dataesr/scientific-paragraphs-categorization huggingface dataset:
Conceptualization and methodology of the experiments: L. A. ; experiments investigation and tools contribution: M. M. C. and A. G. ; data analysis: L. A. , A. G. , M. M. C. and S. B. ; field collections preparation: R. C. , P. G. and A. G. ; field collections dissection: A. G. , J. I. and H. N. ; writing-original draft preparation: L. A. , S. B. , M. M. C. and A. G. ; funding acquisition: L. A. , J. B. D. , I. D. and A. L. All authors have read and agreed to the published version of the manuscript. Funding: This work has been supported by the Délégation Générale pour l'Armement (DGA, MSPro-fileR project, Grant no PDH-2-NBC 2-B-2201) and by Regional Health Agency of French Guiana (Conventions n° FIR/ARS/2014/91, n°FIR/ARS/2015/68, and Convention ARS/IPG 2021-2023 -Nov 26th 2021). This work was also partially supported by the European Union FEDER controle Programme under Grant number GY0010695.
(Mel. ) pedroi Mac. 2018, 2019 15 FGMOS2700-20 (12); FGMOS2758-20 (3) 100% / 100% 4 / 2 Cx. (Mel. ) phlogistus Mac. 2019 1 FGMOS1542-20 (1) 100% / 99% 1 / 1 Cx. (Mel. ) portesi Mac. 2018 28 FGMOS2416-20 (28) 100% / 100% 4 / 1 Cx. (Cux. ) quinquefasciatus Cay. 2019, 2020 34 FGMOS2275-20 (34) 100% / 100% 4 / 4 Cx. (Mel. ) rabanicolus Mac. 2018 5 FGMOS2744-20 (5) 100% / 100% 2 / 2 Cx. (Mel. ) spissipes Mac. 2018, 2019 9 FGMOS2701-20 (9) 100% / 97-99% 3 / 2 Cx. (Cux. ) usquatus Mac. , Rem. 2018, 2019 22 FGMOS046-16 (9); FGMOS2284-20 (12) FGMOS049-16 (1); 100% / 99-100% 4 / 4 #BOLD: Barcode of Life Data Systems; COI: cytochrome oxidase subunit 1; §Number of specimens used to create the reference MS database per body part; Cx. : Culex; Cux. : Culex; Mel. : Melanoconion; Mac. : Macouria; Cay. : Cayenne; Rem.
Today, land and ocean observations are crucial for monitoring climate change. The method of GNSS reflectometry is an opportunistic way to provide low-cost observations of many geophysical parameters. However, although this method has been the subject of numerous research studies, work is still in progress to improve its possibilities and fields of application. This paper focuses on GNSS reflectometry using carrier phase measurements for water altimetry measurements. The difficulties in implementing such a method lie in the need to collect a coherent signal and to solve the integer ambiguity value. In this context, the implementation of innovative signal processing is described, including the correlation of the reflected signals in dedicated software and the prolongation of the coherent integration time to enhance signal coherency.
However, this method works only for a reflected signal delay lower than one chip length, i. e. , 293 m for C/A code but only 29. 3 m for L5 signals. In addition, since the method is based on interferences, only a remnant of the reflected signal is used, and the received signal power is then necessarily limited. The second one uses a classical direct antenna along with an antenna pointing to the nadir to receive reflected signals, then the phase shift between direct and reflected signal is measured. This approach is known as carrier phase measurement reflectometry. It is known that a right-handed circular polarization (RHCP) GNSS electromagnetic signal becomes left-handed polarized (LHCP) once reflected, but the amplitude of the signal is drastically reduced. In order to maximize measurement efficiency, work has been carried out to develop a specific maximize measurement efficiency, work has been carried out to develop a specific processing of the reflected signal collected by a high-quality dedicated LHCP antenna with cross-polar isolation and multiple frequencies.
One important feature of this method is to have full control of the processing of the raw reflected GNSS signal sample, as described by Lestarquit in  [7] , using a dedicated software receiver. We are looking to see whether this implementation of carrier phase measurement reflectometry can improve accuracy and robustness for altimetry measurements. This paper describes how this implementation of the method was performed in the air over the Carcans-Hourtin Lake in France to study its performance. Airborne reflectometry gave us the opportunity to collect data from more satellites since there is no mask to interfere with the propagation of the signals. Moreover, it allowed us to explore different measurement altitudes that can introduce geodesic effects in the computation, as well as different states of the reflection surface, such as calm or rough water.
