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High‐entropy alloys (HEAs) materials, as promising nanomaterials, have garnered significant attention from researchers due to their excellent performance in the field of hydrogen evolution reaction (HER). The four core effects of HEAs, including the high‐entropy effect, severe lattice distortion effect, sluggish diffusion effect, and cocktail effect, are pivotal in underpinning their remarkable mechanical and thermodynamic properties. Nevertheless, the intricate geometric and electronic structures of HEAs make their catalytic mechanisms exceptionally complex and challenging to decipher. In particular, a thorough analysis of the underlying factors responsible for the outstanding catalytic activity, selectivity, and the ability to maintain stable hydrogen production, even at high current densities, in HEAs is lacking. To provide a systematic exploration of the design and application of HEAs in HER systems, this review commences with an examination of the physicochemical properties of HEAs. It covers a wide range of topics, including the synthesis methods of HEAs, and the major reaction mechanisms of HERs, and presents innovative methods and approaches for designing HEAs specifically in the context of HERs. [ABSTRACT FROM AUTHOR]

Hyperspectral image classification (HSIC) has garnered increasing attention among researchers. While classical networks like convolution neural networks (CNNs) have achieved satisfactory results with the advent of deep learning, they are confined to processing local information. Vision transformers, despite being effective at establishing long-distance dependencies, face challenges in extracting high-representation features for high-dimensional images. In this paper, we present the multiscale efficient attention with enhanced feature transformer (MEA-EFFormer), which is designed for the efficient extraction of spectral–spatial features, leading to effective classification. MEA-EFFormer employs a multiscale efficient attention feature extraction module to initially extract 3D convolution features and applies effective channel attention to refine spectral information. Following this, 2D convolution features are extracted and integrated with local binary pattern (LBP) spatial information to augment their representation. Then, the processed features are fed into a spectral–spatial enhancement attention (SSEA) module that facilitates interactive enhancement of spectral–spatial information across the three dimensions. Finally, these features undergo classification through a transformer encoder. We evaluate MEA-EFFormer against several state-of-the-art methods on three datasets and demonstrate its outstanding HSIC performance. [ABSTRACT FROM AUTHOR]

The work focuses on the optimization of management strategies of energy supply chain in oil–gas enterprises to find out the optimal management strategy and overcome the fragility of the traditional energy supply system. Back propagation neural network (BPNN) is applied to energy supply predicting and energy security early warning in oil–gas enterprises, and the security early warning system is designed based on the auxiliary cognitive system. The security early warning system is used to predict the peak value of oil and gas supply, and then the detection performances and the data security transmission performances of different models are compared and analysed. Finally, a comprehensive evaluation model for security management of oil–gas supply chain based on the neural network is established. It is found that compared with convolutional neural network (CNNs), the improved BPNN model algorithm proposed shows increased accuracy by 5.4%, reduced model training time and test time, and improved accuracy and recall rate. The accuracy, recall rate, and F1 value of the proposed model are 88.48%, 75.88%, and 96.21%, respectively, which were obviously higher than those of the CNN. It suggests that the improved security management model of oil–gas supply chain shows better recognition and prediction accuracy. Based on the quantitative and qualitative analysis results, the optimization suggestions for management strategies in oil–gas enterprises are put forward, which is of strategic significance for improving the security of oil–gas supply. [ABSTRACT FROM AUTHOR]

The research on the ecological protection of cultivated land has gradually become a focus and frontier of cultivated land protection. Constructing an ecological network of a cultivated land system is important to improve the effect of cultivated land ecological protection. In this study, the supply-demand ratio of five ecosystem services was calculated from 2000 to 2020 in Shandong Province, a major grain-producing area in China. The morphological spatial pattern analysis (MSPA) model was used to construct the minimum cumulative resistance (MCR) model. The conclusions of the study are as follows: (1) the areas with a higher supply and demand ratio of various ecosystem services of cultivated land in Shandong Province are distributed in southern and southwestern areas. (2) The ecological source of cultivated land in Shandong Province has decreased by about 7000 km2 from 2000 to 2020, and the fragmentation trend is obvious. (3) The total length of the ecological corridors is 798.5 km2. The majority of these corridors are located in central and southern Shandong. The ideas and conclusions of this study are important for the formulation of ecological protection policies for regional cultivated land systems. [ABSTRACT FROM AUTHOR]

Accumulated evidence indicates that geniposide exhibits neuroprotective effects in ischemic stroke. However, the potential targets of geniposide remain unclear.

