dataesr/scientific-paragraphs-categorization huggingface dataset:
a. d. , suggesting that the earliest molecular events leading to branching occur within a day (Figure  1A ). To capture a comprehensive overview of the transcriptional changes associated with early branch development, we next analyzed the transcriptome of whole gametophores collected 2, 6, 12 and 24 h. a. d. , and used gametophores collected immediately after decapitation (0 h. a. d. ) as a reference. The proportion of genes whose transcription was affected increased up to 12 h. a. d. , leading to a maximum of 5028 and 4734 genes down-and up-regulated, respectively, suggesting that major transcriptional changes occur before the first branches become visible (Table  S1 , Figure  S1  and S2 ). Gene ontology (GO) term analyses identified distinct enriched functional categories in both datasets (Table  S2 ), but auxin-related GO terms were not significantly enriched, which was unexpected as published data suggest an important role for auxin in gametophore branching control 2.
Decapitation rapidly alters TAR and YUC auxin biosynthesis gene expression We reasoned that our dataset would provide a reference to identify genes controlling apical dominance, both in an auxin-dependent and independent manner (see Discussion). In this context, we sought to test further the hypothesis that gametophore apices are sites of auxin biosynthesis. Auxin is derived from tryptophan (TRP)-independent and dependent biosynthetic pathways  13. In the latter, the dominant pathway in plants, TRP is converted to indole-3-pyruvic acid (IPyA) by TRYPTOPHAN AMINO-TRANSFERASE (TAA/TAR) enzymes, that is then converted to auxin by YUCCA FLAVIN MONOOXYGENASE-LIKE (YUC) enzymes  [14] [15] [16] [17]. Auxin metabolite profiling in PpTAR and PpYUC overexpression lines, and pptar mutants have suggested that both steps of auxin biosynthesis are essential and identified TRP-to-IPyA conversion as a rate-limiting step in P.
The temporal delay between TAR and YUC gene activation and the differences in their spatial expression domains at early stages of branch initiation suggest that IPyA might be transported from the meristematic cells where it is synthesized to adjacent hair cells to be converted to auxin by YUC enzymes. Alternatively, these differences might be explained by the short-range movement of TAR or YUC mRNA, or corresponding proteins; the involvement of YUC genes co-expressed with TARA and TARC but not differentially expressed in our RNA-seq dataset, such as YUCE (Figure  S4 ); or unmask a putative YUC-independent function of IPyA. Together, the above data suggest that TRP-dependent auxin biosynthesis occurs primarily in the main apex and the basal portion of gametophores, and resumes locally in leaf axils when new branches initiate.
In the future, our dataset will help us to decipher how auxin is integrated at the molecular level to regulate branching. For instance, GO analyses revealed a significant enrichment of the "DNA binding transcription factor activity" term in gene sets up-and down-regulated after decapitation (Table  S2 ). thaliana PUCHI and DORNRÖSCHEN-LIKE, which are regulated by auxin and involved in meristem identity and lateral organ initiation control  [29] [30] [31] [32]. We also found that GO terms related to "trehalose metabolism" were significantly enriched in the "up-regulated 2 h. a. d. " gene set. Interestingly, trehalose 6-phosphate (T6P) metabolic genes regulate branching and apical dominance in flowering plants  [33] [34] [35]. For example, shoot decapitation in Pisum sativum (pea) triggers an increase of T6P levels in a few hours, which contributes to promoting branch outgrowth  33.
Besides, we found that TPS genes were IAA-induced and decapitation-repressed, and GO terms related to trehalose metabolism were associated with genes both induced by auxin and up-regulated after decapitation (Table  S3 ), consistent with TPP and TRE expression profiles 2 and 6 h. a. d. (Figure  S6 ). This suggests that apicallyproduced auxin regulates the activity of T6P metabolic genes in the gametophore, which has not been evidenced yet in flowering plants. Further studies will be needed to determine the biological function of T6P metabolic genes and AP2/ERF DNA-binding transcription factors, and explore their crosstalk with auxin in the moss P. patens. Finally, it must be stressed that our dataset contains a large number of differentially expressed genes with uncharacterized function, representing an untapped reservoir of novel putative regulators of shoot architecture.
