dataesr/scientific-paragraphs-categorization huggingface dataset:
To reduce computational burden, we assumed that trees whose height is lower than H/2 were not directly affected by wind. To account for canopy height heterogeneity and neighbor tree sheltering, we calculated canopy height H for every 20 × 20 m quadrat by taking the arithmetic mean of the top leaf-containing voxel layer of each pixel within the quadrat. From the CRU-NCEP reanalysis dataset, we obtained empirical atmospheric wind speed values (cf. the "Climate forcing" section below), which are conventionally measured at 10 m above ground or above a displacement surface. In order to obtain the horizontal wind speed experienced by each tree, we applied the following equation, derived from eq. (1a), to convert horizontal atmospheric wind speed at d + 10, u(d + 10), into top-canopy wind speed u(H) within each 20 × 20 m quadrat: u (H) = u d + 10 × ln H-d z 0 ln 10 z 0 = u(d + 10) × 1.
Climatic Forcing The TROLL model requires the following monthly mean climatic variables: daytime and nighttime mean temperature, daytime mean irradiance, and daytime mean vapor pressure deficit (VPD). We used the CRU-NCEP dataset to provide the monthly climate forcing, a global gridded (0. 5 • × 0. 5 • ) sub-daily (6hourly) climate product spanning the 1901-2016 period (version 8; version 7 archived 1 )  (Viovy, 2018). It was constructed by combining observation-based CRU TS 3. 2 data  (Harris et al. , 2014)  and model-based NCEP-NCAR data  (Kalnay et al. , 1996). We used CRU-NCEP variables for the 1980-2016 period, for which the most observations are available  (Kistler et al. , 2001). We estimated the monthly average frequency of cyclones occurring within a sufficiently close distance to the study site.
For this, we used the IBTrACS dataset (International Best Track Archive for Climate Stewardship database; v04r00, archived 2 ), which contains records of global tropical cyclones occurring since 1945  (Knapp et al. , 2010). A common measure of the spatial extent of tropical cyclones is the mean radius of gale-force winds, by convention defined as 17. 5 m s -1. Based on the range of reported values in the literature, we assumed the mean radius of gale-force winds (R 17 , km) to be 150 km  (Chan and Chan, 2012; Weber et al. , 2014; Lu et al. , 2017). Thus, for a given site, we calculated the monthly mean frequency of recorded tropical cyclones occurring within a 150-km distance from the site over the period of 1987-2020. At Fushan, cyclone occurrence frequency was highest from July to September (∼ 0.
, 2007). Species-specific plant functional traits that TROLL requires as input parameters include leaf mass per area (LMA, g m -2 ), nitrogen and phosphorus content (N mass and P mass , mg g -1 ), wood density (WD, g cm -3 ), maximum DBH (cm), DBH-height allometric parameters and regional relative abundance. These traits were measured at Fushan according to ForestGEO protocol  (Iida et al. , 2014)  for 94 species, covering ca. 90% of the censused trees. Climatic variables were extracted from the CRU-NCEP dataset at the geographic coordinates closest to Fushan FDP (24 • 45 N, 121 • 32 E). Stem density and aboveground biomass (AGB) estimations were available at Fushan from census data. In order to estimate on-site tree mortality, we used data from the annual tree mortality survey, which has been conducted at Fushan following ForestGEO protocol since 2017.
The mortality survey records the number of tree deaths and cause of death (standing, uprooted, or broken) of a subset of censused trees  (Arellano et al. , 2021). Using the mortality data at Fushan spanning 2017 to 2020, we calculated the mean annual mortality rate for all trees DBH > 10 cm, as well as the proportion of mortality attributed to treefall. For the detailed protocol of attribution of mortality factors, see Supplementary Appendix A. Unless otherwise specified, for all simulations, we simulated forest regeneration from bare soil for a reference plot area of 4 hectares (200 m × 200 m) for a duration of 500 years with a monthly timestep. Since we aimed to examine the effects of extreme wind on mature forest, the wind-induced tree mortality sub-model was activated after a 100-year spin up phase.
Field Mortality Survey Data In this study, we retrieved data from the annual mortality surveys conducted in ForestGEO sites, in the hope of calibrating the model using empirical mortality data as a complement to inventory data. The survey data could inform us on the number of tree deaths and treefall that occur within the forest plot, and simulated mortality rates showed good correspondence with observed values calculated from the survey data, but inferring the cause of tree death from the observed damage modes of the dead trees has proven to be difficult. A tree may experience multiple damage successively or simultaneously, which would all contribute to causing treefall, or it may experience damage after its death: these different scenarios could not be distinguished by post hoc records of observed damage on dead trees.
