dataesr/scientific-paragraphs-categorization huggingface dataset:
By searching for the presence of the ULP1 domain in the proteomes of these bacteria, we identified a total of 228 SUMOproteases present in 91 strains, all of them containing a T3SS reinforcing the idea that these SUMO-proteases correspond to T3Es (Supplementary Dataset S5). A phylogenetic analysis on the 149 available full-length SUMOprotease sequences (Supplementary Dataset S5) revealed that Bel2-5 and Sup3 belong to two distinct clades (Fig. 3E ). Plotting on this tree the symbiotic properties of the corresponding strains reveals that strains of the Sup3 cluster predominantly nodulated A. indica. Furthermore, among these strains, four of them, in addition to NAS96. 2, lack ernA, suggesting that the presence of a Sup3-like SUMO-protease might confer the ability to nodulate A. indica. To support this hypothesis, we constructed sup3 (Ωsup3) and T3SS (ΩrhcN) insertional mutants in the B.
To investigate its subcellular localization in planta, Sup3 WSM1744 N-terminally fused with enhanced GFP and a triple hemagglutinin epitope tag (eGFP-3Flag-Sup3 WSM1744 ) was transiently expressed in tobacco leaf cells. As previously observed with ErnA  [12] , eGFP-3Flag-Sup3 was found to be targeted to the plant nucleus, suggesting that Sup3 triggers nodule organogenesis by interfering with host nuclear components (Fig. 7B ). Identification of three putative ET-Nods, Ubi1, Ubi2 and Ubi3, in Bradyrhizobium icense LMTR13 Except for the PAC68 strain, all ten tested strains from the B. jicamae supergroup nodulated A. indica while all lack an ernA homolog and most also lack a sup3 homolog, suggesting the existence of other ET-Nod(s) within the strains of this phylogroup (Fig. 2 ). Comparative analysis of the effectome of these different strains identified only one common T3E (NopM) present specifically in nodulating strains (Supplementary Dataset S4).
The absence of a mutant with (nearly) completely abolished nodulation capacity, as observed for the ernA or sup3 mutants in other strains, leads us to hypothesize that these three putative T3Es act synergistically. Ubi1, Ubi2 and Ubi3 show no homology to known functional domains, but a nuclear localization signal can be predicted for each of them using Localizer  [43]  suggesting that these T3Es are also targeted to the host nucleus (Fig. 8B ). Their distribution is relatively restricted; Ubi1, Ubi2 and Ubi3 homologs can be detected respectively in 8, 7 and 13 strains of the bradyrhizobia collection used here, among which a large proportion of strains belong to the B. jicamae supergroup (Fig. 2  and Supplementary Dataset S3). Ubi1, Ubi2 and Ubi3 share common conserved motifs found in the C-terminus of ErnA As the three new putative ET-Nods are proteins with unknown function, we used the MEME suite tools  [44]  to investigate if Ubi1, Ubi2 and Ubi3 share common motifs with other known and characterized T3Es.
The addition of the SUMO (SUMOylation process) or its cleavage by SUMO-proteases (deSUMOylation process) can  In the M2-1 motif, the mutagenesis was focused on the QL residues (deletion) considering that these residues are absent in the ErnA sequences clustering with ErnA USDA110 shown to be less active in nodulation triggering on A. indica (Supplementary Fig. S2 ). Box plots show the results of one of the two experiments performed independently (8 plants each). *p ≤ 0. 01, **p < 0. 005, significant differences between the wild-type strain and each mutant using a nonparametric Kruskal-Wallis test, NS: not significant. D Schematic representation of the different tested mutated forms of ErnA. The indicated SSS motif was identified as a putative DNA binding site using the software program DP Bind  [45].
