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Abstract Solid fuel combustion exposure is a leading global health risk factor, yet evidence on its effects, especially on vulnerable children, is sparse. This large-scale, multi-center prospective study aimed to address this gap by involving 9997 schoolchildren across China between 2013 and 2015. Here we show that lung function levels exhibited a marginally significant decline among children exposed to solid fuel usage. Specifically, FVC and FEV1 decreased by 21.2 mL (95% CI: −15.7, 58.1) and 24.1 mL (−8.4, 56.6), respectively. Additionally, PEF, FEF25 and FEF75 decreased by 25.7 mL/s (−46.5, 98.0), 32.7 mL/s (−42.7, 108.2), and 35.4 mL/s (−5.9, 76.7), respectively. Persistent exposure to solid fuel usage in children led to greater lung damage. Children with allergy history were more susceptible to solid fuel exposure. Our study highlights the adverse impact of solid fuel usage on children and the need to promote clean fuel usage for this vulnerable population.

Abstract Background The DELLA proteins, a class of GA signaling repressors, belong to the GRAS family of plant-specific nuclear proteins. Members of DELLA gene family encode transcriptional regulators with diverse functions in plant development and abiotic stress responses. To date, DELLAs have been identified in various plant species, such as Arabidopsis thaliana, Malus domestica, Populus trichocarpa, and other land plants. Most information of DELLA family genes was obtained from A. thaliana, whereas little is known about the DELLA gene family in blueberry. Results In this study, we identified three DELLA genes in blueberry (Vaccinium darrowii, VdDELLA) and provided a complete overview of VdDELLA gene family, describing chromosome localization, protein properties, conserved domain, motif organization, and phylogenetic analysis. Three VdDELLA members, containing two highly conserved DELLA domain and GRAS domain, were distributed across three chromosomes. Additionally, cis-acting elements analysis indicated that VdDELLA genes might play a critical role in blueberry developmental processes, hormone, and stress responses. Expression analysis using quantitative real-time PCR (qRT-PCR) revealed that all of three VdDELLA genes were differentially expressed across various tissues. VdDELLA2 was the most highly expressed VdDELLA in all denoted tissues, with a highest expression in mature fruits. In addition, all of the three VdDELLA genes actively responded to diverse abiotic stresses. Based on qRT-PCR analysis, VdDELLA2 might act as a key regulator in V. darrowii in response to salt stress, whereas VdDELLA1 and VdDELLA2 might play an essential role in cold stress response. Under drought stress, all of three VdDELLA genes were involved in mediating drought response. Furthermore, their transiently co-localization with nuclear markers in A. thaliana protoplasts demonstrated their transcriptional regulator roles. Conclusions In this study, three VdDELLA genes were identified in V. darrowii genome. Three VdDELLA genes were closely related to the C. moschata DELLA genes, S. lycopersicum DELLA genes, and M. domestica DELLA genes, respectively, indicating their similar biological functions. Expression analysis indicated that VdDELLA genes were highly efficient in blueberry fruit development. Expression patterns under different stress conditions revealed the differentially expressed VdDELLA genes responding to salt, drought, and cold stress. Overall, these results enrich our understanding of evolutionary relationship and potential functions of VdDELLA genes, which provide valuable information for further studies on genetic improvement of the plant yield and plant resistance.

Abstract Approximately 90% of Parkinson’s patients (PD) suffer from dysarthria. However, there is currently a lack of research on acoustic measurements and speech impairment patterns among Mandarin-speaking individuals with PD. This study aims to assess the diagnosis and disease monitoring possibility in Mandarin-speaking PD patients through the recommended speech paradigm for non-tonal languages, and to explore the anatomical and functional substrates. We examined total of 160 native Mandarin-speaking Chinese participants consisting of 80 PD patients, 40 healthy controls (HC), and 40 MRI controls. We screened the optimal acoustic metric combination for PD diagnosis. Finally, we used the objective metrics to predict the patient’s motor status using the Naïve Bayes model and analyzed the correlations between cortical thickness, subcortical volumes, functional connectivity, and network properties. Comprehensive acoustic screening based on prosodic, articulation, and phonation abnormalities allows differentiation between HC and PD with an area under the curve of 0.931. Patients with slowed reading exhibited atrophy of the fusiform gyrus (FDR p = 0.010, R = 0.391), reduced functional connectivity between the fusiform gyrus and motor cortex, and increased nodal local efficiency (NLE) and nodal efficiency (NE) in bilateral pallidum. Patients with prolonged pauses demonstrated atrophy in the left hippocampus, along with decreased NLE and NE. The acoustic assessment in Mandarin proves effective in diagnosis and disease monitoring for Mandarin-speaking PD patients, generalizing standardized acoustic guidelines beyond non-tonal languages. The speech impairment in Mandarin-speaking PD patients not only involves motor aspects of speech but also encompasses the cognitive processes underlying language generation.

