dataesr/scientific-paragraphs-categorization huggingface dataset:
| Detecting heterogeneous error To look for heterogeneity in precision across our survey points, we calculated precision values for each target species at each survey point and computed the sample coefficient of variation (CV) using the envStatS R package  (Millard, 2013). However, we were concerned that the random subsampling approach used to select Test Set 1 may result in a non-representative subsample. In particular, selecting too few true-positive data points (e. g. instances in which rarer species were present) at intervals across the gradient of the predictor variable to be sensitive to variance in error. Consequently, the second test set (Test Set 2) was subsampled from the study dataset after classification, and consisted of 50 15 s files from each survey point per target species, with sampling based on the probability distribution of the classification score of the target species, using the createDataPartition function in the caret R package  (Kuhn, 2021).
All files were manually assessed for the presence or the absence of the predicted target species. Optimal thresholds were determined in the same manner, and Precision, Recall and F 0. 5 -Score were calculated. However, while this approach allows a more accurate assessment of the number of true and false positives, it comes at the expense of precise estimation of true and false negatives, which are necessarily excluded from the test dataset. To compensate, the recall scores for each species generated for Test Set 2 were multiplied by the recall score for Test Set 1 prior to the application of a threshold (hereafter 'corrected recall'). To see if heterogeneity of precision error detected by Test Set 2 varied from Test Set 1, we used Levene's test with Benjamini-Hochberg correction to compare the variance in precision score of each survey point for each species.
We used this feature set to build a distributed random forest classifier in the H2O package  (LeDell et al. , 2020) , splitting the data into training (70%) and test (30%) datasets. Although random forests can handle a large number of predictor variables  (Ross & Allen, 2014) , we used the H2O variable importance function to ascertain relative variable importance, and rebuilt a final model with variables of an importance greater than 0. 05, to avoid overtraining on unimportant predictors. Final models used a mean of 214 ± 140 variables (range 51-399)-see SOM Appendix 3 for more details of selected variables. Every 15 s file in which the Tadarida classifier had predicted the presence of a target species was then reclassified with a contextual classifier. We used a CV-optimised thresholding approach and calculated precision, corrected recall and F-score at the optimal threshold for each species.
There is no reason to think heterogeneity of error is unique to this dataset, or even to random forest classifications. It is likely to occur broadly in supervised learning classification methods, with parallels to image classification in camera-trapping  (Wearn et al. , 2019) , including convolutional neural networks currently producing the best classification accuracy. Covariate shift from training datasets can be caused by underlying ecological factors impacting the soundscape varying across a range of gradients including space, time, light and temperature  (Royle, 2018; Yip et al. , 2017). We therefore strongly recommend that future studies explicitly test for, and take measures to reduce variance in error. The methods proposed here are just two possibilities, and are not necessarily optimal.
to reduce heterogeneity of error, for instance with a suitably large initial training set, a naive classifier could be trained at short time-scales and then the naive classification scores used as features in a contextual classifier at longer time-scales with boot-strapping to maintain independence. This approach would remove the need to generate a second training dataset, although would probably make the collection of a suitable initial dataset more challenging. Other machine learning methods, or even ensembles, could outperform Distributed Random Forest algorithms, and other useful contextual variables could be used-for instance acoustic indices to better characterise and contextualise the soundscape. For those wishing to work within an occupancy model framework, it would be useful to undertake further research to compare the efficacy of methods to resolve precision and recall error within the occupancy model (e.
We injected 50 µl per sample. Desulphated GSLs were separated with a reverse-phase C 18 column (4. 6 × 150 mm, 3 μm, Thermo Fisher Scientific, Schwerte, Germany) connected with a C 18 pre-column (10 × 4. 6 mm, 5 μm particle size) using the parameters described in  Grosser and van Dam (2017). After separation, the identification of desulphated GSLs was carried out based on retention time and UV spectra compared to commercially available reference standards (Phytoplan, Heidelberg, Germany). Desulphated GSLs were quantified using sinigrin as an external standard and response factors as described in  Grosser and van Dam (2017). Data were processed using Thermo Scientific Chromeleon Chromatography Data System software vs 7. 2 SR5 MUa (Thermo Fisher Scientific, Waltham, MA, United States).
