dataesr/scientific-paragraphs-categorization huggingface dataset:
CONCLUSION We have shown that that extant sporomorphs can be successfully classified using the chemical signature of sporopollenin. Diagenetic repolymerization of sporopollenin happens relatively soon after burial, however, meaning that chemical datasets from extant and fossil sporomorphs are challenging to integrate directly. Further differences between extant and fossil material can occur via the processing methods used to isolate sporopollenin from fresh sporomorphs. Taken together, these results show that linking extant and fossil sporomorph chemistry datasets is not a trivial task. In late Quaternary settings sporopollenin appears chemically highly similar to extant material, suggesting great potential to integrate across datasets if the laboratory processing methods are similar. This supports the idea of using FTIR-based chemotaxonomy to increase the taxonomic resolution in morphologically cryptic groups  (Julier et al.
Acknowledgements. We are deeply indebted to  Antoine Cleef  for collecting the grass sample material from  Naturalis Biodiversity Center in Leiden. We also thank  Annemarie Philip  and  Malcolm Jones  for sample processing, and  Caixia Wei  for pollen photography. Sally Thomas and four anonymous reviewers are thanked for detailed reviews that greatly increased the quality of this paper. This research was funded by  ERC  grant  MAGIC 649081  (GDN), and  NERC  grant  NE/K005294/1  (WDG, WTF, BHL). BHL and WTF's ongoing work on sporopollenin chemistry is currently supported by  NERC  grants  NE/P013724/1  and  NE/ R001324/1. PEJ is currently funded by  DFG  grant  443701866.
T A B L E 1. Overview of the sporomorph datasets used in this study. Dataset Taxon Age and type Locality Treatment 1 Poaceae Extant (herbarium specimens) Amazonia/Andes Acetolysis 2 Poaceae Fossil (early Miocene to Western Amazonia Na 4 P 2 O 7 and heavy liquid separation (clastics) Pliocene) plus Schultze reagent (lignite) 3 Poaceae Fossil (late Miocene to Amazon Fan HCl, Na 4 P 2 O 7 , acetolysis and heavy liquid Pleistocene) separation 4 Poaceae (Sub)fossil (late Pleistocene) Lake Bosumtwi, Ghana HCl, HF, KOH and acetolysis (splits of eight samples processed without acetolysis) 5 Nitraria Extant (herbarium specimens) Global Acetolysis 6 Nitraria Fossil (Eocene) Tarim and Xining basins, HCl and HF, then heavy liquid separation for China Xining sample only 7 Conifers Extant (live plants) Yunnan and Hunan provinces, Acetolysis China 8 Conifers Fossil (Paleocene) Nangqian Basin, China HCl, HF and heavy liquid separation Subfamily Number of genera/ Number of C 3 /C 4 species in dataset species in dataset Anomochlooideae 1/1 1/0 Aristidoideae 1/1 0/1 Bambusoideae 1/3 3/0 Chloridoideae 3/5 0/5 Danthonioideae 1/2 2/0 Oryzoideae 1/1 1/0 Panicoideae 15/24 6/18 Pharoideae 1/2 2/0 Pooideae 8/11 11/0 T A B L E 2.
T A B L E 3. Maximum subfamily and genus-level classification success rates for the training dataset, for different data treatments, where w is the Savitzky-Golay smoothing window. F I G. 3. PCA of Panicum and F I G. 2. Principal components F I G. 4. Comparison of extant Paspalum FTIR spectra. A, sym- analysis (PCA) of extant South and fossil South American grass bols coloured by species; B sym-bols coloured by photosynthetic Processing Subfamily American grass pollen FTIR spec-pollen. A, PCA of extant and fos-Genus tra. A, symbols coloured by sub-sil South American grasses, with pathway. Circles = Panicum; trian-gles = Paspalum. Values in paren-theses provide the percentage of variance explained by each prin- Maximum classification success family. B, symbols coloured by w Maximum extant grass pollen grains shown photosynthetic pathway.
Values in as orange circles, Amazon Fan classification parentheses provide the percent-fossil grains shown as light blue success age of variance explained by each triangles, and western Amazonia w cipal component. Unprocessed Savitzky-Golay smoothing, 1st derivative Savitzky-Golay smoothing, 2nd derivative 90% principal component. Subfamily NA 80% fossil grains shown as dark blue 96% 9 89% colours in A are the same as those triangles; values in parentheses in Figure 1. provide the percentage of variance explained by each principal 97% 25 92% component. B, stacked mean spectra for the three datasets, with colours as in A and shaded regions showing AE1 standard NA 7 15 deviation about the mean. C, loadings plot for PC 1.
