dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Processing Chain Details of the data processing pipelines are shown in Figs. 3(a ) and 3(b). Without MC, the pipeline follows four step process as illustrated in Fig. 3 (a) as a row of gray-scale images. This data processing chain first retrieves the position of all detected photons in the 32 × 32 × 500;000 dataset, pToF, estimates the photon flux for each single photon (estimate Φ) and noise is reduced by applying a filter (see Sec. 3. 2. 2). We tested different linear and statistical noise filter. Then, we correct the pixel values due to a previous determined dark current map (DCR pattern) and apply a DNN approach to upscale the resulting data frames. With MC applied, see Fig. 3 (b), the data processing pipeline is bypassed through additional processing steps [in Fig. 3 (a) at Ã to ‡] and we compensate motion through an align and merge process.
Upscaling to super-resolution It is known that image upscaling can be done by application of convolutional neural networks. There are several approaches published and their codes and even trained network are available. In our approach, we used the upscaling function of OpenCV  54  in Python. The upscaling algorithm is based on a pretrained DNNs with a scaling factor of 4×, to scale 32 × 32 low-resolution to 128 × 128 super-resolution frames. In our algorithm, we use the enhanced deep super-resolution network approach from Lim et al. 48 e also investigated upscaling prior to an MC step as in Ref. 22. In this approach, the raw data are nearest-neighbor upscaled before applying align and merge MC. This gives sub-pixel resolution because each SPAD pixel will sample along a continuous motion curve.
In the fan scene, we used the euclidean motion model to compensate motion, while for the balloon we used optical flow analysis. Further, we tried out different scaling methods to obtain super-resolution by combining prior and posterior scaling of the data sets with linear and SR-DNN methods. A first MC approach works similarly to the pixelwise algorithms above. MC was used on the datasets with native resolution and resulting frames were later scaled to 4× resolution by employing the pretrained DNN. The results of the two processing steps are shown in the fourth column. In both scenarios, the impact of noise is significantly reduced. Further, shapes and the balloon's ripping edge are reconstructed well. However, the resolution of the letters on the fan appears to be similar to the results of the previous pixelwise processing algorithms.
In a further MC approach, fifth column, we combined a linear scaling (2×) of the datasets before application of MC and 2× DNN scaling. With this approach, it is possible to obtain effective noise reduction and a high contrast. Further, due to the prior scaling of the datasets, it is possible to reveal subpixel information in areas of continuous motion that is, for instance, the rotating fan. In these areas, we can identify details of the letters typeface 66 such as the serifs (in "I" and "L"), hairlines (in "S" and "L"), and bows (in "U" and "W") which were not visible before. On the other hand, in the balloon scene, we observe pixelated representation of areas with no or slow motion (e. g. , balloon surface and dropping dart). Here, the pixelwise processing methods obtain much better resolutions.
Finally, in a the third MC approach (sixth column), we employed only the prescaling (4×) of the datasets before application of MC. Again, it is possible to obtain effective noise reduction and a high contrast. In the fan scene, again, we obtain very detailed subpixel resolution in areas with continuous motion. For instance, the representation of the typefaces appears to be much clearer than in the aforementioned approaches. However, the algorithm results are very pixelated in areas with no or few motion (e. g. , balloon body). Moreover, the ripping edge, although having huge amount of motion, is reconstructed pixelated and does not appear clearly in the resulting frame. Figures  7  and 8  show still images of videos illustrating our processing pipelines. Video 1 (Fig. 7 ) depicts the "bursting balloon" scene and shows raw timestamp frames, pixelwise estimation of the instantaneous photon flux, and the super-resolution reconstruction using CPF denoising and DNN upscaling.
Discussion In our analysis, we have seen that we can apply various noise reduction and super-resolution algorithms to passive single-photon timing data to obtain a super-resolution reconstruction of the instantaneous photon flux. However, the quality of the reconstruction depends on the amount of motion contained in the scene and the reconstruction filter applied. Nevertheless, it is possible to obtain a reconstruction frame rate identical to the sampling frame rate. In our experimental datasets, we were able to reconstruct an original frame rate of 65. 8 kHz. This rate is limited by the achievable frame rate of the hardware used. For further discussion, we focus on the super-resolution results obtained in the rotating fan scene. An overview can be seen in Fig. 9 , which shows two sets of magnified sections  representing the fan blades with the letter "S" and "U.
