dataesr/scientific-paragraphs-categorization huggingface dataset:
The long wavelengths in these high-AE cases often have AOD that is too low to contain sufficient information content on particle absorption. The AERONET aerosol retrieval algorithm employs constraints of the first derivatives of the IPRI wavelength dependence (m = 1) in which case these constraints relate the values of the IPRI at neighboring pairs of wavelengths. Throughout the course of algorithm development, different approximations were used for Lagrange multipliers γ s 3 while assuming equal weights to all the pairs of wavelengths and thus replacing weighing matrices W m,n with unit matrices. In version 1 (V1) of the AERONET database, it was assumed that the IPRI for all the aerosol types is spectrally flat. This was physically justified by the flat spectral dependence of the principal fine mode aerosol absorber, black carbon (BC), in this wavelength range.
For mineral dust aerosols the impact will be insignificant, while for biomass burning aerosol dominated by BC the changes will be relatively small. The application of this new REL constraint on the spectral variation in the IPRI is being planned for future retrievals in Version 4 of the AERONET database. These retrievals will therefore have increased sensitivity to absorption by BrC in fine-mode-dominated aerosol than is currently available in the Version 3 database. Additionally, future retrievals with the added wavelengths of 380 and 500 nm will enable more accurate and robust monitoring of BrC absorption in fine mode aerosols and iron oxides in coarse mode dust aerosols.
Acknowledgements. The  AERONET  project at  NASA GSFC  is supported by the Earth Observing System Project Science Office cal-val,  Radiation Sciences Program at NASA headquarters , and various field campaigns. Resources supporting this work were provided by the  NASA   High-End Computing (HEC) Program  through the  NASA Center for Climate Simulation (NCCS)  at the  Goddard Space Flight Center. Review statement. This paper was edited by  Alexander Kokhanovsky  and reviewed by three anonymous referees.
COVERAGE The ciclesonide arm was opened on 29 December 2020. On 9 June 2021, the DSMB decided to run an interim analysis to ascertain whether the magnitude of the effect of ciclesonide observed in COVERAGE was within the range of that reported for budesonide in the PRINCIPLE trial  [15]. The DSMB reviewed this interim analysis on 2 July 2021 and advised the SAB that continuing the ciclesonide arm would be futile. Based on this recommendation and blind to the results, the SAB decided to halt the ciclesonide arm on 22 July 2021. The arm was closed on 23 July 2021. Reported here are the data from the participants randomized to the ciclesonide arm and those randomized to the control arm in the period when the ciclesonide arm was open.
Statistical analysis For the pilot phase, the analysis is detailed in Appendix S2. For the efficacy phase, the primary analysis compares the proportion of participants reaching the primary endpoint by Day 14 in the two arms and is intention-to-treat (ITT) with a missing-equals-failure approach. Sensitivity analyses include an ITT analysis of observed data, excluding participants with missing data for the main outcome; a modified ITT (mITT) analysis of observed data, additionally excluding participants wrongly included; an ITT maximum bias analysis; and a per-protocol analysis of observed data. It was decided a priori that all secondary analyses would be descriptive and not subject to statistical testing. The analyses were carried out using SAS version 9. 4.
Discussion Our data should be considered alongside others from randomised trials of inhaled corticosteroids in COVID-19 outpatients. In a phase 2 trial conducted by Song et al. [16] , 61 adult patients of any age with COVID-19 were randomized to inhaled ciclesonide or standard care (SC). The number (%) of participants reaching the primary endpoint (SARS-CoV-2 RT-PCR negativation by Day 14) was 10 (32. 3%) in the ciclesonide group and 1 (5. 0%) in the SC group (p ¼ 0. 021)  [16]. In the phase 2 STOIC trial, 146 adults of any age were randomized to inhaled ciclesonide or SC. The number (%) of participants reaching the primary endpoint (COVID-19-related emergency department assessment or hospitalization) was 2 (3%) in the ciclesonide group and 11 (15%) in the SC group (p ¼ 0. 009)  [17]. In a phase 2 trial conducted by Clemency et al.
