dataesr/scientific-paragraphs-categorization huggingface dataset:
Thiovulum ES, all genes required for C fixation via the reductive TCA cycle were identified. in the new freshwater and marine Thiovulum genomes. The oxidative TCA cycle was found to be complete as well in all the Thiovulum species with the citrate synthase gene (EC 2. 3. 3. 1)replaced by ATP-citrate lyase. (EC2. 3. 3. 8)(Fig. 6,SupplementaryFigs. 3-8, Supplementary Dataset 1-6). Sulfur metabolism. All annotation approaches (Supplementary Datasets 1-6) revealed only few genes involved in dissimilatory.
Analysis plan All analyses were pre-registered unless stated. Analyses were conducted using SPSS version 26 (frequentist analyses) and JASP version 0. 16. 4 (Bayesian analyses). Main effects and interactions were assessed against an alpha of p <. 05 for primary and p <. 01 for secondary analyses to account for multiple comparisons. Bayes Factors were computed using default priors; r scale fixed effects = 0. 5, r scale random effects = 1, and r scale covariates = 0. 353. We report BFincl and use conventional cut offs as evidence for the alternative hypothesis (1 -3: anecdotal; 3 -10: moderate; 10 -30: strong; 30 -100 very strong; > 100 extreme; with inverse values indicative of the same degree of evidence for the null) (40).
Sample size. Power calculations were conducted using G*Power 3. 1. For Study 1, we calculated that a minimum sample of 46 participants was required to detect a small-to-medium main effect of portion size or interaction with SEP on daily energy intake (f=0. 175, p <. 05, 85% power, within-subject correlation of 0. 7 estimated from (  31 )). We powered Study 2 to detect the same sized effects and interactions on daily energy intake (f=0. 175, p <. 05, 85% power, within-subject correlation of 0. 75 (conservatively used given Study 1 r = 0. 84]), resulting in a minimum N=40. As specified in the protocol, we planned to recruit slightly above minimum sample sizes (10-15%) to account for any missing data and maximise statistical power. Recruited n = 54 (Study 1) and n = 47 (Study 2).
Statistical tests that evaluate these models would require human cohorts with exposure and outcome data, genetic data, and methylation, gene expression or other 'omics data. The process of finding, obtaining, harmonizing, and analyzing individual-level data from human cohorts presents a challenge, as these data are 'controlled-access' and require numerous regulatory steps for the researcher, the researcher's institution and the entity managing access to the data. On the other hand, it is now common to publish summary-level association statistics from human cohorts. Therefore, an analysis of both genetic and non-genetic data that relies on summary data alone is particularly valuable in this context. To this end, we leveraged multiple summary-level association datasets, both genetic and epigenomic, to evaluate possible mechanisms of gene-environment interactions effects.
Observational data Observational data is considered to contrast results from models and analyze their seasonal forecasting skill. We employ the monthly precipitation values from GPCPv2. 3 with a spatial resolution of 2. 5º × 2. 5º. They are provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, and are available at the website: https:// psl. noaa. gov/ data/ gridd ed/ data. gpcp. html  (Adler et al. 2003). Additionally, we also consider the monthly SSTs data from HadIS-STv1. 1 with 1º × 1º of resolution  (Rayner et al. , 2003). The study is focused on July-August-September (JAS), which is the season when the monsoon takes place  (Rodrigue-Fonseca et al. 2015; Nicholson 2013; Thorncroft et al. 2011). In order to check the sensitivity of the results to the choice of the observational dataset, the analysis is also performed using SSTs from ERSSTv5 (with a resolution of 2º × 2º,  Huang et al.
2017 ) and precipitation from CRU TS 4. 03  (Harris et al. 2020). CRU TS is derived by the interpolation of monthly climate anomalies from extensive networks of weather station observations. However, GPCP considers data from rain gauge stations, satellites, and sounding observations from 1979 to present. Regarding SSTs, HadISST uses reduced space optimal interpolation applied to SSTs from the Marine Data Bank (mainly ship tracks) and the International Comprehensive Ocean-Atmosphere Dataset (ICOADS) through 1981 and a blend of in-situ and adjusted satellite-derived SSTs for 1982-onwards. ERSSTv5 uses new data sets from ICOADS Release 3. 0 SSTs, Argo floats above 5 m and Hadley Centre Ice-SST version 2 (HadISST2) ice concentration. Given that conclusions are not altered, in this work we only show the results obtained by contrasting model simulations with observations from GPCPv2.
