Kelch-like proteins in the gastrointestinal tumors.

Gastrointestinal tumors have become a worldwide health problem with high morbidity and poor clinical outcomes. Chemotherapy and surgery, the main treatment methods, are still far from meeting the treatment needs of patients, and targeted therapy is in urgent need of development. Recently, emerging evidence suggests that kelch-like (KLHL) proteins play essential roles in maintaining proteostasis and are involved in the progression of various cancers, functioning as adaptors in the E3 ligase complex and promoting the specific degradation of substrates. Therefore, KLHL proteins should be taken into consideration for targeted therapy strategy discovery. This review summarizes the current knowledge of KLHL proteins in gastrointestinal tumors and discusses the potential of KLHL proteins as potential drug targets and prognostic biomarkers.

Arctigenin impairs UBC12 enzyme activity and cullin neddylation to attenuate cancer cells.

Neddylation is a type of posttranslational protein modification that has been observed to be overactivated in various cancers. UBC12 is one of two key E2 enzymes in the neddylation pathway. Reports indicate that UBC12 deficiency may suppress lung cancer cells, such that UBC12 could play an important role in tumor progression. However, systematic studies regarding the expression profile of UBC12 in cancers and its relationship to cancer prognosis are lacking. In this study, we comprehensively analyzed UBC12 expression in diverse cancer types and found that UBC12 is markedly overexpressed in most cancers (17/21), a symptom that negatively correlates with the survival rates of cancer patients, including gastric cancer. These results demonstrate the suitability of UBC12 as a potential target for cancer treatment. Currently, no effective inhibitor targeting UBC12 has been discovered. We screened a natural product library and found, for the first time, that arctigenin has been shown to significantly inhibit UBC12 enzyme activity and cullin neddylation. The inhibition of UBC12 enzyme activity was newly found to contribute to the effects of arctigenin on suppressing the malignant phenotypes of cancer cells. Furthermore, we performed proteomics analysis and found that arctigenin intervened with cullin downstream signaling pathways and substrates, such as the tumor suppressor PDCD4. In summary, these results demonstrate the importance of UBC12 as a potential therapeutic target for cancer treatment, and, for the first time, the suitability of arctigenin as a potential compound targeting UBC12 enzyme activity. Thus, these findings provide a new strategy for inhibiting neddylation-overactivated cancers.

CLIP-Based Adaptive Graph Attention Network for Large-Scale Unsupervised Multi-Modal Hashing Retrieval.

With the proliferation of multi-modal data generated by various sensors, unsupervised multi-modal hashing retrieval has been extensively studied due to its advantages in storage, retrieval efficiency, and label independence. However, there are still two obstacles to existing unsupervised methods: (1) As existing methods cannot fully capture the complementary and co-occurrence information of multi-modal data, existing methods suffer from inaccurate similarity measures. (2) Existing methods suffer from unbalanced multi-modal learning and data semantic structure being corrupted in the process of hash codes binarization. To address these obstacles, we devise an effective CLIP-based Adaptive Graph Attention Network (CAGAN) for large-scale unsupervised multi-modal hashing retrieval. Firstly, we use the multi-modal model CLIP to extract fine-grained semantic features, mine similar information from different perspectives of multi-modal data and perform similarity fusion and enhancement. In addition, this paper proposes an adaptive graph attention network to assist the learning of hash codes, which uses an attention mechanism to learn adaptive graph similarity across modalities. It further aggregates the intrinsic neighborhood information of neighboring data nodes through a graph convolutional network to generate more discriminative hash codes. Finally, this paper employs an iterative approximate optimization strategy to mitigate the information loss in the binarization process. Extensive experiments on three benchmark datasets demonstrate that the proposed method significantly outperforms several representative hashing methods in unsupervised multi-modal retrieval tasks.

Graph Sampling-Based Multi-Stream Enhancement Network for Visible-Infrared Person Re-Identification.

