dataesr/scientific-paragraphs-categorization huggingface dataset:
This is truly the advantage of using time-surfaces that encode in a compact representation both spatial and temporal information. The system also relies on a very small number of meta parameters to tune. We did not require long parameter adjusting processes for all the considered databases. Once the parameters of the network match those of the observed object, the same set can apply regardless to the dataset, and we were able to use the same parameters for all the gestures datasets, while obtaining state-of-the-art accuracy scores on the DvsGestures classification task. We believe this is the first time that time-surfaces were used at their true potential. Indeed, in previous work like HOTS  [13]  or HATS  [25]  the decay times used were set to values thousands times higher than the duration of the stimulus.
All the SAXS data were collected at a wavelength of 0. 99 Å using a sample-to-detector (PILATUS 1 M, DECTRIS) distance of 2. 81 m. The scattering of pure water was used to calibrate the intensity to absolute units  (44). Data reduction was performed automatically using the EDNA pipeline  (45). Frames in regions of stable R g were selected and averaged using PRIMUS  (46)  to yield a single averaged frame corresponding to the scattering of individual SEC species. Analysis of the overall parameters was carried out by PRIMUS from ATSAS 2. 8. 4 package  (47)  and by Sc Åtter 3. 0 software. The pair distance distribution function, P(r), and maximum diameter of the particle (D max ) were calculated in GNOM using indirect Fourier transform method  (48). The information on data collection and derived structural parameters is summarized in Supplementary Table  S1.
NMR resonance assignments Sequential assignment of the MvaT dimer at 20 • C was performed using the triple resonances HNCACB, CBCA-coNH, HNCA, HNcoCA, HNCO and HNcaCO experiments on a 15 N, 13 C MvaT F36D/M44D NMR sample (0. 5 mM in 20 mM Bis-Tris, 150 mM KCl, 1 mM EDTA, pH 6). The NMR spectra were recorded on a Bruker Avance III (HD) 600 MHz spectrometer, equipped with TCI cryoprobe, processed by TopSpin 3. 5 (Bruker Biospin) and analysed with Sparky software  (49). The peak list of MvaT dimer was used to assign the MvaT dimer K31C mutant HSQC spectrum. The assignments of the MvaT F36D/M44D spectra are available from BMRB under entry 28010.
NMR titration experiments The NMR titration of the MvaT dimer and DBD with the 12 bp DNA substrate was performed on 95 and 100 M 15 N isotopically labelled protein samples, respectively, in 20 mM Bis-Tris buffer, 50 mM KCl or 300 mM KCl, pH 6 and 6% D 2 O. A series of 1 H- 15 N HSQC spectra were acquired by gradually increasing the DNA:MvaT molar ratio from 0. 1 or 0. 2 to 1. 6. The experiments were recorded at 20 • C on a Bruker Avance III (HD) 600 MHz spectrometer, equipped with TCI cryoprobe, processed by TopSpin 3. 5 (Bruker Biospin) and analysed by CCPnmr software  (50). The MvaT dimer titration with KCl was conducted using the same procedure. The reference titration points were collected in a solution with 20 mM Bis-Tris, 50 mM KCl, pH 6 and the potassium chloride concentration was increased in steps to 100, 150, 200, 250 and 300 mM KCl.
MvaT structure homology modeling The full-length MvaT dimer was constructed by combining a homology model of the dimerization domain (residues 1-78) and the NMR structure of the DNA binding domain (PDB ID: 2MXE) (53) (residues 79-124), using Modeller version 9. 15. The homology model for the dimerization domain was obtained by aligning residues 1-78 of MvaT to the crystal structure of the N-terminal domain of P. putida TurB (PDB ID: 5B52)  (54).
To assess the agreement between the observed PREs and the PREs back-calculated from the simulated ensembles generated in each run, we calculated the Q factor (59), equation (3): Q = i obs 2,i -calc 2,i 2 / i obs 2,i 2 (3) where calc  2,i is given by equation (4): calc 2,i = 1 N N j = 1 2,i j ( 4 ) where N is the ensemble size and 2,i j is the PRE backcalculated for the amide proton of residue (i) of the ensemble member (j). The agreement with the experimental SAXS data in each run was assessed by calculating 2 with the calcSAXS helper program  (57) , which is part of the Xplor-NIH v 2. 49 package used in this work, assuming equal conformer populations in the simulated ensembles. The lowest energy structures of the MvaT dimer obtained from the ensemble modeling were fitted into the ab initio molecular shapes obtained by DAMMIN using UCSF Chimera software  (60).