Transparency declaration The anonymized individual data and the data dictionary of the study will be made available to other researchers by the coordinating investigator, Professor Denis Malvy (denis. malvy@chubordeaux. fr), after approval of a methodologically sound proposal and the signature of a data access agreement. All authors declare no conflict of interest. The trial protocol was registered in the European database EudraCT (2020-001435-27) and on clinicaltrials. gov (NCT043 56495). The trial was supported by grants from the French Ministry of Health (PHRC-N COVID, 2020, COVID-19-20-0100), the French National Research Agency (ANR: ANR-20-COVI-0040-01), the University of Bordeaux, and Inserm/REACTing. CHU de Bordeaux is the sponsor of the study. The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.
Mean square displacement For all MSD experiments, time-lapse imaging was performed every 6 s, taking a Z-stack of 3 lm spread through 1 lm slices for 5 min, with a 512 × 512 pixels format with a 1-2× zoom factor. All images were analyzed using Fiji software (NIH, Bethesda, MD, http://rsb. info. nih. gov/ij/; Sage et al, 2005)  and with the plugin SpotTracker 2D (obtained from http://bigwww. epfl. ch/sage/soft/spottracker). All our time series were corrected for XY drift and nuclear movement using the plugins Stackreg and SpotTracker 2D, respectively. All the details of our image analyses pipeline were done as described in  Meschichi and Rosa (2021).
Acknowledgements We thank  Susan Gasser  and  Anais Cheblal  for the SpotTracking and  Excel Macro ,  Charles White  for RAD51-GFP line,  Anne Britt  for the sog1-1 mutant,  Sashihiro Matstunaga  for RAD54-eYFP line,  Masaaki Umeda  and  Shiori Aki  for the  Cytrap line and Confocal Microscopy Platform  from  SLU, Uppsala, Sweden. We thank  Marion Orsucci  for help with the statistical analysis. We thank  Lauriane Simon  for comments on the manuscript. We would like to thank  COST   ACTION CA16212 INDEPTH  for the inputs and comments. AM and SR were supported by the  Swedish Research Council (Vetenskapsr adet)  grant number  2018-04101  and  Knut and Alice Wallenberg Foundation  ( KAW 2019-0062 ). FP is supported by the  "Laboratoires d'Excellences (LABEX) "  TULIP  ( ANR-10-LABX-41 ) and/or by the  " Ecole Universitaire de Recherche (EUR) "  TULIP-GS  ( ANR-18-EURE-0019 ).
CC is closest to the harbor mouth with high ow and sandy sediment and (along with WP) has the coolest water temperature of these sites. DB is our warmest site about 2˚C warmer in mean temperature than CC and WP and is also the site least impacted by human presence (all other sites are routinely visited by recreation clam shers. In the summer of 2015, the mean temperature of these sites ranged from 15. 9˚C to 18. 1˚C (instantaneous temperature across sites temperature ranged from 12. 4˚C to 21. 8˚C by HOBO loggers) (See Supplemental Table  S11  for temperature data by site). We took collected plants to Bodega Marine lab, standardized shoot and rhizome length, and attached plants to vexar mesh blind to site of origin. Then we planted twelve vexar mesh screens with 16 plants at each of the four sites within an existing eelgrass bed with 1m in between screens (plants growing immediately below screens were removed).
Molecular Methods We extracted DNA with the MoBio PowerSoil DNA kit from the surface of the leaves and roots and from scaled samples of samples. To get the surface of the leaves and roots only, we vortexed each frozen sample with 500ul of MilliQ water and then added that liquid to the bead tubes and proceeded with the standard extraction protocol (full protocol available at github. com/mkardish/Transplants/Lab_Protocols). We lost DNA extractions for leaf samples transplanted to CC from MM and WP and have therefore not been included in further analyses that follow. At the Integrated Microbiome Resource at Dalhousie University, we ampli ed and sequenced the V4-V5 region of the 16S rRNA gene on an Illumina MiSeq on an Illumina MiSeq to identify bacteria present with primers 515F and 926R  50, 51.
Bioinformatic Analysis We ran all bioinformatic and statistical analyses in R (version 3. 6. 1). We used a standard dada2 pipeline to error check our reads and to identify amplicon sequence variants  52. We used only forward reads in our subsequent analyses (280 base pairs). We identi ed ASV taxonomy based on the SILVA database  53  and built a phylogeny of ASVs using alignments built with DECIPHER  54  then a tree built with FastTree2 55 then converted to ultrametric 56. We then rooted the bacterial tree with an archeal outgroup  57, 58.