Study Site: Lake of Carcans-Hourtin (France) In September 2021, the National Centre for Space Studies (CNES) conducted a major airborne measurement campaign on the French Atlantic coast with the support of SAFIRE, the French instrumented aircraft service for environmental research. Multiple study sites were flown over with a Piper PA-23 in order to cover a large variety of water reflection surfaces subject to phenomena such as swell or tides. Flights at 300, 500, 1000 and 2000 ft (~91, 152, 305 and 610 m) were performed to highlight geodetic effects such as the Earth's curvature or the geoid slope. Initial antenna heights are expressed in feet for aeronautical convention. The data presented in this paper were collected over the Carcans-Hourtin site, a lake 18 km long and 5 km wide located in the Gironde department, northwest of the city of Bordeaux (Figure  1 ).
Innovative Data Processing Once digitized, RF signals are processed through three main steps. The first one is performed using a GNSS software receiver and consists of standard processing for the direct signal and open-loop correlation for the reflected one. The second step aims to filter the output of the software receiver and to extend the coherent integration time in order to increase the coherency of the signal. Finally, the last step is to estimate the heights of the reflection surfaces using a least-squares estimate after fixing the integer ambiguity values.
First Step: Dedicated Reflectometry Software In order to process the raw reflected signal from GNSS satellites, the CNES developed a dedicated software receiver that includes a specific processing block for reflected signals. The dedicated receiver is a modified version of GNSS-SDR v0. 0. 15, an open-source GNSS software receiver developed by the Technological Center of Telecommunications of Catalunya (Spain) based on the signal processing development toolkit GNU Radio  [9]. The initial structure of the software implements a classical direct signal processing block with parallel channels for each satellite. Each channel comprises an acquisition and a tracking block as well as a telemetry decoder block to read the navigation message. The tracking loops generate three code replicas named Early (E), Prompt (P) and Late (L), respectively, and two carrier replicas named In-phase (I) and Quadrature (Q), respectively, which are multiplied by the incoming signal to form six parallel correlation channels.
Second Step: Prolongation of the Coherent Integration Considering the simplest model with a plane reflection surface and satellite at an infinite distance as shown in Figure  4 , the elongation ρ AB of the reflected signal collected by the reflectometry antenna B with respect to the direct signal received by the direct antenna A is defined as follows  [7] : ρ AB = 2h• sin( i ) ( 1 ) where h is the height between the aircraft and the reflection surface and i is the elevation of satellite i. In classic GNSS processing, the coherent integration time (CIT) for the C/A code is limited to the data message bit length (20 ms) due to sign changes that can prevent signal coherency. Thus, to improve the performance of carrier-phase altimetry and its range of use, the CIT can be increased. To do so, navigation data must be corrected for sign changes using an operation of data wipe-off expressed, for each correlator output i, as: D Ii (t k ) = sign[D IP (t k )] × D Ii (t k ) (2a) D Qi (t k ) = sign[D IP (t k )] × D Qi (t k ) (2b) R Ii (t k ) = sign[D IP (t k )] × R Ii (t k ) (2c) R Qi (t k ) = sign[D IP (t k )] × R Qi (t k ) (2d) where sign[.
Lever Arm Correction In our calculation process, data from both antennas are compared. One problem is that the reflected and direct antennas are not positioned in the same place on the aircraft fuselage. So, in order to correct the lever arm, the positions of both antenna reference points were accurately measured with a laser tracker in the reference frame of the inertial unit. In the same reference frame, the phase centers of the antennas are considered to be constantly in the up direction. As the INS also provides parameters known as the attitude For the direct signal, we extend the coherent integration by applying the apodization window to each correlator output. The complex output of the ith correlator after extending the coherent integration over M samples centered on the apodization window W M (u) around time t k is defined as:          D Mi (t k ) = ∑ M-1 2 u=-M-1 2 W M (u)•D i (t k+u ) i f M odd D Mi (t k ) = ∑ M 2 u=-M 2 +1 W M (u)•D i (t k+u ) i f M even (5) The resulting signal can again be broken down into a real and an imaginary part as follows: D Mi (t k ) = D I,Mi (t k ) + j•D Q,Mi (t k ) ( 6 ) The processing is the same for the reflected signal and the resulting signal can also be decomposed into a real and an imaginary part, thus giving: R Mi (t k ) = R I,Mi (t k ) + j•R Q,Mi (t k ) (7) Subsequently, decimation is applied to the data obtained from the previous equations to reduce calculation time while maintaining good accuracy of water level estimates.
