dataesr/scientific-paragraphs-categorization huggingface dataset:
Consequently, the developed method is pathology specific. Moreover, by using advanced methods such as DL, a specific signature of a given pathology can be automatically and efficiently learned. However, such approaches suffers from a lack of generalization usually related to overfitting on the training database  [8] ,  [16]. Moreover, with the advent of DL methods, interpretation of the results and explanation of the underlying decision-making process is far from being straightforward  [15]. In this paper, we present an alternative framework combining advantages of both strategies: an easy interpretation and an accurate classification. To this end, we propose a novel method able to detect patients with AD using both normal and pathological lifespan models. First introduced in  [17] , lifespan modelling of AD provides an useful and easily interpretable tool to capture the heterogeneity of AD signature.
Testing dataset To validate our model, we built a testing dataset based on two open access databases (AIBL and MIRIAD) to perform AD vs. CN diagnosis task. Therefore, we validated the generalization capacity of our method and its robustness to domain shift. In addition, we used subjects with Mild Cognitive Impairment (MCI) from ADNI to estimate the capability of our models on prognosis task (see Table  2 ). Consequently, we validated the generalization of our models to unseen related tasks. As in  [8] , the MCI group was split into stable MCI (sMCI) over three years and progressive MCI (pMCI) who will convert to AD within 36 months following the baseline visit. Finally, we used the ClinicaDL software 1   [8]  to define the groups of AD and CN groups in AIBL, and the pMCI and sMCI groups in ADNI.
Image processing All the considered images were processed using AssemblyNet software 2   [18]. Based on collective artificial intelligence, AssemblyNet is able to produce fine-grained segmentation of the whole brain in 15 minutes. The AssemblyNet preprocessing pipeline was based on several steps: image denoising  [19] , inhomogeneity correction  [20] , affine registration to the MNI space, automatic quality control (QC)  [21] , a second inhomogeneity correction in the MNI space  [22]  and a final intensity standardization step  [10]. After preprocessing, the brain was segmented into several structures using 250 DL models (see  [18]  for details). All the segmentations were based on the Neuromorphometrics protocol which comprises 132 structures  [23]. In this protocol, the segmentation of the subcortical structures follows the "general segmentation protocol" as defined by the MGH Center for Morphometric Analysis 3.
Lifespan model estimation To create our lifespan models, we estimated normal and pathological trajectories of structure volumes across the entire lifespan. To this end, for each considered structure, models were estimated on two different groups to generate CN and AD trajectories. For CN trajectories, we used the N=2655 subjects from 9 months to 94y of the training dataset as done in  [25]. For the AD trajectories, we used N=2251 subjects. As done in  [17] , we mixed AD patients with young CN. More precisely, we used 377 AD patients (from 55y to 96y) and all the CN younger than 55y available in the training dataset (i. e. , 1874 subjects) assuming that neurodegeneration is a slow and progressive process. To estimate the volume trajectories, we considered several low order polynomial models: • Linear model 𝑣𝑜𝑙(𝐴𝑔𝑒) = 𝛽 ! + 𝛽 " 𝐴𝑔𝑒 + 𝜀 • Quadratic model 𝑣𝑜𝑙(𝐴𝑔𝑒) = 𝛽 ! + 𝛽 " 𝐴𝑔𝑒 + 𝛽 # 𝐴𝑔𝑒 # + 𝜀 • Cubic model 𝑣𝑜𝑙(𝐴𝑔𝑒) = 𝛽 ! + 𝛽 " 𝐴𝑔𝑒 + 𝛽 # 𝐴𝑔𝑒 # + 𝛽 $ 𝐴𝑔𝑒 $ + 𝜀 As in  [17] ,  [25] , a polynomial model was considered as a potential candidate only when simultaneously F-statistic based on ANOVA (i.
Classification using volume trajectories Once the AD and CN lifespan trajectories were estimated for each structure using the training dataset, we used them to perform subject classification. To classify each subject of the testing dataset, we simply estimated the closest lifespan trajectory in terms of Euclidean distance to assign the class of the subject under study. Moreover, in order to provide easily interpretable non-binary scores to the user about the probability of the subject's status (and to be able to estimate Area Under Curve), we proposed new scores of being an AD patient (respectively a CN subject) based on the distance to the models. This score was built to ensure that when AD score is higher than 50%, the closest model is the AD model. Moreover, we ensured that an AD score of 50% (i.
