dataesr/scientific-paragraphs-categorization huggingface dataset:
In some cases, a high proportion of the selected periods was not annotated. The selected period was long enough to include all the information required for interpretation. For example, we assumed that MHR interference was captured by the Doppler FHR sensor for one minute. During this minute and the preceding 30 min, the MHR sensor was not in place. The selected period had to include all the signals starting a few minutes before the MHR sensor was removed, so that the model could estimate the range of possible MHRs during the annotated period. On datasets (A), (B), and (C), the selected periods were fully annotated by experts blinded to the models' outputs. Datasets (D) and (E) were annotated after the initial models had been trained, and so only parts that appeared to be more difficult to interpret or where the models were wrong (or not confident enough) were annotated.
Thus, the dataset grew progressively as the models' complexity increased. Recordings were randomly attributed to the training dataset (80% of the total duration) or the validation dataset (20%). This attribution was performed for the FSDop/FSMHR datasets together and then for the FSScalp datasets separately. The validation dataset was used to stop the training early (to avoid overfitting) and to compare the various models and hyperparameters. The final evaluation was carried out with the test datasets described in Section 3.
The training datasets were analyzed simultaneously and consensually by one or two experts: an engineer and a senior obstetrician, both of whom performed research on and gave lectures on FHR analysis. The engineer's involvement was important because it resulted in faster, more accurate use of the interface and provided a better understanding of (i) how an FS can arise and (ii) aspects of the recording that are important for machine learning. The interface could also display a model's results by using a color gradient for the FHR (TS = blue, FS = red) and the MHR (TS = violet, FS = cyan). Thus, the experts could annotate recordings while being aware of the mistakes made by the previously trained models. The interface is available as open-source code in the FHRMA MATLAB ® toolbox  [22]. The FHRMA toolbox also included the model recoded in MATLAB ® because the training was performed with Python ® 3.
To estimate the size of the error generated by these time lags, we measured the mean difference in the absolutes value before and after compensation between (a) the signal measured during the MHR FS period with the Doppler FHR sensor and (b) the MHR measured with finger oximeter. For six recordings and a total of 60 min free of accelerations/decelerations, the mean difference was 5. 0 bpm before correction and 3. 5 bpm afterwards. Moreover, the difference was much greater when measured during accelerations or decelerations, which are often critical periods for FSs. Thus, this correction is very important. Unfortunately, we did not record the type of MHR sensor in our database until May 2020. However, the type of sensor could be easily determined retrospectively because the MHR measured with the tocometry belt had a resolution of 0.
Preparation of the Input Matrix We first normalized each signal (MHR or FHR) in beats per minute (bpm) by performing HR = HR-120 60. Next, we coded MSs on an independent channel as MS FHR or MS MHR : the absence of a signal was scored as 0 and the presence of a signal was scored as 1. During MSs, the corresponding FHR or MHR was set to 0. The FSDop model's input comprised the Doppler FHR signal and the MHR signal, whereas the FSHMHR and FSScalp model's inputs comprised only the single, corresponding signal. Since there were no FSs for the MHR on the scalp ECG channels in our dataset, we did not input the MHR into the FSScalp model. We added a channel to code the stage of delivery: for each sample, 0 corresponded to the first stage or antepartum, and 1 corresponded to the second stage. The second stage of delivery features more MHR accelerations, more FHR decelerations, more MSs, and many more FSs; hence, the delivery stage is important for adjusting the probabilities to the period.
Before DL can occur, the data have to be formatted in tables [batch size × time samples × channels]. Unfortunately, the duration differs from one recording to another. Although zero padding is the conventional way of handling differences in duration, our training dataset was mostly composed of short recordings (5 to 30 min) and a few longer signals (up to 4 h). Zero-padding short signals to 4 h would have created a high proportion (85%) of useless zero calculations, and cutting up the 4 h recordings into shorter parts would have prevented the models from detecting long-term features. We therefore grouped the short signals together into packages of approximately 4 h, and concatenated the signals inside a package to create a "unit". A mini-batch was then composed of several units of the same duration (4 h).
