dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  Brady Callahan  for his assistance in accessing Oregon imagery data and the  Portland Metro  for their work collecting the Portland Street Trees dataset. This research was supported by the  Gordon and Betty Moore Foundation's Data-Driven Discovery Initiative  through grant  GBMF4563  to  E. P. White  and by the  National Science Foundation  through grant  1926542  to  E. P. White ,  S. A. Bohlman ,  A. Zare ,  D. Z. Wang , and  A. Singh. We thank  Alina Zare ,  Aditya Singh , and  Dylan Sinnickson  for feedback during model and software development and comments on the manuscript. The authors declare no conflicts of interest.
Data Availability DeepForest source code is available on GitHub (https://github. com/weecology/DeepForest) and archived on Zenodo (https://doi. org/10. 5281/zenodo. 2538143). The code for the case studies is available in a separate repo (https://github. com/weecology/DeepForest_demos). The indevelopment version of the NEONTreeEvaluation benchmark is available online (https://github. com/weecology/NeonTreeEvaluation) and will continue to be updated as more images are annotated. The Oregon RGB imagery was provided by the Oregon Statewide Imagery Program 2018: 1 foot orthophotography of western Oregon: State of Oregon data release, https://www. oregon. gov/geo/Pages/imagery_data. aspx.
was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made //doi. org/10. 5281/zenodo. 2538143). All DeepForest functions are documented online with reproducible examples (https://deepforest. readthedocs. io/) and video tutorials. bioRxiv preprint doi: ; https://doi. org/10. 1101/2020. 07. 07. 191551 this version posted July 8, 2020. The copyright holder for this preprint (which. CC-BY-ND 4. 0 International license available under a (https: 4.
For kinetic measurements, sensorgrams were obtained by passing various concentrations of the analytes over the ligand surface, at a flow rate of 5μl/min, with a 3-min association phase and a 6-min dissociation phase. The sensor surface was regenerated between each experiment with a single injection of 35 mM NaOH and 1. 3 M NaCl at a flow rate of 50 μl/min for 30sec. Identical injections over blank surfaces ran in parallel (and giving a value of 0 RU) were subtracted from the data for kinetic analysis. Binding kinetics were evaluated by linearization using "fit kinetics Langmuir binding type" with BiaEvaluation software (Biacore). Relative Pearson's chi-squared tests assessing goodness of fit were always below 10, which indicated that the models used for fitting adequately describe recorded data.
15 software  [56]. The conserved disulfide bond of the loop between residues C68 (C599) and C74 (C605) was added as a constraint. This bond was proved to be critical to the furin recognition site of HIV-1 gp160  [57]. The crystal structure of the 6-Helix bundle HIV-1 gp41, including both fusion peptide and membrane proximal external regions  [37] , was retrieved from the RCSB Protein Data Bank (http://www. rcsb. org, code: 2X7R). The solution NMR structure of ectodomain of SIV gp41  [58]  was retrieved from the RCSB Protein Data Bank (http://www. rcsb. org, code: 2EZO). Due to the high sequence similarity, the multiple sequence alignment used to build the 6-Helix bundle structure of HIV-1 gp41 was generated using T-Coffee  [50].
(S4 Fig). Pre fusion clade B gp41. The structure of the trimeric gp41 ectodomain of HIV-1 in its pre-fusion state was built using the only available crystallographic structure of the pre-fusion HIV-1 gp41(33) (http://www. rcsb. org, code: 4TVP). Missing loops were modeled using DaR-eUS-Loop which identifies loop candidates in large collections of structures using a new similarity approach based on a Binet Cauchy (BC) kernel  [39]. Pre fusion and 6-Helix bundle clades A and C gp41. The clade A and clade C gp41 sequences were retrieved from http://www. hiv. lanl. gov (code: AF484478 and code: AF110967, respectively) aligned (S5 and S6 Figs) and mapped onto the structures modeled for clade B using the MODELLER v9. 15 software  [56]. We used all atom molecular dynamic (MD) simulations to inspect the behavior of the 6 trimeric structures (pre-fusion: clades A, B and C, 6-Helix bundle: clades A, B and C) and explore their conformational space.
