dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental cryo-ET data: nucleosomes in situ We applied our method on a dataset comprising 650 in situ subtomograms of nucleosomes collected from a cell of a Drosophila embryonic brain, whose conformational variability was detected but not fully explored in a previous work  Eltsov et al. (2018). The subtomograms had the size of 64 3 voxels and the voxel size of 4. 4 Å3. A density map obtained with classical subtomogram averaging (without taking into account conformational heterogeneity) was used as the reference density map for HEMNMA-3D (Figure  10(C) ). The resolution of this reference density map is around 2 nm (as determined by Fourier Shell Correlation between the reference density map and the density map from the atomic nucleosome structure PDB:3w98  Iwasaki et al. (2013)  shown in Figure  10(B) ).
The significant difference between the four group averages (Figure  10(D) ) and the reference density map (Figure  10(C )) as well as the motion observed along the two directions D1 and D2 (Figure  11 , supplementary Movie S1 and Movie S2) can be described, mainly in terms of opening the nucleosome by increasing the distance between the two gyres of the DNA superhelix. This result consents the previous findings, observed but not fully explored in a previous study (manual analysis) of the nucleosome conformational variability  Eltsov et al. (2018). The group averages are also compared with the atomic nucleosome structure PDB:3w98 in supplementary material Figure  S2.
DISCUSSION AND CONCLUSIONS This article presents HEMNMA-3D, the first cryo-ET subtomogram data analysis approach to study continuous conformational variability of biomolecular complexes, which maps a set of subtomograms into a space of conformations using a reference model and its normal modes. The conformational space permits i) grouping (and averaging) subtomograms with similar conformations and revealing hidden conformations and ii) recording animated displacements of the reference model along the densest regions of the space, along trajectories identified by curve fitting of the data in these regions. These HEMNMA-3D outputs could be valuable to cryo-ET studies of molecular mechanisms involved in conformational changes of complexes in vitro and in situ. HEMNMA-3D is thoroughly tested using synthetic subtomograms and applied to a cryo-ET experimental dataset (nucleosome subtomograms recorded in situ in Drosophila interphase nucleus).
It provides promising results coherent with previous findings. An open-source software with a graphical user interface is provided for this method with a C++ backend and a Message Passing Interface parallelization scheme. Both HEMNMA-3D and NMA-based flexible fitting are NMA applications concerned with estimating the molecular conformation in density maps based on an atomic or pseudoatomic reference. However, the purpose of HEMNMA-3D is different from that of the classical NMA-based fitting methods. Classical NMA-based fitting methods aim at determining an atomic representation of an EM density map, which is done by flexible fitting of a given atomic structure into that EM map. The purpose of HEMNMA-3D is to get a low-dimensional representation of the heterogeneity of a given set of EM maps such as subtomograms.
g. , systematic alignment errors such as a wrong biased alignment to one or the other conformation and systematic rotational or translational errors). Taking into account the independent analysis of each individual subtomogram, HEMNMA-3D should be able to recover any other conformational distribution, with similar errors to those obtained with the conformational distributions used in this article. The tests with synthetic data using a pseudoatomic structure from a simulated density map as a reference were used to demonstrate the ability of the method to retrieve the ground truth conformations with a comparable accuracy to the accuracy achieved using an atomic reference despite that i) the pseudoatomic reference was not used to synthesize the data (the datasets were synthesized using the atomic reference), ii) pseudoatomic coordinates unlikely coincide with atomic coordinates (pseudoatomic coordinates are obtained through volume-to-pseudoatoms conversion, which does not use any prior information about atoms), and iii) the method for calculating normal modes is different for a pseudoatomic reference (Cartesian method) and an atomic reference (RTB method).
In experimental cases, one could obtain pseudoatoms from a density map of higher resolution than the data itself, if such density map is already available (e. g. , an EMDB map obtained by high-resolution single particle cryo-EM reconstruction) or if it can be simulated from an available atomic structure (as in our synthetic data experiments). However, a preferred choice for the reference density map should be a density map from the data itself, which can be obtained by classical subtomogram averaging (without taking into account conformational heterogeneity), as was the case in the nucleosome experiments shown in this article. The synthetic subtomogram datasets used in this work do not account for crowded molecular environments, radiation dose accumulation during tilting, and differences in CTF defocus over the tilted planes.
