dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of Transformation Products (TPs) Produced by HRC and T. asperellum To identify the TPs obtained after biological treatment, a suspect list of possible TPs and metabolites was compiled according to the common phase I and II reactions included in the Compound Discoverer feature. In addition, other previously identified plant endogenous conjugation molecules, such as sesquiterpene lactones (e. g. , lactucin, lactucopicrin, and their derivates), stress response molecules (e. g. , abscisic acid), amino acids (e. g. , pterin, methylsalicylate), and sugars (cladinose), were also included  [23, [37] [38] [39]. All compounds from the suspected list detected in the full-scan chromatograms were subjected to further confirmation based on their DDA-MS 2 spectrum. Online databases such as m/z cloud (https://www.
mzcloud. org/ (accessed on 27 October 2022)) and MassBank (https://massbank. eu/MassBank/ (accessed on 14 November 2022)) already contain MS 2 spectra of some biotransformation products. However, due to the limited availability of analytical standards, these are rather limited cases. In the presented study, 5 out of 30 TPs (i. e. , SMX294, OFL376-a, OFL348, TMP305, and TMP307) were identified by matching their MS 2 spectra with the online databases (Figure  3B1, B2 ). For the rest of the compounds, the assessment of their molecular structures was based on the interpretation of MS 2 spectra (Figure  3A ) and application of an enzymatic reaction (β-glucosidase). as m/z cloud (https://www. mzcloud. org/ (accessed on 27. 10. 2022. )) and MassBank (https://massbank. eu/MassBank/ (accessed on 14.
11. 2022. )) already contain MS 2 spectra of some biotransformation products. However, due to the limited availability of analytical standards, these are rather limited cases. In the presented study, 5 out of 30 TPs (i. e. , SMX294, OFL376-a, OFL348, TMP305, and TMP307) were identified by matching their MS 2 spectra with the online databases (Figure  3B1, B2 ). For the rest of the compounds, the assessment of their molecular structures was based on the interpretation of MS 2 spectra (Figure  3A ) and application of an enzymatic reaction (β-glucosidase). Analysis of the TP-based diagnostic evidence obtained from MS 2 spectra is a crucial technique, even though it is a time-consuming process. For 10 compounds, confirmation level 2b according to the Schymanski scale was reached  [40]. That is to say, for these compounds, sufficiently rich MS 2 spectra were obtained, which provided enough evidence to suggest their probable molecular structure.
Experimental data In all resampled scenarios reported below, we reanalyzed RT data from a functional magnetic resonance imaging (fMRI) experiment. Data and scripts for performing bootstrapping for all scenarios are available at https:// osf. io/ 9vs3y. The fMRI analyses and results are not reported here. In this experiment, participants were asked to choose between two lotteries that differed in the probability of winning a certain monetary outcome, as well as the value of the monetary outcome  (Couto et al. , 2020). In this setup, in the absence of explicit instructions, multiple strategies are available for participants to choose, such as computing and comparing the expected value of the options, or using a heuristic or rule of thumb (e. g. , focus only on the probability of winning). In this study, to isolate the strategy that involves computing expected values, we explicitly instructed participants-and incentivized them accordingly-to either choose the option with the highest expected value or choose their preferred option, in a blocked design, resulting in different strategies.
Participants Participants were recruited from the laboratory's participant database (www. lab. uva. nl) of the University of Amsterdam. Participants provided all the necessary written forms before participating in the experiment (i. e. , informed consent for the experiment itself and all the forms concerning safety which are required for fMRI experiments). Participants were rewarded with two research credits (RC) for their participation, with the possibility of a maximum monetary reward of €10, depending on two randomly chosen trials at the end of the experiment. All the experimental procedures followed the guidelines imposed (and approved) by the local Ethics Committee of the University of Amsterdam, Psychology Department (2019-PML-11490). The sample consisted of 48 participants, but four participants were excluded from the analyses for not completing the task, leaving 44 participants in the reported analyses (29 female, mean age = 21.
The expected contributions from SPS, DPS and TPS processes to the total triple-J/ψ cross-section amount to about 6%, 74% and 20%, respectively. This confirms the conclusion of ref. 28 that triple-J/ψ SPS representing about 4% of the total production. In terms of prompt and non-prompt contributions, the theoretical expectation for the production of three promptly produced J/ψ mesons is about 5% of the total yield, whereas the percentage expected for three non-prompt-J/ψ mesons is about 45%. The remaining half of the triple-J/ψ events are expected to be due to the combination of J/ψ mesons produced promptly and from beauty hadron decays. This result is consistent, within the large statistical uncertainties of the present dataset, with the combination of prompt and non-prompt-J/ψ mesons derived from the decay length of each dimuon candidate.
