dataesr/scientific-paragraphs-categorization huggingface dataset:
These small differences still have a significant effect on the bulk optical properties. The ARTS sector snowflake provides less scattering than the Liu equivalent, and simulations of very thick clouds using the ARTS sector snowflake and the  Field et al. (2007)  PSD can be up to 20 K warmer around 300 GHz (shown in Fig. 9  later). Using an identical a and b it is possible to eliminate this difference. However, it is preferred to retain the values previously used with the Liu database to ensure full back-reproducibility, but for the ARTS database to use the coefficients that are supplied with that database. Further discussion on the importance of the mass-size relation is found in Sects. 3. 1 and 3. 2. 4. In this work, it is important to realise that when the bulk optical properties of a particle habit are discussed, this is the net result of both the physical characteristics of the individual particles and the effect of the corresponding mass-size relation on the PSD.
For particles from the Liu or ARTS databases, the integration size range is taken from Table  3 , with. Numerical integration options illustrated using the reconstruction of the implied water content (Eq. 15), for the F07 T PSD with the ARTS large plate aggregate and T = 223 K. The mass distribution is a(Dg) b ng(Dg), using the PSD before renormalisation. The specified water content is (a) l = 1×10 -6 kg m -3 and (b) l = 1×10 -4 kg m -3. The resulting renormalisation factor (r, see Sect. 3. 2. 2) is indicated in the legend. The integration options are the new integration with Dmin = 1×10 -4 m (100 µm) cutoff (black crosses), the new integration extended down to Dg =5×10 -6 m (black line), or the old integration (red line). two exceptions. First is that the size range can optionally be extended down to the relevant D min from Table  2.
If selected, the relevant optical properties are computed using Mie theory, assuming this is valid for particles smaller than the minimum particle size (see Sect. 2. 1). If that option is not selected, then a minimum size D min = 1×10 -4 m is applied when the F07 PSD is used with the ARTS shapes, to avoid extrapolating the PSD. Note that as described in Sect. 2. 1, for the implementation of the Liu database in the hydrotable generator, the D min = 1×10 -4 m constraint was imposed unilaterally in Table  3 , meaning that it affects the Liu particles no matter which PSD is chosen (this behaviour is undesirable but is preserved for back-compatibility). The numerical integration methods are illustrated in Fig. 5  using the computation of the implied water content (Eq. 15, next https://doi. org/10.
Representing preferentially oriented particles The Liu and ARTS particles available in the hydrotable generator (and obviously the Mie sphere) represent only randomly oriented particles. However, ice hydrometeors are often preferentially oriented, as revealed by polarisation signatures in the high-frequency channels of microwave imagers (e. g. Defer et al. , 2014; Gong and Wu, 2017). The ARTS database has recently been extended with a more advanced representation of particle orientation, giving particles a preferred canting angle, but retaining random orientation in azimuth  (Brath et al. , 2020). However, this generates optical properties that are fully polarised, i. e. scattering can transfer energy from one polarisation to another. To model such fully-polarised optical properties would require the whole Stokes vector to be represented in the radiative transfer, but this is not available in a scalar fast model like RTTOV-SCATT.
However it is not yet possible to specify α as a function of frequency. The polarisation ratio approach works best for conical microwave imagers with zenith angles around 50 and will require further scientific development. Further, an approach for radar backscattering needs to be developed. The "full" channel representation provides a framework for future support of single-particle optical property databases based on oriented particles (e. g. Brath et al. , 2020). 4 Effect of optical properties on brightness temperatures.
The effect of cloud ice in this scenario is to warm the brightness temperatures, since cloud ice has relatively low SSA. This warming effect of, for example, cirrus over a strongly scattering lower cloud has surprised a number of investigators (e. g. Xie et al. , 2020; Barlakas et al. , 2020). Where the dashed and solid lines join is where the clouds become optically thick and where below-cloud radiation becomes irrelevant. An interesting feature is that around 300 GHz to 500 GHz, snow and graupel clouds produce their lowest scattering TB depressions. Above this frequency of maximum scattering, these clouds start emitting more radiation again and brightness temperatures are higher. Figure  9  shows brightness temperatures for the standardised ice or snow cloud using the F07 tropical PSD with all options from the ARTS and Liu databases.