Statistical analysis We determined differences among groups of samples based on Euclidean distances after phylogenetic isometric log-transform implemented in the R-package "philr" order to analyze the compositional changes in our dataset based on phylogenetic similarity  59. This analysis tests the differences in weighting of various regions of a phylogenetic tree rather than considering each ASV independently. We used PERMANOVA to determine differences among sample types (among sample types, sample timepoint, sample origin site and destination site) on ASVs present in at least 2 samples. We tested homogeneity of group dispersions with the betadispr function in 'vegan'. To measure bacterial richness, we rari ed all samples to 1980 reads, calculated the number of "Observed ASVs", repeated this 200 times, and then used the average as our measure of bacterial richness in a sample 60.
Pairwise-posthoc analyses were run to determine differences observed among groups. We examined compositional differences among sites and treatments, by assessing phylogenetic clustering and overdispersion of groups relative to random draws from regional and local pools. We assume that many bacterial traits, especially those associated with major metabolic processes, are phylogenetically conserved, and thus conclude that phylogenetic clustering is associated with environmental ltering and dispersion with competitive forces, resource partitioning, or cross-feeding. We also examined the individual balances driving the differences in the communities we observed by identifying balances that made communities distinct from others. We present data on the net relatedness index (NRI) of a community which is the negative of the standardized effect size of the calculated mean pairwise distance (MPD) compared to a null distribution.
The NRI thus represents the average phylogenetic distance between all members of a community relative to an expected null value 61. We did this through the R package MicEco which implements a parallelized version of picante  62, 63. We used an independent swap null model in order to maintain sample richness and species occurrence frequency and compared MPD from our samples to 999 random draws. We only show data from abundance weighted models, but non-abundance weighted models show the same patterns. We tested for differences in clustering and dispersal among sites using ANOVAs or Kruskal Wallis tests when the residuals were not normal. In addition to the clustering analysis, we also assessed which phylogenetic balances differed between groups of interest. This analysis uses multinomial logistic regression implemented in glmnet in order to identify which branches of the phylogeny differ in their representation among groups (Supplemental Figure  S1 )  59, 64.
Acknowledgements We thank  J. Eisen  and  E. Grosholz  for their comments on this manuscript. We thank  A. Alexiev  for assistance with extractions and  A. Firl  for assistance in sequencing. We would like to thank the  Stachowicz Lab  for their expertise and assistance in eld work. This work was funded by the  UC Davis Center for Population Biology , the  National Science Foundation Graduate Research Fellowship  (to  M. Kardish ), a grant from the  Gordon and Betty Moore Foundation  (to J. Eisen and JJS), and a grant from the  NSF Biological Oceanography program  (Grant#  OCE 1829992  to JJS). This work used the  Extreme Science and Engineering Discovery Environment (XSEDE)  on the  Comet at SDSC  through an allocation to  M. Kardish   TG-DEB160008. Our collections and experiments complied with relevant institutional, national, and international guidelines and legislation.
Elastic material We report the first four global resonant frequencies and modal loss factors of the analysed cantilever beam. One can see that the results of the presented approach are in good agreement with the full discretized model solved in Abaqus as well as those available in the literature with relative errors less than 1% for the resonant frequencies. Significant to be reported that only one iteration and 64 seconds are needed to compute the sixteen modal properties using the proposed approach when 33 hours are needed to perform the same computation using a commercial software like Abaqus.
Acknowledgements The authors from  Luxembourg Institute of Science and Technology  acknowledge the financial support of  Fonds National de la Recherche, Luxembourg (FNR)  through the Grant DeeMA:  INTER/MERA20/ 15011099. The authors from the  University of Lorraine  acknowledge the financial support from  French State program  ''Investment in the future'' operated by the  Agence Nationale de La Recherche (ANR)-LabEx DAMAS.
e. , mono-and oligosaccharides). For each of these compounds, we searched databases (ChemSpider, PubChem) for potential structural candidates corresponding to mono-and oligosaccharides, considering that these raw formulas originated from different cases:  A table of 120 potential compounds was generated with possible structures generated by a Metlin m/z match search in negative mode on the basis of (M-H) -and (M-H2O-H) -ions, and a list of gross formulae corresponding to neutral mass losses (M-H2O) was also computed. This database was completed by adding the most characteristic motifs/substituents of monosaccharides (Table  S2 ) and typical mass losses corresponding to classical fragmentations of monosaccharides (Table  S3 ). Some fragmentation patterns revealed the type of linkage of monosaccharides (1.
