dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2008). To statistically test this arrangement, we compared the average population values and their variance. SOS were identified by calculating the average and standard deviation of F ST values for each population and identifying the regions that have values higher than the average plus three standard deviations (mean F ST ± 3 SD)  (Porto-Neto et al. , 2014). Gene annotation was performed using ENSEMBL comparative genomic resources and further filtering, was carried out in the Panther database based on gene biological functions  (Mi et al. , 2016).
| Sequencing and bioinformatic analysis Large-scale sequencing was performed on an Illumina MiSeq platform (Illumina, Inc. ) with a 2 × 150 bp paired-end run (for tnrL amplicons) and 2 × 300 bp paired-end run, (for COX1 amplicons) following the Nextera DNA sample preparation protocol at the Genomix4Life Srl (http://www. genom ix4li fe. com/it/). Sequencing data analysis was conducted as previously described in  Buglione et al. (2020)  and the Blast results were filtered in relation to the regional list of plant  (Fenu et al. , 2010; Maxia et al. , 2003;  www. actap lanta rum. org) and animal (personal database) species to increase the accuracy of the automatic taxonomic assignation.
| Microbiota characterisation A total of 25 individuals (seven wild boar, 10 feral pigs and eight domestic pigs) were used for gut microbiota analyses. Partial 16S rDNA gene sequences were amplified using the primer pair Probio_Uni and Probio_Rev, which target the V3 region as previously described in  Milani et al. (2013). Gene sequencing was performed an Illumina MiSeq platform at the DNA sequencing facility of GenProbio srl (www. genpr obio. com) according to the protocol reported in  Milani et al. (2013). Following sequencing and demultiplexing, the obtained reads of each sample were filtered to remove low quality and polyclonal sequences. All quality-approved, trimmed and filtered data were exported as fastq files and processed using a script based on the QIIME software suite  (Caporaso et al.
Nature offers a huge diversity of glycosidic derivatives. Amongst numerous structural modulations, the nature of the ring size of hexosides may induce significant differences on both biological and physicochemical properties of the glycoconjugate of interest. On this assumption, we expect that small disaccharides bearing either a furanosyl entity or a pyranosyl residue would give a specific signature, even in the gas phase. On the basis of the scope of mass spectrometry, two analytical techniques to register those signatures were considered, i. e. the ion-mobility (IM) and the infra-red multiple photon dissociation (IRMPD), in order to build up cross-linked databases. D-Galactose occurs in natural products in both tautomeric forms and presents all possible regioisomers when linked to D-mannose.
Ion mobility-mass spectrometry. We analyzed the synthesized disaccharides using electrospray ionization (ESI) time-of-flight mass spectrometry, hyphenated with a high-resolution cyclic ion mobility (IM-MS) cell (Waters Select Series Cyclic IMS, Wilmslow, UK). 49 3] [4]  Samples were infused in the mass spectrometer at a flow rate of 5µL/min, and mass spectra were recorded in the positive ionization mode on a mass-to-charge ratio (m/z) range of 50-1200. We recorded the arrival time distributions (ATDs) of the [M+Na] + cations or [M+Li] + cations after 4 passes around the cyclic mobility cell, i. e. approximately a path of 4 meters. The resolving power of ion mobility is proportional to the path length of the ions, which, for 4 passes, yields a theoretical resolution of about 200. Data were recorded with the Quartz v.
Acknowledgements The authors thank the  Agence Nationale de la Recherche (ANR)  to financially support the  ALGAIM-MS  project ( ANR-18-CE29-0006-02 , https://algaims-35. webself. net/accueil). The  NMR platform PRISM (Université de Rennes 1 , https://biosit. univ-rennes1. fr/prism-plate-forme-rennaise-dimagerie-etspectroscopie-multimodales) has also to be acknowledged for having recording 1 H and 13 C spectra at 500 and 125 MHz, respectively.
This pattern specifies both the structure (the mean position of all atoms) and its dynamics (fluctuations, both independent and collective) over the evolutionarily selected reaction coordinate. But, proteins are held together by an extraordinarily subtle balance of forces that produce marginally stable structures  [92] , and deducing the net value of residue interactions remains a challenge for experimental and computational approaches. An added complication is that long-range couplings fundamentally arise from the nonlinear (epistatic) interactions of amino acid residues  [91, 93]. The full theoretical combinatorial complexity of such interactions is inaccessible to any scale of experimental analysis. In recent years, a different approach to understand allostery has been to leverage the growing databases of protein sequences to make statistical models for the pattern of amino acid interactions.