Antibiotics have received extensive attention due to their sophisticated effects on human health and ecosystems. However, there is an extreme scarcity of information on composition, content, geographic distribution, and risk of riverine antibiotics at a large spatial scale. Based on a systematic review of over 600 pieces of literature (1999–2021), we established a global dataset containing more than 90,000 records covering 169 antibiotics and their metabolites in surface water and sediment across 76 countries. The occurrence of prioritized antibiotics largely depended on socioeconomic developmental levels, and the current “hotspots” of polluted rivers were found mostly in less developed countries or emerging economies (e.g., some in Africa, South America, and Asia). By developing the screening protocol for risk-based prioritization of antibiotics, we advanced a rank list of antibiotics for guiding formulation of region-specific strategies, which highlighted the importance of whole life cycle management of antibiotics in health maintenance of the world’s rivers.

With the rapid progress of urbanization in China, the real estate industry, characterized by a long industrial chain, has become a pillar industry for economic development. Therefore, we inspect the nexus between land finance, housing prices, and economic growth. For this purpose, we use the panel data of 277 cities at the prefecture level or above in China from 2011 to 2019, and empirically examine it by using the Panel Vector Auto Regression (PVAR) model. The results show that there is a causal relationship between housing prices and economic growth. Housing prices promote economic growth in the short term and inhibit it in the long term. Both economic growth and housing prices have a significant impact on land finance. The economic growth show a significantly positive impact, while housing prices promote land finance in the short term with a long-term trend from positive to negative. This is the first study that tries to probe the relationship between urban housing prices, land finance, and economic growth by considering 277 prefecture-level and above cities in China. To promote the stable development of the regional economy, local governments need to overcome their dependence on the housing market and land finance and promote the healthy development of the housing market. [ABSTRACT FROM AUTHOR]

In this study, an overview of two years of research findings concerning the 2022 Hunga Tonga–Hunga Ha'apai (HTHH) volcanic eruption in the stratospheric environment is provided, focusing on water vapor, aerosols, and ozone. Additionally, the potential impacts of these changes on aviation equipment materials are discussed. The HTHH volcanic eruption released a large amount of particles (e.g., ash and ice) and gases (e.g., H2O, SO2, and HCl), significantly affecting the redistribution of stratospheric water vapor and aerosols. Stratospheric water vapor increased by approximately 140–150 Tg (8–10%), with a concentration peak observed in the height range of 22.2–27 km (38–17 hPa). Satellite measurements indicate that the HTHH volcano injected approximately 0.2–0.5 Tg of sulfur dioxide into the stratosphere, which was partially converted into sulfate aerosols. In-situ observations revealed that the volcanic aerosols exhibit hygroscopic characteristics, with particle sizes ranging from 0.22–0.42 μm under background conditions to 0.42–1.27 μm. The moist stratospheric conditions increased the aerosol surface area, inducing heterogeneous chlorine chemical reactions on the aerosol surface, resulting in stratospheric ozone depletion in the HTHH plume within one week. In addition, atmospheric disturbances and ionospheric disruptions triggered by volcanic eruptions may adversely affect aircraft and communication systems. Further research is required to understand the evolution of volcanic aerosols and the impact of volcanic activity on aviation equipment materials. [ABSTRACT FROM AUTHOR]

The mechanism of the pulsed hollow cathode arc welding (HCAW) process was revealed using a fully coupled model with a hollow cathode. We solved the governing equations with the Marangoni effect to study the dynamic behaviors of a molten pool with a square pulsing current (200~400 A, 900 Hz) and varying O2 content; the dynamics of the arc plasma and the weld pool in the HCAW process were investigated quantitatively. The results show that the intensity of the arc plasma was more significantly weakened by the design of the hollow cathode in HCAW than that in GTAW with a solid hollow cathode. We could obtain a stable molten pool even with a large pulsing current section (200 A–400 A) at higher frequencies. The flow dynamics of the molten pool were mainly dominated by the Marangoni effect with varying oxygen content, and we could promote penetration by increasing O2 content in HCAW. [ABSTRACT FROM AUTHOR]