Acknowledgements We congratulate our colleagues in the  CERN accelerator departments  for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the  Worldwide LHC Computing Grid  and other centres for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the  LHC , the CMS detector, and the supporting computing infrastructure provided by the following funding agencies:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , and  FAPESP (Brazil) ;  MES  and  BNSF (Bulgaria) ;  CERN ; CAS, MoST, and  NSFC (China) ;  MINCIENCIAS (Colombia) ;  MSES and CSF (Croatia) ;  RIF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC PUT and ERDF (Estonia) ;  Academy of Finland, MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3 (France) ;  BMBF ,  DFG , and  HGF (Germany) ;  GSRI (Greece) ;  NKFIA (Hungary) ;  DAE and DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP and NRF (Republic of Korea) ;  MES (Latvia) ;  LAS (Lithuania) ;  MOE and UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MOS (Montenegro) ;  MBIE (New Zealand) ;  PAEC (Pakistan) ;  MSHE and NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON ,  RosAtom ,  RAS ,  RFBR , and  NRC KI (Russia) ;  MESTD (Serbia) ;  SEIDI ,  CPAN ,  PCTI , and  FEDER (Spain) ;  MOSTR (Sri Lanka) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ;  ThEPCenter ,  IPST ,  STAR , and  NSTDA (Thailand) ;  TUBITAK and TAEK (Turkey) ;  NASU (Ukraine) ;  STFC (UK) ;  DOE  and  NSF (USA).
| INTRODUC TI ON Along with the ever-growing large volumes of heterogeneous observational datasets in ecology (big-data), terrestrial biosphere models (TBMs) have been growing in complexity in order to model ecosystems as realistic as possible (big-model;  Fisher & Koven, 2020). This evolution comes with an increasing number of model parameters and computational demand. High computational cost is now the bottleneck in the big-data-big-model era in global change ecology. It hampers the applications of model-data assimilation systems needed to optimize model parameters, the assessment of model uncertainties, the refinement of ecological processes, and model applications at fine spatiotemporal resolution. As a consequence, arguably little progress has been made regarding the reliability and robustness of these models  (Prentice et al.
Note that the actual time savings depend on the machine infrastructure and software and might deviate from the theoretical one (Data S5). Based on the reported length of a spin-up conv for a precursor version of ORCHIDEE  (Krinner et al. , 2005) , we approximate the time consumption of a spin-up conv for ORCHIDEE v2. 2 without the use of the version-specific acceleration procedure instead of performing such a simulation. (Belitz & Stackelberg, 2021).
We found that top five key predictors in this ranking are generally the same as in the training using a subset of pixels (Figure  6 ) indicating a sufficient representativeness of the selected sites. However, for boreal forests and grasslands we found differences (i. pools are highest for pixels with rare soil suborders (i. e. Andisols, Entisols, Gelisols, and Oxisols; Figure  S13 ) in the training dataset, which provides additional evidence that the selected sites insufficiently cover the variation in edaphic conditions.
DATA AVA I L A B I L I T Y S TAT E M E N T The data that support the findings of this study are openly available at Mendeley Data https://data. mende ley. com/datas ets/8p5xy hv8j9/ 1. The raw data from the TBM simulation are archived at TGCC, and is available upon request. The source code of the land surface models versions are openly available in IPSL data catalog at https://doi. org/10. 14768/ 20200 407002. 1, ht t ps://d oi. o r g /10. 1476 8/ 39182 5a e-d257-4 365-9820 -3 0 e a1 940914c, http://forge. ipsl. jussi eu. fr/orchi dee/wiki/Group Activ ities/ CodeA valai bilit yPubl icati on/ORCHI DEE_ 2. 2_ gmd_ 2022. The implementation of the approach in python for ORCHIDEE TBMs is openly available in ZENODO at https://doi. org/10. 5281/ zenodo. 7503092.
g Summary of measures of beta cell function between family members in (f) during a 2-h 75 g glucose oral glucose tolerance test. Carriers of the p. X186 allele are in bold. HbA1c, hemoglobin A1c; HOMA-IR, homeostatic model assessment of insulin resistance (value > 2 indicates insulin resistance); DI, disposition index (Matsuda); ND, not done. Data are presented as mean ± SEM in (b-d). Statistical analyses were performed using two-tailed unpaired t test. *p < 0. 05, **p < 0. 01. Source data is provided in the Source Data File.
