dataesr/scientific-paragraphs-categorization huggingface dataset:
Read mapping and variant calling Sequencing reads were mapped to the rock pigeon reference genome assembly (Cliv_2. 1; [18]  with BWA-MEM  [34]  and default settings, followed by duplicate removal using PICARD (http://broadinstitute. github. io/picard). Variant calling was performed by means of the Bayesian haplotype-based method implemented in Freebayes v1. 3. 1 (https://github. com/ekg/ freebayes). We modified the following additional parameters relative to the default settings: minimum mapping quality of 30, a minimum base quality of 30, turned off the left-alignment of indels, required at least three observations supporting the alternative allele, and required output of genotype qualities. We identified a total of 20,060,908 variants, including single nucleotide polymorphisms (SNPs), indels, multi-nucleotide polymorphisms, and complex polymorphisms.
Association mapping For the genome-wide association analysis, we filtered the raw variant dataset extensively. First, we retained only SNPs. Second, only variants with quality scores of 500 or greater were retained. Third, we required a minimum coverage of 4 reads per individual and a maximum of 90 reads (i. e. , three times the average coverage of the individual with higher coverage), otherwise a given genotype would be coded as missing. Fourth, all genotypes with genotype quality < 20 were coded as missing. Fifth, all variants with more than 20% missing data were removed. Finally, we excluded all variants with a minor allele frequency (MAF) > 0. 1. All these filters were applied in this specific order using VCFtools  [36]  and resulted in a total of 4,411,065 SNPs for further analysis. Prior to performing the association analysis, we carried out genotype imputation using BEAGLE v5.
A subset of the birds sampled for RNA extraction (two pearl-eye, three heterozygous wildtype and one homozygous wild-type) were used for RNA sequencing. From each individual strand-specific Illumina libraries were prepared using the LM-seq method  [40]  and sequenced using 150 bp paired-end reads. Sequencing quality was checked using FastQC v0. 11. 8. Reads were corrected with Rcorrector v1. 0. 3. 1  [41]  to exclude read pairs with at least one unfixable read, and Trim Galore! v0. 6. 0 (https://www. bioinformatics. babraham. ac. uk/projects/trim_ galore) to remove adapters, low quality bases (Phred score <5), and reads smaller than 36 bp. We generated a total of 558,644,610 reads with an average of 93,107,435 reads per individual (S2 Table ). RNA-seq data are available in the Sequence Read Archive (www.
de novo transcriptome assembly The pigeon reference assembly and annotation were derived from a pearl-eye individual, and are thus based on SLC2A11B transcripts harboring our candidate nonsense mutation. To investigate possible differences in isoforms between pearl-eye and wild-type pigeons, we assembled transcriptomes for one homozygous bird of each phenotype. For the pearl-eye phenotype, we selected the individual with the highest number of reads. Before assembly, reads were mapped with Bowtie2 v2. 3. 5  [42]  to the SSUParc and LSUParc fasta files from the database SILVA  [43]  (https://www. arb-silva. de/; downloaded April 2019) using the very-sensitivelocal option. Reads that were positive hits were discarded from our dataset to remove contamination with ribosomal RNA. For assembly, we used Trinity v2.
8. 4  [44]  with kmer size 35, 45, 55, 65, 75 and 85, using only paired-end reads. Of the resulting transcripts, we excluded those that were smaller than 1,000 bp. To identify SLC2A11B transcripts, we mapped the remaining transcripts to the reference genome with HISAT2 v2. 2. 1  [45]  and retained only those that mapped to the expected genomic region on scaffold AKCR02000030. 1. The relative abundance of each transcript (transcripts per million, TPM) in the original RNA-seq reads of each of the two individuals was estimated using the quasi-mapping approach implemented in Salmon v1. 2. 1  [46] , with the options seqBias and useVBOpt. For each individual, we retained only transcripts that represented more than 10% of the overall abundance. Resulting transcripts were manually aligned using BioEdit v7.
Analysis of differential expression using RNA-seq We used RNA-seq reads from two pearl-eye and four wild-type pigeons to quantify overall gene expression patterns in the iris. From each individual, we estimated the relative abundance of each transcript (TPM) from the published reference transcriptome using Salmon v1. 2. 1, with the options seqBias and useVBOpt. Calculation of differential gene expression was performed with edgeR, through DEApp  [49]. We excluded transcripts with <1 TPM per million in at least two samples. We used a false discovery rate (FDR) of 5% and considered only transcripts with log-fold change over 1. 5.
