dataesr/scientific-paragraphs-categorization huggingface dataset:
A. Description of Usecase In this section, we present an industrial use-case based on Ikusi application : Airport Management. It aims to provide a centralized-operational management for airports management services. It involves the coordination of a group of processes, where both human and IT system interactions are required. It is a classical 3-tier web architecture i. e. , a HTTP frontend (tier-1), an application server (tier-2) and a database (tier-3). This architecture is deployed on top of an IaaS Cloud and is provided to end-users through a SaaS model. Moreover, one instance of the application server is launched for each client i. e. , for each airport. Services provided by the architecture include the management of an operational data repository for each airport operator and passenger, the real time management of flight status updates, and the dynamic allocation and optimization of assigned resources according to data from air flight companies and airport operators.
It is based on message exchange modules, on resource allocation and on billing management airport services to provide airlines with an operational platform based on Cloud computing technology. It also incorporates enhanced security solutions based on a network of secure element developed in the SEED4C project. The use-case is presented in Fig. 5. Four different kinds of machines or VMs are involved. First, the machine ctseed1 is the client machine. It is the device that is used within the airport to access the airport's services. Secondly, the reverseproxy VM (i. e. , tier-1) is a proxy used by the enduser to access the airport's services. The musik VMs (both musik1 and musik2) belong to an airport (MAD 1  or EAS 2  ) and are accessed by the end-user machine through the proxy (i. e. , an instance of the application server, tier-2).
D. Assurance The Assurance Model used in the airport management usecase is based on the security policy and focuses on monitoring the effectiveness of the security mechanisms. The model checks that deployed security mechanisms (e. g. , SELinux, Iptables, and OpenSSH tunnel) are running as expected. It also checks that the security properties are fulfilled, in terms of data integrity, data confidentiality, and data availability. For instance, the enforcement of the property Integrity (ConfigAODB, AdminRoot) (line 5 of the security policy in Section IV-B) can be checked using the script from listing 1. This script is generated by the SE E during the enforcement step (Section IV-C), depending on the properties of the security policy. 1 $ cat BM_fileInt -1. 1. sh 2 #!/ bin / bash 3 RET = $XCCDF_RESULT_PASS 4 check_write () { su -c " test -w " $1 "" $2 ; return $ ?;} The script BM_fileInt-1.
INTRODUCTION Generally, social applications are read intensive whatsoever the write operations are so important. For instance, Facebook may face more than 1. 6 billion reads per second and 3 million writes per second during peak hours. These read and write operations derive from users interactions. Users interactions are sequences of transactions and we assume that each transaction reads and writes data owned by one or several users. Even though, transactions are usually short, the volume of required data is extremely low regarding the size of the whole database. Moreover, many transactions may attempt to access the same dataset simultaneously (i. e. , at the same time), which generates temporal load peaks on some hot data. Such a situation, more known as a net effect, has the drawback to slow user interactions.
The proposed approach for overcoming limits in existing solutions, is guided by the fact that grouping transactions with similar patterns of data access might save a significant amount of work. We leveraged on the ability to process a group of concurrent transactions that is faster than processing one transaction at a time and it reduces the number of messages sent to the database node. More precisely, if every application directly connects to the database, then the latter may become a bottleneck and overloaded. Rather, application requests can be gathered and grouped at a middleware stage and thereby, database connection requests are minimized. The proposed solution works in two steps: a scheduling step followed by an execution step. The scheduling step aims at grouping all transactions accessing the same data into a block regardless where they are issued.
RELATED WORK Our work is linked to transaction processing for scalable data stores. Large scale solutions for managing data are facing a consistency/latency tradeoff as surveyed in  (Abadi, 2012). Today several solutions relax consistency for better latency  (Silberstein et al. , 2012; Lakshman and Malik, 2010; Vogels, 2009)  and do not provide serializable execution of concurrent transactions. Other solutions provide strong consistency but only allow transactions restricted to a single node (Oracle, 2014) 1  ,  (Chang et al. , 2006). In a multi-tenant database context we have Elas-TraS  (Das et al. , 2013) , which is a system providing a mechanism to face load peaks and avoid distributed transactions. In fact, ElasTraS uses a data migration mechanism couple with some load balancing schemes to face load peaks on database nodes.