This database was completed by adding the most characteristic motifs/substituents of monosaccharides (Table  S2 ) and typical mass losses corresponding to classical fragmentations of monosaccharides (Table  S3 ). Some fragmentation patterns revealed the type of linkage of monosaccharides (1. 2, 1. 3, 1. 4, 1. 6)  [19]. In total, a table of 180 neutral mass-loss patterns was generated, and a list of 32 m/z reflecting these mass losses was used to construct a distance matrix among these m/z values. Then the list of mass losses (neutral patterns) was used to search for patterns in the distance matrix with a precision of 5 decimal places; Table  S4  shows an example of such a pattern search in the distance matrix. The neutral mass loss patterns and potential structures assigned to the m/z list allowed for the reconstruction of the structure of potential fragments; an example is shown in Figure  S6.
The bands observed at 1751, 1371, and 1236 cm -1 in the FTIR spectrum are thus in line with the acetylation of sugar moieties. A band may also contribute at 1047 cm -1 , but probably superimposes with the dominating contributions from the sugar ring modes at 1066-1026 cm -1. The FTIR data corroborate the presence of OAc moieties in the EPS structure, as suggested by NMR and FT-ICR-MS analyses. The FTIR data are also in line with the presence of glucuronic acid, since β-Dglucuronic acid is characterized by intense bands at 1729 cm -1 (νC=O), 1089-1065 cm -1 -1 Figure  7. FTIR spectrum of the extracted EPS from R. tataouinensis TTB310 cysts. The spectrum was recorded in the 3900-400 cm -1 range with a 4 cm -1 resolution using an ATR device (Sen-sIR technologies, CT) equipped with a 9-bounce diamond microprism and ZnSe optics.
In this study, the equivalent size is defined as the ratio between the width and the length of hemp particle which has been measured using "Image J" software  [45]. A 10 g sample of hemp shivs was prepared and two-dimensional VD imaging is conducted by taking physical sections of the material and imaging the exposed face using a digital camera. To identify the shivs within the images, a threshold filter has been used to segregate the smoothed image, thus producing a binary picture which serves to measure the width and the length for hemp particles. The equivalent size distribution of hemp particles is shown in Fig. 1c. As shown in Fig. 2 , the SEM micrographs of the cross-section show a porous and complex structural morphology with size ranged from 10 to 50 µm of the cells. It can also be observed that the pores are organized in capillary form, thus making the reinforcement specimen highly performed as regards thermal insulation.
Micromagnetic Modeling We conducted micromagnetic modeling of individual tetrataenite grains using the Micromagnetic Earth Related Robust Interpreted Language Laboratory (MERRILL version 1. 6. 4) software  (Conbhuí et al. , 2018). MERRILL is an open-source software package that uses finite-element modeling to simulate the three-dimensional magnetic structure of a tetrahedrally-meshed grain. MERRILL finds the magnetization state by solving for the orientation of an array of dipoles that has the minimum magnetic energy associated with exchange, anisotropy, Zeeman, and internal demagnetization fields. A full description of MERRILL can be found in  Conbhuí et al. (2018). Cuboid grain meshes were created using the meshing software MeshRRILL  (Conbhuí et al. , 2018)  with a tetrahedral element size of 3 nm, which is the magnetic exchange length of tetrataenite as defined by Equation  12 in  Conbhuí et al.
The tetrataenite grains can be hundreds of nm to a few μm in size. We collected X-ray photoemission electron microscopy (XPEEM) images of duplex plessite in the ungrouped iron meteorite Bacubirito using Beamline 11. 0. 1 at the Advanced Light Source, Lawrence Berkeley National Laboratory (Supporting Information S1). Our X-ray circular magnetic dichroic (XMCD) images produced from the XPEEM data  (Stöhr et al. , 1998)  show the magnetization of kamacite and individual taenite/tetrataenite grains along the X-ray beam direction (Figure  7 ). We applied a magnetic field of increasing intensity in steps of ∼50 mT up to 324 mT and collected XMCD images at each step. We find that the uniformly-magnetized individual taenite/ tetrataenite grains have coercivities between 250 and 324 mT (Figure  S4  in Supporting Information S1 and Figure  5 ).
