dataesr/scientific-paragraphs-categorization huggingface dataset:
In this regard, we have developed AimSeg, a bioimage analysis tool for axon, inner tongue and myelin segmentation. Aided by machine learning classifiers trained on transmission EM (TEM) images of tissue undergoing remyelination, AimSeg can be used either as an automated workflow or as a user-assisted segmentation tool. Validation results on TEM data from both healthy and remyelinating samples show good performance in segmenting all three fibre components, with the assisted segmentation showing the potential for further improvement with minimal user intervention. This results in a considerable reduction in time for analysis compared with manual annotation. AimSeg could also be used to build larger, high quality ground truth datasets to train novel deep learning models. Implemented in Fiji, AimSeg can use machine learning classifiers trained in ilastik.
Several bioimage analysis approaches have been developed to analyse myelin thickness  [32] [33] [34] [35] [36] [37] [38]. Many of these approaches are implemented in semi-automated workflows that frequently require several post-processing steps. Deep learning approaches have also been applied  [39, 40]  to segment the individual fibres and their corresponding compacted myelin. Additionally, there are methods available for the analysis of 3D EM images  [41]. However, their wide application by researchers has been limited, as still only few are publicly available, well documented, and/or the code made accessible through open-source licensing. Therefore, analysis of myelin thickness from EM images is still largely performed manually by investigators, which is timeconsuming and prone to selection bias, thus contributing to limited reproducibility.
The workflow code for AimSeg-implemented as a Groovy script-is open-source and the pre-trained ilastik classifiers are publicly available together with user documentation. the inner edge of the compact myelin, thus ignoring the contribution of the area occupied by the inner tongue (diagonal orange stripes). The myelin g-ratio is determined by assimilating the axon and fibre areas to circles to estimate their respective diameters. This conventionally disregards any area contribution from the inner tongue. https://doi. org/10. 1371/journal. pcbi. 1010845. g001 AimSeg training was conducted on transmission EM (TEM) images of corpus callosum tissue samples obtained from mice that were undergoing remyelination following a unilateral toxin-induced demyelination lesion in the corpus callosum (see Fig  1B ).
We have tested Aim-Seg on both i) a validation dataset that includes TEM images of remyelinating mice and ii) a control dataset composed of images from a healthy specimen, obtaining similar results. The corpus callosum was chosen as it is a highly myelinated white matter region within the CNS, commonly affected in CNS diseases and therefore used often in preclinical studies. Furthermore, segmentation of the compacted and uncompacted myelin is more technically challenging after remyelination compared with the relatively straightforward task of segmenting normal myelinated fibres. As a result, our training and validation data takes into account a variety of features, including myelinated and unmyelinated axons, and a wide range of fibres with different myelin and inner tongue thickness, thereby extending the utility of AimSeg for the segmentation of myelinated fibres cross-sections from TEM images.
Our strategy relies on supervised ML methods based on random forests, which have been demonstrated to be useful to analyse complex images such as those acquired through EM  [44, 45]. AimSeg can be applied as a fully automated image processing workflow or enable an assisted segmentation approach that includes interactive user-editing. Our workflow makes use of open-source bioimage analysis software (ilastik  [42]  and Fiji  [43] ). The AimSeg core pipeline is a Fiji script that takes as an input a series of files previously generated using ML classifiers trained using ilastik (see  Fig 3). The workflow starts with two classifiers (pixel and object classification; see Fig 3A  -3D ) trained using supervised ML methods implemented within ilastik-although AimSeg has the potential to use classifiers trained by means of any other ML toolkit.
For this work, we trained our classifiers on TEM images acquired on remyelinating tissue; specifically, the dataset includes five images of the corpus callosum from four adult mice after a demyelinating lesion was induced. Users can directly apply the ready-to-use classifiers pre-trained for this work, improve them by adding their own raw data and annotations, or train new classifiers from scratch by following the guidelines provided within the AimSeg documentation. The AimSeg pipeline is divided in three sequential stages aimed to segment each of the three fibre components from each of the fibre cross-sections in the image. In Stage 1, the myelin probability map is processed to segment the inner region (see Fig 3E  -3H ), which is used as a seed to get the fibres in Stage 2 (see Fig 3I  -3L ).