Comparison with state-of-the-art methods Second, as shown in  [8] , most of the proposed deep learning methods suffer from data leakage resulting in biased reported performances. In addition, most of the published studies used the same dataset for training and testing that produce over-optimistic performance of the methods  [8] ,  [16]. Consequently, we decided to report the score of the well-evaluated methods proposed in  [8]  as state-of-the-art deep learning methods since the training was well-designed and that the proposed methods were wellvalidated on external datasets. We selected a ROI-based Convolutional Neural Network (CNN) focused on hippocampal area, one subject-based CNN method using the entire image and one patch-based CNN processing the whole image patch by patch. These three strategies are a good representation of current deep learning frameworks for AD detection and prognosis.
We used the same ClinicaDL software to create the testing databases. Consequently, the selection criteria were similar although the number of subjects per cases were not exactly the same. Finally, since  [8]  demonstrated that classical machine learning methods (i. e. , SVM) can perform similarly and sometimes better than deep learning methods, we decided to include two classical classifiers in our comparison. First, we used the nonlinear SVM with RBF kernel of Matlabâ with default parameters. Second, we used the logistic regression with LASSO regularization of Matlabâ with default parameters. The z-score of normalized volumes were used as input features.
Detection of the most discriminant structures First, we selected all the multi-scale brain areas (i. e. , tissues, regional tissues, lobes and structures) for which CN and AD models significantly diverged (i. e. , confidence intervals stop overlapping at some point across lifespan). Thanks to this analysis, we obtained 33 areas. Using these 33 selected areas, we performed a screening to detect the most discriminant ones in terms of classification accuracy on the training ADNI dataset in order not to use testing data during method development. This analysis showed that amygdala, hippocampus and inferior lateral ventricle were the most discriminant structures for AD vs. CN classification (see Table  3 ). These three structures obtained AUC>80% and thus were selected to build our AD-specific hybrid lifespan models.
Sensitivity analysis to training domain Finally, as a sensitivity analysis, in order to evaluate the consistency and the robustness of HAVAs to training domain, we performed an additional experiment using AIBL, OASIS and MIRIAD databases in the training dataset while removing the AD and CN subjects of the ADNI database from training and used them as testing dataset. First, Table  7  shows the results obtained by HAVAs, amygdala, hippocampus and inferior lateral ventricles. The obtained results are similar to the results previously obtained on AIBL. This result highlights the robustness of the proposed HAVAs strategy to training domain selection and the good generalization capability of our method. Moreover, Figure  4  presents the graphical results obtained using HAVAs score in the same condition.
These results obtained using AssemblyNet were in line with recent studies based on other segmentation protocols, software or frameworks  [17] ,  [28] -  [31]. During our experiments, we showed that models combining several structures (i. e. , HAVAs and HAV) outperformed models based on a single structure. This demonstrates the advantage of combining volumes of key structures to improve AD detection. Moreover, our results suggests that methods based on amygdala provide higher accuracy than models based only on hippocampus. The important role of amygdala at the early state of AD has been already observed in the past  [17] ,  [30] ,  [32]. Finally, we showed that using several models had beneficial impact for improving classification accuracy compared to single-based model normative approach.
Moreover, the simplicity of HAVAs make it fast and easy to reimplement. A software package including AssemblyNet pipeline and HAVAs estimation will be made freely available as a downloadable Docker 5  as well as an online pipeline on the volBrain platform 6  after paper acceptance. -Second, HAVAs is based on a very low number of parameters and hyperparameters. The use of low order polynomial models for trajectory results in few learnable parameters per trajectory. Thus, using less than ten parameters, HAVAs is able to outperform CNN models involving more than ten million parameters. Moreover, thanks to our volume normalization procedure compensating for inter-subject and interstructure variabilities, no hyper-parameter is needed to combine hippocampus, amygdala and inferior lateral ventricle volumes.
