dataesr/scientific-paragraphs-categorization huggingface dataset:
It appears, therefore, that automatic detection through (deep) machine learning of the different lesion morphologies occurring in tauopathies and particularly in AD is an emerging and burgeoning field of research. Our previous work introduced novel open-source methods for quantifying NFTs and NPs based on our own studies and developments  [13, 20]. These methods were recognized as pioneering contributions in recent medical image computing conferences  [13] , but still lead to suboptimal performances. Therefore, in the present work, we first developed, using a widely distributed and user-friendly software (Visiopharm®), new strategies to optimize and fasten human annotations while preserving quality of analysis by setting up a "human-in-the-loop" pipeline that may reduce the annotation burden when preparing data training sets (ground truth databases) to be used in DL models.
Slide digitization and annotation All sections were digitized to obtain whole slide images (WSIs) using two scanners: Hamamatsu NanoZoomer S60 (n = 4 cases digitized) and Hamamatsu NanoZoomer 2. 0-RS (n = 11 cases). Images were acquired at 200x magnification and saved in ndpi format. All WSIs were uploaded to the Visiopharm analysis software (Visiopharm®, v2022. 02, Hørsholm, Denmark) that was used for object annotation, detection-segmentation, review, and correction. Neuritic plaques (NPs) and neurofibrillary tangles (NFTs) in neuronal somata were annotated separately within regions of interest (ROI) of the cortical grey matter. All objects (NFTs or NPs) were outlined manually using Visiopharm's integrated drawing tools. All annotated objects were verified and validated by the expert annotators (LI, LS, SB).
Ground truth dataset built by iterative AI-driven object annotations. The identification of all objects of interest within a given region is often challenging for a human annotator especially within heavily stained tissue areas. Thus, following the initial human annotation of NPs and NFTs, all annotated slides were submitted to an iterative AI-driven, human-in-the-loop, object correction procedure in order to find "overlooked" or "wrongly-annotated" objects by the experts and/or to refine their outlines (Figures  1 2 ). For this purpose, an initially annotated data set performed by human experts was used to train a U-Net convolutional neuronal network, which was a part of the AI-Architect module of Visiopharm image analysis software. The algorithm relied on 1024x1024 pixels sized training windows as well as several data augmentation steps including image rotation and flips, and hue and intensity variations.
Annotations obtained after the three initial rounds of human-AI adjustments were used to train a 4th U-Net algorithm. Each annotated and/or AI-detected object was individualized, and a Dice score was calculated. Individual objects and associated Dice scores were uploaded to the integrated Calculator module of Visiopharm. This module allows at the same time to visualize the Dice scores of all individual objects and the corresponding objects' outlines on the annotated slide (Figure  1C ). Using this procedure, we focused the final corrections only on objects with suboptimal Dice scores (<0. 75) to optimize the workload for the human corrector. In addition to creating ground truth datasets, we also evaluated strategies to further reduce, in the future, the annotation burden for human experts during initial labeling of objects.
We tested several options for the objects' outlining simplification and evaluated the different conditions on AI-based automated segmentation (see Supplementary data and supplementary Figure  3 ). Algorithm training and testing for object detection and segmentation. After creation of the ground truth using the iterative AI guided corrections, two distinct algorithms were trained for the detection and segmentation of NPs and NFTs. For the NPs algorithm, we divided our initial set of fifteen annotated slides in a training set containing twelve annotated slides, bearing a total of 4643 annotated and expert-verified NPs, and a test set with three slides which included a total of verified 1133 NPs (Supplementary Figure  1 , Supplementary table  2 ). The detection/segmentation algorithm was generated in Visiopharm® using available U-Net CNN applied on WSI with 100x magnification and a 1024x1024 pixels field of view.
