dataesr/scientific-paragraphs-categorization huggingface dataset:
Although, recently reported methods of post-imaging data processing demonstrate resolutions at the sub-nanometer scale  [11] , observations are still confined to the conformational dynamics of the scanned surface and the interpretation of images remains generally challenging. On the other side, high-resolution molecular structures of proteins are known  [12]  and conformational dynamics can be obtained from multi-scale molecular modelling  [13] [14] [15]. The enormous amount of available high-resolution protein data offers the great opportunity to better understand resolution-limited AFM scanning experiments. As an important step in that direction, we have recently developed the BioAFMviewer software platform which provides an interactive interface for simulated AFM scanning of biomolecular structures and conformational dynamics  [16].
The simulated scanning method computationally emulates scanning of the molecular structure to produce pseudo AFM images. It has been employed in several studies to interpret experimental AFM images of proteins  [8, [17] [18] [19] [20] [21] [22] , and in molecular simulations of flexible fitting biomolecular structures to experimental images  [23]  or to deduce information on the AFM tip shape  [24]. The strengths of the stand-alone BioAFMviewer software are its user-friendly versatile interface and rich functionality. The visualization of molecular structures and simulated scanning to display corresponding AFM graphics proceeds in a synchronized way, even for very large protein structures. Scanning parameters such as tip-shape and spatial resolution, as well as the displayed range of topography heights, can be conveniently adjusted.
In that regard, our independent work distinguishes itself by the convenient implementation into the user-friendly BioAFMviewer software interface (see Discussion). Apart from that, our approach to fitting was aimed to go beyond rather inefficient unbiased search, providing faster implementations described next. Local search-Quick fit function. While the global search function samples molecular structures along a grid of globally available orientations, we also aimed to provide a more sophisticated fitting method employing a refined search strategy. The Quick Fit function performs a local search of rigid-body orientations confined to the neighborhood of a given initial molecular orientation, and identifies a single molecular structure which best fits the target AFM image. We applied a computationally efficient method which resembles a simplified Metropolis algorithm of optimization (see S1 Text).
Topography analysis tools Image comparison methods provide a quantitative assessment of the overall similarity between simulated and experimental AFM images. Beyond that, it is also desirable to have tools available which allow a comparison on a more detailed level, e. g. , those analyzing the height topography of the scanned biomolecular surfaces. For the analysis of biological images convenient software plugins within the ImageJ platform are routinely employed  [25]. We have implemented the Line tool, Cross tool, and Peak tool, which all enable the user to conveniently select specific regions for which topography analysis shall be conducted within simulated and experimental AFM images.
The existence of this allosteric communication provides a possible structural rationale for the relationship between ligand binding, transmembrane voltage modulation, and channel activation observed in functional assays of SthK  [30]  and other eukaryotic homologue channels  [31, 32]. This application demonstrates well the purpose of the BioAFMviewer automatized fitting function. Employing available structural templates allows to obtain 3D atomistic conformations from AFM surface scans. Thus, within the limitations of this approach (see Discussion section), a molecular-level understanding of functional mechanisms beyond the information provided by the original topographic AFM images becomes available. In Fig  3  further applications are provided, including fitting of the actin filament all-atom structure to an experimental HS-AFM image, and those of the ClpB chaperone and F1-ATPase motor which we have previously considered in our first report of the software  [16].
BioAFMviewer 2 and online video manuals Since the BioAFMviewer software is already used by many AFM groups worldwide, and valuable response helping to improve its functionality has been received, we use this opportunity to mention some of the important improvements we implemented in the BioAFMviewer 2 version. The loading of large PDB files, displaying the molecular structures, and, computing the corresponding simulated AFM images, has been highly accelerated to enable a more efficient handling of large protein structures. The integrated molecular viewer now also provides the option to view the loaded structure in the Backbone View, in addition to the standard Vander-Waals (VdW) view. This representation helps in the recognition of secondary structure motifs when biomolecules are compared and fitted to AFM images.
