dataesr/scientific-paragraphs-categorization huggingface dataset:
ACE, however, has two main improvements concerning linking code-like terms with code elements. First, they developed an island parser based on constructs in the Java Language Specification in order to identify code-like terms from free-form text and code fragments. Island parsers are generated from island grammars-grammars that consist of detailed productions that describe the language constructs of interest (the islands), and liberal productions that catch the remainder (the water)  [55, 56]. Therefore, ACE is able to identify code-like terms from source code that did not compile, different to RecoDoc that uses PPA creating a dependence on the Eclipse Java compiler. Second, ACE does not rely on a list of code elements previously known as RecoDoc does. Instead, ACE identifies code elements and creates an index of valid elements based on the elements contained in the collection of documents.
Then, ACE reparses each document extracting unqualified, ambiguous terms and resolves these for their corresponding code elements by using the context of the terms. Therefore, ACE is also able to identify code elements in informal documents from multiple APIs. Subramanian et al. [57]  proposed Baker, a tool for automatically generating links between type references, method calls, and field references identified in source code examples to API documentation. To solve ambiguities, they proposed a process called deductive linking. It generates an incomplete abstract syntax tree (AST) for the source code being analyzed, and uses information from an oracle (dictionary) for Java and Javascript to deduce facts about the AST. Baker performs this deduction iteratively, where in each iteration, the tool performs a depth-first traversal of the AST and examines all nodes of interest: nodes involved in declarations, invocations, and assignments.
NEEDS For the reasons outlined above, new monitors are currently being developed within the Radiation Instrumentation and Logistics section in the framework of 'CROME project'. Included in the scope of the project may cite: • Development of novel read-out electronics that will be capable of measuring very low dose rates at levels beyond the present state of the art whilst being able to measure radiation over an extensive range without any auto scaling. The continuous measurement is streamed towards a central software supervision system. • As radiation protection equipment, the system shall be capable to trigger visual and audible alarms if the monitored dose rate exceeds a preset threshold (Cf. Figure  1 ). Furthermore, the system shall be capable to interlock machinery in case of high radiation levels (i.
SYSTEM ARCHITECTURE The CROME project is one of the projects of the RAMSES program and aims at developing high performance, cost effective, low maintenance radiation monitors for CERN. The project encompasses the design, the conception, the development and the series production of a new generation of read-out electronics for radiation protection monitors (CROME Measuring and Processing Unit, CMPU) as well as alarm units (CROME Alarm Unit, CAU) and uninterruptible power supply (CROME Uninterruptible Power Supply, CUPS) as illustrated in Figure  1.
The FPGA part of the SoC  (4)  is commonly referred as Programmable Logic, while the dual ARM Cortex-A9 is referred as the Processing System. The first part is used for low level or low latency functionalities: • Communication with digitalisation circuits and fast commutation chips. • Instrumentation of environmental sensors such as temperatures or humidity. • Generation of interlock signals. • Triggering of alarms. The FPGA ensures the extremely short response time functions, unobtainable with a simple microcontroller. The processing part integrates high-level functions, manages the TCP/IP communication and the logging of data on a mass media storage. It runs a reliable and fully customisable distribution of Linux for embedded system. Parameters and data are exchanged between the processing system and programmable logic using a dedicated AXI bus.
Such a mechanism would require a highly dynamic structure. Both our live-cell data and a previous report  20  provide strong experimental evidence to show the highly dynamic nature of NEMO oligomers. Indeed, this may further explain the modest reduction (Fig. 6a ) in open-ended lattices in IL-1-treated cells, in which we would expect to observe some latency in the reformation of the lattice. Instead, a rapid and highly dynamic feedback mechanism is implicated in the reformation of the NEMO lattice structure on IL-1 induction. It would therefore be highly valuable to further investigate the nature of this dynamic interaction between inactive and activated cellular states with the advancement of such technologies in a broader study. In particular, NEMO ubiquitination-which corresponds to a covalent attachment of non-degradative polyubiquitin chains (linear and K63) to NEMO-could considerably enhance the conversion of preformed open-ended NEMO lattice structures into activating centres, where specific ubiquitin ligases are presumably present.