Therefore, we proposed a new AD score based on hippocampal-amygdalo-ventricular volume called HAVAs. This score is based on the distances between the volume of the subject under study and the AD and CN lifespan trajectories. During the validation of HAVAs on three external datasets, we showed that our strategy enables accurate detection of subject having AD, or MCI who will convert to AD in the next 3 years (i. e. , pMCI). Finally, we demonstrated the competitive proposed HAVAs compared to usual normative modelling, classical ML and recent DL methods.
We impose the noslip condition at the ground and on the obstacle and an outflow condition at the other boundaries (see  [26]  for more details). When the turbulent flow is fully developed a concentrated odor source is added at 0. 58 m from the ground, i. e. 20 cm above the center of the obstacle. The source is defined by a Gaussian profile with radius σ ∼ 5η, where η is the smallest scale of turbulent eddies (see Table  S1 ). We set adsorbing boundary conditions at the inlet, on the ground and on the obstacle and zero gradient conditions on the sides and top. The simulation was realized by customizing the opensource software Nek5000  [27]  developed at Argonne National Laboratory, Illinois. The three dimensional volume of the channel is discretized in a finite number of elements and Nek5000 solves the Navier-Stokes and scalar transport equations within every element with a spectral element method.
ACKNOWLEDGMENTS G. R was partially supported by the  NSF-Simons Center for Mathematical & Statistical Analysis of Biology at Harvard  (award number # 1764269 ) and the  Harvard Quantitative Biology Initiative. This work received support from: the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 101002724 RID-ING ); the  Air Force Office of Scientific Research  under award number  FA8655-20-1-7028 ; the  National Institutes of Health (NIH)  under award number  R01DC018789. The authors are grateful to the OPAL infrastructure from  Université Côte d'Azur  and the  Université Côte d'Azur's Center for High-Performance Computing  for providing resources and support. N. R. is thankful for the support of  Instituto Nazionale di Fisica Nucleare (INFN) Scientific Initiative SFT.
Loss of photoreceptors in atrophic age-related macular degeneration (AMD) results in severe visual impairment. Since the low-resolution peripheral vision is retained in such conditions, restoration of central vision should not jeopardize the surrounding healthy retina and allow for simultaneous use of the natural and prosthetic sight. This interim report, prespecified in the study protocol, presents the first clinical results with a photovoltaic substitute of the photoreceptors providing simultaneous use of the central prosthetic and peripheral natural vision in atrophic AMD. In this open-label single group feasibility trial (NCT03333954, recruitment completed), five patients with geographic atrophy have been implanted with a wireless 2 x 2 mm-wide 30 µm-thick device, having 378 pixels of 100 µm in size.
Results Five patients with GA were implanted in Paris during 2017-2018 (NCT03333954). In four of them, the implant was placed in the subretinal space, but in one it ended up inside the choroid due to patient's accidental movement during surgery. In one of the four patients, the implant accidentally shifted by about 2 mm from the central position after the fluid-air exchange since the patient did not keep the head in a prone position post implantation. Due to wireless nature of the implant, surgical procedure was relatively short-about 2 h 11. As shown in Table  1 , residual natural acuity in the operated eye did not decrease in any of the subjects. Interestingly, in some patients, acuity improved compared to baseline, which could be attributed to either a neurotrophic benefit of subretinal surgery  12  or of electrical stimulation  13  or just improvement with eccentric fixation after training.
To mimic the crowding effect of the letter charts, the Landolt rings were surrounded by a square frame (Fig. 3a ). At each trial, subjects reported the font orientation (up, down, left, or right), and its size was then adjusted, depending on the response. The visual acuity was determined using the Freiburg Visual Acuity Test (FrACT) software  15, 16. For a stable perception under pulsatile illumination, 30 Hz repetition rate was applied. In the first set of the tests, computer-generated Landolt optotypes were projected into the eye directly from the AR glasses without using a camera. As shown in Table  1 , patients #2 and #5 demonstrated prosthetic acuity at the level similar to that observed with VR glasses in the first phase of the trial (20/500, 20/460), but patient #4 improved compared to the earlier result-from 20/800 to 20/438.