ACKNOWLEDGMENTS Simulations were run on the OLYMPE cluster at CALMIP, Toulouse. We thank  Dr. Chia-Hao Chang-Yang  at the  National Sun Yat-sen University  for helping to provide us with the mortality survey data at Fushan FDP. The data at Fushan FDP results from long-term fieldwork and research supported by the  Taiwan Forestry Bureau , the  Taiwan Forestry Research Institute  and the  Ministry of Science and Technology of Taiwan. We would like to express our gratitude to all field and students who helped with the implementation and recensus of Fushan Forest Dynamics plots. We also thank the staff at  Fushan Research Center  for providing logistic support.
FUNDING This work was supported by the " Investissement d'Avenir " grants managed by the  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01 ;  TULIP, ref. ANR-10-LABX-0041 ;  ANAEE-France :  ANR-11-INBS-0001 ). This work was also funded by the  administrative region of Occitanie (France)  and  Centre National d'Études Spatiales (CNES).
10 3 Hz, Maximum Horizontal Sync: 100. 10 3 Hz)  14. The movements of the walking bee on the treadmill were recorded by two infrared optic-mouse sensors (Logitech M500, 1000 dpi, Logitech, Lausanne, Switzerland) placed on the ball support perpendicular to each other. Experiments were conducted under 3D closed-loop conditions, i. e. , rotations of the ball displaced the visual stimuli not only laterally but also towards the bee. To generate these conditions, we developed a custom software by means of the Unity engine (version 2018. 3. 11f1). The open-source code is available at https:// github. com/G-Lafon/BeeVR. The software updated the position of the bee within the VR every 0. 017 s. A displacement of 1 cm on the ball corresponds to an equivalent displacement in the VR landscape. Moving 1 cm on the ball towards an object increased the visual angle of the object by ca.
5 μl of 10 μmol of each primer and 2 μl of ultrapure water. The reaction conditions were as follows: 95 °C for 30 s followed by 40 cycles of 95 °C for 10 s, 55 °C for 30 s, and a final step at 95 °C for 10 s followed by a melt curve from 55 °C to 95 °C with 0. 5 °C per second. The reaction was performed in a CFX384 Touch Real-Time PCR Detection System (Bio-Rad) and analyzed with the software Bio-Rad CFX Manager. Each sample was run in triplicates. If the triplicates showed too much variability (SD > 0. 3), the furthest triplicate was discarded. If the two remaining triplicates still showed too much variability (SD > 0. 3) the sample was discarded. The samples were subjected to relative quantification and normalization. First, for each sample and for each reference gene per brain region, the relative quantity (Qr) was computed using the difference between the mean Ct value of each sample and the highest mean Ct value (ΔCt), using the following formula: Qr = (1 + E)  ΔCt  (with E = efficiency of the reaction).
Tortuosity was calculated as the ratio between the total distance walked and the distance between the first and the last point of the trajectory connected by an imaginary straight line. When the ratio was 1, or close to 1, trajectories were straightforward while higher values corresponded to sinuous trajectories  91. The analysis of these continuous variables was done using a linear mixed model (lmer function) in which the individual identity (Bee ID) was a random factor and the experimental condition (Condition) and trial number (Trial) were fixed factors  91. Statistical analyses were performed using R 3. 5. 1  92. The package lme4 was used for GLMMs and LMMs. Gene-expression data. Statistical differences in gene expression were assessed for reference genes to check for stability and for target genes within a given brain region using One-Factor ANOVA for independent groups in the case of multiple comparisons or two-sample t-test in the case of dual comparisons.
Acknowledgements We thank three anonymous reviewers for constructive criticisms and  Shiori Iino  and  Takeo Kubo  for providing useful information on the timing of IEG expression. We also thank  Benjamin H. Paffhausen ,  Marco Paoli , and  Dorian Champelovier  for valuable discussions. This work was supported by an  ERC   Advanced Grant  ('Cognibrains') to M. G. , who also thanks the  Institut Universitaire de France (IUF) , the  CNRS  and the  University Paul Sabatier  for support.
We use this method to study coupling between cerebral blood flow, amplitude of low frequency fluctuations, and local connectivity in 803 subjects ages 8 through 22. We demonstrate that coupling is spatially heterogeneous, varies with respect to age and sex in neurodevelopment, and reveals patterns that are not present in individual modalities. As availability of multi-modal data continues to increase, principalcomponent-based IMCo (pIMCo) offers a powerful approach for summarizing relationships between multiple aspects of brain structure and function. An R package for estimating pIMCo is available at: https://github. com/hufengling/pIMCo.