" It is obvious that very different reconstruction qualities are acheived with the different algorithms. However, the algorithms with 4× SR-DNN upscaling result in blurred reconstructions, while applying MC to prescaled datasets achieves sharp reconstruction of subpixel information. To compare the reconstruction performance of the different algorithms, we calculated the structural similarity (SSIM)  67, 68  and mean square error (MSE) by comparison to a scene photo (see Appendix B, Fig. 11 ). Both evaluation metrics were applied to the whole images and to 35 × 35 cut-out sections. For the comparison, we normalized the data using a min-max normalization such as the MSE range from zero to one (MSE ∈ ½0;1). By definition, the SSIM value can range from 1 (good similarity) to -1. Due to the fact that we do not have a real ground truth, but a very similar photo, we do not expect to obtain an SSIM of 1.
Acknowledgments We would like to acknowledge financial funding through the  General Funding of the French-German Research Institute of Saint-Louis  (ISL) by the  République Française (France)  and the  Bundesrepublik Deutschland (Germany). Support for this research was provided by the  University of Wisconsin-Madison , Discovery to Product ( D2P ) with funding from the  State of Wisconsin. This material was based upon work supported by the  Department of Energy/National Nuclear Security Administration  under Award Number  DE-NA0003921 , the  National Science Foundation  ( GRFP DGE-1747503 ,  CAREER 1846884 ). The authors declare no conflict of interests. Disclaimer: This report was partly prepared as an account of work sponsored by an agency of the  United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights.
Castration alters expression of glycosaminoglycan enzymes in a PDX model of prostate cancer progression The patient-derived xenograft (PDX) trans-differentiation model (LTL331/LTL331R) is a typical hormone-naïve AR/PSA-positive adenocarcinoma that upon castration initially regresses, but eventually relapses as terminally-differentiated AR-negative neuroendocrine prostate cancer (NEPC) (LTL331R) (Fig. 1a ), adequately reflecting the clinical scenario in humans following ARPI  37, 38. We performed transcriptomic analysis on LTL331/LTL331R tumors from different disease stages pre-and post-castration (Fig. 1a ) (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE59986), followed by an ingenuity pathway analysis of glycosylation-associated pathway alterations. The analysis revealed that GAG-related pathways, in particular those leading to chondroitin sulfate and heparan sulfate synthesis, were upregulated after murine castration and in the NEPC relapse disease stage (Fig.
Prostate tumors with loss of AR activity display elevated CHST11 and chondroitin sulfate levels We next validated the results from the PDX discovery model in prostate cancer patients. We first determined the relationship between the identified 29 enzyme-coding genes (Fig. 1c ) and androgen signaling in a clinical RNA-Seq dataset of 101 prostate cancer patient tumors at different disease stages (VPC cohort)  39. We correlated expression of the 29 genes with an expression of five validated AR-regulated genes KLK3, NKX3. 1  40  , TMPRSS2  [41] [42] [43]  , FKBP5  44  , and TMEFF2  45  as readouts for AR signaling activity. Of the top-three genes (i. e. , HS3ST5, HS6ST2, and CHST11), only CHST11 showed a consistent negative correlation with all five AR-regulated genes (Fig. 2a  and Supplementary Fig.
1a ). The result was corroborated in a second validation cohort of n > 196 mixed stage patients (IST cohort) (Supplementary Fig. 1b ), supporting an inverse relationship between CHST11 expression and AR signaling in prostate cancer. To further validate that idea, we analyzed a third cohort comprised of 138 CRPC patients (University of Washington; UW cohort) (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE147250). Here, CHST11 expression alone was able to segregate patients based on loss of AR activity (Fig. 2b ). We next analyzed a cohort of prostate cancer patients pre-and post-treatment with ADT and with respect to risk and metastatic disease. Here, CHST11 expression was elevated in patients treated with ADT (Fig. 2c ), in high-risk tumors (Fig. 2d ), and in patients with metastatic disease (Fig.
CHST11 is an AR repressed gene The upregulation of CHST11 expression in patient tumors after ARPI hints that the CHST11 gene might be repressed by AR. To test this, we first assessed the CHST11 gene signature in an AR chromatin-IP (ChIP) sequencing dataset of LNCaP cells cultured with or without androgens  and/or treatment with the clinical-grade AR inhibitor, enzalutamide  50. The analysis identified a strong peak in the first intron of the CHST11 gene located at 12q23. 3 (104625195-229) in LNCaP cells treated with Dihydrotestosterone (DHT) and blocked by enzalutamide (Fig. 3a ). The peak found in LNCaP cells was also observed in androgen-sensitive VCaP cells cultured in the presence of DHT (Fig. 3a ), indicating that AR directly binds the CHST11 gene. We next confirmed the binding of AR to the CHST11 gene by ChIP-PCR in LNCaP cells using three nonoverlapping primer sets flanking the 104625195-229 region of 12q23.
