dataesr/scientific-paragraphs-categorization huggingface dataset:
ASSOCIATED CONTENT The Supporting Information is available free of charge on the ACS Publications website. NMR studies about BrCN-mediated cyclization, diastereoselective reduction of ketone by DIBAL-H 1 H,  13  C NMR spectra, Mitsunobu reaction with inversion of configuration, synthetic pathways and NMR characterization of cpd 22, 33, NMR and HRMS comparison between 1 and data from Barrosa et al. 14  Experimental procedures for LogD determination and metabolic stabilities. 1 H and  13  C NMR for all new compounds. FAIR Data is available as Supporting Information for Publication and includes the primary NMR FID files for compounds [1, 8,  19a, 19b, 20a, 20b, 21]  Deposition Numbers CCDC 2007599-2007601 contains the supplementary crystallographic data for this paper. These data can be obtained free of charge from The Cambridge Crystallographic Data Centre via www.
Cell Counting and Laser Microdissection H&E stained serial sections were analyzed for cell recognition and counting before LMD. [12] Stained tissue sections were scanned using a slide scanner Hamamatsu Nanozoomer 2. 0HT (Hamamatsu Phototonics, Hamamatsu, Japan) and analyzed with open source digital analysis software (QuPath)  [14]  to detect regions with an equivalent number of epithelial and stromal cells (Figure  1 ). The goal was to identify areas containing about 6000 epithelial and 12 000 stromal cells mainly containing immune cells. A classifier was trained with 12 regions containing uniquely immune cells and 12 regions containing uniquely epithelial cells under the guidance of a pathologist. The results of the classifier were verified by a pathologist. The settings for cell and nuclei detection in QuPath are described in M1, Supporting Information.
Nanoflow Liquid Chromatography-Tandem Mass Spectrometry Analyses NanoLC-MS/MS analyses were performed using an Ultimate 3000 nanoflow high performance LC system (Thermo Fisher Scientific), on-line coupled to a high-resolution tandem mass spectrometer (Q-Exactive, Thermo Fisher Scientific). The Q-Exactive mass spectrometer was calibrated in positive ion mode using a high-grade purity protein standard (Pierce LTQ Velos ESI Positive Ion Calibration Solution, Thermo Fisher Scientific). Chromeleon Xpress software (Thermo Fisher Scientific) was used to set up the nanoLC-MS/MS system, and Tune 2. 5 and Excalibur 2. 2 software (Thermo Fisher Scientific) to pilot the mass spectrometer. Method optimizations were performed before analyzing the UC and CD cohorts of microdissected tissues as described in the M3, Figures  S1  and S2 , and Tables  S2  and S3 , Supporting Information.
Data Processing Raw files were analyzed using MaxQuant version 1. 5. 2. 8 (Max Planck Institute of Biochemistry, Planegg, Germany). Andromeda search engine (Max Planck Institute of Biochemistry) was used for the analysis of MS/MS spectra with settings described in M4, Supporting Information. Perseus version 1. 5. 0. 15 (Max Planck Institute of Biochemistry) was used for statistical assessment. All "only identified by site" and "reverse" entries were removed prior to analysis. All data were log2 transformed. Two sample t tests (false discovery rate (FDR) < 0. 05, S0 value = 1) were used to identify the significantly more abundant proteins in one group compared to the other. Among the t-test significant proteins, a minimum t-test difference of 1. 77 was found and a minimum -log test difference of 1.
A total of 404 common proteins could be identified in all the 18 samples (no NaN value). In addition, the nanoLC-MS/MS datasets corresponding to the cleaning sequence of the nanocolumn did not reveal significant carry-over peptides between successive analyses of UC and CD samples. t-test comparisons between UC and CD revealed eight proteins that were significantly more abundant in CD. From the highest to the lowest fold change between CD and UC, we found: Keratin 4 (KRT4), Neutrophil elastase (ELANE), Lactotransferrin (LTF), Lysozyme C (LYZ), Protein S100 A9 (S100A9), Protein S100 A8 (S100A8), Cathepsin G (CTSG) and the protein group Ig mu chain C region and Ig mu heavy chain disease protein (IGHM) (Table  1 ). As described in a previous work,  [18]  some proteins that are found not t-test significant can nonetheless be completely absent from samples of one condition and present in most of the samples from the other condition.