With the increasing demand for person re-identification (Re-ID) tasks, the need for all-day retrieval has become an inevitable trend. Nevertheless, single-modal Re-ID is no longer sufficient to meet this requirement, making Multi-Modal Data crucial in Re-ID. Consequently, a Visible-Infrared Person Re-Identification (VI Re-ID) task is proposed, which aims to match pairs of person images from the visible and infrared modalities. The significant modality discrepancy between the modalities poses a major challenge. Existing VI Re-ID methods focus on cross-modal feature learning and modal transformation to alleviate the discrepancy but overlook the impact of person contour information. Contours exhibit modality invariance, which is vital for learning effective identity representations and cross-modal matching. In addition, due to the low intra-modal diversity in the visible modality, it is difficult to distinguish the boundaries between some hard samples. To address these issues, we propose the Graph Sampling-based Multi-stream Enhancement Network (GSMEN). Firstly, the Contour Expansion Module (CEM) incorporates the contour information of a person into the original samples, further reducing the modality discrepancy and leading to improved matching stability between image pairs of different modalities. Additionally, to better distinguish cross-modal hard sample pairs during the training process, an innovative Cross-modality Graph Sampler (CGS) is designed for sample selection before training. The CGS calculates the feature distance between samples from different modalities and groups similar samples into the same batch during the training process, effectively exploring the boundary relationships between hard classes in the cross-modal setting. Some experiments conducted on the SYSU-MM01 and RegDB datasets demonstrate the superiority of our proposed method. Specifically, in the VIS→IR task, the experimental results on the RegDB dataset achieve 93.69% for Rank-1 and 92.56% for mAP.

Matrix-Directed Mineralization for Bulk Structural Materials.

Mineral-based bulk structural materials (MBSMs) are known for their long history and extensive range of usage. The inherent brittleness of minerals poses a major problem to the performance of MBSMs. To overcome this problem, design principles have been extracted from natural biominerals, in which the extraordinary mechanical performance is achieved via the hierarchical organization of minerals and organics. Nevertheless, precise and efficient fabrication of MBSMs with bioinspired hierarchical structures under mild conditions has long been a big challenge. This Perspective provides a panoramic view of an emerging fabrication strategy, matrix-directed mineralization, which imitates the in vivo growth of some biominerals. The advantages of the strategy are revealed by comparatively analyzing the conventional fabrication techniques of artificial hierarchically structured MBSMs and the biomineral growth processes. By introducing recent advances, we demonstrate that this strategy can be used to fabricate artificial MBSMs with hierarchical structures. Particular attention is paid to the mass transport and the precursors that are involved in the mineralization process. We hope this Perspective can provide some inspiring viewpoints on the importance of biomimetic mineralization in material fabrication and thereby spur the biomimetic fabrication of high-performance MBSMs.

EPI Light Field Depth Estimation Based on a Directional Relationship Model and Multiviewpoint Attention Mechanism.

Light field (LF) image depth estimation is a critical technique for LF-related applications such as 3D reconstruction, target detection, and tracking. The refocusing property of LF images provide rich information for depth estimations; however, it is still challenging in cases of occlusion regions, edge regions, noise interference, etc. The epipolar plane image (EPI) of LF can effectively deal with the depth estimation because of its characteristics of multidirectionality and pixel consistency-in which the LF depth estimations are converted to calculate the EPI slope. This paper proposed an EPI LF depth estimation algorithm based on a directional relationship model and attention mechanism. Unlike the subaperture LF depth estimation method, the proposed method takes EPIs as input images. Specifically, a directional relationship model was used to extract direction features of the horizontal and vertical EPIs, respectively. Then, a multiviewpoint attention mechanism combining channel attention and spatial attention is used to give more weight to the EPI slope information. Subsequently, multiple residual modules are used to eliminate the redundant features that interfere with the EPI slope information-in which a small stride convolution operation is used to avoid losing key EPI slope information. The experimental results revealed that the proposed algorithm outperformed the compared algorithms in terms of accuracy.

Transcriptome sequencing analysis of sorghum callus with various regeneration capacities.

