One-Step Synthesis of 3D Graphene Aerogel Supported Pt Nanoparticles as Highly Active Electrocatalysts for Methanol Oxidation Reaction.

Nowadays, two of the biggest obstacles restricting the further development of methanol fuel cells are excessive cost and insufficient catalytic activity of platinum-based catalysts. Herein, platinum nanoparticle supported graphene aerogel (Pt/3DGA) was successfully synthesized by a one-step hydrothermal self-assembly method. The loose three-dimensional structure of the aerogel is stabilized by a simple one-step method, which not only reduces cost compared to the freeze-drying technology, but also optimizes the loading method of nanoparticles. The prepared Pt/3DGA catalyst has a three-dimensional porous structure with a highly cross-linked, large specific surface area, even dispersion of Pt NPs and good electrical conductivity. It is worth noting that its catalytic activity is 438.4 mA/mg with long-term stability, which is consistent with the projected benefits of anodic catalytic systems in methanol fuel cells.. Our study provides an applicable method for synthesizing nano metal particles/graphene-based composites.

Synthesis of Superelastic, Highly Conductive Graphene Aerogel/Liquid Metal Foam and its Piezoresistive Application.

Graphene aerogel (GA) has important application potential as piezoresistive sensors due to its low density, high conductivity, high porosity, and good mechanical properties. However, the fabrication of GA-based sensors with good mechanical properties and excellent sensing performance is still challenging. Herein, liquid- metal-modified GAs (GA/LM) are proposed for the development of an excellent GA-based sensor. GA/LM with three-dimensional interconnected layered structure exhibits excellent compressive stress of 41 KPa and fast response time (<20 ms). While generally flexible GA composites cannot be compressed beyond 80 % strain without plastic deformation, GA/LM demonstrates a high compressive strength of 60 kPa under a strain of 90 %. A real-time pressure sensor was fabricated based on GA/LM-2 to monitor swallowing, pulse beating, finger, wrist and knee bending, and even plantar pressure during walking. These excellent features enable potential applications in health detection.

Aerobic exercise-induced circulating extracellular vesicle combined decellularized dermal matrix hydrogel facilitates diabetic wound healing by promoting angiogenesis.

Background: Insufficient blood supply results in unsatisfactory wound healing, especially for challenging wound repair such as diabetic wound defects. Regular exercise training brings a lot of benefits to cardiovascular fitness and metabolic health including attenuation of T2DM progression. Circulating extracellular vesicles (EVs) are postulated to carry a variety of signals involved in tissue crosstalk by their modified cargoes, representing novel mechanisms for the effects of exercise. Prominently, both acute and chronic aerobic exercise training can promote the release of exercise-induced cytokines and enhance the angiogenic function of circulating angiogenic cell-derived EVs. Methods: We investigated the possible angiogenesis potential of aerobic exercise-induced circulating EVs (EXE-EVs) on diabetic wound healing. Circulating EVs were isolated from the plasma of rats subjected to 4 weeks of moderate aerobic exercise or sedentariness 24 h after the last training session. The therapeutic effect of circulating EVs was evaluated in vitro by proliferation, migration, and tube formation assays of human umbilical vein endothelial cells (HUVECs), as well as in vivo by quantification of angiogenesis and cutaneous wound healing in diabetic rats. Results: The number of circulating EVs did not change significantly in exercised rats 24 h post-exercise in comparison with the sedentary rats. Nevertheless, EXE-EVs showed remarkable pro-angiogenic effect by augmenting proliferation, migration, and tube formation of HUVECs. Furthermore, the findings of animal experiments revealed that the EXE-EVs delivered by decellularized dermal matrix hydrogel (DDMH) could significantly promote the repair of skin defects through stimulating the regeneration of vascularized skin. Discussion: The present study is the first attempt to demonstrate that aerobic exercise-induced circulating EVs could be utilized as a cell-free therapy to activate angiogenesis and promote diabetic wound healing. Our findings suggest that EXE-EVs may stand for a potential strategy for diabetic soft tissue wound repair.

