dataesr/scientific-paragraphs-categorization huggingface dataset:
Modelling approach based on Digital Signal Processing using Fast Fourier Transform (FFT). innov'SAR requires only, as input data, an initial dataset with the primary sequences of protein variants and their values for a biological activity, to generate a predictive model. This model can be used to find the activity of new mutants, outside of the initial dataset. In contrast to many other rational methods used to find new interesting mutants, innov'SAR can be used without knowledge of the 3D structure. Therefore, innov'SAR can be used for proteins where the crystal structure could not be obtained. 3D structural data could still be used to validate the results from innov'SAR. innov'SAR consists of 3 phases, namely, the encoding phase, the modelling phase and the predictive phase. In the first phase, the encoding phase, innov'SAR must encode the alphabetic protein sequence into a numerical sequence, understandable by the modelling tools.
This phase takes into account only the protein sequences. innov'SAR uses two steps for the encoding (Fig. 3 ). First, innov'SAR uses the indexes of the AAindex database [20] to encode the primary protein sequence into a numerical chain, where each letter of amino acid is replaced by a value. This database holds more than 500 numerical indices representing various physicochemical and biochemical properties for the 20 standard amino acids and correlations between these indices are also listed. The second step comprises a Fast Fourier Transformation (FFT) of the encoded sequences from the first step. FFT is a digital signal processing technique that is used to convert numerical signals into an Energy versus frequency representation (equation 1). After this step, a spectral form of the protein, called the protein spectrum, is generated.
The use of FFT and protein spectra are the cornerstones for the prediction of biological activity by innov'SAR approach. ∑ = π = - - S k s n e ( ) ( ) (1) n N i k 0 1 ( 2 ) N n With s the input signal (encoded sequence) of length N, S the output spectrum (complex numbers), n the position in the input signal, k the frequency in the spectrum and i the complex number such that i 2 = -1. A representation of a protein spectrum is shown in Fig. 3a  as a plot Energy = f(frequency). The protein spectrum allows to take into account the impact of mutations on the whole spectrum and does not focus on local fitness alone. Therefore, a single point mutation impacts the whole protein spectrum in a similar fashion a single point mutation can impact the whole structure of a protein. At the end of the encoding phase all the variants from the initial dataset will have a protein spectrum.
The validation step consists to test the accuracy of the model in order to check if the learning step was efficient. The root mean squared error (RMSE) and the coefficient of determination (R 2 ) are the performance parameters to assess a regression model, during the validation step. RMSE values varies between 0 and +∞. R 2 value varies between 0 and 1. An accurate regression model has an RMSE close to 0 and a R 2 close to 1. One particularity of innov'SAR approach, in the modelling phase, is to evaluate multiple encoding indexes to find the best for the construction of models. innov'SAR uses the initial dataset (training set) to construct a predictive model for each encoding index. For each model, innov'SAR calculates the value of the performance parameters in two stages. The first stage is a standard cross validation.
The next stage is a modelling integrating the full set in the learning step. The performances from the two stages are analysed to evaluate and to check the robustness and the validity of a model. In the first stage, the cross-validation stage, the initial dataset is split into k equal portions. The number k varies according to the size of the initial dataset. We use low k value if the dataset size is high and high k value in the opposite case. We use k-1 portions as the learning dataset and the remaining one as the test dataset. The procedure is repeated k more times until each portion is used as the testing dataset once. The cross-validation allows to avoid potential overfitting problem and to optimize some modelling parameters. The method of cross-validation used for this study is the Leave-One-Out Cross-Validation (LOOCV), where k is equal to the number of sequences.
In the second stage, the full set stage, the whole initial dataset is used as a learning dataset and a test dataset will be tested with the optimized parameters from the first stage. This stage checks the accuracy of the predictions for learned sequences. At the end of the modelling phase, a set of accurate models and their associated encoding indexes are selected and kept. In the predictive phase, the sequences of the new variants are pre-treated by an encoding phase with a selected encoding index, determined in the modelling phase. Once all the new variants have a protein spectrum, innov'SAR employs a model associated to the encoding index and selected in the modelling phase. Next, the model predicts the values of the activity of the new variants from their protein spectrum (see Fig. 3b ).
