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Cross-view image clustering (CIC) showcases immense potential in recognizing image patterns due to the power to aggregate information between views without labels. However, most CIC ignore intricate coupling relationships between category and redundant features in aggregating complementary information of cross-view images, which may restrict performance in recognizing patterns of images. To this end, a cross-view mutual information decoupling based deep generative clustering approach is proposed for recognizing image patterns (DMID-UIC), which contains information maximization deep generative module and self-supervised posterior inference module. Specifically, the former maximizes the mutual information between data and semantics within the generative adversarial network to decouple category and redundant features hidden in cross-view images, which guarantees the separation of different semantic distributions in the data space. The latter models the distribution fitting between generated data and the prior semantic code as a classification task, via treating partitioning results of common representations between views as self-supervised labels. Meanwhile, to better optimize the model, an EM optimization strategy is designed to enhance the above two module learning in an iterative manner. Finally, comprehensive results verify the superiority and effectiveness of DMID-UIC. DMID-UIC improves ACC by 10.1% on the Caltech 101-7 dataset, compared to the second-best result.

Abstract Circularly polarized room temperature phosphorescence materials represent a state-of-the-art frontier of optical materials and exhibit promising applications in various fields. Herein, we fabricate a series of full-color circularly polarized room temperature phosphorescence materials, based on anionic cellulose derivatives and achiral luminophores. The ionic achiral substituents promote the spontaneous formation of chiral helical structure of cellulose derivatives via the electrostatic repulsion effect. There are multiple interactions between anionic cellulose derivatives and the doped luminophores, thus the chirality is transferred to luminophores and the non-radiative transition is inhibited. The resultant materials can be easily processed into large-scale film and flexible 3D objects with repeatable folding and curling properties. In addition, their phosphorescence performance shows to be excitation-dependence, time-dependence, visible-light excitation, and multi-responsiveness to humidity, temperature as well as pH value. Importantly, they recognize many enantiomers in an instrument-free visual mode, including amino acids, hydroxyl acids, organic phosphate and hydrobenzoin. These results provide insights into design of advanced optical materials which can be applied in multilevel information handling and chiral sensing.

Abstract Background China has the third largest number of TB cases in the world, and the average annual floating population in China is more than 200 million, the increasing floating population across regions has a tremendous potential for spreading infectious diseases, however, the role of increasing massive floating population in tuberculosis transmission is yet unclear in China. Methods 29,667 tuberculosis flow data were derived from the new smear-positive pulmonary tuberculosis cases in China. Spatial variation of TB transmission was measured by geodetector q-statistic and spatial interaction model was used to model the tuberculosis flow and the regional socioeconomic factors. Results Tuberculosis transmission flow presented spatial heterogeneity. The Pearl River Delta in southern China and the Yangtze River Delta along China’s east coast presented as the largest destination and concentration areas of tuberculosis inflows. Socioeconomic factors were determinants of tuberculosis flow. Some impact factors showed different spatial associations with tuberculosis transmission flow. A 10% increase in per capita GDP was associated with 10.2% in 2010 or 2.1% in 2012 decrease in tuberculosis outflows from the provinces of origin, and 1.2% in 2010 or 0.5% increase in tuberculosis inflows to the destinations and 18.9% increase in intraprovincial flow in 2012. Per capita net income of rural households and per capita disposable income of urban households were positively associated with tuberculosis flows. A 10% increase in per capita net income corresponded to 14.0% in 2010 or 3.6% in 2012 increase in outflows from the origin, 44.2% in 2010 or 12.8% increase in inflows to the destinations and 47.9% increase in intraprovincial flows in 2012. Tuberculosis incidence had positive impacts on tuberculosis flows. A 10% increase in the number of tuberculosis cases corresponded to 2.2% in 2010 or 1.1% in 2012 increase in tuberculosis inflows to the destinations, 5.2% in 2010 or 2.0% in 2012 increase in outflows from the origins, 11.5% in 2010 or 2.2% in 2012 increase in intraprovincial flows. Conclusions Tuberculosis flows had clear spatial stratified heterogeneity and spatial autocorrelation, regional socio-economic characteristics had diverse and statistically significant effects on tuberculosis flows in the origin and destination, and income factor played an important role among the determinants.

Abstract While the anaerobic-anoxic-oxic (AAO) process is the most widely applied biological wastewater treatment process in municipal wastewater treatment plants (WWTPs), it struggles to meet the increasing demands on biological toxicity control of the treated effluent. To tackle this challenge, this study develops machine learning (ML)-based models for optimizing the AAO treatment process towards improving its toxicity reduction efficacy for the effluent. The water quality parameters, treatment process parameters, and biological toxicity information (based on the nematode bioassay) of the effluent collected from 122 WWTPs in China are used to train the models. The validated models accurately predict the effluent’s quality parameters (average R2 = 0.81) and the biological toxicity reduction ratio of treatment process (R2 = 0.86). To further improve the toxicity reduction, we developed a multiple objective optimization framework to optimize the AAO process via unit process recombination. In the short-range unit combination, the toxicity reduction ratio of the four-unit combined processes (up to 79.8% of anaerobic-aerobic-anaerobic-aerobic) is significantly higher than others. After optimization, it helps to improve the average toxicity reduction efficacy of 122 WWTPs from 48.6% to 70.7%, with a maximum of 87.5%. The methodologies and findings derived from this work are expected to provide the foundation for the optimization, expansion, and technical transformation of biological wastewater treatment in WWTPs.

