dataesr/scientific-paragraphs-categorization huggingface dataset:
FSGS was investigated by means of a PubMed query with three different gene-disease text mining association algorithms, still operational as of 14 January 2021: ConQuR-bio; http://conqur-bio. lri. fr/ [accessed on 14 January 2021] (10), polysearch2  (32) , and Genie; http://cbdm-01. zdv. uni-mainz. de/~jfontain/cms/ [accessed on 14 January 2021] (23). Two of these three algorithms (ConQuR-bio and Polysearch2) enabled human gene ranking by text mining for co-occurrences with the MesH term introduced during the PubMed query, and the last one (Genie) employed a mixed method combining machine learning steps with text mining process (Figure  2 ). Preparations for the single cell analysis were made in the Ubuntu 18. 04 LTS operating system with R environment version 3. 53 and Seurat R-package version 3.
1. 3 (33). The "CreateSeuratObject" Seurat function was implemented on the GSE140989 dataset  (34)  after fixing a threshold of 3 for the minimal number of cells and of 200 for the minimal number of features by cell. The resulting Seurat object with metadata on 24 different kidney samples included a total of 19,622 features across 22,268 cells (Figure  3A ). The "NormalizeData" Seurat function allowed us to log normalized input sequencing data, and 2,000 variable features were identified before data scaling and dimensional reduction by principal component analysis was performed. The variance of principal component analysis was estimated by performing an Elbow plot on thirty principal components (Figure  3B ). Twenty components were found to be important to explaining the heterogeneity of the cells in this dataset.
Subsequently, UMAP dimensional reduction was performed with twenty dimensions within the analyses. The sample distribution in the first UMAP analysis revealed a satisfactory distribution between subjects on the totality of cell distribution (Figure  3C ), suggesting good integration of the data in this multi-experiment analysis. Downstream clustering analysis with the construction of a KNN graph, based on the Euclidean distance in PCA space, refined the edge weights between any two cells based on the shared overlap in their neighborhoods (Jaccard similarity) in the same way as scRNA-seq data  (35)  and CyTOF data with the Phenograph algorithm  (36). We then applied modularity optimization techniques such as the Louvain algorithm to cluster the cells. Twenty-one cell clusters were found to be representative of this Seurat object (Figure  3D ).
ONLINE RESULT VISUALIZATION AND THE DEVELOPMENT OF AN INTERACTIVE WEB INTERFACE Omics analyses are united by their common feature of describing large numbers of biomolecules relevant to the experimental system under investigation. Various frameworks have been developed to highlight biomolecules that are expected to be the most appropriate for more intensive follow-up study. Transcript This diagram explains the processes that we implemented during this integration of text mining tools to interpret single cell transcriptome data. On the left side of the diagram, we can see the text mining process used to find the genes associated with the term: Focal Segmental Glomerulosclerosis. To make this query, three different text mining algorithms were used: ConQuR-bio (CQRB), polysearch2 (PS2) and GENIE.
Common list: (union of two algorithms) Seurat R-package: subset cells of interest   quantifications with omics experiments have been revolutionized during the past 15 years by different technological approaches, such as microarrays, followed by RNA-sequencing and more recently, single cell RNA-sequencing  (37). The single cell approach enables us to understand the heterogeneity of cellular expression at the whole transcriptome level thus allowing detailed characterization of the cell subtypes that make up a tissue. Several studies have tried to gain a clearer understanding of tissue cell composition to develop different methods for data visualization. For example, using python language, the Scanpy library  (38)  was developed to merge processes for single cell visualization already known in different R packages, such as Seurat for clustering  (33) , monocle for cell trajectories  (39)  and pagoda for splicing  (40).
