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Abstract Background Several publications suggest that UTP11 may be a promising gene engaged for involvement of hepatocellular carcinoma (HCC) pathology. However, there are extremely limited biological, mechanistic and clinical studies of UTP11 in HCC. Methods To anayze the UTP11 mRNA expression in HCC and normal clinical samples and further investigate the correlation between UTP11 expression and pathology and clinical prognosis via the Cancer Tissue Gene Atlas (TCGA) database. The protein levels of UTP11 were checked using the Human Protein Atlas (HPA) database. GO-KEGG enrichment was performed from Cancer Cell Line Encyclopedia (CCLE) database and TCGA dataset. The levels of UTP11 were tested with qRT-PCR and western blotting assays. Cell viability, immunofluorescence and flow cytometry assays and animal models were used to explore the potential involvement of UTP11 in regulating HCC growth in vitro and in vivo. The correlation of UTP11 and tumor stemness scores and stemness-associated proteins from TCGA database. The mRNA stability was treated with Actinomycin D, followed by testing the mRNA expression using qRT-PCR assay. Results UTP11 was highly expressed in HCC samples compared to normal tissues from TCGA database. Similarly, UTP11 protein expression levels were obviously elevated in HCC tissue samples from HPA database. Furthermore, UTP11 levels were correlated with poor prognosis in HCC patient samples in TCGA dataset. In addition, the UTP11 mRNA levels was notably enhanced in different HCC cell lines than in normal liver cells and knocking down UTP11 was obviously reduced the viability and cell death of HCC cells. UTP11 knockdown suppressed the tumor growth of HCC in vivo experiment and extended the mice survival time. GO-KEEG analysis from CCLE and TCGA database suggested that UTP11 might involve in RNA splicing and the stability of mRNA. Further, UTP11 was positively correlated with tumor stemness scores and stemness-associated proteins from TCGA database. Knockdown of UTP11 was reduced the expression of stem cell-related genes and regulated the mRNA stability of Oct4. Conclusions UTP11 is potentially a diagnostic molecule and a therapeutic candidate for treatment of HCC.

Abstract The sluggish kinetics of oxygen evolution reaction (OER) and high iridium loading in catalyst coated membrane (CCM) are the key challenges for practical proton exchange membrane water electrolyzer (PEMWE). Herein, we demonstrate high-surface-area nano-metal diborides as promising supports of iridium-based OER nanocatalysts for realizing efficient, low-iridium-loading PEMWE. Nano-metal diborides are prepared by a novel disulphide-to-diboride transition route, in which the entropy contribution to the Gibbs free energy by generation of gaseous sulfur-containing products plays a crucial role. The nano-metal diborides, TaB2 in particular, are investigated as the support of IrO2 nanocatalysts, which finally forms a TaOx/IrO2 heterojunction catalytic layer on TaB2 surface. Multiple advantageous properties are achieved simultaneously by the resulting composite material (denoted as IrO2@TaB2), including high electrical conductivity, improved iridium mass activity and enhanced corrosion resistance. As a consequence, the IrO2@TaB2 can be used to fabricate the membrane electrode with a low iridium loading of 0.15 mg cm−2, and to give an excellent catalytic performance (3.06 A cm−2@2.0 V@80 oC) in PEMWE―the one that is usually inaccessible by unsupported Ir-based nanocatalysts and the vast majority of existing supported Ir-based catalysts at such a low iridium loading.

In this work, flexible Cu2−xS films on nylon membranes are prepared by combining a simple hydrothermal synthesis and vacuum filtration followed by hot pressing. The films consist of Cu2S and Cu1.96S two phases with grain sizes from nano to submicron. Doping Se on the S site not only increases the Cu1.96S content in the Cu2−xS to increase carrier concentration but also modifies electronic structure, thereby greatly improves the electrical properties of the Cu2−xS. Specifically, an optimal composite film with a nominal composition of Cu2−xS0.98Se0.02 exhibits a high power factor of ~150.1 μW m−1 K−2 at 300 K, which increases by ~138% compared to that of the pristine Cu2−xS film. Meanwhile, the composite film shows outstanding flexibility (~97.2% of the original electrical conductivity is maintained after 1500 bending cycles with a bending radius of 4 mm). A four-leg flexible thermoelectric (TE) generator assembled with the optimal film generates a maximum power of 329.6 nW (corresponding power density of 1.70 W m−2) at a temperature difference of 31.1 K. This work provides a simple route to the preparation of high TE performance Cu2−xS-based films.