However, only having several friends that have a high degree can make an individual an influential person. In this sense, eigenvector centrality is a measure used to quantify the influence of a node in a network  (Zaki & Meira, 2014). Eigenvector centrality was used before to assess key residues to effector binding in IGPS  (Negre et al. , 2018). Motivated by the IGPS results, we calculated eigenvector centrality of each residue in the dynamics network of SARS-CoV-2 and SARS-CoV Mpro in order to analyze only the dimeric states. We utilized NetworkX (https://networkx. github. io/) and correlationplus (https://github. com/tekpinar/correlationplus) Python packages for eigenvector centrality calculations.
Fifty early stage mutations of SARS-CoV-2 Mpro reported in GISAID database  (Elbe & Buckland-Merrett, 2017 ) and analyzed by Sheik Amamuddy, Verkhivker, et al. (  2020 ) are marked as vertical solid lines in Figure  6. Similarly, when mutations of SARS-CoV Mpro are also considered (dotdashed lines), we observe that most of the early state mutations are in low eigenvector centrality locations. However, residue 7 and residue 116 have relatively high eigenvector centralities. Both an experimental and a computational study showed that a mutation in this residue can impact the dynamics and enzymatic activity of SARS-CoV and SARS-CoV-2 Mpro  (Wei et al. , 2006; Sheik Amamuddy, Verkhivker, et al. , 2020). Similarly, a mutation in residue 116 caused a noticeably large difference in Ca RMSD. Since the mutations reported in GISAID are from clinical isolates, we must still assume that the mutated forms are still functional.
Funding This work was granted access to the  Jean-Zay HPC resources of IDRIS  under the allocation 2020-AP010711656 made by  GENCI. Also, we utilized  GPU-based computing resources of Inception program  ( Investissement d'Avenir grant  [grant no. ANR-16-CONV-0005 ]. MT thanks to  Programme PAUSE  of  College de France  for their support.
Software description The FRUITNIR-GUI was built utilising the application builder in MATLAB version 2018b (The Mathworks, Natick, MA, USA). All the functions included in the toolbox are either built-in in the programming environment or codes developed in-house. The application can be downloaded and installed in MATLAB (preferred versions: 2018b or more recent), can be run through the '. mlapp' files in the MATLAB command line or can be used as a stand-alone executable. If users do not have a MATLAB version recent enough (from 2018b onwards), it is recommended to install the free MATLAB runtime tool and run the app as standalone. All the executables and MATLAB functions can be downloaded from (https://github. com/puneetmishra2/FRUITNIR). In the GitHub repository, the standalone toolbox executable files can be downloaded as 'FRUITNIR.
zip'(https://github. com/puneetmishra2/ FRUITNIR/raw/master/FRUITNIR. zip) and the function for running the tools in command line as 'Fruitnir_functions. zip'. The dataset corresponding to the case-study discussed in this article can be obtained from the publisher of original data set  (Teh et al. , 2020). All the files are available at the link (https://github. com/puneetmishra2/FRUITNIR). To run the toolbox from the command line, users should use the toolbox folder as the current folder and type T1 on the command line, so to start the main graphical user interface. The users should input the password: 'welovenirs' (without quote marks) and click run. Then, users can load data and run the analysis. The GUI supports. csv,. xlsx and. mat data formats. A summary of the software architecture is presented in Fig.
Dataset for demonstrating the use of FRUITNIR-GUI The demonstration of the GUI was performed on a desktop computer equipped with a 3. 60 GHz Intel® Xeon® W-2133 processor (Intel Corporation, Santa Clara, CA) and 64 GB RAM, running Microsoft Windows 10 operating system (Microsoft Inc. , Redmond, WA) at 64-bit and MATLAB 2018b (The Mathworks, Natick, MA). To demonstrate the functionality of the GUI, a dataset related to the prediction of DM in apples harvested in two different seasons was used  (Teh et al. , 2020). As explained in the original work  (Teh et al. , 2020) , the NIR and DM measurements were carried out in 2015 and 2016 and involved assessment of 2252 fruits from 58 accessions at three orchard sites of Washington State University apple breeding program (WABP). The 58 accessions included 34 WABP apple selections and five commercial cultivars (i.
e. , Cripps Pink, Fuji, Gala, Golden Delicious and Honeycrisp). As explained in the original work  (Teh et al. , 2020) , fruits were stored at 2 • C for two months. After storage, the samples were stabilized for a week at room temperature (25 • C) and five apples were randomly selected for NIR measurements. NIR spectra were acquired using F-750 Produce Quality Meter (Felix Instruments, Camas, WA, USA). As explained in the original work  (Teh et al. , 2020) , the DM measurements were done by sampling and dehydrating a cylindrical core in a food dehydrator. More specific details on the dataset are summarized in Table  1. In this study, since the aim of the methods implemented in the toolbox is to reduce/eliminate the experimental variability ascribable to the batch effect, the models were built on data from year 2015 (training set) and were tested on year 2016 data.
