dataesr/scientific-paragraphs-categorization huggingface dataset:
5 from the experimental ones, with both tools reaching ∼ 90% of success. According to the ssNA type, the RNA sequences dataset benefits at a lower extent from the inclusion of suboptimal predictions as compared to the ssDNA sequences dataset (Fig. 2 , Additional files 4 and 5). For what concerns this latter, we observed an improvement of ∼ 10% and ∼ 25% in the percentage of correctly predicted structures ( Apta D = 0 ) for mfold and RNAfold, respectively, when considering the top 5 subopti- mal predictions as compared to the single MFE structure. Extending the analysis to the top 10 predicted structures has no clear impact, except when using RNAfold under the Mathews DNA model that leads to a further improvement of ∼ 11%. It is interesting to observe that, when considering only the MFE structure, the prediction accuracy of mfold and RNAfold are equivalent; conversely, when suboptimal solutions are included in the analysis, RNAfold can correctly predict a higher number of ssDNA secondary structures ( 72% ) as compared to mfold ( 55% ) (Fig.
2b ). When the close to experiments predictions are included ( Apta D ≤ 1. 5 ), mfold poorly benefits from the computation of suboptimal solutions, with an improvement < 10%. Conversely, the percentage of RNA- fold predicted structures close to the experimental ones increases by 21% , reaching an excellent percentage of 90% when using the Mathews DNA model. A closer look at the experimental structures can explain the remaining wrongly predicted structures. For example, the structures extracted from 5GWL, 5GWQ, 6J37, 6M0B, and 6M0C PDB IDs correspond to a recently characterized DNA folding, called mini-dumbbell  [38]. This folding is hardly detected as a possible folding by mfold or RNAfold because its thermodynamics parameters have been determined in the late '90/early 2000. In addition, this folding is characterized by loops with a length of 2 nucleotides, which are not allowed by both mfold and RNAfold, as previously mentioned.
5 (Fig. 5 , Additional file 3). CONTRAfold is based on a machine learning model tuned on experimental data. It is therefore not surprising that it struggles in correctly predicting the secondary structures of short sequences ( ≤ 15 nucleotides), which are much less frequently characterized. In addition, we found 32 predictions with consecutive open and closed brackets, which is normally penalized by a thermodynamics algorithm by attributing a high energetic score penalty. This is not taken into account by CONTRAfold, and results in predictions with this illegal base pairing pattern, heavily affecting the secondary structure prediction of 5 of the sequences included in the dataset. In addition, like MFE-based methods, CONTRAfold fails in some cases (58, among which 3KTW, 3HXO, 3WC1, 5FJ4, and 6D12) to correctly predict the secondary structure of ssNAs whose experimental secondary structure corresponds to the proteinbound one.
Moreover, CONTRAfold does not make use of the extended dot-bracket notation and, therefore, cannot correctly predict structures containing pseudoknots. This is also a limit of MXfold2  [8] , which implements a deep neural network algorithm combined to Turner's nearest-neighbor free energy parameters. Indeed, it does not implement the extended dot-bracket notation, leading to the incorrect prediction of pseudoknots, and it incorrectly predicted the mini-dumbbell motif. In addition, it sometimes fails in correctly predicting the secondary structures of ssNAs in complex with proteins. Nevertheless, it is the tool providing the best performances, with ∼ 48% of predictions identical to the reference ( Apta D = 0 ) and only ∼ 11% incor- rectly predicted ssNAs ( Apta D > 1. 5 ) (Fig. 5 ). Interestingly, MXfold2 is the only tool capable to output a folding for all the ssNA sequences included in the dataset, making it a good option regardless of the length of the ssNA.
Among the selected tools, 3 are able to handle pseudoknots, namely MC-fold, Ufold, and SPOT-RNA. Nevertheless, MC-fold can predict only H-type pseudoknots. In our dataset, this type of structure is present in 17 out of the 77 structures containing pseudoknots. Without specifying the "pseudoknot" keyword (i. e. under the default parameters), none of the H-type pseudoknots was predicted with an Apta D ≤ 1. 5 , whilst 6 of the other classes of pseudoknots structures were predicted as close to the reference one. The inclusion of the "pseudoknot" keyword allowed to improve the prediction of 8 of the 17 H-type pseudoknots ( Apta D ≤ 1. 5 ), but it reduced to 3 the other types of pseu- doknots predicted with an acceptable distance from the reference. Moreover, it is worth mentioning that many structures have not been predicted by MC-fold due to errors during execution.
