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A comprehensive understanding of the distribution pattern and driving factors of population aging at the country level in China is fundamental for enhancing the governance capacity of the governing authorities and implementing a national strategy to actively cope with the aging society. On the basis of the 2000, 2010, and 2020 census data on the Chinese population, we determined the distribution pattern of population aging in China over the past 20 years at the county level and adopted a fractional response model to identify the main influencing factors of such spatial distribution from three perspectives: the natural environment, socioeconomic factors, and population migration. The following observations were made: 1) During the past 20 years, most counties in China have entered into an "aging society," some counties have entered into an "aged society," and counties in the Chengdu-Chongqing region, central Inner Mongolia, and peripheral Yangtze River Delta have entered into a "hyper-aged society." 2) The spatial structure of the distribution of population aging exhibited various patterns. Overall, the Hu Line is a clear demarcation for the distribution, with the degree of aging of the counties in the southeastern half of the line being generally higher than that of the counties in the northwestern half and maintaining a certain degree of stability. During the past 20 years, the aging population has shown a gradient diffusion of the characteristics of the Eastern monsoon region―Northwest arid region―Qinghai-Tibet Plateau region. From a local perspective, population aging presents various structural characteristics, such as "homogenization," "reverse core-edge," and "core-edge" spatial structures. 3) Significant regional differences exist in population aging. Vast differences in population aging between different ecological regions, between urban and rural areas, and between ethnic and non-ethnic autonomous regions are obvious, and these differences tend to expand further. 4) Natural factors have laid the macro pattern of the distribution of population aging. Socioeconomic factors are the main driving force of the aging process, and population migration has played an important role in restructuring the aging space pattern. This study provides a scientific basis for optimization of the spatial allocation of pension resources, and different regions can actively respond to the formulation and improvement of differentiated policies for population aging.

Simple Summary: We explored the role of exercise in promoting hippocampal neurogenesis and memory function in T2DM mice through the Irisin/TLR4/MyD88/NF-κB signaling pathway. Ten weeks of exercise training significantly reduced inflammation in the DG region caused by T2DM and promoted hippocampal neurogenesis and memory function. However, these positive effects could be reversed by treatment with Cyclo RGDyk, which blocks irisin receptor signaling. The results suggest that exercise may be an effective strategy for alleviating cognitive decline caused by diabetes by activating the irisin signaling pathway. Neuroinflammation is a major feature of type 2 diabetic mellitus (T2DM), adversely affecting hippocampal neurogenesis. However, the precise mechanism is not fully understood, and therapeutic approaches are currently lacking. Therefore, we determined the effects of exercise on neuroinflammation and hippocampal neurogenesis in T2DM mice, with a specific focus on understanding the role of the irisin and related cascade pathways in modulating the beneficial effects of exercise in these processes. Ten-week exercise significantly decreased T2DM-induced inflammation levels and markedly promoted hippocampal neurogenesis and memory function. However, these positive effects were reversed by 10 weeks of treatment with cyclo RGDyk, an inhibitor of irisin receptor signaling. Additionally, exercise helped reduce the M1 phenotype polarization of hippocampal microglia in diabetic mice; this effect could be reversed with cyclo RGDyk treatment. Moreover, exercise markedly increased the levels of fibronectin type III domain-containing protein 5 (FNDC5)/irisin protein while decreasing the expression of Toll-like receptor 4 (TLR4), myeloid differential protein-88 (MyD88), and nuclear factor kappa-B (NF-κB) in the hippocampus of T2DM mice. However, blocking irisin receptor signaling counteracted the down-regulation of TLR4/MyD88/NF-κB in diabetic mice undergoing exercise intervention. Conclusively, exercise appears to be effective in reducing neuroinflammation and enhancing hippocampal neurogenesis and memory in diabetes mice. The positive effects are involved in the participation of the irisin/TLR4/MyD88/NF-κB signaling pathway, highlighting the potential of exercise in the management of diabetic-induced cognitive decline. [ABSTRACT FROM AUTHOR]