Acellular dermal matrix decorated with collagen-affinity peptide accelerate diabetic wound healing through sustained releasing Histatin-1 mediated promotion of angiogenesis.

Treating diabetic ulcers is a major challenge in clinical practice, persecuting millions of patients with diabetes and increasing the medical burden. Recombinant growth factor application can accelerate diabetic wound healing via angiogenesis. The local administration of recombinant growth factors has no robust clinical efficiency because of the degradation of append short duration of the molecules in the hostile inflammatoryenvironment.The present study focused on the pathophysiology of impaired neovascularization and growth factor short duration in the diabetic wound. We prepared a collagen-binding domain (CBD)-fused recombinant peptide (C-Histatin-1) that had both pro-angiogenesis capacity and collagen-affinity properties. Next, we created a biocompatible acellular dermal matrix (ADM) as a drug delivery carrier that featured collagen-richness, high porosity, and non-cytotoxicity. C-Histatin-1 was then tethered on ADM to obtain a sustained-release effect. Finally, a functional scaffold (C-Hst1/ADM) was developed. C-Hst1/ADM can sustain-release Histatin-1 to promote the adhesion, migration, and angiogenesisof vascular endothelial cells in vitro. Using a diabetic wound model, we showed that C-Hst1/ADM could significantly promote angiogenesis, reduce scar widths, and improve extracellular collagen accumulation. Therefore, the results of this study provide a foundation for the clinical application of C-Hst1/ADM covering scaffold in the treatment of diabetic wounds.

Constructing Tissue-Engineered Dressing Membranes with Adipose-Derived Stem Cells and Acellular Dermal Matrix for Diabetic Wound Healing: A Comparative Study of Hypoxia- or Normoxia-Culture Modes.

Diabetes foot ulcer (DFU) is a serious complication of diabetes, characterized by impaired vascular function, limited angiogenesis, and chronic inflammation. Direct stem cell injection on treating DFU is far from satisfactory in clinical practice, as this therapy neither protects nor localizes the injected cell suspension at the chronic ulcer site. Meanwhile, most of injected cells gradually perished within several days due to senescence or apoptosis. Acellular dermal matrix (ADM) has the potential to act as excellent cell delivery vehicles, considering it is highly biomimetic to native dermal tissue, has low immunogenicity, and suitable for stem cell attachment and proliferation. Hypoxia culture has significantly enhanced effects on the survival ability of in vitro cultured stem cells, indicating this culture mode is a suitable way for inhibiting the senescence or apoptosis of transplanted cells. In the current study, we, respectively, culture adipose-derived stem cells (ADSCs) on an ADM membrane under a hypoxia or normoxia condition to construct two kinds of tissue-engineered dressing membranes (H-ADSCs/ADM and N-ADSCs/ADM) and then comparatively evaluated their efficacy on DFU healing using a diabetic rat model. In vitro results showed that hypoxia precondition could stimulate the ADSCs secreting VEGF-A, and the culture medium from hypoxia-preconditioned ADSCs could enhance the proliferation, migration, and angiogenesis of HUVECs. In vivo results indicated that compared to the N-ADSCs/ADM membrane, the transplanted cells in the H-ADSCs/ADM membrane can survive longer at the chronic ulcer site, consequently improve angiogenesis, inhibit inflammation, and increase extracellular matrix remodeling, eventually accelerating DFU closure. This study provides an innovative covering graft for the treatment of DFU in the clinic.

Computational framework for steady-state NLOS localization under changing ambient illumination conditions.

Non-line-of-sight (NLOS) imaging of hidden objects is a challenging yet vital task, facilitating important applications such as rescue operations, medical imaging, and autonomous driving. In this paper, we attempt to develop a computational steady-state NLOS localization framework that works accurately and robustly under various illumination conditions. For this purpose, we build a physical NLOS image acquisition hardware system and a corresponding virtual setup to obtain real-captured and simulated steady-state NLOS images under different ambient illuminations. Then, we utilize the captured NLOS images to train/fine-tune a multi-task convolutional neural network (CNN) architecture to perform simultaneous background illumination correction and NLOS object localization. Evaluation results on both stimulated and real-captured NLOS images demonstrate that the proposed method can effectively suppress severe disturbance caused by the variation of ambient light, significantly improving the accuracy and stability of steady-state NLOS localization using consumer-grade RGB cameras. The proposed method potentially paves the way to develop practical steady-state NLOS imaging solutions for around-the-clock and all-weather operations.

