dataesr/scientific-paragraphs-categorization huggingface dataset:
Furthermore, the lack of divergence in GBC changes induced by each treatment was unexpected, given that the interferon-α and anti-TNF treatments modulated systemic inflammatory markers and behaviour in opposite directions  (Davies et al. , 2020). Though we have interpreted these findings as likely reflecting different underlying mechanisms it is also possible that this may also have been influenced by differences in the post-administration time we acquired the data (4 h in the interferon-α dataset and 24 h in the anti-TNF dataset) or even an interaction between treatment and the pre-existing conditions. Nevertheless, in line with previous studies, our work dovetails with the idea that changes in brain function associated with inflammation are not idle but respect a spatial pattern of regional vulnerability, which differ between interferon-α and anti-TNF treatments and might be underpinned by distinct biological pathways.
Correlations with changes in peripheral inflammation markers and symptoms in the anti-TNF dataset. Summary of correlations between the extent to which each subject manifests the spatial pattern of the first component of our partial least square regression model (PLS 1 ) in their pattern of global brain connectivity changes induced by the anti-TNF treatment and changes in depression, fatigue, and peripheral inflammatory markers. We used partial spearman correlations, which accounted for sex and age. Both uncorrected p-values and p-values FDR corrected for multiple testing are reported. Note that for the peripheral inflammation markers we used only data from samples acquired concomitantly with the fMRI data to maximize comparability. Abbreviations: HADS -Hospital Anxiety and Depression Scale.
Funding DM, OD, MV,  FET  and SCRM are supported by the  NIHR Maudsley's Biomedical Research Centre  at the  South London and Maudsley NHS Trust. MF is funded by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  ( ERC Advanced Grant  agreement No  694665 :  CoBCoM -Computational Brain Connectivity Mapping ).
A simple and low-cost image acquisition system The ears were imaged with an automaton developed and assembled by Phymea Systems (www. phymea-syste ms. com-Montpellier, France). Individual ears are manually positioned in the system (Fig. 2 ), which acquires images stored in a generic hard drive. Images are uploaded to an independent analysis station where the associated software is installed for output retrieval. The automaton works in independent acquisition sessions to easily separate experiments, genotypes or varieties, and treatments. The ears or ear lots are individually identified by the A B Fig. 2  Pictures of the Earbox system. A The Earbox acquisition system, which allows the simultaneous acquisition of six maize ears on six sides via the rotation of the ear by motorized rollers. The identification of individual ears or ear lots is done by a keyboard or a barcode scanner.
The system consists of aluminum profiles, compact laminates and assembly parts supplied by Elcom SAS or manufactured by Phymea Systems (CNC machining or 3D printing). The acquisition system is composed of two Raspberry Pi, each driving a Pi NoIR (V2. 0) camera module, and a custom-made Arduino like board (ATMEGA 328P), to control the lighting and the two stepper motors (door and rollers) via two A4988 drivers. The master Rapsberry Pi (model 3 B+) hosts the main Python program, which centralizes all the functions of the system: the graphical user interface via the PyGame library, the control of the slave Rapsberry Pi (model B+) via SSH protocol, the communication with the Arduino like board for motor control, and the saving of the pictures to an external hard drive. B The Earbox system imaging cabin.
A B Examples of scatterred ears (doors and rollers). A master raspberry Pi was set to centralize all custom functions: host the main python program of the user interface developed with the PyGame python library, control the slave raspberry Pi via SSH protocol, control the Arduino board, and save the images to an external hard drive. The entire system was designed to be affordable and open source, and costs a total of 2500 € in equipment and hardware, excluding labour and development costs. Ear peduncles at ear base were cut off prior to image acquisition, and husk-free ears were scrubbed and cleaned of silks and/or fungi with a brush so that all grains were accessible to the camera. The ear cleaning procedure appears to be the most variable and timeconsuming, up to 10 s or more for an ear covered with fungus.
