dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding Statement The PARKADD study was supported by a grant from the  French National Research Agency  ( ANR-SAMENTA call  for proposal, 2012, decision number:  ANR 12 SAMA 013 01 ). It received additional funding as part of labeling by the  Atlanpole Biotherapies competitiveness cluster. This research was conducted at the initiative of and coordinated by the  UIC "Psychiatrie et Santé Mentale" of Nantes University Hospital. Nantes University Hospital  is the sponsor of this study. There were no constraints on publishing.
Ulcerative colitis is a chronic disease characterized by bleeding and ulcers in the colon. Currently, the gastroenterologist reviews the colonoscopy video to assess the disease severity using an endoscopic score. This task is time-consuming and does not consider the size and the number of lesions. Consequently, automatic detection methods were proposed enabling fine-grained assessment of lesion severity. However, they depend on the quality of the training set, and its specificity to the application context. To suit the local clinical setup, we opted for an internal training dataset containing only rough bounding box annotations around lesions. Color information is the primary indicator used by specialists to recognize the lesions. Thus, we propose to use linear models in suitable color spaces to detect lesions.
The presented work focuses on bleeding and ulcer detection from colonoscopy videos obtained in the context of UC disease during an ongoing collaboration with the Bichat-Beaujon Hospital in Paris, France. Although the lesions' appearance is similar between UC and CD, we anticipate that the currently available methods are biased to their training set, i. e. to well-delineated video-capsule images (  [12] -  [16] ) as opposed to complete colonoscopy videos obtained on the local instruments. Consequently, we built a custom dataset of colonoscopy videos of real patients, called Vatic specific to this collaboration. To minimize the burden of the annotation process, we propose that doctors use an interface inspired by the Vatic software  [17]  that allows the delineation of the lesions by bounding boxes instead of a precise delineation of lesion boundaries.
Classical machine learning or Convolutional Neural Network (CNN) approaches for lesion detection usually depend on the quality of the training dataset, which naturally affects the accuracy of the detector. Consequently, using complex machine learning algorithms such as CNNs in our case may generate unsatisfactory detection results. Therefore, we decided to alternatively employ linear models. Due to their simplicity, these models can provide valuable insights and interpretable results. Following  [7] ,  [8] ,  [12] ,  [18] ,  [19] , we decided to use linear models in convenient color spaces for bleeding and ulcer detection. We also propose an efficient sampling scheme to explore the set of linear models that rejects trivial classifiers that classify all the pixels into the same class. Since the bleeding and ulcer lesions are of variable and complex geometric shapes, their delimitation by bounding boxes is quite imprecise.
Indeed, the pixels surrounding the lesion were included in the annotation although they are healthy pixels. To deal with this problem, we propose to take into account annotation errors to compute the sensitivity of the detector. Specifically, we consider the mislabeled pixels within the bounding box annotations as correctly identified abnormal pixels rather than considering them as false negatives. Finally, we analyze the performance of the best models across the initial training and additional testing datasets by considering only small subsets of endoscopic images. The contributions of this paper are threefold: • Proposition of a sampling strategy to effectively explore the set of linear models by only considering nontrivial models. • Introduction of a sensitivity performance that can deal with bounding box annotations imprecision enhancing detection models accuracies.
e. {aCr + b ≤ Y for (a, b) ∈ R 2 } to detect ulcer lesions. This corresponds to finding a straight separation line between the histograms of normal and lesions' pixels. Let's take the example of endoscopic figures given in Fig. 3. The best bleeding detector should lead to a minimum overlap ratio between normal (Fig. 3c ) and bleeding pixels (Fig. 3e ) in the (R, G) color space. On the other hand, the best ulcer detector should lead to a minimum overlap ratio between normal (Fig. 3d ) and ulcer pixels (Fig. 3f ) within the (Cr, Y) color space. 3) Proposed sampling strategy: The model search process consists of exploring all the linear models of the color spaces (R, G) and (Cr, Y) for bleeding and ulcer detection respectively. In Fig  4 , we give the histograms of normal pixels of the training dataset (Table  I ).
