dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Direct measurements of the geomagnetic field are available through ground observatories and navigators' data for the last 400 years  (Jonkers et al. , 2003)  and satellite measurements for the last decades. For earlier periods, the data are obtained through the archeomagnetic study of archeological baked materials and volcanic rocks that acquire, during their last cooling, a thermoremanent magnetization (TRM) whose direction is parallel, and intensity is proportional to the intensity of the local geomagnetic field. 10. 1029/2021GC009969 2 of 13 is to construct local reference curves for a given area in direction and/or intensity (e. g. ,  Hervé et al. , 2017; Kovacheva et al. , 2014; Molina-Cardın et al. , 2018; Tema & Lanos, 2021). There has been a long tradition of archeomagnetic studies in Northern and Central America, with the first studies in the mid 60s (e.
Also, only a few dozens of data, generally acquired on volcanic flows are full vector (e. g. ,  Mahgoub, Juárez-Arriaga, Böhnel, Siebe, & Pavón-Carrasco, 2019). Another weakness of the Northern and Central American data set is the inhomogeneous data quality, which clearly impacts the recovery of the secular variation in these regions (e. g. ,  Hervé, Perrin, Alva-Valdivia, Tchibinda Madingou, et al. , 2019; Jones et al. , 2020). A large proportion of intensity data do not fulfill modern quality criteria, such as the necessity to appropriately correct for TRM anisotropy and cooling rate effects, at least for archeological baked clays  (Hervé , Perrin, Alva-Valdivia, Tchibinda Madingou, et al. , 2019; Mahgoub, Juárez-Arriaga, Böhnel, Manzanilla, & Cyphers, 2019). As a new step to improve the Central American archeomagnetic database, nine pottery kilns were sampled in the archeological site of Casas de Fuego in Chihuahua state, Northern Mexico.
Rock Magnetism The magnetic carriers and their thermal stability were estimated by different rock magnetic experiments. Susceptibility versus temperature (K/T) curves were done with an AGICO Multi-frequency Kappabridge (MFK1-CS3). Two successive heating-cooling cycles, up to 350 and 620°C, were performed on powder specimens of ∼200 mg. Curie temperatures (Tc) were estimated at the point where the curvature of the concave part of the heating curve is maximal. First-order reversal curves (FORC) experiments were measured with a regular grid and a 200 ms averaging time on selected samples. Data were processed by the FORCinel routine  (Harrison & Feinberg, 2008)  with VARIFORC smoothing parameters  (Egli, 2013). Most kiln walls present a change in color from reddish when the wall was in contact with the fire to blackish inside the wall (Figure  3 ).
Directional Analysis All magnetic moments were measured with a 2G Enterprises cryogenic magnetometer. Alternating field (AF) demagnetization was done with an AF device in line with the cryogenic magnetometer, from 4 to 90 mT (3-5 mT steps). Thermal demagnetization was performed in an MMTD80A oven with temperatures ranging from 100 to 585°C (20-50°C steps). Sixteen pilot sister-specimens were demagnetized with both methods. As they give similar results, the rest of the specimens were AF demagnetized. Results were interpreted with the Puffin Plot software  (Lurcock & Wilson, 2012) , and the mean directions per kiln were calculated by Fisher statistic with the PmagPy software  (Tauxe et al. , 2016). Two-third of the specimens present a single component of magnetization (Figures  4a  and 4c ), while the others also have a small secondary component (Figures  4b  and 4d ) partly maybe of viscous origin.
Discussion Altogether nine mean directions and eight mean intensities were obtained in this study. The differences, especially in declination, suggest that the activity of the kilns did not stop at the same time. These new data are the first full vector data obtained on archeological material in Mexico and Western North America. We compared them to intensities (Figure  7 ) and directions (Figure  8 ) published from sites located within 2,000 km from Casas de Fuego in the 0-2000 CE period. Data were retrieved from GEOMAGIA50 (Brown et al. , 2021) Open (full) circles on Zijderveld plots correspond to a vertical (horizontal) plane projection, respectively. and from regional compilations  (Hervé, Perrin, Alva-Valdivia, Tchibinda Madingou, et al. , 2019; Jones et al. , 2020 Jones et al. , , 2021;; Mahgoub, Juárez-Arriaga, Böhnel, Manzanilla, & Cyphers, 2019; Soler-Arechalde et al.