Methods Structural modelling of NEMO. A modelling of the human dimeric NEMO protein (residues 49-419) with K63 di-Ub chains was undertaken based on COILS  49  and PAIRCOIL2 (ref. 50) coiled-coil prediction algorithms and the following crystal and NMR structures of NEMO fragments: kinase-binding domain (PDB ID: 3BRV, residues 49-109); Helical domain 2 (HLX2, PBD ID: 3CL3, residues 193-249), coiled-coil domain 2-leuzine zipper (CC2-LZ, PDB ID: 2V4H and 4BWN, residues 257-344) and ZF (PDB ID: 2JVX, residues 392-419). All regular heptad phases (abcdefg) n within the predicted fragment of NEMO (residues 49-364), as well as coiled-coil discontinuities such as stutters (deletion of three residues) and stammers (deletion of four residues), were correctly predicted using PAIRCOIL2 when compared with the heptad phases identified from available crystal structures by the coiled-coil analysis programme SOCKET  51.
This disordered fragment was manually generated and refined from initial atomic coordinates derived from a I-TASSER-generated model based on threading algorithm  53. The NEMO:IKKb:K63-di-Ub complexes (WT and Mu) were built by homology modelling using as structural templates the crystal structure of the mouse CC2-LZ in complex with a K63 di-Ub chain (PDB ID: 3JSV  54  ) or the modelled NEMO ZF in complex with a K63 di-Ub chain  55. This structural modelling of NEMO is consistent with a recent in vitro EPR study showing that NEMO forms a parallel coiled-coil dimer in solution  20. The incomplete crystal structure of the human IKKb (residues 16-665, PDB ID:4E3C) and its short C-terminal fragment bound to the NEMO N terminus (NDB, residues S705-Q743, PDB ID:3BRV) were previously described  15, 56.
Image acquisition. Samples were imaged on a custom-built PALM/STORM microscope as described before  57. For each coverslip, preselected fields of view were chosen typically containing one cell. We developed the automated acquisition sequence based on the open-source Micro-Manager microscopy acquisition software  58. First, the field of view was centred to include as much of the cytosol as possible, to maximize the area acquired over the flatter cell membrane. Cells were then imaged by TIRF illumination with a 488 nm, 568 nm and/ or 647 nm laser. We then performed a SRLM image-stream acquisition for the 647 channel (635 nm laser-excitation at 1. 7 kW cm À 2 , 662-690 nm emission) or the 568 channel (561 nm laser excitation at 2. 4 kW cm À 2 , 589-625 nm emission), each composed of 20,000 images acquired at 33 ms intervals.
Dual dSTORM images were generated by first performing an acquisition as described above in the 647 channel, followed by the 568 channel. Imaging parameters were set using Micro-Manager running on a desktop PC. Laser control was achieved with custom software  57. Before particle detection and localization, fiduciary beads were identified and marked, typically 1-5. Sample drift was then calculated by tracking the group displacement of selected beads throughout the acquired image sequence. After particle detection and localization, the position of each particle was readjusted by subtracting the drift identified at the corresponding time point. The diffraction limited z-stack acquisition was performed at 300 nm z-steps encompassing the size of the cells. This was performed to determine the proper localization and expression levels of the proteins of interest in the analysed cells.
Final super-resolution reconstructions were then generated in Fiji  59  through the QuickPALM algorithm  57  by creating images with 20 nm pixel size and additively superimposing a Gaussian kernel of 20 nm full-width half-maximum to each particle. Live-cell super-resolution image reconstruction. Final super-resolution reconstructions were generated in Fiji 59 through a custom-made SOFI-based algorithm using fourth-order SOFI and generating images with 40 nm pixel size. Superresolution Movie 1 analysed through the custom NanoJ-SRRF V1. 0 ImageJ plugin is available upon request to the authors. Quantitative analysis. Protein cluster segmentation was performed through a dedicated algorithm implemented in Jython, running within the Fiji software  59. Here the algorithm uses a Huang threshold method  60  to segment clustered particle detections, this approach showed optimal performance in the identification of NEMO particle clusters.
