dataesr/scientific-paragraphs-categorization huggingface dataset:
This operation is applied for each channel. However, this is not enough as this only takes care of colorimetry. Brands and vehicle appearances still matter a lot, as illustrated by the better results in Christchurch, which is a town in New Zealand with cars and pick-ups with an American look. Vehicle brands and different land covers around the cars might influence the classifiers by introducing unforeseen perturbations. This is particularly true for rarer vehicles such as pick-ups and trucks, which significantly differ in the US and Europe. More comprehensive regularization during fine-tuning and/or training on a more diverse dataset would help alleviate this phenomenon. More generally, the transfer learning issue relates to unsupervised domain adaptation  [42] , which is still under heavy investigation for remote sensing data, with techniques such as  [43].
Traffic Density Estimation Now that we have extracted individual vehicles from the image, the most basic task that we can perform is estimating the number of objects in a designated area. For fair comparison, we subdivide the testing sets for the two datasets into 1000 × 1000 grids (i. e. , 125 × 125 m 2 area) and we compute the relative errors in the vehicle count by comparing the number of vehicles extracted from SegNet's mask against the ground truth, i. e. : | # predicted vehicles -# actual vehicles | # actual vehicles. (2) Averaged results (rounded to the nearest integer) for each dataset are detailed in Table  9. On both ISPRS Potsdam and NZAM/ONERA Christchurch datasets, the vehicle count is less than 10% (predictions are right ±5 vehicles). Christchurch estimations have a higher error, although they might still be accurate enough for a first approximation.
Conclusions In this work, we presented a three-step framework segment-before-detect to segment, detect and classify vehicles from aerial RGB images using deep learning. More precisely, we showed that deep networks designed for semantic segmentation such as SegNet are useful for scene understanding of remote sensing data and can be used to segment even small objects, such as cars and trucks. Moreover, we showed that vehicle detection came without effort from this high resolution segmentation using simple connected component extraction, with results superior to previously used expert methods. We illustrated this fine segmentation on two challenging remote sensing datasets on the cities of Potsdam and Christchurch in various environments. Using a simple morphological approach for connected components extraction, we showed that these high resolution semantic maps were sufficient to extract object-level boundaries that outperform traditional vehicle detection methods.
Future work on this step could involve integrating contour prediction in the segmentation network  [34]  or moving to direct instance prediction using recurrent attention  [39, 44]. In addition, we presented a simple deep learning based method to further improve this analysis by classifying the different types of vehicles present in the scene. We trained several deep CNN on the VEDAI dataset and transferred their knowledge to the Potsdam and Christchurch datasets. Although with some difficulties due to challenging unsupervised domain adaptation tasks, our models managed to classify vehicles with an average accuracy of more than 67% on the ISPRS Potsdam dataset and 80% on the NZAM/ONERA Christchurch dataset. This could be even further improved thanks to better regularization methods, such as unsupervised representation learning  [45]  on larger datasets or domain adaptation techniques such as optimal transport  [43].
Acknowledgments: The Potsdam dataset was provided by the  German Society for Photogrammetry ,  Remote Sensing  and Geoinformation (DGPF): http://www. ifp. uni-stuttgart. de/dgpf/DKEP-Allg. html. The Vehicle Detection in Aerial Imagery (VEDAI) dataset was provided by  Sébastien Razakarivony  and  Frédéric Jurie. The  NZAM/ONERA Christchurch dataset uses imagery from New Zealand 's Land Information Office, licensed under a Creative Commons Attribution 3. 0 New Zealand License with Crown copyright reserved. Image data are downloadable from the LINZ website: http://www. linz. govt. nz/land/maps/linz-topographic-maps/imagery-orthophotos/christchurch-earthquake-imagery. Ground truth is property of ONERA/DTIS and available upon request: bertrand. le_saux@onera. fr)OpenStreetMap Data c OpenStreetMap contributors are available under the Open Database Licence-http://www.
