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Abstract Objective This study investigates the comparative short-term and long-term efficacy of endoscopic lipolysis and liposuction versus traditional open excision in the treatment of gynecomastia. Methods A total of 140 male patients diagnosed with gynecomastia and admitted to our hospital from April 2021 to May 2022 were enrolled in this study. Patients were randomly assigned to two groups based on the surgical treatment method: the control group (traditional open excision, n = 70) and the observation group (liposuction under endoscope, n = 70). Comprehensive demographic and clinical data were collected for both groups. Surgical indicators, postoperative complication rates, and pain levels measured using the Visual Analog Scale (VAS) one month post-surgery were observed and compared. Additionally, recurrence rates and patient satisfaction scores were evaluated one year after the procedure. Results There were no significant differences in demographic and clinical characteristics between the two groups (P > 0.05). The observation group exhibited shorter incision lengths, reduced operation times, and decreased hospital stays compared to the control group (P 0.05). However, the observation group achieved higher scores in terms of chest appearance, wound scarring, nipple and areola aesthetics, and overall satisfaction (P

Semantic change detection (SCD) extends the traditional change detection (CD) task to simultaneously identify the change areas and their corresponding land cover categories in bi-temporal images. This “from-to” change information holds significant value in numerous practical applications and is increasingly garnering attention in the remote sensing domain. However, prevalent challenges such as loss of detail and class imbalance significantly hinder the efficacy of SCD applications. To address this challenge, we propose a novel semantic information collaboration network (SIC-Net). This network incorporates a detail capture path and a spatial-temporal semantic coordination module aiming to effectively execute SCD by fusing detailed information with contextual features and harnessing synergies between binary CD and semantic segmentation. Additionally, we introduce a pseudo-label growth algorithm to mitigate the substantial loss of sample category information. The experimental results on two widely used SCD datasets demonstrate that SIC-Net outperforms other methods across various evaluation metrics, achieving SeK of 23.96% and 61.29%, respectively. These findings not only validate the effectiveness of the SIC-Net but also provide new ideas and directions for future research in the field of remote sensing, particularly in SCD.

This study introduces an innovative UAV cruise data collection path planning approach using the manta ray foraging optimization (MRFO) algorithm to enhance efficiency and energy utilization in forest ecosystem monitoring. Traditionally reliant on costly manual patrols, this method leverages UAVs and ground-based sensors for data collection. The approach begins with a self-organized clustering algorithm for sensors, minimizing communication between UAVs and sensors. It then refines the UAV’s energy consumption equation by integrating propulsion energy needs, actual terrain data, and wind effects. Compared to other heuristic algorithms, the MRFO algorithm demonstrates superior performance in path planning, particularly for complex engineering optimization problems, displaying heightened adaptability and efficiency. Comparative experimental results on real terrain data and MATLAB r2018b simulation show that the error between the corrected energy calculation equation and the actual value is controlled within 5%, and the accuracy is improved by 10% over the original equation. Meanwhile, the ability of the MRFO algorithm to quickly construct approximate high-quality solutions with shortest path lengths in a limited number of iterations validates its potential in practical applications. The α-hop clustering algorithm used in this paper has a huge advantage in space and time complexity compared with existing clustering algorithms, and the accuracy of data extraction is relatively improved by 7.57% and 6.95%. Real forest digital elevation model (DEM) terrain data was introduced in this study, and the method improves the energy utilization of UAV data collection and also provides a comprehensive and detailed solution to the existing challenges faced in the field of forest data collection. Future research could consider combining the MRFO algorithm with other evolutionary classes of algorithms to take advantage of the algorithm’s fast convergence and high-precision properties to further enhance the application prospects in different scenarios.

