dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Nishita Desai ,  Suchita Kulkarni ,  Ursula Laa  and  Wolfgang Waltenberger  for helpful discussions. Moreover, very special thanks go to  Wolfgang Waltenberger  for helping with the public release of the code. This work is supported by the  German Research Foundation DFG  through the research unit "New physics at the LHC". S. K. is supported by the  IN2P3  project " Théorie -LHC-iTools " and the  CNRS-FAPESP  collaboration grant  PRC275431. A. L. is supported by the  Sao Paulo Research Foundation (FAPESP) , projects  2015/20570-1  and  2016/50338-6.
Super-resolution microscopy (SRM) has become essential for the study of nanoscale biological processes. This type of imaging often requires the use of specialised image analysis tools to process a large volume of recorded data and extract quantitative information. In recent years, our team has built an open-source image analysis framework for SRM designed to combine high performance and ease of use. We named it NanoJ-a reference to the popular ImageJ software it was developed for. In this paper, we highlight the current capabilities of NanoJ for several essential processing steps: spatio-temporal alignment of raw data (NanoJ-Core), super-resolution image reconstruction (NanoJ-SRRF), image quality assessment (NanoJ-SQUIRREL), structural modelling (NanoJ-VirusMapper) and control of the sample environment (NanoJ-Fluidics).
Several SRM image processing packages are available, such as ThunderSTORM  [6] , LAMA  [7]  and SIMcheck  [8]  but each of these is focused on a specific type of SRM modality. Here, we present NanoJ, a highly versatile set of image acquisition and analysis methods developed to improve the reliability and quantifiability of microscopy experiments, with a particular focus on the demands of livecell SRM. NanoJ is available as a series of ImageJ-based plugins which can be used independently or concomitantly. NanoJ (figure  1 ) is comprised of the following modules: NanoJ-Core-general image correction tools including drift correction and channel registration, both based on crosscorrelation analysis; NanoJ-SRRF-an analytical approach capable of extracting super-resolution data from a short sequence of diffraction-limited images, which can be acquired using most microscopes  [9, 10] ; NanoJ-SQUIRREL-an algorithm to evaluate resolution and the presence of artefacts in super-resolution images  [11] ; NanoJ-VirusMapper-a single particle analysis method to generate nanoscale models of biological structures such as viruses  [12] [13] [14] ; NanoJ-Fluidics-a hardware and software framework to control fluidics devices, enabling automation of multiplexed experiments  [15].
The NanoJ framework NanoJ has been designed to integrate with the popular ImageJ or Fiji image analysis software  [16, 17]  and is easily installed as a standard set of plugins. NanoJ is also fully open-source and user-friendly. The graphical user interfaces (GUIs) are straightforward to use and its routines can be easily integrated within larger image analysis pipelines through the ImageJ macro language. NanoJ is designed to be an accessible tool for both nonexpert users and developers. Each of its modules possesses its own separate manual and documentation. NanoJ is implemented in both Java (https://java. com/) and OpenCL (https:// khronos. org/opencl), the latter language being used for high-performance analysis of image data through the use of graphical processing units (GPUs). To date, it encompasses four Java ARchive (JAR) packages (NanoJ-SRRF, NanoJ-SQUIRREL, NanoJ-VirusMapper, NanoJ-Fluidics) that all depend on a central package (NanoJ-Core).
NanoJ breaks the task of drift correction into two distinct parts: estimation, followed by translation. As a first step, NanoJ-Core estimates the linear drift between two images by calculating their crosscorrelation matrix (CCM) (figure  2(b) ). The location of the peak intensity in the CCM determines the linear shift between the two images, and precise sub-pixel accuracy is achieved by up-scaling the CCM using a bicubic spline interpolation. Depending on the type of acquisition, the reference frame can either be the first frame of the raw data or the immediately preceding frame. Figure  2(d)  shows the drift in a 100-frame dataset as measured with respect to the first frame. Once drift is estimated, the dataset can be directly corrected by analytically translating each individual frame using a bicubic spline interpolation (figure  2(c) ).
