dataesr/scientific-paragraphs-categorization huggingface dataset:
The data are independent but each squaring requires to load input and to store output. Table  9  shows the performance of squaring in F 2 n. The squaring using PCLMULQDQ is the most efficient. For the same reasons that for the multiplication in F 2 n , the best modular squaring is the one using only sse2 modular reduction. This is the default setting in MQsoft. All measured functions in this Section are available is our library. They are implemented in constant-time. The modular reduction by pentanomials is also available to make the library more complete, but is not yet vectorized (excepted for n ∈ {184, 312, 448, 544}).
To choose the best addition chain is not easy, because it depends on the performance of multiplication, squaring and multi-squaring. Moreover, there is a large set of possible addition chains. This problem is studied in  [36] , which proposes a software generating an efficient C++ inversion code. This software searches the addition chain which maximizes the performance of the generated code. However, the generator does not proposes implementation of multi-squaring tables in constant-time. For the moment, MQsoft uses Algorithm 5, but we propose in Appendix B examples of addition chains chosen to minimize the number of field multiplications. We have improved Algorithm 5 with multi-squaring tables to compute the variable tmp when i is zero or one. Because multi-squaring tables are huge, we use it only for the parameters of GeMSS, Gui, DualModeMS, and for the values of n used to evaluate the performance of MQsoft.
However, the optimal choice to put each mask in a 256-bit register is not trivial. On the one hand, we can store 256-bit masks in the buffer mask. In this way, one load permits to create the Compute p i,j x j. end for Table  15  shows the performance of the evaluation that uses avx2. To improve the performance, we use the option -funroll-loops of gcc which unrolls loops to improve the use of the pipeline. The factor of performance between variable-time and constant-time implementation depends on m: the factor is two for small values of m and four for high values. The performance is affected by cache penalty when the public-key is too large. For m = n + v = 256, we compare our code with the efficient implementation of  [20] , by using a similar processor (ServerH). We have similar times for constant-time implementation, and a speed-up of 1.
Performance of GeMSS, Gui and DualModeMS In this section, we show the speed-ups obtained for GeMSS, Gui and DualModeMS thanks to MQsoft. The difference between Gui and GeMSS are explained in Section 1. 5. MQsoft uses the SHA-3 function from the Keccak Code Package  [26]  and the SHA-2 function from OpenSSL. Random elements ar generated by the determinist random bytes generator provided by the NIST during the competition (which is based on AES from OpenSSL). The original implementation of GeMSS requires NTL library to compute the root finding. In MQsoft, NTL has been completely removed. DualModeMS is based on GeMSS implementation and so is naturally supported by MQsoft.
Finally, we use this buffer to measure the performance of the verifying process. The code was compiled with gcc -O4 -mavx2 -mpclmul -mpopcnt -funroll-loops. Here, we use the version 6. 5. 0 of gcc. In Table  20 , we present the results for the 128-bit security level, with three significant digits. The complete table is on our website  [2]. In this section, we only measure the Gui implementation from libpqcrypto. The main difference with the original implementation is the correction of a mistake in the way to compute the Frobenius map (as explained in the previous section). This change impacts only the performance of the signing process of Gui-448. Then, we have updated MQsoft to achieve the EUF-CMA security property in order to be comparable to libpqcrypto. The EUF-CMA version implies a slow down of the signing which permits to decrease the size of the public-key, but by increasing the size of the signature.
This initial work  [2]  took the DSD and Mie theory into consideration. However, since these measurements were made on natural fogs, few data were collected: only 600 measurements over eight wavelengths between 350 and 10,000 nm were obtained, compared to our 4456 measurements over 213 wavelengths between 360 and 2490 nm. In addition, most of the observations made were in hazy conditions (very light fog) and not in heavy fog conditions. The conclusions of this work are mixed: some fogs have constant extinction for wavelengths between 350 and 1700 nm, while others are said to have lower extinction for higher wavelengths. It was therefore observed that, beyond the visible range, fogs with the same MOR can have different extinction coefficients for different wavelengths. In addition, it was also observed that the extinction coefficient depends on the wavelength.
