dataesr/scientific-paragraphs-categorization huggingface dataset:
Segmentation from manual correction Although time consuming and tedious, manual correction remains optimal for achieving accurate results in cell segmentation. To simplify the manual correction, a first tool was developed to superpose the segmentation mask generated by Ilastik and CellProfiler softwares and the patches of the IMC images. The control of opacity improves the visibility of the cell borders (Figure  2 ). A second tool was developed that provides a black-andwhite brush to manually correct segmentation errors. In the example shown in Figure  2 , the Ilastik and CellProfiler softwares segmented two cells instead of one in the largest rectangle and three cells instead of one in the smallest rectangle. A manual correction was thus necessary to obtain a single cell for both rectangles.
Metrics for evaluating the YOUPI tool To evaluate the performance of the YOUPI tool, we checked the value of the Intersection over Union (IoU) metric, also known as the Jaccard index. The IoU is one of the most frequently used metrics for evaluating a model of image segmentation  (24). Mathematically, it represents the proportion of area overlapping between the target mask and the prediction output. Its value can vary between 0 and 1. The mean IoU of a patch is computed as the mean of the IoU of each cell in the patch. Graphical interface of the YOUPI software. An overview of the graphical interface and the ease-of-use functions of the YOUPI software.
To further global quality, we sought an additional metric. We developed biological metrics, including the number of cells, the percentage of real cells, and the rate of false-positive cells. Real cells are objects characterized by at least a tenth percentile of the grayscale nuclei signal, below they are considered as non-existent cells, while false-positive cells correspond to objects co-expressing exclusive markers. Eighteen new ROIs from different tissues not used in the training data set and from different batches of data (two ROIs of one batch and two of another batch from salivary glands of patients with Sjögren's syndrome, six from lung cancer, and eight from intestinal cancer) were segmented with the combination of Ilastik and CellProfiler, with QuPath and with YOUPI software, and the results were analyzed.
Though not significantly different, the highest number of cells was found with the combination of Ilastik and CellProfiler software but was associated with the segmentation of non-existent cells (Figure  7A ) and with the significantly lowest rate of real cells (91. 2 ± 13. 5%) compared with those detected with the YOUPI software (99. 0 ± 2. 1%, p< 0. 01). There was no significant difference between the number of cells detected with the QuPath software and the YOUPI software. However, there were also significantly fewer real cells with the QuPath software (96. 6 ± 6. 5%) than with the YOUPI software (p< 0. 05). Gating on the real cells, the rate of CD20+ B cells (Figure  7B ), CD3+ T cells (Figure  7B ), CD68+ macrophages (Figure  7C ) and PanKeratin+ epithelial cells (Figure  7D ) were not significantly different between YOUPI software and Ilastik and CellProfiler, and QuPath softwares.
Only Ilastik and CellProfiler seems to detect fewer CD3+ T cells. The rates of CD20 and CD3 double-positive cells were also evaluated. Since CD20 and CD3 markers belong to B and T lymphocyte lineages, respectively  (25) , one single lymphocyte cannot express both markers. As shown in Figure  7E , no significant difference in the rates of false double-positive cells for Ilastik and CellProfiler, QuPath, and YOUPI software (3. 9 ± 6. 3% vs. 4. 0 ± 5. 4% vs. 4. 2 ± 5. 5%, p > 0. 05). Similarly, the rates of PanKeratin and CD8 double-positive cells were evaluated. PanKeratin is an epithelial cell marker (26), while CD8 is a T-cell subset marker  (25). Both cannot be co-expressed by a single cell. Again, the rate of false double PanKeratin and CD8 positive cells was low, and there were no differences among the three methods of segmentation (0.
Discussion We introduced YOUPI, a cell segmentation tool for images generated by the Hyperion IMC intended for non-computer-friendly users. YOUPI works with U-Net. Three post-processing steps were added to obtain better control over the final segmentation mask. This mask provides access to single-cell data via CSV files for downstream supervised (manual gating strategy) or unsupervised (t-SNE, Phenograph, etc. ) analysis whose performance falls between that of Ilastik and that of QuPath (Supplementary Figure  5 ). The cell segmentation of tissue images can thus be performed using existing software, such as Ilastik and CellProfiler, or QuPath. Our experiences indicate that they require specific skills in image processing (e. g. , image format conversion) and analysis to efficiently achieve cell segmentation.
