dataesr/scientific-paragraphs-categorization huggingface dataset:
They may therefore be neglected. Kartezio could be trained at low human cost on small datasets to automatically extract predefined features (geometrical information from images, intensity of staining from flow cytometry data, activation of death pathways in target cells, single cell mRNAseq data, etc). These extracted data would feed large pre-analyzed data bases that could be used by data scientists to address new questions using mathematical and ML approaches. An additional important benefit of Kartezio's application to microscopy concerns the necessity of limiting the subjectivity of the experimentalists who analyze and scores the images. A way to bypass the problem of subjective image scoring is the use of highthroughput techniques such as high-content imaging and imaging flow cytometry (e.
Indeed, characterization of drug vectors and bioactive particles requires the implementation of methods that allow researchers to effectively evaluate and optimize parameters such as particle size and composition. In this sense, Kartezio offers a first stepping stone to address this issue by rapidly creating pipelines for analysis of subcellular structures, such as CTL-derived extracellular particles (Fig. 4 ). Further optimization of Kartezio is required to make it a reliable "pharmacologist assistant" for validation of nanometer/ micrometer size therapeutical preparations. In summary, Kartezio offers a robust, flexible, and groundbreaking approach to automated image processing. It facilitates the generation of specialist models starting from small or very small datasets. Combined with its low hardware requirements, this confers upon Kartezio an exceptional level of versatility and frugality, broadening its potential applications to include contexts in which large datasets or powerful hardware are unavailable.
Mathematical modeling Kartezio was developed using Python3  53  programming language and built mainly over NumPy 54 , OpenCV  55  , Scikit-image  56  , and Scikit-learn  36. For the visualization, images and plots were generated using Matplotlib 57 , Seaborn 58 , OpenCV  55  , and Fiji  59. Mathematical notations and parameters utilized throughout this manuscript are shown in Table  1.
All functions from the library must be determinist and must return one 2D image which is identical to the input image in dimensions and type (generally unsigned integer encoded on 8bits, u8). Most of the time, one node operation consists of one function call from the OpenCV Python package. All functions must be image-size independent, which implies that the input and output for a given image is equivalent. However, there is no image size constraint for the model in both training and test datasets. The first ι nodes, noted as X:{x 1 ⋯x ι } are the receiving inputs (e. g. image channels) while the last o nodes represents the intermediate outputs Ẑ : fẑ 1 Á Á Á ẑo g. An example of genotype G ι = 3, η = 4, o = 2, α = 2, ρ = 2 ½ with its nodes is illustrated in Fig. 1.
Cartesian Genetic Programming: Non-evolvable Preprocessing Node The first stage of a standard image processing pipeline is the preprocessing or/and formatting of the raw data. Depending on the input images (e. g. transmission light images, fluorescent images, IHC tissues images, etc. ) and the expected outcomes (e. g. cell identification, event counting, cell distance and interaction, etc. ) needed to address a given biological question, the user can impose a preprocessing of the dataset. When loading the dataset, the preprocessing stage is done on-thefly and produces a formatted input X for each entry of the dataset. For example, the default behavior for a standard bitmap image is X rgb : ½x 1 = r, x 2 = g, x 3 = b. For specific needs of certain datasets, any function can be used for preprocessing (e.
Cartesian Genetic Programming: Evaluation To evaluate a genotype G, this genotype is first decoded as describe previously. The resulting graph is executed on each image of the training dataset X train to obtain a set of predictions Ŷ X. Predictions are compared to the ground truth of the dataset Y train using a specific fitness function F which evaluates the prediction errors. This function is domain-specific (e. g. AP50 for IS or IoU for SS) and must be defined for each task Kartezio is used for. The genotype evaluation fitness G is finally given by the average of prediction error of each dataset entry (Eq. 7). fitness G = 1 N × X N i = 1 F ðY traini , Ŷ X i Þ ð7Þ Cartesian Genetic Programming: Selection The objective is to minimize the prediction error over many iterations. The best genotype is therefore selected among the offspring and the parent by selecting the smallest prediction error.