5 ). In particular, in both cases most of the predictions resulted in an Apta D ≤ 1. 0 (Additional files 13 and 14), or even ≤ 0. 5 for UFold, indicating that a very few base pairs were incorrectly predicted. For example, the oligonucleotide with PDB ID 3WC2: the UFold predicted structure has an Apta D = 0. 091 , because the bases involved in the pseudoknot are shifted by only one position. The SPOT-RNA prediction has a slightly higher Apta D of 0. 915, because the experimentally detected pseudoknot is shifted by 4 positions and an additional pseudoknot between positions 8 and 14 is predicted (Additional file 15). The slightly better performances of UFold in dealing with pseudoknots, as compared to SPOT-RNA, were somehow expected since it already proved its accuracy for this kind on structures  [9].
This is observable also on the whole dataset since Ufold was able to correctly predict 33% of the ssNAs, while SPOT-RNA provided 21. 9% of predictions with Apta D = 0 (Fig. 5 ). Therefore, if we focus only on the exact predictions, these two tools are globally less performing than MFE-based tools and MXfold2. This is probably due to the fact that these latter methods make use at different extent of thermodynamics parameters, which can take into account relevant factors, such as temperature and salt concentration, known to be fundamental in ssNA folding. In addition, deep learning methods strongly depend on the training dataset, and, therefore, on the available data. Nevertheless, when including predictions close to the reference ( Apta D ≤ 1. 5 ), the UFold and SPOT-RNA performance increases to 89 and 87% , respectively, making them comparable to mfold, RNAfold and MXfold2 (Fig.
Accuracy of approaches applicable to both MFE-and knowledge-based methods In addition to MFE-based and knowledge-based approaches, algorithms implementing modifications in the original approaches exist. For example, Linearfold  [7]  implements an algorithm with a linear time complexity, which makes it suitable for the prediction of long ssNA structures. It is built on both MFE-based and knowledge-based models and it has been explicitly developed for long sequences, for which traditional tools, such as mfold or CON-TRAfold, might be time-consuming. Indeed, both Linearfold-C and Linearfold-V equally succeed to find structures close to the reference ( Apta D ≤ 1. 5 ) for long ssNAs (PDB ID 1GRZ (247 nucleotides) and 2R8S (159 nucleotides)), where others have failed, although they have low predictive performance on short sequences ( ≤ 15 nucleotides, ∼ 10% of the data- set), which are predicted as completely unstructured.
Therefore, they can represent a good alternative to either mfold/RNAfold or CONTRAfold when dealing with long sequences. Like Linearfold, CentroidFold  [6]  can be associated with both MFE-based and knowledge-based models, although, as recommended by the developers, we only tested it under the model implemented by RNAfold. The coupling of this model with the γ-centroid estimator was able to correctly predict 36 of the ssNA structures contained in the dataset. This percentage increase to ∼ 77% if we include the predictions showing an Apta D ≤ 1. 5. This suggests that the CentroidFold γ-centroid estimator is slightly less performing compared to the MFE or MEA estimators in finding the optimal solution (i. e. identical to the experiments), although it is as able to provide a solution close to the experimental one ( Apta D ≤ 1.
To conduct this evaluation, we considered 538 ssNAs secondary structures retrieved from the PDB and NDB databases. The dataset included both ssDNA and RNA sequences, with a length range from 7 to 1509 nucleotides, and a wide range of structural motifs spanning from simple hairpins to pseudoknots. We also included ssNAs whose structure has been determined in complex with their target protein. We used as comparison metrics AptaMat, a ssNA secondary structure comparison algorithm we have recently developed. We observed that only 4. 3% of the considered ssNAs secondary structures (PDB IDs: 2F87, 1ESH, 1I46, 1JZC, 6FK5, 1IK1, 2Y95, 1UUT, 2LPA, 1JWC, 4QIL, 1ATV, 3Q0A, 1A1T, 1K6G, 4A4S, 1JVE, 1YTB, 2LUP, 4OOG, 6U82, 3THW) are correctly predicted by all the aforementioned prediction tools. These ssNAs share a common secondary structure, which includes a hairpin/stem-loop, with a long hairpin stem of 4 to 14 base pairs, and a hairpin loop of 3 to 5 nucleotides.