They rely on multiple third-party tools (ImageJ, Python scripts, etc. ) and are thus barely accessible to nonbioinformatician users. Ease-of-use has guided our development of YOUPI. In contrast to the other tools, the all-in-one graphical interface can be used instinctually by non-computer scientists. In addition, two CSV files are generated. The first is a CSV file containing the mean intensity of markers, which scientists commonly use. However, aberrant high-intensity pixels are frequently identified in IMC images. The second is a CSV file containing the median intensity allowing to ignore these artifacts that can impact downstream analysis. It was added to the YOUPI software during the post-processing step to remove aberrant pixels. Furthermore, among all existing tools or pipelines allowing for cell segmentation, the Ilastik and CellProfiler analysis of a given ROI requires approximately four hours of work, and QuPath software requires thirty minutes.
With YOUPI, an inexperienced computer user obtains the segmentation mask in less than 10 minutes, including choosing markers of interest, thus making YOUPI a tool that is easy to use and delivers results quickly. The segmentation results of the U-Net network are obtained with a set of essential steps. Manual segmentation is a crucial aspect of training to obtain reliable data. To obtain prebuilt binary images, we decided to use masks generated by Ilastik and CellProfiler softwares. Thus, QuPath was not useful since it does not consider membrane markers. Several weeks were required to manually correct the binary image patches. Although manual errors could have been made, the U-Net learned to perform segmentation as efficiently as Ilastik and CellProfiler, and as QuPath software at detecting the number of cells and of false-positive cells based on the corrected images.
It proved to be the most efficient at detecting real cells. Correlation analyses of all tissues together, as well as, analyses on separated tissues, indicate that the Ilastik and CellProfiler detection method hardly match with QuPath and YOUPI software for the detection of real cells and some cell subsets. Segmentation results with YOUPI and QuPath are similar. The mean IoU value confirmed the robustness of the cell segmentation results obtained with YOUPI. Although CD3, CD20, CD8, and PanKeratin molecules are expressed on distinct populations of cells  (25, 26) , some CD3 and CD20 double-positive cells or PanKeratin and CD8 double-positive cells could nevertheless be detected. It has recently been found that CD3+ T lymphocytes can express CD20 in blood and tissue  (27)  FIGURE  6  Overview of cell ID to obtain centroids and the mean/median of gray levels per marker.
U-Net is a network specifically developed to analyze biomedical images, which allows for good performance from a restricted annotated dataset  (18). Nonetheless, segmentation coming from a neural network will never be flawless. However, with the inclusion of additional postprocessing steps (Figure  5 ), obvious mistakes the neural network could make are controlled; thus, YOUPI is safe. YOUPI's overall performance is comparable with that of other segmentation tools, despite relying on different approaches with different flaws. The naive mathematical approaches of Ilastik and CellProfiler can lead to the identification of non-existent cells, while the empirical threshold approach of QuPath can result in oversized shapes. Different intensities of image colorization can impact the quality of segmentation masks generated by YOUPI's U-Net.
SUPPLEMENTARY FIGURE 4 Cell segmentation performance of YOUPI on separate tissues. Four ROI from salivary glands, six ROI from lung and eight ROI from intestine were segmented with Ilastik and CellProfiler, QuPath and YOUPI software. The number of total cells, the percentage of real cells, the percentage of CD20+ B cells, the percentage of CD3+ T cells, the percentage of CD68+ macrophages, the percentage of PanKeratin+ epithelial cells, and the percentage of double CD3+CD20+ cells, and the percentage of double PanKeratin+CD8+ cells were calculated for each tissue and compared. *p< 0. 05, **p< 0. 01, ns, non-significant.
SUPPLEMENTARY FIGURE 5 Unsupervised downstream analysis following cell segmentation. Four ROI from salivary glands were segmented with Ilastik and CellProfiler, QuPath and YOUPI software. (A) The CSV files generated by the three approaches were merged for dimension reduction analysis with tSNE. (B) Clustering was performed with phenograph analysis, and the abundance of the clusters with each software and the phenotype of the cell clusters were determined.
Funding YS was supported by  HORIZON 2020  under Grant Agreement Number  831434  ( 3TR  project) and the  Conseil Reǵional de Bretagne. LBAI was supported by the  Agence Nationale de la Recherche  under the " Investissement d'Avenir" program  with the Reference  ANR-11-LABX-0016-001  ( Labex IGO ).