However, we had to tell the model to reset the internal status at each change of recording. To this end, we added first a reset sample (containing 0) between two recordings and then a "reset channel", which was set to 0 most of time and to 1 on reset samples. The layers' handling of this channel and these samples are described in Section 2. 8. 5 and Appendix B. Thus, Figure  2  shows the decomposed matrix obtained for a unit used as the input for FSDop. For the FSDop dataset, the input matrix sizes were: 80 × 64800 × 6 (training dataset) and 20 × 64800 × 6 (validation dataset). These sizes enabled training on either TPU v3 (batch size: 40) or on Colab Pro ® with GPU T4 or P100 (batch size: 20). The batch size was limited to avoid memory saturation. For FSScalp, the input sizes were 40 × 30600 × 4 for the training and validation datasets, since we worked with a batch size of 40 on either TPU v2 or on Colab Pro ® with GPU T4 or P100.
For FSMHR, the input sizes were 22 × 29000 × 4 for the training and validation datasets, since we worked with a batch size of 22 on Colab Pro with GPU T4 or P100. Our initial trials used a constant window length of 30 min; shorter periods were padded with zeros. Unfortunately, the level of performance decreased when the method was applied to longer recordings; hence, we developed this recording concatenation system to train the models on longer periods. We decided to not subsample the recording because we did not know whether the models would be able to extract information from highfrequency signals; for example, a change in variability might suggest a switch between the FHR and the MHR.
• Each MS sample had a weight of 0. • For each specific period, the weight of annotated samples is set to 1/Ratio o f annotated samples over the period. Thus, if a period is fully annotated, all the samples have a weight of 1. If a period of 25 min contains only 1 min of annotation, however, this annotated period is probably more important, and each annotated sample will have a weight of √ 25 (i. e. , 5). The total weight of this period is then 5 times lower than that of an entirely annotated period but 5 times greater than that of a selection containing the annotated part only. We considered that the two types of error (false positives and false negatives) were equally important and thus gave the annotated FS and annotated TS the same weightseven though the classes on the training dataset were highly unbalanced, as shown in Section 4.
Evaluation Methods Performance was evaluated on data unseen by any model. Test datasets were created after the final model was established, so that we were not tempted to improve the models once we had seen the results with the test data (avoiding the risk of biased evaluation). Two evaluation systems and datasets were developed: (i) A test dataset for routine clinical practice: An evaluation of the three models on 30 recordings per model, selected at random and fully annotated by the experts. This evaluation was intended to provide an idea of the model's performance with data obtained from routine clinical practice and that were not biased by selection criteria. (ii) A dual-signal test dataset: FSDop was evaluated on 34 recordings with the simultaneous scalp ECG sensor and Doppler sensor.
The scalp ECG was used by two experts to set the ground truth. Two other experts analyzed the same periods but were blinded to the scalp ECG signal. This dataset was used to compare FSDop's performance with that of the experts. For each dataset and for each model, we measured the accuracy (setting a threshold of P = 0. 5 for each classifier), the contingency table, the area under the receiver operating characteristic curve (AUC), and the CE. These metrics were also calculated for the training and validation datasets and for each stage (antepartum, the first stage of delivery, and the second stage of delivery). Given that a Doppler recording is not always accompanied by a simultaneous MHR recording in routine clinical practice (due to an obsolete CTG monitor, ill-trained staff or difficulties positioning the sensor), we also assessed the performance without considering the MHR channel.
The Test Dataset for Routine Clinical Practice In the training and validation datasets, the proportion of FSs is higher than in the routine clinical practice since the periods were selected because they contained FSs or at least FS ambiguities. Thus, this test dataset was intended to assess the model's performance without selection bias. Thirty Doppler sensor recordings (containing the first stage of delivery and, unless cesarean section, the second stage) were selected at random from among those recorded in 2019. This dataset was used to evaluate FSMHR and FSDop. Thirty scalp electrode recordings (during the first stage of delivery and, in some cases, the second stage) were selected at random from among those recorded between 1 January 2019, and 31 May 2021. This dataset was used to evaluate FSScalp.