Over the past several decades, deep neural networks have been extensively applied to medical image segmentation tasks, achieving significant success. However, the effectiveness of traditional deep segmentation networks is substantially limited by the small scale of medical datasets, a limitation directly stemming from current medical data acquisition capabilities. To this end, we introduce AttEUnet, a medical cell segmentation network enhanced by edge attention, based on the Attention U-Net architecture. It incorporates a detection branch enhanced with edge attention and a learnable fusion gate unit to improve segmentation accuracy and convergence speed on small medical datasets. The AttEUnet allows for the integration of various types of prior information into the backbone network according to different tasks, offering notable flexibility and generalization ability. This method was trained and validated on two public datasets, MoNuSeg and PanNuke. The results show that AttEUnet significantly improves segmentation performance on small medical datasets, especially in capturing edge details, with F1 scores of 0.859 and 0.888 and Intersection over Union (IoU) scores of 0.758 and 0.794 on the respective datasets, outperforming both convolutional neural networks (CNNs) and transformer-based baseline networks. Furthermore, the proposed method demonstrated a convergence speed over 10.6 times faster than that of the baseline networks. The edge attention branch proposed in this study can also be added as an independent module to other classic network structures and can integrate more attention priors based on the task at hand, offering considerable scalability. [ABSTRACT FROM AUTHOR]

Despite notable advancements achieved on Hyperspectral (HS) pansharpening tasks through deep learning techniques, previous methods are inherently constrained by convolution or self-attention intrinsic defects, leading to limited performance. In this paper, we proposed an Attention-Interactive Dual-Branch Convolutional Neural Network (AIDB-Net) for HS pansharpening. Our model purely consists of convolutional layers and simultaneously inherits the strengths of both convolution and self-attention, especially the modeling of short- and long-range dependencies. Specially, we first extract, tokenize, and align the hyperspectral image (HSI) and panchromatic image (PAN) by Overlapping Patch Embedding Blocks. Then, we specialize a novel Spectral-Spatial Interactive Attention which is able to globally interact and fuse the cross-modality features. The resultant token-global similarity scores can guide the refinement and renewal of the textural details and spectral characteristics within HSI features. By deeply combined these two paradigms, our AIDB-Net significantly improve the pansharpening performance. Moreover, with the acceleration by the convolution inductive bias, our interactive attention can be trained without large scale dataset and achieves competitive time cost with its counterparts. Compared with the state-of-the-art methods, our AIDB-Net makes 5.2%, 3.1%, and 2.2% improvement on PSNR metric on three public datasets, respectively. Comprehensive experiments quantitatively and qualitatively demonstrate the effectiveness and superiority of our AIDB-Net. [ABSTRACT FROM AUTHOR]

In this paper, we studied a Hadamard-type fractional Riemann-Stieltjes integral boundary value problem. The existence of nontrivial solutions was obtained by using the fixed-point method when the nonlinearities can be superlinear, suberlinear, and have asymptotic linear growth. Our results improved and generalized some results of the existing literature. [ABSTRACT FROM AUTHOR]

Background: The 4-hydroxysesamin (4-HS, a di-tetrahydrofuran lignin) is a modified sesamin that was prepared in the laboratory. This preclinical study was designed to preliminarily investigate the neuroprotective properties of 4-HS. Methods: In vitro, neuronal injury and inflammation were simulated by oxygen-glucose deprivation and lipopolysaccharide (LPS) exposure in mouse hippocampal neuronal HT22 cell line, and treated with 4-HS and/or metformin (MET, MAPK pathway activator for exploring mechanism). CCK-8, flow cytometry, and enzyme-linked immunosorbent assay were performed to evaluate cell viability, apoptosis, and inflammation. Apoptosis- and pathway-related proteins were detected by Western blotting. Middle cerebral artery occlusion (MCAO) was constructed as a stroke model and treated with 4-HS for in vivo confirmation. Histological staining was used for in vivo evaluation of 4-HS properties. Results: The 4-HS showed similar anti-inflammatory activity to sesamin but did not affect the cell viability of HT22 cells. In vitro, 4-HS improved the cell viability, ameliorated neuronal apoptosis, along with the reversion of apoptotic proteins (Bax, cleaved-caspase 3/9, Bcl-2) expression and inflammatory cytokines (IL-6, TNF-α, IL-10) in LPS-treated HT22 cells. The 4-HS suppressed the phosphorylation of ERK, JNK, and p38 but the addition of MET reversed 4-HS-induced changes of phenotype and protein expression in LPS-treated cells. In vivo, 4-HS showed apparent improvement in cerebral infarction, brain tissue morphology, neuronal architecture, apoptosis, and inflammation of MCAO mice, and also showed inhibiting effects on the phosphorylation of ERK, JNK, and p38, confirming in vivo results. Conclusion: In this first pre-clinical study on 4-HS, we preliminarily demonstrated the neuroprotective properties of 4-HS both in cell and animal models, and proposed that the underlying mechanism might be associated with the MAPK pathway. [ABSTRACT FROM AUTHOR]