Assessment of the automated and supervised segmentation First we evaluated the learning efficiency of the ML methods used to generate the pixel and object classifiers. The results obtained comparing ilastik classifiers trained on different numbers of samples (1-5 images) showed a limited effect on the final AimSeg output. Small and sparsely annotated datasets generated results close to classifiers trained with more data. This suggests that the classifiers reach their maximum precision with few annotations; further training does not seem to improve the results, and may even be counterproductive (S1 Fig). To quantitatively assess the performance of our strategy, we compared the segmentation output obtained using our workflow to analyse the images of the validation ground truth (five images from four mice), manually annotated by an expert (see  Fig 2).
Performance on non-remyelinating samples Tissue undergoing remyelination contains axons whose myelin sheath may present a wide variety of states and, additionally, a high proportion of unmyelinated axons. Therefore, it seemed suitable data for training and validating AimSeg. We also tested its performance on control, healthy, more uniform tissue (without a demyelinating lesion) (see S4 Fig). Overall, the segmentation metrics and the biological baseline proved to be very similar to the validation dataset (see Fig  6A). The statistical analysis performed on the identified objects within the control dataset (with a 5. 9% false negative rate and a 6. 6% false positive rate) revealed a CCC of 0. 9953 (95% CI 0. 9943-0. 9962) for fibre areas. Fibre areas detected using AimSeg were, on average, 0.
Computation time The time required to manually annotate the validation ground truth per image was approximately one hour compared to an average of 6. 34 seconds with automatic processing with Aim-Seg. We further assessed the computational time required for each automated step of the AimSeg core workflow. The time required for the automated processing steps (i. e. , excluding parameterisation, data import and user supervision) of each stage per image was: 0. 74 s (Stage 1), 1. 97 s (Stage 2), and 2. 01 s (Stage 3) and 1. 62 s for post-processing and quantification. Therefore, the computational time for automated processing across the three stages was negligible when compared with the highly time-consuming endeavour of annotating the images manually.
Discussion The g-ratio is the gold standard for the assessment of the optimal myelination of axons. However, the calculation of this highly used metric neglects the existence of the uncompacted myelin of the inner tongue; a fibre component with relevance during myelination and remyelination, and whose thickness variation may contribute to the identification of both physiological and pathological processes. In fact, our dataset on remyelinating white matter clearly shows that fibres of similar cross-section diameter, but differing in the presence of inner tongue would often render almost identical classic myelin g-ratio values evidencing the risk of overlooking relevant biological conditions. The lack of bioimage analysis tools accounting for the inner tongue makes its quantification a tedious task, requiring the manual annotation of EM images by experts: this is a common bottleneck that often hinders the quantification of larger datasets.
Here we present AimSeg, a bioimage analysis tool for axon, inner tongue, and myelin segmentation of fibre-cross sections from EM images. The AimSeg workflow was built using open-source bioimage analysis software to combine supervised ML with an image processing pipeline to facilitate the annotation of the fibre compartments. To this end, it takes advantage of the user-friendly and interactive ML tools provided by ilastik (which are readily accessible for users without coding experience), the versatility of Fiji, and the interoperability of both toolkits. A post-processing pipeline corrects some common annotation errors and establishes a hierarchical relationship between different ROI sets before quantifying key myelin metrics. In this context, we propose differentiating between the myelin g-ratio, which corresponds to the classic g-ratio, and the axon g-ratio, which takes into account the inner tongue area in addition to the myelin sheath.
Moreover, the AimSeg pipeline can readily be adapted to use other pixel and object classifier outputs, for example generated using standard supervised deep learning-based approaches. Future work will explore training more generalised deep learning models for fibre compartment segmentation and classification, based on training data gathered from different laboratories. This has the potential to make deployment even easier, by removing the need for classifier training on a per-lab, per-modality basis. The efficient, computer-assisted annotation features currently within AimSeg will help in generating the ground truth for such models at scale, while the full AimSeg pipeline will remain important to translate the deep learning outputs into biologically meaningful quantitative results. Any future work, whether conducted by us or the scientific community, can benefit from AimSeg's ground truth data along with the QuPath scripts for the generation of compatible datasets, all openly shared.
