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Qi-Ge Qi,1,* Ya Tuo,2,* Li-Xue Liu,2,* Cong-Xiang Yu,2,* Ai-Ning Wu2 1Department of Clinical Laboratory and Pathology, The 969th Hospital of P.L.A., Hohhot, Inner Mongolia, 010051, People’s Republic of China; 2Department of Obstetrics and Gynecology, The Affiliated Hospital of Inner Mongolia Medical University, Inner Mongolia University, Hohhot, Inner Mongolia, 010050, People’s Republic of China*These authors contributed equally to this workCorrespondence: Ai-Ning WuDepartment of Obstetrics and Gynecology, The Affiliated Hospital of Inner Mongolia Medical University, Inner Mongolia University, No. 1 Of Tongdao North Road, Hui District, Hohhot, Inner Mongolia, 010050, People’s Republic of ChinaTel +86 0471-6636630Email wuaining_vu@163.comObjective: The present study aimed to evaluate and analyze the results of karyotyping by amniocentesis and next generation sequencing (NGS)-based noninvasive prenatal DNA testing (NIPT) for the prenatal diagnosis of fetal chromosomal disorders.Methods: A total of 2267 high-risk pregnant females with the indications for prenatal diagnosis who met the enrollment criteria between January 2015 and May 2019 at the Affiliated Hospital of Inner Mongolia Medical University were included and underwent NGS-based NIPT in the present study. Amniocentesis, chromosome karyotyping by cell culture, and follow-up of the pregnancy outcomes were also conducted in the NIPT-positive pregnant females to assess the consistency between NIPT and results of karyotyping by amniocentesis.Results: Among the 2267 cases, 29 cases were positive for NIPT, including 10 cases with a high risk of trisomy 21, 2 cases with a high risk of trisomy 18, 2 cases with a high risk of chromosome 13, and 20 cases with sex chromosome abnormalities. All the above NIPT-positive cases underwent amniocentesis, and 20 cases were eventually diagnosed. The sensitivity and specificity of NIPT for the diagnosis of trisomy 21, trisomy 13, and trisomy 18 were 100%, 99.96%, 100%, and 99.96%, 100%, 100%, respectively, and the positive predictive values were 91.67%, 66.67%, and 100%, respectively.Conclusion: NGS of the fetal free DNA from the peripheral blood of pregnant females was an important complement to the prenatal diagnosis of chromosomal disorders represented by fetal chromosome aneuploidy with high sensitivity and specificity. In combination with the traditional karyotyping by amniocentesis, it could improve the diagnostic efficacy for fetal chromosomal disorders.Keywords: next generation sequencing, karyotyping by amniocentesis, prenatal diagnosis, prenatal screening, fetal free DNA

We report a facile but general method to prepare highly water-soluble and biocompatible photoinitiators for digital light processing (DLP)-based 3D printing of high-resolution hydrogel structures. Through a simple and straightforward one-pot procedure, we can synthesize a metal-phenyl(2,4,6-trimethylbenzoyl)phosphinates (M-TMPP)-based photoinitiator with excellent water solubility (up to ∼50 g/L), which is much higher than that of previously reported water-soluble photoinitiators. The M-TMPP aqueous solutions show excellent biocompatibility, which meets the prerequisite for biomedical applications. Moreover, we used M-TMPP to prepare visible light (405 nm)-curable hydrogel precursor solutions for 3D printing hydrogel structures with a high water content (80 wt %), high resolution (∼7 μm), high deformability (more than 80% compression), and complex geometry. The printed hydrogel structures demonstrate great potential in flexible electronic sensors due to the fast mechanical response and high stability under cyclic loadings.

Deformable Convolutional Networks (DCNs) are proposed to solve the inherent limited geometric transformation in CNNs, showing outstanding performance on sophisticated computer vision tasks. Though they can rule out irrelevant image content and focus on region of interest to some degree, the adaptive learning of the deformation is still limited. In this paper, we delve it from the aspects of deformable modules and deformable organizations to extend the scope of deformation ability. Concretely, on the one hand, we reformulate the deformable convolution and RoIpooling by reconsidering spatial-wise attention, channel-wise attention and spatial-channel interdependency, to improve the single convolution’s ability to focus on pertinent image contents. On the other hand, an empirical study is conducted on various and general arrangements of deformable convolutions (e.g., connection type) in DCNs. Especially on semantic segmentation, the study yields significant findings for a proper combination of deformable convolutions. To verify the effectiveness and superiority of our proposed deformable modules, we also provide extensive ablation study for them and compare them with other previous versions. With the proposed contribution, our refined Deformable ConvNets achieve state-of-the-art performance on two semantic segmentation benchmarks (PASCAL VOC 2012 and Cityscapes) and an object detection benchmark (MS COCO).

Stretchable strain sensor detects a wide range of strain variation and is therefore a key component in various applications. Unlike traditional ones made of elastomers doped with conductive components or fabricated with liquid conductors, ionically conductive hydrogel-based strain sensors remain conductive under large deformations and are biocompatible. However, dehydration is a challenging issue for the latter. Researchers have developed hydrogel-elastomer-based strain sensors where an elastomer matrix encapsulates a hydrogel circuit to prevent its dehydration. However, the reported multi-step approaches are generally time-consuming. Our group recently reported a multimaterial 3D printing approach that enables fast fabrication of such sensors, yet requires a self-built digital-light-processing-based multimaterial 3D printer. Here, we report a simple projection lithography method to fabricate hydrogel-elastomer-based stretchable strain sensors within 5 minutes. This method only requires a UV projector/lamp with photomasks; the chemicals are commercially available; the protocols for preparing the polymer precursors are friendly to users without chemistry background. Moreover, the manufacturing flexibility allows users to readily pattern the sensor circuit and attach the sensor to a 3D printed soft pneumatic actuator to enable strain sensing on the latter. The proposed approach paves a simple and versatile way to fabricate hydrogel-elastomer-based stretchable strain sensors and flexible electronic devices.