, 2019). Data were relocated to Casas de Fuego via the Virtual Geomagnetic Pole (VGP) and the Virtual Axial Dipole Moment (VADM) assumptions for directions and intensities respectively. The non-dipolar component of the geomagnetic field introduces a relocation error that could reach a few degrees or a few μT at the maximum  (Casas & Incoronato, 2007; Goguitchaichvili et al. , 2020). We also compared the predictions, at Casas de Fuego, of global models that have been recently constructed with archeological and volcanic data (e. g. , ARCH10k. 1, Constable et al. , 2016; COV-ARCH, Hellio & Gillet, 2018; BIGMUDI4k. 1, Arneitz et al. , 2019; SHAWQ2k, Campuzano et al. , 2019). The predictions of these models being reasonably consistent, we choose to present on Figures  7  and 8  only the most recent SHAWQ2k model that has the advantage of weighting data according to their quality.
In (c), the brown data were acquired on burnt material carrying a TRM and the beige data on unburnt lime-plasters. In (b-d), the red curves with their 95% error envelope are the regional curves of  Hagstrum and Blinman (2010)  for Western North America and of Soler-Arechalde et al. (  2019 ) for Mexico. The prediction at the archeological site of SHAWQ2k global model  (Campuzano et al. , 2019)  is plotted in black with its 95% error envelope. All data and curves are relocated to Casas de Fuego via VGP. Böhnel, Siebe, & Pavón-Carrasco, 2019; Morales et al. , 2006; Pérez Rodriguez et al. , 2019)  and 39 baked clays  (Alva-Valdivia et al. , 2021; Hervé, Perrin, Alva-Valdivia, Tchibinda Madingou, et al. , 2019; Mahgoub, Juárez-Arriaga, Böhnel, Manzanilla, & Cyphers, 2019). Other high-quality archeomagnetic data exist in Mexico, but they are either beyond the 2,000 km limit (e.
g. ,  Fanjat et al. , 2013)  or older than 2 kyrs (e. g. ,  Duran et al. , 2010; Hervé, Perrin, Alva-Valdivia, Rodríguez-Trejo, et al. , 2019). For Western North America, 47% of the data set passed the selection criteria, with one lava flow  (Champion, 1980)  and 23 data on ceramics  (Bowles et al. , 2002; Jones et al. , 2020; Sternberg, 1982). As well in Mexico as in Western North America, the most discriminant criterion was the anisotropy correction, usually not performed in early studies (e. g. ,  Bucha et al. , 1970; Lee, 1975). While the selection discards the highest and lowest intensity values (Figure  7b ), the Mexican and Western North American data sets remain consistent, even though data are now missing for some periods as around 500-600, 1200, and 1500-1700 CE. Between 1200 and 1500 CE, besides Casas de Fuego, five Western North American and six Mexican data are available with intensities ranging from 80 to 130 ZAm 2.
, 2019). Most of them are located in Central Mexico along the Trans Mexican Volcanic Belt and in the Maya area. There are no directions from North of 21°N, emphasizing even further the interest of our work for the Mexican database. The Mexican data set is less homogeneous than in Western North America, with a wider range of inclination values and some data with declinations up to 40°W (Figure  8c ). To test the geomagnetic origin of this dispersion, we also performed a critical analysis of the directional data with (a) at least five specimens; (b) an α 95 lower than 5°; and (c) an age uncertainty less than ±200 years. A peculiarity of the Mexican data set is the presence of lime-plasters covering house floors, which mix water, lime, and lithic clasts  (Hueda-Tanabe et al. , 2004). Due to the volcanic environment of Central Mexico, the lithic clasts are rich in ferromagnetic particles, and a remanent magnetization is acquired by the alignment of the particles parallel to the local geomagnetic field when the plaster is mixed, therefore carrying a detrital remanent magnetization that does not record the ancient geomagnetic field as accurately as a TRM.
g. , lime-plaster data and Tula415) in the calculation of the mean directions by the moving windows technique, and likely does not reflect a geomagnetic feature. The Mexican secular variation curve over the last millennium needs to be re-established with new reliable data. For the moment, we suggest to use the Western North America directional curve  (Hagstrum & Blinman, 2010)  for archeomagnetic dating in Northern Mexico. If applied to Casas de Fuego, the fairly steady increase in declination between 1100 and 1800 CE may indicate a relative chronology between our kilns, with CF1-1 being the earliest in activity and CF1-6 the latest.
