dataesr/scientific-paragraphs-categorization huggingface dataset:
Magnin index thresholds for observed outcomes after a vaginal birth attempt are as follows: normal, > 23 cm; favorable, > 22 cm; uncertain, 21-22 cm; poor, 20-21 cm; and very poor, < 20 cm. A childbirth simulation was performed using PREDIBIRTH™ software  15  (Patent Cooperation Treaty: PCT/ IB2011/002573), facilitating the generation of a simulated trial of labor (STOL) score, which could potentially assist in the decision-making process. The STOL score represents the level of fetal brain compression that occurs during the childbirth simulation, expressed as a percentage of fetal head molding measured after the virtual birthing process is complete compared with the initial fetal head size and shape before entering the superior inlet of the birth canal. The maximum (greatest risk) score is 8 points.
a "failed trial", which was any CS except for a scheduled CS. The area under the curve (AUC) and Youden's J index were calculated and compared, with confidence intervals of differences generated by bootstrapping logistic models and recalculating STOLs. A non-parametric Kruskal-Wallis H test with Delivery as the dependent variable and Vcat as the grouping variable was performed to test the hypothesis that birth outcomes would be distributed differently according to STOL category. Statistical analyses were performed using R software packages (R Core Team, 2017, Vienna, Austria; https:// www. R-proje ct. org/)  17. The figures showing our results were created using Microsoft Excel, R package ggplot2  18  , and Visualizing Categorical Data 19 (R package version 1. 4-4)  20. Ethical approval. Institutional Review Board approval was not required because it was a retrospective study.
Discussion Currently, the main application of childbirth simulation is in the training of medical professionals  21, 22. To the knowledge of the authors, this software represents a novel 3D simulation tool that can be used to detect cephalopelvic disproportion and support clinical decision-making regarding the optimal route of delivery. The use of such tools could reduce the number of unnecessary scheduled CS procedures, emergency CS procedures, and traumatic childbirths. The STOL score generated by PREDIBIRTH is simple to use and can assist in the classification of patients into 3 delivery categories: favorable with a high probability of a low-risk delivery (scores of 1, 2, or 3); neutral, requiring a physician to be in attendance and at an increased risk for instrumental delivery (scores of 4, 5, or 6); and unfavorable, with the potential for complications associated with cephalopelvic disproportion or likely brain compression (scores of 7 or 8), indicating that a scheduled CS could be a preferable alternative to attempted vaginal delivery.
Many of the patients in the emergency CS group were found to have cephalopelvic disproportion, and the PREDIBIRTH test is able to predict the presence of a smaller pelvis or cephalopelvic disproportion. However, the PREDIBIRTH test also demonstrated that pelvic size is not the only criterion that determines the occurrence of cephalopelvic disproportion, and only simulating the movement of the fetal head into the maternal pelvis using 3D simulation software allows for a more accurate assessment of the risks associated with childbirth. The vast majority of traumatic deliveries in obstetrics are related to cephalopelvic disproportion, which can result in increased postpartum bleeding  23, 24  , urinary incontinence  25  , fecal incontinence  26  , pelvic prolapse  27  , obstetric fistulas  28  , risk to the infant's brain  29  , and risk to the mother's perineum  30.
This information is medical and should, of course, be delivered by a professional rather than a computer, and the physician and patient can have a rational discussion regarding delivery options to select the best choice for the mother and child. The use of PREDIBIRTH software, which in vast majority of cases found no brain compression in this series of patients referred by their obstetrician for pelvimetry, can help to calm fears associated with cephalopelvic disproportion for both patients and physicians and help physicians guide patients toward potentially safer options. Future versions of PREDIBIRTH software will include recent observations regarding changes to the maternal urogenital sinus during the second phase of labor, as measured by MRI  41  , and will evaluate the stretching of the levator ani when the fetal head molding has been intense by incorporating MRI data obtained during labor.
