dataesr/scientific-paragraphs-categorization huggingface dataset:
The Partek practical required a conventional local install of software and was identical and equally straightforward in all locations after initial, relatively painless setup of a local licence server. However, it was also clear after all courses in all countries that the overwhelming majority of participants (although not all) would simply not be able to purchase commercial software. The third short-course transcriptomics practical added in 2014 was a remote analysis of RNAseq data in DNAsubway at iPlant (http://dnasubway. iplantcollaborative. org)  [17]. This required the students to (optionally) register with a valid email address during the practical in order to access the RNAseq file store. This was fast and easy to perform and all students appreciated the advantages of remote computation and large data file store over local installation.
The main difficulty experienced in remote computing was a surprisingly variable compatibility and functionality for different browsers (Chrome, Firefox, IE) and more critically the reliance of the remote providers on particular Java versions. In a foreign teaching environment, particularly with short local preparation times, upgrade permissions and software versions can be a game-changing barrier. The history of bioinformatics is littered with conflicts between enhanced functionality through incremental upgrades vs universal compatibility. This is just as acute now as ever in association with remotely delivered content. Cloud-oriented services may like to consider providing widely scalable solutions if they are to fully engage with the broadest possible user base. This particularly extends to the greater availability of cheap tablet computing, which is currently overtaking desktop use in all countries as an arena where many cloud based bioinformatics resources are still in their (often incompatible) infancy.
In general, the short-course experiences in Windhoek and UNIM did not throw up any major novel issues that had not already occurred in CCC China. Given that all sites had good network availability the use of remote resources was less problematic than would have been experienced by relying on high network speeds. As long as large file transfer to local machines was reduced or circumvented then the analysis of large datasets was no more difficult for one geographic location over another. Hardware/software heterogeneity and firewall selectivity were probably the most critical issues but none were insurmountable, even local networks could be replaced/supplemented by 3G/4G data networks which were available in all the LMIC locations experienced. Remote computing actually makes a lot more sense in a teaching or research environment than local compute power for many researchers regardless of location.
• The use of memory sticks for software should be considered. • Support from the local institute in terms of making labs available and providing rooms and computers of a sufficient standard is critical. Moving beyond this, scaling up to a much larger series of summer schools is the next logical step and can provide a means for closing the gap in providing LMIC researchers with a background in data science and cloud computing. It is possible to develop a curriculum that runs in this time-scale; and can make repeated use of teaching materials while at the same time allowing integration of specific domain knowledge. The failure rate for Information Systems projects in LMIC is much higher than in the Developed World  [21]  and is a more general feature of development projects. A key contributory factor is that sustainability is not engineered into these projects from their starting point.
Any future series of schools must take this into consideration. In terms of employing the large data sets discussed at the beginning of this article it is clear that LMIC researchers will need to move beyond locally installed clouds and make use of commercial cloud resources. The cost-effectiveness of analysing large data sets for researchers in LMIC has been discussed at length elsewhere  [22, 23]. However, while the costs of using cloud computing platforms for developed world researchers is comparatively small, they can be prohibitively high for LMIC researchers. In order to close this gap, access would need to be provided for LMIC researchers at a much lower and perhaps even zero cost. This bears similarities to the findings of Nathan Eagle who enabled nurses to provide data on blood bank supplies in Kenya by paying for the texts he received from them; which represented a substantial barrier cost for the nurses  [24].
In a large family with an inheritance pattern consistent with an autosomal dominant myopathy/neuropathy, we identified 10 shared variants in three affected individuals, of which 9 did not segregate with the disease. A novel variant in DNAJB5 (c. C43T; p. P15S) was the only rare variant that co-segregated with the phenotype (Figure  1C ). This rare variant was observed in four other independent individuals in our exome database of ~3,000 individuals; however no phenotypic information is available for these individuals. The variant is also present in the heterozygous state in a single individual in the Exome Aggregation Consortium (ExAC) compiled dataset (MAF=0. 00004858). This DNAJB5 variant affects a highly conserved amino acid in the DnaJ domain of the protein. A homozygous mutation in DNAJB2 was identified in a large family segregating recessive distal hereditary motor neuropathy of early adulthood onset  [Blumen et al.
