dataesr/scientific-paragraphs-categorization huggingface dataset:
differential between White and Hispanic defendants. Figure  2  also suggests no significant discrimination in district-month pairs where Democratic-appointed judges are in the majority. This echoes previous results by Cohen and Yang (2019) who finds that Republican-appointed judges give longer sentences to Black offenders rather than Democratic-appointed judges. 6. 3. 2 Data from the JUSTFAIR database (Ciocanel et al. , 2020)   Ciocanel et al. (2020) have recently combined data from multiple sources that allow identifying, for a large subset of the observations used in our benchmark analysis (310,269 observations), the federal judge that sentenced each case. Table  A. 12 in the Appendix reproduces our benchmark estimates on the subset of observations included in the JUSTFAIR database, and then it presents the results obtained when adding a federal judge fixed-effect to Eq.
In the dataset, 22,033 observations correspond to citizens sentenced for an immigration offense (mostly human smuggling), and these observations are concentrated in the five districts along the border. Around two-thirds of these observations are related to Hispanics; more apprehensions increase the future workload of federal judges, and sentences for human smuggling (whose frequency is positively correlated with apprehensions) could deteriorate judges' attitudes toward all Hispanic defendants. Notice that the vast majority of apprehensions results in a detention by the US Immigration and Customs Enforcement that does not involve federal prisons, 56 so that apprehensions  55  The size of our effect of interest is larger with the more demanding structure of fixed-effects, where we interact the judge dummies with either geographical (district) or temporal dummies, or both.
Olivier Santoni  for providing excellent research assistance, and to  Thomas Renault  for sharing data on media;  S. Bertoli  and  M. Laouénan  acknowledge the support received from the  Agence Nationale de la Recherche of the French government  through the program " Investissements d'avenir" , grant  ANR-10-LABX-14-01  for  S. Bertoli , and grants  Labex LIEPP  ( ANR-11-LABX-0091 ,  ANR-11-IDEX-0005-02 ) and  IdEx Université Paris Cité  ( ANR-18-IDEX-0001 ) for M. Laouénan; the usual disclaimers apply. This work was supported by the  LABEX IDGM+  ( ANR-10-LABX-0014 ) within the program " Investissements d'Avenir " operated by the  French National Research Agency (ANR). CERDI Working Papers are available online at: https://tinyurl. com/2xwfzw8s.
Thus, the Red Stone dark microbiome may be composed by truly novel extant species not found anywhere else on Earth, but it may also be the case that such dark microbiome in fact represents the relict community of microbial species which used to inhabit the Red Stone delta in the distant past, of which no extant relatives are to be found in the existing sequence databases. A culture-dependent approach allowed us to obtain only nineteen unique bacterial, and two fungal, isolates from the whole set of Red Stone samples (Fig. S6 ), with extremely low numbers of colony forming units (CFU) (1. 1 × 10 1 -9. 0 × 10 1 , Table  S2 ), and almost all of the bacterial species belonging to the Bacillaceae family of heterotrophic bacteria, in agreement with NGS findings. The majority of these species uniquely matched the mineralogy of the samples from where they were isolated (i.
X-Ray diffraction Powder X-Ray Diffraction of bulk samples was performed using a Bruker D8 Eco Advance with Cu Kα radiation and Lynxeye XE-T linear detector. Samples were scanned between 5°(2θ) and 60°(2θ) using a step size of 0. 05°(2θ) and a count time of 1 s. The phase identification was performed by comparing the measured diffraction pattern with patterns of the PDF Database with the DIFFRAC. EVA software (Bruker AXS). Afterwards, the clay fraction was obtained by decantation according to Stokes' law. The determination of clay involved treatment: air dried, solvation with ethylene glycol, and heating at 350 and 500 °C during 2 h. Then Samples were scanned with a 0. 02°(2θ) step size over the range 2-30°(2θ) with a 1 s collection time at each step.
Biodiversity analyses Raw sequences were processed in MOTHUR software v. 1. 43. 0 67 , using a custom script based upon MiSeq SOP  68  , as previosuly employed before  [69] [70] [71] [72] [73]. Microbial OTU richness (S) and Shannon diversity index (H') were calculated with R language for statistical computing (R Core Team, 2019), by using "vegan" package v. 2. 5-6  74. For the better visualization of the bacterial community composition, a heatmap was constructed by means of STAMP software v. 2. 1. 3  75.