Fast ICCD Imaging and Optical Emission Spectroscopy Since guided streamers are transient and low emissive phenomena, the observation of a single streamer would require a streak camera with a temporal resolution close to (or better than) 800 fs  [34]. Since this equipment is not in our facilities, an alternative is to study a train of guided streamers, i. e. , collecting at regular time intervals a large number of guided streamers, here 25 000, and summing their emissions on a single image. The radiative emission of the plasma jet is therefore collected by an intensified charge-coupled device (ICCD) camera from Andor company (model Istar DH340T). It has a 2048 × 512 imaging array of 13. 5 µm × 13. 5 µm pixels) and an optical gate width lower than 2 ns. The Solis software enables such operations combining the "kinetic series" acquisition mode and the "DDG" gate mode.
Boolean networks (BNs) are discrete dynamical systems with applications to the modeling of cellular behaviors. In this paper, we demonstrate how the software BoNesis can be employed to exhaustively identify combinations of perturbations which enforce properties on their fixed points and attractors. We consider marker properties, which specify that some components are fixed to a specific value. We study 4 variants of the marker reprogramming problem: the reprogramming of fixed points, of minimal trap spaces, and of fixed points and minimal trap spaces reachable from a given initial configuration with the most permissive update mode. The perturbations consist of fixing a set of components to a fixed value. They can destroy and create new attractors. In each case, we give an upper bound on their theoretical computational complexity, and give an implementation of the resolution using the BoNesis Python framework.
• P4 : Source-marker reprogramming of attractors: after reprogramming, all the configurations of all the attractors that are reachable from the given initial configuration match with the given markers. MP fixed points match with the fixed points of the global Boolean map of the BN (and are thus identical to the fixed points of the (a)synchronous update modes). MP attractors match with so-called minimal trap spaces of the BN, which are the smallest sub-hypercubes closed by the Boolean map. Problem P1 has been already addressed in the literature, notably by  Biane and Delaplace (2019)  with the ActoNet method and by  Moon et al. (2022a) , based on bilevel integer programming. To our knowledge, none of the other problems have been addressed completely in the literature. The software BoNesis (github.
com/bnediction/bonesis ) provides a generic environment for the automated construction of BNs with MP update mode from a specified structural and dynamical properties. The properties are translated into a logic satisfiability problem, expressed in Answer-Set Programming (ASP). Initially, BoNesis has been designed for performing BN synthesis  (Chevalier et al. , 2019) : solutions of the logic model correspond to BNs that possess the specified structural and dynamical properties. Leveraging this generic declarative specification of properties, BoNesis is a versatile tool for reasoning on BNs in general, with the MP update mode: besides synthesis, it can be used to do model checking, identify fixed points and attractors in ensemble of BNs, and identifying reprogramming strategies. In this paper, we show how the software BoNesis can be employed to solve P1, P2, P3, and P4 in the scope of locally-monotone BNs, where each local function is unate , i.
BoNesis BoNesis (github. com/bnediction/bonesis ) is a Python library which has been primarily designed for identifying BNs satisfying user-given dynamical properties among a given domain of BNs and with the MP update mode. It takes as input (1) a domain of BNs F, and (2) a set of Boolean dynamical properties φ, and can enumerate the BNs f ∈ F such that f |= φ, i. e. , f verifies the properties φ. Currently, the domain of BNs F can be one of the following: • A singleton locally-monotone BN F = {f }. In that case, BoNesis can be employed as a model checker to verify that f has the specified dynamical properties. In this paper, this is the main setting we will consider, in order to predict perturbations to reprogram the attractors of f. • An explicit ensemble of locally-montone ::::::::::::::: locally-monotone BNs F = {f 1 , • • • , f m }.
• Any locally-monotone BN matching with a given influence graph G: F = {f | G(f ) ⊆ G}. An influence graph is a signed digraph between components, i. e. , of the form ({1, • • • , n}, V ) with V ⊆ {1, • • • , n} × {+1, -1} × {1, • • • n}. The influence graph of a BN f , denoted by G(f ) has an edge i s -→ j if and only there exists a configuration x ∈ B n such that f j (x 1 ,. , x i-1 , 1, x i+1 ,. , x n ) - f j (x 1 ,. , x i-1 , 0, x i+1 ,. , x n ) = s. • Any locally-monotone BN matching with a partially-defined BN following the AEON framework  (Beneš et al. , 2021). BoNesis offers a Python programming interface to declare the dynamical properties over BNs, including reachability, fixed points and trap spaces. BoNesis relies on Answer-Set Programming (ASP) and the ASP solver clingo for the enumeration of solutions.