The amount of brightness temperature depression from the ARTS particles roughly follows the amount of extinction in Fig. 2  and hence the progression broadly from low density aggregates and snowflakes to dense rimed particles. The Evans snow aggregate is again the least scattering, with TB dropping only to 150 K, and again the ICON hail is the most scattering particle, dropping to 90 K. The frequency of maximum scattering varies from around 500 GHz for the Evans snow aggregate to around 200 GHz for the ICON hail. Between the extremes of light aggregate and dense hail, ARTS database provides a good spectrum of potential scattering properties. As mentioned before, the Liu database has bigger gaps and some of the particle shapes are almost redundant. The least-scattering Liu particle is the dendrite snowflake, but this still generates a relatively deep TB depression (as low as 130 K at 340 GHz).
By contrast the ARTS database provides the column aggregate and Evans aggregate, which produce even less scattering. This provides an important new capability to reproduce cloud ice signatures. Hence the ARTS column aggregate was required in the study of  Geer (2021b)  and is used in the new default RTTOV-SCATT configuration (Table  1 ). At the strong scattering end in Figure  9b , the Liu columns and thick hex plate are more strongly scattering than the ICON hail from the ARTS database, giving TBs as low as 70 K around 180 GHz. Hence these Liu shapes continue to provide a capability that is not available from the ARTS database. However it is interesting that the biggest variations in brightness temperature depression are in frequencies below 200 GHz. The studies of  Geer and Baordo (2014)  and  Geer (2021b) , based on SSMIS observations, have already benefitted from a spectral region of great sensitivity to particle habit and PSD.
Conclusion This work has summarised the process of generating bulk hydrometeor optical properties based on physical assumptions about the sizes, masses, habits and orientations of cloud and precipitation particles. It documents the hydrotable generator for microwave and sub-mm scattering radiative transfer in version 13. 0 of RTTOV (Radiative transfer for TOVS,  Saunders et al. , 2018 Saunders et al. , , 2020)) , a widely used satellite simulator, hopefully with relevance to the users of many similar tools. The work has overviewed the bulk optical properties and brightness temperatures generated by Mie spheres and two databases of nonspherical ice particles  (Liu, 2008; Eriksson et al. , 2018). A focus has been the existing and newly supported particle size distributions (e. g. McFarquhar and Heymsfield, 1997; Petty and Huang, 2011; Heymsfield et al.
In the event that a much richer microphysical representation is required, RTTOV-SCATT allows an unlimited number of hydrometeor categories that could be used to represent almost any level of microphysical complexity. Hence the tools are already available, but the underlying issue is whether is possible to appropriately specify this complex microphysics. There are many ways to further improve the representation of bulk scattering from hydrometeors, most of them applicable generally: -RTTOV (and many other codes) use a different mechanism and different physical assumptions for the optical properties of clouds in the IR and solar regions. A major future development should be to produce optical properties from the microwave to the UV using the same approaches as much as possible; this requires non-spherical scattering databases to be expanded to cover the whole range, a process that is only starting (e.
g. Yang et al. , 2013; Ding et al. , 2017; Baran et al. , 2018). -More work is needed to unpick the tight coupling between the particle shape model, mass-size relation and the particle size distribution. Better ways of mapping bulk hydrometeor mass to particle ensembles would be very welcome. -More work needs to be done to standardise and/or interface the physical assumptions, such as shape, PSD and mass-size relation, with assumptions made in atmospheric models. This would particularly support efforts to learn better physical models of cloud and precipitation, directly from observations (e. g. Schneider et al. , 2017; Geer, 2021a). -A global effort is ongoing to standardize and package many of the different scattering databases that are becoming available (see  Kneifel et al. , 2018) ; once complete, interfaces will be added to allow the user a much broader choice of particle models.
g. Johnson et al. , 2016; Leinonen and von Lerber, 2018)  needs to be included. This will be particularly important for simulating radar backscatter in the melting layer (the bright band). It would also improve brightness temperature simulations, which may otherwise be too low in channels and situations sensitive to the melting layer, possibly by up to 8 K  (Bauer, 2001). This survey has also revealed a few issues more specific to the RTTOV hydrotable generator: -The lowest temperature bin, at 203 K, is at least 20 degrees higher than the lowest tropospheric temperatures. Hence, in a future version, it is necessary to extend the lower temperature range of frozen particles permitted by the table generator. Note that, although the lowest temperature point in the ARTS database is 190 K,  Eriksson et al.