That is, a transaction is exactly linked to one and only one master list, and when a SN node sends a transaction to another SN node, it removes it from its master list. After the master list is established, the SN node identifies the different blocks that compose it. Each block is related to one access class. Furthermore, the number of queries that a SN receives must remain bounded. To this end, we efficiently balance the load over all SNs regardless of the data requested by a transaction. We finally assess the number of SNs according to the entire workload submitted to the system, expressed in number of clients, and according to the number of transaction requests that a SN is able to handle in a unit of time.
The Storage Layer This layer is a set of nodes called execution node (XN) that access directly to black-boxed datastore. Each XN node manages the access class that is a partition stored via the datastore. Plus, the XN node executes the block of transactions that are linked to them. Since transactions are ordered in a serial way within a block, the concurrency is already controlled at the scheduling layer. Therefore, the XNs guarantee consistent execution of transactions without locking. The XN node chosen for executing a block is obviously the one that is responsible of the access class required by transactions within the block. When blocks contain transactions requiring several access classes, thus a set of XN nodes are chosen, and Section 3. 4 points out how the executions are done. Considering a database divided into n partitions {p 0 , p 1 ,.
Remote, rural and urban regions of China are all represented in the study. The contents of this article include the following: (1) a description of the sites, data processing, and data quality control, (2) an evaluation of the spatial characteristics of AOD and Ångström exponent over different regions of China, and (3) an analysis of the monthly AODs at 440 nm and the Ångström exponents between 440 to 870 nm (AE) at all 50 sites, (4) a discussion of the longterm (12 years) variations of AOD at 12 CARSNET sites. To provide a record of CARSNET results, monthly and yearly average AODs and AEs for the 50 CARSNET sites are provided as an Appendix. We hope this database will be used for future investigations of aerosols, climate, and the atmospheric environment of China. 2 Site description, instruments, and data.
, 2009c). The requirements for inter-comparison calibration protocol for the CARSNET field instruments were as follows: (a) only raw data collected from 02:00 to 06:00 a. m. (GMT + 08:00) on clean and clear days were used; (b) the AODs at 500 nm (the master instruments have nine wavelengths at  340, 380, 440, 500, 670, 870, 940, 1020, and 1640  nm. Thus the masters could be used for the inter-calibration of all types of Cimel field instruments of CARSNET. ) on calibration days had to be less than 0. 20 and without major fluctuations; (c) the time intervals between the measurements made with two masters and the instruments to be calibrated had to be less than 10 s. The AODs obtained from un-calibrated instruments differed by 4. 5 to 15. 3 % compared with those measured by reference instruments.
Data processing and quality control The AODs were calculated using the ASTPWin software provided by the manufacturer of the sunphotometers. This software can provide Level 1. 0 AOD (raw results without cloud screening), Level 1. 5 AOD (cloud-screened AOD based on the work of  Smirnov et al. , 2000)  and Ångström Exponents (AE) between 440 to 870 nm. For the present study, all AOD data were computed by interpolation of the intercalibration coefficients. The interpolation was performed for daily averages based on the annual inter-calibration coefficients. And the cloud-screened AOD based on the method of  Smirnov et al. (2000)  also were obtained. We note that the cloud-screening technique has not been completely val- idated although the procedure tested favorably using experimental data obtained under different geographical and optical conditions  (Smirnov et al.
, 2000). To further ensure the data quality, all the data were checked manually site by site, and any unreasonable data were deleted. For example, some exceptionally large AOD points were caused by the clouds, and this was determined after checking the MODIS Level-1B granule (MOD02_1km) images, which are available from http://modis-atmos. gsfc. nasa. gov/IMAGES/. Daily average AODs were the first values computed, and any cases where measurements were made less than 10 times in a day were eliminated. This processing procedure eliminated ∼ 20 % of the daily data. Finally, the monthly and yearly averages of the AODs were calculated at each wavelength as were the Ångström exponents between 440 and 870 nm. Che et al. (2009c)  validated AODs from CARSNET-CAMS stations through comparisons with data from the AERONET stations in Beijing.
