dataesr/scientific-paragraphs-categorization huggingface dataset:
Accuracy of the baseline estimation The accuracy of the baseline estimation is measured as the average difference between the baseline annotated by the consensus of obstetricians and the estimated baseline, over the 26 recordings in the FHRMA test dataset which have been excluded from the calibration of the baseline estimation. Table  2  compares the accuracy of the estimated baseline in different settings: -Baseline estimation with a constant probability of stability p stab ¼ 1. In this setting, the baseline estimation is performed using a simple weighted median filter with weights depending on jt À t 0 j only. -Baseline estimation with a probability of stability p stab calibrated on the FHRMA dataset and a fixed half-duration of the window. -Baseline estimation with a probability of stability p stab calibrated on the FHRMA dataset and a variable half-duration of the window.
Selection of the features fed to the model 3. 3. 1. Univariate analysis We have studied the individual performance of the 25 features extracted from the CTG signals by estimating a univariate logistic regression model for every one of those features. Table  3  shows the AUC of those univariate models as well as the p-value and the sign of the coefficient in the logistic regression. A positive sign means that the higher the variable the higher the risk of fetal hypoxia is. Those models are trained and evaluated on all available cases from the three datasets. The minimum and maximum value of the FHR baseline are both significant, unlike the median value. All features based on accelerations and decelerations are statistically significant, and the best performing ones in terms of AUC are the area covered by accelerations and the area covered by decelerations.
Multivariate model selection In a second step, several multivariate logistic regression models have been trained and evaluated on the three datasets using a k-fold cross-validation strategy with five folds. Table  4  gives the AUC of four multivariate models, both on the training and validation cases: The results confirm that the model fed with the 25 features is the best performing one on the training cases (AUC = 0. 793), however the evaluation on the validation cases leads to a significant decrease in performance (AUC = 0. 750). The best performing model on the validation cases is the fourth one (AUC = 0. 757), which is also the simplest as it is fed with four features only. In the rest of the paper, the prediction models are all fed with those four features: b min , b max , acc area and dec area.
Evaluation of the model Table  5  shows, for each dataset, the AUC of the model when it is evaluated on the cases in this dataset and trained on the cases from the other datasets. For example, when evaluated on the CTU-UHB dataset, the model is trained on the Beaujon and SPaM datasets, and when evaluated on the Beaujon dataset, it is trained on the CTU-UHB and SPaM dataset. This methodology ensures that the evaluation is representative of the performance that could be reached when using the model in a new center. Figure  2  shows the corresponding ROC curves. The three centers forming the SPaM dataset have been grouped because the ROC curves on every center were too noisy due to the low number of cases of fetal hypoxia (20 in each center). The model reaches a similar performance on the CTU-UHB and Beaujon datasets (AUC = 0.
Comparison of the model with other published models Table  6  compares the performance obtained by our model with other models published in the literature. All papers included in this comparison report the AUC obtained on the CTU-UHB dataset or a subset of it. For our model, we show a 95% confidence interval  obtained by bootstrapping the CTU-UHB dataset with 100 bootstraps. A confidence interval was available in one other study only  (23). We identify three main limitations to this comparison that are highlighted in Table  6 : -The outcomes predicted by the model are not the same in all papers: three papers use an outcome based on the fetal pH, one of them with a threshold different than 7. 05, another paper uses an outcome based on both the fetal pH and the Apgar at one minute, and another one predicts the delivery mode (vaginal or caesarean).
-Three papers evaluate the model on a subset of the CTU-UHB cases. Fergus et al. (22)  randomly select 101 cases to form the evaluation dataset, while Gatellier et al. (18)  and Abry et al. (19)  remove the cases with the most missing points in the FHR signals, which may bias the evaluation. For the two other papers, it is not explicitly stated whether the evaluation is performed on a subset of the cases or on all cases. -Three papers train the model on the CTU-UHB dataset as well, which can significantly bias the performance evaluated on the same dataset. Our model achieves a slightly better AUC than the 2 other models based on a logistic regression or a support vector machine (AUC = 0. 74 vs. AUC = 0. 72 or 0. 73). The only model giving a better performance is the one published by Petrozziello et al.