Both instruments produced similar zooplankton variables by stations and by taxa and described similar zooplankton community compositions and biogeographical patterns, on the large mesozooplankton size range, i. e. , [0. 3-3. 39] mm ESD. We conclude that the ZooCAM and the ZooScan data can be combined to generate long term or spatially resolved zooplankton time series. Our study shows that benchmarking imaging instruments or techniques (1) offers a robust assessment of interoperability between instruments, mitigating possible instrumental biases, and (2) may be of great interest in the case of instrumental obsolescence or breakdown, to choose the most conservative replacement solution in a long term time series framework. Metazoan zooplankton (hereafter referred to as zooplankton) is a key biological compartment in marine ecosystems.
These newly developed imaging instruments rely on different imaging sensors and techniques such as commercially available digital cameras, flatbed scanners, linescan camera or holographic camera, e. g. , and could be based on different imaging methods such as steady imaging, in-flow imaging, or shadow imaging. These imaging instruments also differ in their image resolutions, sampled volumes, organisms' size range target, and whether they are able to image live organisms, preserved samples, or both. In-depth reviews of zooplankton imaging instruments conditions of use, specifications and capabilities can be found in  Lombard et al. (2019)  and  Romagnan et al. (2016). Modern imaging instruments often generate thousands to millions individual images of precisely sized organisms and particles per dataset.
2022)  as well as improved the reconstruction of long time series by enabling the integration of non-analyzed samples from the past in on-going series (see  Irisson et al. 2022  for a thorough review of the combination of zooplankton imaging and machine-learning). Finally, the use of commercially available instruments producing standardized datasets is favorable to collaborative work between institutions  (Gorsky et al. 2010 ). Zooplankton imaging is now considered a robust and cost effective approach to large-scale studies, long time series reconstruction and monitoring, in order to improve zooplankton ecology knowledge  (Lombard et al. 2019). However, the variety of zooplankton image collection methods and techniques, and achieved taxonomic resolution may make it difficult to compare, aggregate, and analyze zooplankton datasets originating from different imaging sources.
2018). It is used during the PELagic GAScogne (PELGAS) cruise in the Bay of Biscay for imaging spatially resolved zooplankton and fish eggs samples since 2016  (Doray et al. 2018). The zooplankton samples collected during PELGAS before 2016 were preserved and subsequently analyzed at the lab with the ZooScan. The extension of the historical PELGAS zooplankton time series with recent (and on-going) ZooCAM data thus depends on the interoperability of the two instruments. A preliminary study showed that both instruments output to similar zooplankton communities' compositions, total abundances and size spectra slopes using nine samples collected in shallow coastal area (Iroise Sea) in the Northern Bay of Biscay  (Colas et al. 2018). The reduced number of samples and the homogeneity of the sampling location do not totally exclude uncertainties on the interoperability of the two instruments for samples originating from more hydrologically diverse environments.
Taxonomic identification and selection of taxa All individual vignettes from both instruments were classified with the online application Ecotaxa  (Picheral et al. 2017). This online tool enables the automatic classification of all the individual vignettes into taxonomic categories. The classification was then visually inspected and manually validated or corrected when necessary. A total of 113,628 vignettes from the ZooScan and 184,335 vignettes from the ZooCAM, for objects larger than 0. 3 mm ESD, were initially sorted into 35 zooplankton and non-zooplankton categories. Non-living and nonzooplankton categories (i. e. , detritus, fibers, bubbles, artifacts, and phytoplankton) and overlapping objects were removed from those initial datasets (Table  1 , gray lines). All remaining zooplankton individuals were used for an assessment of the instruments interoperable size range based on the computation of Normalized Biovolume-Size Spectra (NB-SS, see "Computation of zooplankton variables" and the "Assessment" sections below).
This analysis led to the removal of a few dozens of very large objects from the initial datasets, resulting in working datasets composed of 113,390 and 184,202 individuals, for ZooScan and ZooCAM, respectively. Then, poorly represented taxa for which occurrences at stations combining the two instrument datasets were smaller than 30 (maximum occurrences is 122, i. e. , twice 61 stations), as well as nontaxonomically resolved objects (identified as "Other") were not taken into account in the zooplankton variables computation and in community structure analyses. Finally, the zooplankton communities as seen by the two instruments were compared using 27 taxonomic groups (Table  1 , Fig. 2 ). Some taxonomic groups aggregate a diversity of organisms. "Hydrozoa" include gelatinous medusa-like organisms, except the siphonophores, which were sufficiently well identified to constitute a separate group.
