dataesr/scientific-paragraphs-categorization huggingface dataset:
Genomic DNA was extracted using the EZ1 biorobot and the EZ1 DNA tissue kit (Qiagen) and then sequenced on a MiSeq sequencer (Illumina, San Diego, France) with the Nextera Mate Pair sample prep kit (Illumina) and Nextera XT Paired End (Illumina), as previously described  [46]. The assembly was performed using Spades V. 3. 15  [47]  and trimmed (using Trimmomatic v. 0. 36)  [48]. Scaffolds of < 800 bp and scaffolds with a depth value < 25% of the mean depth were removed. The best assembly was selected using criteria such as the number of scaffolds, N50, and number of N. All assembled genomes were annotated using Prokka v 1. 14. 5 (Fontainebleau, France)  [49]. The gff3 output files were used to construct a core genome alignment using Roary v3. 13. 0 (USA) using default parameters  [50]. Comparative analysis was performed for the five placental isolates and reference strains such as RSA493 (Genbank: NC_002971.
Statistical Analysis Statistical analysis was performed with GraphPad Prism 6 (Graphpad Software Inc. , San Diega, CA, USA). Bacterial entry into cells, transcription and production of cytokines, and the number of bacteria in explants and mice tissue according to bacterial isolates and the reference NM strain were compared using the one-way ANOVA test and Tukey's multiple comparisons test. Bacteria survival in cells and mouse body weight were analysed using the two-way ANOVA test and Dunnett's multiple comparisons test. Hierarchical clustering of gene expression was analysed using the ClustVis webtool and an ANOVA test with a Sidak's multiple comparisons test. Values represent the mean ± standard deviation. The limit of significance was set up at p < 0. 05.
Accurate simulations of snow emission in surface-sensitive microwave channels are needed to separate snow from atmospheric information essential for numerical weather prediction. Measurements from a field campaign in Trail Valley Creek, Inuvik, Canada during March 2018 were used to evaluate the Snow Microwave Radiative Transfer (SMRT) Model at 89 GHz and, for the first time, frequencies between 118 and 243 GHz. In situ data from 29 snow pits, including snow specific surface area, were used to calculate exponential correlation lengths to represent the snow microstructure and to initialize snowpacks for simulation with SMRT. Measured variability in snowpack properties was used to estimate uncertainty in the simulations. SMRT was coupled with the Atmospheric Radiative Transfer Simulator to account for the directionally-dependent emission and attenuation of radiation by the atmosphere.
The purpose of this study is to demonstrate that SMRT can sufficiently explain the behaviour of observed airborne TB at these frequencies, required to improve assimilation of satellite data in numerical weather prediction. A novel component of this is the coupling of SMRT with the Atmosphere Radiative Transfer Simulator (ARTS)  (Buehler et al. , 2018)  to account for emission and attenuation by the anisotropic atmosphere at these higher frequencies. Data used in this study were taken as part of the MACSSIMIZE (Measurements of Arctic Clouds, Snow and Sea Ice nearby the Marginal Ice ZonE) field campaign in Trail Valley Creek (TVC), NWT, Canada in March 2018. During the campaign, multiple ground based profiles of snow specific surface area were obtained and other stratigraphic physical properties measured at multiple snow pit locations across the study area.
4. 'Base' SMRT simulations describe default parameterisations and neglect within-layer measurement variability or other potential sources of error considered later in this study. These base simulations were constructed from the three-layer dataset described in section 2. 1 and shown in Figure  2. Observations of layer thickness, temperature and density were used directly to create SMRT layers. However, SMRT requires microstructure model parameters rather than the SSA observed in the field. To link with previous studies  (Harlow and Essery, 2012; King et al. , 2018; Vargel et al. , 2020) , an exponential microstructure model was chosen for this study. SSA was used to derive the required exponential correlation length with the modified Debye relationship  (Mätzler, 2002; Montpetit et al. , 2012) : l ex = α db 4(1 -ρ/ρ i ) SSAρ i (1) where the Debye modification parameter α db is assumed to be 0.
Acknowledgements. This project was funded by  NERC  (Grant No. NE/S009280/1 :  Arctic Emissivity of Snow for Operational Prediction of Weather: AESOP ). Data collection was made possible thanks to  NERC Arctic Office UK  and the  Canada Arctic Partnership Bursaries Programme  (to NR and RE),  Wilfrid Laurier University  ( Phil Marsh  and  Branden Walker ) and  Environment and Climate Change Canada. The radiometric surface-based measurements have been supported by the  Natural Sciences and Engineering Research Council of Canada (NSERC)  and by  Polar Knowledge Canada. We thank  Arvids Silis ,  Branden Walker  and  Evan Wilcox  for indispensable field logistics and measurement support and  Chris Derksen  for help in planning the fieldwork. The MACSSIMIZE campaign was part of the  Year of Polar Prediction  effort, coordinated by the  WMO Polar Prediction Project.
