Xylogenesis,the process through which wood cells are formed,results in the long-term storage of carbon in woody biomass,making it a key component of the global carbon cycle.Understanding how environmental drivers infl...Xylogenesis,the process through which wood cells are formed,results in the long-term storage of carbon in woody biomass,making it a key component of the global carbon cycle.Understanding how environmental drivers influence xylogenesis during the growing season is therefore of great interest.However,studying shortterm drivers of wood production using xylogenetic data is complicated by the usual sampling scheme and the influence of eccentric growth,i.e.,heterogeneous growth around the stem.In this study,we improve xylogenesis research by introducing a statistical approach that explicitly considers seasonal phenology,short-term growth rates,and growth eccentricity.To this end,we developed Bayesian models of xylogenesis and compared them with a conventional method based on the use of Gompertz functions.Our results show that eccentricity generated high temporal autocorrelation between successive samples,and that explicitly taking it into account improved both the representativeness of phenology and intra-ring variability.We observed consistent short-term patterns in the model residuals,suggesting the influence of an unaccounted-for environmental variable on cell production.The proposed models offer several advantages over traditional methods,including robust confidence intervals around predictions,consistency with phenology,and reduced sensitivity to extreme observations at the end of the growing season,often linked to eccentric growth.These models also provide a benchmark for mechanistic testing of short-term drivers of wood formation.展开更多
The genus Clematis(Ranunculaceae)comprises over 300 species with remarkable morphological and ecological diversity worldwide.Despite its horticultural,medicinal,and ecological importance,a well-resolved phylogeny and ...The genus Clematis(Ranunculaceae)comprises over 300 species with remarkable morphological and ecological diversity worldwide.Despite its horticultural,medicinal,and ecological importance,a well-resolved phylogeny and coherent infrageneric classification are still lacking.Here,we reconstruct a robust phylogeny for Clematis using a phylogenomic approach and revise its infrageneric taxonomy.We incorporated 198 samples representing 151 species,two subspecies,and 12 varieties,covering all subgenera and most sections worldwide,obtained from both fresh and herbarium material.Nuclear single nucleotide polymorphisms(SNPs)and complete plastid genomes were assembled for phylogenetic analyses.We also prepared a nuclear ribosomal ITS(nrITS)dataset comprising 171 species,two subspecies,and 12 varieties(217 samples)to include as many species as possible for phylogenetic inference.Phylogenies based on plastid genomes and nrITS exhibited limited resolution and modest support,highlighting challenges in resolving certain relationships.Nuclear SNP analyses yielded a robust phylogenetic tree with 22 well-supported clades corresponding to 22 sections,with most previously recognized subgenera and sections not recovered as monophyletic.Ancestral state reconstruction of 12 key morphological characters revealed multiple independent origins of character states.This study presents the first comprehensive sectional classification for Clematis based on robust phylogenomic evidence,redefines morphological characteristics for each section,and resolves long-standing taxonomic ambiguities.Our results establish a framework for future studies on the evolution,ecology,and horticultural potential of this globally significant genus.展开更多
Episodic memory,our ability to recall past experiences,is supported by structures in the medial temporal lobe(MTL)particularly the hippocampus,and its interactions with fronto-parietal brain regions.Understanding how ...Episodic memory,our ability to recall past experiences,is supported by structures in the medial temporal lobe(MTL)particularly the hippocampus,and its interactions with fronto-parietal brain regions.Understanding how these brain regions coordinate to encode,consolidate,and retrieve episodic memories remains a fundamental question in cognitive neuroscience.Non-invasive brain stimulation(NIBS)methods,especially transcranial magnetic stimulation(TMS),have advanced episodic memory research beyond traditional lesion studies and neuroimaging by enabling causal investigations through targeted magnetic stimulation to specific brain regions.This review begins by delineating the evolving understanding of episodic memory from both psychological and neurobiological perspectives and discusses the brain networks supporting episodic memory processes.Then,we review studies that employed TMS to modulate episodic memory,with the aim of identifying potential cortical regions that could be used as stimulation sites to modulate episodic memory networks.We conclude with the implications and prospects of using NIBS to understand episodic memory mechanisms.展开更多