Background The COVID-19 pandemic has lasted for more than 1 year, causing far-reaching and unprecedented changes in almost all aspects of society. This study aimed to evaluate the long-term consequences of the COVID-19 pandemic on depression and anxiety, and explore the factors associated with it. Methods A cross-sectional study using an online survey was conducted to assess mental health problems from February 2 to February 9, 2021 by using patient health questionnaire-9 (PHQ-9) and generalized anxiety disorder-7 (GAD-7). The insomnia severity index (ISI), demographic data and COVID-19 related variables were measured by a self-designed questionnaire. The factors associated with depressive and anxiety symptoms were identified by Pearson chi-square test and binary logistic regression analysis. Results In the study that 1171 participants enrolled, the overall prevalence of depressive and anxiety symptoms among general people was 22.6 and 21.4% respectively in the present study. Living alone was a potential risk factor for depressive symptoms, while regular exercises was a potential protective factor. The prevalence of depressive and anxiety symptoms was significantly associated with the severity of insomnia symptoms and the negative feelings about pandemic. Conclusion COVID-19 pandemic- related chronic stress has brought about profound impacts on long-term mental health in the general population. The level of insomnia and a negative attitude towards the pandemic are significantly correlated with unfavorable mental health. However, we failed to found a significant association of age and gender with the mental health symptoms, although they were recognized as well-established risk factors during the outbreak by some other studies. This discrepancy may be because the acute and chronic effects of the pandemic are influenced by different factors, which reminds that more attention should be paid to the intrinsic psychological factors and physical reactions towards COVID-19.

Regional seismic damage analysis of building clusters based on time history analysis enables damage evolution and distribution to be obtained for structural performance evaluation, seismic loss estimation, and post-earthquake emergency response decision support. Soil–structure cluster interaction (SSCI) effects have been shown to affect significantly seismic damage to buildings due to the influence of neighboring buildings through underlying soils. However, SSCI effects in current regional seismic damage simulations are either ignored or obtained with exceptionally high computation cost due to the common discretization of large scale soil domains using finite elements. To this end, lumped parameter models (LPMs) are presented in this work as a simple effective tool for regional seismic damage simulation of SSCI systems, which is particularly useful for parametric analysis and also enables fast regional damage assessments. The LPMs combine story-based multiple-degree-of-freedom buildings with ‘foundation springs’ that are used to represent soil–foundation interaction and foundation–soil–foundation interaction. Seismic damage analysis of 320 buildings on Wangjiang campus of Sichuan University in China is used as a case study. The results of the specific case study show, that in most cases, SSCI effects result in lower damage to buildings, especially on soft soils. However, the SSCI effects may lead to higher structural damage to buildings in some cases, e.g. those on stiff soils. The results also show that using nominal inter-story drift ratio which includes foundation rocking rotation as the damage index may underestimate story seismic damage to lower stories of tall buildings.

Improving thermal transport between substrate and transistors has become a vital solution to the thermal challenge in nanoelectronics. Recently 2D WTe2 has sparked extensive interest because of heavy atomic mass and low Debye temperature. Here, the thermal transport of supported WTe2 was studied via Raman thermometry with electrical heating. The supported 30 nm WTe2 encased with 70 nm Al2O3 delivered 4.8 W·m−1·K−1 in-plane thermal conductivity along zigzag direction at room temperature, which was almost 1.6 times larger than that along armchair direction (3.0 W·m−1·K−1). Interestingly, the superior and inferior directions for thermal transport are just opposite of those for electrical transport. Hence, a heat manipulation model in WTe2 FET device was proposed. Within the designed configuration, waste heat in WTe2 would be mostly dissipated to metal contacts located along zigzag, relieving the local temperature discrepancy in the channel effectively and preventing degradation or breakdown. Our study provides new insight into thermal transport of anisotropic 2D materials, which might inspire energy-efficient nanodevices in the future.

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.

This article explores the relationships between image element pairs and label pairs and extends label diffusion to label pair diffusion for the interactive image segmentation task. Compared with label diffusion, more accurate relationships between unlabeled and labeled data can be captured on a tensor product graph (TPG) by using higher order information, and more complex interactions among image elements and finer relationships between image element pairs and label pairs are explored in label pair diffusion (LPD) process. We first establish a prior label estimation framework to measure the label pair prior probability. Then, a probability learning process on TPG is designed to smooth the label prior. The learning process is equivalent to an iterative LPD process on the original graph, which makes the proposed algorithm maintain computational efficiency. Finally, the unary label probabilities can be obtained by a total-probability-theorem-based conversion from the binary relationships. Experiments on popular segmentation data sets demonstrate the superior performance of the proposed method.

Maxillofacial space infection (MSI) experience review is beneficial for its management. To identify potential risk factors predisposing to the exacerbation of MSI and a prolonged length of stay (LOS). We performed a comprehensive retrospective review of medical records of 222 MSI patients admitted in Center of Stomatology during 1993–2019. About 63.5% of 222 patients had an odontogenic infection, and submandibular space was the most involved space. Streptococcus spp. was the most common organism isolated (72.4%). Multiple-space cases had more systemic diseases, respiratory difficulty, and life-threatening complications and exhibited worse clinical characteristics (higher white-blood-cell-count, higher body temperature, and restricted mouth opening) than single-space cases (P

Blind image deconvolution is a fundamental task in image processing, computational imaging, and computer vision. It has earned intensive attention in the past decade since the seminal work of Fergus et al. [1] for camera shake removal. In spite of the recent great progress in this field, this paper aims to formulate the blind problem with a simpler modeling perspective. What is more important, the newly proposed approach is expected to achieve comparable or even better performance towards the real blurred images. Specifically, the core critical idea is the proposal of a pure gradient-based discriminative prior for accurate and robust blur kernel estimation. Numerous experimental results on both the benchmark datasets and real-world blurred images in various imaging scenarios, e.g., natural, manmade, low-illumination, text, or people, demonstrate well the effectiveness and robustness of the proposed approach.

