dataesr/scientific-paragraphs-categorization huggingface dataset:
Further sensitivity tests with B4F forced with higher CO 2 levels suggest that NPP could be up to 8 PgC yr -1 higher at certain time slices (see Supplement Fig. S1 ). Changes in NPP will likely affect terrestrial carbon storage, which in turn influences the stable carbon isotope composition (δ 13 C) of seawater because terrestrial organic carbon is depleted in 13 C. Various early modelling studies and databased reconstructions produced a range of 270-1100 PgC decrease in terrestrial carbon storage during the LGM compared with pre-industrial time (see summary Table  1  in  Köhler and Fischer, 2004 ). These early estimates were unreliable, however, because (a) they do not account for variation in carbon storage within biomes and (b) they neglect the substantial influence of atmospheric CO 2 concentration on carbon storage (see  Prentice and Harrison, 2009 , for a fuller discussion).
Acknowledgements. W. A. Watts  is thanked for his contribution to counting the Monticchio pollen record. The authors would like to acknowledge funding from the  NERC QUEST programme  (grant  NE/D001803/1 ) and  NCAS-Climate. J. S. Singarayer  and P. J. Valdes acknowledge the  British Broadcasting Corporation (BBC)  for funding initial climate simulations. Edited by:  H. Fischer.
Service providers were represented by their geocoded professional addresses (latitude, longitude) through Batch Geocoder [http:// dehaese. free. fr/Gmaps/testGeocoder. htm]. In addition, the positional accuracy of geocoded addresses is given by the variable "accuracy". We were satisfied where "accuracy" is greater than or equal to 6, for which we have location information accurate down to street precision. Where this is not the case, we refine the address and repeat the procedure. Street level result is a precise geocode according to The Google Geocoding API [https://developers. google. com/maps/documentation/geocoding/].
Distance computing The travel distance between offer (healthcare providers) and demand (patient) is a key component in computing accessibility using the E2EFCA method. We chose the car travel time between the location of each health professional and the IRIS centroid (defining the location of the patients), calculated by Google Maps. Since we had more than 24 million origins (IRIS centroid)On the basis of the E2SFCA algorithm, two steps/destination (one given healthcare provider) location pairs, we used the FILENAME statement and the URL access method within SAS to access Google Maps, and extracted both the driving time and distance each time the site was accessed  [26].
Integration of generic GigE vision cameras The necessity of pixel stream access meant that rather than using the closed-source Imperx SDK, we instead opted to use an open source GigE vision library (Aravis) with modification to provide low latency pixel stream access, i. e. the ability to access User Datagram Protocol (UDP) packets (and thus begin data processing) as soon as they arrive, rather than waiting for the full frame. The GigE Vision protocol is based on UDP packets, which are inherently unreliable: the Linux Kernel can drop packets if processing load gets too high. Therefore, after investigation, it was determined that to operate these cameras reliably, a hard real-time kernel was necessary, and that the compute thread responsible for reading camera pixels must be restricted to run on CPU cores directly attached to the network card (by setting the thread affinity), with an elevated priority.
Integration of the OCAM2S LGS WFS Due to the urgent need to replace the Bobcat camera because of high readout noise, and to the very limited time available, a pragmatic approach was taken to integrate the OCAM2S camera. This camera has a Camera Link interface, and was (for the frame grabbers that we had available) restricted to full frame operation, i. e. pixel stream access was not possible. We therefore used a Camera Link to 10GigE Vision converter (an iPort CL-Ten from Pleora), and so could use our existing GigE Vision interface module for DARC. The additional latency introduced by the iPort was negligible, at the micro-second level, and the maximum CANARY frame rate remained restricted by the NGS WFS.
Missing camera data and dropped frames For an AO system with multiple camera inputs, it should be assumed that least occasionally, WFS data will fail to arrive at the RTCS: even in the case of perfect hardware, random events such as cosmic ray events could interfere. During CANARY phase A commissioning, we discovered that the NGS WFSs would regularly deliver partial frames, or insert an extra pixel into a frame. Initially, this occurred about once every 4 minutes. However, in later phases of CANARY, the frequency increased, and became less regular. Our understanding is that this is due to the implementation of the non-standard pFPDP interface, and is beyond our control. Therefore, a software fix, within the RTCS, was required. When using UDP-based cameras (i. e. GigE Vision), packet loss is inevitable at some point, and so must be planned for.