We used the ΔΔG ‡ values for the construction of innov'SAR models, in order to follow and determine the enantioselectivity of the ANEH variants of this study. The energy of the 9-single point mutations and the WT constitute a first learning dataset of protein sequences that was used by innov'SAR and named dataset A (Table  S2 ). A first model, named model DSA_FFT, was generated with dataset A to predict the ΔΔG ‡ of double, triple and quadruple point mutants and to appraise the modelling done by innov'SAR with only single point mutants. After the evaluation of multiple encoding indexes by innov'SAR, described previously, the encoding index ZHOH040101  54  was identified as one of the best to generate an accurate model by innov'SAR and selected to build the model DSA_FFT. The ΔΔG ‡ values of all possible combinations of these single point mutations, 2  9  = 512 possible variants, were thus predicted.
We created a validation dataset with the 28 multiple point mutants (Table  S3 ), including the mutant LW202, that were previously reported (among a total of 512)  50  , in order to check the accuracy of our models based on only 9 single point mutations. The high value of the coefficient of determination, R 2 = 0. 81 from the prediction of the validation dataset, indicates the good accuracy of our model (Fig. 4 ). The model uses a learning dataset based only on single point mutations, with relatively high value of ΔΔG ‡ , i. e. , low values of enantioselectivity. However, it can determine the multiple point mutants with high experimental values of E-value such as the mutant LW202. The incorporation of the 28-multiple mutation variants, including the mutant LW202, into the dataset A should improve the accuracy of the model for the prediction of the 512 possible variants.
This incorporation formed a new dataset, named dataset B, by gathering all the 37 mutants and the WT (Table  S4 ). A new model, named model DSB_FFT, was constructed with the dataset B. After the evaluation of multiple indices from the AAindex database, the index RACS820104  55  was used as encoding index due to its higher modelling performance on the new learning dataset. We compared the performances of the model DSA_FFT and the model DSB_FFT for the prediction of the 28 multiple point mutants. As the model DSB_FFT used already the 28 multiple mutants in the learning step, we performed a Leave-One-Out Cross-Validation (LOOCV) for the validation of the second model. In the LOOCV, the predicted value of each mutant was calculated by removing the mutant from the learning dataset. We obtained an excellent R 2 value (0.
6 ). Among the 28 multiple point mutants, the model DSA_FFT gives better results for 18 mutants than the prediction based only on the addition of mutations (Fig. 6  and Table  1 ). The innov'SAR model allows a distinct capture of epistasis for these mutants, which is a powerful characteristic of this machine learning approach. We then tested if model DSB_FFT, with the incorporation of the 28-multiple point mutants into the 9-single point mutant model could bring improvements to the prediction of epistasis for the other mutants where the model DSA_FFT fails to fully capture epistasis. Among the 28 multiple point mutants, the model DSB_FFT generates more accurate predictions for 22 mutants, better results than the one based only on the addition of mutations (see Fig. 7  and Table  1 ). An improvement is observed in the ability of innov'SAR to capture epistatic effects compared to the use of only single mutants in the learning dataset.
As already pointed out, this fact is also shown by the higher R 2 value (0. 96) for the LOOCV training set. The LOOCV shows that the model identifies more epistatic effects from the learning dataset. The generation of protein spectra is a key feature of innov'SAR methodology. To get a better idea why innov'SAR approach could predict epistatic effects, we ran different kinds of modelling, with either single and/or multiple mutants and with or without FFT during the encoding phase of innov'SAR (see Fig. 3a ). Models were built without applying FFT during the encoding phase. When a model based on dataset A and without FFT, named model DSA_noFFT, the ΔΔG ‡ predictions perfectly fit with the pure additive values (Fig. 8 ). In this case, we obtained the same performance as when only the addition of mutations is used to predict the value of ΔΔG ‡ (R 2 = 0.