Cartesian Genetic Programming: Benchmarking We trained the models with a η = 30 nodes, λ = 5 offspring over K = 20,000 iterations. If the parent reached a score of 0, the evolution process stopped as no prediction error was made on the training dataset. The mutation probability was μ = 0:15 for the functional nodes and ν = 0:2 for the outputs. 35 independent runs were made for each experiment for statistical purposes. They were run on a bi Intel Xeon @6140 (2 × 18 cores, 2. 30 GHz -3. 70 GHz) with 192GB of memory.
Standardized Testing Using Published Cell Image Library We compared Kartezio to three state-of-the-art IS algorithms: Cellpose, StarDist and Mask R-CNN. To this end, the dataset and the results for these three algorithms were taken from published data  23, 28. The dataset was composed of 100 images of in vitro cultured neurons stained for phalloidin and DAPI (as shown in Supplementary Fig. 2a , reproduced with permission) and imaged using an epifluorescence microscope  23, 28. This 100-image dataset was split into 89 images for training and 11 images for testing  23  ; the training/test split was retained in this manuscript in order to permit direct comparison of Kartezio with Cellpose under standardized conditions. No data augmentation was necessary for our approach. In this study case, Kartezio was composed of ι = 2 inputs, corresponding to the a-tubulin and DAPI channels, and o = 2 outputs, corresponding to the mask and markers necessary for the Watershed Transform non-evolvable endpoint.
Generated syntactic graphs were evaluated using the Average Precision (AP = TP TP + FP + FN ) metric calculated with the standard IoU between the predicted mask and the ground truth. A threshold of t = 0:5 was applied to decide the correctness (true-positive) of the predicted mask, as described  23. Kartezio was benchmarked on different training dataset sizes, ranging from 1 image to 89 images. For each size of dataset, images were selected at random from the original 89 training images. Each trained solution was then evaluated using the previously described dataset of 11 test images  23. 35 independent training runs were performed, each time randomly selecting different training images from the original set of 89 to control for the impact of training split composition. The AP scores reported in Fig.
Standardized Testing Using Melanoma Cell Nodules To extend these results, we tested whether using a specific nonevolvable endpoint (namely the MCW Endpoint) improved Kartezio performance beyond that which was achieved in the absence of a nonevolvable endpoint, using Connected Component to obtain evaluable labels. In the first case (Fig. 2c , green dots), the model must optimize 2 heuristics: the mask and the markers aimed at producing the best IS (see Fig. 1 ). In the second case (Fig. 2c , magenta dots), the model must learn to distinguish the masks (future labels) directly, which is similar to the CGP-IP version for segmentation. In Fig. 2c , we demonstrate statistically (one-way ANOVA with multiple comparisons) the importance of MCW, on a melanoma nucleus dataset (nuclei stained as orange, Fig.
2 , Supplementary Fig. 2b ). We also compared the use of different pre-processing steps (RGB, HSV, or HED) to generalize the comparison. For each of the six conditions, we performed 10 independent runs. Finally, we evaluated the predictions of the best Cellpose pre-trained model (model CPx, on blue channel) in order to estimate the generalist model performance on the same test set. Among the 30 MCW models, we selected a model with less than 5 active nodes to illustrate Fig. 1. This same model was converted into a Python class (Supplementary Fig. 3 ) and finally its predictions are illustrated on a test image (512×512) shown in Fig. 2d. In this figure, the first panel shows the manual annotations (green), the second panel shows the Kartezio model predictions (magenta) on top of the annotations and similarly for the last panel with the Cellpose model predictions.
Statistics and Reproducibility In this manuscript, individual models were evolved in parallel using Kartezio and this process produced populations of independentlyderived models, each of which is thus considered an independent experiment. The number of models (n) generated in each Use Case is indicated in the corresponding Figure Legend. Statistics reported in this manuscript represent the mean and standard deviation of the population of independent models (of size n) on a given dataset of images, unless otherwise indicated. All statistical analyses were performed using GraphPad Prism 9 and are two-sided unless otherwise indicated. Means were compared using a one-way ANOVA with multiple comparisons. Means compared against a single fixed value were evaluated using a one-sample t-test. Biological replicates are indicated where applicable in the corresponding Methods section.
Software Utilized Image handling was performed using ImageJ (1. 53o), Zen Black (14. 0. 27. 201) and CellSens (XV 3. 26). Data was visualized using Matplotlib, Seaborn, and OpenCV (Python packages) as well as GraphPad Prism (9. 5. 0). Statistical analysis was performed using GraphPad Prism (9. 5. 0).