Population genomic analyses The species genetic structure was inferred using STRUCTURE  (Pritchard, Stephens & Donnelly, 2000)  v2. 3. 4. This approach requires unlinked markers, thus only one variant by locus, randomly selected, has been kept. The STRUCTURE analysis was carried on the datasets generated by each tool. Simulations were performed with genetic cluster number (K) set from 1 to 10. Best K was identified using  Evanno, Regnaut & Goudet (2005)  method. We used 20,000 MCMC iterations after a burn-in period of 10,000. The output is the posterior probability of each sample to belong to each of the possible clusters. For C. lophota species, a multivariate analysis were used to investigate intra-specific genetic structure using adegenet R package  (Jombart, 2008).
Robustness with respect to parameters In DiscoSnp-RAD, the main parameter is the size of k-mers, used for building the dBG. As shown Fig. 3 , DiscoSnp-RAD results are robust with respect to k, the main parameter, and its fine choice is thus not crucial. This figure also the results robustness with respect to other parameters such as the maximal number of predicted SNPs per bubble (5 by default), the maximal number of substitutions authorized when mapping reads on bubble sequences (10 by default), and the maximal number of symmetrically branching crossroads (also 5 by default). Concerning this last parameter, Fig. 3  also shows the advantages of the "high_precision" mode which sets this parameter to zero, leading to a precision of nearly 100%. We enriched results presented in this figure with two additional simulated datasets, following the protocol presented in the "Simulation protocol" section, but introducing SNPs at rates of 0.
5% and 2%, instead of 1%. Results are presented in the Fig. S2. They also highlight the robustness of results and the rational for the default parameter choices with two times more and two times less simulated diversity. vcf file obtained with the 259 samples, 40 C. samples were extracted and the same filters, that is, MAF, missing etc. , were applied on this C. lophota dataset. We obtained 1,306 SNPs by selecting one variant per locus extracted from 4,364 variants identified in this species. The multivariate analysis of this identify three populations comprising respectively 31, 5 and 4 samples (Fig. 4 ). Notably, the genetic structure follows the geographic distribution of the samples, with samples from one population originating from western locations, another population from eastern locations and an intermediate population.
Easy parameter choice Another substantial advantage of using DiscoSnp-RAD is the that parameters are not directly linked to the level of expected divergence of the compared samples. In fact, they impact the number and type of detected variants, but are not related to the subsequent clustering step. As a result, parameters can used whatever the type of analysis (e. g. , or inter-specific), contrasting with classical tools in which parameters govern loci recovering. Indeed, in STACKS, the parameters governing the merge of the stacks can compromise the detection of relevant variants if they are not adapted to the studied dataset  (Shafer et al. , 2017). Therefore, the authors recommend to perform an exploration of the parameter space before downstream analyses  (Paris, Stevens & Catchen, 2017).
This ability of DiscoSnp-RAD to handle reads that do not necessary start at the same genomic position makes it particularly well to analyze datasets produced by another of genome-reduction techniques, namely sequence capture approaches  (Grover, Salmon & Wendel, 2012). In these techniques, DNA shotgun libraries are subject to enrichment using short commercially-synthesized  (Faircloth et al. , 2012)  or in-house made  (Suchan et al. , 2016)  DNA or RNA fragments acting as "molecular baits", that hybridize and allow separation of homologous fragments from genomic libraries. One of such promising approaches is HyRAD, a RAD approach combining the molecular probes generated using ddRAD technique and targeted capture sequencing, designed for studying old and/or poor quality DNA, likely to be too fragmented for RAD-sequencing  (Suchan et al.
Results on real data In this section, we an application of the DiscoSnp-RAD implementation on ddRAD sequences obtained from the anthomyiid flies from the Chiastocheta genus. In this genus, classical mitochondrial markers are not suitable for discriminating the morphologically described species  (Espíndola, Buerki & Alvarez, 2012). Although RAD-sequencing dataset phylogenies supported the species assignment  (Suchan et al. , 2017) , the interspecific relationships between the taxa could not be resolved with high confidence due to high levels of incongruences in gene trees  (Gori et al. , 2016; Suchan et al. , 2017). The dataset is composed of 259 sequenced individuals from 7 species. Results obtained on DiscoSnp-RAD were compared to the prior work of Suchan and colleagues, based on pyRAD analysis  (Suchan et al.