80). Without FFT, the model DSA_noFFT supposes only the addition of mutation and could not reproduce the epistasis for the prediction. Next, we then performed the modelling with the dataset B and without FFT to generate the model DSB_ noFFT. This model has 0. 88 as value of R 2. Figure  9  shows that the model DSB_noFFT can predict some epistatic interactions, but with lower accuracy than the model DSB_FFT, using FFT in the encoding phase (Fig. 7 ). Finally, as shown in Fig. 7 , a model including FFT improves the accuracy of the predictions of the epistatic interactions of the multiple point mutations. FFT is an improving factor for the predictions of mutants with epistasis in the models generated by innov'SAR. Prediction of new improved ANEH mutants. The best experimental mutant previously described  49  , named LW202, contains all 9 single point mutations in its sequence and has an E-value of 115.
Experimental characterization of predicted ANEH mutants. In a next step, we made an arbitrary selection of the predicted improved mutants and tested them in wet lab for enantioselectivity. The predicted and experimental values are listed in Table  2. We decided to select 5 mutants comprising a large spectrum of E-values ranging from E-values that are close to the WT and to mutants with E-values that are outside the highest value of the training set. We were able to demonstrate that the 5 mutants have properties that corresponded quite well to the predictions. We also observed that innov'SAR not only allows accurate predictions of E-values that are inside the learning dataset but can also be used to predict and identify superior variants of ANEH. Gratifyingly, a R 2 value of 0. 94 was obtained.
Accordingly, ASRA identifies the underlying regularity of the protein property landscape, in this case enantioselectivity. Consequently, it is not a QSAR-type approach, but a machine learning process very different from innov'SAR. The present study is the first report that describes the application of innov'SAR in directed enzyme evolution. In this particular case we have limited the application to recombine learned mutations which are contained in the train set. We could identify and experimentally confirmed extremely fit mutants out of 512 possible mutational combinations by using a dataset made of only 7% of the mutants contained in the total fitness landscape. In a next step we are considering going further and to test to what extent our approach is able to predict the properties of non-learned mutations on learned or non-learned positions.
The gold-standard method for quantifying neutralizing antibody responses to many viruses, including dengue virus (DENV), is the plaque reduction neutralization test (PRNT, also called the immunofocus reduction neutralization test). The PRNT conducted on 96-well plates is high-throughput and requires a smaller volume of antiserum than on 6-or 24-well plates, but manual plaque counting is challenging and existing automated plaque counters are expensive or difficult to optimize. We have developed Viridot (Viridot package), a program for R with a user interface in shiny, that counts viral plaques of a variety of phenotypes, estimates neutralizing antibody titers, and performs other calculations of use to virologists. The Viridot plaque counter includes an automatic parameter identification mode (misses <10 plaques/well for 87% of diverse DENV strains [n = 1521]) and a mode that allows the user to fine-tune the parameters used for counting plaques.
However, to achieve the target number of pfu/well, plaques must be smaller to prevent plaque overlap  [3]  and thus are more difficult to count manually. Automated counters for immunostained plaques do exist, but current versions also present multiple challenges. With commercial plaque counting software such as the ImmunoSpot and Biospot software on CTL Analyzers (Cellular Technology Ltd. ), researchers typically optimize a small panel of strains for plaque intensity and size and develop programs or macros to count plaques for each of these strains  [15, 16]. However, for researchers working with diverse strains with varied plaque phenotypes, such programs are often difficult to optimize and standardize. Additionally, commercial counters are often proprietary and expensive. Free, open-source general image processing tools are available that allow for plaque counting on a personal computer, such as ImageJ  [17] , but these programs require users to navigate through a series of menu bars to identify relevant image processing tools or write their own macros; further, as the code is written in JavaScript, many researchers may struggle to adapt image-processing code beyond what is available.
