dataesr/scientific-paragraphs-categorization huggingface dataset:
Assessing the accuracy of the observation-based, super-resolved maps is not trivial, as large-coverage, high-resolution observations of the surface topography/currents are not presently available. As such we followed a double approach: on the one hand, we performed a qualitative analysis of the reconstructed patterns, looking at areas of intense mesoscale activity visible in the satellite SST L4 data and comparing the (sub)mesoscale eddies identified by the AMEDA automatic detection algorithm in original and super-resolved data; on the other hand, we built a match-up database with the surface current estimates provided by surface drifters (see Section 2) and used it to compute the statistics of the differences with respect to the geostrophic currents estimated from standard altimeter ADT and super-resolved ADT field.
Acknowledgments: We thank  Emanuela Clementi  for providing support on the numerical simulation data used for the setup of the observing system simulation experiment and  Milena Menna  for providing support on the drifter database. We also wish to thank  Claudia Cesarini  for critical reading of the paper and useful suggestions on the presentation of the results. We acknowledge the use of imagery from the  NASA  Worldview application (https://worldview. earthdata. nasa. gov,last accessed  14 January 2022 ), part of the NASA Earth Observing System Data and Information System (EOSDIS).
Funding: This work has been partly supported by the  Copernicus Marine Environment Monitoring Service Multi Observations Thematic Assembly Center (CMEMS MOB TAC ), funded through subcontracting agreement no. CLS-SCO-18-0004 between  Consiglio Nazionale delle Ricerche and Collecte Localisation Satellites (CLS) , the latter of which is presently leading the CMEMS MOB TAC. The 83-CMEMS-TAC-MOB contract was funded by  Mercator Ocean  as part of its delegation agreement with the  European Union , represented by the  European Commission , to set up and manage CMEMS.
Data Availability Statement: The dADR-SR code is available at: https://github. com/bbuong/ dADR-SR (last accessed 14 January 2022) and training/test datasets are available at: https://doi. org/ 10. 5281/zenodo. 5815330 (last accessed 14 January 2022). The AMEDA code and description are freely accessible through https://github. com/briaclevu/AMEDA (last accessed 14 January 2022).
Image Analysis and Statistics Fiji software (https://fiji. sc/#, accessed 3 October 2022)  [70]  was used for image processing and western blot quantification. Statistical significance was analyzed by two-way ANOVA with post hoc Bonferroni's multiple comparisons test, or student-t test for comparisons of two groups using GraphPad Prism version 8 (GraphPad Software, San Diego, CA, USA). At least three biological replicates were utilized in each group for statistical analysis. Results are shown as mean ± SEM. , p < 0. 05 was reported as statistically significant.
org/10. 5281/zenodo. 6644859. To get a rough estimate of the model uncertainty, we run an ensemble of three present-day and future simulations. For ensemble member A, the ice shelf and seabed topography is extracted from BedMachine-Antarctica-v1. 33  (Morlighem et al. , 2020) , while members B and C are based on BedMachine-Antarctica-v2. 0  (Morlighem, 2020). Additionally, B and C include a representation of grounded tabular icebergs, in particular B22A (Antarctic Iceberg Tracking Database,  Budge & Long, 2018) , whose ungrounded parts are treated as an ice shelf, and the line of icebergs grounded on Bear Ridge  (Bett et al. , 2020; Nakayama et al. , 2014). The ocean-ice-shelf heat exchange coefficient (Γ T ) is 2. 21 × 10 -2 in A versus 1. 88 × 10 -2 in B-C, while the ocean-ice-shelf salt exchange coefficient is always defined as Γ S = Γ T /35.
Acknowledgments This study was funded by the  European Union 's  Horizon 2020 research and innovation programme  under Grant agreements  869304  ( PROTECT  contribution number  46 ),  820575  (TiPACCs), and  101003826  (CRiceS). It was also partly funded by the  French National Research Agency  under Grant  ANR-19-CE01-0015  (EIS). This work was granted access to the HPC resources of CINES under the allocation  A0100106035  attributed by  GENCI.
Data Availability Statement The model version and set of parameters used to run our experiments are provided in https://doi. org/10. 5281/zenodo. 6644859. The main model outputs (climatological values) are provided in https:// doi. org/10. 5281/zenodo. 7225758. All the python scripts used to build the figures are provided in https://doi. org/10. 5281/zenodo. 7224252 and are mainly based on the Xarray  (Hoyer & Hamman, 2017) , Numpy  (Harris et al. , 2020)  and Matplotlib  (Hunter, 2007)  packages.
