dataesr/scientific-paragraphs-categorization huggingface dataset:
4  In this systematic review, our main aim was to assess the quality of reporting of adverse events in clinical trials of all drugs used in the treatment of covid-19 disease (except vaccines and glucocorticoids). Secondary objectives were comparison of the quality of reporting of adverse events according to the design of the clinical trial. We also examined reporting of serious adverse events in journal articles of covid-19 drug trials compared with their trial summaries on the ClinicalTrials. gov website.
Data sources, search strategy, and study selection The PubMed online bibliographic database was searched from 11 to 17 February 2022 for articles published between 1 December 2019 and 17 February 2022. Online supplemental table 2 shows the search strategy terms. For some drugs, not many clinical trials were published at that time, so the search was performed with keywords (eg, "sotrovimab") to avoid being too specific and potentially missing relevant studies. Based on the search strategy terms, titles, abstracts, and full texts of articles were identified. For selection of article titles, we searched for the name of the drug and covid-19. For selection of abstracts, we searched in the methods sections for the study design (ie, clinical trial). For the selection of full texts, we searched for the name of the drug and covid-19, and the study design, if this information was not reported in the title or abstract.
Secondary objectives Secondary objectives were comparisons between the number of serious adverse events reported in published journal articles listed in PubMed and those reported in clinical trial summaries on the ClinicalTrials. gov website. For each clinical trial summary and journal article, we compared the number of serious adverse events and number of patients with serious adverse events in the treated group. The analysis period for ClinicalTrials. gov data was modified from the original protocol and extended by one year. The extra year gave us more time to evaluate the data. To assess the risk of bias for each study, we used the Cochrane risk of bias tool for randomised trials (RoB 2), which covers sequence generation, allocation concealment, blinding, incomplete outcome data (eg, dropouts and withdrawals), and selective outcome reporting.
Assessment of quality of adverse event reporting Based on the terms listed in the search strategy (online supplemental table 2), we identified 1962 randomised clinical trials assessing the efficacy and safety of drugs used to treat covid-19, published in the PubMed database. We excluded 1906 studies (seven duplicates, 1814 after screening the title, 65 after screening the   7 ). The remaining 56 clinical trials were included in our assessment: 15 clinical trials assessed monoclonal antibodies (two for sotrovimab, five for bamlanivimab, three for bamlanivimabetesevimab, and five for casirivimab-imdevimab), 17 assessed antiviral agents (11 for remdesivir, five for molnupiravir, and one for nirmatrelvirritonavir), and 24 assessed immunomodulatory drugs (14 for tocilizumab, five for sarilumab, two for ruxolitinib, one for tofacitinib, and two for baricitinib).
Reporting of serious adverse events in journal articles versus trial summaries Among the 56 journal articles identified in the review, only 20 trial summaries were available in ClinicalTrials. gov one year after the research period (figure  3 ). The 20 trial summaries were for eight different drugs: sotrovimab, one journal article and one trial summary; bamlanivimab, three journal articles and trial summaries; remdesivir seven journal articles and trial summaries; molnupiravir two journal articles and trial summaries; nirmatrelvir one journal article and trial summary; tocilizumab, four journal articles and trial summaries; sarilumab one journal article and trial summary; and baricitinib one journal article and trial summary. We found that journal articles under-reported 51% of serious adverse events compared with trial summaries.
2%) did not appear in the associated articles. 59  This finding was highlighted in another study that found that reporting was substantially more complete in summaries on the ClinicalTrials. gov website than in published articles for serious adverse events (99% v 63%). 60  These safety data are the main scientific sources for researchers and clinicians, and our results indicate that relying on journal articles for information on covid-19 drugs might miss important information on adverse events. Several studies in our evaluation had moderateto-low quality reporting of adverse events. This finding might be explained in the context of the pandemic where researchers favoured the evaluation of effectiveness over adverse events. Even in the absence of an epidemic, however, risk assessment of drugs has been reported to be low, possibly because different pharmaceutical companies and countries have different requirements for reporting of adverse events in clinical trials.