Intratumoral heterogeneity (ITH) is a fundamental property of cancer; however, the origins of ITH remain poorly understood. We performed single-cell transcriptome profiling of peritoneal carcinomatosis (PC) from 15 patients with gastric adenocarcinoma (GAC), constructed a map of 45,048 PC cells, profiled the transcriptome states of tumor cell populations, incisively explored ITH of malignant PC cells and identified significant correlates with patient survival. The links between tumor cell lineage/state compositions and ITH were illustrated at transcriptomic, genotypic, molecular and phenotypic levels. We uncovered the diversity in tumor cell lineage/state compositions in PC specimens and defined it as a key contributor to ITH. Single-cell analysis of ITH classified PC specimens into two subtypes that were prognostically independent of clinical variables, and a 12-gene prognostic signature was derived and validated in multiple large-scale GAC cohorts. The prognostic signature appears fundamental to GAC carcinogenesis and progression and could be practical for patient stratification. Single-cell analysis of gastric cancer samples tracks the cell of origin of metastatic lesions and identifies an independent prognostic signature of the clinical outcome., Author(s): Ruiping Wang [sup.1] , Minghao Dang [sup.1] , Kazuto Harada [sup.2] [sup.12] , Guangchun Han [sup.1] , Fang Wang [sup.3] , Melissa Pool Pizzi [sup.2] , Meina Zhao [sup.2] [...]

Alternanthera sessilis is considered the closest relative to the invasive weed Alternanthera philoxeroides in China, making it an important native species for studying the invasive mechanisms and adaptations of A. philoxeroides. Chloroplasts play a crucial role in a plant's environmental adaptation, with their genomes being pivotal in the evolution and adaptation of both invasive and related species. However, the chloroplast genome of A. sessilis has remained unknown until now. In this study, we sequenced and assembled the complete chloroplast genome of A. sessilis using high-throughput sequencing. The A. sessilis chloroplast genome is 151,935 base pairs long, comprising two inverted repeat regions, a large single copy region, and a small single copy region. This chloroplast genome contains 128 genes, including 8 rRNA-coding genes, 37 tRNA-coding genes, 4 pseudogenes, and 83 protein-coding genes. When compared to the chloroplast genome of the invasive weed A. philoxeroides and other Amaranthaceae species, we observed significant variations in the ccsA, ycf1, and ycf2 regions in the A. sessilis chloroplast genome. Moreover, two genes, ccsA and accD, were found to be undergoing rapid evolution due to positive selection pressure. The phylogenetic trees were constructed for the Amaranthaceae family, estimating the time of independent species formation between A. philoxeroides and A. sessilis to be approximately 3.5186–8.8242 million years ago. These findings provide a foundation for understanding the population variation within invasive species among the Alternanthera genus. [ABSTRACT FROM AUTHOR]

Purpose: Though exercise generates beneficial effects on diabetes‐associated cardiac damage, the underlying mechanism is largely unclear. Therefore, we prescribed a program of 8‐week treadmill training for type 2 diabetes mellitus (T2DM) rats and determined the role of irisin signaling, via interacting with AMP‐activated protein kinase (AMPK), in mediating the effects of exercise on myocardial injuries and mitochondrial fission. Methods: Forty 8‐week‐old male Wistar rats were randomly divided into groups of control (Con), diabetes mellitus (DM), diabetes plus exercise (Ex), and diabetes plus exercise and Cyclo RGDyk (ExRg). Ex and ExRg rats received 8 weeks of treadmill running, and the rats in the ExRg group additionally were treated with a twice weekly injection of Cyclo RGDyk, an irisin receptor‐αV/β5 antagonist. At the end of the experiment, murine blood samples and heart tissues were collected and analyzed with methods of ELISA, Western blot, real‐time quantitative polymerase chain reaction, as well as immunofluorescence staining. Results: Exercise effectively mitigated T2DM‐related hyperglycemia, hyperinsulinemia, lipid dysmetabolism, and inflammation, which could be diminished by Cyclo RGDyk treatment. Additionally, exercise alleviated T2DM‐induced myocardial injury and excessive mitochondrial fission, whereas the beneficial effects were blocked by the administration of Cyclo RGDyk. T2DM significantly decreased serum irisin concentrations and fibronectin type III domain‐containing protein 5 (FNDC5)/irisin gene and protein expression levels in the rat heart, whereas exercise could rescue T2DM‐reduced FNDC5/irisin expression. Blocking irisin receptor signaling diminished the exercise‐alleviated mitochondrial fission protein expression and elevated AMPK phosphorylation. Conclusion: Exercise is effective in mitigating diabetes‐related insulin resistance, metabolic dysfunction, and inflammation. Irisin signaling engages in exercise‐associated beneficial effects on myocardial injury and excessive mitochondrial fission in diabetes rats involving elevated AMPK phosphorylation. [ABSTRACT FROM AUTHOR]

