dataesr/scientific-paragraphs-categorization huggingface dataset:
(5, 16,. The most commonly used software in biomedical research for phasor analysis is SimFCS,  (64)  developed by Gratton et al. (17)  and which is freely distributed. Recently, Schrimpf et al. (65)  introduced PAM, an opensource software package written in MATLAB that also includes phasor analysis of FLIM data, whereas Gao et al. (10)  integrated phasor transformation in the ImageJ plugin FLIMJ. While companies have been increasingly developing interfaces to analyze FLIM data acquired with their systems, to date, an opensource and easy-to-use software in Python for phasor analysis of FLIM data is not available. Here, we developed Fluorescence Lifetime Ultimate Explorer (FLUTE) that is designed to fill this gap, and allow simple, quick, and interactive visualization of FLIM data acquired in the time domain.
The user-friendly graphical user interface (GUI) accelerates the FLIM analysis pipeline and allows for analysis of large FLIM datasets through bulk processing. FLUTE will be helpful in microscopy labs to perform real-time phasor analysis during experiments while also allowing for post-processing by exporting the data. It is designed to be accessible for researchers of different backgrounds who may not be specialized in FLIM. Moreover, since FLUTE is open-source and programmed in Python, an extension of the included analysis options and integration of existing code in other open-source Pythonbased platforms are possible and desired.
GUI development and implementation FLUTE is programmed in Python, which offers a significant benefit thanks to extensive support libraries and large user base. This facilitates software "extensibility," enabling the integration of supplementary functionalities and modules, or allowing further integration in other open-source Python-based image analysis platforms like napari. (66)  Furthermore, Python's syntax is more straightforward compared to other programming languages, rendering it comprehensible and adaptable for researchers with limited programming background. We used PyQt5  (67)  for the GUI, while phasor analysis (Section 3. 1) and data processing are performed using NumPy  (68)  and SciPy (Figure  1 ). Both the Python code and the executable file are free, open-source, and available on our GitHub repository  (69)  (see the Supplementary Material and Supplementary Figures  S1  and S2 ).
The easiest way to use FLUTE is to run the available "FLUTE. exe" file, which has been tested to work on Windows 7, 10, and 11 and does not require Python to be installed. The code "main. py" can also be run after installing Python (tested with Python 3. 10) and installing all the necessary packages (PyQt5, numpy, opencv-python, matplotlib, and scikit-image). Running "main. py" has been tested to work on different operating systems: Windows, Linux, and MacOS including M1 and M2 chips. A description of an easy procedure to run main. py with a Mac and/or Windows by downloading all the necessary files from FLUTE's GitHub page, as well as troubleshooting to run main. py with Linux, can be found in the Supplementary Material (see Section 1. 2 of the Supplementary Material). FLUTE performs phasor analysis on FLIM data in the time domain, either acquired with a timecorrelated single photon counting (TCSPC) electronic cards or with the time-gating technique, provided that an entire period of the laser repetition is recorded and the lifetime decay it is not truncated.
Data processing • Calibration (with a reference standard) • Phasor transformation at different harmonics • Filters (median filter, thresholds, etc. ) applied in a reversible way    c, d ) FLUTE offers a range of tools for interactive, straightforward, and reproducible data visualization and lifetime estimation such as cluster analysis with cursors of adjustable size and applying various colormaps (distance from molecular species and lifetime contrast TauP and TauM) interactively using range sliders or thresholding entry boxes. These FLIM data were calibrated with fluorescein (4 ns) acquired in solution: Fluorescein_Embryo. tiff.
Batch processing • Processing and analyzing large FLIM datasets. FLUTE introduces novel tools and features that distinguish it from previous software. These additions enable interactive, straightforward, and reproducible FLIM analysis. In particular, we introduced (a) an interactive visualization of lifetime displays TauP and TauM (Figure  2d ); (b) a precise mapping of the distance from known molecular species by defining their location in the phasor plot, allowing to calculate the fraction of molecular species; and (c) interactive simultaneous display of the color map of phasor plot and FLIM images using thresholding entry boxes or range sliders (Figure  2c, d ). 2. 2. FLIM data processing FLUTE has been programmed to perform phasor analysis of FLIM data (Section 3. 1) acquired in the time domain through an FFT (Figure  2a, b ).