To facilitate the exploration of FSGS-related biomarkers found by text mining, an interactive web interface was developed and uploaded at the following address: https://fsgstx. shinyapps. io/textmining/ (last accessed on 14 January 2021). Prior to unsupervised analysis, 1,468 glomerular cells were isolated from the GSE140989 normal human kidney dataset (Figure  4 ). The pre-processed and scaled digital matrix of single cell analysis was restricted to the set of genes identified as being related to FSGS (Table  1 ). Unsupervised analysis using t-SNE (t-distributed stochastic neighbor embedding) was processed to be displayed in the interface. The website was built with a flexdashboard and shiny application inclusion and with graphical interactivity displayed by R plotly. This data dashboard enabled exploration of the expression of FSGS biomarkers in the three glomerular cell subpopulations: podocytes (POD, n=182), vascular smooth muscle cells and mesangial cells (SMCMG, n=713), and glomerular capillary endothelial cells (GCEC, n=753) (Figure  5 ).
CONCLUSION The public PubMed database is a collection of all scientific publications updated periodically and whose web API can be targeted by crossing information with respect to molecular identifiers. Many bioinformatics tools based on text mining algorithms have been developed to target this type of query. During our work we observed that it was interesting to use these tools in order to interpret omics data by reducing their dimensions to those elicited by these text mining algorithms. This methodology reduces the error of false discovery in these high dimensional biological experiments. The development of the WEB new generation currently makes it possible to develop online graphical interfaces that can interact with the user. In our case, we were able to develop a web interface allowing the visualization of single cell transcriptome data which were initially selected by text mining tools.
Microsatellite data analysis We examined the resulting electropherograms using GENEMAPPER v5. 0 (Applied Biosystems), performed automated allele scoring, followed by verifying each call manually. We binned the raw sizing data using AUTOBIN v0. 9 Excel Macro (https://www6. bordeaux-aquitaine. inra. fr/biogeco_eng/Scientific-Production/Computer-software/Autobin). A consensus homozygous genotype was only recorded if the same allele amplified in at least three replicates out of the four, whereas we considered a consensus heterozygote if at least two replicates produced the same two sets of alleles  (Morin et al. , 2018 (Morin et al. , , 2016;; Ruiz-González et al. , 2013; Sawaya et al. , 2011). We discarded any genotype with two different sets of heterozygous consensus calls.
Microsatellite marker characteristics We computed success rates for the individual microsatellite markers as the percentage of successful genotypes produced out of the total number of PCR. We estimated the polymorphism information content (PIC) of the markers using MolKin v3. 0  (Gutiérrez et al. , 2005). Frequencies of null alleles were calculated using the software FreeNA  (Chapuis and Estoup, 2007)  employing the EM algorithm  (Dempster et al. , 1977) , while R package diveRsity  (Keenan et al. , 2013; RCore Team, 2019)  was used to compute observed (H O ), expected (H E ) and unbiased expected heterozygosities (uH E ), allelic richness (AR) as well as to perform exact tests to detect departure from Hardy-Weinberg equilibrium (HWE). We calculated genotyping error rates in a maximum likelihood approach with 100,000 search steps using PEDANT v1.
Genetic diversity and population genetic structure We computed the genetic diversity and differentiation parameters using the R package diveRsity  (Keenan et al. , 2013; RCore Team, 2019)  for each sampled population. We used a Microsoft Excel macro, GenAlEx 6. 5  (Peakall and Smouse, 2012, 2006) , to identify private alleles (A P ) and to calculate their frequencies. We performed Analysis of Molecular Variance (AMOVA)  (Excoffier et al. , 1992; Michdaki and Excoffier, 1995)  to infer the extent of population structuring using ARLEQUIN 3. 5. 2. 2  (Excoffier et al. , 2005). To examine the patterns of genetic structuring of elephants, we also employed multiple Bayesian approaches, viz. STRUCTURE v2. 3  (Pritchard et al. , 2000) , TESS v2. 3. 1  (Chen et al. , 2007)  and GENELAND v4. 0. 8  (Guillot et al.