Nevertheless, it is easy to reach a total acquisition rate of 120-220 ears per hour for a team of 2 people, depending on the storage methods, the identification method, and the appearance of the ears. In this study, a total set of 9492 images (791 ears) were taken from both panels, by one person with a rate of 128 ears per hour. A combination of empirical segmentation and deep learning to build a robust routine workflow for ear and grain segmentation RGB and IR images acquired from both panels of ears were first pre-processed (Fig. 4A -Ground for deep learning) using conventional image analysis tools (dilate, open, close, gaussian blur and watershed) and merged to normalize the data for all ear and grain colours (Fig. 4 , Step 1), resulting in a pre-processed image set (4746 images: 6 images per ear for 791 ears, hereafter referred to as the 'dataset').
B -Deep learning grains mask Grains organisation Grains characteristics phenotypic data (Fig. 4 , Step 4). All images were then processed with the fitted neural network (Fig. 4 , step 5) and used to estimate grain phenotypic traits and characterize grain organization on the ear (Fig. 4 ,  Step 6). Data are extracted for each individual ear side pictures (6 sides per ear) and averaged to represent the whole ear. The whole data extraction pipeline (from treatment of acquired ear images with the EARBOX acquisition system to phenotypic variables extraction) was coded in MATLAB and Python. The code for the analysis using a Graphical User Interface in MATLAB is fully available on a public repository (https:// github. com/ Phymea-Syste ms/ Earbox). It is used in combination with a Python code applying the trained neural network to extract the DL2 images, also available in the same public repository.
A benchmark test to assess image analysis capabilities was performed with parallel computing (using MATLAB parallel loop functions, one image per CPU Core) for the whole pipeline for all 791 ears from both panels with medium capacities laptops: The first step, 'ear masking': validating images presence, counting ears on each image, comparing to scanned user-scanned codes, resizing images, and extracting the ear from initial EARBOX images (from 3 ear per image to 1 ear per image on a black background) took approximately 21 secs per ear (Performed on CPU with parallel computing: 11th Gen Intel(R) Core(TM) i7-11800H @ 2. 30 GHz-16 Gb Ram). The second step, 'Deep Learning' (DL2): extracting the bounding boxes on grains, correcting for overlapping bounding boxes and resizing took approximately 54 s per ear (Performed on CPU and GPU: AMD Ryzen 7 3700x-NVIDIA GeForce RTX 1080 Desktop GPU, 8 Gb VRAM-32 Gb RAM).
The analysis, as developed, requires relatively little supervision by the user and can thus run overnight or outside of working hours, and is therefore not determinant in the cost analysis, allowing for a more efficient use of resources. If accounting for data handling and transfer, as a reference one can therefore estimate an approximate time of analysis of 38 h for a set of 1000 ears with medium capacity hardware, highly scalable. A ground-truth dataset was built prior to any analysis by manually segmenting grains from a set of images from 79 ears (10% of the dataset; not used to train the Neural Network). These ears were randomly selected from each category of grain colour and shape based on its frequency in the entire dataset. Selected ear images were treated manually using adobe photoshop software and a graphic tablet to mark grains by surrounding and filling them on a mask (on top of the image) and converting this mask into a binary image (black and white), similar to the outputs of the processing algorithms used.
A preliminary step of empirical segmentation was performed and used as an automatic annotation to drive deep learning iterations. The architecture of the empirical segmentation was developed to detect grains for a large portion of the dataset, so that all features can be learned and improved in Deep Learning run. RGB and IR images were processed with an algorithm developed by Phymea-Systems (Fig. 4 , step 2) using only trial and error (using morphological image processing toolkits) to produce a grain mask that was precise enough to characterise the grains from images with various colours of grains and cob. The images were pre-processed with conventional image analysis tools to enhance their quality and then merged to retrieve complementary contrast and shapes. The cross-checking of the two pieces of information allowed a precise selection of the grains to produce an initial grain segmentation, which was in turn corrected by image analysis to refine the grain shapes and recover the over-segmented grains.
After this step, the processed images were sorted to assess the quality of the output. The output masks were scored by two independent individuals to evaluate the quality of segmentation with a score from 0 (bad segmentation) to 3 (good segmentation). Ears with uniform grain colour, strong colour contrast between cob and grain colour and non-scattered grains were mostly correctly segmented, with only minor problems. Most low scores were encountered for scattered grains (overly segmented), and for ears with similar grain and cob colour, which made it more difficult for the algorithm to distinguish. A deep learning neural network was trained to segment the grains on ear masks. It is a powerful tool for high-throughput plant phenotyping, yielding valuable results when large datasets are available  [25].