Phthalates are endocrine disruptors of increasing concern for human health; however, previous studies have only assessed the association between internal exposure and human health. We aimed to assess the non-carcinogenic and carcinogenic risks of non-dietary exposure to phthalates in indoor environments among primary school children and their correlations with health indicators. A study involving 54 children was conducted in Jinan, Shandong Province, China. Questionnaires and health examinations were conducted, dust in hard-to-clean corners of students’ classrooms and homes was collected, and airborne phthalates in the middle of classrooms and family living rooms were sampled. The gas-phase phthalate concentrations, individual exposure, and non-carcinogenic and carcinogenic risks were calculated. Associations were estimated using linear mixed models. The findings revealed that phthalates posed a non-carcinogenic risk to 7.4 % of the children and a moderate carcinogenic risk to 27.8 % of the children, with higher non-carcinogenic and carcinogenic risks to girls than to boys. Five phthalates were negatively correlated with body mass index, dimethyl phthalate and diethyl phthalate (DEP) were significantly correlated with waist circumference, and di-iso-butyl phthalate (DiBP) was negatively correlated with hip circumference. DiBP, di-n-butyl phthalate, and DEP, were significantly correlated with cardiovascular disease, DEP and di (2-n-butoxyethyl) phthalate were correlated with decreased lung function, and di-n-octyl phthalate influenced airway inflammation. The findings indicated that phthalate exposure may negatively impact children's health, thereby warranting further comprehensive research on the health effects of these chemicals.

Many studies have confirmed that PM2.5 exposure can cause a variety of diseases. Because people spend most of their time indoors, exposure to PM2.5 in indoor environments is critical to population health. Large-population, long-term, continuous, and accurate indoor PM2.5 data are important but scarce because of the difficulties in monitoring the indoor air quality on a large scale. Model simulation provides a new research direction. In this study, an advanced machine learning model was constructed using environmental health big data to predict the daily indoor PM2.5 concentration data in 22 typical air pollution cities in China from 2013 to 2017. The test R2 value of this model reached as high as 0.89, and the RMSE of the model was 9.13. The predicted annual indoor PM2.5 concentrations of the cities ranged from 54.6 μg/m3 to 82.7 μg/m3, and showed a decreasing trend year by year. The pollution level exceeds the recommended AQG level of PM2.5 and has potential impact on human health. The results could take a breakthrough in obtaining accurate big data of indoor PM2.5 and contribute to research on the indoor air quality and human health in China. Synopsis: This study established a machine learning model and predicted indoor PM2.5 big data, which could support the research of indoor PM2.5 and health.

Abstract Background WOX genes are a class of plant-specific transcription factors. The WUSCHEL-related homeobox (WOX) family is a member of the homeobox transcription factor superfamily. Previous studies have shown that WOX members play important roles in plant growth and development. However, studies of the WOX gene family in blueberry plants have not been reported. Results In order to understand the biological function of the WOX gene family in blueberries, bioinformatics were used methods to identify WOX gene family members in the blueberry genome, and analyzed the basic physical and chemical properties, gene structure, gene motifs, promoter cis-acting elements, chromosome location, evolutionary relationships, expression pattern of these family members and predicted their functions. Finally, 12 genes containing the WOX domain were identified and found to be distributed on eight chromosomes. Phylogenetic tree analysis showed that the blueberry WOX gene family had three major branches: ancient branch, middle branch, and WUS branch. Blueberry WOX gene family protein sequences differ in amino acid number, molecular weight, isoelectric point and hydrophobicity. Predictive analysis of promoter cis-acting elements showed that the promoters of the VdWOX genes contained abundant light response, hormone, and stress response elements. The VdWOX genes were induced to express in both stems and leaves in response to salt and drought stress. Conclusions Our results provided comprehensive characteristics of the WOX gene family and important clues for further exploration of its role in the growth, development and resistance to various stress in blueberry plants.