The automated sequencing of the resulting PCR products was conducted by Macrogen DNA Sequencing Inc. (Seoul, Korea). Sequences were checked for quality using the BioEdit software (Ibis Therapeutics, Carlsbad, USA) and end-trimmed before using the Megablast option for highly similar sequences of the BLASTN algorithm against the National Centre for Biotechnology Information nonredundant database (www. ncbi. nlm. nih. gov) to search for the closest species of each of the isolates obtained. Phylogenetic analysis of 16 S rRNA and 18 S rRNA isolate gene sequences was performed by aligning sequences by multiple sequence comparison by log-expectation, analyzed with jModelTest and then by Phylip NJ (bootstrap 10,000), all tools of the freely available Bosque phylogenetic analysis software (version1.
Laser induced breakdown spectroscopy analyses Five observations of 30 laser shots each were made on the samples with the lab replica of the ChemCam instrument. Samples were situated 1. 6 meters from the instrument, contained in a chamber surrounded by 7 Torr of CO2 to imitate the Mars atmosphere. LIBS data were pre-processed using the methods described in  78  to remove nonlaser background and continuum, and to calibrate for wavelength. TRLS (time-resolved luminescence spectroscopy) observations were made with a lab replica of the SuperCam instrument.
Acknowledgements The research leading to these results is a contribution from the  Project  " MarsFirstWater ", funded by the  European Research Council , Consolidator Grant no  818602  to AGF, and by the  Human Frontiers Science Program  grant  n°RGY0066/2018  to A. A. -B. Additional funding provided was provided by  MINECO  grant  PID2019-107442RB-C32  (O. P. -B. and A. M. ),  Grants-in-Aid for Scientific Research  from the  Japan Society for Promotion of Science  grant numbers  17H06458  and  21H04515  (K. F. ), grant numbers  17H06456 ,  17H06458 ,  20H00195 , and  21H04515  (K. F. and Y. S. ),  Consejería de Educación e Investigación, Comunidad Autónoma de Madrid/European Social Fund program (MAFM) , grant  n°ESP2017-87690-C3-3-R (DC) ,  Ramón y Cajal  grant  n°RYC2018-023943-I (L. S.
Data availability Data and materials availability: all data and samples are freely available upon request to AAB. All NGS raw sequence data were deposited at the NCBI Sequence Read Archive (SRA, http://www. ncbi. nlm. nih. gov/sra) under accession number PRJNA908755. All isolate genes sequences have been deposited at the NIH genetic sequence database (https:// www. ncbi. nlm. nih. gov/genbank/) both for 16 S rRNA (OP955639 to OP955657) and 18 S rRNA sequences (OP954719 and OP962462). Source data are provided with this paper.
Pre-imaginal survival Pictures of host eggs made on day (D+5) were processed with the ImageJ software  (Schneider et al. , 2012; Schindelin et al. , 2012) , specifically with the plugin CODICOUNT  (Perez et al. , 2017). A dedicated macro was written to automatically identify and process each tube in the picture of an entire rack of tubes (code available upon request). The CODICOUNT plugin counted, for each tube, the number of dark and bright pixels in the patch of host eggs, using a segmentation threshold adjusted using manual annotations on a subset of pictures. The two categories of pixels corresponded respectively to black (parasitized) or white (unparasitized) host eggs. Earlier tests and uses of the method suggested a close linear relationship between the number of pixels and the number of eggs  (Burte et al.
Olfactometry bioassays Still images were extracted from the video files, using the software FFmpeg: one picture every two minutes for the control videos (three pictures in total) and one picture each three minutes for the treatment videos (six pictures in total). Each picture was manually counted with the assistance of a custom ImageJ macro  (Schneider et al. , 2012; Schindelin et al. , 2012). The location of all parasitoids was marked, as well as the exact location of the chamber (central hole and four extreme points, Fig  3 ). The ImageJ macro then automatically attributed (x,y) coordinates to each parasitoid, and assigned it to one of 16 portions of the olfactometer chamber, defined with respect to the presence/absence of odors and the shape of the air (see Fig  3  ; code available upon request).
