dataesr/scientific-paragraphs-categorization huggingface dataset:
The model does not consider any isotopic fractionation and thus the 14 C fluxes scale just as the  12  C fluxes. With this a general expression for all boxes at any time can be achieved. Nðt þ ΔtÞ ¼ NðtÞ þ ðΛNðtÞ þ F T ðtÞ1 À FðtÞ1 þ PðtÞ þ P st ÞΔt ð6Þ F ¼ F 12 ðtÞ The Δ 14 C of each box for the simulation is calculated by the following expression: Δ 14 C i ðtÞ Where N 12;14 TN;st are the steady state  12, 14  C contents of the northern troposphere. To get a reasonable start state at a given time the production rate for the whole IntCal13 record has been calculated and the simulated state can be loaded at any time before 1950 CE. The events were evaluated by generating 1000 realizations of the data distributed with the errors of the measurements. For each realization a Gaussian shaped production spike was fitted and the additional production was extracted by integrating the Gaussian production.
This correction factor was further applied to compare the strength of all studied events to the standard conditions, as shown in Table  1. Reconstructions of the geomagnetic field in the past is a difficult task, and the result can be uncertain. To cover a possible range of VADM values we took a conservative approach and considered two paleomagnetic reconstructions for the last millennia, as shown in Supplementary Fig. 5 : a recent GGF100k paleomagnetic model by Panovska et al. 40  and a reconstruction by Knudsen et al. 41  based on the GEOMAGIA50 database. Although there are many other paleomagnetic reconstructions, these two cover the full range of uncertainties. The uncertainties of the corrected for the VADM changes  14  C production (last two columns in Table  1 ) are mostly defined by the uncertainties of the actual production (column 3) and VADM uncertainties (columns 4 and 5).
Acknowledgements The  Laboratory of Ion Beam Physics  is partially funded by its consortium partners  PSI ,  EAWAG , and  EMPA. N. B. is funded by the  Swiss National Science Foundation  (SNSF grant # SNF 197137 ). The establishment of the BRAMS Facility was jointly funded by the  NERC ,  BBSRC  and the  University of Bristol  and the measurements in this work were partly funded by an  ERC Proof of Concept  grant awarded to R. P. E. and financing E. C. postdoctoral contract (LipDat  H2020 ERC-2018-PoC/812917 ). We thank  Bisserka Gaydarska  for sub-sampling the inter-laboratory replicates from  M49 ,  M234 ,  Q2729  and  Q2750 ,  Cathy Tyers  for reviewing the dating of the Irish and German samples, and  Alexander Land  for assistance in dating sample M49. P. F. received funding from the  SNF   Sinergia  project  CALDERA  (no.
PubMLST Database Analysis of S. aureus lspA The PubMLST database, assessed at https://pubmlst. org/organisms/staphylococcus-aureus [47] , was used to analyze the presence and sequence conservation of lspA (SAUR1197) across the S. aureus population. Alignments were made using the locus explorer of the PubMLST database. LspA gene sequences of 26,036 S. aureus strains were downloaded from the database in February 2021. We excluded 788 whole-genome sequences for data analysis that were unlikely to be S. aureus, contained >300 contigs or an N50 contig length shorter than 20,000 bp or when lspA was located at the end of a contig.
For example, the extracellular location of LspA makes it accessible to drug, while no LspA analogs are found in eukaryotic cells, thereby reducing the risk of offtarget effects  [41, 59, 60]. In addition, we showed that LspA is highly conserved among S. aureus strains with only one amino acid substitution in 96% of the S. aureus collection in the PubMLST database (>26,000 isolates at the time of this analysis). This single and prevalent amino acid substitution did not seem to affect LspA function in relation to hGIIA resistance. Conserved proteins are less likely to mutate, making them ideal targets as the inhibi-DOI: 10. 1159/000527549 tor compounds are longer lasting and more effective  [72]. The natural antibiotics globomycin and myxovirescin A1 specifically inhibit LspA and have similar binding sites on LspA, docking to the catalytic dyad and clustering around 14 conserved residues  [41, 57, 58, 60].
Conflict of Interest Statement The authors have no conflicts of interest to declare. 00112090), the National Research Agency (grants MNaims (ANR-17-CE17-0012-01), AirMN (ANR-20-CE14-0024-01)) and "Investments for the Future" Laboratory of Excellence SIGNALIFE, a network for innovation on signal transduction pathways in life sciences (ANR-11-LABX-0028-01 and ANR-15-IDEX-01), and the Fondation de la Recherche Médicale (DEQ20180339193L) and were used in relation to hGIIA experiments. Part of this work was supported by "Fondation Air Liquide" (Grant: S-CM19006) granted to Lhousseine Touqui to perform studies focused on the role of hGIIA in mouse survival. This study was supported by project 91713303 of the Vidi research program to Nina M. van Sorge and Vincent P. van Hensbergen and 09150181910001 of the Vici research program to Nina M.