Conclusions In one in three clinical trials that assessed the efficacy and safety of covid-19 drugs, we found that the quality of reporting of safety data was low or very low. Also, differences were found in the number of serious adverse events related to covid-19 drugs between trial summaries ( ClinicalTrials. gov) and journal articles. Both sources were limited by incomplete reporting. Authors and editors should pay more attention to methods, by better reporting of safety data for covid-19 drugs in clinical trials. Physicians and health professionals should consider published trial reports alongside summaries in clinical trial registries for a more complete understanding of the adverse events of drugs in clinical trials.
Acknowledgements We thank  Marie-Elise Llau  for her help and availability in the practical implementation of KM's research project. Contributors KM, PO, and FM designed the study. KM, CF, and FM screened citations for inclusion and were involved in data extraction and interpretation. KM and CF were involved is the risk of bias assessments. All authors were involved in the interpretation of the data. FM wrote the draft manuscript. KM and FM revised the new version of the manuscript. All authors approved the final and revised versions of the manuscript. FM is the guarantor of this manuscript and accepts full responsibility for the work and the conduct of the study, had access to the data, and controlled the decision to publish. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.
Funding This project was funded by the  French National Research Agency (ANR Agence Nationale de la Recherche). FM has received funding under the  Vigi-Drugs COVID -19  (No  ANR-21-CO17-0001 ) project from the  French National Research Agency  for the evaluation of  safety data of drugs and vaccines  used in the management or prevention of covid-19 (https://anr. fr/Project- ANR-21-CO17-0001 ). The funder had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication.
This indicates that these enzymes also differ in their regulation in addition to differences in their activities. Therefore, GA1 may somehow degrade to a limited extent pectin backbone during root colonization via its Pel enzymes, without impairing cell wall integrity since we never observed any adverse effect on plant health and growth caused by this beneficial bacterium. We thus hypothesized that besides root exudates, the bacterium may use oligomers of galacturonic acid (GA) generated from HG degradation as an additional carbon source to sustain growth and thereby, favour root colonization. However, according to KEGG database  48  , the pentose and glucuronate interconversion pathway necessary for the assimilation of oligomers of GA is incomplete in B. velezensis (Fig2F). Intriguingly, GA1 possesses the complete pathway to metabolize and use GA monomers as carbon source but is unable to generate GA monomers from HG since it does not synthesize polygalacturonases (EC.
Homogalacturonan sensing impacts the production of bioactive secondary metabolites in B. velezensis We also investigated the possible modulation of SMs production by B. velezensis in response to HG since these small-size chemicals contribute to the rhizosphere fitness of the bacterium and play key roles in its biocontrol activity  14, 15, 66. We first performed an untargeted profiling of metabolites secreted by planktonic GA1 cells in liquid cultures supplemented or not with HG. Cell-free extracts collected at stationary growth phase were analysed by UPLC-qTOF-MS and data processing with MZmine2 67 allowed us to detect features and the corresponding compounds differentially produced upon HG sensing. Beside unknown hits, we could identify some non-ribosomal lipopeptides (LPs) and polyketide (PKs)-type metabolites with production modulated in response to HG but in a specific way depending on the family (Fig5A).
0001). (F) Simplified pathway of HG degradation and assimilation (pentose and glucuronate interconversion, adapted from KEGG pathway database 48 ). Enzymes encoded and not encoded in GA1 genome are marked in green and red respectively. (G) Growth profile of GA1 and ΔpelAΔpelB mutant on minimal medium supplemented with 0. 2% (w/v) HG (black and grey line respectively) or GA (green and red lines respectively) (mean±SD, n=3). Growth rate in each condition is indicated next to the corresponding curve and was determined by calculating the slope of the linear part of ln(OD 600 ).
Table1. Content of enzymes involved in pectin degradation (polygalacturonases,(PG); rhamnogalacturonan hydrolases, (RGH); polygalacturonate lyases, (PL)) and pectin remodelling (pectin methylesterases, (PME) and pectin acetylesterases, (PAE)) in archetype strains of soil-dwelling bacilli. Enzymes are grouped by family following the classification established in the CAZy database  105. * means that one or several protein sequences are referred as domain of unknown function (DUF), unknown protein, hypothetical protein or putative protein. See also TableS1.