Software handling of incorrect camera data Correct detection of incorrect camera data is key to AO performance. There are several cases that we have experienced, and thus consider within the DARC WFS interface modules. (i) Dropped pixels: the data frame will be shorter than expected. This condition will be detected when the next startof-frame signal arrives, after pipeline computation from all other WFSs has completed. (ii) Inserted pixels (e. g. a single pixel being duplicated and thus received twice): the end-of-frame signal will be incorrect. This condition will be detected at the end of the frame, probably after pipeline computation has completed, i. e. wavefront reconstruction will have been performed based on corrupt data. (iii) Complete frame missing: the DARC interface will not receive any data for this frame.
Camera driver issues Over the operational period of CANARY, we have had a large number of different cameras interfaced to the CA-NARY RTCS system. Most of these cameras have relied on closed-source software drivers, and as a result we have experienced incompatibilities between required Linux kernel specifications and software stacks, particularly for older cameras which often do not see the related software updated for newer Linux kernels. For operation at phase A, we obtained (under a nondisclosure agreement) source code for the sFPDP receiver card used for capture of NGS pixels. This was then essential at phase C when we upgraded the RTCS server, to allow the sFPDP interface to continue to work with a newer Linux kernel. EMCCD camera control was performed using the standard camera interface card from Andor Technologies, which has good driver support.
The sCMOS camera has a Camera Link interface, and requires closed source drivers for the frame grabber, as well as for the camera SDK (from different manufacturers). As of 2015, these drivers have remained in active development, and it has been possible to continue to operate the camera with up to date Linux kernels. However, obtaining these drivers can be difficult. The Scimeasure controller for the CCID18 detector uses an AIA frame grabber card (necessary for camera control, even though we use sFPDP for receiving the pixel stream), requiring a PCI interface (which are becoming less common). Drivers are still available, though in binary format from the frame grabber manufacturer. A waveform compiler is also necessary, and exists as a Windows executable (which we use on Linux under Wine). As mentioned previously, our discovery of the opensource Aravis library for GigE Vision cameras, and our modification of it meant that any GigE Vision camera now has an interface to DARC which relies only on the presence of a network interface, rather than a commercial frame grabber.
DM driver issues The low order CANARY and DRAGON DM and tip-tilt mirrors are controlled using custom electronics driven from a commercial PCI DAC card. We have access to the source code for the drivers of this card, which has allowed us to make modifications for newer Linux kernels. The high order CANARY DM is operated using Ethernet, though this solution still required use of a closed source binary (at the user level, i. e. without any kernel drivers necessary). The Boston Kilo DM uses a PCIe fibre card, for which closed-source drivers are available for Linux. Fortunately, the Kilo DM drive electronics are modular, allowing a different DM interface to be used in the future, should the need arise.
Lessons learnt and key points for consideration Our extensive experience with WFSs and DMs for wide-field AO systems has provided us with several key considerations to be taken into account during the design of future AO systems. Many of the problems that we encountered were specific to CANARY, however the lessons that we learned are highly relevant for future AO systems. Closed source drivers and binary SDKs are problematic because of potential future incompatibilities with newer Linux kernels due to changes in the application binary interface specification, and should be avoided where possible. Systems using commonly available hardware interfaces such as Ethernet should be favoured, and pixel stream access will significantly reduce AO latency. Maximum cable lengths should also be given consideration.
ACKNOWLEDGEMENTS This work is funded by the  UK Science and Technology Facilities Council , grant  ST/K003569/1 , and a consolidated grant  ST/L00075X/1. D. Guzman  appreciates support from  FONDECYT  grant  1150369. Supported in France by  Agence Nationale de la Recherche  ( ANR ) program  06-BLAN-0191 ,  CNRS/INSU ,  Obs. de Paris  and  Univ. Paris Diderot  ; supported by  European Commission   FP7: E-ELT Prep. Infrastruct. 2007-1 Grant  211257 , OPTICON program Infrastructures 2008-1 Grant  226604  ( JRA1 ) and 2012-1 Grant  312430  ( WP1 ).
