dataesr/scientific-paragraphs-categorization huggingface dataset:
Data processing-BNSs and resting-states network correspondence Each segmented state was then computed from a vector to a 56 × 56 symmetric matrix. This represents the functional network associated with the BNS. Based on the studies published by  Kabbara et al (2017) , and Shirer et al (2012), we associated each of the 56 nodes with seven RSN: DMN, DAN, SAN, MOT, AUD, VIS, and Other. The affiliation of each node and RSN is summed up in the supplementary material. Then, using Brain Connectivity Toolbox (Rubinov and Sporns 2010), the strength (sum of weights of each link connected to the node) of each node was computed. We then computed the strength of each RSN by summing the values of each node associated with this RSN and dividing it by the number of nodes belonging to the RSN. To identify the dominant RSN for each BNS, we elicited those with a value superior to the sum of the average and standard deviation of the seven RSN strengths.
Data processing-Quantification After the BNS segmentation was performed on the averaged matrix, we returned to the subject level by performing a backfitting. Each sample is associated with the most similar BNS, based on GMD  (Murray et al 2008 , Poulsen et al 2018). As a result, we have a BNS distribution for each subject. To analyze it, the percentage of BNS repartition per subject is computed for each of the 478 matrices. Moreover, two metrics are computed: first, the transition probabilities matrix, revealing the transition probabilities from one state to another, and the occurrence, which indicates the average number of times per second a BNS is dominant (Poulsen et al 2018).
Importantly, proliferation-inhibitory effects were observed in vivo in a xenograft model, indicating the potential clinical application of miR-361-3p inhibition. RNA-seq of tumour xenografts identified FANCA as a direct miR-361-3p target, and validation suggested miR-361-3p inhibitor effects might be mediated in part through FANCA modulation. Moreover, miR-361-3p inhibition resulted in p53-mediated G1 cell cycle arrest through activation of p21 and reduced BC invasion. Analysis of publicly available datasets showed miR-361-3p expression is significantly higher in primary breast tumours vs paired normal tissue and is associated with decreased overall survival. In addition, miR-361-3p inhibitor treatment of BC patient explants decreased levels of miR-361-3p and proliferation marker, Ki67. Finally, miR-361-3p inhibitor showed synergistic effects on BC growth when combined with PARP inhibitor, Olaparib.
Data analysis and processing of miR inhibitor screening data Log fold change (LFC) of individual miR inhibitors were calculated as LFC = log(  ̅ resistant /MCF7), where  ̅ resistant is the mean of the apoptotic score in LTED and TamR, and MCF7 is the score in MCF7. In addition, robust Z-scores of the LFC of each individual miR inhibitor were calculated. The median of the LFC of all 954 miR-inhibitors (xmedian = 0. 52) was determined in order to calculate median absolute deviation (MAD) as MAD = media (|x i -xmedian|), where xi is the LFC of each miR-inhibitor and xmedian is the mean LFC of all inhibitors. With MAD (=0. 31), the robust Z-score of the LFC (RZ-score(LFC)) of each miR inhibitor was calculated as RZscore i = (0. 6745 (x i -xmedian))/MAD, where i corresponds to each individual miR inhibitor LFC.
MicroRNA-specific reverse transcription and qPCR Of the total RNA, 10-20 ng were reverse-transcribed using the miRCURY LNA RT Kit (Qiagen), cDNA was diluted 1:15-1:30 and qPCR was performed using miRCURY SYBR Green PCR Master Mix (Qiagen) and miRCURY LNA miRNA PCR assays (Qiagen) according to the manufacturer's instructions. Data analysis was performed with the ΔΔCt method, using hsa-Snord48 and U6 for normalisation.
Bioinformatic processing of RNA-sequencing data Sequencing quality was assessed using FastQC software version 0. 11. 8 (Babraham Bioinformatics Institute). Libraries were aligned to UCSC hg19 genome using HiSat2 2. 1. 0 (Kim et al. 2016). Data analysis and visualisation were performed in R version 3. 6. 1 programming environment. Aligned sam files were imported into R and converted into bam files with the Rsamtools R package. The GenomicFeatures R package was used to retrieve genomic locations of exons from the UCSC platform in a TxDb object, and read counts were generated with the SummarizeOverlaps function of the R package GenomicAlignments v1. 8. 4  (Lawrence et al. 2013). The R package edgeR was used to filter low-expressed genes, normalise the reads by the trimmed mean of M-values (TMM) method and determine differentially expressed genes (DEGs)  (Robinson et al.