The energy management of the electricity-heating integrated energy system (IES) is related to the economic benefits and multi-energy complementary capabilities of a park, but it faces the challenges of the randomness of renewable energy and the uncertainty of load. First, in this paper, a mathematical model of the energy management problem for the electricity-heating IES is conducted, and each energy supply subsystem is empowered as an agent. Based on the deep deterministic policy gradient (DDPG) algorithm, a system energy management model is established that comprehensively considers the real-time energy load of the subsystem, the time-of-use pricing, and the output of each equipment. Then, the federated learning technology is used to interact with the gradient parameters of the energy management model of the three subsystems during the training process to synergistically optimize the training effect of the model, which can protect the data privacy of each subsystem while breaking the data barriers. Finally, an example analysis verifies that the proposed federated-DDPG energy management model can effectively improve the economic benefits of the park-level IES.

Abstract Background Prostate cancer (PCa) is the second leading cause of tumor-related mortality in men. Metastasis from advanced tumors is the primary cause of death among patients. Identifying novel and effective biomarkers is essential for understanding the mechanisms of metastasis in PCa patients and developing successful interventions. Methods Using the GSE8511 and GSE27616 data sets, 21 metastasis-related genes were identified through the weighted gene co-expression network analysis (WGCNA) method. Subsequent functional analysis of these genes was conducted on the gene set cancer analysis (GSCA) website. Cluster analysis was utilized to explore the relationship between these genes, immune infiltration in PCa, and the efficacy of targeted drug IC50 scores. Machine learning algorithms were then employed to construct diagnostic and prognostic models, assessing their predictive accuracy. Additionally, multivariate COX regression analysis highlighted the significant role of POLD1 and examined its association with DNA methylation. Finally, molecular docking and immunohistochemistry experiments were carried out to assess the binding affinity of POLD1 to PCa drugs and its impact on PCa prognosis. Results The study identified 21 metastasis-related genes using the WGCNA method, which were found to be associated with DNA damage, hormone AR activation, and inhibition of the RTK pathway. Cluster analysis confirmed a significant correlation between these genes and PCa metastasis, particularly in the context of immunotherapy and targeted therapy drugs. A diagnostic model combining multiple machine learning algorithms showed strong predictive capabilities for PCa diagnosis, while a transfer model using the LASSO algorithm also yielded promising results. POLD1 emerged as a key prognostic gene among the metastatic genes, showing associations with DNA methylation. Molecular docking experiments supported its high affinity with PCa-targeted drugs. Immunohistochemistry experiments further validated that increased POLD1 expression is linked to poor prognosis in PCa patients. Conclusions The developed diagnostic and metastasis models provide substantial value for patients with prostate cancer. The discovery of POLD1 as a novel biomarker related to prostate cancer metastasis offers a promising avenue for enhancing treatment of prostate cancer metastasis.

Abstract This article proposes a line scanning chromatic confocal sensor to solve the problem of limited chromatic confocal measurement due to the small measurement range and low measurement efficiency in the industrial inspection process. To obtain an extensive dispersion range, the advantages of a simple single-axis structure are combined with the advantages of a large luminous flux of a biaxial structure. Considering large-scale measurement, our sensor uses off-axis rays to limit the illumination path and imaging path to the same optical path structure. At the same time, the field of view is expanded, and a symmetrical structure is adopted to provide a compact optical path and improve space utilization. The simulation and physical system test results shows that the sensor scanning line length is 12.5 mm, and the axial measurement range in the 450 nm to 750 nm band is better than 20 mm. The axial resolution of the detector is ±1 µm combined with the subpixel centroid extraction data processing method, and the maximum allowable tilt angle for specular reflection samples is ±7°. The thicknesses of transparent standard flat glass and the wet collagen membrane are measured. The maximum average error is 1.3 µm, and the relative error is within 0.7%. The constructed sensor is of great significance for rapidly measuring the three-dimensional profile, flatness, and thickness in the fields of transparent biological samples, optics, micromechanics, and semiconductors.

Highlights The novel ionic cellulose derivative with cyano-imidazolium cation and chloride anion, designed from natural cellulose, promotes the crystallization process, grain growth, and orientation of perovskite in perovskite solar cells (PSCs). The use of ionic cellulose leads to the suppression of defect formation, reduction of grain boundaries, enhancement of carrier extraction, and inhibition of non-radiative recombination, resulting in a high power conversion efficiency of 24.71% in PSCs. The biopolymer-based approach enables excellent stability of PSCs, which maintain over 91.3% of their initial efficiencies after 3000 h in conventional air atmosphere.