As a further test of this mutational burden observation, we calculated repeatedly the average number of rare, nonsynonymous variants in the 58 neuropathy genes in 40 randomly selected individuals from the BHCMG_EU sample set compared to the 5748 ARIC_EA controls. Upon conclusion of 100 resamplings (with replacement), we only found three instances in which the p-value was lower than the p-value observed in our original US CMT (subtracting the HPMV) vs. ARIC_EA analysis;. These data reinforce the notion that the background mutation load in these 58 neuropathy genes is specific to the population of neuropathy patients. To further investigate our observation of neuropathy gene mutation burden in neuropathy patients, we analyzed WES data from an independent cohort of 32 patients (30 families) from Turkey with a clinical diagnosis of CMT.
Exome sequencing We performed whole-exome next-generation sequencing according to previously published methods  [Lupski et al. , 2013 ; see Supplementary experimental methods for details], producing an average of 9. 25 Gb of raw data per exome and achieving ~93. 5x average depth of coverage (median coverage = 97x) per sample with >90% of the captured bases covered at 20x (Supplementary Table  3 ). Variant data generated will be released and deposited into the NCBI database of Genotypes and Phenotypes (dbGaP: http://www. ncbi. nlm. nih. gov/ gap) as part of the Centers for Mendelian Genomics research initiative.
Variant annotation pipeline Variant calling from the aligned BAM files was performed using the ATLAS  [Shen et al. , 2010]  and SAMtools suites  [Li et al. , 2009]. Annotation was performed using Sacbe, an inhouse developed annotation pipeline  [Gonzaga-Jauregui et al. , 2013]  based on ANNOVAR  [Wang et al. , 2010]  and custom scripts (see Supplementary experimental methods for details).
Acknowledgements This work was supported in part by  US National Institute of Neurological Disorders and Stroke (NINDS)  grant  R01NS058529  to JRL;  US National Human Genome Research Institute (NHGRI)  grant  National Heart, Lung, and Blood Institute (NHLBI)  grant  U54HG006542  to the  Baylor-Hopkins Center for Mendelian Genomics  and  US National Human Genome Research Institute (NHGRI)  grant  U54HG003273  to RAG; by seed funding from the  Center for Human Disease Modeling, Duke University  and by  P50 MH094268  to NK. AA is supported by a grant from the  Muscular Dystrophy Association  ( MDA294479 ). WKW is supported by the  Career Development Award   K23NS078056  from  NINDS. TH is supported by the  T32 GM07526-37   Medical Genetics Research Fellowship Program. LBG was supported by the  NIH   Cellular and Molecular Biology Training Grant  ( GM007315 ), the  NIH.
B. Components The software is built on 4 main components (Figure  [1] ): 1. web server: manages the API and the GUI. Basically it will only store new job requests and manage queries for the current status of the jobs. 2. scheduler: it is in charge of taking pending jobs , order them according to the selected algorithm and submit them to the executor. It is also based on a plugin architecture to add other scheduling algorithms. A basic FIFO algorithm and Fair Share policy algorithm are provided. 3. Watcher: checks the running jobs status, kill requests, and update database accordingly. 4. Executor: implementations for Docker Swarm  [2]  and Mesos[3] are provided. Executor is in charge of executing the job on an available node based on requested resources. Additional executors can be added as a plugin.
Nodes management is left to the used framework (Swarm or Mesos). To record job data, two databases are used: MongoDB  [2]  and Redis. MongoDB is used for long term storage while Redis is used as storage for running jobs for quicker access. InfluxDB [7] is optionally used to record time-based related data (accounting, number of jobs, mean cpu, mean ram, …) Cadvisor [6] is also an optional component to get live usage statistics for the container. At last, a shared directory is used to store job logs and data. A default pair-tree implementation is available but other storage strategies can be easily added.
