An artificial periodic roughness-gradient conical copper wire (PCCW) can be fabricated by inspiration from cactus spines and wet spider silks. PCCW can harvest fog on periodic points of the conical ...surface from air and transports the drops for a long distance without external force, which is attributed to dynamic as-released energy generated from drop deformation in drop coalescence, in addition to both gradients of geometric curve (inducing Laplace pressure) and periodic roughness (inducing surface energy difference). It is found that the ability of fog collection can be related to various tilt-angle wires, thus a fog collector with an array system of PCCWs is further designed to achieve a continuous process of efficient water collection. As a result, the effect of water collection on PCCWs is better than previous results. These findings are significant to develop and design materials with water collection and water transport for promising application in fogwater systems to ease the water crisis.
Paper-based microfluidics is a promising technology to develop a simple, low-cost, portable, and disposable diagnostic platform for resource-limited settings. Here we report the fabrication of ...paper-based microfluidic devices in nitrocellulose membrane by wax printing for protein immobilization related applications. The fabrication process, which can be finished within 10 min, includes mainly printing and baking steps. Wax patterning will form hydrophobic regions in the membrane, which can be used to direct the flow path or separate reaction zones. The fabrication parameters like printing mode and baking time were optimized, and performances of the wax-patterned nitrocellulose membrane such as printing resolution, protein immobilization, and sample purification capabilities were also characterized in this report. We believe the wax-patterned nitrocellulose membrane will enhance the capabilities of paper microfluidic devices and bring new applications in this field.
We performed the first proteogenomic characterization of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) using paired tumor and adjacent liver tissues from 159 patients. Integrated ...proteogenomic analyses revealed consistency and discordance among multi-omics, activation status of key signaling pathways, and liver-specific metabolic reprogramming in HBV-related HCC. Proteomic profiling identified three subgroups associated with clinical and molecular attributes including patient survival, tumor thrombus, genetic profile, and the liver-specific proteome. These proteomic subgroups have distinct features in metabolic reprogramming, microenvironment dysregulation, cell proliferation, and potential therapeutics. Two prognostic biomarkers, PYCR2 and ADH1A, related to proteomic subgrouping and involved in HCC metabolic reprogramming, were identified. CTNNB1 and TP53 mutation-associated signaling and metabolic profiles were revealed, among which mutated CTNNB1-associated ALDOA phosphorylation was validated to promote glycolysis and cell proliferation. Our study provides a valuable resource that significantly expands the knowledge of HBV-related HCC and may eventually benefit clinical practice.
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•Proteomic subgroups stratify patient survival and allocate specific treatments•Alterations of the liver-specific proteome and metabolism in HCC are identified•Multi-omics profile of key signaling and metabolic pathways in HCC is depicted•CTNNB1 mutation-associated ALDOA phosphorylation promotes HCC cell proliferation
Proteogenomic characterization of HBV-related hepatocellular carcinoma (HCC) using paired tumor and adjacent liver tissues identifies three subgroups with distinct features in metabolic reprogramming, microenvironment dysregulation, cell proliferation, and potential therapeutics.
In this paper, we build a multilabel image classifier using a general deep convolutional neural network (DCNN). We propose a novel objective function that consists of three parts, i.e., max-margin ...objective, max-correlation objective, and correntropy loss. The max-margin objective explicitly enforces that the minimum score of positive labels must be larger than the maximum score of negative labels by a predefined margin, which not only improves accuracies of the multilabel classifier, but also eases the threshold determination. The max-correlation objective can make the DCNN model learn a latent semantic space, which maximizes the correlations between the feature vectors of the training samples and their corresponding ground-truth label vectors projected into this space. Instead of using the traditional softmax loss, we adopt the correntropy loss from the information theory field to minimize the training errors of the DCNN model. The proposed framework can be end-to-end trained. Comprehensive experimental evaluations on Pascal VOC 2007 and MIR Flickr 25K multilabel benchmark data sets with four DCNN models, i.e., AlexNet, VGG-16, GoogLeNet, and ResNet demonstrate that the proposed objective function can remarkably improve the performance accuracies of a DCNN model for the task of multilabel image classification.
The low response rate and adaptive resistance of PD‐1/PD‐L1 blockade demands the studies on novel therapeutic targets for cancer immunotherapy. We discovered that a novel immune checkpoint TIGIT ...expressed higher than PD‐1 in many tumors especially anti‐PD‐1 resistant tumors. Here, mirror‐image phage display bio‐panning was performed using the d‐enantiomer of TIGIT synthesized by hydrazide‐based native chemical ligation. d‐peptide DTBP‐3 was identified, which could occupy the binding interface and effectively block the interaction of TIGIT with its ligand PVR. DTBP‐3 showed proteolytic resistance, tumor tissue penetrating ability, and significant tumor suppressing effects in a CD8+ T cell dependent manner. More importantly, DTBP‐3 could inhibit tumor growth and metastasis in anti‐PD‐1 resistant tumor model. This is the first d‐peptide targeting TIGIT, which could serve as a potential candidate for cancer immunotherapy.
The d‐peptide DTBP‐3 was identified, which could effectively block TIGIT/PVR interaction. DTBP‐3 could inhibit tumor growth and metastasis in anti‐PD‐1 resistant tumor model and could serve as a potential candidate for cancer immunotherapy.
