dataesr/scientific-paragraphs-categorization huggingface dataset:
176G>A [p. Gly59Glu] [GenBank: NM_002140. 4]; P19 HNRNPK variant, c. 173T>C [p. Ile58Thr] [GenBank: NM_002140. 4]), who did not classify as having the AKS signature, which is in contrast to the missense variants in the AKS intermediate signature group. While it is possible these individuals have a condition that is unrelated to HNRNPK, we also wonder whether there could be a separate, non-AKS, but still HNRNPK-associated, condition. Additional individuals will be required to clarify this possibility. The functional roles of the genomic regions within the AKS-associated HNRNPK signature are of great importance, as they demonstrate enrichment in CpG island shores where DNA methylation levels are susceptible to change under several conditions, such as tissue differentiation, reprogramming, aging, and disease.
for P9 (HNRNPK variant, c. 213þ5G>A [GenBank: NM_002140. 4]), all the individuals with an intermediate DNAm score harbored an HNRNPK missense variant. It is clear, however, that this intermediate DNAm signature is still well within the spectrum of AKS and not associated with a separate HNRNPK related condition, as multiple individuals who clearly have AKS by diagnostic criteria classified with an intermediate DNAm score (P5, 6, 7, 9, 10, 12, and 22). Furthermore, one of the recurrent HNRNPK variants, c. 137G>T (p. Arg46Leu) (GenBank: NM_002140. 4),classified as positive for the AKS signature in one individual (P2), whereas it classified as intermediate for the AKS signature in a different individual (P7). There are few published reports of DNA methylation variation for recurrent sequence variants.
Acknowledgments We are grateful to all the families who participated in this research and to the many clinicians who recruited them into the study. We acknowledge the technical assistance of  Youliang Lou  and  Chunhua Zhao. This work was supported by a  Canadian Institutes of Health Research (CIHR)  grants to R. W. ( PJT-178315 ) and the  Ontario Brain Institute (Province of Ontario Neurodevelopmental Disorders [POND] network  [ IDS11-02 ]) grants to R. W. This work was also supported by a grant from  SFARI  ( 887172  for R. W. ). F. A. , H. A. , and Z. R. acknowledge the  King Salman Center for Disability Research  for funding this work through Research Group no. RG-2022-010. J. T. -C. , J. N. , and P. L. were funded through  FEDER  funds  PI20/01053. The work of G. E. was supported by the  Jacob Goldfield Foundation  and the  RTW Charitable Foundation.
7, 8 Furthermore, alteration of cortical development also involves normotopic (i. e. , normally located) and apparently normal cortical regions,  9, 10  resulting in complex and variable organization of the epileptogenic zones across patients. Intracranial EEG data are nonetheless in agreement on a higher involvement of the normotopic cortex compared to NH. 3, 11  In a recent study of 19 patients with NH recorded with SEEG, seizure onsets were mostly normotopic (48. 5%) or normoheterotopic (45. 5%), whereas only 6% were purely heterotopic. 3 However, all of the normotopic regions were not equally involved, and differentiating epileptogenic from non-epileptogenic normotopic cortices is challenging. The commonly used anatomical classification between "local" (same lobes as the NH) and "distant" normotopic cortices is questionable, especially for patients with posterior periventricular multiple NH, which face the temporoparieto-occipital junction, and for whom most cortical regions could be considered "local" while separated by several centimeters.
18 A few case reports used cortico-cortical evoked potentials (CCEPs) to study NH functional connectivity  19, 20  or to help localize small NH scattered in the white matter. 21 Some noninvasive reports, using fMRI and magnetic transcranial stimulation with simultaneous scalp EEG, suggested that the existence of functional connectivity between NH and normotopic structures could predict the epileptogenicity of the network,  17, 22  but this hypothesis has not been formally evaluated using invasive EEG recordings. To address this issue, we analyzed the SEEG data of patients with NH in whom single-pulse electrical stimulation (SPES) had been performed within the NH. Our objectives were: (1) to describe NH functional connectivity using CCEPs, and (2) to evaluate if the normotopic structures functionally connected with the NH would be more epileptogenic than unconnected structures.