When considering predictions close to the experimental structure ( Apta D ≤ 1. 5 ), this percentage increases to 56. 9%. In addition, at least one of the secondary structure prediction tools can provide a prediction identical and close to the experimental secondary structure in 63. 9% and 98. 7% of the cases, respectively. The MFE-based tools, namely mfold and RNAfold, remain among the best performing prediction tools, with ∼ 46% and 47% of exact predictions for mfold and RNAfold, respectively. These percentages increase to 83% and 82% , when including predictions close to the experiments ( Apta D ≤ 1. 5 ). Nevertheless, a knowledge-based tool, CON- TRAfold, and a recently published software, MXfold2, that is based on deep learning coupled with thermodynamics models, showed similar results.
This indicates that, as it has been shown for many other bioinformatics challenges  [39] [40] [41] , artificial intelligence has a high potential to solve the question of ssNAs folding prediction. This is also shown by two recent tools exploiting deep learning methods, namely UFold and SPOT-RNA: although for our dataset they provided a low percentage of exact predictions (33 % and 21. 9% , respectively), they predicted 89% and 87% , respectively, of structures close to the experimental one ( Apta D ≤ 1. 5 ), performing slightly better than the MFE-based methods. Overall, particular structural motifs are a major limitation to the performance of the prediction tools. In particular, most of them do not implement the extended dot-bracket notation and models capable to describe pseudoknots. UFold, and at a slighter minor extent SPOT-RNA, are the only tools providing acceptable predictions for this kind of structures, with 65 out of 77 and 59 out of 77 predictions with an Apta D ≤ 1.
5 , respec- tively. Conversely, MC-fold could not provide any significant improvement in the predictions of the pseudoknotted structures of the dataset, and it has a high runtime, making it the least performing prediction tool. For MFE-based methods, including the possibility of handling pseudoknots would require a huge effort. Therefore, in our opinion the only way to deal with this kind of structure is to use artificial intelligence, thanks to the increasing number of experimentally available ssNAs structures. Mini-dumbbell, a quite recently characterized ssNA motif, is also hard to correctly predict since the number of mini-dumbell structures is limited and adequate parameters are yet to be determined. Moreover, among the tools developed for the prediction of RNA secondary structures, only mfold and RNAfold propose parameters specific to ssDNA sequences.
Discussion A systematic review of the literature including eighty-four references was performed to analyze the complications after different prostate biopsy approaches (3). The most frequent complications were hematuria and hematospermia, irrespective of the biopsy approach. The urinary retention rate was higher after transperineal approach (4-11). In the European Randomized Study for Prostate Cancer (ERSPC) study, the incidence of hematuria lasting more than three days was 22. 6%  (12). In our study, hematuria lasting more than two days was considered to be a late side effect, for which the incidence was 17. 1% for the standard approach and 12. 2% for standard plus targeted approach. Data for the impact of the number of cores on hematuria is conflicting. In our study, the number of cores was associated with the rate of early side effects but not that of late side effects.
Independent predictors of complications requiring hospitalization were age and comorbidities  (19). The retrospective analysis of Nam et al reported an overall hospitalization rate of 1. 4%, with no significant differences based on age  (14). The study Loeb et al from the SEER database reported a 6. 9% hospitalization rate (4) and the Rotterdam section of the ERSPC a 0. 8% hospitalization   (33) , whereas one found a re-admission rate of 0. 7%  (11) , and several a re-admission rate of 1% (Table  VI )  (34) (35) (36) (37) (38). Recently, a multicenter retrospective cohort study evaluated MRI-targeted prostate biopsies, comparing targeted transrectal and transperineal biopsies. They found that the transperineal MRI-targeted prostate biopsy approach may increase the detection of clinically significant prostate cancer relative to the transrectal MRI-targeted prostate biopsy approach, in particular, for cancers located in the apex, transition/central zone, and anterior zone  (2).
Data Analysis To allow for a comparison of the types of fishing gear, the fishing effort was expressed as the number of fishing days at sea, as obtained from the fishermen's interviews. The mean sea turtle bycatch per unit effort (BPUE), according to the fishing gear, was expressed as the number of turtles caught per vessel per day. When available, the national fishery statistics were related to the BPUE to give an estimate of the global annual impact of one gear métier on sea turtles in Tunisia. A rough estimate of the global impact of fishing gear was subsequently obtained by multiplying the yearly mean number of sea turtles captured per vessel by the number of vessels recorded in the Tunisian national statistics. For each major type of gear (longlines, nets, and trawls), the generalized linear model (GLM)  [26, 27]  and the generalized linear mixed model (GLMM) were used to establish the relationships between the sea turtle bycatch and the different covariates related to the gear characteristics, fishing operations (including fishing efforts), and seasonal and geographical variation.