Lever Arm Correction In our calculation process, data from both antennas are compared. One problem is that the reflected and direct antennas are not positioned in the same place on the aircraft fuselage. So, in order to correct the lever arm, the positions of both antenna reference points were accurately measured with a laser tracker in the reference frame of the inertial unit. In the same reference frame, the phase centers of the antennas are considered to be constantly in the up direction. As the INS also provides parameters known as the attitude of the aircraft, which are pitch, roll and yaw measured in radians, it is possible to calculate a correction value for a given satellite of known elevation and azimuth.
Chemical shift tolerances were set to 0. 045 for protons and 0. 4 ppm for the bound heteroatoms. Phi and psi dihedral angles were predicted with TALOS-N  (Shen and Bax, 2013) , and predictions classified as "strong" or "good" were incorporated as dihedral angle restraints. The structure ensemble was visualized and inspected with PyMOL (The PyMOL Molecular Graphics System, Version 2. 0 Schrödinger, LLC), their quality was evaluated with PROCHECK-NMR  (Laskowski et al. , 1996)  and the PSVS server  (Bhattacharya et al. , 2007). The atomic coordinates of PulMCTD dimer and restraints used in the calculation were deposited in the Protein Data Bank (PDB ID: 7ZE0).
Data deposition Atomic coordinates and structure factors have been deposited in the RCSB Protein Data Bank under the accession codes 8A9W (PulLCTD), 7ZE0 (PulMCTD, NMR), 8A9X (PulMCTD, X-ray) and 8AB1 (PulLCTD-PulMCTD). PulM 13 C/ 15 N/ 1 H resonance assignments were deposited to the BMRB under accession number 34719.
Acknowledgements: This work was funded by the  French Agence Nationale de la Recherche  ( ANR   Synergy - T2SS ANR-19-CE11-0020-01 ) and the  Fondation pour la Recherche Médicale  (Equipe FRM  2017M. DEQ20170839114 ). YL was funded by the  Pasteur Paris University (PPU ) international PhD program and the  China National Biotec Group Company Limited , and by a  doctoral fellowship  from the  China Scholarship Council. We thank  Ingrid Guilvout  and  Maylis Lejeune  for their constant help. We acknowledge  Iñaki Guijarro ,  Rémy Le Meur ,  Bertrand Raynal ,  Sébastien Brûlé  and  Christophe Thomas  of  C2RT  for their help and assistance. The 800-MHz NMR spectrometer and the optima AUC of the Institut Pasteur were partially funded by the  Région Ile de France  (SESAME 2014 NMRCHR grant no  4014526 ) and DIM one health, respectively.
This study showing induction of the SWEET-type sugar transporter in AM symbiosis opens a number of questions regarding regulation under natural conditions with a multitude of interactions. Quantification of root colonization by AM fungi is often needed for such studies and so far commonly used by manual techniques. An innovative method is proposed by Muta et al. presenting in a methodological article a software-based implementation called TAIM (Tool for Analyzing root images to calculate the Infection rate of arbuscular Mycorrhizal fungi). A similar approach was recently developed as a standalone application by  Evangelisti et al. (2021)  called "AMFinder" for plant root analyses using deep learningbased image processing. The novel TAIM method, described here in detail, is easily accessible from an Web-based online repository, and has the potential to revolutionize the routine in many laboratories by changing a critical activity from tedious and error-prone to rapid and reliable.
Particle tracking for future experiments at colliders is an incredible challenge both in terms of sensor technology and readout systems. Simulation tools represent a crucial ingredient to investigate new layouts able to cope with harsh radiation conditions and, at the same time, provide valuable timing information for track finding algorithms. D. Brundu et al. present two software packages developed within the TimeSPOT collaboration, to simulate detailed and realistic energy deposits as well as sensor and front-end electronics responses in semiconductor 3D sensor devices. The induced current is assessed at the sensor electrodes due to the motion of charge carriers created by an initial energy deposit via the Ramo's theorem. The developed software packages allow for a significant speed-up in simulation time without losing the results accuracy.