PAX4 p. Arg192His and p. Tyr186X alleles reduce the expression and/or function of PAX4 protein The PAX4 protein consists of two functional domains, paired and homeodomain, that are responsible for DNA binding, and two nuclear localization sequences (NLS) (Fig. 5a )  31, 32. Both p. Arg192His and p. Tyr186X variants are located within the functional homeodomain of the PAX4 protein (Fig. 5a ). As the crystal structure of PAX4 protein has not been elucidated, we obtained the predicted three-dimensional molecular arrangement of PAX4 protein (AF-O43316-F1-model_v2) from the AlphaFold database  33, 34. The p. Arg192 residue is located within a hydrophobic pocket (Supplementary Fig. 6a ), suggesting that substitution to an uncharged histidine may alter the DNA-binding function of the PAX4 protein.
6. Source data is provided in the Source Data File. PAX4 variants, and donor-derived gene-corrected hiPSCs)  30  demonstrated that all differentiating cells shared a similar trajectory towards pancreatic islet-like cells. Knockout of PAX4 did not result in the ablation of human beta cells, but rather, resulted in compromised beta cells with elevated expression of multiple endocrine hormone markers and lowered expression of genes associated with beta cell functional maturation. These observations were similarly replicated across donor-derived hiPSCs of three independent genotypes (p. Arg192His, p. His192His and p. Tyr186X), whereby SC-islets carrying PAX4 alleles demonstrated increased polyhormonal gene expression and reduced total insulin content. Our molecular assessments confirmed p. X186 to undergo NMD, while the p.
We sought to strengthen our findings through the aggregation of exome-sequencing data from multiple publicly available datasets, which both provided nominal evidence for the role of rare coding variation in elevated diabetes risk and support mounting evidence that PAX4 is not a monogenic diabetes gene  11. The loss of beta cell identity and the acquisition of polyhormonal cells have been reported in the pancreatic islets of individuals with diabetes  54, 55. The transdifferentiation of metabolically stressed beta cells to express GCG has been proposed by several groups as a mechanism underlying beta cell failure in T2D  [55] [56] [57]. In the current study, we demonstrate how coding gene variants in PAX4 can influence pancreatic beta cell development, identity, and function, thereby predisposing East Asian carriers to higher risks of developing T2D.
Statistical analysis of clinical data Analyses were carried out using SPSS software version 28. 0 (SPSS Inc. , Chicago, IL, USA). AIRg, Si, DI, HOMA-B, and HOMA-IR were log transformed to reduce skewness. Independent t-test and Chi-square tests were used to compare continuous and categorical variables between carriers and controls, respectively. A multiple linear regression model was used with adjustment by age, sex and BMI. Data are shown as the means (SD) or means (95% confidence intervals) and a p-value of <0. 05 was considered statistically significant. Our study had >80% power to detect a 35% difference in AIRg assuming a one-sided alpha of 0. 05 between carriers and noncarriers of the p. His192 allele. The recall-by-genotype dataset was enriched for individuals who carried the p. His192 allele.
Cell culture All mammalian cells were routinely tested to be mycoplasma free using a MycoAlert TM PLUS mycoplasma detection kit (Lonza Bioscience, LT07-710). All mammalian cells were cultured in a 5% CO 2 humidified incubator at 37 °C. Unless otherwise stated, cells were passaged using 0. 25% trypsin. Alpha TC clone 9 mouse pancreatic adenoma cells (αTC1. 9) (ATCC, CRL-2350™, RRID:CVCL_0150) were cultured in low glucose DMEM (1. 0 g/L) supplemented with an additional 1. 0 g/L glucose (final glucose concentration to be 2. 0 g/L), 10% FBS, 15 mM HEPES (ThermoFisher Scientific, 15630080), 1% NEAA (Gibco, 11140-50), 0. 02% Bovine serum albumin (BSA) (Sigma-Aldrich, A9418) and 1. 5 g/L sodium bicarbonate (ThermoFisher Scientific, 25080094). AD293 (Agilent, 240085, RRID:CVCL_KA63) and 293FT (Invitrogen, R70007, RRID:CVCL_6911) human embryonic kidney cell lines were cultured in high glucose DMEM supplemented with 10% FBS and 1% NEAA.
