dataesr/scientific-paragraphs-categorization huggingface dataset:
A simpler replacement with new features We decided to replace the tape server software with a new one, unencumbered from constrains initially present at the time of SHIFT to CASTOR transition. This new tape server software is a drop-in replacement of the previous stack, which was consolidated in a single daemon for easier operations. It has a pipelined architecture to minimize blocking. Asynchronous instruction fetch, data read, data write and result reporting all run in separate threads. The new tape server introduces several features. In depth performance counters enable fast discrimination between disk servers and tape hardware slowness. Full SCSI tape alerts reporting delivers early warnings for drive and media failures  [5]. A watchdog function unblocks stuck sessions and cleans up after failures.
Special care was taken to integrate the new tape software with the existing CERN tape logging system in order to improve preventive maintenance procedures. Those new features permit reducing the workload for tape operators. The features of the previous software stack were also included, like buffered file marks to ensure write performance for any size of files  [6]. The tape file format and the communication protocols between the tape server and the other daemons were kept unchanged. The new tape server daemon forks a separate process for each request to mount a tape. The forked process contacts a scheduling daemon in order to get instructions for the mount session. Having one process per session allows for the detection and killing of stuck sessions. This problem can arise when disk servers stop responding.
Reading and writing to disk The data can be read from disk using various protocols. The disk access classes provide an uniform interface for the disk tasks to use, as described in 3. 5. The disk tasks are run by a pool of threads, draining a shared FIFO. This allows disk transfers to run in parallel, as long as memory and disk threads are available. The number of threads is configurable by the operator, and the default with which we run in production is 10 threads. This ensures making use of a few gigabytes of memory when accessing file of around 300 MB, the current average size in CASTOR. When migrating, the disk tasks send data and errors to the input FIFO of the corresponding tape tasks. When recalling, the disk tasks send the final file transfer results to the result packer.
Reading and writing to tape Tape is a serial medium onto which CASTOR writes one complete file after another. Access to tape is therefore managed by a single thread. The tape thread is responsible for mounting the tape, checking the tape is in fact the correct one, positioning the tape, validating the position of the tape, transferring data to and from tape and memory, and unmounting the tape at the end of the session. The timing statistics of the tape thread are used as the primary performance indicator for tape operators. The data structures on tape have been left unchanged so tapes can be read and written interchangeably by both the current and previous software. The file format is based on ANSI user label  [8]   [9]. The tape thread can get stuck for various reasons, the most common being a stuck disk transfer.
SCSI access layer A set of classes were developed to reduce the repetitive implementation of boilerplate code associated with SCSI development. These classes also provide a generic approach to endianness and status handling. The SCSI data structures, which are defined at the bit level, were implemented using C bit fields. This allows clear coding when using them and automatic display in debuggers. Particular care was taken to strictly adhere to the naming conventions of the SCSI specification in order to ease cross referencing [10]  [11].
Tape library layer There are two daemons to support tape library control. One for SCSI base tape libraries (tested in-house with IBM TS3500 and Spectra Logic libraries) [10]  [11]  [14]  [15] , and one for proprietary Oracle based libraries (tested with SL8500 libraries)  [16]. The daemon supporting SCSI based libraries was kept as-is from the previous software stack. The daemon for Oracle based tape libraries was created to allow for the optional deployment of support for such libraries. Both tape library daemon use a unified client library.
Threading Necessary threading primitives were implemented to wrap the pthread library and POSIX semaphores with a more convenient C++ interface. The design of the threading layer was inspired by the threading primitives of Qt  [20]. The inter-thread FIFOs were implemented as templated classes relying on STL containers, and semaphore and mutex classes provided by the threading layer. The overall design of the FIFOs was focused on code maintainability and ease of use. This design is simple enough to grant a local re-implementation. The resulting performances were more than adequate given the low performance requirement of 2 kHz data block flow rate.
