dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements EK acknowledges funding by the  Academy of Finland. LP acknowledges financial support from The Vilho, Yrjo ¨and Kalle Va ¨isa ¨la ¨Foundation of the  Finnish Academy of Science and Letters. SM  acknowledges financial support from  Knut and Alice Wallenberg Foundation  and the  COST Action CA18212 -Molecular Dynamics  in the GAS phase (MD-GAS), supported by  COST (European Cooperation in Science and Technology). KK acknowledges funding by  Academy of Finland and Estonian Research Council  (grant  PRG551 ). We acknowledge  MAX IV Laboratory  for time on  Beamline FinEstBeAMS  under proposal 20190446. Research conducted at MAX IV, a Swedish national user facility, is supported by the  Swedish Research council  under contract  2018-07152 , the  Swedish Governmental Agency for Innovation Systems  under contract  2018-04969 , and  Formas  under contract  2019-02496.
Study assessment The study was approved by the "East III" ethical committee (IDRCB: 2019-A03332-55). Clinicians will inform all patients enrolled in the study that their biological samples could be used for research purposes and patients will express their non-opposition. All donors participating may object at any time, leading to the prompt disposal of their tissue and any derived material, as well as the cessation of data collection. The enrollment period of the study will be 3 years, each patient will be monitored for 5 years and study length will be 10 years.
In the case of evaluation of the response to immunotherapies, PDTO will be co-cultured with PDTO specific T cells previously generated (see co-culture of PDTO with immune cells) at a 5:1 ratio. Treatments (such as Nivolumab or Pembrolizumab) will be added directly in the co-culture. A condition containing an MHC-I blocking antibody will be added to control for antigen specific killing. PDTO morphology will be monitored by taking images during the required time using Incucyte S3 (Sartorius). At the endpoint, PDTO response will be assessed using CellTiter-Glo 3D cell viability assay (Promega) according to the manufacturer's instruction and luminescence will be measured using GloMax Discover GM3000 (Promega) with the associated software. Results will be normalized to the control condition. IC50 will be calculated with GraphPad software.
After obtaining the double stranded cDNA library, the library is purified with magnetics beads and amplified. During the library amplification the barcodes and sequences required for cluster generation (index i7 in 3′ and index i5 in 5′) are introduced due to Illumina compatible linker sequences. Library is amplified with 14 PCR cycles. The final library is purified and deposed on High sensitivity DNA chip to be controlled on Agilent bioanalyzer 2100. The library concentration and the size distibution is checked. Each library is then pooled equimolarly and the final pool is also controlled on Agilent bioanalyzer 2100 and sequenced on NovaSeq 6000 (Illumina) with 100 cycles chemistry aiming to obtain a minimum of 20 M reads by sample. To eliminate poor quality regions and poly(A) of the reads, we use the program fastp with quality score threshold of 20.
We also remove reads shorter than 25 pb. The read alignments are performed using the program STAR with the genome reference human (GRCh38) and the reference gene annotations (Ensembl). The UMI (Unique Molecular Index) allowed to reduce errors and quantitative PCR bias are processed using fastp and umi-tools. Based on reads alignements, we counted the numbers of molecules by gene using FeatureCount. Others programs are performed for quality control as qualimap, fastp, FastQC and Mul-tiQC. Statistical analysis is realized with the R/bioconductor package DESeq2. Copy number variations analysis by low-pass whole genome sequencing WGS will be performed using Illumina DNA PCR Free prep kit, starting with 500 ng of DNA. Library will be sequenced on a NovaSeq 6000 system aiming at 6x depth. Copy Number Analysis will be performed with HMMcopy and ichorCNA.
Transcriptome and CNV analysis A preliminary analysis of intra reproducibility and differences between original tumors and PDTO assessed by PCA and unsupervised hierarchical clustering for each omic data after normalization will be done. For each sample we will plot barplot based on the normalized count of reads or number of aberration (CGH) for specific pathways to evaluate heterogeneity in each type of samples. Studied pathways will be defined with ingenuity pathways analysis (proliferation, apoptosis, etc). Each PDTO will be compared to original tumors data. Targeted sequencing by NGS "Mutational status (SNV, indels, CNV and translocations)" will be established on original tumor samples and PDTO derived from the primary tumor by targeted re-sequencing of a panel of 580 genes involved in cancer for DNA and of a panel of 57 genes implicated in protein fusion for RNA.