(26) , which is trained on a much larger dataset with more than 35. 000 cases and using deep learning methods. However, those differences in accuracy are very probably not statistically significant because of the important width of the confidence intervals due to the relatively small size of the CTU-UHB dataset. We have compared the performance of our model with the available obstetricians' annotations (Figure  3 ). The model is evaluated on the CTU-UHB dataset and trained on the other datasets with an outcome consistent with the annotations (pH < 7. 15). The annotations differ significantly from an obstetrician to the other, confirming the important interobserver variability reported in past studies  (5, 35). The outcome predictions from the college of obstetricians are quite conservative, with a low false positive rate around 25%, but a low true positive rate around 45%.
Impact of the inclusion of cesareans in the training and evaluation datasets The mode of delivery (natural, operative or caesarean) is available for every birth in CTU-UHB and Beaujon datasets (Table  7 ). The rate of births with pH < 7. 05 is much higher for cesareans (16% vs. 6% for other delivery modes). While the inclusion of cesareans in the training dataset does not impact the results significantly, their evaluation on cesareans only cases leads to a slightly worse performance (AUC = 0. 74 vs. 0. 76) (Table  8 ).
Discussion In this paper, we have introduced DeepCTG ® 1. 0, a model for predicting fetal hypoxia from the CTG signals. We have brought a significant attention to the methodology used to preprocess the CTG signals and to extract the features which are fed to the model. The methodology applied to fill the missing points in the signals did not have a significant impact on the model's performance, as mentioned in other studies  (34) , and we have used a simple approach based on linear interpolation. The most complex part is the computation of the FHR baseline, which is also a prerequisite for the computation of the features based on accelerations and decelerations. The baseline estimation methodology aims at reproducing as good as possible the annotations of baselines, accelerations and decelerations provided by a consensus of obstetricians in the FHRMA dataset.
This gives our model the important characteristic of being interpretable: it is based on features which are known and understood by practitioners, and the influence of each feature on every prediction can be evaluated. While being relatively simple, it performs well on the three datasets used in this study. Very importantly, the performance on each dataset is evaluated when the model is trained on the remaining datasets, making it representative of the performance the model could reach when deployed in new centers without any specific adaptation. The performance obtained on the Beaujon dataset (AUC = 0. 74) is representative of the performance that could be achieved in a clinical context, because it includes all pathological cases occurring at Beaujon hospital over a time period and non-pathological cases have been filtered for practical reasons, but the filtering methodology described previously should not bring any bias in the dataset.
We do not know how the 552 cases in the open CTU-UHB dataset have been selected, but the performance of the model is very close on those cases. The performance is significantly higher on the SPaM dataset (between 0. 77 and 0. 87). This dataset has been built as part of a challenge and the cases may have been carefully selected, as the lower share of missing points in the FHR and UC signals in the SPaM dataset suggests. Also, the high difference in fetal pH between the pathological cases (pH < 7. 05) and the non-pathological ones (pH in the range 7. 25-7. 30) very probably helps the model to classify the cases. This explains the higher performance observed on those cases. We have compared the performance of the model with other published models and with obstetricians' annotations. The comparison with other models published is not straightforward, because the models are often evaluated on a different set of clinical cases or using different outcomes than pH < 7.
05. Despite those limitations, we conclude that our model performs slightly better than other models based on relatively simple statistical models (like logistic regression or support vector machines) trained on datasets of similar sizes. The only model performing significantly better is the one published by Petrozziello et al. (26) , which is based on a more complex deep learning model trained on a much larger dataset with more than 35. 000 cases. Its complexity makes it harder to deploy in clinical practice and to provide interpretable indicators for practitioners. However, it is worth noting that those differences in accuracy are very probably not statistically significant because of the relatively small size of the CTU-UHB dataset. When compared with the annotations of the CTU-UHB cases by nine expert obstetricians, the model performs significantly better than every individual expert and the majority vote.