EndoC-βH1 cells  58  (Univercell Biosolutions EndoC-βH1, RRID:CVCL_L909) were cultured according to the manufacturer's recommendations. Briefly, tissue culture plates were precoated with high glucose DMEM supplemented with 2 µg/mL fibronectin (Sigma-Aldrich, F1141) and 1% ECM (Sigma-Aldrich, E1270) at least 30 min prior to cell plating. Low glucose DMEM (Gibco, 11885084) supplemented with 2% BSA, 10 mM nicotinamide (Sigma-Aldrich, N0636 or N3376), 2 mM GlutaMAX TM (Gibco, 35050061), 50 µM beta mercaptoethanol, 5. 5 μg/mL transferrin (Sigma-Aldrich, T8158) and 6. 6 ng/mL sodium selenite (Sigma-Aldrich, 214485). Cells were passaged weekly with 0. 05% or 0. 25% Trypsin and neutralized with 20% FBS in DPBS and plated at a density of 70,000 cells/cm 2. The SB Ad3. 1 hiPSC line derived from human skin fibroblasts from a Caucasian donor with no reported diabetes (Lonza CC-2511, tissue acquisition number 23447) was obtained from the Human Biomaterials Resource Centre, University of Birmingham.
6. 0. DE, EPs and SC-islets were collected on D3, D20 and D35, respectively, following differentiation with Protocol B before being dissociated into single cells using TrypLE™ Express (Gibco, 12605-010). Cells were passed through a 40 µm cell strainer and single cells were fixed with 4% PFA for 20 min on ice. Antigen blocking and cell permeabilization were performed using DPBS supplemented with 5% FBS (HyClone, SV30160. 03) and 0. 1% Triton-X-100 for 30 min on ice. Cells were stained with primary antibodies for 1 h at room temperature. The cells were then washed three times with DPBS and incubated with corresponding secondary antibodies for 1 h at room temperature. Flow cytometry analyses were performed with BD® LSR II Flow Cytometer (BD Biosciences) and data was analyzed using FlowJo TM software (BD Biosciences).
RNA sequencing and analysis Total RNA was extracted from samples generated using differentiation Protocol A at the end of Stage 1 (DE), 4 (PE), 5 (EP), and 7 (SC-islets) using RNeasy Mini Kit following the manufacturer's instructions. Polyadenylated transcripts were isolated using NEBNext PolyA mRNA Magnetic Isolation Module (New England Biolabs, E7490). Sequencing libraries were prepared using the NEBNext Ultra Directional RNA Library Kit with 12 cycles of PCR and custom 8 bp indexes (New England Biolabs, E7420). Libraries were multiplexed and sequenced on the Illumina NovaSeq 6000 as 150-nucleotide paired-end reads. Reads were mapped to human genome build hg19 (GRCh37) using STAR v. 2. 5  59  , with GENCODE v19 (https://www. gencodegenes. org/human/ release_19. html) as the transcriptomic reference.
featureCounts from the Subread package v1. 5 (http://subread. sourceforge. net/) was used to perform gene-level quantification. Differential expression analysis was performed per stage using DESeq2 60 comparing PAX4 WT/WT and PAX4 KO/KO cell lines. First, the model was fit using a likelihood ratio test with genotype as a factor of interest and experiment as a covariate. From this, genes not within the top 5000 most significant genes were used as an empirical control (affected only by unwanted experimental variation) and the estimated factor of unwanted variation (k = 1) was calculated using the RUVg function from RUVSeq 61. To identify differentially expressed genes, DESeq2 was performed using a likelihood ratio test and including the factor from RUV as a covariate along with the technical replicate (experiment).
Significance was determined by padj <0. 05. Sashimi, TPM, and volcano plots were generated using ggplot2 in RStudio. Total RNA was extracted from samples generated using differentiation Protocol B on day 0 (hiPSC), day 13 (PP2), day 20 (EP) and day 35 (SC-islets). Poly-A mRNA (10 -100 ng) was used to construct multiplexed strand-specific RNA-seq libraries (NEXTflexTM Rapid Directional RNA-SEQ Kit, dUTP-Based, v2). The quality of individual libraries was assessed and quantified using Agilent 2100 Bioanalyzer and Qubit 2. 0 fluorometer before pooling for sequencing using a HiSeq 2000 (1×101 bp read). Prior to cluster formation, pooled libraries were quantified using the KAPA quantification kit (KAPA Biosystems). The processing of raw RNA sequencing data was performed in collaboration with Molecular Engineering Laboratory (A*STAR) to remove lowquality sequence reads.