Summary and conclusions In this study, data collected from the CARSNET network from 2002 to 2013 were used to characterize the aerosol optical properties over a large area of China. The AODs at 440 nm generally increased from north to south, and very low AODs (< 0. 20) were found only in remote regions, including the Tibetan Plateau and parts of northwestern China. Large AODs (> 0. 60) mainly occurred in central and eastern China where heavy industrial and other anthropogenic emissions led to high aerosol loadings. AOD levels of 0. 20-0. 40 were observed in semi-arid and arid regions as well as at some regional background areas in northern and northeastern China. Large AEs between 440 and 870 nm (> 1. 20), which are caused by large proportions of fine particles, were observed at the sites in the southern reaches of the Yangtze River and at the clean sites in northeastern China.
More detailed work is needed in the future, and we have developed a set of recommendations for continued monitoring efforts. First, CARSNET should continue collaborating with other international networks, including AERONET, PHOTONS, RIMA, SKYNET, etc. to ensure consistency in calibration protocols as well as cloud-screening and data retrieval processing procedures. Second, the network should be expanded and the studies extended in time to obtain more detailed information on the aerosol optical properties over China. Instrument and site maintenance should be top priorities as these efforts would improve data quality and minimize gaps in the data. Third, in addition to AOD and AE, other key parameters such as single scattering albedo, size distributions and the optical properties of fine mode particles should be measured.
Acknowledgements. This research was funded by the  National Key Project of Basic Research  ( 2011CB403401 ), the  Project  ( 41275167  and  41130104 ) supported by the  National Natural Science Foundation of China ,  Strategic Priority Research Program  of the  Chinese Academy of Sciences  ( XDA05100301 ),  CAMS Basis Research Project  ( 2014R17 ) and the  Climate Change Special Fund of CMA  ( CCSF201504 ). The research leading to these results has received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 262254. Edited by:  S. Gong.
Monthly averaged AODs at 440 nm at urban sites. The solid and dash horizontal hairlines indicate the annual averages for the subgroup and the whole database, respectively. 1. 8 1. 8 1. 8 1. 4 1. 6 Urumqi 1. 4 1. 6 Lanzhou 1. 4 1. 6 Yinchuan. 4. 6. 8 1. 0 440 nm AOD 1. 2 AOD 440 nm. 4. 6. 8 1. 0 1. 2. 4. 6. 8 1. 0 440 nm AOD 1. 2. 2. 2. 2 0. 0 0. 0 0. 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1. 8 1. 8 1. 8 1. 4 1. 6 Xi'an 1. 4 1. 6 Beijing 1. 4 1. 6 Tianjin AOD 440 nm. 4. 6. 8 1. 0 1. 2 AOD 440 nm. 4. 6. 8 1. 0 1. 2 AOD 440 nm 1. 0. 4. 6. 8 1. 2. 2. 2. 2 0. 0 0. 0 0. 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1.
Cohort ANM is a European multi-center study aiming to develop biomarkers for AD  [18]. Subjects with an AD diagnosis as well as those with MCI and healthy con- trols were recruited from centers based in Kuopio, Lodz, London, Perugia, Thessaloniki, and Toulouse. Details of study design and enrollment are provided by Lunnon et al. [10]. Subjects for the DCR were recruited from the Maudsley and Kings Healthcare Partners, which incorporates the Alzheimer's Research UK (ARUK) cohort  [19]  from whom gene expression data has not previously been reported. The present study used data from 748 subjects: 614 subjects from ANM and 134 subjects from DCR.
