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Ionic conductive elastomers (ICEs) are emerging stretchable and ionic conductive materials that are solvent-free and thus demonstrate excellent thermal stability. Three-dimensional (3D) printing that creates complex 3D structures in free forms is considered as an ideal approach to manufacture sophisticated ICE-based devices. However, the current technologies constrain 3D printed ICE structures in a single material, which greatly limits functionality and performance of ICE-based devices and machines. Here, we report a digital light processing (DLP)-based multimaterial 3D printing capability to seemly integrate ultraviolet-curable ICE (UV-ICE) with nonconductive materials to create ionic flexible electronic devices in 3D forms with enhanced performance. This unique capability allows us to readily manufacture various 3D flexible electronic devices. To demonstrate this, we printed UV-ICE circuits into polymer substrates with different mechanical properties to create resistive strain and force sensors; we printed flexible capacitive sensors with high sensitivity (2 kPa

Effective exploration of useful contextual information in multi-modal images is an essential task in salient object detection. Nevertheless, the existing methods based on the early-fusion or the late-fusion schemes cannot address this problem as they are unable to effectively resolve the distribution gap and information loss. In this paper, we propose an adaptive multi-level deformable fusion network (AMDFNet) to exploit the cross-modality information. We use a cross-modality deformable convolution module to dynamically adjust the boundaries of salient objects by exploring the extra input from another modality. This enables incorporating the existing features and propagating more contexts so as to strengthen the model’s ability to perceiving scenes. To accurately refine the predicted maps, a multi-scaled feature refinement module is proposed to enhance the intermediate features with multi-level prediction in the decoder part. Furthermore, we introduce a selective cross-modality attention module in the fusion process to exploit the attention mechanism. This module captures dense long-range cross-modality dependencies from a multi-modal hierarchical feature’s perspective. This strategy enables the network to select more informative details and suppress the contamination caused by the negative depth maps. Experimental results on eight benchmark datasets demonstrate the effectiveness of the components in our proposed model, as well as the overall saliency model.

Low‐light image enhancement is rapidly gaining research attention due to the increasing demands of extreme visual tasks in various applications. Although numerous methods exist to enhance image qualities in low light, it is still undetermined how to trade‐off between the human observation and computer vision processing. In this work, an effective generative adversarial network structure is proposed comprising both the densely residual block (DRB) and the enhancing block (EB) for low‐light image enhancement. Specifically, the proposed end‐to‐end image enhancement method, consisting of a generator and a discriminator, is trained using the hyper loss function. The DRB adopts the residual and dense skip connections to connect and enhance the features extracted from different depths in the network while the EB receives unique multi‐scale features to ensure feature diversity. Additionally, increasing the feature sizes allows the discriminator to further distinguish between fake and real images from the patch levels. The merits of the loss function are also studied to recover both contextual and local details. Extensive experimental results show that our method is capable of dealing with extremely low‐light scenes and the realistic feature generator outperforms several state‐of‐the‐art methods in a number of qualitative and quantitative evaluation tests.

Thermally responsive shape memory polymers (SMPs) used in 4D printing are often reported to be activated by external heat sources or embedded stiff heaters. However, such heating strategies impede the practical application of 4D printing due to the lack of precise control over heating or the limited ability to accommodate the stretching during shape programming. Herein, we propose a novel 4D printing paradigm by fabricating stretchable heating circuits with fractal motifs via electric-field-driven microscale 3D printing of conductive paste for seamless integration into 3D printed structures with SMP components. By regulating the fractal order and printing/processing parameters, the overall electrical resistance and areal coverage of the circuits can be tuned to produce an efficient and uniform heating performance. Compared with serpentine structures, the resistance of fractal-based circuits remains relatively stable under both uniaxial and biaxial stretching. In practice, steady-state and transient heating modes can be respectively used during the shape programming and actuation phases. We demonstrate that this approach is suitable for 4D printed structures with shape programming by either uniaxial or biaxial stretching. Notably, the biaxial stretchability of fractal-based heating circuits enables the shape change between a planar structure and a 3D one with double curvature. The proposed strategy would offer more freedom in designing 4D printed structures and enable the manipulation of the latter in a controlled and selective manner.