BackgroundEarly childhood caries (ECC) are a prevalent chronic disease in young children. However, there has been limited research on the microbiota in different tissue levels of the same tooth in children with ECC. This study aimed to investigate the dynamic changes in bacterial diversity during the progression of Severe Early Childhood Caries (S-ECC) within the same tooth, from the tooth surface to the root canal, by collecting tissue samples from different areas of the affected tooth.MethodsTwenty primary teeth with periapical periodontitis were selected from 20 children aged 3–5 years, with 100 samples collected from the different layers: uncavitated buccal enamel surface without white spot lesion (surface), the outermost layer of the dentin carious lesion (superficial), the inner layer of carious dentin (deep), necrotic pulp tissue (pulp), and root exudate (exudate). The taxonomy of each OTU representative sequence was analyzed against the 16S rRNA database. Comparisons of alpha diversity between groups were performed. The number of shared and unique genera between groups counted. Beta diversity was contrasted to evaluate differences in bacterial community composition, and the relationships between the microbiota and samples were analyzed. The heatmap analysis of the 30 most abundant genera was used, which highlighted their relative distribution and abundance. The significantly abundant taxa (phylum to genera) of bacteria among the different groups were identified. The differences of relative abundance between bacterial genera among the five groups were analyzed. Significant Spearman correlations were noted, and visualization of the co-occurrence network was conducted.ResultsBacterial 16S rRNA gene sequencing showed that most genera were present in all layers, with the number of shared genera increasing as the disease advanced. The bacterial communities and core genera in the co-occurrence network changed with progression to severe ECC.ConclusionAn increase in both the quantity and complexity of bacterial interactions was observed. This study emphasized the importance of paying attention to the relationship between microbial species rather than just checking changes in bacterial species structure when investigating the role of bacteria in disease progression.

Coal spontaneous combustion (CSC) brings great danger to mine safety. Although traditional colloidal fire-fighting materials can reside at high fire source points, their permeability and fluidity are poor, and such materials focus on their water absorption, but pay less attention to water retention and resistance, which makes their fire-fighting effect not good. This paper proposed a new type of mining fire prevention colloid material (CPS-α), which had good solid water gel formation, viscosity, fluidity, and blocking properties to compensate for traditional colloids' shortcomings. According to the single-factor experiment, polymeric polyacrylamide (PAM) as the base material, sodium carboxymethyl cellulose (CMC) as the coagulant, modified starch (SS) as the flame retardant and reduced ascorbic acid were determined. After gel treatment, compared with raw coal, the critical temperature point of spontaneous combustion of coal was increased by about 28 °C, the time to reach the critical temperature point was delayed by 190s, and the characteristic temperature points were significantly increased; the overlap area of coal and water increases by about 20 % after adding gel, and the change slope of the diffusion coefficient of water decreases, which indicated that CPS-α had good flame retardant effect.

Specific leaf area (SLA) and leaf dry matter content (LDMC) are key leaf functional traits often used to reflect plant resource utilization strategies and predict plant responses to environmental changes. In general, grassland plants at different elevations exhibit varying survival strategies. However, it remains unclear how grassland plants adapt to changes in elevation and their driving factors. To address this issue, we utilized SLA and LDMC data of grassland plants from 223 study sites at different elevations in China, along with climate and soil data, to investigate variations in resource utilization strategies of grassland plants along different elevational gradients and their dominant influencing factors employing linear mixed-effects models, variance partitioning method, piecewise Structural Equation Modeling, etc. The results show that with increasing elevation, SLA significantly decreases, and LDMC significantly increases (P < 0.001). This indicates different resource utilization strategies of grassland plants across elevation gradients, transitioning from a “faster investment-return” at lower elevations to a “slower investment-return” at higher elevations. Across different elevation gradients, climatic factors are the main factors affecting grassland plant resource utilization strategies, with soil nutrient factors also playing a non-negligible coordinating role. Among these, mean annual precipitation and hottest month mean temperature are key climatic factors influencing SLA of grassland plants, explaining 28.94% and 23.88% of SLA variation, respectively. The key factors affecting LDMC of grassland plants are mainly hottest month mean temperature and soil phosphorus content, with relative importance of 24.24% and 20.27%, respectively. Additionally, the direct effect of elevation on grassland plant resource utilization strategies is greater than its indirect effect (through influencing climatic and soil nutrient factors). These findings emphasize the substantive impact of elevation on grassland plant resource utilization strategies and have important ecological value for grassland management and protection under global change.

The advancement of intelligent manufacturing technology in the era of Industry 5.0 has propelled the intelligence and automation of manufacturing production, while also exerting a significant impact on sustainable development of the manufacturing industry. However, the challenges and enablers faced by the transformation of intelligent manufacturing technology in the context of sustainable development of Industry 5.0 are still unclear. Based on literature review and expert opinions, this study uses the Likert scale to determine the challenges and enablers of the implementation of intelligent manufacturing technology in social, environmental and economic sustainability. The fuzzy-DEMETAL and AISM are used to analyze the logical relationship and hierarchical relationship between the above factors, and the MICMAC matrix is used to determine the key influencing factors. The research conclusions show that the most important challenges affecting the implementation of intelligent manufacturing technology are cost and funding, and the most important enabler is social benefits and public service improved. This research will provide insights for industry practitioners and decision makers in the management and decision-making process of implementing the transformation and upgrading of manufacturing intelligent manufacturing, thereby enhancing the sustainability of manufacturing development.

Spontaneous combustion of residual coal in the goaf is one of the main disasters in coal mines. Understanding the law of spontaneous combustion of residual coal can effectively prevent coal mine fires. This paper establishes a dynamic evolution model of overburden caving and porosity in the goaf using PFC numerical simulation software, and compares and analyzes the dynamic changes in porosity and the spontaneous combustion zone at advancing distances of 50 m, 70 m, and 100 m. The results show that when the working face advances 100 m, the porosity in the caving zone of the goaf decreases from 0–0.6 to 0.16–0.3. The range of the spontaneous combustion zone on the return air side expands from 42m-50 m to 39m–61 m, and on the inlet side, it expands from 0 to 22m–50 m. With increased exposure time, the spontaneous combustion zone range shifts slightly forward to 32m–55 m on the inlet side and 21m–49 m on the return side. This study reveals the dynamic evolution law of the spontaneous combustion zone in the goaf under the influence of different advancing distances and exposure times, which can assist in preventing goaf fires.