Acknowledgements We warmly thank  Lydia Ottenwaelder  for her help in some toxicity experiments. We are grateful to  Robin Laugier  for estimating T. evanescens sex-ratio. We thank  Mélina Cointe  for helpful discussions and comments. This work was financially supported by  INRAE's Department of Plant Health and Environment  and the  Université Côte d'Azur  ( IDEX "Investissements d'Avenir UCAJEDI " ,  project reference  ANR-15-IDEX-01 ). The Trichogramma strains were provided by the  Biological Resource Center EP-Coll  that is a part of  BRC4Env , the pillar " Environmental Resources " of the  Research Infrastructure AgroBRC-RARe.
The largest and most recent GWAS of executive function investigated the common executive function factor score (cEF) derived from multiple tasks in the UK Biobank dataset and found 129 independent lead variants mainly associated with fast synaptic transmission  [54]. SNP-heritability studies indicate that common genetic variation explains a substantial fraction of variance in working memory (h 2 SNP = 0. 3)  [52]  and processing speed (h 2 SNP = 0. 11-0. 19)  [51, 52]  suggesting that with enough power one can expect to identify more genomewide significant associations that could inform the genetic mechanisms of different executive functions, including inhibitory control. Here we performed the first trans-ancestry GWAS meta-analysis of inhibitory control in a general population sample of up to 14,877 participants, focusing on executive control measures derived from the stop-signal task.
METHODS Participants In this study we aggregated data across eight independent samples from the general population [Spit1, Spit2, Adolescent Brain Cognitive Development℠ Study (ABCD Study ® ), MELBOURNE, IMAGEN, COLOR-ADO, Michigan-ADHD-1000, Oregon-ADHD-1000] and four ancestral groups [African (AFR), East Asian (EAS), European (EUR), South Asian (SAS)], totalling to 14,877 participants. Spit For Science (Spit1, Spit2) is an ongoing study at The Hospital for Sick Children in Toronto (Canada) aiming to investigate the genetics of cognition, physical health and well-being in children aged 6-17 years  [30, 56]. The ABCD Study is a publicly available longitudinal dataset from the USA containing participants aged 9 to 10 years at their baseline assessment, focusing on cognition, brain development, and mental and physical health  [57, 58].
The Melbourne sample (MELBOURNE) is derived from an ongoing study at Monash University in Melbourne, Australia that is designed to systematically assess neurocognition, psychopathological symptoms, genetics, as well as brain structure and function in a large sample of healthy young adults aged 18-50 years  [59]. The IMAGEN sample was derived from the longitudinal IMAGEN dataset collected across eight centres in Europe combining brain imaging, genetics and psychiatry to understand brain development and behaviour in adolescents aged 14 years at baseline  [42]. The Colorado sample (COLORADO) includes same sex monozygotic (MZ) and dizygotic (DZ) twins recruited from the Colorado Longitudinal Twin Sample that was designed to investigate genetic and environmental influences on cognitive and emotional development  [60, 61].
Genotyping and imputation Samples were genotyped on a variety of arrays that are listed in Supplementary Table  S1. For Spit 1&2 studies, only participants for which all 4 grandparents shared the same ancestry (either EUR, EAS or SAS) were genotyped. For the ABCD Study, we restricted analyses to non-Hispanic EUR, EAS, SAS and AFR ancestries. Recruitment for all other study cohorts was restricted to participants of EUR ancestry. Genotyping quality control (QC) was performed by different study centres according to their own best practice and pipelines (for genotyping and QC details for each site see Supplementary Text S3). Imputation was performed separately for all studies and genotyping arrays, using data from phase 3, version 5 of the 1000 Genomes project for reference. Data for Spit 1&2 and ABCD Study[Go] were imputed using Beagle v4.
1  [70]. Data for MELBOURNE, IMAGEN and ABCD Study[SSRT] were imputed using minimac v4 on the Michigan imputation server  [71]. The COLORADO sample was imputed on the Michigan Imputation Server using minimac v4, Eagle v2. 4 for phasing. Dosage data were used for all these sites. For both OREGON and MICHIGAN, non-genotyped SNPs were imputed with the same procedure using IMPUTE2  [72] ; autosomal chromosomes were pre-processed and phased using SHAPEIT  [73]. Variant positions and alleles were checked against the reference panel and SNPs that were missing or mismatches were removed. Genotype probabilities for these two sites were converted to best-guess genotypes with the genotype set to missing if the probability was <0. 8.