Results Ground truth generation: 1) Enhancing annotation quality of neuritic plaques and neurofibrillary tangles through AI-driven iterative approach and object-by-object human validation. We used six slides with different densities of lesions to generate ground truth for NPs objects. Our iterative process demonstrated the importance of combining the strengths of AI and human experts to achieve a more accurate and comprehensive dataset of NPs and NFTs, providing a valuable resource for further research in the field of digital neuropathology. Ground truth generation: 2) Dice scores filtering allows fast and precise annotation dataset correction. As a supplementary and final step, we used individual object Dice scores (for NFTs or NPs) to guide and optimize annotation correction. We collected individual Dice scores for each annotated object and first corrected objects with the lowest Dice scores (0 value), corresponding to AI-segmented but not human-annotated objects.
This included the false positive objects (i. e. , objects that were recognized as neuritic plaques by AI but do not belong to this category according to the pathologist) as well as "overlooked" objects (i. e. , objects correctly recognized as neuritic plaques by AI and that were missed by the pathologist). These "overlooked" objects were outlined and added to the object dataset, leading to an 18. 8% (p<0. 0001) reduction in null Dice score objects (Figure  3 ). Objects with low (0. 05 -0. 45) and moderate (0. 45 -0. 75) individual Dice scores represented sub-optimal AI recognition and were manually corrected by pathologists. However, applied corrections led to only minor improvement in NPs detections (+4. 1% for low Dice score objects and -1. 6% for moderate Dice score objects). In contrast, there was a significant improvement (+18.
In conclusion, by correcting objects with low and moderate Dice scores and adding "overlooked" objects to the dataset, we were able to substantially enhance the robustness of the NPs and NFTs databases. This approach decreased the time and effort required for expert correction, thereby increasing efficiency. Ground truth generation: 3) improved annotation strategy. In parallel with our main study, we evaluated strategies to reduce the annotation burden on pathologists through outline simplification. We compared the accuracy of U-Net based algorithm segmentation of neuritic plaques derived from human annotations with precise, rectangular, or larger outlines. Our results (see Supplementary data) demonstrate that simplified rectangular outlined objects yielded high accuracy and correctly segregated peripheral background and object limits with the same performance as highly-detailed and precise manual outlines.
Segmentation of neuritic plaques (NPs) using AI-improved object database To segment neuritic plaques (NPs), we used our training set of twelve annotated whole-slide images (WSIs) containing 4643 expert-validated NPs (see part (2) of supplementary Figure  1 ). We implemented a U-Net detection and segmentation algorithm, which achieved a loss function of 0. 003 after 95,000 iterations. The trained algorithm was then tested on three additional WSIs containing 1133 annotated objects. The detection capacity of the algorithm was evaluated using the F1 score. NPs were detected with a mean F1 score of 0. 86 (±0. 04). The algorithm was evaluated on a test set of 1133 annotated NPs. It detected 1079 objects, of which 959 were true positives (TP) and 120 were false positives (FP). The recall and precision of the algorithm for NPs detection were 0.
86 and 0. 89, respectively, indicating a low rate of both false negative and false positive errors. The segmentation accuracy was evaluated using the Dice score and achieved a value of 0. 77. Of the total segmented area, 81% was correctly classified as belonging to the NPs category (precision: 0. 81). Of the total annotated NPs area, 73% of segmented pixels were correctly classified as belonging to this category (recall: 0. 73). These data are summarized in Table  1. In summary, our algorithm allowed for high-precision detection and segmentation of NPs, enabling calculation of number, load, and density as well as numerous morphology-associated measurements that could be derived from object segmentation. In conclusion (Table  1 ), our algorithm demonstrated precise segmentation capabilities for NFTs within the evaluated slide set.
Building reliable datasets for training DL models Variations in object morphology combined with technical issues promoting heterogeneity in histological preparations (e. g. , tissue conservation, stain intensity, artifacts etc. ) represent a major challenge for efficient generalization of AI-based segmentation protocols. These variations are exacerbated when comparing images across institutions that do not use identical protocols for tissue preparation and staining. Several initiatives have therefore been launched to create online databanks containing whole slide images (WSI) that can be used to train AI-based algorithms to exclude artifacts and minimize the impact of color variations  [8, 38]. In our study, we first addressed the challenge of creating a robust and extensive annotation dataset for complex objects like NPs and NFTs.
