available to sufficiently constrain any dust model globally. The spatial resolution is nominally 2 • , and the temporal resolution is one month (twelve time steps per year). In reality, dust deposition is highly sporadic  (Aumont et al. , 2008; Rijkenberg et al. , 2008). Dust events are expected to be highly local and there is sufficient horizontal mixing to dilute the Al diss distribution, such that there is no significant concentration change. This has also been observed in the North Atlantic Ocean  (Rijkenberg et al. , 2012). Furthermore, our aim is to look at averages of [Al diss ] over at least several months, and therefore it is acceptable to use monthly averaged dust deposition fields.
Observations The recent IPY-Geotraces observations in the Arctic  (Middag et al. , 2009) , North-east Atlantic  (Middag, 2010, Chapter 5) , West Atlantic  (Middag et al. , in preparation)  Oceans and the Atlantic section of the Antarctic Ocean  (Middag et al. , 2011)  are used for a detailed comparison and optimisation of the model parameters. See the upper part of Table  3  for these datasets and Fig. 3      For a worldwide global ocean comparison one has to rely on data that was collected in the era before the reference samples of SAFe and Geotraces were available. Inevitably the definition of criteria for selecting such previously published datasets is less rigorous, see Electronic supplement S-3 for the criteria used for each of the selected datasets. The selected datasets are listed in Table  3 , lower part, and positions are shown in Fig.
However, significant improvement might be possible by (1) developing a more sophisticated model for ocean sediment source of Al, (2) adding diatom Al incorporation, and (3) fine tuning the free parameters and constraining them by means of field studies and laboratory experiments to better understand the role for these processes. noaa. gov/Ferret/). Other noteworthy free software used is the GNU Operating System and Climate Data Operators (cdo). This research is funded by the Netherlands Organisation for Scientific Research (NWO), grant  Nr. 839. 08. 414. This work is licensed under Creative Commons Attribution-ShareAlike 3. 0 Unported License (CC BY-SA). This note overrides other licenses, giving you true freedom for this preprint.
Acknowledgements The comments of two anonymous reviewers significantly improved this manuscript. The authors are grateful to those who have been proved useful in discussion, among which  Micha Rijkenberg ,  Laurent Bopp ,  Jack Middelburg  and  Wilco Hazeleger. Furthermore, we want to thank our colleagues from the  IMAU  institute for their critical and useful feedback to the model configuration and output. We also want to thank the people who kindly provided their datasets that are mentioned in this paper. Christoph Heinze  kindly dug up the model data from Gehlen et al. (2003) and made it available to us. Jasmijn van Huis improved the language by proofreading several article sections. The authors wish to acknowledge use of the Ferret free software program for analysis and graphics in this paper.