Key Points • Single-pulse electrical stimulation is a useful tool to assess the epileptogenic network in nodular heterotopias. 1 ). For the other eight patients included in Pizzo et al. ,  3  only high-frequency (50 Hz) stimulations were performed and were therefore not eligible for the current study. All 12 patients had a comprehensive phase I surgical evaluation including surface EEG, neuropsychological testing, routine MRI, and [18F]-fluorodeoxyglucose positron emission tomography. The patients underwent SEEG exploration during long-term video-EEG monitoring, as part of normal clinical care. We collected and analyzed clinical and raw data of SEEG recordings. All patients were fully informed of the purpose and risks of the SEEG procedure and gave their written consent. The local ethics committee approved this study.
| SEEG recordings SEEG was routinely performed according to Talairach's stereotactic method  23  or using a frameless stereotactic surgical robot. The brain regions to be investigated by depth electrodes were determined for each patient, based on individual presurgical data and most likely origin of seizure onset, independently from this study. Electrodes were 0. 8 mm in diameter and included 5-18 recording contacts, 2 mm in length, 1. 5 mm apart (Dixi Medical in Lyon, ALCIS in Marseille). They were implanted stereotactically after a peroperative angiography. We recorded EEG signals using a video-EEG monitoring system-Micromed (Lyon) or a Coherence-Natus (Marseille)-at a sampling rate of 256, 512, or 1024 Hz. The referential electrode was a contact located within the white matter or the skull, as far as possible from the suspected epileptogenic zones.
| Ictal recordings: Visual analysis, computation of the epileptogenicity index, and seizure classification As described in the previous study,  3  seizure data were collected and visually analyzed, with special attention to the seizure-onset patterns and to the structures involved at seizure onset. We computed the epileptogenicity index (EI) for all selected seizures by using our in-house plugin, implemented in the AnyWave software,  26, 27  available respectively at: https://meg. univ-amu. fr/wiki/AnyWa ve:Plug-ins and https://meg. univ-amu. fr/wiki/AnyWave. The EI is a method computed on SEEG signals that aims to quantify the epileptogenicity of brain structures. 3, 28 In summary, the EI is intended to quantify two important features of the transition from preictal to ictal activity on the SEEG signals: (1) the signal energy redistribution from lower frequency bands (theta, alpha) toward higher frequency bands (beta, gamma, high gamma, and ripple), and (2) the delay in appearance of these high-frequency components in a given structure with respect to the first structure involved in a "rapid discharge" mode of seizure onset.
| Connectivity analysis Outbound connectivity was studied for all patients. Two patients (C and D) showed no connection; their data were then withheld from further analysis. Among the remaining 10 patients, we found 135 outbound connections (4-36 per patient) revealing 92 connected normotopic regions (3-19 per patient). The latencies of outbound CCEPs were [mean ± SD] 36 ± 15 ms and amplitudes were 130 ± 96 mA. Three patients (E, F, rK) each showed one outbound connection with contralateral normotopic cortex, linking a posterior periventricular NH to contralateral temporal or parietal lateral cortices (Table  2 ). Inbound connectivity was available for only the six patients who had SPES performed within normotopic cortices. We found 72 inbound connections (7-30 per patient) revealing 38 connected regions; 33 of those (86%) were also outbound connected regions.
This approach is at the basis of the GREAT software and webserver  (McLean et al. , 2010) , which uses the resulting associations between cis-regulatory elements and genes to infer functional enrichments for sets of regulatory elements. This tool is very popular, as illustrated by the large number of scientific articles that cite it (3196 citations in Google Scholar as of March 2022). GREAT is widely used to predict functional enrichments, including but not limited to Gene Ontology enrichments, for sets of cis-regulatory elements that are of interest in a given study -for example, enhancer elements that are specifically active in response to an external stimulus or in a given tissue. The assumption of genomic proximity between enhancers and target genes is based on reasonable that many enhancers regulate their closest neighboring genes  (Gasperini et al.
