dataesr/scientific-paragraphs-categorization huggingface dataset:
Neisseria meningitidis bacteria growth. With this dataset, we want to follow bacteria cell division dynamics and how the morphology of single bacteria evolves during the cell cycle. For the quantitative assessment, we took a subset of frames of the data shown in Figure  2b  and Movie 2. The objects to track are small and round but elongate before cell division. Due to many division cycles, the number of objects to follow goes from 4 to 400. Therefore we need to use a detector that can accommodate shape changes and a tracker that can detect cell divisions. We tested the ilastik detector with a model trained to segment the cell space inside the membrane. Because the membranes are fluorescently labeled, the cells form an edge object suitable for segmentation with the MorphoLibJ detector. We also included the LoG and DoG detector used on the inverted image.
Glioblastoma-astrocytoma cells. Here we want to follow cell shape changes over time (see Supplementary Manual). This dataset was imaged using brightfield microscopy at high resolution. There are only a few cells to track in the movie. In addition, the cells do not cross or overlap, simplifying the linking step. However, the cells have a complex shape. To strive for the best possible segmentation, we trained a custom cellpose model. This model was trained outside of TrackMate using the cellpose ZeroCostDL4Mic notebook and the Cyto2 pre-trained model as a starting point (see Methods). As TrackMate is integrated in Fiji and given the excellent image quality, we also tested a more basic segmentation strategy. We first preprocessed the source image by applying a variance filter (with a radius of 9).
Then, we used the Thresholding detector in TrackMate to track the cells. In this case, we filtered out spurious spots with an area lower than 1000 μm2. Finally, as the cells display some texture that can elicit a weak response from the LoG and DoG filters, we also used the detectors based on these two filters as a basis for comparison. As expected, the cellpose detector used with a custom model yields a satisfactory SEG score on this dataset. The cell borders have such a low contrast that the other detectors fail to achieve better segmentation accuracy. However, the cellpose detector sometimes misses cells that touch the image's border. In turn, the missing detections negatively affect the metrics that require complete tracks, such as TRA, TF, and CT. The Thresholding detector on the variance image offers better results for these metrics.
Movie 8 -Using cellpose and TrackMate to track migrating cancer cells MCF10DCIS. com cells expressing lifeact-RFP, labeled with Sir-DNA, were recorded using a spinning-disk confocal microscope. Cells were segmented using cellpose, and label images were tracked using TrackMate. The color indicates ID. Movie 9 -Using StarDist 2D within TrackMate to generate 3D labels MCF10 DCIS. com spheroids were imaged using a spinning-disk confocal microscope. To generate 3D labels, nuclei were detected and tracked across the Z volume using StarDist implemented in TrackMate. The 3D rendering was performed using the FPBioimage software.
Acknowledgments The integration of existing algorithms as new detectors in TrackMate has been made possible thanks to the high quality of the code, documentation, and support provided by their respective authors. In particular, we would like to thank  Anna Kreshuk ,  David Legland ,  Dominik Kutra ,  Ignacio Arganda-Carreras ,  Carssen Stringer ,  Marius Pachitariu ,  Martin Weigert ,  Siân Culley , and  Uwe Schmidt. We can only hope for TrackMate to reach such a standard of quality to become a better tool of Science. We are also grateful for the support and help of the bioimage analysis community, in particular  Curtis Rueden ,  Jan Eglinger ,  Nicolas Chiaruttini ,  Romain Guiet ,  Olivier Burri ,  Valdimír Ulman ,  Tobias Pietzsch , and  Pavel Tomancak. We thank  Helen Blau  for giving us the permission to use the "mouse hematopoietic stem cells in hydrogel microwells" dataset made available on the Cell Tracking Challenge website.
The authors thank  Dr. Hellyeh Hamidi  for her critical reading of the manuscript. This study was supported by  France BioImaging  ( Investissement d'Avenir ;  ANR-10-INBS-04 , JYT), the  Academy of Finland (GJ) , the  Sigrid Juselius Foundation (GJ) , the  Cancer Society of Finland (GJ) , the  Åbo Akademi University Research Foundation  (GJ,  CoE CellMech ), the  Drug Discovery and Diagnostics strategic funding to Åbo Akademi University (GJ)  and the  European Union 's  Horizon 2020 research and innovation program  under  Marie Sklodowska-Curie grant agreement 841973  (JRWC). JWP was supported by  Health Campus Turku 2. 0 funded by the  Academy of Finland. RFL was supported by an  MRC   Skills development fellowship  ( MR/T027924/1 ). The Cell Imaging and  Cytometry Core facility  ( Turku Bioscience, University of Turku, Åbo Akademi University, and Biocenter Finland ) and  Turku Bioimaging  are acknowledged for services, instrumentation, and expertise.