Derivatives. Derivatives are used to reveal the underlying peaks  (Savitzky and Golay, 1964). FRUITNIR-GUI allows to calculate 1st or 2nd derivatives with pre-defined settings, or to select a specific order of the derivative (together with setting custom values for the meta-parameters, e. g. , the degree of the interpolating polynomial and the data point window, if using the Savitzky-Golay algorithm) as a 3rd pre-processing step. The algorithms for this operation are implemented by means of an in-house code  ((Roger et al. , 2020) ).
Partial least-squares regression PLS regression is a common chemometric technique for calibration problems involving NIR spectroscopic data  (Wold et al. , 2001). In particular, PLS deals with the multi-collinearity in the multivariate signal by projecting the data onto a subspace of latent variables (LVs) and compressing the relevant information in the X block into a few orthogonal scores, which are extracted so to have maximum covariance with the response(s). This guarantees at the same time that the scores are explanatory (i. e. , provide a "good summary") of the variance in X, and that they are relevant for predicting the response(s) Y. A more detailed description of the method can be found in  (Geladi and Kowalski, 1986; Wold et al. , 2001). In the GUI, PLS models are calculated by means of the MATLAB's 'plsregress', which has been integrated with a function performing a 10-fold cross validation procedure approach is integrated for the selection of the optimal model complexity (number of LVs defining the subspace for data projection).
Dynamic orthogonal projection Dynamic orthogonal projection (DOP) is a model maintenance method developed to deal with physical, chemical and environmental affects in spectroscopic modelling  (Zeaiter et al. , 2006). The approach is based on the correction of the calibration dataset based on the new reference measurements performed in different physical, chemical and environmental conditions. The correction is performed using orthogonal projections based on the subspace defined by the difference of the calibration spectra and the new condition spectra. Let rbe a set of samples measured in the new conditions. Let Y r be the reference values and X r the measured spectra of these samples. The DOP method starts by estimating virtual standards, i. e. , the spectra X * r that should have been measured in correspondence with Y r , if the calibration conditions had not varied.
Data loading and pre-processing to the GUI The FRUITNIR-GUI provides the possibility to load two sets of data together. The two datasets can be either a training/test set pair, or they can be data from two seasons, two temperature conditions or two instruments. The GUI for loading the dataset is shown in Fig. 2. The first step is to select the type of file to be loaded. There are currently three file type options, namely. csv,. xlsx and. mat formats; this choice is based on their being among the most popular data output formats in many NIR spectrometers or, in the case of. mat, to allow MATLAB users to directly input their data. Once the data are loaded, the three pre-processing drop-down menus can be used for smoothing, scatter correction and differentiation. It is recommended to follow these steps while loading data: first load the calibration dataset, select the pre-processing strategy and apply it and then load the test dataset; then, the same pre-processing will be applied to the test dataset automatically, once it is loaded.
4A ), indicating that a batch effect might be present and could have affected the application of the PLS model when tested on data from a new season. Similar model failure was also highlighted in some recent studies such as in relation to prediction of DM and SSC in pear fruit under different storage conditions, where the standard PLS regression model attained high error  (Mishra et al. , 2021). Similarly, standard PLS regression models have been reported to lead to high RMSEP in the prediction of DM in mango fruit of different seasons  (Mishra and Nikzad-Langerodi, 2020; Mishra et al. , 2020b). Previous results and the results obtained in the present study suggest that PLS alone is not Fig. 2. GUI for loading and pre-processing datasets. Two different batches can be loaded through the same interface.
Selecting variables with CovSel Variable selection allows to generalize the model performance by retaining key variables that relate the most with the property of interest. Fig. 5  shows the results of CovSel analysis performed on the apple dataset. Fig. 5A  shows the 20 variables selected by CovSel, most of  which are related to the 3rd overtones of OH bonds, indicative of the moisture in the fresh fruit  (Walsh et al. , 2020b). Fig. 5B  shows the results of calibration using the 20 selected variables and Fig. 5C  shows the predictions of the calibrated model on the test set. The results indicate that CovSel variable selection reduced the RMSEP by 18 %, respectively, compared to the standard PLS regression modelling, suggesting that variable selection could be an approach to optimize prediction models.