MAIN CONCLUSION: The possible molecular mechanisms regulating sorghum callus regeneration were revealed by RNA-sequencing. Plant callus regeneration has been widely applied in agricultural improvement. Recently, callus regeneration has been successfully applied in the genetic transformation of sorghum by using immature sorghum embryos as explants. However, the mechanism underlying callus regeneration in sorghum is still largely unknown. Here, we describe three types of callus (Callus I-III) with different redifferentiation abilities undergoing distinct induction from immature embryos of the Hiro-1 variety. Compared with nonembryonic Callus III, Callus I produced only some identifiable roots, and embryonic Callus II was sufficient to regenerate whole plants. Genome-wide transcriptome profiles were generated to reveal the underlying mechanisms. The numbers of differentially expressed genes for the three types of callus varied from 5906 to 8029. In accordance with the diverse regeneration abilities observed for different types of callus and leaf tissues, the principal component analysis revealed that the gene expression patterns of Callus I and Callus II were different from those of Callus III and leaves regenerated from Callus II. Notably, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses, pharmacological treatment, and substance content determinations revealed that plant ribosomes, lignin metabolic processes, and metabolism of starch and sucrose were significantly enriched, suggesting that these factors are associated with callus regeneration. These results helped elucidate the molecular regulation of three types of callus with different regeneration abilities in sorghum.

Fast depth estimation with cost minimization for structured light field.

Depth estimation is a fundamental task in light field (LF) related applications. However, conventional light field suffers from the lack of features, which introduces depth ambiguity and heavy computation load to depth estimation. In this paper, we introduce phase light field (PLF), which uses sinusoidal fringes as patterns and the latent phases as the codes. With PLF and the re-formatted phase-epipolar-plane-images (phase EPIs), a global cost minimization framework is proposed to estimate the depth. In general, EPI-based depth estimation tests a set of candidate lines to find the optimal one with most similar intensities, and the slope of the optimal line is converted to disparity and depth. Based on this principle, for phase-EPI, we propose a cost with weighted phase variance in the candidate line, and we prove that the cost is a convex function. After that, the beetle antennae search (BAS) optimization algorithm is utilized to find the optimal line and thus depth can be obtained. Finally, a bilateral filter is incorporated to further improve the depth quality. Simulation and real experimental results demonstrate that, the proposed method can produce accurate depth maps, especially at boundary regions. Moreover, the proposed method achieves an acceleration of about 5.9 times over the state-of-the-art refocus method with comparable depth quality, and thus can facilitate practical applications.

Robust depth estimation for multi-occlusion in light-field images.

Occlusion is one of the most important issues in light-field depth estimation. In this paper, we propose a light-field multi-occlusion model with the analysis of light transmission. By the model, occlusions in different views are discussed separately. An adaptive algorithm of anti-occlusion in the central view is proposed to obtain more precise consistency regions (unoccluded views) in the angular domain and a subpatch approach of anti-occlusion in other views is presented to optimize the initial depth maps, where depth boundaries are better preserved. Then we propose a curvature confidence analysis approach to make depth evaluation more accurate and it is designed in an energy model to regularize the depth maps. Experimental results demonstrate that the proposed algorithm achieves better subjective and objective quality in depth maps compared with state-of-the-art algorithms.

Multi-anchor spatial phase unwrapping for fringe projection profilometry.

Phase unwrapping is a necessary step in fringe-projection profilometry that produces accurate depth maps. However, the original wrapped phase is often corrupted by errors, and thus conventional spatial unwrapping suffers from error propagation, such as scanline-based unwrapping, and high complexity, such as quality-guided methods. In this paper, we propose a fast and robust spatial unwrapping method called multi-anchor scanline unwrapping (MASU). Different from previous work, when unwrapping each pixel, MASU refers to multiple anchors in the scanline, where each anchor has a threshold adapting to its location. In such a manner, a set of fringe order candidates are predicted by the anchors according to phase smoothness assumption, and the one with the highest number of votes is chosen. After that, with the obtained fringe order, the absolute phase and depth are computed. Simulation and experiments have shown that even corrupted by severe phase errors, the proposed MASU can still produce robust unwrapped results. In addition, MASU is thousands of times faster than quality-guided unwrapping with comparative or even superior depth accuracy.

Inhibition of M2-like macrophages by all-trans retinoic acid prevents cancer initiation and stemness in osteosarcoma cells.

