dataesr/scientific-paragraphs-categorization huggingface dataset:
Individual frames are then calibrated for astrometry using the Two Micron All Sky Survey (2MASS) catalogue  (Skrutskie et al. 2006). For stacking, we adopt the AstrOmatic suite 2 by E. Bertin to derive a fine astrometry (SCAMP) and resample the images for alignment on a given grid and stacking (SWarp). The sky 2 www. astromatic. net background is subtracted as two-dimensional (2D) plane fits to individual images. In most cases, stacks consist of four images. Despite this small number of data points, good rejection of bad pixels, cosmic rays, and spurious signals is achieved with sigmaclipping using the detector noise parameters (gain and noise). Remnant signals from previous observations of heavily saturated stars are mostly removed during this stacking process but can be easily identified on averaged stacks.
Photometric calibration Information on photometric calibration in the Y-band is scarce due to the limited use of this band. Direct photometric calibration of the Y-band using Vega-like A0 stars has been performed by for example  Hillenbrand et al. (2002). The Y-band UKIDSS (UKIRT Infrared Deep Sky Survey) -WFCAM (Wide Field Camera) data were anchored to the Two-Micron All-Sky Survey (2MASS) and calibrated in the Vega system by zeroing the colours of blue stars present in the data  (Hodgkin et al. 2009). Considering the connection between the CFHQSIR and CFHTLS datasets, we chose to anchor the CFHQSIR photometric calibration to the CFHTLS and to treat the Y-band extension as an extrapolation in wavelength. For convenience, we decided to perform the photometric calibration directly on the image stacks (WIRCam stacks) as described in the previous section.
3. When available, the 2MASS (profile-fitting) magnitudes in the J, H, and K s bands are also used in the fits. 4. For each star, we fit the optical photometric data with the Pickles (1998) library of stellar spectra and we derive the Yband magnitude from these fits. To this aim, we use the Le Phare  (Arnouts et al. 1999; Ilbert et al. 2006 ) photometric redshift sofware. 5. We intentionally use a limited number of spectra representative of the most common stars likely to be present in our samples, and we exclude cold stars that have broad absorption features in the near-IR, therefore preventing a reliable extrapolation of their spectra. We therefore limit the spectral types used for our fitting procedure from G0 to K7. We also limit the photometric bands used in the fit to the riz[JHKs] bands to avoid the sensitivity to metallicity of the u and g bands.
We exclude poor fits as measured by the Le Phare internally derived χ 2 values. An example of a fit with a stellar spectrum to the spectral energy distribution of a star for which there are ugriz CFHTLS and JHKs 2MASS photometry is shown in Fig. 5. The u and g bands data points are shown but are not used in the fit. In total, we use 45,500 stars to perform this photometric calibration over the four CFHQSIR fields, leading to an average photometric zero point per quadrant (detector) of each WIRCam stack. We tentatively observe a less than 1% difference between the zero points derived with or without 2MASS data, not significant enough to be corrected for. After this first pass the relative dispersion of the calibration coefficients is equal to 10%. This dispersion includes photometric variations between stacks and spatial variations within one quadrant and between quadrants.
Spatial variations of the zero points due to optical distortions and/or sky concentration are introduced during the flat-fielding process (see e. g. Regnault et al. 2009 ). These variations can be seen on Fig. 6 , which shows the zero points projected onto a single WIRCam image in pixel coordinates. From this image, we generate a 2D fit per quadrant (Fig. 7 ), usually referred to as illumination-correction map. This map is subsequently used to correct all the WIRCam stacks. After correction for the illumination map, we re-compute all the zero points for the whole dataset. For each WIRCam stack, we derived the final zero point by averaging with sigma-clipping rejection the zero points measured for each star present in the stack. We require that a minimum of six zero points be measured in each stack.
When there are less than six zero points, which happened for nine stacks only over a total of 1445), we assigned  the average zeropoint of the corresponding CFHTLS field. For these nine stacks, this may represent an additional photometric error of less than 10%. The illumination correction reduced the overall dispersion of the zero points from 10% to 7%, consistent with the amplitude of the illumination correction map. Finally, for consistency with the CFHTLS data  (Hudelot et al. 2012) , we normalize each stack by setting the zero point to a value of 30. 0 in the AB system.