Acknowledgements The ORGAPRED core facility 'Tumor organoids for research and predicting response to treatment' is supported by the  Normandy County Council , the  European Union  within the framework of the  Operational Programme ERDF/ ESF   2014-2020  and the French state which was conducted as part of the planning contract 2015-2020 between the State and the  Lower Normandie Region  (ORGAPRED, POLARIS and EquipInnovCaen2022 projects). We thank the  Lions clubs of Normandy  and the  Solidarité don d'espoir association  for their support of the projects carried out by our teams on PDTO. We are grateful to  Inserm, University of Caen Normandy  and the  Comprehensive Cancer Center François Baclesse  for their support in the implementation of these activities.
LR, 2 WA. UP, and 2 BS. LR C) (SOM 1 -Tables  S3  and S4 ). Samples from Facies III, VII, VI-III, and I-IV were excluded from the analyses for having only one representative sample. When significant differences were observed between certain groups, Dunn's pairwise comparison test with Bonferroni adjustments, a post-hoc, pairwise multiple comparison method, was also used to check for significant differences between groups. Nonetheless, the results obtained are tentative due to the small sample size. All statistical analyses used JMP®, Version 16. SAS Institute Inc. , Cary, NC, 1989e2021. Fourier Transform Infrared Spectroscopy (FTIR) was used to identify the bulk mineral components of the archaeological sediments to shed light on fire use and site occupation patterns, and better assess the state of preservation of the phytolith assemblages  (Weiner, 2010).
Taphonomic analyses of the kind used here are informative in that they can be used to pinpoint specific layers that might have suffered physical modification, for example in the form of trampling. Our study thus demonstrates that the combination of phytoliths and FTIR are valuable proxies to aid further stratigraphic and facies refinement at Border Cave. Although grasses and other graminoid-and monocot-associated phytoliths dominate, we observed that the variability observed in our data cannot be explained strictly by differences between types of deposits (combustion features, bedding, and light brown deposits). These variations in the ⁓60 ka deposits could reflect: (i) different unidentified anthropogenic activities taking place at the site; (ii) different human preferences for certain plants that changed over time; or (iii) changes in the surrounding plant communities.
Acknowledgements IE has received funding from the  postdoctoral fellowship programme Beatriu de Pin os  ( 2019 BP00247 ), funded by the  Secretary of Universities and Research (Government of Catalonia ) and by the  Horizon 2020 programme of research  and innovation of the  European Union  under the  Marie Skłodowska-Curie  grant agreement No. 801370. Laboratory materials for sample processing at the  University of Barcelona  were funded by the  Ministerio de Ciencia e Innovaci on -PID   2020-119773 GB-I00. Visit  to the site for sampling collection for this study was sponsored by a  NRF Y-Rated Research Grant  (No. 116358 ) to DS. PdlP was supported by a Poroulis grant through  Cambridge University. PdlP thanks the action PID 2019-1049449 GB-I00 funded by  Spanish FEDER/Ministry of Science and Innovation.
Samples 2. 1. 1. Human brain samples Human brain samples (from parietal and frontal cortex) and associated data were obtained from Tissu-Tumorothèque Est Biobank (CRB-HCL Hospices Civils de Lyon) authorized by the French Ministry of Research (authorizations DC2008-72 & AC2015-2576): • A 51-year-old male patient with genetic (APP duplication) AD (postmortem delay 36 h); • An 82-year-old female patient with sporadic AD (post-mortem delay 11 h). Both had pronounced Aβ pathology (Thal-Braak stage V, CERAD score 3), and had been previously diagnosed as free from prion disease. For each selected region, a pair of snap-frozen samples was collected. One sample was kept frozen for sectioning, while the other was fixed with formaldehyde 4 % (as described in the tissue preparation section below). The other half was sectioned while frozen, fluorescently stained to locate Aβ plaques, and analyzed in 2D using µFTIR (for β-sheet load) and µXRF (for metal level).
XPCT image analysis Ring artifacts were removed using an in-house tool implemented by the company NOVITOM (France; https://www. novitom. com/en/)  [19]. Samples were isolated from the background (air, glass tube and ethanol) and rotated within the AMIRA 3D software (release 2021. 1, Thermo Fisher Scientific, USA). Aβ plaques were segmented semi-automatically using a random forest algorithm from the IPSDK software (Reactiv'IP, France; https: //www. reactivip. com/). The generated masks were screened for false positive segmentations and ∼ 20 plaques were selected, along with ROIs of the background. Signal intensity measurements -as a ratio of the mean intensities of the plaques and the mean intensities of the background ROIs -were retrieved with MorphoLibJ  [37] , an ImageJ plugin.