Code and data availability. Code and data to repeat these simulations are available at git@github. com:mjsandells/AESOP_paper. git. Meteorological data for Figure  9  can be downloaded from https://climate. weather. gc. ca/historical_data/search_historic_data_e. html for station Trail Valley (WMO ID 71683), March 2018. Author contributions. MS, NR, SF designed the study and wrote the AESOP proposal; MS and KW performed the SMRT simulations and data analysis; KW and SF coupled ARTS to SMRT; CH, NR, PT and RE planned and coordinated the combined airborne and ground-based field campaign; NR, RE, AR 2 and PT made ground-based field observations; SF and CH made airborne observations; GP assisted with analysis and interpretation. All contributed to writing this paper.
Data We extend the receiver function (RF) data sets of  Boyce and Cottaar (2021)  and  Pugh et al. (2021)  using data recorded up until October 2021 downloaded from the Incorporated Research Institutions for Seismology (IRIS) Data Management System for teleseismic earthquakes with magnitude (M W ) ≥ 5. 5 at epicentral distances of 40-90°. We capitalize on the new TRAILS data set in the Turkana Depression (I. D. Bastow, 2019; Ebinger, 2018; Kounoudis et al. , 2021)  and a further data set in northeast Uganda  (Nyblade, 2017)  where there is a paucity of station coverage in our RF data set along the East African Rift (EAR). This results in ∼200,000 event-station pairs   (Dziewonski & Anderson, 1981 ) calculated using the TauP toolkit  (Crotwell et al. , 1999). AF, Afar; CVL, Cameroon Volcanic Line; EARS, East African Rift System;  ER, Eastern Rift; and WR, Western Rift.
Data Availability Statement The facilities of IRIS Data Services, and specifically the IRIS Data Management Center, alongside the AUSPASS, GEOFON, ORFEUS, and RESIF data centres were used for access to waveforms, related metadata, and/or derived products used in this study. IRIS Data Services are funded through the Seismological Facilities for the Advancement of Geoscience (SAGE) Award of the National Science Foundation under Cooperative Support Agreement EAR-1851048. The waveform data used in this study are from the following networks: 1C  (Velasco et al. , 2011) , 2H  (Keir & Hammond, 2009) , 2L  (Lange & Soler, 2019) , 3D  (Thomas, 2010) , 4H  (Hammond et al. , 2011) , 5H  (Hammond, 2011) , 6A  (Heit, Yuan, Jokat, et al. , 2010) , 6H  (Helffrich & Fonseca, 2011) , 6R (I. D. Bastow, 2019) , 7C  (Vergne et al.
Acknowledgments This project received funding from the  Natural Environment Research Council  (Grant  NE/L002507/1 ) awarded to  S. Pugh  and the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Grant agreement No. 804071-ZoomDeep ) awarded to  S. Cottaar. C. J. Ebinger  acknowledges  NSFGEO-NERC  award  1824417. I. Bastow  acknowledges support from  Natural Environment Research Council  Grant  NE/S014136/1.
Finally, the sample is cooled to room temperature in the growth atmosphere. X-ray diffraction is performed on a Rigaku SmartLab diffractometer equipped with a Ge (220) monochromator us-ing Cu Kα 1 radiation (1. 5406 Å). Lattice parameter and film thickness were fitted to the measured data using the Inter-activeXRDFit MATLAB tool  [19]. Topography images were obtained on a Park Systems NX10 atomic force microscope and evaluated using GWYDDION 2. 58.
C. XAS and XMCD X-ray absorption spectroscopy (XAS) and XMCD measurements were carried out at the BOREAS beamline at the ALBA synchrotron in Barcelona, Spain  [20]  and the X-Treme beamline at the Swiss Light Source SLS in Villigen, Switzerland  [21]. Data was acquired in grazing incidence (60 • to the sample normal) using total electron yield detection. No samples on DyScO 3 (DSO) substrates were measured, as the strong paramagnetism of these substrates leads to the risk of losing the sample in the cryostat at high applied fields. The 300 K XAS data is obtained by taking the sum of absorption data from linearly vertically and horizontally polarized light, which is subsequently normalized to the most intense peak. The Mn reference spectra were obtained on the following powder samples: MnCl 2 (Mn 2+ ), Mn 2 O 3 (Mn 3+ ), and SrMnO 3 (Mn 4+ ).