Statistical analysis Statistical significance was analyzed by the two-tailed Student's t-test and multiple comparisons were performed using the non-parametric Mann-Whitney U test using Prism 5 (Graph-Pad, San Diego, CA) software. Heatmap was established using the heatmapper web site (http:// www. heatmapper. ca/expression/) using the complete linkage clustering method and nonparametric spearman correlations as distance measurement method. P values <0. 05 were considered significant. Epitope sequences specific for 43 and 177 FabA clones. Epitopes are derived from in silico analysis of each set of FabA 43-and 177-specific mimotopes on pre-fusion and 6-Helix bundle gp41 structures. Amino acid numbers correspond to numbering of the full HIV envelope. Only regions with a PEPsurf score of more than 0.
This study was supported by l 'Agence Nationale de Recherches sur le Sida et les Hepatites Virales (ANRS)  ( AO2015-2-17046 ) and SIDACTION ( 15CONV03 ), and  Fondation pour la Recherche Medicale (Equipe FRM :  EQU201903007830 ) funds to M. B. M. K. was supported by  SIDACTION , and L. X. by the  China Scholarship Council. P. T. was supported by l' Agence Nationale de la Recherche  for calculations performed on the  RPBS platform  ( ANR-14-2011-IFB ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
e. low, mid and high-level  [8]. Low-level data fusion implies taking all the data together and performing an analysis in a similar way as for data from a single sensor. Midlevel fusion involves some preliminary data refining step where interesting features are extracted from each dataset and subsequently fused together. High-level data fusion is the fusion of the conclusions drawn from the output of the models created on each dataset, using decision rules such as majority voting or averaging. The main aim of data fusion in the machine learning domain is to improve the model accuracy with less attention being paid to the background process. However, in the chemometrics domain, data fusion, or multi-block data analysis, aims not only to improve the model accuracy but also to have a better understanding of the underlying characteristics involved.
The aim is to identify the common and the specific hidden factors between and within multiple blocks of measurements, and to subsequently use them to build explainable and explanatory models [9-14]. There are two main tasks that need to be accomplished in multi-block data analysis, i. e. , to enhance the data visualisation and improve the predictive performance of models. For data visualisation, a summary and comparison of methods can be found in  [9, 11, 12]  In the present work, the first version of a MATLAB-based graphical user interface for multiblock data analysis (MBA-GUI) is presented. The MBA-GUI can perform data visualisation, sequential regression and variable selection on up to 4 different data sources. However, the algorithms implemented in the GUI are not limited to 4 data sources and can be used for any number of data sources.
In the toolbox, we have provided a collection of common pre-processing methods. Data smoothing reduces high-frequency noise from datasets prior to modelling. In the toolbox, several techniques are provided for performing smoothing in the variable domain. starts by normalising each block, X i , by its Frobenius' norm so that they all have the same total variance. Details regarding the algorithm can be found in  [22, 35]. Application of the original ComDim (which, for reasons which will become apparent later, we will call ComDim-PCA here) in the case of 2-blocks is presented in Fig. 3. The common components (CCs) are extracted sequentially in an iterative fashion, by extracting the eigenvector associated with the largest eigenvalue from a matrix W, which is the created by concatenating the weighted individual matrices, X i.
Datasets for MBA-GUI demonstration Use of the MBA-GUI is demonstrated with three datasets. All the three datasets can be accessed in the same GitHub repository. Operating procedure and demo analysis The operating procedures are presented in separate sections to demonstrate the use of the MBA-GUI for the analysis of different datasets so that a person with minimal experience can repeat the analysis and use the GUI in their day-to-day multi-block data analysis tasks. The sections are data loading and pre-processing, data visualisation, regression, classification, variable selection and SPORT. All the figures presented in the analysis come directly from the MBA-GUI. Fig. 5A  shows the MBA-GUI interface for loading datasets. Currently, two data formats can be loaded, i. e. ,. xls and. csv. It is currently possible to load up to 4 blocks of data.
The datasets can either come from several sensors or can be the same dataset imported multiple times to perform SPORT fusion. The datasets should be loaded in a logical order since sequential loading of the dataset may improve the performance of sequential methods where the natural order of blocks can provide meaningful insights into the datasets. Once the data are loaded, the figure in the MBA-GUI will be updated to show the data. Fig. 5B  shows an example where two data blocks are loaded. Each data block can be separately pre-processed. The pre-processing can be performed in three steps -smoothing, normalisation or scatter correction, and derivatives. Derivatives are of particular use for NIR data where they can reveal underlying peaks. An example of pre-processing is shown in Fig. 5B. The preprocessing choice in Fig.
