dataesr/scientific-paragraphs-categorization huggingface dataset:
Also, the virtual z-stacking procedure used to constitute the (simplified) reference database from large FOV FPM images for training and evaluation is indicated. Finally, section 4 is devoted to the presentation of experimental results. In contrast to SSIM or image quality metrics traditionally used to evaluate the consequence of image correction, here the DL compensation is discussed quantitatively by studying its impact on a DNN-based parasitized red blood cell classifier. Sensitivity and specificity of parasites detection are evaluated with or without U-Net image enhancement. At last, perspectives of this work are indicated in section 5.
Deep model We introduce a strategy based on a deep-neural network model that is combined to virtual z-stacking calculations for the reference database constitution. Once trained, the DNN is in charge of correcting images locally (i. e. at sub-cellular compartments scale) rather than globally, in order to produce contrasted and sharp details of parasites. The locality of the correction is an important contribution of the model since, most of the time, focus needs to be adjusted depending on the cell position within the FOV of FPM. The definition of what is a sharp and contrasted is here learned intrinsically by examples rather than defined by formal properties of the image  [28]. This is particularly advantageous for phase images. Furthermore, and in contrast to the possible image corrections discussed in section 2.
3, the neural network approach proceeds differently. It does not seek to identify the optimal focal plane per modality but rather produces a clear image for each of the two modalities (intensity and phase) in a single step. The neural network model statistically learns the transformation that needs to be applied on the reconstructed images thanks to a database of reference images. This learning database is composed of pairs of reconstructed images associated with their corresponding refocused references. Once the parameters of the network, namely its weights have been learned, the network can be used on FPM images of any size.
Training flowchart The U-Net model needs to be trained in order to optimize its weights. All the steps involved for the construction of the training dataset are indicated in figure  4. In a first step the optimal position of the microscope objective lens is found with FPM central led. This defines the reference z-position, 𝑧 = 0. The blood smear is then acquired for various focus conditions (z ranging from 𝑧 = -8𝜇𝑚 to 8𝜇𝑚 with a 2𝜇𝑚 step) and using each time 13 successive angles of illuminations. During the 3 𝑟 𝑑 step, the FOV restricted to its central 256x256 pixels (after cropping raw images) is exploited. These dimensions have been determined by considering the largest region over which no focus variation is visually appreciable and hence uniform. Since the defocus aberration law is spatially invariant, the training realized from this particular region is also applicable on the whole FOV.
It is also the region where the other aberrations are generally the smallest, as beneficial for building reference images of the highest quality. As is appreciable, each cropped image contains a sufficiently large amount of red blood cells (typically ∼ 100) to learn the properties of optimal red blood cell images (with or without parasites). After step 3, the data flowchart is divided in two branches: the left one is dedicated to the reconstruction of the images at varied 𝑧 (step 4a-5a) with Δ𝑧 𝑛𝑢𝑚 = 0; the right one is dedicated to the production of the reference images (step 4b-5b). Reconstructed images of the reference branch are here explored by varying finely Δ𝑧 𝑛𝑢𝑚 reconstruction parameter. The selection of reference images for intensity and phase modalities is finally determined by a parasitologist (step 5b).
The flowchart of figure 4 has been conceived to limit the burden of the production of the training dataset using only coarse positioning of the microscope objective lens over a large range of 𝑧 values. It is completed with virtual z-stacking calculations for various Δ𝑧 𝑛𝑢𝑚 for z=0. In test phase, the network is applied on complete FOV (I, Φ) images to produce corrected ones. We insist on the fact that even if only a restricted region of the FOV is used for the construction of the training dataset, the resulting trained neural network is general. In particular and as will be shown in the following section, the neural network is able to correct images over the complete microscope FOV and not only over its central zone.
Training database In this study, anonymized blood-smear slides from two infected patients were used for the U-Net training. Although limited in terms of patient diversity, the database that has been realized is representative of parasites encountered in real situations in terms of size and age. For each patient 14 FOV central regions (of 512x512 pixels after reconstruction) were acquired. 20 FOV central regions were used for training, 4 for validation and 4 for test. Each reconstructed region has then been split into 25 small patches of 128x128 pixels. This choice has been made in order to constraint the processing time and to ensure a sufficient quantity of training data. Data were further augmented through random rotations and flipping to increase the size of the training dataset. The final size of the training database (including out-of-focus images and geometrical transformations and data augmentation) is 18000 patches.