2(c ), attests to a two-dimensional growth mode of our thin films. The full RHEED intensity vs time graph is shown in Supplemental Material Fig. S3  [23]. The period of the RHEED oscillations is in agreement with the film thickness fitted from XRD. To probe the electronic structure of the NNMO films, we resort to XAS. This allows us to determine the oxidation states on an element basis. In Figs. 2(d ) and 2(e) we present XAS data recorded around the Mn and Ni L 3,2 absorption edges in the top panels together with reference data for selected Mn and Ni oxidation states in the bottom panels. Note that no NNMO/DSO sample was measured. Looking at the Mn XAS, we conclude that all films feature predominantly Mn 4+ independent of strain. However, we can also appreciate a small contribution of Mn 3+ to the measured XAS spectra at 641.
To disentangle the different contributions to the total magnetization, we focus on an exemplary sample grown on STO in Fig. 3(c ). The top panel shows the total magnetization hysteresis loop. In contrast to Fig. 3 (b), this data was recorded at ∼2 K, well below the onset of the downturn shown in Fig. 3(a). For proper substrate removal, we measured the hysteresis loop of the bare substrate prior to the film growth and subtracted it from the film measurement. The result shows a hysteretic opening around zero field, as is typical for ferromagnets. However, unlike ferromagnets, we do not observe saturation of the magnetic moment at high applied fields. Instead, we find a linear paramagnetic behavior above ∼3 T. In the lower panels of Fig. 3(c ), we present element-specific XMCD asymmetry loops recorded at the indicated absorption edges at 2 K.
ACKNOWLEDGMENTS This research was supported by the  Swiss National Science Foundation  under Projects No. PP00P2_170564  and No. 206021_150784. XAS and XMCD measurements were performed at  ALBA BL29 BOREAS  under proposal  2020024223-2  and at  SLS   X07MA XTreme  under proposal  20210318. J. S. , G. D. L. , and M. G. conceptualized this project and designed the experiments. The samples were grown and characterized (XRD, AFM, magnetization) by J. S. with the help of S. J. Synchrotron measurements were performed by J. S. , G. D. L. , S. J. , and M. G. , supported by C. P. at PSI, and J. H. and C. G. at ALBA. J. S. and C. M. M. R. performed the scanning SQUID microscopy with support from H. H. J. S. and M. G. wrote the first manuscript. All authors contributed to the final version of the paper.
The largest trial of neoadjuvant immunotherapy for MIBC is PURE01 (NCT02736266), in which 143 patients were treated with three cycles of pembrolizumab before cystectomy  [7] [8] [9]. PURE01 reported a complete pathologic response (ypT0N0) of 36%. Increased CD8+ immune infiltration, PD-L1 status, immune-infiltrated basal subtype, and tumor mutation burden (TMB) were all associated with favorable pathologic response to pembrolizumab  9. ABACUS (NCT02662309)  10  , a Phase II trial of neoadjuvant atezolizumab in patients with muscle invasive urothelial cancer, reported similar efficacy (31% complete pathologic response), but the response was not associated with TMB or PD-L1. While identifying biological features associated with response or resistance to CPI could help clinicians stratify patients who should receive neoadjuvant chemotherapy instead of immunotherapy or identify potential combination treatments to improve response and, ultimately, survival, the features associated with response to CPI remains an area of investigation.
Here, we dissected the heterogenous cancer cell program associated with response and resistance to CPI using multi-omics analysis of pre-treatment and matched post-treatment tumors from PURE01 (82 pre-and 31 posttreatment). We identified five MIBC expression subtypes with distinct genomic, transcriptomic, and pathologic profiles. We developed a single-patient classifier, which we applied to identify the leastresponsive subtype (S1, Luminal-Excluded/LumE) in an independent neoadjuvant CPI trial, ABACUS. Further investigation of S1/LumE tumors, which were enriched for FGFR3 mutations, identified KDM5B as a negative regulator of an immune-related gene network. Enhanced KDM5B activity was unique to S1 tumors, in contrast to the other CPI-resistant subtype, S4. Targeted inhibition of either FGFR3 or KDM5B enhanced immunogenicity in S1-like urothelial cells, suggesting a potential combination treatment for tumors classified as S1/LumE.