In order to ease the exposition, we introduce a deterministic algorithm which from a (randomized) regular algebraic program P and an input pair (X, k), produces a computation trace of P on input (X, k): T ← ComputTrace(P, X, k). This computation trace contains the leaking tuples of all the instructions executed by P on input (X, k). Concretely, ComputTrace initializes T to the empty list. Then, it sequentially evaluates each instruction in P and adds its leaking tuple to T. Specifically, • for an index instruction of the form ind i1 ← op(ind i2 , ind i3 ), ComputTrace adds the pair (ind i2 , ind i3 ) to T ; • for an algebraic instruction of the form X i1 ← Op(X i2 , X i3 ), ComputTrace adds the tuple (X i2 , X i3 , i 1 , i 2 , i 3 ) to T ; • for a randomization operation of the form X i1 ← R(X i2 ) ComputTrace adds the tuple (X i1 , X i2 , i 1 , i 2 ) to T.
On the other hand, the simulation experiment calls the simulator Sim on input Π, X and F which produces a simulated leakage output (ℓ 1 ,. , ℓ z ). ExpReal 1 (A, Π, F): Figure  5 : Game 1. This game is similar to Game 0 but the (deterministic) ComputTrace algorithm is replaced by a (probabilistic) ComputTrace ′ algorithm. In the latter, the computation trace is produced T is produced in a similar way as with the former algorithm, that is by evaluating the program on the inputs X and k. But this time, the output of each randomization operation R is randomly sampled from A (according to the hiddenness property of the leakage). ExpReal 2 (A, Π, F ′ ): 1. (X, k) ← A() 2. k ← Enc(k) 3. T ← ComputTrace ′ (Π, X, k) 4. (ℓ1,. , ℓz) ← LeakageSampler(T , F ′ ) 5. Return (ℓ1,. , ℓz) ExpSim 2 (A, Π, F ′ , Sim): 1.
operations, either inside the inner loop for j ̸ = j * i , or outside the inner loop, completely leak their input values. We explain hereafter how to complete a perfect simulation of such a leakage from Π (the program description) and X the input tuple of algebraic variables. The corresponding simulator is denoted Sim * (Π, X, J ) where J = {j * 0 ,. , j * n-1 } is the set of non-leaking indexes. All the operations in Step 1 (the preprocess procedure) have inputs and outputs derived from X which makes all the leakage in this step perfectly simulable from X (by simply evaluating preprocess on X). Then, for the first iteration i = 0 of the main loop, the leakage of Step 3 can also be perfectly simulated from X. The simulation Sim * then generates k j 0 uniformly at random from [0, 2 w ) for j ∈ [0, d]\{j * 0 }.
RNAseq data analysis The quality of the raw RNA-seq data was evaluated with the FastQC tool,  51  and Trimmomatic 52 was applied to remove the adaptors sequences at the beginning of the reads. The E. coli strain K-12 MG1655 genome fasta and gff files ( GCF_000005845. 2_ASM584v2_genomic ) were downloaded from the National Center for Biotechnology Information website ( https://www. ncbi. nlm. nih. gov/). The genome was indexed using Bowtie2,  53  and the mapping was performed using TopHat. 54 Read counts were obtained using HTSeq count script  55  and normalized considering transcript length and sequencing depth to obtain RPKM. 56 The ARSyN method  57  was applied to remove batch effects, and effective batch effect removal was checked by principal component analysis. Differential expression was assessed using the Limma package, applying the Voom transformation.
58 ignificant differentially expressed genes were called at an false discovery rate ( FDR ) < 0. 05. All scripts are available at https: //github. com/vdclab/RNAseq-analysis. Gene IDs were obtained from EcoGene 3. 0 ( ecogene. org, accessed on December 2017 )  59  and Gene Ontology ( GO ) term annotations ( Gene Ontology Consortium, 2021 ) were obtained from the EcoCyc database ( ecocyc. org, accessed on December 2017 ). 60 Raw data ( fastq files ) and processed data have been deposited in NCBI's Gene Expression Omnibus  61  and are accessible through GEO Series accession number GSE181239.