Abstract Background RNA methylation modifications, such as N1-methyladenosine/N6-methyladenosine /N5-methylcytosine (m1A/m6A/m5C), are the most common RNA modifications and are crucial for a number of biological processes. Nonetheless, the role of RNA methylation modifications of m1A/m6A/m5C in the pathogenesis of renal interstitial fibrosis (RIF) remains incompletely understood. Methods Firstly, we downloaded 2 expression datasets from the GEO database, namely GSE22459 and GSE76882. In a differential analysis of these datasets between patients with and without RIF, we selected 33 methylation-related genes (MRGs). We then applied a PPI network, LASSO analysis, SVM-RFE algorithm, and RF algorithm to identify key MRGs. Results We eventually obtained five candidate MRGs (WTAP, ALKBH5, YTHDF2, RBMX, and ELAVL1) to forecast the risk of RIF. We created a nomogram model derived from five key MRGs, which revealed that the nomogram model may be advantageous to patients. Based on the selected five significant MRGs, patients with RIF were classified into two MRG patterns using consensus clustering, and the correlation between the five MRGs, the two MRG patterns, and the genetic pattern with immune cell infiltration was shown. Moreover, we conducted GO and KEGG analyses on 768 DEGs between MRG clusters A and B to look into their different involvement in RIF. To measure the MRG patterns, a PCA algorithm was developed to determine MRG scores for each sample. The MRG scores of the patients in cluster B were higher than those in cluster A. Conclusions Ultimately, we concluded that cluster A in the two MRG patterns identified on these five key m1A/m6A/m5C regulators may be associated with RIF.

Abstract Recent developed interfacial solar brine crystallizers, which employ solar‐driven water evaporation for salts crystallization from the near‐saturation brine to achieve zero liquid discharge (ZLD) brine treatment, are promising due to their excellent energy efficiency and sustainability. However, most existing interfacial solar crystallizers are only tested using NaCl solution and failed to maintain high evaporation capability when treating real seawater due to the scaling problem caused by the crystallization of high‐valent cations. Herein, an artificial tree solar crystallizer (ATSC) with a multi‐branched and interconnected open‐cell cellular structure that significantly increased evaporation surface is rationally designed, achieving an ultra‐high evaporation rate (2.30 kg m−2 h−1 during 2 h exposure) and high energy efficiency (128%) in concentrated real seawater. The unit cell design of ATSC promoted salt crystallization on the outer frame rather than the inner voids, ensuring that salt crystallization does not affect the continuous transport of brine through the pores inside the unit cell, thus ATSC can maintain a stable evaporation rate of 1.94 kg m−2 h−1 on average in concentrated seawater for 80 h continuous exposure. The design concept of ATSC represents a major step forward toward ZLD treatment of high‐salinity brine in many industrial processes is believed.

Abstract Background Soil microbiomes are considered a cornerstone of the next green revolution, and plant growth-promoting bacteria (PGPB) are critical for microbiome engineering. However, taking plant-beneficial microorganisms from discovery to agricultural application remains challenging, as the mechanisms underlying the interactions between beneficial strains and plants in native soils are still largely unknown. Increasing numbers of studies have indicated that strains introduced to manipulate microbiomes are usually eliminated in soils, while others have reported that application of PGPB as inocula significantly improves plant growth. This contradiction suggests the need for a deeper understanding of the mechanisms underlying microbe-induced growth promotion. Results We showed PGPB-induced long-term plant growth promotion after elimination of the PGPB inoculum in soils and explored the three-way interactions among the exogenous inoculum, indigenous microbiome, and plant, which were key elements of the plant growth-promoting process. We found the rhizosphere microbiome assembly was mainly driven by plant development and root recruitments greatly attenuated the influence of inocula on the rhizosphere microbiome. Neither changes in the rhizosphere microbiome nor colonization of inocula in roots was necessary for plant growth promotion. In roots, modification of DNA methylation in response to inoculation affects gene expression related to PGPB-induced growth promotion, and disruptions of the inoculation-induced DNA methylation patterns greatly weakened the plant growth promotion. Together, our results showed PGPB-induced DNA methylation modifications in roots mediated the promotion process and these modifications remained functional after elimination of the inoculum from the microbiome. Conclusion This study suggests a new mechanism in which PGPB affect DNA methylation in roots to promote plant growth, which provides important insights into microbiome–plant interactions and offers new strategies for plant microbiome engineering beyond the perspective of maintaining inoculum persistence in soils. Video abstract Graphical abstract

