dataesr/scientific-paragraphs-categorization huggingface dataset:
The relationships between them, and between direct/indirect effects and disease severity in the presence of helper strains in vivo, were analyzed using linear regressions. These analyses were carried out using the R 3. 6. 3 program (www. r-project. org) and Sigma Plot (V. 12. 5). To further consider the growth inhibition of R. solanacearum, and disease suppression, we fitted a linear model to estimate the relative importance of direct effects versus indirect effects on the density of R. solanacearum both in vitro and in vivo, and on disease severity. This model considered the interaction scenario where rhizobacterial strains inhibited both the pathogen and its helpers (see the R script "Model" in the supplementary information). These analyses were performed in R version 3. 6. 3 [26]  in conjunction with the package car, readxl and dplyr, and tidyverse 1.
ACKNOWLEDGEMENTS This research was financially supported by the  National Key Research and Development Program of China  ( SQ2021YFD1900024 ),  National Natural Science Foundation of China  ( 31972504 ,  42090060 ,  41922053 , and  42007038 ), the  Fundamental Research Funds for the Central Universities  ( KY2201719 ,  KYT201802 ,  KYXK202010 ,  KJQN202116-KJQN202117 ), the  Natural Science Foundation of Jiangsu Province  ( BK20190518 ,  BK20180527  and  BK20200533 ), and technically supported by the  Bioinformatics Center of Nanjing Agricultural University. ML was supported by  Chinese Scholarship Council (CSC).
Besides, other approaches have been proposed to estimate the orientation. Mahony et al. [18]  proposed an algorithm which employs a nonlinear complementary filter. Fourati et al. [6]  combined a quaternion-based nonlinear filter with the Levenberg Marquardt Algorithm. The algebraic quaternion algorithm (AQUA) has been also discussed  [32]. In 2011, Madgwick et al. proposed a computaionally efficient MARG orientation filter  [17] , based on an optimised gradient descent algorithm (GDA). It exploits quaternion representation. A detailed explanation of this algorithm is out of the scope of this paper, but we can sum it up in three steps. In the first step, data is read from the MARG sensor. Then, the GDA is applied to compute orientation increment (gradient step) using acceleration data, and magnetometer data for magnetic dis- tortion compensation.
Acquired raw data are integers. The acceleration values are multiplied by a constant to convert them into g units, the angular velocity values by to convert them into • ∨́̄ , and the magnetic field values by to transform them into Gauss. These constants are equal to 2 ∕2 , where is the sensor measurement range and is the sensor resolution. Figure  5  illustrates the flowchart of the proposed algorithm, and the corresponding instructions and commands are detailed below. The algorithm begins by reading data, converting them, and storing them in arrays. Quaternions are updated using Madgwick's orientation filter, and Euler angles are then calculated, when an acceleration value is read (Type='A'). After acquisitions, the 3D graph is updated by rotating the midpoints between the opposite vertices and the labels around the axes.
Experiments The proposed approaches were tested to validate their efficiency and prove their biomedical interest. A wrist-worn device developed by RF-Track society (located in Cesson-Sévigné, France) was used to perform some rehabilitation exercises under supervised conditions. The corresponding configuration of the device is = = 60 Hz and = 40 Hz. Moreover, we resorted to a public dataset called FallAllD  [29]. Three devices were considered while simulating ADLs, namely wrist-worn, waist-worn, and necklace devices. Here, = = 238 Hz and = 80 Hz. The acceleration and angular velocity signals were down-sampled by a factor of 4 (∼ 60 Hz) and magnetic fields by a factor of 2 (∼ 40 Hz).
HAR Module FallAllD dataset was used in this experiment, taking both the necklace and waist-worn devices into account (body trunk). For transient ADLs, i. e. 2 , 3 , and 6 , five 6-second windows were considered: the one centered on the transition moment, and four others shifted by ± 500 ms and ± 1 s. Note that 2 consisted of four transitional movements, namely (i) walk-to-sit, (ii) stand-to-sit, (iii) sit-tostand, and (iv) sit-to-walk. Moreover, 6 consisted of falls without recovery, from three different initial body postures, namely (i) fall while walking (standing), (ii) fall while sitting, and (iii) fall from bed (lying). Now, for 1 , the same body postures were considered, i. e. (i) inactive while standing, (ii) inactive while sitting, and (iii) inactive while lying. These factors are important to fairly assess the discrimination power of the proposed recognition process.