Discussion We report simulation AFM as a computational framework to obtain 3D atomistic information from resolution-limited topographic AFM images by applying automatized rigid-body fitting of biomolecular structures, and provide the convenient implementation of developed methods into the BioAFMviewer software platform. Applications ranging from single molecular machines, protein filaments, to large-scale assemblies of protein lattices, demonstrate how the obtained full atomistic information advances the molecular understanding beyond the original AFM topography image. We have shown that the molecular arrangement of functional domains in multimeric proteins and even their nucleotide-state can be disambiguated from AFM surface scans. In particular, applications to AFM images of 2D lattices for the cyclic nucleotide-gated SthK channel stand out to exemplify the explanatory power of atomic reconstruction.
The rigid-body fitting to AFM images was recently presented in another work  [19] , where also the tip-shape geometry was parameterised to obtain best agreement to an experimental image (available as a script file afmize without a graphical interface). While our Global Search method is similar, the fitting approach presented here was aimed to go beyond inefficient exhaustive unbiased search. The interactive BioAFMviewer interface allowed us to implement the Quick Fit function for highly efficient fitting to AFM images, which can also consider the tip-shape dependence on fitting. As a further distinction, the BioAFMviewer interface allows applications beyond static PDB files. Simulation AFM can be conducted for entire molecular movies, e. g. , obtained from modeling simulations  [16]. This opens the opportunity to employ data of functional conformational dynamics for fitting to experimental AFM images  [22].
Its user-friendly environment and rich functionality establishes the BioAFMviewer software package as a convenient platform for the broad Bio-AFM community to employ the enormous amount of existing structural and modelling data to facilitate the interpretation of resolutionlimited AFM images. Application of simulation AFM can reach much beyond just the interpretation of what is observed in an AFM image. We have previously shown that new structural models with atomistic resolution can be retrieved for higher oligomeric arrangements of the Annexin V protein seen under HS-AFM  [20]. Simulation AFM was recently applied in a demonstration study of a membrane transporter protein, showing that based on topographic imaging of only one membrane side, the functionally coupled conformational state at the remote opposite site can be predicted  [22].
g. ,  Rankin et al. 2017, and references therein) , and the field could significantly benefit from a large comprehensive analysis derived from high signal-to-noise ratio (S/N) observations. Additional areas of pulsar astronomy that would benefit from such a dataset include probing the Galactic magnetic field with rotation measure (RM) measurements (e. g. ,  Han et al. 2018; Sobey et al. 2019) , improving Galactic electron density models with new independent distance measurements (see, e. g. ,  Matthews et al. 2016; Yao, Manchester, & Wang 2017) , and constraining models of the NS equation of state through MSP mass measurements  (Özel & Freire 2016). Estimates of the total MSP  (Levin et al. 2013 ) and binary NS-NS populations (e. g. ,  Burgay et al. 2003 ) rely upon our understanding of the luminosity function, which in turn depends upon measured MSP distances, flux densities and their pulse shapes.
This list is updated regularly, to add new pulsars found to have high timing precision and to remove those found not to contribute significantly to the PTA. There are currently 89 that are in the MPTA, and these, excluding one which was included in this analysis due to issues with the ephemeris, are denoted by an asterisk in Table  1  below. In total, we analysed 3 966 observations g of 189 MeerTime MSPs from 2019 February to 2021 February. This number includes observations performed for the MeerTime Relativistic Binaries sub-project  (Kramer et al. 2021) , with which there is considerable overlap of sources; data are shared between MeerTime sub-projects to maximise the science output of the entire Large Survey Project. The processing of all observations for this project used PSRCHIVE  (Hotan, van Straten, & Manchester 2004; van Straten, Demorest, & Oslowski 2012) , in a pipeline developed by  Parthasarathy et al.
