Search

Wetland is one of the three ecosystems in the world, and collaborative monitoring using hyperspectral images (HSIs) and light detection and ranging (LiDAR) has been important for wetland ecological protection. However, because of the domain shift of different images, cross-scene wetland classification of HSIs and LiDAR is a practical challenge, necessitating the development of models trained solely on the source domain (SD) and directly transferred to the target domain (TD) without retraining. To address this issue, an instance-wise domain generalization network (IDGnet) is proposed for HSI and LiDAR cross-scene wetland classification. An instance-wise random domain expansion module (IWR-DEM) is developed to simulate the domain shift, establishing the extended domain (ED). Specifically, the original HSI and LiDAR data are separated as semantic and background information in the frequency domain, a random background shift is applied to the HSI, and a semantic random shift is deployed to LiDAR. The HSI and LiDAR fusion features are extracted from the SD and ED by a weight-shared network. Multiple condition constraints are proposed for domain and class alignment, learning the domain-invariant and class-specific information and improving model generalization. Experiments conducted on two wetland datasets demonstrate the superiority of the proposed IDGnet for cross-scene wetland classification with HSI and LiDAR data. The codes will be available from the website: https://github.com/bigshot-g/IEEE_TGRS_IDGnet.

Activated carbon can be used for NOxremoval from low to medium temperature flue gas, but SO2inhibits NO adsorption. Isothermal adsorption experiments and temperature-programmed desorption experiments were conducted to investigate the effect of SO2on NO adsorption at low temperatures (−20 to 30 °C). Quantum chemical calculations were performed to reveal the active sites and specific reaction pathways involved in the effect of SO2. As the temperature dropped from 30 to −20 °C, NO adsorption enhanced 4.18-fold. However, the presence of SO2reduced the NOxadsorption amount by 96.5 to 99.1% and inhibited NO2formation in the product gas. SO2significantly suppressed both physical and chemical adsorption of NOxand altered the chemical adsorption forms of NOxon activated carbon. In the presence of SO2, the desorption peaks corresponding to adsorbed NO3–completely disappeared, and the peaks corresponding to adsorbed C-ONO and C-NO2decreased dramatically. The adsorption and desorption experiments demonstrated that SO2inhibits the oxidation of NO during the adsorption process and affects the subsequent conversion of NO2. Quantum chemical calculations indicated that gaseous SO2and NO competitively oxidize at the same active sites near hydroxyl functional groups, with comparable reaction barriers. However, the oxidation products of SO2are difficult to desorb, leading to site deactivation and hindering the NO oxidation reaction. Gaseous SO2also reacts with adsorbed C-ONO on the activated carbon surface, further reducing NOxadsorption. Enhancing activated carbon’s catalytic oxidation activity for NO via surface modification could mitigate SO2’s inhibitory effect on NO adsorption.

In recent years, deep learning technology has emerged as the primary research focus in the field of hyperspectral anomaly detection (HAD) and has demonstrated satisfactory detection performance. Existing deep learning-based methods mainly utilize reconstruction errors as criteria for anomaly detection. However, they lack effective suppression of anomaly information in the background reconstruction process, and encounter challenges in addressing scenarios involving the coexistence of multiscale anomalies, which limits the performance of HAD. In order to reconstruct clean and reliable background images, this article proposes a Triple-UNet with dual-window convolution called TDWCNet for HAD. Specifically, we introduce a dual-window convolution that shields pixels within the inner window and only utilizes pixels between the inner and outer windows to reconstruct the central pixel. Based on the dual-window convolution, we construct the DWCBlock module, which serves as the core component for background reconstruction. To address the coexistence of multiscale anomalies, the Triple-UNet structure is designed, which organically combines three DWCBlock modules to gradually eliminate abnormal pixels that are inadvertently reconstructed due to inappropriate convolution kernels. Furthermore, adaptive mean-squared error (mse) and structural similarity index (SSIM) losses are employed to suppress anomaly reconstruction. Extensive experiments conducted on four publicly available datasets demonstrate that TDWCNet achieves satisfactory detection performance. The codes of this work will be available for the sake of reproducibility at: https://github.com/szc2277/TDWCNet-HAD.