The crystal structure of the H-NS N-terminal domain (1-83) has shown that site 1 is formed by a 'handshake fold' topology between ␣1 and ␣2 and part of ␣3. Site 1 is stabilized by a hydrophobic core and salt bridges  (31, 32)  (Figure  1A ). In the short members (MvaT; MvaU; TurA and TurB), these two N-terminal helices are missing (Figure  1B ; Supplementary Figure  S1 ). The crystal structure of the TurB N-terminal domain (1-61) has revealed that site 1 is formed by a 'coiled-coil motif' between the N-terminal helices ␣1 (corresponding to H-NS ␣3) of two monomers (54) (Figure  1B ). Using the TurB N-terminal domain crystal structure, we have built a homology model of the MvaT dimerization site which was connected via the flexible linkers to the NMR structure of the protein DNA binding domains (PDB ID: 2MXE) (53), thus obtaining a full-length structural model of the MvaT protomer (Figure  1B ).
In the middle panel is the TurB N-terminal domain crystal structure (1-64) (PDB ID: 5B52) (54). Lower panel is the MvaT protomer structural homology model (see M&M). The color code is as in (A). (C) The electrostatic potential surfaces of H-NS and MvaT are depicted on full-length protomer structural models of the proteins using a red/white/blue color gradient. The negative and positive patches on the proteins' surfaces are indicated. In the lower panel is the five amino acid averaged charge window of the proteins protomers primary sequences. Positive, negative and neutral charged amino acid fragments are shown in blue, red and white bars, respectively. (D) Primary sequence alignment of selected members of the H-NS family of proteins from different bacterial organisms. The conserved positions of the charged amino acids in the protein's primary sequences are highlighted in blue (positive residues) in red (negative residues).
ACKNOWLEDGEMENTS We thank  Dr Navraj Pannu  for establishing the contact with  Dr Gabriele Giachin  at the  European Synchrotron Radiation Facility  to perform the SAXS experiment. Maud Bre-Nucleic Acids Research, 2020, Vol. 48, No. 4 2171 mer is acknowledged for making the homology structural model of MvaT. Author contributions: L. Q. , F. B. B. , G. G. and P. v. S. performed the experiments. L. Q. , F. B. B. , Y. G. J. S. , A. N. V. , J. V. , G. G. , M. U. and R. T. D. contributed to data analysis and discussion. F. B. B. , M. U. and  R. T. D. supervised the project. F. B. B. and  R. T. D. wrote the manuscript. All authors reviewed and corrected the manuscript.
Data normalization using DeSeq miRNA counts Data were normalized with bank Size Factors (SF) obtained by using [DESeq geometrical normalization (Galaxy-Version 1. 0. 1, owner: artbio)] with miRNA counts obtained using [miRcounts (Galaxy-Version 1. 3. 2)], allowing 0 mismatch (MM). Then, 1/SF values were used in Galaxy small RNA maps (Sup. Fig. 5 ).
, 2017) , these algorithms rely to a much smaller degree on empirical relationships than the abundance based approaches. Ocean color remote sensing is limited to obtain information only under sun-lit, cloud and ice free conditions and of surface waters only. The latter means that satellite information only covers approximately the upper 20% of the layer in which phytoplankton is present (the so-called first optical depth;  Gordon and McCluney, 1975; Morel and Berthon, 1989). For a complete assessment of the distribution and abundance of PGs in situ measurements with sufficient spatial and temporal resolution are also urgently required to complement ocean color remote sensing  (Brotas et al. , 2013). The continuous operation of optical instruments could tremendously enhance the resolution of available PG data sets, both vertically and horizontally.
A few examples have shown this potential which should be further explored. Chase et al. (2013)  and  Liu et al. (2019)  have derived high spatial resolution data on various pigments' surface concentrations from underway flow-through hyperspectral spectrophotometric measurements combined with HPLC based phytoplankton pigment point measurements. In both studies Gaussian decomposition was used to retrieve different types of chlorophylls besides the pigment groups of photosynthetic and photoprotective carotenoids. From their data sets only one specific PG Chla, chlorophytes, could be retrieved. Liu et al. (2019)  have additionally applied the matrix inversion method by  Moisan et al. (2011)  and successfully derived different types of carotenoids, among them marker pigments for diatoms and haptophytes.