Code and data availibility An R package for calculating pIMCo images is available at: https://github. com/hufengling/pIMCo. All code for analysis is available at: https://github. com/hufengling/IMCo_analyses. The following software and packages were used for pre-processing, pIMCo calculation, or analysis -R Core Team (2021); PNC data are publicly available in raw format at https://www. ncbi. nlm. nih. gov/projects/gap/cgi-bin/study. cgi?study_id=phs000607. v3. p2.
For example, in a two-dimensional neighborhood, it could be possible that there is a strong quadratic relationship between modalities, but the data is not well-summarized by one eigenvalue and its corresponding eigenvector. In such cases, an IMCo technique based on manifold learning concepts could be a useful improvement that picks up on otherwise undetected intermodal relationships. Finally, pIMCo is designed to estimate coupling in cross-sectional multi-modality datasets. However, there is also rich covariance information in longitudinal datasets with one or multiple modalities. Future implementations of pIMCo suitable for longitudinal data could investigate whether IMCo changes over time in neurodevelopment or disease, and if so, whether such changes may be useful in identifying biomarkers for clinical phenotypes.
Acknowledgements This study was supported by grants from the  National Institute of Mental Health  ( R01MH112847  to TDS and RTS;  R01MH120482  and  R01MH113550  to TDS;  R01 MH123550-01  to RTS;  R01MH107235  to RCG;  2T32MH019112-29A1  to EBB;  R01MH120174 ,  R01MH119185 , and  R56AG066656  to DRR). FH was supported by  NIH   Medical Scientist Training Program   T32 GM07170. SMW was supported by the  National Science Foundation Graduate Research Fellowship Program (NSF GRFP). The PNC data used was funded by  NIMH   RC2 grants  ( MH089983  and  MH089924  to.
Abstract. Over the last decades, the Amazon rainforest has been hit by multiple severe drought events. Here, we assess the severity and spatial extent of the extreme drought years 2005, 2010 and 2015/16 in the Amazon region and their impacts on the regional carbon cycle. As an indicator of drought stress in the Amazon rainforest, we use the widely applied maximum cumulative water deficit (MCWD). Evaluating nine state-of-the-art precipitation datasets for the Amazon region, we find that the spatial extent of the drought in 2005 ranges from 2. 2 to 3. 0 (mean = 2. 7) ×10 6 km 2 (37 %-51 % of the Amazon basin, mean = 45 %), where MCWD indicates at least moderate drought conditions (relative MCWD anomaly < -0. 5). In 2010, the affected area was about 16 % larger, ranging from 3. 0 up to 4. 4 (mean = 3.
6) ×10 6 km 2 (51 %-74 %, mean = 61 %). In 2016, the mean area affected by drought stress was between 2005 and 2010 (mean = 3. 2 × 10 6 km 2 ; 55 % of the Amazon basin), but the general disagreement between datasets was larger, ranging from 2. 4 up to 4. 1 × 10 6 km 2 (40 %-69 %). In addition, we compare differences and similarities among datasets using the self-calibrating Palmer Drought Severity Index (scPDSI) and a dry-season rainfall anomaly index (RAI). We find that scPDSI shows a stronger and RAI a much weaker drought impact in terms of extent and severity for the year 2016 compared to MCWD. We further investigate the impact of varying evapotranspiration on the drought indicators using two state-of-the-art evapotranspiration datasets. Generally, the variability in drought stress is most dependent on the drought indicator (60 %), followed by the choice of the precipitation dataset (20 %) and the evapotranspiration dataset (20 %).
, 2015; Jiang et al. , 2020; Marengo and Espinoza, 2016). For assessing the severity; the spatial extent; and, in particular, the impacts of such drought events on existing ecosystems, different gridded precipitation datasets are available, which in some cases differ strongly in magnitude and spatiotemporal distribution of precipitation amounts  (Golian et al. , 2019). Typical problems of precipitation data for South America encompass the underestimation of extreme rainfall events in both dry or wet seasons  (Blacutt et al. , 2015; Giles et al. , 2020). Therefore, while for the Amazon region, the recent drought events have been assessed in terms of severity  (Jimenez et al. , 2018; Jiménez-Muñoz et al. , 2016)  and impacts  (Feldpausch et al. , 2016; Lewis et al. , 2011; Phillips et al. , 2009)  based on single precipitation datasets, a systematic analysis of how the most frequently used precipitation datasets differ regarding the spatial extent, location and severity of recent extreme drought events is currently missing.