The FLIM data are converted to the phasor plot with an FFT and referenced using the fluorescence calibration standard of known single lifetime (Figure  2a ,b and Supplementary Figure  S4 ). We note that it is crucial to acquire the FLIM image with the same experimental parameters that were used for calibration (i. e. , laser excitation wavelength, phase settings in the TCSPC software, as well as temporal bin number and bin width), as using different parameters would lead to an incorrect absolute position of the FLIM data in the phasor plot. FLUTE allows for analysis of FLIM data acquired with different parameters such as temporal bin number, bin width, laser repetition rates (in megahertz), and different harmonics of the FFT transformation. These parameters need to be specified in the GUI in the "Load Calibration" (Figure  2a  and Supplementary Figure  S4 ) during the calibration step.
FLIM data visualization and lifetime estimation We designed FLUTE to be an intuitive GUI for flexible data exploration and easy data visualization (Figure  2c, d ). We introduced novel tools and features that enable interactive, straightforward, and reproducible FLIM analysis. Figure  2d  illustrates an example of FLIM data visualization using FLIM data from the autofluorescence of a Zebrafish embryo at 3 h post-fertilization (3hpf), that is uploaded on our Zenodo repository (https://zenodo. org/record/8324901) as Embryo. tiff stack. This example highlights the capabilities of FLUTE software to explore complex multi-exponential FLIM data without a priori knowledge on the tissue content and on the type of fluorophores present in tissues. Figure  2c  also shows the corresponding FLUTE user interface that is used to perform this analysis.
If the users want to calculate the fraction f A of molecular species A with respect to B, the distance d B from molecular species B calculated by FLUTE needs to be manually normalized by the distance between species A and B (see Section 3. 1) by using (16). An example of quantification of the distance from mCherry in a live Zebrafish embryo at 5 days postfertilization (5dpf) from an H2B-mCherry line (see Section 3. 2) is represented in Figure  3. The tail of the H2B-mCherry zebrafish line is excited at 1,100 nm, and FLIM images are collected with different emission filters to selectively collect mCherry from cell nuclei and/or SHG from collagen. ZF-1100_noEF. tif, ZF-1100_607-70_filter. tif, and ZF-1100_550-49_filter. tif that correspond to Figures  2b-d , respectively, are uploaded in our Zenodo repository (https://zenodo.
FLIM analysis of NADH autofluorescence in live cells We tested FLUTE on a FLIM dataset acquired in human mesenchymal stromal cells (hMSCs) in vitro  (61)  to demonstrate the ability of the software to visualize the autofluorescence of the intrinsic biomarker NADH  (73, 79)  and quantify metabolic states associated with subcellular compartments (Figure  5 ) and metabolic shifts associated with drug treatments (Figure  6 ). Figure  5  illustrates an example using FLIM data from hMSCs that is uploaded on our Zenodo repository (https://zenodo. org/record/8324901) as hMSC-ZOOM. tif stack. We measured multiexponential fluorescence intensity decays of NADH with a shorter lifetime in the nucleus with respect to the mitochondria (Figure  5a ). Using FLUTE, we estimated and visualized the NADH lifetime and the relative fraction of bound/free NADH in hMSCs by mapping both the images (Figure  5b ) and the corresponding phasor plot (Figure  5c ) with TauP (τ φ ), TauM (τ m ) lifetimes, and the distance from free NADH contrasts calculated with (  9 ), (10), and (15), respectively.
This analysis reveals subcellular metabolic heterogeneity on a pixel level and a shorter NADH lifetime and a smaller distance from free NADH in the nucleus with respect to the mitochondria (Figure  5b-d ), as previously observed in the literature. (80)  Maps of NADH lifetime and distance from free NADH can be exported with FLUTE and used for further image processing. For example, we measured the profiles of the abovementioned parameters along a line across mitochondria and nucleus (Figure  5b, d ). The values of τ φ vary from 1 ns (in the nucleus) and 1. 3-1. 7 ns in the mitochondria, the values of TauM, τ m , vary from 2. 2 to 3. 4 ns in the mitochondria, whereas the distance from free NADH vary from 0. 45 (in the nucleus) to 0. 55 (in the mitochondria). Figure  6  illustrates an example using FLIM data from the NADH autofluorescence of mesenchymal stromal cells treated with the metabolic drug rotenone, and the data are uploaded on our Zenodo repository  (81)  as hMSC_control.
