dataesr/scientific-paragraphs-categorization huggingface dataset:
Figure7. ERRMOD (black) and ERRMIX immediately after the insertion (3 h after, green), one week (next observation, blue) and one month (4th observation after, red) for snow depth, density, grain shape and WetC. The distance between MMIX and MMOD one month after insertion is also shown (brown). Dataset of the three stations and 15 seasons(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015). For box plot definition, see Figure6.
, 2017). To further characterize the structure of SIRT6 in a solution, we used size exclusion chromatography-multi-angle light scattering (SEC-MALS) and small-angle X-ray scattering (SAXS). Importantly, both methods showed that SIRT6 tends to aggregate; however, when using SEC-MALS, we noted that the aggregation was significantly reduced by the presence of DNA oligomers (Figure  3 -figure supplement 2A), which suggests that SIRT6 is stabilized by (and favors) DNA interactions. SAXS data provide a low-resolution structure of SIRT6, presumably corresponding to a tetramer (Figure  3 -figure supplement 1B-D), supporting the model suggested by the EMSA results (with dimers at the 5 0 and 3 0 , a tetramer). The result obtained by SAXS does not exclude the presence of SIRT6 dimers or trimers in solution (see Table  2 ).
Tethering assay U2OS cells containing 256X LacO sequence repeats in their genome were transfected with plasmids of chimeric LacR-DDR enzyme-GFP/Cherry proteins. Cells were either co-transfected with a second plasmid of a fluorescent/Flag-tagged protein or immuno-stained (see 'Immunofluorescence') for an endogenic protein. Cells expressing both proteins of interest and exhibiting visible foci of LacR-DDR-GFP/Cherry at LacO sites were located using an Olympus IX73 fluorescent microscope, whereas co-localization between both proteins was assessed visually using Olympus CellSens Software. Co-localization is defined as the common localization of large foci of the two proteins of interest at the LacO site. Colocalization was assessed as either positive (1) or negative (0). From this analysis, the percentage of cells that exhibit co-localization (positive cells) was calculated, and defined as 'percentage of colocalization between two proteins'.
Spots were bleached with an argon laser of 488 nm with a power of 1 mW in the back aperture of the objective. Images were taken at 3 s intervals, with three baseline images taken before bleaching. Acquisition before bleaching was used for normalization of each cell intensity (average of the baseline intensity of the whole cell nucleus prior to DNA damage). Images analysis and fluorescence assessment were performed using ImageJ 1. 52i software. To assess protein amounts and to compare between the different conditions, area under the curve was calculated using a MATLAB pipeline.
Exonuclease assay DR-GFP plasmid was cut with restriction enzymes generating linear DNA with blunt (EcoRV) or overhanging ssDNA (SalI or KpnI). DNA cleavage was confirmed by agarose gel electrophoresis. 10 mg of the restricted DNA was incubated with BSA, NBS1, MRE11 or SIRT6 purified proteins in NEB exonuclease buffer for 0 to 20 min. ExoI was then added to the samples. Samples of each reaction were taken at 0, 10 and 20 min. DNA was purified using a Qiagen PCR purification kit. The purified DNA was run on 0. 8% agarose gel, and the amount of DNA was assessed by image analysis using ImageJ 1. 52i software and normalized to the amount of the DNA at the 0 0 time point.
SAXS SAXS data were collected at BioSAXS beamline BM29 (ESRF, Grenoble, France), possessing a Pilatus 1M detector. The scattering intensity was recorded in the interval 0. 0035 < q < 0. 49 A ˚À1. The measurements were performed at 20˚C. SIRT6 (alone or in the presence of dsDNA) was measured at a concentration of 0. 5 mg/ml, as it tends to aggregate at higher concentrations. The scattering of the buffer was also measured and subtracted from the scattering of the samples by using Primus  (Konarev and Svergun, 2018). Konarev and Svergun (2018)  PyMOL (https://pymol. org/) was used to extract the structures of the SIRT6 dimer and tetramer from the available crystal structure (PDB ID code: 3pki). CRYSOL  (Svergun et al. , 1995)  was then used to compute the artificial SAXS spectra of each protein species.