The 60 recordings were analyzed by three experts (two obstetricians and a midwife, all of whom performed research on and gave lectures on FHR analysis). Each expert analyzed a third of the recordings. On the Doppler/MHR dataset, both the MHR and the FHR were fully annotated. Each sample of an FHR or an MHR from the start of the recording through to delivery was annotated as a TS, an FS, or an uncertain signal. On the scalp ECG dataset, the scalp ECG FHR channel was fully annotated as a TS, an FS, or an uncertain signal. The scalp FHR recording was generally shorter than the entire recording because the scalp electrode was applied as a second-line measure, after the Doppler sensor.
The Dual-Signal Test Dataset The evaluation on test dataset for routine clinical practice is limited by potential errors made by the expert. Moreover, one cannot say whether the model is better or worse than the expert. Lastly, not all the recordings contained periods that the model might fail to analyze. We therefore built a second test dataset by selecting all periods of a least 10 min between 1 January 2019, and 31 May 2021 (this inclusion period is after that of the training dataset A) on which both Doppler and scalp sensor signals were simultaneously recorded. This yielded 45 periods in 37 recordings. Next, to form the solid ground truth, two experts (a senior obstetrician and an engineer) consensually annotated the Doppler channel with the help of the scalp ECG signal. These experts annotated all samples in a period as a TS, FS or uncertain signal, even when scalp ECG was temporarily missing.
Statistical Analysis All the results for the training and test datasets are summarized in Figure  8. The left and right parts of the table correspond to the datasets and models, respectively. The most intuitive metric is accuracy, which was usually greater than 99%. However, since most of the signal samples are TSs, trivial classification of all samples as TSs would also produce a relatively high accuracy. This trivial model would have an accuracy corresponding to the "Percentage of "true" among annotated" column. An accuracy that is lower than the percentage of TSs means that the model removes more TSs than FSs. Hence, if we assume arbitrarily that a false negative has the same importance as a false positive (as in Section 2. 7), the model would be of no use. This does not mean, however, that the method performs worse than chance, which will be measured with other metrics.
Results with the Test Dataset for Routine Clinical Practice The accuracy values for this dataset showed that all three models are highly effective (FSDop: 99. 66%, FSMHR: 99. 92% and FSScalp: 99. 93%). The percentage of TSs on the corresponding dataset were respectively 97. 2%, 99. 86% and 99. 90%. The fact that the accuracy was greater than the percentage of TSs means that the models indeed rejected more FSs than TSs. This good performance is also confirmed by the respective AUCs (0. 9992, 0. 971 and 0. 9792 for FSDop, FSMHR and FSScalp) and the respective CE (0. 0112 (vs. 0. 128 for the trivial model), 0. 0044 (vs. 0. 0108) and 0. 0032 (vs. 0. 0074)). FSDop was the most useful model for routine clinical practice because the latter recordings contain many more FSs; the AUC of 0. 9992 is impressive.
The FSMHR and FSScalp models were less useful for this dataset, given to the very small number of FSs. However, the AUCs of FSMHR and FSScalp were respectively 0. 971 and 0. 978, which correspond to good, much-better-than-chance performance. Even though FSMHR and FSScalp have little effect (since there are very few FSs on these channels), it would make sense to implement them on the central monitor or as preprocessing steps. FSDop's performance fell to 97. 88% for the second stage of delivery (Figure  8 ). This was expected because the second stage is often more complicated, with more MSs, more FSs, more FHR decelerations (which can be mistaken for the MHR), and more MHR accelerations (which can easily be confused with the FHR). The percentage of FSs in the second stage was 4. 3%, although 12.