Abstract Background Chronic obstructive pulmonary disease (COPD) is a significant global health issue, suspected to elevate the risk for various cancers. This study sought to discern whether COPD serves as a risk marker or a causative factor for prevalent cancers. Methods We employed univariable MR (UVMR) analyses to investigate the causal relationship between COPD and the top ten common cancers. Sensitivity analyses were performed to validate the main findings. Multivariable MR (MVMR) and two-step MR analyses were also conducted. False-discovery-rate (FDR) was used to correct multiple testing bias. Results The UVMR analysis demonstrated notable associations between COPD and lung cancer (odds ratio [OR] = 1.42, 95%CI 1.15–1.77, FDR = 6.37 × 10–3). This relationship extends to lung cancer subtypes such as squamous cell carcinoma (LUSC), adenocarcinoma (LUAD), and small cell lung cancer (SCLC). A tentative link was also identified between COPD and bladder cancer (OR = 1.53, 95%CI 1.03–2.28, FDR = 0.125). No significant associations were found between COPD and other types of cancer. The MVMR analysis that adjusted for smoking, alcohol drinking, and body mass index did not identify any significant causal relationships between COPD and either lung or bladder cancer. However, the two-step MR analysis indicates that COPD mediated 19.2% (95% CI 12.7–26.1%), 36.1% (24.9–33.2%), 35.9% (25.7–34.9%), and 35.5% (26.2–34.8%) of the association between smoking and overall lung cancer, as well as LUAD, LUSC, and SCLC, respectively. Conclusions COPD appears to act more as a risk marker than a direct cause of prevalent cancers. Importantly, it partially mediates the connection between smoking and lung cancer, underscoring its role in lung cancer prevention strategies.

Long-term exposure to high surface ozone (O3) concentrations, a complex oxidative atmospheric pollutant, can adversely impact human health. Based on O3 monitoring data from 261 cities worldwide in 2020, generalized additive model (GAM) and spatial data analysis (SDA) methods were applied in this study to quantitatively evaluate the spatiotemporal distribution of O3 concentration, exposure risk, and dominant meteorological factors. Results indicated that over 40% of the cities worldwide were exposed to harmful O3 concentration ranges (40–60 µg/m3), with most cities distributed in China and India. Moreover, significant seasonal variations in global O3 concentrations were observed, presenting as summer (45.6 µg/m3) > spring (47.3 µg/m3) > autumn (38.0 µg/m3) > winter (33.6 µg/m3). Exposure analysis revealed that approximately 12.2% of the population in 261 cities were exposed to an environment with high O3 concentrations (80–160 µg/m3), with about 36.32 million people in major countries. Thus, the persistent increase in high O3 levels worldwide is a critical factor contributing to threats to human health. Furthermore, GAM results indicated temperature, relative humidity, and wind speed as primary determinants of O3 variability. The synergy of meteorological factors is critical for understanding O3 changes. Our findings are important for enforcing robust air quality policies and mitigating public risk.

Adipose tissue (AT) serves as an energy-capacitive organ and performs functions involving paracrine- and endocrine-mediated regulation via extracellular vesicles (EVs) secretion. Exosomes, a subtype of EVs, contain various bioactive molecules with regulatory effects, such as nucleic acids, proteins, and lipids. AT-derived exosomes (AT-exos) include exosomes derived from various cells in AT, including adipocytes, adipose-derived stem cells (ADSCs), macrophages, and endothelial cells. This review aimed to comprehensively evaluate the impacts of different AT-exos on the regulation of physiological and pathological processes. The contents and functions of adipocyte-derived exosomes and ADSC-derived exosomes are compared simultaneously, highlighting their similarities and differences. The contents of AT-exos have been shown to exert complex regulatory effects on local inflammation, tumor dynamics, and insulin resistance. Significantly, differences in the cargoes of AT-exos have been observed among diabetes patients, obese individuals, and healthy individuals. These differences could be used to predict the development of diabetes mellitus and as therapeutic targets for improving insulin sensitivity and glucose tolerance. However, further research is needed to elucidate the underlying mechanisms and potential applications of AT-exos.