Early quantitative assessment of liver fat content is essential for patients with fatty liver disease. Mounting evidence has shown that magnetic resonance (MR) technique has high accuracy in the quantitative analysis of fatty liver, and is suitable for monitoring the therapeutic effect on fatty liver. However, many packaging methods and postprocessing functions have puzzled radiologists in clinical applications. Therefore, selecting a quantitative MR imaging technique for patients with fatty liver disease remains challenging.To provide information for the proper selection of commonly used quantitative MR techniques to quantify fatty liver.We completed a systematic literature review of quantitative MR techniques for detecting fatty liver, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol. Studies were retrieved from PubMed, Embase, and Cochrane Library databases, and their quality was assessed using the Quality Assessment of Diagnostic Studies criteria. TheForty studies were included for spectroscopy, two-point Dixon imaging, and multiple-point Dixon imaging comparing liver biopsy to other imaging methods. The advantages and disadvantages of each of the three techniques and their clinical diagnostic performances were analyzed.The proton density fat fraction derived from multiple-point Dixon imaging is a noninvasive method for accurate quantitative measurement of hepatic fat content in the diagnosis and monitoring of fatty liver progression.

Transparent glass with metal mesh is considered a promising strategy for high performance transparent glass heaters (TGHs). However, the realization of simple, low-cost manufacture of high performance TGHs still faces great challenges. Here, a technique for the fabrication of high performance TGHs is proposed using liquid sacrificial substrate electric-field-driven (LS-EFD) microscale 3D printing of thick film silver paste. The liquid sacrificial substrate not only significantly improves the aspect ratio (AR) of silver mesh, but also plays a positive role in printing stability. The fabricated TGHs with a line width of 35 µm, thickness of 12.3 µm, and pitch of 1000 µm exhibit a desirable optoelectronic performance with sheet resistance (R

Shape reconfiguration is ubiquitous in nature and widely used in many applications such as soft robotics, metamaterials, energy absorption and tissue engineering. Shape reconfigurable soft actuators, due to their ability to adapt and adjust in complex and unpredictable working environment, have been designed by the use of various delicate structures and active materials. However, soft actuators that exhibit reconfigurable helical deformation have not been proposed; they have the advantage of integrating both bending and twisting actuations in one deformation mode. In this work, we present a thermal-induced shape reconfigurable soft actuator that shows reversible actuations with vastly shape differences under thermal stimulus. It exhibits helical deformation at lower temperature and mainly in-plane bending at relatively higher temperature. The reversible shape transition is controlled by a thermal stimulus that changes the anisotropy of the structure, which consists of shape memory polymer fibers embedded in a homogeneous elastic matrix. A theoretical model is proposed based on the minimum potential energy that incorporates the thermomechanical behavior of the shape memory polymer fibers. Experiments are conducted and the results agree well with the theoretical modeling. Using the theoretical model, we establish design principles for reconfigurable soft actuators whose functional response is programmable given the architecture and external stimulus. A six-handed helical soft actuator, constructed to demonstrate its programmable deformation, is utilized to catch a living fish in water.

Cylindrical microlens arrays (CMLAs) play a key role in many optoelectronic devices, and 100% fill-factor CMLAs also have the advantage of improving the signal-to-noise ratio and avoiding stray-light effects. However, the existing preparation technologies are complicated and costly, which are not suitable for mass production. Herein, we propose a simple, efficient, and low-cost manufacturing method for CMLAs with a high fill-factor via the electric-field-driven (EFD) microscale 3D printing of polydimethylsiloxane (PDMS). By adjusting the printing parameters, the profile and the fill-factor of the CMLAs can be controlled to improve their optical performance. The optical performance test results show that the printed PDMS CMLAs have good image-projecting and light-diffraction properties. Using the two printing modes of this EFD microscale 3D-printing technology, a cylindrical dual-microlens array with a double-focusing function is simply prepared. At the same time, we print a series of specially shaped microlenses, proving the flexible manufacturing capabilities of this technology. The results show that the prepared CMLAs have good morphology and optical properties. The proposed method may provide a viable route for manufacturing large-area CMLAs with 100% fill-factor in a very simple, efficient, and low-cost manner.

