dataesr/scientific-paragraphs-categorization huggingface dataset:
These dimensions are meant to provide a correct order of magnitude for NanOx calculations, which should be accurate enough for hadrontherapy applications. The impact on NanOx predictions of varying the set of standard parameters, including the sensitive volume dimensions was previously investigated by our team  [22]. Combining the modeling of local lethal and global events, cell survival fractions were computed for the three cell lines. The results are presented in Figure  8 , in which we compare experimental data (symbols) with NanOx predictions (lines). Precisely, we calculated survival fractions for 5-10 doses, which are represented by a LQ fit for the sake of readability. The decision of benchmarking NanOx predictions with experimental data of carbon ion irradiations was motivated not only for the clinical relevance of this ion, but also because of the availability of cell survival curves in the literature.
Note that the cell survival curves plotted in Figure  8  correspond to predictions for LET values different than those in the experimental dataset used to tune the effective local lethal function of the NanOx model. Unfortunately, error bars are rarely reported in cell survival experiments, which makes it impossible to evaluate the agreement between the NanOx model predictions and experiments through a statistical test. Thus, we quantified the agreement by means of the following estimator: χ 2 ϵ ( ) 1 N N i 1 S i n 1 + ϵ ( )-S i exp S i n 2 , ( 47 ) where N is the number of experimental points in each survival curve; S i n and S i exp denote the calculated and experimental cell survival fractions, respectively; and ϵ is a parameter introduced to account for the global relative deviation, ascribed to either pure NanOx errors or systematic uncertainties in experimental data.
The direct outcome of the NanOx model is the cell survival fraction for a given configuration c K (with K ion impacts in the volume of influence). Then, mean cell survival fractions over all configurations covered by the experimental conditions can be computed in order to compare the predictions with experimental data. For instance, it is possible to calculate cell survival curves as a function of the dose and derive α and β coefficients (with LQ fits) that can be implemented in any treatment planning system. As a matter a fact, α and β tables calculated with the NanOx model have been implemented in the GATE Monte Carlo simulation software  [28]  to compute biological dose distributions in spread-out Bragg peaks for pre-clinical and clinical beams  [29]. The NanOx model was first presented in 2017 in a concise manner  [13].
Funding This work was performed in the framework of the  LabEx PRIMES  ( ANR-11-LABX-0063 ) of the  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). We acknowledge the financial support of the  French National Institute of Health and Medical Research (Inserm) , through the grant " Apports à l'oncologie de la physique, de la chimie et des sciences de l'ingénieur ", no. 20CP176-00.
The 6-min walk test (6MWT) can be useful for defining changes in exercise tolerance, because it is easy to perform, reproducible and correlates with daily activity performance. Typically, during the 6MWT, the distance covered during the test (6MWD), oxygen saturation, heart rate and Borg dyspnoea scale are recorded. Although it has been widely used in studies investigating new treatments for PAH, 6MWT outcomes in ILD patients can be more difficult because any limitation reflects both the vascular component and parenchymal involvement. Furthermore, the causes underlying the exercise limitation cannot be extrapolated from data collected by the test. The negative correlation between 6MWD and mortality in ILD is controversial [9,  [55] [56] [57] [58] [59] [60] [61] [62] ; however, oxygen saturation during the test, slow heart rate recovery during the first minute of rest and alterations in chronotropic response appear to provide prognostic insight regarding mortality in this group of patients.
It will be important to investigate whether this finding is related to a change in pulmonary vessel compliance or due to a direct antifibrotic effect of treprostinil as observed in vitro and in mice  [94, 95]. The TETON trial is currently underway, investigating the safety and efficacy of inhaled treprostinil in IPF patients, using the absolute FVC as the primary end-point (clinicaltrials. gov identifier NCT04708782). The use of systemic treatments approved for patients with PAH in group 3 pulmonary hypertension has always raised concerns due to the potential for worsening ventilation/perfusion (V′/Q′) matching. Interestingly, as observed in other studies, inhaled drugs preferentially drive blood flow to the lung zones with better ventilation, reducing the risk of a V′/Q′ mismatch  [96, 97].