, 2005; RCore Team, 2019). We implemented discriminant analysis of principal components (DAPC) using the R package ADEGENET  (Jombart, 2008; RCore Team, 2019)  without any underlying assumptions of population genetic models such as HWE  (Vergara et al. , 2015). Additionally, we used the genetic landscape shape interpolation (GLSI) technique to visualize diversity patterns using the software ALLELES IN SPACE (AIS)  (Miller, 2005). To understand the interrelationship of the seven sampled populations, we computed pairwise Nei's standard genetic distance (Ds)  (Nei, 1978)  at the population level using the software POPULATIONS v1. 2. 32  (Langella, 2002). We used FIGTREE v1. 4. 2  (Rambaut, 2014)  to visualize and annotate the resulting dendrogram. We provide a detailed account of computational parameters, model selection and justification for all analytical methods mentioned in this section with the Supplementary Materials available online.
Acknowledgements The authors would like to acknowledge the academic and administrative authorities of the establishments and organizations involved in this collaborative study for providing the necessary infrastructure. The state forest departments of  Uttarakhand, Uttar Pradesh, West Bengal, Assam, Odisha, Karnataka, Kerala  and  Tamil Nadu  are appreciated for granting research permits and providing support in sample collection. We sincerely thank  Mr. Suvankar Biswas  and  Mr. A Madhanraj  for their logistical support. This research was partially funded by the  Ministry of Environment Forests and Climate Change, Government of India, Operation Eye of the Tiger, India, WWF -Asian Rhino and Elephant Action Strategy, Nepal  (Grant ID:  WU04 ),  US Fish and Wildlife Service Asian Elephant Program  (Grant ID:  FWS F12AP01186 ) and the  International Elephant Foundation.
Note that E[u(t, x, ω) ] is a function of (t, x) only: the expectation operator converts a random variable into a deterministic variable. Then, the symbolic computation of the second-order fluctuation-mean interaction dynamics in Eq. (  16a ) is per-formed, thanks to SymPy, by following the steps as described in Sect. 2. 2. 1. In particular, the computation also leads to the tangent-linear dynamics of the error in Eq. (  6 ), from which it is possible to compute the dynamics of the variance in Eq. (16b) and of the metric tensor in Eq. (16c) (or its associated aspect tensor version). Applying these steps and the appropriate substitutions, this is achieved when calling the in_metric or in_aspect Python property of an instance of the class SymbolicPKF. This is shown for the Burgers equation in Fig.
8   the ensemble is finite, sampling noise is visible, e. g. , in the variance at the initial time that is not strictly equal to V h. In this simulation, it appears that the PKF (solid line) coincides with the ensemble estimation (dashed lines), which shows the ability of the PKF to predict the forecast error covariance dynamics. Note that the notebook corresponding to the Burgers experiment is available in the example directory of SymPKF. While this example shows an illustration of SymPKF in a 1D domain, the package also applies in 2D and 3D domains, as presented now.
(  31c ) is similar to the dynamics of the conformation tensor in viscoelastic flow  (Bird and Wiest, 1995; Hameduddin et al. , 2018). We do not introduce any numerical simulation of the PKF dynamics in Eq. (  30 ) or Eq. (  31 ), but interested readers are referred to the 2D numerical PKF assimilation cycles of  Pannekoucke (2021b) , which have been made thanks to SymPKF. This example illustrates a 2D situation and shows the multidimensional capabilities of SymPKF. Similarly to the simulation conducted for the Burgers equation, it is possible to automatically generate a numerical code able to perform numerical simulations of the dynamics in Eq. (31) (not shown here). Hence, this 2D domain example showed the ability of SymPKF to apply in dimensions larger than 1D. Before concluding, we would like to present a preliminary application of SymPKF in a multivariate situation.
g. , the geostrophic balance. This constitutes another topic of research development for the PKF, facilitated by the use of symbolic exploration. Note that the expectation operator as introduced here can be used to compute Reynolds equations encountered in turbulence. This opens new perspectives for the use of SymPKF for other applications that could be interesting, especially for automatic code generation. O. Pannekoucke and P. Arbogast: SymPKF (v1. 0)  Appendix A: Splitting for the computation of the parametric dynamics In this section we show that using a splitting strategy is possible for the design of the parametric dynamics. For this, it is enough to show that given dynamics written as ∂ t X = f 1 (X ) + f 2 (X ), (A1) the dynamics of the error, the variance, the metric, and the aspect all write as a sum of trends depending on each process f 1 and f 2.