Urease phasing Experimental phasing was performed with SHELXC, SHELXD and SHELXE  (Sheldrick, 2010). A partially refined model was obtained by molecular replacement in Phaser  (McCoy et al. , 2007)  using PDB entry 4ceu  (Benini et al. , 2014) , followed by refinement in phenix. refine (Adams et al. , 2010). Known S-atom positions were obtained using this model and ANODE  (Thorn & Sheldrick, 2011). Phase errors against this structure were computed in SHELXE.
Hierarchical cluster analysis (HCA) The merging of sub-data sets based on hierarchical cluster analysis was performed as described by  Giordano et al. (2012)  using a new GUI  (Santoni et al. , unpublished work). This program reads the output of XDS (XDS_ASCII. HKL), calculates the correlation coefficients between each pair of data sets and saves them as a distance matrix. From this matrix the clustering dendrogram is generated and presented in an interactive GUI. By the selection of different nodes in this dendrogram, different combinations of data sets are generated and automatically processed in the background with XSCALE. The final correlation coefficient cutoffs are specified in Table  2.
Paired refinement Paired refinement was performed as described previously  (Karplus & Diederichs, 2012; Diederichs & Karplus, 2013)  except that resolution increments were selected to keep the numbers of reflections similar. Refinement of atomic positions and individual atomic displacement parameters was performed using phenix. refine, with simulated annealing in the first round of refinement. Evaluation of R values (without refinement) was performed using phenix. model_vs_data and the 'high_resolution' keyword  (Adams et al. , 2010). The same free reflections were used to calculate R free at high and 'low' resolution. The starting PDB entries used were 4zb5  (Lobley et al. , 2016) , 3tt8 with copper(II) removed (B. Prugovecki & D. Matkovic-Calogovic, unpublished work) and 5a3y  (Zander et al.
Genetic algorithm GAs apply concepts of biological natural selection to maximize or minimize a target function. The problem being optimized is encoded into one or more chromosomes, which are contained in a population of randomly initialized individuals. Diversity is introduced into the population via random mutation and crossover events. As a proof of concept, a GA for the grouping of sets of data sets has been implemented in a Python script. The DEAP package (https://github. com/deap/ deap) offers a complete set of tools for the facile development of a GA and has therefore been used. Furthermore, the SCOOP package (https://github. com/soravux/scoop/) has been used for thread-level and host-level parallelization. In our implementation, a chromosome is an array of integers of length n, where n is the number of sub-data sets (Fig.
This fitness is a combination of the inner-shell R meas value, the inner-shell hI/(I)i, the outer-shell CC 1/2 , the overall completeness and the overall multiplicity. In cases where anomalous signal is present, a term for the anomalous signal can also be included in the scoring function, which is the addition of the inner-shell mean anomalous differences in standard deviations above the mean (SigAno in XSCALE/XDS). A second option for anomalous optimization exists, which is the resolution at which the SigAno remains above 1. 0 and the anomalous correlation ('% of correlation between random half sets of anomalous intensity differences'; XSCALE output) remains above 30%. Grouping and merging statistics. Three columns are used for each system, with the first listing data resulting from merging all sub-data sets, the next from the best GA run and the last from the best HCA cluster.
The R meas from merging all data is particularly poor, with inner-shell and overall values of 44. 8 and 35. 3%, respectively. HCA identified a set of 19 sub-data sets from this pool with a significantly better R meas values of 7. 6 and 10. 2% for the inner shell and overall, respectively. The hI/(I)i for the inner shell and overall were, however, lower than those on merging all data. The GA also selected a set of sub-data sets with considerably improved R meas and signal to noise. The R meas for the GA demonstrated an improvement over both merging all data and the HCA set, with an inner-shell value of 7. 2% and an value of 9. 7%. In contrast to the HCA set, the GA retained an inner-shell and overall hI/(I)i of comparable strength to merging all the data, and indeed showed somewhat higher values in the low-resolution bin.
The CC 1/2 values for merging all data were already very good, with CC 1/2,outer and CC 1/2,overall values of 77. 4 and 99. 3%, respectively. This was actually better in the outer shell than the HCA set (77. 4% versus 67. 3%). The GA produced CC 1/2,outer and CC 1/2,overall values that were better in both the outer and overall shells compared with merging all data and with the HCA (CC 1/2,outer = 79. 1% and CC 1/2,overall = 99. 8%). Despite the significant improvements to R meas,inner , paired refinement saw very modest differences in the high-resolution cutoff: 1. 48, 1. 52 and 1. 52 A ˚for the GA, HCA and overall data, respectively. This result is consistent with the smaller improvements seen in CC 1/2 and hI/(I)i, lending further credence to the idea that these latter metrics are more useful than R values, as suggested previously  (Karplus & Diederichs, 2015).