RNA-seq data production and analysis For each biological replicate and condition, RNA were pooled from 5-10 gametophores (Gransden 40 strain). Three independent biological replicates were produced. Total RNA was extracted using an RNeasy Plant Mini Kit (Qiagen, Hilden, Germany) and treated with DNAse according to the supplier's instructions. RNA-seq libraries were made using the TruSeq_Stranded_mRNA_SamplePrep_Guide_15031047_D protocol (Illumina, California, U. S. A. ). The RNA-seq samples have been sequenced in paired-end (PE) with a sizing of 260 bp and a read length of 75 bases, using an Illumina NexSeq500 technology (IPS2, POPS platform, Gif-sur-Yvette). 24 samples were processed by lane of NextSeq500 using individual 13 bar-coded adapters, generating approximately 30 millions of PE reads per sample.
All experimental steps, from growth conditions to bioinformatic analyses, were deposited in the CATdb database  41  (http://tools. ips2. u-psud. fr/CATdb/, ProjectID NGS2017_09_Moss1) according to the MINSEQE 'minimum information about a high-throughput sequencing experiment' (https://doi. org/10. 25504/FAIRsharing. a55z32). To facilitate comparisons, all samples were processed similarly from trimming to count. RNA-Seq pre-processing included trimming library adapters and performing quality controls. Raw data (fastq) were trimmed with fastx toolkit for Phred Quality Score Qscore >20, read length >30 bases, and ribosome sequences were removed with sortMeRNA  42. The mapper Bowtie 43 (version 2) was used to align reads against the Physcomitrium (Physcomitrella) patens transcriptome (with local option and other default parameters).
The 32926 genes were extracted from Phytozome database (Physcomitrella patens transcripts v3. 1 gene model). The abundance of each gene was calculated by a local script which parses SAM files and counts only paired-end reads for which both reads map unambiguously one gene, removing multi-hits. According to these rules, around 82,7% of PE reads were associated to a gene, 5-6% PE reads were unmapped and 12-13% of PE reads with multi-hits were removed. Choices for the differential analysis were made based on the article by Rigaill et al  44. Genes which did not have at least one read after a counts per million (CPM) normalization in at least one half of the samples were discarded. Library size was normalized using the TMM method and count distribution was modeled with a negative binomial generalized linear model.
Acknowledgements YC acknowledges  Fabrice Besnard ,  Joe Cammarata  and  Teva Vernoux  for constructive discussions and suggestions on a draft version of this article,  Stéphanie Hallet  for technical support, and the  CNRS (ATIP-Avenir programme)  for research funding. The sequencing platform (POPS) benefited from the support of the  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ).
Meteorological data Meteorological data were obtained from the Met Éireann website (The Irish National Meteorological Service 2020). Weather stations in both Carlow in Oak Park (52°51′40. 0ʺ N, 6°54′55. 0ʺ W, 22. 5 km from sampling site, 62 masl), and Dublin in Dublin Airport (53°25′40. 0ʺ N 6°14′27. 0ʺ W, 11 km from the sampling site, 74 masl) provided the following daily datasets of major meteorological parameters. All parameters represent daily means unless otherwise stated. For Carlow, the available parameters were: Mean Temperature.
Pollen calendar Data from unpublished pollen monitoring campaigns from  1978 -1980 , 2010 -2011 and from the POMMEL monitoring campaign (2017 -2019) )  were combined to construct the first pollen Calendar for Dublin. The mean daily pollen values for all years were calculated for 21 reoccurring pollen types that were present for all sampling years. Daily values for each month were further divided into 5 sections per month, containing 6 days each. The mean value for each section was then calculated. This was repeated for each pollen type. Early, late and main flowering periods were calculated using the method suggested by  Werchan et al. (2018). The main flowering period was defined as the 10-90% interval, beginning once the pollen interval reached 10% of the APIn and ending once 90% was reached.