Comparison with State-of-the-Art Techniques In this sub-section, we will compare our approach to state-of-the-art techniques, in order to justify our choice regarding the orientation filter, and the feature fusion architecture combining CNN, Dense layers, and LSTM (CDL). Orientation Estimation: we implemented the aforementioned orientation filters in sub-section 2. 3, namely Extended Kalman Filter (EKF), Mahony, Madgwick, AQUA, and Fourati, using Python 3. 6. The scripts were executed on an Intel(R) Xeon(R) Gold 5118 2. 3-GHz processor with 64 GB RAM, to estimate quaternions of a human fall over 20 s. The execution time was measured in seconds. Table  2  illustrates the results. Madgwick's filter has the fastest response, with an execution time equal to 0. 17 s. This filter outperforms all the others in terms of complexity, including AQUA and Mahony.
TESS Photometry Alpha Mensae falls in the TESS SCVZ and thus, was observed for the entire first year of the nominal mission. Alpha Men was observed in 2-minute cadence for all thirteen sectors, for a total baseline of 351 days. We used the light curve files produced by the TESS Science Processing Operations Center (SPOC;  Jenkins et al. 2016 ) that were made publicly available on the Mukulski Archive for Space Telescopes (MAST 1  ). We downloaded all SPOC 2-minute light curves and stitched individual sectors together using the SPOCprocessed PDCSAP (Pre-Data Conditioning Standard Aperture Photometry) flux. Upon initial inspection of the light curve (Figure  1 ), we noticed two sectors with increased scatter by a factor of at least two. We suspect that this is due to instrumental systematics and therefore removed these data before further analysis.
Broadband Photometry & Gaia Parallax Due to its brightness (V = 5. 1) α Men A is saturated in many large photometric surveys. For optical magnitudes, we relied on B T and V T magnitudes from the Tycho-2 catalog  (Høg et al. 2000). Cutri et al. (2012)  reported reliable quality flags for J, H, K S photometry in the extended 2MASS catalog, although we note the higher uncertainties due to the choice of aperture needed for saturated stars. The European Space Agency's (ESA) Gaia Data Release 2 (DR2 3  ; Gaia  Collaboration et al. 2018 ) estimated a mean Gaia magnitude G = 4. 850 for alpha Men. Brighter sources in DR2 photometry (G < 6) suffer from systematic errors due to saturation  (Evans et al. 2018). Using the empirical correction in  Evans et al. (2018) , we calculated a corrected Gaia magnitude, G = 4.
The response function, R(ν), is an attenuation factor that affects the observed spectral amplitudes due to the sampling rate (or cadence) in a time series. The attenuation is greater for oscillations that occur near the Nyquist fre- quency, which for TESS 2-minute data is ν Nyq = 4166. 67 µHz. The last two terms in Equation  1 refer to contributions from the stellar granulation background B(ν) and the Gaussian envelope of oscillations G(ν). To determine the stellar background contribution, we used the publicly available Background 6  , which is a software extension of DIAMONDS 7. Initially created for more robust asteroseismic analyses, DIAMONDS is a nested sampling Monte Carlo (NSMC) algorithm for Bayesian parameter estimation and model comparison  (Corsaro & De Ridder 2014). The background model built into this framework has the functional form: B(ν) = ζ n i=1 σ 2 i /ν i 1 + (ν/ν i ) 4 , ( 2 ) where ζ is a normalization factor (2 √ 2/π), σ i is the amplitude and ν i is the characteristic frequency for n Harvey-like components  (Harvey 1985).
We refer the reader to  Corsaro & De Ridder (2014)  for more details about the software. Ultimately, the data did not provide enough evidence for DIAMONDS to converge on reliable results for more complex models (i. e. multiple Harvey-like terms), and therefore no model comparison was needed. We attempted to model the granulation component but it was mostly unconstrained or resulted in very small amplitudes. This is likely because the amplitude of the granulation signal is comparable to or less than the white noise level in the power spectrum. The final background fit is shown in Figure  2a  as a solid blue line, which is the summed contributions from a white noise component (blue dashed line) and a meso-granulation term (blue dotted line). 4  are added in red, where shaded regions are equal to ±1σ for each mode.