µXRF elemental analysis Spectra were processed using PyMca  [41]. First, the raw spectral maps were summed to locate the plaques more precisely within the scanned field of view -from 20 × 20 µm 2 to 80 × 80 µm 2. Regions of Interest (ROIs) were drawn at the location of each plaque. Then, the measures on the three SRMs and on a blank SiRN membrane helped the calibration of the software -using the fundamental parameter method -and the batch-fitting tool computed the mass fraction maps for each element. Reported mass fractions in µg g -1 or "parts per million" (ppm) were obtained by averaging within the drawn ROI. One can easily retrieve the corresponding surface concentration: 10 ppm ≈ 1 ng cm -2 ; because 9 e dried tissue ≈ 1 µm and ρ dried tissue = 1. 04 g cm -3 (the density of dried brain tissue reported by Stedman and Spyrou  [42] ).
Comparison between human and rodent datasets Figure  7  shows a qualitative comparison of the multimodal dataset obtained with XPCT, µFTIR and µXRF, between transgenic rodents and humans. XPCT (left row) highlighted 3 distinct types of Aβ plaques: small hyperintense plaques in J20 mice and TgF344; larger, hypointense plaques in ArcAβ and APPPS1; and similarly large, but diffusely hyperintense plaques in the genetic case of AD. All types of plaques exhibited an increased level of β-sheet content which matched the fluorescence signal, as measured by µFTIR (central row), and which tended to be increased in the human case compared to animal models. The accumulation of endogenous metals (right row) was mainly restricted to the (hyperintense) core of the plaque in rodents, while it appeared consistently diffuse over the whole µXRF FOV in the human plaques.
Acknowledgements The authors acknowledge  Synchrotron SOLEIL (Saint-Aubin, France)  for allocation of beamtime within the scope of proposal # 20200258  and we would like to thank the staff for assistance in using beamlines ANATOMIX ( M. Scheel ,  J. Perrin ), SMIS and  NANOSCOPIUM  ( K. Medjoubi ,  A. Somogyi ). ANATOMIX is an Equipment of Excellence (EQUIPEX) funded by the  Investments for the Future program  of the  French National Research Agency (ANR) , project  NanoimagesX , grant no. ANR-11-EQPX-0031. We would like to thank  Clément Tavakoli  ( STROBE, Univ. Grenoble-Alpes, Grenoble; Univ. Lyon 1, Lyon ) for his help during the acquisitions at SOLEIL. The study was further supported by the  European Synchrotron Research Facility (ESRF, Grenoble, France ; beamline, ID-19; proposals  MD1018  and  IN1041 ) and by the  ALBA Synchrotron Light Source (ALBA-CELLS, Barcelona, Spain ; beamline  MIRAS ; proposal  2018082957 ).
This study was performed within the framework of  LABEX PRIMES  ( ANR-11-LABX-0063 ) of  Université de Lyon , within the  "Investissements d'Avenir" program  ( ANR-11-IDEX-0007 ) of the  French National Research Agency (ANR). We are grateful to  Corinne Perrin  from the  Tumorothèque Est  tissue bank,  CRB-HCL (Lyon, France ), for managing human brain samples. We also thank  Jérémy Verchère  ( ANSES, Lyon, France ) for the management of APPPS1 brains. p-FTAA was a gift from the laboratory of chemistry at  ENS-Lyon. S. Singh  received support from the  Auvergne-Rhône-Alpes region  ( FUI GigaQuant  project).
Acknowledgments. DA was supported by  NSF   Postdoctoral Fellowship Grant  No. 2002023  and  Simons Foundation  Grant No. 816048. DA is also grateful to  ENS Paris  for partially supporting his academic visit to Paris during which this research was initiated. CP is partially supported by the  Agence Nationale de la Recherche , project  BORDS , grant  ANR-16-CE40-0027-01 , project  SINGFLOWS , grant  ANR-18-CE40-0027-01 , project  CRISIS , grant  ANR-20-CE40-0020-01  and by the  CY Initiative of Excellence , project  CYNA.