In addition, among members of this family, mutated TERTp creates binding sites in CNS tumors for ETS1/2 [13,14] and GA-binding proteins (GABP)  [12]. The binding sites for the ETS transcription factor were specified based on the JASPAR CORE database containing a set of profiles derived from published collections of experimentally determined transcription factor binding sites  [15]. These TERTp mutations create new binding sites for E-26 family transcription factors tryptophan cluster factors class (ETS/TCF) and cause a two-to four-fold increase in transcription of the messenger RNA of the telomerase catalytic subunit  [12]. In addition, among members of this family, mutated TERTp creates binding sites in CNS tumors for ETS1/2 [13,14] and GA-binding proteins (GABP)  [12]. The binding sites for the ETS transcription factor were specified based on the JASPAR CORE database containing a set of profiles derived from published collections of experimentally determined transcription factor binding sites  [15].
At present, it is unclear exactly how TERTp mutations occur in early oncogenic lesions and completely transformed cells. Although the TERT overexpression level is typically observed in tumors, those with TERTp-mut have shorter telomeres than non-cancerous tissues. This fact indicates that TERTp mutations can represent a later event in oncogenesis (second phase) when telomere length has already been depleted  [17, 18]. Another hypothesis suggests that somatic mutations in cells are accumulating at a constant rate throughout life  [29]. Whole genome sequencing data analysis of the Cancer Genome Atlas data of adult diffuse glioma did not show that TERTp mutations are associated with increased telomere length in grade II-III-IV diffuse gliomas [30] which argues for an early oncogenic step in this lesion.
Prognostic nomograms were developed based on predictive molecular groups and clinicopathological data to predict progression-free survival (PFS) and OS. Results showed that an image fusion model including radiological signatures from T1-weighted contrast-enhanced imaging (cT1WI) and apparent diffusion coefficient (ADC) achieved AUCs of 0. 884 and 0. 669 to predict IDH and TERT status, respectively  [83]. Currently, the perfusion method plays an essential role in estimating the degree of glioma malignancy. This approach allows the evaluation of the effectiveness of histogram analysis of MRI with dynamic susceptibility contrast (DSC) and dynamic contrast enhancement (DCE) in distinguishing the states of molecular biomarkers and survival in patients with glioma. Zhang and colleagues included 43 patients with glioma for whom MRI with DCE and DSC was performed.
High-resolution (HR) surface soil moisture (SM) observations are important for applications in hydrology and agriculture, among other purposes. For instance, the S 2 M P (Sentinel-1/Sentinel-2 derived Soil Moisture Product) algorithm was designed to retrieve surface SM at agricultural plot scale using simultaneously Sentinel-1 (S1) backscatter coefficients and Sentinel-2 (S2) NDVI (Normalized Difference Vegetation Index) as inputs to a neural network trained with Water Cloud Model simulations. However, for many applications, including future climate impact assessment at regional level, a resolution of 1 km is already a significant improvement with respect to most of the publicly available SM data sets, which have resolutions of about 25 km. Therefore, in this study, the S 2 M P algorithm was adapted to work at a resolution of 1 km and extended from croplands (cereals and grasslands) to herbaceous vegetation types.
A target resolution of 1 km also allows to explore the use of NDVI derived from Sentinel-3 (S3) instead of S2. The algorithm improvements are evaluated both over Europe and other regions of the globe, for which S1 coverage is poorer. Two sets of SM maps at 1-km resolution were produced with S 2 M P over six regions of ∼ 10 4 km 2 in the southwest and southeast of France, Spain, Tunisia, North America, as well as Australia from 2017 to 2019. The first set of maps was derived from the combination of S1 and S2 data (S1+S2 maps), while the second one was derived from the combination of S1 and S3 (S1+S3 maps). S1+S2 and S1+S3 SM maps were compared to each other and to those of the 1-km resolution Copernicus Global Land Service (CGLS) SM and Soil Water Index (SWI) data sets as well as to the SMAP+S1 product.