Loess is one of the problematic soils with a worldwide distribution for its collapsibility. To study structural strength and parameters of collapsible loess, a number of double-oedometer t...

Conventional 4D printing technologies are realized by combining 3D printing with soft active materials such as shape memory polymers (SMPs) and hydrogels. However, the intrinsic material property limitations make the SMP or hydrogel-based 4D printing unsuitable to fabricate the actuators that need to exhibit fast-response, reversible actuations. Instead, pneumatic actuations have been widely adopted by the soft robotics community to achieve fast-response, reversible actuations, and many efforts have been made to apply the pneumatic actuation to 3D printed structures to realize passive 4D printing with fast-response, reversible actuation. However, the 3D printing of soft actuators/robots heavily relies on the commercially available UV curable elastomers the break strains of which are not sufficient for certain applications which require larger elastic deformation. In this paper, we present two simple approaches to tune the mechanical properties such as stretchability, stiffness, and durability of the commercially available UV curable elastomers by adding: (i) mono-acrylate based linear chain builder; (ii) urethane diacrylate-based crosslinker. Material property characterizations have been performed to investigate the effects of adding the two additives on the stretchability, stiffness, mechanical repeatability as well as viscosity. Demonstrations of fully printed robotic finger, grippers, and highly deformable 3D lattice structure are also presented.

In order to improve the accuracy of emotional recognition by end-to-end automatic learning of emotional features in spatial and temporal dimensions of electroencephalogram (EEG), an EEG emotional feature learning and classification method using deep convolution neural network (CNN) was proposed based on temporal features, frequential features, and their combinations of EEG signals in DEAP dataset. The shallow machine learning models including bagging tree (BT), support vector machine (SVM), linear discriminant analysis (LDA), and Bayesian linear discriminant analysis (BLDA) models and deep CNN models were used to make emotional binary classification experiments on DEAP datasets in valence and arousal dimensions. The experimental results showed that the deep CNN models which require no feature engineering achieved the best recognition performance on temporal and frequency combined features in both valence and arousal dimensions, which is 3.58% higher than the performance of the best traditional BT classifier in valence dimension and 3.29% higher than that of BT classifier in arousal dimension.

In this paper, we propose a hierarchical bidirectional Gated Recurrent Unit (GRU) network with attention for human emotion classification from continues electroencephalogram (EEG) signals. The structure of the model mirrors the hierarchical structure of EEG signals, and the attention mechanism is used at two levels of EEG samples and epochs. By paying different levels of attention to content with different importance, the model can learn more significant feature representation of EEG sequence which highlights the contribution of important samples and epochs to its emotional categories. We conduct the cross-subject emotion classification experiments on DEAP data set to evaluate the model performance. The experimental results show that in valence and arousal dimensions, our model on 1-s segmented EEG sequences outperforms the best deep baseline LSTM model by 4.2% and 4.6%, and outperforms the best shallow baseline model by 11.7% and 12% respectively. Moreover, with increase of the epoch’s length of EEG sequences, our model shows more robust classification performance than baseline models, which demonstrates that the proposed model can effectively reduce the impact of long-term non-stationarity of EEG sequences and improve the accuracy and robustness of EEG-based emotion classification.

The existing saliency detection models based on RGB colors only leverage appearance cues to detect salient objects. Depth information also plays a very important role in visual saliency detection and can supply complementary cues for saliency detection. Although many RGB-D saliency models have been proposed, they require to acquire depth data, which is expensive and not easy to get. In this paper, we propose to estimate depth information from monocular RGB images and leverage the intermediate depth features to enhance the saliency detection performance in a deep neural network framework. Specically, we rst use an encoder network to extract common features from each RGB image and then build two decoder networks for depth estimation and saliency detection, respectively. The depth decoder features can be fused with the RGB saliency features to enhance their capability. Furthermore, we also propose a novel dense multiscale fusion model to densely fuse multiscale depth and RGB features based on the dense ASPP model. A new global context branch is also added to boost the multiscale features. Experimental results demonstrate that the added depth cues and the proposed fusion model can both improve the saliency detection performance. Finally, our model not only outperforms state-of-the-art RGB saliency models, but also achieves comparable results compared with state-of-the-art RGB-D saliency models.