This will replace the current use of RAID. This will stripe individual files over several disk servers, ensuring an even disk performance without the need for explicit disk scheduling. During LHC run 1, the experiments recorded their raw data to the disk pools and tapes of CASTOR, with analysis running on EOS  [31]. For run 2, a new trend is coming where the data goes straight to EOS, and one gets transferred to CASTOR for custodial copy recording to tape. We intend to leverage the flexibility of the tape server software to add support for direct EOS access with the addition of a new request queuing system. This will eliminate the need for the disk staging area of CASTOR in this scenario.
The second keynote, by Dr. Jeroen de Ridder, underlined the critical importance of scale in biological data sets. Depending on the scale used to analyze and interpret data, the features and patterns that emerge can change quite dramatically; this is comparable to the change in perception we have of a landscape when we are flying over it or walking in it. Through an array of working examples Dr. de Ridder guided the audience into understanding that meaningful new insights in molecular data analyses can be achieved by accounting for the importance of scale and using scale-aware analyses tools. Finally, the keynote from Dr. Lars Juhl Jensen concerned the efforts needed to collect and combine data from different sources into a single, biologically meaningful, network. In his talk, Dr. Jensen detailed the efforts and techniques that were necessary to construct the STRING database  [3].
Acknowledgements The success of an event the size of the European Student Council Symposium depends on the commitment of many. We are greatly indebted to ECCB 2014 conference chairs  Marie-Dominique Devignes  and  Yves Moreau  for giving us the opportunity to have the 3rd  European Student Council Symposium in Strasbourg. We are especially thankful for the logistical support and invaluable advice of the ECCB organizing committee; specifically the Workshops and Tutorials chairs  Olivier Poch  and  Mario Albrecht , and our  ECCB intermediary Magali Michaut. We deeply appreciate their continued support of the  ISCB Student Council  and the symposium. Further, we would like to acknowledge the support of the  ISCB Board of Directors  and their trust in our vision. The Student Council would also like to thank our keynote speakers;  Dr.
Martens ,  Dr. de Ridder  and  Dr. Jensen , for volunteering their time to contribute to the success of the symposium and to promote the next generation of computational biologists. Furthermore, we would like to thank everyone on the organizing committee, without them, there would have been no symposium. Also we would like to thank the SCS2014 chairs,  Farzana Rahman  and  Tomas Di Domenico , for the synergetic symposium collaboration. In addition, we would like to thank the  BMC Bioinformatics editorial office  for their help in publishing this report. We are also extremely grateful for the financial support that we received from our sponsors. This year ESCS was supported by  GdrBIM, IMGT ,  Syngenta ,  Novartis ,  BASF  and  Roche. Without their support many of the opportunities that we offered to the delegates at the 3 rd European Student Council Symposium would not have been possible.
Organisms were carefully separated one by one manually with a long wooden needle, in order to avoid overlapping. Each image was then run through the ZooProcess plug-in using the image analysis software Image J  (Grosjean et al. , 2004; Gorsky et al. , 2010). Several measurements of each organism were then computerized. Organism size is given by its equivalent circular diameter (ECD) and can then be converted into biovolume, assuming each organism is an ellipsoid (more details in  Grosjean et al. , 2004). The lowest ECD detectable by this scanning device is 300 µm. To discriminate between aggregates and organisms, we used a training set of about 1000 objects which were selected automatically from 40 different scans. This protocol allows discrimination between aggregates and organisms by building the initial train-ing set of images.
Data analysis The effect of stations and dates (n = 12) on zooplankton abundance and biomass was tested statistically using oneway ANOVA with the statistical software Statistica v7. The statistical significance was tested at the 95 % confidence level. Community patterns for taxa abundance were explored using the Primer (V6) software package which has been shown to reveal patterns in zooplankton communities (e. g. Clarke and Warwick, 2001; Wishner et al. , 2008). Data sets were power-transformed (fourth root), and the Bray-Curtis dissimilarity index between stations  (Bray and Curtis, 1957)  was calculated employing all taxonomic categories that contributed at least 1 % to any sample in that data set. Different groups of zooplankton (BC groups) were individualized based on their taxonomic composition.