6 ). I ) in the RG space (on the left) and CrY space (on the right). Among the set of 100 random linear models, 8% do not cross the RG histogram in opposite to 23% for the CrY histogram. Trivial models are represented in orange color. T N + F P or true negative (TN) rate measures the proportion of normal pixels correctly identified as such, and sensitivity T P T P + F N or true positive (TP) rate measures the correctness of abnormal pixels detection. However, evaluating specificity and sensitivity hinges on reliable pixel annotations by gastroenterologists. Unfortunately, as previously discussed, the gastroenterologists' annotations in our database contain many errors because the regions of interest are provided as bounding boxes, whereas bleeding and ulcers have more complex shapes (see Fig.
5 ). Direct observation also suggests that many dark red pixels were not labeled as bleeding, and white pixels were not labeled as ulcers. Consequently, we expect over-inflated levels of FP and FN based on the database annotations. This will hide the correct classifier, and decrease the confidence in our results. In Fig. 5 , we illustrate the results of bleeding detection (in red) using a chosen random linear model, G ≤ 0. 3R + 1 and ulcer detection (in orange) using the linear model Y ≥ 0. 5Cr + 8. We report the performance metrics of the models in terms of TP, TN, FP, and FN computed on the pixel level in Table  II. It can be seen that the model is able to correctly identify most of the annotated pixels (see the last row). However, as gastroenterologist annotations are usually wider than the actual lesion, some annotated pixels are not detected by our algorithm, then the false negative ratio is very high resulting in decreased sensitivity values (cf Table  II ).
Based on the 3 best models, we estimate that around 90% of bleeding annotations are incorrect, and 80% of the ulcer annotations (see Table  III ). As a result, training with the standard sensitivity would provide nonsensical models, whereas we can achieve good performance with Sensitivity A. In Table  IV , we summarize the results of our models compared to two methods found in the literature. As we are interested in detecting annotations, we apply two simultaneous color ratios as done in  [21]  and find the optimal parameters m = 5. 95 and n = 3. 75 to detect bleeding ROIs. The KNN algorithm has not been further employed as the authors did. On the other hand, for ulcer detection, an SVM model with an RBF kernel and 10-fold cross-validation was trained on our dataset using two color bands Cr and Y as done in  [8].
Based on a grid search within the values range (-8, 7, 6,. , 6, 7, 8), we find that the optimal parameters are C = 0. 79 in terms of regularization constant and γ = 3. 03 in terms of kernel hyper-parameter. We then computed the detection performance for both resulted models on our training dataset (cf Table  I ) using the standard specificity and the proposed Sensitivity A (cf section III-B. 4). Reported results show that linear models exhibit better compromise between specificity and Sensitivity A compared with  [8]  and  [21]. SVM model fails on abnormality detection, here the ulcers found in Vatic. We thus tried to make data augmentation on the ulcer pixels to maintain a balance in the training dataset, but Sensitivity A remained low. In Fig. 7 , we present some annotated frames with the corresponding detection using our models as well as the models computed based on  [8]  and  [21].
V. DISCUSSION As discussed in Section II-A, the RGB color space, and especially the Red and Green channels, has previously been used successfully for bleeding detection, whereas the YCbCr color space was used for ulcer detection. The information present in the pixel color is not altered by a change of color space, but a suitable color space presents this information more straightforwardly, and dimension reduction methods such as PCA can automatically perform this. In this manuscript, choosing the right colorspace based on the previous literature (see  [5] ,  [9] ,  [13] ,  [22]  for bleeding and  [8] ,  [11] ,  [19]  for ulcers) enables us to work with 2D linear models instead of 3D models. The use of bounding box annotations in our database (see Fig. 3 ) entails a considerable quantity of ground truth errors because annotations do not match the arbitrary and complicated shapes of the lesions.