2010). Volcano plots were generated with the ggplot2 and tidyverse R packages. Venn diagrams were drawn with the VennDiagram package, and principal component analysis (PCA) plots were generated with the pcaMethods R package. Finally, gene set enrichment analysis (GSEA) was performed using the Molecular Signatures Database (MSigDB) 'Hallmark' gene set collection  (Liberzon et al. 2015).
Analysis of TCGA and METABRIC small RNA-seq data In the TCGA-BRCA dataset, matched normal adjacent and primary tumour tissue small RNA-seq data were available for 110 invasive ductal carcinoma (IDC) BC patients, and matched metastatic tumour and primary tumour tissue small RNA-seq data were available for 7 patients. Clinicopathological values such as the molecular BC subtype were obtained from  Liu et al. (2018). The normalised miR expression levels for TCGA-BRCA were downloaded using the TCGAbiolinks R package. In addition, normalised miRNA gene expression from the Molecular Taxonomy and Breast Cancer International Consortium (METABRIC) cohort was downloaded with pyega3 and the aspera system. A total of 86 paired normal adjacent and primary IDC BC tumours were analysed. Mature miR-361-3p expression was examined in the abovementioned samples using R version 3.
2A  and B ). miR-346 inhibition similarly decreased Bax, CIAP, FasL and Survivin and increased p21 and p27 compared to NC-transfected cells (Fig. 2C ). Western blotting was performed to validate antibody array findings, confirming that miR-361-3p and miR-346 inhibitor transfection reduced XIAP levels in MCF7 cells, and CIAP, XIAP and Smac protein levels in TamR cells vs NC. Both inhibitors were shown to increase p21 protein levels, with greater effect observed in TamR cells. To confirm the relevance of miR-361-3p to clinical BC, we examined miR-361-3p expression in the METABRIC and TCGA-BRCA patient data sets. It was found that miR-361-3p levels were increased in primary tumour tissue as compared to normal adjacent tissues (Fig. 3A ), and across all BC sub-types (Fig. 3B ) in the METABRIC dataset.
256:3 Journal of Endocrinology e220229 361-3p inhibitor significantly decreased BC cell invasion (Fig. 6F ). We next sought to identify miR-361-3p target genes most relevant to ASO-361-3p phenotype. The list of 194 DEGs likely comprises both direct and indirect miR-361-3p targets. Identification of potential targets directly regulated by miR-361-3p in BC tumours in vivo was achieved by integrating DEGs (P < 0. 05) from xenograft RNA-seq analysis with 1075 predicted and/or validated miR-361-3p targets. Predicted targets were obtained from miRDB and TargetScan databases  (Agarwal et al. 2015 , Wong & Wang 2015) , whilst validated targets were derived from MCF7 AGO-PAR-CLIP-seq data  (Hamilton et al. 2016 ) (see Supplementary Table  6 ) and miRTarBase. From this, nine genes differentially expressed in response to ASO-361-3p in vivo were found to be direct miR-361-3p targets (Fig.
To further investigate the effect of miR-361-3p inhibitor and olaparib co-treatment, Combenefit software was used to study the interaction between the treatments (Di  Veroli et al. 2016). As shown in Fig. 8D , this identified a synergistic effect between miR-361-3p inhibitor (at 5 and 7. 5 nM) and olaparib (at 5 and 8µM) using the highest single agent (HSA) model as reference (n = 4).
Journal of Endocrinology e220229 the METABRIC data set, suggestive of potential oncogenic activity. This is further supported by the trend toward reduced survival of BC patients with high miR-361-3p tumour levels. Inhibition of miR-361-3p shows considerable potential as a BC therapeutic, as systemic administration of ASO-361-3p significantly repressed breast tumour growth in vivo, with no evidence of overt toxicity. Importantly, our data show efficient target regulation: miR-361-3p levels are reduced by over 90% in all ASO-361-3p-administered tumours. However, ASO-361-3p was also able to significantly reduce miR-361-3p levels in the spleen and lungs of treated mice. This may support investigations into the targeted delivery of ASO-361-3p. Notably, whilst ASO-361-3p significantly reduced xenograft cell proliferation (as evidenced by reduced MCM4 protein levels), no alterations in cleaved PARP were observed.