Data availability statement The version of TrackMate described here is available in the Fiji software 8  by simply updating it. TrackMate is documented on the ImageJ wiki: https://imagej. net/plugins/trackmate/ and the documentation for the new features can be accessed from https://imagej. net/plugins/trackmate/trackmate-v7-detectors. We also provide 14 test datasets that are made available via a dedicated Zenodo collection (https://zenodo. org/communities/trackmate/).
We envision that this systematic approach will benefit medium and high-throughput automatic tracking studies. By optimizing the tracking parameters on one movie, users will find optimal tracking parameters for the rest of their dataset. Here, we used TrackMate-Helper to assess the performance of TrackMate on four datasets that cover a wide range of biological and imaging situations: 1. Migrating cancer cells imaged using fluorescence microscopy to visualize their nuclei. In this dataset, the cells are densely packed and divide during the experiment (Figure  2a , Movie 1).
Acknowledgements We thank:  Anna Dewar ,  Kevin Foster ,  Andy Gardner ,  Ashleigh Griffin ,  Asher Leeks ,  Matishalin Patel ,  Tom Scott , and  Daniel Unterwegger  for their helpful comments and suggestions;  St. John's College, Oxford (GAC) , the  French National Research Agency (ANR)  under the Investments for the  Future (Investissements d'Avenir) program  (grant  ANR-17-EURE-0010  to the IAST; J. P. ) and the  ERC (Horizon 2020  Advanced Grant  834164 ; S. A. W. ) for funding. We would like to acknowledge the use of the  University of Oxford Advanced Research Computing (ARC)  facility in carrying out this work. (https:// doi. org/10. 5281/zenodo. 22558).
Medical Images For our medical application we used the HAM10000, "Human Against Machine with 10,000 training images"  [45, 46]. The dataset is composed of dermoscopic images for pigmented lesions, see an example in Figure  5a. The dataset contains dermoscopic images of melanocytic nevi, melanoma, dermatofibroma, actinic keratoses, basal cell carcinoma, and benign keratosis  [45]. As suggested by medical doctors, the most significant comparison is that between melanoma and melanocytic nevi. The target of the medical application in our study is to try to differentiate the deadliest type of skin cancer, melanoma, from the benign melanocytic nevi. These two widespread types of pigmented skin lesions are often mistaken in diagnosis and detection, especially in their early stages. Moreover, early diagnosis and excision could vastly increase the patients' survival rate  [29] [30] [31].
Medical Image Analysis We calculated FuzEnC 2D , FuzEnV 2D , and FuzEnM 2D for 40 melanoma images and 40 melanocytic nevi images from the HAM10000 dataset  [45]  in the color spaces RGB, HSV, and YUV. In order to determine the region of interest (ROI) of melanoma and melanocytic nevi images, the lesions were segmented as shown in Figure  5. Then, the central region of 128 × 128 × 3 pixels was selected, see Figure  5d. By adopting this procedure, we ensured that the same number of pixels were processed (equally sized images) and that no region outside the lesion was included. The parameters r and m were set to 0. 15 and 2, respectively. The images were normalized by subtracting their mean and dividing by their standard deviation. To validate the statistical significance of FuzEnC 2D , FuzEnV 2D , and FuzEnM 2D in differentiating melanoma from melanocytic nevi images, we used the Mann-Whitney U test.
4%, 94. 5%, 93%. On the other hand, FuzEnV 2D , reached an accuracy of 93. 7% and AUC of 96. 4%. In addition, FuzEnM 2D showed an accuracy of 91. 2% and AUC of 95. 0%. Moreover, FuzEnV 2D and FuzEnM 2D outperformed both FuzEnC 2D and the classical descriptors, Haralick features, in differentiating the two similar malignant melanoma and benign melanocytic nevi dermoscopic images. These preliminary results could be the groundwork for developing an objective computer-based tool for helping medical doctors in diagnosing melanoma that is often mistaken for a benign melanocytic nevi or is properly diagnosed only in its late stages. We limited our investigation to three-channel colored images and, consequently, future work could be directed towards multi-spectral color images and towards more adapted applications for each color space and extending our study to a larger dataset.