TEM dataset Experimental protocols involving mice were performed under UK Home Office project licence PADF15B79 (A. W. ) issued under the Animals (Scientific Procedures) Act. Adult mouse corpus callosum tissue from remyelinating and healthy specimens was obtained and processed for EM as described in  [18, 46]. TEM images used in this study were collected on a JEOL JEM-1400 Plus TEM with GATAN OneView camera at 7. 1 K magnification with image dimensions 8. 62 μm x 8. 62 μm (4096 x 4096 pixels; pixel size 2. 1046 x 10 -3 x 2. 1046 x 10 -3 μm).
Data preprocessing AimSeg includes a preprocessing command that can be used to resize and/or normalise the image dataset. If the normalisation option is checked, images are converted to 32-bit and their pixel values are normalised to be floats in a 0 to 1 range. Data normalisation is conducted using ImageJ's ContrastEnhancer class. Bit conversion uses ImageJ's ImageConverter class. The TEM dataset used to train the ilastik classifiers and validate AimSeg was normalised enabling a 1% of saturated pixels and resized using a downsampling factor of 4, generating new data with image dimensions 8. 62 μm x 8. 62 μm (1024 x 1024 pixels; pixel size 8. 4182 x 10 -3 x 8. 4182 x 10 -3 μm).
Validation and control ground truth Evaluation was performed on corpus callosum tissue samples obtained from independent mice undergoing remyelination different from those selected for training the classifiers. Additional tests were conducted on control samples to assess AimSeg performance on samples that had not undergone demyelination. Manual annotations of the three ROIs (axon, inner region, and fibre) were done by a single expert in QuPath  [47] , thus generating the ground truth for the respective ROI set of entire images. The validation ground truth consists of five TEM images from four mice. The control ground truth consists of three TEM images from a healthy specimen. Annotations were exported using QuPath's scripting language to generate three independent instance masks (axon, inner region, and fibre images) and one semantic mask (axon, inner tongue, and compact myelin pixels) per image applying a downsampling factor of 4.
Image processing methods implemented in AimSeg AimSeg is implemented as a Fiji workflow and maintained through an update site. AimSeg handles the segmentation output as three different types of data, including binary masks, ROIs and label masks. Basic binary operations (erode, dilate, open, close, fill) are implemented as modifications of ImageJ 1. x source code  [48]. Logical operators use ImageJ's ImageCalculator class. Binary reconstruction is part of the morphological operations provided at Fiji's Morphology update site  [49]. Connected components from binary masks are detected, filtered and converted into ROIs with the ImageJ ParticleAnalyzer plugin. AimSeg handles different ROI sets by means of independent ImageJ RoiManager instances. The RoiManager is also used to transform ROIs into binary or label masks.
We excluded IoU values below 0. 5 to avoid the conflict of pairing a T object with two P objects, or vice versa. Notably, a perfect overlap is practically unattainable, even when comparing the annotations of two human operators. Therefore, we used a range from 0. 5 to 0. 9 with increments of 0. 05. The average precision, recall and F1 score is calculated as the mean of all the scores obtained at all the IoU thresholds previously described. Additionally, the Jaccard index average is calculated as the IoU mean of all T individual objects. The assessment of AimSeg segmentation performance (IoU, precision, recall, and F1 score) has been computed using a customised script based on the evaluation pipeline implemented by Caicedo et al  [51].
Correlation and bias analysis for AimSeg myelin quantification The CCC was used to determine the level of agreement in measurements between myelin metrics obtained from AimSeg segmentation masks and the manual segmentation performed by an expert (validation and control ground truth). Unlike other methods, CCC relies on concordance, not just linearity. Therefore, all the spots in the scatter plot comparing two samples with a perfect CCC are expected to fall on the x = y line. The expected bias was computed by means of a Bland-Altman analysis. The validation of myelin analysis was conducted using a customised script based on the validation pipeline implemented by Matthews et al  [52]. Image processing and myelin analysis (fibre area, myelin g-ratio, and axon g-ratio) for correlation and bias analysis were carried out using Python's skimage  [53] , numpy  [54]  and pandas  [55]  libraries.
