dataesr/scientific-paragraphs-categorization huggingface dataset:
Data sharing statement All gene expression and DNA methylation data were uploaded in GEO under accession numbers: GSE184653, GSE185391 and GSE185027, which include sample basic information including sample source, age, sex, time point and viral rebound. Additional information can be found in the supplementary material of this article. Additionally, the R code applied in the analyses of this article can be found in the GitHub link: https:// github. com/hostimmuneOMICS/BCN02_OmicsAnaly sis.
We apply a novel statistical analysis to measurements of 16 elemental abundances in 34,410 Milky Way disk stars from the final data release (DR17) of APOGEE-2. Building on recent work, we fit median abundance ratio trends [X/Mg] vs. [Mg/H] with a 2-process model, which decomposes abundance patterns into a "prompt" component tracing core collapse supernovae and a "delayed" component tracing Type Ia supernovae. For each sample star, we fit the amplitudes of these two components, then compute the residuals ∆[X/H] from this two-parameter fit. The rms residuals range from ∼ 0. 01 -0. 03 dex for the most precisely measured APOGEE abundances to ∼ 0. 1 dex for Na, V, and Ce. The correlations of residuals reveal a complex underlying structure, including a correlated element group comprised of Ca, Na, Al, K, Cr, and Ce and a separate group comprised of Ni, V, Mn, and Co.
(2019). In §2 we describe the 2-process model, which is similar to that of  W19 and Griffith et al. (2019)  but with adjust-2 IMF = Initial Mass Function ments that make the model more flexible and easier to generalize. In §3 we describe our selection of APOGEE stars from SDSS Data Release 17 (DR17): red giants in a restricted range of log g, T eff , and [Mg/H] intended to minimize statistical and differential systematic errors while sampling the disk in the range 3 ≤ R ≤ 13 kpc and |Z| ≤ 2 kpc. Section 4 presents median abundance trends from this sample and uses them to infer the CCSN and SNIa 2-process vectors, i. e. , the abundance of each of the APOGEE elements associated with these two processes at a given metallicity. In §5, the heart of the paper, we fit each sample star's abundances with the 2-process model and examine the distributions and correlations of the abundance residuals.
Inferring the 2-process vectors from median sequences Similar to W19, we infer the process vectors q X cc (z) and q X Ia (z) from the observed median sequences of [X/Mg] vs. [Mg/H] for the low-Ia and high-Ia stellar populations. We do this separately in each bin of [Mg/H], and we will henceforth drop the z-dependence from our notation with the understanding that q X cc and q X Ia can change from bin to bin. In principle we could perform a global χ 2 fit to the abundances of stars in each [Mg/H] bin, but inferring the process vectors from the median sequences is much easier, and it is also more robust because outliers (whether physical or observational) have minimal impact on median values in a large data set. The median values of [Fe/Mg] are significantly different between the two populations even at high [Mg/H], so there is sufficient leverage to separate the CCSN and SNIa contributions.
(2021a)  and  Hasselquist et al. (2021)  and the examples in Figure  22. A third generation of the APOKASC catalog  (Pinsonneault et al. 2014 (Pinsonneault et al. , 2018;; Pinsonneault et al. in prep. )  will soon provide asteroseismic masses, ages, and evolutionary states for ∼ 15, 000 APOGEE stars in DR17. This sample can be used to look for more sub-tle trends of residual abundances with age, to look for trends with evolutionary state or internal rotation that could be signatures of non-standard mixing processes, and to disentangle C+N into separate C and N components (see  Vincenzo et al. 2021b). Combinations of APOGEE and GALAH data will provide cross-checks on common elements and a wider range of elements tracing a greater variety of nucleosynthetic origins. In combination with Gaia space velocities, residual abundances should be well suited to the program of Orbital Torus Imaging  (Price-Whelan et al.
g. ,  Bilitewski & Schönrich 2012; Pezzulli & Fraternali 2016 ), but we again suspect that they will alter mean trends without adding scatter in residual abundances. Instead we expect that explaining the observed residual covariances will require models that incorporate localized star formation and gradual ISM mixing, and it may also require stochastic sampling of the supernova and AGB populations. Recent galactic evolution models offer steps in this direction  (Armillotta et al. 2018; Krumholz & Ting 2018; Kamdar et al. 2019). Our results provide a quantitative testing ground for such models. Over a decade of observations and increasingly sophisticated data analysis, APOGEE has obtained an unprecedented trove of high-precision, high-dimensional stellar abundance data, probing all components of the Milky Way and several of its closest neighbors.