Ideally, the system has a sensitivity of 1 and a specificity of 1. In addition, we introduce 4 other metrics in order to produce more robust comparisons: the AUC (area under the ROC curve), Accuracy (percentage of overall (Infected and healthy RBCs) good classification, Precision (performance of the model at predicting the Infected class) and F1-score (harmonic mean of the model's precision and sensitivity) with F1-score= 𝑇 𝑃 𝑇 𝑃+ 1 2 (𝐹 𝑃+𝐹 𝑁 ) The detection algorithm that is implemented in the automaton relies on a conventional YOLO  [32]  neural network evaluated with two training datasets (Refer to appendix 1 and 2 for further details). In order to benefit from the full information that is available on the sample, the intensity and phase images pair is used as input of the YOLO. Table  1.
Plasmodium falciparum detection performances on the test dataset, obtained on raw FPM reconstructed images with and without U-Net compensation module (for 𝑧 = 0𝜇𝑚 and 𝑧 = 2𝜇𝑚). with U-Net without U-Net The sensitivity evaluated with and without U-Net for 𝑧 = 0 and 𝑧 = 2𝜇𝑚 are plotted figure  7a ) as a function of a confidence threshold. The plain curves have been obtained with the application of the U-Net, the dashed curves without refocalisation. As could be expected, the best sensitivity curves are obtained when U-Net compensation module is implied in the workflow. Interestingly, the results obtained for 𝑧 = 2𝜇𝑚 are very close to those obtained with 𝑧 = 0𝜇𝑚. Comparatively and when no U-Net image correction is used, the sensitivity decreases even for 𝑧 = 0𝜇𝑚. It is very degraded for 𝑧 = 2𝜇𝑚.
Recalling that 𝑧 = 2𝜇𝑚 in the center of the FOV and the fact that slide holder is slightly tilted (see colormap of fig. 6 ) the tolerance is roughly evaluated to ∼ ±5𝜇𝑚. To analyze further these results, errors in the detection of red blood cells are indicated in figure  8. The complete test-dataset related to 𝑧 = 2𝜇𝑚 is here considered. The crosses identify the positions of the undetected red blood cells in the FOVs. When no U-Net correction is implied (fig. 8a ), most of the errors are located near the left-up corner of the FOV, region where z is more important. The situation changes radically when images are corrected with the U-Net (figure  8b ). Here errors are very few and almost homogeneously spatially distributed. Moreover, the black box in figure  8  indicates the region where the focus is natively correct.
In this region and without any U-Net compensation, the density of the errors is small as could be expected. It is slightly further decreased after U-Net compensation. This suggests that the quality of images is indeed enhanced even though the focus was already correct. Of course, the neural network classifier could benefit further optimizations (with a larger training dataset and an optimization of some of its internal parameters). Nevertheless, the presented results already demonstrate quantitatively the positive impact of the image correction brought by the U-Net treatment: it extends importantly the tolerances of the positioning of the microscope objective lens and permits also to extend the exploitable surface of the FOV which can be beneficial to reduce the sample acquisition with FPM apparatus over large surfaces.
Z= 0 Z= 0 00 Fig. 7. a) Evolution of Plasmodium falciparum detection sensitivity as a function of the threshold parameter on the test database. The dashed curves correspond to results obtained on raw FPM reconstructed images without U-Net compensation module (for 𝑧 = 0𝜇𝑚 and 𝑧 = 2𝜇𝑚). Plain curves correspond to results after U-Net compensation of images. b) ROC curve revealing the best operating points (circles) defined by the best trade-off between sensitivity and specificity.
Better measurements are needed to understand the D/H variability and its implications. To rectify the uncertainties in the D/H measurements due to poor or nonexistent H I Lyα data, we embarked on a program in HST Cycle 18 to obtain much better H I Lyα spectra using the Space Telescope Imaging Spectrograph (STIS;  Kimble et al. 1998; Woodgate et al. 1998)  on HST. This spectrograph provides vastly better spectra of the Lyα line (see, e. g. , Figure  4  in  Sonneborn et al. 2002)  than IUE and enables precise measurement of N(H I) even in cases where the interstellar Lyα is blanketed with narrow stellar lines  (Sonneborn et al. 2002). Moreover, the new STIS spectra have high spectral resolution and enable measurement of a variety of metal column densities including species such as O I, Mg II, P II, Cl I, Mn II, Ni II, and Ge II, so the new STIS data also enable improvements in the metal measurements.