8 and hI/(I)i overall = 20. 1]. The R meas , while an improvement over that on merging all the data, was rather high (R meas,inner = 25. 4%, R meas,overall = 98. 3%). By increasing the number of groups to eight from the default of three, and down-weighting the multiplicity term by 3, the merging statistics were dramatically improved, with R meas,inner = 9. 4%, hI/(I)i inner = 99. 4 and hI/(I)i overall = 17. 0. The hI/(I)i was dramatically higher for GA versus HCA, while the overall R meas was slightly higher than for the HCA data set, but was still a significant improvement over that on merging all data. The CC 1/2 values, in contrast, were somewhat lower than for both HCA and overall data, although increasing the weight for the CC 1/2 term could produce a data set with an overall CC 1/2 of 98.
1% overall as well as an hI/(I)i of 11. 8 in the low-resolution shell and 7. 8 overall. We wondered whether enough completeness and/or multiplicity could be sacrificed in order to improve both the R meas and signal to noise, and whether there was a selection that could yield acceptable values for all parameters. The GA selected a set of data with significantly improved merging statistics. R meas,inner was improved to 18. 8%, hI/(I)i inner was increased to 16. 3, hI/(I)i overall was improved to 9. 9 from 7. 8 and the overall completeness was still quite acceptable at 98. 0% (compared with 99. 9% for all data). As with the GA, HCA also improved the R meas values compared with the overall data set, but not as significantly as with the GA, with R meas,inner = 35. 1%. While the GA improved the CC 1/2 of the inner shell and that for the outer shell was worse than for the overall data, the HCA data set had an improved CC 1/2 for both the inner shell and overall data.
While we have not employed such a concept in this initial study, we are also looking into the use of a 'free' set of reflections similar to R free  (Bru ¨nger, 1992)  or CC free  (Karplus & Diederichs, 2012). Finally, it might also be possible to use the GA approach to optimize the merging of still diffraction frames generated by XFEL data collections. Thus, there are numerous improvements envisioned for this approach based on this proof of concept. However, even in this initial state, the GA is a promising technique for treating SX data and offers a complementary approach to existing methods for treating SX data.
Acknowledgements The authors would like to acknowledge the allocation of beam time (' SSX BAG ') for this and other ongoing SSX projects at  ESRF  by the  ESRF MX Beamtime Allocation Panel. MC thanks  Professor Stefano Ciurli  ( University of Bologna, Italy ) for making available the crystals of urease used in this work. MC also thanks the  EMBL Hamburg Outstation  for general support of the long-wavelength work. We thank  Dr Gianluca Santoni  ( ESRF ) and  Dr Sasha Popov  ( ESRF ) for making an early version of their ccCluster HCA software available to us for comparison with the  GA. A Python  script demonstrating the concepts described in this manuscript is available at https:// sites. google. com/site/codgas1.
For Cimex lectularius, we sequenced four libraries of nominal insert sizes 180, 500, 3 and 8 kb at genome coverages of Â 62. 4, Â 77. 9, Â 44. 42 and Â 21. 21, respectively (based upon the 650. 47-Mb genome size of the assembled genome). These raw sequences have been deposited in the National Center for Biotechnology Information (NCBI) SRA: SRS580017, BioSamples ID: SAMN02649412 and SAMN02434893. To prepare the 180-and 500-bp libraries, we used a gel-cut paired-end library protocol. Briefly, 1 mg of the DNA was sheared using a Covaris S-2 system (Covaris, Inc. Woburn, MA) using the 180-or 500-bp program. Sheared DNA fragments were purified with Agencourt AMPure XP beads, end repaired, dA tailed and ligated to Illumina universal adaptors. After adapter ligation, DNA fragments were further size selected by agarose gel and PCR amplified for six to eight cycles using Illumina P1 and Index primer pair and Phusion High-Fidelity PCR Master Mix (New England Biolabs, Ipswich, MA).