The combination of 2-process modeling and residual abundance analysis is one way to exploit the rich complexity of this data set, taking advantage of its high dimensionality and helping to disentangle the intertwined impacts of nucleosynthetic yields and Galactic enrichment history. Systematic application of these tools to APOGEE and its brethren, and comparison to a range of theoretical models, will teach us much about the physics of nucleosynthesis in stars and supernovae, about the processes that distribute elements through the ISM and into new stellar generations, and about the particular events that have shaped our galactic home.
ACKNOWLEDGMENTS DHW gratefully acknowledges the hospitality of the IAS and financial support of the W. M. Keck  and  Hendricks Foundations  during much of this work. DHW and JAJ are also supported by  NSF  grant  AST-1909841. JAH acknowledges the support of  NSF  grant  AST-1909897. YST gratefully acknowledges support of  NASA   Hubble Fellowship  grant  HST-HF2-51425. 001  awarded by the  Space Telescope Science Institute. DAGH acknowledges support from the  State Research Agency (AEI)  of the  Spanish Ministry of Science, Innovation and Universities (MCIU)  and the  European Regional Development Fund (FEDER)  under grant  AYA2017-88254-P. Funding for the  Sloan Digital Sky Survey IV  has been provided by the  Alfred P. Sloan Foundation , the  U. S. Department of Energy Office of Science , and the Participating Institutions.
SARS-CoV-2 Sequence Analysis After the sequencing runs, fast5 data files were base-called using Guppy (version 4. 3. 4, Oxford Nanopore Technologies, Oxford, United Kingdom) to generate fastq files (available at https://data-dataref. ifremer. fr/bioinfo/ifremer/obepine/lsem/ data/dna-sequence-raw/). Successfully base-called reads were further analyzed following the ARTIC nCoV-2019 pipeline version 1. 2. 1, 2 which included demultiplexing, read filtering, primers and barcode trimming. The resulting alignment file was used for single nucleotide variants (SNVs) calling using LoFreq version 2. 1. 5 with minimum base quality of 20 and 20× coverage, relative to Wuhan-Hu-1/2019 reference genome (GenBank: MN908947. 3). Short indels calling was also performed using Lofreq after a preprocessing step to insert indel qualities.
Samtools was used to read alignment files and an Awk-based script enabled to extract genome coverage percentages at depth 10, 30, and 100. Samtools also allowed the extraction of mean 2 https://artic. network/ncov-2019 genome coverage across the distinct amplicons trimmed for primer and overlapping sequences, for each sample. For VOC analysis, we excluded samples with depth 30 coverage <70%. On the basis of previous studies  (Martin et al. , 2020; Izquierdo-Lara et al. , 2021) , single nucleotide variants (SNVs) and indels with coverage < 30, average quality < 30, frequency < 5%, and homopolymer run > 4 (for indels only) were excluded. The detected SNVs were filtered by position and compared with the signature mutations for alpha, beta, gamma and delta VOC described in https://nextstrain. org and listed in Supplementary Table  3.
Statistical Analysis GraphPad Prism v 9. 0. 0 (GraphPad Software, San Diego, CA, United States) was used for data representation and statistical analysis. Comparisons for evaluation of the impact of each adaptation line during the adaptation process and freezing on the adapted sequencing protocol were performed using Kruskal-Wallis test followed by Dunn's multiple comparisons test. Correlations between SARS-CoV-2 genomic concentrations and genome coverage for all samples and each group individually (i. e. , fresh or frozen RNA and frozen wastewater) were assessed using the Spearman non-parametric test. Differences were considered statistically significant when p < 0. 05. *Objective of the sequencing run: protocol adaptation for WW samples (runs 1-4), prospective sequencing of fresh WW samples (run 4), retrospective sequencing of stored WW samples (runs 5 to 8).