Methods Patients. The aim of this study (NCT03333954) was to test functionality of the PRIMA system in 5 patients with atrophic AMD. The study adhered to the Declaration of Helsinki and received the ethics committee approval from the Comité de Protection des Personnes Ile de France II and approval by the Agence Nationale de Sécurité du Médicament et des Produits de Santé in France (ASNM). Study participants were above 60 years of age and had advanced atrophic AMD with an atrophic zone of at least three optic disc diameters and best corrected visual acuity of ≤20/400 in the worse-seeing study eye; no foveal light perception (absolute scotoma) but visual perception in the periphery, with preferred retinal locus determined by micro-perimetry; absence of photoreceptors and presence of the inner retina in the atrophic area as confirmed by optical coherence tomography (OCT); absence of choroidal neovascularization verified by retinal angiography.
Acknowledgements The authors thank the patients who participated in the study; the  Pixium Vision team  who designed, fabricated, and tested the PRIMA system; the  Scientific and Medical Advisory Board of Pixium Vision  for its guidance on the clinical trial design; and all the scientific, research and development, medical, and clinical research staff who continue the patient care, rehabilitation, and evaluation. Studies were supported by:  Pixium Vision SA ; the  Sight Again project (via Structural R&D Projects for Competitiveness and Investment for the Future funding managed by BpiFrance)  and the  Clinical Investigation Center at the Quinze-Vingts National Hospital , which is supported in part by the  Inserm-DGOS , France and by  LabEx LIFESENSES  ( ANR-10-LABX-65 ) and  IHU FOReSIGHT  ( ANR-18-IAHU-01 ) grants.
Data availability All the data supporting the findings of this study are available within the paper and its supplementary information files. The visual acuity data analysis software can be downloaded from the publicly available database  21. Protocol for the studies of simultaneous perception of prosthetic and natural vision can be provided upon a reasonable request. Pixium Vision is responsible for approval of the clinical study protocol and for reporting the results to the regulatory authorities in Europe and in US. As such, it was informed about the study on a continuous basis and performed its own data analysis. The paper was written by its authors based on their data analysis, and they are responsible for its content.
| Data collection Demographic data including sex and age at implantation, hearing loss onset, duration of profound hearing loss before implantation, and previous stapes surgery were collected. Implant data were collected for each patient and number of active electrodes at each control visit, and FNS occurrence at 1 month, 6 months, and yearly after implantation. The statistical unit of the study was the implanted ear, and in case of bilateral implantation, both ears of the same patient were included in the study.
The amino acid residues and peptide chains that are exposed on the sides of structurally distinct fibrils also define whether alpha-synuclein may be post-translationally modified and, as a consequence, their interactomes that depend on post-translational modifications. Finally, the amino acid residues and peptide chains that are exposed on the sides of alpha-synuclein amyloids Fig. 1  Structure of the amyloid core of the different fibrillar polymorphs alpha-synuclein forms under different experimental conditions and post-translational modifications. The structure of fibrillar alpha-synuclein obtained by solid-state NMR (pdb ID# 2n0a  [30]  is represented on the top. The structures of full-length or truncated wild type or mutant alpha-synuclein derived from cryo-electron microscopy are represented on the two bottom lines (PDB id# 6cu7  [24, 25] , 6h6b  [22] , 6flt  [22] , 6a6b  [24, 25] , 6cu8  [24, 25] , 6sst, 6ssx  [23] , 6l1t  [31].
The respective pdb identities are given. The identity of the amino acid residues located at the N-and C-terminal sides of alpha-synuclein molecule within the resolved fibrillar structures are indicated determine with what efficiency they are degraded by the cellular clearance machinery and/or extracellular proteases while those exposed at their growing ends define the rate at which incorporated alpha-synuclein molecule is lost from their tips. Alpha-synuclein fibrils binding to the cell membrane have been shown to affect membrane fluidity and geometry/curvature  [32]. The lateral diffusion of alpha-synuclein fibrils in the plane of the plasma membrane together or independently of membranous components such as receptors, channels, and adhesion molecules, contributes to the build-up of large fibrillar clusters and the redistribution and/or coalescence of protein partners at the surface of the cell  [30] [31] [32] 35].
Funding Research carried out in  R Melki  laboratory is supported by grants from the  EU Joint Programme on Neurodegenerative Disease Research and Agence National de la Recherche  (contracts  PRO-TEST-70 ,  ANR-17-JPND-0005-01  and  Trans-PathND ,  ANR-17-JPND-0002-02 ),  France Parkinson , the  JiePie  award and the  Fondation Bettencourt-Schueller.