User interactions are generally utilized to impose the hard constraint and estimate the label prior probability in the interactive image segmentation methods. The conventional interactive approaches cannot work well when the user inputs contain incorrect marks. The existing error tolerance methods mainly focus on the interaction itself and try to eliminate the influence of incorrect hard constraint, which however ignore the label prior estimation. This paper mainly focuses on solving the label prior estimation problem when error marks appear. The prior probability is generally defined as the matching degree between pixels and the cluster centers of marked pixels. Incorrect interaction leads to the formation of incorrect clusters. Therefore, a reliability learning model is constructed in this paper by 1) assigning smaller weighting factors to incorrect clusters, 2) assigning larger weighting factors to correct clusters with higher matching degree. Accurate label prior probability can be obtained by the weighted averaging. Then referring to the existing methods, an error-tolerant segmentation model is designed as a ratio energy function, which can both overcome the hard constraint and the label prior limitation with error marks. Experiments on the challenging Berkeley segmentation data set and Microsoft GrabCut database demonstrate the effectiveness of the proposed method.

Background Hepatocellular carcinoma (HCC) is one of the most common tumors in recent days with high mortality and low early diagnosis rate, resulting in the fourth leading cause of deaths globally. This study aimed to evaluate the feasibility, efficacy, and safety between transcatheter arterial embolization (TAE) therapy followed by multi-applicator ablation and transarterial chemoembolization (TACE) for the treatment of large HCC. Methods An intervention study conducted at the Cancer Center of Sun Yat-sen University, Guangzhou, China, with whom had large HCC. A comparison between the two groups (intervention and control group) was evaluated at different time dimensions by repeated-measures analysis of variance, Mann-Whitney U rank-sum test, where Kaplan-Meier and log-rank test calculated for the overall survival (OS), tumor response (TR), and progression-free survival (PFS). Results This study found the improved survival rate (SR) and PFS after 1-year treatment in the observation group (Group-I) by 56.4% and 12.8%, compared to 38.3% and 8.5% in the control group (Group-II). Likewise, 1- and 2-year OS rates (OSRs) in Group-I were 66.7% and 51.4%, wherein Group-II were 48.9% and 30.0%, respectively. The short-term efficacy of Group-I, such as complete response (CR), partial response (PR), stable disease (SD), progressive disease (PD) were 28.21%, 56.41%, 0, 15.38%, while in Group-II was 2.13%, 68.79%, 6.38% 22.70% respectively, which were significant. The short-term efficacy was more excellent in the Group-I than Group-II. The quality of life (QOL) was better in Group-I than that of Group-II. Conclusions TAE combined with multi-applicator ablation therapy is safer, effective with prolonged survival, and less severe adverse reactions compared to TACE therapy for the treatment of large HCC.

A novel bolt-connected precast concrete wall panel structural system has recently been proposed for low-rise buildings in rural areas. The system features replaceable distributed bolt-conne...

The design of a compressible battery with stable electrochemical performance is extremely important in compression-tolerant and flexible electronics. While this remains challenging with the current battery manufacturing method, the field of 3D printing offers the possibility of producing free-standing 3D-printed electrodes with various structural configurations. Through the simple and scalable strategy, various structural configurations can be produced. Herein, we demonstrate a 3D-printed quasi-solid-state Ni-Fe battery (QSS-NFB) that shows excellent compressibility, ultrahigh energy density, and superior long-term cycling durability. Through a rational design and adjustment of chemical components, two electrodes consisting of ultrathin Ni(OH)2 nanosheet array cathode and holey α-Fe2O3 nanorod array anode are achieved with a ultrahigh active material loading over 130 mg cm-3 and excellent compressibility up to 60%. It is noteworthy that the compressible QSS-NFB demonstrated an excellent cycling stability (∼91.3% capacity retentions after 10000 cycles) and ultrahigh energy density (28.1 mWh cm-3 at a power of 10.6 mW cm-3). This work provides a simple method for producing compression-tolerant energy-storage devices, which are expected to have promising applications in next generation stretchable/wearable electronics.

Two-dimensional (2D) semiconductors have been a central focus for next-generation electronics and optoelectronics owing to their great potential to extend the scaling limits in a silicon transistor. However, due to the lack of surface dangling bonds in most 2D semiconductors, such as graphene and transition metal dichalcogenides (TMDs), the direct growth of the high-κ film on these 2D materials via an atomic layer deposition (ALD) technique often produces dielectrics with poor quality, which hinders their integration in the modern semiconductor industry. Here, we comprehensively investigate the ALD growth of the Al2O3 layer on 2D exfoliated black phosphorus (BP). Intriguingly, we found that the 2D BP with "silicon-like" characteristics possesses a native surface oxide layer PxOy after air exposure. The PxOy-induced surface dangling bonds enable the spontaneous integration of the high-quality Al2O3 layer on the BP flake without any pretreatments to functionalize the surface. Additionally, the Al2O3 layer could effectively passivate BP to prevent its degradation in ambient conditions, which addresses the most serious problem of the BP material. Moreover, the Al2O3-encapsulated BP field-effect transistor (FET) exhibits good electrical transport performance, with a high hole mobility of ∼420 cm2 V-1 s-1 and electron mobility of ∼80 cm2 V-1 s-1. Moreover, the high-quality Al2O3 layer can also be integrated into the top-gated BP transistor and inverter. Our findings reveal the silicon-like characteristics of BP for the high-κ ALD dielectric growth technology, which promises the seamless integration of 2D BP in the modern semiconductor industry.

