A photoresponsive amphiphilic gold nanoparticle (AuNP) is achieved through the decoration of AuNP with hydrophilic poly(ethylene glycol) (PEG) and hydrophobic photoresponsive polymethacrylate containing spiropyran units (PSPMA). Owing to the photoresponsive property of spiropyran units, the amphiphilic AuNPs can easily achieve the controllable assembly/disassembly behaviors under the trigger by light. Under visible light, spiropyran units provide weak intermolecular interactions between neighbored AuNPs, leading to isolated AuNPs in the solution. While under UV light irradiation, spiropyran units in the polymer brushes transform into merocyanine isomer with conjugated structure and zwitterionic state, promoting the integration of adjacent AuNPs through π-π stacking and electrostatic attractions, further leading to the formation of Au oligomers. The smart reversible AuNP oligomers exhibited switchable plasmonic coupling for tuning surface-enhanced Raman scattering (SERS) activity, which is promising for the application of SERS based sensors and optical imaging.

upon a change in relative humidity of the surrounding air. [ 13 ] Sun et al. have demonstrated the fabrication of an energetic walking device driven by a powerful humidity responsive bilayer actuator comprising an action layer of cross-linked poly(allylamine hydrochloride)/poly(acrylic acid) (PAH/PAA) fi lms and a supporting layer of UV-cured Norland Optical Adhesive (NOA)-63. [ 8 ] Considering that graphene has many extraordinary characteristics, such as high thermal conductivity and excellent mechanical properties, it has also served as the smart water responsive actuators. [ 20 ] Ruoff et al. presented a macroscopic actuator based on graphene oxide/carbon nanotubes (GO/CNTs) bilayer fi lm to show obvious actuation that depends on variation of humidity onto water responsive materials of GO. [ 21 ] We have reported previously the asymmetric graphene/ graphene oxide (G/GO) fi ber structures obtained by positioned laser reduction of GO fi bers display well-controlled motion in a predetermined manner once exposed to moisture. [ 22 ] To realize the desired performance in a predefi ned fashion, bilayer materials or structures with different swelling behavior was necessary to mimic the strain transition among different material layers. However, as a moving component, bilayer actuators may also suffer from poor interlayer adhesion during frequent bending and complex fabrication process, etc. Moreover, concerns with respect to toughness, elasticity, and chemical/ physical stabilities also constitute a main barrier for the development of smart actuators. In this regard, from a manufacturing point of view, rational design and fabrication of actuators by refi ned control of the lateral microstructures of a solo material or even a single piece of thin fi lm might be a solution to the above-mentioned problems, but obviously, still remains big challenging. To overcome this limitation and further take advantage of the bilayer design of actuators, we present an alternative strategy in this article that a moisture gradients responsive reduced graphene oxide/polydopamine (RGO-PDA) thin fi lm, the uniform materials can serve as highly effi cient actuator driven by water absorption induced in situ formation of the bilayer structures in swelling difference. During the trigger of RGO-PDA fi lm by water with a gradient from one side, humidity-responsible hydrophilic PDA can absorb water on the surface layers of the RGO-PDA fi lm and act as soft "muscle" to active the swelling locomotion. The existing of rigid RGO sheets with relatively hydrophobic nature inside the fi lm hinders or delays the diffusion of water across the fi lm and construct the "skeleton" of the actuator which hold the PDA "muscle" to transfer the energy of water gradients to a mechanical motion.As energy transducers, actuators can provide controllable mechanical response into 2D or 3D motions upon the trigger by external stimuli, such as electric fi elds, [1][2][3][4] heat, [ 5,6 ] light, [ 7,8 ] and moisture. [ 9,10 ] Therefore, the fabrication of various actuators...

Lead is a metal that has toxic effects on the developing nervous system. However, the mechanisms underlying lead-induced neurotoxicity are not well understood. Non-coding RNAs (ncRNAs) play an important role in epigenetic regulation, but few studies have examined the function of ncRNAs in lead-induced neurotoxicity. We addressed this in the present study by evaluating the functions of a long non-coding RNA (named lncRpa) and a circular RNA (named circRar1) in a mouse model of lead-induced neurotoxicity. High-throughput RNA sequencing showed that both lncRpa and circRar1 promoted neuronal apoptosis. We also found that lncRpa and circRar1 induced the upregulation of apoptosis-associated factors caspase8 and p38 at the mRNA and protein levels via modulation of their common target microRNA miR-671. This is the first report of a regulatory interaction among a lncRNA, circRNA, and miRNA mediating neuronal apoptosis in response to lead toxicity.Electronic supplementary materialThe online version of this article (doi:10.1007/s00204-016-1837-1) contains supplementary material, which is available to authorized users.

In recent years, there have been multiple works of literature reviewing methods for automatically segmenting multiple sclerosis (MS) lesions. However, there is no literature systematically and individually review deep learning-based MS lesion segmentation methods. Although the previous review also included methods based on deep learning, there are some methods based on deep learning that they did not review. In addition, their review of deep learning methods did not go deep into the specific categories of Convolutional Neural Network (CNN). They only reviewed these methods in a generalized form, such as supervision strategy, input data handling strategy, etc. This paper presents a systematic review of the literature in automated multiple sclerosis lesion segmentation based on deep learning. Algorithms based on deep learning reviewed are classified into two categories through their CNN style, and their strengths and weaknesses will also be given through our investigation and analysis. We give a quantitative comparison of the methods reviewed through two metrics: Dice Similarity Coefficient (DSC) and Positive Predictive Value (PPV). Finally, the future direction of the application of deep learning in MS lesion segmentation will be discussed.