Two lipid membrane sculpting BAR domain proteins, PICK1 and ICA69, play a key role early in the biogenesis of peptide hormone secretory vesicles and are critical for normal growth and metabolic homeostasis.

Landscape change caused by ecological restoration projects has both positive and negative influences on human livelihoods, yet surprisingly little research on the cultural consequences of ecological restoration in agricultural landscapes has taken place. Cultural consequences can be captured in the ecosystem services framework as cultural ecosystem services (CES). However, assessment and valuation of these services to support decision-making for this essential ecosystem is lacking. To help fill this gap, we assessed the opinions of Chinese rural communities about CES and the changes in their perception under the Grain for Green program (GFG), a nationwide program to relieve the pressure on ecosystems (soil erosion and land degradation) by converting cultivated land or barren land on steep slopes into grassland and forests. We used Guyuan City in China's Ningxia Hui Autonomous Region as a case study, using a workshop to identify the CES provided by the agricultural landscape, followed by semi-structured household interviews to quantify perceptions of these CES. We found that all eight CES types identified by the workshop were perceived by the rural communities. Reforestation changed their perceptions of CES directly due to land cover change and indirectly due to the resulting economic changes and migration of mostly young workers in search of better jobs. Cultivated land was perceived as more important than forest for CES provision. In addition, residential areas were perceived as providing significant CES because of local traditions that produce close and highly social neighborhood bonds in agricultural landscapes.

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Recovering the low-rank and sparse components from a given matrix is a challenging problem that has many real applications. This paper proposes a novel algorithm to address this problem by introducing a sparse prior on the low-rank component. Specifically, the low-rank component is assumed to be sparse in a transform domain and a sparse regularizer formulated as an 1 -norm term is employed to promote the sparsity. The truncated nuclear norm is used to model the low-rank prior, rather than the nuclear norm used in most existing methods, to achieve a better approximation to the rank of the considered matrix. Furthermore, an efficient solving method based on a two-stage iterative scheme is developed to address the raised optimization problem. The proposed algorithm is applied to deal with synthetic data and real applications including face image shadow removal and video background subtraction, and the experimental results validate the effectiveness and accuracy of the proposed approach as compared with other methods.