Acknowledgments We thank  Dr Jonathan Cedernaes  and all members of the  Bass lab  for helpful discussions. This research was supported by the  National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)  grants  R01DK090625 ,  R01DK113011 ,  R01DK100814  and  R01DK050203 ,  The National Institute on Aging  grant  P01AG011412 , the  Chicago Biomedical Consortium S-007  and the  University of Chicago Diabetes Research and Training Center  grant  P60DK020595  (JB).
Acknowledgments SW gratefully acknowledges funding from the  Dean of Physical Sciences  and the  Bhaumik Institute for Theoretical Physics at UCLA. Acknowledgments I thank  Olivier Rivoire ,  Arvind Murugan , and members of the  Ranganathan lab  for discussions and comments. RR acknowledges support from  NIH  Grant  RO1GM12345 , a  Data Science Discovery award  from the  University of Chicago , and the  Center for Physics of Evolving Systems at the University of Chicago. Acknowledgments This material is partially supported by the  National Aeronautics and Space Administration  through the  NASA Astrobiology Institute  under Cooperative Agreement No. NNA13AA91A  issued through the  Science Mission Directorate  and by the the Ministry Phys. Biol. 18 (2021) 041502  Roadmap of Science and Technology, Taiwan  under No.
Primers and probes design Primers (Sigma Aldrich, France) and probes (TaqMan, Applied Biosystem, France) were designed using the Primer Express v. 3. 0. 1 software (Applied Biosystem, California) in order to: (1) avoid primers dimers and with a melting temperature (Tm) 10°C lower than the probe (intended at 70°C); (2) obtain the same in silico amplification conditions for all qPCRs; (3) allow the possibility of multiplexing; (4) discriminate the variants of family genes (e. g. , bla CTX-M groups and variants) with genotyping assays. For the SNPs visualization, a Clus-talW alignment was performed on the nucleotide sequences using BioEdit v. 7. 2. 5 software. The nucleotide sequences and accession number for each gene were retrieved from the Genbank database (Tables  1 2 3 ). Probes were labeled with a fluorophore (JUN, or VIC, or ABY, or FAM) at the 3'-end and a quencher (MGB or QSY) at the 5'-end.
Reaction conditions of qPCRs The qPCR reactions were performed in 384-wells plates in 5 mL final volume containing 2. 5 mL of TaqPath ProAmp Multiplex Master Mix (Applied Biosystem, Lithuania), 0. 5 mL of forward and reverse primer (1 mM), respectively, 0. 25 mL of TaqMan probe (50 nM), 1. 25 mL of sterile H 2 O and 1 mL of DNA. For the genotyping assays, primers were used at 200 mM final concentration, whereas probes (only with MGB quencher) were used at 100 mM  (Malkki et al. , 2012). For both qPCRs and genotyping assays, amplification was performed in fast mode using QuantStudio 5 Real-Time PCR System (ThermoFisher Scientific) (Table  S1 ). Data were collected at the annealing-amplification step and analyzed using the QuantStudio Design & Analysis Software v1. 4. 3. (ThermoFisher Scientific).
Funding support This work and  Tony Rocheg€  ue thesis was supported by the  LABEX ECOFECT  ( ANR-11-LABX-0048 ) of  Universit e de Lyon , within the  program ''Investissements d'Avenir''  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR)  and proper funding from  ANSES.
Dust emission is the main foreground for cosmic microwave background (CMB) polarization. Its statistical characterization must be derived from the analysis of observational data because the precision required for a reliable component separation is far greater than what is currently achievable with physical models of the turbulent magnetized interstellar medium. This letter takes a significant step toward this goal by proposing a method that retrieves non-Gaussian statistical characteristics of dust emission from noisy Planck polarization observations at 353 GHz. We devised a statistical denoising method based on wavelet phase harmonics (WPH) statistics, which characterize the coherent structures in non-Gaussian random fields and define a generative model of the data. The method was validated on mock data combining a dust map from a magnetohydrodynamic simulation and Planck noise maps.