J o u r n a l P r e -p r o o f Multi-block data visualisation is a challenging task where the interest is not only in the extraction of latent variables but also in how different blocks are linked with each other or contribute to the extraction of the global LVs. In the present case, the objective is also to limit the extraction of redundant information from the different blocks. In the MBA-GUI, multiple multi-block data visualisation methods are implemented. The presentation here will be limited to the use of ComDim-PLS, which is a supervised common dimension extraction method requiring a response variable to orient the decomposition of the blocks. It will be applied to the olive oil dataset. block that contributed to the CCs. In this case, the higher saliences for block 2 show that CC1 was dominated by the information from that block, whereas CC2 and CC3 were dominated by the information from block 1.
69 and 0. 66 % dried matter, respectively. It should be noted that when only one block of data is used, the R 2 is lower and the error is higher (as shown in single block analysis in Fig. 10 ), showing the benefit of multi-block regression. J o u r n a l P r e -p r o o f Variable selection is a key step to identify the predictive variables that are most responsible for explaining the response variables. Variable selection can give a better understanding of the important parameters and, in many cases, help in the development of cheap multi-spectral sensor systems. In this work, a demonstration of multi-block variable selection using SO-CovSel is given. The analysis was carried out on the olive fruits dry matter content dataset, where two data blocks in the NIR range are used to predict the dry matter in olives.
The reference dry matter was measured by the hot air oven method by noting the fresh and the dry weight of the samples. More details on the olive fruits data can be found in[44]. The third dataset to demonstrate the classification task consists of the NIR spectra of mayonnaise samples made from oils of 6 different origins. The classification task can be understood as a 6-class problem. The mayonnaise dataset was obtained from the official website of ChemHouse (www. chemproject. org). To make the data fit for multi-block analysis, each dataset was split into two blocks in the spectral domain. A further description of the datasets is provided in Table1. Description of the datasets used in the demonstration.
ConclusionA MATLAB based GUI for multi-block data analysis (MBA-GUI) is presented. The toolbox can perform a range of common pre-processing methods on blocks of multivariate data. Multi-block data analysis for regression, classification, visualisation, variable selection and SPORT are proposed. The performance of the MBA-GUI for each of the data analysis tasks was demonstrated with several data sets. The results showed that the MBA-GUI performed well, and all the options are fully functional. The main advantage of the toolbox is that it can be easily understood and used by non-experts. The first version of the GUI can be downloaded at (https://github. com/puneetmishra2/Multi-block. git). Other features will be added to the GUI with the development of new methods. All the data analysis presented in this work can be replicated with the supplied data.
multi-blockThe first one is the multi-block toolbox by the from (https://github. com/puneetmishra2/Multi-block. git). In the GitHub repository, the standalone toolbox executable files can be downloaded as 'Multi-block_toolbox. zip' and the function for running the tools in command line as 'Toolbox. zip'. The dataset demonstrated in this article can be downloaded as 'Dataset. zip' from the same GitHub repository. All three files are available in the link (10/June/2020). To run the toolbox from command line user should use the toolbox folder as the current folder and type T1 on the command line which University will start the main GUI interface. The user should put the password: 'welovedata' without of Copenhagen, Denmark (http://www. models. life. ku. dk/~courses/MBtoolbox/mbtmain. htm), which, having been last parallel and sequential partial least-squares regression' toolbox by NOFIMA [20].
g. , Standard Normal Variate -SNV -Multiplicative Scatter Correction -MSC -etc. ). It is intuitive and very well-conceived. The imported datasets can afterwards be processed by a remarkable collection of multi-block latent variable-based dimensionality reduction methodologies permitting tasks of various nature to be carried out:1. Variable selection (Sequential Orthogonalised Covariance Selection and Sequential MATLAB The MBA-Orthogonalised Covariance Selection-Linear Discriminant Analysis); 2018b run time compiler of the f th t of rsion o lti-block. git). O block. git) p r o methods. All th ods. A ied data. The G data. Th e to notify the a o notify x, so that the t so that tly installed in installed 018b b J o u r n a l P r e -p r o o f works. com/prod ks. com/p J o veda 2. data exploration/visualisation (Common Dimension -ComDim -Principal tool at Component Analysis, ComDim Canonical Correlation Analysis, ComDim Independent Component Analysis and ComDim Partial Least Squares regression); (https:Validation 3.
