Hydrophobic interaction chromatography of proteins: Studies of unfolding upon adsorption by isothermal titration calorimetry

Heat of adsorption is an excellent measure for adsorption strength and, therefore, very useful to study the influence of salt and temperature in hydrophobic interaction chromatography. The adsorption of bovine serum albumin and β‐lactoglobulin to Toyopearl Butyl‐650 M was studied with isothermal titration calorimetry to follow the unfolding of proteins on hydrophobic surfaces. Isothermal titration calorimetry is established as an experimental method to track conformational changes of proteins on stationary phases. Experiments were carried out at two different salt concentrations and five different temperatures. Protein unfolding, as indicated by large changes of molar enthalpy of adsorption Δh_ads, was observed to be dependent on temperature and salt concentration. Δh_ads were significantly higher for bovine serum albumin and ranged from 578 (288 K) to 811 (308 K) kJ/mol for 1.2 mol/kg ammonium sulfate. Δh_ads for β‐lactoglobulin ranged from 129 kJ/mol (288 K) to 186 kJ/mol (308 K). For both proteins, Δh_ads increased with increasing temperature. The influence of salt concentration on Δh_ads was also more pronounced for bovine serum albumin than for β‐lactoglobulin. The comparison of retention analysis evaluated by the van't Hoff algorithm shows that beyond adsorption other processes occur simultaneously. Further interpretation such as unfolding upon adsorption needs other in situ techniques.     

Engineering Materials for Electrochemical Sweat Sensing

Human sweat contains vast physiological information, which has been a promising resource for on-body and real-time health monitoring. Wearable sweat sensors have recently attracted an ever-increasing interest due to their promising capabilities for continuously tracking changes in health status. However, the commercialization of sweat sensors is seriously hindered by drawbacks of materials including high manufacturing and consumables costs, complex integration technology, as well as limited electrochemical signal transduction. In this review, sweat sensing principles are elaborately interpreted, and the latest advances in functional materials for biomarkers sensing in sweat are systematically summarized. Subsequently, the complex structure–activity relationships between various functional materials and sensing capabilities are further elucidated by coupling chemical structures, geometrics, electrochemical properties, and approaches for materials manufacturing. Furthermore, the integration of each component into sensing device for sweat detection and analysis is also discussed. Finally, challenges and opportunities for wearable sweat sensors are delineated in the development of future personalized and predictive healthcare.     

Stretchable and Shape-Adaptable Triboelectric Nanogenerator Based on Biocompatible Liquid Electrolyte for Biomechanical Energy Harvesting and Wearable Human–Machine Interaction

The significant demand of sustainable power sources has been triggered by the development of wearable electronics (e.g., electronic skin, human health monitors, and intelligent robotics). However, tensile strain limitation and low conformability of existing power sources cannot match their development. Herein, a stretchable and shape-adaptable liquid-based single-electrode triboelectric nanogenerator (LS-TENG) based on potassium iodide and glycerol (KI-Gly) liquid electrolyte as work electrode is developed for harvesting human motion energy to power wearable electronics. The LS-TENG demonstrates high output performances (open-circuit voltage of 300 V, short-circuit current density of 17.5 mA m^–2, and maximum output power of 2.0 W m^–2) and maintains the stable output performances without deterioration under 250% tension stretching and after 10 000 cycles of repeated contact-separation motion. Moreover, the LS-TENG can harvest biomechanical energy, including arm shaking, human walking, and hand tapping, to power commercial electronics without extra power sources. The LS-TENG attached on different joints of body enables to work as self-powered human motion monitor. Furthermore, a flexible touch panel based on the LS-TENG combined with a microcontroller is explored for human–machine interactions. Consequently, the stretchable and shape-adaptable LS-TENG based on KI-Gly electrolyte would act as an exciting platform for biomechanical energy harvesting and wearable human–machine interaction.     

4D Printing of Multi-Stimuli Responsive Protein-Based Hydrogels for Autonomous Shape Transformations

Stimuli responsive hydrogels that can change shape in response to applied external stimuli are appealing for soft robotics, biomedical devices, drug delivery, and actuators. However, existing 3D printed shape morphing materials are non-biodegradable, which limits their use in biomedical applications. Here, 3D printed protein-based hydrogels are developed and applied for programmable structural changes under the action of temperature, pH, or an enzyme. Key to the success of this strategy is the use of methacrylated bovine serum albumin (MA–BSA) as a biodegradable building block to Pickering emulsion gels in the presence of N-isopropylacrylamide or 2-dimethylaminoethyl methacrylate. These shear-thinning gels are ideal for direct ink write (DIW) 3D printing of multi-layered stimuli-responsive hydrogels. While poly(N-isopropylacrylamide) and poly(dimethylaminoethyl methacrylate) introduce temperature and pH-responsive properties into the printed objects, a unique feature of this strategy is an enzyme-triggered shape transformation based on the degradation of the bovine serum albumin network. To highlight this technique, protein-based hydrogels that reversibly change shape based on environmental temperature and pH are fabricated, and irreversibly altered by enzymatic degradation, which demonstrates the complexity that can be introduced into 4D printed systems.     

