dataesr/scientific-paragraphs-categorization huggingface dataset:
From gene models to SNP models Although the unit of analysis in GWAIS is the SNP, biological interactions are often characterized at the gene level. Hence, we mapped all SNPs in the dataset to genes using FUMA  [47] , a post-GWAS annotation tool. We created an artificial input where every SNP is significant in order to perform such mapping on all the SNPs. We performed three SNP-gene mappings using FUMA's SNP2GENE: positional, eQTL and 3D chromatin interaction (Table  1 ). In the Positional mapping, we mapped a SNP to a gene when the genomic coordinates of former was within the boundaries of the latter ± 10 kb. The eQTL mapping uses eQTLs obtained from GTEx  [16]. We mapped an eQTL SNP to its target gene when the association P-value was significant in any tissue (FDR < 0. 05). Lastly, in the Chromatin mapping, we mapped a SNP to a gene when a contact had been observed between the former and the region around the latter's promoter in the 3D structure of the genome (250 bp upstream and 500 bp downstream from the transcription start site) in any of the Hi-C datasets included in FUMA (FDR < 10 -6 ).
Co-function gene and SNP networks We used Biofilter 2. 4  [34]  to obtain candidate gene pairs to investigate for epistasis evidence. Biofilter generates pairs of genes susceptible to interact (gene models) with evidence of co-function across multiple publicly available biological databases. It includes genomic locations of SNPs and genes, as well as known relationships among genes and proteins such as interaction pairs, pathways and ontological categories, but does not use trait information. As per Biofilter's default, we used gene models supported by evidence in at least 2 databases. Additionally, we removed self-interactions, as detection of within-gene epistasis requires special considerations and is beyond the scope of this paper. Given this set of gene models, and three different ways of obtaining SNP models from it, we removed all the SNPs that did not participate in any SNP model.
Subsequently, we created six datasets. In one dataset no filter was applied (Standard analysis), i. e. no Biofiltering nor any SNP-to-gene mapping. Hence, the original SNP set was used. We also created one dataset exclusively for each SNP to gene mapping (Positional, eQTL and Chromatin). Lastly, we created two datasets using joint mappings: one with all the mappings (Positional + eQTL + Chromatin); and one with only the functional ones (eQTL + Chromatin). Since the main objective of this protocol is to increase the biological interpretability of epistasis findings, we have excluded other combinations that mix functional and non-functional information (Positional + eQTL and Positional + Chromatin). We discarded SNP models involving rare variants (MAF < 5%) or in Hardy-Weinberg equilibrium (Pvalue < 0.
SNP-level epistasis detection and multiple testing correction We used PLINK 1. 9 to detect epistasis through a linear regression on the population structure adjusted phenotypes with the option --epistasis: Y = β 0 + β 1 g A + β 2 g B + β 3 g A g B , where g A and g B are the genotypes under additive encoding for SNPs A and B respectively, Y is the adjusted phenotype, and β 0 , β 1 , β 2 , and β 3 are the regression coefficients. PLINK performs a statistical test to evaluate whether β 3 = 0. It only returns SNP pairs with a P-value lower than a specified threshold. We used the default 0. 0001. Only SNP models were considered, apart from the Standard approach. To correctly account for multiple testing, the P-value threshold of significance had to be dataset-dependent as the number of tested SNP pairs changed from dataset to dataset (Section 2.
2. 1). We obtained these thresholds through permutations as in Hemani et al. [19]  (Fig. 1 ). More specifically, for each dataset, we permuted the phenotypes 400 times and fitted the aforementioned regression-based association model. This produced a null distribution of the extreme P-values for this number of tests given the LD structure in the data. For each dataset, we took the most extreme P-value from each of the 400 permutations and set the threshold for 5% family-wise error rate (FWER) to be the 5% percentile of these most extreme P-values. Posterior experiments showed that a higher number of permutations, 1 000, barely changed the empirical threshold (data not shown). Hence, 400 was a sufficient number of permutations to obtain an adequate threshold.