The interaction of light with fog is discussed in Section 2 considering two models for the wavelength range of 350 nm-2450 nm. The two models used are the Mie model  [8]  and the MODTRAN software model (which includes Mie diffusion)  [9]. The method used to study the influence of wavelength on light transmission in fog is given in Section 3. This method is based on a theoretical approach, based on DSD measurement, for calculating the extinction coefficient for different wavelengths, in comparison to measurements with a spectroradiometer in the range of 350 nm-2450 nm obtained in controlled fog conditions. The results are given in Section 4 and discussed in Section 5.
As the Mie model remains partial (without molecular impact and in the context of single scattering), MODTRAN software  [9]  was also used to avoid these two latter assumptions. This moderate resolution atmospheric transmission model solves the radiative transfer equation  [14]  by computing line-of-sight (LOS) atmospheric spectral transmittances and radiances for participating media (molecular and clouds): ∂L λ ∂u (r, u) = -k(λ)L λ (r, u) + β(λ) 1 4π 4π L λ (r, u i )φ λ (u i , u)dΩ i , (10) where L λ (r, u) is the spectral radiance for the wavelength λ at point r and in direction u, expressed in W•m -2 •sr -1 •m -1 , and dΩ i is the elementary solid angle. The terms β(λ) and k(λ) are respectively the scattering coefficient and the extinction coefficient given by the Mie theory. The function φ is the scattering phase function making it possible to take into account energy gain by in-scattering  [14].
In the MODTRAN model, the medium is modeled in one dimension: it is assumed to be homogeneous in the other two dimensions. The software computes the spectral radiance received by an observer from the black body radiance emitted by a source. Users set the source temperature, the Mie coefficients of the participating media (including the scattering phase function), and the distance between the source and the observer. In this work, we used the black body source, whose spectrum is plotted in Figure  4 , and a molecular composition of a typical continental atmosphere at ground level. It is important to note that when we omit the second term of the right-hand side of the Equation  (10).
During the tests, carried out from 6 December 2018 to 14 January 2019, 6754 spectra were measured with the spectroradiometer and 25,590 DSDs were measured with the PSA. For greater clarity, we limited the MOR presented in this paper to the following list: 15, 20, 30, 40, 50, 70, 100, 125, 150, and 175 m. Only 4456 of 6754 spectra were then used. As the data from the various sensors were perfectly synchronized, the data from each of them could then be processed simultaneously.
Estimation of the Extinction Coefficient Three devices measuring three different quantities were used during the tests carried out. In order to perform the analysis, it was necessary to link the data of the three sensors by their time stamp. A database containing the following information was created: For both types of fog, the radiance as a function of wavelength λ, MOR, and distance D could therefore be obtained. The radiance is therefore expressed in the following form: Radiance = Rad(λ, V, D) (11) The measurements were then grouped by MOR, in increments of 5 m (plus or minus 2 m) from 10 m-200 m  (10, 15, 20,. , 200 m). For a fixed spectroradiometer/light source distance and a fixed fog type, several measurement points were obtained. However, this number of measurements was not constant as a function of MOR.
This was because fog dissipated slowly in the platform when MOR was low (dense fog), and then, the rate of fog dissipation increased with increasing MOR (light fog). This is shown in Table  1. A sufficient number of measurements was still obtained for all the cases tested. In order to determine the extinction coefficient k(λ) for each wavelength, the following procedure was applied. Owing to the fact that the spectroradiometer resolution was 8 nm for wavelengths between 1000 nm and 1900 nm, all spectral measurements were grouped every 10 nm. Furthermore, in order to reduce the database, MORs were grouped every 5 m. Each MOR class (e. g. , the class for 10 m) contained the MOR value of the class, as well as those plus or minus 2 m of this value (e. g. ,  [8; 12 m] ). All radiance spectra measured by the spectroradiometer for a three-tuple "source/receiver distance", "fog type", and "MOR class" were collected and sorted.