Stellar Models In order to take the stellar contributions to the Lyα line profile and the placement of the continuum into consideration, we computed synthetic spectra using stellar atmosphere models. We used the stellar atmosphere and spectrum synthesis codes TLUSTY 10    (Hubeny & Lanz 1995)  and Synspec 11  to compute nonlocal thermodynamic equilibrium (NLTE) stellar atmosphere models and synthetic spectra. Both codes were developed by I. Hubeny and T. Lanz. A detailed description and use of the codes are presented in a series of three papers by  Hubeny & Lanz (2017a , 2017b , 2017c). Synthetic spectra are calculated from models of stellar atmospheres that describe the surface properties of stars and are based on the results of spectroscopic data analysis. Table  2  gives the atmospheric parameters of the 16 stars considered in this study.
For this task we used the amoeba software routine in IDL. In the χ 2 calculation that minimizes the outcome for N(H I) we assigned greater weight to the Lyα region than to the continuum regions because we did not want difficulties in the continuum fit to compromise the important Lyα fit, in particular in continuum regions far from the Lyα line, which are unimportant for the determination of the H I column density. We now consider the proper spectral region of the damped absorption wings used to determine N(H). We want to select regions where the model spectrum most sensitively deviates from the observed spectrum due to errors in the modeled value of N(H I). Figure  3  shows a closeup of the continuumnormalized Lyα region of WD1034+001 with no other interstellar or stellar absorption lines.
Data Analysis The deuterium column densities N(D I) on the five lines of sight were measured by Voigt profiles fits of the interstellar spectral absorption lines. We used the profile-fitting method presented in detail by  Hébrard et al. (2002) , which is based on the procedure Owens. f, developed by Martin Lemoine and the French FUSE Team  (Lemoine et al. 2002). We split each spectrum into a series of small subspectra centered on absorption lines and fitted them simultaneously with Voigt profiles using χ 2 minimization. Each fit includes D I lines, as well as those of other species blended with them. Due to the redundancy in the FUSE spectral coverage, a given transition might be observed in several segments and with one or two slits. These different observations allow some instrumental artifacts to be identified and possibly averaged out.
Summary In this investigation we observed 11 targets at medium spectral resolution (∼30 km s -1 ) and 5 more at high resolution (∼2. 6 km s -1 ) in order to obtain high-S/N absorption spectra of the H I Lyα absorption line arising in the nearby ISM. These targets range in distance from 189 to 2200 pc. With these data we reach the following conclusions. 1. We computed an atmospheric model for each star in our program. These models include temperature, gravity, and a large number of metal lines of various ionization states. In some cases the models were better constrained than previous ones in the literature due to accurate distances provided by Gaia DR3. 2. We fit the Lyα absorption profile against the stellar flux model in order to compute N(H I). We demonstrated that the most sensitive spectral region for constraining N(H I) is where the damped profile lifts up from the saturated core region.
The SP2 data were processed using a Python code following the method used in the SP2 Toolkit from the Paul Scherer Institute  (Gysel et al. , 2009). The SP2 used in this study measured BC cores over a size range between 90 and 580 nm. The observed size distributions showed an increase in M BC at diameters less than 90 nm (Figure  S1  in the Supplement). Because of these small-mode particles below the SP2 detection window, the SP2 M BC measurements could be underestimated. To compensate the missing mass the observed BC size distributions have been fitted using the sum of three individual lognormal distribution to extrapolate BC size distribution in the range 10 to 1000 nm. The position of the three modes were constrained in the following ranges : Mode 1 : 50 < d g < 100 nm and 1. 2 < σ g < 3; Mode 2 : 150 < d g < 250 nm and 1.
