dataesr/scientific-paragraphs-categorization huggingface dataset:
We envision that this pipeline can also be applied to any circular or oval-shaped objects. However, we acknowledge that using brightfield images may not always work directly with our pipeline, especially if cells display complex and interchanging shapes since StarDist is mostly designed to detect round or compact shapes. In this case, other tools, such as Usiigaci, could also be considered  8. Still, brightfield images could also be artificially labeled using deep learning, transforming the brightfield dataset into a pseudo-fluorescence one, as can be done with ZeroCostDL4Mic already  15. The pipeline described here is currently limited to the tracking of objects in 2D. However, a similar workflow can be applied to 3D datasets as both StarDist and TrackMate can accommodate 3D images  12, 13, 18.
Material and methods. Breast cancer cell dataset  15. MCF10DCIS. com cells were described previously  19. DCIS. COM lifeact-RFP cells were incubated for 2h with 0. 5 µM SiR-DNA (SiR-Hoechst, Tetubio, Cat Number: SC007) before being imaged live for 14h using a spinning-disk confocal microscope (1 picture every 10 min). The spinning-disk confocal microscope used was a Marianas spinning disk imaging system with a Yokogawa CSU-W1 scanning unit on an inverted Zeiss Axio Observer Z1 microscope (Intelligent Imaging Innovations, Inc. ) equipped with a 20x (NA 0. 8) air, Plan Apochromat objective (Zeiss). T cell dataset 17. Lab-Tek 8 chamber slides (ThermoFisher) were coated with 2 µg/mL ICAM-1 or VCAM-1 overnight at 4°C. Activated primary mouse CD4+ T cells were washed and resuspended in L-15 media containing 2 mg/mL Dglucose.
T cells were then added to the chambers, incubated 20 min, gently washed to remove all unbound cells, and imaged. Imaging was done using a 10x phase contrast objective at 37°C on a Zeiss Axiovert 200M microscope equipped with an automated X-Y stage and a Roper EMCCD camera. Timelapse images were collected every 30 sec for 10 min using SlideBook 6 software (Intelligent Imaging Innovations). Flow chamber dataset. AsPC1 pancreatic cancer cells (500 000 cells/ml in PBS) were perfused at a speed of 300 µm/sec using a peristaltic pump (ISMATEC MS12/4 analogic) and a homemade tubing system (Ismatek 3-Stop tubes and Ibidi® tubings and connectors) in a microchannel (Ibidi® µ-slides400 LUER). Images were acquired with a brightfield microscope (Zeiss Laser-TIRF 3 Imaging System, Carl Zeiss) and a 10X objective.
DETAILED PROTOCOL EQUIPMENT/ EQUIPMENT SETUP • A computer with access to the internet. • A Google account. You can create a free account here. • The ZeroCostDL4mic StarDist 2D notebook. The latest version of this notebook is available here. • A Fiji script to batch analyze movies. The latest version of this script is available here. • The latest version of Fiji. TrackMate and its dependencies are already installed in Fiji. You can download Fiji here. • The Laboratory for Optical and Computational Instrumentation (LOCI) Fiji plugin.
1. 1: Generate a training dataset for StarDist To train a StarDist model, you need matching images of nuclei/or cells and their corresponding masks. These images need to have the same name and to be saved in two different folders. The mask images can be generated in Fiji, either manually or semi-automatically. The semi-automatic approach can be used if an existing StarDist model performs reasonably well on your images. Alternatively, you can manually annotate 20 or so images, train your StarDist model, and then use the semi-automated annotation method to increase your training dataset size rapidly.
Alternative A: Manual annotations using Fiji and the LOCI plugin • Open one image in Fiji. • Open the ROI manager [Analyze-> Tools -> ROI Manager]. • Using the freehand selection tool, draw outlines around each nucleus manually. • Add each outline to the ROI manager (key shortcut "t"). • Once all the nuclei outlines are stored in the ROI manager, use the LOCI plugin to create an ROI map [Plugins-> LOCI-> ROI map]. This is your mask image. • Rename the mask image so that its name matches the corresponding nuclei image and save it inside another folder. • Choose the number of EPOCH to use to train StarDist. We recommend that you train new models for at least 100-200 EPOCH. • Run the cell. • Optional -Use data augmentation to increase the size of your training dataset artificially (Section 3.