This is a major difficulty in our context, regardless of the type of model or machine-learning approach. To ease the annotation burden, semi-automatic region selection algorithms have been proposed. In the work of Sainju et al. [28] , the authors use the growing region algorithm  [29]  to create homogeneous bleeding regions from consecutive capsule endoscopy frames. A seed is manually selected by the user and then enlarged by adding 8-connected neighbors, and the new centroid is taken as the seed for the following frame. This method extracts only one region per lesion, which can unbalance the normal and bleeding regions in the training database. In addition, it does not perform well in the absence of lesions due to forward and backward camera movements or in patients with mild forms of UC.
In  [5] , the authors use a similar method to extract the bleeding regions but keep only a single frame rather than the complete sequence. In this paper, we propose to adjust the performance criterion of lesion detection rather than automatically annotate the dataset. We chose to work with linear models instead of more sophisticated approaches in order to provide results that are easy to interpret and use in clinical practice. In addition, the good performance obtained in this and previous studies ( [5],  [7] ,  [8] ,  [11] ,  [18] ,  [19] ) suggests that clinical validation of the approach is the critical step, as opposed to more sophisticated approaches such as SVM or neural networks. To evaluate the validity of our results, we did not perform cross-validation, but show the results of computing specificity and Sensitivity A on a random subset of frames in each video in Fig.
8. Cross-validation selects random subsets and finds the Fig. 7 : Annotated frames from Vatic database (first column), bleeding detection with our best linear model (second column), bleeding detection by two simultaneous linear models  [21]  (third column), ulcer detection with our best linear model (fourth column), ulcer detection using SVM  [8]  (last column). best model for each subset. Consequently, it selects different models at each run and evaluates the performance of the optimization algorithm. For clinical practice, we are interested in the performance of specific models, their reliability, and their generalization to new patients. Fig. 8  shows the performance of the 3 best models for the patients in the training dataset (left) and 5 new patients (right). For each patient, we estimated specificity and Sensitivity A on 20 random subsets of a video, each containing 10% of the frames.
Only three points are drawn, but the size of the ellipses is computed from the standard deviations of the 20 subsets. Fig. 8  shows that specificity and Sensitivity A are estimated precisely, even on a fraction of the frames. This suggests that computational time can be reduced by using only a small subset of the video. However, the performance varies a lot between patients, even inside the training set. This means that the selected models are not universal and that specific models should be trained for each patient. This observation was not reported in previous works because the datasets used contain frames that are not organized "by patient". Consequently, methodological advances are necessary to make colonoscopy videos comparable, in order to apply trained models to new patients.
VI. CONCLUSION This paper studies the automatic detection of bleeding and ulcers in colonoscopy videos for UC severity assessment based on a training dataset containing many annotation errors. We decided to deal with the annotations problem rather than proposing a sophisticated machine learning algorithm to improve detection performance as done by current studies. As in previous studies, we explore the set of linear classifiers and propose an efficient optimization method based on sampling the contour of the color histogram. This allows us to eliminate around 25% of trivial models which leads to focusing only on interesting models i. e. those giving nonzero true negative and true positive ratios. By adjusting the definition of sensitivity, we can circumvent the effect of the annotation errors using bounding boxes, and select good pixel-level lesion detectors.
ACKNOWLEDGMENT This work was performed in the context of the  Investissements d'Avenir programme   ANR-11-IDEX-0005-02  and  10-LABX-0017 ,  Sorbonne Paris Cité ,  Laboratoire d'excellence INFLAMEX. Safaa Al-Ali  received funding from the  Paris Region Fellowship Programme , attributed by the  DIM Math-Innov. We express our sincere thanks to  Eric Ogier-Denis  and  Xavier Treton  from  CRI, Inserm 1149  and  APHP Beaujon Hospital  for their insight during this collaboration.
Exclusion criteria were the existence of neurological (including a history of traumatic brain injury) or motor disorders at the level of the upper limb (history of tendinous disease, arthritis, surgery). Healthy participants were recruited via local association, while stroke ones were recruited at the beginning of a rehabilitation protocol (ReArm project, Clinical trial identi er: NCT04291573, 2nd March 2020). Table  1  provides detailed participant information, including gender, age, lesioned side, laterality, and clinical scores (refer to the clinical assessments section for additional details). For the stroke group, Table  2  presents all patients' demographic data and clinical history. In accordance with the Declaration of Helsinki, this study was approved from the French Research Ethics Committee, (Comité de Protection des Personnes-CPP SUD-EST II, N°ID-RCB: 2019-A00506-51, http://www.