"Occurrence" is the number of times a taxa or group is present at a station, combining the two instruments datasets (maximum occurrences is 122, i. e. , twice 61 stations). The taxa highlighted in gray were not used for the community analyses, either because being non-living, imaging artifacts (e. g. , multiples), not zooplanktonic (e. g. , phytoplankton), unidentifiable (e. g. "other"), or too poorly represented in the datasets (Occurrence ≤30).
Dashed lines: mean NB-SS slopes. The stations' names are on the x-axis. The slopes were calculated in the [0. 3-3. 39] mm ESD size range. slopes by stations between the ZooScan and ZooCAM data without taking into account those with a significant difference (n = 58) had an estimated slope of 0. 88 (p-value <0. 05, R 2 = 0. 56) indicating a small difference between datasets (Table  2 ). The spatial patterns over the Bay of Biscay continental shelf were similar for the two instruments (Supporting Information Fig. S1 ).
Mean sizes and abundances by stations Then, the two instruments mean individuals sizes and total counts calculated by station were compared. Linear regressions were performed on mean sizes and log-transformed total counts between the ZooScan and the ZooCAM data. Spearman correlation tests were run to estimate the correlations between the values obtained with both instruments. For each variable, a procedure to find the stations where the ZooScan and the ZooCAM values were abnormally different was applied before performing the regressions and the correlation tests. The data points for which such an abnormal difference was detected were not included in the analyses, albeit being shown in the figures. The procedure is described in details in the Supporting Information section "Abnormal differences between instruments test procedure.
" The mean sizes (ESD) calculated at each sampling station ranged from 0. 49 to 0. 76 mm (mean = 0. 57 AE 0. 06 mm) in the ZooScan data, and from 0. 51 to 0. 73 mm (mean = 0. 58 AE 0. 05 mm) in the ZooCAM data. Mean size values by station were consistent between instruments and only one station had a ZooCAM mean size much larger than the ZooScan's (Sta. U0286, closed symbol in Fig. 5a , and in Supporting Information Fig. S4a, b , excluded from the linear regression and the correlation test). The linear regression was fitted to the 60 remaining data points and the two datasets were significantly correlated (r = 0. 94, Table  2 ), and depicted similar spatial patterns (Supporting Information Fig. S4 ). The total abundances by stations ranged from 919 individuals m À3 to 14,140 individuals m À3 (mean = 4,840 AE 3,550 ind m À3 ) in the ZooScan data and from 361 individuals m À3 to 19,525 individuals m À3 (mean = 5,330 AE 4,245 ind m À3 ) in the ZooCAM data.
Three stations showed abnormally different total counts between instruments. One station (U0203) showed higher ZooCAM total counts, while  the two other stations (U0315 and U0320) showed higher ZooScan total counts (Fig. 5b ). These stations were excluded from the linear regression and the correlation test. The linear regression was fitted to 58 remaining data points and revealed a robust agreement and a significant correlation between the two datasets (Table  2 ), and depicted similar spatial patterns (Supporting Information Fig. S5 ).
The ZooScan and ZooCAM total counts and mean sizes calculated by taxa were also compared. The procedure to detect abnormally large differences between ZooCAM and ZooScan taxa data was applied. The procedure is described in details in the Supporting Information section "Abnormal differences between instruments test procedure. " The mean ESD by taxa were very similar between instruments (Fig. 6a ). The Harosa and the Hydrozoans were the two taxa identified showing abnormally large differences between ZooCAM and ZooScan data. They both displayed smaller sizes with the ZooScan data, and were excluded from the linear regression and the correlation test. A linear regression was fitted to the 25 remaining taxa data points and revealed a robust agreement and a significant correlation between the two mean sizes datasets (Table  3 ).