Results E2F1 transcription factor is a major player in miRNA regulation during Salmonella infection. To investigate the importance of E2F1 downregulation to the miRNA expression changes occurring during Salmonella infection, we have performed a comparative analysis of miRNA expression datasets obtained from small RNA sequencing of Salmonella-infected HeLa cells and E2F1 knockdown cells. HeLa cells are an epithelial cell line widely used to study infection by bacterial pathogens, including Salmonella. Upon Salmonella infection, 258 miRNAs (47% of the total detected miRNAs) were downregulated and 112 miRNAs (20% of total) were upregulated (at least 1. 4-fold compared to mock-treated cells; Fig. 1a ). In E2F1 knockdown cells, 245 miR-NAs (44% of total) were downregulated and 41 miRNAs (7. 5% of total) were upregulated (at least 1.
e. 33%) were commonly downregulated in Salmonella-positive and bystander populations (at least 1. 4-fold compared to mock-treated cells; Fig. 1g, h ). A panel of eight miRNAs (miR-15a-5p, miR-15b-5p, miR-16-5p, miR-22-3p, miR-421, miR-744-5p, let-7i-3p, and let-7i-5p) was selected for further validation by quantitative reverse transcription PCR (qRT-PCR). The selection of these miRNAs was motivated by the miRNA expression datasets described above, our analysis of miRNA function during Salmonella infection  27, 28  , as well as on having been previously described as E2F1-dependent miRNAs  22, 23, 25. In perfect agreement with E2F1 downregulation (Fig. 1e , f), we observed that the expression of these miRNAs was decreased both in Salmonella-positive and bystander cell populations (Fig. 1i ).
RNA-seq datasets from three independent experiments are shown. Only miRNAs with a number of reads ≥20 in both controls are shown. d Comparison of miRNA expression changes in Salmonella-infected cells and naive cells treated with the secretome of Salmonella-infected cells. Color code for datapoints identical to panel c, based on average values. e Expression levels of the mature forms of selected miRNAs, determined by qRT-PCR in naive HeLa cells treated with the secretome of Salmonella-infected cells, analyzed at 24 h post-treatment. Results are normalized to naive cells treated with secretome collected from mock-treated cells. f E2F1 protein levels, determined by western blot, in naive HeLa cells treated with the secretome of Salmonella-infected or mock-treated cells and digested or not with proteinase K, analyzed at 24 h post-treatment.
m-p Levels BiP protein (m), IRE1 phosphorylation (n), ATF6 protein (o), and PERK phosphorylation (p), determined by western blot, in naive HeLa cells treated with the secretome of Salmonella-infected or mock-treated cells. Tunicamycin treatment was used as a positive control for ATF6 and PERK activation. Salmonella infection was performed at MOI 100. Results are shown as mean ± s. e. m. of n = 5 (b, e, f) biologically independent experiments; western blots are representative of n = 3 (h) or n = 5 (b, f, i-p) biologically independent experiments; **P < 0. 01 and ***P < 0. 001 (statistical analysis is detailed in Supplementary Data 1); Source data are provided as a Source Data file. confirmed by quantifying XBP1 splicing in cells treated with tunicamycin (Supplementary Fig. 4k ). According to the literature, the activation of the JNK pathway by the IRE1 pathway is independent of its endonuclease activity  48.
Signals were detected using SuperSignal West Dura Extended Duration Substrate (Pierce, 34075) using an ImageQuant LAS 4000 CCD camera (GE Healthcare). Of note, long-term treatment with tunicamycin results in a blockade of Nlinked glycosylation, which results in a fully unglycosylated ATF6 detectable as a faster migrating form of ATF6 (approx. 100 kDa). This unglycosylated ATF6 form cannot undergo proteolytic processing into the active ATF6 transcription factor  82, 83. Quantification of western blots was performed with ImageJ. Uncropped images of immunoblots are included as Source Data. Sample preparation, mass spectrometry data acquisition, and analysis. Twenty-two milliliters of cell culture medium from HeLa cells mock-treated or infected with Salmonella (MOI 100) were collected at 14 hpi and processed as described above.
