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Electrochemical water splitting is one of the most economical and sustainable methods for large-scale hydrogen production. However, the development of low-cost and earth-abundant non-noble-metal catalysts for the hydrogen evolution reaction remains a challenge. Here we report a two-dimensional coupled hybrid of molybdenum carbide and reduced graphene oxide with a ternary polyoxometalate-polypyrrole/reduced graphene oxide nanocomposite as a precursor. The hybrid exhibits outstanding electrocatalytic activity for the hydrogen evolution reaction and excellent stability in acidic media, which is, to the best of our knowledge, the best among these reported non-noble-metal catalysts. Theoretical calculations on the basis of density functional theory reveal that the active sites for hydrogen evolution stem from the pyridinic nitrogens, as well as the carbon atoms, in the graphene. In a proof-of-concept trial, an electrocatalyst for hydrogen evolution is fabricated, which may open new avenues for the design of nanomaterials utilizing POMs/conducting polymer/reduced-graphene oxide nanocomposites. The development of low-cost and earth-abundant non-noble-metal catalysts for the hydrogen evolution reaction remains a challenge. Here, the authors report and evaluate a catalyst based on a two-dimensional coupled hybrid of molybdenum carbide and reduced graphene oxide.

Synthetic aperture radar (SAR) ship detection based on deep learning has the advantages of high accuracy and end-to-end processing, which has received more and more attention. However, SAR ship detection faces many problems, such as fuzzy ship contour, complex background, large scale difference and dense distribution of small targets. To solve these problems, this paper proposes a SAR ship detection method with ultra lightweight and high detection accuracy based on YOLOX. Aiming at the problem of speckle noise and blurred ship contour caused by the special imaging mechanism of SAR, a SAR ship feature enhancement method based on high frequency sub-band channel fusion which makes full use of contour information is proposed. Aiming at the requirement of light-weight detection algorithms for micro-SAR platforms such as small unmanned aerial vehicle and the defect of spatial pooling pyramid structure damaging ship contour features, an ultra-lightweight and high performance detection backbone based on Ghost Cross Stage Partial (GhostCSP) and lightweight spatial dilation convolution pyramid (LSDP) is designed. Aiming at the characteristics of ship scale diversity and unbalanced distribution of channel feature information after contour enhancement in SAR images, four feature layers are used to fuse contextual semantic information and channel attention mechanism is used for feature enhancement, and finally the improved ship target detection method based on YOLOX (ImYOLOX) is formed. Experimental tests on the SAR Ship Detection Dataset (SSDD) show that the proposed method achieves an average precision of 97.45% with a parameter size of 3.31 MB and a model size of 4.35 MB, and its detection performance is ahead of most current SAR ship detection algorithms.

To investigate whether diabetes contributes to mortality for major types of diseases. Six National Health and Nutrition Examination Survey data cycles (1999 to 2000, 2001 to 2002, 2003 to 2004, 2005 to 2006, 2007 to 2008, and 2009 to 2010) and their linked mortality files were used. A population of 15,513 participants was included according to the availability of diabetes and mortality status. Participants with diabetes tended to have higher all-cause mortality and mortality due to cardiovascular disease, cancer, chronic lower respiratory diseases, cerebrovascular disease, influenza and pneumonia, and kidney disease. Confounder-adjusted Cox proportional hazard models showed that both diagnosed diabetes category (yes or no) and diabetes status (diabetes, prediabetes, or no diabetes) were associated with all-cause mortality and with mortality due to cardiovascular disease, chronic lower respiratory diseases, influenza and pneumonia, and kidney disease. No associations were found for cancer-, accidents-, or Alzheimer's disease-related mortality. The current study's findings provide epidemiological evidence that diagnosed diabetes at the baseline is associated with increased mortality risk due to cardiovascular disease, chronic lower respiratory diseases, influenza and pneumonia, and kidney disease, but not with cancer or Alzheimer's disease.