To our knowledge, there are not programs that include integrated plaque counting and neutralization titer estimation functionality. We have designed Viridot (Viridot package), a free, open-source, automated package in R  [18]  for immunostained viral plaque counting and neutralizing antibody titer estimation with a user interface in shiny  [19]  and image processing tools from the EBImage package  [20]. The R statistical framework is open source statistical software with a large community of users that contribute packages enabling a multitude of specific functions. Viridot is designed to be relatively simple to set up and learn for new users without coding experience as well as easy to optimize to each assay. Among other features, Viridot includes plaque counting with an automatic parameter identification mode; a single interface that enables easy access to all modifiable parameters at once; tools to break down each image-processing step to aid in the optimization process; counting of single or multiple plates; and the ability to save images with circled plaques as well datasets of plaque sizes, parameter settings, and plaque counts in 96-or 24-well plate format.
The Viridot plaque counter can be used for images acquired with different light settings as well as spots of different stain colors. Viridot also has a built-in neutralizing antibody titer estimation program with many modifiable parameters and programs for estimating virus concentrations from virus titration assays as well as the dilution factor to achieve the correct number of pfu/well in a neutralization test. Here, we describe the Viridot programs, explain the process for standardizing manual and Viridot plaque counting methods, and evaluate the performance of Viridot on a variety of plaque images. We show that for the same set of wells, a single analyst can achieve plaque counts with Viridot that are as similar to their own manual count as to the manual count of a different analyst. Further, we show that neutralizing antibody titers estimated from Viridot plaque counts are highly similar to those estimated from manual counts.
Ethics statement This study followed the National Institutes of Health guidelines for the humane treatment of laboratory animals and was approved by the NIAID Animal Care and Use Committee (11DEN33 and 14DEN34, parent protocol NIAID ASP LID 9). Human sera from NIH monovalent and tetravalent DENV vaccines trials (ClinicalTrials. gov identifiers: NCT00473135, NCT00920517, NCT00831012, NCT01072786) were performed under an investigational new drug application reviewed by the U. S. Food and Drug Administration and approved by the Institutional Review Board at the University of Vermont and Johns Hopkins University. Johns Hopkins Bloomberg School of Public Health approved the study and collection of a DENV-positive human antiserum and coordinated the IRB review conducted by Western Institutional Review Board.
Viridot development and distribution Viridot was written in R 3. 3. 2 (R Foundation for Statistical Computing  [18] ). We used shiny  [19]  and shinyFiles  [21]  for creating the user interface and the EBImage package  [20]  for many of the image processing tools. Viridot is available on GitHub at: https://github. com/ leahkatzelnick/Viridot. Installation instructions for Mac and Windows are available on the GitHub webpage and in the Viridot manual (S1 Supporting File).
Image processing pipeline The user has the option of seeing how the image data is affected by the parameter settings for each image-processing step with the Show what is done at each step option as part of the optimization process (S1 Fig). The image-processing steps are as follows. The Viridot plaquecounting program analyzes pixel intensity values ranging from 0-1, requiring conversion of the raw image data into a single channel that can be analyzed as a grayscale image. The amount and type of light used for taking the well image determines the optimal color channel that provides the most information on the difference between background (should be closer to values of 0, or dark) and plaque (closer to values of 1, or light). The user can select the red, green, or blue channels, hue, saturation, or value channels (rgb2hsv function in the grDevices package), or a combination of these channels.
Images can then be smoothed to reduce noise by applying a Gaussian blur (gblur, EBImage package  [20] ) with a sigma value (radius in pixels for the Gaussian transformation) defined by the user. An extra option allows removal of strings or fibers in image that are a different color from plaques. Because well edges may contain reflections, excess liquid, and other distortions that can interfere with the plaque counting, images can be trimmed so that only pixels more than a certain distance from the well edge are included in the analyses. To increase the difference between background and plaque, pixel data can be transformed by multiplying and/or exponentiating the image pixel data by constants, ensuring that the subsequent threshold step is applied to the pixel difference between background and plaque, and not in some other minimum in pixel intensities.
Neutralizing antibody titer estimation The neutralizing antibody titer estimation program creates dose-response curves (2-or 3-parameter logistic regression with drm in drc package  [23] ) from a table of plaque counts based on a plate template specifying the positions of samples, dilutions, and controls as defined by the user (Viridot manual in S1 Supporting File). The user can select from multiple methods for estimating neutralization titers, write in sample and experiment names, define the dilution series, designate the PRNT titer cut-off (PRNT 10 -PRNT 90 ), or run only single titrations at once. Control well plaque counts can be inserted manually or estimated from specified wells on the plate. The program outputs the PRNT titers with 95% confidence intervals, the curve slope, and a plot showing the neutralization curves with 95% confidence intervals, with customizable color schemes and methods for plotting error bars of the raw data.