As previously shown  [35] , the relationship between the prescribed voltage V and the vibration velocity V a is found to be linear over the range 0 to 60 Volts. For each tested frequency, we proceeded a calibration between voltage and acoustic velocity. The obtained images are treated with the open-source software ImageJ (https://imagej. net/). The streaming velocity field in the plane (x, y) is determined from the relative displacement of particles at a given phase during several vibration periods. Successive frames are converted into displacement vectors and vorticity maps by the software PIVlab (see : https://pivlab. blogspot. com/).
Data preprocessing The finger position was sampled at 60Hz, recorded as a sequence of time stamped x and y coordinates, and transformed into a fixed sampling rate of 100Hz using cubic spline interpolation. The x,y coordinates were then separately smoothed with Gaussian weighting (σ = 20 ms). For each time point in the finger trajectory, θt was defined as the direction between the finger coordinates at times t -10 ms and t, and the implied endpoint (iEP) was defined as the position on the number line that the finger would reach if it keeps moving in direction θt. Implied endpoints were cropped so not to exceed each end of the number line by more than 5% its length, and were undefined for sideways movement (|θt| > 80°). The trial endpoint is the position where the finger crossed the number line.
Both digits disappeared 500 ms after the unit digit onset. Note that at t = 0, a visual change occurred in both decimal positions, in order to minimize the between-digit differences in attentional, alerting or spatial bias. The finger trajectory data of each SOA condition was separately submitted to the regression analysis and analyzed for between-condition delays as described in Section 2. 4. In the D0 block, we suspected that b[U] may not converge to the same value in all SOA conditions (due to an effect of SOA on the amount of processing the '0' placeholder). As we shall see below, this was indeed the case. Thus, the delay estimations were not based on the raw b values but on the ratio between b[U] and its endpoint value, as explained in Section 2. 4. 2. 4. (a) Task design: the fixation was XX.
Vector Distribution and Population For each year, we obtained data on establishment of Ae. albopictus mosquitoes at the regional level (third level of the Nomenclature of Territorial Units for Statistics  [10] ) from the VectorNet database  (11)  and the French Ministry of Health website  (12)  and the population in reporting countries from Eurostat  (13). We calculated the percentage of the population in regions in which the vector was established (population living in regions in which Ae. albopictus mosquitoes were established × 100/total population in the country). We grouped countries per geographic regions according to the United Nations Statistics Division definitions  (14).
Inclusion Criteria The applied inclusion criteria (Appendix Figure, https://wwwnc. cdc. gov/EID/article/26/6/19-0490-App1. pdf) aimed to account for possible errors in gathering or reporting travel history/exposure of case-patients and lack of specificity of IgM serologic testing  (15, 16). We included probable and confirmed travel-related cases. Cases related to the French overseas territories (e. g. , Martinique, French Polynesia) were considered as travel-related cases and those overseas territories as countries of infection. We also included reporting countries that submitted data every year and provided country of infection for >50% of their cases (arbitrary cutoff value) over the study period. When multiple countries of infection were reported for 1 case, the country of infection was changed to unknown.
These biases were likely to be constant over time and place and therefore did not affect interpretation of our results. We considered that all reported case-patients had viremia while in the EU and therefore could have contributed to secondary transmission. However, a study published in 2007 estimated that 63% of infected travelers were viremic upon return to their home country  (39) , which would suggest that we overestimated the number of potential index cases. Several studies that assessed risk for travelers related to chikungunya and dengue used travelers data from the United Nation World Tourism Organization  (22, 40)  or national databases 41,42). As highlighted in another report  (43) , there are major differences in the number of travelers between databases. The International Air Transport Association collects the number of flight passengers from 1 departure airport to an arrival airport, whereas other databases also include multicountry journeys and other types of travel mode, such as cruise ships; in addition, some databases might count travelers on the basis of their nationality instead of place of departure.