BackgroundBladder carcinoma is a common malignancy of the urinary system. The previous study showed that EPDR1 expression was significantly related to the carcinogenesis and progression of bladder carcinomaMethodsWe retrospectively reviewed the records of 621 patients who were newly diagnosed with bladder carcinoma between January 2018 and August 2020 at The Affiliated Hospital of Zunyi Medical University. We conducted immunohistochemistry of EPDR1 in tumor tissues. Meanwhile, tumor budding evaluation was also carried out by 2 independent experienced pathologists.Results80 patients were included in this study with a median age of 66 years (range; 42–88 years). 45% of the patients (36/80) were non-muscle-invasive bladder carcinoma patients, while 55% of muscle-invasive bladder carcinoma(44/80). The follow-up time was from 6 months to 36 months. We found that there were significant differences in expression of EPDR1 in the tumor pT stages(p

Methylation has a close relationship with immune reactions, metastasis, and cancer cell growth. Additionally, RNA methylation-related proteins have emerged as potential cancer therapeutic targets. The connection between the tumor microenvironment (TME) and methylation-related genes (MRGs) remains unclear. We explored the expression patterns of the MRGs in the genome and transcriptional fields of 796 prostate cancer (PCa) samples using two separate data sets. We identified a relationship between patient clinicopathological characteristics, prognosis, TME cell infiltrating qualities, and different MRG changes, as well as the identification of two distinct molecular groupings. Then, we formed an MRGs model to predict overall survival (OS), and we tested the accuracy of the model in patients with PCa. In addition, we developed a very accurate nomogram to improve the MRG model’s clinical applicability. The low-risk group had fewer tumor mutational burden (TMB), greater tumor immune dysfunction and exclusion (TIDE) ratings, fewer mutant genes, and better OS prospects. We discuss how MGRs may affect the prognosis, clinically important traits, TME, and immunotherapy responsiveness in PCa. In order to get a better understanding of MRGs in PCa, we could further explore the prognosis and create more effective immunotherapy regimens to open new avenues.

Semantic change detection (SCD) is a challenging task in remote sensing, which aims to locate and identify changes between the bi-temporal images, providing detailed “from-to” change information. This information is valuable for various remote sensing applications. Recent studies have shown that multi-task networks, with dual segmentation branches and single change branch, are effective in SCD tasks. However, these networks primarily focus on extracting contextual information and ignore spatial details, resulting in the missed or false detection of small targets and inaccurate boundaries. To address the limitations of the aforementioned methods, this paper proposed a spatial-temporal semantic perception network (STSP-Net) for SCD. It effectively utilizes spatial detail information through the detail-aware path (DAP) and generates spatial-temporal semantic-perception features through combining deep contextual features. Meanwhile, the network enhances the representation of semantic features in spatial and temporal dimensions by leveraging a spatial attention fusion module (SAFM) and a temporal refinement detection module (TRDM). This augmentation results in improved sensitivity to details and adaptive performance balancing between semantic segmentation (SS) and change detection (CD). In addition, by incorporating the invariant consistency loss function (ICLoss), the proposed method constrains the consistency of land cover (LC) categories in invariant regions, thereby improving the accuracy and robustness of SCD. The comparative experimental results on three SCD datasets demonstrate the superiority of the proposed method in SCD. It outperforms other methods in various evaluation metrics, achieving a significant improvement. The Sek improvements of 2.84%, 1.63%, and 0.78% have been observed, respectively.

Traditional models that employ CNNs as encoders do not sufficiently combine high-level features and low-level features. However, high-level features are rich in semantic information but lack spatial detail, while low-level features are the opposite. Therefore, the integrated utilization of multi-level features and the bridging of the gap between them is crucial to promote the accuracy of semantic segmentation. To address this issue, we presented communicating mutual attention (CMA) and communicating self-attention (CSA) modules to enhance the interaction and fusion of different levels of feature maps. On the one hand, CMA aggregates the global context information of high-level features into low-level features and embeds the spatial detail localization characteristics of low-level features in high-level features. On the other hand, the CSA module is deployed to integrate the spatially detailed representation of low-level features into the attention map of high-level features. We have experimented with the communicating attention network (CANet), a U-net-like network composed of multiple CMA and CSA modules, on the ISPRS Vaihingen and Potsdam datasets with mean F1-scores of 89.61% and 92.60%, respectively. The results demonstrate that CANet embodies superior performance in the semantic segmentation task of remote sensing of images.