8% of the second-stage samples were annotated as "uncertain" by the experts. This confirms that the second stage is more complicated to analyze, and the true proportion of FSs was probably around 10%-much more than the 2. 8% in the first stage of delivery. When we removed the MHR from the model's input, the performance fell from 99. 75% to 99. 08% for the first stage of delivery, and from 97. 88% to 94. 72% for the second stage. Thus, the model is still effective for the first stage of delivery. However, for the second stage (and even though the AUC was 0. 83), the model removed more TSs than FSs and so might not be relevant. For example, if these models were applied to the CTG-UHB public dataset  [27] , the absence of MHR data would limit their value. The difficulty of interpreting the second stage of delivery in the absence of an MHR signal was confirmed by the experts during their annotation of the study's datasets; hence, this was not a method-specific problem.
We encourage practitioners to check that the MHR is recorded well during the second stage of delivery, since poor obstetric decisions prompted by FHR/MHR confusion are probably more common than thought. We also suggest that the MHR could be recorded with a smartwatch, which might be more comfortable for the mother and possibly more reliable; we hope that CTG monitor manufacturers will study this possibility. In this dataset, the experts were able to annotate features as "uncertain", and so we expected very few incorrect (false) annotations. However, some of the models' remaining errors might still be expert errors. The second test dataset was designed to overcome this problem but could be applied to FSDop only.
Results for the Dual-Signal Test Dataset The dual-signal test dataset was more difficult to interpret than the routine clinical practice dataset. Nevertheless, the FSDop model's accuracy was 97. 29%, which was still higher than the proportion of true samples in the dataset (96. 6%). The AUC was 0. 965, which corresponds to good classification performance. The accuracy rates for the two experts (who analyzed the recordings under the same conditions as the models, i. e. , blinded to the scalp ECG signal) were 96. 91% and 97. 29%; hence, the model was better than one expert and worse than the other. The degree of randomness in these statistics is difficult to assess. However, the statistics confirmed our impression that the method was as accurate as a competent practitioner although a well-trained expert who understands the FS mechanism described in Appendix A could probably do slightly better than the models.
Results with the Validation Dataset As expected, performance on the validation dataset was slightly worse than performance on the training data. One cannot compare the levels of performance between the validation dataset and the test datasets because they do not have the same selection criteria. The validation dataset provided greater precision (but not greater accuracy), because there were more data in general and more FS data in particular. However, the results were probably influenced by evaluation bias, induced by our choice of the hyperparameters' values in this dataset. The models' accuracies were very satisfactory (99. 30% for FSDop, 98. 68% for FSMHR, and 99. 90% for FSScalp) and much higher than the percentage of TSs (89. 5%, 89. 3%, and 99. 4%, respectively); hence, the number of errors was divided by a factor of 15.
0 for FSDop, 8. 1 for FSMHR, and 6. 0 for FSScalp. The FS rate was much lower in the FSScalp dataset (0. 6%) than in the FSDop dataset (10. 5%), and so FSDop is likely to be of use more frequently in practice. The FS rate for the MHR was high (10. 7%) but this was mainly due to selection bias during annotation. We did not consider it necessary to develop a test dataset for antepartum recordings because the latter do not present difficulties absent from the first stage of delivery. This was confirmed by the accuracy on the antepartum validation dataset (99. 37%), which was approximately equivalent to the accuracy on the first stage of delivery (99. 49%) for much the same percentage of TS (88. 2% vs. 89. 4%, respectively). The only details which did not work with the first models we developed were present in a recording with an FHR baseline above 210 bpm.
Our results suggest that the MHR sensor is very important (and often essential) for recognizing the FS -particularly during the second stage of delivery. However, the MHR is often missing during the second stage of delivery. One simple, comfortable solution would be to add a smartwatch with a wireless connection. We also strongly encourage CTG monitor manufacturers to compensate for the delay between the FHR and the MHR (5 s or 12. 5 s on a Philips system); this would make it easier to distinguish the MHR on the Doppler FHR channel, and this function could be patched (as display software) relatively easily into central monitoring stations. The present work contributes to the field by (i) describing and formalizing the problem of automatic FS detection, (ii) offering a large, annotated dataset, and (iii) offering the first ever effective solution to the problem.