One trend in the latest bottom-up approaches for arbitrary-shape scene text detection is to determine the links between text segments using Graph Convolutional Networks (GCNs). However, the performance of these bottom-up methods is still inferior to that of state-of-the-art top-down methods even with the help of GCNs. We argue that a cause of this is that bottom-up methods fail to make proper use of visual-relational features, which results in accumulated false detection, as well as the error-prone route-finding used for grouping text segments. In this paper, we improve classic bottom-up text detection frameworks by fusing the visual-relational features of text with two effective false positive/negative suppression (FPNS) mechanisms and developing a new shape-approximation strategy. First, dense overlapping text segments depicting the ‘`characterness’' and ‘`streamline’' properties of text are constructed and used in weakly supervised node classification to filter the falsely detected text segments. Then, relational features and visual features of text segments are fused with a novel Location-Aware Transfer (LAT) module and Fuse Decoding (FD) module to jointly rectify the detected text segments. Finally, a novel multiple-text-map-aware contour-approximation strategy is developed based on the rectified text segments, instead of the error-prone route-finding process, to generate the final contour of the detected text. Experiments conducted on five benchmark datasets demonstrate that our method outperforms the state-of-the-art performance when embedded in a classic text detection framework, which revitalizes the strengths of bottom-up methods.

Four-dimensional (4D) printing of shape memory polymer (SMP) imparts time responsive properties to 3D structures. Here, we explore 4D printing of a SMP in the submicron length scale, extending its applications to nanophononics. We report a new SMP photoresist based on Vero Clear achieving print features at a resolution of ~300 nm half pitch using two-photon polymerization lithography (TPL). Prints consisting of grids with size-tunable multi-colours enabled the study of shape memory effects to achieve large visual shifts through nanoscale structure deformation. As the nanostructures are flattened, the colours and printed information become invisible. Remarkably, the shape memory effect recovers the original surface morphology of the nanostructures along with its structural colour within seconds of heating above its glass transition temperature. The high-resolution printing and excellent reversibility in both microtopography and optical properties promises a platform for temperature-sensitive labels, information hiding for anti-counterfeiting, and tunable photonic devices., Four-dimensional (4D) printing of shape memory polymer (SMP) imparts time responsive properties to 3D structures. Here, the authors explore 4D printing of a SMP in the submicron length scale, extending its applications to nanophononics.

Soft biomaterials have a wide range of applications in many areas. However, one material can only cover a specific range of mechanical performance such as the elastic modulus and stretchability. In order to improve the mechanical performance of soft biomaterials, lattice structures are embedded to reinforce the biomaterials. In this paper, rectangular and triangular lattice structures formed by modified horseshoe microstructures are used because their mechanical properties are tunable and can be tailored precisely to match the desired properties by adjusting four geometrical parameters, the length L, radius R, width w and arc angle θ0. A theoretical design framework for the modified horseshoe lattice structures is developed to predict the dependence of the mechanical behaviors on geometrical parameters. Both experiments and finite element simulations on lattice structures are conducted to validate the theoretical models. Results show that a wide range of design space for the elastic modulus (a few kPa to hundreds of MPa), stretchability (strain up to 180%) and Poisson ratio (ranging from -0.5 to 1.2) can be achieved. Experiments on lattice-hydrogel composites are also conducted to verify the reinforcement effect of lattice structures on the hydrogel. This work provides a theoretical method to predict the mechanical behaviors of the lattice structures and aid the rational design of reinforced biomaterials, which has applications in tissue engineering, drug delivery and intraocular lenses.

