Morphogen concentration gradients that extend across developmental fields form by dispersion from source cells. In the Drosophila wing disc, Hedgehog (Hh) produced by posterior compartment cells distributes in a concentration gradient to adjacent cells of the anterior compartment. We monitored Hh:GFP after pulsed expression, and analyzed the movement and colocalization of Hh, Patched (Ptc) and Smoothened (Smo) proteins tagged with GFP or mCherry and expressed at physiological levels from bacterial artificial chromosome transgenes. Hh:GFP moved to basal subcellular locations prior to release from posterior compartment cells that express it, and was taken up by basal cytonemes that extend to the source cells. Hh and Ptc were present in puncta that moved along the basal cytonemes and formed characteristic apical-basal distributions in the anterior compartment cells. The basal cytonemes required diaphanous, SCAR, Neuroglian and Synaptobrevin, and both the Hh gradient and Hh signaling declined under conditions in which the cytonemes were compromised. These findings show that in the wing disc, Hh distributions and signaling are dependent upon basal release and uptake, and on cytoneme-mediated movement. No evidence for apical dispersion was obtained.

No abstract

Land cover mapping (LCM) in complex surface-mined and agricultural landscapes could contribute greatly to regulating mine exploitation and protecting mine geo-environments. However, there are some special and spectrally similar land covers in these landscapes which increase the difficulty in LCM when employing high spatial resolution images. There is currently no research on these mixed complex landscapes. The present study focused on LCM in such a mixed complex landscape located in Wuhan City, China. A procedure combining ZiYuan-3 (ZY-3) stereo satellite imagery, the feature selection (FS) method, and machine learning algorithms (MLAs) (random forest, RF; support vector machine, SVM; artificial neural network, ANN) was proposed and first examined for both LCM of surface-mined and agricultural landscapes (MSMAL) and classification of surface-mined land (CSML), respectively. The mean and standard deviation filters of spectral bands and topographic features derived from ZY-3 stereo images were newly introduced. Comparisons of three MLAs, including their sensitivities to FS and whether FS resulted in significant influences, were conducted for the first time in the present study. The following conclusions are drawn. Textures were of little use, and the novel features contributed to improve classification accuracy. Regarding the influence of FS: FS substantially reduced feature set (by 68% for MSMAL and 87% for CSML), and often improved classification accuracies (with an average value of 4.48% for MSMAL using three MLAs, and 11.39% for CSML using RF and SVM); FS showed statistically significant improvements except for ANN-based MSMAL; SVM was most sensitive to FS, followed by ANN and RF. Regarding comparisons of MLAs: for MSMAL based on feature subset, RF achieved the greatest overall accuracy of 77.57%, followed by SVM and ANN; for CSML, SVM had the highest accuracies (87.34%), followed by RF and ANN; based on the feature subsets, significant differences were observed for MSMAL and CSML using any pair of MLAs. In general, the proposed approach can contribute to LCM in complex surface-mined and agricultural landscapes.

For identification of forested landslides, most studies focus on knowledge-based and pixel-based analysis (PBA) of LiDar data, while few studies have examined (semi-) automated methods and object-based image analysis (OBIA). Moreover, most of them are focused on soil-covered areas with gentle hillslopes. In bedrock-covered mountains with steep and rugged terrain, it is so difficult to identify landslides that there is currently no research on whether combining semi-automated methods and OBIA with only LiDar derivatives could be more effective. In this study, a semi-automatic object-based landslide identification approach was developed and implemented in a forested area, the Three Gorges of China. Comparisons of OBIA and PBA, two different machine learning algorithms and their respective sensitivity to feature selection (FS), were first investigated. Based on the classification result, the landslide inventory was finally obtained according to (1) inclusion of holes encircled by the landslide body; (2) removal of isolated segments, and (3) delineation of closed envelope curves for landslide objects by manual digitizing operation. The proposed method achieved the following: (1) the filter features of surface roughness were first applied for calculating object features, and proved useful; (2) FS improved OPEN ACCESSRemote Sens. 2015, 7 9706 classification accuracy and reduced features; (3) the random forest algorithm achieved higher accuracy and was less sensitive to FS than a support vector machine; (4) compared to PBA, OBIA was more sensitive to FS, remarkably reduced computing time, and depicted more contiguous terrain segments; (5) based on the classification result with an overall accuracy of 89.11% ± 0.03%, the obtained inventory map was consistent with the referenced landslide inventory map, with a position mismatch value of 9%. The outlined approach would be helpful for forested landslide identification in steep and rugged terrain.