Identification of air mass origins The Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT)  (Draxler and Hess, 1997)  was used to calculate air masses backtrajectories. This model uses 3-hourly atmospheric data from the Global Data Assimilation System (GDAS) of the National Center for Environmental Prediction (NCEP) in a 1°×1°spatial resolution. More information can be found on https://www. ready. noaa. gov/index. php. One backtrajectory was run every 24h going 72h back in time at 12h for the two-year period of the campaign. Every backtrajectory arrived at the PDM altitude and coordinates. Precipitation rates along the back trajectories were also computed from the HYSPLIT calculations, in order to classify days where the air masses arriving at PDM encountered precipitations or not in the past 72h.
Acknowledgements. This work received funding from the  French national program LEFE/INSU and Météo-France. Observation data were collected at the  Pyrenean Platform  for Observation of the Atmosphere P2OA (http://p2oa. aero. obs-mip. fr). P2OA facilities and staff are funded and supported by the  University Paul Sabatier Toulouse 3, France , and  CNRS (Centre National de la Recherche Scientifique). We especially thank the staff of the  Pic du Midi platform (Observatoire Midi-Pyrénées)  for their technical assistance. We acknowledge the  SNO ICOS-France  and  ACTRIS-France  forsupporting greenhouse gases and aerosol observations at PDM, data collection, processing and dissemination. We also acknowledge the use of the HYSPLIT model and READY websitefrom the  NOAA Air Ressources Laboratory  (http://ready.
Development of a deep learning framework for ccRCC diagnostic images We first developed prediction models that provide high resolution, quantitative, and humanunderstandable representations of ccRCC hematoxylin & eosin (H&E) WSIs to identify established pathology features like tumor tissue and nuclear grade at scale  16, 17  (Fig. 1A ; Methods, Extended Data Figures  1 2 3 4 5 6 7 8 ). After quality control analysis, we examined WSIs from 1102 ccRCC patients (n = 421 TCGA-KIRC, 439 CM-025, 208 DFCI-PROFILE, 21 multiblock nephrectomy cases, 13 paired mIF ccRCC cases; Methods). We next trained a second CNN classifier to distinguish low (G2) from high (G4) grade cases in the DFCI-PROFILE cohort, achieving high accuracy at the patient level when evaluated on the TCGA-KIRC dataset using an ensemble of four models (AUROC=0.
Microheterogeneity and response to ICI In addition to prognostic clinical value, we assessed whether this computer vision derived feature may be predictive for certain ccRCC therapeutics. We assessed spatial microheterogeneity patterns within both treatment arms of CM-025, a phase III randomized clinical trial cohort that compared anti-PD1 blockade (nivolumab) to mTOR inhibition (everolimus) in anti-angiogenic refractory metastatic ccRCC patients (Methods)  13, 23. Presence of microheterogeneity was associated with improved OS and PFS in the ICI arm, but not in the mTOR inhibitor arm (Fig. 4A ; Extended Data Fig. 17 ). Given that continuous grade score correlated with OS for each trial arm, we also examined whether microheterogeneous cases correlated with changes in survival due to having lower overall grade scores.
Taken together, these findings suggest that tumor and immune features of ccRCC can be jointly considered in a spatially aware manner to guide biological and clinical investigations using widely available H&E WSIs. There are several challenges and limitations to this analysis. The histological data analyzed from CM-025 consisted of pre-treatment primary tumor samples, and thus may differ from the tumor state at the time of trial accrual due to ongoing tumor evolution. As such, the specimens we analyzed may be uncoupled from eventual metastatic progression. Similarly, larger sample sizes in additional clinical cohorts are necessary to generalize these findings to the evolving combination treatment landscapes of ccRCC, and additional histologic features could be added to our model framework (e.
Clinical Cohorts Three distinct patient cohorts were used in the analysis: TCGA-KIRC, the CheckMate 025 (CM-025) phase III clinical trial (NCT01668784), and the DFCI-PROFILE of ccRCC patients from the Dana-Farber Cancer Institute (also under DFCI IRB #20-293 and 20-376). Additional datasets include a set of multiblock nephrectomy samples from Dana-Farber/Brigham (under DFCI IRB #20-293 and 20-376), and multiplexed immunofluorescence data from Dana-Farber/Brigham the ImmunoProfile project (under DFCI IRB #20-293 and 20-376).