The distribution of π * under the null hypothesis can again be determined through permutations  [13]. In our procedure, which is detailed below for a given gene pair, we used B = 999 permutations and τ ∈ {0. 001, 0. 01, 0. 05}. Remarkably, and following the suggestion of Becker and Knapp  [2] , the null distribution includes both the statistic from the observed dataset, and from the 999 permutations. 2. For each value of τ and b, compute the test statistic W (τ ). 3. For each value of τ and b, estimate the P-value : π b (τ ) = B l=0 I(W b (τ )≥W l (τ )) B+1. 4. For each value of b, compute π * b = min τ π b (τ ).
Pathway analysis A pathway enrichment analysis on the neighborhood of a significant gene model can inform about the broader framework in which gene epistasis occurs. To define such neighborhoods, we adapted the network neighborhood search protocol from Yip et al. [50]. We computed the neighborhood of two genes as the list of all genes that (1) participate in any of the shortest paths between the two studied genes in the Biofilter network, once the direct link between them is removed; and (2) are also involved in a significant interaction with at least one other gene on these paths. We restricted our attention to neighborhoods containing at least 3 genes, including the 2 from the considered gene model. For each of these, we conducted a gene set enrichment analysis in relevant gene sets from the Molecular Signature Database (MSigDB version 7)  [24, 41].
We performed the enrichment analysis using a hypergeometric test, which compares the obtained overlap between two sets to the expected overlap from taking equally-sized random sets from the universe of genes. We favored the hypergeometric test over the chi-square test used in Yip et al. [50]  because the sample sizes of the neighborhoods were small and because chi-square is an approximation whereas the hypergeometric test is an exact test. The universe set was analysis dependant. It contained all genes in an annotated pathway and that can be mapped via genomic proximity to a SNP of the dataset for the Standard analysis, and genes present in Biofilter gene models, in an annotated pathway and that can be mapped via the appropriate SNP to gene mapping to a SNP of the dataset for the other analysis.
Type I error In this article, we propose a protocol for epistasis detection using a gene co-function network (Section 2. 2). Due to its multi-stage nature, type I error needs to be controlled. For that purpose, we performed a permutation analysis based on 1 000 permutations for each of the datasets, permuting the phenotypes and running the entire protocol to detect significant gene interactions (Table  2 ). This permutation procedure is independent of the one used in the proposed protocol to compute significance thresholds. When at least one significant gene interactions was observed in a permutation, that permutation was considered a false positive (FP). This allowed us to compute the type I error rate as # FP 1000. Type I error was under control in all tested experimental settings, with estimates ≤ 6.
The Positional analysis does not recover any SNP interaction Using the aforementioned SNP-gene mappings, and combinations of them (cross-mappings), yielded six datasets in which we analyzed SNP models (Section 2. 2. 3). The resulting epistatic SNP-SNP networks are described in Table  3. Strikingly, while the Standard analysis generated the largest SNP-interaction network (55 nodes/SNPs and 57 edges/interactions), the eQTL one was the largest by number of interactions (64). The Positional analysis produced no significant interactions at all. Notably, the significant SNP interactions tended to be located in nearby genomic regions and to overlap with GWAS main effects loci (Fig. 3A ). To investigate whether main effects could be driving some of the signals, even when in imperfect LD with epistatic SNP pairs (a phenomenon sometimes referred to as "phantom epistasis"  [10] ), we conducted a linear regression based test as in Section 2.
Biofilter boosts discovery of interpretable hypotheses Searching for epistatic interactions exclusively across edges of the Biofilter network greatly reduces the number of tests. Yet, this gain in statistical power might not lead to greater discoveries as it potentially disregards new interactions absent from databases. Hence, we tested whether exhaustively searching for epistasis on the datasets not reduced for Biofilter models but using each mapping, led to similar results. At the SNP level (Fig. 6B , upper panel), only a small proportion of the significant interactions were still detected when the network was not used. Strikingly, that difference got smaller at the gene level (Fig. 6B , lower panel). This suggests that the significant SNP models, even if fewer in number, are strong enough to lead to the detection of the gene eQTL Chromatin # sign.