Discussion Examination of the recorded pollen data from both sampling locations provided important information about the prevalent pollen types and seasonal trends of the Irish environment. Pollen seasonal trends were similar at both locations and can be described as bimodal, starting in January/February with the releases of Corylus and Alnus pollen, and ending in late September/early October with the decline of Poaceae and Urticaceae pollen. This observed trend is comparable to aerobiological studies from the United Kingdom  (Emberlin et al. , 1990 (Emberlin et al. , , 1993 (Emberlin et al. , , 2007) )  and notably different from those recorded in other European regions such as Mediterranean  (Camacho, 2015; Galán et al. , 1995; Giner et al. , 1999; Puljak et al. , 2016; Rodríguez et al. , 2015; Subiza et al.
Acknowledgments We thank  Livia Recchia (PixLR)  for the design of Figure 2. We sincerely apologize for publications that could not be cited due to space limitation or due to our 'adult' bifidobacterial focus. D. v. S. is a member of  APC Microbiome Ireland  funded by  Science Foundation Ireland (SFI) , through the  Irish Government's National Development Plan  (Grant no. SFI/12/RC/2273-P1  and  SFI/ 12/RC/2273-P2 ). F. T. was supported by progetto  Ricerca Finalizzata 2018 ,  Ministero della Salute  ( RF GR-2018-12365988 ).
For this reason, it has been proven useful to produce local and regional chronologies and further combine them to supra-regional chronologies especially when working on dendroarchaeological material. The use of various dendrochronological databases assists the compilation of individual chronologies which can be (re)assembled for specific research questions. Successful dating of archaeological and historical wood is usually confirmed by several independent reference chronologies. Constant improvement of the replication allows periodic updates and improvement of reference chronologies.
SOURCES AND CURRENT STATE OF DENDROARCHAEOLOGY IN EUROPE Chronologies Annually resolved and absolutely dated millennia-long treering width chronologies from living and relict wood have been developed in Europe for the Austrian Alps (9111 years;  Nicolussi et al. , 2009) , northern Germany (8000 years;  Leuschner et al. , 2002) , Ireland (6939 years;  Baillie, 2009) , northern Sweden (7400 years;  Grudd et al. , 2002) , and Finish Lapland (7519 years;  Eronen et al. , 2002). The initial "Holocene Oak Chronology" by  Becker (1993) , has been revised and extended by the "Preboreal Pine Chronology"  (Spurk et al. , 1998; Friedrich et al. , 1999 Friedrich et al. , , 2004)). This composite dataset covers 12. 460 years and reaches back to the Late Glacial Period with a continuous coverage, which is unique globally.
, 2019). Socio-economic decline at the end of the Roman Empire is reflected by a decrease in building activity and therefore, a limited number of dendroarchaeological evidence in the late 4th and 5th centuries CE  (Rzepecki et al. , 2019). In the majority of archaeological sites in Europe, no organic tissue is preserved. Here, dendroarchaeological studies are limited to chemically modified wood, e. g. , charcoal or mineralized wood. These highly fragmented finds rarely show traces of the original object surface and hardly provide a sufficient number of tree rings. In most cases, they are not suitable for technological or dendrochronological analyses. However, taxonomical identification is still possible and often the minimum diameter of the used trunk can be estimated. With large charcoal datasets it is possible to answer research questions of local vegetation cover, anthropogenic land use and forest exploitation as well as dendrochronological dating  (Cichocki, 2007; Nelle et al.
Forest History Comprehensive dendroarchaeological datasets established by European laboratories provide information on the anthropogenic impact on forest environments. The natural composition of species in European forests depends on various factors, including soil properties, environmental and climate conditions, the ecological amplitude of different tree species, the timing of species occurrence as well as the inter-species competition within a forest ecosystem  (Ellenberg, 1996). However, human societies have influenced the natural forest composition in large parts of Europe throughout the Holocene. First indirect human impact on post-glacial forests might have already happened in the Mesolithic period through hunting pressure on large herbivores as well as facilitating the distribution of species through gathering, e.
The frequency of occurrence of the modalities of each activity was then computed. The activities with a modality used by all respondents were considered as non-discriminant and removed from the subsequent analyses. Two activity tables were then created with respondents in rows and activities (nominal variables) in columns. To establish the viticultural and winemaking typologies we used a multiple correspondence analysis (MCA) of the activity tables. Only the modalities with five or more occurrences were kept in this analysis. MCA codes data by creating several binary columns for each variable with the constraint that only one of the columns will obtain the value 1. This coding schema creates artificial additional dimensions, because one categorical variable is coded with several columns. Consequently, the inertia (i.