Acknowledgments This work was supported by  ANR - CONACYT   SVPIntMex  project  ANR-15-CE31-0011-01  coordinated by  M. Perrin  and  L. M. Alva-Valdivia  and by  PAPIIT-UNAM IN101521  research project. G. Hervé also benefited from a support of  Campus   France PRESTIGE program  ( PRESTIGE-2018-1-0002 ). The authors thank the staff of the  BCCT-UNAM (head, Saúl Armendariz)  for their technical assistance. The manuscript was improved thanks to the reviews of  L. Tauxe  and  S. Jones.
These tools eliminate the bias of investigator assessment and multicenter variability, allowing their implementation in large multisite trials. Artificial intelligence could be used to combine previously cited biomarkers to build robust prognostic/predictive models. One challenge is that building robust models using artificial intelligence requires creating large datasets, hence the need to aggregate data from multiple institutions to avoid overfitting  (48, 49). Eventually, deep learning could contribute to determine radiomics signature correlated with survival.
In contrast, Clpp -/-MEL cells synthesized increased quantities of heme (Fig. 1E ). As heme is an important transcriptional regulator of globin  (19) (20) (21) (22) , we analyzed the effect of Clpx or Clpp deficiency on globin expression. Consistent with a heme defect in Clpx -/-cells, differentiating Clpx -/-cells experienced a significant decrease in Hbb-b1 and Hbb-b2 mRNA expression. Undifferentiated Clpx -/-cells also had a significant decrease in Hbb2 expression (Fig. 1F ). These data confirm previous observations that CLPX is required for heme synthesis, while CLPP is not, and confirm that CLPX regulates heme independent of its role in the CLPXP proteolytic complex. Clpx deficiency also decreased globin mRNA expression, contributing to the hemoglobinization defect. To understand the role of Clpx in erythropoiesis in vivo, we characterized a zebrafish clpxb mutant (clpxb sa38141 ) obtained from the Zebrafish International Resource Center (ZIRC).
Data Processing Raw reads were processed using microSysMics(https://bio. tools/microSysMics, 20 April 2020), a workflow that relies on the Quantitative Insights into Microbial Ecology 2 (Qiime2) toolbox  [50]. Calculations were performed on the BiRD platform (Nantes). The R package DADA2 v1. 14 was used to demultiplex, quality filter, chimera filter, denoise the sequence reads, and call amplicon sequence variants (ASVs)  [51]. A phylogenetic tree was generated against the SILVA v132 reference database using FastTree and MAF-FTalignment in Qiime2. Diversity metrics were calculated on the rarefied ASV matrix. We chose a subsampling depth of 34,610 sequences per sample, for a final rarefied dataset of.
Data Processing Raw reads were processed using microSysMics (https://bio. tools/microSysMics, 20 April 2020), a workflow that relies on the Quantitative Insights into Microbial Ecology 2 (Qiime2) toolbox  [50]. Calculations were performed on the BiRD platform (Nantes). The R package DADA2 v1. 14 was used to demultiplex, quality filter, chimera filter, denoise the sequence reads, and call amplicon sequence variants (ASVs)  [51]. A phylogenetic tree was generated against the SILVA v132 reference database using FastTree and MAFFTalignment in Qiime2. Diversity metrics were calculated on the rarefied ASV matrix. We chose a subsampling depth of 34,610 sequences per sample, for a final rarefied dataset of 47 samples (>90% of samples, 13 HC-34 ASD). Alpha diversity was visualized and estimated by the metrics of observed ASVs, Pielou's and Shannon's indexes, using R packages dplyr v0.
8. 5, ggplot2 v3. 3. 0 and ggstatsplot v0. 4. 0. Beta-diversity ordinations were calculated on the Bray-Curtis dissimilarity matrix, Jaccard distance, and (un)weighted UniFrac distances, and PCoA were visualized using phyloseq v1. 30. 0 and ggplot2 packages. The difference in microbial β-diversity between classes was tested using permutational multivariate analysis of variance (PERMANOVA) using vegan 2. 5-6. The relative abundance of microbial ASVs was calculated from the nonrarefied ASV matrix. ASVs were filtered based on a prevalence of 20%. Community structure and metadata factors that influence it were studied by using omeClust 1. 1. 8 [52]  on the Bray-Curtis dissimilarity matrix. Differential abundance analysis was performed using Phyloseq v1. 30. 0 and DESeq2 v1. 26  [53]. The estimation of size factor was set to use "poscounts" as suggested for microbiota analysis.