Our Standard analysis highlighted two potentially new epistasic interactions involving percentage of gene pairs detected with the entire population that are recovered in the ten replicates with of the individuals Discoveries in the proposed protocol are guided by plausible biological interactions. Hence, every significant gene model can be traced back to a biological database, therefore producing biological hypotheses. For instance, the gene model MST1 -MST1R is significant in multiple pipelines. Both genes have been linked to IBD, both by themselves  [3, 6]  and in interaction with other genes  [48]. MST1R is a surface receptor of MST1, and, through physical interaction, they play a role in the regulation of inflammation.
The proposed pipeline increases reproducibility GWAIS results are notoriously hard to reproduce. Hence, we studied whether our proposed pipeline led to more stable results. For that purpose, we ran the whole protocol again on a random subset of the data containing 80% of the samples. In each subset, 49% of the individuals were cases, respecting the initial proportion of cases and controls of the entire dataset. We repeated this experiment 10 times for each SNP-gene mapping. Conservatively, we used the same SNP and gene significance thresholds as for the corresponding entire dataset. The Standard pipeline, which does not include Biofilter network-information, produced on average 11. 4 significant gene models (standard error (SE) 1. 1). With the eQTL (respectively Chromatin) analysis, we detected on average 5.
DISCUSSION In this article we proposed a new protocol for epistasis detection, based on a variety of functional filtering strategies (Section 2. 2), and studied its application to GWAS data for Inflammatory Bowel Disease (Section 2. 1). The protocol included several components to control for type I error, hereby strengthening our belief in the discovered genetic interactions. A common theme in the interpretation of epistasis results consists on linking the associated variants to an altered gene function. In this article, we considered 3 different such SNP-gene mappings. Notably, the number of SNP-gene correspondence provided by each mapping differed by orders of magnitude. Moreover, the different mappings unevenly described genes; for instance, genes that had most SNPs mapped by using a chromatin contact map, had comparatively few eQTL SNPs.
This motivated combining multiple mappings into an analysis (e. g. eQTL + Chromatin) in order to combine different perspectives of the epistasis process. For the most part, these complementary mappings improved the analyses, by recovering most of the interactions significant in the analyses that used one mapping at a time. Importantly, our results display the benefits of going beyond one single SNP-gene mapping (often, genomic position) to interpret epistasis results. Restricting the tested interactions to functionally plausible pairs of genes and SNPs joins two faces of epistasis: searching for statistical epistasis, yet exclusively on plausible candidates for biological epistasis. This has several advantages. First, a more targeted input dataset reduces the number of tests and, in consequence, the multiple testing burden.
In contrast, the high dimensionality of GWAIS data requires a much more stringent multiple testing correction and limits the detection of epistasis with low effect sizes. As we observed in Section 3. 2, adopting one of the proposed analyses may reduce the number of SNP interactions to test by more than half. Yet, the Standard analysis, which does not use Biofilter, produces the most significant gene models. Second, the proposed protocol addresses the reproducibility issues widespread in GWAIS by producing results that are consistent at the gene and pathway levels (Section 3. 5). Indeed, we observed an increased analytic robustness when using Biofilter gene models, in line with previous reports  [4]. In particular, eQTL and Chromatin mappings, separate or in combination, increased said robustness.
For instance, several interactions involved RNA genes of unknown function (e. g. LOC101927272 or LINC02178 ). Remarkably, while the Standard analysis produced rich results, the Positional analysis did not lead to any significant SNP models. They both use genomic position to map SNPs to genes, but Positional is restricted to gene models in Biofilter. We note that the Positional analysis does not coincide with how Biofilter is typically used on GWAS data for epistasis detection. The latter involves pooling all SNPs that are mapped to genes which occur in Biofilter proposed gene interaction models, and subsequently exhaustive screening those SNPs for pairwise interactions. These pairs may also involve gene pairs that were not highlighted by Biofilter, in contrast to our Positional analysis. We evaluated the impact of biofilter on the final resuls.