This work was supported by the  Fundacão para a Ciência e Tecnologia (FCT , https:// www. fct. pt)through  POPH-QREN  funds from the  European Social Fund and Portuguese MCTES  ( CEECINST/00014/2018/CP1512/CT0002 ); by Portuguese national funds to R. J. L. (Transitory Norm contract [ DL57/2016/CP1440/CT0006 ]); by research fellowships attributed to M. A. G. ( PD/BD/ 114042/2015 ) and C. I. M. ( SFRH/BD/147030/2019 ) in the scope of the  Biodiversity, Genetics , and  Evolution (BIODIV)  PhD program; by  ERC  Consolidator Grant number  681396  to M. T. P. G. (https://erc. europa. eu);by an  EMBO Short Term  Fellowship number  8033  to M. A. G. (https://www. embo. org); by the project  "PTDC/BIA-EVL/31569/ 2017 -NORTE -01-0145-FEDER-30288 ", cofunded by NORTE2020 through Portugal  2020  and  FEDER Funds , and by  National Funds  through FCT and by  National Funds through FCT  in the scope of the project  UIDB/50027/2020  (Base).
4 cases per 100,000 people (range, 0. 8 to 2. 3 cases per 100,000 depending on POTW; Table  2 ). In the POTW service areas included in this project, these rates correspond to between 1. 0 and 26. 3 cases per sewershed depending on sewershed (Dav and Sac are represented by the minimum and maximum report in the range, respectively). This estimation is corroborated by measurement of N gene at concentrations above 750 cp/g when there were 1. 3 recorded cases at Dav, although data in this study do not reach the detection limit and future data can lend more insight to these predictions.
io (21) (22) (23) , and results of wastewater measurements have been provided to stakeholders in real time on a website visualizing the data (wbe. stanford. edu). Four POTWs serve populations of Santa Clara County, CA (SJ, Gil, Sun, and PA), and the others each serve a portion of San Mateo County (SVCW and PA), San Francisco County (Ocean), Sacramento County (Sac), and Yolo County (Dav). The eight POTWs serve between 66,000 and 1,500,000 residents and have permitted flows between 8. 5 and 181 million gallons per day (Table  3 ). Sample collection. Samples were collected by POTW staff using sterile technique in clean, labeled bottles provided by our team. POTWs were not provided additional compensation for participation. Approximately 50 ml of settled solids was collected each day from each sewage treatment plant between mid-to-late November 2020 and 31 March 2021.
ACKNOWLEDGMENTS This work is supported by a gift from the  CDC Foundation. Numerous people contributed to sample collection and case data acquisition, including  Srividhya Ramamoorthy  (Sac),  Michael Cook  (Sac),  Ursula Bigler  (Sac),  James Noss (Sac) ,  Lisa C. Thompson  (Sac),  Payak Sarkar  (SJ),  Noel Enoki (SJ) , and  Amy Wong  (SJ),  Alexandre Miot  (Ocean),  Lily Chan (Ocean) , the Oceanside plant operations personnel,  Karin North  (PA),  Armando Guizar  (PA),  Saeid Vaziry (Gil), Chris Vasquez (Gil) ,  Alo Kauravlla  (Sun),  Maria Gawat  (SVCW),  Tiffany Ishaya  (SVCW),  Eric Hansen  (SVCW), and  Jeromy Miller  (Dav). We thank  Lauren Baehner ,  Brooke Bregman ,  David Rocha ,  Tony Fristachi , and  Elana Silver  at  CDPH  for their help in preparing case data. This study was performed on the ancestral and unceded lands of the Muwekma Ohlone people.