We estimated the performance of NanoJ-Drift correction (see supplementary note 3 and figure S1a (stacks. iop. org/JPhysD/52/163001/mmedia)) at varying levels of signal-to-noise ratio (SNR) and established that the errors remain within 30 nm even at very low SNR and drop quickly below 10 nm for SNR > 6, appropriate for SRM. The interpolation process will, however, change the noise properties of the resulting dataset  [20] , which can have an influence on further analysis requiring specific assumptions in noise properties such as maximum likelihood estimation (MLE) single-molecule localization analyses. For the specific case of SMLM datasets with sparse blinking, there will only be a weak correlation across frames, as there is little observable structure conserved between consecutive time points.
One common strategy to alleviate this low correlation is to add fiduciary landmarks to the sample, such as static fluorescent beads. As an option NanoJ-Core can temporally bin images within the dataset, thus increasing the correlation between frames and allowing their shift to be more accurately estimated  [21]. Drift estimation in NanoJ differs from strategies applied by other SRM algorithms, such as ThunderSTORM  [6] , by analysing unprocessed raw data instead of post-processed super-resolution reconstructions. This allows the estimation to be decoupled from the super-resolution image reconstruction algorithm and hence drift-corrected raw data can be analysed using a wider range of methods including SRRF  [9]  and SOFI  [18]. Furthermore, NanoJ-SRRF (as described in a following section) can import the drift curve (figure  2(d )) created by NanoJ-Core Drift Correction and use this information directly during analysis without the need to pre-translate each frame in the raw dataset.
NanoJ-Core: channel registration In multicolour fluorescence microscopy, images acquired in different spectral channels are often misaligned as a result of chromatic aberrations in the optical path and the use of different filter sets for each colour. This misalignment is frequently ignored in conventional microscopy as it typically occurs on a scale smaller than the diffraction limit. However, this effect becomes non-negligible in the context of SRM  [22]. Channel registration is therefore essential for multicolour SRM studies quantifying colocalization or interactions between different structures  [23] [24] [25]. Advanced imaging research groups tend to develop their own analysis scripts for their particular applications, and thus no user-friendly tools are readily available. Therefore, NanoJ-Core channel registration offers a unique tool to perform this registration easily and robustly on a wide variety of datasets.
Given that chromatic misalignment is a fixed property of an optical system, NanoJ-Core can then apply these transforms to realign other multicolour datasets acquired using the same optical path (figure  3 )  [27, 28]. To generate these non-linear channel registration fields NanoJ-Core first calculates local, linear misalignments between channels (figure  3(a) ). This is achieved by dividing the image into small areas ('blocks'). For each block, the shift is calculated by finding the cross-correlation peak position as shown in figures 2(a) and (b). These local shift values are then interpolated across the whole field-of-view using an inverse distance weighting interpolation  [29]. This generates two smooth shift maps describing the misalignments in the horizontal and vertical directions (figure  3(b) ).
For a given channel, each pixel value within the horizontal/vertical shift map indicates the horizontal/vertical displacement that needs to be applied to that pixel to align it with the reference channel. NanoJ-Core can then be used to apply these maps to align channels in the dataset of interest (figure  3 ), provided they have been acquired using the same optical configuration. Here, we measured the target registration error  [28, 30]  (TRE, see supplementary note 3 and figure  S1b ) in typical multi-colour configurations and showed that the TRE remains below 50 nm for all combinations of channels. NanoJ-Core performs the channel registration by creating a new image representing each channel, where the intensity value for each pixel coordinate corresponds to the intensity value from the original image at the equivalent coordinate corrected for local shift.
NanoJ-SQUIRREL: estimating image resolution The purpose of SRM is to resolve finer structural detail than is achievable with conventional diffraction-limited microscopy. It is therefore useful to have an objective measurement of resolution within a super-resolution image, for example to enable meaningful analysis and structural hypotheses that stay in line with the actual precision of the data. The current standard for measuring image resolution in SMLM images is Fourier ring correlation (FRC)  [36]. This method involves comparing two independently acquired super-resolution images of the same field-of-view so that they only differ by their noise components. For SMLM datasets the two SRM images are usually obtained by splitting localizations from odd and even frames. The correlation between these two SRM images is measured at different frequencies in Fourier space; the frequency at which this correlation drops below a set threshold indicates the resolution of the image.