Acknowledgements The authors would like to thank  Prof. Charles Coombes  and his team at  Charing Cross Hospital (London) ,  Lottie Ion ,  Jessica Lin  and  Cristina Realingo  for providing, collecting and processing breast cancer samples. They are are deeply indebted to the breast cancer patients who consented to donate tissue to this project. The results shown here are in part based upon data generated by the  TCGA Research Network : https://www. cancer. gov/tcga.
Funding This work was supported by  Rosetrees Trust Research Grants   M192-F3  and  M192-F4 , an  Imperial College London Research Fellowship  (Fletcher), and a  Leukemia and Lymphoma Society Fellow Award (Hamilton). The authors gratefully acknowledge infrastructure support from  Imperial Experimental Cancer Medicine Centre ,  Cancer Research UK Imperial Centre and National Institute for Health Research (NIHR) Imperial Biomedical Research Centre (BRC).
Methods We conducted a prospective observational study in our tertiary university hospital. We included iTTP patients (> 18-year old) experiencing their first acute event, admitted to our ICU between January 2016 and September 2022. Clinical and biological parameters were recorded. Skin microcirculatory reactivity in the right forearm area was recorded at ICU admission (baseline) and after PE. As a control group for microvascular reactivity, we used data from a previously published cohort of young diabetic patients recorded after the correction of metabolic acidosis  [14]. The local ethical committee approved the protocol (Comité de Protection des Personnes, Hôpital Saint-Louis, France, No 2015/64NI), and the database was registered according to the French legislation (No 2,228,742), and all patients consented to anonymous data use for academic research and publication.
Statistics Continuous variables were presented as mean ± SD or median and 25th-75th interquartile ranges (IQR). Discrete variables were presented as percentages. Comparisons between groups were made with Fisher test for discrete variables and Mann-Whitney U test for continuous variables. Comparisons between admission and post-PE values were made using a paired Wilcoxon signed-rank test. Statistical analysis and graphical representations were performed using GraphPad Prism 10. 2 software (Graph Pad Software Inc. , La Jolla, CA). A twosided P-value of less than 0. 05 was considered statistically significant.
Objectives We aimed to evaluate whether obese patients with psoriatic arthritis (PsA) were less likely to be in remission/low disease activity (LDA). Methods We used data from the ReFlaP, an international multi-centre cohort study (NCT03119805), which recruited consecutive adults with definite PsA (disease duration ≥ 2 years) from 14 countries. Demographics, clinical data, comorbidities, and patient-reported outcomes were collected. Remission/LDA was defined as Very Low Disease Activity (VLDA)/minimal disease activity (MDA), Disease Activity in PSoriatic Arthritis (DAPSA) ≤4/≤14, or by patients' opinion. Obesity was defined as physician-reported and/or body mass index ≥30 kg/m 2. We evaluated the association between obesity and the presence of remission/LDA, with adjustment in multivariable regression models.
METHODS We used the baseline data of an international multicentre study performed in 2018, Remission/Flare in PsA (ReFlaP; NCT03119805). 10  ReFlaP recruited adult patients with physician diagnosed PsA from 21 centres in 14 countries (Austria, Brazil, Canada, Estonia, France, Germany, Italy, Romania, Russia, Singapore, Spain, Turkey, United Kingdom and USA). We recruited patients with at least 2 years' disease duration, as they should have a more comprehensive experience of disease flare or remission.
Data collection We collected demographic data, including age, sex, years of schooling, current use of conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) and/or biologic (b-)DMARDs. Clinical data collected included 66/68 swollen/tender joint counts, enthesitis (Leeds Enthesitis Index) and C reactive protein (CRP). Comorbidities were collected using the Functional Comorbidity Index (FCI), which comprises 17 additional conditions on top of arthritis. 19  Recent data have demonstrated preliminary data on construct validity  20  and known group validity  21  of FCI in PsA. PROs included pain (in numeric rating scale, NRS 0-10, none to worst), PGA of disease activity (NRS 0-10, none to worst), Health Assessment Questionnairedisability index (HAQ-DI) and Psoriatic Arthritis Impact of Disease-12 (PsAID-12).