Poly(A)enriched strand-specific library preparation and RNA sequencing were performed by the Deep Sequencing group of Biotech TU Dresden, Germany on an Illumina NextSeq nextgeneration sequencer. In total, approximately 570 Mio read pairs having a length of 75 bp were generated. The recently published reference genome of D. radicum (of iDiv_Dra_1. 0, GenBank accession number GCA_021234595. 1,  (Sontowski et al. , 2022)  was used as a starting point for the expression analysis in both species. To obtain a reference sequence suitable to analyze D. floralis expression, we first used Illumina RNAseq reads (NCBI BioSamples: SAMN25131701 and SAMN25131702) of D. floralis transcriptomes to polish the existing D. radicum reference genome. Reads were first mapped on the iDiv_Dra_1. 0 D. radicum genome using the Burrows-Wheeler Alignment tool with the BWA MEM algorithm version 0.
5. 0, https://github. com/TransDecoder/ TransDecoder). Annotations were filtered for a proper start and end of protein-coding transcripts by applying the GeMoMaAnnotationFilter (GAF, GeMoMa version 1. 7. 2, Keilwagen et al. , 2016; Keilwagen et al. , 2018)  according to the workflow applied for the annotation of the genome of D. radicum  (Sontowski et al. , 2022). The resulting gene annotations were appended to the liftoff-derived gene annotation file of D. floralis (iDiv_DFl_1. 0. gff) and accordingly, sequences of the new contigs containing these annotations were added to the set of pilonderived genome sequences of D. floralis (iDiv_DFl_1. 0. fasta). Gene expression was assessed for both species using the nfcore/rnaseq pipeline in version 3. 0  (Ewels et al. , 2020) , which is a standard nextflow pipeline for RNA-seq analysis, relying on STAR v2.
radicum and D. floralis larvae were presented with a Venn diagram using jvenn  (Bardou et al. , 2014). All genes were functionally annotated using PANNZER2 (Protein ANNotation with Z-scoRE,  Törönen et al. , 2018). Enrichment of GO terms of differentially expressed genes with regard to all expressed genes was tested using the topGO R package in version 2. 44. 0  (Aibar et al. , 2015). Significantly enriched terms within the "Biological Process" and "Molecular Function" families were selected using a Kolmogorov-Smirnov test and a 5% significance threshold.
Statistical Analyses Other Than for RNAseq Data Statistical analyses of data obtained from the GSL-breakdown, ITC-detoxification, performance experiments and qPCR analyses were run on R version 4. 03 (R Core Team 2020). Homogeneity of variance across groups was tested using Levene's test in the "car" package  (Fox and Weisberg 2019). Normal distribution of the residuals was assessed visually and with the Shapiro-Wilk test. Data of the ITC breakdown experiment and qPCR data were normalized by log 2 transformation before analyses. Measured GSL values were statistically compared using a Student's t-Test. The LC-MS/MS peak areas of ITCs, amines and conjugates retrieved in the ITC breakdown experiments were compared using a one-way ANOVA on each compound separately followed by a Tukey post-hoc test.
For qPCR data, differences between groups were determined using a two-way ANOVA within each species and gene using time and  ITC concentration as independent variables, in combination. Significant differences among conditions within time were identified using Tukey's "Honest Significant Difference" method as post-hoc test. In the performance experiment, differences between treatments were compared using a log-rank test with the "survival" package 3. 2-7 in R  (Therneau and Lumley 2015). To test differences in the female-male distribution when larvae fed on different diets, we used a one-proportion z-test. The weight of the larvae and pupae were compared within single species and among conditions using a Mann-Whitney test with a Bonferroni correction of the resulting p-value.
Isothiocyanates in the Diet Reduce D. radicum and D. floralis Performance More Than Their Breakdown Products In order to determine the possible advantages of metabolizing 2PE-and 4MSOB-ITCs to Delia larvae, we fed ITCs and their major amine detoxification products to the insects in a semiartificial diet. In general, consuming 4MSOB-ITC, 2PE-ITC, 4MSOB-amine or 2PE-amine all negatively affected the survival rate of D. radicum (Figure  6A ; Table  4 ). Among FIGURE 5 | Expression of selected candidate genes related to the mercapturic acid pathway (A-D) and cytochrome P450 (CYP6A1, E) responding to 2phenylethyl isothiocyanate (2PE-ITC) in D. radicum larvae measured by qPCR. Larvae were fed on 0, 1 or 2 µmol 2PE-ITC/g diet for 6, 24 and 48 h and the response of transcripts of a candidate gene coding for glutathione-S-transferase (A), γ-glutamyltransferase (B), dipeptidase (C), N-acetyltransferase (D) and CYP6A1 (E) compared relative to the control sample using 2 -ΔΔCt.
