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Photochemistry is a powerful tool for synthesizing important molecules which are challenging to create without light. We report compelling results which indicate that photochemical reaction rates (oxygenation and cycloaddition) can be notably enhanced by utilizing a very small number of entangled photons. Measurements with the same small number of classical photons show the rate of product formation is considerably lower. This suggests that the reaction rate with entangled photons is enhanced by many orders of magnitude. Theoretical calculations show that classical photons and entangled photons excite the photocatalyst to different final excited states. This chemical synthesis approach with entangled photons could have a large impact on our understanding of chemical reactivity and provide new insights into photochemical processes. 

With rapid innovations in drone, camera, and 3D photogrammetry, drone-based remote sensing can accurately and efficiently provide ultra-high resolution imagery and digital surface model (DSM) at a landscape scale. Several studies have been conducted using drone-based remote sensing to quantitatively assess the impacts of wind erosion on the vegetation communities and landforms in drylands. In this study, first, five difficulties in conducting wind erosion research through data collection from fieldwork are summarized: insufficient samples, spatial displacement with auxiliary datasets, missing volumetric information, a unidirectional view, and spatially inexplicit input. Then, five possible applications—to provide a reliable and valid sample set, to mitigate the spatial offset, to monitor soil elevation change, to evaluate the directional property of land cover, and to make spatially explicit input for ecological models—of drone-based remote sensing products are suggested. To sum up, drone-based remote sensing has become a useful method to research wind erosion in drylands, and can solve the issues caused by using data collected from fieldwork. For wind erosion research in drylands, we suggest that a drone-based remote sensing product should be used as a complement to field measurements. 

Cryo-electron Tomography (cryo-ET) generates 3D visualization of cellular organization that allows biologists to analyze cellular structures in a near-native state with nano resolution. Recently, deep learning methods have demonstrated promising performance in classification and segmentation of macromolecule structures captured by cryo-ET, but training individual deep learning models requires large amounts of manually labeled and segmented data from previously observed classes. To perform classification and segmentation in the wild (i.e., with limited training data and with unseen classes), novel deep learning model needs to be developed to classify and segment unseen macromolecules captured by cryo-ET. In this paper, we develop a one-shot learning framework, called cryo-ET one-shot network (COS-Net), for simultaneous classification of macromolecular structure and generation of the voxel-level 3D segmentation, using only one training sample per class. Our experimental results on 22 macromolecule classes demonstrated that our COS-Net could efficiently classify macromolecular structures with small amounts of samples and produce accurate 3D segmentation at the same time.