The field of remote sensing (RS) image change detection (CD) has made significant progress, largely due to the powerful feature representation abilities of deep learning. However, traditional methods have not fully exploited the valuable information in differences. These methods often treat deep models as tools to extract features from individual images, which limits their ability to effectively describe differences. Additionally, many approaches tend to focus on spatial differences, while neglecting variations in the channel dimension. In this study, we introduce a novel Summator–Subtractor network for CD ( ${S}^{2}$ CD), which adeptly captures subtle differences within both the spatial and channel aspects of bi-temporal images. The initial spatial and channel differences are derived through summation and subtraction operations on the bi-temporal images. The summator computes initial channel variations, while the subtractor captures initial spatial disparities. Transformers are then used to pull out meaningful differences in both spatial and channel patterns, allowing for a more nuanced understanding than methods relying solely on features from individual images. Finally, a heterogeneous modulation block integrates channel and spatial difference features, thus amplifying overall differences. Through extensive experimentation on four widely acknowledged CD benchmark datasets, our proposed ${S}^{2}$ CD method outperforms existing techniques, showcasing its superior performance and promising potential. The codes of this work will be available for the sake of reproducibility at: https://github.com/qianday/SSCD-CD.

With the development of Earth observation technology, hyperspectral image (HSI) and light detection and ranging (LiDAR) data collaborative monitoring has shown great potential in the ecological protection and restoration of coastal wetlands. However, due to the different working principles adopted by the HSI sensor and the LiDAR sensor, the data obtained by them have different distribution characteristics. The distribution difference limits the fusion of HSI and LiDAR data, bringing a great challenge for coastal wetland classification. To tackle this problem, a multisource feature embedding and interaction fusion network (MsFE-IFN) is proposed for coastal wetland classification. First, the HSI and LiDAR data are embedded in the same feature space, where the feature distribution of multisource remote sensing is aligned to alleviate data distribution differences. Second, the aligned HSI and LiDAR features interact information in channels and pixels, which is able to establish the relationship of spectral, elevation, and geospatial. Third, the HSI and LiDAR features are sent into the feature fusion network, in which the low-frequency residual is retained to enrich intraclass features. Finally, the fused feature is applied for final class prediction. Experiments conducted on three coastal wetland HSI-LiDAR datasets created by ourselves demonstrate the superiority of the proposed MsFE-IFN for coastal wetland classification. The codes will be available from the website: https://github.com/bigshot-g/IEEE_TGRS_MsFE-IFN.

This article presents the preparation of Kevlar aramid fiber (AF) braided fabric/epoxy resin (EP) composite laminates through plasma treatment and a hand-folding process. The electrical and mechanical properties of these composite laminates are compared and analyzed. The modified AF braided cloth is examined using scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and Fourier transform infrared spectroscopy (FT-IR). The results confirm an increase in the number of C=O bonds in the surface layer of the AF braided cloth, along with the generation of −COOH groups. SEM characterization reveals that plasma-modified aramid braided fabric leads to the “fibrinization” of AF, where the cortex falls off. This phenomenon promotes the physical linking between AF and EP matrix materials, thereby improving the mechanical properties of the composite material. isothermal surface potential decay (ISPD) measurements demonstrate that the modified Kevlar fiber braided cloth/EP composite exhibits higher deep trap density and improved insulation strength. Furthermore, molecular dynamics (MD) simulation is employed to analyze the impact of plasma modification on the interface characteristics of the AF/EP composites. The findings indicate that plasma treatment enhances the interaction energy density between the AF and EP, primarily on account of the formation of numerous hydrogen bonds between the surface of the modified AF and EP.

We mainly study the influence of surface charge on the movement and distribution characteristics of metal particle dust (MPD) in dc gas insulated switchgears/lines (GIS/GIL). Based on the improved boundary element algorithm (BEM), we establish the movement model of MPD. Combined with experiments, we obtained the distribution characteristics of MPD. The research results show that under negative voltage, the metal particle contamination is more serious at the convex side of the spacer. Homopolar surface charge enhances the adhesion of MPD near the spacer, but it also enhances the repulsion between the spacer and MPD in the chamber. Heteropolar surface charge weakens the adhesion of MPD near the spacer, but it also weakens the repulsion between the spacer and MPD in the chamber. Therefore, we suggest that surface regulation should be adopted to make the spacer surface accumulate homopolar surface charge. Meanwhile, particle traps should be installed near the spacer and in the middle of the chamber to achieve more comprehensive metal particle suppression.