Methods of data collection An interview with the mothers of the children in the weighing and immunization centers was the main means of data collection. This interview was done through a collection forms designed using as a basis the WHO indicators for assessing infant and young child feeding practices  [18]. The selection of mother-child pairs was carried out by the systematic sampling method. The general information collected was the name, the age of mother and child, the gender of the child, the marital status of mother, the education level and the occupation of the responding mothers. The recall of the last 24 hours was obtained from the responding mothers and allowed the collection of dietary information, including the description of the dishes consumed by the child the previous day and all ingredients used.
Acknowledgements We would like to thank all the participants who contributed to this study. Author details 1  Laboratory of Applied Biochemistry   and Immunology, (LaBIA)/UFR-SVT/ University Joseph KI-ZERBO ,  03 PO Box 7021 Ouagadougou ,  Burkina Faso. 2 Laboratory of Biochemistry, Biotechnology, Food Technology and Nutrition (LABIOTAN)/UFR-SVT/University Joseph KI-ZERBO , 09  PO Box 848 Ouagadougou ,  Burkina Faso. 3  Department of Food Technology (DTA) ,  Research Institute of Applied Sciences and Technology (IRSAT) , 03  PO Box   7047   Ouagadougou ,  Burkina Faso.
Nasopharingeal swabs are easily executable and affordable and current standard in diagnostic setting; their accuracy in literature is influenced by the severity of the disease and the time from symptoms onset and is reported up to 73. 3%  [25]. Current position papers from radiological societies (Fleischner Society, SIRM, RSNA)  [1, 10, 16]  do not recommend routine use of imaging for Covid-19 diagnosis; however, it has been widely demonstrated that, even at early stages of the disease, chest x-rays (CXR) can show pathological findings. In the last year, many works attempted to tackle this problem, proposing deep learning-based strategies  [20, 18, 3, 12, 21]. All of the proposed approaches include some elements in common: the images collected during the pandemic need to be augmented with non-Covid cases from publicly available datasets; some standard pre-processing is applied to the images, like lung segmentation using U-Net  [15]  or similar models  [20]  or converting the pixels of the CXR scan in Hounsfield units; the deep learning model is trained to predict the final diagnosis using stateof-the-art approaches for deep neural networks.
Despite some very optimistic results  [3, 12, 21, 13, 19] , the proposed approaches exhibit significant limitations that deserve further analysis. For example, a recent work  [20]  showed that augmenting Covid-19 datasets with negative cases from publicly-available datasets can inject dangerous biases, where the trained model learn to discriminate different data sources rather than actual radiological features related to the disease. These unwanted effects are difficult to spot when using a black box model like deep neural networks, without having control on the decision process. In this work, we propose an explainable approach, mimicking the radiologists' decision process. We break-down the Covid-19 diagnosis problem into two separate sub-problems. First, we train a model to detect anomalies in the lungs.
Datasets In this section we introduce the datasets that we employed for building our proposed method. As shown in recent works such as  [20] , augmenting COVID datasets with negative cases from publicly-available datasets (such as ChestXRay or RSNA) can drive the models towards spourious correlations, such as discriminating only between healthy and unhealthy lung, or also be influenced by information related to the image acquisition sites. This is why we focus on the recognition of objective pathologies and radiological findings (CheXpert), which we then use for training a model to elaborate the final Covid-19 diagnosis (CORDA). Fig. 2 : Radiological findings provided by CheXpert, conforming to the medical known categorization  [6]. We now provide a short description of the main datasets, additional information can be found in the supplementary material.
CheXpert. This is a large dataset comprising about 224k CXRs. This dataset consists of 14 different observations on the radiographic image: differently from many other datasets which are focused on disease classification based on clinical diagnosis, the main focus here is "chest radiograph interpretation", where anomalies are detected  [9]. The learnable radiological findings are summarized in Fig. The data collection is still in progress, with other 5 hospitals in Italy willing to contribute at time of writing. We plan to make CORDA available for research purposes according to EU regulations as soon as possible. 3 Radiological report In this section we describe our proposed method for detecting and classifying radiological findings from CXRs. For this task, we leverage the large scale dataset CheXpert, which contains annotation for different kinds of common radiological findings that can be observed in CXR images (like opacity, pleural effusion, cardiomegaly, etc.