With the successive launches of the C-band SARs onboard Sentinel-1 A (S1-A, 2014) and Sentinel-1 B (S1-B, 2016), SM can be estimated at high spatial resolution and with a revisit time better than 6 days over Europe. Three operational high resolution (HR) SM data sets using S1 exist such as the S1 1-km SM and Soil Water Index (SWI) products from the Copernicus Global Land Service  (CGLS, Bauer-Marschallinger et al. , 2018 , 2019)  and the SMAP+S1 downscaled product  (Das et al. , 2020)  with resolutions of 1 or 3 km. SM estimates at a very high resolution (plot scale) at some locations in Europe (as well as in Lebannon and Morocco) are also distributed by the French continental surfaces data center (THEIA, https://www. theia-land. fr). In contrast to the CGLS data set, the THEIA SM data set is obtained making a synergistic use of S1 and Sentinel-2 (S2) using the S 2 M P algorithm (El  Hajj et al.
, 2017). This data set has been evaluated against in situ data in comparison to SMAP, SMOS and ASCAT coarse resolution data sets  (El Hajj et al. , 2018)  and with respect to the CGLS S1 SM data set  (Bazzi et al. , 2019) , both in the south of France. The S 2 M P SM showed the lowest unbiased root mean squared errors with respect to in situ measurements but also a moderate correlation, lower than that obtained for SMAP and ASCAT data sets  (El Hajj et al. , 2018). In this region, the S 2 M P SM showed better performances with respect to in situ measurements than the CGLS SM for the classical metrics  (Bazzi et al. , 2019). Taking into account the importance of having accurate HR SM data sets, a more comprehensive evaluation of existing products must be done over different types of surfaces and regions.
The S 2 M P S1+S2 and S1+S3 maps were compared to those of the SMAP+S1 product as well as those of the CGLS SM and SWI data sets. The comparison was carried out over the 6 study regions on a per pixel basis and the results were analysed according to pixel homogeneity for areas covered by croplands and herbaceous vegetation. In addition, the HR time series were evaluated against in situ data along with those of coarser resolution SM data sets from SMAP, SMOS, and ESA CCI. The paper is structured as follows. Section 2 presents the different remotely sensed and ground based data that are used in this study. Section 3 describes the methodology used. Section 4 shows the results of the comparisons of the HR maps as well the assessment against in situ measurements. Section 5 discusses the interest of the S 2 M P algorithm.
Section 6 draws the conclusions of the study. The S 2 M P (Sentinel-1/Sentinel-2 derived Soil Moisture Product) algorithm  (El Hajj et al. , 2017)  estimates surface SM at the scale to agricultural plots using synergistically radar signal and optical images from S1 and S2 satellites as inputs to a neural network. The neural network is trained using a synthetic database gathering (i) SAR C-band backscatter coefficients in the VV polarization (ii) incidence angles (from 20 to 45 degrees), and (iii) NDVI (Normalized Difference Vegetation Index) as input and SM examples as target. This synthetic database is built using a Water Cloud Model  (Baghdadi et al. , 2017)  combined with an Integral Equation Model  (Baghdadi et al. , 2006 (Baghdadi et al. , , 2011) )  that are specially modified and optimized for this application.
In the framework of this study, the trained neural networks were applied to a real database gathering SAR and NDVI measurements computed from S1 and S2 over croplands and herbaceous vegetation areas at 100-m resolution using the CGLS land cover map. It does not consider retrievals for other types of land cover. Afterwards, the data were averaged at 1-km resolution to provide HR SM maps (hereafter S 2 M P S1S2 ) over the six ∼ 10 4 km 2 regions of the study. The S 2 M P algorithm was also adapted to work directly at 1-km resolution using NDVI from S3 instead of S2 to produce maps for the same six regions (hereafter S 2 M P S1S3 , see Sect. 3. 1).
Discussion The four HR data sets evaluated in this study have been produced with different approaches. Two products were obtained by merging S1 data with ASCAT (CoperSW I) and SMAP (SM AP S1), respectively. S 2 M P S1S2 and CoperSSM are based on Sentinel-only. The last one is computed from local temporal variations of the S1 backscatter coefficients time series following the method of  Wagner (1998). In contrast, S 2 M P S1S2 uses electromagnetic modelling to compute a database predicting backscatter coefficients from surface characteristics such as SM and vegetation status (approximated by NDVI). This database is in turn used to train a neural network to predict SM from S1 backscatter coefficients and HR NDVI estimations. Initially, the S 2 M P algorithm by El  Hajj et al. (2017)  was limited to croplands and the use of NDVI derived from S2.