A simulation model of needle-plate electrode discharge in mineral transformer oil is established on COMSOL finite element simulation software. In order to investigate the influence of multi/single ionized molecular clusters on streamer discharge in oil, numerical calculations were conducted for the charged particles in the discharge model, considering different ionized molecular clusters, voltage peaks, and voltage rise times. The electric field distribution along the $z$ -axis and branching direction, as well as the distribution characteristics of the charged particles, were analyzed. The simulation results indicate that under different ionized molecular cluster conditions, streamer branching forms near the needle electrode. When considering multiple ionized molecular clusters, the morphology of the streamer is determined by the low ionization potential molecular clusters, while high ionization potential molecular clusters affect the electric field strength inside the streamer channel. The development of the streamer exhibits a staged propagation, which is more significant when considering only high ionization potential molecular clusters. Higher voltage peak will cause the streamer to propagate farther and form secondary streamer more easily. Faster voltage rise times facilitate the formation and propagation of the streamer, and the thickness of the streamer head is positively correlated with the voltage rise time. This study contributes further understanding of the mechanisms behind streamer discharge in liquid dielectrics, with mineral transformer oil serving as a representative medium.

Two-dimensional (2D) nanosheet arrays with unidirectional orientations are of great significance for synthesizing wafer-scale single crystals. Although great efforts have been devoted, the growth of atomically thin magnetic nanosheet arrays and single crystals is still unaddressed. Here we design an interisland-distance-mediated chemical vapor deposition strategy to synthesize centimeter-scale atomically thin Fe3O4arrays with unidirectional orientations on mica. The unidirectional alignment of nearly all the Fe3O4nanosheets is driven by a dual-coupling-guided growth mechanism. The Fe3O4/mica interlayer interaction induces two preferred antiparallel orientations, whereas the interisland interaction of Fe3O4breaks the energy degeneracy of antiparallel orientations. The room-temperature long-range ferrimagnetic order and thickness-tunable magnetic domain evolution are uncovered in atomically thin Fe3O4. This strategy to tune the orientations of nanosheets through the an interisland interaction can guide the synthesis of other 2D transition-metal oxides, thereby laying a solid foundation for future spintronic device applications at the integration level.

Hyperspectral anomaly detection (HAD) is crucial for identifying and analyzing abnormal objects in various domains. While existing methods have shown promising results by designing detection methods tailored to specific anomaly characteristics, there is a need for a highly versatile approach that can effectively handle anomalies, particularly those with large spatial sizes. In this article, we propose an end-to-end corner-visible network (CVNet) for unsupervised HAD. Specifically, we introduce a corner-visible convolution that leverages the statistical dependencies of the background within the receptive field while eliminating spatial correlations with potential anomalies for background generation. To address the grid effect caused by the corner-visible convolution, a background smoothing module is employed by using the conventional convolution. Furthermore, adaptive mean-squared error (MSE) and structural similarity index (SSIM) losses are employed to suppress anomaly reconstruction, resulting in a reliable reconstructed background map. Anomalies are identified through the residual of the original hyperspectral image (HSI) and the reconstructed background. Extensive experiments conducted on three publicly datasets demonstrate the effectiveness of our proposed method in handling different types of anomalies. The state-of-the-art performance showcases the versatility and applicability of CVNet in HAD. The codes of this work will be available for the sake of reproducibility at https://github.com/Cloudynewbee/CVNet-HAD.

With the development of deep learning (DL), research on ship classification in synthetic aperture radar (SAR) images has made remarkable progress. However, such research has primarily focused on classifying large ships with distinct features, such as cargo ships, containers, and tankers. The classification of SAR fishing vessels is extremely challenging because of two main reasons: 1) the small size and minor interclass differences of fishing vessels make learning fine-grained features difficult and 2) determining fishing vessel types is difficult, resulting in a lack of labeled data. Hence, after designing a process framework for vessel tagging, we construct a high-resolution fine-grained fishing vessel classification dataset (FishingVesselSAR), which contains 116 gillnetters, 72 seiners, and 181 trawlers. We then propose a novel DL model (FishNet) that aims to strengthen feature extraction and utilization. In FishNet, we introduce four innovative modules to ensure superior performance in SAR fishing vessel classification: a multipath feature extraction (MUL) module, a feature fusion (FF) module, a multilevel feature aggregation (MFA) module, and a parallel channel and spatial attention (PCSA) module. Furthermore, we design an adaptive loss function to achieve better classification performance by mitigating the effects of class imbalance. In this article, we report extensive ablation studies conducted to confirm the efficacy of the five improvements listed above. Sufficient comparisons with 33 advanced methods from the DL and SAR target classification communities demonstrate that FishNet achieves an SAR fishing vessel classification accuracy of 89.79%, which is 6.77% higher than that of the second-best method.