However, the pigment data sets of the two studies are too limited to further allow the retrieval of all PG being relevant to explain the total Chla (TChla) in the investigated areas, thereby restricting their further application. The continuous underwater operation of instruments which directly enable the derivation of hyperspectral inherent optical properties (IOPs) requires that independent data are easily attainable to ensure frequent calibration  (IOCCG Protocol Series, 2019). To date the development and validation of PG and PSC algorithms developed for optical data rely mostly on HPLC pigment-based proxies of taxonomic composition or size structure which require verification by additional analyses including flow cytometry, imaging, microscopy and others  (Bracher et al. , 2017). Some of these later methods, inline flow cytometer or imaging systems, have shown promising capabilities to retrieve horizontally highly resolved surface PG data.
(2006)  to predict the water column integrated TChla and its distribution among PSC. The method was developed generically, so other data of chlorophyll fluorimeter sensors linked to depth profiling platforms can be analyzed and TChla and PSC data sets can be produced. However, the abundance based retrieval only allows to retrieve the expected PSC based on global patterns. PG data derived by making the most out of their spectral signatures (see above) are preferred as observations to be linked to PFTs  (Bracher et al. , 2017). To employ spectral algorithms for quantitative PG retrieval, we require high spectrally resolved data sets. Measuring apparent optical properties (AOPs) with radiometers has the advantage over IOP measurements, that these measurements are less sensitive to absolute calibration.
By calculating AOPs, which are gradients of measured irradiance between different depths when deriving the diffuse attenuation, or ratios for the reflectance and the transmission, most instrumental effects cancel out  (Miller et al. , 2005). In this study we have exploited the potential of deriving a geospatial highly resolved (horizontally and vertically) data set on major PGs Chla and TChla from hyperspectral radiometric data. We obtained these data continuously along specific transects within the Atlantic Ocean measured by a radiometer mounted on a large undulating system towed behind the ship. Previous studies by  Taylor et al. (2013)  and  Bracher et al. (2015)  have retrieved from underwater radiometric measurements, either upwelling radiance or remote sensing reflectance, concentrations of various phytoplankton pigments using empirical orthogonal function (EOF) analysis on the spectral data.
Xi et al. (2020)  have further utilized the method to directly retrieve the Chla of six PGs which explain most of the TChla. In the present study we further adapted this EOF based retrievals by using AOPs derived from downwelling underwater irradiance profile measurements. We tested whether robust PG Chla retrievals are possible even under high profiling velocities (∼1 m/s). We investigated the quality of the PG Chla data by evaluating their potential to assess the phytoplankton composition dynamics within the different biogeochemical provinces crossed by our research cruise.
We followed  Losa et al. (2017, Supplementary Material)  for the pigment specific coefficients as applied in  Booge et al. (2018) : The fractions of seven main phytoplankton groups (as listed above) were calculated based on the weighted sum of specific diagnostic pigments. These weights were based on coefficients derived from a large in situ pigment database excluding the Southern Ocean to convert each diagnostic pigment concentration into a group specific Chla. The Chla of Prochlorococcus was directly given by the divinyl-Chla. The total Chla (TChla) was determined from the sum of monovinyl-and divinyl-Chla and chlorophyllide a concentration.
In the beginning of every transect the platform was hold for several minutes in the subsurface, then the platform was undulating between surface (varying with a minimum depth between 1 and 20 m) and about 250-300 m. The depth was recorded continuously by the pressure sensor of a Seabird CTD (Sea-Bird Electronics, United States) attached to the Triaxus and the inclination in either dimension was measured by the Triaxus hardware. E d (z, λ) was measured by the RAMSES sensor and the average speed to lower or lift the platform was about 1 m/s. For more details on the operation of the Triaxus we refer to  Strass (2018) and von Appen et al. (2020). The sensor meta data is available at https://hdl. handle. net/10013/sensor. 5c126f5b-86de-469c-adf7-251789e54362 and the repository of the raw data is von  Appen et al.
Finally, these data were interpolated on discrete intervals of 1 m. As surface waves strongly affect measurements in the upper few meters, deeper measurements that are more reliable can be further extrapolated to the sea surface  (Mueller et al. , 2003). Following  Stramski et al. (2008) , each profile was checked and an appropriate depth interval z was defined (for station data mostly 7-22 m and for the Triaxus casts mostly 7-30 m, sometimes even 7-60 m). This was used to calculate the mean diffuse attenuation coefficients for downwelling irradiance over this depth interval [ Ǩd (λ)]. By using Ǩd (λ), the subsurface irradiance E d (0 -, λ) for each profile was extrapolated from the profiles of E d (z, λ) within the respective depth interval. Then two other types of AOP were calculated: • The hyperspectral transmission at each depth was calculated as in Eq.