The BeiDou-3 system (BDS-3) broadcasts PPP-B2b signals to provide real-time PPP service. Compared with the traditional PPP technique, the PPP-B2b service broadcasts corrections through satellite links, covers a wide area, and is independent of the internet. With the PPP-B2b service, users can obtain high-precision positioning information through the real-time PPP. Many studies have been conducted to evaluate the positioning performance of PPP-B2b. In theory, high-precision timing information could also be obtained through PPP-B2b. With the development of science and technology, the need for highly accurate time measurement, even at the sub-nanosecond level, is experiencing significant growth. However, the GNSS standard timing service can hardly meet these requirements. This contribution analyzes the timing performance of the PPP-B2b service and proposes a sub-nanosecond precise timing method of Coordinated Universal Time (UTC) based on the PPP-B2b service. BDS-3 and GPS observations from nine tracking stations and real-time collected PPP-B2b corrections over 516 days were used to analyze the performance of the proposed timing method. The results show that: (1) The difference between the PPP-B2b-restored UTC, which was realized by one-way timing with the UTC offsets in broadcast ephemeris, and UTC (NTSC), fluctuates within a few ns. (2) The timing uncertainty of the zero baseline based on the proposed method is better than 0.2 ns. (3) Compared with the post-processed PPP time transfer, the UTC dissemination uncertainty of the short and long baseline with the proposed method is better than 0.7 ns. The experiment results verified the feasibility of the proposed sub-nanosecond level precise UTC dissemination method based on the PPP-B2b service. [ABSTRACT FROM AUTHOR]

The filamentation temperature-sensitive H (FtsH) gene family is critical in regulating plant chloroplast development and photosynthesis. It plays a vital role in plant growth, development, and stress response. Although FtsH genes have been identified in a wide range of plants, there is no detailed study of the FtsH gene family in soybean (Glycine max). Here, we identified 34 GmFtsH genes, which could be categorized into eight groups, and GmFtsH genes in the same group had similar structures and conserved protein motifs. We also performed intraspecific and interspecific collinearity analysis and found that the GmFtsH family has large-scale gene duplication and is more closely related to Arabidopsis thaliana. Cis-acting elements analysis in the promoter region of the GmFtsH genes revealed that most genes contain developmental and stress response elements. Expression patterns based on transcriptome data and real-time reverse transcription quantitative PCR (qRT-PCR) showed that most of the GmFtsH genes were expressed at the highest levels in leaves. Then, GO enrichment analysis indicated that GmFtsH genes might function as a protein hydrolase. In addition, the GmFtsH13 protein was confirmed to be localized in chloroplasts by a transient expression experiment in tobacco. Taken together, the results of this study lay the foundation for the functional determination of GmFtsH genes and help researchers further understand the regulatory network in soybean leaf development. [ABSTRACT FROM AUTHOR]