Acknowledgements This work was funded by the  Department of Science and Technology of South Africa. A. F. S. is funded by a  Young Researcher Establishment Fund ,  CSIR  ( YREF 2015 009 ) and J. S. by the  National Research Foundation Professional Development Programme Fund  ( NRF PDP ). M. M. M. and R. H. are funded by  PTDC/SAU-GMG/115652/2009  from the Fundac¸a ˜o para a Ciencia e a Tecnologia (FCT, Portugal). R. H. is funded by grants from the  UK Medical Research Council  ( MR/K015826/1 ) and  UK Biotechnology and Biological Sciences Research Council  ( BB/M022374/1 ). F. A. acknowledges funding from  Institut Pasteur : Citech, Global care initiative and Institut Carnot Pasteur MI. We thank  Practical Fishkeeping  (accessible at http://www. practicalfishkeeping. co. uk/) for permission to modify their image as a thumbnail image.
( 3 ) The kurtosis is the fourth-order standardized moment. When calculated instantaneously on the energy it is given by the following equation: 𝐾 (𝑡) = 𝐸 [𝑒 (𝑡) -𝜇 𝑒 (𝑡)] 4 𝜎 𝑒 (𝑡) 4 , (4) where   () and   () are the instantaneous mean and standard deviation of the energy while [] denotes the expected value. In order to complete the detection on the skewness and kurtosis signals, a threshold has to be set in order to pick up the peak signals. We decided to establish a databased threshold for the skewness and kurtosis signals from their respective means   and   and respective standard deviations   and. This threshold is defined as   =   +   for skewness and   =   +   for kurtosis, where  is a parameter whose value is adjusted using an optimization training phase in a manner that increases the system's sensitivity and specificity (refer to Results section).
The Holter System and the Protocol TCD is a noninvasive, nonionizing, inexpensive, portable, and safe technique, which renders it as a convenient tool for the detection of cerebral microemboli. Long time probe positioning and the short effective examination duration are the main limitations of traditional TCD systems. The Transcranial Holter (TCD-X, Atys Medical, Soucieu en Jarrest, France) shown in Figure  3  allows prolonged patient monitoring (higher than 5 hours) with the patient no longer attached to a TCD and does not need to be laying on a bed but rather can be monitored under naturalistic conditions. The Holter is equipped with a robotized automatic probe that helps find the best TCD signal and tracks it automatically during the whole recording. A database obtained from the Centre Hospitalier Régional Universitaire (CHRU) de Lille (2 Avenue Oscar Lambret, 59000 Lille, France) is used.
Results The different detectors are tested through algorithms we developed using the numerical calculation software Matlab (Mathworks, Natick, MA, USA). Our database is composed of 18 recorded signals divided into two categories. The first (i) Sensitivity (or Detection Rate) calculated as the number of true positive detections/the number of gold standard detections. True positive detection refers to the detection of an embolus recorded in the gold standard. (ii) Specificity calculated as 1 -False Alarm Rate (FAR) the latter being the number of false positive detections/the total number of detections. False positive detection refers to the detection of an embolus not recorded in the gold standard or in other words an embolus which has not crossed the sample volume.
Acknowledgments This research was financially supported by the  Doctoral School of Sciences and Technology, Lebanese University, Lebanon , the  National Council for Scientific Research CNRS, Lebanon , and the  French Institute of Health and Medical Research INSERM, France. The authors would like to sincerely thank  Marilys Almar  and  Benoit Guibert  for the Holter TCD equipment. They also acknowledge  Corinne Gautier  for the Holter Doppler recordings.
1  are given in the Appendix B. The Type 1 MUSICA NDACC/FTIR product consists of water vapour profiles for the lower, middle, and upper troposphere (DOFS of almost 3). The Type 2 product offers consistent H 2 O-δD pairs, which are sensitive to the lower and the middle troposphere, whereby it is possible to reasonably separate both altitude regions (degree of freedom for signal, DOFS, is typically about 1. 8). Error estimations are discussed in detail in  Schneider et al. (2012). The random error is about 2% for H 2 O and 25‰ for δD. Already for small uncertainties of 1% and 2% for line intensity and pressure broadening parameters the systematic errors in H 2 O and δD "profiles" can reach 10% an 150‰, respectively. An empirical study indicates that for the v2015 data the bias is actually much smaller (see Sect.