Acknowledgments: The data presented here were collected thanks to funding received from the Rufford foundation, a Ford environmental grant, and SWOT. We are grateful to those organizations. Special thanks to all the fishermen involved in the survey who collaborated with us and consented to answer during the interviews in different ports. We are indebted to the personnel in different administrations in the ports who facilitated our field work. Special thanks to RASTOMA (Réseau des Acteurs de la Sauvegarde des Tortues Marines en Afrique centrale) and NGB (Notre Grand Bleu association).
Our first objective is to present the original ice-supersaturation/contrails parameterization, based on results from mesoscale simulations and calibrated according to expert judgment. We also detail how we have modified it to reach a better agreement with results from previous studies. Evaluation of the model parameterization performance against different observational datasets is presented. Our second objective is to explore the parametric dependence of contrail radiative forcing to different calibrations of ARPEGE-Climat. We use the approach developed by  Peatier et al. (2022)  with a few minor changes. First, a PPE is generated and evaluated by comparing model climatology with observational/reanalysis data. Then, to more densely sample the parameter space at lower computational cost, statistical emulators based on multilinear regressions are used to predict the contrail radiative forcings and the associated model performances.
Model and Baseline Experiment The PPE is based on ARPEGE-climat version 6. 3  (Roehrig et al. , 2020) , the atmospheric component of CNRM-CM6-1  (Voldoire et al. , 2019). ARPEGE-Climat 6. 3 (ARPEGE thereafter) used in this work has a spatial resolution of about 150 km, with 91 vertical levels from near 6 m to 82 km (or 0. 01 hPa). The ARPEGE released version will be referred to as the ARPEGE baseline version. Alternative versions (meaning with different sets of adjustable parameters) of this model will be called ARPEGE candidates. Experiments under investigation in this work are AMIP-type simulations, where the atmospheric model is forced by observed sea surface temperatures and sea ice concentrations. The emission data used to represent aviation's climate forcing were generated during the project QUANTIFY, an integrated project funded through the EU-Framework Program 6 (https://www.
For instance, the relative humidity with respect to ice is a key control parameter for the formation of cirrus in the upper troposphere. An additional metric built on the relative humidity measurement from the IAGOS database would further strengthen our analysis. In addition, metrics based on cloud properties such as ice crystal effective radius and optical depth could also be introduced thanks to the MODIS data (https://modis. gsfc. nasa. gov/data/). Finally, the same optimization algorithm could also be applied to other atmospheric models with different contrail parameterizations to assess whether the key control atmospheric parameters are dependent on the contrail representation.
Acknowledgements. The study was supported by the  OCTAVIE  project from the  Occitanie (France) region  (grant  n°249586 ). This work benefited from previous work by  Odile Thouron  and  David Saint-Martin  on the adaptation of the cloud scheme for modeling condensation trails. The authors thank  Romain Roehrig  for discussions and support with the definition of the perturbed parameters and  Dominique Bouniol  for providing observational data on high clouds fraction.
3) with Y = P C i , the ith PC of the EOF analysis (from a total N = 5), x j the parameter value, a j the regression coefficient estimated based on the training of the model, R the intercept and K = 22 the number of perturbed parameters. The RF emulator is trained to predict directly the contrail radiative forcing values (in mW. m -2 , calculated as for Equation 3 with Y = RF). During the evaluation process, both emulators are trained on 150 members of the PPE, the 40 other members being used as the test dataset to estimate the out-of-sample error : OSE = 40 i=1 (pred itrue i ) doi. org/10. 5194/egusphere-2023-2478 Preprint. Discussion started: 14 November 2023 c Author(s) 2023. CC BY 4. 0 License.
Patient Material Plasma samples were collected serially (0 and 3 months) from 98 randomly selected patients among those enrolled in the Danish multicenter study Optimized treatment in Early RA [OPERA; registered at ClinicalTrials. gov (NCT00660647)]. Details of the study design have been previously published  [25, 26]. Briefly, treatment-naïve patients with newly discovered RA were randomized to a conventional MTX treatment, plus placebo (DMARD + PLA) or MTX, in combination with adalimumab, an anti-TNFα agent (DMARD + ADA). At entry (baseline), patients had a disease duration (from first persistently swollen joint) of <6 months (average 3 months) and had moderate to severe RA, defined as a 28-joint DAS with CRP (DAS28-CRP) (four variables, CRP-based) >3. 2. Radiographic progression was assessed by the delta total Sharp score (TSS).