). Given the high heterogeneity and the large size of CheXpert, its use is perfect for our purposes: in fact, once the model is trained on this dataset, there is no need to fine-tune it for the Covid-19 diagnosis, since it will already extract objective radiological findings. CheXpert provides 14 different types of observations for each image in the dataset. For each class, the labels have been generated from radiology reports associated with the studies with NLP techniques, conforming to the Fleischner Society's recommended glossary  [6] , and marked as: negative (N), positive (P), uncertain (U) or blank (N/A). Following the relationship among labels illustrated in Fig. 2 , as proposed by  [9] , we can identify 8 top-level pathologies and 6 child ones.
COVID diagnosis The second step of the proposed approach is building the model which can actually provide a clinical diagnosis for COVID. We freeze the model obtained from Sec. 3 and use its output as input features to train a new binary classifier on the CORDA dataset. We test two different types of classifiers: a decision tree (Tree) and a two-layers fully-connected classifier (FC). The decision tree is trained on the probabilities output of the radiological reports, using the standard CART Algorithm implementation provided by the Python scikit-learn  [14]  package. The fully-connected classifier, comprising one hidden layer of size 512 and the output layer, is instead trained on the encoder latent space. The reason is that training it on the output probabilities would result in a loss of explainability compared to the decision tree, hence it makes more sense to maximize the achievable performance by training on the full latent space as discussed in Sec.
Experiments In this section we compare the COVID diagnosis generalization capability through a direct deep learning-based approach (baseline) and our proposed two-step diagnosis, where first we detect the radiological findings, and then we discriminate   [20]  to our twostep diagnosis. Baseline results are obtained pre-training the model on some of the most used publicly-available datasets. We observe that the best achievable performance is very low, consisting in a BA of 0. 67. A key takeaway is that trying to directly diagnose diseases such as COVID-19 from CXRs might be currently infeasible, probably given the small dataset sizes and strong selective bias in the datasets. We can clearly see how the two-step method outperforms the direct diagnosis: using the same network architecture (ResNet-18 as backbone and a fully-connected classifier on top of it), we obtain a significant increase in all of the assessed metrics.
75 with DN-121+FC vs 0. 68 with Tree), this is the result of a trade-off between interpretability and discriminative power. Using Grad-CAM  [17]  we have hints on the area the model focused on to take the final diagnostic decision. From Fig. 5  we observe that on COVID-positive images, the model seems to mostly focus on the expected lung areas. However this kind of interpretability provides very limited insights when compared to the clinical-based explanation given by our decision tree. Finally, to further test the reliability of our approach, we used our strategy also on CORDA-SLG (which are data coming from a different hospital structure), reaching comparable and encouraging results.
Conclusions One of the latest challenges for both the clinical and the AI community has been applying deep learning in diagnosing COVID from CXRs. Recent works suggested the possibility of successfully tackling this problem, despite the currently small quantity of publicly available data. In this work we propose a multi-step approach, close to the physicians' diagnostic process, in which the final diagnosis is based upon detected lung pathologies. We performed our experiments on CORDA, a COVID-19 CXR dataset comprising approximately 1000 images. All of our experiments have been carried out bearing in mind that, especially for clinical applications, explainability plays a major role for building trust in machine learning algorithms, although better interpretability can come at the cost of a lower prediction accuracy.
[22]loped for the RSNA Pneumonia Detection Challenge, this dataset contains pneumonia cases found in the NIH Chest X-ray dataset[22]. It comprises 20672 normal CXR scans and 6012 pneumonia cases, for a total of 26684 images. [11]estXRay. This dataset contains 5857 X-ray images collected at the Guangzhou Women and Children's Medical Center, Guangzhou, China. In this dataset, three different labels are provided: normal patients (1583), patients affected by bacterial pneumonia (2780) and affected by viral pneumonia (1493). This dataset is granted under CC by 4. 0 and is part of a work on Optical Coherence Tomography[11]. 4RSNA. 5CORDA. This dataset was created for this study by retrospectively selecting chest x-rays performed at a dedicated Radiology Unit in CDSS and at SLG in all patients with fever or respiratory symptoms (cough, shortness of breath, dyspnea) that underwent nasopharingeal swab to rule out COVID-19 infection.