The enhanced near-field amplitude of localized surface plasmon resonance in the proximity of metal nanoparticles can boost the photocatalytic activity of the neighboring semiconductor, which has been ...proven and has attracted wide interest recently. Since the plasmon resonance energy strongly depends on the metal particle size and shape, interparticle spacing, and dielectric property of the surrounding medium, it is available to improve the photocatalytic activity of the neighboring semiconductor by designing and synthesizing targeted metal nanoparticles or assembled nanostructures. In this paper, we propose a Au/TiO2/Au nanostructure with the thickness of the middle layer TiO2 nanosheets around 5 nm, which satisfies the distance needed for the coupling effect between the opposite and nearly touching Au nanoparticles, and thus, it can be used as a “plasmonic coupling photocatalyst”. Compared with the bare TiO2 nanosheet films, the photocurrent density of this favorable nanostructure exhibited a significant improvement in the visible region. The three-dimensional finite-difference time domain was used to quantitatively account for the electromagnetic enhancement of this Au/TiO2/Au heterostructure and substantiated the plasmonic enchancement photocatalytic mechanism further.
•In this study, fCS-Sc was investigated for its macrophage activation effects.•fCS-Sc significantly promote the NO, TNF-α, IL-1β, and IL-6 production.•fCS-Sc activated macrophage through TLR4-NF-κB ...pathways.
Sea cucumbers were nutritional food and traditional Chinese medicine. In this study, fucosylated chondroitin sulfate from sea cucumber Stichopus chloronotus (fCS-Sc), a potential anticoagulant agent and immunological adjuvant, was investigated for its immune activation effects on RAW 264.7 macrophage for the first time. The results indicated that fCS-Sc could significantly promote the proliferation, the pinocytic activity of RAW 264.7 cells, and the production of NO, TNF-α, IL-1β, and IL-6. The fluorescence labeling assay indicated that fCS-Sc could bind to the macrophage. Moreover, the specific pattern recognition receptor inhibition assays showed that toll-like receptor 4 (TLR4) and TLR2 were involved in the recognition of fCS-Sc. Western blot assays indicated that fCS-Sc could induce degradation of cytoplasm IκB-α, and promotion of NF-κB p65 subunit translocation to nucleus, leading to a functional improvement of macrophage through NF-κB pathway. The results suggested that fCS-Sc might served as a promising candidate of immunomodulator.
Smart anisotropic‐unidirectional spreading is displayed on a wettable‐gradient‐aligned fibrous surface due to a synergetic directing effect from the aligned structure and the ratio of hydrophilic ...components.
•This paper is the first to formulates the structured distance relationships into the graph Laplacian form for deep feature learning.•Joint learning method is used in the framework to learn ...discriminative features.•The results show clear improvements on public benchmark datasets and some are the state-of-the-art.
Learning the distance metric between pairs of examples is of great importance for visual recognition, especially for person re-identification (Re-Id). Recently, the contrastive and triplet loss are proposed to enhance the discriminative power of the deeply learned features, and have achieved remarkable success. As can be seen, either the contrastive or triplet loss is just one special case of the Euclidean distance relationships among these training samples. Therefore, we propose a structured graph Laplacian embedding algorithm, which can formulate all these structured distance relationships into the graph Laplacian form. The proposed method can take full advantages of the structured distance relationships among these training samples, with the constructed complete graph. Besides, this formulation makes our method easy-to-implement and super-effective. When embedding the proposed algorithm with the softmax loss for the CNN training, our method can obtain much more robust and discriminative deep features with inter-personal dispersion and intra-personal compactness, which is essential to person Re-Id. We did experiments on top of three popular networks, namely AlexNet 1, DGDNet 2 and ResNet50 3, on recent four widely used Re-Id benchmark datasets, and it shows that the proposed structure graph Laplacian embedding is very effective.
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•SOM can interpret nonlinear and complex hydrogeochemical data at site scale.•The migration of contaminants is investigated by SOM and numerical simulation.•Hydrogeochemical datasets ...at a contaminated site are downscaled and clustered.•The pollution sources are identified using SOM and k-means clustering.
Groundwater contamination at the site has become a very serious problem. A clear understanding of the hydrogeochemical characteristics of groundwater is indispensable for pollution remediation. It requires taking a number of samples and continuous monitoring. However, it is challenging to interpret hydrogeochemical datasets with diverse compositions and wide range of concentration by linear method. In this work, combination of self-organizing map (SOM) and K-means clustering was applied to investigate the hydrogeochemical characteristics at a contaminated site. The results showed that shallow groundwater hydrogeochemical characteristics were performed by 42 neurons and were classified into 5 clusters. The NO3– in cluster 1 widely distributed in the site. The application of fertilizers led to high NO3– concentration in groundwater. Cluster 2 was dominated by Ca2+, Mg2+, Cr(Ⅵ) and NO2– and cluster 3 was characterized by TDS, Na+, Cl−, HCO3– and SO42−. Pollutants were mainly from the migration of components at the chromium slag heap under the effect of convection and dispersion. Cluster 4 was dominated by pH, As and CO32–. Furthermore, the pH with the minimum of 8.3 and the presence of CO32– in groundwater provided a favorable opportunity for arsenic enrichment. Pollutants in cluster 4 originated from rainfall leaching on the chromium slag. Moreover, the migration of components from cluster 4 to cluster 2 was also observed by SOM and numerical simulation. Cluster 5 was mainly dominated by Mn and Fe. Reduced environment and anthropogenic activities caused Fe and Mn to exceed standards. The deep groundwater characteristics were performed using 20 neurons and were identified into 4 clusters. Its contamination was due to the leakage of shallow groundwater. Finally, the Gibbs diagram and the saturation index method performed the chemistry control mechanisms of different clusters. This study demonstrated that SOM could be used to interpret nonlinear and complex contamination datasets.
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