dataesr/scientific-paragraphs-categorization huggingface dataset:
This technique causes the DNA fragments to be overstretched by a factor of 1. 7 to 1. 75  (28). We imaged the labeled DNA with a wide-field microscope. Finally, we extracted the fluorescence intensity signal along the linearized DNA for each individual DNA fragment (Figure  1C ). The simulated data allowed us to investigate the performance of our identification method on expanded sets of species. As outlined before, the simulation model generates DNA maps for these species accounting for the various sources of experimental variation: enzymatic labeling of the DNA, overstretching and shearing of the DNA, fluorescence photo-physics and imaging.
In addition, testing the statistical significance of the matching score allows the whole approach to be robust against biased definitions of the database of reference species. That is to say that if fragments drawn from a species that is not included in the reference database are imaged, we expect our methodology to recognize them as unknowns (i. e. no significant matchings should be returned for any of the reference species under assessment). This is a clear advantage over just assigning these fragments to the species for which a maximum matching score is found as it reduces the impact of false positives on the final outcomes. Similarly, in situations in which DNA maps that are shared by multiple microbial species (due to high evolutionary similarity) are imaged, they will not be necessarily assigned to only one of them, allowing common genomic subsequences to be therefore easily recognized.
This is evident from Supplementary Figure  S13 , which suggests that a minimal labeling efficiency of 70% was needed for achieving a good matching sensitivity with overstretched DNA images collected by wide-field microscopy. As for fragment length, an increase in resolution also relaxes this requirement to ∼60%. To confirm these effects experimentally we imaged overstretched DNA fragments from bacteriophage T7 and lambda using a SIM microscope, which allowed us to obtain both wide-field and SR-SIM images for each DNA fragment (see 'Materials and Methods' section). The resolution improvement is apparent from the experimental data shown in Figure  2A , where wide-field and SR-SIM data are shown side-by-side. We assessed the resolution improvement experimentally to be close to 2 (see Supplementary Section S2.
As can be seen from the results for the simulated overstretched SR-SIM data in Figure  3C , applying the resampling step clearly improved specificity, while having only a minor effect on sensitivity. We observed the same improvement in experi-mental data from bacteriophages lambda (Figure  3F ) and T7 (Figure  3G ). One might wonder whether it is necessary to perform the initial matching significance test at all if the resampling step allows distinguishing between species on its own. Indeed, if the reference database of tested species contains all possible species present in the sample, this approach would yield the same results. However, if no matching score significance test is carried out, the resampling step will always return at least the species with the highest matching score. Thus, if the sample under study contains species that are not present in the reference database, measured maps from such unknown species will always be assigned to wrong species.
Simulated identification of bacterium-sized genomes To investigate if the identification performance varies when the reference species have a much longer genome (a bacterial genome, for example, is about 2 orders of magnitude larger than the one of a bacteriophage), we created artificially large genomes in silico by inserting the sequence of a bacteriophage (lambda and T7) in the middle of the sequence of a bacterial genome (Bacteroides thetaiotaomicron VPI-5482) having similar labeling density (lambda: 2. 5/kb, T7: 2. 8/kb, bacteroides: 2. 8/kb). Next, we used our experimental SR-SIM data for lambda and T7 DNA fragments and matched them to the artificial bacteria, as well as to the bacterium without the inserted phage DNA. We also matched to two other bacteria as a control: Lactobacillus Reuteri and Escherichia Coli (strain K-12 substrain MG1655).
The case of V. Harveyi In order to prove the usefulness and suitability of the proposed methodology for the assessment of more complex bi- ological samples, 601 optical maps of V. Harveyi DNA were recorded based on the experimental procedure described in the 'Materials and Methods' section and analyzed by the developed algorithm. V. Harveyi can be regarded as a naturally 'calibrated' biological system. Every V. Harveyi cell, in fact, contains DNA constituted by two single chromosomes (chromosome #1: accession number CP000789. 1, size 3. 77 Mb -chromosome 2: accession number CP000790. 1, size 2. 20 Mb) and a varying amount of plasmids (accession number CP000791. 1, size 0. 09). As the ratio of occurrence of these two chromosomes does not change across cells, its expected value can be easily estimated as the ratio of their size, which is equal to 3.