The denoised map reproduces the true power spectrum down to scales where the noise power is an order of magnitude larger than that of the signal. It remains highly correlated to the true emission and retrieves some of its non-Gaussian properties. Applied to Planck data, the method provides a new approach to building a generative model of dust polarization that will characterize the full complexity of the dust emission. We also release PyWPH, a public Python package, to perform GPU-accelerated WPH analyses on images.
The mixture of dust and gas is described as a turbulent magnetized fluid, and the grains tend to align statistically with their long axes perpendicular to the local magnetic field, thus polarizing the emission (Planck Collaboration XII 2020). The physical and chemical processes regulating and structuring the diffuse ISM are nonlinearly coupled  (Draine 2011) , leading to the emergence of strong scale couplings, which may be evidenced by the highly non-Gaussian statistics observed for many tracers  (Burkhart et al. 2009 ). The public Python package PyWPH is available at https:// github. com/bregaldo/pywph. Capturing non-Gaussianity is therefore essential to characterize the dust signal. To tackle this issue, several authors have introduced machine learning algorithms that need to be trained  (Aylor et al.
2020; Krachmalnicoff & Puglisi 2021; Petroff et al. 2020; Thorne et al. 2021). Another approach, which does not involve any learning steps, uses statistics defined from nonlinear transforms, namely the wavelet scattering transform and the wavelet phase harmonics (WPH). These describe the couplings between scales in non-Gaussian fields, and they have been applied to ISM data  (Allys et al. 2019; Regaldo-Saint Blancard et al. 2020; Saydjari et al. 2021)  as well to study the largescale structure of the Universe  (Allys et al. 2020; Cheng et al. 2020). They have been successfully used to characterize simulated and observational data with a high signal-to-noise ratio (S/N), and to build generative models for these data. However, these approaches face difficulties with noisy data, which is the case of Planck polarization observations.
In this letter, we introduce a statistical denoising method using the WPH statistics to retrieve the statistical properties of the noise-free dust emission. In Sect. 2 we present our statistical denoising method and show how well it performs on noisy simulated data of the polarized emission from dust. In Sect. 3, we apply it to the Chamaeleon-Musca 353 GHz polarized emission, as observed by the Planck satellite, and discuss our results. In Sect. 4 we conclude and suggest a few perspectives for follow-up studies. This paper also includes two appendices. Appendix A introduces the WPH statistics and the denoising procedure in detail. Appendix B presents a first comparison with other meth-Article number, page 1 of 10 arXiv:2102. 03160v2 [astro-ph. CO] 25 May 2021 A&A proofs: manuscript no. main ods.
Validation on a simulation In this section, we assess the performance of our denoising algorithm by applying it to mock data d = s + n, emulating a noisy Q + iU Planck polarization signal. We show figures of merit based on power spectra and probability distribution functions (PDFs) of the increments of the maps. These are only shown for Q, but they are similar for U. We postpone figures of merit based on other kinds of statistics, including the WPH statistics, to a future study. Here s is a simulated Q + iU map, representing a typical linear polarization signal produced by the thermal emission of dust in the diffuse ISM. It was built from the same magnetohydrodynamic simulation as the one used in Regaldo-Saint  Blancard et al. (2020)  and following the same procedure. We refer to this paper for more details on the construction of this simulated map.
Presentation of the data We now apply our denoising method to a Q+iU polarization map of the Chamaeleon-Musca region observed at 353 GHz with the Planck satellite (PR3 data 3  , Planck Collaboration III 2020). At this frequency, the CMB is negligible and the dominant components are the dust emission and the noise (see Fig. 34  of Planck Collaboration IV 2020). We consider the Q and U maps corresponding to the full mission, and those corresponding to the two half-missions, as well as the 300 end-to-end simulated Q and U maps of the noise and the systematics of the instrument for the full mission (the ones used in Sect. 2). We projected all of these maps on 512 × 512 grids with a pixel size of 2. 35 , centered on the region of the Chamaeleon-Musca clouds at Galactic coordinates (l, b) = (300.
26 • , -16. 77 • ). We used a Gnomonic projection through the HEALPix/healpy 4  package  (Górski et al. 2005; Zonca et al. 2019 ). In Fig. 4 , we show the projected full-mission Q map that we aim to denoise (left), as well as the denoised map discussed in the next section (middle). The same maps for U are given in.