Conclusion In the present work, a GUI has been presented for chemometric modelling of NIR spectra of fruit. Specific algorithms for correcting the batch effect were integrated. The GUI was demonstrated with a realworld dataset related to the prediction of DM in apple fruit. The results showed that the GUI can perform tasks such as PLS regression, variable selection and batch effect correction using either DOP or TCA methods. The results reported indicate that approaches such as variable selection and batch effect corrections with DOP and TCA can improve the performance of models built on NIR spectra collected in multi-batch experiments. The best performance in terms of lowest RMSEP was obtained with the DOP approach, followed by variable selection with CovSel and then TCA. However, a main drawback of the DOP approach is that it requires reference measurements from the new batch to model and remove the external influence.
Model simulations based on real case-studies Currently, the above exact mathematical solution of the generic onecompartment TK model is used in support of the development of the R-package rbioacc 20 as well as its web interface MOSAIC bioacc , which is freely available at https://mosaic. univ-lyon1. fr/bioacc. 21 Both tools allow fitting the TK model to bioaccumulation data in a user-friendly way, providing all necessary outputs to check for goodness-of-fit and obtain bioaccumulation metrics, regardless of the considered speciescompound combination. The database associated to the web interface is also available online at http://lbbe-shiny. univ-lyon1. fr/mosaicbioacc/data/database/TK_database. html, with a wide collection of accumulation-depuration data in support of toxicokinetic modelling.
22 rom this database, we selected three real case-studies from which we got fitting results (especially the joint posterior probability distribution of all kinetic parameters) in order to perform the subsequent model simulations, thus illustrating the use of the generic TK model. All model simulations of the generic set of mathematical solutions (Equations (  3 ), (  10 ), (  15 ) and (  23 )) were performed using R software with the rbioacc package and function predict(). 20 Note that the same simulations can be performed directly online with the MOSAIC bioacc platform and its prediction module (https://mosaic. univ-lyon1. fr/bioacc). We used 500 time points in [0; t f ], where t f stands for the final time of each simulation. These simulations illustrate the use of the generic TK model in three case studies where different compounds are bioaccumulated by different species.
Acknowledgments The authors would like to express their sincere thanks to  Miléna Kaag ,  Yacout Lahlou  and  Nino Molin  who designed the MOSAI-C bioacc prediction tool as part of their 4 th year study project at the  National Institute of Applied Sciences (INSA) in Lyon (France). This work was made under the umbrella of the  French GDR "Aquatic Ecotoxicology " framework, aiming at fostering stimulating scientific discussions and collaborations for more integrative approaches.
Study of the Gap Effect This section focuses on the effect of the distance between electrodes d (Figure  1 ) on the general sensor performance for a constant resonance frequency of 11 kHz. The analytic solutions previously presented (Equation (  27 )) were implemented in a Python programming environment to estimate an optimal value of signal-to-noise ratio for each value of the gap d. Subsequently, for each optimal SNR value, we get the geometrical parameters of the cantilever (width, length, thickness, Figure  11a ) and their corresponding output voltages (Figure  11b ). When d increases, the signal-to-noise ratio increases as the displacement increases due to the decrease of squeeze film damping. At the same time, the signal output voltage decreases due to the increase of the distance between electrodes.
Funding: This research was financially supported by the  French Ministry of Defense  ( DGA-MRIS ),  Region Languedoc Roussillon ,  European Community (FEDER)  and  Renatech ,  Agence Nationale de la Recherche  ( MULTIPAS Project No. [ ANR-16-CE04-0012 ], and  NOMADE  Project  No. [ ANR-18-CE04-0002-01 ]).
Acknowledgements We thank  ISIMIP  for assistance with processing ESM inputs and outputs and the  ESM community  for enabling these impact analyses. This work was supported by the  Jarislowsky Foundation  (D. P. T. ), the  Natural Sciences and Engineering Research Council of Canada   Discovery Grant  programme (D. P. T. , H. K. L. , T. D. E. , W. W. L. C. , J. P. -A. and V. C. );  Australian Research Council (ARC) Discovery  Projects  DP170104240  (J. L. B. and C. N. ), DP190102293 (J. L. B. , C. N. , A. J. R. , J. D. E. and D. P. T. ) and  DP150102656  (J. D. E. ); the  European Union 's  Horizon 2020 research and innovation programme  under grant agreements  817578  (TRIATLAS) (M. C. , J. S. , L. S. , O. M. , L. B. , Y. -J. S. , N. B. and J. R. ), 869300 (FutureMARES) (J. A. F. -S. ,Y. -J.
