dataesr/scientific-paragraphs-categorization huggingface dataset:
Creating the clean masks in such a way ensures that cleaning is mostly limited to real emission, while also speeding up the imaging by limiting the cleaning area. Though the self-calibration can be a significant improvement on the image it can also be potentially unstable. To monitor the stability of solutions, a diagnostic tool for self-calibration produces a report on the variation of relevant statistics such as noise and central source flux density in different steps of calibration. As with calibration, the logs were evaluated for errors and warnings during the self-calibration process and information relevant to assessing the quality of the products, such as percentage of flagged data and theoretical noise limit, were extracted. Imaging is performed using Multi-term Multi-Frequency Synthesis (MTMFS; Rau & Cornwell 2011) deconvolution with four pixel scales (0, 2, 3, and 5 pixels) to model extended emission in the images and two Taylor terms to account for the spectral shape of the sources.
Source finding With thousands of sources expected to be detected in every MALS pointing, we require an automated source finding algorithm to find and characterise these sources. A small number of these are suitable for radio images, and perform comparatively similar  (Hale et al. 2019). Of these, PYBDSF has been used in several recent data releases of large-scale surveys, such as the LOFAR Two-Metre Sky Survey (LoTSS;  Shimwell et al. 2019 ) and the RACS  (Hale et al. 2021). PYBDSF stands out in its ability to model extended emission with its wavelet decomposition module, and provides easy ways to compile source catalogues and assess the quality of the fields. Besides generating catalogues, PYBDSF provides output maps related to the input image, such as the RMS noise images we used in Sect.
We created cutouts from the images and performed visual inspection, which was implemented in a separate module based on PYTHON and CASA 5. PYBDSF assigns each source a flag indicating whether the source is fit by a single Gaussian ('S'), multiple Gaussian components ('M'), or Gaussian component(s) on an island with other sources ('C'). Since all Gaussian components that occupy the same island are always combined into one source, the 'C' flag is not present in our catalogues. For the visual inspection, we considered all sources made up of multiple Gaussian components. As such, all sources that carry the 'M' flag -which make up 8% of the all sources found in the fields -are flagged for visual inspection. Through the visual inspection, we then assigned an additional flag indicating the nature of the source and how well it is described by the PYBDSF model: G: sources that are well described by the Gaussian model.
Though the result from NVSS might indicate any number of problems that could cause the offset, the additional data from RACS rules out most of these assumptions. A likely source of uncertainty would be the assumption of spectral index; however, this would impact the RACS results far more significantly, with its rest frequency of 887 MHz. From the RACS flux density offset we can assume that the 6% offset stems mostly from the residual primary beam effect, but this can only explain part of the offset seen in NVSS. If this offset is persistent, it points to a systematic effect affecting either NVSS or both MALS and RACS. Due to the relatively low sensitivity of the surveys, only about 10% of MALS sources are matched to a counterpart, which makes the error bars on the flux density offset measurement rather large.
4. 4. Once again we also correct number counts using the RMS noise coverage (green squares). For comparison, number counts from the full SKADS simulated sample are also shown, both for the central frequency of 1. 27 GHz and for the full frequency band, accounting for the fact that for most sources flux density is not equal across the band. The number counts derived from the MeerKAT DEEP2 image and NVSS by  Matthews et al. (2021)  are also shown (white diamonds). Error bars are computed taking into account Poisson uncertainties as well as source clustering following  Heywood et al. (2013). The same data from Fig. 24  is tabulated in Table  3 , showing the numerical values of flux bins, raw number counts, and uncorrected and corrected differential number counts. Though it is not taken into consideration in Fig.
In the current literature on the cosmic dipole, there are many examples of systematic effects that limit the sensitivity of these estimates and could not be pushed further due to a lack of information on the inner workings of the surveys that were used. For MALS, we have a complete assessment of the inner workings of the survey, with insight and access into the processing pipeline. Looking forwards, we determine that 100 MALS pointings suffice for a dipole measurement. Paired with the analysis on source characteristics and counts in the pointings, we are poised to perform the most complete dipole estimate of a radio survey thus far. Calibration and imaging of all the data was carried out through ARTIP. After imaging, we separately created spectral index images and performed primary beam correction averaged over the frequency range on these and the continuum images with a primary beam model derived from holographic measurements.