XLVIII 2016), which was used on Planck polarization data and provides a local smoothing kernel in order to optimally remove the noise. For the Wiener approach, we find that neither the Wiener filtered image nor realizations drawn from the Wiener posterior distribution are able to retrieve the PDFs of the increments, even when the true power spectrum was given as input. The comparison with GNILC shows that our method recovers the true power spectrum and the PDFs of the increments better.
Conclusion and prospects We have introduced a new method for the denoising of Planck polarization data based on the generation of random synthetic maps from WPH statistics that characterize the non-Gaussianity of the dust emission. This method takes advantage of the strong statistical differences between the signal of interest (non-Gaussian and regular) and the noise (close to Gaussian and irregular) by performing an optimization that constrains the statistical properties of the denoised map plus noise realizations. We applied our method to mock Q + iU noisy data designed to emulate typical Planck polarization maps of dust in the diffuse ISM. The denoised map has a power spectrum that coincides with the true power spectrum down to a minimum scale where the power of the noise is ten times that of the signal, while being highly correlated with the truth signal.
Such a model derived from the analysis of Planck data may be used to simulate the dust polarization sky. Our denoising algorithm constitutes a step toward this modeling, in the sense that WPH statistics of the dust emission, corrected to a first approximation from noise contamination, may be computed from the denoised map. A natural extension of this work would be to quantitatively assess how well we can recover the WPH statistics of the truth map with this approach. This will be the subject of a future paper. A&A proofs: manuscript no. main Fig. 6. PDFs of the Chamaeleon-Musca Q maps increments for d (noisy), s (denoised), and GNILC map, computed for three logarithmically spaced lags ranging from 4. 7 to 75. 2. For each lag, the increments were normalized by the standard deviation σ l of the Gaussian that fits the core of the PDF of the noisy map.
Acknowledgements. We thank the anonymous referee for a very helpful report. This work was undertaken in parallel to a similar project led by  J. -M. Delouis  on the full sky, and we acknowledge a fruitful interaction with his team. We gratefully acknowledge  P. Lesaffre  for helping us with the computation of the PDFs of the increments. We also acknowledge fruitful discussions with  J. -F. Cardoso ,  S. Mallat , and  T. Marchand. This research was supported by the  Agence Nationale de la Recherche  (project  BxB :  ANR-17-CE31-0022 ). B. Regaldo-Saint Blancard  acknowledges support from the  Centre National d'Etudes Spatiales (CNES).
Neither of the two detected amino-acid substitutions were strainspecific differences that could underlie preference (Supplementary Data 1). Potential phenotypic effects of exon 1. 5 were evaluated by using CRISPR/Cas9 to create a bab gene lacking a complete exon 1. 5. Four homozygous mutant lines originating from E-strain-injected embryos had exon 1. 5 deletions (Supplementary Fig. 6a ), but all four lines had behavioral and electrophysiological responses similar to wild-type E strain males (Supplementary Fig. 6b, c ). Genomic associations of preference in nature. Parallel to QTL mapping, we conducted a GWAS of field collections of sympatric E and Z strain populations to identify specific mutations linked to preference variation. Male preference in the wild was measured as behavioral attraction to either Z or E pheromones released from paired pheromone traps 30 m apart.
After controlling for population structure between two sympatric locations (Landisville, PA, USA: n = 16 males per trap; Rockspring, PA, USA: n = 15 males per trap; n = 62 males total), bab was the only gene on the Z chromosome strongly associated with pheromone blend attraction (Bayes factor > 20 dB, Bayesian P-value (eBP is ) > 2; Fig. 3a ; Supplementary Data 2). None of the associated polymorphisms (SNP, indels, SV) were within bab coding sequence, whereas 98. 7% were located in intron 1A (n = 133 polymorphisms) or intron 1B (n = 96 polymorphisms) (Fig. 3b ; Supplementary Data 2). Three polymorphisms were upstream of bab between bab and not. Preference had its strongest association with a 3-bp indel in intron 1A (BF = 45. 26 dB, eBP is = 3. 27, β = 0. 15). In expanded GWAS analyses that included more individuals and a third sympatric site (Bellona, NY, USA), we compared pool-seq genomic data of males from E pheromone traps homozygous for the E allele at pgFAR (pgFAR-e/pgFAR-e genotypes) (n = 31, 34, 41 males per site, 106 males total) to males in Z pheromone traps homozygous for the Z allele at pgFAR (pgFAR-z/pgFAR-z genotypes) (n = 25, 33, 39 males per site, 97 males total).