Uniform apriori settings For v2015 we work with the same globally constant water vapour isotopologue apriori data for all ground-based retrievals (different globally distributed NDACC/FTIR stations) and for all MetOp/IASI retrievals (for the whole globe). Thereby we assure that observations at different locations are not affected by the use of different apriori and therefore an interpretation of regional differences (e. g. latitudinal gradients) becomes rather straight-forward. This is a further development of the previous retrieval versions, where we used different apriori data for the (sub-)tropics, the mid-latitudes, and the polar regions. The used apriori profiles are mean values of LMDZiso calculations  (Risi et al. , 2012b)  and are as depicted in Fig. 2  of  Lacour et al. (2012). As H 2 O apriori variability we assume 75% in the boundary layer, 150% in the middle and upper troposphere, and 30% in the stratosphere.
Meas. Tech. Discuss. , doi:10. 5194/amt-2015 -330, 2016  Manuscript under review for journal Atmos. Meas. Tech. Published: 18 January 2016 c Author(s) 2016. CC-BY 3. 0 License. In this section we show that the MUSICA v2015 remote sensing products are well calibrated with respect to the reference data. We would like to remind that there is no similar study where the accuracy of simultaneous tropospheric H 2 O and δD remote sensing observations is documented by such direct comparison to coincident reference profiles.
The references are constructed from the in-situ profile measurements (surface up to ceiling altitude of 6-7 km) and a climatology for higher altitudes by convolution with the FTIR averaging kernels (for the climatology at higher altitudes we use the apriori data as discussed in Sect. 2. 3. 3). The technical details for this comparison are the same as for the exemplary study of  Schneider et al. (2015). The large error bars on the reference data are mainly due to the unknown humidity and δD values above the aircraft's ceiling altitude (for a more detailed discussion see Appendix C). We observe a reasonable correlation and no significant bias between the reference data and the MUSICA NDACC/FTIR v2015 data. We also see variations in the FTIR data measured between 8:15 and 9:45 UT on a single day, which cannot be attributed to changes in the averaging kernels (there is no similar variation seen in the smoothed reference data).
These water masses are strongly depleted in HDO, which indicates rain re-evaporation or gradual dehydration (Rayleigh distillation) after evaporation over a warm Ocean. For SAL conditions (red contours) the air is also humid but HDO is significantly enriched if compared to the typical Rayleigh distribution. This can be well explained by the mixing of planetary boundary layer humidity with middle/upper tropospheric humidity. The middle panels show the H 2 O-δD contours as obtained from the remote sensing data sets (Type 2, i. The IASI Type 1 distributions for SAL conditions (red contours) is relatively close to the Rayleigh line, which is again in disagreement to the in-situ reference (there and in the corresponding IASI Type 2 distribution the data points group around a mixing line). Similar observations are made for the NDACC/FTIR Type 1 product.
, 2015). We require that the measured and modelled XCO 2 data agree within 2%. The number of stations contributing to the MUSICA activities is gradually increasing and the data sets have been updated. Table  5  gives an overview of the NDACC/FTIR sites that now contribute to the MUSICA activities. The stations are well distributed from the Arctic to the Antarctic and in some occasions offer data since the late 1990s. A further extension of this data set to other sites or for some stations to measurements made in the beginning of the 1990 is feasible but has not been possible with the funds available for the MUSICA project. The NDACC/FTIR activities complement the surface-based in-situ isotopologue monitoring activities. While the data obtained from the latter represent near surface small-scale variations which are often difficult to be captured by models, the MUSICA NDACC/FTIR isotopologue data are representative for different altitudes and for larger scale processes (the data represent vertical layers averaged over 2-5 km, see typical averaging kernels in Fig.
3  of  Schneider et al. , 2015). Due to their long-term data characteristics the NDACC/FTIR data are in particularly interesting for climatological studies. The upper panels show Type 2 data. For the subtropical North Atlantic (left panel) the seasonal cycle can be well understood by the seasonal distribution of the different pathways as discussed in Sect. 4 in the context of Fig. 4 : in summer (JAS) the vapour is most enriched in HDO. This is due to the SAL events that are then most frequent. After summer/autumn (OND) the free tropospheric vapour concentrations are occasionally as high as during summer, but significantly more depleted in HDO, which can be understood by moisture transport from the lower troposphere of lower latitudes. In winter (JFM) the free troposphere is The lower panel of Fig.