These spectra served as a reference for the reconstitution of experimental SAXS data. Values for the radius of gyration (R g ) and the maximum particle dimension (D max ) were derived from distance distribution function P(r), using in-house script  (Akabayov et al. , 2010). This script was designed to perform an automatic search for the best fitting parameters in GNOM  (Svergun, 1992 ). In the end, DAMMIN  (Svergun, 1999)  was used to reconstruct the molecular envelope on the basis of the best GNOM fit (obtained from the script analysis and refined manually). E models were calculated and averaged using DAMMAVER  (Volkov and Svergun, 2003).
SEC-MALS A miniDAWN TREOS multi-angle light scattering detector, with three detector angles (43. 6˚, 90˚and 136. 4˚) and a 658. 9 nm laser beam (Wyatt Technology, Santa Barbara, CA), with a Wyatt QELS dynamic light scattering module for determination of hydrodynamic radius and an Optilab T-rEX refractometer (Wyatt Technology), were used in-line with a size exclusion chromatography analytical column, Superdex 200 Increase 10/300 GL (GE, Life Science, Marlborough, MA) equilibrated in buffer (50 mM tris, 150 mM NaCl and 4 mM MgCl 2 [pH 8. 0]). Experiments were performed using an AKTA explorer system with a UV-900 detector (GE), at 0. 8 ml/min. All experiments were performed at RT (25˚C). Data collection and mass calculation by SEC-MALS analysis were performed with ASTRA 6. 1 software (Wyatt Technology).
Acknowledgements This work was supported by  ISF   188/17  and by the High-tech, Bio-tech and Chemo-tech scholarship of  Kreitman School of Advanced Research of Ben Gurion University. We appreciate the plasmids kindly donated by  Prof. Misteli  and the  U20S-LacO cells  from  Prof. Greenberg. We thank the staff scientist of beamline BM29 of  ESRF (Grenoble, France)  for providing support and the  Israeli Block Allocation Group (BAG)  for providing access. We thank  Dr Mario Lebendiker  and  Dr Hadar Amartely  from the  Protein Purification Facility Wolfson Centre for Applied Structural Biology -The Hebrew University of Jerusalem , for their help with the SEC MALS experiments. We thank  Prof. Eyal Gur  and  Dr Maayan Korman  from  Ben-Gurion University  for their contribution to the development of the NAD + consumption assay.
Sampling Data were collected in the framework of the MED-ITS bottom-trawl surveys accomplished in the period 1994-2015 at 15 GSAs of the Mediterranean (Fig. 1 ). Sampling included annual sampling at pre-defined stations following a standardized protocol (see  Bertrand et al. 2002 , Spedicato et al. 2019). Trawling was performed by means of the experimental net GOC-73 with a 20-mm square mesh cod-end, and selection of stations was based on a depth-stratified sampling scheme that included five depth strata: 10-50, 51-100, 101-200, 201-500 and 501-800 m. Collected data included number, weight, gonad maturation stage and total length measurements for a wide range of fish, cephalopod and crustacean species. In the present study we analysed biomass data for red and striped red mullets by station, expressed in terms of kg per square km of swept area (kg km -2 ).
Data analysis The analysis utilized generalized additive modelling (GAM) techniques  (Hastie and Tibshirani 1990)  to investigate the effects of station position and depth on species biomass index. The main advantage of GAM over traditional regression methods is its capability to model non-linear relationships (a common feature of many ecological datasets) between a response variable and multiple explanatory variables using non-parametric smoothers. In the present case, the non-linear predictors included sampling position (entered as the latitude-longitude interaction), depth and year of sampling. A preliminary analysis indicated that the presence of red mullets at depths over 400 m was negligible, so the stations below that depth were not considered in the analysis. The final data set included a total of 16716 hauls.
Given the existence of stations with no presence of red mullets, and based on the diagnostic randomized quantile residual plots  (Foster and Branvington 2013) , Tweedie GAM models following a compound Poissongamma approach were applied. This avoids multiplestage modelling of zero-inflated data and allows the probability of presence and the non-zero sampled quantity to be modelled jointly  (Shono 2008 , Lecomte et al. 2013). The smoother function used for the year and depth effects was a penalized cubic regression spline and model fitting was accomplished using the mgcv library  (Wood 2006)  under the R language environment. The procedure automatically selects the degree of smoothing based on the generalized cross-validation score, which is a proxy for the model's predictive performance. However, to avoid dubious relationships, the model was constrained to be at maximum a quartic relationship.