This article proposes an approach of 3D printing structures consisting of stretchable hydrogels bonded with UV curable polymers., Hydrogel-polymer hybrids have been widely used for various applications such as biomedical devices and flexible electronics. However, the current technologies constrain the geometries of hydrogel-polymer hybrid to laminates consisting of hydrogel with silicone rubbers. This greatly limits functionality and performance of hydrogel-polymer–based devices and machines. Here, we report a simple yet versatile multimaterial 3D printing approach to fabricate complex hybrid 3D structures consisting of highly stretchable and high–water content acrylamide-PEGDA (AP) hydrogels covalently bonded with diverse UV curable polymers. The hybrid structures are printed on a self-built DLP-based multimaterial 3D printer. We realize covalent bonding between AP hydrogel and other polymers through incomplete polymerization of AP hydrogel initiated by the water-soluble photoinitiator TPO nanoparticles. We demonstrate a few applications taking advantage of this approach. The proposed approach paves a new way to realize multifunctional soft devices and machines by bonding hydrogel with other polymers in 3D forms.

4D printing is an emerging fabrication technology that enables 3D printed structures to change configuration over "time" in response to an environmental stimulus. Compared with other soft active materials used for 4D printing, shape-memory polymers (SMPs) have higher stiffness, and are compatible with various 3D printing technologies. Among them, ultraviolet (UV)-curable SMPs are compatible with Digital Light Processing (DLP)-based 3D printing to fabricate SMP-based structures with complex geometry and high-resolution. However, UV-curable SMPs have limitations in terms of mechanical performance, which significantly constrains their application ranges. Here, a mechanically robust and UV-curable SMP system is reported, which is highly deformable, fatigue resistant, and compatible with DLP-based 3D printing, to fabricate high-resolution (up to 2 µm), highly complex 3D structures that exhibit large shape change (up to 1240%) upon heating. More importantly, the developed SMP system exhibits excellent fatigue resistance and can be repeatedly loaded more than 10 000 times. The development of the mechanically robust and UV-curable SMPs significantly improves the mechanical performance of the SMP-based 4D printing structures, which allows them to be applied to engineering applications such as aerospace, smart furniture, and soft robots.

We report a dual-stage photocrosslinked polymer network based on sequential ultraviolet (UV)-triggered radical polymerization and thermally activated etherification, applicable to the fabrication of tailorable and programmable high-resolution structures. The first stage involves photoinitiated polymerization of monomer and crosslinker to obtain an intermediate polymer network. As such, sophisticated two-dimensional (2D) and micro-scale three-dimensional (3D) structures can be made by using UV-based advanced manufacturing technologies. These complex structures can then be readily programmed into other desired, permanent shapes, in the second stage, via thermally triggered etherification which results in a highly crosslinked, robust polymer network. The intermediate network (Stage I) is characterized to have a Young’s modulus ranging from 342 to 1146 MPa and a glass transition temperature from 52 °C to 83 °C, depending on the concentration of crosslinker. The same material attains a glass transition temperature ranging from 67 °C to ~105 °C and a Young’s modulus of up to 1607 MPa after the subsequent heating process (Stage II). Originally 3D printed 2D structures can be further programmed into rigid, permanent 2.5/3D ones. Micropatterns fabricated from intrinsically hydrophilic dual-stage crosslinked photopolymers through soft lithography show superhydrophobicity, and can subsequently be molded with different curvatures for practical applications.

Flexible transparent electrodes (FTEs) with an embedded metal mesh are considered a promising alternative to traditional indium tin oxide (ITO) due to their excellent photoelectric performance, surface roughness, and mechanical and environmental stability. However, great challenges remain for achieving simple, cost-effective, and environmentally friendly manufacturing of high-performance FTEs with embedded metal mesh. Herein, a maskless, templateless, and plating-free fabrication technique is proposed for FTEs with embedded silver mesh by combining an electric-field-driven (EFD) microscale 3D printing technique and a newly developed hybrid hot-embossing process. The final fabricated FTE exhibits superior optoelectronic properties with a transmittance of 85.79%, a sheet resistance of 0.75 Ω sq-1 , a smooth surface of silver mesh (Ra ≈ 18.8 nm) without any polishing treatment, and remarkable mechanical stability and environmental adaptability with a negligible increase in sheet resistance under diverse cyclic tests and harsh working conditions (1000 bending cycles, 80 adhesion tests, 120 scratch tests, 100 min ultrasonic test, and 72 h chemical attack). The practical viability of this FTE is successfully demonstrated with a flexible transparent heater applied to deicing. The technique proposed offers a promising fabrication strategy with a cost-effective and environmentally friendly process for high-performance FTE.