The Q-TOF was operated at high Paper | 5 sensitivity ( 4 GHz ) in positive ion mode with the following source conditions: gas temperature 350 ºC, drying gas 13 l/min, fragmentor 360 V. Capillary voltage was manually adjusted between 1800 to 2150 V to maintain a steady nanospray plume. Data were acquired from 300 to 1700 m/z with an acquisition rate of 6 spectra/s in MS mode, and from 50 to 1700 m/z with an acquisition rate of 3 spectra/s in MS/MS mode. A peptide isotope model ( charge state 2 + ) was used to detect a maximum 20 precursors per cycle at a minimum threshold of 25 000 counts/spectra at a narrow isolation window ( ∼1. 3 m/z ). Sloped collision energy ( C. E. ) was used to maximize collision induced dissociation of detected isobarically tagged peptides according to the following rules: charge state 2 + C.
E. slope 4. 2, offset 3. 5; charge states ≥ 3 + C. E. slope 4. 2, offset 4. LC-MS data was extracted and evaluated for quality using the molecular feature extraction ( MFE ) algorithm in MassHunter Qualitative Analysis software ( v B06. 00 ). Test injections ( 3-4 ) from each fraction of the first technical replicate were used to optimize injection volumes for second and third biological replicates with the aim of maximizing the number of extracted molecules with peptide-like features. For each fraction, the MFE list of molecular ions was exported and used to exclude spectral acquisition of these ions in subsequent technical replicates. Each of the 24 fractions from biological triplicates were injected in technical duplicate-spectra generated from technical replicate #1 were acquired without use of an exclusion list, whereas spectra generated from technical replicates #2 were acquired with the exclusion list.
Data from MassHunter Qualitative Analysis were exported to Mass Profiler Professional ( v B03. 00 ) for analysis of technical reproducibility. This process was repeated for all three biological replicates. Mass spectra were processed using Spectrum Mill ( Agilent, v B06. 00 ) and Scaffold Q + ( v Scaffold_4. 8. 8 ) , and quantified protein associations were manually analyzed by binning and averaging peptide quantities across related protein groups in Excel.
Gene-specific codon usage analysis A codon counting algorithm  63  was used to calculate the genespecific codon usage frequencies for all genes in the E. coli genome using proteomics data. Over-or under-representation of a specific codon for a particular gene relative to the genome average was determined by calculating the average Z-score based on a hypergeometric distribution with a cut-off of P < 0. 01. The upregulated and downregulated proteins in the tgt mutant relative to wildtype E. coli were selected based on the log 2 ( tgt /WT ) values, and codon usage analysis was performed for the filtered set of genes. Gene-specific codon usage data were visualized as a heat map using GraphPad Prism 9, as was multivariate statistical analysis. The principal components with the largest eigenvalues that together explain 75% of the total variance were selected for analysis.
Proteomics of the tgt mutant confirms altered biological processes identified in the transcriptomics analysis Total extracts of E. coli WT and tgt mutant strains in LB medium, prepared from the same cultures used for the RNA-seq experiment, were labeled with TMT Isobaric Mass Tags and analyzed by chromatography-coupled Orbitrap mass spectrometry as described in Methods section. This analysis produced 23 326 spectra that were assigned to 855 E. coli proteins ( peptide threshold: < 1% FDR ) across all three experimental replicates ( protein threshold: > 99% confidence, minimum of 2 peptides ). Fold-change values were calculated to obtain relative protein abundance ( tgt _LB/WT_LB ). Proteomics data is available in Supplemental File 4 and was uploaded to the CHO-RUS database ( https://chorusproject.
Another interesting protein with increased levels in the mutant is the putative ATP-dependent protease YcbZ, which is linked to translation. The ycbZ mutant showed increased levels of stop codon readthrough, and the use of a complementation plasmid rescued the fidelity and fitness defects of the mutant. 77 The least abundant protein in the mutant is YoaE. This member of the UPF0053 is not functionally characterized but GEO profiles searches shows that yoaE expression is induced in the fur mutant and repressed in the WT when these strains are exposed to gentamicin ( Profile GDS5162/1768340_s_at, https://www. ncbi. nlm. nih. gov/geoprofiles/). yoaE expression is also induced in a strain deficient for the hydrogen peroxide-scavenging enzymes ( Profile GDS5163/1768340_s_at ). A comparison between gene expression ( transcriptomics ) and protein abundance ( proteomics ) is displayed in Supplemental File 5.