2 ms. The extraction field was set to 2. 9 kV=cm in LXe to reduce localized, intermittent bursts of single electron S2 signals. Despite the lower-than-designed drift and extraction fields, the energy and position resolution, as well as the energy threshold, are comparable to those achieved with XENON1T. The TPC and veto detectors are integrated into a single data acquisition system  [14]. The data acquired by the MV uses the same hardware event trigger as in XENON1T  [15] , whereas data from the TPC and NV are acquired in a "triggerless" mode, with each individual PMT channel recording all signals above a channel-specific threshold of 0. 13 photoelectrons (PE). Published by the American Physical Society under the terms of the Creative Commons Attribution 4. 0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.
7 eV=quantum  [26]. Reconstructed energies using this scale directly give the ER-equivalent energy (keV ER ), while the NR-equivalent energy (keV NR ) requires a model for energy lost to heat, and uses the full NR detector model described later. The science search data were collected from July 6 to November 10, 2021. This period named Science Run 0 (SR0) contains a total of 97. 1 d of data which correspond to a dead-time-and veto-corrected live time of 95. 1 d. The length of SR0 was primarily chosen to investigate the XENON1T ER excess  [25] , leading to a WIMP search exposure of ð1. 09 AE 0. 03Þ ton yr. The detector conditions were stable throughout SR0 with an average LXe temperature of ð176. 8 AE 0. 4Þ K and pressure of ð1. 890 AE 0. 004Þ bar, where the uncertainties represent the corresponding rms over SR0.
PMT gains were monitored by weekly calibrations with a pulsed low-intensity light source, and voltages were adjusted at the beginning of SR0 to achieve 2 × 10 6 gains for all PMTs. The time dependence of the PMT gains was modeled and the signals were corrected, resulting in a gain variation < 3%. In total, 17 PMTs were excluded from analysis due to internal vacuum degradation, instability, light emission, or noise. Five of these PMTs are distributed evenly in the top PMT array. Periods of data taken with an intermittent and localized high rate of S2 emission from single or few electrons are not included in calibration and search data. Calibrations with 83m Kr were performed every second week to correct the detector response for position-and timedependent effects, and to monitor the stability of cS1 and cS2.
This data-driven tagging efficiency is corrected for position-dependent effects using GEANT4  [28]  simulations which account for the full spatial distribution of neutrons emitted by detector materials  [8]. The length of the veto window was set to 250 μs with a fivefold PMT coincidence and a 5 PE event area threshold in the NV. This gives a neutron tagging efficiency of ð53 AE 3Þ%, and a live time reduction of 1. 6%. The ER response model is calibrated with 2051 212 Pb β events from a 222 Rn calibration source  [29] , before SR0 and with events from an 37 Ar source  [30]  collected after SR0, as discussed in Ref. [13]. NR and ER calibration datasets were fitted using the LXe response model and fast detector simulation described in Ref. [31]. For both datasets, a Markov-chain Monte Carlo sampling of the parameter space gives the best-fit point and posterior distribution.
Neutron yields and energies originating from various detector materials are evaluated as in Refs. [8, 31]. A custom interface based on the fitted NR model accepts GEANT4 simulation inputs, and provides observable quanta processed by WFSim to construct the neutron background model  [38]. The neutron rate was estimated based on this full detector simulation and compared against a data-driven method. The data-driven estimate uses a combined Poisson likelihood for MS and SS events tagged by the NV, together with a simulation-driven MS:SS ratio which was validated with 241 AmBe data. The maximum deviation of the MS:SS ratio estimated as a function of the radius between data and simulation was found to be less than 20%. However, a wrong sign in the NV tagging window discovered only after unblinding of the main data meant that the simulation and data-driven estimates found before were no longer in agreement.
Data-driven models are constructed for AC events and surface background events. The AC background is concentrated at low S1 and S2, and is therefore a particular challenge for low-mass WIMP searches. The model is constructed from a synthetic dataset made from isolated S1s and S2s using the method in Ref. [32]. Looser cuts in FIG. 2. Detection and selection efficiency for NR events in this search as a function of the NR recoil energy. The total efficiency in the WIMP search region (black) is dominated by the detection efficiency (green) at low energies and event selections (blue) at higher energies until the edge of the ROI. Normalized recoil spectra for WIMPs with masses of 10, 50, and 200 GeV=c 2 are shown with orange dashed lines for reference. PHYSICAL REVIEW LETTERS 131, 041003 (2023) 041003-5 the near-wire region give a 6 times larger AC rate for this region compared to the rest of the TPC.