Semantic segmentation networks usually utilize special pyramid structure after encoder or combine low-level and high-level feature maps in decoder to capture multi-scale context information, which we term them feature combination and feature connection, respectively. However, both such frameworks are less valid with the fixed geometric structure or unsuitable interim. In this paper, we advocate Dynamic Attention Network (DAN) to solve these problems. First, we design a Deformable Attention Pyramid (DAP) module to perform a self-adjustable descriptor of high-level output, which utilizes deformable function to model geometric transformation. With DAP, semantic information can be captured effectively. Second, we propose a Fusing Attention Interim (FAI) module to guide the back-propagation of long-short range information in each level of decoder. We evaluate DAN on the challenging PASCAL VOC 2012 and Cityscapes segmentation benchmarks and find that it achieves state-of-the-art results without post-processing. Our observation can be concluded that the flexible structure that possesses dynamic attention mechanism is beneficial to learn multi-scale context information.

Reverse cyclic lateral testing was undertaken to investigate the seismic behavior of 1/4 scale steel-plate concrete (SC) composite walls. The experimental program involved seven SC wall pier specimens. A new chamber structure is proposed, using steel diaphragms to connect the two steel faceplates to each other and to divide the SC wall pier into two parts. Conventional wall specimens failed mainly by tensile fracture of the concrete at the junction of the wall side and wall base, crushing of the concrete at the toe of the wall, or buckling of the steel faceplate. Tearing of the welded joints at the steel faceplates and steel diaphragm, buckling of steel, steel diaphragms being pulled out, tensile fracture and crushing of the concrete were the main failure modes of the chamber structure walls. A parametric numerical analysis in ABAQUS was developed to investigate the effects of the stiffening rib, steel web amount, material strength, shear-span ratio, and axial compression ratio on the seismic response of SC walls. The chamber structure of the SC wall piers can improve the peak load, ductility, and energy-dissipating capacity. The steel faceplate thickness and stiffening ribs can improve the behavior of SC wall piers.

Research Question: Does athlete off-field misconduct affect the stock price of sponsors of the athlete's team, stadium and league? No research on the impact of athlete off-field misconduct on indir...

Electrocardiography is a useful diagnostic tool for various cardiovascular diseases, such as myocardial infarction (MI). An electrocardiograph (ECG) records the electrical activity of the heart, which can reflect any abnormal activity. MI recognition by visual examination of an ECG requires an expert’s interpretation and is difficult because of the short duration and small amplitude of the changes in ECG signals associated with MI. Therefore, we propose a new method for the automatic detection of MI using ECG signals. In this study, we used maximal overlap discrete wavelet transform to decompose the data, extracted the variance, inter-quartile range, Pearson correlation coefficient, Hoeffding’s D correlation coefficient and Shannon entropy of the wavelet coefficients and used the k-nearest neighbor model to detect MI. The accuracy, sensitivity and specificity of the model were 99.57%, 99.82% and 98.79%, respectively. Therefore, the system can be used in clinics to help diagnose MI.

Super-resolution of facial images, a.k.a. face hallucination, has been intensively studied in the past decades due to the increasingly emerging analysis demands in video surveillance, e.g., face detection, verification, identification. However, the actual performance of most previous hallucination approaches will drop dramatically when a very low-res tiny face is provided, due to the challenging multimodality of the problem as well as lack of an informative prior as a strong semantic guidance. Inspired by the latest progress in deep unsupervised learning, this paper focuses on tiny faces of size 16 × 16 pixels, hallucinating them to their 8 × upsampling versions by exploring the potentials of Wasserstein generative adversarial networks (WGAN). Besides a pixel-wise L2 regularization term imposed to the generative model, it is found that our advocated autoencoding generator with both residual and skip connections is a critical component for WGAN representing the facial contour and semantic content to a reasonable precision. With the additional Lipschitz penalty and architectural considerations for the critic in WGAN, the proposed approach finally achieves state-of-the-art hallucination performance in terms of both visual perception and objective assessment. The cropped CelebA face dataset is primarily used to aid the tuning and analysis of the new method, termed as tfh-WGAN. Experimental results demonstrate that the proposed approach not only achieves realistic hallucination of tiny faces, but also adapts to pose, expression, illuminance and occluded variations to a great degree.

Since first developed, the conducting materials in wireless communication and electromagnetic interference (EMI) shielding devices have been primarily made of metal-based structures. Here, we present a highly conductive and corrosion-resistant graphene assembled film (GAF) that can be used to fabricate multi-band 5G wireless communication electronics and EMI protection at frequencies ranging from tens of MHz to THz to demonstrate its potential in metal replacement in practical electronics. The GAF based antennas (dipole antenna, ultra-wideband antenna and 5G wireless communication antenna array) are comparable with metal-based antennas in terms of performance and device complexity. The EMI shielding effectiveness of GAF can reach up to 127 dB in the frequency range of 2.6 GHz - 0.32 THz, and a maximum shielding effectiveness per unit thickness is of 6966 dB/mm. Furthermore, the GAF metamaterials exhibit promising frequency selection characteristics and angular stability as flexible frequency selective surfaces that can work at 3.5 GHz and 60 GHz respectively.

Diabetes mellitus (DM), as a chronic disease caused by insulin deficiency or using obstacles, is gradually becoming a principal worldwide health problem. Pueraria lobata is one of the traditional Chinese medicinal and edible plants, playing roles in improving the cardiovascular system, lowering blood sugar, anti-inflammation, anti-oxidation, and so on. Studies on the hypoglycemic effects of Pueraria lobata were also frequently reported. To determine the active ingredients and related targets of Pueraria lobata for DM, 256 metabolites were identified by LC/MS non targeted metabonomics, and 19 active ingredients interacting with 51 DM-related targets were screened. The results showed that puerarin, quercetin, genistein, daidzein, and other active ingredients in Pueraria lobata could participate in the AGE-RAGE signaling pathway, insulin resistance, HIF-1 signaling pathway, FoxO signaling pathway, and MAPK signaling pathway by acting on VEGFA, INS, INSR, IL-6, TNF and AKT1, and may regulate type 2 diabetes, inflammation, atherosis and diabetes complications, such as diabetic retinopathy, diabetic nephropathy, and diabetic cardiomyopathy.