2). Use this section only If your training dataset is small. • Optional -Use Transfer Learning. If a pre-existing StarDist model has been trained on data that are somewhat similar to yours and performs relatively well on your data, consider using it as a starting point for training. This will accelerate the training process and can improve overall performance. • Run section 4. 1 to prepare your images for training • Run section 4. 2 to Start training the network. This step can take several hours to complete. At the end of the training, the model will be automatically saved in GDrive. In addition, the model will be automatically exported so it can be used to make predictions in the StarDist Fiji plugin. • Download the trained StarDist model so it can be used in the StarDist Fiji plugin. Automated tracking is, however not supported via the Fiji StarDist plugin.
ACKNOWLEDGEMENTS This work was supported by grants awarded by the  Academy of Finland  (G. J. ), the  Sigrid Juselius Foundation  (G. J. ),  Åbo Akademi University Research Foundation  ( CoE CellMech ; G. J. ) and by  Drug Discovery and Diagnostics strategic funding to Åbo Akademi University  (G. J. ). G. F. was supported by the  National Cancer Center Finland (FICAN). NHR acknowledges support from the  Cancer Research Institute  and  NIH  ( T32 AR007442 ). R. F. L. acknowledges the support of the  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) , the  Finnish Euro-Bioimaging Node , and  Turku Bioimaging  are acknowledged for services, instrumentation and expertise.
• Error propagation and estimation of co-simulation is poorly understood. The choice of simulation step sizes or synchronization points is therefore not trivial and is still subject of technical developments. Interfacing with the master can be done via various APIs (application program interfaces), one that received broad industry support is called FMI: the Functional Mockup Interface. It is an open standard, based on a C-interface that offers the specification of required functions such as start, stop, step, synchronization, variable exchange, etc. The master algorithm itself is in its core often very simple but associated functionality (such as scenario handling, data logging, distributed computing, etc. ) can be quite complex. Again, there are a few popular master platforms, two of them being HLA (the high-level architecture) and mosaik.
Finally, mosaik was created with a particular intention to serve as a smart grid co-simulation framework, and as such, it is largely in tune with energy system applications  [8]. In contrast to the previously mentioned standards, mosaik is a direct implementation of a master algorithm, and not a standard per se. It is written in Python, based on a discrete event scheduler and is capable of FMU integration. Besides FMU integration, it also provides interfaces for several common tools in the energy system realm (such as DigSilent PowerFactory, PandaPower, etc. ). Since its primary user group are energy engineers, the elaborate co-simulation settings are greatly simplified, which represents mosaik's greatest advantage. A comparison of mosaik and HLA for a co-simulation of a power system control action is performed in  [9].
Communication Network Simulation with Ns-3 In recent years, ns-3 has become very popular in the network simulation community. ns-3 is a highly flexible simulation package, which allows programmers to add new attributes without modifying the core of the source code, or having to deal with a specific, restricted and complex API. The default version of ns-3 comes with an extensive library of models, which can be used to describe the components and other aspects of communication networks (e. g. , devices, channels, interfaces, protocols). A dedicated ns-3 package called fmi-export has been developed, which provides all functionality needed for creating an FMU for C-Simulation from a user-defined ns-3 application (typically referred to as script). An FMU created with the help of this package implements a tool coupling mechanism that allows to control the execution of the ns-3 simulator and to establish a connection for data exchange during run-time.
Control Simulation with MATLAB Despite the popularity and widespread use of the numerical computing environment MATLAB, there is so far only comparably little support within the context of FMI. For instance, the Modelon FMI Toolbox 3  and the FMI Kit for Simulink 4  offer the export of Simulink models as FMUs for Model Exchange, but so far there is no tool available that allows to provide MATLAB's full functionality via an FMI-compliant co-simulation interface. Therefore, the FMI++ MATLAB Toolbox 5  has been implemented that provides two components: a front-end component to be used by the co-simulation master and a back-end component to be used by MATLAB. The corresponding interfaces are tailored to suit the requirements of the FMI specification and they implement the necessary functionality required for a master-slave concept, i.