Emerging evidence indicates that M2-polarized tumor-associated macrophages (TAMs) directly participate in tumor initiation, progression and metastasis. However, to date, few studies have investigated novel strategies for inhibiting TAMs in order to overcome osteosarcoma. In this study, we reported that M2 macrophages were enriched in osteosarcoma tissues from patients, and M2-polarized TAMs enhanced cancer initiation and stemness of osteosarcoma cells, thereby establishing M2-polarized TAMs as a therapeutic target for blocking osteosarcoma formation. We also found that all-trans retinoic acid (ATRA) weakened TAM-induced osteosarcoma tumor formation by inhibiting M2 polarization of TAMs in vivo, and inhibited the colony formation, as well as sphere-formation capacity of osteosarcoma cells promoted by M2-type macrophages in vitro. Furthermore, M2-type macrophages enhanced cancer stem cells (CSCs) properties as assessed by increasing the numbers of CD117+Stro-1+ cells accompanied by the upregulation of CSC markers (CD133, CXCR4, Nanog, and Oct4), which could clearly be reduced by ATRA. Taken together, the results of this study demonstrated the role of M2-polarized TAMs in osteosarcoma initiation and stemness by activating CSCs, and indicated that ATRA treatment is a promising approach for treating osteosarcoma by preventing M2 polarization of TAMs.

Hybrid profilometry using a single monochromatic multi-frequency pattern.

In this paper, we propose a novel profilometry scheme to acquire high quality depth, where only a single shot of a monochromatic pattern is utilized. We design a band-wise pattern consisting of fringe bands spatially modulated with coprime periods. After that, with the designed pattern, depth is obtained in a hybrid manner, where both phase-based profilometry and active stereo are incorporated. To be specific, pixels in smooth regions obtain their depth values through phases analysis. Especially, based on depth smooth property, we propose a novel phase unwrapping algorithm, which avoids the problem of error propagation and yields accurate unwrapping phases. On the other hand, for boundary regions, spatial stereo, which is more robust to depth discontinuities, is utilized to modify incorrect depth values. Both theoretical verification and experimental results demonstrate that the proposed scheme can generate high quality depth maps, even for complex scenes and isolated objects.

Key Targets and Molecular Mechanisms of the Fat-soluble Components of Ginseng for Lung Cancer Treatment.

OBJECTIVE: To analyze the regulatory effects and key targets of the fat-soluble components of ginseng in lung cancer. METHODS: Gas chromatography-mass spectrometry and the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform were used to analyze and identify the fat-soluble components of ginseng. Network pharmacology was used to analyze the therapeutic targets of the fat-soluble components of ginseng in lung cancer and screen key proteins. In vitro assays were conducted to verify the effects of the active fat-soluble components of ginseng on proliferation and apoptosis in lung cancer cells and to verify the regulation of key proteins. RESULTS: Ten active fat-soluble components of ginseng were screened for follow-up. Network pharmacology showed 33 overlapping targets between the active fat-soluble components of ginseng and lung cancer, and functional enrichment of the targets showed involvement of response to nitrogen, hormone response, membrane raft, and positive regulation of external stimulus. Pathway enrichment analysis showed vascular endothelial growth factor (VEGF) signaling, adipocyte lipolysis regulation, chronic myelogenous leukemia, endocrine resistance, and NSCLC-related pathways. A protein-protein interaction network was constructed, and the top 10 targets were selected in accordance with their scores. Ultimately, five target genes (EGFR, KDR, MAPK3, PTPN11, and CTNNB1) were selected in combination with literature mining for subsequent experimental verification. Proliferation assays showed that the growth of lung cancer cells was significantly decreased in a concentration-dependent manner in the fat-soluble components of ginseng intervention group compared with controls. Flow cytometry showed that active fat-soluble components of ginseng promoted apoptosis in a concentration-dependent manner in lung cancer cells. Western blot and quantitative real-time PCR showed that levels of the five key proteins and mRNAs were significantly decreased in the intervention group; furthermore, histone protein and mRNA levels were significantly higher in the high-concentration intervention group compared with the low-concentration group. CONCLUSION: The active fat-soluble components of ginseng inhibited the growth of lung cancer cells and promoted apoptosis. The underlying regulatory mechanisms may be related to signaling pathways involving EGFR, KDR, MAPK3, PTPN11, and CTNNB1.