Four-dimensional (4D) printing that enables 3D printed structures to change configurations over time has gained great attention because of its exciting potential in various applications. Among all the 4D printing materials, shape memory polymers (SMPs) possess higher stiffness and faster response rate and therefore are considered as one of most promising materials for 4D printing. However, most of the SMP-based 4D printing materials are (meth)acrylate thermosets which have permanently cross-linked covalent networks and cannot be repaired if any damage occurs. To address the unrepairable nature of SMP-based 4D printing materials, this paper reports a double-network self-healing SMP (SH-SMP) system for high-resolution self-healing 4D printing. In the SH-SMP system, the semicrystalline linear polymer polycaprolactone (PCL) is incorporated into a methacrylate-based SMP system which has good compatibility with the digital light processing-based 3D printing technology and can be used to fabricate complex 4D printing structures with high resolution (up to 30 μm). The PCL linear polymer imparts the self-healing ability to the 4D printing structures, and the mechanical properties of a damaged structure can be recovered to more than 90% after adding more than 20 wt % of PCL into the SH-SMP system. We investigated the effects of PCL concentration on the thermomechanical behavior, viscosity, and the self-healing capability of the SH-SMP system and performed the computational fluid dynamics simulations to study the effect of SH-SMP solution's viscosity on the 3D printing process. Finally, we demonstrated the self-healing 4D printing application examples to show the merits of the SH-SMP system.

Early detection is the key to improve the prognosis of kidney cancer. This study profiled and identified differentially expressed serum proteins in stage T1a renal cell carcinoma (RCC) using isobaric tags for relative and absolute quantification (iTRAQ)-based mass spectrometry. A total amount of 99 serum samples including 29 patients with ccRCC, 24 patients with a benign kidney mass, 28 patients with another type of urological tumor (20 cases of transitional cell carcinoma and 8 cases of prostate cancer or a male genital tumor), and 18 healthy controls were subjected to iTRAQ-based mass spectrometry. ProteinPilot software was used to identify the differentially expressed serum proteins in RCC compared to the other three populations. Hierarchical clustering analysis according to The Cancer Genome Atlas (TCGA) RCC database was then performed as the cross-platform validation. Immunohistochemistry was performed to verify the expression of selected proteins in tissue samples from these subjects. iTRAQ identified 27 differentially expressed serum proteins in the RCC patients, and 11 of these proteins were cross validated in RCC tissues from the TCGA database. The expression of C1QC, C1QB, S100A8, S100A9, ceruplasmin, and lumican was verified and associated with the tumor stage and/or grade. There were 27 differentially expressed proteins in early-stage RCC identified by iTRAQ; among them, the expression of C1QC, C1QB, S100A8, S100A9, ceruplasmin, and lumican were associated with the tumor stage and/or grade. Further studies are needed to confirm these data for their use as biomarkers for the early detection of RCC.