Phylogenetic and sequence identity analysis All the analysis were carried out on MEGA11 software 108. Phylogenetic analysis was performed on a total of 20 pectin/pectate lyase sequences (classified in PL1 and PL3 family following the CAZy Database 105 ) of soil-dwelling bacilli species retrieved from NCBI protein database. To construct the maximum likelihood tree, protein sequences were aligned by MUSCLE alignment and phylogenetic evolution was inferred using the "Maximum likelihood" method and "Whelan And Goldman" model (WAG) with a discrete Gamma distribution (+G). The best-fit substitution model was selected among all the substitution models implemented in MEGA11 by choosing the one yielding to the lowest BIC score for the dataset analyzed.
Stereomicroscopy acquisitions Pictures of biofilm pellicles of GA1 formed at the air-liquid interface were taken with a Nikon SMZ1270 stereomicroscope (Nikon, Japan) equipped with a Nikon DS-Qi2 monochrome microscope camera and a DS-F 1× F-mount adapter. Pictures were captured in the bright field channel with an ED Plan Apo 1×/WF objective and a 0. 63x magnification of with an exposure time of 40ms. Additionally, pictures of biofilm pellicles at 6h were captured with a 3x magnification and an exposure time of 10ms to get a better view of the structures in formation. Acquisitions were then processed with NIS-Element AR software (Nikon, Japan).
Microscopy acquisitions Microscopy acquisitions were performed using a Nikon Ti2-E inverted microscope (Nikon, Japan) equipped with a ×20/0. 45 NA S Plan Fluor objective lens (Nikon, Switzerland) and a Nikon DS-Qi2 monochrome microscope camera. Images and videos taken in the bright field channel were acquired using a Ti2 Illuminator-DIA and an exposure time of 20ms. GA1 cells strained with RedoxSenor Green (RSG) (BacLight vitality kit, Thermofisher) were visualized by conventional epifluorescence microscopy with a ×100/1 oil NA S Plan Fluor objective lens (Nikon, Switzerland). A lumencor sola illuminator (Lumencor, USA) was used as the source of excitation with an exposure time of 500ms and the GFP-B HC Bright-Line Basic Filter was used. Acquisitions were then processed with NIS-Element AR software (Nikon, Japan).
1 × 50mm × 1. 7μm; Waters, milford, MA, USA) using a gradient of water (solvent A) and acetonitrile (solvent B) both acidified with 0. 1% formic acid as a mobile phase with a constant flow rate 0. 6ml/min and a temperature of 40°C. The gradient was set up to start at 10% B and kept in this condition during 1min before raising to 100% B in 10 min. Solvent B was kept at 100% for 3. 5 min before going back to the initial ratio. MassHunter v10. 0 workstation and the open-source software MzMine2 67 were used for data collection and analysis.
RNA-seq data analysis The analysis of RNAseq data used in this study has been described in details previously  32. In the present study, we focused especially on genes involved in motility and EPS and cohesion protein synthesis. Briefly, raw RNA-seq reads were trimmed with Trimmomatic v0. 39 114 and quality control was assessed using FastQC v0. 11. 8 (Babraham Bioinformatics). The trimmed reads were then aligned to the genome of B. velezensis GA1 (GenBank: CP046386) using BWA-MEM v0. 7. 17  115. Read counts were calculated using the python-based tool HTSeq v0. 9 116 and the tables of fragments per kilobase of transcript per million mapped reads (FPKM) were generated using the Cufflinks function cuffnorm  117. Genes with read counts <25 were excluded. Differential expression analysis was conducted using DESeq2 pipeline 118 , considering the following parameters: P-value<0.
Acknowledgments This work was supported by the  PDR  research project  ID 26084552  from the  F. R. S. -FNRS (National fund for Scientific Research in Belgium ) and by the  EOS  project  ID 30650620  from the  FWO/F. R. S. -FNRS. F. B. , A. A. and A. R. are recipient of a F. R. I. A. fellowship (F. R. S. -FNRS ) and MO is Research Director at the  F. R. S. -FNRS. We gratefully acknowledge  Emmanuel Petit  for providing the oligogalacturonides of high polymerization degree and  Catherine Helmus  for technical help.
