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The glacier surface is composed not only of ice or snow but also of a heterogeneous mixture of various materials. The presence of light-absorbing impurities darkens the glacier surface, reducing local reflectance and thereby accelerating the glacier melting process. However, our understanding of the spatial distribution of these impurities remains limited, and there is a lack of studies on quantifying the dirty degree of glacier surfaces. During the Sentinel satellite overpass on 21 August 2023, we used an ASD FieldSpec3 spectrometer to measure the reflectance spectra of glacier surfaces with varying degrees of dirtiness on the Qiyi glacier, Qinghai–Tibet Plateau. Using Multiple Endmember Spectral Mixture Analysis (MESMA), the Sentinel imagery was decomposed to generate fraction images of five primary ice surface materials as follows: coarse-grained snow, slightly dirty ice, moderately dirty ice, extremely dirty ice, and debris. Using unmanned aerial vehicle (UAV) imagery with a 0.05 m resolution, the primary ice surface was delineated and utilized as reference data to validate the fraction images. The findings revealed a strong correlation between the fraction images and the reference data (R2 ≥ 0.66, RMSE ≤ 0.21). Based on pixel-based classification from the UAV imagery, approximately 80% of the glacier surface is covered by slightly dirty ice (19.2%), moderately dirty ice (33.3%), extremely dirty ice (26.3%), and debris (1.2%), which significantly contributes to its darkening. Our study demonstrates the effectiveness of using Sentinel imagery in conjunction with MESMA to map the degree of glacier surface dirtiness accurately. [ABSTRACT FROM AUTHOR]

As a popular tool for nonlinear models, additive models work efficiently with nonparametric estimation. However, naively applying the existing regularization method can result in misleading outcomes because of the basis sparsity in each variable. In this article, we consider variable selection in additive models via a combination of variable selection and basis selection, yielding a joint selection of variables and basis functions. A novel penalty function is proposed for basis selection to address the hierarchical structure as well as the sparsity assumption. Under some mild conditions, we establish theoretical properties including the support recovery consistency. We also derive the necessary and sufficient conditions for the estimator and develop an efficient algorithm based on it. Our new methodology and results are supported by simulation and real data examples. [ABSTRACT FROM AUTHOR]

Optical remote-sensing derived end-of-summer snowline altitude (SLA) has long been employed on glaciers as an indicator of the equilibrium-line altitude (ELA). In the Tien Shan, northwest China, both accumulation and ablation of glaciers occur mainly in the warm season, making it difficult to obtain the representative snowline (highest snowline) in the area. The high spatio-temporal resolution of HJ-1 satellite images enables the highest snowline to be acquired. In this paper, we compare image-derived SLA and measured in situ ELA for two adjacent glaciers in the Tien Shan over the period 2009-10. Results indicate that (1) in 2009, there was a substantial difference between SLA and ELA on one glacier, suggesting inconsistent applicability in using SLA to identify ELA over a large area; and (2) in 2010, an intense ablation year, the field-data-derived ELA surpassed the glacier peak. In this situation, there is no theoretical relationship between SLA and ELA, and the image-derived snowline actually indicates the boundary between ice and firn from previous years. In summary, errors will arise from the discrepancies between individual glaciers and from intensive ablation when using SLA to identify ELA over a large area. [ABSTRACT FROM AUTHOR]

Coronavirus-infected cells contain double-membrane vesicles (DMVs) that are key for viral RNA replication and transcription, perforated by hexameric pores connecting the vesicular lumen to the cytoplasm. How pores form and traverse two membranes, and how DMVs organize RNA synthesis, is unknown. Using structure prediction and functional assays, we show that the nonstructural viral membrane protein nsp4 is the key pore organizer, spanning the double membrane and forming most of the pore lining. Nsp4 interacts with nsp3 on the cytoplasmic side and with the viral replicase inside the DMV. Newly synthesized mRNAs exit the DMV into the cytoplasm, passing through a narrow ring of conserved nsp4 residues. Steric constraints imposed by the ring predict that modified nucleobases block mRNA transit, resulting in broad-spectrum anticoronaviral activity.