P. C. B. was awarded a  Formacion de Personal Investigador (FPI)  predoctoral contract funded by the  Ministerio de Ciencia e Innovacion (Gobierno de España). Currently, P. C. B. holds a  Margarita Salas  postdoctoral contract ( MS21-057 ), which is funded by the  European Union-NextGenerationEU  through a call from the  Ministerio de Universidades (Gobierno de España)  and the  Universitat de València (Valencia, Spain)  for the requalification of the Spanish university system (Plan de Recuperacion,  Transformacion y Resiliencia ). A. M. R. was awarded a  UK Medical Research Council   Tissue Repair  PhD fellowship. A. W. is funded by the  Multiple sclerosis Society UK, Medical Research Council (MRC) , and the  UK Dementia Research Institute  as UK DRI which was funded by the  MRC ,  Alzheimer's Society and Alzheimer's Research UK.
Accordingly, in our real-world data set of patients with dMMR/MSI-high mCRC, anti-CTLA-4/anti-PD-1 combination seemed to improve the survival over anti-PD-(L)-1 monotherapy especially in patients with adverse prognostic features, including poor ECOG PS related to high disease burden, high systemic inflammation indexes and malignant ascites. 23 24 31 Randomized trials comparing single-agent anti-PD-(L)-1 with combinations with anti-CTLA-4 (NCT04008030; NCT04895722) or with chemotherapy and bevacizumab (NCT02997228) are still ongoing in patients with dMMR/MSI-high mCRC. These studies should be carried out also in patients with dMMR/MSI-high advanced GC, as they potentially allow to explore the efficacy of intensified treatments in key clinical subgroups of interest. Future randomized trials may allow to validate the prognostic or even the predictive role of key clinical factors or molecular biomarkers such as TMB and specific gene expression signatures.
Funding This study was supported by  AIRC  IG  23624 , to FP. Competing interests RC has received personal fees from  AstraZeneca, Bristol-Myers Squibb, Exeliom Biosciences, Enterome Bioscience, MSD Oncology, Mylan Medical, Pierre Fabre, Servier  and non-financial support from  Amgen, Bristol-Myers Squibb ,  Mylan Medical  and Servier outside the submitted work. SJK reports.
Study design and participants All participants were enrolled between March 2016 and November 2019 in the prospective longitudinal Shatau7-Imatau study (NCT02576821-EudraCT2015-000,257-20). The Ethics Committee (Comité de Protection des Personnes Ile-de-France VI) approved the study (Protocole n°  [13] [14] [15]. All subjects provided written informed consent. Patients with AD at the mild cognitive impairment/ mild dementia stages (n = 27) were included according to the following criteria: (i) cognitive phenotype of AD: temporoparietal cognitive deficit characterized by either a predominant amnestic syndrome (n = 18) or a predominant biparietal phenotype (n = 9); (ii) pathophysiological markers suggestive of AD, defined by both CSF AD profile (t-tau/Aß42 > 0. 52)  [19]  and [ 11 C]-PiB PET Global Cortical Index (GCI) > 1.
)  [24]. In addition to the VOI analysis, MRIs were also processed with the pairwise longitudinal registration module of SPM12 to obtain annualized subtraction images (divided by the time interval between the 2 scans) for voxel-to-voxel analysis (http:// www. fil. ion. ucl. ac. uk/ spm). As performed in other works  [25] , we segmented the average T1 image obtained for each subject with the voxel-based morphometry pipeline implemented in the Computational Anatomy Toolbox (CAT12) and multiplied the grey matter in this image by the Jacobian difference map. The resulting image was subsequently normalized in MNI space and smoothed with an 8-mm full-width at half-maximum (FWHM) Gaussian kernel. All corrections (attenuation, normalization, random and scatter coincidences) were incorporated in an iterative OSEM reconstruction.
The partial volume effect (PVE) was corrected by directly modeling the detector spatial resolution properties (point spread function (PSF) modeling) in the image reconstruction algorithm  [26, 27]. Parametric standard uptake value ratio (SUVr) images were created using BrainVisa software (http:// brain visa. info) on averaged images over 40-60 min after injection of [ 11 C]-PiB and over 80-100 min after injection of [ 18 F]-flortaucipir by dividing each voxel by the corresponding value for the eroded (2 mm) supra-tentorial white matter (segmented by Volbrain  [28] , which provided an accurate distinction of the basal ganglia from the white matter). This value of 2 mm is close to the intrinsic resolution of the HRRT PET camera and enables us to be far enough from the neighboring structures without reducing the volume of the region too much.