Acknowledgements. We thank the project coordinator,  S. Blain , and the chief scientist on board,  B. Quéguiner , for inviting us to take part in the  KEOPS2  project and for supplying data. We thank the captain  Bernard Lassiette  and crew of the  R/V Marion Dufresne  for their support aboard. We also thank  G. Guillou LIENSs Laboratory  at the  Université de La Rochelle  for processing stable isotope analysis, and  Tom Trulls  and  Marc Pagano  for valuable comments on the manuscript. We thank the three reviewers for their comments and suggestions, which significantly contributed to improving the quality of the publication, and  M. Paul  for English proof-reading. This work was supported by the  French Research program  of  INSU-CNRS LEFE -CYBER  ("Les enveloppes fluides et l'environnement -Cycles biogéochimiques, environnement et ressources"), the  French ANR (Agence Nationale de la Recherche ,  ANR-10-BLAN-0614  of  SIMI-6 program , and  ANR-09-CEXC-006-01  to  M.
Zhou  and  F. Carlotti ),  LABEX OT-MED  (no. ANR-11-LABX-0061 ), the  French CNES  ( Centre National d'Etudes Spatiales ) and the  French Polar Institute IPEV (Institut Polaire Paul-Emile Victor ). Edited by:  T. Trull  www. biogeosciences. net/12/4543/2015/Biogeosciences, 12, 4543-4563, 2015.
Understanding immune alterations in cancer patients is a major challenge and requires precise phenotypic study of immune subsets. Improvement of knowledge regarding the biology of natural killer (NK) cells and technical advances leads to the generation of high dimensional dataset. High dimensional flow cytometry requires tools adapted to complex dataset analyses. This study presents an example of NK cell maturation analysis in Healthy Volunteers (HV) and patients with Acute Myeloid Leukemia (AML) with an automated procedure using the FLOCK algorithm. This procedure enabled to automatically identify NK cell subsets according to maturation profiles, with 2D mapping of a four-dimensional dataset. Differences were highlighted in AML patients compared to HV, with an overall increase of NK maturation.
NK cells initially differentiate from immature CD56 bright to CD56 dull phenotype, with different functions, including cytotoxicity, cytokine production, and migratory capacities  (14, 19, 20). Subsequently, NK cells lose expression of NKG2A, and sequentially express CD57 and KIR. Accordingly, five states of maturation stages are defined according to expression of these markers. To date, an increasing number of NK cell activation and maturation markers have been described  (17, 21). The improvement of knowledge regarding the biology of NK cells led to an increase of markers required to phenotypically and functionally characterize these cells  (21). Technological advances in the field of flow and mass cytometry led to the development of complex panels to study NK cells, with subsequent generation of high dimensional dataset  (21).
In this context, manual processing of the data presents many limitations. First, generation of gates in two dimensions is time-consuming, and subject to operator subjectivity  (22). Hence, gating strategy can impact on results and conclusions when multiple gates are drawn [  (22) ; Gondois-Rey et al. manuscript in preparation]. Second, the high number of results generated is sometimes hard to interpret and summarize, in particular when large cohorts of patients are analyzed. Another problem is the global comprehension of a complex system and interpretation of results when conclusions are drawn parameter by parameter. New tools are then required to address these problems. Recently, algorithms for automatic gating and 2D mapping of high dimensional dataset have been developed, such as Spade  (23) , viSNE  (24) , flowClust  (22) , or FLOCK  (25, 26).
Flow Cytometry A FACS Canto II (BD Biosciences, San Jose, CA, USA) was used for flow cytometry. NK cells from whole blood EDTA were immunostained with Krome Orange-conjugated anti-CD45, Phycoerythrin cyanin 7 (PC7)-conjugated anti-CD3, allophycocyanin (APC)-conjugated anti-CD56, fluorescein isothiocyanate (FITC)conjugated anti-CD158b1b2j, FITC-conjugated anti-CD158a,h (further referred to as KIR), APC-alexafluor 750 (APC AF 750)conjugated anti-CD159 (NKG2A), pacific blue-conjugated anti-CD57. All the antibodies used in the study were a kind gift of Beckman Coulter, Marseille, France. Red blood cells were lysed with BD FACS Lysing solution (BD Biosciences) before data acquisition.