Triboelectric nanogenerator (TENG) sensors can meet various application‐scenario requirements for their portability and sustainability. However, the multiple steps from manufacturing to assembly are generally tedious and manpower‐consuming, limiting further development of TENGs in industrialization. Herein, a printable heat‐resistant triboelectric layer‐electrode integration ink is prepared by doping MXene into Polyvinylpyrrolidone and methyl cellulose system for the first time, which is simple and environmentally friendly. And a facile method for manufacturing integrated‐forming TENG (IF‐TENG) sensors without additional electrode assembly based on 3D printing technology is reported, the resulting TENG is flexible and heat‐resistant (≤200 °C). On this basis, IF‐TENGs with simple and complex patterns are rapidly constructed without manual assembly, which can perceive different pressures and bending angles of arms efficiently in high‐temperature conditions. To improve the sensing accuracy, the prepared IF‐TENG sensors are implanted on the four main stress positions on an insole. The support vector machine (SVM) is used to process and analyze the output signals to improve the accuracy of gaits recognition. The mean area under the receiver operating characteristic curve (AUC) value of 0.99 is obtained after parameter optimization by the GridSearch algorithm, and the recognization of various gaits achieves a high accuracy of 96.7%. This work provides an advanced fabrication strategy for TENG with low labor costs and thermal stability, demonstrating great prospects of TENG in mass manufacturing and safety motion monitoring in high‐temperature environments. [ABSTRACT FROM AUTHOR]

The data explosion driven by advancements in genomic research, such as high-throughput sequencing techniques, is constantly challenging conventional methods used in genomics. In parallel with the urgent demand for robust algorithms, deep learning has succeeded in various fields such as vision, speech, and text processing. Yet genomics entails unique challenges to deep learning, since we expect a superhuman intelligence that explores beyond our knowledge to interpret the genome from deep learning. A powerful deep learning model should rely on the insightful utilization of task-specific knowledge. In this paper, we briefly discuss the strengths of different deep learning models from a genomic perspective so as to fit each particular task with proper deep learning-based architecture, and we remark on practical considerations of developing deep learning architectures for genomics. We also provide a concise review of deep learning applications in various aspects of genomic research and point out current challenges and potential research directions for future genomics applications. We believe the collaborative use of ever-growing diverse data and the fast iteration of deep learning models will continue to contribute to the future of genomics. [ABSTRACT FROM AUTHOR]

Abnormal electromagnetic radiation associated with seismic activity has been reported across a wide range of frequencies, but its primary energy is concentrated in the super-low-frequency (SLF) and extremely low-frequency (ELF) bands. To estimate the effect of the seismic radiation source, a radiator in a soil foundation was modeled as a horizontal electric dipole (HED), and the propagation characteristics of the electromagnetic fields were studied in the Earth–ionosphere cavity. The expressions of the electromagnetic fields could be obtained according to the reciprocity theorem. Therefore, a new algorithm named the numerical integral algorithm was proposed, which is suitable for both the SLF and ELF bands. The new algorithm was compared with the asymptotic approximation algorithm when the receiving point was not close to the field source and the antipode. The two algorithms were found to be in excellent agreement, confirming the validity of the new algorithm for SLF and ELF bands. [ABSTRACT FROM AUTHOR]

In this paper, for the UAV attitude angle stabilization system under constant flat wind disturbance, the fuzzy adaptive PID control theory is used to establish the equation of wind disturbance attitude angle stability system. After mastering the control rate of the system, the fuzzy controller is designed by using matlab, and the attitude angle dynamic simulation analysis was carried out. The results show that under the interference of flat wind, the longitudinal attitude of the UAV is changed by interference, and the fuzzy adaptive PD is used to control the attitude angle of the aircraft, which has faster tracking performance, smaller adjustment time than the traditional PD control. Thereby achieving better maneuverability and less steady-state error. Therefore, the fuzzy adaptive PD can better control the attitude stability of the UAV, improve the wind resistance of the UAV, and ensure the flight is safe and reliable.