Therefore, the conformational landscape will always be an approximation of the actual conformational landscape. Small sets of selected potentially relevant modes have been shown to produce good approximation of the actual conformational landscape of the nucleosome studied here by HEMNMA-3D as well as of other complexes studied by HEMNMA. In some cases, a single normal mode could be enough such as in the case of 70S ribosomes, where the normal mode describing the rotation between the two subunits 30S and 50S was used to analyze conformational and compositional variability of EF-G bound and unbound 70S ribosome cryo-EM dataset  Jin et al. (2014). HEMNMA-3D uses the same software for NMA and the same numerical optimizer (to estimate the amplitudes of normal modes iteratively) as HEMNMA, and it should thus have similar performance as HEMNMA regarding the determination of the conformational landscape using a smaller number of normal modes.
HEMNMA-3D software (ConinuousFlex plugin of Scipion V3. 0) helps the user decide which normal modes to select, based on the lowest-frequency highest-collectivity criterion and including or not a prior knowledge about the conformational transitions, as is the case with HEMNMA software  Harastani et al. (2020). HEMNMA-3D can deal with larger sets of subtomograms than those shown in this article. Each subtomogram is analyzed independantly of other subtomograms, meaning on a separate computing thread. The same computing time per subtomogram is required for larger and smaller datasets of the same molecular complex and the time required to process the entire dataset varies with the size of the dataset.
FUNDING We acknowledge the support of the French National Research Agency-ANR (ANR-20-CE11-0020-01 to A. L. , ANR-20-CE11-0020-02 to M. E. , ANR-20-CE11-0020-03 and ANR-19-CE11-0008-01 to S. J. ); German Research Foundation (DFG EL 861/1 to M. E. ); the French Microscopy Society (2019 Master internship grant to M. H. ); the Sorbonne University (2019 "Interface pour le Vivant" PhD scholarship grant to M. H. ); and the access to the HPC resources of CINES and IDRIS granted by GENCI (2019-A0070710998, AP010712190, AD011012188 to S. J. ). Table  1. Mean absolute error and standard deviation between the estimated and ground-truth normal-mode amplitudes along with the angular and shift distances obtained with HEMNMA-3D and "Discrete" synthetic dataset, using an atomic structure (Atomic) and simulated EM map (Volume) as input references.
The data points below the p-value of 0. 01 were excluded from the error evaluation based on the Mahalanobis distance measure (few data points differing significantly from the remaining observations, which would not be selected in real-case experiments as being too isolated and far from other points). The number of points used for the error computation is shown in the last column of the table (column Samples) and the region with the kept points (p-value > 0. 01) is shown in Figure  6. Table  2. Mean absolute error and standard deviation between the estimated and ground-truth normal-mode amplitudes along with the angular and shift distances obtained with HEMNMA-3D and "Continuous" synthetic dataset, using an atomic structure (Atomic) and simulated EM map (Volume) as input references. The data points below the p-value of 0.
NUCLEOSOME DATA PREPARATION AND ACQUISITION The Drosophila embryo cryo-sample preparation, vitreous sectioning, tilt series acquisition and tomogram reconstruction were performed as described in  Eltsov et al. (2018). Briefly, Drosophila embryos (Bloomington Stock number 30564) were frozen by high-pressure freezing using an HPM 010 machine (ABRA Fluid AG). The vitreous sections were obtained using a 25 • diamond knife (Diatome) and Leica FC6/UC6 ultramicrotome (Leica Microsystems). Sections were collected on C-flat CF-2/1 grids (Electron Microscopy Sciences) and transferred into a Titan Krios (FEI, Thermofischer, Eindhoven, The Netherlands) operated at 300 kV equipped with a GATAN GIF Quantum SE post-column energy filter and K2 Summit direct electron detector (Gatan, Pleasanton, USA). Tilt series were recorded using Serial EM software  (Mastronarde (2005) ) at a nominal magnification of 64000 × (2.