Data Acquisition For the TCGA-KIRC cohort, we obtained clinical data, and normalized bulk RNA and genomic sequencing from the GDC PanCanAtlas (https://gdc. cancer. gov/about-data/publications/pancanatlas), and downloaded whole-slide H&E stained diagnostic images from the ISB-CGC mirror of TCGA data. For the CheckMate 025 cohort, we directly obtained H&E stained diagnostic images from the Signoretti Lab via an established Bristol Myers Squibb IION agreement, and used clinical and molecular data previously generated by Braun et al. , 2020 (European Genome-Phenome Archive: EGAS00001004290, EGAS00001004291, EGAS00001004292). DFCI-PROFILE images were obtained via the Dana-Farber/Brigham and Women's PROFILE project. Multiplexed Immunofluorescence (and accompanying H&E images) were obtained via the Dana-Farber/Brigham and Women's ImmunoProfile project.
Truncating mutation categorization When considering somatic alterations, we consider mutations only if they are truncating (likely loss of function). Within MAF annotation data, this comprised the following variant categories: {'Nonsense_Mutation', 'Frame_Shift_Ins', 'Frame_Shift_Del', 'Splice_Site'}. Image Quality Control Quality control of H&E whole-slide images was performed using the HistoQC 27 toolkit. A full set of modules used is available as a. ini` file. Custom examples used to train pen detection modules are available in a forked repository (https://github. com/jmnyman/HistoQC)]. Following HistoQC filtration, we further removed small slide images with fewer than 500 tiles (512px at 20X resolution).
Cross Validation Training and validation were performed in the DFCI-PROFILE cohort, with additional testing done in the TCGA-KIRC and CM-025 cohorts. In subsequent finetuning experiments for tumor and grade classification, we utilized 4-fold cross-validation. Each dataset was split into folds at the patient level to ensure no patient-level information bled between training and validation contexts. Datasets were then subsampled to ensure a balanced label composition. Following balanced patient-level fold creation, we then sample a fixed number (per specified hyperparameters) of 512 pixel tiles (20X) from each patient slide to create folds that were label-balanced.
Tumor Classification A model to classify general renal cell carcinoma tumor tissue versus adjacent normal stromal tissue was trained using pixel-level pathologist annotations from the DFCI-PROFILE cohort (n=36 slides). Following quality control, training data slides were split into 512 pixel tiles (20X), and assigned labels according to pathologist annotations (ie, whether a region contained tumorous tissue or not). A pretrained ResNet-50 neural network model was then finetuned using color jitter, and the highest performing model in a series of 4-fold cross validation was selected for subsequent inference. All neural network architecture and training code used the PyTorch and PyTorch-Lightning libraries, and 1-2 NVIDIA Tesla V100 GPU units on Google Cloud VM instances  28, 29. Hyperparameters used for training are available in the project repository.
Tumor Grading A second finetuned ResNet-50 neural network model was trained to distinguish low (G2) from high (G4) ccRCC cases from the DFCI-PROFILE cohort (n=190 slides). This cohort contained samples collected prior to and following the adoption of the WHO/ISUP grading changes, and as such contains both Fuhrman and WHO/ISUP grades 6. These conventions share significant overlap and are generally highly concordant  30. Additionally, manual review for sarcomatoid and rhabdoid (S/R) tumor content was previously performed, and cases with S/R content were upgraded to G4 if previously assigned a lower grade to ensure greater concordance with WHO/ISUP guidelines. Following quality control, training data slides were again split into 512 pixel tiles (20X), and tiles were assigned labels according to pathologist annotation of the source slide.
Tiles were only considered for model training if their predicted probability of containing tumor tissue was >= 0. 7, and slides were restricted to those with at least 500 putative tumor-containing tiles. A pretrained ResNet-50 neural network model was again finetuned using heavy color jitter. An ensemble composed of each model trained in 4-fold cross validation was then used for subsequent inference, taking the average across all model softmax outputs to make predictions. All neural network architecture and training code used the PyTorch and PyTorch-Lightning libraries, and 1-2 NVIDIA Tesla V100 GPU units on Google Cloud VM instances  28, 29. Hyperparameters used for training are available in the project repository. Inference Post-Processing Following tumor and grade inference, tile-level model scores were smoothed using uniform nearest neighbor averaging (n=4 nearest tiles).