3 ). These low skill scores are in striking contrast to the statistically significant ACC skill scores obtained by  Giannini et al. (2020) , especially for the multi-model ensemble, which presented statistically significant skill scores for JAS Sahel precipitation well above the significant threshold for FSTs up to 5 months before. Several factors could contribute to these contrasting results, like the number of models considered (5 vs 15), the zonal domain used to define the Sahel (20ºW-40ºE vs 15ºW-15ºE), the period of study  (1982-2016 vs 1982-2010)  and the type of observational dataset employed to assess skill scores (CHIRPS  (Funk et al. 2015)  vs GPCP and CRU). However, we find that the main reason for such differences is related to the methodology used to preprocess the forecasts. While  Giannini et al.
Acknowledgements We acknowledge the agencies that support the NMME-Phase II system, and we thank the climate modeling groups ( Environment Canada, NASA, NCAR, NOAA/GFDL, NOAA/NCEP, and University of Miami ) for producing and making available their model output. NOAA/NCEP,  NOAA/CTB , and  NOAA/CPO  jointly provided coordinating support and led development of the NMME-Phase II system. Authors also thank the support of the  Spanish Ministry of Science and Innovation  for the support through project  PRE4CAST  ( CGL2017-86415-R ) and the  EU H2020  project  TRIATLAS  (No. 817578 ).
Assembly of Data Sets into Loci, Data Set Cleaning Filtering of raw data and assembly into loci followed  Cruaud et al. (2019). Only UCEs that had a sequence from at least 50% of the samples were retained for analysis (n=1,024). Loci were aligned with MAFFT (-linsi option;  Katoh and Standley 2013). Sites with more Rougerie,  Cruaud, Arnal et al. than 50% gaps and sequences with more than 25% gaps were removed from each UCE using SEQTOOLS (package PASTA;  Mirarab et al. 2015) ). For all loci, two successive rounds of TreeShrink  (Mai and Mirarab 2018)  were performed to detect and remove abnormally long branches in individual gene trees, using the per-species mode and b parameter set to 20. Loci were realigned with MAFFT between and after the two rounds of TreeShrink; gene trees were inferred with IQ-TREE 2.
Best-fit models for the unpartitioned data set and for all partitions were selected with the Bayesian Information Criterion (BIC) as implemented in ModelFinder  (Kalyaanamoorthy et al. 2017). FreeRate models with up to ten categories of rates were included in tests for the unpartitioned data set and for partitions that grouped all core regions or all flanking regions, but only common substitution models were tested for the subsets identified by PartitionFinder. The candidate PHYLOGENY AND BIOGEOGRAPHY OF SATURNIIDAE MOTHS tree set for all tree searches was composed of 98 parsimony trees + 1 BIONJ tree and only the 20 best initial trees were retained for NNI search. Node supports were assessed with ultrafast bootstrap (UFBoot)  (Minh et al. 2013 ) with a minimum correlation coefficient set to 0.
99 and 1,000 replicates of SH-aLRT tests  (Guindon et al. 2010). UFBoot values ≥ 95 and SH-aLRT ≥ 80 were considered as strong support for a node. In addition, we computed gene (gCF) and site (sCF) concordance factors  (Minh et al. 2020)  with IQ-TREE. For ASTRAL analyses, nodes in gene trees with UFBoot support lower than 50, 70, or 90 were collapsed following the approach of Zhu (2014, perl script AfterPhylo. pl) before reconciliation (trees are hereafter referred to as ASTRAL50, ASTRAL70 and ASTRAL90, respectively). Node supports for the ASTRAL trees were evaluated with local posterior probabilities (local PP). RF distances  (Robinson and Foulds 1981)  among recovered trees were calculated with RAxML-NG_v0. 9. 0  (Kozlov et al. 2019)  and Approximately Unbiased (AU) tests  (Shimodaira 2002)  were performed with IQ-TREE (number of bootstrap replicates set to 20,000).