Four-dimensional (4D) printing, which enables 3D printed structures to alter shapes over time, is attracting increasing attention because of its exciting potential in various applications. Among all the 4D printing materials, shape memory polymers (SMPs) have a higher stiffness and faster response rate and therefore are considered as one of the most promising 4D printing materials. However, the current studies of SMP-based 4D printing mainly focused on the deformation behavior and structural design of 4D printed structures. An additional function such as color change is desired for 4D printed structure, which would be potentially beneficial to the applications such as anti-counterfeiting, encryption, and bioinspired camouflage. In this paper, we report an ultraviolet (UV)-curable and thermochromic (UVT) SMP system that enables color-changeable 4D printing. The UVT SMP system is acrylate-based, thus highly UV-curable and compatible with PμSL-based high-resolution 3D printing technique. Thermochromism is imparted by adding the thermochromic microcapsules to the UVT SMP system, which allows the printed structures to reversibly change colors upon heating and cooling. To demonstrate its extraordinary thermochromic and mechanical performance, we use UVT SMP to print QR codes and multilevel anti-counterfeiting patterns which can hide the visible information at room temperature and visualize the information by encrypting, decrypting, and encrypting again steps with the shape-color recovery process. The development of UVT SMP will significantly advance current applications of SMP-based 4D printing, especially for anti-counterfeiting and safe data recording.

With the rapid progress of urbanization, more and more structure clusters are being built in major cities and small towns, where the spacing of buildings is becoming smaller and smaller. The cluster effect among buildings in the system of soil and structure cluster interaction (SSCI) emerges during an earthquake. The concept of cluster effect of SSCI is presented in this paper. This work investigates and fully presents the cluster interaction in the context of SSCI using a shaking table test based on the finite element method (FEM). Several cases are studied: a single building and a structure cluster, with various loading conditions, such as the El Centro ground record and Shanghai artificial wave. The Davidenkov foundation model is used as the constitutive soil model, and this model describes the dynamic nonlinearity of soil and is conducted using an ANSYS parameter design language and a restarting command. The numerical simulations of the shaking table test are in good agreement with the experimental records. Extensive analyses on parametric analyses of SSCI are conducted to determine the influence of some parameters of SSCI, including structure spacing, soil property and structure number. Considering the cluster effect under the similar conditions with the experiment, the laws and the influences of soil and structure cluster interaction are fully summarized as follows: the natural frequency of the system of SSCI is less than that of soil and structure interaction (SSI). Under earthquake excitation, the structures in different locations have different responses, and this is the cluster effect of SSCI. Cluster interaction reduces the acceleration responses of structures, while velocity may be larger or less than that of the single building (SSI), and this is related with the earthquake input and the structure location. The displacement of the central building reduces, but these of the surrounding structures along the vibration increase, and the buildings farthest from the center have the largest displacements. The maximum absolute value of the influence coefficients of the cluster effect for displacement reaches 770.94%. The building number is the most significant factor that affects the cluster effect in SSCI system. The cluster interaction constantly reduce when the structure spacing increase and disappears when the clear spacing is larger than 1.32 times as the size of the building foundation, and the cluster effect is weak. Last, the mechanism of SSCI is studied by the laws of the displacement.

Micro- and nanoscale surface topographies that give rise to superhydrophobic surfaces have been achieved mostly on 2-dimensional planar objects. Increasing interests in superhydrophobic surfaces on consumer products, optics, and biomedical devices demand topographic patterning on free-form nonplanar surfaces via a high throughput manufacturing process such as injection molding. However, successes in high-resolution (submicrometer) injection molding have been limited to flat and planar objects. A challenge associated with achieving submicrometer surface resolution on a 3-dimensional curved object lies in the control of the replication process in a multiscale mold cavity and the nonuniform temperature and pressure distribution over a macroscopically curved mold insert. Here, a two-step simulation approach is employed to investigate the replication of polymer in the macroscopic and submicrometer cavities. Both simulation and experimental data revealed the effects of holding pressure, mold temperature, and ma...

Shape memory alloys (SMAs) have been widely used to fabricate soft actuators by embedding SMA wires into various soft matrices manufactured by conventional moulding methods or novel three-dimensional (3D) printing techniques. However, soft matrices of SMA based actuators are typically fabricated from only one or two different materials. Here, we exploit the great manufacturing flexibility of multimaterial 3D printing to fabricate various bending, twisting and extensional actuators by precisely controlling the spatial arrangements of different printing materials with different stiffnesses. In order to achieve a broad range of deformations, ten different printing materials were characterized and used in the actuators design. In addition, we developed a finite element model to simulate complex deformations of the printed actuators and facilitate the design process. The model incorporates a user defined material subroutine that describes the nonlinear temperature dependant behavior of SMAs. The results show the efficiency and flexibility of multimaterial 3D printing in tailoring the deformed shape of the SMA based soft actuators, which cannot be accomplished using conventional manufacturing methods such as moulding.