The total counts by taxa were also consistent for both instruments but showed four taxa for which differences between instruments were abnormally large (Fig. 6b ). The Annelida larvae and the Metridinidae showed higher total counts with the ZooScan, whereas the Harosa and the Siphonophorae showed higher total counts with the ZooCAM. These taxa were excluded from the linear regression and the correlation test. A linear regression was fitted to the 23 remaining taxa data points and revealed a robust agreement and a  significant correlation between the two total counts datasets (Table  3 ).
The block averaging procedure was realized with the EchoR R library  (Doray et al. 2013). The gridded abundances data were log-transformed to reduce the skewness in their distributions. Two separated Principal Component Analyses (PCA, one analysis for each instrument) were used to summarize the data into principal components accounting for the largest part of the data variability. Then, the spatial structure of the zooplankton community was identified with a spatially constrained Hierarchical Agglomerative Clustering applied on the grid cells' coordinates in each PCA factorial space (Ward's method with euclidean distance matrix). The function hclustgeo in R language was used. Grid cell clusters were mapped to characterize the spatial structure of the zooplankton community. A biplot was also constructed with the grid cells' and the taxonomic groups' coordinates on the two first principal components to identify which taxa correlate to the spatial patterns.
For both instruments, the hierarchical clustering was done on the four first principal components of the abundance PCA, explaining 85% of the abundances datasets total variance. The abundances' spatial patterns were composed of four clusters and are presented with the most characteristic taxa of these spatial structures in Fig. 7. The zooplankton community spatial patterns were highly similar between the ZooScan and the ZooCAM and highlighted a coastal-offshore and a North-South gradients (Fig. 7a, c ). Only few differences were noted in the clusters' limits. The northern coastal cluster (cluster 3), which gathered grid cells along the Brittany and the Vendee coast extended further on the continental shelf with the ZooCAM data. The spatial limit between the southern and northern offshore clusters (clusters 1 and cluster 4, respectively) was located at 46 N with the ZooCAM data and further north, at 47 N, with the ZooScan data (Fig.
We therefore consider that our initial working hypothesis is validated: the ZooScan and the ZooCAM are interoperable. Yet, differences between the two instruments have been identified in both stations and taxa datasets. The two systems were used following different data collection strategies: the ZooCAM was used on-board on live organisms while the ZooScan was used in the lab on the same but preserved samples. Also, each instruments did not image the exact same aliquot of the sample. Therefore, the discrepancies found in the results are discussed hereafter according to three main points: the differences in the sample preparation before the image acquisition, the differences in the imaging technique and the differences in the image quality. How can we explain the observed differences?.
These differences in the sample preparation protocols may have resulted in occasional higher estimates of large organisms in the ZooCAM dataset and contributed to the discrepancies in the largest size classes of the size spectra, flatter NB-SS slopes, and higher mean sizes as well as higher biovolumes, detected at few sampling stations in the ZooCAM dataset. The same reason could explain why ZooScan abundances and biovolumes for Sta. U0315 were three times higher than those of the ZooCAM. Besides differences in the number of objects imaged (2,443 for ZooCAM and 1,645 for ZooScan), the subsampling ratio of the small size fraction aliquot digitized with the ZooScan was four times higher than that of the aliquot imaged with the ZooCAM (1/128 vs 1/32).
In addition, the mineralized protists, such as the acantharians (belonging to the overarching clade Harosa, along with radiolarians and foraminifera), are also degraded through the dissolution of their mineral skeleton if the fixative solution is not oversaturated with their skeleton main mineral  (Beers and Stewart 1970). These protists that can be large and contribute significantly to the biomass of oceanic zooplankton are now considered completely underestimated in preserved seawater samples, as a result of damage by net collection and dissolution in the preservatives  (Biard et al. 2016). The dissolution of mineralized protists, here gathered under the Harosa taxa, may explain their quasi absence in the ZooScan dataset (samples were preserved in buffered formaldehyde for 4 yr before digitization) compared to the much higher abundances, biovolumes and mean sizes estimates obtained when imaged live on-board with the ZooCAM, just after collection (fixative effect).
