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1.
Tree Physiol ; 44(6)2024 Jun 03.
Article in English | MEDLINE | ID: mdl-38769900

ABSTRACT

The effects of rising atmospheric CO2 concentrations (Ca) with climate warming on intrinsic water-use efficiency and radial growth in boreal forests are still poorly understood. We measured tree-ring cellulose δ13C, δ18O, and tree-ring width in Larix dahurica (larch) and Betula platyphylla (white birch), and analyzed their relationships with climate variables in a boreal permafrost region of northeast China over past 68 years covering a pre-warming period (1951-1984; base period) and a warm period (1985-2018; warm period). We found that white birch but not larch significantly increased their radial growth over the warm period. The increased intrinsic water-use efficiency in both species was mainly driven by elevated Ca but not climate warming. White birch but not larch showed significantly positive correlations between tree-ring δ13C, δ18O and summer maximum temperature as well as vapor pressure deficit in the warm period, suggesting a strong stomatal response in the broad-leaved birch to temperature changes. The climate warming-induced radial growth enhancement in white birch is primarily associated with a conservative water-use strategy. In contrast, larch exhibits a profligate water-use strategy. It implies an advantage for white birch over larch in the warming permafrost regions.


Subject(s)
Betula , Larix , Permafrost , Water , Larix/growth & development , Larix/physiology , Betula/growth & development , Betula/physiology , Water/metabolism , China , Climate Change , Taiga , Global Warming
2.
Proc Natl Acad Sci U S A ; 121(10): e2309656121, 2024 Mar 05.
Article in English | MEDLINE | ID: mdl-38408254

ABSTRACT

Inner ear hair cells are characterized by the F-actin-based stereocilia that are arranged into a staircase-like pattern on the apical surface of each hair cell. The tips of shorter-row stereocilia are connected with the shafts of their neighboring taller-row stereocilia through extracellular links named tip links, which gate mechano-electrical transduction (MET) channels in hair cells. Cadherin 23 (CDH23) forms the upper part of tip links, and its cytoplasmic tail is inserted into the so-called upper tip-link density (UTLD) that contains other proteins such as harmonin. The Cdh23 gene is composed of 69 exons, and we show here that exon 68 is subjected to hair cell-specific alternative splicing. Tip-link formation is not affected in genetically modified mutant mice lacking Cdh23 exon 68. Instead, the stability of tip links is compromised in the mutants, which also suffer from progressive and noise-induced hearing loss. Moreover, we show that the cytoplasmic tail of CDH23(+68) but not CDH23(-68) cooperates with harmonin in phase separation-mediated condensate formation. In conclusion, our work provides evidence that inclusion of Cdh23 exon 68 is critical for the stability of tip links through regulating condensate formation of UTLD components.


Subject(s)
Deafness , Hearing Loss , Mice , Animals , Hearing Loss/genetics , Hearing Loss/metabolism , Hair Cells, Auditory/physiology , Deafness/genetics , Hair Cells, Auditory, Inner/metabolism , Cadherins/metabolism , Exons/genetics
3.
Sensors (Basel) ; 24(1)2024 Jan 02.
Article in English | MEDLINE | ID: mdl-38203131

ABSTRACT

In order to achieve the automatic planning of power transmission lines, a key step is to precisely recognize the feature information of remote sensing images. Considering that the feature information has different depths and the feature distribution is not uniform, a semantic segmentation method based on a new AS-Unet++ is proposed in this paper. First, the atrous spatial pyramid pooling (ASPP) and the squeeze-and-excitation (SE) module are added to traditional Unet, such that the sensing field can be expanded and the important features can be enhanced, which is called AS-Unet. Second, an AS-Unet++ structure is built by using different layers of AS-Unet, such that the feature extraction parts of each layer of AS-Unet are stacked together. Compared with Unet, the proposed AS-Unet++ automatically learns features at different depths and determines a depth with optimal performance. Once the optimal number of network layers is determined, the excess layers can be pruned, which will greatly reduce the number of trained parameters. The experimental results show that the overall recognition accuracy of AS-Unet++ is significantly improved compared to Unet.

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