• IDENTIFIER: Include catalog numbers (entered in the column as "Cat#" followed by the number, e. g. , Cat#3879S). Where available, please include unique entities such as RRIDs, Model Organism Database numbers, accession numbers, and PDB, CAS, or CCDC IDs. For antibodies, if applicable and available, please also include the lot number or clone identity. For software or data resources, please include the URL where the resource can be downloaded. Please ensure accuracy of the identifiers, as they are essential for generation of hyperlinks to external sources when available. Please see the Elsevier list of data repositories with automated bidirectional linking for details. When listing more than one identifier for the same item, use semicolons to separate them (e. g. , Cat#3879S; RRID: AB_2255011).
If an identifier is not available, please enter "N/A" in the column. o A NOTE ABOUT RRIDs: We highly recommend using RRIDs as the identifier (in particular for antibodies and organisms but also for software tools and databases). For more details on how to obtain or generate an RRID for existing or newly generated resources, please visit the RII or search for RRIDs. Please use the empty table that follows to organize the information in the sections defined by the subheading, skipping sections not relevant to your study. Please do not add subheadings. To add a row,.
Literature Search We reviewed published, peer-reviewed literature on nutrient management and pest and disease management in smallholder farms and collected data on 35 indicators of climate change mitigation, adaptation, and crop yield for agroecological interventions. Figure  1  shows the steps taken to identify candidate articles. We searched for articles that included key terms related to (i) agroecological practices that were used in LMICs, (ii) indicators of climate change mitigation, adaptation, or related co-benefits, and (iii) scaling up (Fig. 1 ). We excluded articles related only to agroecosystems or agro-ecological zones and articles that were meta-analyses, review, opinion, or perspective articles. The initial review resulted in 138 articles. See  Snapp et al. (2021)  for more information on literature search methodology and Supplementary Tables  S1  and S2  for the Web of Science search strings used.
Co-benefits We tested for climate change co-benefits to yields by examining the association of climate change mitigation and adaptation with sole crop yield. The evidence was inconclusive based on our sample size. We found 145 responses for the subset of data that contained positive responses for Fig. 4  Percentage of responses across sole crop yield and climate change mitigation and adaptation indicators, normalized by the number of cases per input category, with a significant (p < 0. 1) positive (green), significant negative (red), neutral (i. e. , not significant finding; gray), and mixed (yellow) response for baseline inputs to agroecological cases inputs sole yield, though the most frequent responses, and therefore the most evidence occurred for profitability (n = 44), water and nutrient regulation (n = 32) and agricultural diversity (n = 23) categories.
Acknowledgements This work was implemented as part of the  CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) , which is carried out with support from the  CGIAR Trust Fund and  through bilateral funding agreements. The CCAFS Program closed at the end of 2021. More information can be found on the CGIAR website https://www. cgiar. org. The views expressed in this document cannot be taken to reflect the official opinions of these organizations.
It should be noted that multichannel observation opens up new possibilities for remote sensing and/or improves the accuracy of parameter estimation, classification, and object detection. On the one hand, a larger number of RS image components increases the need for more efficient RS data compression. On the other hand, CR in this case can be increased (compared with component-wise compression) using three-dimensional compression of multichannel images that exploits sufficient correlation between component images  [7, 11, 15]. Many papers discuss lossy compression of RS data under the assumption that images are free of noise (see, e. g. , ref. [9, 10, 24, 25]  and references therein). However, this assumption is not always valid in practice. Let us give some examples of remote sensing when noise is visible in acquired images and can be intensive.
A first example is speckle, which is always present in synthetic aperture radar images  [21, 22]. Another example is components of multispectral and hyperspectral RS data, for which the input PSNR can be of the order 20-30 dB  [23]. This can also be the case for so-called night-light images  [26] , etc. Here, we are not interested in the origin of the noise. We only assume that there are three or more components of multichannel RS data for which noise is quite intensive. The specific features of lossy compression applied to noisy images were discovered approximately 25 years ago  [27] [28] [29] [30]. Two features are the most important. First, a noisefiltering effect occurs due to lossy compression. Second, due to this denoising, an optimal operation point (OOP) can be observed for some images and noise properties.