The recently developed dual-stage 3D printing reprocessable thermosets (3DPRTs) utilize acrylate functional groups to enable the ultra-violet based 3D printing (Stage I), and employ bond exchange reaction (BERs) to tailor the network structure for mechanical properties enhancement and impart the reshapeability, reparability, and recyclability into traditionally unprocessable thermosets (Stage II). 3DPRT provides a practical solution to address environmental challenges associated with the rapid increase in the consumption of 3D printing materials. However, due to the nascent state of development, fundamental understanding of the chemomechanics during the processing of 3DPRTs is still lacking. In this paper, we present detailed experimental and theoretical studies to understand the effect of thermal treatment condition at Stage II on the evolution of network structure and thermomechanical properties of 3DPRTs. A chemomechanics theory is defined to link the molecular-level BER kinetics to the macroscale thermomechanical properties of 3DPRTs during the thermal treatment. A thermo-viscoelastic multi-branched constitutive model is then established to capture the elastic and glass transition behaviors during the Stage II processing. The developed theory is able to capture the experimental observations on the mechanical properties enhancement and provide theoretical guidance for the network design and the selection of thermal treatment conditions to tailor the final mechanical properties of 3DPRTs.

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.

Single-image nonparametric blind super-resolution is a fundamental image restoration problem yet largely ignored in the past decades among the computational photography and computer vision communities. An interesting phenomenon is observed that learning-based single-image super-resolution (SR) has been experiencing a rapid development since the boom of the sparse representation in 2005s and especially the representation learning in 2010s, wherein the high-res image is generally blurred by a supposed bicubic or Gaussian blur kernel. However, the parametric assumption on the form of blur kernels does not hold in most practical applications because in real low-res imaging a high-res image can undergo complex blur processes, e.g., Gaussian-shaped kernels of varying sizes, ellipse-shaped kernels of varying orientations, curvilinear kernels of varying trajectories. The paper is mainly motivated by one of our previous works: Shao and Elad (in: Zhang (ed) ICIG 2015, Part III, Lecture notes in computer science, Springer, Cham, 2015). Specifically, we take one step further in this paper and present a type of adaptive heavy-tailed image priors, which result in a new regularized formulation for nonparametric blind super-resolution. The new image priors can be expressed and understood as a generalized integration of the normalized sparsity measure and relative total variation. Although it seems that the proposed priors are simple, the core merit of the priors is their practical capability for the challenging task of nonparametric blur kernel estimation for both super-resolution and deblurring. Harnessing the priors, a higher-quality intermediate high-res image becomes possible and therefore more accurate blur kernel estimation can be accomplished. A great many experiments are performed on both synthetic and real-world blurred low-res images, demonstrating the comparative or even superior performance of the proposed algorithm convincingly. Meanwhile, the proposed priors are demonstrated quite applicable to blind image deblurring which is a degenerated problem of nonparametric blind SR.

Achievement of optimized lateral and vertical resolution is a key factor to obtaining three-dimensional (3D) structural details fabricated through digital light processing (DLP)-based 3D printing technologies which exploit digitalized ultraviolet (UV) or near-UV light to trigger localized photopolymerization forming solid patterns from liquid polymer resins. Many efforts have been made to optimize printing resolution through improving the optical systems. However, researchers have paid comparatively little attention to understand the influences of polymer formulation on the printing resolution and surface quality. Here, we report an investigation on the effects of in-house formulated (meth)acrylate-based photopolymer constituent types and concentrations on the resolution and quality of structures printed on a bottom-exposure DLP-based 3D printing system. We examined a wide variety of resin formulations to determine optimal formulations that yield best printing resolution and surface quality over a reasonably broad range of mechanical properties. We demonstrated the controlled fabrication of sub-pixel conical and aspherical smooth features, whereby the shape and dimensions could be prescribed with the resin formulation and process parameters. Such features hold promising implications in micro-optic and microfluidic fabrication using the DLP-based 3D printing technique. Additionally, we devised di(meth)acrylate-based resin formulations which could exclusively produce optically-clear layers in contrast to opaque, rough surfaces resulting from commonly-used diacrylate-based resins including those available commercially. Use of this solution minimized the ‘stair-stepping’ effect in components printed in a layer-by-layer manner. We also showed that the maximum lateral and vertical resolution attainable using our present system were 7 μm and 4 μm, respectively, while maintaining uniform width and height. Taken together, the present findings provide a basis for optimized photopolymer resin formulations that retain maximum vertical and lateral resolutions and minimal surface roughness and layering artifacts for a versatile range of mechanical and rheological properties suited to novel applications in 3D printing of smooth free-form solids, micro-optics, and direct fabrication of microfluidic platforms with functional surfaces.

Stretchable and flexible electronics conformal to human skin or implanted into biological tissues has attracted considerable interest for emerging applications in health monitoring and medical treatment. Although various stretchable materials and structures have been designed and manufactured, most are limited to two-dimensional (2D) layouts for interconnects and active components. Here, by using projection microstereolithography (PμSL)-based three-dimensional (3D) printing, we introduce a versatile microfabrication process to push the manufacturing limit and achieve previously inaccessible 3D geometries at a high resolution of 2 μm. After coating the printed microstructures with thin Au films, the 3D conductive structures offer exceptional stretchability (∼130%), conformability, and stable electrical conductivity (5% resistance change at 100% tensile strain). This fabrication process can be further applied to directly create complicated 3D interconnect networks of sophisticated active components, as demonstrated with a stretchable capacitive pressure sensor array here. The proposed scheme allows a simple, facile, and scalable manufacturing route for complex, integrated 3D flexible electronic systems.

DNA digital storage provides an alternative for information storage with high density and long-term stability. Here, we report the de novo design and synthesis of an artificial chromosome that encodes two pictures and a video clip. The encoding paradigm utilizing the superposition of sparsified error correction codewords and pseudo-random sequences tolerates base insertions/deletions and is well suited to error-prone nanopore sequencing for data retrieval. The entire 254 kb sequence was 95.27% occupied by encoded data. The Transformation-Associated Recombination method was used in the construction of this chromosome from DNA fragments and necessary autonomous replication sequences. The stability was demonstrated by transmitting the data-carrying chromosome to the 100th generation. This study demonstrates a data storage method using encoded artificial chromosomes via in vivo assembly for write-once and stable replication for multiple retrievals, similar to a compact disc, with potential in economically massive data distribution., A 254 kb artificial chromosome, storing two pictures and one video clip encoded with low-density parity-check codes and pseudo-random sequences, was manufactured and transmitted from generation to generation stably. The digital information residing in the chromosome was fast retrieved, tolerating base insertions and deletions in nanopore readout.