Bioinformatics tools are imperative for the in depth analysis of heterogeneous high-throughput data. Most of the software tools are developed by specific laboratories or groups or companies wherein they are designed to perform the required analysis for the group. However, such software tools may fail to capture "what the community needs in a tool". Here, we describe a novel community-driven approach to build a comprehensive functional enrichment analysis tool. Using the existing FunRich tool as a template, we invited researchers to request additional features and/or changes. Remarkably, with the enthusiastic participation of the community, we were able to implement 90% of the requested features. FunRich enables plugin for extracellular vesicles wherein users can download and analyse data from Vesiclepedia database.
Most of the software tools are developed by specific laboratories or groups or companies wherein they are designed to perform the required analysis for the group. However, the software tools fail to capture "what the community wants in a tool". A "community needs software" may overcome these hurdles and aid in the development of a comprehensive data analysis tool. Here, we report a novel community needs software initiative in the context of functional or gene set enrichment analysis. To achieve this, we initially reached out to the scientific community through editorial  [6] , conference participations, social networking sites and communicated with researchers in the OMICS community via e-mail. FunRich, an open access standalone functional enrichment analysis tool  [7] , was used as a template for this purpose.
By various means of communication, we invited the researchers to request for additional features/changes in FunRich software through the online forum (http://www. funrich. org/forum). We had enthusiastic participation from many researchers who requested for additional features/changes in the existing software. By September 2016, we had 54 unique requests/changes from users worldwide (Supplementary Table  1 ). The features were prioritised based on the number of users per request and were implemented in FunRich tool over the last 18 months. Remarkably, 90% of the requested features have been implemented in the new version of FunRich (Version 3). The updated version of FunRich is now freely available for download (http://www. funrich. org/download) both for academic and commercial users. To gain biological insights, researchers often rely on functional enrichment analysis of large scale data from high-throughput experiments to identify overrepresented classes.
Using FunRich, users can perform functional enrichment analysis with minimal or no support from computational and database experts for more than 13,320 species. The database is integrated from heterogeneous genomic and proteomic resources (>6. 8 million annotations). The background database in any analysis tool is critical for the analysis and needs to be constantly updated  [8]. However, the currently existing functional enrichment analysis tools do not allow the users to control the databases nor to update them in real time  [8]. Using the forum, one of the request from the community pertained to "user controlled databases" and "regular update of background databases". To address this, FunRich now uniquely allows the users to update the background database for 13,320 species from UniProt, Gene Ontology and Reactome in real time (Figure  1 ).
Additionally, the users can build custom databases with tab delimited files and perform the enrichment analysis irrespective of the organism and the type of dataset (e. g. metabolomics). Hence, these database options allows for longer sustainability of FunRich as a tool to perform functional enrichment analysis. Other popular requests that have been implemented in FunRich include miRNA enrichment analysis (requested by most users -Supplementary Table  1 ), customisable heat maps, plugin to analyse extracellular vesicle datasets, comparison of oncogenes using COSMIC database and customisable colour for all the publication quality graphs. In miRNA enrichment analysis, users can submit a list of miRNA and identify biological pathways that may be perturbed. Gene set enrichment analysis is normally performed with the number of input genes/proteins and the quantitative data is often ignored.
In FunRich, users can upload quantitative data and perform enrichment analysis for gene/protein expression values. For instance, total mRNA/protein abundance of genes involved in Wnt signalling pathway is compared between datasets in addition to number of genes. The quantitative data can also be utilised to generate customisable heat maps. Furthermore, users have complete control on all the graphs where the text and colour can be customised. Based on popular requests, FunRich now allows users to automatically download data from Vesiclepedia, an online compendium that hosts RNA and protein data pertaining to extracellular vesicles including exosomes  [9]. The input datasets can be compared with filtered Vesiclepedia data either through enrichment analysis or through Venn diagrams. In addition users can customise the data that can be downloaded from Vesiclepedia by using filters based on extracellular vesicles subtype, sample type, isolation method, cargo type and identification method.