6m telescope: the Coudé Echelle Spectrograph (CES, pre-and post-upgrade) and HARPS, which was already corrected for systemic instrumental offsets in  Zechmeister et al. (2013). We also collected data from the Anglo-Australian Planet Search (AAPS), which perfectly overlapped with the CES and HARPS data. Figure  9  shows the complete RV time series, which covers 22 years. AAPS data after 2011 is plotted as a separate instrument because of an unexplained RV offset. We used a conservative bin size of 60 days to average over the scatter due to stellar rotation, revealing a period which is similar to that observed in the Sun. Using the publicly available GLS code (Zechmeister & Kürster 2009), a generalized Lomb-Scargle periodogram analysis that is better suited for unevenly (and sparsely) sampled data, we detect a period P = 13.
ACKNOWLEDGEMENTS We acknowledge the traditional owners of the land on which the Anglo-Australian Telescope stands, the Gamilaraay people, and pay our respects to elders past, present, and emerging. The authors would like to thank the staff at the  Gemini South Observatory  for follow-up observations. A. C. acknowledges support from the  National Science Foundation  under the  Graduate Research Fellowship Program  ( DGE 1842402 ). D. H. acknowledges support from the  Alfred P. Sloan Foundation , the  National Aeronautics and Space Administration  ( 80NSSC18K1585 ,  80NSSC19K0379 ), and the  National Science Foundation  ( AST-1717000 ). T. A. B. acknowledges support by a  NASA   FINESST award  ( 80NSSC19K1424 ). A. S. is partially supported  MICINN  project  PRPPID2019-108709GB-I00. V. S. A.
acknowledges support from the  Independent Research Fund Denmark  (Research grant  7027-00096B ) and the  Carlsberg foundation  (grant agreement  CF19-0649 ). T. R. B. acknowledges support from the  Australian Research Council  ( DP210103119 ). W. H. B. , W. J. C. and M. B. N. thank the  UK Science and Technology Facilities Council  ( STFC ) for support under grant  ST/R0023297/1. R. A. G. acknowledge the support of the PLATO and GOLF CNES grants. M. S. L. is supported by the  Carlsberg Foundation  (Grant agreement no. :  CF17-0760 ). Funding for the  Stellar Astrophysics Centre  is provided by The  Danish National Re-search Foundation  (Grant  DNRF106 ). S. M. acknowledges support from the  Spanish Ministry of Science and Innovation with the Ramon y Cajal  fellowship number  RYC-2015-17697  and from the grant number  PID2019-107187GB-I00.
Background: Mental health difficulties in children and adolescents are highly prevalent; however, only a minority receive adequate mental health care. Internet-delivered interventions offer a promising opportunity to increase access to mental health treatment. Research has demonstrated their effectiveness as a treatment for depression and anxiety in adults. This work provides an up-to-date examination of the available intervention options and their effectiveness for children and young people (CYP). Objective: In this systematic review and meta-analysis, we aimed to determine the evidence available for the effectiveness of internet-delivered interventions for treating anxiety and depression in CYP. Methods: Systematic literature searches were conducted throughout November 2020 using PubMed, PsycINFO, and EBSCO academic search complete electronic databases to find outcome trials of internet-delivered interventions treating symptoms of anxiety and/or depression in CYP by being either directly delivered to the CYP or delivered via their parents.
Literature Search This systematic review and meta-analysis was completed in line with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement  [24] ; for the corresponding checklist, refer to Multimedia Appendix 1. Its protocol was prospectively registered at PROSPERO (reference number CRD42020220171). A systematic literature search for English language articles was conducted in early November 2020, and the final searches were conducted on November 19, 2020, across three electronic databases: PubMed, PsycINFO, and EBSCO academic search complete. Each database was searched individually with search terms specified by population, presenting the problem, intervention, and intervention medium (refer to Table  1  for examples of the search terms used across databases).
Data Extraction Extracted data from studies included (1) participant characteristics (percentage female, mean age, and age range of the sample), (2) study characteristics (country of setting, recruitment strategy, clinical eligibility criteria implemented, and type of control group), (3) intervention characteristics (intervention focus of anxiety and/or depression, intervention delivery to youth and/or parents, intervention theoretical orientation, intervention support delivered, number of modules in intervention and length of treatment, and average amount of the intervention completed by participants), and (4) means and SDs or equivalent intention-to-treat metrics facilitating the calculation of posttreatment and follow-up between-group effect sizes where applicable. Data were extracted by one researcher (AL or RW) and checked for accuracy by another researcher (NE).