These size-selected circular DNA fragments were then sheared to 400 bp (Covaris S-2), purified using Dynabeads M-280 Streptavidin Magnetic Beads, end repaired, dA tailed and ligated to Illumina PE sequencing adaptors. DNA fragments with adaptor molecules on both ends were amplified for 12-15 cycles with Illumina P1 and Index primers. Amplified DNA fragments were purified with Agencourt AMPure XP beads. Quantification and size distribution of the final library was determined before sequencing as described above. Sequencing was performed on Illumina HiSeq2000s generating 100-bp paired-end reads. Reads were assembled using ALLPATHS-LG (v35218, http://www. broadinstitute. org/software/allpaths-lg/blog/) and further scaffolded and gap-filled using in-house tools Atlas-Link (v. 1. 0) and Atlas gap-fill (v.
2. 2) (https://www. hgsc. bcm. edu/software/). This yielded an assembly of size 650. 47 Mb with contig N50 of 23. 5 kb and scaffold n50 of 7. 17 Mb. The assembly has been deposited in the NCBI: BioProject PRJNA167477. Automated gene annotation using a Maker 2. 0 pipeline adapted for arthropods. The bed bug is 1 of 30 i5K pilot genome assemblies that were subjected to automatic gene annotation using a Maker 2. 0 (http://www. yandell-lab. org/software/ maker. html) annotation pipeline tuned specifically for arthropods. The pipeline is designed to be systematic, providing a single consistent procedure for the species in the pilot study, scalable to handle 100s of genome assemblies, evidence guided using both protein and RNA-seq evidence to guide gene models and targeted to utilize extant information on arthropod gene sets.
The core of the pipeline was a Maker 2 instance, modified slightly to enable efficient running on our computational resources. The genome assembly was first subjected to de novo repeat prediction and CEGMA analysis (http://korflab. ucdavis. edu/datasets/cegma/) to generate gene models for initial training of the ab initio gene predictors (Supplementary Data 33). Three rounds of training of the Augustus (http://bioinf. uni-greifswald. de/augustus/) and SNAP (http://korflab. ucdavis. edu/software. html) gene predictors within Maker were used to bootstrap to a high-quality training set. Input protein data included 1 million peptides from a non-redundant (nr) reduction (90% identity) of Uniprot Ecdysozoa (1. 25 million peptides) supplemented with proteomes from 18 additional species (Strigamia maritima, Tetranychus urticae, Caenorhabditis elegans, Loa loa, Trichoplax adhaerens, Amphimedon queenslandica, Strongylocentrotus purpuratus, Nematostella vectensis, Branchiostoma floridae, Ciona intestinalis, Ciona savignyi, Homo sapiens, Mus musculus, Capitella teleta, Helobdella robusta, Crassostrea gigas, Lottia gigantea and Schistosoma mansoni) leading to a final nr peptide evidence set of 1.
03 million peptides. RNA-seq from C. lectularius adult males and females was used judiciously to identify exon-intron boundaries but with a heuristic script to identify and split erroneously joined gene models. We used CEGMA models for QC purposes: for C. lectularius, of 1,977 CEGMA single-copy orthologue gene models, 1,928 were found in the assembly, and 1,892 in the final predicted gene set. Finally, the pipeline uses a nine-way homology prediction with human, Drosophila and C. elegans, and InterPro Scan5 to allocate gene names. The automated gene set is available from the BCM-HGSC website (https://www. hgsc. bcm. edu/arthropods/bed-bug-genome-project) and at the National Agricultural Library (https://i5k. nal. usda. gov). Community curation of the bed bug genome. Thirty-two groups were recruited through the i5k pilot project to manually curate the MAKER-predicted gene set Clec_v0.
5. 3. These curators selected genes or gene families based on their own research interests. Manual curation occurred via the Web Apollo software, a webbased graphical user interface for gene model curation that allows curators to create and view changes to gene models in real time. A C. lectularius Web Apollo (http://genomearchitect. org/) instance was made available (https://apollo. nal. usda. gov/cimlec/jbrowse/) to display evidence included in the generation of the Clec_v0. 5. 3 gene predictions. This Web Apollo instance also incorporates aligned RNA-seq and transcriptome data sets provided to the scientific community by Zach Adelman (Virginia Tech) that were not included in the MAKER analysis. Curators were provided a webinar-based training session on the Web Apollo software (courtesy of Monica Mun ˜oz-Torres, Lawrence Berkeley National Laboratory) and were asked to adhere to a set of curation rules (https://i5k.