Identifying diabetes from conjunctival images using a novel hierarchical multi-task network.

Diabetes can cause microvessel impairment. However, these conjunctival pathological changes are not easily recognized, limiting their potential as independent diagnostic indicators. Therefore, we designed a deep learning model to explore the relationship between conjunctival features and diabetes, and to advance automated identification of diabetes through conjunctival images. Images were collected from patients with type 2 diabetes and healthy volunteers. A hierarchical multi-tasking network model (HMT-Net) was developed using conjunctival images, and the model was systematically evaluated and compared with other algorithms. The sensitivity, specificity, and accuracy of the HMT-Net model to identify diabetes were 78.70%, 69.08%, and 75.15%, respectively. The performance of the HMT-Net model was significantly better than that of ophthalmologists. The model allowed sensitive and rapid discrimination by assessment of conjunctival images and can be potentially useful for identifying diabetes.

Ancient genome analyses shed light on kinship organization and mating practice of Late Neolithic society in China.

Anthropology began in the late nineteenth century with an emphasis on kinship as a key factor in human evolution. From the 1960s, archaeologists attempted increasingly sophisticated ways of reconstructing prehistoric kinship but ancient DNA analysis has transformed the field, making it possible, to directly examine kin relations from human skeletal remains. Here, we retrieved genomic data from four Late Neolithic individuals in central China associated with the Late Neolithic Longshan culture. We provide direct evidence of consanguineous mating in ancient China, revealing inbreeding among the Longshan populations. By combining ancient genomic data with anthropological and archaeological evidence, we further show that Longshan society household was built based on the extended beyond the nuclear family, coinciding with intensified social complexity during the Longshan period, perhaps showing the transformation of large communities through a new role of genetic kinship-based extended family units.

Data-driven framework for high-accuracy color restoration of RGBN multispectral filter array sensors under extremely low-light conditions.

RGBN multispectral filter array provides a cost-effective and one-shot acquisition solution to capture well-aligned RGB and near-infrared (NIR) images which are useful for various optical applications. However, signal responses of the R, G, B channels are inevitably distorted by the undesirable spectral crosstalk of the NIR bands, thus the captured RGB images are adversely desaturated. In this paper, we present a data-driven framework for effective spectral crosstalk compensation of RGBN multispectral filter array sensors. We set up a multispectral image acquisition system to capture RGB and NIR image pairs under various illuminations which are subsequently utilized to train a multi-task convolutional neural network (CNN) architecture to perform simultaneous noise reduction and color restoration. Moreover, we present a technique for generating high-quality reference images and a task-specific joint loss function to facilitate the training of the proposed CNN model. Experimental results demonstrate the effectiveness of the proposed method, outperforming the state-of-the-art color restoration solutions and achieving more accurate color restoration results for desaturated and noisy RGB images captured under extremely low-light conditions.

Adipose-Derived Stromal Cell-Sheets Sandwiched, Book-Shaped Acellular Dermal Matrix Capable of Sustained Release of Basic Fibroblast Growth Factor Promote Diabetic Wound Healing.