Intravital Confocal Microscopy All images and videos were analyzed by using Image J (https://imagej. nih. gov/ij/, accessed on 1 August 2022) and 4D image analysis software (Imaris). Mice preparation: Mice were anesthetized by injecting i. p. 120 µL of the Xylasin/Ketamine cocktail mixed with PBS (1:1:2); any supplementary injection of anesthesia during the confocal experiment were performed intra-muscularly (i. m). Mice were maintained under observation on the microscope stage in a chamber regulated at 28 • C. These very mild anesthesia conditions were applied for less than 2 h. Static mosaic images and time lapse video acquisitions: We adapted regular intravital confocal imaging as described previously  [32]  to visualize both the epidermis and the dermis of ear skin in typical non-invasive inspections.
For that, we made a trade-off between XY resolution and Z depth of focus and tuned the instrument with a somewhat larger pinhole size than usual (1. 5 to 2 time the Airy disk) as previously established for vital ear skin imaging  [32]. Static mosaic images and time lapse video acquisitions were performed in the ear-skin using Leica TCS SP5 and SP8 microscopes (Leica Microsystems, Wetzlar, Germany). 405 nm, 488 nm, and 561 nm lasers are used for Blue (autofluorescence), eGFP, and tdT excitation, respectively. Typical confocal recording times were 30 to 60 min to acquire complete dual channel 3D and 4D data sets for static mosaic images and video data sets, respectively. Static mosaic images were recorded by assembling a series of juxtaposed dual channel 3D frames (from 20 to 30 frames) using the mosaic routine allowing automatic control of the XYZ motorized stage of the Leica TCS SP5 and SP8 confocal microscopes with a typical duty cycle of 2 to 3 min between the individual frames.
Video confocal image dual channel 4D data sets were recorded by assembling a series of time lapse dual channel 3D frames using the time lapse routine and automatic control of the Z motorized stage of the Leica TCS SP5 and SP8 confocal microscopes, with a typical duty cycle of 4 to 5 min between time points. For each time point, the initial raw X, Y, Z stacks are composed of 20 to 30 individual sections covering the dermal layer. On some occasions, individual sections allowed us to visualize all the details including the presence of nuclei structures, which exclude the fluorescent tdT marker. Next, we processed all raw image acquisition stacks using maximum Z projection of 4D (X, Y, Z, color) data sets acquired for the static mosaic images and dual channel 5D (X, Y, Z, color and time) video acquisitions to visualize on single micrographs all MCs, Tregs and LCs present in different focal planes within the specimens.
tdT + MCs were revealed in the red fluorescence channel and a third blue spectrum fluorescence channel imaging was performed to differentiate the tdT + red only MCs from the yellow auto-fluorescent structures such as hair follicles and associated sebaceous glands present at the basal position around each follicle. eGFP was imaged using the green channel. All images were acquired in 1024 × 1024 format with a 40X HCS PLAPO objective. Dual fluorescence channel 4D data sets were collected over the 30-45 µm thickness with 1. 5 µm Z step size using the 20X PLAPO objective (field of 450 µm × 450 µm, 1 Mpixel image). Maximum Z projections of 3D stacks were obtained at all time points of multi-dimensional dual channel 4D image stacks yielding time lapse dual channels videos animations of the ear-skin.
Briefly, each video Z stack was copied in triplicates using ImageJ software, one copy was converted in blue color, one in green color and one in red color. We then merged the 3 videos, but introduced a time shift of minus one time frame for the blue video and plus one time frame for the red video. In the resulting RGB video, each time frame has a color coding of red for time frame +1, green for the actual time frame and blue for time frame -1. Thus, we were able to identify visually the cell motility in 5D videos as previously performed  [32]. For cell tracking and quantification of cell displacement velocities, we used the Imaris software yielding overlays with white displacement tracking lines over the micrographs. Quantification of cell velocities in µm per min was then calculated using a tracking plug-in (http://rsbweb.