Together, our results strongly suggest that the Alpha VOC or closely related strains were introduced in the studied city during November 2020. This is in agreement with previous studies where SARS-CoV-2 was sequenced in WW samples. In the United Kingdom, Wilton and his co-workers were able to detect the Alpha VOC in WWs from London as early as November 2020 by nested-PCR amplification and sequencing of two regions of the Spike gene  (Wilton et al. , 2021). The Alpha VOC was also detected through WGS by mid-December in WW from Switzerland  (Jahn et al. , 2021) , in December 2020 in Israel  (Bar-Or et al. , 2021)  and in January 2021 in Nice, France  (Rios et al. , 2021). In previous studies, the frequency of the Alpha VOC in SARS-CoV-2 strains infecting the population was estimated from those of signature mutations in WW data  (Jahn et al.
spread). Another important advantage of WGS is that SNV analysis can reveal mutations that were not previously observed in the global database and could also be used to monitor novel mutations. These newly observed mutations could (1) be the result of technical errors, like PCR mistakes or sequencing noise, (2) belong to minority (or even defective) genomes that are overlooked in clinical samples when the consensus sequence is generated, (3) be specific of the intestinal shedding of SARS-CoV-2 while most data are derived from nasopharyngeal swabs, (4) simply not persist in the population due to genetic drift or fitness disadvantage, (5) arise from non-human reservoirs also shedding into sewage  (Smyth et al. , 2022). Additional work on SARS-CoV-2 genetic diversity in different compartments of infected individuals, and in commensal animals, are still needed to better interpret the vast amount of information provided by WW sequencing.
ACKNOWLEDGMENTS We thank  Céline Bressolette  ( CRTI ,  UMR1064 ,  ITUN, Nantes, France ) for kindly sharing reagents and materials. We are also grateful to the  EmerEaUde consortium  for scientific discussions. We also thank  Christophe Rivière  and  Kevin Boucaron  ( Suez, France ) for providing WW samples and entry volume data.
DATA AVAILABILITY STATEMENT The datasets presented in this study can be found in the online repository DataRef from the SeaDataCloud Network: https:// data-dataref. ifremer. fr/bioinfo/ifremer/obepine/lsem/data/dnasequence-raw/, 20210210-run-1, 20210303-run-2, 20210324run-3, 20210413-run-4, 20210505-run-5, 20210507-run-6,  20210708-run-7, and 20210727-run-8.
Phenotyping We studied the same 15 traits in both trials (diversity panel and half-diallel). Phenotypic data were collected in 2011 and 2012 for the diversity panel and between 2013 and 2017 for the half-diallel. Traits were related to (i) berry composition at harvest, with malic acid (mal. ripe), tartaric acid (tar. ripe), shikimic acid (shik. ripe) concentrations, and shikimic to tartaric acid (shiktar. ripe) and malic to tartaric acid (maltar. ripe) ratios, (ii) berry and cluster morphological traits, with mean berry weight (mbw), mean cluster weight (mcw), mean cluster length (mcl), mean cluster width (mcwi) and cluster compactness (clucomp), (iii) phenology traits, with véraison date (onset of ripening, verday), harvest date (samplday) and the interval between véraison and harvest (vermatu), (iv) vigor (vigour).
After harvest, shoots and leaves were dried at 60 • C until the weight stopped decreasing, and then stored in a cold chamber until measurements. For each tissue, spectra measurements for both populations lasted a few weeks. For spectra gathering, a reflectance probe plugged to a visible-infrared spectrometer was used (LabSpec 2500 Portable Vis/Nir spectrometer device ; Analytical Spectral Devices, Inc. , Boulder, CO, US) with its associated software IndicoPro 6. 5. A reference spectrum was taken twice a day, using Spectralon ®. For each wood shoot, two scans were taken, one on each end of the shoot. For each leaf disc, one scan was taken, on the adaxial surface. Thus, for each tissue, four scans were produced per plot (i. e. , per genotype × block combination). Wavelengths ranged from 350 to 2,500 nm, with a 1 nm step.
Heritability assessment Heritability values of phenotypic data were assessed for both populations in Flutre et al. [9]  for the diversity panel and in Brault et al. [4]  for the half-diallel. Broad-sense heritability values ranged between 0. 53 and 0. 91 for the half-diallel and between 0. 72 and 0. 97 for the diversity panel. Heritability values were also assessed for reflectance data at each wavelength, after mixed model fitting. As for phenotypic data, heritability formula was calculated on an entry-mean basis, as described in Piepho et al. [23] : H 2 = σ 2 geno +σ 2 cross σ 2 geno +σ 2 cross + σ 2 geno:year +σ 2 cross:year nyear + σ 2 x +σ 2 y +σ 2 ǫ nrep×nyear , with the variance components previously estimated in the mixed model, n rep. year the mean number of replicates per year, and n year the number of year (one or two, depending on the model).