Abstract Knowledge Graphs (KGs) have become an increasingly important part of artificial intelligence, and KGs have been widely used in artificial intelligence fields such as intelligent answering questions and personalized recommendation. Previous knowledge graph completion methods require a large number of samples for each relation. But in fact, in KGs, many relationships are long-tail relationships, and the existing researches on few-shot completion mainly focus on static knowledge graphs. In this paper, we consider few-shot completion in Temporal Knowledge Graphs (TKGs) where the event may only hold for a specific timestamp, and propose a model abbreviated as FTMO based on meta-optimization. In this model, we combine the time-based relational-aware heterogeneous neighbor encoder, the cyclic automatic aggregation network, and the matching network to complete the few-shot temporal knowledge graph. We compare our model with the baseline models, and the experimental results demostrate the performance advantages of our model.

Since the start of the twenty-first century, China's economy has grown at a high or moderate rate, and air pollution has become increasingly severe. The study was conducted using data from remote sensing observations between 1998 and 2019, employing the standard deviation ellipse model and spatial autocorrelation analysis, to explore the spatiotemporal distribution characteristics of PM2.5 in Henan Province. Additionally, a multiscale geographically weighted regression model (MGWR) was applied to explore the impact of 12 driving factors (e.g., mean surface temperature and CO2 emissions) on PM2.5 concentration. The research revealed that (1) Over a period of 22 years, the yearly mean PM2.5 concentrations in Henan Province demonstrated a trend resembling the shape of the letter “M”, and the general trend observed in Henan Province demonstrated that the spatial center of gravity of PM2.5 concentrations shifted toward the north. (2) Distinct spatial clustering patterns of PM2.5 were observed in Henan Province, with the northern region showing a primary concentration of spatial hot spots, while the western and southern areas were predominantly characterized as cold spots. (3) MGWR is more effective than GWR in unveiling the spatial heterogeneity of influencing factors at various scales, thereby making it a more appropriate approach for investigating the driving mechanisms behind PM2.5 concentration. (4) The results acquired from the MGWR model indicate that there are varying degrees of spatial heterogeneity in the effects of various factors on PM2.5 concentration. To summarize the above conclusions, the management of the atmospheric environment in Henan Province still has a long way to go, and the formulation of relevant policies should be adapted to local conditions, taking into account the spatial scale effect of the impact of different influencing factors on PM2.5.

ST15 Klebsiella pneumoniae (Kpn) is a growing public health concern in China and worldwide, yet its genomic and evolutionary dynamics in this region remain poorly understood. This study comprehensively elucidates the population genomics of ST15 Kpn in China by analyzing 287 publicly available genomes. The proportion of the genomes increased sharply from 2012 to 2021, and 92.3% of them were collected from the Yangtze River Delta (YRD) region of eastern China. Carbapenemase genes, including OXA-232, KPC-2, and NDM, were detected in 91.6% of the studied genomes, and 69.2% of which were multidrug resistant (MDR) and hypervirulent (hv). Phylogenetic analysis revealed four clades, C1 (KL112, 59.2%), C2 (mainly KL19, 30.7%), C3 (KL48, 0.7%) and C4 (KL24, 9.4%). C1 appeared in 2007 and was OXA-232-producing and hv; C2 and C4 appeared between 2005 and 2007, and both were KPC-2-producing but with different levels of virulence. Transmission clustering detected 86.1% (n = 247) of the enrolled strains were grouped into 55 clusters (2–159 strains) and C1 was more transmissible than others. Plasmid profiling revealed 88 plasmid clusters (PCs) that were highly heterogeneous both between and within clades. 60.2% (n = 53) of the PCs carrying AMR genes and 7 of which also harbored VFs. KPC-2, NDM and OXA-232 were distributed across 14, 4 and 1 PCs, respectively. The MDR-hv strains all carried one of two homologous PCs encoding iucABCD and rmpA2 genes. Pangenome analysis revealed two major coinciding accessory components predominantly located on plasmids. One component, associated with KPC-2, encompassed 15 additional AMR genes, while the other, linked to OXA-232, involved seven more AMR genes. This study provides essential insights into the genomic evolution of the high-risk ST15 CP-Kpn strains in China and warrants rigorous monitoring.