IntroductionProgrammed cell death (PCD) is a fundamental biological process crucial for plant development. Despite recent advancements in our understanding of PCD’s molecular mechanisms, the intricate orchestration of this process within plant cells remains enigmatic. To address this knowledge gap, the present study focuses on Decaisnea insignis, a plant species renowned for its unique fruit anatomy, including laticiferous canals that secrete latex. While extensive anatomical studies have elucidated the structural features of these canals,molecular insights into their developmental regulation, particularly the involvement of PCD, are lacking.MethodsIn this study, we sequenced the single-cell transcriptomes at two developmental stage of Decaisnea insignis fruit using the technology known as 10x Genomics (S1, S2). Using sequencing technology combining full- length RNA sequencing and single-nucleus RNA sequencing (snRNA-seq) in combination with ultrastructural analyses, our study revealed a cellular map of Decaisnea insignis fruit at the single-cell level and identified different cell types.ResultsIn particular, we identified a possible PCD-mediated cluster of Decaisnea insignis fruit lactiferous canals in epidermal cells and clarified the expression patterns of DiRD21A (a hydrolase) and DiLSD1 (a transcription factor), which may be closely related to the development of laticiferous canals in Decaisnea insignis fruits.DiscussionBy integrating high-resolution gene expression profiling with visual insights into cellular transformations, we sought to more precisely characterize the regulatory role of PCD during the developmental formation of lactiferous canals in Decaisnea insignis fruit.

In light of continuous advancements in science and technology, the global economy is experiencing rapid growth. However, this growth has been accompanied by significant depletion of natural resources and environmental degradation. Consequently, there is a burgeoning global emphasis on energy conservation, emissions reduction, and sustainable development. In this study, based on the Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI) databases from 1990 to 2022, a statistical analysis of energy conservation and emission reduction in alignment with Sustainable Development Goals (SDGs)-related publications was undertaken using biblimometric methods. The findings reveal that (1) In recent years, there has been a discernible increase in global research on this subject, especially since 2009, with a sustained trend of exceeding 100 publications per annum. China prominently contributing to this domain, the proportion reached 34.2%, reflecting a growing emphasis on eco-friendly development trends. (2) Due to the burgeoning significance of energy conservation and emission reduction, there has been a notable escalation in research efforts pertaining to “Energy and Fuels,” “Environmental Science” and “Green and Sustainable Science and Technology” and other related subjects. (3) Regarding the keyword analysis, “renewable energy” as the most frequently encountered term, often paired with “CO2 emissions.” This association underscores the pivotal role of renewable energy technologies in advancing green development initiatives and mitigating emissions. (4) China, United States and United Kingdom occupy central positions in terms of both paper publication volume and collaborative networks, collectively accounting for about 54.7%, and these countries are pivotal contributors to the scholarly discourse on sustainable development and environmental conservation. (5) From 1990 to 2022, the top 20 cited articles predominantly address diverse sub-goals of Sustainable Development Goal 7, with a common emphasis on enhancing energy efficiency, sustainability and renewable energy. These findings furnish valuable analytical insights for subsequent researchers investigating energy conservation and emission reduction as well as sustainable development endeavors.

Abstract Background Acute arterial embolism due to tumor embolus is a rare complication in cancer patients, even rarer is lung tumor embolization leading to acute myocardial infarction. We report a patient who had a diagnosis of acute myocardial infarction(AMI)which was brought on by a coronary artery embolism by a metastatic lung cancer tumor. Clinicians need to be aware that tumor embolism can result in AMI. Case presentation An 80-yeal-old male patient presented with persistent chest pain for 2 h and his electrocardiogram(ECG)showed anterior ST-segment elevation myocardial infarction. Instead of implanting a stent, thrombus aspiration was performed. Pathological examination of coronary artery thrombosis showed that a few sporadic atypical epithelial cells were scattered in the thrombus-like tissue. Combined with immune phenotype and clinical history, metastatic squamous cell carcinoma is more likely. Conclusions We report a rare case of a patient who was diagnosed of AMI due to a coronary artery embolism by a metastatic mass from lung cancer. Since there is no evidence-based protocol available for the treatment of isolated coronary thrombosis, we used thrombus aspiration to treat thrombosis rather than implanting a stent.

Assessing the effects of future urban expansion on ecosystem services (ESs) is essential for the sustainability of cities worldwide. Nonetheless, evaluating these effects of future urban expansion on ESs remains challenging due to the uncertainties associated with socioeconomic development and the intricate nature of urban expansion. In this research, we initially integrated the localized Shared Socioeconomic Pathways (SSPs) and the Land Use Scenario Dynamics-urban (LUSD-urban) model to project the urban expansion of the Yangtze River Delta urban agglomeration (YRDUA). Subsequently, we quantified the impacts of urban expansion on ESs utilizing the Integrated Valuation of ESs and Tradeoffs (InVEST) model. The outcomes indicate that the urban land in the YRDUA is projected to expand by 1,020.19–12,282.04 km2 at a growth rate of 3.71–44.67% from 2022 to 2050. Simultaneously, habitat quality (HQ), carbon storage (CS), water retention (WR), and air purification (AP) are expected to decline by 0.34–4.24%, 0.48–5.82%, 0.39–4.75% and 0.20–2.45%, correspondingly. Most importantly, the primary cause of ES losses is the conversion of cropland to urban land, accounting for more than 90% of the total ES losses. The results offer crucial contextual insights to support future synergistic development policies for urbanization and ecological conservation in the YRDUA under increased climate change.