IV. JOBS IN CONTAINERS When a job is executed, it is executed in a Docker container on an execution node, selected according to the job requirements (cpu, ram, queue constraints). A container runs an image (selected by user) on the node host, but the image runs the selected operating system (running on the host Linux Kernel). This choice provides the user the possibility to execute jobs in different OS/configurations or images, containing the software needed for their job, and use the increasing number of containers available. In the container, the executed command can create new processes, they will run in the same container and within the same boundaries. This prevents different jobs overlapping or over using the same resources. If job is killed by user, for example, all container processes will be killed, leaving the node in a clean state.
Go-Docker offers the possibility for the user to run the task as root or with his Linux id/gid in the container (left to administrator choice). If user is root in the container, he can install new libraries etc. in the system. If user decides to be root, different ACLs will be used and the volumes (directories) selected by the user to be mounted in the container may be either rejected or mounted in read-only mode. Several directories can be mounted (according to configuration) in the container. This will usually map to user home directory and other common directories (common data, …). They can be mounted as read-write or read-only. The authentication plugin will check the user requirements and adjust them. An additional directory is mounted, unique, specific to the job. As root, this is the only place where user can write persistent data.
Assuming the relevant Higgs boson couplings are independent of the energy scale of the Higgs production, a combination with the on-shell measurements of ZZ and WW in the same dataset yields an observed (expected) 95% CL upper limit on Γ H /Γ SM H in the range 4. 5-7. 5 (6. 5-11. 2) under the same variations of the background K-factor. Assuming the value of R B H * =1 and under the assumptions reported above, this translates into an observed (expected) 95% CL upper limit on the Higgs boson total width of 22. 7 (33. 0) MeV. Assuming that the total width of the Higgs boson is as expected in the SM, the same combination can be interpreted as a limit on the ratio of the off-shell to the on-shell couplings to gluons R gg = κ 2 g,off-shell /κ 2 g,on-shell. An observed (expected) 95% CL upper limit on R gg in the range 4.
Acknowledgments We are very thankful to  M. Bonvini ,  J. Campbell ,  S. Forte ,  F. Krauss ,  K. Melnikov ,  G. Passarino , and  M. Spannowsky  for their essential input in the estimation of uncertainties in the theoretical predictions for the signal and background processes and their interference. We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,.
3D datasets from the first experiment revealed excellent visualisation of the 50 μm beams and various discrepancies from the planned delivery dose were found. The optical CT PVDR measurements were found to be consistently 30% of the expected Monte Carlo values and deconvolution of the microbeam profiles was found to lead to increased noise. The reason for the underestimation of the PVDR by optical CT was attributed to lack of spatial resolution, supported by the results of the DOF characterisation. Solutions are suggested for the outstanding challenges and the data are shown already to be useful in identifying potential treatment anomalies. Keywords: optical CT, x-ray microbeam therapy, 3D dosimetry, PRESAGE ® S Online supplementary data available from stacks. iop. org/PMB/61/320/ mmedia (Some figures may appear in colour only in the online journal).
Results High quality image datasets were acquired for each sample and the microbeams are easily visualised. The reconstructed 3D datasets can be visualised in different ways to provide an assessment of the irradiation. Figure  2  shows a maximum intensity projection (MIP) image of a reconstructed image volume of sample 1. 1, with the full dataset available in movie format as online supplementary material (stacks. iop. org/PMB/61/320/mmedia). This type of visualisation is very powerful, allowing the user to assess quickly that the pencil beam irradiation has been delivered successfully and is well centred within the sample. Confirming this would be very difficult using 2D dosimeters. Figure  3  shows a reconstructed slice through sample 1. 2, irradiated with an interlaced dose pattern. More views are available in supplementary video material (stacks.
25) mm. Using the full matrix size of the new camera across the width of the sample allows significantly higher sampling frequency over previous reports, with an isotropic reconstructed voxel size of (5. 2 μm) 3. A smaller matrix height of 256 pixels was chosen for reduced acquisition and reconstruction times. The large computer RAM of 256GB allows readout of the large amounts of data simultaneously making these scans a manageable 1 hour long. The projections were acquired with the largest DOF setting (smallest NA, setting A1), reflecting the 'ideal' focal situation of constant resolution across the entire sample. The samples were scanned at depths of 0. 3 cm, 1 cm and 4 cm for comparison with Monte Carlo and film measurements of PVDR (Martinez-Rovira et al 2012).