Experimental design Each participant engaged in an hour-long session in a quiet isolated room. The participants were equipped with the fNIRS-fEEG neuroimaging systems and performed two functional UL tasks while seated: a paced reaching arm task and a circular steering task. The setup permitted synchronized recording of UL kinematics and brain activity (fNIRS and fEEG) using lab streaming layer (LSL, https://github. com/labstreaminglayer/App-LabRecorder). More comprehensive details about the functional motor task methodology can be found in our recent methodological paper (see Figure  5  in 42).
Circular steering task This task was based on the speed-accuracy trade-off  (43). Participants were seated on a chair in front of a horizontal graphic tablet (A3 size; Wacom, Kazo, Japan) equipped with a stylus a xed to a mouse pad, facing a 24-inch vertical screen projecting a circular target (33-inch circumference) with a 2 cm tunnel. A Kinect was placed above the graphic tablet at the height of 1. 70 m. The task was delivered using a labmade software, the LSL-Mouse (https://github. com/KarimaBak/LSL-Mouse). Participants were instructed to move a cursor as fast as possible in a clockwise direction. During the familiarization phase, participants were instructed to accelerate if errors (any instances outside the 2 cm circular tunnel boundaries) were below 15% (based on pilot testing). The task comprised three blocks for each arm (20 seconds of task with 20 seconds of rest), commencing with their non-dominant/paretic hand.
Task performance The paced reaching and circular steering task kinematics analysis was done based on previous work  (3, 4, 47)  and LSL-Kinect software (LSL-KinectV2: https://github. com/KarimaBak/LSL-KinectV2). For the paced reaching task, we calculated the proximal-arm non-use (%) and the hand mean velocity (mm/s). For both tasks, we calculated as trunk compensation parameter, the range of trunk anterior exion (°) representing the use of the trunk to realize the reaching movement. And, we calculated, as arm use parameters the range of elbow extension (°) representing the use of whole arm to perform the movement.
Search strategy The review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Before data extraction, the review was registered with the International Prospective Register of Systematic Reviews PROSPERO (Registration No CRD42023391278). The articles cited in this systematic review were obtained querying the PubMed database, Scopus, Web of Science and EMBASE filtered by the English language. No additional filters were applied to the search strategy. The research was started in January 2023 and completed in March 2023. The keywords used were "uterine transposition" or "uterine fixation" or "uterine ventrofixation" AND "rectal cancer"; "anal cancer"; "cervical cancer"; "vaginal cancer"; "sarcoma" AND "radiotherapy". The term "AND" was used to find the intersection.
Data extraction After removing duplicate publications at the title/abstract level, MP and NB independently reviewed titles, abstracts and keywords for first selection purpose. In case of differences in the selection, the final decision was taken through a discussion with a third author (DQ). In all articles potentially suitable for the purposes of this analysis, the full text was examined independently by MP and NB. In the event of discrepancies, we proceeded as described above. Studies were selected according to the criteria based on the following items: 1) description of uterine transposition surgical technique 2) patients with pelvic malignancies with life conceive possibility (<44 y. o. ) 3) the type of outcome assessment of interest. In Table  1  are reported the selected studies.
Summary of main results Uterine ventrofixation and uterine transposition techniques as options to spare uterine function in patients undergoing radiotherapy for rectal or anal cancer have been described by various authors for over a decade but are not frequently used in clinical practice. From the result of the dose simulations the uterine transposition was the most protective from radiation doses in both simulated cases (rectal and anal cancer) with a maximum dose of approximately 3. 05 and 7. 9 Gy, respectively. As per ventrofixation, in both cases, none of the simulated US ventrofixation positions received a Dmean surpassing 14 Gy, and the US volumes receiving 14 or 20 Gy for all simulated ventrofixation positions were remarkably small. Few patients were analyzed in the reported studies, and even fewer obstetric data were recorded.