Abstract Solid backfilling in coal mining refers to filling the goaf with solid materials to form a support structure, ensuring safety in the ground and upper mining areas. This mining method maximizes coal production and addresses environmental requirements. However, in traditional backfill mining, challenges exist, such as limited perception variables, independent sensing devices, insufficient sensing data, and data isolation. These issues hinder the real-time monitoring of backfilling operations and limit intelligent process development. This paper proposes a perception network framework specifically designed for key data in solid backfilling operations to address these challenges. Specifically, it analyses critical perception objects in the backfilling process and proposes a perception network and functional framework for the coal mine backfilling Internet of Things (IoT). These frameworks facilitate rapidly concentrating key perception data into a unified data centre. Subsequently, the paper investigates the assurance of data validity in the perception system of the solid backfilling operation within this framework. Specifically, it considers potential data anomalies that may arise from the rapid data concentration in the perception network. To mitigate this issue, a transformer-based anomaly detection model is proposed, which filters out data that does not reflect the true state of perception objects in solid backfilling operations. Finally, experimental design and validation are conducted. The experimental results demonstrate that the proposed anomaly detection model achieves an accuracy of 90%, indicating its effective detection capability. Moreover, the model exhibits good generalization ability, making it suitable for monitoring data validity in scenarios involving increased perception objects in solid backfilling perception systems.

Accurate and prompt determination of fire types is essential for effective firefighting and reducing damage. However, traditional methods such as smoke detection, visual analysis, and wireless signals are not able to identify fire types. This paper introduces FireSonic, an acoustic sensing system that leverages commercial speakers and microphones to actively probe the fire using acoustic signals, effectively identifying fire types. By incorporating beamforming technology, FireSonic first enhances signal clarity and reliability, thus mitigating signal attenuation and distortion. To establish a reliable correlation between fire type and sound propagation, FireSonic quantifies the heat release rate (HRR) of flames by analyzing the relationship between fire-heated areas and sound wave propagation delays. Furthermore, the system extracts spatiotemporal features related to fire from channel measurements. The experimental results demonstrate that FireSonic attains an average fire type classification accuracy of 95.5% and a detection latency of less than 400 ms, satisfying the requirements for real-time monitoring. This system significantly enhances the formulation of targeted firefighting strategies, boosting fire response effectiveness and public safety.

Background and purpose: Anatomical labeling of the cerebral vasculature is a crucial topic in determining the morphological nature and characterizing the vital variations of vessels, yet precise labeling of the intracranial arteries is time-consuming and challenging, given anatomical structural variability and surging imaging data. We present a U-Net-based deep learning (DL) model to automatically label detailed anatomical segments in computed tomography angiography (CTA) for the first time. The trained DL algorithm was further tested on a clinically relevant set for the localization of intracranial aneurysms (IAs).Methods: 457 examinations with varying degrees of arterial stenosis were used to train, validate, and test the model, aiming to automatically label 42 segments of the intracranial arteries [e.g., 7 segments of the internal carotid artery (ICA)]. Evaluation metrics included Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD). Additionally, 96 examinations containing at least one IA were enrolled to assess the model’s potential in enhancing clinicians’ precision in IA localization. A total of 5 clinicians with different experience levels participated as readers in the clinical experiment and identified the precise location of IA without and with algorithm assistance, where there was a washout period of 14 days between two interpretations. The diagnostic accuracy, time, and mean interrater agreement (Fleiss’ Kappa) were calculated to assess the differences in clinical performance of clinicians.Results: The proposed model exhibited notable labeling performance on 42 segments that included 7 anatomical segments of ICA, with the mean DSC of 0.88, MSD of 0.82 mm and HD of 6.59 mm. Furthermore, the model demonstrated superior labeling performance in healthy subjects compared to patients with stenosis (DSC: 0.91 vs. 0.89, p < 0.05; HD: 4.75 vs. 6.19, p < 0.05). Concurrently, clinicians with model predictions achieved significant improvements when interpreting the precise location of IA. The clinicians’ mean accuracy increased by 0.04 (p = 0.003), mean time to diagnosis reduced by 9.76 s (p < 0.001), and mean interrater agreement (Fleiss’ Kappa) increased by 0.07 (p = 0.029).Conclusion: Our model stands proficient for labeling intracranial arteries using the largest CTA dataset. Crucially, it demonstrates clinical utility, helping prioritize the patients with high risks and ease clinical workload.