Grouted Sleeve Connections (GSC) play an important role in assembly of structural members for precast concrete structures. With many experimental work and parametric study have been done on the tension load-slip behavior of GSC, the mechanical behavior for the tension load-slip responses of GSC should also be studied and explained. The failure modes for GSC in tensile tests with different material properties are firstly analyzed here by investigations on 326 GSC specimens. To study the mechanism of the failure modes, a partial-interaction numerical model is introduced to simulate the load-slip responses of GSC with provided bond-slip characteristics. The different boundary conditions for long and short embedment lengths are described respectively. More than 400 data points in load-slip responses are collected from tensile tests of GSC. The verification between theoretical results with test results shows good agreement. Hence, with the partial-interaction numerical model, the corresponding GSC load-slip responses could be obtained by given material and dimensional properties. Furthermore, parametric study shows that load-slip responses of GSC in tensile behavior change with embedment lengths of reinforcing bars. The CT scan results also show that embedment lengths for reinforcement are suggested to be longer than the designed size by considering the original defects of the sleeves and the eccentricity of reinforcement in construction.

Abstract Background Southwest China is one of the largest karst regions in the world. Karst environment is relatively fragile and vulnerable to human activities. Due to the discharge of sewage and domestic garbage, the karst system may be polluted by pathogenic bacteria. The detection of bacterial distribution and identification of phage capable of infecting them is an important approach for environmental assessment and resource acquisition. Methods Bacteria and phages were isolated from karst water in southwest China using the plate scribing and double plate method, respectively. Isolated phage was defined by transmission electron microscopy, one-step growth curve and optimal multiplicity of infection (MOI). Genomic sequencing, phylogenetic analysis, comparative genomic and proteomic analysis were performed. Results A Klebsiella quasipneumoniae phage was isolated from 32 isolates and named KL01. KL01 is morphologically identified as Caudoviricetes with an optimal MOI of 0.1, an incubation period of 10 min, and a lysis period of 60 min. The genome length of KL01 is about 45 kb, the GC content is 42.5%, and it contains 59 open reading frames. The highest average nucleotide similarity between KL01 and a known Klebsiella phage 6939 was 83.04%. Conclusions KL01 is a novel phage, belonging to the Autophagoviridae, which has strong lytic ability. This study indicates that there were not only some potential potentially pathogenic bacteria in the karst environment, but also phage resources for exploration and application.

Abstract Ulcerative colitis (UC) is a chronic, recurrent inflammatory bowel disease. UC confronts with severe challenges including the unclear pathogenesis and lack of specific diagnostic markers, demanding for identifying predictive biomarkers for UC diagnosis and treatment. We perform immune infiltration and weighted gene co-expression network analysis on gene expression profiles of active UC, inactive UC, and normal controls to identify UC related immune cell and hub genes. Neutrophils, M1 macrophages, activated dendritic cells, and activated mast cells are significantly enriched in active UC. MMP-9, CHI3L1, CXCL9, CXCL10, CXCR2 and S100A9 are identified as hub genes in active UC. Specifically, S100A9 is significantly overexpressed in mice with colitis. The receiver operating characteristic curve demonstrates the excellent performance of S100A9 expression in diagnosing active UC. Inhibition of S100A9 expression reduces DSS-induced colonic inflammation. These identified biomarkers associated with activity in UC patients enlighten the new insights of UC diagnosis and treatment.

Abstract Spatial clustering is an essential method for the comprehensive understanding of a region. Spatial clustering divides all spatial units into different clusters. The attributes of each cluster of the spatial units are similar, and simultaneously, they are as continuous as spatially possible. In spatial clustering, the handling of spatial outliers is important. It is necessary to improve spatial integration so that each cluster is connected as much as possible, while protecting spatial outliers can help avoid the excessive masking of attribute differences This paper proposes a new spatial clustering method for raster data robust to spatial outliers. The method employs a sliding window to scan the entire region to determine spatial outliers. Additionally, a mechanism based on the range and standard deviation of the spatial units in each window is designed to judge whether the spatial integration should be further improved or the spatial outliers should be protected. To demonstrate the usefulness of the proposed method, we applied it in two case study areas, namely, Changping District and Pinggu District in Beijing. The results show that the proposed method can retain the spatial outliers while ensuring that the clusters are roughly contiguous. This method can be used as a simple but powerful and easy-to-interpret alternative to existing geographical spatial clustering methods.

Abstract Background Acetyl-CoA acetyltransferase 2 (ACAT2) is a lipid metabolism enzyme and rarely was researched in epithelial ovarian cancer (EOC). Methods ACAT2 expressions were confirmed in two pairs of cell lines (A2780 and A2780/DDP, OVCAR8 and OVCAR8/DDP) from Gene Expression Omnibus database by bioinformatics analysis, and in A2780 and A2780/DDP cell lines by quantitative real-time polymerase chain reaction and western blotting. Tissue samples were stained by immunohistochemistry and scored for ACAT2 expression. The relationships between ACAT2 expression and clinicopathological characteristics were analyzed by χ2 test. The prognosis of ACAT2 was analyzed by the log-rank tests and Cox regression models. Results ACAT2 was remarkably upregulated in the above drug-resistant cell lines by mRNA (all P