Full mass scan was acquired with mass range 350-1400m/z in profile mode in the FT with a resolution of 70,000. The filling time was set at a maximum of 100 ms with a limitation of 3 × 10 6 ions. Data-dependent acquisition was performed with the resolution of the Orbitrap set to 35,000, with a fill time of 120 ms and a limitation of 2 × 10 5 ions. A normalized collision energy of 32 was applied. A loop count of 10 with count 1 was used and a minimum AGC trigger of 2e 2 was set. Dynamic exclusion time of 30 s was used. The peptide match algorithm was set to "preferred" and charge exclusion "unassigned", charge states 1, 5-8 were excluded. MS2 data were acquired in profile mode  86. IsobarQuant  87  and Mascot (v2. 2. 07; Matrix Science, London, UK) were used to process the acquired data, which was searched against the Uniprot Homo sapiens proteome database (UP000005640) containing common contaminants and reversed sequences.
The following modifications were included into the search parameters: Carbamidomethyl (C) and TMT10 (K) (fixed modification), Acetyl (N-term), Oxidation (M), and TMT10 (N-term) (variable modifications). For the full scan (MS1) a mass error tolerance of 10 ppm and for MS/MS (MS2) spectra of 0. 02 Da was set. Further parameters were set as follows: trypsin as a protease with an allowance of maximum two missed cleavages; a minimum peptide length of seven amino acids; at least two unique peptides were required for protein identification. The false discovery rate on peptide and protein level was set to 0. 01. The raw output files of IsobarQuant (protein. txt-files) were processed using the R programming language. Only proteins that were quantified with at least two unique peptides were considered for the analysis.
Moreover, only proteins that were identified in two out of two mass spec runs were kept. Nine hundred and forty-eight proteins passed the quality control filters. Raw signal-sums (signal_sum columns) were first cleaned for batch effects using the "removeBatchEffect" function of the limma package (v. v3. 34. 5)  88  and further normalized using vsn (variance stabilization normalization  89  ). The four replicates of the HeLa wild-type condition were normalized separately to maintain the higher protein abundance in this condition. Proteins were tested for differential expression using the limma package. A protein was annotated as a hit with a false discovery rate (fdr) smaller 5% and a fold change of at least 100% and as a candidate with an fdr below 20% and a fold change of at least 50%. Functional analysis of the proteins significantly increased in the secretome of Salmonella-infected cells compared to mock-treated cells (722 proteins) was conducted using the Ingenuity Pathway Analysis Software (IPA, Ingenuity Systems).
Datasets were uploaded into IPA and analyzed for functional enrichment in terms of "Canonical pathways", based on the information in the Ingenuity Knowledge Base. Enrichments were calculated by IPA using multiple hypothesis correction based on the Benjamini-Hochberg method; corrected P values are shown. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  90  partner repository with the dataset identifier PXD018026 (http://www. ebi. ac. uk/pride/archive/projects/PXD018026). Fluorescence staining and confocal microscopy. Cells seeded in glass coverslips in 24-well plates and treated as described above were fixed with 4% paraformaldehyde (PFA) for 15 min at RT, permeabilized with 0. 5% Triton-X-100 in PBS for 10 min. Nuclei were counterstained with Hoechst 33342 (1:5,000; Life Technologies, H3570).
0 Fluorometer (Invitrogen). Small RNA-seq libraries were prepared manually from 250 ng of total RNA using the NEBNext Multiplex Small RNA Library Prep kit (New England Biolabs, E7300L). Obtained libraries that passed the QC step, which was assessed on the Agilent Bioanalyzer system, were pooled in equimolar amounts. Ten picomolar solution of each pool of libraries was loaded on the Illumina sequencer HiSeq2500 and sequenced uni-directionally, generating ~190 million reads per run, each 62 bases long. For data analysis, the quality of the reads was analyzed using FastQC (version 0. 11. 8). The reads were trimmed and adapter sequences were removed using Trimmomatic (version 0. 38)  91. The reads in fastq format were converted to fasta format using fastq_to_fasta commands in the FASTX Toolkit (version 0.
0. 13). Further read processing, including polyA-tail removal, size filtering (minimal length 12 nt. after clipping), alignment of the reads, computation of alignment statistics, and annotation, was performed using the READemption pipeline (version 0. 4. 3) 92 with default parameters, with Segemehl (version 0. 2. 0)  93. Reads were mapped against the human mature miRNA sequences provided by miRBase (release 22). For normalization, read counts of each miRNA species were normalized by those of miR-29a-3p (a miRNA that does not change with Salmonella infection or E2F1 knockdown). This normalization strategy has been previously used for the normalization of small RNA-seq datasets in which global changes of miRNA expression are observed  94. The demultiplexed FASTQ files and coverage files have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession numbers GSE147362 (Salmonella-infected cells), GSE147361 (E2F1 knockdown samples), and GSE147363 (cells treated with secretome).