Microscopy, Quantification, and Statistical Analysis Fluorescence and brightfield images were obtained with an Imager M2 microscope (Zeiss). Images were processed using Zen (Zeiss, Oberkochen, Germany) and Photoshop CS5 (Adobe) software. Quantification of labelled cells was performed by manual counting. Three to ten sections per retina were analysed, and an average number was calculated. For measurements of staining areas, 1 to 3 sections were analysed per retina; the total labelled surface was quantified using Image J software after setting an optimal intensity threshold. For thickness quantification, the length of 10 labelled photoreceptors was measured on one representative section per retina. All experiments were performed at least in duplicate. Figures show the results from one representative experiment.
Acknowledgements We gratefully acknowledge the  French Ministry of Research and the CNRS  for financial support. S. M. thanks the  French Ministry of Research  for PhD fellowships. The authors thank  Dr. Pierre de Frémont  for crystallographic structure refinement of compound 12. The MCAT company is also acknowledged for providing gold catalyst precursors (www. mcat. de). Notes and references ‡ Crystallographic data for 12 have been deposited in the CCDC under accession number  2111055.
Background: Statins and metformin are commonly prescribed for patients, including those with prostate cancer. Preclinical and epidemiologic studies of each agent have suggested anti-cancer properties. Methods: Patient data from three randomised, double-blind, placebo-controlled, phase III studies evaluating enzalutamide (AFFIRM, PREVAIL and PROSPER) in patients with castration-resistant prostate cancer were included in this analysis. This post hoc, retrospective study examined the association of statin and metformin on radiographic progression-free survival (rPFS), metastasis-free survival (MFS), toxicity and overall survival (OS). After adjusting for available clinical prognostic variables, multivariate analyses were performed on pooled data from AFFIRM and PREVAIL, all three trials pooled, and each trial individually, to assess differential efficacy in these end-points associated with the baseline use of these medications.
Patients and study design Patients from three previously published randomised, double-blind, placebo-controlled, phase III studies were included in the analysis; the study designs have been previously described  [23,25e28]. AFFIRM (NCT00974311) enrolled patients with mCRPC previously treated with docetaxel who were randomised 2:1 to receive enzalutamide 160 mg per day or placebo. The primary end-point was OS. Radiographic progressionfree survival (rPFS), defined as time from randomisation to radiographic progression, assessed by conventional imaging or death due to any cause specified by Prostate Cancer Clinical Trials Working Group 2 criteria, was a secondary end-point  [26, 29]. PREVAIL (NCT01212991) enrolled patients who were chemotherapy-naı ¨ve with mCRPC who were on continuing ADT and were randomised 1:1 to receive enzalutamide 160 mg or placebo once daily  [25].
The coprimary end-points were rPFS, defined as time from randomisation to radiographic progression or death due to any cause within 168 days after treatment discontinuation, whichever occurred first, and OS. PROSPER (NCT02003924) enrolled patients with non-metastatic CRPC with a PSA doubling time of 10 months who were on continuing ADT and were randomised 2:1 to receive 160 mg of enzalutamide or placebo once daily  [23]. The primary end-point was metastasis-free survival (MFS), defined as the time from randomisation to radiographic progression, assessed by conventional imaging, or death due to any cause within 112 days after treatment discontinuation, whichever occurred first. OS was a secondary end-point. All studies were conducted in accordance with the Declaration of Helsinki and were approved by the ethics committee at each participating center.
Data sets All analyses were carried out retrospectively on randomised patients with non-missing covariates who received the study drug. All analyses from AFFIRM were performed on data from the cut-off date of 25th September 2011  [26]. In PREVAIL, the rPFS analysis was performed on data from the cut-off date of 6th May 2012, and the OS from the data cut-off date of 16th September 2013  [25]. MFS and OS analyses from PROSPER were performed on data from the cut-off dates of 28th June 2017 and 20th December 2019, respectively  [23, 28]. Patients categorised into statin or metformin use were defined as those patients who were receiving a statin or metformin at baseline or post-baseline. Statin and metformin use was derived from a review of the baseline medications table in clinical study reports.