Air temperature ( T a ) is a common meteorological element involved in many fields, such as surface energy exchange and water circulation. Consequently, accurate T a estimation is essential for the establishment of hydrological, climate, and environmental models. Unlike most studies concerned with the estimation of daily T a from land surface temperature, this study focused on the estimation of instantaneous T a from Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric profile products aboard the Terra and Aqua satellites. The applicability of various estimation methods was examined in two regions with different geomorphological and climate conditions, North and Southwest China. Specifically, the spatiotemporal trend of T a under clear sky conditions can be reflected by the atmospheric profile extrapolation and average methods. However, the accuracy of T a estimation was poor, with root mean square error (RMSE) ranging from 3.5 to 5.2 °C for North China and from 4.0 to 7.7 °C for Southwest China. The multiple linear regression model significantly improved the accuracy of T a estimation by introducing auxiliary data, resulting in RMSE of 1.6 and 1.5 °C in North China and RMSE of 2.2 and 2.3 °C in Southwest China for the Terra and Aqua datasets, respectively. Since atmospheric profile products only provide information under clear sky conditions, a new multiple linear regression model was established to estimate the instantaneous T a under cloudy sky conditions independently from atmospheric profile products, resulting in RMSE of 1.9 and 1.9 °C in North China and RMSE of 2.5 and 2.8 °C in Southwest China, for the Terra and Aqua datasets, respectively. Finally, instantaneous T a products with high accuracy were generated for all-weather conditions in the study regions to analyze their T a spatial patterns. The accuracy of T a estimation varies depending on MODIS datasets, regions, elevation, and land cover types. [ABSTRACT FROM AUTHOR]

Simple Summary: To further explore the host recognition mechanism of the monophagous insect Agasicles hygrophila Selman and Vogt (Coleoptera: Chrysomelidae), scanning electron microscopy images were used to examine and compare the external morphology and distribution of the sensilla of this species in both male and female adults. The results showed that there were 12 types and 46 subtypes of sensilla, some of which displayed sexual dimorphism. We found a new type of sensor, reported here for the first time, and named it sensilla petal-shaped (Sps) based on its morphological characteristics. This sensor may be related to host recognition. In addition, the potential functions of the structure are discussed. This information extends the study of the sensorium of monophagous insects and lays a morphological foundation for the exploration of the host recognition mechanism of A. hygrophila. Agasicles hygrophila Selman and Vogt (Coleoptera: Chrysomelidae) is the key natural enemy of Alternanthera philoxeroides (Mart.) Griseb, an invasive weed worldwide. To understand the morphology of A. hygrophila and further explore the specific host localization mechanism, scanning electron microscopy was used to observe and study the morphological characteristics of sensilla on the head appendages, tarsi, and external genital segments of A. hygrophila. Twelve types and forty-six subtypes of sensilla were observed. These contain various types of head appendices, including sensilla chaetica, sensilla trichodea, sensilla basiconca, sensilla coeloconica, sensilla styloconica, Böhm bristles, sensilla campaniform, sensilla terminal, sensilla dome, sensilla digit-like, sensilla aperture, and many subtypes. A new type of sensor was reported for the first time, which may be related to host plant recognition. This sensor was located on the distal segment of the maxillary palps of A. hygrophila and was named as sensilla petal-shaped based on its morphological characteristics. Sensilla chaetica, sensilla trichodea, and sensilla basiconca are also found on the tarsi and external genital segments. In addition, sensilla basiconica 4, sensilla coeloconica 1 and 2, sensilla styloconica 2, Böhm bristles 2, and sensilla campaniform 1 were only found in females. On the contrary, sensilla styloconica 3, sensilla coeloconica 3, and sensilla dome were only found in males. Numbers and sizes of the sensilla were also different between males and females. The potential functions related to structure were discussed in comparison with previous investigations on beetles and other monophagous insects. Our results provide a microscopic morphological basis for further research on the localization and recognition mechanism of A. hygrophila and its obligate host. [ABSTRACT FROM AUTHOR]