, 2022). Thus, there is growing evidence that the "genomic proximity" assumption is challenged by the presence of a large proportion of long-range regulatory interactions between enhancers and genes. In light of this, we developed an alternative to the GREAT software which does not rely on this assumption. Here, we report a new tool named GOntact, which uses PCHi-C data to infer associations between cisregulatory elements and genes, and which uses these associations to infer gene ontology enrichments for sets of regulatory elements. We apply GOntact on multiple enhancer and PCHi-C datasets from human and mouse and show that it generates functional annotations that are coherent with the patterns of activity of enhancers. We find that there is substantial overlap between functional predictions obtained with GREAT and GOntact but that each method can yield unique testable hypotheses, reflecting the underlying differences in target gene assignment.
Overall, individual genes are assigned substantially more enhancers with GOntact than with GREAT (Figure  1. B). The median number of ENCODE enhancers assigned to a gene is 24 with GREAT and 51 with GOntact, for human (Wilcoxon rank sum, p-value < 1e-10, Figure  1. B). The number of gene-enhancer relationships inferred by GOntact likely depends on the quality and quantity of PCHi-C data used as an input (N = 910069 interactions in total for human across 26 samples, N = 823539 for mouse across 14 samples). We restricted the number of chromatin contacts by considering only those detected in at least 2 PCHi-C samples (N = 539794 for human, N = 434305 for mouse). With this reduced dataset, the number of regulatory relationships inferred with GOntact decreases (median number of 33 enhancers per gene in the human ENCODE dataset), but remains significantly higher than the one inferred with GREAT (Wilcoxon rank sum test, p-value < 1e-10, Figure  1.
D. Distribution of the distances between genes and enhancers predicted to be in regulatory interactions with the GREAT, GOntact or fixed window approach. Color code as in B and C. than 100 kb and 8% are separated by more than 500 kb (Figure  1. D). With GOntact, distances between pairs of enhancers and promoters are generally larger: only 22% are separated by less than 100 kb, and 16% are separated by more than 500 kb (Figure  1. D). We note that there is an excess of enhancers situated within 50 kb of the TSS even when we define gene-enhancer pairs with a "fixed window" approach, that is, when we assign to genes all enhancers present within a maximum distance of 1 Mb of the TSS (Figure  1. D). In contrast, regulatory associations inferred from PCHi-C data are less frequent in close proximity to genes, which is at least in part due to the technical difficulty of distinguishing genuine from spurious chromatin contacts at small distances on the linear genome  (Cairns et al.
Gene ontology enrichment tests Beyond defining putative target genes for cis-regulatory elements, the aim of both GOntact and GREAT is to test for the enrichment of functional annotations associated with a set of cis-regulatory elements. Here, we apply both methods to search for enrichments of Gene Ontology (GO) annotations, in a set of enhancers. With both approaches, each enhancer is assigned all GO categories that are associated with its predicted target genes. For each GO category, we then compute the proportion of enhancers that are associated with it, among all enhancers in the dataset of interest (hereafter termed "foreground set"). We then test for an enrichment by comparing this proportion with the proportion of enhancers associated with the same GO category within a background set of elements, with a one-sided binomial test (Methods).
We found that all 5 methods yielded GO enrichment results that were coherent with the patterns of activity of the input enhancer datasets. For example, for embryonic heart enhancers, all methods reveal significant enrichments for GO categories related to muscle functions (Figures  2, 3 ). Likewise, for embryonic midbrain enhancers, all 5 methods reveal significant GO enrichments related to neural differentiation (Figures  2, 3 ). Although there is considerable overlap between the significant GO categories found by all methods (Figure  4. ), each of them appears to prioritize a different subset of biologically relevant GO annotations. For example, for embryonic heart enhancers, GOntact methods tend to bring forward categories related to the early development of the organ (e. g. , lateral mesoderm formation, embryonic heart tube elongation), whereas GREAT seems to prioritize categories related to the physiology of the embryonic and adult organ, such as the regulation of striated muscle contraction or the regulation of blood circulation (Figures  2, 3 ).