With the development of deep neural networks, hyperspectral image (HSI) classification systems have achieved a significant improvement. These systems require numerous and accurately labeled hyperspectral data to be adequately trained. However, noisy labels are inherent in real-world hyperspectral systems, resulting in unreliable decisions. To handle noisy labels in hyperspectral classification, an end-to-end attentive-adaptive network (AAN) is proposed for robust HSI classification training. The goal is to build a classifier with strong generalization capabilities that can be applied to both clean and noisy training sets without explicit noise label pretreatment. Specifically, a spectral stem network with a nonadjacent shortcut is exploited initially to redistribute the sensitive layers for noisy labels to achieve robust spectral representation. Then, a group-shuffle attention module is proposed to capture the discriminative and robust spatial–spectral features in the presence of noisy labels. Finally, an adaptive noise-robust loss (ANRL) function is developed to fight against noisy labels by learning a parameter to balance the normalized cross entropy (NCE) and reverse cross entropy (RCE). Experimental results on three HSI benchmark datasets with simulated noisy labels demonstrate the effectiveness of AAN on HSI classification.

A plethora of studies have shown that both DNMT1 and EZH2 have great effects on the progression of a variety of cancers. However, it remains unclear whether the expression profiles of these two epigenetic enzymes are molecularly intertwined in prostate cancer (PC), especially in castration-resistant prostate cancer (CRPC). Here, we found that DNMT1 is highly expressed and facilitates PC cell proliferation and migration. Importantly, we demonstrate that the abrogation of DNMT1 expression can induce the decreased expression of EZH2, resulting in the less aggressive capacity of PC cells. Mechanistically, we discovered that DNMT1 promotes PC tumorigenesis and metastasis by inhibiting TRAF6 transcriptional expression and subsequent TRAF6-mediated EZH2 ubiquitination. Finally, we confirmed that there is a negative correlation between DNMT1 and TRAF6 expression and a positive correlation between DNMT1 and EZH2 expression in PC patients. In this study, we first disclose that there is a direct crosstalk between DNA methyltransferase DNMT1 expression and histone methyltransferase EZH2 expression in tumorigenesis and cancer metastasis in vitro and in vivo. Our results also show that targeting DNMT1 with its inhibitor decitabine (an FDA-approved drug) is an appealing treatment strategy for CRPC patients through epigenetic suppression of both DNMT1-mediated DNA methylation and EZH2-modulated histone methylation.

We report for the first time the use of experimental electron density (ED) in the Protein Data Bank for modeling of noncovalent interactions (NCIs) for protein–ligand complexes. Our methodology is based on reduced electron density gradient (RDG) theory describing intermolecular NCIs by ED and its first derivative. We established a database named Experimental NCI Database (ExptNCI; http://ncidatabase.stonewise.cn/#/nci) containing ED saddle points, indicating ∼200,000 NCIs from over 12,000 protein–ligand complexes. We also demonstrated the usage of the database in the case of depicting amide−π interactions in protein–ligand binding systems. In summary, the database provides details on experimentally observed NCIs for protein–ligand complexes and can support future studies including studies on rarely documented NCIs and the development of artificial intelligence models for protein–ligand binding prediction.

ABSTRACTTripartite motif-containing 21 (TRIM21) has been reported to have a cancer-promoting or anticancer effect in various tumors; however, its role in ovarian cancer (OC) remains to be elucidated. In this study, we explored the biological function of TRIM21 in OC progression and investigated the potential mechanisms. We found that TRIM21 was remarkably decreased in OC tissues and cell lines compared with adjacent-cancerous tissues and normal ovarian epithelium cell. Decreased expression of TRIM21 in OC patients was significantly correlated with shorter overall and disease-specific survival by The Cancer Genome Atlas database (TCGA) analysis. Functional assays revealed that TRIM21 inhibited the migration and invasion of OC cells; and that TRIM21 also obviously impaired cell proliferation by inhibiting cell cycle progression in vitroand in vivo. Taken together, our results suggest that TRIM21 may be a promising biomarker and target for OC diagnosis and treatment.