The continued evolution and emergence of novel influenza viruses in wild and domestic animals poses an increasing public health risk. Two human cases of H3N8 avian influenza virus infection in China in 2022 have caused public concern regarding the risk of transmission between birds and humans. However, the prevalence of H3N8 avian influenza viruses in their natural reservoirs and their biological characteristics are largely unknown. To elucidate the potential threat of H3N8 viruses, we analyzed five years of surveillance data obtained from an important wetland region in eastern China and evaluated the evolutionary and biological characteristics of 21 H3N8 viruses isolated from 15,899 migratory bird samples between 2017 and 2021. Genetic and phylogenetic analyses showed that the H3N8 viruses circulating in migratory birds and ducks have evolved into different branches and have undergone complicated reassortment with viruses in waterfowl. The 21 viruses belonged to 12 genotypes, and some strains induced body weight loss and pneumonia in mice. All the tested H3N8 viruses preferentially bind to avian-type receptors, although they have acquired the ability to bind human-type receptors. Infection studies in ducks, chickens and pigeons demonstrated that the currently circulating H3N8 viruses in migratory birds have a high possibility of infecting domestic waterfowl and a low possibility of infecting chickens and pigeons. Our findings imply that circulating H3N8 viruses in migratory birds continue to evolve and pose a high infection risk in domestic ducks. These results further emphasize the importance of avian influenza surveillance at the wild bird and poultry interface.

This study focuses on the development of a new framework for evaluating bikeability in urban environments with the aim of enhancing sustainable urban transportation planning. To close the research gap that previous studies have disregarded the dynamic environmental factors and trajectory data, we propose a framework that comprises four sub-indices: safety, comfort, accessibility, and vitality. Utilizing open-source data, advanced deep neural networks, and GIS spatial analysis, the framework eliminates subjective evaluations and is more efficient and comprehensive than prior methods. The experimental results on Xiamen, China, demonstrate the effectiveness of the framework in identifying areas for improvement and enhancing cycling mobility. The proposed framework provides a structured approach for evaluating bikeability in different geographical contexts, making reproducing bikeability indices easier and more comprehensive to policymakers, transportation planners, and environmental decision-makers.

To solve the problems that the background reference frames are seriously contaminated by the foreground, and the bit rate increases suddenly incurred by the one-time transmission of the background frames, a progressive background frame generation method with image block as the basic unit was proposed for surveillance video application.An image block codebook model based on clustering was formulated.The image blocks at the same position in the video sequence were effectively clustered by using perceptual hash-based element matching.The background symbol was accurately detected by using the characteristics of the background image area.A complete background frame was produced by extracting the background blocks in different frames based on the codebook model.Experimental results demonstrate that the proposed method achieves 17.89% coding efficiency for luma component compared with standard HM16.20, and can effectively improve the quality of the produced background reference frame.Besides, the proposed method complexity meets the real-time requirements of video applications.

This study evaluated the effect of different enzymatic hydrolysis and different microwave pretreatment durations on the sugar yield of Chinese spirits distillers residues (CSDR). Addition of both α-amylase and cellulase in the enzymatic hydrolysis of distillers grains resulted in a higher yield of reducing sugar (145.74 ± 4.20 mg·g–1). Fourier transform infrared spectroscopy (FTIR) revealed that different enzyme treatments degraded different functional groups of the cellulose structure. Subsequently, the highest reducing sugar yield (181.33 ± 6.42 mg·g–1) was achieved at microwave pretreatment time of 6 min and was 1.29-fold higher than that of the untreated sample. The differences in the structural features of CSDRs under various microwave pretreatment durations were characterized by scanning electron microscopy, FTIR spectroscopy, and X-ray diffraction. The data revealed that the optimal microwave pretreatment time at 6 min could obviously remove lignin, and then obtain the highest CrI value, achieving the highest reducing sugar yield.