Association analysis Association analyses were performed within each study and within each ancestral group, focusing on SNVs with MAF > 1% and imputation quality r 2 > 0. 80. Most studies used allele dosage, while data in OREGON and MICHIGAN samples were based on the best-guess genotype calls (i. e. from reading vcf files into plink). To account for relatedness between participants, we used linear mixed models implemented in GEMMA v0. 98. 1  [74]. All traits (mean GoRT, GoRT SD, SSRT) were analysed on the natural log scale. We used sex, age, age 2 and age x sex as covariates, as well as the first 3 principal components constructed from the SNP data. An example from the Spit1 study demonstrates that 3 principal components were sufficient to cluster regional ancestries within continental ancestries (see Supplementary Fig.
S2 ). Each study is weighted according to the inverse of the variance of its effect size. Significance is obtained from testing a = b 1 = b 2 = b 3 = 0, in which case the observed effect sizes in each study are no different from random residuals (ϵ s ). The original implementation of MR-MEGA can only analyse complete data, so we implemented our own regression in R to allow for missing results in some of the studies that arose due to frequency or imputation quality thresholds. We verified that results from our code and MR-MEGA agree for complete data. Axes of genetic variation were calculated using MR-MEGA from SNPs with complete data. Gene-based analysis was performed using MAGMA version 1. 10 [78], using the auxiliary files available on the software's website  (19,427 genes). Since the analysis depends on LD patterns, it was performed separately within each ancestry group, then the results were combined using Stouffer's method.
Pathway analysis was also performed with MAGMA, using curated gene sets (collection C2) downloaded from Molecular Signatures Database (MSigDB), v2023. 1. Hs  [79]  (https://www. gseamsigdb. org/gsea/msigdb/), restricting to sets with 10 to 1000 genes (5637 sets). A Bonferroni correction was used to account for multiple testing for the gene-based and pathway-based analyses.
SNP heritability, genetic correlations and polygenic scores We assessed SNP heritability (h 2 SNP ) for each phenotype (mean GoRT, GoRT SD and SSRT) as well as the genetic correlation between each of those phenotypes using LD score regression as implemented in LDSC v1. 0. 0  [80]. We restricted these analyses to SNVs with complete data to ensure that results were not affected by imbalances in power between studies. We used the LD scores as provided within LDSC v1. 0. 0 for EUR and EAS samples and performed our own calculations of scores for AFR and SAS samples using the same methods. We investigated trans-ancestry genetic correlations using POPCORN (installed from git commit #facdfbc)  [81]. Polygenic scores (PGS) were constructed from summary statistics derived from GWAS for ADHD  [76] , ASD  [82] , and SCZ  [83]  using a pruning and thresholding approach as implemented in PLINK  [84]  and PRSice v1.
25  [85] , clumping SNPs for LD (using default r 2 < 0. 1 in 250 kb windows). Analysis of the European ancestry cohorts excluded the data from COLORADO, a twin study. Summary statistics were also available in EAS samples for SCZ, which were used with the EAS ancestry samples from Spit1, Spit2 and ABCD Study. PGS were tested for association in our samples with our traits, evaluated at the p-value thresholds 0. 001, 0. 05, 0. 10, 0. 20, 0. 30, 0. 40, 0. 50. We restricted these analyses to SNVs with imputation quality r 2 > 0. 8. PGS effect sizes between studies were metaanalysed using fixed-effect, inverse variance methods. To account for testing multiple correlated PGS derived from the p-value inclusion thresholds, we calculated an effective number of independent PGS from the data and applied a Bonferroni correction with respect to that number (for a description, see Supplementary Text S5).
ACKNOWLEDGEMENTS MAB was supported by a  National Health and Medical Research Council of Australia Senior Research Fellowship. AA was funded by a grant from the  Australian Research Council (ARC)  under its  Linkage  Project scheme ( LP160101592 ). This work was in part supported by operating grants from the  Canadian Institutes of Health  ( MOP-106573  to P. D. A. ,  MOP-93696  to R. J. S. ,  PJT-159462  to J. C. and  PJT-165876  to J. C. ). A part of the data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development SM (ABCD) Study (https://abcdstudy. org), held in the  NIMH  Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the  National Institutes of Health  and additional federal partners under award numbers  U01DA041048 ,  U01DA050989 ,  U01DA051016 ,  U01DA041022 ,  U01DA051018 ,  U01DA051037 ,  U01DA050987 ,  U01DA041174 ,  U01DA041106 ,  U01DA041117 ,  U01DA041028 ,  U01DA041134 ,  U01DA050988 ,  U01DA051039 ,  U01DA041156 ,  U01DA041025 ,  U01DA041120 ,  U01DA051038 ,  U01DA041148 ,  U01DA041093 ,  U01DA041089 ,  U24DA041123 ,  U24DA041147.