Clinical, pathologic, and molecular information Patients were enrolled in the PURE01 clinical trial, a Phase II prospective study of neoadjuvant pembrolizumab before radical cystectomy (NCT02736266). We used tumor samples from 82 patients as a biomarker-evaluable subset representative of the overall study cohort of 114 patients. The median follow-up for 68 patients without progression was 22 months, and 14 patients had recurrence after a median of 11 months. A detailed summary of clinical and histopathological information is provided in Supplementary Data 1. Initial reports from the PURE01 trial included limited mutation and transcriptomic analysis  [7] [8] [9]. We have now performed a comprehensive multi-omics analysis of 82 pre-treatment tumors. In addition, we have expanded our cohort to include 31 post-treatment tumors (27 matched pre-and post-pembrolizumab specimens), which may identify features associated with treatment response.
atezolizumab), differences in trial design, and inclusion characteristics. To further validate our findings, we evaluated a third cohort, the adjuvant IMvigor010 trial of 670 patients with MIBC  23, 24  (NCT02450331). In this Phase III MIBC trial, participants were randomized to atezolizumab or observation after radical cystectomy. When we applied the classifier to cystectomy specimens of the cohort, we found that the "responsive" subtypes (S2, S3, and S5), treated with 12 months of atezolizumab, had an improvement in outcomes compared to "unresponsive" subtypes (S1 and S4) (p = 0. 046) (Fig. 2g ). In the observation cohort we identified no difference in recurrence between subtypes (Supplementary Fig. 2h ). Collectively, validation in two additional trials suggest that MIBCs may be evaluated by transcriptional subtype, which may help to identify CPI-responsive tumors.
By ESTIMATE and MCP-counter, S5 tumors had higher ImmuneScores and StromalScores, and higher levels of fibroblasts and other immune cell types, confirming the immune-excluded phenotype observed by histology (Supplementary Figs. 7a, 9a ). In addition, S5 tumors had the highest expression of cancer-associated fibroblast (CAF) markers (Supplementary Fig. 9b ). Tumor-infiltrating B-cells have long been associated with improved prognosis and clinical outcomes  29. Applying the Immuno-Mind platform's R package immunarch to TRUST4-processed bulk RNA-Seq data, we tested PURE01 pre-treatment B-cell repertoires for specific immunoglobulin signatures associated with response to neoadjuvant pembrolizumab. Gene usage analysis of the IGH, IGK, and IGL segments showed that some V(D)J genes were used more frequently.
Yet, the mechanistic differences between clinical and pathologic responses require further investigation. Collectively, our data identified expression signatures that were associated with intrinsic resistance to CPI, providing further granularity into features associated with response to checkpoint immunotherapy. We integrated bulk transcriptome and spatial protein data profiles to identify features of non-responsive tumors (Fig. 7 ). Spatial analysis of the tumor microenvironment from a non-responding S2 sample identified elevated protein expression of the immune suppressive checkpoint marker IDO1. The multi-faceted role of IDO1 in the suppression of T-cell responses is well-studied in several different cancers  41. IDO1 inhibition in combination with anti-PD-1 therapy (nivolumab) and chemotherapy is being tested in a Phase III clinical trial for MIBC (NCT03661320)  42, 43.
g. , FGFR3-related pathways. If an S1 tumor were identified before treatment, it might be possible to change its susceptibility to CPI by using an inhibitor targeting FGFR3 or KDM5B. In contrast, S7 tumors were associated with greater immune activation, and CMap analysis identified the MAPK pathway and WEE1 as potential targets in these tumors. A combination of WEE1 inhibitors with CPI is currently being evaluated in a Phase Ib study in patients with muscle invasive bladder cancer (NCT02546661  47  ). These examples further support using an RNA-based platform to identify potential new therapeutics. We acknowledge the limitations of this study. We developed tumor subtypes from a unique cohort with limited sample numbers and attempted to validate the subtypes in two cohorts of tumors from different clinical trials.
Tumor morphometry To quantify the amount of tumor in each bladder specimen, the H&Estained slides were scanned at low magnification and digitized using the TissueFAXS system (TissueGnostics, Los Angeles, CA, USA). The acquired images were analyzed using TissueGnostics' HistoQuest cell analysis software. Prior to morphometry, the tumor was marked by a GU pathologist (BC) with a black marker. We then used the automated region of interest (auto-ROI) function to identify the borders of the specimen and manually excluded any non-urinary bladder components that auto-ROI had included. Finally, in the HistoQuest software, we traced the area indicated as tumor by our pathologist and recorded the total tumor area (mm 2 , including necrotic tumor) for each slide.