Wound repair was assessed daily during 5 consecutive days using a camera-coupled optical microscope (× 10 magnification; Olympus CK2; Olympus, Norfolk, USA). Repaired areas were determined by subtracting cell-free areas at various timepoints from initial cell-free areas (ImageJ software, NIH, Bethesda, USA). Values of filled areas were normalized relative to control areas. Proliferation assays. For proliferation assays, 4T1 cells (3 × 10 3 ) were seeded in 96-well semipermeable plates pre-coated with type I collagen, in medium with or without 5 µM macitentan, and submitted to a 2-day N or IH exposure. Cell viability was then assessed using MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) staining. Transwell migration assays. 4T1 cells  (10 6  ) were seeded in the top chamber of Transwell 8.
They were then anesthetized (isoflurane 4% for induction and 1. 5% thereafter) and placed in the BLI system (IVIS Kinetic, PerkinElmer, Villebon-sur-Yvette, France), in supine position. Signal quantifications were carried out by drawing regions of interest on the primary tumor and thorax areas (LivingImage software; PerkinElmer, Villebon-sur-Yvette, France). Results were expressed as photons per second (ph/s).
A meta-analytical method was performed on repeated independent in vivo experiments to evaluate the global Hedge's g effect size (see supplementary methods for details)  34. By convention, overall effect sizes of 0. 2, 0. 5 and 0. 8 standard deviations are considered as "small", "medium" and "large", respectively. Statistical analysis and meta-analysis were performed using SPSS Statistics (version 20. 0, IBM, Armonk, USA) and RStudio (version 1. 2. 1335, RStudio, Boston, USA) software, respectively.
Moreover, the results of the present study are strengthened by in vitro data showing that proliferation, migration and expansion were higher in tumor cells submitted to IH compared to normoxic exposure. This provides the first demonstration that in vivo breast tumor expansion by IH could be explained by both enhanced proliferation and increased migration capacity. Intermittent hypoxia exposure increases lung breast cancer metastasis. Thoracic metastasis, assessed by in vivo bioluminescence, was enhanced in mice exposed to IH. This is in accordance with our in vitro results showing increased migration and proliferation of breast cancer cells exposed to IH as shown by wound healing, transwell and proliferation assays. This is consistent with the in vitro study of Chen et al. showing that IH exposure increases the expression of genes known to predict lung metastasis in breast cancer  39.
Funding This work was supported by  University Grenoble Alpes, INSERM, CHU Grenoble Alpes , the  Agir pour les Maladies Chroniques Foundation  and the  French League against Cancer. Macitentan was graciously provided by  Actelion Pharmaceuticals, Switzerland. The Optimal imaging platform is supported by  France Life Imaging  ( French program  " Investissement d' Avenir " grant; " Infrastructures d'avenir en Biologie Santé ",  ANR-11-INBS-0006 ) and the  IBISA French consortium "Infrastructures en Biologie Santé et Agronomie ". The imaging systems used in this study were purchased thanks to the France Life Imaging grant and to a  Contrat Plan Etat-Région Auvergne-Rhone-Alpes grant. The funding bodies did not have any role in designing the study, interpreting data or writing the manuscript.
Statistics Data were analysed with GraphPad (GraphPad Prism8, San Diego, CA, United States). Normality of data distribution was assessed using d'Agostino and Pearson normality test. Results were given as mean ± SEM when data complied to normality or as median and interquartile range [Q1; Q3] when normality was not detected. Statistical analyses of electrophysiological data were performed on data averaged over a 5-min time-period. The value of fR was expressed in absolute values (c. min -1 ) in the text. Statistical comparisons of body weight between CONT and PH pups were assessed using a Mann and Whitney test. Statistical comparisons of fR between basal conditions and drug challenges for all groups (MS or PMS, P0-1 or P3-4, CONT or PH) were assessed with one-way repeated measures analysis of variance (ANOVA) followed by the post hoc Holm-Sidak's multiple comparison test or with Friedman test followed by the post hoc Dunn's multiple comparison.