We have challenged the inclusion of cesareans in the datasets used to build the model, as this is a questionable decision. On one hand, for those births, there is probably a lower correlation between the available CTG signals and the fetal pH at birth. Indeed, there may be a delay between the end of the CTG segment and the time of delivery, as in practice the CTG signal does not end at delivery but instead when the decision of doing the cesarean is made. On the other hand, removing those cases could create a very strong selection bias harming the performance of the model when it is used in clinical practice. As far as we know, this point is not discussed specifically in the literature and more research should be done to integrate those births consistently when building the model by using the reason why the cesarean was performed.
Our study shows that the inclusion of cesareans in the training dataset does not significantly impact the accuracy of the system, suggesting that for most cesarean cases, the last available CTG signal can be reliably used as a proxy of the state of the fetus just after delivery. The definition of the CTG segment used to compute the features is a key parameter of the system: in this study, we choose the last 30 min before delivery. We have shown a high dependence of the performance of the system on the length of the segment: we choose 30 min, which achieves a good performance while making the model usable quickly after the CTG recording starts. Using the latest available signal before delivery is consistent with the fact that the outcome is based on the fetal blood pH measured right after delivery.
, some of whom are published in open-access, have led to a surge of research works about computerized CTG analysis using recent machine learning and deep learning techniques. Gatelier et al. and Abry et al. have trained and evaluated multivariate machine learning models on the open CTU-UHB dataset (18, 19). Other systems are based on deep learning methods which input the raw CTG signals instead of features extracted from the signals: Petrozziello et al. and Mohannad et al. have built such systems on proprietary databases with more than 30. 000 births (20, 21), while Fergus et al. and Ogasawara et al. have built similar systems on the CTU-UHB dataset.
Accuracy of the baseline estimation methodology on the FHRMA test dataset. -A model fed with the best performing features in each category: the minimum and maximum value of the baseline (noted b min and b max ), the area covered by accelerations and decelerations (noted acc area and dec area ), the area covered by late decelerations outside contractions and the time difference between late decelerations and contraction peaks (noted late dec no contraction area and time diff late dec contraction), and the STV and LTV (noted stv and ltv). We have not included the features based on contractions which are not statistically significant. -The same model after removing the features corresponding to the joint interpretation of contractions and decelerations. -The same model after removing the STV and LTV features as well.
(5)< 7. 15) based on the CTG signals(5). We also have defined a college of obstetricians which consists in considering for every case the most frequent labor outcome prediction among the nine obstetricians. Some annotations are missing: the nine obstetricians have classified in average 456 cases each over 552. Ben M'Barek et al. 10. 3389/fped. 2023. 1190441 -The SPaM dataset, introduced as part of the Workshop on Signal Processing and Monitoring in Labor (24), contains 300 cases collected from three participating centers (Lyon, Brno and Oxford). Each center provided 100 cases: 80 with a fetal pH within 7. 25-7. 30 and 20 corresponding to a pathological outcome (pH < 7. 05). For every case, the CTG recordings during delivery (FHR and UC) as well as the binary outcome (pH < 7. 05 or pH within 7.
25-7. 30) are available. -The Beaujon dataset contains 675 CTG recordings collected at the University Hospital Beaujon (Clichy, France), with corresponding maternofetal data and fetal outcomes. All deliveries with pH < 7. 15 between January 2020 and December 2022 have been included, and for any such delivery, the latest delivery with pH > 7. 15 has been included. This inclusion methodology has been defined to limit the total number of cases (because the extraction of the CTG signals was manual and time-consuming) while ensuring that the dataset contains the highest possible number of cases of fetal hypoxia. An additional dataset shared by Boudet et al. (25), named the FHRMA dataset (for FHR morphological analysis) in the rest of the paper, has been used to calibrate our FHR baseline estimation methodology.