FRC has been previously implemented in ImageJ  [36] , but only gives a single resolution measurement for the entire field of view. However, resolution is not necessarily homogeneous across the SRM image (figure  5(c )). This is particularly true for SMLM methods as localization accuracy depends strongly on labelling density and laser illumination intensity, which can both vary considerably within a single field of view. Furthermore, FRC can generate biased measurements for certain fluorophore distributions such as point-like patterns. Therefore, an additional feature of the NanoJ-SQUIRREL plugin is local mapping of FRC resolution across an image. To do this, the user provides an image stack comprising two independent renderings of the same dataset (e. g. through the odd/ even frames splitting as described above).
Here, we illustrate this with models of three distinct vaccinia virus substructures  [42] : core, lateral bodies (LBs) and membrane. All three substructures were labelled on viral particles and imaged with SIM (figure  6 (a), top). VirusMapper can explicitly incorporate information from multiple fluorescence channels to enable multi-component modelling. Here, the images of the LBs and the core were used to find the orientations of each particle in the dataset. This allowed us to create simultaneous models of the three components of the virus (figure  6 (a), bottom). VirusMapper can also be used to separately model different 3D orientations of a structure. Here, we highlight this capability on a study of the division ring in the thermoacidophilic archaeon Sulfolobus acidocaldarius, strain DSM639 (figure 6(b)).
NanoJ-Fluidics: sample liquid exchange NanoJ-Fluidics is a hardware and software framework for precise and accurate automated liquids exchange  [15]. It was developed to enable automation of sample treatment and labelling of live or fixed specimens directly on the microscope stage  [15, 43]. The NanoJ-Fluidics hardware component is composed of customisable, low-cost and robust LEGO ® syringe pumps and a liquid removal peristaltic pump, all controlled by simple Arduino ® electronics. It is compatible with off-theshelf imaging chambers, without the need for any microfabrication. Its control software (the NanoJ-Fluidics software) is ImageJ-based and can be fully integrated with microscopy acquisition software. We have demonstrated the applicability of NanoJ-Fluidics in multiple experimental contexts, including in-situ correlative live-to-fixed super-resolution imaging, multimodal super-resolution imaging and event-driven fixation  [15].
The approach can also be easily extended to proto col optimisation (e. g. titrating antibody concentrations or adjusting imaging buffer composition) or liquid exchange protocols integrated with the imaging (e. g. drug delivery or automated event-driven fixation). Here, we demonstrate NanoJ-Fluidics by acquiring a high quality multicolour SMLM dataset, where STORM and DNA-PAINT  [44]  imaging strategies are combined into a single workflow (figure  7(a) ). This approach is particularly suited to multi-target imaging  [32] , which is difficult to achieve with standard sample preparation techniques due to the low number of suitable fluorophores available for SMLM microscopy. With NanoJ-Fluidics, we can seamlessly perform all labelling steps in an automated and reliable manner directly on the microscope stage.
We showcase this using a four-channel acquisition of actin with STORM, and mitochondria, vimentin and clathrin with DNA-PAINT (figure  7(b) ). NanoJ-Fluidics' highly customisable nature has already spawned several alternative designs from the community (https://github. com/ HenriquesLab/NanoJ-Fluidics/wiki), which are in constant development. NanoJ-Fluidics makes imaging protocol automation readily available to researchers, hence improving not only the reliability and repeatability of the protocols, but also the range of protocols that are achievable.
Finally, the NanoJ framework provides tools to quantitatively assess SRM image quality by spatially mapping unbiased quality metrics, artefacts (errors) and FRC resolution (NanoJ-SQUIRREL), with the aim to improve standards in assessing and reporting microscopy data. The NanoJ framework gives access to user-friendly yet robust analytical methods in tandem with new approaches to perform live-cell SRM experiments. A typical SRM live-cell imaging experiment using the NanoJ framework would entail acquiring a SRRF-compatible raw dataset in any modern fluorescence microscope; performing drift correction and channel registration; followed by SRRF analysis for SRM image reconstruction; and finally, quality check using SQUIRREL. Used concomitantly in this manner, these tools can be used to address a large variety of biological questions and can be seamlessly repeated for imaging protocol optimisation and complemented using NanoJ-VirusMapper and/or NanoJ-Fluidics.