Cluster Identification Procedure FCS files were read, compensated, transformed, and exported using flowCore (R, Bioconductor)  (27). FLOCK algorithm was then applied to each exported data file  (26). Resulting gated data were imported with R. Centers of populations were extracted, assembled, and exported as a unique tabulated text file using R. MeV permitted heatmap visualization and hierarchical clustering  (28). Centers were clustered using euclidean distance. The tree of centers was cut at a threshold that results in clusters with homogeneous mean fluorescence intensity (MFI) profile. Those clear populations were annotated using expert knowledge. The automated gating and cluster identification procedure was described by Gondois-Rey et al. (manuscript in preparation).
Automatic Gating with FLOCK Provides Reliable Results Multiparametric flow analysis of NK cell subsets has become complex over recent years with the identification of several subsets of NK cells depending on classical activating receptors, inhibitory receptors, and maturation markers. More importantly, it has become evident that rare subsets may be under or overestimated during manual analysis by different investigators and different software. We have, therefore, searched for a more reliable tool to provide unbiased analysis of NK cell subsets in the peripheral blood of AML patients. First, PBMCs from healthy donors (N = 18) and AML patients at diagnosis (N = 18) were isolated and stained for CD56, CD3, KIR (see PATIENTS AND METH-ODS), NKG2A, and CD57. Then CD3 -CD56 + live NK cells were  initially manually pre-gated, exported, and then analyzed with FLOCK algorithm.
In accordance with previous studies, KIR positivity inversely correlates with NKG2A expression, in both HV and AML patients  (18, 29). We then considered the repartition of NK cells within the different clusters. On average, the percentage of CD56 bright cells was found to be significantly lower in AML patients compared to HV (1. 3 ± 3. 2% vs. 6. 4 ± 5. 8%, respectively; P = 0. 001) (Figures  2C, D ). Overall, 2D mapping of NK maturation profiles enabled the visualization of high dimensional dataset as well as fast and reliable identification of NK cell subsets. With this unsupervised automated gating of NK cells with four parameters, the algorithm was able to distinguish all the NK subsets that were previously described in the literature, but did not identify any new population. Notably, NKG2A was not informative in NK cell cluster definition by the algorithm.
One additional difference in the context of AML is that NK cells maturate in close contact with tumor cells. This could explain the high proportion of highly maturated NK cells. However, some authors also described CD56 dim CD57 + enrichment in tumor-infiltrated lymph nodes in patients with metastatic melanoma, with significant impact on patients' survival  (33). Technical advances in flow and mass cytometry now enable the dissection of NK cell biology with high precision, with the subsequent need for tools adapted to the analysis of datasets with unprecedented dimensionality  (21, 25, 39). In our study, we used an automated procedure using the FLOCK algorithm and hierarchical clustering, which enabled unsupervised identification of NK subsets and patients profiling based on NK parameters.
Automatic gating algorithms are powerful and reliable tools adapted to high dimensional dataset analysis  (25, 40)  with potential limitations highlighted in the case of rare populations  (40, 41). In the case of immunomonitoring studies on large cohorts of patients, the development of such approaches is of primary importance for data analysis standardization. First, the high number of subjects included in these studies requires automated gating in order to reduce the time of analysis. Second, visualization of all the clusters allows fast and unsupervised identification of cell populations. Moreover, the hierarchical classification of patients according to maturation profiles enables the discovery of distinct patterns or specific subgroups among patients. The clinical consequences of such observations should be evaluated on larger cohorts of patients.
The software POPS performs inference of population genetic structure using multilocus genotypic data. Based on a hierarchical Bayesian framework for latent regression models, POPS implements algorithms that improve estimation of individual admixture proportions and cluster membership probabilities by using geographic and environmental information. In addition, POPS defines ancestry distribution models allowing its users to forecast admixture proportion and cluster membership geographic variation under changing environmental conditions. We illustrate a typical use of POPS using data for an alpine plant species, for which POPS predicts changes in spatial population structure assuming a particular scenario of climate change.