ACKNOWLEDGEMENTS This work was supported by a grant from the  French National Research Agency  (" ET-Nod ";  ANR-20-CE20-0012 ), the  French Laboratory of Excellence  project " TULIP"  ( ANR-10-LABX-41 ;  ANR-11-IDEX-0002-02 ) and the  "Ecole Universitaire de Recherche (EUR) "  TULIP-GS  ( ANR-18-EURE-0019 ). AC was supported by a PhD fellowship from the  French Ministry of National Education, Higher Education and Research. We thank all the researchers who sent us their Bradyrhizobium strains:  Neung Teaumroong ,  Panlada Tittabutr ,  Kiwamu Minamisawa ,  Xinhua Sui ,  Christian Staehelin ,  Fabricio Cassan ,  Pekka Oivanen ,  Penny Hirsch ,  Kristen DeAngelis ,  Eric Roden ,  Els Vercoutere ,  Nadine Tollens ,  Hirohito Tsurumaru ,  Julia Vorholt ,  William Mohn ,  Luciane Passaglia ,  Dale Pelletier ,  Tania Wade ,  Jinjin Tao ,  Stephanus Venter ,  Ederson da Conceição Jesus ,  Eliana Gertrudes de Macedo Lemos ,  Matthew Parker ,  François Lefort ,  Djamel Chekireb ,  Eoin Brodie ,  Hans Martin Fischer ,  Mike Sadowsky ,  John Ferguson.
Study area and population Vietnam has eight national sentinel sites for JE surveillance, two of which were selected as centers for recruitment: Vietnam National Children's Hospital (VNCH) in Hanoi and Pasteur Institute in Ho Chi Minh City. These sites represent different geographical areas and approximately 52% of JE cases from around the country  [10]. Researchers examined databases at the two sites to identify eligible participants. Prospective participants were grouped by province and provincial subregions. The study team began contacting potential participants in the province with the largest case numbers and continued to reach the target sample size. The study sought to understand the costs of JE treatment during three phases: acute JE, initial rehabilitation and sequelae care, and long-term rehabilitation and sequelae care.
g. , cancer, HIV) that could inhibit ability to differentiate cost of care. In Laos, the study team examined the Mahosot Hospital database. We acknowledged the limitation of sourcing the cases from the tertiary hospital database, which may mean that the severity level may not be reflective of JE severity in the community; however, it is the only facility to provide laboratory-confirmed JE cases. The study team identified and contacted potential participants for recruitment until reaching the target sample size or running out of cases. There was not an adequate number of eligible cases in the database for the acute and initial sequalae groups, so the study team dropped the acute group to focus on the cost of initial and long-term sequelae care in Laos. The team did attempt to obtain hospitalization costs in the acute stage whenever available.
Sample sizes The study team used the following formula to estimate sample sizes for the acute and two sequelae groups:  [19] n ¼ ceiling precision 2 CV 2 � Z 2 1À a=2 þ 1 N 0 ! À 1 " # Precision is set at +/-10% of the mean cost, with coefficient of variation assumed to be 0. 5. Considering the estimated 252 annual JE cases per year since 2011 and Vietnam case fatality rate of 12%, the estimated total sample size is 240 in Vietnam  [7, 10]. According to the Mahosot Hospital database, there are 20 new laboratory-confirmed JE attributable AES cases per year in Laos. With the estimated mortality rate of 18%,  [11]  the total estimated sample size for the initial and long-term sequelae group is 79 in Laos. The number of cases in each study group are presented in S1 Table.
Data collection Data collection occurred between November 2021 and April 2022. Data collection procedures varied slightly between the acute and sequelae groups, although patient/caregiver consent was the initial step in all cases. In the acute costs group in Vietnam, study participants were interviewed using a structured questionnaire covering basic demographic information, socioeconomic status, direct medical and non-medical costs, and indirect non-medical costs incurred before, during, and up to 90 days after hospitalization. Study staff also undertook a review of hospital records to extract the direct medical costs associated with each case. The severity of each JE illness was captured by the Glasgow Coma Score (GCS) or a doctor's note on severity if GCS was not available in the patient's hospital records.
Expenses related to the disease were also of great concern to the families and were the cause of considerable stress and fear. The results of this study should be considered in the context of some limitations. First, the data were mainly self-reported by respondents and recall bias is unavoidable. However, we extracted clinical and expense data from hospitals to put forward evidence from the hospital perspective and supplement the household data. The study was conducted during the COVID-19 pandemic when most participants could not have a follow-up visit within 90 days. Although we asked the participants to compare their current access to care with pre-pandemic access, our estimation on expenses for re-examination of JE patients at hospitals might not fully reflect the counterfactual situation of non-pandemic.