The calibration is performed with a reference sample that is usually a fluorophore with a known fluorescence lifetime, for example, fluorescein that has a decay of 4 ns or with an instrument response function like the SHG measurement of 0 ns. Two examples of fluorescein decay are uploaded on our Zenodo repository  (81)  as Fluorescei-n_embryo. tif and Fluorescein_hMSCs. tif; they are used to calibrate the FLIM data in the embryo and in hMSCs, respectively. Figure  8  demonstrates the principle of the phasor calibration with Fluorescein: a correction on the phase is performed applying an offset ofΔφ, while correction on the modulation is performed applying a multiplication constant Δm as described in the following formulas: If the FLIM image contains a combination of two non-interacting fluorescent molecular species (i.
Conclusion In this article, we presented FLUTE, a highly interactive GUI that removes barriers in FLIM analysis and makes the phasor approach more accessible for applications in biology and the biomedical field. FLUTE performs phasor data analysis acquired in the time domain, both acquired by TCSPC and time-gaiting, provided that the fluorescence decay is properly acquired over the entire period of the laser repetition rate. As FLUTE is open-source and written in Python, it allows other programmers to extend the FLUTE code with complementary advanced developments, introduce an online version with Jupiter notebook, and integrate phasor analysis in other Python open-source image analysis platforms such as napari. (66)  FLUTE efficiently provides phasor FLIM data processing and visualization tools (Figure  2 ) such as calibrating the FLIM data, displaying the phasor plot, calculating the distance from known molecular species and the fraction of molecular species, evaluating lifetime, displaying the phasor plot clouds and the FLIM images with different lifetime contrast simultaneously, performing interactive reciprocity analysis with cursors, applying different types of filters and thresholding, and calculating average lifetime values in masks.
The final edited datasets after applying the desired filters and thresholds can be exported for further user-specific analysis (Figure  4 ). FLUTE enables simultaneous analysis of multiple images, and it has also been designed to perform batch processing of large FLIM datasets. We demonstrated that FLUTE is a useful GUI to explore complex multi-exponential FLIM data such as the autofluorescence in a 3hpf zebrafish embryo (Figure  2d ) without a priori knowledge on the tissue content and on the type of fluorophores present. We also demonstrated the use of FLUTE to map and quantify the distance from mCherry in the tail of a 5dpf zebrafish embryo (Figure  3 ) and for metabolic imaging in cells culture to map and quantify the lifetime of NADH and the distance from free NADH at the subcellular level and with drug treatment (Figures  5  and 6 ).
In terms of speeds, FLUTE processes and analyzes FLIM data in the order of seconds (~1. 1 and ~2. 5 s for a phasor transformation and analysis in the bulk process of a 512 × 512 pixels FLIM image). FLUTE phasor processing time represents a significant increase with respect to multi-exponential fitting analysis  (19)  and performs similarly (in the order of seconds per image) with respect to the open-source phasor software based on central processing units. (10, 17, 65)  It will be possible to increase FLUTE speed with parallel computing capabilities of graphics processing units by using specialized Python libraries such as Numba  (88)  and CuPy. (89)  Further accelerating the processing times to the milliseconds range will allow real-time phasor analysis and representation during experiments.
Acknowledgments. The authors thank  Ana Maria Pena  for feedback on the GUI,  Emmanuel Beaurepaire  for the choice of the GUI name,  Clement Caporal  for help with Python environment,  Seongbin Lim ,  Clement Caporal ,  Anatole Chessel , and  Stefano Zacchiroli  for discussion and advice on open-source software,  Pierre Mahou  for help on the microscope,  Isabelle Lamarre  for technical help in cell culture, and  Emilie Menant  for the fish husbandry. The authors also thank  Becker & Hickl ,  ISS , and PicoQuant companies for discussion and help on exporting raw FLIM data and reading their data formats. This article has been submitted to bioRxiv preprint ( BIORXIV/2023/534529 ).
