More Like this

1. The L _p chord Minkowski problem

   https://doi.org/10.1515/ans-2022-0041  

   Xi, Dongmeng; Yang, Deane; Zhang, Gaoyong; Zhao, Yiming (January 2023, Advanced Nonlinear Studies)

   Abstract Chord measures are newly discovered translation-invariant geometric measures of convex bodies in R n {{\mathbb{R}}}^{n} , in addition to Aleksandrov-Fenchel-Jessen’s area measures. They are constructed from chord integrals of convex bodies and random lines. Prescribing the L p {L}_{p} chord measures is called the L p {L}_{p} chord Minkowski problem in the L p {L}_{p} Brunn-Minkowski theory, which includes the L p {L}_{p} Minkowski problem as a special case. This article solves the L p {L}_{p} chord Minkowski problem when p > 1 p\gt 1 and the symmetric case of 0 < p < 1 0\lt p\lt 1 . 

   more » « less
2. Gridded road network statistics and settlement age surfaces for core-based statistical areas in the conterminous U.S.

   https://doi.org/10.6084/m9.figshare.19593496.v1  

   Uhl, Johannes H.; Burghardt, Keith (January 2022, figshare)

   These geotiff files represent road network statistics for each core-based statistical area (CBSA) in the conterminous U.S., within grid cells of 1km x 1km. The road network statistics are based on the National transportation dataset (USGS-NTD) v2019. These statistics include: gridcell_stats_azimuthvariety_1km_all_cbsas.tif: The number of unique road angles (azimuth / orientation) in bins of 10 degrees per 1 sqkm grid cell. gridcell_stats_deadendrate_1km_all_cbsas.tif: The proportion of dead ends (nodes of degree 1) of all nodes per 1 sqkm grid cell. gridcell_stats_kmroad_1km_all_cbsas.tif: The approximate total road network length per 1 sqkm grid cell. This is based on the road segment length appended to each road segment centroid and may be biased for very long road segments. gridcell_stats_meandegree_1km_all_cbsas.tif: The average nodal degree of all nodes per 1 sqkm grid cell. gridcell_stats_meangriddedness_1km_all_cbsas.tif: The average griddedness of all nodes per 1 sqkm grid cell. gridcell_stats_nodedensity_1km_all_cbsas.tif: The number of nodes per 1 sqkm grid cell. gridcell_stats_nodesperkmroad_1km_all_cbsas.tif: The number of nodes per km road within each 1 sqkm grid cell. gridcell_stats_firstbuiltup_1km_all_cbsas.tif: The approximate settlement age per 1 sqkm grid cell. This layer is derived from the HISDAC-US First-built-up year (FBUY) layer, which is derived from Zillow's Transaction and Assessment Dataset (ZTRAX). The FBUY data is available here: Leyk, Stefan; Uhl, Johannes H., 2018, "FBUY.tar.gz", Historical settlement composite layer for the U.S. 1810 - 2015, https://doi.org/10.7910/DVN/PKJ90M/BOA5YC, Harvard Dataverse, V2  gridcell_stats_1km_all_cbsas_arcmap10.8.mxd: ESRI ArcMap 10.8 MXD file for quick visualization of the gridded surfaces. Spatial resolution: 1x1km Spatial reference: SR-ORG:7480, USA_Contiguous_Albers_Equal_Area_Conic_USGS_version Source data: USGS-NTD, HISDAC-US. File format: GeoTIFF. Spatial coverage of the road network metrics: All CBSAs in the conterminous U.S. Spatial coverage of the "first built-up year" surface: all U.S. counties that are covered by the HISDAC-US  historical settlement layers. This datasets includes around 2,700 U.S.  counties. In the remaining counties, construction year coverage in the  underlying ZTRAX data (Zillow Transaction and Assessment Dataset) is  low. See Leyk & Uhl (2018) for details. All data created by Johannes H. Uhl, University of Colorado Boulder, USA. Code available at https://github.com/johannesuhl/USRoadNetworkEvolution. References: Burghardt, K., Uhl, J., Lerman, K.,  & Leyk, S. (2022). Road   Network Evolution in the Urban and Rural  United States Since 1900.   Computers, Environment and Urban Systems. Leyk, S., & Uhl, J. H. (2018). HISDAC-US, historical settlement   data  compilation for the conterminous United States over 200 years. Scientific data, 5(1), 1-14. DOI:  https://doi.org/10.1038/sdata.2018.175  

   more » « less
3. Exploring FlyBase Data Using QuickSearch

   https://doi.org/10.1002/cpz1.731  

   Marygold, Steven J.; the FlyBase Consortium (April 2023, Current Protocols)