Interestingly, for embryonic midbrain enhancers, both GREAT and GOntact find stronger enrichments for processes related to gene expression regulation, rather than to brain development (Figures  2, 3 ). Enrichment of meaningful functional annotations with a simple "fixed window" approach Surprisingly, the simplest method used in the assignment of putative enhancer target genes (the fixed 1 Mb window approach) also reveals numerous biologically relevant GO category enrichments, at least with the enhancer datasets that we tested (Figure  2. ). For heart enhancers, this approach reveals significant enrichments for metabolic processes, but also for muscle adaptation and muscle hypertrophy (Figure  2. ). In fact, for this set of human enhancers, the fixed window approach is the most sensitive: it uniquely identifies more than 300 significantly enriched GO categories that are not shared with the other approaches (Figure  4.
) and are also coherent with the expression patterns of the selected sets of enhancers (Figure  3. ). A key factor for the success of the GOntact approach is evidently the input PCHi-C data. Ideally, chromatin contacts and cis-regulatory element activity should be assessed in the same biological conditions. However, PCHi-C data is not yet available or efficiently produced for many cell types or tissues. As an alternative, we used the subset of chromatin interactions that were detected in at least two PCHi-C samples (Methods). This restricted set of interactions should be enriched in widespread (or even ubiquitous) chromatin contacts, which are likely relevant for the regulatory program of the tissue or cell type in which enhancer activity was assayed. A disadvantage is that the number of interactions.
4: Comparison between Gene Ontology enrichment results obtained with different methods : A. GREAT (default implementation, red), fixed window (black), GOntact (using all PCHi-C data, dark orange), GOntact (using chromatin contacts observed in at least 2 PCHi-C samples, purple), GOntact hybrid mode (25 kb fixed window around TSS + chromatin contacts from all PCHi-C data, blue). The bars represent the number of significantly enriched GO categories (maximum FDR 0. 05, minimum enrichment 1. 5) in the 32 possible combinations of the 5 cited methods. The colored dots below the bars represent for each method whether it was included in each combination. The enrichment was computed by comparing a set of 141 experimentally validated human enhancers active in the heart with the entire ENCODE enhancer dataset.
B. Same as A, for 279 enhancers active in the brain. C. Boxplots depicting the proportions of ENCODE enhancers that are associated with each significantly enriched GO category, detected with the 5 methods described above. D. Same as C, for 279 enhancers active in the brain. that can be used is substantially reduced, which likely affects our power to detect statistically significant functional enrichments. Indeed, we found much fewer significantly enriched GO categories with this conservative variant of GOntact than with all available PCHi-C data or with the GREAT approach (Figure  4. ). However, these functional annotations remain biologically relevant: biological processes related to cardiac cell development and muscle adaptation were found for the embryonic heart enhancers (Figure  3. B), and categories related to neural cell differentiation were identified for the embryonic brain (Figure  3.
Discussion Both GOntact and GREAT aim to predict putative biological functions for a set of cis-regulatory elements of gene expression. This functional annotation can serve as a basis for further experimental validations, and can thus be seen as a source of testable hypotheses. We are indeed unable to perfectly assess the validity of our results because we lack a "golden truth". Instead, we can only judge whether the results that we obtain are biologically meaningful, given what is known about the input dataset. Here, we applied our methods on sets of experimentally validated enhancer elements, whose pattern of activity was ascertained in mouse embryos with enhancer trap experiments  (Visel et al. , 2007). We thus expect an enrichment for biological processes associated with the development and the physiology of the organs in which enhancer activity was detected.
, 2015) , they are not restricted to this type of interaction. The chromatin contact datasets that we used as an input for GOntact are likely to include false positives arising as artifacts of the computational or biochemical methodologies that were employed, as well as genuine chromatin contacts that are not involved in gene expression regulation. Despite these drawbacks, we believe that GOntact nevertheless provides a useful alternative to the regulatory relationships inferred by the classical "genomic proximity" approach implemented in GREAT. A major advantage of the regulatory relationships inferred by GOntact is that they are to a large extent independent of the genome organization, in contrast with the methods implemented in GREAT. The definition of the regulatory domains directly depends on the TSS coordinates of the neighboring genes, upstream and downstream of the gene of interest.