Growth on nonpolar group III-nitride semiconductor surfaces has been suggested to be a remedy for avoiding detrimental polarization effects. However, the presence of intrinsic surface states within the fundamental bandgap at nonpolar surfaces leads to a Fermi-level pinning during growth, affecting the incorporation of dopants and impurities. This is further complicated by the use of ternary, e.g., Al x Ga 1 − x N layers in device structures. In order to quantify the Fermi-level pinning on ternary group III nitride nonpolar growth surface, the energy position of the group III-derived empty dangling bond surface state at nonpolar Al x Ga 1 − x N (10 1 ¯ 0) surfaces is determined as a function of the Al concentration using cross-sectional scanning tunneling microscopy and spectroscopy. The measurements show that the minimum energy of the empty dangling bond state shifts linearly toward midgap for increasing Al concentration with a slope of ≈ 5 meV/%. These experimental findings are supported by complementary density functional theory calculations. [ABSTRACT FROM AUTHOR]

Landslides are a common geological disaster, which cause many economic losses and casualties in the world each year. Drawing up a landslide list and monitoring their deformations is crucial to prevent landslide disasters. Interferometric synthetic aperture radar (InSAR) can obtain millimeter-level surface deformations and provide data support for landslide deformation monitoring. However, some landslides are difficult to detect due to the low-coherence caused by vegetation cover in mountainous areas and the difficulty of phase unwrapping caused by large landslide deformations. In this paper, a method based on multi-direction phase gradient stacking is proposed. It employs the differential interferograms of small baseline sets to directly obtain the abnormal region, thereby avoiding the problem where part of landslide cannot be detected due to a phase unwrapping error. In this study, the Sentinel-1 satellite ascending and descending data from 2018 to 2020 are used to detect landslides around Zhouqu County, China. A total of 26 active landslides were detected in ascending data and 32 active landslides in the descending data using the method in this paper, while the SBAS-InSAR detected 19 active landslides in the ascending data and 25 active landslides in the descending data. The method in this paper can successfully detect landslides in areas that are difficult for the SBAS-InSAR to detect. In addition, the proposed method does not require phase unwrapping, so a significant amount of data processing time can be saved. [ABSTRACT FROM AUTHOR]

Background: Human health is seriously threatened by antibiotic-induced intestinal disorders. Herein, we aimed to determine the effects of Autoinducer-2 (AI-2) combined with Lactobacillus rhamnosus GG (LGG) on the intestinal barrier function of antibiotic-induced intestinal dysbiosis neonatal mice. Methods: An antibiotic-induced intestinal dysbiosis neonatal mouse model was created using antibiotic cocktails, and the model mice were randomized into the control, AI-2, LGG, and LGG + AI-2 groups. Intestinal short-chain fatty acids and AI-2 concentrations were detected by mass spectrometry and chemiluminescence, respectively. The community composition of the gut microbiota was analyzed using 16S rDNA sequencing, and biofilm thickness and bacterial adhesion in the colon were assessed using scanning electron microscopy. Transcriptome RNA sequencing of intestinal tissues was performed, and the mRNA and protein levels of HCAR2 (hydroxycarboxylic acid receptor 2), claudin3, and claudin4 in intestinal tissues were determined using quantitative real-time reverse transcription PCR and western blotting. The levels of inflammatory factors in intestinal tissues were evaluated using enzyme-linked immunosorbent assays (ELISAs). D-ribose, an inhibitor of AI-2, was used to treat Caco-2 cells in vitro. Results: Compared with the control, AI-2, and LGG groups, the LGG + AI-2 group showed increased levels of intestinal AI-2 and proportions of Firmicutes and Lacticaseibacillus, but a reduced fraction of Proteobacteria. Specifically, the LGG + AI-2 group had considerably more biofilms and LGG on the colon surface than those of other three groups. Meanwhile, the combination of AI-2 and LGG markedly increased the concentration of butyric acid and promoted Hcar2, claudin3 and claudin4 expression levels compared with supplementation with LGG or AI-2 alone. The ELISAs revealed a significantly higher tumor necrosis factor alpha (TNF-α) level in the control group than in the LGG and LGG + AI-2 groups, whereas the interleukin 10 (IL-10) level was significantly higher in the LGG + AI-2 group than in the other three groups. In vitro, D-ribose treatment dramatically suppressed the increased levels of Hcar2, claudin3, and claudin4 in Caco-2 cells induced by AI-2 + LGG. Conclusions: AI-2 promotes the colonization of LGG and biofilm formation to improve intestinal barrier function in an antibiotic-induced intestinal dysbiosis neonatal mouse model. [ABSTRACT FROM AUTHOR]