rrBLUP vs GBLUP model type • In rrBLUP, we fitted the following model: y = Xβ + ǫ , with y the vector of genotypic BLUPs from phenotypic data, X the matrix for marker genotypes (additively coded as in Brault et al. [4] ) or wavelength data (from NIRS BLUPs for each of the nine above-mentioned model combinations), β the marker or wavelength effects and ǫ the residual effects. This model was fitted using R/glmnet package version 4. 1-2  [11]. In rrBLUP, marker or wavelength effects are shrunk towards zero, according to a regularization parameter, chosen by an inner cross-validation (CV). • In GBLUP model (using the NIRS or the genomic relationship matrix), we fitted the following model: y = u + ǫ , with y the vector of genotypic BLUPs from phenotypic data, u the random effects for genomic or phenomic estimated breeding value, with u ∼ N (0, σ 2 u K ) , K being the relationship matrix from markers or spectra, σ 2 u the genetic variance, and ǫ the random residual effect, ǫ ∼ N (0, σ 2 ǫ I) , I being the identity matrix.
Funding Partial funding of  CB 's PhD was provided by  ANRT  (Grant Number  2018/0577 ), IFV and  Inter-Rhône. Spectra collection was partially supported by the project OASIs funded the  French ministry of Agriculture and Food (CASDAR ,  C 2020-5 ). The preparation of this manuscript was partly supported by the project  Sel-GenVit  funded by the  French National Research Agency  ( ANR-19-ECOM-0006 ).
Availability of data and materials All analyses were conducted using free and open-source software, mostly R. Genotypic values and genotypic data for half-diallel and diversity panel populations are available at https:// doi. org/ 10. 15454/ PNQQUQ. Spectra, R scripts and result tables have been deposited in the INRAE data portal: https:// doi. org/ 10. 15454/ BICRFX.
Sample Collection and Study Design This retrospective observational study included 4400 SARS-CoV-2-positive nasopharyngeal swabs obtained from COVID-19-positive patients referred to Italian Centres participating in the SCIRE (SARS-CoV-2 Italian Research Enterprise) collaborative group (see the map for the involved centres' distribution all over the Italian territory: https://arcg. is/19f9mn2, accessed on 3 November 2022) during the period from 1 April to 31 December 2021. The distribution of samples through the different months was as follows: 357 (April), 267 (May), 184 (June), 304 (July), 443 (August), 409 (September), 323 (October), 764 (November), and 1349 (December). All the genotypic, demographic, epidemiologic, and clinical data used for the analyses were collected at each centre as a part of routine variant surveillance or for research purposes.
Virus Amplification and Sequencing Genotypic data were obtained by using different methods: RT-PCR variant screening assays (n = 2535), spike Sanger (n = 938), next-generation sequencing (NGS, n = 46), and whole-genome sequencing (WGS, n = 881). These data, stratified according to the different methodologies and timing of sample collection, are shown in Table  1. SARS-CoV-2 swabs were collected from the respiratory tract of individuals who were either hospitalised or tested in screening programs. Viral RNA was extracted using different commercial kits, such as the QIAsymphony DSP Virus/Pathogen Midi kit on the QIAsymphony automated platform (QIAGEN, Hilden, Germany), the NucleoMag 96 Virus (Macherey-Nagel, Dueren, Germany) on automated KingFisherTM ml Magnetic Particle Processors (Thermo Fisher Scientific, Waltham, MA, USA), and manually with the QIAamp Viral RNA Mini Kit (QIAGEN, Hilden, Germany).
, Hayward, CA, USA). Sequencing was performed on the Illumina iSeq (n = 99), Miseq (n = 644), and Nextseq (n = 138) platforms for all samples. The results were mapped and aligned to the reference genome obtained from GISAID (https://www. gisaid. org/, accession ID: EPI_ISL_406800, accessed on 3 November 2022), using the Geneious Prime software v. 11. 1 (http://www. geneious. com, Biomatters, Auckland, New Zealand, accessed on 3 November 2022) or BWA-mem, and rescued using Samtools alignment/Map (Hinxton, UK) (v. 1. 9). The SARS-CoV-2 lineage was attributed to all sequences using the Pangolin COVID-19 Lineage Assigner v. 4. 1. 1 (https://pangolin. cog-uk. io/, accessed on 3 November 2022) and Nextclade v. 2. 4. 1 (https://clades. nextstrain. org/, accessed on 3 November 2022). Mutations were identified using Nextclade.