Abstract Background Bladder cancer (BLCA) is a malignant urothelial carcinoma and has a high mortality rate. EPDR1 (ependymin related 1) is a type II transmembrane protein and related to calcium-dependent cell adhesion. Methods We explored the potential oncogenic roles of EPDR1 in BLCA basing on the multiple public datasets. Results We found that EPDR1 expression had a significant difference in BLCA and adjacent normal bladder tissues, and the level of EPDR1was up-regulated with advanced tumor stage and metastasis in BLCA. Meanwhile, the high expression group of EPDR1 had a shorter OS compared to the low or medium expression-group. Furthermore, EPDR1 expression was associated with tumor-infiltrating immune cells (TIICs), including NK cells, CD8 + T cells, CD4 + T cells, Macrophages cells, and so on. Moreover, EPDR1 also involved in several signaling pathways as well as PI3K/AKT pathway, Cytokine receptor interaction, and apoptosis. Conclusion EPDR1 can be used as a novel prognostic biomarker as well as an effective target for diagnosis and treatment in BLCA.

Heavy metals (HMs) have become a major environmental pollutant threatening ecosystems and human health. Although hyperaccumulators provide a viable alternative for the bioremediation of HMs, the potential of phytoremediation is often limited by the small biomass and slow growth rate of hyperaccumulators and HM toxicity to plants. Here, plant growth-promoting bacteria (PGPB)-assisted phytoremediation was used to enhance the phytoremediation of HM-contaminated soils. A PGPB with HM-tolerant (HMT-PGPB), Bacillus sp. PGP15 was isolated from the rhizosphere of a cadmium (Cd) hyperaccumulator, Solanum nigrum. Pot experiments demonstrated that inoculation with strain PGP15 could significantly increase the growth of S. nigrum. More importantly, strain PGP15 markedly improved Cd accumulation in S. nigrum while alleviating Cd-induced stress in S. nigrum. Specifically, PGP15 inoculation significantly decreased the contents of H2O2, MDA, and O2·− in S. nigrum, while the activities (per gram plant fresh weight) of SOD, APX, and CAT were significantly increased in the PGP15-inoculated plants compared with the control sample. These results suggested that the interactions between strain PGP15 and S. nigrum could overcome the limits of phytoremediation alone and highlighted the promising application potential of the PGPB-hyperaccumulator collaborative pattern in the bioremediation of HM-contaminated soils. Furthermore, the PGP15 genome was sequenced and compared with other strains to explore the mechanisms underlying plant growth promotion by HMT-PGPB. The results showed that core genes that define the fundamental metabolic capabilities of strain PGP15 might not be necessary for plant growth promotion. Meanwhile, PGP15-specific genes, including many transposable elements, played a crucial role in the adaptive evolution of HM resistance. Overall, our results improve the understanding of interactions between HMT-PGPB and plants and facilitate the application of HMT-PGPB in the phytoremediation of HM-contaminated soils.

DNA methylation is a rapid response strategy promoting plant survival under heavy metal (HM) stress. However, the roles of DNA methylation underlying plant adaptation to HM stress remain largely unknown. Here, we used pokeweed, a hyperaccumulator of manganese (Mn) and cadmium (Cd), to explore responses of plant to HM stress at phenotypic, transcriptional and DNA methylation levels. Mn- and Cd-specific response patterns were detected in pokeweed. The growth of pokeweed was both inhibited with exposure to excess Mn/Cd, but pokeweed distinguished Mn and Cd with different subcellular distributions, ROS scavenging systems, transcriptional patterns including genes involved in DNA methylation, and differentially methylated loci (DML). The number of DML between Mn/Cd treated and untreated samples increased with increased Mn/Cd concentrations. Meanwhile, pretreatment with NADPH oxidase inhibitors prior to HM exposure markedly reduced HM-induced reactive oxygen species (ROS), which caused reductions in expressions of DNA methylase and demethylase in pretreated samples. The increased levels of HM-induced demethylation were suppressed with alleviated ROS stress, and a series of HM-related methylated loci were also ROS-related. Taken together, our study demonstrates that different HMs affect different DNA methylation sites in a dose-dependent manner and changes in DNA methylation under Mn/Cd stress are partly mediated by HM-induced ROS.