Data sets The automatic-parameter identification mode training set and test set 1 each consisted of 1521 well images of PRNT titrations conducted on C6/36 cells (Aedes albopictus, NIAID/Novavax MCB) (full PRNT protocol in S2 Supporting File). The PRNT titrations were of 47 genetically diverse DENV1-4 strains (GenBank accession numbers KT382187, KT452791, GQ868619, FJ639680, AY145121, AY780643, EU482591, AF180817, KT382186, FJ461335, KT382189, KT452795, GU131927, GQ868638, KT382188, FJ538920, FJ467493, AF038403, EU482756, EU482684, AY158339, EF105384, AY744147, KT452797, KT452796, EU482672, KT452792, KT452799, L11422, KT452798, HQ541806, KT452800, EU529698, EU660409, FJ432743, KT452794, KT452793, KF543272, KT452802, AF326573, JN022608, KT452801, JF262780, KT452803, EU854297, FJ882599, and AY780644) titrated against de-identified human biorepository samples from prior clinical trials of monovalent components of a live attenuated dengue vaccine  [24]  or African green monkey (Chlorocebus sabaeus, NHP) antisera  [25].
The training set consisted of well E7 from each plate; test set 1 was well H9. Test set 2 consisted of 421 additional well images randomly drawn from the control antiserum titration plate for PRNT titrations of the African green monkey antisera against 107 Thai DENV1-4 strains (subset of strains in  [26] , Gen-Bank accession numbers KY586306 to KY586946) that had not been used in the training set or in test set 1. The manual plaque-counting methods comparison (n = 72 wells) and Viridot plaquecounting methods comparison (n = 32 wells) used 96-well plates of C6/36 cells inoculated with a DENV3 strain (GenBank accession number HQ541806) at a target of 40 pfu/well. Comparison of standardized manual and Viridot plaque-counting methods consisted of 168 wells of the DENV3 strain titrated against DENV-positive antiserum (three titrations on the same plate, three titrations on different plates in different experiments).
Plaques were stained with peroxidase-labeled antibody and TrueBlue peroxidase substrate (S2 Supporting File). All plates were scanned using a Zeiss Axio Imager. M1 microscope with a 2. 5X lens and the AxioCam MRc mounted camera controlled by the software KSElispot (Windows 95). Well images were saved as. TIF images of 1040 by 1040 pixels. We also imaged plates from the top (well-side) and the bottom of 96 well plates using camera phones. A set of 45 well images from clear plates (placed on white paper) were taken using the iPhone 7 plus version 11. 4. 1, model MN5L2LL/A, without flash. The camera phone was balanced on a plastic test tube holder 9cm above the plate, with bench lighting 39 cm above plate. Five additional well images taken from the top of white 96 well plates were acquired using the Samsung Galaxy S7, model SM-G930V, also without flash.
The image was taken with Zeiss Axio Cam Imager. M2 scope with the AxioCam MRc camera, 5x objective lens (image courtesy of J. Misplon). A well of the total and DENV4-specific IgG+ memory B cells were from an ELISPOT-based Multi-Color FluoroSpot assay  [31]. Spots were detected by adding either a TRITC-labeled IgG-specific antibody or antigen of each serotype and virusspecific antibodies each labeled with a fluorescent Qdot of a different color. Images were acquired using the ImmunoSpot Analyzer (images courtesy of P. Andrade). Wells of plaques of recombinant lymphocytic choriomeningitis virus (LMCV) on Vero E6 cells were resolved by applying crystal violet and ethanol in water  [32]. Plates were imaged using Alpha Innotech digital camera with the Computar H6Z0812M motorized zoom lens and divided into individual well images using the Viridot image formatting program (images courtesy of C.