No significant SNP interaction were detected in Positional analysis. In the analysis without biofiltering (dataset reduced to mappable SNPs using genomic proximity, but not reduced to biofilter gene pairs), 62 pairs were significant. Also, on the 86 SNP interactions that passed the experimental threshold in the Standard analysis (dataset not reduced to mappable using genomic proximity, nor biofilter gene pairs), only 57 are mappable to gene interactions using genomic proximity. Hence, 66% of significant SNP pairs are mappable via genomic proximity in the Standard analysis. An important component of our protocol is the conversion of SNP-based epistasis to gene-based epistasis. The most popular approach consists in aggregating SNP-level P-values into gene-level statistics, which can be done in different ways (see  [28]  for some early examples, and  [46]  for recent developments).
One of the motivations was its proven ability to highlight meaningful interactions in a narrower alternative hypothesis space, at the expense of leaving parts of the interaction search space unexplored. The database that Biofilter built contained 37 266 interactions. This is notably smaller than other gene interaction databases, like HINT  [9] , 173 797 interactions), or STRING  [42] , 11 759 455 interactions). Testing gene interactions with other (combinations of) biological interaction networks was beyond the scope of this paper. Furthermore, Biofilter analysis or exhaustive screening may lead to non-overlapping results. An example within a regression context is given in  [4].
CONCLUSION In this study we presented a protocol to enhance the interpretation of epistasis screening from GWAS. It includes gene-level epistasis discoveries with type I error under control, as well as a network-guided pathway analysis. Moreover, it improves the robustness of the results, making epistasis detection more reproducible. Aggregating SNP-level results into gene-level epistasis is challenging, allows to include information from biological interaction databases. Based on that, we conducted multiple analyses that use different sources of prior biological knowledge about SNP-to-gene relationships and gene interaction models, as well as rigorous statistical approaches to assess significance. Each of them offers a different, albeit complementary view of the disease, which leads to additional insights.
We thank the  International IBD Genetics Consortium  for data collection and processing and for interesting discussions. Computational resources have been provided by the  Consortium des Équipements de Calcul Intensif (C ÉCI) , funded by the  Fonds de la Recherche Scientifique de Belgique (F. R. S. -FNRS)  under Grant No. 2. 5020. 11and by the  Walloon Region. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  666003  and  813533. KVS acknowledges opportunities and funding provided by  WELBIO (Walloon Excellence in Life sciences and BIOtechnology). C-A. A. acknowledges funding from  Agence Nationale de la Recherche  ( ANR-18-CE45-0021-01 ).
Unlike conventional channel-based audio and scene-based audio, an object-based approach is adopted not only in the audio, but also in other media components, such as videos and position data of instruments. The complete media data can be controlled and managed by abstracting a series of processes from recording to playback, and OBA can interpret and express viewing objects existing in the real world. In this paper, we present Co-Sound, an interactive audio-visual medium with WebAR. Such an audio-visual media platform is ideal for reproducing softwaremanaged object audio. By measuring the real-time response of multiple people to the system and the QoE (Quality of Experience) of the application using this system, we confirmed that Co-Sound create new and enhanced user experiences. The main findings of these experiments were that the delay of spatial synchronization with WebRTC was lower than that with WebSocket and the accuracy of AR-marker detection and calibration could deteriorate the QoE even when the WebAR media application was rated highly.
Design We propose a platform that enables multiple people to view and manipulate the same content by playing an object-based music event using interactive AR on the web. Co-Sound satisifes the following requirements. Co-Sound derives the audio data of the music event based on SDM ontology from the database, and centrally manages the displayed virtual objects. Viewers input video information and touch actions, and Co-Sound outputs binaural audio with camera images of virtual objects superimposed on them. Marker detection from the input video estimates coordinates of the camera, and those of each virtual object are determined by referring to the position information of the recorded data. Real-time rendering of AR images and sounds in response to touch actions realizes user interactivity. Moreover, Co-Sound synchronizes the virtual space with other devices by communicating the serialized object data.