25) mm. The lens aperture was set to A4 which had the largest DOF for 10 μm resolution (see table 2). When images were reconstructed, in principle a bead at point (x, y, z) has experienced the same optical blur as at point (x, y, z + Δz). Assuming this, a bead in the centre of the sample in good focus was used as a PSF during Richardson-Lucy deconvolution of the microbeam profile axially above it at a depth of 0. 3 cm in PRESAGE ®. Deconvolution was performed in the IDL software environment (Exelis Visual Information Solutions, Boulder, CO) using the 'deconv' tool  (Varosi et al 1993) , assuming Poisson noise. The PVDR from the resulting deconvolved profile was calculated for different numbers of deconvolution iterations.
• Further improvements in image SNR and extensions to the dynamic range of acquired data would be achievable via the acquisition of multiple datasets with different levels of illumination  (Krstajic and Doran 2007, Thomas et al 2011). The results of section 3 suggest that accurate PVDR measurements are not possible with the current optical CT arrangement. Given the results of the knife-edge measurements of MTF along the optical axis, we believe the underestimation of the PVDR by optical CT is primarily due to lack of true spatial resolution. Attempts to improve the resolution by increasing the numerical aperture and deconvolution using a measured PSF lead to unreliable and noisy results, making these methods unsuitable for clinical measurements of PVDR. It remains an open question as to whether the optical CT technique will in future prove capable of making measurements of PVDR that are competitive with other methods such as those employing 2D TLD films or MOSFET detectors.
Acknowledgments The authors gratefully acknowledge the allocation of beam time at  ESRF  and associated funding for experiment MI-949,  Chris Bunton  for machining samples and creating the sample holder,  CRUK  and  EPSRC  support to the  Cancer Imaging Centre at ICR  and  RMH  in association with  MRC and Department of Health   C1060/A10334 ,  C1060/A16464  and  NHS  funding to the  NIHR Biomedicine Research Centre  and the  Clinical Research Facility in Imaging.
Several quality levels are available in the AERONET database (http://aeronet. gsfc. nasa. gov/); in this study the level 2. 0 is used. The PM 10 surface concentrations are measured by the EEA (European Environmental Agency  Guerreiro et al. , 2013)  running the AirBase database. It contains hourly surface concentration measurements and information submitted by the participating countries throughout Europe (http://www. eea. europa. eu/).
These aerosols are represented using nine bins with diameters ranging from 40 nm to 20 µm. The life cycle of these aerosols is completely represented, with nucleation of sulfuric acid, coagulation, adsorption/desorption, wet and dry deposition and scavenging. The scavenging is represented both by coagulation with cloud droplets and precipitation. The formation of SOA is also taken into account. The anthropogenic emissions are estimated using the same methodology as the one described by  Menut et al. (2012)  but with the HTAP (Hemispheric Transport of Air Pollution) masses as input data. These masses were prepared by the EDGAR Team, using inventories based on MICS-Asia (Model Intercomparison Study Asia), EPA-US/Canada (Environmental Protection Agency), and TNO (Nederlands Institute Toegepast Natuurwetenschappelijk Onderzoek) databases (http://edgar.
jrc. ec. europa. eu/htap_v2/index. php?SECURE=123). Biogenic emissions are calculated using the MEGAN scheme  (Guenther et al. , 2006 ) that provides fluxes of isoprene, terpene, and pinenes. In addition to this version, several processes were improved and added in the framework of this study. First, the mineral dust emissions are now calculated using new soil and surface databases, as described by  Menut et al. (2013b). Second, emission fluxes produced by vegetation fires are estimated using the new high resolution fire model presented by  Turquety et al. (2014). And finally, the photolysis rates are explicitly calculated using the FastJ radiation module  (Wild et al. , 2000)  fully described by  Mailler et al. (2015).