In this article, a second-order time discrete algorithm with a shifted parameter $ \theta $ combined with a fast time two-mesh (TT-M) mixed finite element (MFE) scheme was considered to look for the numerical solution of the nonlinear fractional hyperbolic wave model. The second-order backward difference formula including a shifted parameter $ \theta $ (BDF2-$ \theta $) with the weighted and shifted Grünwald difference (WSGD) approximation for fractional derivative was used to discretize time direction at time $ t_{n-\theta} $, the $ H^1 $-Galerkin MFE method was applied to approximate the spatial direction, and the fast TT-M method was used to save computing time of the developed MFE system. A priori error estimates for the fully discrete TT-M MFE system were analyzed and proved in detail, where the second-order space-time convergence rate in both $ L^2 $-norm and $ H^1 $-norm were derived. Detailed numerical algorithms with smooth and weakly regular solutions were provided. Finally, some numerical examples were provided to illustrate the feasibility and effectiveness for our scheme.

Abstract Recently, advanced adenoma (AA) has been recognized as a target for colorectal cancer (CRC) screening. However, the fecal occult blood test (FOBT), the primary non-invasive screening method, shows limited sensitivity in detecting AA. This study investigates the relationship between adenoma characteristics and FOBT false-negative results. In a retrospective cohort study conducted from 2015 to 2022, we examined 342 inpatients with AA who underwent colonoscopy and received qualitative FOBT. FOBT sensitivity was analyzed about various adenoma characteristics, and logistic regression models were employed to investigate the relationship between adenoma features and FOBT false-negative outcomes. FOBT sensitivity in AA inpatients was 52.63%. Significant differences in sensitivity were observed based on adenoma location (left vs. right), morphology (with or without pedunculation), and size (≤ 10 mm vs. > 10 mm). After adjusting for several potential confounders, FOBT showed a reduced false-negative rate in AA with large-sized (OR, 0.49; 95% CI 0.31–0.77), left-sided location (OR, 0.53; 95% CI 0.31–0.89), and pedunculated morphology (OR, 0.73; 95% CI 0.43–1.24). AA with large size, left-sided location, and pedunculated morphology independently contribute to a decreased rate of FOBT false-negative results. However, these adenoma characteristics are not actively modifiable. Therefore, novel non-invasive methods are needed to improve AA detection accuracy.

Abstracts: Several pre-cooling methods for bok choi were used, such as natural convection pre-cooling (NCPC), strong wind pre-cooling (SWPC), vacuum pre-cooling (VPC), cold water pre-cooling (CWPC), electrolyzed water pre-cooling (EWPC), and fluid ice pre-cooling (FIPC), in order to determine the most suitable precooling method. It was found that VPC reduced the respiration rate, inhibited the increase of malondialdehyde (MDA) and relative electrolyte leakage, and significantly decreased the total bacterial count. This may be due to the rapid decompression process during vacuum pre-cooling, which disrupts the microbial structure and has a certain sterilizing effect. Bok choi pre-cooled by VPC had the best color, hardness value, chlorophyll, titratable acid (TA) content, vitamin C (VC) content, total phenolic (TP) content, soluble sugar content, superoxide dismutase (SOD) activity, ascorbate peroxidase (APX) activity, and catalase (CAT) activity. Therefore, the most suitable pre-cooling method for bok choi among the above pre-cooling methods was the VPC method.

ABSTRACT: A 2-dimensional (2D) egg-shape equation can be used to construct a 3D egg geometry based on the hypothesis that an egg is a solid of revolution, which helps to calculate egg volume and surface area. The parameters in the 2D egg-shape equation are potentially valuable for providing a clue to the ecology and evolution of avian eggs. In this study, the 5-parameter Preston equation (PE), the 4-parameter Troscianko equation (TE), and another 2 egg-shape equations, were compared in describing real 2D egg-shape data of 300 Gallus gallus domesticus eggs and additional 50 eggs that represented the variation in avian egg geometries. Adjusted root-mean-square error was used to quantify each equation's prediction error. Given that the 4 equations are nonlinear, relative curvature measures of nonlinearity were used to assess the extent of nonlinearity in each equation. PE was found to be the best among the 4 equations in terms of adjusted root-mean-square error and minimizing nonlinearity. The empirically determined egg volumes using a graduated cylinder were compared with the predicted egg volumes using the formula for a solid of revolution based on 2D predictions from the 4 egg-shape equations. There were negligible differences in the predicted egg volumes and surface areas among the 4 equations, indicating that these equations are all valid in calculating egg volume and surface area. In addition, we proposed a 5-parameter TE and found that it outperformed the above 4 equations in describing the 2D egg shape of G. gallus, but was less general than PE for other egg shapes. This work provides statistical evidence to show which equation is the best for describing the geometry of avian eggs and nondestructively calculating their volume and surface area, helping to classify poultry eggs into different grades according to the morphological characteristics of the eggs.