Panoramic images are widely used in many scenes, especially in virtual reality and street view capture. However, they are new for street furniture identification which is usually based on mobile laser scanning point cloud data or conventional 2D images. This study proposes to perform semantic segmentation on panoramic images and transformed images to separate light poles and traffic signs from background implemented by pre-trained Fully Convolutional Networks (FCN). FCN is the most important model for deep learning applied on semantic segmentation for its end to end training process and pixel-wise prediction. In this study, we use FCN-8s model that pre-trained on cityscape dataset and finetune it by our own data. Then replace cross entropy loss function with focal loss function in the FCN model and train it again to produce the predictions. The results show that in all results from pre-trained model, fine-tuning, and FCN model with focal loss, the light poles and traffic signs are detected well and the transformed images have better performance than panoramic images in the prediction according to the Recall and IoU evaluation.

Power systems faces significant uncertainty during operation owing to the increased integration of renewable energy into power grids and the expansion of the scale of power systems, these factors lead to higher load-loss risks; therefore, realization of a fast online load-loss risk assessment is crucial to ensuring the operational safety and reliability of power systems. This paper presents an online load-loss risk assessment method for power systems based on stacking ensemble learning. First, a traditional load-loss risk assessment method based on power flow analysis was constructed to generate risk samples. The label of the sample is load-loss risk assessment index and the features are multiple operational variables of the power system. And the recursive feature elimination using cross validation (RFECV) was adopted for feature selection. Second, four different machine learning models, including support vector regression (SVR), extremely randomized trees (ET), extreme gradient boosting (XGBoost) and elastic network (EN) were used to form a stacking ensemble learning model for sample training. Moreover, to further improve the model performance, the particle swarm optimization (PSO) algorithms was used for parameter optimization. Finally, based on this model, the online load-loss risk assessment of a power system was realized. The application of the proposed method on IEEE test systems demonstrated that the proposed method was more accurate than methods based on individual machine learning models, from which the stacking was designed, while still maintaining a significant advantage in terms of runtime compared to the traditional risk assessment method.

This study introduces a machine learning-based framework for mapping street patterns in urban morphology, offering an objective, scalable approach that transcends traditional methodologies. Focusing on six diverse cities, the research employed supervised machine learning to classify street networks into gridiron, organic, hybrid, and cul-de-sac patterns with the street-based local area (SLA) as the unit of analysis. Utilising quantitative street metrics and GIS, the study analysed the urban form through the random forest method, which reveals the predictive features of urban patterns and enables a deeper understanding of the spatial structures of cities. The findings showed distinctive spatial structures, such as ring formations and urban cores, indicating stages of urban development and socioeconomic narratives. It also showed that the unit of analysis has a major impact on the identification and study of street patterns. Concluding that machine learning is a critical tool in urban morphology, the research suggests that future studies should expand this framework to include more cities and urban elements. This would enhance the predictive modelling of urban growth and inform sustainable, human-centric urban planning. The implications of this study are significant for policymakers and urban planners seeking to harness data-driven insights for the development of cities.

Traditional video compression technology based on pixel correlation has limited performance improvement space, semantic compression has become the new direction of video compression coding, and video image reconstruction is the key link of semantic compression coding.First, the video image reconstruction methods for traditional coding optimization were introduced, including how to use deep learning to improve prediction accuracy and enhance reconstruction quality with super-resolution techniques.Second, the video image reconstruction methods based on variational auto-encoders, generative adversarial networks, autoregressive models and transformer models were discussed emphatically.Then, the models were classified according to different semantic representations of images.The advantages, disadvantages, and applicable scenarios of various methods were compared.Finally, the existing problems of video image reconstruction were summarized, and the further research directions were prospected.

Random cross-validation (CV) is often used to evaluate geospatial machine learning models, particularly when a limited amount of sample data are available, and collecting an extra test set is unfeasible. However, the prediction locations can be substantially different from the available sample, leading to over-optimistic evaluation results. This has fostered the development of spatial CV methods. Yet these methods only focus on spatial autocorrelation and cannot sufficiently guarantee that the validation subset is a good proxy of the test set with significant differences. In this paper, we propose the spatial+ cross-validation (SP-CV) method. This method, which considers both the geographic and feature spaces, is composed of two stages. The first stage addresses spatial autocorrelation issues by using agglomerative hierarchical clustering to divide the available sample into blocks. The second stage deals with multiple sources of differences. It uses cluster ensembles to split the blocks into training and validation folds based on the locations of the sample data and the values of the covariates and target variable. The proposed method is compared against random and block CV methods in a series of experiments with Amazon basin above ground biomass and California houseprice datasets. Our results show that SP-CV provided the smallest error differences with respect to the reference error. This means that SP-CV produced more representative splits and led to more reliable model evaluations. It suggests that a reliable model evaluation requires to consider both the geographic and the feature spaces in a comprehensive manner.