, 2019) , they seem to use different mechanisms to overcome the ITCs. Our experiments showed that gut extracts from both species produced 2PE-amine when incubated with 2PE-ITC. After adding 4MSOB-ITCs to gut extracts, we detected three breakdown products, the 4MSOB-ITC-cysteine conjugate, a cyclic 4MSOB-ITC-cysteine product formed by intramolecular cyclization of the linear 4MSOB-ITCcysteine conjugate  (Falk et al. , 2014) , and 4MSOB-amine. The latter amine appears to be formed directly via hydrolysis of the TABLE 3 | Two-way ANOVA and Tukey post-hoc of transcripts in Delia radicum or D. floralis larvae treated with different ITCs at different time points (6, 24, 48 h), relative to untreated controls. Each condition and time point were represented by 4 till 5 replicates, depending on the survival of the larvae.
DATA AVAILABILITY STATEMENT The datasets generated and analyzed in this study can be found in the National Center for Biotechnology (NCBI, https://www. ncbi. nlm. nih. gov). BioSample metadata are available in the NCBI BioSample database (http://www. ncbi. nlm. nih. gov/ biosample/) under accession numbers SAMN25131701 and SAMN25131702 for D. floralis and SAMN19640657 and SAMN19640660 for D. radicum. Corresponding raw read.
floralis. Pilon detected and corrected 81,723 SNPs and indels after aligning 28. 5 M of D. floralis Illumina RNAseq reads on the D. radicum reference genome. De novo assembly of the unmapped reads allowed us to reconstruct 36,573 transcripts clustered in 25,064 clusters. In total, 1,948 of those contigs were annotated to contain 2160 protein-coding open reading frames and were added to the edited genome of D. radicum to yield a comprehensive genome for D. floralis (https://doi. org/10. 5281/ zenodo. 6044094). RNAseq sequencing yielded on average 46. 8 M read pairs for D. floralis (min: 41. 0 M, median: 47. 5 M, max: 52. 0 M) and 48. 2 M read pairs for D. radicum (min: 40. 9M, median: 49. 8M, max: 53. 0 M) per sample. On average, 87. 63% of the reads were correctly aligned for D. floralis (min: 86.
files are available in the NCBI sequence read archive (https:// www. ncbi. nlm. nih. gov/sra)under accession numbers:  SRS11844094 ,  SRS11844093 ,  SRS9199199 , and  SRS9199198. Analyzed genome sequences of D. radicum are available via NCBI under the GenBank accession number  GCA_021234595. 1. Genome sequences of D. floralis, corresponding gene annotation and functional annotation, unmapped-readassembly results, expression values, and DESeq2 results are available via Zenodo (https://doi. org/10. 5281/zenodo. 6044094).
ACKNOWLEDGMENTS We thank  Franziska Beran  from the  MPI for Chemical Ecology  for sharing her experience and knowledge about detoxification in insects and  Heiko Vogel  from the  MPI for Chemical Ecology  for his advice on the project design and discussions about the results. We thank  Axel Touw  and  Dominik Jacob  for their assistance with the insect rearing. Publication and open access funding supported by the  MPG.
The work is organized as follows. Section 2 describes our model of LBM for solid aiming static equilibrium simulations. Section 3 presents numerical implementations and key algorithms of the method, and the integration in the Parallel Lattice Boltzmann Solver (PaLaBoS)  [32]  framework. Section 4 is dedicated to benchmarks and comparisons with Finite Element Analysis (FEA) using COMSOL software  [33] , in order to validate the method. Section 5 discusses the results and their openings, and Section 6 concludes the paper.
e. , which uses only PaLaBoS as dependence: the LBMS library. The LBMS library is, thus, an easily accessible setup. This strategy leads to a non-intrusive coding task where solid classes and solid objects are called beside PaLaBoS without modifying its functions or methods. The LBMS library algorithms and the results presented here show their ability to solve solid static equilibrium with the LBM. Library source code is available online  [36] , and main used numerical ingredients and scheme properties are thus freely accessible. Moreover, we refer the readers to Section 4 for more numerical details.