Statistical analysis Patients with available data for remission (by indices and patients' opinion) were included in this analysis. Demographic and clinical variables were described as mean (SD) or frequencies for continuous or categorical variables as appropriate; comparisons were made between patients with obesity versus non-obese. No imputation Psoriatic arthritis Psoriatic arthritis Psoriatic arthritis of missing data was performed; data were analysed on complete cases. We compared the rates of obesity according to each remission/LDA definition. We constructed generalised linear models (binary logistic type), for the outcome variables, that is, the three remission/LDA definitions. In addition to obesity, variables that were not components of remission criteria were considered to be included as adjustment variables, that is, age, sex, years of schooling, duration of PsA, use of current cs/b-DMARDs and other comorbidities.
These variables were chosen as they have previously been reported to be associated with remission in PsA. 16 17 26  Comorbidities other than obesity were considered using the FCI without the obesity category to avoid circularity. Although disease activity variables were the major contributor of remission/LDA as we previously reported,  26  we did not consider them in analysis models as they are the components of remission/LDA definitions. Variables associated with remission/LDA outcomes with p value ≤0. 10 in the univariable models were included in the multivariable logistic regression models. As female sex is known to be associated with remission/LDA,  27  we explored the interaction of sex and obesity in exploratory models. A sensitivity analysis limiting to patients with available data for BMI was performed, with obesity defined by measured BMI ≥30 kg/m 2.
28  Obesity represents a state of low-grade systemic inflammation, and elevated CRP has been repeatedly described across different population, across sex and age groups. 29  Adipokines secreted by adipose tissue are increasingly recognised to regulate various immune responses. For instances, the most described adipokine, leptin, promotes pro-inflammatory signals including interleukin (IL)-1α, IL-17 and tumour necrosis factor (TNF)-α. 30  It has been reported that PsA patients who are obese may have higher disease activity and poorer response to treatment. PsA patients who were obese had higher disease burden measured by PsAID and Routine Assessment of Patient Index Data (RAPID3). 31  In a longitudinal cohort, PsA patients with obesity had a lower probability of achieving sustained MDA. 16  Among patients initiating TNF inhibitor treatment, obesity was a strong predictor of not achieving Clinical Disease Activity Index remission,  17  while weight loss was associated with a higher rate of achieving MDA.
For instance, higher BMI may adversely affect the volume of drug distribution, leading to insufficient dosing and reduced efficacy, particularly for TNF blockers. 28 36  Currently, infliximab, golimumab and ustekinumab can be given as weight-based regimen, while other biologics are usually given in fixed doses. 37  In addition to emphasis on weight reduction, adjustment of dosing of therapeutics may be considered for individual patients. We acknowledge several limitations of our study. First, patients were recruited from tertiary referral centres and results may not be generalisable to milder PsA. The crosssectional analysis does not allow study on changes in remission status or causality. Although we collected data for one follow-up visit, the small sample size precluded longitudinal analysis.
Data from this study do not preclude obese patients having high disease activity and impact at baseline, and still have good potential to improve and be satisfied with T2T strategy overtime. We may not be able to account for variables we did not collect, such as fibromyalgia, which could be an important interaction with obesity in the models. Comorbidities in this study were collected through FCI, which was developed to predict functional outcomes rather than mortality. 19  Finally, obesity was defined based on either BMI or physician report, which led to some discrepancies. In conclusion, PsA patients with concurrent obesity had lower odds of being in remission by indices, highlighting the importance of managing obesity to improve the care of patients with PsA. Shared decision-making is important for an individualised target of management.
Acknowledgements The ReFlap study was funded by  Pfizer  through an investigator-initiated research grant. Pfizer  played no role in the study itself and did not have access to the analyses or the paper prior to publication. YYL is supported by the  Singapore National Medical Research Council. LCC is supported by the  National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). The views expressed are those of the author(s) and not necessarily those of the NHS, the  NIHR  or the  Department of Health. We acknowledge the support of the  National Institute for Health Research Clinical Research Network (NIHR CRN). Collaborators The  ReFlap study  group. Contributors Substantial contributions to study conception and design: YYL, LCC, MdW, JSS, A-MO, LG; substantial contributions to analysis and interpretation of the data: YYL, LE,  A-MO , LCC, MdW, JSS, UtK, PP, JDC, RS, AB, ED, SM, MS, UmK, LG; drafting the article or revising it critically for important intellectual content: YYL, LE, A-MO, LCC, MdW, JS, UtK, PP, JDC, RS, AB, ED, SM, MS, UmK, LG; LG; final approval of the version of the article to be published: YYL, LE, A-MO, LCC, MdW, JSS, UtK, PP, JDC, RS, AB, ED, SM, MS, UmK, LG; Guarantor of the work: LG.