Cancer has become one of the most important causes of human death. In particular, the 5 year survival rate of patients with digestive tract cancer is low. Although chemotherapy drugs have a certain efficacy, they are highly toxic and prone to chemotherapy resistance. With the advancement of antitumor research, many natural drugs have gradually entered basic clinical research. They have low toxicity, few adverse reactions, and play an important synergistic role in the combined targeted therapy of radiotherapy and chemotherapy. A large number of studies have shown that the active components of Paris polyphylla (PPA), a common natural medicinal plant, can play an antitumor role in a variety of digestive tract cancers. In this paper, the main components of PPA such as polyphyllin, C21 steroids, sterols, and flavonoids, amongst others, are introduced, and the mechanisms of action and research progress of PPA and its active components in the treatment of various digestive tract cancers are reviewed and summarized. The main components of PPA have been thoroughly explored to provide more detailed references and innovative ideas for the further development and utilization of similar natural antitumor drugs. [ABSTRACT FROM AUTHOR]

There have been considerable efforts in generating tree crown maps from satellite images. However, tree localization in urban environments using satellite imagery remains a challenging task. One of the difficulties in complex urban tree detection tasks lies in the segmentation of dense tree crowns. Currently, methods based on semantic segmentation algorithms have made significant progress. We propose to split the tree localization problem into two parts, dense clusters and single trees, and combine the target detection method with a procedural generation method based on planting rules for the complex urban tree detection task, which improves the accuracy of single tree detection. Specifically, we propose a two-stage urban tree localization pipeline that leverages deep learning and planting strategy algorithms along with region discrimination methods. This approach ensures the precise localization of individual trees while also facilitating distribution inference within dense tree canopies. Additionally, our method estimates the radius and height of trees, which provides significant advantages for three-dimensional reconstruction tasks from remote sensing images. We compare our results with other existing methods, achieving an 82.3% accuracy in individual tree localization. This method can be seamlessly integrated with the three-dimensional reconstruction of urban trees. We visualized the three-dimensional reconstruction of urban trees generated by this method, which demonstrates the diversity of tree heights and provides a more realistic solution for tree distribution generation. [ABSTRACT FROM AUTHOR]

Background: Green tea has been reported to be potentially protective against the development of cardiovascular disease (CVD). This study aimed to investigate the association between green tea consumption and incident CVD in type 2 diabetes (T2D) patients with overweight/obesity. Methods: A total of 4756 Chinese overweight/obese T2D patients were recruited and followed up for 6.27 years. Information on green tea consumption was collected at baseline using interviewer-administered questionnaires. Hazard ratios (HRs) and 95% confidence intervals (CIs) for incident CVD according to green tea consumption were estimated using the Cox proportional hazards model. Results: Compared with non-habitual consumers, participants who consumed > 5 g/day of green tea leaves reduced the risk of CVD by 29% (95%CI: 0.55–0.92), stroke by 30% (95%CI: 0.51–0.95) and coronary heart disease (CHD) by 40% (95%CI: 0.40–0.89). Similarly, participants who consumed green tea for ≥ 40 years reduced the risk of CVD by 31% (95%CI: 0.54–0.88), stroke by 33% (95%CI: 0.50–0.90) and CHD by 39% (95%CI: 0.42–0.88). Among participants with < 5-year history of T2D, > 5 g/day of tea leaves and > 40 years of tea consumption were associated with 59% (95%CI: 0.23–0.72) and 57% (95%CI: 0.26–0.74) reduced risk of stroke, respectively. However, among participants with ≥ 5-year history of T2D, > 5 g/day of tea leaves and > 40 years of tea consumption were associated with a 50% (95%CI: 0.30–0.82) and 46% (95%CI: 0.35–0.85) reduced risk of CHD, respectively. Conclusions: Green tea consumption is associated with reduced risk of CVD, stroke, and CHD in overweight/obese T2D patients. [ABSTRACT FROM AUTHOR]