Registration to the CFHTLS format For ease of use in connection with the CFHTLS data, we decided to produce one-square-degree image tiles similar to the CFHTLS data format, in addition to the WIRCam stacks. We Fig. 7 : Map used to correct the spatial non-uniformity of the photometric response. The peak-to-peak relative amplitude variation in this image is 20 %. use SCAMP  (Bertin 2006)  to determine the geometrical transformation between the reference z-band CFHTLS tile and the nine WIRCam stacks corresponding to this tile. In practice, the transformation is determined for each of the four quadrants of the WIRCam stacks using a three-degree polynomial. We then use SWarp  (Bertin et al. 2002)  to apply these transformations using a LANCZOS3 (Lanczos-3 6-tap filter) interpolation kernel and to reformat the whole dataset to the CFHTLS tile format.
Comparison with the VIDEO survey For the sake of assessing the performance of the CFHQSIR survey, we compare the CFHQSIR and VIDEO (VISTA Deep Extragalactic Observations Survey;  Jarvis et al. 2013 ) number counts. VIDEO is approximately two magnitudes deeper than CFHQSIR albeit over a much reduced area. We use the XMM1 and XMM3 fields, which are both in the W1 footprint and cover a total area of three square degrees. The VIDEO data in these fields have a 5σ depth of Y ∼ 24. 9 in 2 diameter apertures. We use the SExtractor MAG_AUTO magnitudes reported in the fourth data release (DR4) of the VIDEO catalogue. Because our catalogue is optically selected, we apply a cut in SNR in the Y-band in order to keep objects well detected in this band. After visual and qualitative tests we adopt SNR ≥ 6.
Photometric redshifts Following  Erben et al. (2013)  and  Moutard et al. (2016) , we build a multi-wavelength dataset based on SExtractor isophotal apertures (corresponding to SExtractor MAG_ISO). By construction, ISO apertures contain pixels characterized by a high signalto-noise ratio, which tends to make them better matched to the shape of the sources than traditional circular apertures while delivering less noisy measurements than Kron-like apertures (corresponding to SExtractor MAG_AUTO). This leads to more precise measurements of the colours and, therefore, more accurate photometric redshifts  (Hildebrandt et al. 2012). Following the method developed by  Moutard et al. (2016) , we compute the total magnitude for each source by rescaling the MAG_ISO magnitudes to the MAG_AUTO magnitude.
The addition of the Y-band clearly improves the overall robustness of the photometric redshifts at z 1. 0 despite the shallow CFHQSIR sensitivity. For instance, the redshift range where σ ∆z/(1+z) < 0. 06 is extended from z 1. 05 to z 1. 2, where the use of the Y-band maintains an outlier rate η < 8. 8% against η < 13. 8% without the Y-band. The CFHQSIR data therefore extends the effective redshift range probed by the CFHTLS for studies based on photometric redshifts, for example galaxyevolution or weak-lensing analyses.
Conclusion We have conducted a new Y-band imaging survey covering the four fields of the CFHTLS-Wide, as part of the CFHQSIR. The average image quality of the data is 0. 70, with a standard deviation of 0. 08. The observations reach a limiting magnitude Y AB ∼ 22. 4 (5σ/0. 93) over 130 square degrees. We combined the CFHTLS-T0007 optical data and the CFHQSIR wide band data to produce a multi-wavelength catalogue. We found good agreement between the number counts from this catalogue and the deeper VIDEO data up to a magnitude of Y AB ∼ 21. 3 at SNR ≥ 6. We tested the improvement of the CFHTLS photometric redshifts with the addition of the Y-band. We derived photometric redshifts for more than 40,000 sources in the W1 and W4 fields and compared them to their spectroscopic redshifts. We found that the addition of the Y-band improves the accuracy of photometric redshifts at z 0.