Results We show how the general declarative approach of BoNesis can be instantiated to compute the complete solutions to the P1, P2, P3, and P4 reprogramming problems on BNs, and also extend the reasoning to ensembles of BNs. Importantly, note that BoNesis currently supports only locally-monotone BNs. Alternatively, the computation of reprogramming perturbations from single Boolean networks can be performed using the command line programbonesis-reprogramming, provided alongside the bonesis Python package. We detail its usage in each case.
Source-marker reprogramming of attractors (P4 ). In each case, we briefly study the complexity of the associated decision problem (existence of a perturbation given the desired reprogramming property), and give the Python and command line recipe to identify the perturbations with BoNesis. The following table summarizes the results, with the complexity in the locally-monotone case and command line usage: Problem Complexity Command line P1 ∃∀ [base] --fixpoints P2 ∃∀ [base] --fixpoints --reachable-from z P3 ∃∀∃ [base] P4 ∃∀∃ [base] --reachable-from z where [base] is the command line bonesis-reprogramming model. bnet M k, with model. bnet the path to a file in BooleanNet format , M specifies the marker as a JSON map, k is the maximum number of simultaneous perturbations, and z is the initial configuration as a JSON map.
(2) With the BoNesis Python interface, this reprogramming property can be declared as follows, where f is a BN, M the marker (specified as Python dictionary associating a subset of components to a Boolean value), and k the maximum number of components that can be perturbed (at most n): In The line bo = bonesis. BoNesis(f) instantiates a BoNesis object bo with the domain f. In this section, we assume that f is a single Boolean network. It can be either the path to a BooleanNet file, a minibn object, for instance as returned by the biolqm. to minibn function for importing models from various formats, or a Python dictionary, associating components to a Boolean function. Examples are given below. Then, the line P = bo. Some(max size=k) declares a variable that will consist of a map associating at most k components to a Boolean value (the perturbation to be identified).
The solution {'A': 0, 'C': 1} is not returned as {'A': 0} is sufficient to acquire the desired dynamical property. In our BoNesis code snippet defined above, by default we ensure that the perturbed BN possesses at least one fixed point. When relaxing this constraint, we obtain that the empty perturbation is the (unique) minimal solution, as f has no fixed point. In the following, we demonstrate how to perform the same computation with the command line. By default, the reprogramming of fixed points adds the constraint that at least one fixed point must exist. In Adding the option --allow-no-fixpoint would return an empty perturbation as unique minimal solution.
∃P ∈ M k , ∀x ∈ B n , ((f /P )(x) = x ∧ x ∈ TS P (z)) =⇒ x |= M. (4) As with the previous case, in practice we may also want that there exists at least one fixed point reachable from z. With the BoNesis Python interface, this reprogramming property is declared as follows, where f is a BN, z the initial configuration (Python dictionary), M the marker, and k the maximum number of components that can be perturbed (at most n): Compared to the previous code snippet, this function relies on specific operators to restrict the properties to the fixed point reachable from z. The instruction ~bo. obs(z) refers to a specific configuration matching with z; ~bo. obs(z) >> "fixpoints" ^{bo. obs(M)} specifies that all the fixed points reachable from such configuration have to match with at least one configuration given in the set {bo.
e. , of the minimal trap spaces of the BN. With the BoNesis Python interface, this reprogramming property is declared as follows, where f is a BN, M the marker, and k the maximum number of components that can be perturbed (at most n): The idea of the above code snippet is to declare the property of being a bad perturbation (coP). In the context of this perturbation, we declare with x=bo. cfg() a configuration. Then, bo. in attractor(x) imposes that x belongs to an attractor (minimal trap space) of the perturbed BN; and the x != bo. obs(M) instruction adds the constraint that x must no ::: not : match with M. The. complementary assignments() method performs the computation of the complement of solu- The (only) fixed point of the network indeed has C active. However, it possesses another (cyclic) attractor, where C is inactive.