To solve the problem that the traditional ambiguity function cannot well reflect the time-frequency distribution characteristics of linear frequency modulated (LFM) signals due to the presence of impulsive noise, two robust ambiguity functions: correntropy-based ambiguity function (CRAF) and fractional lower order correntropy-based ambiguity function (FLOCRAF) are defined based on the feature that correntropy kernel function can effectively suppress impulsive noise. Then these two robust ambiguity functions are used to estimate the direction of arrival (DOA) of narrowband LFM signal under an impulsive noise environment. Instead of the covariance matrix used in the ESPRIT algorithm by the spatial CRAF matrix and FLOCRAF matrix, the CRAF-ESPRIT and FLOCRAF-ESPRIT algorithms are proposed. Computer simulation results show that compared with the algorithms only using ambiguity function and the algorithms only using the correntropy kernel function-based correlation, the proposed algorithms using ambiguity function based on correntropy kernel function have good performance in terms of probability of resolution and estimation accuracy under various circumstances. Especially, the performance of the FLOCRAF-ESPRIT algorithm is better than the CRAF-ESPRIT algorithm in the environment of low generalized signal-to-noise ratio and strong impulsive noise.

Purpose Epithelial to mesenchymal transition (EMT) can contribute to gastric cancer (GC) progression and recurrence following therapy. Tumor-associated neutrophils (TANs) are associated with poor outcomes in a variety of cancers. However, it is not clear whether TANs interact with the EMT process during GC development. Methods Immunohistochemistry was performed to examine the distribution and levels of CD66 + neutrophils in samples from 327 patients with GC. CD66b + TANs were isolated either directly from GC cell suspensions or were conditioned from healthy donor peripheral blood polymorphonuclear neutrophils (PMNs) stimulated with tumor tissue culture supernatants (TTCS) and placed into co-culture with MKN45 or MKN74 cells, after which migration, invasion and EMT were measured. Interleukin-17a (IL-17a) was blocked with a polyclonal antibody, and the STAT3 pathway was blocked with the specific inhibitor AG490. Results Neutrophils were widely distributed in gastric tissues of patients with GC and were enriched predominantly at the invasion margin. Neutrophil levels at the invasion margin were an independent predictor of poor disease-free survival (DFS) and disease-specific survival (DSS). IL-17a + neutrophils constituted a large portion of IL-17a-producing cells in GC, and IL-17a was produced at the highest levels in co-culture compared with that in TANs not undergoing co-culture. TANs enhanced the migration, invasion and EMT of GC cells through the secretion of IL-17a, which activated the Janus kinase 2/signal transducers and activators of transcription (JAK2/STAT3) pathway in GC cells, while deprivation of IL-17a using a neutralizing antibody or inhibition of the JAK2/STAT3 pathway with AG490 markedly reversed these TAN-induced phenotypes in GC cells induced by TANs. Conclusions Neutrophils correlate with tumor stage and predict poor prognosis in GC. TANs produce IL-17a, which promotes EMT of GC cells through JAK2/STAT3 signalling. Blockade of IL-17a signalling with a neutralizing antibody inhibits TAN-stimulated activity in GC cells. Therefore, IL-17a-targeted therapy might be used to treat patients with GC.

Automotive millimeter-wave (MMW) radar is essential in autonomous vehicles due to its robustness in all weather conditions. Traditional commercial automotive radars are limited by their resolution, which makes the object classification task difficult. Thus, the concept of a new generation of four-dimensional (4D) imaging radar was proposed. It has high azimuth and elevation resolution and contains Doppler information to produce a high-quality point cloud. In this paper, we propose an object classification network named Radar Transformer. The algorithm takes the attention mechanism as the core and adopts the combination of vector attention and scalar attention to make full use of the spatial information, Doppler information, and reflection intensity information of the radar point cloud to realize the deep fusion of local attention features and global attention features. We generated an imaging radar classification dataset and completed manual annotation. The experimental results show that our proposed method achieved an overall classification accuracy of 94.9%, which is more suitable for processing radar point clouds than the popular deep learning frameworks and shows promising performance.

Single-image dehazing suffers from severe information loss and the under-constraint problem. The lack of high-quality robust priors leads to limited generalization ability of existing dehazing methods in real-world scenarios. To tackle this challenge, we propose a simple but effective single-image dehazing network by fusing high-quality semantic priors extracted from Segment Anything Model 2 (SAM2) with different types of advanced convolutions, abbreviated SAM2-Dehaze, which follows the U-Net architecture and consists of five stages. Specifically, we first employ the superior semantic perception and cross-domain generalization capabilities of SAM2 to generate accurate structural semantic masks. Then, a dual-branch Semantic Prior Fusion Block is designed to enable deep collaboration between the structural semantic masks and hazy image features at each stage of the U-Net. Furthermore, to avoid the drawbacks of feature redundancy and neglect of high-frequency information in traditional convolution, we have designed a novel parallel detail-enhanced and compression convolution that combines the advantages of standard convolution, difference convolution, and reconstruction convolution to replace the traditional convolution at each stage of the U-Net. Finally, a Semantic Alignment Block is incorporated into the post-processing phase to ensure semantic consistency and visual naturalness in the final dehazed result. Extensive quantitative and qualitative experiments demonstrate that SAM2-Dehaze outperforms existing dehazing methods on several synthetic and real-world foggy-image benchmarks, and exhibits excellent generalization ability.

