dataesr/scientific-paragraphs-categorization huggingface dataset:
mamane were included among the 20 components. Interestingly, it seems that several compounds produced by C. albicans in monocultures are detected with a much lower intensity in co-culture with CM. It seems that several unknown compounds (undetected with HRMS database) produced by CA are no longer produced during co-culture. On the contrary, two compounds produced by CM (botryosulfuranol A and sapinopyridione) in monoculture are highly detected in co-culture, suggesting a potential role of these compounds in the antifungal mechanism of CM. Botryosulfuranol A is a member of thidikeopiperazines  [8] , and is an important fungi-derived chemical with antifungal action  [19, 20]. Sapinopyridione was isolated from Sphaeropsis sapinea and demonstrated antimycotic activity  [21].
Prediction of the antifungal mechanism of C. mamane Evaluation of the metabolites produced by axenic C. albicans and that were not detected in co-culture with C. mamane (Table  1 ) provides support for the hypothesis that C. mamane employs an inhibitory mechanism. The presence or absence of these compounds initially identified in the data matrix was manually confirmed using Xcalibur. Several metabolites that are essential for the survival and virulence of C. albicans including C20 sphinganine 1-phosphate, myo-inositol, farnesol, gamma-undecalactone, and MG (12:/0:0/0:0) were not produced during co-culturing with C. mamane. Sphinganine is a simple sphingolipid precursor involved in the synthesis of complex sphingolipids  [22]. The loss of C20 sphinganine 1-phosphate may affect the activities of the sphingolipid biosynthetic pathway, resulting in sensitivity to C.
Fungal strains Cophinforma mamane E224 strain was isolated from Bixa orellana leaves and identified using barcoding (PCR amplification of the ITS with ITS5 and ITS4 primers)  [32] , sequencing, and sequence comparison with Gen-Bank databases). C. mamane was was taken in Iquitos (national reserve of Allpahuayo Mishana, Amazonian rainforest) in November 2013, GPS coordinates: 3° 58′ 02. 3 S, 73° 25′ 03. 9 W). In the UMR 152 Pharmadev laboratory collection, the strain was cryopreserved in 30% glycerol at -80 °C. This strain was procured in compliance with all applicable laws and regulations. The C. mamane sequence can be found in the GenBank database (accession number MG457709). C. albicans ATCC 90028 strain was obtained from Thermo Fisher Scientific France (Illkirch-Graffenstaden, France) and was propagated in Sabouraud's agar media according to the supplier's protocol.
Peak analysis MS-DIAL (data-independent MS/MS deconvolution for comprehensive metabolome analysis) version 4. 90 was utilized to process the UHPLC-HRMS data  [34]. Automatic feature detection was performed between 0. 3 and 13 min, with positive and negative mode mass signal extraction ranging from 100 to 1500 Da. MS1 and MS2 tolerances were set to 0. 01 and 0. 025 Da, respectively, in centroid mode. The optimal detection threshold for MS1 was set at 2 105, and for MS2 it was set at 10. The peaks list from MS-DIAL was then transferred to Excel to generate a matrix of m/z ratios, retention times, and peak intensities. Using the MetaboAnalyst 5. 0 metabolomics data analysis and interpretation software  [35] , the matrix was transformed into a comma-separated value format. If the sample peak appeared in at least two of the three sample peaks, it was retained.
However, the relationships differ with path elevation and orientation. Results do not show any significant relationship between volumes and meteorological or snowpack conditions for the spring season. Focusing on deposits that disturbed the road network in winter and spring reveals a distinct influence of meteorological and snow variables compared to the overall dataset, with snowfall intensity as the predominant control variable of deposit volumes leading to road cuts. When the same analysis is conducted by considering Guil valley separately or by aggregating the Haute-Maurienne with Guil valley area data, results do not show any significant relationship, highlighting the specific local nature of relations between deposit volumes and meteorological and snowpack conditions.
| Avalanche-deposit volumes The analysis is based on SDV estimated from paths monitored by the Enquête Permanente sur les Avalanches (EPA)  (Mougin, 1922) , a system of descriptive reports on avalanche events. With 0. 8 avalanches per path and per year between 1946 and 2005  (Eckert, Parent, et al. , 2010) , the Haute-Maurienne region exhibits high avalanche activity compared to other regions of the French Alps. The EPA monitors avalanches as exhaustively as possible on more than 3000 avalanche paths in the French Alps since the beginning of the 20th century  (Mougin, 1922). For each avalanche, a volume is provided by an estimation of the length, width and mean depth of each deposit. However, to develop a more comprehensive SDV database, further corrections and completions works were completed until 2017  (Kern et al.