Wheat stem rust, caused by the fungus Puccinia graminis f. sp. tritici (Pgt), poses a substantial threat to global wheat production. Utilizing stem rust resistance (Sr) genes represents an economically viable, effective, and environmentally friendly approach to disease control. In this study, gene postulation, molecular testing, and pedigree analysis were used to identify the presence of Sr genes in 45 wheat cultivars. In addition, the resistance of these cultivars was evaluated against two predominant Pgt races, 34MRGQM and 21C3CTHTM, at the adult-plant stage during 2021–2022. The results identify seven Sr genes (Sr31, Sr38, Sr30, SrTmp, Sr22, Sr19, and Sr5) within 35 wheat cultivars. Among these, 23 cultivars contained Sr31, whereas Sr5 and SrTmp were present in four cultivars each. Han 5316, Shimai 15, Shiyou 20, and Kenong 1006 exhibited the presence of Sr19, Sr22, Sr30, and Sr38, respectively. Molecular studies confirmed the absence of Sr25 and Sr26 in any of the wheat cultivars. During field evaluation, 37 (82.2%) and 39 (86.7%) wheat cultivars demonstrated resistance to races 34MRGQM and 21C3CTHTM, respectively. Moreover, 33 wheat cultivars (73.3%) exhibited resistance to all the tested races. These study findings will significantly contribute to future research in wheat pre-breeding and abiotic stress tolerance. [ABSTRACT FROM AUTHOR]

Background: Scarce research has reported the efficacy and safety of laparoscopic distal gastrectomy in elderly patients. This retrospective study aimed to compare the outcomes of laparoscopic and open distal gastrectomy for advanced gastric cancer in elderly patients. Methods: A total of 303 elderly patients who underwent distal gastrectomy for advanced gastric cancer from June 2017 to June 2021 were enrolled. Variables used to calculate propensity score matching included sex, age, body mass index, American Society of Anesthesiologists, history of diabetes, and history of hypertension. The statistical significance of continuous variables was tested using an independent sample t test. chi-square or Fisher's exact tests were used for categorical variables. Kaplan–Meier curve and log-rank test were used for the evaluation of 3-year overall survival and recurrence-free survival. Results: After performing 1:1 propensity score matching, 248 patients were included for analysis (laparoscopic = 124, open = 124). Compared with the open group, the laparoscopic group showed significant advantages in estimated blood loss (P < 0.001), pain scale on the first postoperative day (P = 0.002), time to first flatus (P = 0.004), time to first liquid diet (P = 0.005), hospital stays (P < 0.001), and total complications (P = 0.011), but devoted much more operation time (P < 0.001). No statistical difference was observed between the two groups in 3-year recurrence-free survival (P = 0.315) or overall survival (P = 0.159). Conclusions: Our analysis demonstrated that laparoscopic surgery had the advantages of less intraoperative blood loss, fewer postoperative complications, and faster postoperative recovery in distal gastrectomy for advanced gastric, indicating that laparoscopic distal gastrectomy is safe and effective for treating elderly patients with distal gastric cancer. [ABSTRACT FROM AUTHOR]

In response to the problem that current multi-city multi-pollutant prediction methods based on one-dimensional undirected graph neural network models cannot accurately reflect the two-dimensional spatial correlations and directedness, this study proposes a four-dimensional directed graph model that can capture the two-dimensional spatial directed information and node correlation information related to multiple factors, as well as extract temporal correlation information at different times. Firstly, A four-dimensional directed GCN model with directed information graph in two-dimensional space was established based on the geographical location of the city. Secondly, Spectral decomposition and tensor operations were then applied to the two-dimensional directed information graph to obtain the graph Fourier coefficients and graph Fourier basis. Thirdly, the graph filter of the four-dimensional directed GCN model was further improved and optimized. Finally, an LSTM network architecture was introduced to construct the four-dimensional directed GCN-LSTM model for synchronous extraction of spatio-temporal information and prediction of atmospheric pollutant concentrations. The study uses the 2020 atmospheric six-parameter data of the Taihu Lake city cluster and applies canonical correlation analysis to confirm the data's temporal, spatial, and multi-factor correlations. Through experimentation, it is verified that the proposed 4D-DGCN-LSTM model achieves a MAE reduction of 1.12%, 4.91%, 5.62%, and 11.67% compared with the 4D-DGCN, GCN-LSTM, GCN, and LSTM models, respectively, indicating the good performance of the 4D-DGCN-LSTM model in predicting multiple types of atmospheric pollutants in various cities. [ABSTRACT FROM AUTHOR]