Liquid crystals are a vital component of modern photonics, and recent studies have demonstrated the exceptional sensing properties of stimuli-responsive cholesteric liquid crystals. However, existing cholesteric liquid crystal-based sensors often rely on the naked eye perceptibility of structural color or the measurement of wavelength changes by spectrometric tools, which limits their practical applications. Therefore, developing a platform that produces recognizable sensing signals is critical. In this study, we present a visual sensing platform based on geometric phase encoding of stimuli-responsive cholesteric liquid crystal polymers that generates real-time visual patterns, rather than frequency changes. To demonstrate this platform’s effectiveness, we used a humidity-responsive cholesteric liquid crystal polymer film encoded with a q-plate pattern, which revealed that humidity causes a shape change in the vortex beam reflected from the encoded cholesteric liquid crystal polymers. Moreover, we developed a prototype platform towards remote humidity monitoring benefiting from the high directionality and long-range transmission properties of laser beams carrying orbital angular momentum. Our approach provides a novel sensing platform for cholesteric liquid crystals-based sensors that offers promising practical applications. The ability to generate recognizable sensing signals through visual patterns offers a new level of practicality in the sensing field with stimuli-responsive cholesteric liquid crystals. This platform might have significant implications for a broad readership and will be of interest to researchers working in the field of photonics and sensing technology.The proposed visual remote sensing platform utilizes geometric phase encoding of stimuli-responsive cholesteric liquid crystal polymers to generate intuitive image signals, showcasing its proof of concept by real-time humidity monitoring.

Artemisia argyi Lévl. et Vant essential oil could be used as a good antimicrobial flavouring agent and applied in the food industry. In this study, three methods, including simultaneous distillation-extraction (SDE), subcritical extraction and hydrodistillation, were applied to extract A. argyi essential oil. Compared with subcritical extraction (1%) and hydrodistillation (0.5%), SDE gave a higher yield (1.2%). Components of the essential oils were analysed with gas chromatography-mass spectrometry (GC-MS), and the most abundant ingredients were caryophyllene oxide, neointermedeol, borneol, α-thujone and β-caryophyllene. These five components accounted for 82.93%, 40.90% and 40.33% for SDE, subcritical extraction, and hydrodistillation, respectively. Based on agar disc diffusion and minimum inhibitory concentration (MIC) assays, SDE oil showed a significant inhibitory effect towards Listeria monocytogenes, Escherichia coli, Proteus vulgaris, Salmonella enteritidis and Aspergillus niger. Furthermore, electron microscope observations (SEM) confirmed that SDE oil could obviously deform cell morphology and destroy the structure of cell walls. Performances showed that SDE was a promising process for extracting A. argyi essential oil with both high yield and antimicrobial activity.

The base sequences of DNA are endowed with the rich structural and functional information and are available for the precise construction of the 2D and 3D macro products. The hydrogels formed by DNA are biocompatible, stable, tunable and biologically versatile, thus, these have a wide range of promising applications in bioanalysis and biomedicine. In particular, the stimuli-responsive DNA hydrogels (smart DNA hydrogels), which exhibit a reversible and switchable hydrogel to sol transition under different triggers, have emerged as smart materials for sensing. Thus far, the combination of the stimuli-responsive DNA hydrogels and multiple sensing platforms is considered as biocompatible and is useful as the flexible recognition components. A review of the stimuli-responsive DNA hydrogels and their biosensing applications has been presented in this study. The synthesis methods to prepare the DNA hydrogels have been introduced. Subsequently, the current status of the stimuli-responsive DNA hydrogels in biosensing has been described. The analytical mechanisms are further elaborated by the combination of the stimuli-responsive DNA hydrogels with the optical, electrochemical, point-of-care testing (POCT) and other detection platforms. In addition, the prospects of the application of the stimuli-responsive DNA hydrogels in biosensing are presented. Graphical abstract