S. and M. C. ) and 862428 (MISSION ATLANTIC (J. A. F. -S, Y. -J. S. and M. C. ); the  Spanish National Project ProOceans  ( PID2020-118097RB-I00 ) (M. C. and J. S. ); the  Open Philanthropy Project  (C. S. H. ); the  United Kingdom Research and Innovation (UKRI) Global Challenges Research Fund (GCRF) One Ocean Hub  ( NE/S008950/1 ) (K. O. -C. and L. S. ); the  Simons Foundation  (nos. 54993 ,  645921 ) (G. L. B. ); the  Belmont Forum and BiodivERsA  under the  BiodivScen ERA-Net COFUND programme  ( SOMBEE  project,  ANR-18-EBI4-0003-01 ) (Y. -J. S. and N. B. ); the  MEOPAR   Postdoctoral Fellowship Award   2020-2021  and the  Ocean Frontier Institute (Module G)  (A. B. -B. ); the  French ANR  project  CIGOEF  (grant  ANR-17-CE32-0008-01 ) (O. M. , L. B. and J. R. ); the  California Ocean Protection Council  Grant  C0100400 , the  Alfred P.
Protocol for multicolor three-dimensional dSTORM data analysis using MATLAB-based script package Grafeo This protocol describes the step-by-step analysis of the multicolor (one-, two-, or three-color) two-and three-dimensional dSTORM data using MATLAB-based script package Grafeo. Grafeo primarily uses pointillist data visualization and analysis frameworks, including the nearest neighbors, approach, Voronoi tessellation, Delaunay triangulation, Ripley's (K, L) and two-point correlation functions, and graph-based clustering.
The current version of the Grafeo is 3 (v3). b. Extract the Grafeo package and copy all files to the desired folder on your computer, e. g. , ''C:yUsersyDocumentsyProgramsyMatlabyGrafeo_v3''. Note: This protocol uses a Matlab desktop environment, which requires a license. You have to install Matlab before going to step c). To run Grafeo v3, you need to install the following Matlab toolboxes: Curve Fitting Toolbox, Image Processing Toolbox, Statistics and Machine Learning Toolbox, Signal Processing, and Optimization Toolbox. Grafeo can also use a third-party function InPolygon that can be downloaded from Matlab file exchange (see the key resources table). For large polygons, InPolygon can perform faster than a build-in Matlab function inpolygon. However, the installation of InPolygon requires Matlab C compiler to compile mex files, and it does not work on the Windows 32 bits.
In the absence of the InPolygon function, Grafeo uses Matlab built-in inpolygon function. c. Open your Matlab desktop. d. In the Matlab ''Command Window'' type: addpath(''fullpathToGrafeoFolder''), using the example above, addpath(''C:yUsersyDocumentsyProgramsyMatlabyGrafeo_v3''), and press ''enter''. Opening Grafeo in Matlab. e. In the Matlab ''Command Window'' type in grafeo_v3, and press "enter". This will open the Grafeo graphical user interface (GUI) (Figure  1 ). grafeo_v3 is the name of the main Grafeo function. If you rename it or the version changes, use the update name, e. g. , grafeo_v4, for the Grafeo version 4. f. Installing 'InPolygon' third party function. First, check if you have a C language compiler compatible with Matlab installed on your machine. For this type mex -setup c in the Matlab command window.
If you have, e. g. , a free C compiler installed for Matlab (MinGW), the following message will appear: ''MEX configured to use 'MinGW64 Compiler (C)' for C language compilation'', where 64 stands for the Windows 64 bits. If no C compiler is installed, an error message will be displayed: ''No supported compiler was found''. Download MinGW compiler from the Matlab website (https://fr. mathworks. com/support/requirements/ supported-compilers. html) and follow the installation instruction. Once the installation of C compiler is completed, copy the downloaded InPolygon folder to the desired location on your computer and change the Matlab current directory (cd) by typing in the command window cd("InPolygonDirectory"), where ''InPolygonDirectory'' is a full path on your computer, e. g. , ''C:yDocumentsymatlabyInPolygon''.