Investigation of Q influence on codon usage Given the role of wobble Q in modulating reading of NAU and NAC synonymous codons by G 34 U 35 N 36 anticodons on tRNAs corresponding to Asn, Asp, His, and Tyr,  1  we assessed the codon usage patterns using our transcriptomics and proteomics data. Exploring our transcriptomics dataset, we analyzed the differences in the relative frequencies of the codons NAC and NAU ( of the amino acids Tyr, His, Asp, and Asn ). We observed that the frequencies of these codons are not consistently elevated or depleted in the genes with differential expression when compared with their genomic frequencies ( data not shown ). Using proteomics fold-change data for proteins upregulated and downregulated by the loss of Q-inserting tgt ( Supplemental File 4 ) , we calculated codon usage frequency relative to genome average for the top 37 up-and 33 down-regulated proteins, and then analyzed this data relative to protein up-and downregulation values by principal components analysis.
Expression of the iron import system is repressed in the Q -strain While FNR and ArcA activities seem to be repressed, an induction of the activity of the master regulator Fur is clear in the tgt mutant. Most of the E. coli iron homeostasis genes belong to the Fur regulon [dataset] ( https://regulondb. ccg. unam. mx/)  84 and many of these genes are repressed in the tgt mutant ( Fig. 5 C , Table  1  ). This includes genes involved in iron import and synthesis of siderophores, such as feoABC , fepABCEG , fhuACDE , nfeF ( yqjH ) , entED , cirA , and tonB/exbBD. Consistently with most of Fur regulon genes, downregulation of ryhB expression was observed in the tgt mutant. RyhB is a small RNA that represses the expression of iron-containing proteins, hence reducing iron consumption under low-iron conditions.
Loss of tgt makes E. coli more sensitive to aminoglycosides A few links between Q biosynthesis genes and antibiotics treatment have been already observed. A TnSeq fitness study showed that, in organisms like Pseudomonas simiae and Desulfovibrio vulgaris , insertions in the Q related genes tgt , queC , queF, and gluQ are usually detrimental to the growth phenotype under gentamicin stress  67  [dataset] ( https://fit. genomics. lbl. gov/cgi-bin/orthCond. cgi?expGroup= stress&condition1=Gentamicin%20sulfate%20salt ). The expression levels of yhhQ were increased in E. coli exposed to sublethal Fig. 6  Oxidative stress phenotypes and investigation of ROS levels. ( A-D ) Growth curves of E. coli WT and tgt strains monitored in Bioscreen C Analyzer at 37°C with constant shaking. Error bars showing standard deviation for biological triplicates.
Data availability The data underlying this article are available in the article and in its online supplementary material. For high throughput data: RNAseq scripts are available at https://github. com/vdclab/ RNAseq-analysis RNAseq raw data ( fastq files ) and processed data have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE181239. Available at https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE181239 Proteomics data was uploaded to the CHORUS database under ID 3655. Available at https://chorusproject. org/anonymous/ download/experiment/2a0b77c87014485a993df01e7744a5a6.
While unsaturated zone thickness is not considered during Step 1 (potential recharge equals recharge), it is expected that Step 2 reveals its importance. As developed below, we focus on water table and recharge anomalies, here called "fluctuations". These fluctuations are obtained by removing the mean value from input data (potential recharge and pumping rates used during Step 1) over the study period. Consequently, simulated WTFs obtained during Step 1 will be compared to observed fluctuations. Finally, RFs are obtained from observed WTFs (Step 2) and will be compared to potential RFs. In the next sections, we describe (1) the analytical GW flow model deployed to estimate recharge from observed WTFs, (2) the soil models used to estimate potential recharge as input to the GW model, (3) the GW parameter calibration strategy and (4) the analytically inverted GW model.
Groundwater-level data While first GW-level data at Ploemeur date back to 1991, we focus our analysis on the 1996-2017 period to avoid potential transient responses to the pumping setup. At the Guidel site, data are available from 2009 to 2017. Water levels are recorded at minute to daily time steps and decimated to daily timescales for our analysis. Hydraulic heads measured in boreholes are corrected from atmospheric pressure variations. In Ploemeur, GW levels are relatively deep due to the pumping (depth ∼ 7 to 30 m; see Fig. C1 ) but still contain seasonal and interannual variability (Fig. 3 ) due to both pumping and climate variations. Note that WTFs in Ploemeur boreholes have quite similar patterns. Seasonal variations decrease with the distance to the pump: the amplitude is 5 m at well F11, 2.