The software includes basic functions, like extracting important data from the raw recordings and interpolating all parameters to the same sampling rate, performing a diurnal correction using data from a stationary base station, computation and application of the compensation parameters, and calculation of discrepancies at flight-line and tie-line crossings necessary for assessing the quality of the magnetic data. Here, we introduce the software and apply it to a dense sUAS magnetic survey to assess the final precision using different sensor types (vector and scalar) and sensor-platform configurations (tethered and fixed frame).
Software Here we present MagComPy-software that was developed for working with data from the above-described magnetometer systems  (Kaub, 2021). It was designed to be adaptable also to other sensor systems that are used in aerial magnetometry. MagComPy is fully written in Python and therefore platform independent and easy to install. A user-friendly interface allows access to these modules, but they also can be used in user-developed scripts. The software can be used for many tasks that are usually done with airborne magnetic recordings, including the merging of drone and base-station magnetometer recordings, compensating magnetic perturbations that originate from the aircraft, and computing cross-over differences. All results presented in this paper were computed using MagComPy. Any further analysis and interpretation of the data (e.
File Preparation The two magnetometer systems used for this study record data in sensor-specific binary formats. Therefore, the raw recordings are first converted to a comma-separated values (CSV) format with software tools provided by the manufacturers. These converted files contain signals from different sensors (magnetometer, GNSS, IMU) that are merged but sampled at different frequencies. The "file preparation" tool resamples the different signals at a common frequency and optionally merges these data with data from a base station magnetometer for diurnal corrections. If available, the data can also be merged with GNSS data from the sUAS's flightlog, which are generally of higher accuracy than the magnetometer GNSS data. The tool is designed to be easily adapted to changes in the input file formats that might come with firmware updates of the magnetometer systems or sUAS.
The software is designed to facilitate the processing of data from other magnetometers, sUAS, and attachment configurations. Even though the magnetometer systems work with fundamentally different sensors, the general tasks are similar for both the vector and the scalar systems. In a first step, the tool reads the recording coordinates and plots the flight path. During this step, the user can trim the data to remove recordings that are not part of the intended survey (e. g. , acquired during takeoffs KAUB ET AL. Table  1  Selected Specifications of the Two Magnetometer Systems  (Geometrics Inc. , 2019; SENSYS, 2018)  and landings or while the sUAS is on the ground) or to separate multiple surveys gathered in one data file. Trimming the data has proven to be a valuable feature as it significantly decreases the size of the output file and significantly expedites the remaining processing.
In a second step, the tool extracts the important data from the recording and resamples all signals to the same sampling rate-typically the sampling rate of the magnetometer. Finally, the tool converts the longitude and latitude coordinates into projected coordinates for a given coordinate system chosen by the user. Base station data, if available, are interpolated to the common sampling rate and used for diurnal correction of the magnetic field data. The option to use GNSS data from the sUAS is currently limited to platforms from DJI which let you retrieve flightlog files with data from the GNSS/IMU units that we convert to a CSV format with a third-party software tool (https://datfile. net/ DatCon/intro. html). For higher precision positioning data, differential GNSS could be used by mounting an external GNSS sensor on the aircraft and installing a GNSS base-station.
Cross-Over Differences A good way to assess the quality of the compensation is to calculate the differences of the magnetic field values at intersections of flight lines (e. g. ,  Telford et al. , 1990) , since the magnetic field intensity measurements should be independent of the flight direction. Differences at these intersections can be caused by errors of the magnetic sensors (heading errors or drifts), incomplete compensation of the magnetic fields arising from the aircraft, and errors in the positioning (horizontal and vertical) of either the magnetometer system or the sUAS. As part of our software, we created a tool that can be used to calculate differences at crossover points, called cross-over differences. The input data can be any file (CSV format) containing projected coordinates and the corresponding signals to assess.