Selection and Inclusion of Studies The database searches resulted in 1014 articles, whereas an additional 11 results were obtained from other sources (refer to the Literature Search section). Duplicate removal left 874 articles, 645 of which were excluded based on the title. The full-text versions of the remaining 236 articles were assessed for potential eligibility, of which 213 did not meet the eligibility criteria and were excluded. Finally, 23 studies were deemed eligible for the systematic review, and an additional 7 studies were excluded from the meta-analysis because of not having conducted an RCT. A total of 16 studies met the inclusion criteria for meta-analysis. The study selection process and reasons for exclusion are summarized in Figure  1.
Soil microbial community analyses Soil DNA was extracted from (i) initial rhizosphere soils used for microbial consortium selection, (ii) Cons A and Cons C consortia, and (iii) soils sampled at the end of the pot experiment (T28). For soil samples, 0. 5 g of soil were used, and for consortia, 1. 5 mL of culture were centrifuged to obtain the pellet. DNA was extracted using the FastDNA™ Spin Kit for Soil (MP Biomedicals, USA) according to the manufacturer's recommendations. Microbial DNA concentrations were measured using a NanoDrop (NanoDrop 1000 spectrophotometer, ThermoFisher Scientific, Watham, USA). For soil DNAs analysis through quantitative PCR (qPCR) at the end of the pot experiment, soil microbial DNA concentrations were adjusted to 1 ng•μL -1 and qPCR targeting 16S rRNA genes for bacterial biomass quantification was performed using a CFX ConnectTM Real-Time PCR Detection System (Bio-Rad, France) and the following thermocycling conditions: 95 °C for 3 min, 35 cycles of 95 °C for 30 s, 60 °C for 30 s, 72 °C for 30 s and a data acquisition step at 80 °C for 30 s at each cycle.
Sequences were clustered into OTUs (Operational Taxonomic Units) using SWARM and an aggregation distance of 3. After chimera removal and filtering for OTU abundance (threshold of 0. 00005%), the remaining OTUs were affiliated using BLASTn and the Silva 132 16S database. A total of 1,325,398 sequences were retrieved, made up of 897 OTUs. Samples contained between 52,658 and 106,321 sequences, and between 21 and 764 OTUs. Next, sequence abundance was normalized through random resampling in order to obtain an equal number of 52,658 sequences per sample. FROGSSTAT was used to calculate the alpha and beta diversities, after normalization of the data. Finally, the affiliation of the most abundant OTUs (relative abundance >5%) was verified using BLASTn (https://blast. ncbi. nlm. nih. gov/Blast. cgi).
Crystallogenesis, data collection and structure determination Structure Screens 1 and 2 and Stura Footprint Screen (Molecular Dimensions Ltd) were used for initial screening. Trials were assessed in SWISSCI 3 Lens Crystallization Plates with three wells per reservoir using 400, 300 and 200 nL drops. The drops contained increasing volumes (100, 200 and 300 nL) of protein solution at a concentration of 8-9 mg/mL in complex with or without 2. 5 mM SFX (from 50 mM stock in 100% DMSO) and 100 nL mother liquor. Crystal hits were observed only in presence of SFX, in 0. 1M MES pH 6. 5, Polyethylene glycol (PEG) 20K 12%. Optimization of crystal-growth conditions led to the following condition: 0. 1M MES pH 5. 7 -6. 7, PEG 20K 7. 5/15 %. Crystals appeared after 24 h. They were soaked in 25% (v/v) PEG 200 added to the mother liquor and cooled in liquid nitrogen.
X-ray diffraction data were collected at European Synchrotron Radiation Facility, Grenoble (ESRF). The structure was solved by molecular replacement using Phaser with PV-2C-ΔN-3Mut structure (PDB code: 5Z3Q, D chain) as the searching model and was refined to a resolution of 1. 4 Å. The crystal belongs to a space group of P212121, with 1 copy of CV-B3 Δ116-2C in the asymmetric unit (ASU). In addition, crystals were also obtained in another crystallization solution containing 0. 1M MES pH 6. 3 -6. 7, PEG 5 000 MME 23/33 %, 0,2M ammonium sulphate. Crystals in mother liquor were soaked with 0. 3 µL of 20 mM ATP overnight at 20°C. The datasets were collected at Proxima-2A-Synchrotron SOLEIL. The structure was solved by molecular replacement using Phaser with the structure of CV-B3 Δ116-2C in complex with SFX as a search model and refined to a resolution of 1.