The shape memory alloy-shape memory polymer (SMA-SMP) composite actuators are the novel smart actuator designs which capable of large recoverable actuation deformation as well as retaining shape in the actuated state without consuming extra energy. This is particularly doable because of the recent progresses in additive manufacturing technologies. The 4D printed actuators designed in the previous study [7] are fabricated by multi-material inkjet 3D printing and is able to deposit various range of digital materials with different elastic stiffness in micro-scale. In this study, we have developed the numerical simulation of the 4D-printed composite actuator hinges to determine important actuation parameters and develop a design tool for future applications. The three-dimensional finite element models were conducted in ABAQUS and the nonlinear temperature dependence behaviour of shape memory alloy (SMA) and shape memory polymer (SMP) materials are incorporated through the user material subroutines (UMATs). The finite element results then have been compared with the experimental measurements and illustrate the accuracy of our numerical analysis. Therefore, the validated computational framework could be used for further development of actuators that could generate complex deformations.

Some cancers such as human breast cancer, prostate cancer, and lung cancer easily metastasize to bone, leading to osteolysis and bone destruction accompanied by a complicated microenvironment. Systemic administration of bisphosphonates (BP) or denosumab is the routine therapy for osteolysis but with non-negligible side effects such as mandibular osteonecrosis and hypocalcemia. Thus, it is imperative to exploit optimized drug delivery systems, and some novel nanotechnology and nanomaterials have opened new horizons for scientists. Targeted and local drug delivery systems can optimize biodistribution depending on nanoparticles (NPs) or microspheres (MS) and implantable biomaterials with the controllable property. Drug delivery kinetics can be optimized by smart and sustained/local drug delivery systems for responsive delivery and sustained delivery. These delicately fabricated drug delivery systems with special matrix, structure, morphology, and modification can minimize unexpected toxicity caused by systemic delivery and achieve desired effects through integrating multiple drugs or multiple functions. This review summarized recent studies about optimized drug delivery systems for the treatment of cancer metastatic osteolysis, aimed at giving some inspiration in designing efficient multifunctional drug delivery systems.

In this work, we propose a new method termed Semantic Frustum Based VoxelNet (SFB-VoxelNet) for 3D object detection. Since previous frustum-based methods are greatly affected by the accuracy of 2D image proposal, we directly alleviate this influence by reducing the probability of omissions and reinforcing the discriminating ability of the 3D detector. Specifically, first, we use semantic map, generating by the method of semantic segmentation, to keep up semantic information and stuff of interest. Second, by projecting the semantic map to LIDAR space, the frustum is generated and then sent to our detector to estimate 3D object classification and bounding box location. Our method not only cuts down most of search space but also assigns a reasonable arrangement for the two steps of frustum-based methods. In addition, our method is evaluated on KITTI 3D object detection benchmark to verify its superiority while maintaining a real-time inference speed, comparing to other state-of-the-art literatures.

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.

By 3D printing lattice structure with active materials, the structures can exhibit shape and functional changes under external stimulus. However, the programmable shape changes of the 3D printed lattice structures are limited due to the complex geometries, nonlinear behaviors of the active materials, and the diverse external stimuli. In this work, we propose a design framework combining experiments, theoretical modeling, and finite element simulations for the controllable shape changes of the 3D printed horseshoe under thermal stimulus. The theoretical model is based on a phase evolution model that combines the geometrical nonlinearity and the material nonlinearity. Results show that the shapes with positive or negative Poisson's ratio and bending intermediate shapes can be programmed by tuning the geometrical parameters and the temperature distribution. This work provides a method to aid the design of 3D printed functional lattice structures and have potential applications in soft robotics, biomedicine, and energy absorbing fields.

We report a method to prepare highly stretchable and UV curable hydrogels for high resolution DLP based 3D printing. Hydrogel solutions were prepared by mixing self-developed high-efficiency water-soluble TPO nanoparticles as the photoinitiator with an acrylamide-PEGDA (AP) based hydrogel precursor. The TPO nanoparticles make AP hydrogels UV curable, and thus compatible with the DLP based 3D printing technology for the fabrication of complex hydrogel 3D structures with high-resolution and high-fidelity (up to 7 μm). The AP hydrogel system ensures high stretchability, and the printed hydrogel sample can be stretched by more than 1300%, which is the most stretchable 3D printed hydrogel. The printed stretchable hydrogels show an excellent biocompatibility, which allows us to directly 3D print biostructures and tissues. The great optical clarity of the AP hydrogels offers the possibility of 3D printing contact lenses. More importantly, the AP hydrogels are capable of forming strong interfacial bonding with commercial 3D printing elastomers, which allows us to directly 3D print hydrogel–elastomer hybrid structures such as a flexible electronic board with a conductive hydrogel circuit printed on an elastomer matrix.