Duck clavicle is very popular among consumers as snack food, however, the technical parameters about its shelf life are insufficient. In this paper, the changes of pH, TVB-N, water content and total bacterial count were studied under room temperature storage. On this basis, the volatile flavor compounds of duck clavicle were detected using headspacegas chromatography-ion mobility spectroscopy (HS-GC-IMS) combined with principal component analysis (PCA), and the flavor fingerprints around shelf life were established. The results showed that the pH appeared downtrend, and the total number of colonies, TVB-N, water were on the increasing following the growth of storage time. On the 6th day, the content of TVB-N was 21.1±1.21 mg/100 g and the total number of colonies on the 6th day was 5.82±0.08 lg CFU/g. Both of them exceeded the maximum safety limit value, so its shelf life was determined to be no more than 6 days. A total of 52 flavor substances were identified by GC-IMS technology, including 12 aldehydes, 13 alcohols, 7 ketones, 8 esters, 6 acids, 2 pyrazines, 2 furans, 1 amine and 1 hydrocarbon. Propanal, 2-methylbutyric acid, propyl acetate, ethyl butyrate and heptanal were determined as the key flavor compounds of samples at the initial stage of storage (0, 2 d). After the samples exceeded the shelf life (6 d), 2,3-butanedione, 3-hydroxy-2-butanonedimer, 2,3-pentanedione, N-nitromethylethylamine, butyric acid,propionic acid were the main flavor compounds. These volatile substances could be used as characteristic markers substances to determine the shelf-life of duck clavicle product. The shelf-life fingerprint based on GC-IMS technology would provide new technical parameters for production companies.

Cumin essential oil chitosan nanocapsules (CENPs) were prepared through the ionic gelation method by blending chitosan (CS) with cumin essential oil (CEO) in different proportions (1:0.8, 1:1, 1:2, 1:3, 1:4). Subsequently, these nanocapsules were characterized and evaluated for their antibacterial properties to determine the optimal cumin essential oil encapsulation and antibacterial efficacy. The outcomes demonstrated that the highest encapsulation efficiency of CENPs was 52%, achieved with a 1:3 CS/CEO ratio. At this point, the nanoparticles had the smallest particle size (584.67 nm) and a regular spherical distribution in the emulsion. Moreover, the CENPs could release the encapsulated CEOs slowly, leading to efficient inhibition of E. coli and L. monocytogenes over a relatively extended period (24–36 h) compared to the CS and CEO. This research offers a promising approach for the use of nanocapsules in food preservation.

Unmanned aerial vehicles (UAVs) have gained much attention from academic and industrial areas due to the significant number of potential applications in urban airspace. A traffic management system for these UAVs is needed to manage this future traffic. Tactical conflict resolution for unmanned aerial systems (UASs) is an essential piece of the puzzle for the future UAS Traffic Management (UTM), especially in very low-level (VLL) urban airspace. Unlike conflict resolution in higher altitude airspace, the dense high-rise buildings are an essential source of potential conflict to be considered in VLL urban airspace. In this paper, we propose an attention-based deep reinforcement learning approach to solve the tactical conflict resolution problem. Specifically, we formulate this task as a sequential decision-making problem using Markov Decision Process (MDP). The double deep Q network (DDQN) framework is used as a learning framework for the host drone to learn to output conflict-free maneuvers at each time step. We use the attention mechanism to model the individual neighbor's effect on the host drone, endowing the learned conflict resolution policy to be adapted to an arbitrary number of neighboring drones. Lastly, we build a simulation environment with various scenarios covering different types of encounters to evaluate the proposed approach. The simulation results demonstrate that our proposed algorithm provides a reliable solution to minimize secondary conflict counts compared to learning and non-learning-based approaches under different traffic density scenarios.

This work investigated the changes in properties of saliva-participating emulsions during different oral processing stages (the whole process from intake to swallowing was divided into five stages, i.e., 20 %, 40 %, 60 %, 80 % and 100 % stage). The stewed pork with brown sauce was masticated and the emulsion was collected for the determination of emulsion stability, droplet size, ζ-potential, interfacial tension, and microstructure. The results showed that the emulsion stability increased gradually during the oral processing and reached the highest level near the swallowing point. The droplet size of emulsion showed a significant downward trend (P < 0.05). Microstructure observations also found different degrees of reduction in fat droplets size at different stages of oral processing. In addition, the ζ-potential of food boluses emulsion was decreased from −16.4 mV to −41.2 mV and the interfacial tension decreased by 52.6 % before and after oral progressing. In conclusion, the oral processing of stewed pork with brown sauce was essentially a process in which fat was constantly emulsified, and saliva might act as an emulsifier. This study provides new insights on understanding the oral processing process and sensory changes of fat.