Acknowledgements The authors would like to thank the following people for providing specific input for the various sections of the review:  Jeryl Adcock , Laser Lines Ltd; Trudy Auty, Kent;  Elaine Blackwood , Photonic Solutions Ltd;  Steve Blake ,  STFC Rutherford Appleton Laboratory ;  Debbie Brindley , M-Squared Lasers Ltd;  Thomas Butcher ,  STFC Rutherford Appleton Laboratory ;  Gareth Cairns ,  AWE ;  Bob Collins ,  Magna-Power ;  William Dillon-Gray , Coventry;  Terry Dines ,  ICOS Ltd ;  Chris Dorman ,  Coherent Scotland ;  Rob Eason ,  University of Southampton ;  Gianluca Gregori ,  University of Oxford; Tim Grey Morgan, Amersham ;  Aaron Grimes ,  Tykma Electrox ;  Doug James , Consultant, Ottawa,  Canada ;  Eddie Judd ,  Beck Optronic Solutions ;  Ken Lipton ,  Founder and MD Rofin Sinar  UK 1997-2018 retired;  Mark Middleton ,  Crystran Ltd ;  Michelle Mitchell ,  University of Southampton ;  Paul Morris ,  Gooch & Housego  plc;  Ian Musgrave ,  STFC Rutherford Appleton Laboratory ;  Peter Norreys ,  University of Oxford / STFC Rutherford Appleton Laboratory; Miles Padgett FRS, University of Glasgow ;  Sir David Payne ,  University of Southampton ;  Robin Pitman , retired;  Alex Robinson ,  STFC Rutherford Appleton Laboratory ;  Harvey Rutt ,  University of Southampton ;  Daniela Stozno ,  Laserlab-Europe ;  Greg Tallents ,  University of York ;  Paul Treadwell ,  AWE ;  Brian Thomas , AWE retired;  Chris Varney ,  Laser Components  (UK), Ltd; and  Claes-Goran Wahlstrom, Lund University, Sweden.
Bulk optical properties can be derived from a range of particle models including Mie spheres (liquid and frozen) and non-spherical ice habits from the Liu and Atmospheric Radiative Transfer Simulator (ARTS) databases, which include pristine crystals, aggregates and hail. The effects of different PSD and particle options on simulated brightness temperatures are explored, based on an analytical two-stream solution for a homogeneous cloud slab. The hydrometeor scattering "spectrum" below 1000 GHz is described, along with its sensitivities to particle composition (liquid or ice), size and shape. The optical behaviour of frozen particles changes in the frequencies above 200 GHz, moving towards an optically thick and emission-dominated regime more familiar from the infrared. This region is previously little explored but will soon be covered by the Ice Cloud Imager (ICI).
This gives the hydrometeor a descriptive name and indicates whether it is frozen or liquid. It also associates a density and size range D min to D max , which are mainly relevant when the Mie sphere approximation is used to compute the optical properties. If optical properties are taken from a scattering database, then the density and integration size range are instead implied by the settings for that database. Any placeholder type can be associated with any hydrometeor from the databases. The number of hydrometeor types in the lookup tables is unlimited, so the placeholder types can be re-used multiple times. Their names should not be taken too literally, such as in the use of the "snow" placeholder type with large-scale snow, and the "graupel" placeholder for frozen https://doi. org/10. Dmin [m] Dmax [m] Frozen Rain 1000 1×10 -4 1×10 -2 F Snow 100 1×10 -4 2×10 -2 T Graupel 400 5×10 -4 3×10 -3 T Aggregate 900 5×10 -3 2×10 -2 T Cloud water 1000 5×10 -6 1×10 -4 F Cloud ice 900 5×10 -6 1×10 -4 T Totalice 500 5×10 -6 2×10 -2 T T=true; F=false particles in convective clouds.
3. 3) and in this case, the full channel set, including each distinct polarisation, is represented in the data file. Given that over the width of a channel, bulk scattering properties vary slowly with frequency, there is no attempt to represent the exact spectral response function. Instead, channels are specified by the central frequency and optical properties are evaluated at this exact frequency. In the case of double-sideband channels such as 183±7 GHz, the calculations are done at each of the two frequencies, e. g. 174 GHz and 190 GHz, and then averaged. Within the main RTTOV-SCATT radiative transfer code, the atmospheric profile is specified, at each model level, in terms of variables including temperature, humidity, and the sub-grid fraction and grid-box average mixing ratio of each of the hydrom-   Bauer, 2001).
Single-particle optical properties Single-particle optical properties are derived using either Mie theory, which assumes spherical particles, or from scattering databases that summarise the properties of non-spherical particle habits, which have been computed using more sophisticated methods such as DDA. Currently, the  Liu (2008)  and ARTS  (Eriksson et al. , 2018)  databases are available within the RTTOV-SCATT hydrotable generator.
This is primarily due to excessive forward scattering from the idealised spherical particle (Sect. 4). However, the Mie capability is still used, where appropriate, to fill gaps in the scattering databases. Specifically, this supports an optional extension of the size ranges below the smallest available particles from the databases. It is also used to fill the gap below 3 GHz where the  Liu (2008)  database does not provide data. Frozen Mie spheres are assumed to be composed of a mixture of air and ice at the relevant density from Table  2 , with pure ice assumed to have a density of 917 kg m -3 and air a density of 1. 225 kg m -3. Alternatively, formulations of density as a function of particle diameter can be used, from  Wilson and Ballard (1999) ,  Jones (1995)  or  Brown and Francis (1995).