Experimental settings Here we describe the details of the implementation and the setting used in our experiments. We have implemented Y-PT and PY-PT algorithms in Java and our code is publicly available at  [4]. Note that in our implementations the parameter k is not part of the input, this enables the use of these methods as iterators, able to return a next earliest arrival itinerary as long 4 When scanning the connections starting with c i arr stop after c i arr time , the journey M Let J = (f 0 , c 0 , • • • , c φ , f r ) 10: if ζ = 1 (J is simple) C dev ← {c s. t. there is a journey J in Output starting with (c 0 , • • • , c i-1 , c)} 21: T = (S \ S π , T, C \ C dev , F ) 22: Q ← CSA(T , c arr stop , d, c arr time , t max ) 23: if Q exists then 24: J new ← (f 0 , c 0 , • • • , c j , Q) 25: add (J new , j, ζ = 1) to Candidates 26: return Output as one exists.
Networks setting We have evaluated the performances of our algorithms on two train networks (Germany and Switzerland) and three public transit networks (Paris, Berlin and Stockholm). The characteristics of these networks are presented in Table  1. This dataset is publicly available via a GTFS feed (https://transitfeeds. com/), we downloaded this dataset in October 2019. The public transit networks are more dense than the train networks, i. e. the connections to stops ratio is smaller on train networks than public transit networks. This can be easily explained because the train networks can only use trains whereas the public transit networks can use buses, trains, ferries and many other means of transportation. Therefore, we will show the performances of our algorithms on those two types of networks.
*    This work has been supported by the  French government , through the  UCA jedi Investments  in the  Future  project managed by the  National Research Agency (ANR)  with the reference number  ANR-15-IDEX-01 , the  ANR  project  MULTIMOD  with the reference number  ANR-17-CE22-0016 , the  ANR  project  Digraphs  with the reference number  ANR-19-CE48-0013 , and by  Région Sud PACA.
, 2021; Ruiz et al. , 2021; Venkatesan et al. , 2021). Experimental three-dimensional image recordings (e. g. , microscopy and medical) are typically acquired in limited quantities  (Matthews, 2018). Additionally, these few acquisitions are subject to variability which make for difficult streamlining of data analysis. To address this reality we require, first, the appropriate means to visualize, interact with, and manipulate data and, second, an ability to rapidly perform quantitative assessments on these data. The first challenge can be tackled via visualization with VR. By rendering image stacks into a VR environment, users can easily navigate and interact with their 3D data. In turn, VR enables the user to grasp an intuitive understanding of the dataset being visualized. However, multiple issues are associated with this task: 1) finding proper ways to represent diverse image stacks originating from different imaging modalities with varying signal to noise ratios, 2) providing versatile tools to explore and interact with the VR representation, and 3) finding procedures that can handle large data sets.
The second challenge is addressed by employing human-inthe-loop  (Patel et al. , 2019)  data treatment procedures. The idea here is to couple user interactions with data analysis for extracting relevant information from the data. In the context of this work, this implies 1) defining procedures to select data within the VR environment, 2) performing the required computations for analysis without significantly impacting the VR rendering performance, and 3) allowing corrections to be performed in an iterative fashion. In the following sections, we discuss related works involving VR software for image stack visualization. We introduce our approach, DIVA Cloud, which allows visualization and interaction in VR combined with cloud computing. Finally, we show how this approach can be effectively utilized in data annotation for microscopy and medical images.
Large images of entire nervous systems can now be acquired with optical and electron microscopy (EM). Tracing complex neuronal structures is essential as there is a link between structure, connectivity and functions of neural circuits. Some initiatives already use VR to address this  (Peng et al. , 2010; Usher et al. , 2017). Applications at the frontiers of microscopy and neurosurgery have also been demonstrated in literature  (de Mauro et al. , 2009; Wang et al. , 2019; Wisotzky et al. , 2019). VR interactions from multiple users on the same data have been introduced in Naviscope  (Shattuck, 2018). Additionally, some projects address topic-specific challenges in microscopy, such as colocalization  (Theart et al. , 2017). Companies developing software for microscopy image analysis are now adding VR compatibility for visualization and treatment.