By integrating the definition of the IN-ATTRACTOR property, we obtain the following ∃∀∃-expression: ∃P ∈ M k , ∀x ∈ B n , x |= M ∨ x / ∈ ρf/P mp (z) ∨ ∃y ∈ B n , y ∈ TS P (x), TS P (y) = TS P (x) (11) The resolution of the problem can be approached in a very similar way to P3, i. e. , by solving its complement. The equation is almost the same, with the addition that x must be reachable from z, leading to the ∃∀-expression: ∃P ∈ M k , ∃x ∈ B n , x ∈ TS P (z) ∧ x |= M ∧ ∀y ∈ B n , y ∈ TS P (x) =⇒ TS P (y) = TS P (x). (12) With the BoNesis Python interface, this reprogramming property is declared as follows, where f is a BN, z the initial configuration, M the marker, and k the maximum number of components that can be perturbed (at most n): is the maximum number of perturbations, and z is the initial configuration as a JSON map.
Discussion In this paper, we demonstrated how the BoNesis Python library can be employed to fully characterize permanent perturbations which guarantee that all the fixed points or all minimal trap spaces of the perturbed BN have a subset of components fixed to desired values (marker). For the sake of simplicity, we ::: We : focused on reprogramming for achieving elementary dynamical properties, that are the fixed points or attractors, optionally reachable from a given configuration. It should be noted that ::::::::::: Nevertheless, : the snippets shown in this paper can be extended to account for more complex or specific dynamical properties after mutation(, : e. g. , existence of additional trajectories, considering multiple initial configurations). Moreover,. uncontrollable : components which must not be perturbed (exclude option for the Some object, or --exclude for the command line, taking a list of components which should be excluded from the candidate perturbations).
colomoto : -::::::: docker :: -: V ::::::::::: 2023-02-01 then open http://127. 0. 0. 1:8888 and upload the notebook from the Jupyter interface. See github. com/bnediction/reprogramming-with-bonesis for further help. In [1]: %DIFDELCMD < #!pip install --user bonesis # uncomment to install bonesis locally %DIFDELCMD < %%% #!pip install --user bonesis # uncomment to install bonesis import bonesis In [2]: from colomoto_jupyter import tabulate # for display import pandas as pd # for display import mpbn # for analyzing individual Boolean networks with MP update mode from colomoto. minibn import BooleanNetwork.
This is an executable paper which demonstrates the use of BoNesis for the reprogramming of BNs. The corresponding notebook can be downloaded from nbviewer. org/github/bnediction/reprogramming-withbonesis/blob/release/paper. ipynb. Its execution requires the Jupyter notebook system, Python , and the Python package bonesis to be installed, see github. com/bnediction/bonesis for instructions. Alternatively, the notebook can be executed within the CoLoMoTo Docker distribution (Naldi et al. , 2018) , using the Docker image colomoto/colomoto-docker:2022-07-01 ::::::::::: 2023-02-01, which can be launched as follows: pip install colomoto-docker colomoto-docker -V 2022-07-01 ::::::::.
Acknowledgements The scientific cooperation is supported by the  French Ministry  of foreign affairs and the  French Ministry of higher education and research  (project number:  46275WC ) and by  Scientific & Technological Cooperation (S&T Cooperation)  grant, funded by funds of the  Federal Ministry of Education, Science and Research (BMBWF) Austria  (project  ID :  FR 12/2021 ). Fig. 7. TL glow curve measured using the TL-wideband filter after different bleaching times.
The basic idea of the approach is to uncover 21-bps sequences specific to a given virus strain (in this case SARS-CoV-2 or a variant), rank them by their suitability as primers, and measure their capacity of discriminating samples belonging to the target virus from other strains. In a previous work, we generated a set of primers to detect SARS-CoV-2 with Convolutional Neural Networks (CNNs)  25  using 52,645 SARS-CoV-2 sequences from the GISAID database and 20,572 sequences of other taxa from the National Center for Biotechnology Information (NCBI) database  27. Then, we tested the resulting sequences in silico using the software Primer3Plus 28 to verify their suitability as primer sets using the SARS-CoV-2 reference genome NC045512 from NCBI  29. The process generated a primer set in the ORF3a gene of the SARS-CoV-2 genome, that was named UtrechtU-ORF3a, and was successfully tested in clinical settings with 10 patients' samples  25.