Nephron progenitor cells (NPCs) self-renew and differentiate into nephrons, the functional units of the kidney. Here, manipulation of p38 and YAP activity allowed for long-term clonal expansion of primary mouse and human NPCs and induced NPCs (iNPCs) from human pluripotent stem cells (hPSCs). Molecular analyses demonstrated that cultured iNPCs closely resemble primary human NPCs. iNPCs generated nephron organoids with minimal off-target cell types and enhanced maturation of podocytes relative to published human kidney organoid protocols. Surprisingly, the NPC culture medium uncovered plasticity in human podocyte programs, enabling podocyte reprogramming to an NPC-like state. Scalability and ease of genome editing facilitated genome-wide CRISPR screening in NPC culture, uncovering genes associated with kidney development and disease. Further, NPC-directed modeling of autosomal-dominant polycystic kidney disease (ADPKD) identified a small-molecule inhibitor of cystogenesis. These findings highlight a broad application for the reported iNPC platform in the study of kidney development, disease, plasticity, and regeneration.

Enhancer of zeste homolog 2 (EZH2), a key histone methyltransferase and EMT inducer, is overexpressed in diverse carcinomas, including breast cancer. However, the molecular mechanisms of EZH2 dysregulation in cancers are still largely unknown. Here, we discover that EZH2 is asymmetrically dimethylated at R342 (meR342-EZH2) by PRMT1. meR342-EZH2 was found to inhibit the CDK1-mediated phosphorylation of EZH2 at T345 and T487, thereby attenuating EZH2 ubiquitylation mediated by the E3 ligase TRAF6. We also demonstrate that meR342-EZH2 resulted in a decrease in EZH2 target gene expression, but an increase in breast cancer cell EMT, invasion and metastasis. Moreover, we confirm the positive correlations among PRMT1, meR342-EZH2 and EZH2 expression in the breast cancer tissues. Finally, we report that high expression levels of meR342-EZH2 predict a poor clinical outcome in breast cancer patients. Our findings may provide a novel diagnostic target and promising therapeutic target for breast cancer metastasis.

The reciprocal interactions among the different embryonic kidney progenitor populations lay the basis for proper kidney organogenesis. During kidney development, three types of progenitor cells, including nephron progenitor cells, ureteric bud progenitor cells, and interstitial progenitor cells, generate the three major kidney structures—the nephrons, the collecting duct network, and the stroma, respectively. Epigenetic mechanisms are well recognized for playing important roles in organism development, in fine‐tuned control of physiological activities, and in responses to environment stimuli. Recently, evidence supporting the importance of epigenetic mechanisms underlying kidney organogenesis has emerged. In this perspective, we summarize the research progress and discuss the potential contribution of novel stem cell, organoid, and next‐generation sequencing tools in advancing this field in the future. Representative scheme of epigenetic regulation of kidney progenitors within a nephrogenic niche during kidney development. Epigenetic mechanisms involved in kidney organogenesis include DNA methylation, histone acetylation, chromatin remodeling complexes, and versatile noncoding RNAs. These mechanisms are mediated by special epigenetic modifiers and play important roles in the regulation of self‐renewal maintenance and differentiation of three types of kidney progenitors during kidney development

To separate the aging oil emulsions on offshore platforms, a series of polyether demulsifiers based on proanthocyanidins were synthesized, and a dehydration ratio of 94.4% can be obtained at a dosage of 100 mg/L and 70 °C within 120 min. The exceptional performance is due to the multibranched structure and multiaromatic core of the demulsifier, as revealed in our previous work. Meanwhile, the moderate molecular weight, high hydrophilicity, and high interfacial activity of the demulsifier also improve its performance. To understand the distribution of the demulsifier molecules after demulsification and elucidate the demulsification mechanism, partition coefficient (Kpc) measurements were performed using the fluorescence labeling method. The results demonstrate that the demulsifier molecules are mainly present in the oil phase and the high Kpcvalues favor demulsification. Furthermore, a high dehydration ratio can be achieved when the demulsifier has a strong ability to reduce the elastic modulus of aging oil emulsions, as illustrated by the rheological results. To confirm the practical applicability of the demulsifiers on offshore platforms, pilot production on a ton scale was carried out and the pilot product was found to exhibit excellent performance that was essentially the same as that of the laboratory sample.