Statistical Analysis Descriptive analyses of the demographic and clinical data are presented as a median and an inter-quartile range (IQR) when continuous and as a frequency and a proportion (%) when categorical. To compare normally distributed, non-normally distributed continuous, and categorical variables, parametric tests (t-test and ANOVA), nonparametric tests (Mann-Whitney and Kruskal-Wallis), and the Pearson χ 2 test (or Fisher exact test, when necessary) were used, respectively. Significance was established at p < 0. 05. Data analysis was performed using the IBM SPSS Statistics version 25.
Here, we describe a heating system that is compatible with most microfluidic devices, microscopes, and objectives, without affecting the image quality, while allowing to precisely maintain a homogeneous temperature along the sample area for extended periods of time, in a cost-effective and easily reproducible manner. The system is based on a heating, transparent glass that can be put in contact with the top of PDMS-based microfluidic devices. An infrared probe facing the glass slide of the device is used to directly read the temperature of the sample, and the whole system is controlled with a simple, Arduino-based electronic circuit and program (Figure  1 ).
Design files summary Sample holder: 3D files relative to the 3D printed sample holder (depicted on figure  3 ). This part is used to hold the microfluidic device and can be attached to a standard microscope slide holder. Tightening legs: 3D files relative to the 3D printed tightening legs (depicted on figure  3 ). They are small plastic pieces used to hold the ITO glass against the PDMS, and hold the PDMS device in place onto the sample holder. Print in PLA or similar plastic. Probe holder: 3D files relative to the 3D printed probe holder (depicted on figure  3 ). It is used to hold the infrared temperature probe onto the objective of the microscope. Different variants are already available, but the design can be adapted to fit specific needs. Arduino programs: Program used to read the temperature from the infrared probe and compute an output power using a Proportional Integral Derivative controller.
The probe will then be able to send signals to the Arduino that can be interpreted by using the associated library (see code of the Arduino program), including the temperature of the target and the ambient temperature. b. Connect pin 11 (the pin number is defined in the Arduino program) to the gate of a MOSFET with a threshold voltage inferior to 5V, and to a 10kΩ pull-up resistor. c. Connect the source of the MOSFET to the ground and the drain to one of the two cables of the ITO glass. d. Connect the other pole of the ITO glass to the VCC pin of the micro USB (or USB C) port and the GND pin of the port to the ground. Explanation of the circuit: The Arduino will compute an output number from the inputs from the temperature probe. This output number will be sent to pin 11 as a PWM signal between 0 and 5V, which will close or open the gate (resp.
) of the MOSFET, hence modulating the average current passing through the heating glass, from the power supply. 11) Adjust the parameters of the Arduino program if needed (such as the Proportional and Integral coefficients, the setpoint, or the reading frequency, see comments in the code to see the effects of each parameter), and upload it to the Arduino card, using the Arduino software. Step 3: Insert the probe holder around the objective of the inverted microscope (d). (B) Step 4: Place the microfluidic device (e) onto the sample holder (f). Step 5: Place the heating glass (g) onto (e). (Steps 4 and 5 are interchangeable according to the user's needs and preferences). Step 6: Lock (g) against (e) using 4 M3 screws (h) passing through 4 tightening legs (i) and screwed inside (f). (C) Representative view of the assembled device.
Altogether, this confirms that the infrared probe is precisely measuring the temperature of the glass slide, where the sample of interest is located. In addition, this experiment allowed us to measure the response dynamics of the feedback system. In its configuration (PDMS thickness = 5mm, P coefficient = 50, I coefficient = 10, D coefficient = 0), the 99% equilibrium state was reached after ~12min, with an overshoot of 0. 5°C (Figure  7B ). The heating speed at full power in this configuration was 1. 5°C/min and at equilibrium, and the power used to maintain the sample at 30°C, with a room temperature of 24°C, was 0. 4W. These performances are similar to that of heating systems heating the sample from the sample holder with Peltier modules or heating resistors  [8]. Of note, modifying the P and I coefficients in the Arduino program (see comments in the code to see the effect of each parameter), or the PDMS thickness, affects the equilibrium time, the heating speed, and the overshoot.
Probe holder 3D files (. stl and. f3d) CC BY-SA 4. 0 https://3dprint. nih. gov/discover/3DPX-017678 Arduino program. ino text file CC BY-SA 4. 0 https://doi. org/10. 5281/zenodo. 7316523 and https://github. com/TAspert/HeatChips (Optional) read_HeatChips. m. m text file (MATLAB) CC BY-SA 4. 0 https://doi. org/10. 5281/zenodo. 7316523 and https://github. com/TAspert/HeatChips Arduino Software Free software from Arduino. cc GNU GPL https://www. arduino. cc/en/software.