With large-scale access to distributed new energy, the power flow form of the distribution network tends to be complex. The distribution loop network rate increases, resulting in serious circulation of the distribution network, which places significant pressure on the loss of the distribution network. The problem of network loss change caused by distributed new energy grid connection, coupled with the inconsistency between load fluctuation and output power change of distributed new energy, will have a significant impact on the safety, reliability, and economic operation of the distribution network. Therefore, it is urgent to have good economic and technical means of power flow control. This paper proposes a minimum network loss optimization control strategy for active distribution networks based on a distributed power flow controller. First, according to the characteristics of the distribution network load and the new energy power supply along the distribution line, the multiple sub-modules of the distributed power flow controller are arranged in sections in the distribution loop network line. The regulation effect of the distributed power flow controller on the voltage and power flow of the distribution loop network is analyzed. The loop network current equation and power loss equation after the distributed power flow controller are constructed, and the power equation with the minimum active loss of the active distribution loop network is derived. Second, the control strategy for minimum network loss of the active distribution network based on the distributed power flow controller is proposed. Finally, the simulation model of the active distribution loop network is constructed by PSCAD/EMTDC. The loss of the loop network before and after the distributed power flow controller is simulated and analyzed, and the effectiveness of the proposed strategy is verified.

Introduction: In recent research, the expansion in the use of Mg alloys for biomedical applications has been approached by modifying their surfaces in conjunction with micro-arc oxidation (MAO) techniques which enhance their abrasion and corrosion resistance.Methods: In this study, combining laser texturing and MAO techniques to produce the dense ceramic coatings with microstructures. On the surface of the AZ31 Mg alloy, a micro-raised annulus array texture has been designed in order to increase the surface friction under liquid lubrication and to improve the operator’s grip when holding the tool. For this work, the micro-morphology of the coatings was characterised, and the friction properties of the commonly used scalpel shank material 316 L, the untextured surface and the textured surface were comparatively analysed against disposable surgical gloves.Results and discussion: The results show that the Laser-MAO ceramic coating grows homogenous, the porosity decreases from 14.3% to 7.8%, and the morphology after friction indicates that the coating has good wear resistance. More specifically, the average coefficient of friction (COF) of the three types of gloves coated with Laser-MAO ceramic was higher than that of the 316 L and MAO ceramic coatings under the action of the annulus-integrated texture under the lubrication conditions of physiological saline and defatted sheep blood, which achieved the goal of increasing friction for the purpose of helping to prevent the problem of tool slippage from the hand.

Quorum-sensing bacteria (QSB) are crucial factors for microbial communication, yet their ecological role in wastewater treatment plants (WWTPs) remains unclear. Here, we developed a method to identify QSB by comparing 16S rRNA gene sequences. QSB in 388 activated sludge samples collected from 130 WWTPs across China primarily were identified as rare taxa and conditionally rare taxa. A co-occurrence network shared by all sludge communities revealed that QSB exhibited higher average clustering coefficient (0.46) than non-QSB (0.15). Individual sludge networks demonstrated that quorum sensing microbiomes were positively correlated with network robustness and network complexity, including average clustering coefficient and link density. We confirmed that QSB keystones and QSB nodes have a positive impact on network complexity by influencing network modularity through a structural equation model. Meanwhile, QSB communities directly contributed to maintaining network robustness (r = 0.29, P

Tomato spotted wilt virus (TSWV) is ranked among the top 10 most destructive viruses globally. It results in abnormal leaf growth, stunting, and even death, significantly affecting crop yield and quality. Phytohormones play a crucial role in regulating plant-virus interactions. However, there is still limited research on the effect of TSWV on phytohormone levels, particularly growth hormones and genes involved in the phytohormone pathway. In our study, we combined phytohormone metabolomics and transcriptomics to examine the impact of TSWV infection on phytohormone content and gene expression profile. Metabolomic results showed that 41 metabolites, including major phytohormones and their precursors and derivatives were significantly altered after 14 days of TSWV inoculation tobacco plants cvK326, with 31 being significantly increased and 10 significantly reduced. Specifically, the levels of abscisic acid (ABA) and jasmonoyl-isoleucine (JA-Ile) were significantly reduced. The levels of indole-3-acetic acid (IAA) have remained unchanged. However, the levels of cytokinin isopentenyladenine (iP) and salicylic acid (SA) significantly increased. The transcriptome analysis revealed 2,746 genes with significant changes in expression. Out of these, 1,072 genes were significantly downregulated, while 1,674 genes were significantly upregulated. Among them, genes involved in ABA synthesis and signaling pathways, such as 9-cis-epoxycarotenoid dioxygenase (NCED), protein phosphatase 2C (PP2C), serine/threonine-protein kinase (SnRK2), and abscisic acid responsive element binding factor (ABF), exhibited significant downregulation. Additionally, expression of the lipoxygenase gene LOX, Jasmonate ZIM domain-containing protein gene JAZ, and transcription factor gene MYC were significantly down-regulated. In the cytokinin pathway, while there were no significant changes in the expression of the cytokinin synthesis genes, a significant downregulation of transcriptionally active factor type-B response regulators (type-B RRs) was observed. In terms of SA synthesis and signaling pathways, the isochorismate synthase gene ICS1 and the pathogenesis-related gene PR1 were significantly upregulated. These results can strengthen the theoretical foundation for understanding the interaction between TSWV and tobacco and provide new insights for the future prevention and control of TSWV.

Disturbances to the power system may lead to relative swing between synchronous generator rotors, causing low-frequency oscillation problems in the absence of damping. In this article, based on the single-machine infinity system containing flexible distribution transformer and synchronous generator, the linearized state equations of the system is deduced by the small-signal modeling. The mechanism of flexible distribution transformer on the rotor speed of synchronous generator is analyzed. The influence factors of flexible distribution transformer on the rotor speed of synchronous generator are analyzed. The equation of the additional damping provided by the flexible distribution transformer to this system is constructed. It was concluded that by controlling the output voltage of the regulating converter in the flexible distribution transformer, the rotor speed of the synchronous generator can be varied to increase the system damping. An additional damping control strategy for flexible distribution transformer is proposed. The simulation verifies that the proposed control strategy can effectively suppress the oscillation amplitude of each electrical quantity and shorten the oscillation duration when the system is subjected to large and small perturbations. The proposed additional damping control strategy of flexible distribution transformer can effectively improve the damping of the system and alleviate the problem of low frequency oscillation.