(2021)  which includes RFI excision with a modified version of the COASTGUARD software  (Lazarus et al. 2020 ) that is optimised for the RFI present at MeerKAT (see, e. g. ,  Bailes et al. 2020). Observations taken prior to mid-April 2020 were polarisation-calibrated with the PSRCHIVE tool PAC using post-facto derived calibration solutions provided by the South African Radio Astronomy Observatory (SARAO), whereas later observations are polarisation-calibrated at the telescope prior to the data being received by the PTUSE computers (see  Serylak et al. 2021 , for a full description of both calibration methods). All polarisation values and derivatives follow standard pulsar conventions h as described in  van Straten et al. (2010).
Software was written to cross-correlate the template with each profile, and the rms residual of the phase-aligned profiles in the baseline region was evaluated. There are sections of the MeerKAT band that are rarely affected by RFI (see  Bailes et al. 2020) , and these were used to determine the flux density in those bands. We found that many of the high-DM MSPs that would be expected to have consistent epoch to epoch flux densities often exhibited dips in their mean spectra where RFI is regularly present in the fourth of the 8 sub-bands. As an example, for PSR J1017 -7156, the off-pulse rms was roughly 5-21% higher in this band with a mean 10% higher after our attempts at RFI removal with COASTGUARD. Scaling the flux densities of all channels by the ratio of their off-pulse rms counts to that of the median off-pulse rms counts led to mean spectra largely free of this dip in channel 4 and was performed on all the data.
Timing results For all pulsars in this study, we list basic parameters in Table  1 , and provide our measured median ToA uncertainties. The templates used for our standard timing procedure are produced from summed, temporally-integrated observations with 232 frequency channels across the 775. 75-MHz band. From these high-S/N observations, we then derive pulse 'portraits' using the PULSEPORTRAITURE software  (Pennucci 2019 ) and produce noise-free templates with 8 sub-bands for timing. p We use the 'Fourier Domain with Markov chain Monte Carlo' (FDM) fitting algorithm from PAT in PSRCHIVE to produce ToAs from archives partially-integrated to 256-s sub-integrations and 8 subbands across the 775. 75-MHz band (∼97-MHz sub-bands). We compute 'typical' ToA uncertainties per pulsar by first normalising the measured uncertainties to the nominal minimum observing time, 256 s, then calculating the error on the weighted mean ToA for each sub-integration as 1 σ 2 sub = i 1 σ 2 sub,i , ( 4 ) where i represents the frequency channels, and finally returning the median of the resulting σ sub values as the typical ToA uncertainty.
Timing forecast In assembling a large dataset of MSP data across a minimum timespan of six months, we are able to determine the timing potential for each source and inform future studies. The potential for MeerTime to contribute to global efforts to detect GWs p True wideband timing with the Pennucci (2019) method is left to future work. with the IPTA can be estimated from these data. As mentioned in Section 2, we continue to regularly observe pulsars that have low ToA uncertainties, σ τ < 1 µs, calculated using frequencyscrunched templates q and observations with integration times, 256 < T obs < 2000 s. To compare the sensitivity of PTA observations with MeerKAT to other current programmes, and its potential impact on global efforts, we consider the detection of a GW background (assuming a strain spectrum of h c (f ) = 1.
For sources in multiple PTAs, the data set with the longest time span is used. We show how the S/N for this background increases with time in Figure  6. For the NANOGrav sensitivity curves, we have assumed that the Arecibo timing programme ceased in 2020 August and that the pulsars are not observed elsewhere, which demonstrates the importance of Arecibo to international pulsar timing efforts. The modelling includes red noise in the sensitivity estimates, for pulsars where red noise has previously been detected  (Arzoumanian et al. 2020; Goncharov et al. 2021). Undetected red noise is likely to be present in some of the pulsars timed by MeerKAT, which will affect our sensitivity estimates. These calculations imply that the MPTA will be comparable in sensitivity to the PPTA by 2023, NANOGrav by 2024, and the EPTA by ≈ 2025.