Target gene inference for cis-regulatory elements with GOntact To infer enhancer target genes using chromatin contact data, we first analyzed the overlap and the distance between enhancer coordinates and restriction fragments. We used only interactions involving a baited (promoter) and an unbaited restriction fragment, in cis, within a maximum distance of 1 Mb. An enhancer is said to be in chromatin contact with a baited promoter if it overlaps with or if it is found within a certain maximum distance of the restriction fragments that interact with the baited fragment. The maximum distance between enhancers and restriction fragments is a command-line argument of the GOntact program and with a default value of 5 kb.
B. Boxplots representing the numbers of enhancers assigned to genes with GREAT, with GOntact (orange: using all available PCHi-C data; purple: using chromatin contacts shared across at least 2 PCHi-C samples) or with the hybrid GOntact-GREAT approach (Methods). C. Boxplot representing the numbers of enhancers attributed to genes with a fixed window approach (1Mb around the TSS; Methods). D. Distribution of the distances between genes and enhancers predicted to be in regulatory interactions with the GREAT, GOntact or fixed window approach. Color code as in B and C.
Acknowledgements We thank  C. Berthelot ,  Y. Ghavi-Helm ,  F. Picard ,  C. François  and  D. Mouchiroud  for discussions and advice on the project. Computational analyses were performed using the computing facilities of the  CC LBBE/PRABI  and the Core Cluster of the  Institut Français de Bioinformatique (IFB)  ( ANR-11-INBS-0013 ). This work was funded by the  French National Research Agency  ( ANR-17-CE12-0019-01  "LncEvoSys").
Code and data availability All methods described here were implemented in a package named GOntact, written in OCaml. The source code and the R scripts used to generate graphics and statistical analyses are freely available in a GitLab repository: https://gitlab. in2p3. fr/anamaria. necsulea/GOntact. For reproducibility, we provide all data, source code and program binaries in a Singularity image. Processed data and supplementary files are also available here: http://pbil. univ-lyon1. fr/members/necsulea/GOntact.
Camera-traps have revolutionized the way ecologists monitor biodiversity and population abundances. Their full potential is however only realized when the hundreds of thousands of images collected can be rapidly classified with minimal human intervention. Machine learning approaches, and in particular deep learning methods, have allowed extraordinary progress towards this end. Trained classification models remain rare however, and for instance are only emerging for the European fauna. This can be explained by the technical expertise they require but also by the limited availability of large datasets of annotated pictures, which are key to obtaining successful recognition models. In this context, we set-up the DeepFaune initiative (https://deepfaune. cnrs. fr), a largescale collaboration between dozens of partners involved in research, conservation and management of wildlife in France.
The aim of DeepFaune is to aggregate individual datasets of annotated pictures to train species classification models based on convolutional neural networks, an established deeplearning approach. Here we report on our first milestone, a two-step pipeline built upon the MegaDetector algorithm for detection (discarding empty pictures and cropping the animal) and a classification model for 18 species or higher-level taxa as well as people and vehicles. The classification model achieved 92% validation accuracy and showed > 90% sensitivity and specificity for many classes. Most importantly, these performances were generally conserved when tested on an independent out-ofsample dataset. In addition, we developed a cross-platform graphical-user-interface that allows running the pipeline on images stored locally on a personal computer.
, 2015)  to identify 48 African mammals, yet all data was collected from one large study area in Serengeti National Park as part of the same monitoring project. Although this can lead to highly accurate models, when tested on data collected in the same sites, and even provide models which retain sufficient accuracy on out-of-sample data to be useful for others working at other sites, such approaches may not always be possible or desirable. For instance, we are not aware of any European research group that has either collected such a large camera-trap image dataset or trained an equally-powerful image classification model on European fauna up to this day. We believe there is potential in aggregating multiple small datasets from a large number of partners to obtain sufficiently large training, validation and test datasets to develop efficient classification models.