Disease activity was evaluated by the simple disease activity index (SDAI), Clinical disease activity index (CDAI), and DAS28-CRP. DAS28-CRP is based on CRP, the number of swollen  (28 and 40)  and tender joints  (28 and 40) , and a physician's global assessment of the disease activity. The baseline DAS28-CRP was 5. 75 (CI 5. 54-5. 96), which was reduced to 2. 1 (1. 8-3. 2) after 3 months in both treatment arms and to 2. 0 (1. 8-2. 7) after 24 months (Table  1 ). Clinical and laboratory data were acquired at the same time as the plasma samples were collected, but also after 24 months wherefrom, plasma samples were not assessed. In addition, we obtained a paired set of plasma (P) and synovial fluid (SF) from patients with established RA (n = 18) in a single-center cross-sectional study at the outpatient clinic at Aarhus University Hospital.
Flow Cytometry FLSs were blocked with 0. 1 mg/mL mouse IgG (Jackson ImmunoReseach, West Grove, PA, USA) and 0. 1 mg/mL rat IgG (Jackson ImmunoReseach) for 15 min and then surface stained with CD34-PerCP-eFlour710 (Clone: 4H11, Thermo Fisher Scientific, Waltham, MA, USA), CD45-APC-Cy7 (Clone: HI3, Biolegend), THY-1-PE-Cy7 (Clone: 5E10, Biolegend, San Diego, CA, USA), PDPN-PE (Clone: NZ-1. 3, eBioscience, Thermo Fisher), and fixable LIVE/DEAD nIR Dead Cell Stain Kit (Life Technologies, Thermo Fisher Scientific). Data were acquired by NovoCyte Quanteon ® (ACEA Bioscience Inc. , San Diego, CA, USA) within 24 h of surface stain and processed in FlowJo (FlowJo software version 10. 5. 3). The spectral overlap was compensated using antibody-coated beads (eBioscience, San Diego, CA, USA). Gating was performed on live cells using fluorescence minus one (FMO).
Immunofluorescence of RA Synovial Tissue Paraffin-embedded and formalin-fixed RA synovial tissue samples were cut (5 µm sections) and subjected to standard deparaffinization and rehydration steps prior to antigen retrieval by heat. Sections were blocked for unspecific binding (PBS supplemented with 0. 5 % BSA and 10 % donkey serum). Gal-9 expression was evaluated using the rabbit antihuman Gal-9 1:50 (Cat. PA5-32252; ThermoFisher, Waltham, MA USA) antibody followed by an Alexa 647-conjugated secondary anti-rabbit antibody 1:50 (Cat. Jackson 711-606-152). Primary antibodies were omitted for the negative controls, otherwise performed in the same way. Slides were mounted in Prolong Gold Antifade Mounting media with DAPI (Life Technologies, Carlsbad, CA, USA). Representative images were acquired using an Olympus VS120 slide scanner at 10× and 40× and processed using the imaging software packed CellSens Standard (Olympus, Tokyo, Japan).
In a control set-up, Gal-9 was omitted from the set-up otherwise performed in the same way. The binding signals were directly quantified after the incubation and slides were scanned with a GenePix 4300A microarray scanner (Molecular Devices, Sunnyvale, CA, Cells 2023, 12, 327 6 of 13 USA), photomultiplier (PMT): 450, Laser Power (LP): 25. Spot-based signal intensities were quantified using GenePix Pro 7 (Molecular Devices, San Jose, CA, USA). The raw data from the software was processed using Microsoft Excel to obtain the backgroundsubtracted mean relative fluorescence intensity (RFU) for the four replicates, as previously described  [34].
Statistics Statistical analyses and graphs were performed using GraphPad Prism 7 for Mac (GraphPad Software, La Jolla, CA, USA). The amount of MCP-1 secreted in the cultures was calculated as ratios comparing stimulated cells with untreated cells, due to data normalization and donor variations. The differences were compared with the corresponding controls. Data were analyzed with a paired or unpaired t-test. Correlations were studied using Spearman's ρ test. In all tests, the level of significance was a two-sided p value of less than 0. 05. Graphics are presented as the mean, with standard error of the mean (SEM) if not otherwise stated.
Ethics The OPERA study was approved by the Danish Medical Agency (2612-3393), the Danish Data Protection Agency (2007-41-0072), and the Regional Ethics Committee (1-10-72-82-15). The use of human cells from the INART biobank was approved by the Danish Data Protection agency and the Ethics Committee, protocol number: 20-121-329. The subjects' informed and written consent was obtained, according to the Declaration of Helsinki.