Study populations and data collection For the Dutch families all available information from 1993 to 2015 was used from the prospectively-collected database of a consecutive series of families with familial aneurysms at the University Medical Center Utrecht, the Netherlands  [4]. For the Finnish families the saccular intracranial aneurysm database of Kuopio University Hospital (KUH), Kuopio, Finland was used which includes all sporadic and familial intracranial aneurysm patients admitted to KUH between 1980 and 2017  [5]. The KUH solely serves a defined catchment population in Eastern Finland. Last, for the French families data of the ICAN project were used which is a non-interventional nationwide research program including large pedigrees with familial forms of intracranial aneurysms  [6].
Statistics We used Shapiro-Wilk test to assess the normality of the outcome data, and Levene's test to test equality of variances. According to these test data was non-normal and the variances between the groups were different, so nonparametric Kruskal-Wallis test was used to compare groups. If the result is statistically significant (p-value < 0. 05), then we carried out the post hoc analyses to compare groups pairwise using Dunn's test. P-values of post hoc (ph) analyses were adjusted using Holm multiple testing correction method. Statistical analyses and figures were made using Graph pad Prism 6 (graph Pad Prism Software Inc. , San Diego, CA) and R statistical software (R Core Team (2019).
Acknowledgements We thank  Minna Oksanen  for expert assistance. We thank university of  Helsinki EM core  for helping us with electron microscopy. This study was supported by  KLTO  ( Doctoral School for Clinical Research programme at the University of Helsinki ),  Jane and Aatos Erkko Foundation, HUCH Research Funds (EVO) ,  Sigrid Juselius Foundation ,  Finnish Cancer Organizations, University of Helsinki ,  Novo Nordisk Foundation ,  Päivikki and Sakari Sohlberg Foundation , The  Finnish Society of Sciences And Letters, TILT Biotherapeutics Ltd ,  European Commission Marie Curie Innovative Training Network VIRION  ( H2020-MSCA-ITN-2014 643130 ). We thank  Albert Ehrnrooth  and  Karl Fazer  for research support.
Statistical analysis Statistical analysis was made using Statistica 13. 1 software (TIBCO Software, USA). Pearson's chisquared test of independence was used whether observations consisting of measures on variables are independent of each other. U-Mann-Whitney test was used to compare medians of analyzed parameters in different groups. Spearman correlation test was used to determine the relationship between particular factors in studied groups. ROC curve (Receiver Operating Characteristic) analysis was made to assess the utility of HER2 CNV assessment in liquid biopsy as a diagnostic test. The results were considered statistically signi cant at p<0. 05.
They used the chemiluminescent immunoassay test for HER2 detection in serum. However, they have no data regarding the molecular status of HER2 gene in serum. Liu et al. studied the plasma HER2 gene ampli cation in gastric cancer patients by droplet digital PCR (ddPCR) during 12 months of chemotherapy with uorouracil and oxaliplatin combined with trastuzumab  [21]. They observed that the concordance rate of results of HER2 gene examination in plasma and FFPE tissue samples by ddPCR was 81. 4%, with a sensitivity of 76% and a speci city of 84%. Signi cant decrease in the plasma HER2 gene copy number was found after two months of treatment  [20]. Researchers indicated that serum HER2 gene assessment may serve as a good marker for monitoring the effectiveness of trastuzumab therapy in patients with gastric cancer.
Statistical Analysis Data management and statistical and graphical analysis were performed using IBM SPSS Statistics Version 22. 0. 02 (IBM Corporation, New York, NY, USA). Statistical analysis included the use of chi-square tests for categorical variables or Fischer's exact test, as appropriate, as well as the Mann-Whitney U test for continuous variables. Normal and binomial 95% confidence intervals (CIs) are presented for univariate statistics where appropriate. The LOWESS (locally weighted scatterplot smoothing) local regression was used to describe the operative time. Results are expressed as a mean with standard deviation and medians with an interquartile range. A standard alpha of 0. 05 indicated statistical significance.
Lastly, the framework for data assessment in this study did not allow to identify a significant difference with global benchmark cutoffs value. We acknowledge that because of the small number of patients in this data set, we could only report perioperative outcomes within the benchmarks but the level of significance of this result could not be established. Likewise, a reduction in operative time related to the learning effect in the anesthesia procedure could not be established because operative time was measured from the incision to the dressing of the wound.