Sentences in the US Federal Criminal Justice System The main data source is represented by the United States Sentencing Commission (USSC), which keeps a case-level record for all offenders sentenced in the US federal courts in each fiscal year (except those receiving a death penalty since 2006). We consider all observations from October 2001 to September 2017 in 90 district courts located in all states and in the District of Columbia. 13  We do not use for the analysis the data from four district courts corresponding to insular areas (Guam, Puerto Rico, Northern Marina Islands, and the Virgin Islands). This dataset includes the district courts where the defendant was sentenced, the sentencing month and year, 14 the final sentence (in months). It also provides information on the case, such as whether it was settled by plea agreement or trial, the primary offense type, the final offense level, and the final defendant's criminal history category.
Database Given the disproportionately high number of apprehensions at the southwest border, we restrict the analysis only to the nine sectors along the terrestrial border with Mexico as depicted in Figure A. 5 in the Appendix. 25  Indeed, from 2000 to 2017, 98 percent of all the apprehensions occurred along this border, so most apprehensions are related to Hispanic illegal aliens. As a matter of fact, Mexicans accounted for 98 percent of the total number of apprehensions at the border in 2000. While this number decreased to 42 percent in 2017, the rest of the illegal aliens apprehended at the border still originated from Latin American countries such as Guatemala (21 percent), El Salvador (  16 ), and Honduras (15). We build on these data to gauge the extent of the exposure of different federal districts in the United States to border apprehensions as follows: Border app cmy = 9 s=1 1 dist cs × Border app smy , (1) where Border app is the total number of border apprehensions in sector s in month m in year y and dist cs the geodesic distance between the district court centroid c and the border sector headquarter s.
This reasoning, then, naturally calls for a placebo, which requires estimating Eq. (  3 ) on a sample including non-Hispanic (White and Black) citizens and Hispanic non-citizens. As reported in Table  A. 2 in the Appendix, the dataset includes a similar number of Hispanic citizens (101,103) and of Hispanic non-citizens (108,069) charged with non-immigration offenses, so that the size of this alternative sample is similar to the one used in the benchmark. We thus remove from the estimation sample defendants who are Hispanic citizens, and add defendants who are Hispanic non-citizens. We find no significant effect of the variable of interest on the differential in sentence length between Hispanic non-citizens and non-Hispanic citizens in Table  7 , irrespective of whether we also differentiate the latter group between White and Black defendants.
Judges' characteristics The USSC does not allow, for confidentiality reasons, to identify the judge who sentenced each case. Cohen and Yang (2019) use confidential data from the Transactional Records Access Clearinghouse (TRAC), to which we do not have access, to link individual defendants with their sentencing judge, and they analyze the influence of judge political affiliation on sentencing decisions in the Federal Justice System. They find that Republican-appointed judges give longer sentences to Black offenders than Democratic-appointed colleagues. Interestingly, they find no significant disparities for Hispanics. 52 Using the American Community Survey between 2001 and 2017 (Ruggles et al. , 2020), we find that the average Hispanic non-citizen is a male (51. 8 percent), aged 39. 4 years old, and with some secondary education (48.
Data from the Biographical Directory of Federal Judges We take advantage of the information on judges' characteristics from the Biographical Directory of Federal Judges, provided by the Federal Judicial Center. 53  These data provide biographical information on presidentially appointed judges since 1789, including the district(s), and the corresponding time period(s), to which each judge was assigned to, his or her ethnicity, and the political affiliation of the President who appointed them. We can use these variables to reconstruct the set of judges serving in each district in a given month, as in McConnell and Rasul (2021), and then compute the month and district-specific share of Hispanic or Republican-appointed judges. As judges are randomly assigned to cases, a higher share of, say, Republican-appointed judges, corresponds to a higher probability that a defendant was sentenced by a Republican-appointed judge.