Overall, with the involvement of researchers in the phase of software development, we have developed a comprehensive tool for functional enrichment analysis. As databases are not regularly updated in most of the functional enrichment analysis tools  [8] , the community requested features pertaining to automatic database update and custom database feature will allow for the continuous use of FunRich. Though we have completed 90% of the requests from researchers, we are constantly implementing the newer requests. We envision the development of a web-based version of FunRich and implementation of metabolomic analysis in the near future. With the advent of large volumes of data, it is critical to build such comprehensive software tools for data analysis. Based on this fruitful experience, we strongly encourage community driven software development for research purposes so as to build comprehensive software tools and to curtail software duplications.
Acknowledgements We would like to thank  Tejaswee P. Shah  for her valuable input in the development of FunRich. We would like to thank  Jan Lotvall  for helping us in this effort by circulating the email within his group. We thanks many other users who requested for features/changes in FunRich though the e-mail and the online forum. Suresh Mathivanan is supported by  Australian Research Council DECRA  ( DE150101777 ) and Award  U54-DA036134  supported by the  NIH Common Fund  through the  Office of Strategic Coordination/Office  of the  NIH Director.
In the breast cancer trial, the adaptive lasso penalty selected a prediction model with 4 clinical covariates, the main effects of 98 biomarkers and 24 biomarker-by-treatment interactions, but there was high variability of the expected survival probabilities, with very large confidence intervals. Conclusion: Based on our simulations, we propose a unified framework for: developing a prediction model with biomarker-by-treatment interactions in a high-dimensional setting and validating it in absence of external data; accurately estimating the expected survival probability of future patients with associated confidence intervals; and graphically visualizing the developed prediction model. All the methods are implemented in the R package biospear, publicly available on the CRAN.
Estimation of confidence intervals of the expected survival probability We compared two strategies to estimate the confidence intervals at level 1 -θ of the expected survival probabilities: one based on analytical expressions deriving from the normal approximation of the estimator and one based on a non-parametric bootstrap approach. The analytical approach  [21]  consists in estimating the variance of the cumulative risk Ĥ i (t) = Ĥ(t, T i , X i ) based on the Breslow estimator  [22]  in the Cox model. Thus, the confidence interval of Ŝ i (t) is approximated as CI 1-θ Ŝi t ð Þ À Á ¼ qθ 2 Ŝi t ð Þ À Á ; q 1-θ 2 Ŝi t ð Þ À Á h i with q α Ŝi t ð Þ À Á ¼ exp -Ĥ i t ð Þ þ z α ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi c var Ĥ i t ð Þ À Á q : This can be directly implemented via the R-package survival  [23].
Data generation and scenarios For each simulated data set, we generated p = 500 Gaussian unit-variance variables, including prognostic biomarkers and/or treatment-effect modifiers. Data were generated for a total of n = 1500 patients, randomly assigned to the experimental or control group with equal probability of 0. 5. We generated random survival times t with constant hazard. Table  1  shows the three null and three alternative scenarios considered in the simulations. Simulation parameters such as the desired baseline (i. e. x = (0, …, 0) T ) survival probability S 0 (⋅) at the horizon τ (5 years in our simulations) and the regression parameters α, β and γ were chosen on the basis of the early breast cancer trial presented in the section "Application". Thus, we fixed S 0 (5) = 77%. All the scenarios included censoring and an autoregressive correlation structure  [28]  between biomarkers ρ jj 0 ¼ 0:8 j-j 0 j j within 25biomarker blocks.