Microbiota-Metabolite Association Heatmaps were generated to assess the correlation between microbiota-derived metabolites and genuslevel taxa in HC and ASD. ASVs were aggregated to the genus-level. Samples with missing metabolite concentration values were removed and a dataset of 14 HC and 28 ASD was used for the correlation analysis. Only the genera and metabolites seen in at least 30% of the samples per group (HC and ASD) were considered. A heatmap matrix was generated from spearman correlations and hierarchical clustering was used to cluster genera separately for HC and ASD using tidyverse v1. 3. 0, phyloseq v1. 30. 0, and microbiome v1. 8. 0 packages. Heatmaps and statistical analysis were generated using the associate function of the microbiome package. Only results displaying an adjusted p-value below 0.
Histological Analysis Murine colonic tissues were fixed with 4% paraformaldehyde in 0. 1 M phosphatebuffered saline for 3 h at RT, embedded in paraffin, microtome-sectioned (3 µm thick), mounted on glass slides and stained with hemalun, phloxin, and saffron (HPS). Images were acquired with a slide scanner (Hamamatsu Nanozoomer 2. 0HT, Massy, France) and viewer software (NDPViewer). Colonic tissue integrity was monitored by scoring the mucosal architecture, cellular infiltration, and muscle thickening, as 0, 1, 2, 3 (from best to less preserved). Multiplication of the score by 1, 2, 3, or 4 was applied if the observed damage covered 25%, 50%, 75%, or 100%, respectively, of the analyzed tissue fragment.
Socio-Demographic Data Thirty-six adults with ASD without mental retardation were included (ten women, twenty-six men). They were aged 33 ± 1 year on average and their score at the ADOS was 10 ± 0. 7 (Figure  1A ). Fifteen healthy controls (HC) were included (five women, ten men). They were aged 28 ± 2 years on average (Figure  1A ). The two groups were statistically similar with respect to age and sex distribution according to the Student t-test (p = 0. 61) or Fisher exact test (p = 0. 74).
Acknowledgments: We thank  S. Billon-Crossouard  from the  Mass Spectrometry Platform of SFR Bonamy  ( Nantes, France ) for SCFA and BA analysis. We thank  S. Blandin  and  M. Malloci  from the  Platform MicroPicell of SFR Bonamy (Nantes)  for tissue processing and staining. We thank the bioinformatics core facility of  Nantes (BiRD-Biogenouest ) for technical support. We thank  Erwan Delage  for his help in microbiota analyses.
Binding calculations High nonspecific binding was a limiting factor in the analysis of affinity especially when attempting to determine kinetic constants that were compromised also by a large component of mass transport during association and ligand rebinding during dissociation. Consequently, the steady-state value for association of each FN3Con-anti-VEGFR2 concentration was determined at 190 s after injection, and a K D was calculated using a plot of RU values at this point as a function of concentration with GraphPad Prism and a simple Langmuir type fit (Y = Bmax*X/(Kd + X) + NS*X + Background, where Y is the RU, X is the concentration in nM, and Background is constrained to 0). The ELISA data was also limited by nonspecific binding; however, the aim of this experiment was to provide a comparison between Adnectin-anti-VEGFR2 and FN3Con-anti-VEGFR2.
Acknowledgments-We would like to thank  Neelam Shah  and  Jackie Wilce  for assistance with Biacore experiments. Author contributions-P. G. C. , B. T. P. , and A. M. B. designed the study and drafted the paper. P. G. C. and L. L. T. performed data acquisition and interpretation. J. S. G. , B. T. R. , S. S. B. , D. E. H. , and M. B. assisted in data analysis, acquisition, and interpretation. D. E. H. , R. J. F. , T. P. M. , C. J. J. , and A. M. B. contributed to writing and conceptual advances.
Acknowledgments We would like to thank the participants in the ARAMIS trial and their family members and caregivers. We would also like to thank the trial site staff who cared for and supported the participants in the trial. Without all of these people working together, clinical trials could not move forward. Medical writing and editorial assistance in the development of this article were provided by  Brandis Pickard  and  Kimbra Edwards, PhD  of  The Center for Information & Trial on Clinical Research Participation (CISCRP) , a non-profit focused on educating and informing the public about clinical research participation. Medical writing assistance was also provided by  Adeline Rosenberg of Oxford PharmaGenesis, UK  and supported by  Bayer.