ACKNOWLEDGEMENTS This work was funded by  Burgundy Regional Conseil, Bureau Interprofessionnel des Vins de Bourgogne (BIVB) ,  France  and  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  programa "Ciência sem Fronteiras", Brazil. The authors wish to warmly thank the wine professionals from Chablis who participated in this study. The authors would also like to thank  Monique Jouano  for her help in the interview transcriptions.
0, 3. 5, 4. 0, 5. 0 and 6. 0 (adjusted with sodium hydroxide) were used. Similarly, ethanol was added to the original synthetic grape juice model (pH = 3. 5) to obtain final ethanol concentrations of 0, 5, 10 and 15% (v/v). Determination of the inhibitory effect of sulfur dioxide, ascorbic acid and glutathione. Analogous testing assays applying only (-)-epicatechin as substrate were performed to determine the inhibitory effect of the three most frequently used wine antioxidants: SO 2 , ascorbic acid and reduced glutathione. To this end SO 2 (in the form of Na 2 S 2 O 5 ) concentrations of 0, 10, 20 and 30 mg/L, ascorbic acid concentrations of 0, 50 and 100 mg/L, and reduced glutathione concentrations of 0, 20, 50 and 100 mg/L were added to the reaction media. Statistical analysis. All data are expressed as mean values ± standard deviation of three replicates.
| A SS E SS ING AIR P OLLUTI ON: RE S E ARCH INFR A S TRUC TURE S AND ME THODOLOG IE S FOR FORE S T MONITORING Regional and national air quality directives and emissions control policies (e. g. , Japanese Air Pollution Control  Act 1968 Act /1970;; European Council Directive 2008/50/EC; United States Federal Register, 2015)  led to the development of air quality monitoring stations. Monitoring data are collated within national or regional databases, such as the Acid Deposition Monitoring Network in East Asia, the European Environment Agency Airbase system, and the Australia Air Quality Network (AUSAQN;  Schultz et al. , 2017). Despite efforts to monitor air quality in South America, the spatial distribution of monitoring stations is still heterogeneous and insufficient to represent the pollutant levels  (Peláez et al.
Expertise warrants the collection, management, and analysis of spatiotemporally diverse datasets, such as DEIMS (Drupal Ecological Information Management System), a central platform providing information on sites and networks with ecological long-term monitoring and experimentation at European and global scales. Currently, ILTER encompasses 39 countries which together operate more than 600 sites  (Maass et al. , 2016). Some sites maintain advanced continuous measurements, such as tower-based eddy covariance assessments of CO 2 and H 2 O fluxes. The ILTER network includes the Terrestrial Ecosystem Research Network (TERN), established in Australia, which provides a comprehensive metadata portal containing information on continental scale datasets of measurements describing fauna, flora, terrestrial ecosystems, ecological dynamics, land surfaces, soils, agricultural ecosystems, coasts, climate observations and fluxes  (Karan et al.
, 2016). Similarly, the Chinese National Ecosystem Research Network (CNERN) is an integrated platform of field stations supervised by various Chinese ministries. CNERN represents a science and technology system that conducts network observation and experimentation across China's ecosystems, cutting across governmental departments, industrial sectors, regions, and jurisdictions, and seeks to integrate observation equipment and data resources and standardize research methods, tools, and protocols. As a result, CNERN serves as a nexus for national ecological research, promotes data sharing, and creates an educational center and collaborative base for ecological research. ILTER networks are also present in Korea and Taiwan. Another "network of regional networks" is represented by FLUXNET, which is coordinating regional and global analyses conducted at micrometeorological tower sites (eddy covariance) to investigate the exchanges of carbon dioxide (CO 2 ), water vapor, and energy between terrestrial ecosystems and the atmosphere  (Pastorello et al.