Thus, the NanoJ-enabled scientific pipeline allows any user, regardless of the experience level, to seamlessly obtain SRM quantitative data of the highest scientific quality. The NanoJ framework was developed to facilitate access and expand the options of research groups using fluorescence microscopy. Hence, speed, reliability, performance and cross-compatibility are of paramount importance. In this context, NanoJ has been designed for high-performance image analysis, using GPU computing, ensuring the quick processing of large data volumes  [45] [46] [47] [48] [49]. Further, NanoJ's modular and open-source nature and its integration within ImageJ allow it to be used in conjunction with other analysis software packages  [50] [51] [52] [53]. We are continuously supporting, adapting and expanding the framework to include new approaches, such as 3D imaging.
Software and hardware availability NanoJ follows opensource software and hardware standards. Each of its modules can be installed by enabling the corresponding code repository in Fiji or by following the instructions on the corresponding websites: • https://gith ub. com/HenriquesLab/NanoJ-Core • https://git hub. com/HenriquesLab/NanoJ-SRRF • https://bitbuc ket. org/rhenriqueslab/NanoJ-SQUIRREL • https://bitbuck et. org/rhenriqueslab/NanoJ-VirusMapper • https://git hub. com/HenriquesLab/NanoJ-Fluidics.
Acknowledgments First and foremost, we would like to thank the  ImageJ and Fiji open-source development community. Their astonishing work has been the inspiration to the creation of  NanoJ. There has been a large number of beta-testers and NanoJ users whose feedback helped us to continually update and improve its code, to the best of our capacity we keep an updated list of significant contributors in the website corre sponding to each NanoJ module. We thank  Professor Ralf Jungmann  at  Max Planck Institute of Biochemistry Munich  for reagents and advice. This work was funded by grants from the  UK Biotechnology and Biological Sciences Research Council  ( BB/M022374/1 ;  BB/P027431/1 ;  BB/R000697/1 ;  BB/S507532/1 ) (R H, P M P and R F L), the  UK Medical Research Council  ( MR/K015826/1 ) (R H), the  Wellcome Trust  ( 203276/Z/16/Z ) (S C, R H and B B), Core funding to the  MRC Laboratory for Molecular Cell Biology, University College London  ( MC_UU12018/7 ) (J M), the  European Research Council  ( 649101-UbiProPox ) (J M) and the  Centre National de la Recherche Scientifique  ( CNRS ATIP-AVENIR program   AO2016 ) (C L).
N G and  R D M G  were funded by the  Engineering and Physical Sciences Research Council  ( EP/L504889/1 ). P A was supported by a PhD fellowship from the  UK's Biotechnology and Biological Sciences Research Council. Research by B B was supported by  UCL ,  Cancer Research UK  ( C1529/A17343 ),  BBSRC  ( BB/P001440/1 ) and  MRC  ( MC_CF12266 ). K L T and G T R are supported by a 4 year  MRC   Research Studentship. D A is presently a  Marie Curie fellow (Marie Sklodowska-Curie 750673 ). A C L and F H were supported by the  Swedish Research Council , grant  621-20134685.
This indicates that uncertainty about the evolutionary and biogeographic history of the genus remains. Understanding the fossil history of a group is critical if we are to provide a timescale to its evolutionary and biogeographic history. Known fossils relating to Corylus belong to stem group taxa  13  or closely related extinct genera  14, 15. Fossilized pollen and mesofossils have been putatively classified as belonging to Corylus or other Betulaceae genera  [16] [17] [18]  , but more study using techniques such as electron microscopy must be done to confirm this to make them reliable enough for molecular dating  19. We thus consider that there is an adequate fossil record available for preliminary dating of divergence events in this group. Here we present new phylogenetic trees of the hazel genus, Corylus, inferred from chloroplast (cpDNA) and nuclear DNA (nrDNA) sequences available on GenBank.
