As one of the most important parameters of the nanomotors' motion, precise speed control of enzyme-based nanomotors is highly desirable in many bioapplications. However, owing to the stable physiological environment, it is still very difficult to in situ manipulate the motion of the enzyme-based nanomotors. Herein, inspired by the brakes on vehicles, the near-infrared (NIR) "optical brakes" are introduced in the glucose-driven enzyme-based mesoporous nanomotors to realize remote speed regulation for the first time. The novel nanomotors are rationally designed and fabricated based on the Janus mesoporous nanostructure, which consists of the SiO2@Au core@shell nanospheres and the enzymes-modified periodic mesoporous organosilicas (PMOs). The nanomotor can be driven by the biofuel of glucose under the catalysis of enzymes (glucose oxidase/catalase) on the PMO domain. Meanwhile, the Au nanoshell at the SiO2@Au domain enables the generation of the local thermal gradient under the NIR light irradiation, driving the nanomotor by thermophoresis. Taking advantage of the unique Janus nanostructure, the directions of the driving force induced by enzyme catalysis and the thermophoretic force induced by NIR photothermal effect are opposite. Therefore, with the NIR optical speed regulators, the glucose-driven nanomotors can achieve remote speed manipulation from 3.46 to 6.49 μm/s (9.9–18.5 body-length/s) at the fixed glucose concentration, even after covering with a biological tissue. As a proof of concept, the cellar uptake of the such mesoporous nanomotors can be remotely regulated (57.5–109 μg/mg), which offers great potential for designing smart active drug delivery systems based on the mesoporous frameworks of this novel nanomotor.
CapsNet (Capsule Network) was first proposed by~\citet{capsule} and later another version of CapsNet was proposed by~\citet{emrouting}. CapsNet has been proved effective in modeling spatial features with much fewer parameters. However, the routing procedures in both papers are not well incorporated into the whole training process. The optimal number of routing procedure is misery which has to be found manually. To overcome this disadvantages of current routing procedures in CapsNet, we embed the routing procedure into the optimization procedure with all other parameters in neural networks, namely, make coupling coefficients in the routing procedure become completely trainable. We call it Generalized CapsNet (G-CapsNet). We implement both "full-connected" version of G-CapsNet and "convolutional" version of G-CapsNet. G-CapsNet achieves a similar performance in the dataset MNIST as in the original papers. We also test two capsule packing method (cross feature maps or with feature maps) from previous convolutional layers and see no evident difference. Besides, we also explored possibility of stacking multiple capsule layers. The code is shared on \hyperlink{https://github.com/chenzhenhua986/CAFFE-CapsNet}{CAFFE-CapsNet}.
Abstract The catalytic therapy based on the nanozymes has received increasing interest in cancer treatment. However, the catalytic capabilities of standalone nanozymes are relatively limited, necessitating the development of a nano‐bio composite system that integrates both nanozymes and natural enzymes. This construction often inevitably leads to interference between natural enzyme and nanozymes, resulting in reduced synergistic performance. Herein, a cascade catalysis system featuring the “core@paratroopers” structure is proposed, wherein hollow manganese dioxide (HMnO 2 ) serves as “core” and ultra‐small hybrid single‐micelle (H‐micelle) encapsulated with glucose oxidase (GOx) as “paratroopers” (H‐micelle‐GOx). The outer SiO 2 layer of the H‐micelle can effectively protect the GOx. Under hypoxic conditions, HMnO 2 reacts with endogenous H 2 O 2 to produce O 2 , thereby enhancing the catalytic efficiency of GOx for starvation therapy. Simultaneously, the generated H 2 O 2 boosts the catalytic efficiency of HMnO 2 , accelerating local O 2 generation and alleviating tumor hypoxia. Additionally, this system exhibits rapid degradation in the tumor microenvironment characterized by high glutathione (GSH) expression, facilitating the release and deep penetration of the ultra‐small H‐micelle‐GOx “paratroopers” within the solid tumor.
Summary Rapid development of cloud-based computations and storage infrastructure is driving the digital transition in the energy industry at an unprecedented pace. To expedite the transition, it is critical to enable the large-scale cloud data ingestion of seismic data. Seismic data files saved in SEG-Y format are often massive, up to several hundred giga bytes (GB) or even several terra bytes (TB). The metadata present in the textual header, binary header, and trace headers in the SEG-Y file capture the geographic location and survey geometry of the seismic data. The metadata in the textual header that captures the above-mentioned information is often found incorrect or absent. In this scenario it becomes imperative to validate and if necessary, extract the missing textual header metadata from the trace headers, to ingest the SEG-Y file. Traditionally, ingesting SEG-Y files requires great manual effort from a geophysical technician. We have developed end-to-end workflow that includes data-driven and rule-based algorithms to automatically ingest 3D poststack seismic data into cloud database. The end-to-end seismic data ingestion workflow improves productivity by automating ingestion and greatly accelerates the digital transition in the energy industry by enabling large scale SEG-Y data migration to cloud.
A novel degradation‐restructuring induced anisotropic epitaxial growth strategy is demonstrated for the synthesis of uniform 1D diblock and triblock silica mesoporous asymmetric nanorods with controllable rod length (50 nm to 2 µm) and very high surface area of 1200 m 2 g −1 . The asymmetric diblock mesoporous silica nanocomposites are composed of a 1D mesoporous organosilicate nanorod with highly ordered hexagonal mesostructure, and a closely connected dense SiO 2 nanosphere located only on one side of the nanorods. Furthermore, the triblock mesoporous silica nanocomposites constituted by a cubic mesostructured nanocube, a nanosphere with radial mesopores, and a hexagonal mesostructured nanorod can also be fabricated with the anisotropic growth of mesopores. Owing to the ultrahigh surface area, unique 1D mesochannels, and functionality asymmetry, the obtained match‐like asymmetric Au‐NR@SiO 2 &EPMO (EPMO = ethane bridged periodic mesoporous organosilica) mesoporous nanorods can be used as an ideal nanocarrier for the near‐infrared photothermal triggered controllable releasing of drug molecules.
A near-infrared (NIR) induced decomposable polymer nanocapsule is demonstrated. The nanocapsules are fabricated based on layer-by-layer co-assembly of azobenzene functionalized polymers and up/downconversion nanoparticles (U/DCNPs). When the nanocapsules are exposed to 980 nm light, ultraviolet/visible photons emitted by the U/DCNPs can trigger the photoisomerization of azobenzene groups in the framework. The nanocapsules could decompose from large-sized nanocapsule to small U/DCNPs. Owing to their optimized original size (ca. 180 nm), the nanocapsules can effectively avoid biological barriers, provide a long blood circulation (ca. 5 h, half-life time) and achieve four-fold tumor accumulation. It can fast eliminate from tumor within one hour and release the loaded drugs for chemotherapy after NIR-induced dissociation from initial 180 nm capsules to small 20 nm U/DCNPs.
Strongly coupled interfaces in the epitaxial growth heteronanocrystals (HNCs) provide advanced functionalities regarding interface connection, electron transfer, and carrier separation. However, the majority of current nanocomposites primarily focus on a single heterojunction involving only two subunits, which hinders the achievement of optimized synergy energy transfer among more than two components. Herein, ternary NaGdF
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