We derived training datasets from slides, obtained from four different European brain banks, and presenting large heterogeneity in terms of density of immunostained objects and overall staining levels. The success of AI-based segmentation algorithms is directly dependent on the quality of ground truth annotations  [18]. However, even overtrained, and experienced human annotators (e. g. , pathologists) face challenges when using WSI due to the large number of required annotations and difficulty in explicitly annotating all objects of interest in a given area. This caveat becomes critical when performing annotation work in densely stained WSI comprising thousands of objects, as often seen in AT8 stained brain sections of patients with late-stage AD. We hypothesized that an AI-driven iterative procedure could increase the precision and speed-up human-made annotations of dense and heterogeneous objects.
45) suggesting bad agreement between AI and pathologists. This refinement made the correction process faster and less error-prone, leading to further improvements for both NPs (-18. 8% for Dice score 0 objects) and NFTs (-39. 6% for Dice score 0 objects) datasets. The resulting final training datasets were validated by two pathologists (LS, SB) and deemed suitable for final training of AI-based segmentation protocols. During training datasets generation, we confirmed that even trained human annotators can overlook many objects during the initial annotation phase, which may lead to imprecise datasets and propagation of errors to subsequent AI-based segmentation algorithms. To solve this issue, we showed that AI-based algorithms can effectively identify overlooked objects and significantly improve annotation quality to provide refined training datasets and establish a reliable ground truth.
Tailored open-source and explainable algorithms From a technical point of view, one major disadvantage of commercial software is the user's lack of traceability, interpretability, and explainability on the trained deep learning models  [29]. Therefore we started testing in-house traceable tools for NFT and NPs segmentation and detection  [13]. These tools, also derived from the dataset used in the present study, are based on CNNs and UNet architectures. To enhance the segmentation performance, we used attention mechanisms that suppress irrelevant regions in the input patches and highlight salient features of the NFTs and NPs. We evaluated our tools using cross-validation and cross-testing, which ensure the robustness and generalization of our results followed by the use of visual explanation methods, such as attention maps, to provide insights into the segmentation process of the deep learning models.
Conclusions In conclusion, our data indicate that artificial intelligence-based algorithms can be effectively applied to histological material for two main purposes. First, these algorithms can be used to identify overlooked objects in tissue sections and significantly improve the quality of annotations for creating labeled datasets (ground truth). Second, they allow to generate deep learning models capable of detecting and segmenting discrete brain lesions (such as plaques (NPs) and neurofibrillary tangles (NFTs)) with high precision in an unbiased and automated way. Objects with moderate Dice scores (0. 5-0. 75) were also reduced. Concomitantly, the precision of the segmentation of objects with highest Dice sores (>0. 75) was improved by more than 15%.
The goal was mainly to evaluate the impact of a U-Net based model assistance on the discovery of true objects that were overlooked by human annotators. Two experts (LS, LI) manually drew 1069 annotations of NPs, agreeing on both the location and outline of the objects. This human-annotated set was used to train a first model, the output of which was then validated or corrected by human annotators (LS, LI, SB). The AI assistance and Human-in-the-loop allowed to identify objects initially overlooked by experts. Three subsequent rounds of AI training and human corrections were performed, using the initial set of slides, which yielded an increase of the number of expert-validated NPs by 43. 9% after three correction rounds compared to the initially hand-annotated dataset (Database 1). In addition to the six initially annotated slides, nine new (non-annotated) slides were included in the annotationcorrection procedure to constitute a 5013-objects expert validated ground truth dataset for NPs.
We applied the same procedure to NFTs annotations, starting with five slides presenting variable NFTs burden. This dataset included 1939 human-annotated and verified NFTs annotations. Training a first U-Net model and performing subsequent three rounds of AI-Human iterative validation/correction yield 2325 NFTs annotations, representing an improvement of 19,9% of segmented objects compared to the initial "human-only" annotated dataset. In addition to the five initially annotated slides, four new (non-annotated) slides were included in the annotation-correction procedure to constitute a 4927-objects expert validated ground truth dataset for NFTs.