DataChannel, which is one of the types of WebRTC for binary data transport, adopts Stream Control Transmission Protocol (SCTP), and can ensure reliable sequential transport of messages with congestion control. Santos-González reported that its packet transmission rate is higher than Real Time Streaming Protocol  [7]. We employed SkyWay v2. 0. 1 4  , a platform as a service (PaaS) designed as a real-time interactive multimedia service. SkyWay provides a signaling server for WebRTC connections, TURN server for packet relay, and WebSocket server. These servers are publicly stated to have been located in Tokyo. Two types of communication methods and protocols were implemented for the comparison experiments: (1) mesh type connection using WebRTC, and (2) start type connection using WebSocket. The open source of SkyWay JavaScript software development kit (SDK) implements WebSocket for room-type binary data communication; for this reason, we improved it to build a mutual DataChannel connection between peers even in room type.
Although the evaluation of the QoE in spatial shared AR has not been determined yet, Nishibori's study on delay recognition in music sessions over the Internet reported that the delay is recognized at 30 ms or more, and the performance becomes difficult at 50 ms or more  [12]. Vlahovic reported that the player's score and QoE decrease over 100 ms in first-person-shooting games in VR  [11]. The results of these experiments show that the average delay for WebRTC communi-  cation is less than 50 ms, and P2P in the same LAN could reduce the overhead by using SCTP and retain a lower latency than that using HTTP. Moreover, the transmission delay was independent of the message size and the number of devices within the range measured in the experiments. Even when the payload of AR spatial data became longer because of the increase in the number of AR objects and the complexity of the attributes, Co-Sound could be considered to be highly scalable with the real-time synchronization.
We also investigate the ability of our detector to deal with new backgrounds that appear in the validation set. Experiments, conducted on both aerial and satellite data sets , show that YOLO-fine significantly improves the detection performance w. r. t. state-of-the-art detectors, better deals with various backgrounds to detect known objects, and it is able to perform real-time prediction. In the remainder of the paper, Section 2 briefly goes through literature works in object detection in remote sensing with a focus on small objects. Section 3 revisits the one-stage YOLO detector family and describes our proposal, called YOLO-fine. In Section 4, we report our experimental studies on three different datasets, including two aerial (VEDAI  [31]  and MUNICH  [32] ) and one satellite (XVIEW  [33] ) remote sensing datasets.
In the remote sensing community, the detection of small objects has been mostly tackled by exploiting two-stage object detectors thanks to their capacity to generally provide more accurate detection performance as compared to one-stage detectors. In this scenario, the region-based approach appears to be more relevant since the first stage whose aim is to search candidate objects could be set to focus on small regions and ignore large ones. Many recent works have exploited state-of-the-art two-stage detectors, such as Faster R-CNN  [22, [41] [42] [43] , deconvolution R-CNN  [44] , and deformable R-CNN  [45, 46]  to detect e. g. , small vehicles, airplanes, ships, man-made structures, in remote sensing datasets collected mostly from aerial or Google Earth images. In  [41] , the RPN (Region proposal network) of Faster-RCNN was modified by setting appropriate anchors and leveraging a single high-level feature map of finer resolution for small region searching.
Authors also incorporated contextual information with the proposal region to further boost the performance of detecting small objects. In  [44] , Deconv R-CNN was proposed by setting a deconvolution layer after the last convolutional layer in order to recover more details and better localize the position of small targets. This simple but efficient technique helped to increase the performance of ship and plane detection compared to the original Faster R-CNN. In  [46] , an IoU-adaptive deformable R-CNN was developed with the goal of adapting IoU threshold according to the object size, to ease dealing with small objects whose loss (according to the authors) would be absorbed during training phase. Therefore, such an IoU-based weighted loss was adopted to train the network and improve the detection performance on small objects in the large-scale aerial DOTA dataset  [47].