Acknowledgements. We thank the  CFHT  staff, and in particular  Todd Burdullis , QSO Operations Specialist, and  Karun Thanjavur , previously resident astronomer at CFHT, for their dedication and efforts in accompanying the CFHQSIR observations and early data reduction. This paper uses data from the  VIMOS Public Extragalactic Redshift Survey (VIPERS ). VIPERS has been performed using the  ESO Very Large Telescope , under the " Large Programme "  182. A-0886. The participating institutions and funding agencies are listed at http://vipers. inaf. it. This paper uses data from the  VIMOS VLT Deep Survey (VVDS ) obtained at the  ESO Very Large Telescope  under programmes  070. A-9007  and  177. A-0837 , and made available at the  CeSAM data centre, Laboratoire d'Astrophysique de Marseille, France.
To achieve a rigorous control of diabetes (children should not undergo this control type), the patient should give more attention to their diet and exercise  [11]  and measure their blood glucose levels more frequently, at this point is where enter the scene new technologies  [12] , which now can be used for many utilities. In this paper the development and subsequent Quality of Experience (QoE) evaluation of a mobile app to self-control type 2 diabetes is shown. This mobile app has been developed for one of the main operating systems on mobile devices such as Android and has been used C# programming language. To carry out this development has been used Xamarin development platform which allows interpreting the C # code and converting it to the main development language of Android, Java, in addition to IDE Visual Studio.
Final application The development carried out has resulted in a tracking application, capable of receiving data on personal measurements and storing in telephone database and visualizing both graphically (bar diagrams) and in the history form. At the app start is displayed for 2 seconds a welcome screen where you can read "Welcome to my diabetes app" along with the application logo and a background image representing a blood sample to measure glucose, a very representative image of the disease. In Figure  2a  is shown the application main screen. The New Registry window (Figure  2b ) implements a button to add a new annotation to each of the 5 parameters that can be tracked. All the windows that open when press their respective button have a similar appearance, but with some details that differentiate.
Metagenomic sequencing has the potential to transform microbial detection and characterization, but new tools are needed to improve its sensitivity. We developed CATCH (Compact Aggregation of Targets for Comprehensive Hybridization), a computational method to enhance nucleic acid capture for enrichment of diverse microbial taxa, and implemented it in a publicly available software package. CATCH designs compact probe sets that achieve full coverage of known microbial sequence diversity and that scale well with this diversity. Using CATCH, we designed and synthesized multiple probe sets, including one to capture whole genomes of the 356 viral species known to infect humans, and conducted a rigorous evaluation of their performance. Capture with these probe sets enriched unique viral content on average 18× in sequencing libraries from patient and environmental samples and allowed us to assemble viral genomes that we could not otherwise recover.
The key to determining the final probe set is then to find an optimal probe set s(d, θ d ) for each input dataset. Briefly, CATCH creates "candidate" probes from the target genomes in d and seeks to approximate, under θ d , the smallest set of candidates that achieve full coverage of the target genomes. Our approach treats this problem as an instance of the well-studied set cover problem  22, 23  , the solution to which is s(d, θ d ) (Fig. 1a ; see Methods for details). We found that this approach produces substantially fewer probes than previously used approaches and scales well with increasing diversity of target genomes (Fig. 1b , Supplementary Fig. 2 ). Figure  1  -Using CATCH for probe set design. (a) Sketch of CATCH's approach to probe design, shown with three datasets (typically, each is a taxon).
For each dataset d, CATCH generates candidate probes by tiling across input genomes. Then, it determines a profile of where each candidate probe will hybridize (the genomes and regions within them) under a model with parameters θ d (see Supplementary Fig. 1b  for details). Using these coverage profiles, it approximates the smallest collection of probes that fully captures all input genomes (described in text as s(d, θ d )). Given a constraint on the total number of probes (N) and a loss function over the θ d , it searches for optimal θ d. (b) Number of probes required to fully capture increasing numbers of HCV genomes. Approaches shown are simple tiling (gray), a clustering-based approach at two levels of stringency (red), and CATCH with three choices of parameter values specifying varying levels of stringency (blue).