We also performed analyses with the eroded (4 mm) cerebellar grey matter (to avoid including the superior part of the cerebellar vermis, which is a site of [ 18 F]-flortaucipir offtarget binding, and to avoid PVE from the CSF or occipital cortex) as a reference region, which was also used for PiB PET analyses as previously described  [29, 30]. For the longitudinal tau-PET voxelwise analyses, we established annualized subtraction images, representing the annual change in SUVr values, by subtracting the images from the 2-year and baseline PET exams (Tau2 -Tau1) divided by the time interval between the two scans. To ensure the best alignment between the two PET images, we implemented a longitudinal pipeline as described in Schwarz et al. [30]. Briefly, we first created a linear template from the two PET exams using Advanced Normalization Tools (ANTs) software and then aligned each PET image (Tau1 and Tau2) to this template.
Characteristics of the participants The data were analyzed using R version 3. 6. 1 (R Core Team, 2017). Differences between subjects' groups at baseline were assessed using analysis of covariance (ANCOVA), and the trajectories of cognitive decline and imaging progression over 2 years in AD patients and controls were compared using linear mixed effects models (interaction between the group, i. e. , AD or control, and time, with subjects as a random intercept). Age and sex were included as covariates.
Acknowledgements The authors are grateful to the chemical/radiopharmaceutical and nursing staff of the  Service Hospitalier Frédéric Joliot  for the synthesis of [ 11 C]-PIB and [ 18 F]-flortaucipir and patient management during PET acquisition, respectively, and the staff of the  Centre de Neuroimagerie de Recherche (CENIR), Salpêtrière Hospital  for patient management during MRI acquisition. We are also indebted to  AVID Radiopharmaceuticals, Inc. , for their support in supplying the flortaucipir precursor and chemistry production advice.
TOM systems interact with both brokers and these multi-messenger alert distribution systems. While there is inevitably some redundancy with brokers especially, the focus of TOMs is to be a centralized database whereby coordination of follow-up observations of identified candidates (such as those passing the filters run within brokers) and support of astronomer science (such as integrating classification frameworks to be used on the resulting data sets) is possible. These TOM systems involve both human and algorithmic interactions, such as for the prioritization and submission of follow-up requests for target lists. TOM's provide an important conduit for information flow within this ecosystem. While many of these services are developed independently, they rely on well-defined interfaces such as Application Programming Interfaces (APIs) to interact.
Alert Brokering Several SkyPortal instances rely on alert brokers to populate their databases with candidates that users can scan and save as sources (or reject) for their groups. In the case of fritz, it uses Kowalski 8 , an open-source, multi-survey data archive and alert broker  (Duev et al. 2019) , for performing filtering and cross-matches, including rejection of bogus objects  (Duev et al. 2019) ; some science groups also rely on AMPEL  (Nordin et al. 2019 ). In the case of icare, it relies on ZTF-Fink  (Aivazyan et al. 2022) , including its filtering capabilities targeting fast transients, to select promising candidates.
Kowalski 5 https://www. wis-tns. org/ 6 https://minorplanetcenter. net/ 7 To be sure, standards like VOTable enable unified data storage and exchanges but are generic and flexible enough to the point that the same data may be stored in a multitude of ways, ie. , without a unified standard. 8 https://github. com/skyportal/Kowalski Kowalski comprises a non-relational (NoSQL) MongoDB 9  database to store ZTF alerts and other catalogs, and an API layer that allows external users (such as fritz itself, or the users using it) to interact with it. Among other benefits, MongoDB provides fast execution times (∼ log(N )) for standard operations, allows for efficient positional queries, and saves individual entries in the binary JSON format-well-matched with the AVRO format of ZTF alerts. A dedicated Kafka consumer in Kowalski consumes the ZTF alert stream at IPAC in real-time and ingests alerts into the database, where they are stored as "documents.