Acknowledgments Authors are grateful to the Chir 4 nursing team for their dedicated work,  Christian Toso , director of the  Geneva Unit of Visceral Surgery, Amélie , and  Claude Giudicelli  for their valuable assistance with manuscript editing,  Christopher Burel  and  Guy Temporal , professionals in medical English proofreading for their assistance in the linguistic revision of the manuscript. We acknowledge that reprints will not be available from the author(s). Funding Open access funding provided by  University of Geneva. Minoa K. Jung  reports grants from  Intuitive Surgical , personal fees from  Johnson & Johnson , outside the submitted work. Dr. Diana  reports grants from  ARC Foundation for Cancer Research , personal fees from  Diagnostic Green , outside the submitted work.
Protein 3D structures support their biological functions. As the number of protein structures is negligible in regards to the number of available protein sequences, prediction methodologies relying only on protein sequences are essential tools. In this field, protein secondary structure prediction (PSSPs) is a mature area, and is considered to have reached a plateau. Nonetheless, proteins are highly dynamical macromolecules, a property that could impact the PSSP methods. Indeed, in a previous study, the stability of local protein conformations was evaluated demonstrating that some regions easily changed to another type of secondary structure. The protein sequences of this dataset were used by PSSPs and their results compared to molecular dynamics to investigate their potential impact on the quality of the secondary structure prediction.
Nowadays DSSP can be considered as a gold standard; it had been recently modified to change the assignment priority in the helical regions, which slightly increased the occurrence of the π-helix  [16]. The number of protein sequences is incredibly higher than the number of avalaible protein structures (millions against >169. 000 in the Protein DataBank  [17] ). Protein secondary structure predictions (PSSPs) remain essential, as it is not conceivable to have experimental structures associated to each known sequence. PSSPs are useful for instance for predicting protein folds, for building structural models of proteins, and in functional prediction methods such as binding site prediction. The first generation of PSSPs was based on simple analyses  [18]  and classical statics, e. g. Chou-Fasman and GOR approaches  [19, 20] , leading to a prediction rate around two thirds.
5% for 310-helices. Similarly, the infrequent π-helices go to β-turn, bend and coil conformations, but not to α-or 310-helices. Only 310-helices goes to α-helices  [6]. Additional analyses underlined that hydrogen bonded turn went more frequently to helical conformation while the non-bonded turn preferred to go to coil conformations  [39]. These finding raised a simple question: 'Can the dynamics of the protein explain (partially) erroneous protein secondary structure prediction?' To answer to this question the protein MD dataset was used  [6, [39] [40] [41]. Their sequences were predicted by PSSPs and compared with the results of simulations to see if the dynamics can have some influence the prediction.
Data sets. A databank of 169 X-ray structures, taken from Protein Data Bank (PDB)  [17]  was extracted using ASTRAL 2. 03 at 40% sequence identity  [42, 43]  (PDB ids and corresponding chain provided in Supplementary Data 1 of  [39] ). The databank was filtered out based on structure resolution better than 1. 5 Å, and without presence of heteroatoms (other than water), without alternate, without missing or modified residues in the chain. Only globular proteins, with complete chain length ranging between 50 and 250 residues, were selected. In-house parser was used to filter out and to fetch the information  [44]. The 169 domains represent a rather equilibrated repartition among the different SCOP classes: all-α represents 18. 9% of the chains, all-β 29. 6%, α/β 24. 8% and α+β 26. 7%. This distribution is well distributed among all types of protein folds.
Molecular dynamics simulations. Three molecular dynamic (MD) simulations were performed for each protein structure with GROMACS 4. 5. 7 software  [45] , using AMBER99sb force field  [46]. Each protein structure was put in a periodic dodecahedron box, using TIP3P water molecules  [47] , and neutralised with Na + or Cl -counter ions. The system was then energetically minimised with a steepest-descent algorithm for 2000 steps. The MD simulations were performed in isotherm-isobar thermodynamics ensemble (NPT), with temperature fixed at 300 K and pressure at 1 bar. A short run of 1ns was performed to equilibrate the system, using Berendsen algorithm for temperature and pressure control  [48]. The coupling time constants were equal to 0. 1 ps for each physical parameter. Then, a production step of 50 ns was done using Parrinello-Rahman algorithm  [49]  for temperature and pressure control, with coupling constants of T=0.