Statistical consideration was not used to determine the animal sample size. Animals were randomly assigned to experimental groups; data blinding was not performed. Statistical analysis. Unless otherwise indicated, data are presented as mean ± standard error of the mean (s. e. m. ), with the exact number of experiments performed indicated in figure legends. Statistical analysis was performed using Prism Software (GraphPad). Normal distribution of the data was assessed by the Shapiro-Wilk test. For statistical comparison of datasets from two conditions, twotailed Student's t-test or multiple t-test corrected for multiple comparison using the Holm-Sidak method were used; for data from three or more conditions/groups, one-way ANOVA with Tukey's or Dunnett's post hoc test or two-way ANOVA with Tukey's multiple comparison test was used.
Acknowledgements C. A. is a recipient of a fellowship from the  Bayerischen Gleichstellungsförderung (BGF)  through the  SCIENTIA Program  and S. C. is a recipient of a Ph. D. scholarship from the  Portuguese Foundation for Science and Technology  ( 2020. 06572. BD ). The authors acknowledge the  EMBL Proteomics Core facility  (in particular  Mandy Rettel  and  Frank Stein) , the  EMBL Genomics Core facility  (in particular  Vladimir Benes  and  Nayara Trevisan Doimo de Azevedo) , and the  Animal facility at the University of Leon, Spain  (in particular  Ana Carvajal  from the  Department of Animal Health ). This work was supported by grants from the  Bavarian Ministry of Sciences, Research and the Arts in the framework of the Bavarian Molecular Biosystems Research Network (BioSysNet) ,  DFG  project  BR 4837/1-1 ,  ERDF -European Regional Development Fund  through  COMPETE 2020  and the  Portuguese Foundation for Science and Technology  ( POCI-01-0145-FEDER-007440 ,  UIDB/ 04539/2020 , and  FCT Investigator Programme IF/01105/2015 ) to A.
Data availability The data supporting the findings of this study are available from the corresponding author upon reasonable request. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD018026. The demultiplexed FASTQ files and coverage files have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession numbers GSE147362 (Salmonella-infected cells), GSE147361 (E2F1 knockdown samples), and GSE147363 (cells treated with secretome). The Uniprot Homo sapiens proteome database (UP000005640) can be accessed at https://www. uniprot. org/ proteomes/UP000005640. The human mature miRNA sequences can be accessed at http://www. mirbase. org/. Source data are provided with this paper.
3 2Fig. 1 E2F1 31 Fig. 1 E2F1 plays a crucial role in miRNA regulation in Salmonella-infected and bystander cells. a Heat map showing miRNA expression changes in Salmonella-infected cells and E2F1 knockdown cells. Results are shown as log2 fold change compared to mock-treated cells, or cells transfected with control siRNA, respectively. RNA-seq datasets from three independent experiments are shown. Only miRNAs with a number of reads ≥20 in both controls are shown. b Comparison of miRNA expression changes in Salmonella-infected cells and E2F1 knockdown cells. Color code for datapoints identical to panel a, based on average values. c E2F1 protein levels, determined by western blot, in HeLa cells infected with Salmonella or mock treated, analyzed at 1, 4, 8, and 20 hpi. d Schematic representation of the experimental design.
RNA-seq datasets from three independent experiments are shown. Only miRNAs with a number of reads ≥20 in the mock-treated cells are shown. h Comparison of miRNA expression changes in Salmonella-positive and bystander cells. Color code for datapoints identical to panel g, based on average values. i Expression levels of the mature forms of selected miRNAs (miR-15a-5p, miR-15b-5p, miR-16-5p, miR-22-3p, miR-421, miR-744-5p, let-7i-3p, and let-7i-5p) determined by qRT-PCR in the total population, Salmonella-positive and bystander cells. Results are normalized to mock-treated cells. j Expression levels of the mature forms of selected miRNAs determined by qRT-PCR in HeLa cells transfected with control or E2F1 siRNAs. Results are normalized to cells transfected with control siRNA. Salmonella infection was performed at MOI 100.