Stability issues and high material cost constitute the biggest obstacles of a perovskite solar cell (PVSC), hampering its sustainable development. Herein, we demonstrate that, after suitable surface modification, the low-cost cerium oxide (CeO ) nanocrystals can be well dispersed in both polar and nonpolar solvents and easily processed into high-quality electron transport layers (ETLs). The inverted PVSC with the configuration of "NiMgLiO/MAPbI/[6,6]-phenyl-C-butyric acid methyl ester (PCBM)/CeO " has achieved a high efficiency up to 18.7%. Especially, the corresponding devices without encapsulation can almost keep their initial PCEs in 30% humidity-controlled air in the dark for 30 days and also show no sign of degradation after continuous light soaking and maximum power point tracking for 200 h in a N atmosphere. These results have been proved to be associated with the dual functions achieved by the PCBM/CeO bilayer ETLs in both efficient electron extraction and good chemical shielding. Furthermore, an all inorganic interfacial layer based PVSC with the configuration of "NiMgLiO/MAPbI/CeO " has also achieved a promising efficiency of 16.7%, reflecting the potential to fabricate efficient PVSCs with extremely low cost.

No abstract

Perovskite solar cells (PSCs) have attracted much attention in the past decade and their power conversion efficiency has been rapidly increasing to 25.2%, which is comparable with commercialized solar cells. Currently, the long‐term stability of PSCs remains as a major bottleneck impeding their future commercial applications. Beyond strengthening the perovskite layer itself and developing robust external device encapsulation/packaging technology, integration of effective barriers into PSCs has been recognized to be of equal importance to improve the whole device’s long‐term stability. These barriers can not only shield the critical perovskite layer and other functional layers from external detrimental factors such as heat, light, and H2O/O2, but also prevent the undesired ion/molecular diffusion/volatilization from perovskite. In addition, some delicate barrier designs can simultaneously improve the efficiency and stability. In this review article, the research progress on barrier designs in PSCs for improving their long‐term stability is reviewed in terms of the barrier functions, locations in PSCs, and material characteristics. Regarding specific barriers, their preparation methods, chemical/photoelectronic/mechanical properties, and their role in device stability, are further discussed. On the basis of these accumulative efforts, predictions for the further development of effective barriers in PSCs are provided at the end of this review.

Cell polarity is fundamental to the development of both eukaryotes and prokaryotes, yet the mechanisms behind its formation are not well understood. Here we found that, phytohormone auxin-induced, sterol-dependent nanoclustering of cell surface transmembrane receptor kinase 1 (TMK1) is critical for the formation of polarized domains at the plasma membrane (PM) during the morphogenesis of cotyledon pavement cells (PC) in Arabidopsis. Auxin-induced TMK1 nanoclustering stabilizes flotillin1-associated ordered nanodomains, which in turn promote the nanoclustering of ROP6 GTPase that acts downstream of TMK1 to regulate cortical microtubule organization. In turn, cortical microtubules further stabilize TMK1-and flotillin1-containing nanoclusters at the PM. Hence, we propose a new paradigm for polarity formation: A diffusive signal triggers cell polarization by promoting cell surface receptor-mediated nanoclustering of signaling components and cytoskeleton-mediated positive feedback that reinforces these nanodomains into polarized domains.