2 Å/pixel), and a target defocus of -3. 5 µm. The dose-symmetric recording scheme  (Hagen et al. (2017) ) was applied within an angular range from 60 • to +60 • , with a starting angle 0 • and an angular increment of 2 •. The electron dose was set to 1. 5 e -/ Å2 for individual tilt images, corresponding to the total dose of 91. 5 e -/ Å2 for the complete tilt series. A marker-less tilt series alignment was done in IMOD  (Kremer et al. (1996) ), three dimensional CTF correction and weighted backprojection with the voxel size of 4. 4 Å were performed using EmSART script  (Kunz and Frangakis (2014, 2017) ) provided by Achilleas Frangakis. A slice of the experimental nucleosome tomographic data is shown in Figure  S1. The reconstructed volumes were denoised using 3D non-linear anisotropic diffusion filter of Etomo of IMOD (k = 1, 15 iterations).
ADDITIONAL SYNTHETIC DATA TESTS WITH DIFFERENT NOISE LEVELS Additional tests were performed on HEMNMA-3D using synthesized datasets at different noise levels of conformationally heterogeneous subtomograms that mimic continuous conformational variability. The noise levels were chosen as A) without noise, B) SNR = 0. Here, the amplitude value of normal mode 7 is chosen randomly in the range {-300, 300} and the amplitude value mode 8 as half of the value of mode 7. More details on the synthesis of the data will be found in the main article manuscript Section 3. 1. The goal in this experiment is to find a solution for the inverse problem of finding the conformation of the structure in each subtomogram using the combined elastic and rigid-body alignment of a reference model with the subtomograms in the different-in-noise datasets.
Figure6. Plots showing the output of the 3D-to-3D elastic and rigid-body alignment module of HEMNMA-3D with "Discrete" dataset (synthetic subtomograms are simulating discrete conformational heterogeneity). (A) Use of the atomic structure (chain A of PDB:4AKE) and its normal modes to estimate the conformational parameters (normal-mode amplitudes) and rigid-body parameters (orientation and shift) of the molecules in the input synthetic subtomograms. (B) Use of a pseudoatomic structure (from a simulated density map) and its normal modes to estimate the conformational and rigid-body parameters of the molecules in the input synthetic subtomograms. The goal was the retrieval of the ground-truth relationship between the amplitudes along normal modes 7 and 8; ideally, all data should lay in one of the following three clusters of normal-mode amplitudes: (mode 7, mode 8) ∈ {(-150, 0), (150, 0), (0, 150)}; each point in the plot represents a subtomogram and close points represent similar conformations.
Figure8. Plots showing the output of the 3D-to-3D elastic and rigid-body alignment module of HEMNMA-3D with "Continuous" dataset (synthetic subtomograms are simulating continuous conformational heterogeneity). (A) Use of the atomic structure (chain A of PDB:4AKE) and its normal modes to estimate the conformational parameters (normal-mode amplitudes) and rigid-body parameters (orientation and shift) of the molecules in the input synthetic subtomograms. (B) Use of a pseudoatomic structure (from a simulated density map) and its normal modes to estimate the conformational and rigid-body parameters of the molecules in the input synthetic subtomograms. The goal was the retrieval of the ground-truth relationship between the amplitudes along normal modes 7 and 8 (ideally linear relationship, with equal amplitudes of normal modes 7 and 8); each point in the plot represents a subtomogram and close points represent similar conformations.
ACKNOWLEDGMENTS MH and SJ thank the  Scipion team (CNB-CSIC, Madrid, Spain)  for the help with porting the HEMNMA-3D code from Scipion  V2. 0  to Scipion V3. 0, and  Ilyes Hamitouche  and  Rémi Vuillemot  ( IMPMC -UMR 7590, Paris, France ) for constructive discussions on HEMNMA-3D development. ME thanks  Diana Grewe  for cryo-sectioning, and  Achilleas Frangakis  ( BMLS, Frankfurt, Germany ) for providing scripts for subtomogram averaging, and the access to computational resources.
DATA AVAILABILITY STATEMENT The original contributions presented in the study are included in the article/supplementary files. HEMNMA-3D software code is publicly available on Github (https://github. com/scipion-em/scipionem-continuousflex) and is also part of the open-source ContinuousFlex plugin of Scipion V3. 0. The nucleosome data used in this article have been deposited in EMPIAR and EMDB databases under the accession codes EMPIAR-10679 and EMD-12699, respectively. Further inquiries can be directed to the corresponding author.
