Spatial relationships are core components in the design and definition of spatial queries.A spatial relationship determines how two or more spatial objects are related or connected in space.Hence,given a spatial datas...Spatial relationships are core components in the design and definition of spatial queries.A spatial relationship determines how two or more spatial objects are related or connected in space.Hence,given a spatial dataset,users can retrieve spatial objects in a given relationship with a search object.Different interpretations of spatial relationships are conceivable,leading to different types of relationships.The main types are(i)topological relationships(e.g.overlap,meet,inside),(ii)metric relationships(e.g.nearest neighbors),and(iii)direction relationships(e.g.cardinal directions).Although spatial information retrieval has been extensively studied in the literature,it is unclear which types of spatial queries can be defined using spatial relationships.In this article,we introduce a taxonomy for naming,describing,and classifying types of spatial queries frequently found in the literature.This taxonomy is based on the types of spatial relationships that are employed by spatial queries.By using this taxonomy,we discuss the intuitive descriptions,formal definitions,and possible implementation techniques of several types of spatial queries.The discussions lead to the identification of correspondences between types of spatial queries.Further,we identify challenges and open research topics in the spatial information retrieval area.展开更多
The raphe nucleus is critical for feeding, rewarding and memory. However, how the heterogenous raphe neurons are molecularly and structurally organized to engage their divergent functions remains unknown. Here, we gen...The raphe nucleus is critical for feeding, rewarding and memory. However, how the heterogenous raphe neurons are molecularly and structurally organized to engage their divergent functions remains unknown. Here, we genetically target a subset of neurons expressing VGLUT3. VGLUT3 neurons control the efficacy of spatial memory retrieval by synapsing directly with parvalbumin-expressing GABA interneurons(PGIs) in the dentate gyrus. In a mouse model of Alzheimer's disease(AD mice),VGLUT3→PGIs synaptic transmission is impaired by ETV4 inhibition of VGLUT3 transcription. ETV4 binds to a promoter region of VGLUT3 and activates VGLUT3 transcription in VGLUT3 neurons. Strengthening VGLUT3→PGIs synaptic transmission by ETV4 activation of VGLUT3 transcription upscales the efficacy of spatial memory retrieval in AD mice. This study reports a novel circuit and molecular mechanism underlying the efficacy of spatial memory retrieval via ETV4 inhibition of VGLUT3 transcription and hence provides a promising target for therapeutic intervention of the disease progression.展开更多
The leaf area index(LAI) is a critical biophysical variable that describes canopy geometric structures and growth conditions.It is also an important input parameter for climate,energy and carbon cycle models.The scali...The leaf area index(LAI) is a critical biophysical variable that describes canopy geometric structures and growth conditions.It is also an important input parameter for climate,energy and carbon cycle models.The scaling effect of the LAI has always been of concern.Considering the effects of the clumping indices on the BRDF models of discrete canopies,an effective LAI is defined.The effective LAI has the same function of describing the leaf density as does the traditional LAI.Therefore,our study was based on the effective LAI.The spatial scaling effect of discrete canopies significantly differed from that of continuous canopies.Based on the directional second-derivative method of effective LAI retrieval,the mechanism responsible for the spatial scaling effect of the discrete-canopy LAI is discussed and a scaling transformation formula for the effective LAI is suggested in this paper.Theoretical analysis shows that the mean values of effective LAIs retrieved from high-resolution pixels were always equal to or larger than the effective LAIs retrieved from corresponding coarse-resolution pixels.Both the conclusions and the scaling transformation formula were validated with airborne hyperspectral remote sensing imagery obtained in Huailai County,Zhangjiakou,Hebei Province,China.The scaling transformation formula agreed well with the effective LAI retrieved from hyperspectral remote sensing imagery.展开更多
基金financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil(CAPES)-Finance Code 001.Anderson C.Carniel was supported by Google as a recipient of the 2022 Google Research Scholar program.
文摘Spatial relationships are core components in the design and definition of spatial queries.A spatial relationship determines how two or more spatial objects are related or connected in space.Hence,given a spatial dataset,users can retrieve spatial objects in a given relationship with a search object.Different interpretations of spatial relationships are conceivable,leading to different types of relationships.The main types are(i)topological relationships(e.g.overlap,meet,inside),(ii)metric relationships(e.g.nearest neighbors),and(iii)direction relationships(e.g.cardinal directions).Although spatial information retrieval has been extensively studied in the literature,it is unclear which types of spatial queries can be defined using spatial relationships.In this article,we introduce a taxonomy for naming,describing,and classifying types of spatial queries frequently found in the literature.This taxonomy is based on the types of spatial relationships that are employed by spatial queries.By using this taxonomy,we discuss the intuitive descriptions,formal definitions,and possible implementation techniques of several types of spatial queries.The discussions lead to the identification of correspondences between types of spatial queries.Further,we identify challenges and open research topics in the spatial information retrieval area.
基金supported by the National Natural Science Foundation of China (31721002, 81920208014, 31930051, 81800133)China Postdoctoral Science Foundation Funded Project (2018M642853)。
文摘The raphe nucleus is critical for feeding, rewarding and memory. However, how the heterogenous raphe neurons are molecularly and structurally organized to engage their divergent functions remains unknown. Here, we genetically target a subset of neurons expressing VGLUT3. VGLUT3 neurons control the efficacy of spatial memory retrieval by synapsing directly with parvalbumin-expressing GABA interneurons(PGIs) in the dentate gyrus. In a mouse model of Alzheimer's disease(AD mice),VGLUT3→PGIs synaptic transmission is impaired by ETV4 inhibition of VGLUT3 transcription. ETV4 binds to a promoter region of VGLUT3 and activates VGLUT3 transcription in VGLUT3 neurons. Strengthening VGLUT3→PGIs synaptic transmission by ETV4 activation of VGLUT3 transcription upscales the efficacy of spatial memory retrieval in AD mice. This study reports a novel circuit and molecular mechanism underlying the efficacy of spatial memory retrieval via ETV4 inhibition of VGLUT3 transcription and hence provides a promising target for therapeutic intervention of the disease progression.
基金supported by the National Natural Science Foundation of China(Grant Nos.91025006,40871186,40730525)National Basic Research Program of China(Grant No.2007CB714402)National High Technology Research and Development Program of China(Grant Nos.2009AA12Z143,2009AA122103)
文摘The leaf area index(LAI) is a critical biophysical variable that describes canopy geometric structures and growth conditions.It is also an important input parameter for climate,energy and carbon cycle models.The scaling effect of the LAI has always been of concern.Considering the effects of the clumping indices on the BRDF models of discrete canopies,an effective LAI is defined.The effective LAI has the same function of describing the leaf density as does the traditional LAI.Therefore,our study was based on the effective LAI.The spatial scaling effect of discrete canopies significantly differed from that of continuous canopies.Based on the directional second-derivative method of effective LAI retrieval,the mechanism responsible for the spatial scaling effect of the discrete-canopy LAI is discussed and a scaling transformation formula for the effective LAI is suggested in this paper.Theoretical analysis shows that the mean values of effective LAIs retrieved from high-resolution pixels were always equal to or larger than the effective LAIs retrieved from corresponding coarse-resolution pixels.Both the conclusions and the scaling transformation formula were validated with airborne hyperspectral remote sensing imagery obtained in Huailai County,Zhangjiakou,Hebei Province,China.The scaling transformation formula agreed well with the effective LAI retrieved from hyperspectral remote sensing imagery.