Simple Summary: Protozoa that parasitize within host cells can avoid direct damage from the host immune system. In this review, we have revealed some key factors and signaling pathways that inhibit host cell apoptosis, regulate cell apoptosis signal transduction, and interfere with the molecular mechanisms of cell apoptosis. These findings provide important clues to further understand the infection mechanism and pathogenesis of Apicomplexan protozoa and a theoretical basis for the development of new therapeutic strategies and preventive measures. Apicomplexan protozoa, which are a group of specialized intracellular parasitic protozoa, infect humans and other animals and cause a variety of diseases. The lack of research on the interaction mechanism between Apicomplexan protozoa and their hosts is a key factor restricting the development of new drugs and vaccines. In the early stages of infection, cell apoptosis is inhibited by Apicomplexan protozoa through their interaction with the host cells; thereby, the survival and reproduction of Apicomplexan protozoa in host cells is promoted. In this review, the key virulence proteins and pathways are introduced regarding the inhibition of cell apoptosis by the interaction between the protozoa and their host during the early stage of Apicomplexan protozoa infection. It provides a theoretical basis for the development of drugs or vaccines for protozoal diseases. [ABSTRACT FROM AUTHOR]

Cigarettes contain various chemicals that cause damage to nerve cells. Exposure to cigarette smoke (CS) causes insulin resistance (IR) in nerve cells. However, the mechanisms for a disorder in the cigarette-induced insulin signaling pathway and in neurotoxicity remain unclear. Therefore, we evaluated, by a series of pathology analyses and behavioral tests, the neurotoxic effects of chronic exposure to CS on C57BL/6 mice. Mice exposed to CS with more than 200 mg/m3 total particulate matter (TPM) exhibited memory deficits and cognitive impairment. Pathological staining of paraffin sections of mouse brain tissue revealed that CS-exposed mice had, in the brain, neuronal damage characterized by thinner pyramidal and granular cell layers and fewer neurons. Further, the exposure of SH-SY5Y cells to cigarette smoke extract (CSE) resulted in diminished insulin sensitivity and reduced glucose uptake in a dose-dependent fashion. The PI3K/GSK3 insulin signaling pathway is particularly relevant to neurotoxicity. microRNAs are involved in the PI3K/GSK3β/p-Tau pathway, and we found that cigarette exposure activates miR-153-3p, decreases PI3K regulatory subunits PIK3R1, and induces Tau hyperphosphorylation. Exposure to an miR-153 inhibitor or to a PI3K inhibitor alleviated the reduced insulin sensitivity caused by CS. Therefore, our results indicate that miR-153-3p, via PIK3R1, causes insulin resistance in the brain, and is involved in CS-induced neurotoxicity. [ABSTRACT FROM AUTHOR]

Hot-deformation pre-bonding is a newly developed method to avoid smelting defects in large stainless steel billets, in which the high-quality interfacial connection needs to be realized as a key guarantee. In this work, a novel electromagnetic shocking treatment (EST) method, as a special way to apply an external physical field, was proposed to introduce the hot-deformation pre-bonded austenitic stainless steel. It is demonstrated that EST can efficiently optimize the hot-deformation pre-bonded interface and restore the tensile properties of pre-bonded stainless steel within several seconds. The interface healing behaviors induced by EST were analyzed via the in situ observation of an optical micrograph, scanning electron micrograph, as well as electron backscattering diffraction. The optimization of the interface connection is mainly attributed to the fact that EST can act on the interface locally, leading to the healing of voids at the interface accompanied by a reduction in stress and the decomposition of oxide. In addition, EST plays another important role in adjusting the texture difference at both ends of the interface, which can further improve the mechanical properties. The results suggest that EST exhibits great potential in promoting the hot-deformation pre-bonding of large billets. [ABSTRACT FROM AUTHOR]

As for the problem that the traditional single depth prediction model has poor strain capacity to the prediction results of time series data when predicting lake eutrophication, this study takes the multi-factor water quality data affecting lake eutrophication as the main research object. A deep reinforcement learning model is proposed, which can realize the mutual conversion of water quality data prediction models at different times, select the optimal prediction strategy of lake eutrophication at the current time according to its own continuous learning, and improve the reinforcement learning algorithm. Firstly, the greedy factor, the fixed parameter of Agent learning training in reinforcement learning, is introduced into an arctangent function and the mean value reward factor is defined. On this basis, three Q estimates are introduced, and the weight parameters are obtained by calculating the realistic value of Q, taking the average value and the minimum value to update the final Q table, so as to get an Improved MIMO-DD-3Q Learning model. The preliminary prediction results of lake eutrophication are obtained, and the errors obtained are used as the secondary input to continue updating the Q table to build the final Improved MIMO-DD-3Q Learning model, so as to achieve the final prediction of water eutrophication. In this study, multi-factor water quality data of Yongding River in Beijing were selected from 0:00 on July 26, 2021 to 0:00 on September 5, 2021. Firstly, data smoothing and principal component analysis were carried out to confirm that there was a certain correlation between all factors in the occurrence of lake eutrophication. Then, the Improved MIMO-DD-3Q Learning prediction model was used for experimental verification. The results show that the Improved MIMO-DD-3Q Learning model has a good effect in the field of lake eutrophication prediction. [ABSTRACT FROM AUTHOR]