These options https://doi. org/10. 5194/gmd-2021-73 Preprint. Discussion started: 14 June 2021 c Author(s) 2021. CC BY 4. 0 License. were explored by  Doherty et al. (2007). Permittivity of ice uses the  Mätzler (2006)  formulation, consistent with the ARTS scattering database  (Eriksson et al. , 2018). Earlier versions of the table generator followed the  Mätzler and Wegmüller (1987)  formulation, which differs only slightly; the option is retained in case exact backward comparison is required. The permittivity of the ice-air mixture is computed using the Fabry and Szyrmer (1999) mixing rule.
Liu (2008) non-spherical frozen particles Use of the  Liu (2008)  scattering database for non-spherical ice particles revolutionised the quality of microwave scattering simulations made by RTTOV-SCATT  (Geer and Baordo, 2014). Table  3  lists the options, of which the sector snowflake was the previous default choice for snow. The other options are a dendrite snowflake and a variety of hex-plates, columns and rosettes. These habits are geometric models of ice particles which are rescaled and then discretised into a 3D grid of polarizable points for input to DDA scattering calculations. To create the database, single-particle scattering properties have been averaged over a large number of random orientations, and these averages have been tabulated at a range of particle sizes  (Liu, 2008, their  Table  2 ), frequencies from 3 to 340 GHz and temperatures from 233.
15 to 273. 15 K. The database offers linear interpolation and extrapolation to a specified frequency, temperature and size. However, this extrapolation is not used within the hydrotable generator: For frequencies below 3 GHz, Mie sphere results are substituted; above 340 GHz, use of the Liu database is forbidden. For temperatures below 233. 15 K the optical properties at 233. 15 K are substituted. Finally, the available particle sizes define the size range over which the PSD is integrated. This integration range is shown in Table  3 , with D max being the largest size available from the database and D min the smallest, but bounded at 100 µm on the assumption that the  Field et al. (2007)  PSD will be used (see  Sect. 3. 1). The geometric particle model also provides the link between the particle size (the geometric diameter or maximum dimension D g ) and its mass m, via the mass-size relation: 1.
9  (or see also  Kulie et al. , 2010; Geer and Baordo, 2014). A limitation of the Liu database is the relatively low diversity among the bulk optical properties achievable using the different habits. For example, the 4-, 5-and 6-bullet rosettes give similar results to the sector snowflake. Then, there is a big gap to the next-most scattering particle, the 3-bullet rosette, and another big gap to the intensely scattering hex. plates and columns which provide similar results as each other. However, these five hex. particles with b = 3 provide a uniquely strong bulk extinction that cannot be obtained from the ARTS database (further discussion in Sect. 4).
ARTS database The ARTS scattering database  (Eriksson et al. , 2018)  was created to support sub-mm frequencies and to provide a broader range of non-spherical ice particles, including a variety of aggregates and densely rimed particles (e. g. hail and graupel). Table  3  summarises the options available. The current default frozen particles in RTTOV-SCATT are based on ARTS particles (see Table  1 ). The ARTS database provides optical properties at 34 frequencies from 1 to 886 GHz, at three temperatures  (190, 230, and 270 K) , and at least 34 sizes per habit. The ARTS standard habits are used here; these simplify the application of the database by ensuring a full coverage of size, temperature and frequency. The size issue is that in the underlying database, the smallest size of aggregate habits can exceed 200 µm, so where necessary the standard habits consist of a habit mix.
In these cases, the small size range is covered by a single crystal habit having similar shape as the constituents of the aggregate (see Table. 3). For example, the "Large Plate Aggregate" habit is complemented with the "Thick plate" habit. To avoid discontinuities, there is a linear transition between the two habits over a certain size range. These "mixed" standard habits are throughout named after the habit covering the main size range and contain at least 42 particle sizes. Remaining standard habits are essentially a copy of the original ones. To improve temperature coverage in the standard habits, points at 210 and 250 K are added by a second order interpolation, in order to decrease the error by subsequent linear temperature interpolation. Finally, due to limitations in DDA, there are some gaps in the database for combinations of large size and high frequencies.