Mosaik for Scenario Development and Simulation Orchestration The mosaik  6  framework is an easy-to-deploy software package that facilitates the integration of new simulators as well as the creation of co-simulation experiments. This is achieved via a lightweight software core based purely on Python, a special Component-API for simulator integration, and a Scenario-API for flexible simulator coupling. The mosaik framework is still under active development and new features are being introduced based on activities within the smart grid testing and validation community.
FMI Support As an example, the FMI++ Python Interface 7  and the mosaik framework have been successfully combined for the co-simulation of FMUs. Several examples of importing FMUs have been implemented using the FMI++ Python Interface to interact with the FMU mosaik's high-level component API to integrate it into the co-simulation. For this, especially the functionality for conveniently handling FMUs was extensively used, such as extracting the FMU, parsing its model description or the ability to refer to input/output variables by name (rather than the numerical value reference associated to each variable).
It monitors the voltages on both AC and DC sides to sense fault conditions and shifts from normal control mode to FRT control, post-FRT mode,   5  Modified IEEE 9-bus system acting as a test system for co-simulating PowerFactory with Matlab/Simulink using the functional mock-up interface and back to normal control accordingly. During the FRT mode, the FRT controller increases the reactive current infeed and blocks active power flow to address the voltage dip. In post-FRT mode, the FRT controller sets a maximum ramping rate for restoring the active current reference back to pre-fault conditions.
Experiment Setup and Objectives The co-simulation is set up as follows. The AC grid including the wind part array is modelled and simulated in DIgSILENT PowerFactory. Inside PowerFactory, the wind turbine is modelled as a Norton equivalent source, the current injection of which can vary in time and is provided by the parameter event functionality as discussed in Sect. 3. 1. 1. This can be considered as a proxy model of the actual converter dynamics by the converter controller and FRT controllers, which are developed in Simulink and Matlab respectively. During runtime of the co-simulation, all FMUs are synchronised using fixed macro time step-sizes of 10 ms. A 3-phase short circuit event is simulated to study the FRT capability. The cosimulation is orchestrated by a Python script, which uses the FMI++ toolbox.
Eventually the overall system under test is split into three FMUs. The three FMUs being: One FMU for the entire power system model in PowerFactory, one for the FRT controller and one for the converter controller. The arrangement of the FMUs and the co-simulation orchestration by Python and FMI++ is shown in Fig. 6. The simulation itself is centred around the dynamic response of the WPP and the IEEE 9-bus system that is subject to a self-cleared 180 ms 3-phase short circuit starting at t = 1 s (Bus 6 of Fig. 5 ). The main objective is to study the FRT capability  and reactive power control of the WPP. These ensure that the voltage at the PCC does not dip beyond the FRT voltage versus time profile, and quickly ramp the voltage to pre-fault levels after fault clearance (i. e. , grid code compliance).
e. , cyclic dependencies); • Implementation of an FMI++ adapter in PowerFactory (RMS mode) complying with the FMI for co-simulation specification. An application example has been discussed in Sect. 5; • Development of an FMI++ export package for ns-3 based on FMI for cosimulation; • Implementation of the FMI++ MATLAB toolbox based on FMI for co-simulation; and • Proof-of-concept of continuous-time, discrete-event, and mixed simulator coupling; • Assessment of the scalability of the applied approaches; and • Application of the holistic testing methodology for simulation-based assessment methods. Notwithstanding these innovations in terms of applications of co-simulations, the approach is not very suitable for the day-to-day engineer yet. Parameterisation of simulator interfaces, master algorithm configuration, distributed execution, and harmonisation of semantics of the overall simulation are examples that require a lot of manual work and are still subject to technological development.