DeepMethylation: a deep learning based framework with GloVe and Transformer encoder for DNA methylation prediction.

DNA methylation is a crucial topic in bioinformatics research. Traditional wet experiments are usually time-consuming and expensive. In contrast, machine learning offers an efficient and novel approach. In this study, we propose DeepMethylation, a novel methylation predictor with deep learning. Specifically, the DNA sequence is encoded with word embedding and GloVe in the first step. After that, dilated convolution and Transformer encoder are utilized to extract the features. Finally, full connection and softmax operators are applied to predict the methylation sites. The proposed model achieves an accuracy of 97.8% on the 5mC dataset, which outperforms state-of-the-art methods. Furthermore, our predictor exhibits good generalization ability as it achieves an accuracy of 95.8% on the m1A dataset. To ease access for other researchers, our code is publicly available at https://github.com/sb111169/tf-5mc.

Thymidine kinase 1 as a target is regulated by the hsa-let-7b-5p/LINC00665 axis and affects NSCLC prognosis.

Background: In the past, multiple studies have offered incremental evidence that indicates that competitive endogenous RNA (ceRNA) regulatory networks are involved in tumor growth and present novel therapeutic targets. Herein, we investigated the impact of thymidine kinase 1 (TK1)-related ceRNA networks on the prognosis of non-small cell lung cancer (NSCLC). Methods: TK1 expression data in NSCLC and normal tissue samples were retrieved from the Cancer Genome Atlas (TCGA) database and were then compared. Thereafter, the findings of the immunohistochemical staining experiments and clinical follow-up data derived from patients with NSCLC were used for conducting prognostic analysis. The starBase database was searched to determine TK1-targeted microRNAs and long non-coding RNAs, and clinical data from TCGA were used for survival analysis to construct a ceRNA network associated with TK1 expression and prognosis. Finally, the roles played by methylation and immunity in the prognosis and treatment of NSCLC were analyzed. Results: Our findings revealed that the cancer tissues expressed significantly higher TK1 levels than normal tissues, and the follow-up clinical data revealed that the prognosis was generally worse in the high-expression patients than in the low-expression patients. In addition, clinical data collected from the starBase and TCGA databases showed that the LINC00665/has-let-7b-5p/TK1 network could influence the growth and prognosis of NSCLC. It was also noted that the TK1 methylation site was correlated with the prognosis of NSCLC, and immunoprognostic analysis further indicated that patients with higher TK1 expression levels displayed a worse prognosis. Conclusion: When the regulatory network of LINC00665/has-let-7b-5p/TK1 was assessed, it was observed that elevated TK1 levels may affect the prognosis of NSCLC. Therefore, it could be considered a prognostic biomarker and a probable therapeutic target for predicting NSCLC prognosis.

Deformable hard tissue with high fatigue resistance in the hinge of bivalve Cristaria plicata.

The hinge of bivalve shells can sustain hundreds of thousands of repeating opening-and-closing valve motions throughout their lifetime. We studied the hierarchical design of the mineralized tissue in the hinge of the bivalve Cristaria plicata, which endows the tissue with deformability and fatigue resistance and consequently underlies the repeating motion capability. This folding fan-shaped tissue consists of radially aligned, brittle aragonite nanowires embedded in a resilient matrix and can translate external radial loads to circumferential deformation. The hard-soft complex microstructure can suppress stress concentration within the tissue. Coherent nanotwin boundaries along the longitudinal direction of the nanowires increase their resistance to bending fracture. The unusual biomineral, which exploits the inherent properties of each component through multiscale structural design, provides insights into the evolution of antifatigue structural materials.

Nanograded artificial nacre with efficient energy dissipation.