Acknowledgements This research has received funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 Research and Innovation Programme  (Grant agreement No. Syn-951329 ; SV and MD). This work was performed using HPC resources of the CALMIP supercomputing center under the allocation 2022-[P16043]. This work was supported by the  AI Interdisciplinary Institute ANITI , funded by the  French program "Investing for the Future -PIA3"  (grant agreement no. ANR-19-PI3A-0004 , SCB). This work was also supported by grants from the  Laboratoire d'Excellence Toulouse Cancer (TOUCAN)  (contract  ANR11-LABEX ; SV). This work also received support from an  ERC Marie Sklodowska Curie Actions /UK Engineering and Physical Science Research Council fellowship  (E. Pantano/X023907/1 , MD and OS).
The GOODS-ALMA sample GOODS-ALMA is a blind 1. 1 mm galaxy survey of the deepest part of the Great Observatories Origins Deep Survey South (GOODS-South;  Dickinson et al. 2003; Giavalisco et al. 2004)  field. The survey covers a continuous area of 72. 42 arcmin 2  with ALMA Band 6 observations using two array configurations, providing high-and low-angular-resolution datasets at a homogenous average sensitivity  (programs 2015. 1. 00543. S and 2017. 1. 00755. S; PI: D. Elbaz). The high-resolution dataset was presented in  Franco et al. (2018;  GOODS-ALMA 1. 0), while the low-resolution dataset and its combination with the high resolution was presented in Gómez-Guijarro et al. (2022a; GOODS-ALMA 2. 0). The combined mosaic reaches an average point-source sensitivity of 68. 4 µJy beam - 1  at an average angular resolution of 0.
447 × 0. 418. We refer the reader to  Franco et al. (2018)  and  Gómez-Guijarro et al. (2022a)  for details of the survey observations, data processing, and source catalogue. In this work, we use the GOODS-ALMA 2. 0 source catalogue presented in  Gómez-Guijarro et al. (2022a,b) , which is composed of 88 sources. In particular, we focus on the subset of galaxies with a Herschel counterpart (69/88), as presented in  Gómez-Guijarro et al. (2022b) , but discarding galaxies labelled as optically dark or faint (also known as HST-dark) in the GOODS-ALMA 2. 0 catalogue. The sample is composed of 65 galaxies with continuous ultraviolet (UV) to mm photometry coverage, which is needed for the purpose of this work. In the following, we adopt the redshifts reported in the GOODS-ALMA 2. 0 source catalogue.
Inclusion of non-parametric star formation histories in CIGALE We use the SED modelling code CIGALE 1  (Boquien et al. 2019 ), which combines a set of modules modelling the SFH, the emission of the stellar populations, the attenuation from dust, the dust emission, and the AGN contribution from X-ray to radio. Regarding the SFH, two main approaches are already implemented in CIGALE. The first is the possibility of using any file containing an SFH, such as the output of a simulation for instance. The second approach is to use a parametric model, such as an exponentially declining or rising SFH, or τ-delayed SFH, adding a recent burst or quenching. In this study, we add a third possibility: a non-parametric set of SFHs. For this, we use the 2022. 1 version of CIGALE customised for the purpose of this work.
Contrary to the parametric SFHs, the non-parametric SFHs do not assume any analytic function to model the SFH. This approach assumes a given number of time bins in which the SFRs are constant. We take advantage of the versatility of CIGALE and implement a new SFH module, sfhNlevels. Based on the results of  Ocvirk et al. (2006) ,  Leja et al. (2019)  defined seven redshift bins in lookback time  [0, 30, 100, 330, 1100, 3600, 11 700, 13 700]  (in Myr). The number and time limits of these bins were extensively tested by  Leja et al. (2019)  and used in further works (e. g. ,  Leja et al. 2021; Tacchella et al. 2020 Tacchella et al. , 2021b;; Lower et al. 2020). As in the other SFH modules of CIGALE, the age of the galaxy is a free parameter. For each age value, the time interval is divided into seven bins, conserving the logarithmic scaling used in  Leja et al.