Data acquisition KAGRA is composed of nineteen suspended mirrors and many optical components  [1]. All mirrors and optics are controlled by a digital control system. The data-acquisition system is integrated into this digital control system, and it records more than 100,000 channels. The recorded channels contain not only the main interferometer signals but also signals from physical environmental sensors, many test points in the control loop of the main interferometer, local control signals from all the suspended mirrors, and so on. All data are recorded as discrete time-series signals with various sampling intervals. The total data rate reached 12 MB/s during the O3GK observing run on KAGRA. This data set was obtained at the Kamioka site and was transferred to KAGRA's main data center at Kashiwa.
Some indicators, called "data-quality state vectors", are provided if these criteria are satisfied. This process is performed by the digital control system and the vectors are recorded in bit-string format. The process of data-quality evaluation is also performed offline in order to correct for mistakes and errors in the real-time process. Because the amount of data reaches 1PB/yr for each detector, it's difficult to transfer all data between overseas. From the view point of data storage, there is no enough storage for keeping all KAGRA, LIGO and Virgo's data. Therefore only some important channels are shared with overseas. In order to reduce the amount of data, only the main interferometer signals and the data-quality (DQ) state vector are shared with LIGO and Virgo. In addition, a list of an GPS times when glitch is occurred that define "science segments" in which the data can be used for searching for GW signals is provided.
The DQ state vector was used to the data selection in order to estimate duty factor, the detection range of binary neutron stars and so on. duty factor and the detection range is basically estimated only with the detector data which is in the science mode. On the other hand, these quantities are also computed for the data which is not in the science mode though the interferometer is locked in order to investigate behavior of interferometer in various states. This "segment" information is provided to indicate the noise status in order to evaluate the status of the interferometer.
13/33 To search for GWs reliably, it is important to reject false events from among the GW candidate events. Each GW search pipeline evaluates false alarm probability from the background noise behavior of the GW channel. Other auxiliary channels are not usually used in the GW search pipelines; instead, they are analyzed using "glitch pipelines" and other noise-evaluation methods. Glitch pipelines are tools that detect bursts of excess power, used to identify transient noise. They are applied not only to GW channel but also many auxiliary channels. Detected transient noise in the GW channel and auxiliary channels are evaluated for coincidence and used to remove false events from GW event candidates. Data quality is assessed not only for reliable detection, but also for improving the sensitivity and stability of the detector.
Data-Quality State Vector Interferometer status is evaluated from many auxiliary channels. Because this evaluation result is used in many cases such as the decision of interferometer control strategy, interferometer noise evaluation, various GW searches, etc. , status evaluation is performed as a real-time process in automatically and its results are recorded as a simple indicator such as "OK" or "Not OK". In order to satisfy the various situations, several types of indicators were prepared during O3GK. These indicators are merged as one bit-string named as "Data Quality (DQ) state vector". This DQ state vector helps us to use same criteria for each search pipeline and to reduce CPU costs for re-evaluation. The definition of DQ state vector is shown in Table  1. The most important flag is the science-mode flag, because GW searches are performed only for data indicated to be science-mode data.
Data-Quality Segment Segment information is generated to indicate multiple data periods that are suitable for use in gravitational wave searches. One segment is recorded between the two GPS times when a science mode starts and ends. In addition to the science mode, segment information is provided about overflow periods and various types of noise status. Such information is generated every 15 minutes, based on the data-quality state vector, which is recorded as timeseries data with bit information at a 16 Hz sampling rate. KAGRA's segment information is sent to a database server called "DQSEGDB"  [45]  at the California Institute of Technology (CIT) and is stored together with segment information from LIGO, Virgo and GEO. Some search pipelines use such segment information from multiple detectors to perform coincidence and coherence searches.