72; 95% CI 0. 59e0. 89 and HR 0. 75; 95% CI 0. 55e1. 03, respectively, Fig. 1d  and Table  2 ). ECOG PS was associated with reduced OS for all three individual trials (Fig. 1cee ). Prior CVD was significantly associated with reduced OS in PREVAIL (HR, 1. 58; 95% CI 1. 26e1. 98) but not in PROSPER. Patients with visceral metastases were at higher risk of death compared with lymph node-only metastases in AFFIRM (HR, 1. 49; 95% CI 1. 24e1. 80; Fig. 1c ) and PREVAIL (HR, 1. 43; 95% CI 1. 13e1. 79; Fig. 1d ). Disease site data were not available for the PROSPER trial as the study population consisted of patients with non-metastatic disease. Other factors associated with slight improvements in OS were albumin (AFFIRM and PREVAIL) and haemoglobin (AFFIRM), reduced OS was associated with lactate dehydrogenase (for AFFIRM and PREVAIL), PSA (for PREVAIL and PROSPER) and alkaline phosphatase (for AFFIRM and PREVAIL).
Safety Across the three trials, patients with statin use reported a higher incidence of cardiac AEs compared with those without statin use (any grade exposure-adjusted rates ranged from 0. 06 to 0. 28 per patient-year; Grade !3 exposure-adjusted rates ranged from 0. 01 to 0. 15 per patient-year), except in the placebo arm of PREVAIL, where the rates were similar (any grade rates of 0. 15 per patient-year in both cohorts; Grade !3 rates 0. 03 versus 0. 05 per patient-year, respectively, Fig. 2a ). At the 2019 PROSPER data cut-off, exposure adjusted rates were 0. 03e0. 09 and 0. 01e0. 06 for any grade and Grade !3 cardiac AEs, respectively (Supplementary Fig. 7A ). The relationship between exposure-adjusted cardiac AEs and metformin use was less consistent (any grade rates ranged from 0. 06 to 0.
Discussion Despite multiple preclinical and epidemiological studies [3e8], clinical evidence of benefit of the use of metformin and statins in patients with advanced or metastatic prostate cancer is lacking. Here, we analysed three global, randomised, phase III trials to examine whether statin or metformin use was associated with improved radiographic endpoints or OS in patients with CRPC, ranging from mCRPC post-docetaxel (AFFIRM, NCT00974311), pre-docetaxel (PREVAIL, NCT01212991) and finally high-risk nonmetastatic CRPC (PROSPER, NCT02003924), all of which achieved superiority over their respective control arms  [23,25e28,33]. Even with access to data from three of the largest clinical trials in advanced prostate cancer, our ability to detect a modest effect is limited and is complicated by the non-random nature of the decision to prescribe statins or metformin.
Health imbalances, particularly cardiovascular and metabolic conditions, between statin and metformin users could not be fully accounted for. Additional limitations to this analysis include lack of data about continuation of treatment, including ADTs, or new prescriptions after enrolment and inability to assess the exposure duration. Thus, although the pooled multivariate analysis suggests that statins are associated with improved OS after adjustment for enzalutamide treatment and common prognostic factors, this finding is subject to significant limitations. There are prospective clinical trials of each agent currently underway (PEACE-4 for atorvastatin and aspirin [NCT03819101] in patients with mCRPC, STAMPEDE for metformin [NCT00268476] in patients with mCRPC and MAST for metformin in patients receiving active surveillance [NCT01864096]), and it remains plausible that the benefits of the drugs in unselected populations will be too minor to reach a level of clinical utility.
01). This effect seemed to be most pronounced in patients with high-risk localised disease and nodepositive disease and marginal in those with lowvolume metastatic disease, and there seemed to be no benefit in those with high-volume metastatic disease  [36]. Thus, if this and the STAMPEDE results confirm a benefit to metformin, these data suggest that efficacy results may differ for concurrent metformin use in the HSPC versus the CRPC settings. While it is possible that drugs such as statins or metformin have their greatest effect in early mCRPC, or possibly in hormone-sensitive disease, the inconsistency of effect in earlier stages of disease (PROSPER) or within the control arm suggest this effect, if any, is not robust or deep enough in the population already receiving these drugs for pre-existent conditions to be clinically indicated.
and  WCG Oncology ;  Board of Directors' Member of Asterias Biotherapeutics ;  Intellectual Property Rights BioNTech, Elucida Oncology, MaBVAX, Y-mAbs Therapeutics, Inc  and institutional research funding from  Epic Sciences, Illumina ,  Janssen Diagnostics ,  Menarini Silicon Biosystems , and  ThermoFisher. JDB  has served on advisory boards and received fees from  Amgen ,  Astellas ,  AstraZeneca ,  Bayer , Bioxcel Acknowledgments This study was sponsored by  Pfizer Inc. (New York, NY, USA)  and  Astellas Pharma, Inc. ( Northbrook, IL, USA ), the co-developers of enzalutamide. Medical writing and editorial support funded by the sponsors were provided by  Stephanie Vadasz, PhD , and  Dena McWain  of  Ashfield MedComms  (an  Ashfield Health company ),  Lauren Rainer, BSc , and  Julie B. Stimmel, PhD , of  Onyx (a Prime Global agency ).