We choose to adopt ν = 2 following  Leja et al. (2019)  and σ = 1 following  Tacchella et al. (2021b). Indeed, as shown in  Tacchella et al. (2021b) , this prior will allow us to reach a wider range of specific SFR (sSFR) compared to the simple continuity prior with ν = 2. In the sfhNlevels module, the SFR of the first bin is set to 1, and the SFRs of the following bins are then obtained by randomly choosing x in the Students-t distribution. The obtained SFH is then normalised to 1 M and used to build the modelled SED. CIGALE provides output parameters obtained from the best-fit models as well as derived from a Bayesian-like analysis, that is, computed from the probability distribution function (PDF) of a given parameter. The SFR in each bin can be obtained from the PDF analysis as well. The age of formation, age form , which is the time at which the galaxy has formed half of its stellar mass is also an output of the module.
Accuracy of the non-parametric SFH We test the ability of non-parametric SFHs to model the SED of galaxies using a set of 100 SFHs of simulated z = 1 typical main sequence galaxies obtained with the semianalytical code Galform  (Cole et al. 2000; Bower et al. 2006; Benson & Bower 2010 ) already used in  Ciesla et al. (2015)  to test several parametric SFHs. CIGALE is used to produce SEDs associated to the Galform SFHs assuming different models. In this test, to focus only on the effect of the SFH model used to perform the SED fitting, we build and fit the SED using the same single stellar population model  (Bruzual & Charlot 2003) , attenuation law  (Calzetti et al. 2000) , and dust emission  (Dale et al. 2014). This test was carried out by  Ciesla et al. (2015)  to quantify the ability of exponential and τ-delayed SFHs to recover the stellar mass and SFR of simulated galaxies.
In both panels, the solid line represents the results obtained when the full observed UV-mm SED is fitted and the dashed line shows results from when no FIR data are used for the fit. a burst and a sudden quench. However, in their test, the sudden quench occurred 1 Gyr ago and they found that the continuity prior provides a good estimate of the SFR. We try to refine the test to see the limits of the SFH sensitivity. As we adopt the bursty continuity prior  (Tacchella et al. 2021a ) we evaluate the extent to which we can recover this burstiness. To perform our test, we simulate a set of mock SEDs with CIGALE. We assume a constant SFH with a final instantaneous burst or quenching. The time when this instantaneous variation occurs varies from 10 Myr to 1 Gyr. Its strength, which is the ratio between the actual SFR and the SFR just before the variation, is set to between 0.
Results from SED modelling We ran CIGALE on the GOODS-ALMA sample using the sfhNlevels SFH module, stellar populations models of  Bruzual & Charlot (2003) , a modified  Calzetti et al. (2000)  starburst attenuation law module, and the  Dale et al. (2014)  IR templates. The parameters used for the two runs are provided in Table  1. The code uses energy balance to fit the entire SED from UV to submm, as we rely on the results of  Seillé et al. (2022)  who tested it extensively on the highly perturbed and star-forming Antennae system. These authors showed that the results of the UV-submm SED modelling of the entire system using energy balance are consistent with the sum of the results of the UV-submm SED modelling of individual regions, although they exhibit highly different levels of attenuation.
Mock analysis As a standard procedure, we analyse the ability of the code to constrain the key parameters necessary to this study. The mock analysis procedure has been extensively described in previous works (e. g. ,  Buat et al. 2019; Boquien et al. 2019; Ciesla et al. 2021). Although its principle is the same as in the tests performed in Sects. 2 and 3. 2, the difference here is that the mock SEDs are created using the SED fit of the GOODS-ALMA galaxies of our sample. The best models of this first run are used as mock galaxies representative of the GOODS-ALMA population for which all parameters are known. Thus, the input parameters are representative of our galaxy population. A second run of CIGALE is used to see how well we constrain these parameters. The results of the mock analysis are shown in Fig.
Acknowledgements. L. C. warmly thanks  M. Boquien  and  Y. Roehlly  for their help in implementing the non-parametric SFHs in CIGALE and  O. Ilbert  for useful discussions. This project has received financial support from the  CNRS  through the  MITI interdisciplinary programs. S. J. acknowledges the  Villum Fonden  research grants  37440 ,  13160  and the financial support from  European Union 's  Horizon research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No. 101060888. M. F. acknowledges  NSF  grant  AST-2009577  and  NASA   JWST GO Program   1727. H. I. acknowledges support from  JSPS KAKENHI  Grant Number  JP19K23462. G. E. M. acknowledges the  Villum Fonden  research grants  13160  and  37440  and the  Cosmic Dawn Center of Excellence  funded by the  Danish National Research Foundation  under the grant No.