More specifically, the Mask-RCNN neural network is commonly used as a framework for instance segmentation  [26]. It is a highly flexible, trainable framework that has been widely validated in many scientific domains, including plant science  [12, [27] [28] [29] [30] [31] [32]. More specific to maize, Kienbaum et al. [19]  have trained mask R-CNN for cob segmentation on images and have highlighted the interest of this type of framework over classical image analysis techniques: its robustness and accuracy. The entire deep learning (DL) framework was coded in Python 3 using TensorFlow for in-learning visualisation  [33]. The baseline architecture of the deep learning model used in this paper for the kernel segmentation is the Mask-RCNN model with a ResNet50 backbone. Mask-RCNN is a two-stage model, the first stage called Region Proposal Network (RPN) generates object bounding box proposals, then a second stage extracts feature from each proposal, performs classification, does bounding box regression, and in parallel predicts object masks.
The input layer of this model was adapted to receive 4 channel images (RGB + IR) and no pre-trained weights were used, it was trained from the ground up on the maize dataset. The RPN network was set to use 256 anchors per image with power-of-2 side length from 8 to 128 pixels, and a non-max suppression overlap threshold of 0. 9. The smooth-L1 loss function is used for bounding box proposal, and cross-entropy loss is used for both classification and mask prediction. The training was done on a subset containing 2226 images, on random 512*512 pixels crops from original images without resizing, and cross-validated on random samples from a separate subset of the complete dataset. The model was trained for 99 epochs, with 75 cross-validation steps each, and a base learning rate of 0. 001 with momentum optimization (gamma = 0.
9). Detection was done on full-scale images with the non-max suppression overlap threshold of the RPN network set to 0. 7. A set of data augmentation techniques were applied prior to learning. Each image was cropped into a set of 512 by 512 pixels elements on which a random number (between 0 and 2) of augmentation techniques were applied before being introduced into the model. The various augmentation transformations were retrieved from the 'imgaug' package (https:// imgaug. readt hedocs. io/ en/ latest/): flip up down, flip left right, 90 or 180-or 270-degree rotation, pixel value multiplication and gaussian blur. Deep Learning iterations involved 33 learning epochs with a cross-validation using 75 random images unused in the training dataset. The neural network training (Fig. 4 , step 3) included the following steps (Additional file 2: Fig.
S2 ). First, empirical masks with a mean score equal or greater than 2. 5 (2076 images, 43. 7% of the dataset, i. e. , 346 ears) were used to train the neural network (Additional file 2: Fig. S2 , step 3). The resulting DL1 masks outputs were corrected by 'minor' manual corrections (only 'click on grains' to add or remove mis detected grains) for 1926 images (92,8% of DL1 images, i. e. 321 ears) and 'major' corrections (adding grains and reshaping grains for ears with a large number of missing or mis-segmented grains and/or wrong shapes) for the remaining 150 images (7,2% of DL1, i. e. 25 ears-Additional file 2: Fig. S2, step 4 ). Second, a set of 72 images (12 ears, i. e. , 1. 5% of the dataset) from ears incorrectly segmented in the initial empirical segmentation, were manually corrected in the same way as the 'major' corrections seen above.
Corrected images from DL1 and initial empirical segmentation were used in a second Deep Learning iteration (DL2 Additional file 2: Fig. S2 , step 5) with 2148 images, i. e. , 358 ears (45. 3% of the dataset). The 'mean Average Precision' (mAP) was used to estimate the quality of the Deep Learning output  [34]  and calculated as defined by the latest evaluation's techniques of the COCO dataset  [35]. The literature usually considers an algorithm to be highly efficient for mAP values of 0. 4. After several steps of learning, small input image correction, re-calibration of the neural network parameters, the resulting network with fitted weights (DL2) was used to extract segmented grains from all acquired images, i. e. , the dataset (Fig. 4. , step 5).