The use of social media has changed since the outbreak of coronavirus disease 2019 (COVID-19). However, little is known about the gender disparity in social media use for nonspecific and health-specific issues before and during the COVID-19 pandemic. Based on a gender difference perspective, this study aimed to examine how the nonspecific and health-specific uses of social media changed in 2017–2020. The data came from the Health Information National Trends Survey Wave 5 Cycle 1–4. This study included 10,426 participants with complete data. Compared to 2017, there were higher levels of general use in 2019 and 2020, and an increased likelihood of health-related use in 2020 was reported among the general population. Female participants were more likely to be nonspecific and health-specific users than males. Moreover, the relationship of gender with general use increased in 2019 and 2020; however, concerning health-related use, it expanded in 2019 but narrowed in 2020. The COVID-19 global pandemic led to increased use of social media, especially for health-related issues among males. These findings further our understanding of the gender gap in health communication through social media, and contribute to targeted messaging to promote health and reduce disparities between different groups during the pandemic. [ABSTRACT FROM AUTHOR]

For the attitude active disturbance rejection control (ADRC) system of quadrotor unmanned aerial vehicle (UAV), when the feedback signal of attitude angle measurement is polluted by sensor noise, the extended state observer (ESO) of the traditional nonlinear nonsmoothed fal function is not accurate enough. Based on this problem, a new nonlinear smooth galn function is constructed to redesign ESO, and the performance is verified by Mallab/Simulink simulation software. The simulation results show that compared with the traditional fal function, the galn function constructed is smooth and has better convergence, and can better reflect the engineering idea of "large error and small gain". The improved ESO based on galn function has stronger error following capability. The quadrotor attitude ADRC system designed by improved ESO has better tracking and anti-jamming capability. [ABSTRACT FROM AUTHOR]

Fault diagnosis of industrial equipments is extremely important for the safety requirements of modern production processes. Lately, deep learning (DL) has been the mainstream fault diagnosis tool due to its powerful representational ability in learning and flexibility. However, most of the existing DL-based methods may suffer from two drawbacks: Firstly, only one metric is used to construct networks, thus multiple kinds of potential relationships between nodes are not explored. Secondly, there are few studies on how to obtain better node embedding by aggregating the features of different neighbors. To compensate for these deficiencies, an advantageous intelligent diagnosis scheme termed AE-MSGCN is proposed, which employs graph convolutional networks (GCNs) on multi-layer networks in an innovative manner. In detail, AE is carried out to extract deep representation features in process measurement and then combined with different metrics (i.e., K-nearest neighbors, cosine similarity, path graph) to construct the multi-layer networks for better multiple interaction characterization among nodes. After that, intra-layer convolutional and inter-layer convolutional methods are adopted for aggregating extensive neighbouring information to enrich the representation of nodes and diagnosis performance. Finally, a benchmark platform and a real-world case both verify that the proposed AE-MSGCN is more effective and practical than the existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Aiming at the typical faults in the coal mills operation process, the kernel extreme learning machine diagnosis model based on variational model feature extraction and kernel principal component analysis is offered. Firstly, the collected signals of vibration and loading force, corresponding to typical faults of coal mill, are decomposed by variational model decomposition, and the intrinsic model functions at different scales are obtained. Then, the eigenvectors consisting of feature energy and sample entropy in these functions are respectively calculated, and the kernel principal component analysis is used for noise removal and dimensionality reduction. Finally, the kernel extreme learning machine model is trained and tested with the dimension reduced feature vector as input and the corresponding coal mill state as output. The results show that the variational model decomposition extraction can improve the input features of the model compared with the single eigenvector model, and the kernel principal component analysis method can significantly reduce the information redundancy and the correlation of eigenvectors, which can effectively save time and cost, and improve the prediction performance of the model to some extent. The establishment of this model provides a new idea for the study of coal mill fault diagnosis. [ABSTRACT FROM AUTHOR]