Acknowledgements. This study was partly funded by the  French Ministry in charge of Ecology. We acknowledge  Francois Dulac  (IPSL/LSCE) and  Marc Mallet  for their coordination of the  CHARMEX program  and the  ADRIMED  project, respectively. We thank the  EEA  for maintaining and providing the AirBase database of pollutant surface concentrations over Europe. We thank the principal investigators and their staff for establishing and maintaining the AERONET sites used in this study:  Didier Tanré for Banizoumbou ,  Capo Verde  and Dakar;  Bernadette Chatenet  and  Jean-Louis Rajot  for Zinder and Cinzana;  Daniela Meloni  and  Alcide Di Sarra  for Lampedusa. We acknowledge the  Service d'Observation PHOTONS/AERONET  and the AERONET-ACTRIS TNA supporting the AERONET activity in Europe. We acknowledge the E-OBS data set from the  EU-FP6  project  ENSEMBLES  (http://ensembles-eu.
The code was tested and, with the same parameters as in Ref. [20] , we have reproduced their results. However, their combined fit of the charmonium data taken at the Tevatron and the LHC cannot reproduce well the low-energy data measured by PHENIX collaboration  [51]  at RHIC. Since the collision energy of RHIC √ s = 200 GeV is very close to the centre-of-mass energy of the fixed-target experiment at the LHC (AFTER@LHC), i. e. √ s = 115 GeV, we prefer to use the PHENIX data alone to determine the parameters in Eq. (9). A fit of d 2 σ/dP T dy to the PHENIX data  [51]  for J/ψ and ψ(2S ) production gives the χ 2 results presented in Tab. 2a having fixed n = 2 and P T = 4. 5 GeV. We also show the comparisons of the P T spectra in Fig. 1a-c. The large χ 2 for the single J/ψ production can be reduced to 55.
Acknowledgements This work is supported in part by the  France-China Particle Physics Laboratory (FCPPL)  and by the  French CNRS  via the grants  PICS-06149   Torino-IPNO ,  FCPPL-Quarkonium4AFTER & PEPS4AFTER2. H. -S. Shao  is also supported by the  ERC  grant  291377  "LHCtheory: Theoretical predictions and analyses of LHC physics: advancing the precision frontier".
, 2007]. However, none of these stations is situated in continental tropical regions. Spaceborne measurements of COS by other instruments are rare or cover low latitudes only poorly  [Farmer et al. , 1987; Barkley et al. , 2008; Kuai et al. , 2014]. So far, observations of COS by the spaceborne Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) have been reported for a restricted time period (August 2003) only  [Burgess et al. , 2004]. We analyze the global distribution of COS and its seasonal cycle by the use of the whole MIPAS data set covering the years 2002-2012. We compare the MIPAS observations with simulations of the Parameterized Chemical Transport Model (PCTM)  [Kawa et al. , 2004; Berry et al. , 2013]  and of the European Centre/Hamburg/Modular Earth Submodel System Atmospheric Chemistry (EMAC) model  [Jöckel et al.
10. 1002/2015GL066293 Observations campaign  [Wofsy et al. , 2012] , the Arctic Research of the Composition of the Troposphere from Aircraft and Satellite (ARCTAS) campaign  [Fuelberg et al. , 2010] , the Intercontinental Chemical Transport Experiment-Phase B (INTEX-B) campaign  [Molina et al. , 2008] , and with additional airborne data of the National Oceanic and Atmospheric Administration (NOAA) obtained above the midcontinental United States. Generally, the COS amounts measured by MIPAS agree well with the airborne data. The deviations are mostly less than 20 parts per trillion by volume (pptv) and increase to 50 pptv or more on rare occasions only. A more detailed presentation of these intercomparisons is given as supporting information.
Acknowledgments The authors like to thank the European Space Agency for giving access to MIPAS level-1 data. Meteorological analysis data have been provided by ECMWF. This work is a contribution to the EU Strato-Clim project (grant 603557). Part of this work was supported by the German Federal Ministry of Education and Research through the project ROMIC-THREAT (BMBF-01LG1217B). We acknowledge provision of airborne in situ COS data of the HIPPO-campaign (E. L. Atlas, S. A. Montzka, and J. W. Elkins), the ARCTAS-and INTEX-B campaigns (D. R. Blake), and of the National Oceanic and Atmospheric Administration (S. A. Montzka and C. Sweeney). MIPAS COS data can be accessed via http://www. imk-asf. kit. edu/english/sat. php.