Acknowledgments We would like to express our gratitude to the study participants and families in Laos and Vietnam for their involvement in this study. We acknowledge  Ashley Latimer  from  PATH  for the review and important inputs to the final manuscript;  Dr. Pham Van Khang  and  Dr. Tong Thi Thu Ha  from the  National Institute of Hygiene and Epidemiology ;  Ms. Nguyen Thi Huyen  from  Pasteur Institute Ho Chi Minh City ;  Dr. Pham Thi Lan Lien  from  Vietnam National Children Hospital ; and  Ms. Vayouly Vidhamaly ,  Dr. Xaykhamphet Phommavanh ,  Dr. Khanxayaphone Phakhounthong , and  Mrs. Anisone Chanthongthip  from  Lao-Oxford University-Mahosot Hospital-Wellcome Trust Research Unit  for helping in data collection, processing the samples, and administrative coordination. We deeply thank  Prof.
Furthermore, we report the diversity of genes encoding H3 lysine 36 (H3K36) trimethylation-specific methyltransferase in microalgae leveraging diverse sequencing resources including the Marine Microbial Eukaryote Transcriptome Sequencing Project database (MMETSP). Our study expands the repertoire of epigenetic marks in a model microalga and provides valuable insights into the evolutionary context of epigenetic-mediated gene regulation. These findings shed light on the intricate interplay between histone modifications and gene expression in microalgae, contributing to our understanding of the broader epigenetic landscape in eukaryotic organisms.
In this study, we used chromatin immunoprecipitation coupled with deep sequencing to investigate the distribution pattern and functional implications of H3K27ac and H3K36me3 in the model diatom P. tricornutum. Additionally, we sought to broaden our understanding of the H3K36me3 diversity in diatoms by analyzing the occurrence and evolution of H3K36me3-specific methyltransferase from other species using the Marine Microbial Eukaryote Transcriptome Sequencing Project database (MMETSP)  [42]. Furthermore, we undertook a re-examination of the epigenetic code that was previously established in P. tricornutum  [19, 43, 44]  and analyzed the mapping profiles of H3K27ac and H3K36me3 in relation to other histone marks. Our aim was to determine their co-occurrence pattern and effect on transcriptional regulation of genes and transposable elements in this unicellular organism.
Profile hidden Markov models (HMMs) for SETD2 genes was built using multiple sequence alignment data obtained from other model species. The robustness of the HMMs was supported by high bootstrap values, indicating a significant degree of conservation among SETD2 homologs in these species (Supplementary Figure  S1 ). Subsequently, we scanned the profile against published diatom genomes including P. tricornutum and T. pseudonana, in addition to transcriptome libraries such as the MMETSP database  [42]  (refer to the "Materials and Methods" section) and seven other diatom transcriptomes, namely Shionodiscus, Synedra, Pleurosigma, Navicula, Haslea, Guinardia, and Minidiscus, that were sequenced by Genoscope. A maximum likelihood phylogenetic tree was then constructed to understand the evolutionary divergence of SETD2 among unicellular eukaryotes (Figure  1B ).
Phylogenetic Analysis and Motif Analysis A representative set of published reference sequences of H3K36me3-specific methyltransferase was selected, and a multiple sequence alignment (MSA) was generated using the MUSCLE alignment tool in the Geneious 11. 0. 5 software, then a maximum likelihood tree was constructed with default parameters. Next, a profile Hidden Markov Model (HMM) was built using HMMER 3. 3. 2  [69]  to capture conserved sequence patterns and structural features of the protein family or domain. The HMM was then employed to search the MMETSP database for homologous sequences, and the results were filtered and curated to remove false positives and sequences with low similarity scores. To analyze motifs, the homologs search results were subjected to MEME-suite 5. 5. 3. org, an online software tool.
ChIP-Seq Analysis and Expression Analysis Pair-end sequencing of H3K27ac and H3K36me3 ChIP and input samples was performed on Illumina NovaSeq platform with read length of 2 × 150 bp. Previously published ChIP sequencing for H3K9me2, H3K9me3, H3K4me2, H3K27me3, H3K9/K14Ac and H3K4me3 were retrieved from NCBI's Gene Expression Omnibus accessions GSE68513 and GSE139676  [19, 25]. Raw reads were filtered and low-quality read pairs were discarded using Trim Galore 0. 6. 7 (https://doi. org/10. 5281/zenodo. 5127899 (accessed on 23 July 2021)) with a read quality (Phred score) cutoff of 20 and a stringency value of 3bp. Using the 25 to 25 telomere assembly published in 2021 as a reference genome, the filtered reads were mapped using Bowtie2 2. 4. 5  [73]. We then performed the processing and filtering of the alignments using Samtools 1.