Moreover, in  El Hajj et al. (2018) , SM estimates from the S 2 M P algorithm were only derived over croplands while in our study, the S 2 M P SM retrievals were performed both for croplands and herbaceous vegetation. However, the results of the current study are in good agreement with those of El  Hajj et al. (2018)  in terms of bias. In both studies, S 2 M P S1S2 shows low bias compared to in situ measurements, similar to or lower than the CR data sets. Regarding the S 2 M P S1S2 and CoperSSM comparison by  Bazzi et al. (2019) , it is interesting to note that S 2 M P S1S2 is better correlated to in situ data than CoperSSM , which is in agreement with the results of our study (Table  3 ). Taking into account the results discussed in the previous section, HR merged products (SM AP S1, CoperSW I) provide better estimations or temporal agreement with in situ data than the HR Sentinel-only data (S 2 M P S1S2 , CoperSSM ) (Tab.
3) but still show lower performances than the CR data. This was also found by  Bauer-Marschallinger et al. (2019) , who demonstrated that 25-km resolution SM estimates from ASCAT were better correlated to in situ measurements within the Italian Umbria region that those of the 1-km resolution CoperSSM. The results of our study are also in perfect agreement with the findings by  Ojha et al. (2021) , who showed that two merged products, SM AP S1 and SMAP + DISPATCH  (Merlin et al. , 2012) , were better correlated to in situ data than CoperSSM over several regions in France and Spain. When aggregated to CR, the performances of the SM AP S1 1-km data increase to values similar to those of the SM AP L3 data. This implies that the gain in resolution brought by merging data of different resolutions comes at the expense of introducing uncertainties in the resulting HR data set.
Obviously, this study was limited to comparisons over six regions of 10 4 km 2 within a 3-years time period, so the results can not be straightforwardly extended to a global scale. However, it is noticeable that the HR products provide SM estimates using C-band measurements while the CR data sets used in this study are computed using L-band measurements. Indeed, SMOS and SMAP were specifically designed to measure surface SM, which was not the case for the Sentinel satellites.
In contrast to S 2 M P , the CGLS surface SM is a local index of SM variations and for many applications it has to be scaled against a reference to transform the variation index to actual SM in volumetric units (m 3 m -3 ) before being used. S 2 M P was also compared to two HR merged data sets, i the SWI from CGLS combining S1 and ASCAT measurements as well as ii the SMAP+S1. As for the surface SM data set, the SWI data had to be scaled into absolute SM values. CGLS products only provide retrievals over the European continent and the Mediterranean basin. The results showed that the S 2 M P data set is well correlated to the 1-km surface SM product provided by CGLS in large areas of the 6 regions of study: R ∼ 0. 7-0. 8. However, the correlations significantly drop in some areas of the 6 domains when the S 2 M P maps are compared to those of the SWI product and those of SMAP+S1.
Over almost all the pixels within the 6 regions, the standard deviation of the difference (ST DD) between S 2 M P and the CGLS products are lower than 0. 06 m 3 m -3 while the values obtained with respect to SMAP+S1 can reach 0. 1 m 3 m -3. Then, the bias between S 2 M P and the other HR data sets differ significantly inside a same region and can be strongly dry or wet (± 0. 1 m 3 m -3 ). The correlations between S 2 M P and the other HR data sets improve over croplands when the 1-km pixels are homogeneous but a similar behaviour was not found for pixels where the dominant land cover class is herbaceous vegetation. The HR products were also evaluated with respect to in situ measurements along with coarser resolution data sets from SMOS, SMAP and ESA CCI. The coarse resolution (CR) products show higher correlations (0.
65 ≤ R ≤ 0. 68) than the HR data sets (0. 44 ≤ R ≤ 0. 64), and the HR merged data sets showed higher correlations than the HR Sentinel-only ones. However, as several SMAP and SMOS data sets, S 2 M P also showed a very low bias with respect to in situ measurements. Finally, the ST DD differ according to the data set as well as the spatial resolution and range from 0. 04 to 0. 08 m 3 m -3 for the ESA CCI product to the SWI product. The S 2 M P algorithm gave promising results over the 6 regions of study. Of course, for some applications the approach should be extended to consider other land cover types than croplands and herbaceous. For instance, a 1 km resolution SM data sets over all land cover types could be used to asses climate impact at regional level in the future when 1 km SM times series are longer.