3. The Mie theory proposed in the previous section did not take into account molecular absorption and scattering or the phase function in the radiative transfer function. The data from the PSA were then also processed with MODTRAN to estimate the extinction coefficient with greater accuracy  [9]. The corresponding two graphs were then added to each sub-figure of Figure  9  (continuous line for the MODTRAN model). For each of these, the reference extinction coefficient k re f at λ = 550 nm was calculated from the MOR measured by the transmissometer, considered as a reference. This coefficient k re f was used to normalize all the curves, in order to have the same extinction coefficient for 550 nm. This choice was made by MODTRAN and had the advantage of focusing more closely on the influence of wavelength, particularly for analyzing data from the PSA, without taking into account the total concentration of fog droplets.
Even if such strategies are considered as recent, initial strategies were started in the 80's. Pouchus et al. [6] , Samuelsson et al. [7]  and Cardellina et al. [8] , were among the first people interested in improving the classical bioassay-guided fractionation method for active NP extracts. In 1989, Pouchus developed a mathematical script allowing a better purification of active compounds by calculating their relative quantities in each fraction. The Solid Phase Extraction (SPE) columns, they managed to deduce the chemical properties of active compounds (size, polarity, stability, acido-basic properties…) and therefore develop appropriate and more focused purification strategies. Interestingly, the authors represented their results in an elution matrix with active fractions being highlighted, thus revealing the chemical profiles of the active compounds.
In the past few decades, metabolomics has appeared as a rapidly emerging and developing field in NP chemistry. Extracts analyses mainly by HPLC-MS (n) and NMR spectroscopy has been employed for different purposes such as chemical profiling and dereplication  [10] [11] [12] [13] , biomarker characterization  [14, 15] , quality control  [16] [17] [18]  and also for bioactive drug discovery  [19] [20] [21] [22]. Metabolomics studies generate a huge amount of data related to all detected compounds in a sample and need the use of bioinformatics tools to highlight the information of interest within the collected data. To this purpose many software have been developed such as MZmine  [23] , R Cran packages  [24]  or Python packages  [25]  and are now available for the community. Nevertheless, effective strategies for identification of compounds present in small amounts and their associated biological effect from a complex mixture are crucially lacking.
Fungal strain, culture and extraction Penicillium chrysogenum (MMS5) was collected in November 1994 from cockles in Le Croisic (France) and identified by ITS sequencing (Genbank accession number MK015724). This marine-derived fungal strain was 3point inoculated on PDA medium (Potato Dextrose Agar) in 50 Erlenmeyer flasks (50 mL of media per flask) and incubated for 12 days at 27 °C. The crushed gelose and mycelium were extracted twice with 100 mL of a CH2Cl2/EtOAc (50:50, v/v) solvent mixture after 30 min ultrasound treatment for the first step extraction and after a night at room temperature incubation for the second. The supernatants were combined and filtered over Büchner and reextracted. The filtrates were dehydrated by Na2SO4 and filtered over filter paper and over a 0. 45 μm regenerated cellulose membrane (Sartorius Stedim Biotech) to remove spores.
3 mL/min. The mass spectrometer was operated in full-scan mode. MS data were recorded in the ESI positive mode in the mass range of m/z 100-1000 with a mass accuracy of 7 ppm and a resolution of 10,000 at m/z 500, using the following parameters: heat block and curved desolvatation line temperatures at 200 °C; nebulizing nitrogen gas flow at 1. 5 L min -1 ; interface voltage at (+) 4. 5 kV and detector voltage of the TOF analyser at 1. 6 kV. UV-VIS spectra were detected and collected from 190 to 600 nm. The samples were prepared in MeOH (UPLC/MS grade) at concentrations of 0. 4 mg mL -1 , stored at 4 °C before injection of 5 μL for each. The analyses were performed randomly and included solvent blank samples (pure MeOH) and QCs injected regularly throughout the sequence.