The only difference between snow and graupel in the default configuration (Table  1 , Fig. 1 ) is the use of, respectively, an ARTS large plate aggregate and an ARTS column. The primary resulting difference is the asymmetry, with graupel giving less forward scattering between 50 GHz and 500 GHz. This allows the graupel to generate deeper brightness temperature depressions (see Fig. 8 later ). This greater "scattering" ability led to its selection as a reasonable representation of convective snow  (Geer, 2021b). In Fig. 1  the frozen particles have small oscillations with frequency, particularly obvious in the radar reflectivity at lower frequencies. This is a result of interpolating away from the original temperature, size and frequency steps in the ARTS database. This should not be an issue near the frequencies of typical satellite channels, since the ARTS frequencies have been chosen with this in mind  (Eriksson et al.
, 2018). Figure  2  illustrates the full range of frozen particle representations available from the ARTS database, along with the Mie sphere. The ARTS particles fall into two classes. The first is less dense particles with branched shapes including rosettes, snowflakes and most of the aggregates. These are shown with solid lines and typically generate smaller SSA, extinction, asymmetry and radar reflectivity. The second class is denser and more compact particles including pristine crystals, densely rimed particles (graupel and hail) and the 8-column aggregate. These are shown with dashed lines and typically generate higher values of all the optical properties. Further discussion, and comparison to the  Liu (2008)  particles, is made in terms of brightness temperature in Sec. 4.
( 6 ) This is the mass of the particle m(D g ) divided by the volume of a sphere of diameter D g. For spherical particles, the effective density and true density are equal. The true particle mass is reported in the particle databases and is the mass of ice used in the DDA calculation; these can vary slightly from the fitted mass-size relation (see e. g. Eriksson et al. , 2018, their Fig. 11 ). 2007 ) tropical PSD has been used in all cases, with the old integration (Sect. 3. 2) and a cutoff at Dmin = 1×10 -4 m. A soft ice sphere ("Mie") is also included, with settings as Fig. 1. Bulk reflectivity and extinction have been normalised by those of the ARTS large plate aggregate; some lines are off scale to be able to focus on the most populated areas. The legends are in order of brightness temperature depression at 183 GHz, from most to least scattering (see Fig.
9; Sec. 3. 1) any superdense region will be included. But as explored in Sects. 3. 2. 4 and 3. 2. 2 this is of little practical relevance, and even in the sub-mm the bulk extinction is insensitive to ice particles smaller than 100 µm. In an ideal world, users would impose their own constraints on the mass-size relation. For example, a certain mass-size relation may be assumed within the physics of the forecast model which supplies the cloud and precipitation profiles, and it may be intended to achieve microphysical consistency throughout the modelling chain. Further, there are observational constraints, and for example the  Brown and Francis (1995)  mass-size relation gives a good description of midlatitude stratiform ice cloud  (Hogan et al. , 2012) ; this is shown on Fig. 3. However, there is currently no way of decoupling the mass-size relation from the DDA particle choice in the hydrotable generator; this could only be achieved by choosing an appropriate (and probably different) database particle for each size bin -in other words, a particle ensemble approach, which is not yet supported.
The Mie sphere does allow a free choice of mass-size relation (Sec. 2. 1. 1) but obviously brings many other drawbacks, so this is not advised. However, Fig. 3  shows that the available DDA particles span a wide range of mass-size possibilities; the https://doi. org/10. 5194/gmd-2021-73 Preprint. Discussion started: 14 June 2021 c Author(s) 2021. CC BY 4. 0 License. dendrite particle in the Liu database (Table  3 ) would almost exactly match the  Brown and Francis (1995)  mass-size relation, for example. However, microwave radiances have their strongest sensitivity to convective snow particles, both in the tropics and midlatitudes, so the appropriate mass-size relation remains poorly known. Hence the dominant approach is to explore all potential DDA particle choices and to use the one that provides the best fit between model and observations (e.
g. Geer, 2021b, and references therein). Interestingly, the best choices in that work, reflected in the default RTTOV-SCATT configuration (Table  1 ) seem to be particles with b around 2. 0 -2. 4; particles with b closer to 3 seem to work poorly as a description of convective snow. The sensitivity of optical properties to the mass-size relation is further illustrated by the sector snowflake, which is present in both ARTS and  Liu (2008)  databases and has almost identical optical properties as a function of particle size. However, as shown in Table  3 , the a and b coefficients used with the Liu and ARTS databases are different, due to different, but equally valid methodological choices in fitting those coefficients to the particle masses within the databases (see  Geer and Baordo, 2014,  appendix B for further explanation).