The management of diabetic wounds is a therapeutic challenge in clinical settings. Current tissue engineering strategies for diabetic wound healing are insufficient, owing to the lack of an appropriate scaffold that can load a large number of stem cells and induce the interaction of stem cells to form granulation tissue. Herein we fabricated a book-shaped decellularized dermal matrix (BDDM), which shows a high resemblance to native dermal tissue in terms of its histology, microstructure, and ingredients, is non-cytotoxic and low-immunogenic, and allows adipose-derived stromal cell (ASC) attachment and proliferation. Then, a collagen-binding domain (CBD) capable of binding collagen was fused into basic fibroblast growth factor (bFGF) to synthetize a recombinant growth factor (termed as CBD-bFGF). After that, CBD-bFGF was tethered onto the collagen fibers of BDDM to improve its endothelial inducibility. Finally, a functional scaffold (CBD-bFGF/BDDM) was fabricated. In vitro and in vivo experiments demonstrated that CBD-bFGF/BDDM can release tethered bFGF with a sustained release profile, steadily inducing the interaction of stem cells down to endothelial differentiation. ASCs were cultured to form a cell sheet and then sandwiched by CBD-bFGF/BDDM, thus enlarging the number of stem cells loaded into the scaffold. Using a rat model, the ASC sheets sandwiched with CBD-bFGF/BDDM (ASCs/CBD-bFGF/BDDM) were capable of enhancing the formation of granulation tissue, promoting angiogenesis, and facilitating collagen deposition and remodeling. Therefore, the findings of this study demonstrate that ASCs/CBD-bFGF/BDDM could be applicable for diabetic wound healing.

Enhanced performances of triboelectric nanogenerators by filling hierarchical flower-like TiO2 particles into polymethyl methacrylate film.

In this study, a flower-like TiO2 filled polymethyl methacrylate (PMMA) composite is presented as a positive tribo-material to produce an excellent-performance triboelectric nanogenerator (TENG). By working in conjunction with polydimethylsiloxane (PDMS), the flat-surface PDMS/PMMA-flower TiO2 TENG generates a voltage of 1200 V, a current of 139 mA m-2 and an output power of 34.85 W m-2, showing significant enhancement compared with its counterpart utilizing neat PMMA as the positive tribo-material under the same operating conditions, whose voltage is 620 V, current is 78 mA m-2 and output power is 13.89 W m-2, respectively. The performance of the TENG is highly dependent on filler loadings of TiO2 flower particles in PMMA composites with an optimal filler loading of 40 wt% with the highest performances. The flower TiO2 is vital to the enhanced performances of the TENG, which is due to the modified surface, the tailored dielectric constant and the space charge polarization. The TENG is capable of powering 600 light emitting diodes, a calculator and a digit display, and applied in self-powered electrophoretic deposition of oxide films. This work demonstrates a facile, low-cost approach for obtaining high-performance TENGs utilizing a PMMA-flower TiO2 composite as the positive tribo-material for applications in sustainable power systems.

Ancient genomes from northern China suggest links between subsistence changes and human migration.

Northern China harbored the world's earliest complex societies based on millet farming, in two major centers in the Yellow (YR) and West Liao (WLR) River basins. Until now, their genetic histories have remained largely unknown. Here we present 55 ancient genomes dating to 7500-1700 BP from the YR, WLR, and Amur River (AR) regions. Contrary to the genetic stability in the AR, the YR and WLR genetic profiles substantially changed over time. The YR populations show a monotonic increase over time in their genetic affinity with present-day southern Chinese and Southeast Asians. In the WLR, intensification of farming in the Late Neolithic is correlated with increased YR affinity while the inclusion of a pastoral economy in the Bronze Age was correlated with increased AR affinity. Our results suggest a link between changes in subsistence strategy and human migration, and fuel the debate about archaeolinguistic signatures of past human migration.

Multi-channel residual network model for accurate estimation of spatially-varying and depth-dependent defocus kernels.

Digital projectors have been increasingly utilized in various commercial and scientific applications. However, they are prone to the out-of-focus blurring problem since their depth-of-fields are typically limited. In this paper, we explore the feasibility of utilizing a deep learning-based approach to analyze the spatially-varying and depth-dependent defocus properties of digital projectors. A multimodal displaying/imaging system is built for capturing images projected at various depths. Based on the constructed dataset containing well-aligned in-focus, out-of-focus, and depth images, we propose a novel multi-channel residual deep network model to learn the end-to-end mapping function between the in-focus and out-of-focus image patches captured at different spatial locations and depths. To the best of our knowledge, it is the first research work revealing that the complex spatially-varying and depth-dependent blurring effects can be accurately learned from a number of real-captured image pairs instead of being hand-crafted as before. Experimental results demonstrate that our proposed deep learning-based method significantly outperforms the state-of-the-art defocus kernel estimation techniques and thus leads to better out-of-focus compensation for extending the dynamic ranges of digital projectors.