Acknowledgements The authors thank  A/P Yee Joo Tan  ( IMCB, A*STAR ) for her support with antibodies and  Daniela Moralli  ( University of Oxford ) for her support with hiPSC karyotyping. We thank the  Oxford Genomics Centre at the Wellcome Centre for Human Genetics  (funded by  Wellcome Trust  grant reference  203141/Z/16/Z ) for the generation and initial processing of the sequencing data. Data used in this manuscript has received funding from the  Innovative Medicines Initiative Joint Undertaking  under Grant Agreement number  115439  (StemBANCC), resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and  EFPIA  companies in kind contribution. H. H. L. is supported by the  Institute of Molecular and Cell Biology (IMCB)   Scientific Staff Development Award  (SSDA) for her parttime Ph.
D. N. A. J. K. is supported by the  Stanford Maternal and Child Health Research Institute Postdoctoral  Fellowship. A. L. G. is a  Wellcome  Senior Fellow in Basic Biomedical Science. A. L. G. is funded by the  Wellcome  ( 200837 ) and  National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)  ( U01-DK105535 ,  U01-DK085545 ,  UM1DK126185 ,  U01DK123743 ,  U24DK098085 ) and the  Stanford Diabetes Research Center (NIDDK  award  P30DK116074 ). A. K. K. T. is supported by  IMCB ,  A*STAR ,  Lee Foundation  Grant  SHTX/LFG/002/2018 ,  FY2019   SingHealth Duke-NUS Surgery Academic Clinical Programme Research Support Programme Grant ,  Precision Medicine  and  Personalized Therapeutics Joint Research  Grant 2019, the  2nd A*STAR-AMED Joint  Grant Call  192B9002 ,  HLTRP/2022/NUS-IMCB-02 ,  Paris-NUS 2021-06-R/UP-NUS  ( ANR-18-IDEX-0001 ),  OFIRG21jun-0097 ,  CSASI21jun-0006 ,  MTCIRG21-0071 ,  SDDC/FY2021/EX/93-A147 ,  FY 2022 Interstellar Initiative Beyond  grant,  H22G0a0005  and  I22D1AG053.
Data availability Data reported in this study were deposited into public data repositories, with accession numbers provided. Protocol A PAX4 KO/KO RNAseq data: EGAS00001006036. Data is available through the EGAhttps://ega-archive. org. Applicants should request access to the data using the data access agreement form (available from EGA or through https://med. stanford. edu/genomics-of-diabetes/datasets. html). Upon receipt of completed forms, a decision on access will be made by the data access committee (DAC) within 14 days. The chair of the DAC will inform EGA and EGA will release the data to the account listed in the data access agreement. Forms should be returned to Anna L. Gloyn, Stanford University (agloyn@stanford. edu). Protocol B RNA-seq data: GSE203265. The processed RNA-seq data generated from this study are available as Supplementary Data 1-5.
Acknowledgements. This research was conducted within the  Long-Term Socio-Ecological Research (LTSER)  platform  Lautaret-Oisans , a site of the  European Research Infrastructure eLTER. It received funding from the  Lautaret Garden (Univ. Grenoble Alpes ,  CNRS ,  LAUTARET ,  38000   Grenoble, France) , a member of the  Zone Atelier Alpes  and the  eLTER  network and from  ANR  project  ANR-15-IDEX-02.
Simulation Environment Our previous works  [15, 32]  focused on V2X-based interference mitigation strategies with a Python simulator, which was built to generate realistic traffic scenarios including radars. This framework uses the recognized and widely used SUMO software (SUMO is an open-source continuous multi-modal and highly portable simulation package made with Eclipse. It is designed to handle large road networks and is already used by some V2X studies to provide realistic road traffic scenarios. The software is available at https://www. eclipse. org/sumo/ (accessed on 1 June 2023)) to generate the road traffic (the configuration used in SUMO is available online at https://github. com/Ramboun/dynamic_k_coloring_ data (accessed on 1 June 2023)). SUMO allows for the generation of custom scenarios, but a real traffic scenario could be used via existing real-world databases, such as the US Highway 101 dataset  [33] , or new data acquired with connected vehicles  [34].
Metaheuristic Results The metaheuristic has been applied to the graph extracted from the simulation environment described in Section 3 (the temporal adjacency matrix resulting from the simulation environment is available online at https://github. com/Ramboun/dynamic_k_coloring_data, accessed on 1 June 2023). Simulated annealing has been applied with the parameters presented in Table  1. α c is computed, so cost con f reaches cost end con f by the end of the cool-down phase. With these parameters, a total of a million solutions are tested, yielding the results presented in Table  2  and Figure  11  with 16 ≥ K ≥ 36. Overall, 16 is the lowest value tested as there is a timestep with a max clique of 16, making it impossible to avoid all conflicts with less than 16 colors. The results are compared with a method that changes colors right before a conflict happens and chooses the color that maximizes the amount of time before the next conflict.