This comparison between the Type 1 and Type 2 results again demonstrate the difficulty of correctly interpreting the Type 1 product, because there H 2 O and δD represent different air masses. We strongly recommend the use of Type 2 products where H 2 O and δD represent the same air mass (optimal estimated H 2 O-δD pairs). Water vapour isotopologue remote sensing products are complex data products. In this paper we review the progress achieved within the MUSICA project. The developments made within the MUSICA framework allow for generating remote sensing H 2 O-δD pairs for the troposphere with a high and well-understood data quality and consistent to in-situ references. We present the final version (v2015) of the MUSICA ground-and space-based remote sensing product (generated from NDACC/FTIR and MetOp/IASI spectra, respectively).
Compared to previous versions, v2015 improves the consistency between the different locations (uniform apriori for all retrievals and improved spectral windows for the NDACC/FTIR retrievals). Furthermore, for v2015 we calibrate the spectroscopic parameters in agreement with the uncertainty ranges as given in the HITRAN database. This calibration significantly reduces the δD bias in the remote sensing products. While in the previous MUSICA versions we found strong indications of a bias between +25‰ and +70‰, the bias is almost negligible for the v2015 data. This comprehensive bias assessment has been possible by using the unique H 2 O and δD profile references generated during the summer 2013 MUSICA campaign between the surface and 7 km altitude in the surroundings of Tenerife Island and in coincidence with the remote sensing observations.
We stress the importance of using the optimally estimated H 2 O-δD pairs (we call it Type 2 product, which is obtained from an aposteriori processing) instead of using data obtained by an individual optimal estimation of H 2 O and δD (Type 1 product). Usage of the latter manifests in artificially too shallow slopes in the H 2 O-δD plots, which can easily result in misleading interpretations of the H 2 O-δD plots. This is especially the case for the IASI product due to the limited sensitivity with respect to δD. We show that the space-and ground-based MUSICA v2015 data are rather consistent on global scale. First, there is no significant bias between both data sets and second, the space-and ground-based products consistently detect extremes in the H 2 O-δD distribution at different globally distributed locations.
This suggests that the calibrations and validations with respect to the in-situ references (which are limited to the subtropics) have global validity. While NDACC/FTIR provides long-term data records, which are very important for climatological studies, MetOp/IASI offers high horizontal resolution, on quasi global scale and morning as well as evening observations. We discuss examples of seasonal cycles as obtained from the NDACC/FTIR H 2 O-δD climatology and demonstrate the potential of IASI for detecting the diurnal variability in the moisture pathways using the summertime Sahara as exemplary area. The few examples reveal that the validated and well-characterised ground-and space-based remote sensing H 2 O-δD pairs can make a significant contribution for investigating moisture transport pathways on different scales.
Atmos. Meas. Tech. Discuss. , doi:10. 5194/amt-2015-330,2016 Manuscript under review for journal Atmos. Meas. Tech. Published: 18 January 2016 c Author(s) 2016. CC-BY 3. 0 License. Acknowledgements. This study has been conducted in the framework of the project  MUSICA  which is funded by the  European Research Council  under the European Community's  Seventh Framework Programme  (FP7/2007-2013) /  ERC  Grant agreement number  256961. E. Sepúlveda  is supported by the  Ministerio de Economía and Competitividad of Spain  for the project  NOVIA  ( CGL2012-37505 ). The aircraft campaign has been co-funded by the project  MUSICA  and the  Spanish national project AMISOC  ( CGL2011-24891 ). We are grateful to  INTA Aerial Platforms , a branch of the  Spanish ICTS program , and the  Spanish Air Force  for their efforts in maintaining and operating the aircraft.
The AERONET sun photometer at Izaña (PI:  Dr. Emilio Cuevas ) has been calibrated within AERONET EUROPE TNA supported by the  European Community Research Infrastructure Action  under the  FP7 Capacities program  for Integrating Activities, ACTRIS grant agreement number  262254. The Izaña aerosol in-situ measurements are part of the project  POLLINDUST  ( CGL2011-26259 ) funded by the  Minister of Economy and Competitiveness of Spain. We thank all the personal from the  Izaña Atmospheric Research Center (IARC) of the Agencia Estatal de Meteorología (AEMET). Our study has strongly benefitted from this great support and important measurements have been made in IARC research facilities. We acknowledge the support by the  Deutsche Forschungsgemeinschaft and the Open Access Publishing Fund  of the  Karlsruhe Institute of Technology.