See Methods for details regarding parameter choices. Shaded regions around each line are 95% pointwise confidence bands calculated across randomly sampled input genomes. (c) Number of probes designed by CATCH for each dataset (of 296 datasets in total) among all 349,998 probes in the V ALL probe set. Species incorporated in our sample testing are labeled. (d) Values of the two parameters selected by CATCH for each dataset in the design of V ALL : number of mismatches to tolerate in hybridization and length of the target fragment (in nt) on each side of the hybridized region assumed to be captured along with the hybridized region (cover extension). The label and size of each bubble indicate the number of datasets that were assigned a particular combination of values. Species included in our sample testing are labeled in black, and outlier species not included in our testing are in gray.
Probe sets to capture viral diversity We used CATCH to design a probe set that targets all viral species reported to infect humans (V ALL ), which could be used to achieve more sensitive metagenomic sequencing of viruses from human samples. V ALL encompasses 356 species (86 genera, 31 families), and we designed it using genomes available from NCBI GenBank  24, 25  (Supplementary Table  1 ). We constrained the number of probes to 350,000, significantly fewer than the number used in studies with comparable goals  15, 16  , reducing the cost of synthesizing probes that target diversity across hundreds of viral species. The design output by CATCH contained 349,998 probes (Fig. 1c ). This design represents comprehensive coverage of the input sequence diversity under conservative choices of parameter values, e.
Designing across many taxa Consider a large set of input sequences that encompass a diverse set of taxa (e. g. , hundreds of viral species). We could run CATCH, as described above, on a single choice of parameters θ d such that the number of probes in s(d, θ d ) is feasible for synthesis. However, this can lead to a poor representation of taxa in the diverse probe set; it can become dominated by probes covering taxa that have more genetic diversity (e. g. , HIV-1). Furthermore, it can force probes to be designed with relaxed assumptions about hybridization across all taxa. To alleviate these issues, we allow different choices of parameters governing hybridization for different subsets of input sequences, so that some can have probes designed with more relaxed assumptions than others. We represent a set of taxa and its target sequences with a dataset d, with its own set of parameters θ d.
Let {θ d } be the collection of θ d across all d. We wish to find S({θ d }), the union of s(d, θ d ) across all datasets d. CATCH finds this by solving a constrained nonlinear optimization problem: {θ d } * = arg min {θ d } d L(θ d ) s. t. |S({θ d })| ≤ N. The constraint N on the number of probes in the union is specified by the user; this is the number of probes to synthesize, and might be determined based on synthesis cost and/or array size. CATCH solves this using the barrier method with a logarithmic barrier function. By default, we use the following loss function for each d: L(θ d ) = w d (β 1 m 2 d + β 2 e 2 d ) where m d gives a number of mismatches to tolerate in hybridization and e d gives a cover extension, as defined above. w d allows a relative weighting of datasets, e. g. , if one should have more stringent assumptions about hybridization and thus more probes.
β 1 , β 2 , and the set of {w d }s can be specified by the user. A user can also choose to generalize the search to a different set of parameters: L(θ d ) = w d i β i θ 2 di where θ di is the value of the ith parameter for d and β i is a specified coefficient for that parameter. In practice, we have used the default loss function above, with w We implemented CATCH in a Python package that is available at https://github. com/broadinstitute/catch.
Design of viral probe sets presented here Input sequences for design of probe sets We designed four probe sets using publicly available sequences. The design of V ALL (356 viral species) incorporated available sequences up to June, 2016; V WAFR (23 viral species) up to June, 2015; V MM (measles and mumps viruses) up to March, 2016; and V ZC (chikungunya and Zika viruses) up to February, 2016. Most sequences we used as input for designing probe sets are genome neighbors (i. e. , complete or near-complete genomes) provided in NCBI's accession list of viral genomes 56 and were downloaded from NCBI GenBank 25. We selected a small number of other genomes using the NIAID Virus Pathogen Database and Analysis Resource (ViPR)  57. Supplementary Table  1  contains links to the exact input (accessions and nucleotide sequences) used as input for each probe set.