Materials and Methods phylogenetic inference. We collected sequences from 13 different loci across the 16 Corylus taxa that were available on Genbank (https://www. ncbi. nlm. nih. gov/genbank/ accessed Nov. 2016) and four outgroup species (Alnus incana, Betula papyrifera, Carpinus japonica and Ostrya rehderiana), selecting the longest sequence available per locus per taxon to maximize available information. These loci were; internal transcribed spacer 1 (ITS1), internal transcribed spacer 2 (ITS2) & 5. 8S-rRNA, 5S-rRNA & Non-Transcribed Spacer (NTS), ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit (rbcL), ATPase beta-subunit (atpB-rbcL) intergenic spacer, granule bound starch synthase (GBSSI), maturase K (matK), nitrate reductase (NIA), cytosolic phosphoglucose isomerase (pgiC), tRNA-His -photosystem II protein D1 intergenic spacer (trnH-psbA), ribosomal protein S16 (RPS16), ribosomal protein L16 (RPL16), tRNA-Leu (trnL) and trnL-tRNA-Phe (trnL-trnF) intergenic spacer.
Sequences were aligned using the MAFFT 1. 3. 5 alignment plugin 20 in GENEIOUS 10. 0. 6 (Biomatters). To test if there were any erroneous sequences we built gene trees using the maximum-likelihood approach RAxML 21 (v8. 2. 9). For each run we applied a GTRGAMMA model and used the '-f a' option with 100 replicates for bootstrapping. We visually assessed each gene tree for unusually long branches or unlikely topologies (compared to preliminary trees in this study and those from previous studies) that may be indicative of error in the Genbank sequences. When a potentially incorrect sequence was identified, it was removed from the associated alignment for downstream phylogenetic analyses. We used BEAST (v2. 4. 4)  22  , a Bayesian approach, to build time-calibrated trees. Partitions of nuclear and chloroplast loci were analysed separately so we could examine the differences between trees inferred from nuclear and plastid genomes.
Sequence alignments were then imported into BEAUti (v2. 4. 4)  22. A yule model was used as the tree prior. Each locus was treated as a partition to confer separate site and clock models while trees were linked across all partitions. We used the program modeltest-ng (v0. 1. 3) to determine the appropriate substitution models for each partition based on Bayesian Information Criteria (BIC). We added the +G (gamma rate heterogeneity) parameter to substitution models for matk, rbcL and atpB-rbcL to improve mixing after it was initially poor. Relaxed lognormal clock models were used for each partition to allow rates to vary among branches. We took two separate time-calibration approaches. Trees were first calibrated with three lognormal priors, using published estimated ages of fossil records of C.
Two additional runs were conducted in each case and compared to the initial run to ensure stationarity had been reached at the same point across runs. We examined combined logfiles in TRACER 1. 6  23  to ensure stationarity had been reached (ESS greater than 100) for all parameters. Trees from these three runs were combined using LOGCOMBINER  22  and the relevant proportion of burn-in was discarded. TREEANNOTATOR  22  was then used to infer a maximum clade credibility (MCC) tree from the posterior distribution of combined trees, keeping target heights. Growth form. We used the ace function in the R package 'ape'  24  to reconstruct the evolution of growth form on nrDNA and cpDNA trees. We then used likelihood ratio tests to compare whether a model with equal transition rates (ER) or an all-rates-different (ARD) model fit better.
We first ran models using members of the Corylus genus only and then conducted a further set of runs that included members of the two closest outgroup genera, Ostrya and Carpinus, to understand how the inclusion of these tree form taxa affected the root state of Corylus. Geographic data. Native geographic ranges were taken from Molnar  6  where available. If no range information was available we examined records from the Global Biodiversity Information Facility (http://www. gbif. org/) and assigned taxa to regions based on occurrences. We checked these data and removed clearly erroneous entries and museum/cultivated individuals. For the differences in range between C. sieboldiana and C. sieboldiana var. mandshurica we used data from Chang et al. 25. In the case of Corylus maxima, we used information from the IUCN red list of threatened species (http://www.