Gene expression Whole blood samples (2. 5 ml) were collected after 2 h of fasting into Paxgene Blood RNA tubes (BD) and extracted as in Lunnon et al. [9]. Illumina Human HT-12 Expression BeadChips were used to analyze the whole transcriptome according to the manufacturers protocol. The gene expression analysis was run in two batches at two different sites. Batch 1 contained samples from 356 ANM subjects run on version 3 of the BeadChip, as previously described  [9, 10]. Batch 2 contained samples from 411 subjects: 134 from DCR and 277 from ANM run on version 4 of the Bead-Chip. Samples from 19 subjects were included in both batches. See Fig. 1  for an overview of sample numbers. The raw gene expression data are available as GEO DataSets (Accession number GSE63060 for batch 1 and GSE63061 for batch 2).
Statistical analysis Data pre-processing The data pre-processing performed in this study is different to that used for the original analysis by Lunnon et al. [9, 10]. The data processing pipeline used in this study aims to address the effects of technical data artifacts in gene expression studies  [20]. Raw gene expression data was exported from Illumina's Genome studio and processed in R (version 3. 1. 1) [21]  using the lumi package  [22]  and custom in-house pre-processing scripts (GitHub, http://bit. ly/1vjyKNo). Briefly, raw expression data was subject to a model based background correction for bead array  [23]. This used negative bead expression levels to correct for background noise. The data was then log base 2 transformed and robust spline normalized in lumi  [22]. Outlying samples were iteratively identified using fundamental network concepts and removed, following the methods described by Oldham et al.
[24]. To reduce any batch effects we adjusted for technical categorical variables using ComBat  [25]. Continuous technical artifacts were accounted for by taking the first principal component across housekeeping and undetected probes and regressing this against technical variables. Variables significantly associated with the first principal component were then regressed against expression for each probe, and the mean adjusted residuals taken forward for all further analyses. Finally, the data was reduced to a subset of probes that could be reliably detected in 80% of samples in at least one diagnostic group. Finally, subjects were excluded where there were discrepancies between the recorded sex and sex determined by the XIST (ILMN 1764573), USP9Y (ILMN 2056795) and EIF1AY (ILMN 1755537 and ILMN 2228976) X-and Y-linked genes.
Pathway level analysis of gene expression (PLAGE) Gene level expression data were condensed to sample wise, pathway level scores using PLAGE  [14]. PLAGE groups genes into pathways defined by the Broad Institute Collection of Curated Pathways  [27]  and outputs a score, per sample, for each of these sets. We restricted PLAGE to only include pathways with between 10 and 500 genes. The generation of PLAGE scores was implemented through R package 'GSVA' and is detailed in Supplementary Methods, section 1  [13].
The purpose of the demographic model is to provide a comparator for the gene and pathway models. If models that include blood expression information (as well as demographics) are no more informative than demographic variables alone there is no benefit in including this information. All model building was performed in the statistical software R (Version 3. 1. 1) using the 'caret' package  [28]. Each model was used to predict the diagnostic status of subjects in batch 2. Model statistics including accuracy, sensitivity, and specificity were generated and compared between the demographic model, gene model, and pathway model. Receiver Operator Curve (ROC) analysis was also performed in batch 2 data using R packages ROCR and pROC  [30, 31]. Full details of model building are provided in Supplementary Methods section 2.
Data pre-processing As a result of pre-processing 12 samples in batch 1 and 49 samples in batch 2 failed quality control (QC) and were removed. The majority of these samples failed QC as they were identified as outliers. Additionally, some samples were removed because the sex of the individual recorded in the clinical database did not match the biological sample (2 samples in batch 1 and 7 in batch 2). Samples from 19 subjects were present in both batch 1 and batch 2. Samples from 14 of these individuals passed QC in both batches; only data from batch 1 was used and the other was discarded. Correlation between the two batches was at least 0. 9 for all individuals (Supplementary Figure  3 ). Batch 2 gene expression data contains subjects from the DCR whereas batch 1 does not. This study used the same protocols, staff, and facilities as the London sample collection site within ANM.