DNA extraction, sequencing, and analysis For PURE01, DNA was extracted from FFPE tissue from pretherapy transurethral resection of the bladder (TURB) samples, and a hybridization-capture panel of approximately 400 cancer-related genes was sequenced to a median coverage of 743x, then analyzed for mutations and copy number alterations by Foundation Medicine (Cambridge, MA, USA), as described previously. For ABACUS, we downloaded mutation and copy number data from EGA as dataset EGAD00001006201. We generated oncoprints of mutations and copy number variations using ComplexHeatmap v2. 6. 2  55. For comparisons of mutation frequencies between the different subtypes, we used R's Fisher's Exact test unless otherwise noted. In Fig. 3a , b, we used pvalues that we Bonferroni-corrected for multiple hypothesis testing; in Fig.
Kaplan-Meier plots For time-to-event analysis, we used the R survival package v3. 2-7 to generate Kaplan-Meier (KM) plots and calculate log-rank p-values. For PURE01, we calculated times-to-events as follows: if RELAPSE == 1, time = Time to recurrence; otherwise, time = Time to last follow-up. Times were from the date of the first pembrolizumab to the date of relapse (i. e. , recurrence). Prior to KM calculations, we censored data for status and times-to-event to 24 months. For the ABACUS pretreatment cohort (n = 84), and the Atezolizumab arm of the MIBC samples in the IMvigor010 cohort, we used clinical DFS data.
To test differences in ESTIMATE ImmuneScore and StromalScore for n = 113 subtypes S6 vs. S7 (Fig. 5h ), we used Kruskal-Wallis tests, then applied a Bonferroni (x2) correction for multiple hypothesis testing. We used Kruskal-Wallis tests to assess differences in MCP-counter fibroblast results for n = 113 S6 and S7 vs. S1-to-S5, and to assess differences in ESTIMATE ImmuneScore and StromalScore for n = 82 S5 and S3. Expression of cancer-associated fibroblast (CAF) genes across n = 82 subtypes For five marker genes (CFD, COL1A1, DCN, LUM, PTGDS) for inflammatory CAFs (iCAFs) and five (ACTA2, CALD1, MYL9, RGS5, TAGLN) for mCAFs  56  , we used a custom R script to generate box-whisker plots of FPKMs of each gene across the PURE01 n = 82 subtypes. For S5 and S3, we compared FPKMs for each gene with a Kruskal-Wallis test, then transformed the 10 p-values into adjusted p-values with a Bonferroni correction for multiple hypothesis testing.
Gene usage for IGH, IGK, and IGL subrepertoires We processed RNA-Seq data for n = 82 PURE01 pre-treatment samples with TRUST4 57 v0. 2. 054, then processed the TRUST4 output with the immunarch v0. 6. 5 R package (ImmunoMind, Berkeley CA). In TRUST4's output, we separated the mixed TCR and BCR receptor sequences and removed out-of-frame, partial, and erroneous CDR3 amino acid sequences. While the data contained too few TCR sequences to allow estimating TCR diversity, it contained a large enough number of BCR sequences to support robustly estimating BCR repertoire diversity. We assessed gene usage for IGH, IGK, and IGL subrepertoires, retaining the n = 74, 75, and 66 of 82 samples that had at least 100 clonotypes for each subrepertoire, and fitting generalized linear models against overall response rate (ORR = CR or PR, n = 49) vs.
4b ). For correlation, PCA, and differential abundance calculations, we used a subset of 49 expressed proteins, as follows. We first removed the one 'muscle' record (ROI = AOI) from the dataset. Then, because we have more confidence in proteins whose abundance distributions were above the distributions of the negative controls, we calculated the mean abundance of the three negative controls across all AOIs and retained only signal (i. e. , non-control) proteins whose median abundances were above the median of the negative control average. Taking SMA as a muscle marker, we then removed SMA from all AOIs, leaving 49 of the original 71 signal proteins. We then used protein levels normalized by the signal-to-noise ratio (SNR) (Supplementary Data 21) and only TME AOIs, to do two types of calculations.
Libraries were equimolar pooled based on QC values. The pools were sequenced on an Illumina NovaSeq S4 (Illumina, California, USA) as 150-bp PE reads, generating 20 M read pairs per sample. BCL2fastq was used to convert BCL files generated by the sequencer into fastq format, and fastq files were processed using the Ceto pipeline created by Elizabeth Bartom (https://github. com/ebartom/NGSbartom). Briefly, RNA sequencing reads were aligned to the GRCh38 reference genome using STAR v2. 7. 5. Gene expression was quantified using HTSeq v0. 11. 1, and differential gene expression profiles were generated using edgeR v3. 16. 5  51. HTSeq FPKM values were used for downstream analysis in R v3. 6. 3. Volcano plots and dotplots were generated using ggplot2 v3. 3. 2, and gene set enrichment analysis was performed using tmod v0.