4f ). The increase of CSA chains on LNCaP cells after androgen deprivation was confirmed by immunoblotting of cell-bound rVAR2 (Fig. 4g ). In order to rule out that the observed CSA changes are due to CSPGs core proteins being AR regulated, we examined the expression levels of major CSPGS in different PC cell lines from the dataset published in ref. 55. The analysis showed that while AR expression and activity correlated positively with TMEFF2 and negatively with CD44, it did not correlate with the ten remaining CSPGs known to be able to carry CSA modifications (Supplementary Fig. S4c ). This result was further confirmed on a selected panel of cell lines and CSPGs by qRT-PCR and western blotting (Supplementary Fig. S4d, e ). Together, these data indicate that CSA regulation by AR is independent of the CSPG core proteins.
S9d ), reflecting the complexity and phenotypic diversity of the PC relapsed tumors. However, while there was no significant difference in PSA levels between LNCaP and relapsed castrated LNCaP; castrated LNCaPCHase tumors showed a significant lower level of PSA when compared to uncastrated LNCaPCHase (Fig. 5n  and Supplementary Fig. S9d ). This is corroborated by RNAseq of additional LNCaP and LNCaPCHase tumors ± castration, showing that while AR mRNA levels increase after castration in both LNCaP and LNCaPCHase tumors, mRNA of established AR target genes KLK3/PSA, TMPRSS2, FKBP5, is significantly decreased only in LNCaPCHase tumors after castration (Fig. Supplementary Fig. S9e ). These data indicate that progression to CRPC depends on elevated CS expression. The GalNAc-containing glycocalyx supports cell motility and metastases in AR-negative CRPC We next assessed the importance of the GalNAc-containing glycocalyx for prostate cancer cell motility and metastases in the absence of AR signaling.
Cell culture All human cell lines excluding IGR-CaP1 cells were purchased from ATCC. IGR-CaP1 (RRID: CVCL_A018) cells were provided by ref. 79. All cell lines were confirmed to be free of mycoplasma contamination and were maintained at 37 °C with 5% CO 2. LNCaP (ATCC Cat# CRL-1740, RRID:CVCL_1379) and its derivatives, 22Rv1 (ATCC Cat# CRL-2505, RRID:CVCL_1045), and IGR-CaP1 cells were maintained in RPMI 1640 medium containing 10% fetal bovine serum (FBS) (Gibco Cat# 11875119 & Gibco Cat# A3160401). PC3 (ATCC Cat# CRL-1435, RRID:CVCL_0035), VCaP (ATCC Cat# CRL-2876, RRID:CVCL_2235), and DU145 (ATCC Cat# HTB-81, RRID:CVCL_0105) cells were maintained in Dulbecco's Modified Eagle Medium (DMEM) containing 10% FBS (GE Healthcare Cat# SH30243. 01). NCI-H660 (ATCC Cat# CRL-5813, RRID:CVCL_1576) cells were maintained in RPMI 1640 medium containing 5% FBS, 1X Insulin-Transferrin-Selenium (Gibco Cat# 41400045), 10 nM Hydrocortisone, 10 nM beta-estradiol, and 4mM L-glutamine (add 2 mM extra to the media) (Gibco Cat# 35050061).
Western blotting analysis Western blotting assays were performed as previously described  82. All antibodies used in the present study, their catalog numbers, applications, and used dilution can be found in Supplementary Table  1. Anti-rabbit and anti-mouse IgG secondary antibodies were purchased from LI-COR. Membranes were scanned using the LI-COR Odyssey® Gel Imaging system. W. B intensities were determined using ImageJ software.
Proliferation assay Cells were plated on a 96-well plate (5000 cells per well) in RPMI 1640 medium containing 10% FBS or in phenol-free RPMI medium containing 10% CSS (Gibco Cat# 11835030 & GE Healthcare Cat# SH30068. 03). Cell growth was monitored using the IncuCyte Live-Cell Analysis System (Sartorius). Propidium iodide (500 nM) was added to the media to follow cell death. Ec50 values were calculated using a weighted five-parameters logistic regression and statistical analyses were performed using GraphPad Prism 8 (GraphPad Software Inc. ).