As cloud storage systems increase in scale, hard drive failures are becoming more frequent, which raises reliability issues. In addition to traditional reactive fault tolerance, proactive fault tolerance is used to improve a system's reliability. However, there are few studies which analyze the reliability of proactive cloud storage systems, and they typically assume an exponential distribution for drive failures. This paper presents closed-form equations for estimating the number of data-loss events in proactive cloud storage systems using RAID-5, RAID-6, 2-way replication, and 3-way replication mechanisms, within a given time period. The equations model the impact of proactive fault tolerance, operational failures, failure restorations, latent block defects, and drive scrubbing on the systems reliability, and use time-based Weibull distributions to represent processes (instead of homogeneous Poisson processes). We also design a Monte-Carlo simulation method to simulate the running of proactive cloud storage systems. The proposed equations closely match time-consuming Monte-Carlo simulations, using parameters obtained from the analysis of field data. These equations allow designers to efficiently estimate system reliability under varying parameters, facilitating cloud storage system design.

Background: Sphingomonas is a kind of microbial resources used for biodegradation of aromatic compounds. In computational biology, identifying protein coding domains in Sphingomonas genome is known as a challenging problem. Objective: In this work, to address the challenge, we propose a novel method to predict protein coding regions from Sphingomonas genome by 3-base periodicity. Method: In our method, DNA sequences are firstly transformed into wavelet by a so-called 3-base characteristics strategy. After that, sliding windows with certain fixed lengths are developed to identify protein coding regions, in which the initial size of sliding windows and values of thresholds are set by experimentally verified protein data in NCBI library. Results: As results, an experimental verified protein coding domain in congeneric families of Sphingomonas is identified from Sphingomonas genome. Conclusion: This would be with high possibility to encode the similar functioning proteins. As well, some potential protein coding regions are marked by narrowing the forecast areas, and then an extensible sliding window strategy is used to improve predictive accuracy.

Mounting evidence shows that histone methylation, a typical epigenetic mark, is crucial for gene expression regulation during aging. Decreased trimethylation of Lys 36 on histone H3 (H3K36me3) in worms and yeast is reported to shorten lifespan. The function of H3K36me2 in aging remains unclear. In this study, we identified Caenorhabditis elegansSET-18 as a histone H3K36 dimethyltransferase. SET-18 deletion extended lifespan and increased oxidative stress resistance, dependent on daf-16activity in the insulin/IGF pathway. In set-18mutants, transcription of daf-16isoform a(daf-16a) was specifically upregulated. Accordingly, a decrease in H3K36me2 on daf-16apromoter was observed. Muscle-specific expression of SET-18 increased in aged worms (day 7 and day 11), attributable to elevation of global H3K36me2 and inhibition of daf-16aexpression. Consequently, longevity was shortened. These findings suggested that chromatic repression mediated by tissue-specific H3K36 dimethyltransferase might be detrimental to lifespan and may have implications in human age-related diseases.

ABSTRACTThis paper reconstructs and defends a Husserlian transcendental conception of intentionality. Initially, naturalistic conception of intentionality seems attractive, however, a naturalistic understanding conceals the true meaning of the puzzle and the nature of intentionality. Following Kant and primarily Husserl, this paper tries to determine the conditions a transcendental conception of intentionality must satisfy in order to be qualified as “transcendental”. Additionally, following Husserl, this paper argues that the alternative transcendental conception is not only (1) possible, hence plausible; but also (2) necessary, in the sense that a thorough understanding of intentionality can only be achieved if a transcendental conception is adopted.

This article presents a semi-analytical method to calculate the ultimate strength of inelastic beam-columns with I-shaped cross section using geometrically exact beam theory. A computer code based on this method has been applied to beam-columns under axial compression. The results agree with nonlinear finite element analysis. Compared with previous step-by-step integration approach, this new method is more efficient and can be extended to multi-span beam-columns and other load combinations including lateral pressure. The presented beam-column model is ideally suited for ultimate strength prediction of stiffened steel panels of ships and offshore structures.