EOF Based Prediction of Phytoplankton Groups From Hyperspectral Underwater Data We modified the method developed by  Bracher et al. (2015)  which is similar to  Xi et al. (2020)  to derive continuous profile data of PG Chla and TChla. However, instead of using remote sensing reflectance we tested our three hyperspectral AOP data sets, T(z,λ), K d (z 1 → z 2 ,λ), and Ǩd (λ), as spectral input to the models. We briefly summarize here this procedure and just detail our applied changes to the method by  Bracher et al. (2015). Step 1: We limited the spectral range of our AOP input data to 400-580 nm since the values at wavelengths above were often very noisy. We applied an EOF analysis to our standardized (subtracting the mean and then divided by the standard deviation) spectral AOPs. Standardized AOP spectra were matched for each PG separately with the HPLC based PG Chla data.
Then the singular value decomposition was performed to the spectral AOP matrix X (with M observations × N wavelengths; M may vary among different PGs due to the number of matchups) to obtain the EOF modes by extracting the vectors of the scores associated with the EOF modes (U), the EOF loadings (V, i. e. , spectral patterns) and the singular values of X on the diagonal in decreasing order ( ): X = U V T , x ij = k=l,N u ik σ k v kj (2) Step 2: For each PG, we subsequently developed the corresponding multiple linear regression model using the collocated PG Chla data and the EOF modes extracted from the AOP data, in which the log-transformed PG Chla data derived from the HPLC measured pigment concentrations, ln(C train o ), are expressed as a function of a subset of the EOF scores (U). As in  Xi et al.
(2020) , the EOF modes with standard deviations (singular values from ) that are less than 0. 0001 times the standard deviation of the first EOF mode were considered insignificant and thus omitted. Following  Bracher et al. (2015)  a stepwise routine was applied to search for smaller regression models (for each PG model) based on fewer prediction terms through minimization of the Akaike information criterion. The regression model for PG Chla [ln(C p )] predictions was expressed as: ln (C p ) =a+b 1 u 1 +b 2 u 2 +. +b n u n (3) Step 3: The robustness of the fitted model was estimated following  Bracher et al. (2015)  by a cross-validation of the model fitting using 500 permutations for splitting the collocated data into two subsets, in which 80% of the data was used for model fitting (training), while the rest of the data was used for prediction validation (details in Step 4).
The pairs of observed and predicted PG Chla (C val o and C val p , respectively) of the 500 permutations were recorded for later prediction error statistics. Step 4: To predict PG Chla from the T(z, λ), K d (z 1 → z 2 ,λ), and Ǩd (λ) spectral data for which we do not have corresponding pigment based PG information, we projected these standardized spectral data onto the EOF loadings (V) to derive the new sets of EOF scores (U). The derived U were subsequently used for the prediction with the fitted linear model [Eq. (3) in Step 2], where the regression coefficients were taken from the model developed with the full matchup data set of pigment and AOP [either T(z, λ), K d (z 1 → z 2 , λ), or Ǩd (λ)] data. Step 5: We finally applied a strict data quality control since we encountered large deviations between the TChla directly predicted from its specific EOF model and the sum of the seven PG Chla predictions (SPG-Chla) for some data points.
Cryptophyte Chla was not included because of the failure of reliable predictions and their marginal contributions as derived from the HPLC PG data (details see section "Prediction of Phytoplankton Groups From Hyperspectral Underwater Measurements"). We removed all data points where the deviation between SPG-Chla to TChla was larger than 20%. For Ǩd (λ) related predictions 42 out of 425 data points had to be removed, for the T(z, λ) and K d (z 1 → z 2 , λ) related predictions only one entire profile and only very deep measurements of profiles had to be removed. For K d (z 1 → z 2 , λ) related predictions deepest quality controlled data were 20-30 m above the deepest quality controlled T(z, λ) related predictions. Visual inspection showed that flagged Ǩd (λ) data had resulted from profiles where only very few data points were available within the upper layer and therefore the fitting of K d in the upper depth failed.