LC-MS data treatment LC-MS data obtained for studied mixtures were treated. Automatic feature detection between 0 and 30 min in positive mode in MZmine2. 31 software  [23]  was achieved using the parameters selected according to the TOF-MS detector. Peak detection was performed with the "mass detection" algorithm with a noise level of "1. 6E4" in centroid mode. Then, chromatograms were built for all detected ions with a minimum time span of 0. 1 min and a minimum intensity of "8. 0E4" counts in positive mode, allowing 80 ppm tolerance on m/z values. Peak deconvolution was applied to the generated chromatograms with the "baseline cut-off" algorithm using a min peak height of "8. 0E4", a peak duration range of 0. 1-5 min and a baseline level of 1. 6E4. Deisotoping filter was applied using the "isotopic peaks grouper" module with tolerance parameters adjusted to 0.
1 min and 0. 01 on m/z values. Feature alignment was achieved with the "Join Aligner" module with a m/z tolerance of 0. 01 and a retention time tolerance of 0. 3 min followed by gap filling using the "Gapfiller" module, yielding a combined dataset. The features detected from blank MeOH and non-inoculated culture medium samples (BM) were removed from the generated matrix to focus on the features really corresponding to the fungus production.
Biochemometric analyses To link chemical profiles and associated biological data of all fractions obtained, biochemometrics analyses were performed. The spectral data matrix obtained from Mzmine software  [23]  including m/z, retention time, and peak area for each detected ions was imported to Excel Microsoft office 2011 (© 2010 Microsoft Corporation) and merged with the bioactivity dataset (inhibition percentages at 50 μg mL -1 ) to form the final matrix. The R FiBiCo script developed in the present work is presented in details in the "Results and Discussion" section. All statistical models used and included in the script such as Principal Component Analysis (PCA), Spearman, Focused-Principal Component Analysis (F-PCA), Partial Least Squares regression (PLS) and Partial Least Squares Discriminant Analysis (PLS-DA) were computed using the open source software Rstudio version 0.
Virtual evaluation of the script efficiency The initial data matrix was manually modified to evaluate the ability of the R FiBiCo script to detect potential bioactive compounds. A total of 65 matrices were designed by adding a virtual feature which peak area values in each fraction were defined according to different models of relationships between peak area values and biological activity (linear, exponential, logarithmic and two other relationships with monotonic and nonmonotonic "S-shaped" curves). For all relationships peak area values in each fraction for the virtual feature were given in accordance to the antiproliferative activity on MCF-7 (in %) with lowest peak area value for the less active fraction C18-2 (except for the non-monotonic model) and the highest for the most active fraction SX-5.
Results and Discussion As identifying relevant active molecules from complex mixtures represents a major challenge in NP drug discovery, this study here proposes a complete workflow to target bioactive compounds from a crude extract, based on a combination of fractionation, metabolomics and chemometrics. It is divided into three steps, namely the Explorative-Solid Phase Extraction (E-SPE) followed by the acquisition of biological and chemical data and the biochemometrics analysis with the combination of four statistical models (Fig. 1 ). The last step required the development of the new FiBiCo script as described below and available in Supplementary information S1.
Design of the script This script was written using R Software (CRAN) to perform an automated processing of biochemometrics analyses on MacOS or Windows systems. It was designed to allow any user to perform the analyses on his/her own dataset. Therefore, it was very important that the operator could define and modify some parameters to improve methodology efficiency in accordance with his/her own data. In the same way, the file containing LC-MS and biological activity data to be read by the script was arranged as a simple matrix in a comma separated values (. csv) file. In this matrix, columns contained in the following order: sample names, groups, biological activity and peak areas or intensities of the different features "m/z_RT" detected. The script was divided into five steps (Fig. 2 ) and used "MetaboAnalystR"  [44] , "psy"  [45] , "mdatools"  [46] , "readr"  [47] ,"psych"  [48]  packages.