While mutations of TP53, FLT3, IDH1 and other genes driving CH and tAML are overrepresented across tAML cases, DNMT3A mutations appear overrepresented across CH cases (Fig. 4b ). It is thus possible that mutations in at least some genes of chemotherapy-related CH do not provide the advantage required to undergo the full clonal expansion that characterizes tAML. Finally, we reasoned that if, as in the case of tAMLs, the treatment predated the start of the clonal expansion, platinum-related mutations should be detectable in blood samples of donors exposed to these drugs with traces of CH. However, no platinum mutational footprint (or of any drug) is detected through a mutational signatures extraction approach across the blood samples in the metastasis cohort (Fig. S4 ). To rule out power problems due to the low number of mutations, we then employed mSigAct, a method aimed at recognizing the activity of a particular mutational signature in a dataset of mutations  39.
Datasets of tumor somatic mutations A) WGS AML cohort Sequencing in-house treatment-related AML samples DNA was extracted from six samples of three secondary AML patients. Both samples were bone marrow aspirates corresponding to the diagnosis of the AML and the remission. The short-insert paired-end libraries for the whole genome sequencing were prepared with KAPA HyperPrep kit (Roche Kapa Biosystems) with some modifications. In short, in function of available material 0. 1 to 1. 0 microgram of genomic DNA was sheared on a Covaris™ LE220-Plus (Covaris). The fragmented DNA was further size-selected for the fragment size of 220-550bp with Agencourt AMPure XP beads (Agencourt, Beckman Coulter). The size selected genomic DNA fragments were end-repaired, adenylated and ligated to Illumina platform compatible adaptors with Unique Dual matched indexes or Unique Dual indexes with unique molecular identifiers (Integrated DNA Technologies).
The libraries were quality controlled on an Agilent 2100 Bioanalyzer with the DNA 7500 assay for size and the concentration was estimated using quantitative PCR with the KAPA Library Quantification Kit Illumina® Platforms (Roche Kapa Biosystems). To obtain sufficient amount of libraries for sequencing it was necessary for the low input libraries (0,1 -0,2 ug) to amplify the ligation product with 5 PCR cycles using 2x KAPA-HiFi HS Ready Mix and 10X KAPA primer mix (Roche Kapa Biosystems). The libraries were sequenced on HiSeq 4000 or NovaSeq 6000 (Illumina) with a paired-end read length of 2x151bp. Image analysis, base calling and quality scoring of the run were processed using the manufacturer's software Real Time Analysis (HiSeq 4000 RTA 2. 7. 7 or NovaSeq 6000 RTA 3. 3. 3).
B) WES AML cohort Within the beatAML cohort  36  , a bone marrow sample and a paired skin sample were taken from 261 AML patients. The somatic mutations identified in these AML cases were downloaded from the Genomics Data Commons 58 (GDC) repository provided by the authors of the original paper reporting the sequencing and variant calling of these patients. Of note, the original Varscan2  59  variant calling was used, and mutations that coincided with variants present in gnomAD 60 v2. 1 at allele frequency greater than 0. 005 were removed. Mutations identified in the 347 remaining patients without a paired normal sample, as well as the clinical information of all patients in the cohort, were downloaded from the cbioportal repository  61. While the somatic mutations across the 261 cases with paired samples (deemed more reliably true somatic calls) were employed in the driver discovery (see below), mutations identified in AML driver genes across the 608 cases were subsequently taken into account.
Mutational signatures extraction Mutational signature extraction in the leukemias in WGS AML cohort, the tumors in the metastasis cohort, and the healthy blood samples in the metastasis cohort was performed using a non-negative matrix factorization approach  62  , as reported in refs  5, 38. We employed the SigProfilerJulia (bitbucket. org/bbglab/sigprofilerjulia) implementation carried out in our lab of the algorithm developed by Alexandrov et al  5, 63. The resulting signatures were then compared to the PCAWG COSMIC V3 26 set using the cosine similarity measure. The Hematopoietic Stem Cell Signature (HSC Sig 34 ) was computed as the average the number of mutations observed across the 23 healthy blood samples in each of the 96 tri-nucleotide channels and normalizing them by the total number of mutations observed.