1 ps and P=4 ps. All bond lengths were constrained with LINCS algorithm  [50] , which allowed an integration step of 2 fs. The PME algorithm  [51]  was used for long-range electrostatic interactions using a cut-off of 1 nm for non-bonded interactions. This protocol was applied on each of the 169 protein chains. Conformations were saved every ps. For each MD simulation, the secondary structures were analysed and the structural deviation of each snapshot from the initial structure was measured. Trajectory analyses were done with the GROMACS software, in-house Python and R scripts. Root mean square deviations (RMSD) and root mean square fluctuations (RMSf) were computed on Cα atoms. Normalized RMSfs and normalized B-factors were computed as in  [52].
It is slightly different for SSpro3  [63, 64] , helical state was only 'H', and 'G', extended state was 'E' and 'B', and coil state was ('T', 'S', 'B', 'I', blank). Two distinct prediction modes exist in SSpro, namely with homologues or without homologues. The with homologues mode added a specific search for homologous sequence in the PDB. If one (or more) sequence (s) was (were) found, it was used as supplementary information. The weights depended on the degree of detected homology (i. e. sequence identity). It would therefore improve largely the prediction, especially if highly related sequences are in the PDB. It was consequently an issue for this study as all the proteins included our dataset came from PDB. It biased the analysis, so the mode without homologues was the only one that had been taken into account.
Principle. The principle of the study was to look at the PSSPs and compare their results with results from MDs. Firstly, PSSPs results were independently analysed, namely, PSIPRED, SSPro3 and SSPro8to3 (SSPro8 reduced to 3 states). It was essential to evaluate the quality of prediction on this specific dataset to know their pertinences. Secondly, all PSSPs were directly compared to apprehend their specificities. Thirdly, the predictions have been analysed in the light of MDs results, underlying potential correlations between the quality of the prediction and the dynamics behaviours. Finally, the prediction have been reanalysed in the light of the regions that have changed of secondary assignment during the simulations to see if incorrect predictions were linked to potential dynamical properties.
SSpro3 prediction analysis. SSpro was an interesting tool as it can predict both 3states (SSpro3) and 8-states (SSpro8). Another option of SSpro was to mine PDB and used existing structures to enhance the prediction. In our case, it strongly biased the prediction rate, as all the dataset structures were present in the PDB. The SSpro3 (i. e. with homologues) accuracy Q3 reached 96. 6%, with 97. 9% for the β-sheets, 97. 1% for the α-helix and 95. 3% for the coil (see SI Table  2a ). Hence, the real accuracy prediction of SSpro3 (i. e. without homologues) was of 81. 2%, with 84. 5% for the α-helices, 82. 5% for the coil and 74. 4% for the β-sheets (see Table  1b ). It was slightly less effective than PSIPRED (minus 2. 4%) with an increase of the α-helices prediction rate of 4. 4%, and a decrease of 5.
PSSPs were assessed at Critical Assessment of Methods of Protein Structure Prediction (CASP), but were dropped after CASP 4. As noted by Moult and colleagues  [27] : "It is likely that these methods are as close to accurate as they will ever be, given that secondary structure is partially determined by tertiary factors. Very small improvements continue to be made but probably only as a consequence of increased sequence database size. " Nonetheless, PSSPs are are still particularly used today. For instance, de novo protein structure prediction, protein structural classification, or fold recognition approaches often used PSSP  [22, 24, 25, 30, [84] [85] [86]. Following our analysis of protein dynamics behaviours  [6, 39, 41] , a logical and intuitive question rose: 'Is the prediction of a given secondary state easier when the residue is in a rigid region, while a flexible region has less constraints and so information to predict it?' Two points were important for this study: (i) the choices of PSSPs and (ii) the information for protein dynamics.
g. CABS-flex  [89, 90]. As for the previous criteria, the choice of protein X-ray structures and molecular dynamics was partly personal. It was classical and recognized, but also had shown interesting correlations with previously presented approaches  [91]. It seemed logical to use this protein structure dataset  [6, 39, 41]. This study underlined that more stable was the assignment of secondary structure better was the prediction (see Figure  2 ). For instance, the PSIPRED Q3 accuracy of the residues always assigned to the same secondary structure during all the simulations (100%) equalled to 93. 8% while it drastically dropped to 34. 1% when the initial assignment was conserved less than 10% of the times. This strong relationship was found in the light of MD classes, but also with CIndex classes, using experimental B-factors, and computational RMSf, relative accessibility and Neq.