Assessing the occurrence and status of wheat in late Neolithic central China: the importance of direct AMS radiocarbon dates from Xiazhai.

The introduction of wheat into central China is thought to have been one of the significant contributions of interactions between China and Central Asia which began in the 3rd millennium bc. However, only a limited number of Neolithic wheat grains have been found in central China and even fewer have been directly radiocarbon dated, making the date when wheat was adopted in the region and its role in subsistence farming uncertain. Based on systematic archaeobotanical data and direct dating of wheat remains from the Xiazhai site in central China, as well as a critical review of all reported discoveries of Neolithic and Bronze Age wheat from this region, we conclude that many wheat finds are intrusive in Neolithic contexts. We argue that the role of wheat in the subsistence of the late Neolithic and early Bronze Age of central China was minimal, and that wheat only began to increase in its subsistence role in the later Bronze Age during the Zhou dynasty after ca. 1000 bc.

Cases of endocranial lesions on juvenile skeletons from Longshan cultural sites in Henan Province, China.

Endocranial lesions were recognized on eight out of the 31 juveniles (25.8%) that were recovered from three Neolithic archaeological sites in Henan province. The remains of juveniles were recovered from urn burials at the Jiazhuang site (2200-2030 BCE) and graves at the Pingliangtai (2300-2100 BCE) and Haojiatai sites (2448-1700 BCE). The presence of endocranial lesions on all eight of these juvenile skulls was associated with a range of lesions on other bones, including areas of abnormal porosity and subperiosteal new bone deposition on either the sphenoid, maxilla, mandibular ramus, or orbit, as well as subperiosteal lesions on the postcranial bones. Several plausible explanations for the formation of these endocranial lesions in our eight cases include scurvy, shaken baby syndrome, and intrathoracic disease (such as tuberculosis or pulmonary infection). We show that the presence of endocranial lesions had a strong correspondence with skeletal markers of dietary deficiency, i.e. scurvy, and in one case, anemia. Millet was a key component of the Longshan subsistence in the area, while paleobotanical evidence of fruit and leafy vegetables appears to be limited, likely resulting in a nutrient deficient diet. The coupling of endocranial lesions with skeletal signs of dietary deficiency can be direct, as scurvy favors hemorrhaging, or mediated by physiological or sociocultural factors, and thereby represents comorbidity.

On-the-fly extrinsic calibration of multimodal sensing system for fast 3D thermographic scanning.

The fusion of three-dimensional (3D) geometrical and two-dimensional (2D) thermal information provides a promising method for characterizing temperature distribution of 3D objects, extending infrared imaging from 2D to 3D to support various thermal inspection applications. In this paper, we present an effective on-the-fly calibration approach for accurate alignment of depth and thermal data to facilitate dynamic and fast-speed 3D thermal scanning tasks. For each pair of depth and thermal frames, we estimate their relative pose by minimizing the objective function that measures the temperature consistency between a 2D infrared image and the reference 3D thermographic model. Our proposed frame-to-model mapping scheme can be seamlessly integrated into a generic 3D thermographic reconstruction framework. Through graphics-processing-unit-based acceleration, our method requires less than 10 ms to generate a pair of well-aligned depth and thermal images without hardware synchronization and improves the robustness of the system against significant camera motion.

Versatile functionalization of surface-tailorable polymer nanohydrogels for drug delivery systems.