结合视觉特性的红外图像增强方法

为了增强红外图像的细节信息,改善图像视觉效果,将视觉特性与红外图像特征结合,提出了一种结合视觉特性的图像增强方法。首先,对图像运用Weber定律进行亮度处理,并在对数域角度,利用背景强度与光强梯度呈局部线性关系的特性,模拟视觉系统来分割图像。然后,分别对分割图像进行增强处理,过暗、过亮区域采用改进的自适应直方图均衡算法、Weber区域采用多尺度Retinex算法。最后,三区域构建图像,输出细节突出、亮度适中的红外图像。实验结果表明:本文算法增强了图像细节、缩短了运行时间、有效地消除“光晕”现象,改善了图像的视觉效果,具有较好的应用前景。     

基于双频段FMCW雷达的人体动作识别

针对单频段雷达利用微多普勒特征识别人体动作能力有限的问题,提出了一种基于双频段调频连续波(Frequency Modulation Continuous Wave, FMCW)雷达的人体动作识别方法。首先利用K频段与C频段两部不同频段的FMCW雷达分别对人体不同动作进行探测收集到回波数据,对回波数据进行预处理分别得到距离时间、距离多普勒与微多普勒时频谱图像;然后,运用主成分分析法对图像进行特征提取得到特征向量,对提取到的特征向量进行特征级融合;最后,将融合后的特征作为支持向量机的输入从而实现人体动作识别。采用雷达实测数据的实验结果表明,基于双频段FMCW雷达联合工作的探测方法对五种人体动作的识别正确率为96.25%,优于单个频段FMCW雷达单独工作时的动作识别正确率。     

Stretchable Thermoplastic Elastomer Optical Fibers for Sensing of Extreme Deformations

The design of advanced materials with coupled optical and mechanical properties is an important challenge in materials science; especially, the implementation of soft materials in optics has recently gained significant interest. Soft optical systems are particularly versatile in sensing, where large and repeated deformations require dynamically responsive materials. Here, stretchable step‐index optical fibers, which are capable of reversibly sustaining strains of up to 300% while guiding light, are demonstrated. A continuous and scalable melt‐flow process is used to coextrude two thermoplastic elastomers, thereby forming the fibers' high index core‐low index cladding structure. Deformation of the fibers through stretching, bending, and indentation induces detectable, predictable, reversible, and wavelength‐dependent changes in light transmission. Quantitative knowledge about the coupling of the fibers' mechanical and optical properties forms the basis for the design of fiber‐based sensors that are capable of reliably assessing extreme mechanical stimuli. The fibers utility in sensing scenarios is demonstrated in a knee brace for continuous knee motion tracking, a glove for control of a virtual hand model, and a tennis racket capable of locating ball impacts. Such devices can greatly improve quantitative assessment of human motion in rehabilitation, sports, and anywhere else where large deformations need to be monitored reliably.     

3D Superelastic Scaffolds Constructed from Flexible Inorganic Nanofibers with Self‐Fitting Capability and Tailorable Gradient for Bone Regeneration

Repair of bone defects with irregular shapes or at soft tissue insertion sites faces a huge challenge. Scaffolds capable of adapting to bone cavities, generating stiffness gradients, and inducing osteogenesis are necessary. Herein, a superelastic 3D ceramic fibrous scaffold is developed by assembly of intrinsically rigid, structurally flexible electrospun SiO₂ nanofibers with chitosan as bonding sites (SiO₂ NF‐CS) via a lyophilization technique. SiO₂ NF‐CS scaffolds exhibit excellent elasticity (full recovery from 80% compression), fast recovery rate (>500 mm min^?1), and good fatigue resistance (>10 000 cycles of compression) in an aqueous medium. SiO₂ NF‐CS scaffolds induce human mesenchymal stem cell (hMSC) elongation and differentiation into osteoblasts. In vivo self‐fitting capability is demonstrated by implanting compressed SiO₂ NF‐CS scaffolds into different shaped mandibular defects in rabbits, with a spontaneous recovery and full filling of defects. Rat calvarial defect repair validates enhanced bone formation and vascularization by cell (hMSC) histomorphology analysis. Further, subchondral bone scaffolds with gradations in SiO₂ nanofibers are developed, leading to a stiffness gradient and spatially chondrogenic and osteogenic differentiation of hMSCs. This work presents a type of 3D ceramic fibrous scaffold, which can closely match bone defects with irregular shapes or at different implant sites, and is promising for clinical translation.     

No-Reference Image Quality Assessment Method Based on Visual Parameters

Recent studies on no-reference image quality assessment (NR-IQA) methods usually learn to evaluate the image quality by regressing from human subjective scores of the training samples. This study presented an NR-IQA method based on the basic image visual parameters without using human scored image databases in learning. We demonstrated that these features comprised the most basic characteristics for constructing an image and influencing the visual quality of an image. In this paper, the definitions, computational method, and relationships among these visual metrics were described. We subsequently proposed a no-reference assessment function, which was referred to as a visual parameter measurement index (VPMI), based on the integration of these visual metrics to assess image quality. It is established that the maximum of VPMI corresponds to the best quality of the color image. We verified this method using the popular assessment database—image quality assessment database (LIVE), and the results indicated that the proposed method matched better with the subjective assessment of human vision. Compared with other image quality assessment models, it is highly competitive. VPMI has low computational complexity, which makes it promising to implement in real-time image assessment systems.