The remaining data sets showed a correlation coefficient of 0. 93, 0. 92, and 0. 95 for T(z, λ), K d (z 1 → z 2 , λ), and Ǩd (λ) related predictions of TChla versus SPG-Chla, respectively. Finally the PG Chla (PG i -Chla) was recalculated to agree consistently with TChla predictions, as follows. First, the fraction of each PG (f-PG i ) was determined by Eq. (4): f -PG i = PG i -Chla/SPG-Chla (4) Then the final PG i -Chla was determined by Eq. (  5 ): PG i -Chla = TChla. f -PG i (5).
Statistical Assessment of Model Performance Since the Chla range varied greatly among the different PG, we calculated mainly relative error statistics. Considering the comparison between HPLC observations and AOP data based model predictions, error statistics were calculated for the full collocated data set incorporated into the training (full-fit results). Here, the determination coefficient, R 2 , the root mean square difference (RMSD), the slope and the intercept of the linear regression were based on the log-scaled predicted as compared to the log-scaled observed PG Chla and TChla data, while the median percent difference (MPD), the median percent bias (MPB) were based on the non-log-transformed concentrations. For the cross validation, the R 2 based on ln(C val p ) versus ln(C val o ) was derived for each permutation, and the mean value of the cross-validated R 2 (R 2 cv) for all permutations is calculated.
Temperature, Salinity, and Upper Mixed Layer For further interpretation of our surface phytoplankton data set and clustering into biogeochemical provinces (see section "Classification of the Biogeochemical Provinces"), we compared these to matchup data of surface temperature and salinity obtained continuously during PS113 by the ship's thermosalinograph Seabird SBE 21 equipped with an external thermometer SBE 38 (both Sea-Bird Electronics, United States) installed at the keel of the ship. These data are published in  Strass and Rohardt (2018). Temperature and salinity profiles were obtained using a Seabird SBE 911 CTD (Sea-Bird Electronics, United States) at the discrete CTD stations and another one on the Triaxus platform. The measurements from the two CTD systems agree very well  (von Appen et al.
Classification of the Biogeochemical Provinces Water samples were grouped in clusters according to the results of an unsupervised hierarchical cluster analysis (HCA) using the Euclidian distance as the distance measure and linking clusters following  Taylor et al. (2011). The unsupervised HCA has been proven useful in  Taylor et al. (2011)  based on phytoplankton pigment composition for reflecting the measurements groupings into biogeochemical provinces according to Longhurst  (Longhurst, 2007). As input data, we used the HPLC based fractions of PG Chla to TChla. As in  Taylor et al. (2011) , within clusters, differences in surface TChla, surface temperature and surface salinity were tested following an initial Shapiro-Wilk's W test of normality. Normally distributed data were tested with the independent t-test and non-normally distributed data were tested with a Mann-Whitney-U-test.
Prediction of Phytoplankton Groups From Hyperspectral Underwater Measurements We obtained 227 valid surface data points for PG Chla and TChla derived from HPLC measurements. Twenty-four of these data were sampled at the CTD stations where we also obtained information at five more depths. Details on the range and distribution of TChla and PG fraction are discussed in Section "Phytoplankton Composition Along the Atlantic Transect. " Spectral input data sets were based on 424 valid irradiance profiles for T(z, λ) and K d (z 1 → z 2 , λ) and 383 valid Ǩd (λ) data points. The composition and range of the three AOP type input data, namely the depth resolved T(z, λ) and K d (z 1 → z 2 , λ), and the upper surface layer mean Ǩd (λ) data, are shown as original and standardized spectra (Figure  2 ). Standardized spectra, that were used for the EOF analysis, look very much alike for the latter two AOP data sets, and show inverted spectral features between T(z, λ) and K d (z 1 → z 2 , λ) data.
g. , the EOF-2 mode is the most important term for TChla and nearly all PG Chla models, except for Prochlorococcus and cyanobacteria where the other EOF modes take over and more EOF modes are included in these PG Chla models. This is because for these two groups their Chla does not co-vary with TChla. The results of the statistical assessment of the model performance considering the full fit and cross-validation comparisons are presented in Table  3. Matchups to HPLC PG data sets of the three AOP-derived PG Chla and TChla varied (Table  3 ), because for Ǩd (λ) only surface values, while for the T(z, λ) and K d (z 1 → z 2 , λ) data sets also matchups from CTD stations at five more depths could be considered. Different numbers of matchups for these two depth-resolved data sets resulted from the differences in valid PG data points due to the data quality procedure applied (see section "EOF Based Prediction of Phytoplankton Groups From Hyperspectral Underwater Data, " Step 5).