Analysis of Conflicting Topologies and Bias Exploration Systematic biases were investigated with respect to influences from both loci and taxa. For analyses based on loci, Gene Genealogy Interrogation (GGI)  (Arcila et al. 2017 , Zhong and Betancur-R 2017 , Betancur-R et al. 2019 ) was used to identify UCEs that supported each of the competing topologies. Each UCE was analyzed with IQ-TREE to infer trees using each of the competing topologies as multifurcating constraint trees (the structure of the backbone was fixed, but taxa within identified clades were free to move around). Per-site log-likelihood scores for all constrained trees were then calculated with RAxML-8. 2. 11  (Stamatakis 2014)  and used to perform AU tests in CONSEL  (Shimodaira and Hasegawa 2001). The program makermt in CONSEL was used to generate K = 10 sets of bootstrap replicates with each set  Rougerie, Cruaud, Arnal et al.
consisting of 100,000 replicates of the row sums. Properties of the UCEs that supported each topology were compared to detect whether significant differences could be identified between groups of UCEs and whether any topology could result from analytical bias. Two properties that are known to bias phylogenetic analyses, GC content  (Romiguier and Roux 2017)  and saturation  (Philippe et al. 2001 , Duchêne et al. 2021) , were analyzed. GC content was calculated with AMAS  (Borowiec 2016 ) and saturation (R squared of the linear regression of uncorrected p-distances against inferred distances in individual gene trees) was calculated following  Borowiec et al. (2015). Results were analyzed in R (R Core Team 2018) using the packages ggpubr  (Kassambara 2020)  and rich  (Rossi 2011). For analyses based on taxa, GC content and long branch (LB) score heterogeneity were calculated for each taxon in each UCE and in the concatenated data set.
GC content was calculated with AMAS and LB heterogeneity scores were calculated with TreSpEx  (Struck 2014). In each tree, the per sample LB score measures how the patristic distance (PD) of a sample to all others deviates (as a percentage) from the average PD across all samples  (Struck, 2014). The calculation of the heterogeneity in LB scores can therefore help to reveal possible Long Branch Attraction (LBA) artefacts  (Bergsten 2005). Hierarchical clustering of taxa based on GC content and LB scores was performed with the R package cluster  (Maechler et al. 2018).
Historical Biogeography Species distributions were assigned as presence/absence to the nine following biogeographical areas: Neotropical, West Nearctic, East Nearctic, Afrotropical, Australasian, Madagascar, Oriental, West Palearctic, and East Palearctic (Table  S2 ). Ancestral area estimations were performed using the R package BioGeoBEARS 1. 1. 1 (Matzke 2014). The chronogram built with MCMCtree was used as input but only one specimen per genus/subgenus was included in the analysis and outgroups were removed to avoid artefacts (taxa were pruned from the chronogram with the R package ape  (Paradis and Schliep 2018) ). Rougerie, Cruaud, Arnal et al. Dispersal-Extinction-Cladogenesis (DEC; Ree and Smith 2008) , BAYAREALIKE  (Landis et al. 2013)  and DIVALIKE  (Ronquist 1997 ) models were used with and without considering the jump parameter for founder events (+J; Matzke 2014).
ACKNOWLEDGEMENTS We are thankful to the  Canadian Centre for DNA Barcoding and Centre for Biodiversity Genomics at University of Guelph (Ontario, Canada ) as well as to all the participants to the Saturniidae DNA barcoding campaign for their contributions to the assembly of samples. We thank  Corinne Poitout ,  Hélène Vignes  and  Audrey Weber  ( INRAE  and  CIRAD, AGAP ,.
FUNDING This work was supported by  French National Research Agency  (SPHINX:  ANR-16-CE02-0011-01 ,  GAARAnti :  ANR-17-CE31-0009 ,  LABEX CEBA :  ANR-10-LABX-25-01  and  LABEX TULIP :  ANR-10-LABX-41 );  French Foundation for Research on Biodiversity (FRB)  and  Centre  for the Synthesis and Analysis of Biodiversity (CESAB: ACTIAS grant).
DATA AVAILABILITY Fastq paired reads for analyzed samples are available as a NCBI Sequence Read Archive (ID#PRJNA XXXXX). Custom script for random sampling of sites within data sets is available from https://github. com/acruaud/saturniidae_phylogenomics_2022. Data sets (concatenated UCEs) and trees have been uploaded on Dryad (http://dx. doi. org/10. 5061/dryad. [NNNN]).
g. , South Africa[HS15], northest Australia [HS19]), as the timing of soil moisture excess coincide better with flood timing. Despite above promising result, there is still some space for future improvement. For example, only the timing of the annual 7-day maximum flood was studied, which excludes the fact that there might be more than one flood peak for some stations (e. g. two flood seasons). The regional interpretation of flood timing shifts is influenced by the limited and non-uniform spatial coverage, inconsistency of datasets and number of hydrometric stations, which causes high uncertainty, especially for the data-scarce region. Additionally, the flood timing in each calendar year rather than water year is recorded in GISM, which also increased the uncertainty in certain stations with a sequence of dependent flood events being counted in two successive years.