Finally, a remaining challenge is to provide HR SM data with comparable spatio-temporal coverage and retrieval quality across different land cover types than those of the state-of-the-art coarse resolution products, such as the SMOS, SMAP and ESA CCI products. Table  1. In-situ measurements that were used in this study. The depths are quoted as two numbers: the first one is the upper depth, and the second one is the lower depth of the sensor. Both numbers are equal when the sensor is placed horizontally. The fourth column gives the number of sensors available for each region.
Acknowledgements. This research made use of data from the  Centre Aval de Traitement des Données SMOS (CATDS)  operated for the  Centre National d'Etudes Spatiales  (CNES) by The  Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)  in France, as well as from the  Copernicus Global Land Service (CGLS) , the  National Snow and Ice Data Center (NSIDC)  and the  ESA ' s Climate Change Initiative for Soil Moisture  project. The authors acknowledge partial funding from the  ESA's Climate Change Initiative for Soil Moisture.
aeruginosa. It is important to keep in mind that before proof of concept in a phase II study, no efficacy is proven in patients. Efficacy-safety balance evaluation in P. aeruginosa infected patients is an unavoidable milestone. To dedicate this review only to anti-P. aeruginosa treatments in clinical development, a literature search was conducted with a specific strategy in the PubMed ® database between January 2017 and December 2021. The following criteria were used: (i) keywords related to development (antibacterial pipeline, antibiotic pipeline, clinical development) and treatment (drug, antibiotic, therapy), (ii) selection of phase I to III clinical trials, and (iii) exclusion of research data or preclinical targets (Figure  3 ). Clinical trial registries and websites of companies or organizations with a special interest in the field were consulted to verify or supplement the data (Figure  3 ).
, 2019) , consultation of the clinical trials registry  (ClinicalTrials. gov. , 2021) , and probably due to the recommendation published in the Cochrane Database of Systematic Reviews  (Johansen and Gøtzsche, 2015) , no vaccine is currently in clinical development (Figure  4A ). An effective vaccine should be able to induce an humoral immune response capable of both mediating opsonophagocytic killing by phagocytic cells and neutralizing P. aeruginosa virulence factors  (Sainz-Mejías et al. , 2020). Advances have been made in understanding the interaction between P. aeruginosa and its host and the key role of Th1 and Th17 host immune responses are well established and a cellular immune response mediated by Th17 cells  (Sainz-Mejías et al. , 2020). A deeper understanding of these mechanisms at each potential site and stage of infection would facilitate future vaccine development.
A phase IIa trial evaluating AR101 was completed in 2009 for hospital acquired pneumonia, showing promising results in clinical cure and survival rates (NCT00851435). A phase II trial testing the efficacy, safety and pharmacokinetic of AR105 in addition to standard-of-care (SOC) antibiotics for pneumonia cause by P. aeruginosa was started in 2017 (NCT03027609). MEDI3902, a bispecific mAb, is able to recognize two different targets of P. aeruginosa, each containing a distinct epitope: the T3SS needle-tip protein PcrV, involved in host cell cytotoxicity and the surface exopolysaccharide Psl, involved in epithelial attachment and biofilm formation (Figure  2 ;  Sato et al. , 2011; Ryder et al. , 2007). Designated as a Fast track drug from Food and Drug Administration (FDA), MEDI3902 is under development for the prevention of healthcare-acquired P.
TRL1068, a human mAb targeting DNA Binding protein II (DNABII) is a promising new agent in clinical development (Figure  4A ). By stabilizing extracellular DNA, DNABII is a key structural component of biofilm  (Thi et al. , 2020). Currently being tested in a phase I trial for prosthetic joint infection, TRL1068 is expected to eliminate the pathogen-protecting biofilm, making them more susceptible to antibiotic treatment (NCT04763759). The targeted epitope is highly conserved making the antibody potentially effective against biofilm produced by a wide range of clinically relevant pathogens, including 10 of the 12 WHO priority pathogens 3. Nevertheless, this efficacy could be low with early isolates that does not produce biofilm or are lower producers.
and Rizk, 2020). Promising assessment of cefiderocol for the treatment of cUTI in patients at risk of MDR Gram-negative infections started in phase II trial in 2015  (Portsmouth et al. , 2018). Lipopolysaccharide (LPS) that constitutes the OM outer layer at the surface of Gram-negative bacteria represents also an attractive target against pathogens  (MacNair et al. , 2020). By binding to the LPS transport protein D (LptD), the small cyclic peptide murepavadin causes a specific P. aeruginosa LPS biogenesis alteration  (Martin-Loeches et al. , 2018). A second phase II study of murepavadin coadministered with SOC in VAP due to P. aeruginosa finished in 2017 (NCT02096328).