Composition of the cheese surface virome. The sequence assembly obtained from all the studied samples (15 from the dynamic study and 9 from the persistence study) led to the production of a metavirome composed of 331 vOTUs >2 Kb (Table  1 ). The vast majority of these contigs (284) were detected in samples from both the dynamic and persistence studies. The most abundant phages detected in the dataset were identified as Lactococcus phages from the 949 and 936 groups (Audreyjarvisvirus and Skunavirus genera, respectively) and Glutamicibacter phage Montesquieu (Table  2 ). Of importance, the five virulent phages previously isolated from the same cheese, namely Montesquieu, Voltaire, Rousseau, D'Alembert and Diderot were also detected in this metagenomics survey. Remarkably, the vOTUs with no match (BLAT identity × coverage < 30%) to any known dairy phages (Table  S1 ) represented only 3% of the relative abundance in the dataset, showing that most of the dominant phages present in this ecosystem have already close-relatives that have been isolated and characterized.
When using the complete dataset (no filtering on relative abundance, Figure  4D ), we observed that 56. 6% of the 297 vOTUs present in at least one of the three years were shared in all production years (75. 4% in two different production years). Interestingly, samples from year 2022 had a higher number of unique vOTUs (48, 32. 1% of the total) than samples from years 2017 and 2019 (3 and 11, respectively). We next applied the same analysis only on the most abundant vOTUs (average normalized relative abundance > 5 × 10 -5 , 99 vOTUs in total) (Figure  4E ). The vast majority of the vOTUs, i. e. 89. 9%, were shared among the 3 productions years and this value rose to 97% when considering only two different years. This result indicates that the cheese surface virome was mostly stable in terms of composition (presence/absence) and that dominant phages persist across productions years.
15. 3 (44) , using either the complete dataset of trimmed reads available or after subsampling the dataset to 1. 5 million, 150,000 or 15,000 trimmed reads per sample. We noted that some abundant contigs were assembled into longer, nearly complete contigs, after subsampling. Contigs of length >2kb from all assemblies were selected and clustered following an approach adapted from  (45). Succinctly, a pairwise alignment was first performed for all contigs using BLAT  (46). Then, contigs with a self-alignment > 110% of contig length, corresponding to chimeras, were removed. Remaining contigs were clustered at the species level (90% identity × coverage) and the longest contig within each cluster was selected as the representative sequence. The final contig dataset consisted in 3122 dereplicated contigs.
Viral contigs were selected using a combination of three detection tools: VIBRANT v1. 2. 1 (47), VirSorter2 v2. 2. 4 (48) and CheckV v0. 8. 1  (49). The ones retained in the final virome were those meeting at least one of the following criteria: declared "complete", "high" or "medium" quality by either VIBRANT or CheckV, declared "full" by VirSorter2. The bacterial host of the 332 viral contigs was predicted using iPHoP  (50). Finally, all sequences were compared by BLAT to an in-house database consisting of genome sequences from 32 common dairy phages (listed in Table  S1 ) in order to identify potential related phages with known taxonomy, verified host and lifestyle (30% identity × coverage minimal cutoff). When appropriate, the host genus predicted by iPHoP was replaced by the genus of the bacterial host of the closest relative phage identified by the BLAT search.
One contig, corresponding to the genome of the phage PhiX174 which is routinely used as control in Illumina sequencing runs to monitor sequencing quality, was discarded resulting in a final dataset of 331 viral contigs. In order to evaluate the relative abundance of each viral contig in each metavirome sample, trimmed reads were mapped against the viral contigs using bwa-mem2 v2. 2. 1 (51) and counted with Msamtools v1. 0. 0 profile with the options --multi=equal --unit=ab -nolen (https://github. com/arumugamlab/msamtools). The output files from all samples were joined into an abundance table and processed using the R package phyloseq v1. 38. 0  (52). For each experimental dataset (dynamic study, persistence study), read counts were rarefied to the minimum depth observed in one individual sample.