Conclusions This work provides a study on global flood timing shifts and and contributions from three main influential factors (i. e. , maximum 7-day precipitation, soil moisture excess, and snowmelt). The shifts in flood timing are calculated with circular index based on the observed flood data from 1970 to 2010. The causality relationship of flood timing and its three influential factors were investigated based on visual interpretation. Despite the difference in the spatial distribution of the flood timing shifts, there are some spatial patterns: Earlier floods are more prevailing in high-latitude regions, mainly caused by earlier snowmelt due to global warming. Regions with delayed floods were mainly distributed in the monsoon-affected areas (e. g. , South Africa, India and Japan), where the delayed floods were mainly the interplay of delayed rainstorm and soil moisture excess.
Data and methods Data. In this study, we analyzed a large data set of flood observations to study the shifting in flood timing at 6167 gauging stations from 1970 to 2010. The Global Streamflow Indices and Metadata (GSIM) archive and the Global Runoff Data Centre (GRDC) dataset were jointly used. For climatic variables, daily precipitation data from the Global Historical Climatology Network (GHCN), the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), the CPC Global Unified Precipitation data provided by the NOAA/OAR/ESRL PSL, South America Daily Gridded Precipitation (1979 ~) and the NCEP/NCAR Reanalysis Products 1 were also used. The CRU TS4. 05 maximum and minimum temperature dataset was interpolated to daily scale using spline interpolation method in R Package "stats".
The daily temperature data were used to calculate the potential evapotranspiration and snowmelt. We restricted our analysis to sites with no less than 37 years of data (flood) during period 1970-2010 (allowing for a maximum of 4 years' missing data), resulting in a total of 6167 sites across the world. The study period is selected based on data availability. For Africa and Asia, few gauges were used due to data availability. Note that for the south America, the CPC precipitation only records daily precipitation during 1979 to recent. More details on the datasets and preprocessing are described in Text S1 in Supporting Information. Definition of flood and its influential factors. In this study, floods are represented by the annual maximum 7-day flow. Regarding the flood timing influential factors, we assume floods were only caused by the following climatic factors, i.
Acknowledgements This research was supported by the  National Natural Science Foundation of China  ( 42071046 ,  42130512 ), the  Open Project of Key Laboratory of Xinjiang, China  ( 2022D04043 ) and the  Western Young Scholar of the Chinese Academy of Sciences. The authors gratefully acknowledge the  Youth Innovation Promotion Association of the Chinese Academy of Sciences  (No. 2019431 ). No conflict of interest exits in the submission of this manuscript.
Data availability The flood date data used in this study can be downloaded from https:// doi. panga ea. de/ 10. 1594/ PANGA EA. 887477 and https:// www. bafg. de/ GRDC/ EN/ Home/ homep age_ node. html. The precipitation can be downloaded from https:// www. ncei. noaa. gov/ produ cts/ land-based-stati on/ global-histo rical-clima tology-netwo rk-daily for America, Europe and Australia, from http:// aphro dite. st. hiros aki-u. ac. jp/ produ cts. html for Asia region, from https:// psl. noaa. gov/ data/ gridd ed/ data. cpc. globa lprec ip. html and https:// psl. noaa. gov/ data/ gridd ed/ data. ncep. reana lysis. html for south America. The temperature data can be downloaded from https:// cruda ta. uea. ac. uk/ cru/ data/ hrg/ cru_ ts_4. 05/.
, 1995). We then performed a principal components analysis (PCA) on 10 shape variables (CL*, L1*, L3*, Vol 1/3* , Sh*, Sw*, R1*, R3*, CTh*, and NT*; SOM Table  S2 ) to inspect patterns of shape variation. The PCA was performed in R using the 'ade4' v. 1. 7e16 package  (Dray and Dufour, 2007). We also performed a canonical variate analysis (CVA) on the same shape variables included in the PCA, using the 'Morpho' v. 2. 6 package  (Schlager, 2017)  for R, to assess the closest morphometric affinities of individual fossil specimens by classifying them into various groups defined a priori within the comparative sample. We used modern humans, Neanderthals, australopiths (Australopithecus þ Paranthropus), and Sima de los Huesos humans as a priori defined groups, assuming equally distributed prior probabilities.