We can quote for example the commercial system IFTTT (3). The IIHM team of the LIG laboratory proposes an approach based on a CCBL programming language specifically dedicated to non-programmer users (  [32]  and an article in this issue). Designing systems that are truly usable and accepted by users is the 2 th point among 17 cited in a survey of 31 experts on potential barriers to the development of smart homes  [30]. In first place is the technical reliability of these devices and the ability of a home to behave properly. The problem of technical reliability is linked to the multiplication of devices (often equipped with batteries which low level is not detected), to the multiplication of heterogeneous protocols and operating modes, as well as to the dynamic aspect of the environment (arrival or removal of new sensors and services).
This complexity, this heterogeneity, this dynamicity of the environments, as well as taking into account the users' needs require the implementation of specific architectures and software infrastructures (middlewares for example) allowing to take in charge these various points and to reinforce the quality of the produced software. Becker and al. [3]  propose for instance an overview of existing approaches as well as the challenges to be solved. In the middleware domain, we can mention the work of the Adèle team (which has since joined the M-PSI team) of the LIG laboratory. They propose a service-oriented software component model iPOJO on top of OSGi  [13]  as well as an execution platform ApAM  [8]  allowing to take into account the dynamic and evolutive aspect of environments. Beyond the work on software architecture and runtime support, we can also mention an initial work led by the Vasco team of the LIG aiming at verifying at runtime that the behavior of the intelligent building conforms to a set of properties enacted during the design  [15].
The Maison Intelligente de Blagnac is presented, in particular the technical choices that were made to meet the objectives of a living lab, the experimental methodology deployed, as well as a more complete description of an experiment that was carried out to assess the needs in home automation for the elderly. The second paper, by Demeurre and Caffiau, proposes to put the user, not at the center of the design of new innovative devices, but at the heart of the design of the behavior resulting from the cooperation of the various home automation systems deployed in his home. This problem of programming by the users themselves of the behavior of their smart home is usually addressed in experimental or commercial approaches by "user friendly" languages based on rules Event -Condition -Action. These approaches have a certain number of limitations that the authors resolve by defining a new dedicated language: CCBL (Cascading Contexts Based Language).
All the experiments described in this section were made using a computer with Intel(R)Xeon(R) CPU E5-2650 v2 processor clocked at 2. 60 GHz, 32 cores, 2 threads per core and 252 GB RAM, under Linux operating system. All methods are implemented using the Python API for Cplex, which is run in single-thread mode with a default memory limited to 20 GB. All Cplex parameters were set to their default values. A default time limit of one hour is set for each tested instance. For the initialization heuristic used within the B&P algorithm (cf. Section 6. 3), the time limit is fixed to 900 s. The following settings represent all tested methods in our computational experiments: • PF: The Branch-and-Price implementation of the model PF with the best-performing pricing method (more details are given in Appendix A.
With a well-developed understanding of the principles of GI-WAXS, Section 3 presents examples of GIWAXS data analysis in action. This includes the identification of phase and symmetry changes, and methods to verify structural characteristics via simulations of both the 2D GIWAXS images and their 1D azimuthally integrated profiles. Within this context, there exists a growing need to reliably assess hybrid perovskite materials by means of GIWAXS, to accurately determine the structure and space group of the perovskites when fashioned into thin films, as well as resolve important structure-property relationships. [12]  While several Review articles have recently covered aspects of GI-WAXS on metal halide perovskites,  [4, 9, 10, 13]  our goal here is different from these, as we elucidate the important considerations for completing reliable measurements, their interpretation, and realizing a quantitative analysis of the integrated GIWAXS patterns.
We explore how GIWAXS experiments capture the structural properties of the film, as encoded in the shape, width, and azimuthal angular distribution (𝜒) of Debye-Scherrer diffraction rings (Figure  1 ). Finally, it will become evident that collecting quality GIWAXS data suitable for reliable Rietveld refinement or texture analysis (Section 3) is far from trivial, and our goal is to demonstrate how to carefully shape the design of meaningful GIWAXS experiments. Within this context, we clearly link several key scientific and technical aspects of GIWAXS experiments which are commonly applied within metal halide perovskite research.