This region is indicated in Fig. 3   calibration data is used to construct a model for the signal in cS1 and cS2. After unblinding, the ROI contains 152 events, 16 of which were in the blinded WIMP region. The data are shown in Fig. 3 , and the best-fit expectation values are in Table  I. The binned GOF test indicates no largescale mismodeling (p ¼ 0. 63). At high cS1, PE, we observe more events which are consistent with ER events than our model or calibration data predict, in particular between cS1s of 50 and 75 PE. Of the 16 former blinded events, 13 are found in the upper right half of the horizontal event distribution, with no correlation with the transverse wires observed (see Fig. S3  in Supplemental Material  [34] ). The 220 Rn, 83m Kr, and 37 Ar calibration datasets do not exhibit any asymmetry, nor is any seen in the acceptances evaluated in the X, Y plane for any of the applied cuts.
08 × 10 -47 cm 2 × ½M DM =ð100 GeV=c 2 Þ. In conclusion, a blind analysis of 95. 1 d of science data with a total exposure of ð1. 09 AE 0. 03Þ ton yr has been performed. The best fit to the data is compatible with the background-only hypothesis. The experiment has achieved an ER background level of ð15. 8 AE 1. 3Þ events=ton yr keV, 5 times lower than XENON1T, with comparable detector resolutions, and energy threshold. This results in a sensitivity improvement with respect to XENON1T by a factor of 1. 7 at a WIMP mass of 100 GeV=c 2. Currently, XENONnT continues to take data, with a further reduced 222 Rn ER background, using the radon distillation system with combined gaseous and liquid xenon flow. Subsequent data taking is planned with the NV operating as designed, with Gd-sulphate-octahydrate loaded into the water  [45, 46]  to increase the neutron tagging efficiency to 87% with a lower overall lifetime reduction  [8].
How the mitochondrial genomes may have changed during the selection of the cultivars are not known. In this study, we re-sequenced the genomes of 352 strains representing wild strains from all known geographic distributions and the key commercial cultivars of A. bisporus in the world. For comparative purposes, we also sequenced nine representative strains from four outgroup species. We are interested in the mitogenome variation and how geographic and ecological factors have likely impacted the observed genetic variations. In addition, we aim to utilize the data to infer its evolutionary history, including its origin and divergence time, as well as its potential migration and dispersal patterns, based on its current geographical distribution. Furthermore, we aim to compare the mitogenomes of domesticated and wild populations in order to investigate the differences between them and explore the potential relationship between these differences and the domestication process.
Phylogenomic analysis of A. bisporus mitogenomes Sequence data preprocessing and variant calling The raw data obtained by sequencing was filtered using Trimmomatic to remove adaptor and low-quality reads to obtain clean reads. Clean sequence reads were mapped to the A. bisporus mitochondrial reference genome (JX271275) using the BWA-MEM version 0. 7. 17-r1188  [44]. Reads were then sorted and PCR duplicates removed, using PICARD (version 2. 25. 6) [45]. Then, variants were determined using samtools and bcftools. Last, Snpeff verion 5. 0e  [46]  was used to annotate and predict the effects of genetic variants on genes and proteins. In order to exclude the effect of horizontal transfer of introns, we prepared two datasets, one filtered as above and the other with SNPs in introns removed. Program circos-0.
Population structure analysis, and establishment of divergence times used mitochondrial genome To determine the optimal number of genetic clusters in our sample, we analyzed allelic data at all SNP sites using a block relaxation algorithm implemented in the ADMIXTURE software  [52]. The number and type of haplotypes within each genetic cluster (i. e. , clade) were calculated using DNASP based on the phylogenomic tree and the population structure, and using PopART  [53]  to construct the haplotype network. Principal component analysis was carried out using the glPCA function of R package and using Matlab for graphing. In addition, to exclude the influence of outgroup selection on the inference of the population history of A. bisporus, we removed outgroup strains and used only all A. bisporus strains to infer their population history using DIYABC Random Forest  [54].
Mitochondrial genome structure evolution at the interand intra-specific levels After the mitochondrial genomes are assembled and annotated for each strain, their gene orders and intron positions and types were analyzed at both within A. bisporus and above the species level. For genome structural variation analysis, the mitogenomes of each clade were first analyzed for covariance using mauve to derive the approximate regions of genome structural variation. The manually calibrated annotation results were then converted to standard Genbank file format using mf2sqn per sample, then gene alignment information was exported using Phylosuit  [56] , and finally mapped to each sample in the evolutionary tree using Itol  [57]. For intron distribution analyses, the intron annotation results of all samples were cross-checked against the reference genome JX271275.