The aforementioned monitoring networks may benefit from data derived through remote sensing measurements  (Lechner et al. , 2020). Remotely sensed imagery provides a synoptic view, and is potentially available everywhere at a large range of spatial and temporal scales with a high degree of homogeneity. Furthermore, remote imagery provides digital images that can easily be integrated with other spatial datasets in a geographic information system, and per unit area remote sensing is an inexpensive way to acquire data. The most used remote sensing sensors for assessing and monitoring forest conditions are those on-board satellites, followed by airborne (including Unmanned Aerial Vehicles) and terrestrial systems, or a combination of these platforms  (Torres et al. , 2021). Previous studies have demonstrated the utility of optical remote sensing for assessing a variety of forest health indices, and are commonly used in forest monitoring activities  (Curran et al.
, 1992; Huang et al. , 2019; Parent & Verbyla, 2010). Landsat satellite images are still the most widely used Earth Observation (EO) data in forest health studies  (Torres et al. , 2021) , which provide continuous time series data from the 1970s (i. e. , Landsat 1 mission) until today (i. e. , Landsat 8). Access to Landsat images has been free since 2008, and the recently launched Landsat 9 (September 2021) will be publicly available in early 2022. In addition to Landsat imagery, imagery from sentinel missions from the European Space Agency is also particularly important for forest monitoring because of their high spatial and temporal resolution. Furthermore, the availability of both active (Sentinel-1) and passive (Sentinel-2) sensors might increase the precision of previous analytical methods that rely primarily on optical reflectance indices.
, 2019). The databases for genetic information of tree species have been rapidly increasing, and the most important model systems for forest trees are Populus, Eucalyptus, Quercus, Castanea, Pseudotsuga, Pinus, Picea, and Betula genuses  (Falk et al. , 2018; Salojärvi et al. , 2017). Genetic engineering efforts by forest biotechnology companies have produced transgenic Eucalyptus and Populus trees with enhanced growth and disease-resistant properties  (Naidoo et al. , 2019). Silver birch (Betula pendula Roth) is an excellent model system for elucidating the adaptation and acclimation capacity of forest trees to rapidly changing climate due to its (i) wide latitudinal and longitudinal distribution; (ii) recent advances in population genomics and evolutionary history of birch species  (Salojärvi et al.
, 2018)  make the simulation of species composition changes a decisive function for any model. When dynamic PBMs are used for forecasting biodiversity shifts, they are usually combined with vegetation response models based on species niche suitability and competition  (Belyazid et al. , 2019; Dirnböck et al. , 2018). PBMs have been used at stand (e. g. ,  Collalti et al. , 2016) , landscape  (Shifley et al. , 2017) , regional  (Belyazid et al. , 2019; De Marco et al. , 2020; Santini et al. , 2014) , and global scales (e. g. ,  Krause et al. , 2017). However, their implementation is restricted at larger scales since PBMs need large, detailed input datasets, which are often not available at national or continental scales. At these scales, a currently suitable approach is using new models, mostly empirical, based on currently available large datasets, such as species distribution models (SDMs;  Maréchaux et al.
, 2020; Fabrika et al. , 2019; Maréchaux et al. , 2020). The modeling process might be as complex as needed by risk assessment objectives (Figure  5 ), providing models and data are available and suitable. In general terms, empirical models are good at predicting biomass and forest structure in the shorter term, and consequently producing good management recommendations for the present conditions, but are not reliable in novel situations (i. e. , future air pollution and climate change). PBMs are good at studying effects and underlying processes of change, particularly in the context of global change. However, they still have low feasibility at broad scales, and the calibration and validation processes are highly time-consuming (particularly for the less-modeled species or regions). SDMs are appropriate for early risk assessment on biodiversity conservation at the broadest scales, but still too empirical, which diminishes their reliability in the long term  (Urban et al.
, 2011). Long-term data series can be integrated in existing big databases such as the Global Atmosphere Watch (GAW) Program and the international Tropospheric O 3 Assessment Report (TOAR;  Schultz et al. , 2017; WMO, GAW, 2003). These raw databases can lead to the development of new products for temporal and spatial analysis (data analysis, maps of data distributions, and data summaries) that are freely accessible to the scientific community and other stakeholders. Such databases can be used as tools for mechanistic and diagnostic understanding and upscaling. The need for a global forest monitoring is irrefutable, and "supersites" promote the integration of research communities in a transcontinental collaboration network by upgrading existing ground-based observation networks (e. g. , FLUXNET, ICP, NEON) covering all biogeographic areas (e.