, 6}. I2 = c 2 ( These invariants are integral whenever f is integral. The Igusa-Clebsch invariants are 'invariants for the Siegel moduli space'. Before making this more precise, we recall some facts about the Siegel moduli space. Definition 5. 2. We define Sym 2 (C) = τ1 τ2 τ2 τ3 ∈ Mat2×2(C) , and for τ ∈ Sym 2 (C), we write Im(τ ) > 0 for 'Im(τ ) is positive definite'. Definition 5. 3. The Siegel upper half space is defined to be H2 = τ = τ1 τ2 τ2 τ4 ∈ Sym 2 (C) : Im(τ ) > 0 , and the symplectic group Sp 2 (Z) = γ ∈ GL4(Z) : γ 0 I2 -I2 0 γ tr = 0 I2 -I2 0 acts on H2 via A B C D • τ = (Aτ + B)(Cτ + D) -1. The field of rational functions of the coarse moduli space for hyperelliptic curves of genus 2 can be generated by three Siegel modular functions, as shown by Igusa in  [Igu60]. Following the notation in the Echidna database  [Echidna] , we choose as generators three Siegel modular functions i1, i2, i3 : Sp 2 (Z)\H2 -→ C such that, if C is a curve of genus 2, and [τ ] ∈ Sp 2 (Z)\H2 is the point in the moduli space corresponding to C, then i1(τ ) = (I4I6/I10)(C), (7) i2(τ ) = (I 3 2 I4/I10)(C), (8) i3(τ ) = (I 2 2 I6/I10)(C).
Results and Discussion Widespread Occurrence of Sedimentary NATZ. We retrieved four sediment cores (2. 0 to 3. 6 m long) from the seabed of the Arctic Mid-Ocean Ridge (AMOR) at water depths of 1,653 to 3,007 m (Fig. 1A  and SI Appendix, Table  S1 ) and performed geochemical measurements and microbiological analyses with high vertical resolution. Although the cores are separated by more than 300 nautical miles, they exhibited similar geochemical profiles (Fig. 2  A-C and Dataset S1) summarized as follows: 1) O 2 monotonically decreased and was depleted at a depth of 0. 4 to 1. 2 m below seafloor (mbsf), while dissolved Mn 2+ built up right below the oxygen depletion depths; 2) NO 3 -was abundant in the oxic zone and depleted in layers below the oxygen depletion depth; and 3) NH 4 + was abundant in the deep anoxic sediment but undetectable in the oxic zone.
1 to 3. 1 μM for NO 3 -and 0. 5 to 4. 6 μM for NH 4 + ), suggesting that the modeled profiles and reaction rates provided a realistic estimation of the in situ nitrogen cycling processes (see also Model parameterization and sensitivity analysis in SI Appendix, Supplementary Text). Our model predicts that anammox is restricted to the NATZs across all of the four cores (Fig. 2D ), although from a thermodynamic perspective this process is exergonic at most depths (with Gibbs free energy of up to 200 kJ mol -1 N; SI Appendix, Fig. S3 ). This confined window of anammox in the NATZ is due to the combination of the coexisting reactants (NH 4 + and NO 2 - /NO 3 - ) and the absence of oxygen inhibition. In our compiled dataset of sediment sites (n = 67) with an NATZ, 56 sites (84% of the total sites, including GC04 and GC08 from AMOR) have no upward ammonium efflux from the NATZs (Fig.
1%) below the NATZs (Fig. 2F ). Similar peak occurrences of anammox bacteria in zones with counteropposing gradients of ammonium and nitrite/nitrate were also reported in anoxic water columns of the Mediterranean ocean  (18) , Black Sea  (19) , and the Golfo Dulce, Costa Rica  (20). To exclude the possibility that these relative abundance peaks in the NATZs are the result of a closed compositional dataset (decrease/decay of other microbial taxa), we quantified the absolute abundances of anammox bacteria throughout the four cores by two methods: 1) quantitative PCR targeting the hzo gene encoding the hydrazine dehydrogenase, a marker that has been successfully used to characterize the vertical distribution of Scalindua in oxygen minimum zones (e. g. , refs. 21, 22) ; and 2) calculating their abundance by multiplying the relative abundance of Scalindua in the total community by the total cell numbers determined by 16S rRNA gene quantification.