Subsequently, two data sets were collected on the SFX incubated sample using a Titan Krios Cryo-TEM (Thermo Fisher Scientific) operating at 300 keV. For the first data set, movies were collected using a K2 direct electron detector operating in electron counting mode, at 165,000× magnification corresponding to a calibrated pixel size of 0. 842 Å/pix over a defocus range of -1. 5 to -2. 5 μm. 1527 movies were collected using a dose rate of 8. 8 e -/pix/sec for a total of 4 seconds (40 fractions), resulting in a total exposure of ~50 e -/Å 2 (1. 25 e -/Å 2 /fraction). To account for the preferred orientation exhibited by the 2C hexamers, an alpha tilt of +30 degrees was used for the second data collection. Movies were collected using a K3 direct electron detector operating in super resolution mode, at 64,000× magnification corresponding to a super resolution pixel size of 0.
69 Å/pix over a defocus range of -2 to -4 μm. 6119 movies were collected using a dose rate of 12. 6 e -/pix/sec for a total of 2 seconds (37 fractions), resulting in a total exposure of ~54 e -/Å 2 (1. 45 e -/Å 2 /fraction). All cryo-EM data were acquired using the EPU 2 software (Thermo Fisher Scientific).
Image processing For the untilted data, collected movie stacks were manually inspected and then imported in Relion version 3. 0. 1  55. Drift and gain correction were performed with MotionCor2 56 , and GCTF was used to estimate the contrast transfer function for each micrograph  57. Approximately 200 particles were picked manually and 2D classified. The best resulting class was then used as a template for autopicking in Relion, resulting in 42,206 particles. Fourier binned (2 × 2) particles were extracted in a 240pixel box and subjected to a round of 2D classification after which 3616 particles were retained. Using the 'molmap' command in UCSF chimera, residues 358-628 of the JC polyomavirus helicase (PDB ID: 5J40 41 ) were used to generate a 50 Å resolution starting model. Particles selected from 2D classification were 3D auto-refined (with C6 symmetry), which produced a highly anisotropic map.
Data availability Atomic coordinates and structure factors for CV-B3 Δ116-2C in complex with SFX are deposited in the Protein Data Bank under accession codes 6S3A and 6T3W. The EM density map for the SFX incubated hexΔ116-2C has been deposited to the Electron Microscopy Data Bank under the accession EMD-12798. All reagents and relevant data are available from the authors upon request.
Acknowledgments DLH is funded from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie grant agreement  ( No 842333 ) and holds an  EMBO non-stipendiary long-term Fellowship  ( ALTF 1172-2018 ). PEK is the recipient of a scholarship of the Foundation " Méditerranée Infection ",  Marseille. This work was supported by the  European Union  ( Horizon 2020 Marie Skłodowska-Curie ETN 'ANTIVIRALS' , grant agreement number  642434  to AB, FJMvK and BCo). This work is also supported by  Netherlands Organization for Scientific Research  ( NWO-ECHO-711. 017. 002  to FJMvK) and  NWO-VICI-91812628  to FJMvK), and from the  Life Science Research Network Wales  (grant no. NRNPGSep14008  to AB, an initiative funded through the  Welsh Government 's  Ser Cymru program ).
Correlation among variables To identify the relations between polyphenolic compounds, flavonoids, carotenoids, and antioxidant activity (DPPH), all variables were subjected to bivariate correlation using the Pearson coefficient. Significant correlations at the level of 0. 05 or 0. 01 are summarized in Fig. 4. To distinguish variables in datasets and downplay the number of effective parameters to classify the eight studied date seeds oils (Phoenix dactylifera L. ) based on phenolics profile, phytochemical compounds, and DPPH method, the principal component analysis (PCA) based on correlation was performed. The total variance of 72. 45% was explained by the two first components. Thus, the first component represented 46. 44% of the total variance, while the second component accounted for 26. 01% of the total variance (Fig.
Two-dimensional clustered heatmap A hierarchically clustered heatmaps is one of the numerous analyses that does not require a dimensionality reduction to visualize dataset distribution. It is a widely used technique to analyze complex biological data by displaying network connections in a symmetric adjacency matrix. It was performed to obtain a simplified representation of the date palm seeds oil phytochemical variability within the dataset of sampled palm date. Color-coded two-dimensional heatmaps for both fruit parts are formed with two clusters using Euclidean distance following the Ward method; one is samples-oriented, whereas the other is variableoriented (Fig. 4 ). To better assess the relationship among phenolic acid compounds, phytochemical compounds, and antioxidant proprieties using the DPPH method of date seeds oils from eight date532 varieties, a bivariate correlation analysis using the Pearson coefficient was conducted following a heatmap model (Fig.