基金supported by the Discovery Grants program of the Natural Sciences and Engineering Research Council of Canada(No.RGPIN-2021-03553)the Canadian Research Chair in dendroecology and dendroclimatology(CRC-2021-00368)+3 种基金the Ministère des Ressources Naturelles et des Forèts(MRNF,Contract no.142332177-D)the Natural Sciences and Engineering Research Council of Canada(Alliance Grant No.ALLRP 557148-20,obtained in partnership with the MRNF and Resolute Forest Products)the Fonds de recherche du Qu ebec–Nature et technologies(Partnership Research Program on the Contribution of the Forestry Sector to Climate Change MitigationGrant No.2022-0FC-309064)。
文摘Xylogenesis,the process through which wood cells are formed,results in the long-term storage of carbon in woody biomass,making it a key component of the global carbon cycle.Understanding how environmental drivers influence xylogenesis during the growing season is therefore of great interest.However,studying shortterm drivers of wood production using xylogenetic data is complicated by the usual sampling scheme and the influence of eccentric growth,i.e.,heterogeneous growth around the stem.In this study,we improve xylogenesis research by introducing a statistical approach that explicitly considers seasonal phenology,short-term growth rates,and growth eccentricity.To this end,we developed Bayesian models of xylogenesis and compared them with a conventional method based on the use of Gompertz functions.Our results show that eccentricity generated high temporal autocorrelation between successive samples,and that explicitly taking it into account improved both the representativeness of phenology and intra-ring variability.We observed consistent short-term patterns in the model residuals,suggesting the influence of an unaccounted-for environmental variable on cell production.The proposed models offer several advantages over traditional methods,including robust confidence intervals around predictions,consistency with phenology,and reduced sensitivity to extreme observations at the end of the growing season,often linked to eccentric growth.These models also provide a benchmark for mechanistic testing of short-term drivers of wood formation.
基金funded by the National Natural Science Foundation of China(grant no.31670207).
文摘The genus Clematis(Ranunculaceae)comprises over 300 species with remarkable morphological and ecological diversity worldwide.Despite its horticultural,medicinal,and ecological importance,a well-resolved phylogeny and coherent infrageneric classification are still lacking.Here,we reconstruct a robust phylogeny for Clematis using a phylogenomic approach and revise its infrageneric taxonomy.We incorporated 198 samples representing 151 species,two subspecies,and 12 varieties,covering all subgenera and most sections worldwide,obtained from both fresh and herbarium material.Nuclear single nucleotide polymorphisms(SNPs)and complete plastid genomes were assembled for phylogenetic analyses.We also prepared a nuclear ribosomal ITS(nrITS)dataset comprising 171 species,two subspecies,and 12 varieties(217 samples)to include as many species as possible for phylogenetic inference.Phylogenies based on plastid genomes and nrITS exhibited limited resolution and modest support,highlighting challenges in resolving certain relationships.Nuclear SNP analyses yielded a robust phylogenetic tree with 22 well-supported clades corresponding to 22 sections,with most previously recognized subgenera and sections not recovered as monophyletic.Ancestral state reconstruction of 12 key morphological characters revealed multiple independent origins of character states.This study presents the first comprehensive sectional classification for Clematis based on robust phylogenomic evidence,redefines morphological characteristics for each section,and resolves long-standing taxonomic ambiguities.Our results establish a framework for future studies on the evolution,ecology,and horticultural potential of this globally significant genus.
文摘Episodic memory,our ability to recall past experiences,is supported by structures in the medial temporal lobe(MTL)particularly the hippocampus,and its interactions with fronto-parietal brain regions.Understanding how these brain regions coordinate to encode,consolidate,and retrieve episodic memories remains a fundamental question in cognitive neuroscience.Non-invasive brain stimulation(NIBS)methods,especially transcranial magnetic stimulation(TMS),have advanced episodic memory research beyond traditional lesion studies and neuroimaging by enabling causal investigations through targeted magnetic stimulation to specific brain regions.This review begins by delineating the evolving understanding of episodic memory from both psychological and neurobiological perspectives and discusses the brain networks supporting episodic memory processes.Then,we review studies that employed TMS to modulate episodic memory,with the aim of identifying potential cortical regions that could be used as stimulation sites to modulate episodic memory networks.We conclude with the implications and prospects of using NIBS to understand episodic memory mechanisms.