Calculation of the ventricular volume Ventricle volumes were calculated by manual segmentation using FIJI/ImageJ. Lateral ventricle and 3rd ventricle areas, which appear white in T2 MR images, were measured by manual quantification of the white patches (determined by greyscale) in FIJI, similar to approaches used in previous studies  [17] [18] [19]. All selections were blinded to avoid selection bias. Ventricle CSF volume was calculated by total ventricle area × 0. 6 mm slice thickness. To avoid bias, all images were identified based on mouse date of birth and tag ID without information about genotypes. Calculation was completed for each mouse without knowledge of their genotypes. After all data were documented, a separate file containing mouse ID and genotype information was used to assign genotype to each datapoint-see also  [19].
Micro-CT imaging and analysis of spine morphology Micro-computed tomography (micro-CT) skeletal scans were acquired using an Albira system (Bruker). Animals were lightly anesthetized by 3% isoflurane and placed in a prone position on the micro-CT bed. Special attention was paid to ensure that the spine was not artificially bent, and that all limbs were in a relaxed position and not pinned underneath the torso. Stacks of cross-sectional images were reconstructed with a volume resolution of 125 μm. A custom pipeline written in Python (https:// github. com/ teamn bc/ MouSp ine) was designed to compute three-dimensional spinal trajectories using stacks of micro-CT cross sectional images. The pipeline implements a series of annotation steps as follows. A horizontal maximum intensity projection (MIP) image is first used to place a set of points along the spinal midline.
In the present study, each reference point was associated with the vertebra immediately anterior to it (e. g. , the L6 point was located at the posterior side of L6). At this stage, only the anteroposterior and dorso-ventral coordinates of reference points are defined. The final step consists in interpolating their medio-lateral coordinates using the spline function calculated above and describing the spinal midline, yielding a set of Cartesian coordinates describing the three-dimensional trajectory of the spine (Fig. 3A, right ). For one mouse, the whole annotation process is typically completed in less than 2 min. The person who performed these annotations was blinded to the genotype of the mice. The rest of the analysis was performed with custom code written in R (https:// github. com/ teamn bc/ MouSp ineR, using version 4.
The mice identifier was assigned as a random (intercept) effect to account for the paired measurements by animal across the vertebral segments. Two separate LMMs were fitted, one for values from 6-week-old mice and one for values from 12-week-old mice, using restricted maximum-likelihood estimation (REML) with the function lmer in the lme4 package (v1. [20]. For each model, significance of the main effects and interaction terms was assessed based on Type II Wald chi-square tests using the function Anova in the car package (v3. 1-1), followed by post hoc comparisons of the two genotypes at each vertebra within each sex using the emmeans package (v1. 4. 5) with False Discovery Rate (FDR) correction of p-values. The level of statistical significance was set at p or adjusted p < 0. 05 for all tests.
Sspo-deficiency reduced brain ventricle volumes We compared T2 weighted magnetic resonance imaging (MRI) brain scans of 6-week-old live Sspo +/+ and Sspo -/-mice. MR images were segmented manually by researchers blinded to the animal IDs to calculate lateral ventricle volumes (Fig. 2A ). We analyzed a total of 8 wild type Sspo +/+ mice (3 males and 5 females) and 10 Sspo -/-mice (4 males and 6 females). We found that Sspo -/-mice had on average a ~ 35% reduction in lateral and third ventricle size than Sspo +/+ littermate controls (Fig. 2B-D ). These data were consistent across male and female mice and suggest that the absence of SSPO expression and RF formation during development limited lateral ventricle expansion. The overall brain sizes were also not significantly different as measured by the average brain area obtained from cross sections of MR images (Sspo +/+ : 41.
Our method represents a tradeoff between simplicity of use (high), rapidity of development (high), complexity of the measure (low) and accuracy of the measurement (high). Our code is openly available (https:// github. com/ teamn bc/ MouSp ine) and will be adaptable to other studies with some adjustments to work with multiple data formats and non-prone positions. The companion code for performing the necessary corrections, aligning individual trajectories, computing average trajectories and calculating curvature angles is also available (https:// github. com/ teamn bc/ MouSp ineR). Future application of our approach may answer additional questions such as how spine curvature changes in Sspo -/-mice during embryonic development or during aging. Consistent with the mild anatomical phenotype localized to the thoracic region of the spine, behaviors tested in Sspo -/-mice were largely unaffected.