Acknowledgements. This work is part of the  ANR  project  EQUIPEX CRITEX  (grant  ANR-11-EQPX-0011 ) and relies on the  Ploemeur Critical Zone Observatory  H + (SNO H +, http://hplus. ore. fr/en/ploemeur, last access: 4 May 2022). We are grateful to the OZCAR research infrastructure (http://www. ozcar-ri. org/, last access: 4 May 2022). We thank  Mikhail Smilovic  for revising an earlier version of the manuscript for grammar and syntax. Financial support. This research has been supported by  Université de Rennes 1. Review statement. This paper was edited by  Gerrit H. de Rooij  and reviewed by  Ty P. A. Ferre  and one anonymous referee.
Code and data availability. Data and MATLAB models developed for this study, as well as models with different configurations, are available at https://hplus. ore. fr/en/guillaumot-et-al-2022-hess-data (Géosciences Rennes/Université de Rennes 1, 2022). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/hess-26-5697-2022-supplement.
Ethics statement We received authorisation from the Department of Reproductive Health of the Ministry of Health in Vietnam, and the four participating hospitals ethically approved the research. We obtained ethical clearance for the study from the local and institutional review boards from the Centro Rosarino de Estudios Perinatales of Rosario, Argentina (Record Notice No. We designed the DAT using the Ottawa Decision Support framework  [7]. We devised a 3-step approach to generate and evaluate the tool. We used data collected in hospitals in Vietnam to identify needs for decision support. Then, we conducted an overview of the literature to provide evidence for decision support, while we assessed acceptability in hospitals in Argentina, Burkina Faso, and Thailand. Table  1  shows the characteristics of the participating hospitals in the four countries.
The same method was used to achieve a diverse sample of postpartum women in terms of age, religion, ethnicity and mode of birth (caesarean or vaginal delivery). We recruited and interviewed women and their companion separately, immediately after delivery and before discharge from the study hospitals. Each interview and focus group was facilitated in the participants' respective languages by a female data collector with experience in conducting in-depth interviews or focus group discussions, and audio-recorded if consent was obtained. We conducted focus group discussions separately with obstetricians and midwives to encourage the expression of opinions outside of the clinical hierarchy. We asked women and providers about the possible reasons for the high rates of caesarean section in Vietnam and what they needed to prepare them to discuss the most appropriate planned mode of delivery.
Based on the ecological model to understand factors influencing caesarean rates  [17] , we analysed the recordings and interpreted the data using a thematic analysis approach. Step 2: Providing evidence for a holistic decision support tool We designed the DAT in two sections (S1 Text). The first section aims to inform women during antenatal care (ANC) visits about the risks and benefits of caesarean section and vaginal delivery. The second section aims to help women clarify their values and thus prepare them to discuss their preferences with a health care professional during following visits. We performed an overview of the literature to provide evidence-based information on the risks and benefits of both modes of delivery. We included systematic reviews (SRs), overviews, or agency statements/reports that provide risk estimates for short-and long-term maternal and child outcomes of women who planned for a caesarean section (but in few cases may have had vaginal delivery instead) compared to women who planned for a vaginal delivery (but ended up with either a vaginal delivery or an emergency caesarean section).
Acknowledgments We developed this decision-analysis tool using the format of the decision aid created by  Marylène Dugas ,  Nils Chaillet , and  Allison Shorten  entitled 'Giving birth after a caesarean section: Making an informed choice' (Dugas,  2016 ) and the recommendations of the research group on the decision support tool from the  Research Institute of the Ottawa Hospital (OHRI) , affiliated with the  University of Ottawa.
Motivation and fatigue ratings with time on the task After each minute during testing, participants were asked to report their level of fatigue ('How tired do you feel now?') and motivation ('How motivated do you feel?') using a visual analogue scale. Responses were registered by clicking on the appropriate position on each scale. After completing all ratings, a 'confirm' button appeared at the bottom of the screen, allowing participants to validate their ratings and start the next block. To control the time between blocks and to reduce variance between participants, a 15 s countdown timer was displayed at the top of the screen, and the next block would begin automatically once the timer lapsed. There were seven participants who had missing ratings. The exclusion of these participants did not affect the rating results or behavioural data and there were no group differences in age, gender or any questionnaire measures (P.