Environmental assessments are important tasks for the long-term, sustainable development of cities. With the rapid urbanization in China, it is crucial to establish a City Sustainability Index (CSI) and evaluate the environmental conditions in major cities. However, most of the existing major sustainability indices/indicators are not able to assess cities at diverse levels of development using common axes of evaluation. In this work, we incorporate urban built-up areas extracted from high-resolution remote sensing images as indicators to measure the degree of urban development in city sustainability evaluation and propose a comprehensive index, i.e., the Urban Geographic Environment Index (UGEI). In order to eliminate the impact of urban development levels, UGEI mainly consists of area-averaged indices which are calculated from original indices and urban built-up areas. We adopt a comprehensive weighting method by using the analytic hierarchy process (AHP) method to weigh the high-level indicators and using the entropy weighting method to weigh low-level indicators. We evaluate the environmental conditions of 30 China’s provincial capitals from the aspects of pressure, state, response, and overall status. In addition, we analyze how diverse types of indicators affect the values of UGEIs. The major findings are as follows: (1) About half of the provincial capitals in China have poor sustainability in environmental conditions, and more environmental protection measures should be taken in developing cities; (2) the environmental conditions of the provincial capitals in China present a four-region distribution pattern, namely, the northeastern coastal region, northwest inland region, southwest region, and southeast region; (3) and indices based on urban built-up extents can be common axes of evaluation for cities at diverse levels of development. The proposed UGEI can serve as an effective and reliable index for sustainability evaluation in environmental conditions.

Increasing effort has been devoted to restoring coastal ecosystems to counteract their degradation globally. Restoration success of coastal ecosystems often relies on harnessing biotic interactions that shape the performance of foundation plant species. Crabs acting as essential ecosystem engineers and consumers are commonly present in coastal saltmarshes. Previous work has focused on the grazing effect of crabs on plants. In line with the idea of top-down control, plant performance (in terms of biomass or height) is often used as indicators of plant-animal interactions. However, crabs could also produce facilitative effects through non-consumptive behavior. It remains unclear if and to what extent the net effect of crabs on coastal plants is dependent on environmental context. Therefore, systematic assessments are needed to test if the reliability of plant performance as an indicator of plant-crab interactions is context-dependent. Here, we conducted field survey in two similar intertidal saltmarsh ecosystems along the Yellow Sea coast, eastern China, and examined the effects of crab burrowing, soil properties, and nutrient availability on plant (Suaeda salsa) performance using multiple regression and structural equation models. We found that crab burrow density was significantly correlated with plant height, which signals strong plant-crab interactions. Surprisingly, we observed opposite signs (positive vs. negative) of plant-crab relationships between the two study sites that have highly similar composition and structure of plant communities. A possible explanation is that soil compactness can mediate crab burrowing behavior, resulting in differential facilitative effects on the plants. Thus, the usefulness of plant height serving as an indicator of plant-crab interactions depends on environmental context to a substantial degree. More comprehensive indicators taking into account soil compactness may facilitate robust inference of plant-crab interactions. Our results highlight landscape-scale spatial heterogeneity and context-dependency of plant-animal interactions in coastal saltmarshes, and provide a useful implication to inferring nuanced biotic interactions using compressive biotic and abiotic indicators in coastal ecosystems.

A new definition method of ramp sections is proposed. Firstly, the extreme point extraction algorithm is designed. The results show that the extreme value sequence can not only achieve data compression, but also provide data for the definition of the ramp section. Secondly, several typical recognition methods of wind power ramp are compared. The results show that the ”event” as the unit of analysis is not possible to effectively distinguish between the ramp process and the transient process in the ramp event, which causes the prediction error of the ramp duration. Also, the non-representation of the threshold setting will also cause errors in the prediction and recognition of complex ramp events. In this article, the concepts of ramp section, slope point(SP) and stagnation point(STP) are introduced to realize the segmentation processing and analysis of ramp events, which can effectively distinguish the ramp process and transient process in ramp events and provide a strong guarantee for the accurate prediction of the ramp duration. In addition, the mathematical statistics method of cumulative probability is utilized to discuss the threshold setting. The ramp situation of a wind farm in Yunnan in 2014 is further identified and analyzed, and on this basis, the characteristics of the ramp sections are investigated, whose results show the applicability of this new definition.