For our study, we selected precipitation from nine different datasets. (1, 2) Data from the Tropical Rainfall Measurement Mission (TRMM) versions 6 and 7  (Huffman et al. , 2007)  have been frequently used e. g. to estimate drought impacts on the carbon balance  (Lewis et al. , 2011; Malhi et al. , 2009)  and are assumed to represent precipitation patterns in the Amazon region best, since they are derived from radar measurements  (Huffman et al. , 2007). (  3 ) CHIRPS (Climate Hazards group Infrared Precipitation with Stations;  Espinoza et al. , 2019)  has been used to study regional hydro-climatic and environmental changes in the Amazon basin. These three datasets only provide precipitation and no information about other climatic variables such as temperature or radiation. In addition, we selected five datasets that are often used as drivers for ecosystem models (e.
g. in  Forkel et al. , 2019; Yang et al. , 2015)  and -in contrast to the other datasetsprovide information about more climate variables. (4) Data from the Climatic Research Unit (CRU) have been used in a joint project reanalysis (NCEP, National Centers for Environmental Prediction), known as CRUNCEP (version 8;  Viovy, 2018). (5) The WATCH (Water and Global Change) WFDEI (WATCH Forcing Data methodology applied to ERA-Interim reanalysis data;  Weedon et al. , 2011 Weedon et al. , , 2014) )  dataset was originally derived from global sub-daily observations merged with integrations from a general circulation model. (6) The GSWP3 (Global Soil Wetness Project Phase 3;  Kim et al. , 2022)  dataset is closely related to WATCH WFDEI, relying on a similar forcing but with a different bias-correction method applied.
(7) The newer GLDAS (Global Land Data Assimilation System) 2. 1 is derived from various geostationary infrared satellite measurements and microwave observations  (Rodell et al. , 2004). (  8 ) The latest ECMWF atmospheric reanalysis dataset, ERA5, is the successor to ERA-Interim and provides higher spatial and temporal resolutions and a more recent model and data assimilation system than the previous ERA-Interim reanalysis  (Albergel et al. , 2018). Finally, (9) the GPCC (named after the Global Precipitation Climatology Centre) dataset  (Schneider et al. , 2018)  is based on globally available land stations (rain gauges) combined with an empirical interpolation method  (Willmott et al. , 1985). The datasets were chosen because they are often used to force dynamic global vegetation and hydrological simulation models in climate impact studies.
A more detailed description of the datasets is given in the "Methods" section. We evaluate the precipitation datasets based on the maximum cumulative water deficit (MCWD;  Aragão et al. , 2007) , a well-established drought index that is particularly suitable for estimating drought stress in the Amazon region (e. g. Esquivel-Muelbert et al. , 2019; Lewis et al. , 2011; Malhi et al. , 2009; Phillips et al. , 2009; Zang et al. , 2020). In addition, we included two other measures to complement our analysis: a rainfall anomaly index (RAI), which does account for the mean deviation (in units of standard deviation) of precipitation during the driest months of the year, and the scPDSI (self-calibrating Palmer Drought Severity Index;  Wells et al. , 2004). The scPDSI index has a more complex formulation compared to RAI and MCWD and takes available soil water content into account.
Both RAI and scPDSI have been used in studies describing the recent Amazonian drought events (e. g. Jiménez-Muñoz et al. , 2016; Lewis et al. , 2011). Many studies (e. g. Flack-Prain et al. , 2019; Hubau et al. , 2020)  currently still use a fixed evapotranspiration rate for the calculation of MCWD instead of using evapotranspiration datasets as input. To assess the robustness of a fixed evapotranspiration rate, we include two evapotranspiration datasets GLEAM (Global Land Evaporation Amsterdam Model;  Martens et al. , 2017)  and DOLCE (Derived Optimal Linear Combination Evapotranspiration;  Hobeichi et al. , 2018)  for the calculation of MCWD and scPDSI. The goals of our study are (1) to analyse and quantify the uncertainty in strength, extent and location of three recent Amazon droughts in the years 2005, 2010 and 2015/16 in precipitation from nine state-of-the-art precipitation or climate datasets based on MCWD and (2) to examine differences among these drought events by taking two additional drought indicators (RAI and scPDSI) and two evapotranspiration datasets into account.