Data availability statement. FLUTE is Copyright (C) 2022 FLUTE. FLUTE source code is Free and Open-Source Software released under the terms of the 3-Clause BSD License. The prebuilt FLUTE executable is a combined work that contains both FLUTE and QT library bindings. It is released under the terms of both the 3-Clause BSD License (for the FLUTE part) and the GNU Lesser General Public License (for the QT part). Source code and executable are available on the GitHub repository at https:// github. com/LaboratoryOpticsBiosciences/FLUTE. The FLIM data relative to the publications are in the Zenodo repository at https://zenodo. org/record/8324901.
using 3D Slicer's GrowCut semiautomatic segmentation  [14, 15]. Nonetheless, inter-user variability, as with manual delineation, seems to be a concern and has rarely been studied, especially considering the clinical expertise of the user  [16]. In addition to its reproducibility, the main advantage of the semi-automatic procedures reported in the literature lies in its potential rapidity, which is critical as the delays in PSI production are also a concern  [17, 18]. Such procedures are already integrated into commercial software packages such as Materialise Mimics. In this study, a customized semi-automatic pipeline was built in open-source image processing software: 3D Slicer. The flexibility of open-source software makes it possible to develop customized segmentation adapted to specific surgical procedures.
Materials and methods DICOM from 6 patient cases, including at least one CT and one MRI of a pelvic bone tumor, were selected. The protocol tested included CT/MRI registered images, semi-automatic segmentation, and STLbased CAD modelling. The pipeline used 3D Slicer 4. 11 (http://www. slicer. org)  [19]  for the image processing part (3D Slicer is open-source research software and is not FDA approved for real case PSI AM). Additionally, the following extensions (also open-source) were installed: MarkupsToModel, SegmentEditorExtraEffects, and SlicerElastix  [20, 21]. The models were then evaluated using MeshLab  [22, 23]  to compute Hausdorff distances (segmented models vs reference and between each-other), as well as the DICE similarity coefficient.
Statistic method and ethics The patient data, CT, and MRI images were collected retrospectively and anonymized. We noted the demographic and tumor parameters. Inter-user variability was evaluated on the tumor models using the symmetrical Hausdorff distances (average and maximum) and the DICE similarity coefficient without significance analysis. The duration of the process was compared using an ANOVA one-way test. The alpha risk of all these tests was set at 5%, with a level of significance of p < 0. 05. The data was collected using Microsoft Ò Excel, and the statistical analyses were performed using IBM Ò SPSS Statistics V25 software. According to French legislation regarding anonymized data analyzed retrospectively (articles L. 1121-1 paragraph 1 and R1121-2, French Public Health Code), Nantes University Hospital has confirmed that approval from the ethics committee was not needed due to the non-interventional nature of the study, and thus no ethics committee approval was necessary at the time of the beginning of the study.
Discussion As the use of PSI in oncological orthopedics increases, a number of studies report the use of in-house or outsourced image processing, surgical planning and production of PSI surgical cutting guides  [4, 24, 25]. These procedures are often diverse, with close collaboration between expert clinicians and engineers. The image processing, in particular, often involves both: clinicians performing the manual delineation of the tumor and engineers finishing the segmentation to obtain 3D models of bone and tumor. For this process, both commercial (closed) and open-source software exist. Open-source software offers the possibility of developing customized workflows for specific surgical applications. In this case, 3D Slicer was customized to focus on planning pelvic orthopedic oncology surgery.
Limitations The study presents limitations in the number of cases studied. The focus was on pelvic tumor images presenting at least one CT sequence and one MRI sequence with adequate contrast on the tumor. Extending the database of images might improve the consistency of the results and allow for a more robust statistical analysis. Intra-user measurements were also not taken (comparing two segmentations by the same user performed with a time interval of one month or more). On this point, Dionísio et al. found a 0. 88 ( § 0. 05) DICE similarity coefficient between a manual and semi-automatic segmentation by the same user using 3D Slicer  [15]. Yushkevich et al. found an average 0. 87 ( § 0. 27) DICE similarity coefficient between two segmentation sessions performed by a non-expert user (in clinical practice) with a one-month interval.