Abstract Plaque accumulation and microbial community changes are important causes of periodontal disease. Cleaned plaque microorganisms will reattach to form biofilms, but the recovery and outcome of plaque microbial communities in different periodontal health states remain unknown. In this study, we tracked the biofilm remodeling process in 206 dental plaque samples from 40 healthy periodontal, gingivitis and periodontitis volunteers at 6 time points before and after supragingival scaling. We found that microbial communities of different periodontal states changed asynchronously during the process, and the more severe the periodontal disease condition, the more lagged the recovery of plaque microorganisms to their original state after cleaning; this reflected a higher degree of plaque development in periodontitis samples. The plaque index and bleeding index were significantly correlated with plaque recovery, especially the recovery of bacteria such as Abiotrophia and Capnocytophaga. Meanwhile, we found that the microbial community structure of different periodontal health states was most similar at the Day 3 after plaque cleaning, and the communities gradually differentiated and developed in different directions. Abiotrophia and other bacteria might play an important role in determining the development trend of plaque biofilms. The discovery of specific time points and bacteria was of great value in clarifying the pathogenesis of periodontal disease and in seeking targets for prevention and treatment.

Abstract Background The gut microbiota play important roles in host adaptation and evolution, but are understudied in natural population of wild mammals. To address host adaptive evolution and improve conservation efforts of threatened mammals from a metagenomic perspective, we established a high-quality gut microbiome catalog of the giant panda (pandaGUT) to resolve the microbiome diversity, functional, and resistome landscapes using approximately 7 Tbp of long- and short-read sequencing data from 439 stool samples. Results The pandaGUT catalog comprises 820 metagenome-assembled genomes, including 40 complete closed genomes, and 64.5% of which belong to species that have not been previously reported, greatly expanding the coverage of most prokaryotic lineages. The catalog contains 2.37 million unique genes, with 74.8% possessing complete open read frames, facilitating future mining of microbial functional potential. We identified three microbial enterotypes across wild and captive panda populations characterized by Clostridium, Pseudomonas, and Escherichia, respectively. We found that wild pandas exhibited host genetic-specific microbial structures and functions, suggesting host-gut microbiota phylosymbiosis, while the captive cohorts encoded more multi-drug resistance genes. Conclusions Our study provides largely untapped resources for biochemical and biotechnological applications as well as potential intervention avenues via the rational manipulation of microbial diversity and reducing antibiotic usage for future conservation management of wildlife. Video Abstract

The increasing adoption of renewable energy, particularly photovoltaic (PV) power, has highlighted the importance of accurate PV power forecasting. Despite advances driven by deep learning (DL), significant challenges remain, particularly in capturing the long-term dependencies essential for accurate forecasting. This study presents TransPVP, a novel transformer-based methodology that addresses these challenges and advances PV power forecasting. TransPVP employs a deep fusion technique alongside a multi-task joint learning framework, effectively integrating heterogeneous data sources and capturing long-term dependencies. This innovative approach enhances the model’s ability to detect patterns of PV power variation, surpassing the capabilities of traditional models. The effectiveness of TransPVP was rigorously evaluated using real data from a PV power plant. Experimental results showed that TransPVP significantly outperformed established baseline models on key performance metrics including RMSE, R2, and CC, underscoring its accuracy, predictive power, and reliability in practical forecasting scenarios.

The “leafing intensity premium” hypothesis proposes that leaf size results from natural selection acting on different leafing intensities, i.e., the number of leaves per unit shoot volume or mass. The scaling relationships among various above-ground functional traits in the context of this hypothesis are important for understanding plant growth and ecology. Yet, they have not been sufficiently studied. In this study, we selected four bamboo species of the genus Indocalamus Nakai and measured the total leaf fresh mass per culm, total non-leaf above-ground fresh mass, total number of leaves per culm, and above-ground culm height of 90 culms from each species. These data were used to calculate leafing intensity (i.e., the total number of leaves per culm divided by the total non-leaf above-ground fresh mass) and mean leaf fresh mass per culm (i.e., the total leaf fresh mass per culm divided by the total number of leaves per culm). Reduced major axis regression protocols were then used to determine the scaling relationships among the various above-ground functional traits and leafing intensity. Among the four species, three exhibited an isometric (one-to-one) relationship between the total leaf fresh mass per culm and the total non-leaf above-ground fresh mass, whereas one species (Indocalamus pumilus) exhibited an allometric (not one-to-one) relationship. A negative isometric relationship was found between the mean leaf fresh mass per culm and the leafing intensity for one species (Indocalamus pedalis), whereas three negative allometric relationships between mean leaf fresh mass per culm and leafing intensity were observed for the other three species and the pooled data. An exploration of the alternative definitions of “leafing intensity” showed that the total number of leaves per culm divided by the above-ground culm height is superior because it facilitates the non-destructive calculation of leafing intensity for Indocalamus species. These results not only confirm the leafing intensity premium hypothesis for bamboo species but also highlight the interconnected scaling relationships among different functional traits, thereby contributing to our understanding of the ecological and evolutionary significance of leaf size variation and biomass investment strategies.