In less than four years, the MPTA will be contributing to the IPTA effort. As the MeerKAT timing array programme has observed a large number of pulsars from its start, the sensitivity to the cross-correlated signal (dashed lines) is always comparable to the auto-correlated signal, similar to the EPTA with its 42 pulsars. In contrast, the PPTA project includes only 26 pulsars, and NANOGrav started out with a smaller number of pulsars before gradually increasing in size starting in 2009, and, in the most recent data release (12. 5-yr), they listed 47 pulsars  (Alam et al. 2021b ). The MPTA could be expanded in the future with timing of additional pulsars in other bands or timing of MSPs discovered by MeerKAT and other facilities. Future work by Middleton et al. (in preparation) will examine the sensitivity of the MPTA and potential improvements.
Summary In this work, we demonstrated the outstanding potential of MeerKAT for precision pulsar timing of MSPs with this initial census, analysing polarisation properties, flux densities and spectral indices, and timing precision. We presented a dataset of nearly 4 000 observations of 189 pulsars, spanning 24 months, including polarisation-calibrated profiles and high-precision ToAs. Our timing simulations offer simple predictions of future results, and we predict that the MPTA will be a major contributor to global PTA efforts within the next 5 yr. More work is forthcoming, including the first MPTA data release with full timing and noise analyses  (Miles et al. , in preparation) , an analysis of flux density measurements for MeerTime  MSPs (Gitika et al. , in preparation) , and a study of the sensitivity of the MPTA and potential for improvements  (Middleton et al.
This work used the OzSTAR national facility at Swinburne University of Technology. OzSTAR is funded by Swinburne University of Technology and the National Collaborative Research Infrastructure Strategy (NCRIS). This research made use of ASTROPY, a community-developed core Python package for Astronomy (Price-Whelan et al. 2018), the MATPLOTLIB package (v1. 5. 1; Hunter 2007), and PSRQPY, a Python package for searching the ATNF Pulsar Catalogue  (Pitkin 2018). Data Availability. We have made available all 189 polarisation profiles, as shown in , in PSRFITS format with full phase resolution (1 024 bins), 4 polarisation channels, and 8 frequency channels across the 775. 75-MHz band, as well as the table containing the analysis results. The table, in csv format with 189 rows, contains mean flux densities (and standard deviations) in 8 frequency bands per pulsar, spectral indices and fit S 1400 values with uncertainties, median timing uncertainties as described in Section 5, polarisation fractions and rotation measures, dispersion measures and reference epochs, and basic pulsar properties (rotation properties and positions) for easy reference.
l We found that the 'normal' baseline estimator performed worse than the 'minimum' estimator with custom widths for most pulsars with 95% duty cycles. m IGSG maps for the ionospheric correction were downloaded from NASA's CDDIS database at https://cddis. nasa. gov/archive/slr/data/npt_crd/lageos1 (Noll 2010 ). https://doi. org/10. 1017/pasa. 2022. 19 Published online by Cambridge University Press.
q Note that frequency-dependent profile evolution and scintillation can result in poor ToA fits, and therefore higher uncertainties, using frequency-scrunched templates than when using frequency-resolved templates. This effect, and the differing datasets used in this Census and the MPTA programme, leads to different numbers of pulsars with submicrosecond timing precision. r Shannon et al. (2013)  used the same approach to place the bound on a gravitational wave background.
A. Hardware and Software design Students are in charge of assembling electronics components, designing the object and programming the code. The components are interconnected as depicted in the schematic in Figure  2. The physical implementation also consists of defining a support to contain all the elements of the station in a single portable system but also ergonomic and integrating visually to his environment. This object is designed and realized using the TinkerCAD software and a 3D printer. Students proposed the classic shape of a mushroom, as illustrated in Figure  3. To manage the sensors within the program, a general sensor mother class. It contains all the attributes and functions that all sensors can have in common. Each sensor is then identified as a daughter class in which the conversion and calibration function specific to each sensor is performed.