Acknowledgements. We thank the anonymous referee for their useful comments and feedback. We thank  Charles Walker  for his insightful comments and feedback on the text. J. D. W. acknowledges the support from the  International Max Planck Research School (IMPRS)  for  Astronomy and Astrophysics at the Universities of Bonn and Cologne. E. B. acknowledges support by  NASA  under award number  80GSFC21M0002. P. K. is partially supported by the  BMBF  project  05A17PC2  for  D-MeerKAT II. The MeerKAT telescope is operated by the  South African Radio Astronomy Observatory , which is a facility of the  National Research Foundation , an agency of the  Department of Science and Innovation. The MeerKAT data were processed using the MALS computing facility at IUCAA (https://mals. iucaa. in/releases).
93 +04°40 ′ 28 ′′. 90 420 472. 6 ± 0. 3 -0. 80 ± 0. 09 8. 1 6. 7 -7. 6 1558 29 6. 3 J1133+0015 11 h 33 m 03 s. 12 +00°15 ′ 48 ′′. 90 233 377. 7 ± 0. 9 -0. 01 ± 0. 07 8. 9 6. 7 -15. 3 803 52 15. 9 J1232-0224 12 h 32 m 00 s. 13 -02°24 ′ 04 ′′. 10 1647 1823. 4 ± 5. 2 -0. 31 ± 0. 09 8. 5 6. 7 -9 611 73 20. 8 J1312-2026 13 h 12 m 07 s. 86 -20°26 ′ 52 ′′. 40 778 851. 4 ± 0. 4 -0. 83 ± 0. 09 7. 7 6. 3 -9. 1 2431 21 12. 2 J2023-3655 20 h 23 m 46 s. 21 -36°55 ′ 21 ′′. 20 436 406. 6 ± 0. 5 -0. 11 ± 0. 12 10. 9 6. 8 -88 2160 22 5. 8 known strong RFI are flagged (see also Fig. 2 of Gupta et al. 2021). The resulting dataset has a total of 960 channels, a bandwidth of 802. 5 MHz (869. 3-1671. 8 MHz), and a central frequency of 1. 27 GHz as a result of the edges of the band being flagged.
In this paper, we examine the influence of IRD on the   record of three sedimentary cores from the subpolar North Atlantic and the Antarctic margin. The influence of unsorted IRD on   records is clearly established. To remove this IRD influence on grain-size distributions (GSDs), we propose a new method based on End-Member Analysis approach, and for which a MATLAB script is made available. This method characterizes the GSD of the unsorted IRD input, allowing it to be isolated and discarded, and the current sensitive   variability to be robustly identified. The method therefore allows the recalculation of a modified sediment GSD free of unsorted IRD influence and the construction of modified   and sortable silt percentage (the % of the 10-63 μm in the total <63 μm fraction) records. The application of the method to the three studied cores shows that (a) the unsorted IRD component is correctly removed from the grain-size signal and (b) the new   record is consistent with the XRF-based ln(Zr/Rb) grain-size proxy.
Paleoceanography and Paleoclimatology STEVENARD ET AL. 10. 1029/2022PA004500 2 of 16 An alternative to reconstruct true past current strength variability is the use of  Δ which is the result of the subtraction of the regression between   (phi scale) and sand (%) from the initial    (Hass, 2002). However, the lack of accuracy in most of the regression calculations makes this method poorly precise  (Hoffmann et al. , 2019; Li & Piper, 2015). Jonkers et al. (2015)  have proposed another method to remove IRD influence using inverse modeling or End-Member-Analyses (EMA). The EMA consists in a statistical decomposition of the grain-size datasets resulting in the determination of "End-Members" (EM) and of their respective contributions to all grain-size spectra  (Dietze et al. , 2012; Paterson & Heslop, 2015; Seidel & Hlawitschka, 2015; Weltje, 1997; Zhang et al.
Prior to grain-size measurements, 2 ml of a solution of sodium hexametaphosphate (Calgon, 2 g/L) was added to the sample, then placed in an ultrasonic tank during 2 min. The laser diffraction measurements were run in a deionized water tank with red light, Fraunhofer approximation and no ultrasound during the measurement in order not to warm up the solution and change its optical properties. Two parameters were calculated following previous studies:   , the geometric mean grain-size of the sortable fraction of silt  (McCave et al. , 1995)  and SS%, the percent of non-cohesive silt (10-63 μm) divided by total percent of the fraction smaller than 63 μm  (McCave & Hall, 2006). Both parameters were calculated according to McCave and Andrews (2019) by adding interpolated columns at 10 and 63 μm in grain-size datasets.
