Nearly one in six people worldwide suffer from disorders of the central nervous system (CNS). There is an urgent need for effective strategies to improve the success rates in CNS drug discovery and development. The lack of effective technologies for delivering drugs and genes to the brain due to the blood–brain barrier (BBB), a structural barrier that effectively blocks most neurotherapeutic agents from reaching the brain, has posed a formidable hurdle for CNS drug development. Brain‐homing and brain‐penetrating molecular transport vectors, such as brain permeable peptides or BBB shuttle peptides, have shown promise in overcoming the BBB and ferrying the drug molecules to the brain. The BBB shuttle peptides are discovered by phage display technology or derived from natural neurotropic proteins or certain viruses and harness the receptor‐mediated transcytosis molecular machinery for crossing the BBB. Brain permeable peptide–drug conjugates (PDCs), composed of BBB shuttle peptides, linkers, and drug molecules, have emerged as a promising CNS drug delivery system by taking advantage of the endogenous transcytosis mechanism and tricking the brain into allowing these bioactive molecules to pass the BBB. Here, we examine the latest development of brain‐penetrating peptide shuttles and brain‐permeable PDCs as molecular vectors to deliver small molecule drug payloads across the BBB to reach brain parenchyma. Emerging knowledge of the contribution of the peptides and their specific receptors expressed on the brain endothelial cells, choice of drug payloads, the design of PDCs, brain entry mechanisms, and delivery efficiency to the brain are highlighted. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease

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One major reason for the success of convolutional neural networks (CNNs) is the availability of large-scale labeled data. Effective training of CNNs relies on large annotated data. Unfortunately, large amounts of data with corresponding annotations are too expensive to obtain in some real-world applications. One reasonable alternative is to use data augmentation techniques to automatically generate annotated samples. In this paper, a novel data augmentation framework based on perspective transformation is proposed. This method automatically generates new annotated data without extra manual labeling, thus effectively extends the inadequate dataset. Perspective transformation can produce new images captured from any cameras viewpoints. Therefore, our method can mimic images taken at the angle that the camera cannot reach. Extensive experimental results on several datasets have demonstrated that our perspective transformation data augmentation strategy is an effective tool when using deep CNNs on small or imbalance datasets.

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In order to improve the implement precision of shearer memory cutting, a novel approach based on the coal floor height variation which is taken as a significant factor and fuzzy optimization theory is proposed. The problem of shearer memory cutting is analyzed and the mathematic model is established. Moreover, the key technologies such as fuzzy control model, quantitative factors, and fuzzy control rules are elaborated, and the flowchart of shearer memory cutting method based on fuzzy optimization theory is designed. Finally, a simulation example is carried out and the proposed approach is proved feasible and efficient.

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A polymerized liposome (PLS) was prepared using a synthesized phosphatidylethanolamine with a diacetylene moiety that showed a reversibly precipitable property on addition and removal of salt. To prepare a soluble-insoluble immobilized enzyme, chymotrypsin was covalently immobilized on the outer surface of the PLS. The carbodiimide method was employed for the enzyme immobilization. Coupling was rapid and nearly complete at a weight ratio of enzyme to the PLS of < 0.12. The immobilized enzyme showed favorable activity yields for both low- and high-mol-wt substrates, i.e., 90 +/- 9% for N-benzoyl-L-tyrosine ethyl ester and 59 +/- 5% for casein up to an enzyme coupling density of 0.38 g/g-PLS. The immobilized enzyme was reusable and more stable at high temperature and long-term incubation than the native enzyme.

A high-pressure water jet can break rock efficiently, which is of great potential to overcome the problems of a tunnel boring machine (TBM) in full-face hard rock tunnel digging, such as low digging efficiency and high disc cutter wear rate. Therefore, this paper presented a new tunneling method that is a TBM coupled with a high-pressure water jet. The rock failure mechanism under the coupled forces of a disc cutter and water jet was analyzed at first. Then, the finite element method (FEM) and smoothed particle hydrodynamics (SPH) method were used to establish a numerical model of rock broken by the disc cutter and water jet. Effects of parameters on rock breaking performance were studied based on the numerical model. Moreover, an experiment of the water jet cutting marble was carried out to verify the reliability of the numerical simulation. Results showed that the high-pressure water jet can increase the TBM digging efficiency and decrease the forces and wear rate of the disc cutter. The optimum nozzle diameter is 1.5 mm, while the optimum jet velocity is 224.5 m/s in this simulation. The results can provide theoretical guidance and data support for designing the most efficient system of a TBM with a water jet for digging a full-face hard rock tunnel.