The advantages of implementing an automated system are that it can overcome the need for capturing and measuring the abundant discrete measurements used to regulate the experimental conditions. This can also remove the need for constant human observation which may not be feasible in the long-term, but may be required to program new regulatory operations and make rapid adjustments to the experimentally manipulated conditions. Here, we describe an autonomous salinity and temperature experimental perturbation mesocosm system (SalTExPreS) that can regulate salinity and temperature in real-time. This system was employed outdoors, along with static light filters to mimic the increase in turbidity and associated irradiance attenuation due to glacial melting to perform a two-month long experiment in KongsFjorden, Svalbard, exposing mixed kelp communities to future Arctic conditions.
Automation The automation was performed using 4 Industrial Arduino-based PLCs (Industrial shieldsⓒ Mduino-42+), with a single PLC regulating the control and each treatment 1 -3. Each PLC was responsible for data logging and the regulation of a specific scenario. The PLC regulating the control condition-identified as the Head PLC-was the primary device responsible for communication with the Branched PLCs and the monitoring computer (Fig. A2 ). All monitoring was performed on a PC Windows application (Section 2. 5) and responsible for: (1) reading data received from the PLCs, (2) reading in situ data received from the internet, (3) displaying live data, (4) logging data and sending to an FTP server, and (5) sending settings and commands to the PLCs. Communication between the PLCs and the PC was made using http WebSocket protocol on RJ45 ethernet cables, while communication between the PLCs and the conductivitytemperature and oxygen sensors, flow rate sensors, and regulation valves was executed using half duplex RS485 (2 wires) protocol, an analog 4-20mA signal, and analog 0-10V signal, respectively.
Software The code for the application was written in C/C++, and developed using Visual Studio community (2019 edition) with the vMicro extension and Arduino 1. 8. 13. The code uses publicly available Arduino libraries (https://www. arduino. cc/reference/en/libraries/) as well as originally designed libraries. All code is available on Github (https://github. com/purrutti/FACEIT). The code is divided into two pathways: 'Master. ino' for the Head PLC, and 'Regul_condition. ino' for the Branched PLCs. A description of the main functions applied in the code for programming the system regulation and features are listed in Table  A3. The program application has a user-friendly interface designed to allow real-time monitoring and parameterization of regulation processes (Fig. A3 ). From the interface, the user sets the temperature condition and associated salinity offset, IP address and logging parameters, sensor calibration settings, and pressure setpoints (Fig.
Data Log The 'maintenance' tab is where sensor calibration and communication 'Debug' operations can be executed (Fig. A4 d, e ). Calibration can be performed for each sensor deployed in each mesocosm, and uses a 2-point calibration for temperature and % oxygen. The salinity calibration is done by setting the conductivity value corresponding to a temperature of 25 °C rather than the in situ measured temperature. The conductivity value is programmed as µS cm -1. The communication process for sensor calibration is between 5 to 10 seconds. The final option in the menu is the 'data' tab which displays the historical and live data. The historical data can be interfaced to an html site if desired.
Some of the operational challenges encountered during the 2-month experiment in Svalbard were able to be mitigated or resolved henceforth. The use of pop-up alert windows when a lapse in connection occurred, or when data is not logging, along with secondary coding instructions (as fail-safe instructions) ensuring that the last received in situ data were maintained are examples how improvements made during the experiment facilitate a more robust deployment for the future. These improvements are now incorporated into the available code for programming of the SalTExPreS. These new editions were implemented during a second deployment that occurred in the summer of 2022 under a similar experimental design, and resulted in fewer lapses and frequencies of mis-regulation (Fig. 6 ). Further, we forewent attempting to regulate the control condition (i.
Explanation evaluation metrics Evaluating the explanations is an important challenge, and common practices mostly depend on costly human judgments. Here we rely on the diagnostic properties proposed by  Atanasova et al. (2020)  in the context of text classification. We discarded two measures that cannot be computed: the agreement with human rationales measure, since we do not have access to human annotations for the explanation of political datasets, and the rationale consistency measure, since it is meant to compare an explanation method across different models. We consider that a document is composed of a set of features, and that our explanation method generates a saliency score for each of them. Confidence Indication (CI) When generating an explanation, the feature scores for each possible class can be computed.