Test Survey To test our magnetometer systems and the different suspensions described above, we designed a high-resolution grid survey with 11 flight lines and 10 tie lines covering an area of more than 135 × 150 m located at the campus of the University of California, Santa Cruz (USA). We flew this survey with the SM on three configurations-tethers, on frame, and on landing gear-and with the VM on the frame and on the landing gear. The survey was acquired at a constant altitude of 30 m above the takeoff point to assure that the crossover differences were not affected by a difference in flight altitude. However, due to a mistake while using the flight control software, the tethered configuration was flown about 10 m below the other surveys. We further tested the performance of the compensation methods by repeating some of the surveys in the opposite direction.
Since MagComPy does not include a leveling module, we used commercial software (Oasis montaj, Seequent) to compute leveling corrections by applying a 4-th order trend to all tie-lines and then adjusting the flight-lines to match the leveled tie-lines. This correction systematically led to smaller RMS values (Table  3 ). We also see that after applying this correction our above conclusions (1-4) remain unchanged. Using positioning data from the sUAS did not improve cross-over differences compared to using positioning data from the magnetometer systems. The figures and the cross-over difference analysis shown here are therefore based on the magnetometer systems GNSS data. The sUAS data was however used to analyze the altitude measured by the sUAS, which led to RMS values of altitude cross-over differences below 0.
Then, we compared the quality of the compensated data obtained using both the first and best sets of parameters. Figure  5  and Table  4  show results from sensor 1 (similar to results from sensor 2) with recordings from four different days each spaced two days apart. We see that standard deviations are slightly increased when using the first set of parameters (σ first larger than σ best in Note. Signals were low-pass filtered with filter corner frequencies that resulted in minimum RMS values (see Table  S3 ). For the VM, both sensors as well as a dual sensor average were analyzed. RMS values were computed using MagComPy  (Kaub, 2021) , while leveling corrections and RMS values after leveling ( lev RMS ) were computed using commercial software (Oasis montaj, Seequent). Abbreviation: RMS, Root mean square.
Conclusion We tested two magnetometer systems-scalar (SM) and vector (VM)-that are commercially available and designed for sUAS surveys by flying test surveys with various configurations for attaching each system to the aircraft. We present software that can be used to compensate for the magnetic fields arising from the sUAS. The software is adaptable to any fixed-frame design, platform, or sensor, and is therefore useful for future setups. We also developed a new suspension design using a fixed frame built from carbon fiber tubes and 3D-printed joints that offers more flight stability than the suspension methods recommended by the magnetometer manufacturers (tethers for the SM and attachment to the landing gear for the VM). Combined with algorithms to compensate for magnetic fields that originate from the sUAS, our frame setup also results in magnetic recordings of higher precision than the original suspension methods.
However, we found that this configuration with our UAS platform results in significantly less accurate magnetic recordings. The VM appears to provide more accurate results than the SM in any configuration. On the other hand, the SM directly records the absolute magnetic field intensity while the VM gives only a relative measurement of the intensity. The compensated magnetic field intensities of the VM will in most cases contain an offset which may be problematic when merging different datasets that do not overlap and were acquired at different time periods. This can often be prevented by proper survey planning that includes some overlap with previous surveys. One advantage of the SM over the VM is that the SM does not require manufacturer calibration while regular calibrations performed by the VM manufacturer are recommended.
Data Availability Statement The data used in this study can be accessed online at https://doi. org/10. 5066/P9KIU1OO (Kaub et al. , 2021). The software MagComPy is open-source with a GNU AGPL-3. 0 license and available at http://doi. org/10. 5281/zenodo. 4568692 (Kaub, 2021).
Introduction Recently, several works have shown that factual information can be extracted from large scale language models trained on vast quantities of data  (Radford et al. , 2019; Petroni et al. , 2019; Jiang et al. , 2019; Talmor et al. , 2019). Building on that observation and the advances in pretraining of natural language processing models,  Roberts et al. (2020)  introduced a generative model for open domain question answering. Without relying on external knowledge, this method obtained competitive results on several benchmarks. However, it requires models containing billions of parameters, since all the information needs to be stored in the weights. This makes models expensive to query and train. In this paper, we investigate how much this method could benefit from having access to an external source of knowledge, such as Wikipedia.