Recent extensions of this method to graph structures (39) could also be considered if additional information about local stretching is available. It is fundamental to notice that testing the statistical significance of the matching score allows the whole proposed approach to be robust against biased definitions of the database of reference species. This is a clear advantage over just assigning these fragments to the species for which a maximum matching score is found as it reduces the impact of false positives on the final outcomes. Since currently not all microbial species are sequenced, unknowns are to be expected in real world scenarios. Therefore, not assigning a DNA map will be important when matching experimental maps against a sub-database containing only species of interest (since matching against all known species would be computationally very demanding).
Analysis of shotgun metagenomic reads from microbiome samples is typically very challenging from a computational point of view due to the large quantities of data involved  (37). This issue also applies to optical mapping and the method presented here: all measured DNA maps need to be aligned to the expected DNA maps for all target species. Nevertheless, the selection of the reference DNA sequences is actually not a critical step. The algorithmic procedure proposed here, in fact, performs a separate test for every single species under study in the attempt of assessing whether a particular DNA map belongs to its corresponding genome or not. This way, one can easily reduce the database constructed for identification purposes so as to encompass only few microorganisms of interest while guaranteeing high robustness against optical maps proceeding from unknown species (which, as specified before, ideally would not be assigned).
This would dramatically decrease the computational load and cost in real-world scenarios characterized by the presence of unsequenced/partially sequenced genomes and extreme complexity and heterogeneity (e. g. gut microbiome mapping) without jeopardizing the identification quality and minimizing the number of false assignations. Moreover, alignment of DNA maps by cross-correlation can be implemented very efficiently by converting the signals into the Fourier domain. Due to the convolution theorem, the cross-correlation estimation becomes, indeed, a computationally cheaper multiplication. Moreover, the database of expected DNA maps for all target species needs to be Fourier-transformed only once, after which it can be reused for each alignment. As an example, a single crosscorrelation alignment against a 6.
Background: Resolving population genetic structure is challenging, especially when dealing with closely related or geographically confined populations. Although Principal Component Analysis (PCA)-based methods and genomic variation with single nucleotide polymorphisms (SNPs) are widely used to describe shared genetic ancestry, improvements can be made especially when fine-scale population structure is the target. Results: This work presents an R package called IPCAPS, which uses SNP information for resolving possibly finescale population structure. The IPCAPS routines are built on the iterative pruning Principal Component Analysis (ipPCA) framework that systematically assigns individuals to genetically similar subgroups. In each iteration, our tool is able to detect and eliminate outliers, hereby avoiding severe misclassification errors.
Background Single Nucleotide Polymorphisms (SNPs) can be used to identify population substructure, but resolving complex substructures remains challenging  [1]. Owing to the relatively low information load carried by single SNPs, usually thousands of them are needed to generate sufficient power for effective resolution of population strata due to shared genetic ancestry  [2]. Moreover, in practice with high-density genome-wide SNP datasets, linkage disequilibrium (LD) and haplotype patterns are likely to exist, which can be exploited for the inference of population structure  [3]. On the one hand, exploiting haplotype patterns is potentially informative, but comes with a high computational burden. On the other hand, although removing LD by pruning strategies can eliminate some spurious substructure patterns, it may limit our ability to identify subtle subgroupings.
The test for substructure and clustering is performed iteratively on nested datasets until no further substructure is detected, i. e. until a stopping criterion based on fixation index (F ST ) is satisfied. F ST is commonly used to measure genetic distance between populations. The software developed to perform ipPCA has some shortcomings though. Notably, it is limited to a MATLAB environment, which is not freely available. Also, outliers can severely disturb the clustering analysis. These limitations are addressed in IPCAPS  [7] , which improves the power of fine-scale population structure, while appropriately identifying and handling outliers.