Major advances are found with Arivis AG  (Dekkers et al. , 2019; Conrad et al. , 2020)  syGlass, which include an optimized data interaction interface. Other research applications focus on biomolecule structural information visualizations and interactions  (Doutreligne et al. , 2014; Balo et al. , 2017; Goddard et al. , 2018; Cassidy et al. , 2020). Not all microscopy image analysis software involves raw, fullstack image analyses but instead a deconvolved output  (Betzig et al. , 2006; Manzo and Garcia-Parajo, 2015; Qi et al. , 2016). It is especially the case for single-molecule microscopy, where signals from individual biomolecules are captured and processed to deduce their nanometric positions and dynamic behavior. In these cases, the microscopy image stacks are reduced to point clouds.
Two recent open-source software tools have been introduced to visualize and interact with single-molecule experiments: vLUME  (Spark et al. , 2020)  and ours, Genuage  (Blanc et al. , 2020). Both software offers interfaces to interact with the point clouds and to perform various forms of data analysis  (measuring, counting, cropping, etc. ). Other initiatives on point clouds relate to data tagging for machine learning  (Berge et al. , 2016; Stets et al. , 2018; Ramirez et al. , 2019; Wirth et al. , 2019; Liu et al. , 2020). Mixed applications can be found involving astronomy, such as Gaia Sky  (Sellés, 2013). General visualization and interaction software include PointCloud XR and developments centered on compression to ensure visualization in VR and Augmented Reality (AR)  (Pavez et al. , 2018).
VISUALIZING AND INTERACTING WITH IMAGE STACKS WITHOUT PRE-PROCESSING IN VR We recently introduced DIVA software (El Beheiry et al. , 2020), a user-friendly platform that generates volumetric reconstructions from raw 3D microscopy image stacks and enables efficient visualization, analysis and quantification. The software is available at https://diva. pasteur. fr. DIVA was developed using the Unity game engine (UnityTechnologies), and is based on what we term a lean mapper software architecture. Furthermore, the software uses the Windows-based SteamVR standard, making it compatible with most PC VR headsets, such as the HTC Vive and Oculus Rift S. DIVA renders image stacks and hyperstacks instantaneously as 3D volumes through via GPU-based volume ray-casting  (Engel et al. , 2004 (Engel et al.
Transfer functions in DIVA can be saved and loaded in Javascript Object Notation (JSON) format. In DIVA's VR mode, the user visualizes image stacks rendered as live physical objects as a result of stereoscopy. Physical manipulation of the volume, such as grasping, rotation, or navigation, can be done with the VR controller, which acts as a 3D mouse for interaction (see Figure  1B ). Hence, the data can be observed at any arbitrary angle to understand its structure in detail. To ease the navigation in complex and dense biological images, a clipping tool, presented in Figure  1C , can be activated. It dynamically removes a planar portion of the rendered volume allowing deep structures in the image to be revealed. Tagging, counting, and distance measurement tools are included for basic quantitative measurements (see Figures  1D, E ).
Users can extract all measurement results in a CSV file as well as through screen and movie captures. As shown in Figures  2A-C , users can utilize DIVA for various microscopy modalities with up to four different channels associated with individual transfer functions. The user can reveal structures of interest by modulating the voxel transparency and colors in the transfer function. He can also discard undesired voxels without losing information, which can happen when using segmentation techniques. For example, users can easily remove the significant background noise of EM images. DIVA can also be used to compare raw and segmented data by merging the image stacks together as TIFF files with multiple channels. With VR, the perception of 3D structures in complex data (e. g. , EM images) is enhanced, and measurements are performed quicker than in standard 2D stack viewers.
, 2017; Chen et al. , 2020) , motion or gaze precision  (Martirosov et al. , 2021; Pastel et al. , 2021) , and 3D data labeling prior to machine learning training  (Ramirez et al. , 2020). Medical experts and undergraduate students have reported better visualization of 3D anatomical structures in VR using DIVA, when compared with typical 3D renderings, see Table  1  in  Bouaoud et al. (2020)  and  Raimondi et al. (2021). Medical images are most often stored in the Digital Imaging and Communications in Medicine (DICOM) format and analyzed through a 2D interface (i. e. , a DICOM Viewer). In Figures  2D-F , we show examples of medical images visualized within DIVA. As DIVA is data agnostic, the user can experience both medical and microscopy images.