In this article, we introduce POPS, a software that implements Bayesian clustering algorithms based on genetic, geographic and environmental variables. The principle of POPS is that individuals sharing similar environmental conditions and geographically close to each other are also likely to share genetic ancestry. To achieve this objective POPS assigns individuals or genes to genetic groups after modeling the effects of geography and environment on individual membership and admixture proportions. POPS is based on latent regression models that consider individual ancestry as a hidden response variable regressed on geographical and environmental covariates (e. g. ,  Bandeen-Roche, Miglioretti, Zeger, and Rathouz 1997; Chung, Flaherty, and Schafer 2006; Linzer and Lewis 2011). Hidden regression models are used to obtain predictions of cluster membership and admixture proportions from geographic and environmental variables.
In the context of climate change, POPS can be used to forecast changes in population genetic structure of species in response to global warming  (Jay et al. 2012). POPS is implemented in the C++ programming language and can be run from a commandline engine or a graphical user interface. The program takes input data files in a format compatible with existing clustering algorithms like STRUCTURE  (Pritchard et al. 2000)  and TESS  (Chen, Durand, Forbes, and François 2007). POPS returns textual and graphical results of inferred and predicted membership probabilities and admixture coefficients, allele frequencies in the estimated clusters, and values of criteria for model selection. In Sections 2 and 3, we describe the hierarchical models used by POPS and their implementation using Markov chain Monte Carlo (MCMC) algorithms.
Models We consider a data matrix, x, with N rows and L columns that records genotypic data for a sample of N individuals genotyped at L loci. For haploid species, each entry of x corresponds to the occurrence of a given allele encoded as a categorical variable. In polyploid species there are A × N (A ≥ 2) rows instead of N rows in the data matrix. Each copy corresponds to one of the A chromosomes carried by an organism. To each sampled individual corresponds a geographic sample site where coordinates are recorded. In addition to the genetic and geographical data, we assume that a set of environmental covariates are measured at each sample site. We denote by XS the geographic coordinates of a site, and by XE the subset of measured environmental variables. We denote by X the N × (D + 1) design matrix containing the value 1 in its first column plus D geographical and environmental covariates in its subsequent columns.
Objectives of POPS models The models implemented in the program POPS fall into two main classes according to whether or not genetic admixture is considered. A model without admixture assumes that each individual originates from a unique ancestral group whereas a model with admixture assumes that individuals may share ancestry from more than one source population. In statistical terms, individual membership to a genetic cluster and admixture proportions correspond to nonobserved random variables. For readers not familiar with Bayesian inference of population structure, we recommend to read  Pritchard et al. (2000)  where the software STRUCTURE is introduced and the standard Bayesian model of population genetic structure is carefully explained. Assuming that there are K ancestral groups -also called genetic clusters -models without admixture infer latent group labels corresponding to the membership of each individual, z i , of one of the K clusters.
As with the program STRUCTURE, POPS estimates a matrix of ancestral allele frequencies p = (p k j ), k = 1,. , K, = 1,. , L and j = 1,. , J. Each entry of p corresponds to the frequency of allele j at locus in population k, and J is the number of distinct alleles observed at locus. Models with admixture suppose that each individual genome shares ancestry from K ancestral clusters. Admixture models estimate a matrix, q, where each element, q ik , i = 1,. , N , k = 1,. , K, corresponds to the proportion of individual i's genome that originates from cluster k. In admixture models, POPS estimates cluster labels, z ( ,a) i , for each allele copy, a = 1,. , A, at each locus, = 1,. , L. POPS provides estimates and predictions for the hidden variables z and q. Compared to existing software packages, the basic principle of POPS is that the inclusion of geographical and environmental variables improves the accuracy of inference when there is genuine association between those variables and population genetic structure.