15 "fixmate -m" and "markdup -r" modules  [74]. Two biological replicates for each ChIP were performed and read counts showed a good Pearson correlation by Deeptools multiBamSummary v3. 5. 1 with a bin size of 1000 bp  [75]. To identify regions that were significantly enriched, we used MACS2 v2. 2. 7. 1  [76]  on the combination of the two replicates with "callpeak --qvalue 0. 05 --nomodel --SPMR --bdg" options. In addition, extension size was set to the arithmetic mean of the two IP replicates fragment size for each mark, as determined by MACS2 predicted module with "-m 2 70" MFOLD value. Furthermore, we used MACS2 with calling mode "--broad" to call peaks for H3K37ac and H3K36me3. Output normalized Fold Enrichment signal files were generated with MACS2 v2. 2. 7. 1 "bdgcmp" module and transformed to BigWig using Deeptools bedGraphToBigWig.
Aeolus carried the first Doppler wind lidar to measure wind profiles from space. Aeolus was a European Space Agency explorer mission with the objective to retrieve winds data from the collected atmospheric return signal that is the result of Mie and Rayleigh scattering of laser emitted light by atmospheric molecules and particulates. During the course of the mission the quality of Aeolus winds measured in clear-air conditions from Rayleigh-channel-collected data, so called Rayleigh-clear winds, improved substantially. The same is true for winds measured in cloudy and aerosol-rich atmospheric conditions from Mie-channel-collected data, the so-called Mie-cloudy winds. For the latter conditions, good quality winds can in principle also be obtained from Rayleigh-channel-collected data, the so-called Rayleigh-cloudy winds, if contamination of the purely molecular signal by Mie scattering is well addressed.
In addition, NWP model data have proven to be useful to detect instrumental imperfections and developing schemes to correct for these imperfections. In the context of Aeolus, it was found that the temperature variations of the telescope primary (M1) mirror caused substantial biases in Rayleigh winds, relative to European Centre for Medium-Range Weather Forecasts (ECMWF) model winds, which was not foreseen before launch. This finding was used to develop a correction scheme, based on M1-temperature monitoring data and ECMWF model data, which reduced biases of Rayleigh-clear winds -that is, Rayleigh winds measured in clear-air conditions -to a level acceptable for use in data assimilation as part of NWP  (Weiler et al. , 2021). By comparing Mie winds with ECMWF model winds,  Marseille et al. (2022)  found biases in the Mie winds that could be traced back to imperfections of the in-orbit instrument calibration procedure.
A linear and nonlinear parametrization are considered to quantify the contamination. The dataset used to develop the nonlinear parametrization is discussed in Section 4. Quantification of the parameters in the parametrization and their evolution are presented in Section 5. The resulting Rayleigh-cloudy winds from the parametrization are discussed in Section 6. The potential use of Rayleigh-cloudy winds in NWP is discussed in Section 7. A summary and discussion are provided in Section 8.
This relationship is computed as part of the instrument calibration suite software and includes the convolution of the transmission curves, which are obtained from in-orbit instrument calibration, and a theoretical description of the Rayleigh-Brillouin spectrum, whose shape depends on atmospheric temperature and pressure and its central position on Δf  (Dabas et al. , 2008). The computation is performed for a range of combinations (T i , P 𝑗 , Δf k ). Each combination provides values for N A and N B , and the resulting RR values, RR(T i , P 𝑗 , Δf k ), which are stored in a three-dimensional look-up table. The resulting so-called Rayleigh-Brillouin calibration auxiliary file, in short denoted AUX_RBC, is used as input of the level-2B processing (L2Bp) for HLOS wind estimation as follows. For each Aeolus observation the RR is calculated from the measured photons in Rayleigh channels A and B.
DATASET The dataset used in this study is from the Aeolus second reprocessing activity. The total dataset covers the period June 24, 2019, until October 10, 2020, covering the first part of the operational period of the second laser flight model (FM-B). The second reprocessing was based on level-1 and level-2 processor versions deployed in October 2020 (L1Bp 7. 09. 1, L2Ap 3. 11. 1, L2Bp 3. 40. 2), used for near-real-time generation of baseline 11 data products. The second reprocessing campaign covers a longer FM-B period with a later processor baseline version (B11 instead of B10) compared with the first reprocessing campaign. The N and P setting in this period of the mission was P = 18, N = 30, with P the number of accumulated consecutive pulses within one measurement, which corresponds to a distance of about 2.