Zr/Rb Ratio as a Grain-Size Proxy The elemental composition, including Zr and Rb contents, of the two North Atlantic cores discussed here were obtained using an Avaatech X-Ray Fluorescence (XRF) core scanner, which provides rapid, non-destructive and high-resolution records  (Croudace & Rothwell, 2015; Croudace et al. , 2019; Richter et al. , 2006; Weltje & Tjallingii, 2008). The elemental composition of cores MD03-2679 and MD03-2673, reported here for the first time, were specifically analyzed using the 4th generation Avaatech core scanner of the Laboratoire des Sciences du Climat et de l'Environnement (LSCE). After a careful smoothing, the sediment surface was covered with an Ultralene® foil to avoid the contamination of the sensor. XRF data were collected every 1 cm with downcore and cross-core slits of 1 and 1.
2 cm, respectively, throughout the entire length of the cores. The counting time was 10 s at 10 kV (no filter, under helium flow) for light elements and 30 kV (Pd thick filter, under air flow) for heavier elements, with an X-ray intensity of 140 and 90 μA, respectively. Three replicas were measured every 10 cm to estimate the measurement precision which is 3. 4% and 5. 1% for Zr, and 6. 2% and 7. 8% for Rb, in core MD03-2679 and MD03-2673, respectively. A certified standard SARM4 ("monitor scan") was measured at 10 kV between each section measurement in order to further ensure the reproducibility of the data and verify the presence of reliable technical conditions of measurement, such as the absence of helium leak which impacts light element measurements. XRF core-scanning data are affected by specimen (e.
g. , variable water content, GSDs and presence of burrows) and matrix effects which impact the absorption, enhancement and scattering effects caused by the presence of other elements in a specimen  (Weltje & Tjallingii, 2008). These effects on XRF semi-quantitative core-scanner data may be reduced by the use of a Multivariate Log-ratio Calibration (MLC,  Weltje et al. , 2015). To calibrate the XRF core-scanner data, we measured the elemental composition of discrete sediment samples using powder Energy Dispersive XRF. A total of 96 and 85 discrete samples for cores MD03-2679 and MD03-2673, respectively, was thus analyzed using the PANalytical Epsilon 3 XLE ED-XRF spectrometer available at LSCE. Discrete samples were selected from the XRF scanning profiles in order to cover the entire range of variations of 10 geochemical elements (see Supporting Information S1).
All samples were measured with a counting time of 100 s at 5 kV (no filter, under helium flow), 12 kV (thick Al 50 μm filter, under air), 20 kV (thick Al 200 μm filter, under air) and 50 kV (Ag filter, under air) with X-ray intensities of 700, 320, 140, and 140 μA, respectively. More than 50% of samples were analyzed three times, to estimate the measurement precision (2. 8% and 1. 5% for Zr and 3. 2% and 1. 9% for Rb in cores MD03-2679 and MD03-2673, respectively). These discrete measurements were used to calibrate the core-scanner intensities of 10 geochemical elements (see Figures S1-S4 in Supporting Information S1 for more information) using the GUI Matlab® Xelerate  (Weltje et al. , 2015). As suggested by previous studies  (Aitchison, 1990; Weltje et al. , 2015; Weltje & Tjallingii, 2008) , geochemical ratios are presented in log-ratios.
Paleoceanography and Paleoclimatology STEVENARD ET AL. 10. 1029/2022PA004500 8 of 16 coarse-grained fractions related to IRD. With this hypothesis in mind, isolating the GSD associated with the current-transported fraction and recalculating it in sum to 100% produces a modified GSD free of IRD influence. Our method is focused on the removal of the IRD influence from the entire GSD to construct a modified GSD matrix using the parametric EMA of the GUI Matlab AnalySize  (Paterson & Heslop, 2015). Generally, EMA aims to fix the minimum number of EM related to mixing dynamics that represents a sufficient percentage of the variance (e. g. ,  Dietze et al. , 2012; Paterson & Heslop, 2015; Weltje, 1997). Here, we have decided to use parametric EMA to deconvolve the GSD with the highest number of EM.
This high number of EM allows a better representation of the initial GSD than the use of only two or three EM, especially for samples with plurimodal distributions due to IRD content. The AnalySize GUI Matlab supports four types of parametric distributions  (Paterson & Heslop, 2015). The Skewed Generalized Gaussian (SGG) and Lognormal parametric distributions do not control the shape of distributions  (Paterson & Heslop, 2015). The General Weibull distribution adds an additional location parameter to the Weibull distribution (which have parameters controlling the shape and the scale of the distribution) and produces EM with a larger left-hand tail than Weibull distributions  (Paterson & Heslop, 2015). As our new method needs Gaussian shape and well-constrained EM in term of grain-size range, we decided to run EMA with Weibull distributions.