It then highlights the metabolites for which the presence most probably explains the activity observed for the initial extract. For the fourth step performing all biochemometrics analyses, a first output devised in the script was the generation and exportation in the working directory of one table (in a. csv file) per model with all the results obtained, together with the associated graphs (. pdf) as "Spearman score plot", "F-PCA score plot", "PLS score plot" and "PLS-DA score plot" (Supplementary information S2. 1). To get a first overview of all detected peaks and all highlighted ones according to each model independently, each table was implemented with an additional classification column with importance levels as "High/Medium or Low". A combined graph representing the results of the four models was then created (with the colour code blue for "high", orange for "medium" and grey for "low").
For F-PCA, the classification was carried out based on the graphical results, taking into account the three parameters defined as the correlation score (corresponding to the distance for each feature to the centre of the graph), the sign of the correlation and the significance of the score (corresponding to features localised inside the red circle of the graph). This latter score was calculated as reported by Falissard et al. [52]. Some other parameters were calculated and reported in the table during the script (cf values in italic font in Table  1 ). While they were not used for subsequent classification, they were kept available in the final table, as they could be useful for further in-depth interpretation. For the final step of the script combining all the results from each independent model, it was chosen to allow the operator to define a maximal number of interesting ions for each model to obtain a suitable and easily interpretable list.
Application to the marine-derived P. chrysogenum MMS5 extract Following an initial screening of fungal extracts from our in-house library on MCF-7 breast cancer cell proliferation, extracts from the marine-derived Penicillium chrysogenum MMS5 isolated from cockcles displayed strong inhibition whatever the growth medium used with 57-66% inhibition on CYA,YES and PDA media at 10 µg. mL -1 (data not shown). It was found that the activity of this extract was related to high amounts of ergosterol, which was recently described as cytotoxic on MCF-7 cells  [43]  (Supplementary information S2. 2). The proposed workflow was then applied to this MMS5 extract to evaluate its ability to highlight this bioactive compound.
This allows a more comprehensive description of the chemical composition of each fraction. Thus, automatic peak detection was here performed on raw chemical profiles by using Mzmine2 software  [20] , which allowed to obtain a list of 795 individual features among all samples, corresponding to peaks characterised by their m/z values and associated retention times (referred as "m/z_RT"), with their respective peak areas. The aligned data matrix was amended with biological data (inhibition percentages and groups as defined previously) and applied to the newly developed R FiBiCo script for statistical analyses. After the first two steps consisting of filling needed information, defining appropriate parameters, uploading and normalizing the data, the script proceeded to the third step. The chemical diversity of fractions obtained from the E-SPE was then investigated using principal component analysis (PCA), an unsupervised multivariate statistical approach.
Biochemometrics analysis After running the fourth step of the script corresponding to the biochemometrics analysis, the four statistical models returned very interesting features "highly" related to the biological activity (Table  2 ). This observation was consistent with the possibility to find bioactive metabolites produced by P. chrysogenum. Differences in features importance levels were observed. For example, among the 795 features detected by LC-MS, the script allowed to highlight 50, 42, 38 and 51 features with "high" importance level for Spearman, F-PCA, PLS and PLS-DA, respectively. These features of interest were highlighted in blue ("high" importance level) on the combined graph representing the results of the four models (Fig. 5 ). Spearman score plot (Fig. 5A ) displayed two main groups of chromatographic peaks indicating potential bioactive compounds were either moderately polar (RT=10-15 min) or apolar (RT=20-28 min).
This is where the last step of the FiBiCo script was carried out to provide a "Super list" of most interesting features and their corresponding radar charts (Supplementary information S2. 4 and S2. 5), based on the combination of the top ten of each model. In the case of P. chrysogenum MMS5 a total of 23 highly interesting features was present in the "Super list". The corresponding Venn diagram (Fig. 6 ) displayed the importance of combining the results from the four different statistical analyses as they highlight complementarily the features of importance. In fact, among the 23 highlighted in the "Super list", only 2 where highlighted by all the models, while 13 were selected by only one of them, i. e. 3, 2, 2 and 6 features by Spearman, F-PCA, PLS and PLS-DA respectively (Fig. 6A ). It is important to note that complementarity between the models remained even when increasing the number of features to select in each model to the top-20, as in this case, only 6 (out of 41) were highlighted by all the models (Fig.