Glycocalyx imaging Cells were seeded on glass coverslips and treated with 40 µM of GlcNAz (Thermo Fisher Cat# PI88905) for 48 h. After three washes with cold DPBS (Gibco Cat# A1285801), cells were Cu-click conjugated with AlexaFluor-488-alkyne (Click Chemistry Tools Cat# 12771) and fixed as previously reported (  83 ). Cells were mounted onto glass slides with ProLong glass mounting reagent (Thermo Fisher Cat# P36980). Images were acquired at 60× magnification on the Olympus FV3000RS confocal microscope. The glycocalyx quantification was determined by defining a region of interest around individual cells and then measuring the intensity of the staining in each region using ImageJ software. Intensity plot of the cell membrane was generated and the width at half maximum intensity was considered as glycocalyx thickness.
Tumor growth was monitored weekly using a caliper-measuring tool. The tumor volume was calculated using ellipsoid volume formula; V = (4/3)π(L/2)*(W/2)*(D/2) and mice were euthanized before reaching the maximal tumor size of 1000 mm 3. PC3-LUC cells were pre-treated with 25 µM of Fluorosamine (Synthesized by Life Chemicals as previously described  84  ) for 72 h and collected using CellStripper dissociation reagent. 1 × 10 6 PC3-LUC cells suspended in 100 µl of PBS were injected into the left ventricle of 6week-old Nu/Nu male mice under ultrasound guidance using a 30gauge needle. Metastases formation was monitored using IVIS spectrum CT scanner (Perkin Elmer) and analyzed using IVIS Living Image Software. Metastasis sites were collected and fixed in formalin for pathology studies at humane or experimental endpoint.
Flow cytometry Cells were grown to 70-80% confluency in appropriate growth media and harvested in CellStripper. Cells were incubated with rVAR2 protein (200-25 nM) in PBS containing 2% FBS for 30 min at 4 °C and binding was acquired in a FACS Calibur (BD Biosciences) after a secondary incubation with an anti-V5-FITC antibody. Single cells were gated using FSC-H/FSC-A parameters. FlowJo (v9) and Cyflogic (1. 2. 1) softwares were used for FACS analysis Gating strategy example in Source Data file.
Acknowledgements We would like to thank  Mr. Joseph Lee  and  Dr. Davide Tortora  for their technical support. Miss Sonia Kung  for the illustration of the graphical abstract and  Dr. Elizabeth M. Muir  for providing the chABC plasmid. This work is supported by  NIH Prostate Cancer PNW-SPORE  ( 1016339 ,  223493 ;  5P50 CA097186-17 ); the  Canadian Institutes of Health Research (CIHR)  ( PJT-153092 );  Prostate Cancer Canada (PCC)  ( RS2014-02 ); and MITACS (IT06057).
Data availability Previously published RNA-seq data that were re-analyzed here are available under accession code https://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE147250; https://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE59986. The Chip-SEQ data reanalyzed here were obtained from Cistrome. com; WashU Epigenome Browser using data from refs. 50; Chng et al. (2012). Source Data are provided in this paper. All other data supporting the findings of this study are available in the Source Data file. All unique biological materials are available by request from the corresponding author. Source data are provided with this paper.
A high-resolution map of in vivo cell cycle phosphorylation We took advantage of the naturally synchronous early cell cycles of Xenopus laevis embryos  20, 21  to perform quantitative phosphoproteomics in vivo, using a sensitive phosphopeptide enrichment strategy  22. We collected single embryos at 15-minute intervals while recording visual cues of cell divisions. Phosphopeptides from each embryo were purified, separated by nano-LC and analysed by mass spectrometry (Fig. 1a ). We identified 4583 highconfidence phosphosites mapping to 1843 proteins (Extended data Fig. 1a ; Data S1), most being phosphoserines (Extended data Fig. 1b ). Individual embryo phosphorylation states strongly correlated (Extended data Fig. 1c ). We thus generated a dynamic map of protein phosphorylation from an unfertilised egg to a 16-cell embryo.
3b ). This phosphoproteomics dataset greatly increases the known repertoire of phosphorylation sites upregulated during S-phase  12. We next analysed the cell cycle behaviour of dynamic phosphosites that we found in vivo (Extended data Fig. 3c ). Most embryo cluster A sites were upregulated in both CSF-arrested meiotic extracts and mitotic extracts, highlighting the global similarities of regulation of meiotic and mitotic M-phase, despite the additional activity of the Mos/MEK/MAP kinase pathway in meiosis. Around half of embryo cluster B sites were present only in interphase, while the rest showed a minimum phosphorylation in late S-phase, confirming their dephosphorylation during the first round of DNA replication. As expected, most sites from embryo clusters C and D were part of the in vitro S-phase and mitotic groups, respectively.