This study aimed to investigate the release of volatile compounds in mutton shashliks (named as FxLy, x-fat cubes: 0-4; y-lean cubes: 4-0) with different fat–lean ratios before and during consumption, respectively. In total, 67 volatile compounds were identified in shashliks using gas chromatography/mass spectrometry. Aldehyde, alcohol, and ketone were the major volatile substances, accounting for more than 75% of the total volatile compounds. There were significant differences in the volatile compounds of mutton shashliks with different fat–lean ratios. With the increase of the fat content, the types and content of volatile substances released also increase. However, when the percentage of fat exceeded 50%, the number of furans and pyrazine, which were characteristic of the volatile compounds of roasted meat, was decreased. The release of volatiles during the consumption of mutton shashliks was measured using the exhaled breath test and the results showed that adding an appropriate amount of fat (2:2) shorten the mastication duration and weaken the breakdown of bolus particles in the consumption process, which is not conducive to the release potential of volatile substances. Therefore, setting the fat to lean ratio to 2:2 is the best choice for making mutton shashliks, as it (F2L2) can provide rich flavor substances for mutton shashliks before and during consumption.

This article presents investigation of the flavor profile on 5 different regional Chinese smoked chicken samples using gas chromatography−ion mobility spectrometry analysis methods. Five batches of samples were obtained over the course of 6 mo. A total of 34 flavor substances were identified in the 5 smoked chicken samples, including 10 aldehydes, 7 alcohols, 4 ketones, 2 hydrocarbons, 3 heterocyclic compounds, 4 esters, 2 ethers, and 2 phenolic compounds. The whole spectral fingerprint visually displayed flavor differences and relations in 5 types of smoked chicken with local characteristics. Moreover, the orthogonal projections to latent structures discriminant analysis model revealed that these samples could be separately classified into 5 groups. Multivariate statistical analysis showed that 20 chemicals with higher Variable Importance for the Projection values were the key contributors to the differences of flavor in these 5 kinds of smoked chicken. N-nonanal, heptanal, n-nonanal, heptanal, furfurol, and hexanal were the main common flavor compounds in the 5 types of Chinese smoked chicken, whereas linalool, alpha-terpineol, 1,8-cineole, and anethole were the main characteristic flavor compounds of Goubangzi chicken (No. 1); gamma-butyrolactone, 2-acetylfuran, 2-methoxyphenol, 2-acetylpyrrole, and limonene were determined as the key flavor compounds of Liaocheng chicken (No. 2); the concentration of octanal and n-nonanal was higher in Tangqiao chicken (No. 3); butyl acetate was the key contributor to the flavor compounds of Jinshan chicken (No. 4). 2-Heptanone and 2-pentylfuran had a high correlation with Zhuozishan chicken (No. 5). The different raw materials and ingredients used, especially the method of preparation and cultural differences, in different regions of the country in China were the main reasons leading to the similarities and differences of volatile compounds in the 5 kinds of Chinese traditional smoked chicken.

Aiming at the problem that the Global Navigation Satellite System (GNSS) is confronted with great challenges in terms of continuity, integrity and usability due to the complex electromagnetic environment, an improved signal detecting algorithm of weak interference based on polarization-sensitive array was proposed. Through the joint-processing in space domain, frequency domain and polarization domain of the real-time data received from antenna elements and the descending processing of eigenvalues in each frequency bin, the weak signals in strong interference environment could be accurately separated and identified by the distribution curve of eigenvalues in the whole frequency domain. The results show that the power consumption and computational complexity were reduced greatly through the improved minimum description length (MDL) criterion based on multistage wiener filter, as the step of eigen-decomposition was omitted. Simulation results also demonstrate the efficiency and accuracy of the proposed method. The improved algorithm can be applied to different antenna arrays, keeps highly effective detecting probability even with amplitude and phase errors, and has high engineering practice value.

Significant progress has been made in generating counterfeit images and videos. Forged videos generated by deepfaking have been widely spread and have caused severe societal impacts, which stir up public concern about automatic deepfake detection technology. Recently, many deepfake detection methods based on forged features have been proposed. Among the popular forged features, textural features are widely used. However, most of the current texture-based detection methods extract textures directly from RGB images, ignoring the mature spectral analysis methods. Therefore, this research proposes a deepfake detection network fusing RGB features and textural information extracted by neural networks and signal processing methods, namely, MFF-Net. Specifically, it consists of four key components: (1) a feature extraction module to further extract textural and frequency information using the Gabor convolution and residual attention blocks; (2) a texture enhancement module to zoom into the subtle textural features in shallow layers; (3) an attention module to force the classifier to focus on the forged part; (4) two instances of feature fusion to firstly fuse textural features from the shallow RGB branch and feature extraction module and then to fuse the textural features and semantic information. Moreover, we further introduce a new diversity loss to force the feature extraction module to learn features of different scales and directions. The experimental results show that MFF-Net has excellent generalization and has achieved state-of-the-art performance on various deepfake datasets.