Diabetes mellitus (DM) is a chronic inflammatory disease, and the rapidly increasing DM is becoming a major problem of global public health. Traditional Chinese medicine (TCM) has a long history of treating diabetes. It has been developed and utilized because of its good efficacy and no toxic side effects. Lobelia chinensis is a traditional whole grass herbal. With the continuous deepening of pharmacological research on TCM, the active ingredients of L. chinensis are continuously revealed, which contained the alkaloids, flavonoids, flavonoid glycosides and amino acids that have the good effects of anti-inflammatory, anti-viral and anti-diabetic. In order to further explore the targets of active ingredients and its anti-diabetic mechanism, a feasible network pharmacology analysis model based on chemical, pharmacokinetic and pharmacological data was developed by network construction method to clarify the anti-diabetic mechanism of L. chinensis. The present study conducted by gas chromatography–mass spectrometer (GC/MS), which identified 208 metabolites of L. chinensis, of which 23 ingredients may have effective pharmacological effects after absorption, distribution, metabolism, and excretion (ADME) screening. Network pharmacological analysis on the active ingredients revealed that 5-hydroxymethylfurfural in L. chinensis affects the insulin resistance signaling pathway by acting on GSK3B, TNF, and MAPK1, acacetin affects the diabetic pathway by acting on INSR, DPP4, and GSK3B, that regulate type 2 diabetes, non-insulin-dependent DM, and inflammatory diseases. These results successfully indicated the potential anti-diabetic mechanism of the active ingredients of L. chinensis.

Digital Light Processing (DLP) 3D printing enables the creation of hierarchical complex structures with specific micro- and macroscopic architectures that are impossible to achieve through traditional manufacturing methods. Here, this hierarchy is extended to the mesoscopic length scale for optimized devices that dissipate mechanical energy. A photocurable, thus DLP-printable main-chain liquid crystal elastomer (LCE) resin is reported and used to print a variety of complex, high-resolution energy-dissipative devices. Using compressive mechanical testing, the stress-strain responses of 3D-printed LCE lattice structures are shown to have 12 times greater rate-dependence and up to 27 times greater strain-energy dissipation compared to those printed from a commercially available photocurable elastomer resin. The reported behaviors of these structures provide further insight into the much-overlooked energy-dissipation properties of LCEs and can inspire the development of high-energy-absorbing device applications.

This paper proposes a deep convolutional neural network-based low-light image enhancement method. In order to adaptively enhance the image brightness, a convolutional neural network with convolutional modules is designed. Lowlight image is firstly down-sampled into sub-images. Then an illumination map is obtained from the input image to provide additional information to the network. The network works on a tensor that consists of sub-images and illumination map, achieving a good performance in brightness increasing and structure preservation. The enhanced result is reconstructed from the output sub-images. Experimental results demonstrate the effectiveness of the proposed method in low-light image enhancement.

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.

The performance of conventional interactive image segmentation methods is strongly affected by seed quantity and position, and it is difficult for them to maintain global data coherence due to the bias that is caused by limited interactions. Furthermore, the pixel-level relationships in these methods are too local to capture long-range connectivity cues, which often causes them to obtain under-segmented results. To solve these problems, this paper proposes an interactive segmentation method that is based on likelihood diffusion and perceptual learning. The diffusive likelihood strategy is proposed for accurately estimating the prior label probability from limited user inputs. Superpixel-level grouping cues are utilized to enforce continuity during the segmentation process. The geometrical adjacency and long-range grouping cues are fused in the proposed framework to ensure that the segmentation results maintain proximity and continuity. The final results can be obtained by applying a joint optimization technique to solve a pair of sub-module functions. Experiments on the Berkeley segmentation data set and the Microsoft GrabCut database demonstrate that the proposed method outperforms state-of-the-art methods.

A microlens array has become an important micro-optics device in various applications. Compared with traditional manufacturing approaches, digital light processing (DLP)-based printing enables fabrication of complex three-dimensional (3D) geometries and is a possible manufacturing approach for microlens arrays. However, the nature of 3D printing objects by stacking successive 2D patterns formed by discrete pixels leads to coarse surface roughness and makes DLP-based printing unsuccessful in fabricating optical components. Here, we report an oscillation-assisted DLP-based printing approach for fabrication of microlens arrays. An optically smooth surface (about 1 nm surface roughness) is achieved by mechanical oscillation that eliminates the jagged surface formed by discrete pixels, and a 1-3 s single grayscale ultraviolet (UV) exposure that removes the staircase effect. Moreover, computationally designed grayscale UV patterns allow us to fabricate microlenses with various profiles. The proposed approach paves a way to 3D print optical components with high quality, fast speed, and vast flexibility.

In this paper, we investigate a novel learning-based method for dynamic scene deblurring. Since the inference model is formulated as an encoder-decoder, the core task has turned to learning blur-invariant hidden features from the blurred images to a great degree. To achieve state-of-the-art results in terms of both deblurring accuracy and efficiency, a simple, robust and computationally efficient deep auto-encoder is developed tailored specifically for blind deblurring, which is learned in an adversarial fashion based on use of the recent Wasserstein generative adversarial networks. Thanks to the designed framework, the new model has shown comparable or superior performance both qualitatively and quantitatively to existing state-of-the-art methods. What is more important, instead of exploiting the multi-scale strategy as previous methods, our model is just single-scale capable of achieving 6 times more efficiency than the closest competitor by Nah et al. [1], which is a more complex multi-scale deep method.

In this paper, we built up a desktop digital light processing (DLP) 3D printer and fabricated multiple size soft pneumatic actuators integrally with fast speed and high precision. The printing process is based on the projection microstereolithography method. The composition of the printing system and key parameters during the printing process were presented. Evaluation experiments demonstrate that our printer can print objects with as small as 87.5 μm size features. We first printed single pneumatic net-works (pneu-net) actuators integrally and conducted a series of actuation experiments and finite element method analyses to test the actuators’ deformation capability. We further designed a soft pneumatic gripper containing three micro pneu-net actuators with 0.4 mm wide square air channels as well as 0.2 mm thick chamber walls and fabricated the gripper integrally in less than 30 min using our printer. The grasping capability of the gripper was verified through experiments as well. Results presented in this work prove the performance of the DLP printer we build up and show the convenience of fabricating micro soft pneumatic actuators integrally using DLP 3D printing approach with fast speed and high precision.