In particular, in the input to the design of V ALL we included all sequences in NCBI's accession list of viral genomes  56  for which human was listed as a host, along with all sequences from a selection of additional species (Supplementary Table  1 ). Since genome neighbors for Influenza A virus, Influenza B virus, and Influenza C virus were not included in the accession list, we included a separate selection of sequences for Influenza A virus that encompass all hemagglutinin and neuraminidase subtypes that infect human (in V ALL , 8,629 sequences), as well as sequences for Influenza B (376 sequences) and C (7 sequences) viruses. Furthermore, we trimmed long terminal repeats from all sequences of HIV-1 and HIV-2 used as input to both V ALL and V WAFR. In V ZC we included, along with genome neighbors, partial sequences of Zika virus from NCBI GenBank  25.
Exploring the parameter space across taxa To explore the parameter space in the design of V ALL and V WAFR , we varied m d (number of mismatches) and e d (cover extension) while fixing all other parameters. We pre-computed probe sets over a grid with m d in {0, 1, 2, 3, 4, 5, 6} and e d in {0, 10, 20, 30, 40, 50} when finding optimal parameters. In designing V ALL , we ran the optimization procedure 1,000 times, each with random starting conditions, and picked the choice of the parameter values from the run with the smallest loss. Supplementary Table  1  lists the selected parameter values of each dataset for each probe set, as well as other fixed parameter values.
Design additions for synthesis and probe set data For synthesis of probes in V ALL , the manufacturer (Roche) trimmed bases from the 3' end of probe sequences to fit within synthesis cycle limits. Probe lengths did not change considerably after trimming: of the 349,998 probes in V ALL , which were designed to be 75 nt, 61% remained 75 nt after trimming and 99% were at least 65 nt after trimming. We did not add PCR adapters for amplification to probe sequences in V ALL. We did add adapters to probe sequences in V WAFR , V ZC , and V MM (designed to be 100 nt and synthesized with CustomArray); we used two sets of adapters (20 bases on each end), selected by CATCH for each probe to minimize probe overlap as described above. Furthermore, in these three probe sets we included the reverse complement of each designed 140 nt oligonucleotide in the synthesis.
Analysis of probe set scaling with parameter values and input size In all evaluations of how probe counts grow with respect to an independent variable (Supplementary Fig. 1c , Fig. 1b , and Supplementary Fig. 2 ), we used genome neighbors from NCBI's accession list of viral genomes 56 (downloaded in September, 2017) as input. We trimmed long terminal repeats from HIV-1 sequences. The specific sequences are available at https://github. com/broadinstitute/catch/tree/323b639/hybseldesign/datasets/data. In all of these evaluations, we designed 75 nt probes. In the plots showing probe counts as a function of parameter values (Supplementary Fig. 1c ), we varied only the mismatches (m) and cover extension (e) parameters using the values shown. We set parameters on the longest common substring (lcf ) and island of exact match (i) to their default values: lcf equal to the probe length (75) and i = 0.
Assembly and alignments We performed demultiplexing and data analysis of all sequencing runs using viral-ngs v1. 17. 0  59, 60  with default settings, except where described below. To enable comparisons between pre-and post-capture results, we downsampled all raw reads to 200,000 reads using SAMtools  61. We performed all analyses on downsampled data sets unless otherwise stated. We chose this number as 90% of all samples sequenced on the MiSeq were sequenced to a depth of at least 200,000 reads. For those few low coverage samples for which we did not obtain > 200,000 reads, we performed all analyses using all available reads (Supplementary Table  3 ). Downsampling normalizes sequencing depth across runs and allows us to more readily compare unique content (see below) than an approach such as comparing viral reads per million.
Within-sample variant calling For our comparison of within-sample variant frequencies with and without capture (Fig. 3c , Supplementary Table  5 ), we used 3 dengue virus samples (DENV-SM1, DENV-SM2, and DENV-SM5). We selected these because of their relatively high depth of coverage, in both pre-and post-capture genomes (Supplementary Table  3 ); the high depth in precapture genomes was necessary for the comparison. We did not subsample reads prior to this comparison, in order to maximize coverage for detection of rare variants. For each of the three samples, we pooled data from three sequencing replicates of the same pre-capture library prior to downstream analysis. For each of these samples we performed two capture replicates on the same pre-captured library (two replicates with V WAFR and two with V ALL ), and sequenced, estimated, and plotted frequencies separately on these replicates.