We displayed the negative component in the white channel for each given pixel on the raw image. Independent fields of over 400 × 400 µm surface areas per tissue section were inspected using a numerical aperture 20× objective to visualize Tregs counting a total of 22 Tregs in control experiments and 71 Tregs in PCA experiments. Using the NIH ImageJ software, we extracted the contact points (in white) for each time frame within videos, quantified their occurrence and monitored the duration of each cell-to-cell contact. These results were then plotted considering three categories to assess transient, intermediate and long term contacts.
Of importance, on single sections, MC nuclei are clearly visible as tdT negative holes on most MCs visualized with arrows (Figure  1A ). To visualize all MCs present in different focal planes in the dermis, we processed all further micrographs with a Z maximum fluorescence projection software, with the drawback of a loss of intracellular details such as nuclei holes (Figure  1B-E ). Further figures displaying other eGFP labeled cells such as Tregs and LCs were equally processed with Z maximum projection of 3D and 4D data sets. Next, we performed large field inspections and confirmed using maximal Z projection images, that MCs form a dense layer of dispersed cells present in the dermis at the steady state (Figures  1B  and S1A ). The distribution of MCs in the dermis is well organized constituting a dense network in agreement with their sentinel function.
The results show that upon PC61 mAb-mediated ablation and deletion of Tregs  [35] [36] [37]  the induced inflammatory response, albeit slightly lower, was not significantly different compared to Treg non-depleted control mice (Figure  5 ). Therefore, Tregs do not significantly control the outcome of the fast PCA response induced by MC degranulation in the dermis (Figure  5 ). Ag (DNP-HSA) at the indicated time points. Each quantification was performed in at least two independent fields of skin of over 400 × 400 µm Z projection images from three mice and expressed as the mean ± SEM of the total number of Tregs per mm 2. (B,C,E,F) micrographs of reduced size and resolution in 3D dimensions were recorded with a rapid duty cycle of 40 to 60 sec as detailed in the method section. (B,E) Tregs were tracked with Imaris software (white lines).
(H) Average Treg velocities and (I) MCs-Tregs contact times were measured at 2 h to 3 h after Ag (DNP-HSA) challenge performed in control PBS-sensitized (PBS/Ag) and IgE-sensitized (IgE/Ag) ear skin. (C,F) Contacts between MCs and Tregs were automatically identified with dedicated software on the time lapse videos (arrow heads), tracked with Imaris software and classified (I) into three categories: none or short-lived contacts (0-10 min), intermediate-lived contacts (10-50 min) and sustained long-term contacts (>50 min). Each quantification was performed in at least two independent fields of the ear skin of over 400 × 400 µm Z projection images from three mice. Numbers correspond to the mean ± SEM of Tregs per category. Statistical analysis in G, H and I was based on an unpaired t-test between groups.
Langerhans Cells Remain Sessile While Exhibiting Constant Sprouting of Their Dendrites Finally we also performed real-time imaging of control PBS-sensitized or IgE-sensitized ear skin of RMB x Langerin-eGFP mice 2 h to 3 h post Ag challenge. The data revealed that like MCs (Figure  6A , Videos S4 and S5) epidermal Langerhans cells remain sessile both in control PBS-sensitized ears and after the initiation of PCA in IgE-sensitized ears. In contrast to LCs, some motile dermal Langerin+ cells, which primarily comprise cDC1  [42]  are observed in the dermis (Videos S4 and S5). Close inspection using RGB color code overlay images superimposing time points 0, 7 and 14 min revealed that in contrast.
Acknowledgments : The Red Mast Cell and Basophil reporter (RMB) mice, Langerin-IRES-eGFP (Langerin-eGFP) mice and Foxp3-IRES-eGFP (Foxp3-eGFP) knock-in mice were developed at  Centre d'Immunophénomique (CIPHE)  with the support of the  Investissement d'Avenir program PHE-NOMIN  ( French National Infrastructure for Mouse Phenogenomics ;  ANR-10-INBS-07  to  B. Malissen ). Confocal imaging has been realized in the Cell imaging core facility SFR Necker ( UMS 24 ) shared by  INEM & Institut Imagine, Paris, France. We thank  Meriem Garfa-Traoré  and  Nicolas Goudin  from the  «Plateforme d'Imagerie Cellulaire», SFR Necker  ( UMS 24 ), for their outstanding technical assistance with the confocal instruments as well as with  NIH ImageJ  and Imaris image analysis softwares.