The distributions represented by the sum of all EM distributions deduced from the Weibull EMA approach covers more than 99% of the original GSD for the large majority of datasets used in this study. Then, we group some of the defined EM in order to isolate the current-sorted fraction (hereafter labeled EM CS ) from the coarse "IRD" fraction (hereafter labeled EM IRD ) (Figure  5 ). The difficulty is to decide which EM represents particles transported and sorted by bottom currents and which EM represents grains derived from ice or iceberg rafting. For all cores, the EM CS are always identified in GSD of samples with no IRD influence (e. g. , Figures  5a  and 5b ). We noticed that the percentile 80 (P80) of the average GSD of each core could be a grainsize limit between the two EM groups (Table  2 , Figures  S5a, S5c , and S5e in Supporting Information S1).
EM with a median grain-size higher than the P80 value of the core are placed in the EM IRD group, while those with a median grain size below the P80 value belong to the EM CS group. This P80 limit is reliable for the studied cores but it might not be always the case, so we encourage users to first closely examine EM composing the unimodal distributions of current-sorted samples. In the following, we shall describe, step by step the new approach we propose to correct GSD from the influence of IRD. Note that we produced a MATLAB script (available at https://gitlab. in2p3. fr/nathan. stevenard/ird_corr_ package) to facilitate the application of this method. After running the EMA and defining the n number of EM related to EM CS (see above), the first step of our method is to calculate, for each sample, the GSD of each EM in a structure X using their abundances (EMa) and their densities (EMd) following the equation: , , = EMd , * EMa , 100 where i is the ith EM, j is the jth specimen (sample) and k is the kth grain-size bin.
Acknowledgments We are grateful to the chief scientist ( C. Laj ) and the crew of the R. V. Marion Dufresne  for collecting cores during the  P. I. C. A. S. S. O cruise. We thank  G. Maillet ,  M. Mojtahid  ( LPG-BIAF ) and  C. Skonieczny  ( GEOPS ) for technical support for grain-size analyses and  C. Wandres  ( LSCE ) for technical support for XRF core-scanning. We are grateful to  Prof. N. McCave  for fruitful exchanges. N. S. acknowledges a PhD grant from the  Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA). A. G. acknowledges support from  ANR  (Grant  ANR-18-BELM-0001-06 ). This work was supported by the  CEA , the  Centre National de la Recherche Scientifique (CNRS)  and by the  French National program LEFE (Les Enveloppes Fluides et l'Environnement) , under the project  DECORATING (Dynamique des principaux courants profonds de l'Atlantique lors des périodes chaudes du passé: étude des interglaciaires des derniers 450 000  ans).
Data Availability Statement The Matlab code of the method described in this study is available at https://gitlab. in2p3. fr/nathan. stevenard/ ird_corr_package  (Stevenard, 2023). Geochemical ratio ln(Zr/Rb) grain-size proxy and grain-size data of cores MD03-2679 and MD03-2673 used in this are available in the PANGAEA database Stevenard et al. (2023a Stevenard et al. ( , 2023 Stevenard et al. (   b, 2023c Stevenard et al. ( , 2023d)). Geochemical ratio ln(Zr/Rb) grain-size proxy and data of core ANT30-P1-2 used in this study are available in L. Wu et al. (2020 ) via L. Wu (2020).
These data further support the importance of the metabolic function of the gut microbiome in the susceptibility of individuals to developing chronic diseases and the opportunities to design nutritional and probiotic-mediated solutions that stimulate 4-CS levels in treating this cluster of metabolic diseases. Several limitations exist in this study. First, diagnostic accuracy of the three chronic diseases (T2DM, CKD and NAFLD) might be compromised. Of note, T2DM was determined by self-answered questionnaires, CKD diagnosis was based solely on creatinine level at a single time point, and NAFLD cases were defined only by the FIB-4 score. Nevertheless, board-ranging intermediate phenotypes systematically available for all subjects in the Nagahama population study have provided important clues for analysis of disease risks.