To test for the activity of a mutational signature in a specific sample we used the method so-called mSigAct  39. Given a set of signatures bound to explain the mutational catalogue of the samples (baseline signatures), the method tests whether an additionally given signature (foreign signature) does improve the sample catalogue reconstruction significantly. Briefly, the method models the mutation count data as being negative binomial distributed and conducts a likelihood ratio test comparing the likelihood of the observed catalogue under two competitive models: with/without the foreign signature. The method returns for each sample the fitting exposures attributed to each signature (both baseline and foreign) alongside the significance (p-value) yielded by the likelihood ratio test. In the case of the metastatic samples the footprint was deemed as detectable when both SigProfilerJulia and mSigAct report that the treatment signature was active in the treated sample.
Clonal and subclonal mutations We used the MutationTime. R package developed elsewhere  40  to classify SBS in a tumor as early, late or subclonal. Then, we associated each mutation uniquely with a mutational signature using a maximum likelihood approach  5, 66  , and we computed the proportion of clonal mutations amongst all platinum-associated mutations across metastatic tumors. Subsequently, we computed the fold change between the relative proportions of clonal (grouping early and late clonal mutations) and subclonal mutations associated to the platinum-related signature. We computed this fold change as , (n ) ]/ [ 0 + n 1 / (N ) 0 + N 1 (n /N ) s s where and are, respectively, the number of clonal early and clonal late platinum-related n 0 n 1 mutations, is the number of subclonal platinum-related mutations, , and are, n s N 0 N 1 N s respectively, the total number of clonal early clonal late and subclonal mutations.
Model selection was then performed by computing the Bayesian Information Criterion (BIC) and by cross-validation (30% test size, N=100 randomizations) resulting in an average area under the ROC curve (auROC). Model selection gave precedence to models with low BIC and high auROC (Table  S2 ). In both platinum and 5-FU treatments the best model has the same form. The data employed in the paper is available through different sources. Whole genome sequences of samples in the WGS AML cohort are accessible through the dbGAP phs000159, EGAD00001005028 and EGAXXXXX (to be deposited before publication) collections. Somatic mutations of samples in the WES AML cohort are available through the GDC repository provided by the authors in the original publication of the beat AML cohort ( ttps://pubmed. ncbi.
Acknowledgements N. L-B. acknowledges funding from the  European Research Council  (consolidator grant  682398 ) and  ERDF/Spanish Ministry of Science, Innovation and Universities -Spanish State Research Agency/DamReMap Project  ( RTI2018-094095-B-I00 ) and  Asociación Española Contra el Cáncer (AECC)  ( GC16173697BIGA ). IRB Barcelona is a recipient of a  Severo Ochoa Centre of Excellence Award  from the  Spanish Ministry of Economy and Competitiveness (MINECO; Government of Spain ) and is supported by  CERCA  ( Generalitat de Catalunya ). O. P. is the recipient of a BIST PhD fellowship supported by the  Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia , and the  Barcelona Institute of Science and Technology (BIST). This publication and the underlying research are partly facilitated by  Hartwig Medical Foundation  and the  Center for Personalized Cancer Treatment (CPCT)  which have generated, analyzed and made available data for this research.
However, part of the multifunctional metasurfaces are implemented by changing the polarization state of the incident light, while the others change the polarization state of outgoing light. The application of multifunctional metasurfaces are restricted by these problems in practical applications. In this paper, based on VO 2 phase change material, a general strategy to design a thermal tuning meta-duplex-lens (MDL) in the mid-infrared range (4 µM) is proposed and numerical simulation calculation demonstrated by commercial software FDTD Solutions (Lumerical Inc. in Vancouver, BC, Canada). The designed meta-atoms have the property of realizing transmission and reflection by controlling their temperature. Therefore, in the same structure, transmission or reflection optical focusing can be achieved respectively.
Super-resolution microscopy provides diffraction-unlimited optical access to the intricate morphology of neurons in living brain tissue, resolving their finest structural details, which are critical for neuronal function. However, as existing image analysis software tools have been developed for diffraction-limited images, they are generally not well suited for quantifying nanoscale structures like dendritic spines. We present SpineJ, a semi-automatic ImageJ plugin that is specifically designed for this purpose. SpineJ comes with an intuitive and user-friendly graphical user interface, facilitating fast, accurate, and unbiased analysis of spine morphology.