The renowned mechanical performance of biological ceramics can be attributed to their hierarchical structures, wherein structural features at the nanoscale play a crucial role. However, nanoscale features, such as nanogradients, have rarely been incorporated in biomimetic ceramics because of the challenges in simultaneously controlling the material structure at multiple length scales. Here, we report the fabrication of artificial nacre with graphene oxide nanogradients in its aragonite platelets through a matrix-directed mineralization method. The gradients are formed via the spontaneous accumulation of graphene oxide nanosheets on the surface of the platelets during the mineralization process, which then induces a lateral residual stress field in the platelets. Nanoindentation tests and mercury intrusion porosimetry demonstrate that the material's energy dissipation is enhanced both intrinsically and extrinsically through the compressive stress near the platelet surface. The energy dissipation density reaches 0.159 ± 0.007 nJ/µm3, and the toughness amplification is superior to that of the most advanced ceramics. Numerical simulations also agree with the finding that the stress field notably contributes to the overall energy dissipation. This work demonstrates that the energy dissipation of biomimetic ceramics can be further increased by integrating design principles spanning multiple scales. This strategy can be readily extended to the combinations of other structural models for the design and fabrication of structural ceramics with customized and optimized performance.

Comparison of two different deposition methods of 3-aminopropyltriethoxysilane on glass slides and their application in the ThinPrep cytologic test.

In this work, two different deposition methods of 3-aminopropyltriethoxysilane (APTES) on glass slides were compared in order to study the adhesion effect of cervical exfoliated cells on smear slides. Glass slides were modified by vapor-phase deposition (V-D) and liquid-phase deposition (L-D), respectively. The topographic images and amine density of the modified slides were investigated by using atomic force microscopy, UV-vis spectroscopy and X-ray photoelectron spectroscopy. The numbers of cells adhered on the slides functionalized by V-D and L-D were counted and compared under the microscope. The data showed significant differences between the two methods (t-test: P < 0.05). The results presented here have made it theoretically possible to produce amine slides by V-D method for the ThinPrep cytologic test.

Artificial Nacre with High Toughness Amplification Factor: Residual Stress-Engineering Sparks Enhanced Extrinsic Toughening Mechanisms.

The high fracture toughness of mollusk nacre is predominantly attributed to the structure-associated extrinsic mechanisms such as platelet sliding and crack deflection. While the nacre-mimetic structures are widely adopted in artificial ceramics, the extrinsic mechanisms are often weakened by the relatively low tensile strength of the platelets with a large aspect ratio, which makes the fracture toughness of these materials much lower than expected. Here, it is demonstrated that the fracture toughness of artificial nacre materials with high inorganic contents can be improved by residual stress-induced platelet strengthening, which can catalyze more effective extrinsic toughening mechanisms that are specific to the nacre-mimetic structures. Thereby, while the absolute fracture toughness of the materials is not comparable with advanced ceramic-based composites, the toughness amplification factor of the material reaches 16.1 ± 1.1, outperforming the state-of-the-art biomimetic ceramics. The results reveal that, with the merit of nacre-mimetic structural designs, the overall fracture toughness of the artificial nacre can be improved by the platelet strengthening through extrinsic toughening mechanisms, although the intrinsic fracture toughness may decrease at platelet level due to the strengthening. It is anticipated that advanced structural ceramics with exceeding performance can be fabricated through these unconventional strategies.

3D MOF Nanoarchitecture Membrane via Ultrafast Laser Nanoforging.

Metal-organic framework (MOF) crystals are useful in a vast area of applications because of their unique chemical and physical properties. Manufacturing of an integrated MOF membrane with 3D nanoarchitectures on the surface is especially important for their applications. However, as MOF crystals usually exist as powdery crystals, fabrication of their large area, monolithic, and high-resolution patterns is challenging. Here, it is found that isolated MOF nanocrystals could be directly converted to a monolithic MOF film with designed 3D nanoarchitectures/patterns via an ultrafast laser induced nanoforging without binders. During the nanosecond laser shock, the voids among MOF nanocrystals are eliminated due to the surface amorphization effect, which allows the fusing of the MOF nanocrystals on the grain boundaries, leading to the formation of a dense film while preserving the nature of the pristine MOF. The high strain rate by laser enhances formability of MOFs and overcomes their brittleness to generate arbitrary 3D nanoarchitectures with feature sizes down to 100 nm and high productivity up to 80 cm2 min-1 . These 3D MOF nanoarchitectures also exhibit boosted mechanical strength up to 100% compared with their powdery particles. This method is facile and low-cost and could potentially be used in various fields, such as devices, separation, and biochemical applications.