Acknowledgments This work has been funded by the  Agencia Estatal de Investigaci  on (R þ D þ I projects  PID2020-117289GB-I00  and  PID2020-116908GB-I00 ;  MCIN/AEI/10. 13039/501100011033/ )and the  Generalitat de Catalunya  ( CERCA Programme and Consolidated Research Group   2017 SGR 116 ). We express our gratitude to the  Werner Reimers Foundation in Bad Homburg , which provides the  Gustav Heinrich Ralph von Koenigswald collection as a permanent loan for scientific research  to the  Senckenberg Research Institute and Natural History Museum Frankfurt. We would like to thank  C. Zanolli  for technical and scientific discussion, as well as the following people and institutions for granting access to and/or performing the CT scans of the comparative material: the  University of the Witwatersrand , the  Ditsong National Museum of Natural History , the  Max Plank Institute for Evolutionary Anthropology, Steinmann-Institute of Geology, Mineralogy und Palaeontology at the University Bonn ,  M.
Grape quality is regulated by complex interactions between environments and cultivars. Growing suitable cultivars in a given region is essential for maintaining viticulture sustainability, particularly in the face of climate change. We created a database composed of three different subsets of data. The first subset was created by digitizing and curating the seminal report of Amerine and Winkler (1944), which provided grape harvest dates (GHDs), the quality of musts and wines, and wine tasting notes for 148 cultivars from 1935-1941 across five contrasting climatic regions of California. To put this dataset into a climate change context, we collected GHDs and must sugar content (°Brix) records from 1991 to 2018 for four representative cultivars in one of the five studied regions (Napa). Finally, we integrated meteorological data of the five regions during 1911-2018 and calculated bioclimatic indices important for grape.
For example, an early-maturing cultivar may suffer from heat stress during ripening and cause decrease in grape quality under warming climate condition, while those of late-ripening cultivars may have time to mature fully in areas where they were previously unable to ripen  12, 13. Moreover, earlier budbreak caused by warming climate may bring higher risks of spring frost for grapevines  14. Therefore, phenology is vital in determining the suitability of a given cultivar to a particular climatic region. At present, there are very few comprehensive long-term phenological databases that include a diversity of environments and cultivars. These kinds of databases are needed to understand cultivar by environment interactions, describe the effects of global warming on the viticulture, and develop phenological models capable of predicting the plasticity of cultivar behaviour in the future.
Note that France's long tradition of recording phenology for wine grapes, researchers there have created an open-access database of observed grape harvest dates (GHDs) for a range of cultivars and sites across many decades  15, 16. These types of databases can inform strategies for grape-growers and wine-makers to mitigate the adverse impacts of a warming climate. Phenology data should be integrated with information on grape composition and wine quality in order to accurately evaluate cultivar adaptability to specific environments. Previous researches have used climatic indices based mainly on temperature to establish past and future suitability, explore possible geographical shifts of vineyards, and investigate the relationships between growing season temperature, GHDs, and grape  composition  [17] [18] [19] [20].
This will likely be a huge challenge for the grape industry as both established and new viticultural regions adapt to a changing environment. In present study, we digitized and curated a unique dataset of GHDs, quality records of musts and wines, and wine tasting notes for 148 grape cultivars from 1935 to 1941 across five contrasting climatic regions  24. These data were integrated with the corresponding climate data from 1911-2018 in order to determine cultivar suitability to specific climates. Finally, we complemented the dataset with both GHDs and must sugar content (°Brix) records under the past (1935-1941) and current (1991-2018) climates. This database can be used in the future to assess cultivar suitability, and evaluate climate change impact on GHDs and grape quality for the same set of cultivars across diverse climatic regions.
Method Site description. The respective sites were classified into five climatic regions in California, containing San Cruz and San Rose in region 1, Saint Helena and San Jose in region 2, Livermore and Cloverdale in region 3, Davis, Lodi and Fontana in region 4, Fresno and Bakerfield in region 5 (Fig. 1 ). There were differences in annual mean temperature among five climatic regions, ranging from 14. 3°C to 18. 6°C. In each region, the GHDs, quality of musts and wines, and wine tasting notes were recorded for 148 cultivars from 1935 to 1941. Meanwhile, in region 2, namely in Napa, the GHDs and must sugar content (in °Brix) were recorded for four representative cultivars (Cabernet Sauvignon, Chardonnay, Merlot and Sauvignon Blanc) during 1991-2018. Climate data. The climate data was collected from five stations for over one hundred year-period (1911-2018), including daily average, maximum and minimum temperature (Table  1 ).