Application A retrospective biomarker study was performed on tumor samples from n = 1574 patients in an early breast cancer randomized clinical trial comparing chemotherapy plus adjuvant trastuzumab (arm C + T, n = 779) or not (arm C, n = 795). The comprehensive description of this data set is provided in Pogue-Geile et al. [1]. Gene expression data had been collected for p = 462 genes and normalized as in the original publication. Clinicopathological covariates such as ER and nodal status, and tumor size were also available. Median follow-up time for distant-recurrence free survival (DRFS) was 7. 1 years and the censoring rate was 73% (i. e. 431 events for DRFS). The arm-specific 5-year DRFS was 84% (CI95%: 81%-86%) and 64% (CI95%: 61%-68%) for patients in arm C + T and C, respectively (Fig.
Acknowledgments Nils Ternès acknowledges financial support by  Foundation Philanthropia Lombard-Odier. The authors also acknowledge the  National Surgical Adjuvant Breast and Bowel Project (NSABP)  investigators of the B-31 trial who submitted data from the original study to dbGaP (dbGaP Study Accession: phs000826. v1. p1) and the  NIH data repository. The B-31 trial was supported by:  National Cancer Institute ,  Department of Health and Human Services, Public Health Service , Grants  U10-CA-12027 ,  U10-CA-69651 ,  U10-CA-37377 , and  U10-CA-69974 , and by a grant from the  Pennsylvania Department of Health.
Collaboration between the clinical, clinical molecular diagnostic, and research communities may optimize discovery of disease genes, considering the rarity of specific genetic disorders  [13] [14] [15] [16] [17]. We performed a pilot study for systematic transfer of molecularly "unsolved" exomes from the clinical environment to a research setting, in order to potentially fuel human genetic disease gene discovery. The WES data from 74 probands for whom clinical singleton WES did not reveal a secure molecular diagnosis were augmented with WES from additional family members, where available. Additional bioinformatics filters, database resources, and interpretive analyses were implemented, leveraging systematic studies emerging from the research laboratory. A likely disease contributory gene and potential molecular diagnosis (i.
Depending on the clinical situation and individual availability, additional family members (e. g. parents or affected siblings) were also enrolled. Our pilot study consisted of 74 cases including 63 trios, four quartets, one multiplex family involving three affected siblings, and six singleton cases for which parental samples were unavailable. Prior diagnostic work-up was variable from case to case based on the referring physician's differential diagnosis (e. g. single gene testing, enzyme assays, array comparative-genome hybridization) and included a proband-only WES in all cases. Mitochondrial DNA sequencing was performed for all cases undergoing clinical WES through December 2014. Detailed clinical phenotype data were collected and entered into PhenoDB  [14, 28] , after contacting the families/ patients to obtain informed consent, and did not influence the choice of cases.
The raw sequence data were post-processed using the Mercury pipeline  [31]. First, the raw sequencing data (bcl files) were converted to fastq files using Casava. Then, the Burrows-Wheeler Alignment (BWA) tool was utilized to map short reads to the human genome reference sequence (GRCh37). Finally, the recalibration and variant calling were performed using GATK  [30]  and the Atlas2 suite, respectively  [32]. The Mercury pipeline is available in the cloud via DNANexus (http://blog. dnanexus. com/2013-10-22-run-mercury-variant-calling-pipeline/). For research cases, exome variant analyses were then independently performed in the Baylor Hopkins Center for Mendelian Genomics (BHCMG)  [15]  under a Research Protocol approved by the Institutional Review Board (IRB) for Human Subjects Research at BCM; this protocol enables bi-directional transfer of samples and data between the clinical and research laboratories.
Identification of de novo single nucleotide variants (SNVs) in BHCMG De novo variants were identified by an in-house developed software called DNM (de novo mutation)-Finder (https:// github. com/BCM-Lupskilab/DNM-Finder), available upon request. Parental variants were subtracted in silico from the proband's variants in vcf files, while incorporating read number information extracted from BAM files. Filtering was then implemented using the following criteria: (1) an alternative variant read count greater than 5 in the proband; (2) ratio of alternative variant read count to reference variant read count greater than 30% in the proband; (3) reference variant read count greater than 10 in both parents; and (4) ratio of alternative variant read count to reference variant read count less than 5% in both parents.