Therefore, only south, west and northwest orientations with at least 100 deposits from a minimum of 5 paths were considered for both winter and spring seasons (Figure  5 ). For these regressions, only snow predictors simulated for the same orientation on the 40 slope were considered. For each elevation, there is one value for SAFRAN meteorological variables relative to a flat topography and four values for Crocus snowpack variables, one relative to a flat topography and one for each considered orientation. • Analyses on the avalanche events that affected the road networks were conducted separately from the rest of the dataset because of their very large volume as mentioned earlier. However, path orientation was not considered at this stage, because of a limited number of events. This causes an overrepresentation of certain orientations affecting the road networks.
In detail, 58% of the corridors present a northwest or south orientation. Finally, in order to test the relevance of the local nature of these results, they are compared and combined with data from the Guil valley that display significant avalanche activity in a different geographical setting. First, the stepwise procedure was applied exclusively to the Guil valley. Second, to combine the volume data with the meteorological and snowpack variables from the two areas, multi-area means, derived from local means, were evaluated for the two seasons. Because differences in avalanche activity between the two areas are important (Figure  3 ), analyses were conducted using seasonal weighted mean SDV and covariates to address regional disparities for the two seasons as follows: X t ω ¼ ω t HM X t HM þ ω GV X t GV with ω t HM ¼ N t HM N t HM þN t GV ω t GV ¼ N t GV N t HM þN t GV ¼1Àω t HM 8 < : ð1Þ where X t HM and X t GV are the mean seasonal SDV or the seasonal mean of any of the considered covariates for the year t in the Haute-Maurienne and Guil valleys, respectively.
ACKNOWLEDGMENTS Hippolyte Kern  holds a PhD grant from the  Université Paris-1 Panthéon-Sorbonne. IGE/INRAE  is a member of the  Labex OSUG , and this work has received financial support from  LabEx DynamiTe  ( ANR-11-LABX-0046 ) as part of the " Investissement d'Avenir" program. The numerous people from  ONF -RTM and INRAE that contributed to the EPA survey with the financial support of the  French Ministry of the Environment  are acknowledged. The authors are grateful to the  ONF-RTM  for sharing pictures and information regarding the avalanches of winter 2017-2018.
2017 year. Further work should consider datasets covering longer time frames to strengthen the findings. Concerning SDV disrupting the road network, both winter and spring highlight a different influence of variables on snow deposit volumes compared to the overall path sample. It is further speculated that the road network vulnerability is influenced by a complex combination of meteorological and snow conditions, road proximity and path morphological characteristics, which influence both the occurrence and magnitude of road disruptions. Yet, fitted models show T A B L E 5 Statistical summary of stepwise linear regressions performed between SDV disrupting roads and meteorological and snow conditions in the Haute-Maurienne during winter and spring seasons. Snow and meteorological conditions are for a flat topography and all elevations.
| Comparison of the outflux versus influx approach in estimating biomass dynamics Here, we used forest inventory data to empirically test whether woody quantified using the outflux approach better predicts the observed woody biomass accumulation rates compared with woody using the influx approach. To do so, we investigated the relationships between observed and predicted woody biomass accumulation rates. The observed biomass accumulation rates were quantified as the difference of woody productivity (W_productivity) and woody biomass loss (W_loss) from forest plots at different census intervals. The predicted woody biomass accumulation rates were derived from Equation (1) with woody from Equation (  4 ) proposed in this study as a better approximation of. Time series of W_productivity and W_loss in plots at multiple census intervals were available for North and South America.
| Ensemble of dynamic global vegetation model turnover simulations and CARDAMOM data product Outputs of woody carbon stock and woody mortality flux of six DGVMs (ORCHIDEE, CABLE-POP, JULES, LPJ-GUESS, LPJmL and SEIB-DGVM) are from the carbon turnover intercomparison project https://zenodo. org/commu nitie s/vegc-turno vercomp/?page=1&size=20 (Pugh et al. , 2020). We note that the outputs of non-woody components are available in the six DGVMs and we only used the tree carbon components to quantify woody. These six DGVMs were used because they explicitly reported tree mortality annually, which is required for direct comparison with inventory-based estimates. Moreover, those DGVMs reported their output per vegetation functional type, which allowed us to sample them for forests only. The six DGVMs reported components of tree mortality including abiotic (climate stress or fires) and biotic (competition or human activity) agents.
Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/geb. 13736 by Cochrane France, Wiley Online Library on [25/07/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 5 YU et al. woody and belowground (coarse root) biomass turnover rates are the same-an approach which previous DGVMs or ecosystem models have widely used (De Kauwe et al. , 2014; Xia et al. , 2019; Yang et al. , 2021). Similarly, we use CARDAMOM outputs of woody biomass and wood mortality fluxes to infer woody. We use the global 1 × 1 degree 2001-2010 carbon cycle analysis, simulated by the data assimilation linked ecosystem carbon cycle model (Williams et al. , 2005) , jointly informed in a Bayesian model-data inference framework by (i) meteorological and fire forcing, (ii) satellite-observed leaf area index, (iii) satellite-informed circa 2001 above-and belowground biomass (across tropical ecosystems only), (iv) gridded soil carbon inventory data sets and (v) ecological and dynamical constraints (Bloom & Williams, 2015).
DATA AVA I L A B I L I T Y S TAT E M E N T Forest inventory data of woody aggregated at 0. 25 degree and the final woody maps (mean and standard deviation) at 0. 25 degree are available at 10. 5061/dryad. jq2bvq8f7. The codes of machine learning used to generate woody maps were adapted from https:// github. com/Kaili angYu/ Morta lity-const raint and are available on request from the corresponding author.
14668238, 0 , 0 Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/geb. 13736 by Cochrane France, Wiley Online Library on [25/07/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 5 YU et al. woody and belowground (coarse root) biomass turnover rates are the same-an approach which previous DGVMs or ecosystem models have widely used (De Kauwe et al. , 2014; Xia et al. , 2019; Yang et al. , 2021). Similarly, we use CARDAMOM outputs of woody biomass and wood mortality fluxes to infer woody. We use the global 1 × 1 degree 2001-2010 carbon cycle analysis, simulated by the data assimilation linked ecosystem carbon cycle model (Williams et al.
Patients Clinical and molecular data were collected retrospectively at CHU Lille. In accordance with the European general data protection regulation for a retrospective non-interventional research, a data processing declaration was made to the data protection officer of the Lille University Hospital (n°273), a privacy impact assessment was carried out and informed information was given to living patients in the context of this project; for patients who were not able to give consent for this project, non-opposition was sought in the medical records.
Cell lines All cell lines were cultured in media with 10% foetal bovine serum from Eurobio-scientific and maintained in humidified incubators at 37 °C and 5% CO2. Mycoplasma tests were routinely performed using the MycoAlert mycoplasma detection kit (Lonza). The non-tumorigenic 16HBE14o-(RRID:CVCL_0112) (16HBE) cell line developed by immortalizing primary human bronchial epithelial cell with SV40 Large T antigen, was a kind gift from Pr. Dieter Gruenert and cultured in MEM  [38]. The non-small-cell lung cancer cell line cell lines H226 and H596 were obtained from Dr. Kong-Beltran  [25]. H226 cells (RRID:CVCL_1544) expressing endogenous MET WT and H596 cells (RRID:CVCL_1571), an established lung adenocarcinoma cell line with a homozygous point mutation leading to exon 14 skipping and concurrent activating PIK3CA mutation, were both cultured in RPMI1640.
The MRC5 fibroblasts (RRID:CVCL_0440) were cultured in MEM. MET exon 14 skipping was introduced in 16HBE cells by electroporation with px459V2 vector (Addgene #62988) containing cDNA coding for Cas9 enzyme and sgRNA sequence (5'-TACCGAGCTACTTTTCCAGA-3') targeting the exon 14 splice donor region. After 24 hours, cells were selected in 1 µg/ml puromycin and isolated by limit dilution in 96-well plates. Screening was performed by western blot and PCR using methods described previously  [39]. The parental cell line as well as the METex14 expressing subclones have been authenticated using short tandem repeat profiling in 2021 or 2022 (Eurofins Genomics). In addition, METex14 mutation and absence of alteration in other oncogenic drivers were validated by NGS sequencing using the CLAPv1 targeted NGS panel using methods described previously  [39].