To further clarify this issue additional investigations including precise counting of neutrophils in UC and CD samples are necessary. Furthermore, the effect of different therapeutics has not been evaluated here due to the lack of clinical data in our study and should be taken into account in future studies. To conclude, our LMD-based microproteomic study highlighted eight proteins with significantly different abundances between UC and CD that provide valuable information about differences of UC and CD. The proteins found suggest a differential neutrophil activity between UC and CD and a differential implication of DAMPs between the two entities. The results also suggest that proteins formerly proposed as markers of IBD may also differentiate UC and CD. Differential quantities of AKR1C3 were found between UC and CD and confirmed by IHC.
Acknowledgements Christiane Zgorzelski  is gratefully acknowledged for her excellent technical assistance. The authors also thank the members of the  GIGA Cell Imaging Platform  for their help with laser microdissection. Financial support was provided by the " Association Plateforme BioPark of Archamps" from its "Research & Development" budget. Additionally , this project was supported by the  "Bundesministerium für Bildung und Forschung" (BMBF)  and the  Biobank of the National Center for Tumor Diseases (NCT).
g. , ATM, CDKN1A), cell polarization (SCRIB), proliferation (RNASEL), VEGF expression (MAP3K1, MAP3K6), and in genes encoding growth factors (FGF2) (Table  EV2 ). These variants were predicted to impair protein function by different in silico prediction tools; however, experimental data on the functional relevance of these variants are still inconsistent. Finally, the CTC-ITB-01 line displayed a high degree of loss of heterozygosity (LOH) as assessed by runs of homozygosity (ROH) within the exome. In comparison with the primary tumors which showed homozygosity within ˂ 90 Mb of the genome, 601 Mbs of homozygous sequences were detected for the breast CTC line, potentially indicating heterozygous mutations that became homozygous and contributed to tumor evolution. The 15 largest regions of LOH found solely in CTC-ITB-01 are listed in Table  EV3  and   A CNAs generated from CTCs from the primary blood collection (upper panel) and CTC-ITB-01, indicated as "CL" (medium panel).
The levels of Cleaved NOTCH-1 expressed in CTC-ITB-01 and MCF-7 were similar  (Fig EV5). Our results imply a participation of these pathways in the regulation of stem cell features in the CTC-ITB-01 cell line  (Bocci et al, 2017; Saha et al, 2017). All four samples were analyzed for rare (MAF, minor allele frequency, <1%), functionally relevant variants with an allele frequency of 10% of reads and higher in at least one of the samples. 219 cancer-associated genes curated from the COSMIC, HGMD, and OMIM databases were analyzed, and 9 pathogenic mutations in 6 genes were identified. Gene symbols were used as approved by the HGNC, and location of variants on cDNA and protein (one letter code) level, and allele frequency is shown. del, deletion; ins, insertion; *, stop codon; fs, frameshift. A Western blot analysis of selected protein markers, including ERa, EGFR, ERBB2, EpCAM, K18, K19, K8, E-cadherin, N-cadherin, vimentin, CD44, CD24, SNAIL, SLUG, TWIST1, and a-tubulin (as a loading control) (n = 3 replicates).
CTC-ITB-01 was compared to more mesenchymal ER À Hs578t and epithelial ER + MCF-7 breast cancer cell lines. B ICC staining of CTC-ITB-01 for pan-keratin (orange), ER (green), and DAPI (blue). The scale bar corresponds to 20 lm. Two representative panels are shown for CTC-ITB-01 (1) and (2). MCF-7 cells are depicted as reference cell line. C PAM50 classifier results showing probabilities of pooled CTC-ITB-01 cells matching specific molecular breast cancer subtypes. Starting from lowest probability, CTC-ITB-01 was classified as 1. 02% (s = 2. 5%) normal-like, 3. 11% (s = 6. 6%) Basal-like, 13. 11% (s = 9. 3%) ERBB2-positive, 16. 77% (s = 23. 7%) luminal A, and 65. 22% (s = 16. 6%) luminal B breast cancer subtype. Data were generated from n = 3 replicates. D PAM50 classifier results showing probabilities of the non-adherent and adherent CTC-ITB-01 fractions matching molecular subtypes.
Whole-exome sequencing and WES data analysis Exonic and adjacent intronic sequences were enriched from genomic DNA of the primary tumor, the vaginal metastasis and the CTC line using the Agilent SureSelect Human All Exon V6 enrichment kit and were run on an Illumina HiSeq4000 sequencer by the Cologne Center for Genomics (CCG). Data analysis and filtering of mapped target sequences were performed with the "Varbank" exome and genome analysis pipeline v. 2. 1 (CCG) and data were filtered for high-quality (coverage of more than 6 reads, a minimum quality score of 10) variants. WES data were analyzed for variants in 219 (Appendix Table  S1 ) of the frequently mutated genes based on the COSMIC, HGMD and OMIM databases  (Tate et al, 2019)  and genes involved in hereditary cancer predispositions syndromes.