Reproduced with permission. [19]  Copyright 2019, The Authors, published by American Association for the Advancement of Science. B) Elastic scattering factor of the common elements forming the perovskite structures of interest for different q values. The inset illustrates the relative incoming and outgoing intensity of scattering X-rays in real-space for the example of carbon, that is, as the angle of scatting increases the magnitude of scatting decreases. C) Mass attenuation and normalized X-ray fluorescence emission lines for different elements. The data have been extracted from the X-ray database project: https://github. com/xraypy/XrayDB. D) Origin of atomic X-ray emission: a core electron is excited by synchrotron photons and then decays by emitting a characteristic X-ray photon to fill the core hole.
On the other hand, the angular resolution depends directly on the sample-to-detector distance and inversely to the pixel size, whereby increasing the distance equates to better angular resolution. Thus, there exists a compromise struck between gathering a large range in reciprocal space and obtaining data with high-angular resolution. Direct X-ray detection is the basis of state-of-the-art hybrid pixel array detector (HPAD),  [32]  which directly converts the photon to an electronic signal, acting as a photon counting detector, since each photon directly registers an event. They are based on the electronic excitation of a semiconductor layer (e. g. , Si, Ge, CdTe) and recorded by a complementary metal-oxidesemiconductor (CMOS) system, giving huge versatility and efficiency (unlike older CCD technologies which are still in use).
Here 𝜑 represents the angle of rotation about the film normal axis. D) Comparison of scattering profiles generated from integrated both out-of-plane (without the Ewald sphere correction) and over the whole GIWAXS image shown in (A). These data have been normalized to the (110) peak intensity near q = 10 nm -1 and background corrected (removal of glass signals in this region). E) Azimuthal intensity profiles of the (110) peak shown in (A) using different GIWAXS image formats; the red profile is generated from the (q x,y , q z ) image without Ewald sphere corrections and is an incorrect representation, the black profile is from the (q r , q z ) image which includes the Ewald sphere correction (correct representation), and the blue trace further includes the quantitative correction involving a factor of |sin(𝜒)|.
General Evolution of the GIWAXS Signal with Increasing 𝛼 i As showcased throughout this section, numerous aspects of GIWAXS are fundamentally determined by, and evolve with, the grazing incidence angle. Thus, in practice, it can be advantageous to acquire multiple GIWAXS images over a range of 𝛼 i angles (Figure  22A ), rather than a single frame at a pre-determined 𝛼 i value. Such datasets help manage several anomalous effects, including instrumental broadening, refraction, the missing wedge, and the in-plane scattering horizon. To examine how these features continuously evolve with 𝛼 i, Figure  22B -E presents GIWAXS data recorded from a CsI 0. 05 [FA 0. 83 MA 0. 17 Pb(I 0. 83 Br 0. 17 ) 3 ] 0. 95 perovskite thin film deposited on silicon, scanning over -0. 42°≤ 𝛼 i ≤ 4. 0°. Intended for solar cell applications, this perovskite wafer is relatively large (≈20 × 20 mm 2 ) and thick (500 nm), exaggerating the features identified above.
The preparation of high-quality metal halide perovskite thin films for analysis requires stringent substrate choices based on the application. Topologically rough films are generally   001 ) 2H-PbI 2 reflection at q ≅ 0. 9 Å -1 (2𝜃 ≅ 12. 7°, CuK 𝛼 ). Crystallographic information files (CIFs) used for powder XRD simulations are cited in numeric order with respect to decreasing order of the figure legend (2H, 4H, 6H, …, P63mc, R-3m, P3m1). The CIF files for the alphabetical labels in the figure can be retrieved from Refs. [48, 117, 150, 153, 154, 158]  , and  [165] [166] [167] [168] [169]. Note that we have used the experimentally determined lattice constants from Ref. [167] to update the lattice parameters databased for the 2H-PbI 2. unavoidable in halide perovskite synthesis and occasionally even advantageous for optical scattering in devices.