Epigenetic variation may contribute to traits that are important in domestication, but how patterns of genetic and epigenetic variation differ between cultivated and wild plants remains poorly understood. In particular, we know little about how selection may shape epigenetic variation in natural and cultivated populations. In this study, we investigated 11 natural populations and 6 major cultivated populations using amplified fragment length polymorphism (AFLP) and methylation-sensitive AFLP (MS-AFLP or MSAP) markers to identify patterns of genetic and epigenetic diversity among Corydalis yanhusuo populations. We further explored correlations among genetic, epigenetic, alkaloidal, and climatic factors in natural and cultivated C. yanhusuo. We found support for a single origin for all cultivated populations, from a natural population which was differentiated from the other natural populations. The magnitude of FST based on AFLP was significantly correlated with that for MSAP in pairwise comparisons in both natural and cultivated populations, suggesting a relationship between genetic and epigenetic variation in C. yanhusuo. This relationship was further supported by dbRDA (distance-based redundancy analyses) where some of the epigenetic variation could be explained by genetic variation in natural and cultivated populations. Genetic variation was slightly higher in natural than cultivated populations, and exceeded epigenetic variation in both types of populations. However, epigenetic differentiation exceeded that of genetic differentiation among cultivated populations, while the reverse was observed among natural populations. The differences between wild and cultivated plants may be partly due to processes inherent to cultivation and in particular the differences in mode of reproduction. The importance of epigenetic compared to genetic modifications is thought to vary depending on reproductive strategies, and C. yanhusuo usually reproduces sexually in natural environments, while the cultivated C. yanhusuo are propagated clonally. In addition, alkaloid content of C. yanhusuo varied across cultivated populations, and alkaloid content was significantly correlated to climatic variation, but also to genetic (6.89%) and even more so to epigenetic (14.09%) variation in cultivated populations. Our study demonstrates that epigenetic variation could be important in cultivation of C. yanhusuo and serve as a source of variation for response to environmental conditions.

Aspergillus includes both plant pathogenic and beneficial fungi. Although endophytes beneficial to plants have high potential for plant growth promotion and improving stress tolerance, studies on endophytic lifestyles and endophyte-plant interactions are still limited. Here, three endophytes belonging to Aspergillus, AS31, AS33, and AS42, were isolated. They could successfully colonize rice roots and significantly improved rice growth. The genomes of strains AS31, AS33, and AS42 were sequenced and compared with other Aspergillus species covering both pathogens and endophytes. The genomes of AS31, AS33, and AS42 were 36.8, 34.8, and 35.3 Mb, respectively. The endophytic genomes had more genes encoding carbohydrate-active enzymes (CAZymes) and small secreted proteins (SSPs) and secondary metabolism gene clusters involved in indole metabolism than the pathogens. In addition, these endophytes were able to improve Pi (phosphorus) accumulation and transport in rice by inducing the expression of Pi transport genes in rice. Specifically, inoculation with endophytes significantly increased Pi contents in roots at the early stage, while the Pi contents in inoculated shoots were significantly increased at the late stage. Our results not only provide important insights into endophyte-plant interactions but also provide strain and genome resources, paving the way for the agricultural application of Aspergillus endophytes.

As urbanization has progressed over the past 40 years, continuous population growth and the rapid expansion of urban land use have caused some regions to experience various problems, such as insufficient resources and issues related to the environmental carrying capacity. The urbanization process can be understood using nighttime light data to quickly and accurately extract urban boundaries at large scales. A new method is proposed here to quickly and accurately extract urban boundaries using nighttime light imagery. Three types of nighttime light data from the DMSP/OLS (US military’s defense meteorological satellite), NPP-VIIRS (National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite), and Luojia1-01 data sets are selected, and the high-precision urban boundaries obtained from a high-resolution image are selected as the true value. Next, 15 cities are selected as the training samples, and the Jaccard coefficient is introduced. The spatial data comparison method is then used to determine the optimal threshold function for the urban boundary extraction. Alternative high-precision urban boundary truth-values for the 13 cities are then selected, and the accuracy of the urban boundary extraction results obtained using the optimal threshold function and the mutation detection method are evaluated. The following observations are made from the results: (i) The average relative errors for the urban boundary extraction results based on the three nighttime light data sources (DMSP/OLS, NPP-VIIRS, and Luojia1-01) using the optimal threshold functions are 29%, 20%, and 39%, respectively. Compared with the mutation detection method, these relative errors are reduced by 83%, 18%, and 77%, respectively; (ii) The average overall classification accuracies of the extracted urban boundaries are 95%, 96%, and 93%, respectively, which are 5%, 1%, and 7% higher than those for the mutation detection method; (iii) The average Kappa coefficients of the extracted urban boundaries are 61%, 71%, and 61%, respectively, which are 5%, 4%, and 12% higher than for the mutation detection method.