Assessment of cell size For immunofluorescence staining, cells were seeded on culture slides. Cells were fixed, permeabilized, and blocked overnight. Cytoskeleton was detected using anti-pan-keratin eFluor 570 clone AE1/AE3 (eBioscience, 1:80) and counterstained with DAPI. Fluorescence images were captured using a Zeiss Axioplan 2 epifluorescence microscope equipped with a charge-coupled device camera and AxioVision software. Cell size was measured using ImageJ.
Libraries were pooled and sequenced on a HiSeq 4000 (Illumina) generating 50 bp single-end reads (30-40 Mio reads/sample). Sequencing images obtained after sequencing in the form of. bcl files are demultiplexed and converted to fastq files using Illumina software bcl2fastq v2. 17. 1. 14. Quality check of resulting sequence reads is performed using FastQC version 0. 11. 5. Generated raw reads are aligned to the reference Human genome version hg38 sourced from ENSEMBL database using aligner STAR version 2. 5. BAM files generated from alignment step are used to perform read counting using software featureCounts from subread package version 1. 5. 0. Counts generated are used to perform the differential gene expression analysis in R environment version 3. 4. 3 loading R/Bioconductor package DESeq2 version 1.
Mouse model CTC-ITB-01 cells were fluorescently tagged with a luciferase gene (GFP-fLuc2) using lentiviral technology. Efficiency of the lentiviral transduction lay at around 30-40%. A single-cell suspension of 300,000 cells was injected into the milk duct of NSG mice (N = 4). Mice were kept for 8. 5 months prior to sacrificing. The tumor growth was monitored in real time at multiple time points following transplantation by Xenogen IVIS bioluminescence imaging system 200 (Caliper Life Sciences) in accordance with the manufacturer's recommendations and protocols 12-15 min after intraperitoneal administration of 150 mg/kg luciferin (cat# L-8220, Biosynth AG). Images were analyzed with Living Image software (Caliper Life Sciences, Inc. ). For metastasis ex vivo detection, mice were injected with luciferin 10 min prior to sacrifice and resected organs were imaged immediately.
Afterward, the plate was immediately transferred into the IncuCyte Zoom live cell imaging system (Essen Bioscience) and relative cell confluence was measured every 2 h. The processing definition and confluence mask of the IncuCyte system were created using the provided Incu-Cyte Zoom 2016B software (Essen Bioscience). Accuracy of fit of the confluence mask was verified on representative images at different states of growth during the experiment. Statistical testing was performed with Prism 7. 0a (GraphPad Software Inc. ) using a one-way ANOVA followed by Dunnett's multiple comparisons test. A P < 0. 05 was considered as statistically significant. IC 50 calculation was also achieved with Prism 7. 0a using the implemented non-linear three-parameter logistic curve regression. Concentrations were transformed to common logarithm.
Acknowledgements The authors acknowledge  Malgorzata Stoupiec ,  Cornelia Coith ,  Antje Andreas , and  Olivier Mauermann  from  UKE Hamburg , and  Alexandra Soler  from  CHU Montpellier  for technical assistance. For preparation of the graphical synopsis, the figures were designed with the assistance of and modification to the images provided by the Servier Medical Art database http://smart. servier. com/. Further information pertaining to the license and disclaimer notices can be found here: https://creativecommons. org/licenses/ by/3. 0/. Klaus Pantel  and  Catherine Alix-Panabières  received funding from the  European IMI  research project  CANCER-ID  ( 115749-CANCER-ID ). Klaus Pantel  and  Sabine Riethdorf  received funding from the  Deutsche Krebshilfe  (Nr. 70112504 ). Klaus Pantel has received research funding from the  Deutsche Forschungsgemeinschaft (DFG)   SPP2084  lBone and  ERC   Advanced Investigator Grant  INJURMET (Nr.