Implementation The R package IPCAPS provides one synthetic dataset and seven functions: 1) simSNP: a synthetic dataset containing SNPs and population labels. 2) ipcaps: a function for unsupervised clustering to capture population structure based on iterative pruning. 3) rubikClust: a function for unsupervised clustering to detect rough structures and outliers. 4) cal. PC. linear: a function for linear PCA. 5) fst. hudson: a function for average F ST calculation between two groups. 6) fst. each. snp. hudson: a function for F ST calculation for all SNPs between two groups. 7) plot. 3views: a function to create scatter plots in three views. 8) top. discriminator: a function to detect top discriminators between two groups. See the IPCAPS reference manual for details of the functions, arguments, default settings, and optional user-defined parameters.
The IPCAPS package implements unsupervised strategies that facilitate the detection of fine-scale structure in samples, extracted from informative genetic markers. For general populations, information regarding substructure may come directly from SNPs. For patient samples, general population structure should first be removed via regressing out ancestry informative markers prior to clustering. The latter is incorporated in IPCAPS. Currently, IPCAPS accepts three data input formats: text, PLINK binary (bed, bim, fam), and RData (more details in Table  1 ). In the sequel, we will assume the availability of a sufficiently large SNP panel that is called on a collection of population samples. Prior to clustering with IPCAPS, adequate data quality control (QC) steps need to be taken. These are not supported by IPCAPS itself but can easily be performed in PLINK (1.
9)  [8]. Suggested PLINK parameters include: restrict to founders (--filter-founders), select chromosome 1-22 (--not-chr 0,x,y,xy,mt), perform LD pruning (--indep-pairwise 50 5 0. 2), test for Hardy-Weinberg equilibrium (--hwe 0. 001), use call rate at least 95% (--mind 0. 05), filter out missing SNP above 2% (--geno 0. 02), and remove low minimum allele frequency (--maf 0. 05). The remaining missing genotype values are SNP-wise imputed by medians. Rather than performing two-means clustering in PCA-space, at each iteration, IPCAPS clustering potentially involves the consecutive application of 2 clustering modules. The first, which we call rubikClust, is applied in the 3-dimensional space determined by the first three principal components (axes) at an iteration step. It involves applying rotations in 3D by consecutively performing rotations around PC1, PC2, PC3, and may provide more than 2 clusters.
Notably, this approach also allows for rapid identification of outliers. When samples cannot be divided into 2 groups in this way, the existing R function mixmod (package Rmixmod) is used for latent subgroup detection. In particular, earlier computed PCs (untransformed) at a particular iteration are subjected to multivariate Gaussian mixture modeling and Clustering EM (CEM) estimation  [9] , allowing for up to three clusters at each iteration. The iterative loop of IPCAPS can be terminated automatically by calling one of three possible stopping criteria: the number of subgroups is lower than a minimum, the fixation index (F ST ) is lower than a threshold, and EigenFit is lower than a pre-specified cutoff. The EigenFit criterion is defined by the differences between the logarithms of consecutive eigenvalues, sorted from high to low.
RData format In the case of re-analysis, it is convenient to rerun the function ipcaps using the file rawdata. RData is generated by the function ipcaps itself. This file contains a vector of labels and a matrix of SNPs containing N rows of individuals and M columns of SNPs. All IPCAPS results are saved in a single directory including textual information about cluster allocations, and visual information such as PC plots and hierarchical trees of group membership. Due to memory restrictions in R, large datasets (i. e. , a large number of subjects) may need to be split into multiple files and loaded into computer memory via the IPCAPS option files, which they are internally merged again for iterative PCA. Extra attention is paid on efficient PC calculation  [10] , also relying on the R package rARPACK.
Conclusions Fine-scale resolution of population substructure can be captured using independent SNPs once all redundancies are filtered out. In this work, we have introduced a flexible and efficient R package to accomplish an unsupervised clustering without prior knowledge, in the search for strata of individuals with similar genetic profiles. The tool performs well in fine-scale and broad-scale resolution settings. The IPCAPS routines allow a relatively easy extension to input data derived from transcriptome or epigenome experiments.
Funding This work was supported by the  Fonds de la Recherche Scientifique  ( FNRS PDR T. 0180. 13 )[KC, KVS]; the  Walloon Excellence in Lifesciences and Biotechnology (WELBIO)  [FAY, KVS]; the  French National Research Agency  ( ANR GWIS-AM ,  ANR-11-BSV1-0027 ) [AS], and the  National Science and Technology Development Agency (NSTDA) Chair  grant [ST].