Therefore, there is a need for a robust and versatile tool that can track individual neurons in entire deforming bodies, accurately estimate the neuronal activity from calcium fluorescence, and could also be applied to more simple cases where animals are fixed or minimally moving. Here, we developed TraSE-IN, an end-to-end pipeline (Figure  1 ) that allows the automatic tracking and signal extraction of individual neurons in whole behaving Hydra. This novel pipeline entails both hardware and software improvements over existing tools to create a versatile, robust, and accurate way to automatically detect and track individual neurons in a highly deformable organism while behaving. These improvements include the generation of a two-color transgenic Hydra and imaging method that allows the simultaneous recording of both nuclei and neural activity, and the development of an open-source Python platform (https://github.
com/raphaelreme/trase-in). This platform consists of a tracking module to monitor the calcium activity of all neurons in the behaving animal, and a series of signal processing modules for the robust extraction of individual spikes from calcium traces, paving the way for the analysis of the functional correlates underpinning animal behavior. Altogether, hardware and software developed in the pipeline will significantly enhance our ability to correlate entire organism neural activity with behavior under different conditions, analyze large data sets over longer time scales, and bring us closer towards the goal of cracking the neural code of an entire animal. (III) Each segmented nucleus is then tracked over each frame in the movie by first using probabilistic temporal linking to form short tracklets, which are then stitched together using a cost-based method to determine the final full-length tracks.
The second step of SPT algorithms is the robust linking of extracted positions into coherent tracks representing the trajectories of individual particles. The implemented tracking pipeline can be divided into three main steps: (A) the automatic detection of nuclei spots in tdTomato sequences using analytical methods such as wavelet transform and thresholding or deep learning methods such as StarDist detection, followed by (B) the robust linking of segmented nuclei in coherent tracklets with a state-of-the-art SPT method. Here, the probabilistic method enhanced Multiple-Hypothesis-Tracking (eMHT) method is called from the Icy software. (C) Finally, tracklets corresponding to the same neuron are stitched using a cost-based method. The cost here corresponds to the minimal distance between the forward-and backward-propagated positions of undetected nuclei.
The optimal association between tracklets that minimizes the global cost of association is a linear association problem that we solved with the Jonker-Volgenant (JV) algorithm. The first module of the TraSE-IN platform consists of a tracking pipeline, that we call ByoTrack (https://github. com/raphaelreme/byotrack) (Figure  3 ), which is a series of functions enabling the automatic detection of neurons' nuclei and their tracking along the time-lapse sequence.
Among DL approaches, StarDist is particularly well suited for segmenting and separating roundish objects such as cell nuclei as it uses a star-convex representation of objects' contours and a non-maximum-suppression (NMS) post-processing for separating touching cells  40. However, DL approaches usually require manual annotation of data for model training. In the first part of ByoTrack, we implemented different algorithms for neurons' segmentation and detection (Figure  3A ). In particular, we used an algorithm that executes the DL method StarDist, the training of the model being performed using the open-source Python code of StarDist  40. We also implemented an analytical method based on undecimated wavelet transform and statistical thresholding of the image  41. The wavelet thresholding approach has demonstrated a good performance on images with medium to low SNR  42.
Using tdTomato images extracted from two-color time-lapse sequences we trained a StarDist model and compared its performance with the analytical wavelet method (Figure  4A ). As manual annotation of cell nuclei can be tedious and time-consuming, we trained a StarDist model for an increasing number of tdTomato images (from 2 to 15, corresponding to a maximum of 9422 neurons' nuclei, see Methods) and measured how model accuracy increased with the size of the training dataset (Figure  4B ). In this manner, we could determine how to optimally balance model accuracy and training. Wavelet thresholding is specifically designed for the detection and localization of fluorescent spots, rather than for their accurate segmentation. Thus, the comparison metrics (f1-score) between detected spots was computed using a distance threshold d between the center-of-mass of detected objects, rather than the commonly used intersection-over-union (IOU) between segmented spots.