Acknowledgments We acknowledge and thank funding from  NASA FINESST  Grant  80NSSC20K1366  and  NASA  contract  NNM16AA09C , " Psyche: Journey to a Metal World. "We also acknowledge funding from the  UK Natural Environment Research Council Grants (NERC)  grants  NE/ S011978/1  and  NE/S001018/1  to W. W. C. M. acknowledges the financial support of the  European Marie Slokowska Curie Actions. We are grateful for the help of  Dr. Santiago Benavides  who provided assistance in setting up our modeling on the MIT Engaging Cluster. Additionally, we thank  Les Nagy  for productive conversations and suggestions for implementing MERRILL for this study. Lastly, we thank  Rajesh Chopdekar  and the  Advance Light Source (ALS)  at  Lawrence Berkeley National Lab  for setting up and maintaining the photon beam responsible for gathering our XPEEM data.
Discovery of risk loci by TWAS and MWAS TWAS was performed by implementing the PredictDB pipeline using mRNA expression data from 1,107 colorectal mucosa samples as reference (709 in house, 368 GTEx transverse colon)  9, 10. In addition to associations identified by GWAS or those previously reported by TWAS (PYGL and TRIM4  11, 12  ), we identified 15 novel associations at Bonferroni-corrected significance (P Bonferroni, Table  2 , Supplementary Tables  5  & 6 , Supplementary figure  4 ). We extended the main TWAS to a transcript isoform-wide association study (TIsWAS), both to ascertain whether specific transcripts could account for TWAS associations and to identify previously unreported risk associations (Supplementary Tables  7  & 8 ). For a third of TWAS genes, a significant association with CRC risk was found for a single mRNA isoform (Supplementary Table  7 ).
Effector genes and biological pathways of CRC oncogenesis A major, largely unfulfilled aim of cancer GWAS is to identify genes and functional mechanisms that may ultimately be clinically useful targets, for example in chemoprevention. The large GWAS and TWAS datasets in this study address this aim by enabling a detailed functional analysis of the molecular mechanisms contributing to CRC risk. Since TWAS approaches do not identify causal genes directly, we used our data to compile a set of 155 credible effector genes from the independent associations identified through GWAS, TWAS, TIsWAS and MWAS (details in Supplementary Table  17  and Supplementary Methods). We identified molecular pathways enriched in effector genes using Enrichr (https://maayanlab. cloud/Enrichr/) (Supplementary Table  18 ).
DISCUSSION We report a comprehensive genetic analysis of CRC risk in the general population. To identify the most credible effector genes for each risk variant, we performed detailed annotation using tissuespecific gene expression and other relevant data types. Our study is twice as large as previous CRC GWAS, and also includes participants of both European and East Asian ancestries, demonstrating that most loci are shared across these ancestral groups. This increased power for GWAS, coupled with complementary analyses, including TWAS and MWAS, identified 103 previously unreported risk associations and identified 155 effector genes. These data substantially expand our existing knowledge regarding the impact of common genetic variation on the heritable risk of CRC. The availability of large, multi-omic data sets has allowed us to assign the most likely target/effector genes of GWAS and TWAS associations (Fig.
Indeed, to identify SNPs explaining 80% of the heritable risk of CRC risk loci, thus providing comprehensive biological insights, will require sample sizes in excess of 500,000 cases and at least that number of controls (Supplementary figure  6 ). This is far higher than a previous estimate  23  , which was based on a small subset of the GWAS included herein. Extending GWAS to African and other populations may detect further risk SNPs, including population specific ones. Complementary approaches such as TWAS and MWAS are demonstrably useful for the discovery of further risk loci, especially if, and when, reference data sets from multiple populations are made available. Overall, our findings demonstrate the power of multi-omics to provide new insights into the biological basis of CRC, including both the identification of candidate effector genes and support for previously unsuspected functional mechanisms.
TABLES # indicates the number of novel TWAS loci. z score and effect size are calculated as the mean across S-PrediXcan models from the TWAS reference data sets. n models shows the number of reference data sets for which the S-PrediXcan elastic nets produced genetically-predicted expression models, with the n indep showing the number of those models that were statistically independent. The SNP with the lowest CRC GWAS P-value within 1Mb of the gene is also shown. 2 , SMultiXcan uses a two-sided F-test to quantify the significance of the joint fit of the linear regression of the phenotype on predicted expression from multiple tissue models jointly. All associations shown were transcriptome-wide significant after Bonferroni correction for 27,941 transcripts with an S-MultiXcan model (i. e. P = 0.