Installation protocols for the KAGRA PEM sensors To evaluate the environmental noise, we have installed more than 100 PEM sensors in the KAGRA experimental site (including outside the tunnel). Detailed information about the sensors used for the O3GK observation is summarized in Table  2 , including the sensor type, product name, operating frequency, and number of sensors, and in Fig. 5  with a location map. Signals from the fast sensors (seismometers, accelerometers, microphones, magnetometer, and voltmeter) are acquired by the KAGRA digital system together with the interferometer signals and suspension signals. The slow sensors (thermohygrometers and weather station) have their own data loggers, and the signals are also merged into the KAGRA data through the EPICS 1 system. The PEM sensors are installed for the following protocols in KAGRA: 4.
The segment information which was common format with LIGO and Virgo was also provided and stored DQSEGDB in CIT. Science segment will be used for offline GW searches by using O3GK data. Providing more detailed segments which indicate an existence of loud glitches, is planned O4 observation. The goal of segment production in O4 observation is used not only the data selection for GW searches but also the removal of fake events of GWs. Noise transient investigation by Omicron and hveto is now being performed as the offline noise 30/33 investigation. In the O4 observation, transient noise survey will be performed as both lowlatency and offline analyses. Results of low-latency noise investigation will be used for the veto analysis with GW search results by low-latency pipelines, improvement of contents of SummaryPage, and so on.
One of the most urgent tasks toward O4 observation for KAGRA is to reduce the cadence of noise investigation. The data-acquisition system is integrated with the KAGRA digital control system, and more than 100,000 auxiliary channels were recorded with GW signals. To evaluate the detector health and noise status, a data-quality state vector was prepared and used to identify appropriate science segment. The KAGRA science segments were shared with other international interferometers via DQSEGDB in a data server at CIT. SummaryPages were also prepared to help identify the reason why data were flagged as"bad" (i. e. , unsuitable for GW searches). To investigate and veto transient signals in the gravitational wave channel and auxiliary channels, we implemented the hveto analysis technique. This technique is used in un-modeled GW searches (burst searches).
Acknowledgment This work was supported by  MEXT ,  JSPS Leading-edge Research Infrastructure Program ,  JSPS   Grant-in-Aid for Specially Promoted Research 26000005, JSPS Grant-in-Aid for Scientific Research on Innovative Areas 2905 :  JP17H06358 ,  JP17H06361  and  JP17H06364 ,  JSPS Core-to-Core Program A. Advanced Research Networks, JSPS   Grant-in-Aid for Scientific Research (S)   17H06133 , the joint research program of the  Institute for Cosmic Ray Research, University of Tokyo, National Research Foundation (NRF)  and  Computing Infrastructure Project  of  KISTI-GSDC in Korea, Academia Sinica (AS), AS Grid Center (ASGC)  and the  Ministry of Science and Technology (MoST) in Taiwan  under grants including  AS-CDA-105-M06 , the  LIGO  project, and the  Virgo  project. GIF was also supported by  JSPS   KAKENHI 31/33  Grant Number  JP17H06207 , the  Joint Research Program of the Institute for Cosmic Ray Research (ICRR) ,  University of Tokyo  ( 2019-F19 ), and the  Joint Usage/Research Center program of the Earthquake Research Institute (ERI) ,  University of Tokyo  ( 2019-B-03 ).
Methods: We analyzed the PROSPECT-5 and Invertible Forest Reflectance Model (INFORM) biophysical parameters as retrieved from canopy reflectance data of different forest plots (using Sentinel-2 images for the years 2017, 2018 and 2019) as optical trait indicators (OTIs). We successively tested the Spectral Variation Hypothesis (SVH) for each retrieved OTI using the Rao's Q as heterogeneity index, validating them against Shannon's H values calculated as a tree species diversity index derived from in-situ collected data.
Asner and Martin (2009)  tested how trait variability and spectral variability measured from airborne imaging spectroscopy changed with taxonomic diversity in tropical forest. Durán et al. (2019)  made use of different methodologies combining foliar traits and hyperspectral data to assessed remotely sensed functional diversity in tropical forest across an elevation gradient. Schweiger et al. (2018)  showed that the spectral diversity at the leaf level (derived from leaf spectroscopy) is correlated to functional diversity. They furthermore made use of a metric of spectral diversity that "describes the extent and filling pattern of the spectral space occupied by a plant community and thus its functional complexity"  (Schweiger et al. , 2018). To our best knowledge, very few recent studies explored this type of relationship with multispectral images  (Rossi et al.