Principal components analysis (PCA) was performed across the batch 2 gene expression data from DCR and ANM subjects from London. The first three principal components (accounting for >40% of variation) were linearly regressed against the study the individual was enrolled in (DCR or London ANM) and found to be non-significant. Therefore, it was deemed appropriate to group DCR subjects with London ANM, allowing the model trained in batch 1 data to be simply applied to batch 2 data. After data processing only subjects with either an AD diagnosis at all visits or control status at all visits were analyzed further: 207 subjects in batch 1 and 236 in batch 2. Only gene probes that mapped between the version 3 and version 4 chips used to generate batches 1 and 2, respectively, were used for analysis (5212 probes).
Demographic model The following demographic variables were associated with case/control status in our cohorts (Table  1 ): age, sex, APOE status, years in full time education, and sample collection site. These variables were therefore used in multivariate modeling using RFE. The optimal cross-validated accuracy was found when including all variables; calculation of a tolerance set excluded the variable representing the Lodz sample collection site. Variable importance scores showed age as the most important covariate followed by years in full time education and then APOE status and sample collection site. In batch 2 test data the model achieved an accuracy of 0. 69, sensitivity of 0. 53 and specificity of 0. 84. The area under the ROC curve was 0. 77 (see Table  2  and Fig. 2 ). Additionally, a model that did not contain the sample collection site was built.
The fourteen pathways included in the final pathway model focused around DNA repair, immune response, and regulation of cellular activities. Of particular interest to AD, two pathways containing genes from the calpain gene family were included in the final fourteen pathways. It is thought that amyloid peptides interfere with calpain activity leading to deregulation of the CDK5 gene and in turn hyperphosphorylation of the tau protein. This promotes the death of neurons  [27]. It is encouraging that we have seen relevant pathways in our final pathway model. Overall, the pathways are similar to those identified by Lunnon et al. who studied overall pathway differences using an identical raw dataset that was processed differently  [9]. As we would expect, 12 out of 13 of the genes in the final gene model were present in the genes used for modeling by Lunnon et al.
However, in early exploratory work we are more likely to find results of interest if technical data artifacts are not creating a barrier. The models created in this study all achieved an accuracy of approximately 70% with the pathway model having test sensitivity and specificity results of greater than 60%. The pathway model and gene model did not outperform a model of demographics alone. Any potential increase in concordance from the novel pathway level approach used here has not lead to a greater predictive ability in these datasets. However, we have only tested one method for creating pathway level scores. Further, we have been able to benchmark pathways against genes in datasets that had been extensively harmonized. It is reassuring to see that pathways perform similarly to genes and further work is now needed to see if pathway concordance is more easily detected using other methodological approaches and in data generated by independent groups and platforms.
ACKNOWLEDGMENTS This work was supported by the  Alzheimer's Society, InnoMed (Innovative Medicines in Europe) , an integrated project funded by the  European Union  of the  Sixth Framework program  priority ( FP6-2004-LIFESCIHEALTH-5 ); Alzheimer's  Research Trust UK ; the  John and Lucille van Geest Foundation (AH) ; and the  NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at the South London, Maudsley NHS Foundation Trust and Kings College London , and a joint infrastructure grant from  Guy's and St Thomas' Charity  and the  Maudsley Charity. The research leading to these results has received support from the  Innovative Medicines Initiative Joint Undertaking  under.
Botryllus, whose genome has recently been sequenced  (44) , is also the subject of famous studies on allorecognition and the emergence of an adaptive immune system  (45, 46). Finally, a large fraction of disease-associated genes are conserved in ascidian genomes  (19) , suggesting that ascidians can also be useful human disease models  (47). ANISEED is a sophisticated information system dedicated to the biology of ascidians, and which consists of two classes of inter-linked databases. The Developmental Browser (www. aniseed. cnrs. fr) formalizes, integrates and displays an extensive set of complementary and interrelated molecular and anatomical data for each species  (48) , which can be explored via four main menus giving access to functional annotations of genes and cis-regulatory sequences, to descriptions of anatomical entities, to gene expression data and to literature articles.