Finally, when the fCmin normalized to the EUCAST clinical breakpoint for Pseudomonas aeruginosa (i. e. , fCmin/MIC P. aeruginosa ratio) exceeded 8, a significant deterioration of the neurological status occurred in approximately half of the ICU patients treated with piperacillin/tazobactam and approximately two-thirds of the ICU patients treated with meropenem  [54]. It is of note that an ongoing prospective clinical trial, the OPTIMAL TDM Study (NCT03790631), aims to address the toxicity thresholds of cefepime, imipenem, meropenem, piperacillin, flucloxacillin, amoxicillin and ceftazidime. However, the impact of TDM guided dosing adjustment to prevent harmful BL concentrations and to improve clinical outcome has yet to be determined.
Finally, TDM dosing guidance may be available after several doses, while adequate exposure may be needed right from the of therapy. To overcome the barriers related to antimicrobial TDM-guided dose op zation, innovative approaches using health record data in real time have been prop (Figure  3 ). A computerized approach using dedicated software that can integrate in their rithm the different variables affecting antimicrobial PK such as weight, renal functi age could allow a more precise dose adaptation  [55]. Model informed precision d (MIPD) uses a mathematical model to interpret the measured drug concentration. MIPD is data driven, relying on the individual patient's current characteristics and cl data and drug properties. To integrate the necessary data to inform the modelling, facing with health record data in real time is crucial.
However, MIPD generally req custom-made software tools as generic modelling software are too complex for clini to apply. Until recently, very few studies have focused on individualized and comp ized dose adaptation to optimize antimicrobial therapy in ICU patients  [56] [57] [58] [59] [60] [61]. Thes proaches aim to better understand each patient's unique pharmacological profile. A computerized approach using dedicated software that can integrate in their algorithm the different variables affecting antimicrobial PK such as weight, renal function or age could allow a more precise dose adaptation  [55]. Model informed precision dosing (MIPD) uses a mathematical model to interpret the measured drug concentration. Thus, MIPD is data driven, relying on the individual patient's current characteristics and clinical data and drug properties.
To integrate the necessary data to inform the modelling, interfacing with health record data in real time is crucial. However, MIPD generally requires custom-made software tools as generic modelling software are too complex for clinicians to apply. Until recently, very few studies have focused on individualized and computerized dose adaptation to optimize antimicrobial therapy in ICU patients  [56] [57] [58] [59] [60] [61]. These approaches aim to better understand each patient's unique pharmacological profile.
Population PK data are used to a priori determine the recommended dose likely to achieve a predefined PK/PD target. When measured drug concentrations become available, these data together with existing population PK data can be used to derive the individual PK parameters using Bayesian estimation and predict individualized dosing regimen. The individual PK parameter estimate is referred to as the maximum a posteriori (MAP) Bayesian estimate. These parameters are derived from the characteristics of the patient for whom the optimal dose is required and will therefore have an important influence on the final simulation. Thus, Bayesian forecasting presents the advantage of using TDM data that strengthen the accuracy of dosing recommendations and consider inter-individual variability. The Bayesian approach offers the advantage that it makes optimal use of all information contained in the population model (a priori) combined with the most current pharmacokinetic information from the patient (a posteriori) to develop the patient's most precise regimen  [65].
Clinical Data Supporting Antimicrobial Dosing Software Use Available clinical trials supporting BL dosing software tools are summarized in Table  3. A growing body of evidence indicates, especially in critically ill patients, that betalactam TDM-guided dosing and MIPD may maximize efficacy. Felton et al. analyzed the fitness of BestDose TM software to estimate each individual's pharmacokinetics by com-parison of the observed-versus-predicted piperacillin concentration after 24 h of therapy and its ability to accurately predict piperacillin dosing from the observed piperacillin concentrations  [57]. They found that the dose optimization software predicted a mean piperacillin dosage of 4. 02 g in the eight patients administered piperacillin doses of 4. 00 g when at least two observed piperacillin concentrations were available.