The urbanization level in China has increased rapidly since beginning of the 21st century, and the monitoring and analysis of urban expansion has become a popular topic in geoscience applications. However, problems, such as inconsistent concepts and extraction standards, low precision, and poor comparability, existing in urban monitoring may lead to wrong conclusions. This study selects 337 cities at the prefecture level and above in China as research subjects and uses high-resolution images and geographic information data in a semi-automatic extraction method to identify urban areas in 2000, 2005, 2010, and 2015. City size distribution patterns, urban expansion regional characteristics, and expansion types are analyzed. Results show that Chinese cities maintained a high-speed growth trend from 2000 to 2015, with the total area increasing by 115.79%. The overall scale of a city continues to expand, and the system becomes increasingly complex. The urban system is more balanced than the ideal Zipf distribution, but it also exhibited different characteristics in 2005. Urban areas are mostly concentrated in the eastern and central regions, and the difference between the east and the west is considerable. However, cities in the western region continuously expand. Beijing, Shanghai, Tianjin, and Guangzhou are the four largest cities in China. Approximately 73.30% of the cities are expanding in an extended manner; the urban form tends to be scattered, and land use efficiency is low. The new urban areas mainly come from cultivated land and ecological land.

Objective. To report 3 cases using the Monti ileovesicostomy as a continent catheterizable stoma in women with post-traumatic resulted in loss of the urethra. Materials and Methods. Between April and November 2010, the Monti technique was performed as a reconstructive procedure in 3 female patients at our institution. All cases had history of posterior urethral defect, two motor vehicle accidents, and one pelvic crush injury. Channels were created by transversely reconfigured segment of ileum. The 3 patients age was 20, 41, and 43 years. Indications, technique, complications and outcomes are reported below. Results. Three patients were undergone for transversely re-tubularized ileal segment urinary reconstruction. All of them were evaluated from 5 to 12 months postoperatively. All patients’ postoperative visits to our outpatient gynecological clinic about 3, 6 and 12 months respectively after Monti ileovesicostomy operation revealed no dehiscence, necrosis, stenosis or perforation of the tube. All performed catheterization through the conduit without difficulty. Conclusion. The Monti ileovesicostomy is a new technique for creation of a continent catheterizable stoma. Based on our data, Monti procedure seems to be a valuable technique in patients with complicated urethral defect who cannot be managed with routine urethroplastic techniques. This reconstruction provides another option for a diversion technique in patients with irreparable urethral damage. However, evaluation whether it can be extensively developed still need more investigations with larger number of patients by long-term follow up.

Digital terrain models (DTMs) are considered important basic geographic data. They are widely used in the fields of cartography, land utilization, urban planning, communications, and remote sensing. Digital photogrammetry mainly based on stereo image matching is a frequently applied technique to generate DTMs. Generally, the process of ground filtering should be applied to the point cloud derived from image matching to separate terrain and off-terrain points before DTM generation. However, many of the existing filtering methods perform unsatisfactorily for steep mountainous areas, particularly when residential neighborhoods exist in the proximity of the test areas. In this study, an improved automated filtering method based on progressive TIN (triangulated irregular networks) densification (PTD) is proposed to generate DTMs for steep mountainous areas and adjacent residential areas. Our main improvement on the classic method is the acquisition of seed points with better distribution and reliability to enhance its adaptability to different types of terrain. A rule-based method for detecting ridge points is first applied. The detected points are used as additional seed points. Subsequently, a locally optimized seed point selection method based on confidence interval estimation theory is applied to remove the erroneous points. The experiments on two sets of stereo-matched point clouds indicate that the proposed method performs well for both residential and mountainous areas. The total accuracy values in the form of root-mean-square errors of the generated DTMs by the proposed method are 0.963 and 1.007 m; respectively; which are better than the 1.286 and 1.309 m achieved by the classic PTD method.