This allows the assumption that we know noise variance in component images in advance. Thus, one has the following model of an observed noisy three-channel image I n ijk = I true ijk + n ijk , where I true ijk , i = 1,. , I Im , j = 1,. , J Im , k = 1,. , 3 is the true or noise-free image in a k-th component, n ijk denotes AWGN in the ij-th pixel of a k-th component, and I Im and J Im define the image size. The noise mean is assumed to be equal to zero whilst the AWGN variance is supposed to be equal to σ 2 in all components and it is assumed to be known in advance. The assumption that noise variance is the same in all components is one more simplification dealing with the possible use of variance-stabilizing transforms. For the preliminary study, we use four test three-channel images (Figure  1 ) visualized as color ones from the USC-SIPI image database-Aerials (https://sipi.
usc. edu/database/ database. php?volume=aerials (accessed on 15 March 2023)). Our goal is to study peculiarities of rate/distortion dependencies for images of different complexities where we consider that the image Frisco (Figure  1a ) is of simple structure, the image Earth (Figure  1c ) is of middle complexity, and the images Diego and Diego 2 (Figure  1b ,d, respectively) are complex ones since they contain many small objects (details) and textures. A standard way to characterize the quality of the original noisy image is to use PSNR n = 10log 10 255 2 σ 2 , (1) that relates to the case of equal variance values in all components where images in all components are represented as 8-bit two-dimensional (2D) arrays of integers. In turn, a standard way to obtain and analyze rate/distortion curves is to calculate a metric between the original (in our case, noisy) and compressed images for a set of PCC values.
Certainly, this is impossible in practice when one has a noisy image to be compressed and any noise-free image is absent. However, this is possible in the analysis of simulated data when one has a noise-free image, adds noise to it, and then applies compression with several PCC values  [36, [40] [41] [42] [43] [44] [45] [46]. Such analysis is beneficial since it allows an understanding of how the properties of compressed images change with changes to the PCC values. As an example of such dependencies, Figure  3  presents the plots PSNRtc(Q) for all three considered formats. As it is seen, the plots for simple and middle complexity images Frisco and Earth have obvious maxima (optimal operation points) observed for QOOP = 30. Here, it is worth recalling some properties of the metric PSNR. Although it is not the best metric for characterizing the visual quality of images  [56, 57] , it is possible to state  [57]  that distortions start to become visible if PSNR is approximately 35 dB.
Meanwhile, while solving the curve-fitting task for our application, we have taken into account the following. First, according to visual inspection of the scatter-plot properties, the fitted curves have to be smooth enough. Second, there are special tools available that are designed for curve fitting (regression); we have used a MATLAB Curve Fitting Tool. Third, relying on previous experience  [40] , we have employed rational functions (the fitted curves in Figures 5-8 are obtained using such functions). The function parameters and the fitting criteria are given in Table  1. The scatter-plot in Figure  8c  is similar to those in Figures  6a  and 7c , whilst the scatterplot in Figure  8d  is analogous to the ones in Figures  6b  and 7d. The analysis of the metric ∆MDSI allows the conclusion that the OOP exists if P 2σ ≥ 0.
First, according to visual inspection of the scatter-plot properties, the fitted curves have to be smooth enough. Second, there are special tools available that are designed for curve fitting (regression); we have used a MATLAB Curve Fitting Tool. Third, relying on previous experience  [40] , we have employed rational functions (the fitted curves in Figures  5 6 7 8 are obtained using such functions). The function parameters and the fitting criteria are given in Table  1. Analysis of the data given in Table  1  shows the following. First, for the metric ∆PSNR -H A, the fitting is very good for all formats. The values of R 2 are approximately 0. 97 (from 0. 95 till 0. 99), and RMSE is approximately or less than 0. 5 dB. These are appropriate pre-conditions for accurate prediction. Second, fitting for the metric ∆MDSI is characterized by an R 2 of approximately 0.