This additional wind from accumulating all measurements is denoted Rayleigh-all. The Rayleigh-all wind is experimental and not yet part of the L2Bp, but is shown to be a useful extension to the dataset for estimating the parameters of NLINC. Clearly, the total Mie contamination (and related scattering ratio) of the Rayleigh-all wind is less or equal to the corresponding Rayleigh-cloudy wind.
Estimation of NLINC parameters with use of a priori NWP information Given the Aeolus observation location, date, and time, an estimate of the wind can be obtained from the AUX_MET dataset. Next, the Doppler-shifted frequency corresponding to this NWP model wind, Δf NWP , is obtained following Equations 2-4. AUX_MET contains model temperature and pressure information in addition, which allows one to determine the corresponding RR value for clear air atmospheric conditions from the right panel of Figure  1  and denoted RR 0 (P, T, Δf NWP ). Next, we calculate the RR increment from the difference between the measured Rayleigh response in cloudy conditions and the NWP-based reference for clear-air conditions: ΔRR(𝜌, Δf NWP ) = RR meas -RR 0 (P, T, Δf NWP ). (8) The   2  with corresponding error bars denoting plus/minus two times the standard deviation 𝜎 of the mean value.
For the August 2019 dataset this value is close to -100 MHz, (i. e. , 29 m⋅s -1 HLOS wind velocity); see Figure  3  also. To conclude, it is noted that the RR increment defined in Equation (  8 ) includes instrument noise and Mie contamination from the first term on the right-hand side and NWP model errors from the second term on the right-hand side. Averaging over a large data sample, as done in this section, reduces the impact of instrumental noise and model errors (which are assumed unbiased for a large data sample) on the parameter estimates. Then, the RR increment Equation (  8 ) can be fully attributed to Mie contamination, which can be well parametrized through Equation (  7 ) as shown in this section.
The laser frequency adjustment change caused a change of the zero Doppler-shifted frequency position relative to the atmospheric path FP A and B curves (see left panel of Figure  1 ), and hence a change of the centre of the RR curves. The Rayleigh cover temperature correction changes the shapes of the FP A and B curves, and hence the RR curves in the right panel of Figure  1. The use of a limited dataset explains the larger error bars of the estimated parameters in Figure  5. It should be noted that the vertical distribution of the 24 range bins was not fixed over this 1 year period. In fact, there were changes of the settings in 2019 in mid July, end of September, early October, mid October, and end of December and in 2020 at the end of March. These changes were motivated to improve Aeolus sampling of seasonal effects, such as the sampling of polar stratospheric clouds during the Antarctic winter period and dedicated campaigns as part of Aeolus calibration/validation (Cal/Val) activities; for example, the atmospheric motion vector (AMV) campaign (first half of October 2019) with very dense Mie sampling below 3 km for optimal collocation of Mie winds with AMV winds,  Hoffman et al.
Indeed, the NLINC option better fits a Gaussian pdf, which is broader for small values but shows no tails for large values (not shown). These pdf characteristics explain the smaller SMAD value for LINC but, on the other hand, a larger value for the standard deviation. It was also found that pdf of the LINC option, more winds were rejected for this option (0. 9%) than for the NLINC option (0. 0%). Finally, the use of unbiased observations is a strong prerequisite for data assimilation. In addition, since most data assimilation systems assume observations with errors following a Gaussian distribution, winds from the NLINC option are expected to provide better information to NWP than winds from the LINC option. Figure  8  shows no evidence of a wind-speed-dependent bias for either the LINC or NLINC options.
The range bin settings hardly changed over the 2020 period, and the increase of the random error over this period can largely be attributed to the gradually decreasing atmospheric return signal; that is, related to instrument degradation; for example, see  Lux et al. (2021). From the bottom panel, the choice of either the LINC or NLINC option has hardly any impact on the total number of Rayleigh-cloudy winds. The smaller data count for July 2019 has been explained in Section 5. 2. The variability of the curves is most probably explained by the changing range bin settings over the complete period and to a lesser extent by the gradual reduction of the atmospheric return signal. Recent results from Aeolus flight model A (FM-A) are found in Appendix C, which has the nice characteristics of fixed range bin settings and stable performance of the instrument over the evaluation period.