•Propose a 3D contours measurement method based on gray intensity and phase difference.•In-situ measurement of 3D contours of the fusion area and identify the warping deformation.•Accurate circle fitting and center distance measurement.

EZH2 (the Enhancer of Zeste Homolog 2), as a key epigenetic regulator and EMT inducer, participates in a variety of cancer metastasis. EZH2 stability is regulated by several types of post-translational modifications (PTMs).The long non-coding RNAs (lncRNA) have been implicated to have critical roles in multiple carcinogenesis through a wide range of mechanisms, including modulating the stability of proteins. To date, whether the stability of EZH2 protein is regulated by lncRNAs remains unexplored. Here we report the discovery of ANCR modulating the stability of EZH2, and hence in the invasion and metastasis of breast cancer cells. We determined that ANCR potentiated the CDK1-EZH2 interaction, which then increased the intensity of phosphorylation at Thr-345 and Thr-487 sites of EZH2, facilitating EZH2 ubiquitination and hence its degradation. Moreover, we also uncover ANCR is an important player in breast cancer progression and metastasis mainly through decreasing EZH2 stability. More specifically, we initially found that ANCR level was lower in breast cancer tissues and breast cancer cell lines, in contrast to their normal counterparts. We then demonstrated that knockdown of ANCR induced an EMT program and promoted cell migration and invasion in MCF10A (epithelial cells), whereas ectopic expression of ANCR repressed breast cancer cells migration and invasion. Furthermore, we validated in a nude mouse model that overexpression of ANCR in highly malignant and invasive MDA-MB-231 breast cancer cells significantly reduced the ability of the cells to form tumors and prevented the lung metastasis in vivo. Based on these data, our findings define a new mechanism underlying modulation of EZH2 stability by linking ANCR interaction with EZH2 to promote its phosphorylation that facilitates EZH2 degradation and suppresses breast cancer progression.

Transit-amplifying nephron progenitor cells (NPCs) generate all of the nephrons of the mammalian kidney during development. Their limited numbers, poor in vitro expansion, and difficult accessibility in humans have slowed basic and translational research into renal development and diseases. Here, we show that with appropriate 3D culture conditions, it is possible to support long-term expansion of primary mouse and human fetal NPCs as well as NPCs derived from human induced pluripotent stem cells (iPSCs). Expanded NPCs maintain genomic stability, molecular homogeneity, and nephrogenic potential in vitro, ex vivo, and in vivo. Cultured NPCs are amenable to gene targeting and can form nephron organoids that engraft in vivo, functionally couple to the host’s circulatory system, and produce urine-like metabolites via filtration. Together, these findings provide a technological platform for studying human nephrogenesis, modeling and diagnosing renal diseases, and drug discovery.

This paper proposes a rapid and automatic measurement system to acquire a 3D shape of a human body. A flexible calibration method was developed to decrease the complexity in system calibration. To reduce the computation cost, a GPU–Steger line detector was proposed to more rapidly detect the center of the laser pattern and at subpixel level. The processing time of line detection is significantly shortened by the GPU–Steger line detector, which can be over 110 times faster than that by CPU. The key technologies are introduced, and the experimental results are presented in this paper to illustrate the performance of the proposed system. The system can be used to measure human body surfaces with nonuniform reflectance such as hair, skin, and clothes with rich texture.

Video data has become the largest source of big data. Owing to video data's complexities, velocity, and volume, public security and other surveillance applications require efficient, intelligent runtime video processing. To address these challenges, a proposed framework combines two cloud-computing technologies: Storm stream processing and Hadoop batch processing. It uses deep learning to realize deep intelligence that can help reveal knowledge hidden in video data. An implementation of this framework combines five architecture styles: service-oriented architecture, publish-subscribe, the Shared Data pattern, MapReduce, and a layered architecture. Evaluations of performance, scalability, and fault tolerance showed the framework's effectiveness. This article is part of a special issue on Software Engineering for Big Data Systems.

Due to the process route and the design of the backplane compensation circuit, the Mura of LTPS AMOLED production badly restricts the yield rate climbing. This article has made some progress in experimental research on the timely and effective Mura detection and classification improvement method for the array segment, it means a lot for mass production yield.LTPS AMOLED.