Graphs A-E include all 791 ears from both panels. F Number of grains per ear measured automatically by the Earbox (y) and manually with an automatic grains counter (x) for 257 ears selected from the environmental diversity panel. G Grain width in centimeters, measured along the axis perpendicular to the ear. H Grain height in centimeters, measured along the main axis of the ear. I Assignment of a cohort number to each grain. Data presented in G-I correspond to a set of 809 grains measured and classified automatically by the Earbox (y) and measured manually on the acquisition images (x) for comparison. Green dots: ears from the biological diversity panel; red dots: ears from the environmental diversity panel. Empty red dots: scattered ears of the environmental diversity panel (Fig. 3B ). Grey dots: grain dimensions (one dot = one grain).
Grain dimensions and spatial positions: a potential framework for studying developmental gradients The method adequately estimates grain dimensions (Fig. 6G, H ), ranging from 0. 3 to 1. 3 cm wide (Fig. 6G ) and 0. 1 to 0. 9 cm high (Fig. 6H ) in both panels. The correlation between manual and automatic measurements was indeed high for grain width (R 2 = 0. 88, RMSE = 0. 08 cm; Fig. 6G ), and slightly lower for grain height (R 2 = 0. 67, RMSE = 0. 07 cm; Fig. 6H ). The slight differences may be due to the narrower range of variation observed for grain height versus grain width in the training dataset, which can be easily addressed with further Deep Learning iterations with suitable datasets. Most of the noise comes from isolated grains on scattered ears that tend to have a more circular shape when space is available around them (examples Fig.
Acknowledgements We thank all the staff of the  Station Expérimentale de Mauguio & Saint-Martinde-Hinx  for their special contribution to grow, harvest and provide access to the biological diversity used in the dataset of this study. We particularly thank the  French INRAE agents, especially Serge Malavieille , for their patience during the testing of our prototypes, their feedback and their valuable knowledge that allowed us to successfully complete this project. A special thanks to  Christian Fournier  ( INRAe LEPSE ) for his helpful advice to put in place the first steps of the empirical segmentation. Special thanks to  Romain Chapuis  and  Olivier Turc  for investing time in the project from beginning to end. Finally, we thank  Joubin Ezami  for his patience and his valuable contribution for the 3D modeling and the production of the imaging system.
Funding This study is a joined effort from  Phymea-Systems  and the  INRAe. For Phymea-Systems , the work was largely financed by the company's own resources with a participation from  Région Occitanie funds. For the INRAe participation, this work was supported by the  EU  project  FP7-244374  (DROPS), the projects  ANR-10-BTBR-01  ( Amaizing ) and  ANR-11-INBS-0012  ( Phenome ) and the  EU  project  H2020 731013  ( EPPN2020 ).
Evidence review and development of recommendations For each guideline question, the evidence synthesis team prepared an evidence profile  30, 31  and a GRADE evidence-to-decision (EtD) table  32, 33  using the GRADEpro software. Each EtD table summarizes the results of a systematic review of the literature that was either updated or performed de novo for these guidelines. The EtD table includes the information about the effects of interventions on health outcomes, the values and preferences (ie, relative importance of outcomes), resource utilization (costeffectiveness), health equity issues, acceptability of interventions to stakeholders, and the feasibility of implementation. The guideline panel reviewed the draft EtD tables before and during the guideline meetings making suggestions for corrections and clarifications.
Acknowledgments We would like to acknowledge the contributions of our late colleague  Prof. Mario Sánchez Borges , who was a member of the  WAO DRACMA guideline group. Hospital, Rome, Italy. e Hospital Quironsalud Bizkaia, Bilbao-Erandio, Spain. f  Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. g Allergy and Immunology Section ,  Louisiana State University Health Sciences Center, Shreveport, LA, USA. h  Pediatric Allergy Program  at the  Department of Translational Medical Science , and  ImmunoNutritionLab at Ceinge Advanced Biotechnologies, University of Naples Federico II, Naples, Italy. i  Department of Pediatrics, British Hospital-Perdriel, Buenos Aires, Argentina. j Paris Descartes University, Pediatric Gastroenterology, Necker Hospital, Paris, France.