Engineering multicomponent composite materials into tailored structure is of vital importance for developing advanced sodium ion batteries (SIBs). However, the mechanical stress intensification originating from severe volume expansion upon sodiation induces anisotropic swelling and anomalous structural changes, thus leading to electrode instability and inferior sodium storage performance. Herein, we propose a novel stress-release strategy by inserting of MoSe2 nanosheets onto the surface of yolk-shell Fe7Se8@C composite to accommodate the volume expansion and stabilize the electrode. Bestowed by the unique superiority, the Fe7Se8@C@MoSe2 composite manifests impressive sodium-storage performance in terms of high specific capacity (473.3 mAh g−1 at 0.1 A g−1), excellent rate capability (274.5 mAh g−1 at 5.0 A g−1) and long-term cycling stability (87.1% capacity retention after 600 cycles at 1.0 A g−1). Finite element (FE) simulations confirm that the exterior MoSe2 layer could significantly dissipate the stress caused by the sodiation-induced expansion of Fe7Se8 in the carbon layer. The primary sodium storage mechanisms and structural evolution are further revealed in details by in situ and ex situ investigations. More encouragingly, a practical sodium-ion full cell based on Fe7Se8@C@MoSe2 anode is demonstrated with remarkable performances. This work strengthens the fundamental understanding of mechanical effect for sodium-storage behaviors and sheds light onto designing smart multi-compositional hybrids toward advanced energy storage devices.

In order to investigate the two members of the EF‑hand Ca2+ binding protein S100 family, S100A8 and S100A9, in renal cell carcinoma (RCC), serum samples were collected from patients with RCC, transitional cell carcinoma in the kidney, benign renal masses and normal controls. The samples were analyzed by isobaric tags for relative and absolute quantification technology to identify the differential expression of S100A8 and S100A9 in the respective groups. Hierarchical clustering analysis was then conducted for the samples and the relevant selected gene. The cross‑platform analysis for the external validation was performed by means of The Cancer Genome Atlas database, containing the gene/microRNA expression pattern and clinical information of patients with RCC. Immunohistochemical staining was used to verify the expression of S100A8 and S100A9 in the four groups. As a result, serum and mRNA expression levels of S100A8 and S100A9 were found to be upregulated in patients with RCC compared with the other three groups, which was consistent with the result of the upregulated expression of mRNA levels in RCC tissue. The overexpression of S100A8 and S100A9 in cancer cells was also confirmed by immunohistochemistry. In addition, bioinformatics revealed that let‑7, a microRNA formerly identified as an inhibiting factor of RCC was downregulated in RCC, which contrasted with S100A8. It was also complementary to the sequence at the 3' untranslated region terminal of S100A8. Therefore, indicating that S100A8 and S100A9 may serve as biomarkers for the detection of RCC.

This paper proposes a novel experimental test apparatus that permits direct measurements of tyre/asperity normal contact forces under rolling conditions without interfacial layer. A reduced-sized pneumatic tyre is set rolling on the exterior surface of a cylindrical test rig simulating a smooth road surface except a single asperity of simple geometric shape connected to an embedded force transducer. Distinct asperity geometries lead to similar shapes of force signal but different magnitudes whose relationships with the indentation have exponents close to those in classical analytical solutions. By analyzing the time signals of the contact force and their frequency contents for different rolling speeds, the quasi-static nature of the contact, commonly assumed in numerical models, is verified.

In this paper, zinc magnesium methyl cyclohexylphosphinate double salt (ZnMg (MHP)) was synthesized and used as flame retardant for PA6. The structure and the flammability of ZnMg (MHP) had been studied. The results presented that the LOI of PA6 with 20wt% filler loading improved from 21.2% to 31.4%, and the vertical burning test passed UL94 V-0 rating. TG analyses showed that ZnMg (MHP) had excellent thermal stability. DSC displayed that ZnMg (MHP) melted at 235°C. The tests of flammability revealed that ZnMg (MHP) had a strong inhibition effect on heat release rate (HRR) of the composites.

Optimal path selection is a fundamental problem in tourism, the influence factors of which only including the rout length, but also including weather, transportation and the scenery of attractions and other relevant factors. Therefore, route selection only based on the route length cannot capture the actual requirement. The paper studies the multi-weights (such as weather, route length, attractions scenery and etc.) in route selection, and then proposed an improved ant colony algorithm based on multi-weights (ACA-MW), which uses the multi-weights ant and the genetic variation to search optimal path. Simulated experiment of the ACA-MW shows high performance, the improved algorithm is effective. In tourism, ACA-MW can do well in optimal path selection problem.