3 nM  [31]. Interestingly, for targeted proteins, the optimal affinities for targeting tumors with and without convection, of 16. 4 nM and 625 pM, respectively, for these simulated parameters, appear to be readily achievable, as demonstrated by the superimposition of the frequency distribution of protein affinities reported in the literature and compiled in the kinetics database KOFFI  [30]. When investigating the effect of receptor density for our default conditions (i. e. , using a proteinaceous EpCAM binder with an affinity of 68 pM), we noted a very strong dependency of the therapeutic effect on the receptor density (Supplementary Figure  S3 ). This mirrors the effects of modulating receptor affinity (see Figure  3A ) and reflects the characteristics of the binding-site barrier. Therapeutic effects were highest at around 1-2 × 10 5 receptors/cell (Supplementary Figure  S3 ).
Funding: This research received support of  Radboudumc , through an internal funding program, and of  Institut Pierre-Gilles de Gennes (laboratoire d'excellence , " Investissements d'avenir" program   ANR-10-IDEX-0001-02 PSL  and  ANR-10-LABX-31 ). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Data simulation Genotypes Genotype data at 51,477 loci were available for animals from each of the three dairy cattle breeds: Danish Holstein (HOL), Swedish Red (RED) and Danish Jersey (JER), from which a subset of 1050 (HOL and RED) or 220 (JER) individuals formed the base populations for this study. The SNPs that were fixed for the same allele in all three breeds were removed. For computational reasons, only the SNPs (12,664) on first five chromosomes were considered. A plot summarising the principle component analysis of genomic relationships among all animals was depicted to assess the genetic relationships between the pure breeds (see Additional file 1: Figure  S1 ). In order to establish a data set including multiple pure breeds (i. e. , HOL, RED and JER) and an admixed population (hereafter, MIX), a rotational crossbreeding system was mimicked using simulations, that considered three cycles of rotation (Table  1 ) for nine generations.
Reference and validation populations Generations 6,7 and 8 (G6-G8) were used to form reference populations, while generation 9 (G9) was used to form validation populations. Hence, 660 JER individuals, and 3150 individuals from each of the HOL, RED and MIX were available for forming reference populations to estimate SNP effects. The SNP effects were estimated using different reference populations: (i) a single pure breed (separate for each breed, i. e. , HOL, RED or JER), (ii) combined data of multiple pure breeds (HOL + RED + JER), and (iii) combined data of multiple pure breeds and admixed (MIX) individuals. The MIX dataset was either used as a different "breed", assuming homogeneous SNP effects across all breeds (HOL + RED + JER + MIX), or truly treated as an admixed population considering the breed origin of alleles (BOA) approach and uncorrelated (uncor) or correlated (cor) SNP effects between the breeds (HOL + RED + JER + MIX uncor/cor)  [27, 40, 56, 57].
Prediction accuracy Prediction accuracy was assessed as the correlation between true and predicted breeding values of validation individuals (1050 individuals for HOL, RED, MIX, and 220 individuals for JER) at G9. The accuracies of prediction using different data sets and models to estimate SNP effects were compared for each trait, QTL correlation and region size, separately. The accuracies of prediction for different region sizes were compared for each data set and model, trait and QTL correlation, separately. All comparisons were performed using a two-sided paired t-test, for which accuracies were paired across each replicate for the same validation population. A Bonferroni correction was used to control the type 1 error rate of 0. 05.
Results Accuracies for all scenarios and all region sizes are given in Additional file 2: Tables S1-S4. For readability, only the core results obtained with a QTL effect correlation of 0. 50 are presented in the main text. Accuracies were higher for a high heritability trait than for a low heritability trait (Figs. 1  and 2 ). Within-breed predictions for breeds with large reference populations (HOL and RED) were more accurate than for a breed with a small reference population (JER). For the high heritability trait, within-breed predictions for HOL, RED and JER were 0. 785, 0. 747 and 0. 629, respectively, when the region size was 1 SNP (Fig. 1 ). For this high heritability trait, combining data from multiple pure breeds (HOL + RED + JER) assuming homogenous SNP effects (multi-breed prediction) did not improve, or even decreased (but not always significantly) the accuracies for all breeds.
Including the admixed population's (MIX) data in multi-breed prediction, considering MIX as a different breed (HOL + RED + JER + MIX), yielded higher accuracies compared with combining only the data from pure breeds, and similar to or higher accuracies than using the single breed data alone (within-breed prediction), for genomic prediction of JER. When prediction models were able to accommodate data of admixed individuals by accounting for breed origin of alleles (HOL + RED + JER + MIX uncor/cor), accuracies were generally improved compared to combining all available data, but this depended on the correlation scenario. Across-breed predictions yielded much lower accuracies than within-breed predictions. Accuracies were lowest when using SNP effects from any of the pure breeds to predict the breeding values of admixed individuals.