Egg crates were suspended 15 cm below the water surface of aquaria. A spheric quantic sensor (LI-193SA) coupled to a data logger (1,400 model) was used to measure Photosynthetically Active Radiation (PAR) levels, which varied between 320 and 345 μE. m -2 s -1 (400-700 nm waveband). Water quality parameters including temperature, pH (kept at 8. 3), and salinity (kept at 33 psu) were measured once a day. Ammonium (kept at 0 mg L -1 ), nitrites (kept between 0. 002 and 0. 005 mg L -1 ), nitrates (kept between 0 and 2 mg L -1 ), calcium concentration (kept between 389 and 401 mg L -1 ), alkalinity (kept at 100 mg L -1 ), and oxygen concentration (kept between 6. 5 and 8. 0 mg L -1 ) and oxygen saturation (kept at 104%) were measured once a week. Cleaning routines were performed at least three times a week to avoid algal growth, and these included cleaning the expansion anchors with a toothbrush and replacing egg crates.
Data Analyses Data matrices were first tested for assumptions of normality and homoscedasticity with Shapiro's Wilk and Levene's test, respectively. When assumptions were rejected, data were log-transformed and auto-scaled (i. e. , mean centered and divided by the standard deviation, SD). Coral health condition datasets for individual species were analyzed as percentages, and a MANOVA was then employed to test for effects of (i) temperature and predation, as well as their interactive effects in overall health condition of all coral species combined; whereas Student's t-tests were used to (ii) test the effects of species thermal sensitivity (thermotolerant vs. thermosensitive) in paleness, bleaching, and mortality, using Statistica v8 (Statsoft, United States). To explore biomarker data structure, multivariate analyses were employed.
Principal component analysis (PCA) was performed based on group separation within each independent categorical variable (species, temperature, and predation), followed by heatmaps constructed based on hierarchical cluster analysis of biomarkers, using Euclidean distance and complete linkage algorithm as metrics. Metaboanalyst v5. 0 online software was used to run these analyses (https://www. metaboanalyst. ca/,  Chong et al. , 2019). To identify the main effects and interactions of variables (i) species, (ii) temperature, and (iii) predation in biomarkers, multi-factorial MANOVAs and ANOVAs were employed. Given that our design was incomplete due to 100% mortality of four species at 32°C, for which biomarker measurements are missing in that treatment, we performed two separate sets of tests: one for species that survived moderate stress (30°C, all seven species) and another one for species that also survived severe stress (32°C, three species).
FUNDING Fundação para a Ciência e Tecnologia (FCT, I. P. ) funded this study through the project  WarmingWebs PTDC/ MAR-EST/2141/2012  and strategic projects  UIDB/04292/2020  awarded to MARE,  UIDB/04326/2020  awarded to CCMAR, and  UIDB/04378/2020  and  UIDP/04378/2020  awarded to  UCIBIO , as well as  LA/P/0140/2020  awarded to i4HB. CM was supported by a researcher grant  CEECIND/01526/2018 , and MD was supported by a PhD grant  SFRH/BD/103047/2014 , both awarded by  FCT. This publication was also funded by the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreement  N810139 :  Project Portugal Twinning for Innovation and Excellence in Marine Science and Earth Observation-PORTWIMS.
Wild-type MIC values could be restored for all tested cysteine-containing enzymes by complementation of dsbA (Figure  1  -figure supplements 4 and 5). Moreover, since DsbA acts on its substrates post-translationally, we performed a series of control experiments designed to assess whether the recorded effects were specific to the interaction of the resistance proteins with DsbA, and not a result of a general inability of the dsbA mutant strain to resist antibiotic stress. We observed no decreases in MIC values for the aminoglycoside antibiotic gentamicin, which is not affected by the activity of the tested enzymes (Figure  1B , Figure  1  -figure supplement 6). Furthermore, the β-lactam MIC values of strains harboring the empty-vector alone, or a plasmid encoding L2-1 (Figure  1B ), a β-lactamase containing three cysteine residues, but no disulfide bond (PDB ID: 1O7E), remained unchanged.