Materials and methods The Nagahama Study. Data were obtained from the first follow-up of the Nagahama Cohort for the Comprehensive Human Bioscience, a community-based prospective cohort study conducted in Nagahama City in Shiga prefecture, Japan. A total of 11644 middle-aged to elderly residents participated in the first follow-up from 2012 to 2016. Initially, there were 3645 individuals whose metabolome data were measured. Four individuals without available phenotype information were excluded. Therefore, 3641 individuals were included in this analysis. The details of this cohort have been reported elsewhere  20. This study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the ethics committee of Kyoto University Graduate School of Medicine and by the Nagahama Municipal Review Board (no.
Acknowledgements We would like to thank all the organizing staff and the participants in the Nagahama Study. We also thank the  Metabolomics Advanced Training  and the  International Exchange Program (MATRIX)  for the financial support. H. O. is supported by the  Kyoto-McGill International Collaborative Program in Genomic Medicine. D. G. and F. M. acknowledge financial support from the  INSERM Projet de Recherche International DIABETOMARKERS.
For metabolites that show potentially causal relationship with depression, we study the impact of antidepressant therapy in the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study. Finally, we integrate our findings with those of the Virtual Metabolic Human (VMH) and Assembly of Gut Organisms through Reconstruction and Analysis (AGORA2) databases to identify gut microbiota involved in the metabolism of the identified metabolites (Fig. 1 ).
Metabolites with >20% missing values were excluded. Then, metabolites were log-transformed, imputed and scaled to mean zero and variance 1. Details are provided in the Supplementary Materials. Non-targeted metabolite detection and quantification was conducted by the metabolomics provider Metabolon, Inc. (Durham, USA) on fasting plasma samples of 10,654 participants from the UK Bioresource. The metabolomic dataset measured by Metabolon included 1069 compounds of known structural identity belonging to the following broad categories -amino-acids, peptides, carbohydrates, energy intermediates, lipids, nucleotides, cofactors and vitamins, and xenobiotics. Metabolites data were day-median normalized, and inverse normalized, as the metabolite concentrations were not normally distributed. Metabolites with more than 20% missing values were excluded leaving 722 metabolites of known chemical identity for analysis.
Genotyping For the GWAS of metabolites, genotyping in the UK bioresource was carried out with a high-density array data (Affymetrix UK Biobank Axiom® Array). Genotypes were subsequently imputed using information from the Human Reference Consortium imputation panel (version r1. 1, 2016)  [33]. Only individuals of full European ancestry (N = 8809) were included in the analyzes in the discovery cohort.
Statistical analyzes Metabolites association analysis. Linear regression analyzes were used to test the association between the levels of each metabolite (dependent variable) and depression. Three different models were tested, where the first model (model 1) was adjusted for age and sex only, the second model (model 2) was additionally adjusted for antidepressant medication usage, and the third model was an extension of the second model (model 3) with additional adjustment for lipid-lowering medication (yes/no), antihypertensive medication (yes/no), antidiabetic medication (yes/no), BMI (kg/m 2 ), and current smoking (yes/no). The summary statistics from all cohorts were combined in a sample size-weighted meta-analysis using METAL software  [34]. Sample size weighted meta-analysis was used since the depression measurement scales were different among cohorts.
Further, food sources of metabolites were associated with baseline CRP levels in the UK Biobank cohort in a linear regression analysis adjusted for age, sex, BMI, socioeconomic status and PCs. Metabolite GWAS for Mendelian Randomization (MR) analysis. To test for association between metabolite levels and genotypes, we built linear regression models where the outcome was defined as the transformed level of each metabolite, predicted by the allele dosage at each polymorphic (MAF > 0. 01) genotyped or imputed genetic variant. In addition, analyzes were adjusted for age, sex and BMI. All analyzes were conducted using the PLINK software (https://www. cog-genomics. org/plink/ 2. 0/).
Because of scarcity in GWAS-grade significance for SNPs associated with these metabolites, we used independent SNPs that showed the strongest association with a pvalue < 10 -06 as instruments (Supplementary Table  3 ). The summary statistics for depression for these instrumental variables were extracted from the publicly available dataset (2019 PGC UKB Depression Genomewide; https://www. med. unc. edu/pgc/download-results/mdd/). For the analysis we used the 'mr_allmethods' option of the R (https://cran. rproject. org/) library "MendelianRandomization"  [35]  that reports the results from the median method (simple, weighted and penalized), Inverse variance weighted and Egger methods (penalized, robust and penalized & robust). Effect of antidepressant therapy on metabolites in PReDICT study.