The medium should be changed regularly, and non-viable embryos, dead eggs or chorion remains should be removed at the same time. At around 120 hpf the digestive tract has developed, the anus has opened, the swim bladder is inflated, and larvae are able to swim and feed independently. 17 Early larvae are often maintained in small tanks with the water flow turned off at up to 250 larvae/l. When reaching an appropriate size, the juveniles are transferred to aquaria with water exchange. The period to reach sexual maturity varies between 2 to 4 months depending on the environment, feeding and husbandry conditions. All adult fish in a facility need to be tracked via the use of an appropriate inventory database system that allows central monitoring of animal numbers, breeding history, characteristics of a given strain and other vital information.
Acknowledgements  Zoltan Varga  and  Katy Murray  ( Zebrafish International Research Center, University of Oregon, Eugene, OR ),  Christian Lawrence  ( Childrens Hospital, Harvard University, Boston, MA ),  Robert Kelsh  ( University of Bath, UK), Manfred Schartl (Biocenter, University of Wu¨rzburg, Germany ) and  Uwe Stra¨hle  ( Karlsruhe Institute of Technology, University of Karlsruhe, Germany ) are thanked for critical discussions and many valuable comments to the manuscript. Jan-Bas Prins ,  Ann-Christine Eklo¨f ,  Martina Dorsch  (FELASA),  Uwe Stra¨hle  and  Larissa Kaufmann  (EUFishBioMed) are thanked for support during the process, FELASA for financial support and  Laurent Coen  ( Muse´um National d'Histoire Naturelle, Paris ) for hosting one of the working group meetings.
Molecular dynamics simulations Molecular dynamics (MD) simulations complement experimental methods and modeling. The interpretation of experimental data with the help of MD simulations has evolved into a very large field (e. g. Smith, 1991; Daniel et al. , 2003; Sakai and Arbe, 2009; Smith et al. , 2018)  beyond the scope of this review. The comparison of results from simulated atom trajectories with experimental scattering functions has been achieved using software packages such as nMOLDYN  (Kneller et al. , 1995; Róg et al. , 2003)  or MDANSE  (Goret et al. , 2017). To this effect, these software packages calculate the simulated scattering functions from the computed trajectories, and the possibility to selectively investigate for instance only some molecular groups contributes to the advantages of the simulation approach.
, 1999; Roosen-Runge et al. , 2011) , and modeling is key to extract more detailed information. One approach to model these global contributions to protein dynamics is to calculate the diffusion tensor based on protein structures using software packages such as HYDROPRO (de la  Torre et al. , 2000). This approach accounts for the full anisotropy of the shape and diffusion, but depends on an available protein structure, which furthermore is assumed to be rigid. From the diffusion tensor D, the apparent collective diffusion coefficient D (c)  app (q) is given by  (Biehl et al. , 2011; Stingaciu et al. , 2016 ) D (c) app (q) = 1 P(q) a,b b coh a b coh b e iq•(r a -r b ) q q×r a D q q×r b ( 24 ) where P(q) = a,b b coh a b coh b e iq•(r a -r b ) is the form factor of the particle and b i and r i denote the scattering length and the coordinate of the individual atoms.
Large-scale and domain motions Given the relevance of large-scale motions such as cleft opening for protein function, experimental access to these motions is of great importance. The determination of prevailing large-scale motions in proteins is, however, no trivial task, and depends in general on the availability of protein structures. The most conventional approach for the analysis of experimental data of large-scale protein motions is a normal mode analysis aiming at the low-frequency Brownian modes  (Hinsen, 1998; Doruker et al. , 2000; Hinsen et al. , 2000; Cui and Bahar, 2006) , as implemented in open-source software packages such as the molecular modeling toolkit MMTK  (Hinsen, 2000)  or ProDy  (Bakan et al. , 2011). The basic idea is to assume an elastic network of all atoms in the protein as a model of collective fluctuations around the equilibrium structure, which corresponds to the classical Ornstein-Uhlenbeck process.