The South Korean meteorological and environmental satellite GEO-KOMPSAT-2A (GK-2A) was launched into geostationary orbit at 128. 2 • East on 4 December 2018. The space weather observation aboard GK-2A is performed by the Korea Space Environment Monitor. It consists of three particle detectors, a charging monitor and a four-sensor Service Oriented Spacecraft Magnetometer (SOSMAG). The magnetometer design aims for avoiding strict magnetic cleanliness requirements for the hosting spacecraft and an automated on-board correction of the dynamic stray fields which are generated by the spacecraft. This is achieved through the use of two science grade fluxgate sensors on an approximately one meter long boom and two additional magnetoresistance sensors mounted within the spacecraft body. This paper describes the instrument design, discusses the ground calibration methods and results, presents the post-launch correction and calibration achievements based on the data which were acquired during the first year in orbit and demonstrates the in-flight performance of SOSMAG with two science cases.
The dynamic stray fields from the GK-2A spacecraft, which was built without specific magnetic cleanliness considerations, are reduced up to a maximum factor of 35. The magnitude of the largest remnant field from an active spacecraft disturber is 2. 0 nT. Due to a daily shadowing of the SOSMAG boom, sensor intrinsic offset oscillations with a periodicity up to 60 minutes and peak-to-peak values up to 5 nT remain in the corrected data product. The comparison of the cleaned SOSMAG data with the Tsyganenko 2004 magnetic field model and the magnetic field data from the Magnetospheric Multiscale mission demon-.
It consists of three particle detectors, a charging monitor and a four-sensor Service Oriented Spacecraft Magnetometer (SOSMAG). The functional composition of the KSEM instrument suite is illustrated in Fig. 1. A dedicated Instrument Data Processing Unit (IDPU) is allocated for the three particle detectors  (Seon et al. 2020 ) and the charging monitor to handle scientific data, telemetry and spacecraft command, whereas another independent Data Processing Unit within the SOSMAG electronics box handles data, telemetry and commands for the four-sensor magnetometer. All SOSMAG commands and data are routed via the IDPU. The SOSMAG development was initiated and conducted by the European Space Agency and built by the SOSMAG consortium: Magson GmbH, Austrian Academy of Sciences, Technische Universität Braunschweig and Imperial College London.
The instrument configuration aboard the GK-2A mission contains two science grade fluxgate sensors on the boom and two additional magnetoresistance sensors mounted within the spacecraft body. The two latter sensors are ideally located in the vicinity of dynamic magnetic field sources like reaction wheels, heater elements and magnetic actuators. The measurements of the two spacecraft sensors together with the inner boom sensor enable an automated correction of the dynamic, spacecraft generated disturbances from the measurements of the outer boom sensor. The correction can be done by the on-board software. The first power-on of SOSMAG including successful boom release took place on 4 January 2019. After the commissioning of all KSEM sensors as well as the primary payload of GK-2A, SOSMAG started regular operation on 25 February 2019 with all four sensors taking data at one vector per second.
In this paper, Sect. 2 discusses the science objectives for the magnetic field measurements aboard GK-2A and the derived performance requirements for the SOSMAG instrument. Section 3 explains the instrument design with a brief description of all main hardware elements while Sect. 4 deals with the on-ground calibration. Section 5 outlines the flight operation with a detailed discussion of the removal of the magnetic contamination from the spacecraft as well as the determination of offsets and alignment of the cleaned SOSMAG data. The description of exemplary science observations complement this section. The data product flow of SOSMAG is explained in Sect. 6, and Sect. 7 summarizes this review.
Overview SOSMAG is a multi-sensor magnetometer with an approximately one meter long boom. The configuration as built for the GK-2A spacecraft as part of the KSEM instrument suite contains two science grade, boom mounted fluxgate sensors, two anisotropic magnetoresistance sensors which are mounted within the spacecraft body, a deployable boom, which includes a specific mounting interface to avoid mismatches of the thermal expansion coefficients, and an electronics box with the data processing, power conversion and sensor front-end electronics. The block diagram of the SOSMAG instrument is shown in Fig. 4  and the main instrument parameters are listed in Table  3. Pictures of the SOSMAG qualification model and the boom mounted to the +X sidewall of the spacecraft are depicted in Fig. 5  and Fig.
6 , respectively. The modular concept of SOSMAG would allow for one additional sensor front-end electronics board within the electronics box, which connects to either one more fluxgate sensor or two more Anisotropic Magneto-Resistance (AMR) sensors. SOSMAG measures the magnetic field continuously with a maximum vector rate of 128 vectors per second at all four sensors, and it generates adequate housekeeping information. Within the Data Processing Unit (DPU), the magnetic field data can be filtered and decimated by a commandable integer factor. With e. g. a decimation factor of 128, it is reduced to 1 vector per second. The filter coefficients are equal to one over the decimation factor. It results in a transfer characteristics which is equivalent to a boxcar filter with rectangular windowing.