New colonies of Formosan subterranean termites are founded by monogamous pairs. During swarming season, alates (winged reproductives) leave their parental colony. After swarming, they drop to the ground, shed their wings, and male and female dealates find suitable nesting sites where they mate and become kings and queens of new colonies. The first generation of offspring is entirely dependent on the nutritional resources of the founder pair consisting of the fat and protein reserves of the dealates and their microbiota, which include the cellulose-digesting protozoa and diverse bacteria. Since termite kings and queens can live for decades, mate for life and colony success is linked to those initial resources, we hypothesized that gut microbiota of founders affect pair formation. To test this hypothesis, we collected pairs found in nest chambers and single male and female dealates from four swarm populations. The association of three factors (pairing status, sex of the dealates and population) with dealate weights, total protozoa, and protozoa Pseudotrichonympha grassii numbers in dealate hindguts was determined. In addition, Illumina 16S rRNA gene sequencing and the QIIME2 pipeline were used to determine the impact of those three factors on gut bacteria diversity of dealates. Here we report that pairing status was significantly affected by weight and total protozoa numbers, but not by P. grassii numbers and bacteria diversity. Weight and total protozoa numbers were higher in paired compared to single dealates. Males contained significantly higher P. grassii numbers and bacteria richness and marginally higher phylogenetic diversity despite having lower weights than females. In conclusion, this study showed that dealates with high body weight and protozoa numbers are more likely to pair and become colony founders, probably because of competitive advantage. The combined nutritional resources provided by body weight and protozoa symbionts of the parents are important for successful colony foundation and development. [ABSTRACT FROM AUTHOR]

The introduction of customized bus (CB) service intends to expand and elevate existing transit service, which offers an efficient and sustainable alternative to serve commuters. A probabilistic model is proposed to optimize CB service with mixed vehicle sizes in an urban setting considering stochastic bus arrival time and spatiotemporal demand, which minimizes total cost subject to bus capacity and time window constraints. The studied optimization problem is combinatorial with many decision variables including vehicle assignment, bus routes, timetables, and fleet size. A heuristic algorithm is developed, which integrates a hybrid genetic algorithm (HGA) and adaptive destroy-and-repair (ADAR) method. The efficiency of HGA-ADAR is demonstrated through numerical comparisons to the solutions obtained by LINGO and HGA. Numerical instances are carried out, and the results suggested that the probabilistic model considering stochastic bus arrival time is valuable and can dramatically reduce the total cost and early and late arrival penalties. A case study is conducted in which the proposed model is applied to optimize a real-world CB service in Xi'an, China. The relationship between decision variables and model parameters is explored. The impacts of time window and variance of bus arrival time, which significantly affect service reliability, are analysed., Author(s): Qian Sun [1]; Steven Chien [1,2]; Dawei Hu (corresponding author) [1]; Xiqiong Chen [1] 1. Introduction Public transportation agencies are experimenting with on-demand and shared technologies to augment traditional [...]

β-Ga2O3 nanostructures are attractive wide-band-gap semiconductor materials as they exhibit promising photoelectric properties and potential applications. Despite the extensive efforts on β-Ga2O3 nanowires, investigations into β-Ga2O3 nanotubes are rare since the tubular structures are hard to synthesize. In this paper, we report a facile method for fabricating β-Ga2O3 nanotubes using pre-synthesized GaSb nanowires as sacrificial templates. Through a two-step heating-treatment strategy, the GaSb nanowires are partially oxidized to form β-Ga2O3 shells, and then, the residual inner parts are removed subsequently in vacuum conditions, yielding delicate hollow β-Ga2O3 nanotubes. The length, diameter, and thickness of the nanotubes can be customized by using different GaSb nanowires and heating parameters. In situ transmission electron microscopic heating experiments are performed to reveal the transformation dynamics of the β-Ga2O3 nanotubes, while the Kirkendall effect and the sublimation process are found to be critical. Moreover, photoelectric tests are carried out on the obtained β-Ga2O3 nanotubes. A photoresponsivity of ~25.9 A/W and a detectivity of ~5.6 × 1011 Jones have been achieved with a single-β-Ga2O3-nanotube device under an excitation wavelength of 254 nm. [ABSTRACT FROM AUTHOR]