Single-end reads were assessed for quality using the FastQC program (http://www. bioinformatics. babraham. ac. uk/projects/fastqc). Sequences were then trimmed using Trimmomatic version 0. 35 to remove adapter sequences, bases with low sequence quality, and any reads that were shorter than 36 base pairs. FastQC reports were generated for each file again to confirm posttrimming quality. Mitochondrial DNA and ribosomal RNA sequences were removed using Bowtie2 48 by aligning against known mtDNA sequences and identical reads were collapsed prior to assembly (but counts retained) using the FastX Toolkit version 0. 013 (http://hannonlab. cshl. edu/fastx_toolkit). The transcriptome was assembled de novo using Trinity  46  and a k-mer length of 25. The longest transcript for each component were retained using custom scripts.
Individual resequencing data were collected for 31 E-trapped and 31 Z-trapped individuals from two sites (Rockspring, PA, USA (n = 15 per trap), and Landisville, PA, USA (n = 16 per trap); Supplementary Table  5 ). Landisville, PA, Z-trap data were originally described by Kozak et al. 54  ; all other data are new. Libraries were prepared using Illumina TruSeq (Illumina Inc. ) and sequenced on an Illumina NextSeq using 150 bp paired-end sequencing at Cornell University. Trimmed genomic data were analyzed using the GATK best practices pipeline  [55] [56] [57]  with data aligned to the repeat-masked genome reference (GenBank BioProject: PRJNA534504; Accession SWFO00000000 54 ) using bwa  58  , sorted and filtered using Picard and samtools to remove duplicates and reads with a mapping quality score below 20.
SNPs and small indels were called using GATK Haplotype caller (joint genotyping mode) after realigning around indels and filtered using recommended GATK filters  57. Large structural variants (SV) were called from aligned bam files using information from split paired end reads using split reads and anomalies in pair orientation and insert size in Delly2 59 (https://github. com/dellytools/delly); these structural variants included indels (>300 bp), translocations, and inversions. Delly2 was run on all individual files, these were merged to a consensus SV file and genotypes were reassessed. BayPASS 2. 1  60  was used to identify SNPs associated with pheromone trap while controlling for population demography in the individual resequencing data using allele frequencies for our four populations to test the association with pheromone trap (Z = 1, E = -1) using the STD model.
As described in Kozak et al. 54  , significantly associated polymorphisms had XtX above the 0. 001% quantile of pseudo-observed data of simulated "neutral" loci, BF > 20 dB  61  , and eBP is > 2 (equivalent to P value < 0. 01 for β = 0)  60, 62, 63. We expected the demographic history of the sex chromosome to be different from that of the autosomes, so we ran analyses with only Z chromosome loci. F ST was calculated in vcftools v0. 1. 16  64. Plots were created in R using packages qqman 65 and ggplot2  66. Pooled genome resequencing of field moths. Pooled samples were created by including equal amounts of DNA from all males caught within the same pheromone trap that were also homozygous pgFAR genotypes, as determined by a diagnostic Taq1α restriction digest  67. At each of three sites (Rockspring, PA, Landisville, PA, Bellona, NY), separate E-preferring pgFAR-e/pgFAR-e (E-strain) and Z-preferring pgFAR-z/pgFAR-z (Z-strain) pooled libraries were prepared as above and uniquely indexed E and Z pools from each site were sequenced on a single Illumina sequencing lane (n = 25-41 males per trap per site; 203 males total).
Bellona, NY, and Landisville, PA, pools were sequenced on an Illumina HiSeq3000 at the Iowa State University DNA Facility (Ames, IA) using 150 bp paired-end sequencing while Rockspring, PA, pools were sequenced using 100 bp single end sequencing. Rockspring, PA, and Landisville, PA, data were originally described in ref. 68  , whereas Bellona, NY, data are new. Genomic reads were trimmed using Trimmomatic v. 35 to remove Illumina adapters (TruSeq2 singleend or TruSeq3 paired-end), reads with quality <15 over a sliding window of 4 and reads <36 bp long. Reads were aligned using Bowtie2  48  and genomic positions were determined as described above. Aligned reads were sorted and filtered using Picard and samtools to remove duplicates and reads with a mapping quality score below 20. Samtools was used to identify single nucleotide polymorphisms (SNPs) in populations  58.