We note that certain observations may be influenced by the resolution functions of the instruments  (Magazù et al. , 2017)  as well as by the specific data treatment and models used to describe the data. The respective instrument resolution is also listed in the tables. This section is organized as follows: first, a brief summary of the results obtained on the dynamics of hydrated protein powders will be given, and some recent results will be shortly reviewed in the 'Dynamics of hydrated protein powders' section. Second, the main studies pointing out differences of protein internal dynamics in hydrated powders compared with solutions will be reviewed in the 'From powder to solution: influence of solution conditions on protein dynamics' section. Subsequently, studies regarding a temperature-dependent dynamical transition in solution will be reviewed in the 'The dynamical transition in solution' section, followed by studies on the influence of the protein conformation on the internal macromolecular motions in the 'Comparison of internal protein dynamics in native, molten and denatured states' and 'Relations of protein dynamics to structure: from globular to intrinsically disordered proteins' sections.
The data were fitted with the jump-diffusion model by Singwi and Sjölander (see section 'Localized internal dynamics'), and the diffusion coefficients were found to decrease with increasing protein concentration, whereas the residence times increased. Similar results were observed in solutions of ferritin, a complex consisting of protein units responsible for iron storage  (Häußler, 2008). At low concentrations and high-ionic strength, the diffusion measured by NSE approaches the dilute limit. Increasing the concentration, as expected, slows down the diffusion. The study examined also solutions with low-salt content. In these samples, a structure factor peak appeared as a consequence of the ordering of the ferritin molecules. In the vicinity of the peak, only an approximate analysis was possible, because the q-averaged intermediate scattering function is affected by the slope of the structure factor.
Nevertheless, the data indicated that in low-salt samples both direct electrostatic and indirect interactions influence the ferritin dynamics, especially close to the structure factor. These results essentially confirmed those by Häussler and Farago on apoferritin (ferritin without the iron core) suggesting that, at the structure factor peak, dynamics is slowed down by strong spatial correlations, while at lower scattering vectors (larger length scales), dynamics is hindered by hydrodynamic interactions  (Häußler and Farago, 2003). Recently,  Gupta et al. (2016)  have mimicked a crowded environment by dispersing model globular proteins such as α-lactalbumin and Hb, in aqueous solution of poly(ethylene oxide). By employing NSE and SANS, the corresponding protein dynamics in semidilute polymer solution was measured.
Summary Protein dynamics is a key research area in biophysics with many facets in terms of time and length scales as well as implications for physical properties and biological function. Neutron scattering spectroscopy was employed in a multitude of investigations of protein dynamicsfrom fast, localized motion with TOF and NBS to slow domain motion with NSE, as well as center-of-mass diffusion with NBS and NSE. In this context, neutron spectroscopy has benefited from progress in neutron instrumentation and data analysis during the recent years. With its nondestructive and contact-free access to a wide range of time and length scales accessing the molecular level, including in optically opaque samples, the neutron scattering technique has proven to be complementary to other established biophysical techniques such as NMR, fluorescence spectroscopy and light scattering.
Acknowledgements. The authors are indebted to many collaborators, with whom it has been a pleasure to interact. These include in particular present and former members of the Tübingen-ILL collaboration. The authors would like to thank in particular  H. Schober ,  B. Farago  and  B. Frick  for discussions and support. Fruitful interactions with  A. Stadler ,  R. Biehl ,  I. Hoffmann ,  V. Garcia Sakai ,  G. Kneller ,  J. Peters ,  K. Saalwächter ,  A. Stradner  and  P. Schurtenberger  are also gratefully acknowledged. The authors would like to acknowledge enlightening discussions with numerous colleagues in the field, who cannot all be mentioned. We thank  C. Beck ,  O. Matsarskaia ,  M. Braun ,  M. Oettel ,  R. Roth  and  E. Schäffer  for stimulating discussions. The authors wish to thank the  ILL (Grenoble) , the  JCNS (Jülich) ,  FRM-II (Munich)  and  SNS  ( Oak Ridge ) for making our own neutron-based work on proteins in solution possible.