Surface decoration of nanohydrogels with functional molecules as well as nanomaterials offers a facile approach for developing multifunctional drug nanocarriers. Herein, the surface-tailorable polymer nanohydrogels, with the catechol groups as a universal anchor, were prepared by simple reflux-precipitation polymerization for versatile functionalization as drug delivery systems. The resultant polymer nanohydrogels were not only capable of delivering doxorubicin (DOX) through electrostatic interactions, but also exhibited facile conjugation with magnetic Fe3O4 nanoparticles and anticancer drug bortezomib (BTZ) via the versatile catechol-based coupling chemistry. The DOX and Fe3O4 loaded nanohydrogels (DOX-Fe3O4@NG) exhibited high DOX loading capability and triggered drug release behaviors in the acidic and redox environment. Furthermore, the DOX-Fe3O4@NG achieved improved cellular uptake in the presence of external magnetic field due to the active magnetic targeting properties. As for the dual drug delivery system (DOX-BTZ@NG), the DOX-BTZ@NG also released the drugs in response to the external stimuli including low pH and GSH presence, indicating their intelligent drug delivery properties. In particular, the DOX-BTZ@NG showed higher antiproliferation efficacy to cancer cells in comparison with the single drug loaded nanohydrogels, suggesting a synergistic effect of the dual drug combination therapy. The degradable poly(AA-co-DMA) nanohydrogels with surface-tailorable functionalities are thus a promising versatile platform for conjugation with both nanomaterials and drug molecules.

Exploiting fusion architectures for multispectral pedestrian detection and segmentation.

Recent research has demonstrated that the fusion of complementary information captured by multi-modal sensors (visible and infrared cameras) enables robust pedestrian detection under various surveillance situations (e.g., daytime and nighttime). In this paper, we investigate a number of fusion architectures in an attempt to identify the optimal way of incorporating multispectral information for joint semantic segmentation and pedestrian detection. We made two important findings: (1) the sum fusion strategy, which computes the sum of two feature maps at the same spatial locations, delivers the best performance of multispectral detection, while the most commonly used concatenation fusion surprisingly performs the worst; and (2) two-stream semantic segmentation without multispectral fusion is the most effective scheme to infuse semantic information as supervision for learning human-related features. Based on these studies, we present a unified multispectral fusion framework for joint training of semantic segmentation and target detection that outperforms state-of-the-art multispectral pedestrian detectors by a large margin on the KAIST benchmark dataset.

Single-image-based nonuniformity correction of uncooled long-wave infrared detectors: a deep-learning approach.

Fixed-pattern noise (FPN), which is caused by the nonuniform opto-electronic responses of microbolometer focal-plane-array (FPA) optoelectronics, imposes a challenging problem in infrared imaging systems. In this paper, we successfully demonstrate that a better single-image-based non-uniformity correction (NUC) operator can be directly learned from a large number of simulated training images instead of being handcrafted as before. Our proposed training scheme, which is based on convolutional neural networks (CNNs) and a column FPN simulation module, gives rise to a powerful technique to reconstruct the noise-free infrared image from its corresponding noisy observation. Specifically, a comprehensive column FPN model is utilized to depict the nonlinear characteristics of column amplifiers in the readout circuit of FPA. A large number of high-fidelity training images are simulated based on this model and the end-to-end residual deep network is capable of learning the intrinsic difference between undesirable FPN and original image details. Therefore, column FPN can be accurately estimated and further subtracted from the raw infrared images to obtain NUC results. Comparative results with state-of-the-art single-image-based NUC methods, using real-captured noisy infrared images, demonstrate that our proposed deep-learning-based approach delivers better performances of FPN removal, detail preservation, and artifact suppression.

Depth and thermal sensor fusion to enhance 3D thermographic reconstruction.

Three-dimensional geometrical models with incorporated surface temperature data provide important information for various applications such as medical imaging, energy auditing, and intelligent robots. In this paper we present a robust method for mobile and real-time 3D thermographic reconstruction through depth and thermal sensor fusion. A multimodal imaging device consisting of a thermal camera and a RGB-D sensor is calibrated geometrically and used for data capturing. Based on the underlying principle that temperature information remains robust against illumination and viewpoint changes, we present a Thermal-guided Iterative Closest Point (T-ICP) methodology to facilitate reliable 3D thermal scanning applications. The pose of sensing device is initially estimated using correspondences found through maximizing the thermal consistency between consecutive infrared images. The coarse pose estimate is further refined by finding the motion parameters that minimize a combined geometric and thermographic loss function. Experimental results demonstrate that complimentary information captured by multimodal sensors can be utilized to improve performance of 3D thermographic reconstruction. Through effective fusion of thermal and depth data, the proposed approach generates more accurate 3D thermal models using significantly less scanning data.