For each kinase, documented phosphosites were strongly enriched in IDRs (Extended data Fig. 6c, d ), supporting the idea that phosphorylation of residues in IDRs is kinetically favoured 34. To explain the dominance of CDK-mediated phosphorylation in the cell cycle, we surmised that their substrates might be more disordered than phosphoproteins in general. We therefore determined the percentage of disordered residues of proteins in our datasets, compared to the rest of their respective phosphoproteomes (Data S3). This revealed that, on average, both Xenopus dynamic phosphoproteins and human and yeast CDK substrates contain approximately twice the proportion of disordered amino acids as other phosphoproteins (Fig. 3e , Extended data Fig. 6e ), putting them among the top quartile of proteins with the most disorder in the proteome.
Enrichment of MLO components among CDK substrates We thus reasoned that phosphorylation may have been selected to regulate the functions of IDPs during the cell cycle. IDPs are key components of membrane-less organelles (MLO), many of which (e. g. Cajal bodies, nucleoli, nuclear pore complexes, splicing speckles) are thought to arise by phase separation (PS)  37  , are disassembled in mitosis, and can be regulated by phosphorylation  36, 38, 39. To corroborate our hypothesis, we analysed available data on cellular localisation for each of our curated human CDK substrates. We found that 257 (39. 2%) are present in MLOs, including key IDPs such as coilin (Cajal bodies), nucleophosmin, nucleolin and Ki-67 (nucleoli), 53BP1 (53BP1 bodies), nucleoporins (NPC) and PML (PML bodies) (Fig. 3g ). We then manually curated an MLO proteome from human proteomics studies (Data S4; See Supplementary Methods for sources).
Acknowledgments: We thank  Merlijn Witte  for technical assistance with the Xenopus laevis egg fertilization experiments,  Ariane Abrieu  for a gift of  CSF  egg extracts, and  Markus Raschle  from the  Technical University of Kaiserslautern  for providing the Xenopus laevis protein database. Funding: AJRH and MA acknowledge support from the  Horizon   2020 program  INFRAIA project  Epic-XS (Project 823839 ) and the  NWO funded Netherlands Proteomics Centre  through the  National Road Map for Large-scale Infrastructures program X-Omics  (Project  184. 034. 019 ) of the  Netherlands Proteomics Centre. JMV is supported by scholarships from the  Ministry of Science and Technology of Costa Rica (MICITT)  and the  University of Costa Rica (UCR). PK and MV are funded by the  Oncode Institute  which is financed by the  Dutch Cancer Society  and by the gravitation program CancerGenomiCs.
nlfrom the  Netherlands Organisation for Scientific Research (NWO). DF and LK are Inserm employees. GD is funded by the  Institut National de Cancer, France (INCa)   PRT-K programme (PRT-K17  n°  2018-023 ). The Fisher lab is funded by the  Ligue Nationale Contre le Cancer, France  ( EL2018. LNCC/DF ) and  INCa  ( PLBIO18-094 ). The CBS is a member of  France-BioImaging (FBI)  and the  French Infrastructure for Integrated Structural Biology (FRISBI) , supported by the  French National Research Agency  ( ANR-10-INBS-04-01  and  ANR-10-INBS-05 ).
[ 11 C]-Flumazenil PET Imaging: Acquisition Protocol and Imaging Data Analysis PET imaging experiments were performed on an Inveon microPET scanner (spatial resolution ~1. 6 mm; Siemens Healthcare, Knoxville, TN, USA) in anesthetized mice (1. 5-2. 5% inhaled isoflurane, weight 34. 2 ± 3. 7 g). [ 11 C]flumazenil was formulated in 0. 9% aqueous saline with 3% ethanol (v/v). The radiochemical purity of [ 11 C]flumazenil was >98% and the molar activity at the time of injection was 6. 8 ± 0. 9 GBq. µmol -1. Data acquisition started immediately after intravenous bolus injection of [ 11 C]flumazenil (6. 8 ± 0. 9 MBq) for 60 min. Dynamic PET images were reconstructed using standard OSEM-2D reconstruction algorithm while correcting for radioactive decay, scatter, and attenuation. PET data analysis was performed using the PMOD software (version 3.