Data sources In the following, we briefly describe the nine precipitation datasets applied in our study (see also Table  1 ): the Tropical Rainfall Measuring Mission (TRMM v7) product  (Huffman et al. , 2007)  is a precipitation-only dataset based on multiple microwave-infrared satellite data, developed as a joint product between NASA and the Japan Aerospace Exploration Agency (JAXA). We also included the predecessor, v6, for comparison in our study, because it has been frequently and prominently used to derive drought impacts to the Amazon basin (e. g. Lewis et al. , 2011; Phillips et al. , 2009)  and shows significantly lower precipitation throughout the basin compared to v7  (Seto et al. , 2011). CHIRPS (Climate Hazards group Infrared Precipitation with Station) is a novel dataset  (Funk et al.
2 (Time Series;  Harris et al. , 2014)  dataset. GPCC is mainly based on data from rain-gauge land stations. Similar to CRUNCEP, it is also based on the NCEP Reanalysis dataset and has been used in global drought studies  (Ziese et al. , 2014). Both GPCC and CRUNCEP cover the longest periods of all selected datasets in this study with time spans from 1891 until 2016 and from 1901 until 2016, respectively. WATCH WFDEI  (Weedon et al. , 2011 (Weedon et al. , , 2014) )  is based on the reanalysis ERA-Interim corrected with GPCC precipitation. GSWP3  (Kim et al. , 2022)  is based on the atmospheric reanalysis method "20CR" (Twentieth Century Reanalysis version 2;  Compo et al. , 2013) , which has been dynamically downscaled to 0. 5 • × 0. 5 • resolution. Corrections with observational data have been applied not only to precipitation but also to short-/longwave radiation, air temperature and the daily temperature range.
Both WATCH WFDEI and GSWP end in the year 2010. The GLDAS 2. 1 dataset is built by using the Noah land surface model forced by the Goddard Earth Observing System (GEOS) Data Assimilation System with corrected precipitation and radiation  (Rodell et al. , 2004; Sheffield et al. , 2006). Starting in January 2000 (version 2. 1), it is the dataset with the latest time onset and hence defines the lower-bound time interval considered in this study. For the 2015/16 drought event, only seven datasets were available, as three of the datasets (TRMM 6, GSWP3 and WATCH WFDEI) end before. All datasets were (if not directly available) aggregated to 0. 5 • × 0. 5 • spatial resolution and to monthly time steps.
Evapotranspiration datasets In addition to assuming a constant evapotranspiration of ET(t) = ET fixed = 100 mm for the calculation of MCWD, for the calculation of scPDSI we use the two ET datasets GLEAM and DOLCE. The Global Land Evaporation Amsterdam Model (GLEAM) v3a dataset  (Martens et al. , 2017)  is derived from a set of algorithms incorporating satelliteobserved soil moisture, vegetation optical depth, reanalysis air temperature and radiation, and multiple precipitation datasets. The Derived Optimal Linear Combination Evapo-transpiration (DOLCE;  Hobeichi et al. , 2018)  dataset is derived by combining and weighting multiple other evapotranspiration datasets, also including GLEAM.
Calculation of drought area and extent Each grid cell's area was approximated as a trapezoid to its boundary coordinates (in 0. 5 • × 0. 5 • resolution), resulting in an area between 2900 and 3090 km 2 per grid cell. Accumulating the associated areas over all grid cells resulted in a total area of 5. 94 × 10 6 km 2 representing the Amazon basin. Note that for comparison of our results with  Lewis et al. (2011) , differences in absolute areas arise because of differences in study area size (5. 94 × 10 6 vs. 5. 3 × 10 6 km 2 , respectively). For the calculation of the drought-affected area, we summed up the area of grid cells that matched the respective drought classification (e. g. rMCWD anomaly < -2. 5 for extreme drought stress). The spatial agreement of drought location among datasets was estimated by selecting the grid cells matching the drought classification per dataset and subsequently counting the number of datasets per grid cells showing the respective drought classification.
Comparison of total drought area based on the relative MCWD anomaly We first evaluate differences in rMCWD for 2016 across the datasets (Fig. 1 ). Here, we find that the spatial patterns of the rMCWD anomaly generally match across the available datasets, showing severe and extreme drought stress mainly in the northern Amazon basin. Only GLDAS diverges, showing extreme drought stress in the central and western part of Amazonia (Fig. 1d ), where none of the other datasets show any drought stress during the same year. The other datasets mostly differ in the intensity of the drought stress. While ERA5 and TRMM 7 show values of rMCWD < -2. 5 in the Columbian part of the basin, CRUNCEP and GPCC do show such a strong drought impact only in northern Brazil. The absolute areas of drought stress across different severity levels are similar across most datasets with only GLDAS showing a significantly larger area affected by extreme drought stress of rMCWD < -2.