To measure the accuracy of our tracking pipeline on an experimental dataset, we tracked neurons' nuclei using ByoTrack in 𝑁 = 3 different animals (Figure  5 ). The rhythmic longitudinal contractions of Hydra can drastically hinder the robust tracking of individual neurons. Indeed, the speed of contraction motion can surpass the acquisition rate of the movie (10 Hz typically), blurring the images and making the detection and tracking of nuclei spots difficult. Thus, for each animal, we determined two time-windows of 𝑇 = 1,000 frames where the animal contracts or not. For each of these six extracted movies (2 movies per animal, movies will be uploaded to Dryad), two independent operators chose randomly 𝑛 = 15 individual neurons throughout the Hydra body (5 neurons in the animal's peduncle (foot), central body, and hypostome (head)) and measured, for each neuron, the percentage of frames where it is correctly tracked.
This algorithm, implemented as a module on the TraSE-IN platform, provides reliable single neuron fluorescence data that can be further processed for spike extraction. A second major hurdle to monitoring neuronal activity is the extraction of spikes from calcium fluorescence. Indeed, the fluorescent calcium traces are usually corrupted by background signal and noise, and the dynamics of conformational change of calcium indicators is much slower than actual voltage variations. For all these reasons, elaborate deconvolution methods have been developed over the years to robustly extract individual spikes  [26] [27] [28] 47. These algorithms show good performance on synthetic and experimental datasets but require an input fluorescent signal with good signal-to-noise ratio and no, or moderate, fluctuations of fluorescence baseline.
Calcium Signal and Spike-Time Extraction After filtering and preprocessing the extracted fluorescence signals, we utilized the FOOPSI spike extraction method (Figure  7F ) implemented in the CaImAn Python package  30. This package is intended for use with mouse two photon calcium imaging data. However, since our data has low background noise and uses the same indicator used in mouse studies, we found this method was reliably able to extract spikes from our processed data with only minor tuning. FOOPSI fits an autoregressive model to the provided signal and, using an underlying model of the expected calcium signal dynamics from GCaMP indicators, can predict the underlying fluorescence of GCaMP7s while ignoring the accompanying noise. In addition to extracting fluorescence, the FOOPSI algorithm provides spike times predicting the underlying electrical activity of the neuron from its calcium activity.
Imaging Dual-labeled transgenic Hydra were prepared for imaging as described  16. Imaging was performed using a custom dual-channel spinning disc confocal microscope (Solamere Yokogawa CSU-X1) with a sCMOS camera for each channel (Teledyne-Photometrics Prime-BSI). Samples were simultaneously illuminated with both 488 nm and 561 nm lasers (Coherent OBIS). Emission light was split with a dichroic mirror sending green light to one camera and red light to the other. Cameras were aligned with pinholes and images were registered during processing. Images were captured with a frame rate of 10 frames per second using a 6X objective (Navitar HR Plan Apo 6X/0. 3) and Micro-Manager software.
Segmentation of neuronal nuclei To learn a segmentation model and validate our experiments, we sampled 20 tdTomato images of Hydra on various positions of the animal (e. g. , contracted, elongated). We manually annotated these images using the ImageJ draw tool resulting in 12,705 segmented neurons (~600/image). To measure the segmentation performance, 5 random images were kept as a testing dataset. To account for performance variability, we repeated this process 5 times with different seeds, resulting in different training and testing images. We measured the performance with each seed and report the results as (mean ± std). We used standard instance segmentation metrics (precision, recall, f1-score). But we did not base these metrics on IOU association, where a predicted instance is considered to be a true positive if it overlaps with a ground truth on a sufficiently large area.
We rather based these metrics on distance association, where a predicted instance (spot) is considered valid if its mass-center is sufficiently close to the masscenter of a ground truth instance. This has two benefits: first, it is more meaningful for tracking as we only use the position of each detected instance. Second, it allows fair comparison with detection methods that do not try to produce a faithful segmentation or bounding box of the instances like the wavelet thresholding method. The remaining 15 images were used to train and tune all the parameters of the segmentation model. We also measured the impact of the training data quantity by training the model with fewer images, from 1 to 15. We compared two detection methods: an analytical method based on wavelet decomposition and thresholding, and a deep learning approach, StarDist.