Over the last 20 years, automated systems (i. e. systems that can be operated autonomously for months or years and remotely controlled) such as CytoSense/Sub/Buoy  (Dubelaar et al. , 1999) , FlowCytobot and Imaging FlowCytobot  (Olson et al. , 2003; Olson & Sosik, 2007) , SeaFlow  (Swalwell et al. , 2011) , have produced highly resolved spatial and temporal datasets of numerous phytoplankton groups, including relatively large and rare groups thanks to increasing sample volume analysed. These instruments can be deployed in situ and underwater  (Sosik et al. , 2003; Thyssen et al. , 2008b; Peacock et al. , 2014; Hunter-Cevera et al. , 2016) , on a ship  (Li et al. , 1995; Thyssen et al. , 2009; Bonato et al. , 2015; Thyssen et al. , 2015; Marrec et al. , 2018; Aardema et al. , 2019; Ribalet et al.
, 2019; Fowler et al. , 2020; Louchart et al. , 2020)  or a buoy  (Thyssen et al. , 2014). Imaging in flow systems can be combined with flow cytometry acquisition, affording additional taxonomic identification for microplankton size range. The investigation of machine learning approaches for generating automatic recognition of microorganisms at individual or cluster levels in marine samples has been explored  (Malkassian et al. , 2011; Ribalet et al. , 2011; Hyrkas et al. , 2016; Olusoji et al. , 2021; Fuchs et al. , 2022) , and will benefit from medical field research for additional computational processes and data analysis workflow  (Aghaeepour et al. , 2013) , promising to ensure near real-time data availability. The worldwide increase in marine FCM datasets provides evidence of the technique's utility and reliability in marine research.
Indeed, FCM datasets are now widely accepted as a core parameter and a common reference in oceanographic cruises  (Manti et al. , 2012). Unfortunately, the datasets collected are, in most cases, maintained in local data stores and are not routinely set up for open access through international data portals, despite the need for sharing and disseminating valuable information on ocean plankton functional-group distribution. Consequently, scientists are facing the difficult challenge of handling a large amount of data which needs to be processed rapidly and harmonised before being stored in databases to be accessible to scientific and environmental management communities. Best practices in data management  (Boss et al. , 2018)  have received relatively little attention despite the need for a data flow framework to follow the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles  (Wilkinson et al.
, 2016). In 2016, the SeaDataCloud project (H2020 EU, ID: 730960) supported a first initiative to harmonise FCM datasets via an interoperable vocabulary for the most common phytoplankton and heterotrophic prokaryotic groups. This first step was a valuable attempt to gather input from a large and international panel of FCM experts, but needed maturation as the names of groups first suggested as standards referred to some vague taxonomic descriptions. In addition, the definitions of those groups lacked precision. This paper stems from this common initiative, aiming to promote accessibility to the marine FCM datasets, proposing a consensus vocabulary and its definition for microbial groups discriminated by FCM in marine environments. The virus and microzooplankton groups were excluded from this exercise as they are not yet routinely analysed by these instruments.
Standardized vocabulary and definition Authors of this manuscript provide a commonly agreed standardized vocabulary with definitions of marine microbial groups observed by FCM, resulting from the consultation among FCM users and experts (co-authors of this manuscript) and a literature review spanning the period 1983-2020 (Annex 1 in Supplementary material). Datasets in which FCM groups are identified by the experts are highly informative and are compatible with a consensus nomenclature, unlike raw data which are saved in various binary formats (e. g. cyz for the CytoSense, adc for the Imaging FlowCytobot, evt for the SeaFlow, fcs for more conventional flow cytometers). The interoperable vocabulary needs to apply to a variety of groups present in many marine environments while being specific enough to reveal the functional diversity observed with any flow cytometer.
Additionally, some groups were identified by sorting (Ostreococcus sp. ,  Courties et al. , 1994; Richiella sp. , Zhao et al. , 2012)  or image acquisition (Phaeocystis sp. ,  Rutten et al. , 2005)  as their light scattering and fluorescence signatures are not specific enough to be resolved at a taxonomical level from cytograms only. As said before, these groups are named on a basis of post-treatment procedures by expert's, and should appear in any interoperable database in line with a consensual and interoperable vocabulary. As a best practice recommendation, any manuscript dealing with marine FCM results should use the standardised common vocabulary or include a correspondence table that lists the groups or subgroups identified by a flow cytometry user with the standards, as in Table  1 , and referenced to their sources (Uniform Resource Identifier (URI)).