Acknowledgements We thank members of the  Lehtinen and Wyart labs  for helpful discussions,  Nancy Chamberlin  for critical reading and editing of the manuscript,  Morgan Shannon  and  Aja Pragana  for animal husbandry support,  Mantu Bhaumik  and the  IDDRC Gene Manipulation Core ;  Nathaniel Hodgson  and the  Animal Behavior and Physiology Core ;  Peter Morris ,  Kristina Pelkola ,  Simon Warfield  and the  BCH Small Animal Imaging Laboratory  for assistance with MRI and CT;  BCH IDDRC Cellular Imaging Core ;  Annick Prigent  and the staff of the histology facility of the  Paris Brain Institute (ICM).
In simulated data, COMPARI accurately recovered fiber orientations in the presence of extracellular water. In a dataset of 23 patients with highly edematous brain tumors, the amplitudes of FOD and anisotropic index distribution within the peritumoral region were higher with COMPARI than with a recently proposed multi-compartment constrained deconvolution model. In a selected patient with metastatic brain tumor, we demonstrated COMPARI's ability to effectively model and eliminate water from the peritumoral region. The white matter bundles reconstructed with our model were qualitatively improved compared to those of other models, and allowed the identification of crossing fibers. In conclusion, the removal of isotropic components as proposed with COMPARI improved the bio-physical modeling of dMRI in edema, thus providing information on crossing fibers, thereby enabling improved tractography in a highly edematous brain tumor.
In vivo data acquisition, preprocessing and experiments Twenty-three participants were recruited between 2017 -2020 from an ongoing brain tumors study with varying degrees of peritumoral edema, including 13 patients with a histological diagnosis of glioblastoma (WHO grade 4), 8 with anaplastic gliomas (WHO grade 3 atrocytomas and oligodendrogliomas), and 2 with brain metastasis. Authors had access to information that could identify individual participants during or after data collection. All participants underwent a multi-shell diffusion acquisition with b-values of 300 (15 directions), 800 (30 directions), and 2000 (64 directions) s/mm 2 and 9 b=0 volumes, with TR/TE of 4300/75 ms, along with the pulse duration time δ = 0. 01293 s and pulse separation time ∆ = 0. 03666 s, multi-band factor of 2 and A > P phase encoding direction, followed by an acquisition of 7 b=0 volumes with P > A phase encoding direction.
Data were acquired on a Siemens 3T Magnetom Prisma Fit scanner with either a 32-channel or 64-channel head coil. The data were denoised  [21] , followed by correction for echo planar imaging distortions with TOPUP  [22]  and motion and eddy current correction with EDDY  [23]. Additionally, each patient underwent a routine pre-surgical scan in the clinic on either a Siemens TrioTim, MAGNETOM Vida or MAGNETOM Avanto 3T scanner, yielding T1-weighted (pre-and post-gadolinium contrast), T2-weighted and FLAIR images. These structural images were processed with the pipeline described in  [24]  which included DeepMedic  [25]  segmentation of the tumor and peritumoral area. The T1 image and segmentation were finally registered to the multi-shell DTI acquisition with ANTs software  [26]. To compare FODs in our model, we fitted COMPARI and MSMT-CSD in each patient.
Implementation We implemented COMPARI using the Dmipy  [13]  software package. COMPARI was fitted by employing a brute force optimization technique to sample all possible parameter values within the defined optimization bounds, followed by the application of constrained optimization algorithm L-BFGS-B  [32]  to obtain the locally optimal parameter estimates. Then, the CSD estimation was employed based on the approach proposed by  [10]. The λ ∥ initiated using response function described in  [10]. In addition, we utilized Dipy  [33] , ITK-snap  [34] , MRview  [35] , and Trackvis  [36]  to facilitate visualization. We fitted COMPARI in parallel in each axial slice on a cluster with 40 cores 1. 2 GHz CPU with 128 GB of RAM, and computation time was approximately 5 hours per participant.