Imaging techniques With the ZooScan, the images are acquired along a 2D plane where the objects are set horizontally, which is the best orientation to capture the objects' size and the silhouette. On the contrary, the ZooCAM is an in-flow imaging system in which the position of the object cannot be controlled when the images are captured. The zooplankton orientation in space may have two main effects: (1) make the identification more complicated for the algorithm and the experts when specific features are not visible, and (2) underestimate the size of objects if their longest axis is not captured  (Colas et al. 2018 ). In our study, the Acartiidae, Metridinidae, and Euchaetidae contributed more to the total abundances and biovolumes in the ZooScan dataset than in the ZooCAM dataset. This result was essentially due to the difficulty to spot specific features in these taxa and differentiate them from non-identified calanoids (here gathered under the Calanoida taxa).
The random orientation of copepods in the ZooCAM flow cell may have contributed to the observed slight underestimation of those copepods' abundances and biovolumes in the ZooCAM dataset in comparison to the ZooScan dataset (orientation effect), but have presumably not affected their size measurements, as ZooCAM and ZooScan data agree for those taxa. We conclude that the orientation effect may slightly bias the size measurements, yet non-significantly. This effect seem to be the cause of a loss of taxonomic identification accuracy among calanoid copepods in the ZooCAM dataset. Yet, this loss do not have a strong effect on the observed zooplankton community spatial patterns. Imaging quality: illumination, imaging sensor, and image processing Illumination, imaging sensor and image processing are potential causes of the significant differences in the abundances and biovolumes estimates of the transparent organisms such as Hydrozoans and Siphonophorae.
The benefits of comparing zooplankton imaging instruments The comparison of the ZooScan and the ZooCAM highlights their interoperability on the large mesozooplankton size range, reflecting the possibility to combine zooplankton datasets originating from both instruments to expand their spatial and / or temporal coverage (e. g. ,  Grandremy et al. 2023). Following this example, efforts should be made to inter-compare and inter-calibrate other imaging systems to evaluate their interoperability and therefore the possibility to assemble existing datasets in order to spatially and/or temporally extend zooplankton ecological studies. Also, intercalibration and benchmarking of different imaging methods or instruments may enable to extend as well the size range of target organisms, paving the road toward more ecologically and biology integrated studies (e.
This would be achievable only by benchmarking instruments and methods. Our study highlighted another aspect of the usefulness of benchmarking imaging instruments and methods: instruments can be complementary and help get a better picture of the reality. This is highlighted by the Harosa and Siphonophorae that were better imaged with the ZooCAM methods and technique (live), and with the copepods for which the ZooScan enabled to better identify calanoids to the family level. Since the two instruments are intercomparable, the two datasets could be combined to take into account their advantages, and mitigate the weaknesses of each of them. Finally, benchmarking instruments enables the detection of technical as well as methodological biases that could be overcome by developing instrumental, software or methodological upgrades, and could be addressed when developing new devices.
Data availability statement The ZooScan data can be found in the SEANOE dataportal, in the dataset "PELGAS Bay of Biscay ZooScan zooplankton  Dataset (2004 Dataset ( -2016 )" (doi: https://doi. org/10. 17882/94052). The ZooCAM data can be found in the SEANOE dataportal, in the dataset "PELGAS Bay of Biscay ZooCAM zooplankton Dataset (2016-2019)" (doi: https://doi. org/10. 17882/94040).
The response of a biological neuron depends on the precise timing of afferent spikes. This temporal aspect of the neuronal code is essential in understanding information processing in neurobiology and applies particularly well to the output of neuromorphic hardware such as eventbased cameras. However, most artificial neuronal models do not take advantage of this minute temporal dimension. Inspired by this neuroscientific observation, we develop a model for the efficient detection of temporal spiking motifs based on a layer of neurons with heterogeneous delays which we apply to the computer vision task of motion detection. Indeed, the variety of synaptic delays on the dendritic tree allows to synchronize synaptic inputs as they reach the basal dendritic tree. We show this can be formalized as a time-invariant logistic regression which can be trained using labeled data.