Abstract Objective This study was to compare tonsillectomy with intraoperative suturing (TIS) and tonsillectomy without intraoperative suturing (TsIS) in preventing postoperative tonsillectomy hemorrhage (PTH). Methods The Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines was followed. Articles compare TIS and TsIS in preventing PTH were included. The quality of eligible studies was assessed with the Newcastle‐Ottawa Scale (NOS) by two independent investigators. Random effect models were used to determine odds ratio (OR) with 95% CIs. Results A total of 15 studies were analyzed. The pooled results showed the PTH rate was lower in the TIS group (OR = 0.64; 95% CI, 0.47–0.88). The TIS group had a lower primary and secondary PTH rate than the TsIS group with OR values of 0.44 (95% CI, 0.30–0.64) and 0.70 (95% CI, 0.54–0.90), respectively. However, suturing did not show an advantage in reducing the risk of returning to the operation room for hemostasis (OR = 0.57; 95% CI, 0.13–2.47). Adults might benefit from the intraoperative suturing procedure (OR = 0.31; 95% CI, 0.16–0.60). Patients with more than three stitches on each side had a lower PTH rate (OR: 0.44; 95% CI, 0.32–0.60). Suturing the tonsillar fossa and pillars simultaneously could reduce the PTH rate (OR = 0.47; 95% CI, 0.34–0.64). Conclusions Intraoperative suturing is a good strategy for preventing PTH. More multicenter randomized controlled studies should be conducted to demonstrate the efficacy of this procedure. Level of Evidence 5.

background Detecting and segmenting intracranial aneurysms (IAs) from angiographic images is a laborious task. Objective To evaluates a novel deep-learning algorithm, named vessel attention (VA)-Unet, for the efficient detection and segmentation of IAs. Methods This retrospective study was conducted using head CT angiography (CTA) examinations depicting IAs from two hospitals in China between 2010 and 2021. Training included cases with subarachnoid hemorrhage (SAH) and arterial stenosis, common accompanying vascular abnormalities. Testing was performed in cohorts with reference-standard digital subtraction angiography (cohort 1), with SAH (cohort 2), acquired outside the time interval of training data (cohort 3), and an external dataset (cohort 4). The algorithm's performance was evaluated using sensitivity, recall, false positives per case (FPs/case), and Dice coefficient, with manual segmentation as the reference standard. results The study included 3190 CTA scans with 4124 IAs. Sensitivity, recall, and FPs/case for detection of IAs were, respectively, 98.58%, 96.17%, and 2.08 in cohort 1; 95.00%, 88.8%, and 3.62 in cohort 2; 96.00%, 93.77%, and 2.60 in cohort 3; and, 96.17%, 94.05%, and 3.60 in external cohort 4. The segmentation accuracy, as measured by the Dice coefficient, was 0.78, 0.71, 0.71, and 0.66 for cohorts 1-4, respectively. VAUnet detection recall and FPs/case and segmentation accuracy were affected by several clinical factors, including aneurysm size, bifurcation aneurysms, and the presence of arterial stenosis and SAH. Conclusions VA-Unet accurately detected and segmented IAs in head CTA comparably to expert interpretation. The proposed algorithm has significant potential to assist radiologists in efficiently detecting and segmenting IAs from CTA images. [ABSTRACT FROM AUTHOR]

This paper investigates the path planning problem of the coal mine solid-filling and pushing mechanism and proposes a hybrid improved adaptive genetic particle swarm algorithm (AGAPSO). To enhance the efficiency and accuracy of path planning, the algorithm combines a particle swarm optimization algorithm (PSO) and a genetic algorithm (GA), introducing the sharing mechanism and local search capability of the particle swarm optimization algorithm. The path planning of the pushing mechanism for the solid-filling scenario is optimized by dynamically adjusting the algorithm parameters to accommodate different search environments. Subsequently, the proposed algorithm’s effectiveness in the filling equipment path planning problem is experimentally verified using a simulation model of the established filling equipment path planning scenario. The experimental findings indicate that the improved hybrid algorithm converges three times faster than the original algorithm. Furthermore, it demonstrates approximately 92% and 94% better stability and average performance, respectively, than the original algorithm. Additionally, AGAPSO achieves a 27.59% and 19.16% improvement in path length and material usage optimization compared to the GA and GAPSO algorithms, showcasing superior efficiency and adaptability. Therefore, the AGAPSO method offers significant advantages in the path planning of the coal mine solid-filling and pushing mechanism, which is crucial for enhancing the filling effect and efficiency.