CODE AVAILABILITY Genotyping data processing code for SPIT1, SPIT2 and ABCD Study data is provided at http://bitbucket. org/mathieu-lemire/sk-scripts-qc-genotypingarrays. The custom MR-MEGA code implementation is provided at https://bitbucket. org/mathieu-lemire/ sk_my_mrmega. Genotype simulation code is provided at https://bitbucket. org/ mathieu-lemire/sk_recomb/src/master/.
The various factors and parameters affecting the mechanisms involved, its relative contributions to the improvement of the platform and their dependence explain the need for more investigations on this subject. Besides the experimental studies and based on them, numerical studies can provide access to additional information which is difficult to be quantified during the experimentation. The methods used in geosynthetic reinforcement structure model should be able to describe: 1. the large deformation of the granular soil layer, 2. the mechanical behaviour of the geosynthetic and 3. the interaction between the geosynthetic and its surrounding soil Villard et al. [5]  have proposed coupling techniques to use DEM for the granular material and FEM to describe the geosynthetic behaviour. The used calculation code is the SDEC software (Spherical Discrete Elements Code, Donze and Magnier  [6] ) developed by Frédéric Donzé, then adapted and validated for soil reinforcement applications by geosynthetic sheet  [5].
Scientific context and methodology An experimental campaign, consisting of tests on reinforced and non-reinforced platforms over weak subgrade are carried out. The reinforcement performance is tested under vertical loading using two geotextiles with different tensile strength and two base course thicknesses. In addition, a numerical model using a Spherical Discrete Elements Code (called SDEC) and developed by  [5]  is used and calibrated with the experimental results. This paper presents the results of six cyclic vertical plate loads tests on non-reinforced and geotextile reinforced platforms. This study highlights the benefit obtained from using geotextile at the subgrade-base course interface, the effect of the geotextile tensile stiffness at small strain on these benefits. In addition, the numerical model is calibrated after applying the static load of the first cycle applied experimentally.
Study area and sampling size In this study, we conducted a future estimate of the value of housing assets in the Tokyo metropolitan area of Japan, still one of the largest areas in the word with 36 million inhabitants, and the study findings should be useful for examining the shrinking city problem in large metropolitan areas. The Tokyo metropolitan area includes Tokyo, Chiba, Saitama, and Kanagawa prefectures, and 209 municipalities exist as of 2022. We collected the data by municipality to calculate more thoroughly the housing asset value and because services for elderly households are provided by municipality. The data collection and estimation period are from 1984 (when the bubble economy began in Japan) to 2045. The future population and number of households were projected by a Japanese governmental institute from 2019 to 2045 (National Institute of Population and Social Security Research of Japan, 2018).
The actual value was provided from 1984 to 2018 by municipality to observe the land price boom and bust and to maintain the continuity of the data. Thereafter, the estimated value was projected from 2019 to 2045. The changes in land prices and residential areas due to the merger of municipalities were also adjusted by weighted average land prices and residential areas before the merger. Residential land prices, the surface area of residential areas, population and the number of households are all basic data. Residential area data are based on the summary record of fixed asset prices from the Ministry of Internal Affairs and Communications of Japan, whose data are employed for calculating municipal property tax. Tax base residential area is checked by governments; thus, these data are more reliable than others.
The GIS program relies on ArcGIS software, and utilizes the municipal geocodes from MLIT. Figs. 2 and 3 exhibit the results of an annual analysis (2018 and 2045) of the housing asset value by municipality. For areas farther than 30 km, there are some differences noticed in estimated values. Although there is an overall trend of declining housing asset values, it is difficult to clearly comprehend the changes in housing values by municipality as the total value of housing assets in the Tokyo metropolitan area is already very high. Therefore, we converted asset value to the index basis where the 2018 value is 100 as shown in Fig. 4. The index basis analysis clearly distinguishes between the red area with increased housing value in 2045 and the blue area with reduced housing value. Within 15 km from central Tokyo (approximately within 30 min to commute), the housing asset value will continue to rise or slightly decrease until 2045.