IV. EXPERIMENTAL RESULTS The functionality of the prototype is validated by the good reception of data on Grafana originating from measurements, performed by the object and powered on battery. All the components are then integrated into the 3D support, previously painted in the colors of an Amanite (which will give its name to the node on the network). The Arduino, the LoRa module and the BME680 sensor are housed and camouflaged inside the mushroom foot. The Grove and TMP36 floor sensors are attached to the feet while allowing them to fit into the floor. Finally, the solar battery and the light sensor are positioned facing the sky on the mushroom cap for maximum light performance. When data are sent from the LoRa module of the object, it is received by a LoRa receiver module serving as a gateway to the Internet.
The LoRa node of the object is identified by a node number (here 32) and each sensor measurement is also preceded by an individual identifier (here 70,71,72,73,74,75 and 76) as visible in the sent data frame by the Arduino at LoRa (cf. Figure  7 ). This data is then sorted and stored in a database. The Grafana software allows access to this database and to perform varied visual monitoring as illustrated by the measurements of luminosity in Figure  8. For its real-life test phase, the object is then installed in a planter (cf. Figure  9 ) where radish plants are being developed. The measurements collected by the object are recorded in the database to follow the conditions of development of the plantation. This student project aims at creating a connected object as a solution for precision agriculture, from the conceptual idea to its physical realization of a complete and aesthetic prototype.
High-throughput sequencing has provided the capacity of broad virus detection for both known and unknown viruses in a variety of hosts and habitats. It has been successfully applied for novel virus discovery in many agricultural crops, leading to the current drive to apply this technology routinely for plant health diagnostics. For this, efficient and precise methods for sequencing-based virus detection and discovery are essential. However, both existing alignment-based methods relying on reference databases and even more recent machine learning approaches are not efficient enough in detecting unknown viruses in RNAseq datasets of plant viromes. We present VirHunter, a deep learning convolutional neural network approach, to detect novel and known viruses in assemblies of sequencing datasets.
While our method is generally applicable to a variety of viruses, here, we trained and evaluated it specifically for RNA viruses by reinforcing the coding sequences' content in the training dataset. Trained on the NCBI plant viruses data for three different host species (peach, grapevine, and sugar beet), VirHunter outperformed the state-of-the-art method, DeepVirFinder, for the detection of novel viruses, both in the synthetic leave-out setting and on the 12 newly acquired RNAseq datasets. Compared with the traditional tBLASTx approach, VirHunter has consistently exhibited better results in the majority of leave-out experiments. In conclusion, we have shown that VirHunter can be used to streamline the analyses of plant HTS-acquired viromes and is particularly well suited for the detection of novel viral contigs, in RNAseq datasets.
INTRODUCTION Study of viromes at an unprecedented scale has been enabled by the adoption of high-throughput sequencing (HTS) technologies and is now frequently undertaken across an increasing range of host species. In particular, sequencing of plant viromes has become quite common, partly due to its relevance to the agricultural sector. The acquired datasets help to elucidate important questions such as virus spread among host reservoirs and effects of agriculture on the ecosystems and their biodiversity as well as the identification of novel viruses in crops and natural environments  (Lefeuvre et al. , 2019). These developments are fast advancing our knowledge of viral diversity through the discovery of previously unknown viral species or variants and the identification of new hosts of known viruses  (Roossinck et al.
, 2015; Massart et al. , 2017). Following the classification proposed by  Stobbe and Roossinck (2014) , viruses identified in HTS datasets can be classified into three different groups as follows: 1) viruses that are already known to infect a given host; 2) novel viruses from a known family or known viruses that have not been found previously described to infect a given host; and finally 3) completely novel viruses that share little to no sequence similarity with known viruses already present in the databases. Using an efficient virus detection method, including for the identification of novel viruses, is essential for efficient disease management. Standard diagnostic tests (ELISA assays and PCRbased assays) depend on specific antibodies or primers and thus require prior knowledge of the virus and of its phylogenetic neighbors.