In addition, a cleaned vector can be calculated by the SOSMAG DPU. It is a linear combination of the measurements from all sensors. The data from the magnetic sensors are multiplied with a 3 × 3 calibration matrix and corrected for a static offset value before the cleaned vector is processed. All SOSMAG commands and data are routed via the Instrument Data Processing Unit (IDPU) of the KSEM instrument suite (Fig. 1 ). Also the power switching for SOSMAG is  included in the IDPU electronics. The data routing through the IDPU implies a bandwidth limitation which reduces the maximum possible vector rates transmitted from the GK-2A spacecraft to ground. All operational modes and their respective vector rates are listed in Ta- ble 4. They are defined via the number of sensors, the commanded data rate and the optional transmission of the additionally processed cleaned vector.
In principle, there is no transmission of the cleaned vector foreseen in the commissioning modes except for one specific mode in which the vector rate of the four magnetic field sensors is reduced to eight. Transmission of the cleaned vector is mandatory for the nominal operational modes of SOSMAG. The commissioning modes, which can only be enabled when the other KSEM sensors are turned off, are used for instrument check-out and the characterisation of potential dynamic disturbers especially during the early phase of the mission. The two nominal modes, all four sensors plus cleaned vector with one vector per second or just the cleaned vector with 4 vectors per second, are to be run during the science phase. The data from the first 12 months of nominal operation were studied on ground to characterize the dynamic spacecraft disturbances and to determine correction factors for each specific disturber (see Sect.
Electronics Box and AMR Sensors The SOSMAG electronics is housed in one metal box fabricated from Aluminium. The upper left picture of Fig. 7  shows the flight model of the electronics box with bottom plate, data processing and power distribution frame, two fluxgate front-end electronics frames, AMR front-end electronics frame and lid. The box design allows for a flexible scaling of the number of frames between two and five according to the target application. The fully redundant processing and power distribution unit is accommodated on a single printed circuit board (upper right picture of Fig. 7 ). The power distribution section reduces the supply voltage provided by the KSEM IDPU (Fig. 1 ) from 50 V to less than 30 V via a linear voltage regulator and connects it to the instrument internal interface bus.
7  Pictures of the SOSMAG electronics box (upper left), the combined data processing and power distribution unit (upper right), the front-end electronics for the fluxgate sensors (lower left), which is implemented twice, and the front-end electronics for the two AMR sensors (lower right). The envelope of the electronics box is 170 × 255 × 110 mm 3 feedback coils within the sensor to increase the overall linearity and stability. The primarily digital electronics design was first developed for the Rosetta Lander magnetometer  (Auster et al. 2007 ) and has already been used for a number of other space missions  (Auster et al. 2008). For SOSMAG, two different hybrid chips are used. The ADC hybrid contains the preamplifier and the analogue-to-digital converter (18 bits, 1 MSPS), and the DAC hybrid is made up from two 16-bit digital-to-analogue converters and a current source for the feedback system.
Flight Operation After launch of GK-2A from Kourou in French Guiana on 4 December 2018, SOSMAG was first taken into operation with stowed boom on 4 January 2019. Boom release was performed successfully on the same day. SOSMAG was then kept in commissioning mode with all four sensors taking data with 16 vectors per second to monitor the back ground field of the spacecraft up to 8 Hz. On 7 January 2019, the vector rate was reduced to 1 vector per second for all four sensors (instrument mode Normal 1V according to Table  4 ) to open up enough telemetry bandwidth for the commissioning of the KSEM particle sensors. From 25 January Fig. 14  Drift of the sensor offsets with changing sensor temperature; the maximum drift of the outboard fluxgate (upper plot) and the AMR 1 (lower plot) sensors over a temperature range of about 100 • C is less than 2 nT and 156 nT, respectively until 25 February 2019 all KSEM sensors were powered down for the commissioning of the Advanced Meteorological Imager, the primary payload of GK-2A.
From 25 February 2019, SOSMAG has been operating in Normal 1V almost continuously. The first parameter upload for the on-board data cleaning took place on 2 May 2019. The following subsections describe the required coordinate systems, the removal of the magnetic disturbances from the spacecraft with the four-sensor approach, the determination of the low frequency offsets by a comparison with the Tsyganenko Earth's field model 2004, two examples of scientific events measured by SOSMAG and the data product flow.