Moreover, the approach presented here required several developments with a great degree of optimization (e. g. , data augmentation, kernel/recurrent sparsity, and a symmetric GRU). We also developed a technique for concatenating recordings inside a GRU unit (Appendix B); this avoids useless operations due to zero padding, and might be useful for other problems. To encourage other researchers to work on this problem, we have made all the data and our source code available via the open-access FHRMA project  [22]. The test dataset annotations are not available on an open-access basis. Researchers who want to evaluate their models will have to send us their results for evaluation; hence, a competition has been opened. We also encourage researchers working on FHR signal processing to use our FS detection methods as preprocessing steps and to use our open-source WMFB method (also available in the FHRMA toolbox)  [14]  for FHR baseline estimation.
• Possible FS values: The Doppler FHR sensor has several possible FS values. It can be either the MHR (e. g. , Figure  6a, c, d ), a harmonic of the FHR (double, half or (more rarely) triple or a third) or a harmonic of the MHR (e. g. , Figure  6d ). The mixing (i. e. , the linear combination) of the FHR and the MHR should not be possible. The MHR signal is less subject to FS but it can switch to its own harmonic (double, half, or (more rarely) triple) (e. g. , Figure  6d ). Over short periods, it is also possible for these sensors to take on other values, although the variance is high and the situation does not last for more than a few seconds. The FSs on the scalp ECG sensor are also short and have high variance (often a vertical line) (e. g. , Figure  6b ). In our dataset, we did not observe MHR inference or harmonics on the scalp ECG sensor.
• It might be possible to increase the number of recordings with a solid ground truth (i. e. , better than the analysis by experts, who had to use the same information as the models) by putting two Doppler sensors (rather than one) on the belt. Although this would be less precise than a scalp ECG, adding the latter sensor with no medical justification is ethically problematic. Thousands of recordings could be recorded and annotated automatically by considering that two similar signals are necessarily either both TSs or (less likely) both FSs. If two signals differ, one is likely to be false. Originally, we tried this method for annotating training dataset A (with dual Doppler/Scalp ECG sensors).
For example, the E6 genes of HPV-16 and HPV-18 display a PDZ-binding motif (X-T/S-X-V) that is a specific genomic signature for the HR-HPV strains  [62]. Wholegenome sequencing of HCMV could be one of the best tools to compare the sequences of all HCMV genes and check whether one or several viral genes with specific "HRmotif(s)/domain(s)" could be detected. We already sequenced the genome of the HR-HCMV strains DB and BL with Davison's group (Genbank KT959235 and MW980585, respectively)  [11, 23, 40]. Various techniques, including restriction fragment length polymorphism (RLFP) analysis  [63] , targeted amplicon sequencing  [64] [65] [66] [67] , and whole-genome sequencing  [21, 68] , have been used to describe HCMV genome variability. With the increasing sequencing depth of next-generation sequencing platforms, the detection of low-frequency variants, i.
Importantly, this protection has been reported in heart failure (HF) trials, which also included individuals with CKD stage 4 and those without diabetes  [16]. Although the eValuation of ERTugliflozin effIcacy and Safety CardioVascular outcomes (VERTIS CV) trial (ClinicalTrials. gov registration no. NCT01986881) did not enrol individuals with CKD stage 4 or necessarily include those at risk of CKD progression  [17] , as the authors point out, it is likely that the same principles apply in VERTIS CV  [18]. Regarding the authors' point that 'therapy-related muscle wasting' may conceal the need to start dialysis, changes in body weight and related muscle mass, if present at all, would occur very early following the commencement of SGLT2 inhibitor therapy, and there is no indication that body weight acts as a surrogate for muscle mass changes over time during the course of trials lasting 3-4 years  [17].