   Abstract FlyBase (www.flybase.org) is the primary online database of genetic, genomic, and functional information aboutDrosophila melanogaster. The long and rich history ofDrosophilaresearch, combined with recent surges in genomic‐scale and high‐throughput technologies, means that FlyBase now houses a huge quantity of data. Researchers need to be able to query these data rapidly and intuitively, and the QuickSearch tool has been designed to meet these needs. This tool is conveniently located on the FlyBase homepage and is organized into a series of simple tabbed interfaces that cover the major data and annotation classes within the database. This article describes the functionality of all aspects of the QuickSearch tool. With this knowledge, FlyBase users will be equipped to take full advantage of all QuickSearch features and thereby gain improved access to data relevant to their research. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Using the “Search FlyBase” tab of QuickSearch Basic Protocol 2: Using the “Data Class” tab of QuickSearch Basic Protocol 3: Using the “References” tab of QuickSearch Basic Protocol 4: Using the “Gene Groups” tab of QuickSearch Basic Protocol 5: Using the “Pathways” tab of QuickSearch Basic Protocol 6: Using the “GO” tab of QuickSearch Basic Protocol 7: Using the “Protein Domains” tab of QuickSearch Basic Protocol 8: Using the “Expression” tab of QuickSearch Basic Protocol 9: Using the “GAL4 etc” tab of QuickSearch Basic Protocol 10: Using the “Phenotype” tab of QuickSearch Basic Protocol 11: Using the “Human Disease” tab of QuickSearch Basic Protocol 12: Using the “Homologs” tab of QuickSearch Support Protocol 1: Managing FlyBase hit lists 

   more » « less
4. Deciphering the variability of leaf phosphorus‐allocation strategies using leaf economic traits and reflectance spectroscopy across diverse forest types

   https://doi.org/10.1111/nph.70219  

   Dong, Tingting; Wu, Fengqi; Tsujii, Yuki; Townsend, Philip_A; Yang, Nan; Xu, Weiying; Liu, Shuwen; Swenson, Nathan_G; Lamour, Julien; Han, Wenxuan; et al (May 2025, New Phytologist)

   Summary Allocation of leaf phosphorus (P) among different functional fractions represents a crucial adaptive strategy for optimizing P use. However, it remains challenging to monitor the variability in leaf P fractions and, ultimately, to understand P‐use strategies across diverse plant communities.We explored relationships between five leaf P fractions (orthophosphate P, P_i; lipid P, P_L; nucleic acid P, P_N; metabolite P, P_M; and residual P, P_R) and 11 leaf economic traits of 58 woody species from three biomes in China, including temperate, subtropical and tropical forests. Then, we developed trait‐based models and spectral models for leaf P fractions and compared their predictive abilities.We found that plants exhibiting conservative strategies increased the proportions of P_Nand P_M, but decreased the proportions of P_iand P_L, thus enhancing photosynthetic P‐use efficiency, especially under P limitation. Spectral models outperformed trait‐based models in predicting cross‐site leaf P fractions, regardless of concentrations (R² = 0.50–0.88 vs 0.34–0.74) or proportions (R² = 0.43–0.70 vs 0.06–0.45).These findings enhance our understanding of leaf P‐allocation strategies and highlight reflectance spectroscopy as a promising alternative for characterizing large‐scale leaf P fractions and plant P‐use strategies, which could ultimately improve the physiological representation of the plant P cycle in land surface models. 

   more » « less
5. Bacterial Community Structure Modulates Soil Phosphorus Turnover at Early Stages of Primary Succession

   https://doi.org/10.1029/2024GB008174  

   Wang, Yuhan; Bing, Haijian; Moorhead, Daryl L; Hou, Enqing; Wu, Yanhong; Wang, Jipeng; Duan, Chengjiao; Cui, Qingliang; Zhang, Zhiqin; Zhu, He; et al (October 2024, Global Biogeochemical Cycles)

   Abstract Microbes are the drivers of soil phosphorus (P) cycling in terrestrial ecosystems; however, the role of soil microbes in mediating P cycling in P‐rich soils during primary succession remains uncertain. This study examined the impacts of bacterial community structure (diversity and composition) and its functional potential (absolute abundances of P‐cycling functional genes) on soil P cycling along a 130‐year glacial chronosequence on the eastern Tibetan Plateau. Bacterial community structure was a better predictor of soil P fractions than P‐cycling genes along the chronosequence. After glacier retreat, the solubilization of inorganic P and the mineralization of organic P were significantly enhanced by increased bacterial diversity, changed interspecific interactions, and abundant species involved in soil P mineralization, thereby increasing P availability. Although 84% of P‐cycling genes were associated with organic P mineralization, these genes were more closely associated with soil organic carbon than with organic P. Bacterial carbon demand probably determined soil P turnover, indicating the dominant role of organic matter decomposition processes in P‐rich alpine soils. Moreover, the significant decrease in the complexity of the bacterial co‐occurrence network and the taxa‐gene‐P network at the later stage indicates a declining dominance of the bacterial community in driving soil P cycling with succession. Our results reveal that bacteria with a complex community structure have a prominent potential for biogeochemical P cycling in P‐rich soils during the early stages of primary succession. 

   more » « less