In recent years, sensor technology has been widely used in the defense and control of sensitive areas in cities, or in various scenarios such as early warning of forest fires, monitoring of forest pests and diseases, and protection of endangered animals. Deploying sensors to collect data and then utilizing unmanned aerial vehicle (UAV) to collect the data stored in the sensors has replaced traditional manual data collection as the dominant method. The current strategies for efficient data collection in above scenarios are still imperfect, and the low quality of the collected data and the excessive energy consumed by UAV flights are still the main problems faced in data collection. With regards this, this paper proposes a multi-UAV mission planning method for self-organized sensor data acquisition by comprehensively utilizing the techniques of self-organized sensor clustering, multi-UAV mission area allocation, and sub-area data acquisition scheme optimization. The improved α-hop clustering method utilizes the average transmission distance to reduce the size of the collection sensors, and the K-Dimensional method is used to form a multi-UAV cooperative workspace, and then, the genetic algorithm is used to trade-off the speed with the age of information (AoI) of the collected information and the energy consumption to form the multi-UAV data collection operation scheme. The combined optimization scheme in paper improves the performance by 95.56% and 58.21%, respectively, compared to the traditional baseline model. In order to verify the excellent generalization and applicability of the proposed method in real scenarios, the simulation test is conducted by introducing the digital elevation model data of the real terrain, and the results show that the relative error values of the proposed method and the performance test of the actual flight of the UAV are within the error interval of ±10%. Then, the advantages and disadvantages of the present method with the existing mainstream schemes are tested, and the results show that the present method has a huge advantage in terms of space and time complexity, and at the same time, the accuracy for data extraction is relatively improved by 10.46% and 12.71%. Finally, by eliminating the clustering process and the subtask assignment process, the AoI performance decreases by 3.46× and 4.45×, and the energy performance decreases by 3.52× and 4.47×. This paper presents a comprehensive and detailed proactive optimization of the existing challenges faced in the field of data acquisition by means of a series of combinatorial optimizations. [ABSTRACT FROM AUTHOR]

Music is closely related to soundscape. The present study employs methods and tools for assessing soundscape perception to compare the evaluation of soundscape perception between students who had not been taught general music courses (n = 80, equal into N1, N2) with that of students who had been taught general music courses (n = 40, MU) in four distinct audio-visual environments. According to the results of the questionnaire in four scenes and eight perceptual dimensions, there are significant differences between MU and N1 N2 (4-5 items), whereas there is only a slight difference between N1 and N2 (1 item). The difference in soundscape perception is reflected in those students who had been taught general music courses having a higher evaluation of the diversity of soundscape elements (as eventful), and different emotional judgments of the sound environment (as calm). These differences are visualized using the soundscape analysis tool, Soundscapy. Overall, the findings suggest that taking a general music course promotes richer sound perceptions and clearer affective associations with sound and that these enhancements could be presented using the soundscape perception measurement method. The findings also presented could inform the effectiveness of teaching in a general music course. [ABSTRACT FROM AUTHOR]

Named Entity Recognition (NER) stands as a fundamental task within the field of biomedical text mining, aiming to extract specific types of entities such as genes, proteins, and diseases from complex biomedical texts and categorize them into predefined entity types. This process can provide basic support for the automatic construction of knowledge bases. In contrast to general texts, biomedical texts frequently contain numerous nested entities and local dependencies among these entities, presenting significant challenges to prevailing NER models. To address these issues, we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer (RoBGP). Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors. It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information, effectively addressing the issue of long-distance dependencies. Furthermore, the Global Pointer model is employed to comprehensively recognize all nested entities in the text. We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models. This research confirms the effectiveness of RoBGP in Chinese biomedical NER, providing reliable technical support for biomedical information extraction and knowledge base construction. [ABSTRACT FROM AUTHOR]