Statistics All results are expressed as mean +/-SD. According to their distribution, quantitative variables were compared with a t-test, Mann-Whitney test, or two-way ANOVA. Two-way ANOVA were performed to compare the entire curves of the tumor growth analyses in subcutaneous xenograft models. All statistical testing was conducted at the 2-tailed α level of 0. 05. Data were analyzed with GraphPad Prism software version 9.
We thank the Animal Facilities of the  Institut Pasteur de Lille and of Barts Cancer Institute  for access to instruments and technical assistance and advice. The authors thank  Sara Farrah Heuss ,  Edward Carter ,  Cerian Bolton ,  Shayin Gibson  and  Frédéric Leprêtre  for their kind assistance with the setting up of the CRISPR experiments, the nanodrop spheroid invasion assay and data submission. The work received financial support from the  Cancéropôle Nord-Ouest  (Metropolis-STRUCTURANT 2016),  Agence Nationale de la Recherche  ( ANR-17-CE17-0020 ),  INCa  ( PLBIO-2017-159 ), La Ligue Contre le cancer (LCC AISNE 2019),  Hauts-de-France region  ( C5D1402 ),  Contrat de Plan Etat-Région CPER Cancer   2015-2020 , the  BBSRC (LIDo consortium), OCTIMET Oncology ,  Rosetrees Trust  ( M314 ),  Barts Charity  ( MGU0511 ) and the  MRC  ( MR / R009732 / 1 ).
Data analyses First, we characterized the study stands based on the sampled trees' height and DBH and tested if these phenotypic traits differed significantly between south-and north-facing slopes using a two sample Student's t test on each stand pair run in R v. 4. 1. 2  [42]. Then, based on the SNP data, we estimated genetic diversity parameters such as observed and expected heterozygosity and the fixation index using the R package hierfstat  [43]. After this, we tested whether we could detect significant neutral genetic differentiation between the sampled stands by estimating pairwise FST  [44]  using the complete SNP data set and comparing with neutral expectations from 1,000 permutations. To visualize the neutral population genetic structure inherent to our data, we also performed a Principal Component Analysis (PCA) using the function dudi.
pca implemented in the R package ade4  [45]  and a supervised (i. e. defining each stand as a group) Discriminant Analysis of Principal Components (DAPC) using the dapc function in the R package adegenet  [46]  based on all SNP markers. Additionally, we assessed the fine-scale spatial genetic structure (SGS) within each of the three pairs. First, we estimated the pairwise Loiselle kinship coefficient  [47]  in SPAGeDi v. 1. 5d [48]  between individuals. The average kinship coefficient per distance class was regressed against the logarithm of spatial distances and significance was assessed based on 10,000 permutations of individual locations. The strength of SGS was estimated as Sp =-b/(1 -F1), where b is the regression slope and F1 is the average kinship coefficient in the first distance class  [49].
To identify outlier loci with BayeScanH, we used the full dataset of 4,034 SNPs with MAF > 0. 1, and default parameters with an odds prior of 10. We tested two models, one with the same selection pressure acting between contrasting slopes in the three stand pairs and another one with three independent selection pressures on contrasting slopes within the three pairs. Finally, using the R-script 'paired_GEA. R' from https://gitlabext. wsl. ch/rellstab/genotype-environment-associations, we tested if any of the candidate SNPs identified with Arlequin or BayeScanH showed consistent patterns in population allele frequencies between the paired stands in all replicates. For this, we checked whether the differences in population allele frequency had the same sign in all pairs, i. e. whether the population allele frequency in all north-facing slopes was consistently lower or higher than the allele frequency in all south-facing slopes (i.
e. the strict sign test). Then, we ran a linear mixed model using the function lme implemented in the package nlme  [53] , with population allele frequency as response variable, slope aspect (south/north) as fixed effect and pair as random factor. The sequences flanking SNPs identified as loci potentially under selection, and associated annotation, were retrieved from Plomion et al. [38]. These sequences were newly blasted against the NCBI nucleotide database to check for new functional annotations.
Acknowledgements We thank  Ana Hernández-Serrano ,  Fernando del Caño ,  Delphine Grivet ,  Mario Zabal-Aguirre ,  Natalia Vizcaíno-Palomar ,  Enrique Sáez-Laguna ,  Diana Turrión  and  Miguel Navarro  for help in the field and  Carmen García-Barriga  ( INIA-CIFOR ) for assistance in the laboratory. The present study was funded by projects from the  Spanish Ministry of Science and Innovation (MICINN) :  VaMPiro  ( CGL2008-05289-C02-01/02 ),  LinkTree  ( EUI2008-03713 , under the  ERAnet-BiodivERsA call) , and  TrEvol  ( CGL2012-39938-C02-00/01 ), and the  IdEx Bordeaux  ( EcoGenPin ,  Chaires d 'installation 2015). KBB acknowledges an exchange grant of the  European Science Foundation (ESF)  within the framework of ' Conservation Genomics: Amalgamation of Conservation Genetics and Ecological and Evolutionary Genomics' (ConGenOmics).
Data accessibility GPS coordinates and SNP genotypes of all individuals included in this study are available on Zenodo public database: https://doi. org/10. 5281/zenodo. 6345964. The R-script 'paired_GEA. R' for analyzing population allele frequencies in paired stands with a known environmental contrast can be found at https://gitlabext. wsl. ch/rellstab/genotypeenvironment-associations.
Daily precipitation fields are derived from a 5-km resolution interpolated product POHL ET AL. 10. 1029/2022JD037209 3 of 28 constrained by observational networks, that allows for a detailed analysis of terrain influence on the spatial distribution of daily amounts. In the following, Section 2 presents the data and methods used in this work. Section 3 presents the AR count in each region of ANZ, and their association with WTs. Section 4 next focuses on their separate versus combined influences on daily precipitation, considering both anomalies and daily extremes. Section 5 addressed the co-occurrence of ARs and WTs, how they change from one part of ANZ to another, and assesses to which extent their intrinsic properties are interdependent. Finally, Section 6 summarizes and discusses our main results.
10. 1029/2022JD037209 4 of 28 with the density and quality of the assimilated data. In this work, we use the regular 0. 25° × 0. 25° grids for daily or 6-hourly fields of geopotential height at 1,000 hPa (m, Z1000) and 700 hPa (Z700) and horizontal wind at 700 hPa (m s -1 ), and vertically-integrated moisture transport (IVT: kg m -1 s -1 ) over the period 1979-2019. Daily precipitation data for ANZ come from the National Institute of Water and Atmospheric Research (NIWA) Virtual Climate Station Network (VCSN). Data coverage for the NIWA VCSN  (Tait & Turner, 2005; Tait et al. , 2006 Tait et al. , , 2012) )  is available from 1972 to present for 13 daily climate variables on a 5 × 5 km grid covering the country. The approach to generate the NIWA VCSN uses a thin-plate smoothing spline model for spatial interpolation between in situ station data, incorporating two location variables (latitude and longitude) and a third "pattern" variable.
Geopotential heights are derived from ERA5 for the period 1979-2019. Only anomalies that are statistically different from the climatology according to a one-tailed t test at the 95% level are represented. The inner rectangle represents the original domain used by  Kidson (2000). Letter H indicates local maximum Z1000 anomalies; L indicates local minimum Z1000 anomalies. ERA5 definition of WTs is used. Each WT is labeled with a descriptor that is used for referencing throughout the manuscript. POHL ET AL. 10. 1029/2022JD037209 5 of 28 same WT. However, the composite mean fields are very similar, because day swaps between types are associated with similar patterns that differ only in the location of ACAs. The latter are expected to be more precisely defined by ERA5 data due to its much higher spatial resolution.
Data Availability Statement ERA5 data  (Hersbach et al. , 2020)  can be retrieved from https://cds. climate. copernicus. eu/cdsapp#!/search?-type=dataset&text=era5. The AR catalogs from the ARTMIP (https://www. cgd. ucar. edu/projects/artmip) Tier2  (Marquardt Collow et al. , 2022 ) are available at https://zenodo. org/record/5773269#. Y4Ci4-zMJGA. AR detection is based on the algorithm originally introduced in  Guan and Waliser (2015) , refined in  Guan et al. (2018) , and further enhanced in  Guan and Waliser (2019). Weather types and their descriptors (P21) are available at https:// pohlben. files. wordpress. com/2022/02/ktdescriptors_era5. xlsx and https://pohlben. files. wordpress. com/2022/02/ ktdescriptors_ncep. xlsx. VCSN data  (Tait & Turner, 2005; Tait et al. , 2006 Tait et al. , , 2012 ) can be retrieved from the NIWA website at https://niwa.