Abstract The inequality in leaf and fruit size distribution per plant can be quantified using the Gini index, which is linked to the Lorenz curve depicting the cumulative proportion of leaf (or fruit) size against the cumulative proportion of the number of leaves (or fruits). Prior researches have predominantly employed empirical models—specifically the original performance equation (PE‐1) and its generalized counterpart (GPE‐1)—to fit rotated and right‐shifted Lorenz curves. Notably, another potential performance equation (PE‐2), capable of generating similar curves to PE‐1, has been overlooked and not systematically compared with PE‐1 and GPE‐1. Furthermore, PE‐2 has been extended into a generalized version (GPE‐2). In the present study, we conducted a comparative analysis of these four performance equations, evaluating their applicability in describing Lorenz curves related to plant organ (leaf and fruit) size. Leaf area was measured on 240 culms of dwarf bamboo (Shibataea chinensis Nakai), and fruit volume was measured on 31 field muskmelon plants (Cucumis melo L. var. agrestis Naud.). Across both datasets, the root‐mean‐square errors of all four performance models were consistently smaller than 0.05. Paired t‐tests indicated that GPE‐1 exhibited the lowest root‐mean‐square error and Akaike information criterion value among the four performance equations. However, PE‐2 gave the best close‐to‐linear behavior based on relative curvature measures. This study presents a valuable tool for assessing the inequality of plant organ size distribution.

The vascular veins in photosynthetic leaves play an important role in transporting water and sugars throughout the plant body, and their venation pattern and vein density determine the hydraulic efficiency of the leaf. Likewise, stomatal density (SD) can influence photosynthetic gas exchange. However, the correlation between leaf vein density and SD is seldom reported. Herein, we examined 16 leaves from the hybrid Photinia × fraseri and 16 leaves from one of its parents, P. serratifolia, to explore the correlation between leaf vein density and SD. For each leaf, equidistant lamina quadrats were excised along two longitudinal transects (one along the midrib and another along the leaf margin). For each quadrat, micrographs of 1.2 mm × 0.9 mm stomatal imprints, and 2.51 mm × 1.88 mm micrographs of leaf veins were used to measure total vein area per leaf unit area (VAA) and total vein length per unit area (VLA), as indicators of leaf vein density, to determine the correlation between SD and leaf vein density. For each taxon, there was no significant correlation between SD and VAA, but there was a significant correlation between SD and VLA. The data indicate that SD is not positively correlated with VAA but positively correlated with VLA for both the hybrid and the parent species. This study indicates that future work should focus on the relationships between SD and total vein length per unit area rather than on total leaf vein area per unit area within and across species.

Endoplasmic reticulum (ER) stress-related genes are closely related to the occurrence, development, and immunotherapy response of tumors. This study provides a comprehensive assessment of HSPA5 from a pan-cancer perspective using multi-omics data. We analyzed the function of HSPA5 in multiple tumor types using multiple databases. Finally, immunohistochemistry was used to examine the relationship between HSPA5 expression in tissue microarrays from 100 patients with bladder cancer and the prognosis of patients with bladder cancer. Using the TCGA database, we were able to determine that HSPA5 is significantly elevated in a number of common malignancies and is linked with a bad prognosis. Cox regression analysis showed that the high expression of HSPA5 was correlated with OS, progression free survival (PFS), disease free survival (DFS), and disease special survival (DSS) of adrenocortical carcinoma (ACC). In addition, we discovered significant disparities in HSPA5 methylation and phosphorylation levels between various malignancies and normal tissues. HSPA5 expression was significantly correlated with the levels of infiltrating cells and immune checkpoint genes. HSPA5 is highly expressed in bladder cancer and patients with high HSPA5 expression have a poor prognosis. Our study provides a basis for further understanding of the role of ER stress-related gene HSPA5 in different tumor genesis and development. HSPA5 has also been shown to be a prognostic biomarker for bladder cancer patients.

In order to solve the problem of the power quality caused by distributed power access to the distribution network, this paper proposes a coordinated control strategy of reactive power compensation based on a flexible distribution transformer. First, the working principle of the flexible distribution transformer is analyzed, and the mathematical model of energy acquisition and the regulation converter of the flexible distribution transformer are studied as well. The device-level control strategies of energy acquisition and regulation converters are proposed, respectively. Then, in order to maintain the stability of the bus voltage and quickly respond to the reactive power changes of the system, a coordinated control strategy for the reactive power compensation of flexible distribution transformers is proposed. The priority herein is to maximize the reactive power compensation capacity of the energy harvesting converter. When the energy harvesting converter reaches the compensation upper limit, the control converter is used for reactive power compensation to further suppress the grid voltage fluctuation. Finally, it is verified through simulations that the flexible distribution transformer can realize the reactive power compensation of the distribution network and effectively improve the power quality of the distribution network.

Due to nutrient stress, which is an important constraint to the development of the global agricultural sector, it is now vital to timely evaluate plant health. Remote sensing technology, especially hyperspectral imaging technology, has evolved from spectral response modes to pattern recognition and vegetation monitoring. This study established a hyperspectral library of 14 NPK (nitrogen, phosphorus, potassium) nutrient stress conditions in rice. The terrestrial hyperspectral camera (SPECIM-IQ) collected 420 rice stress images and extracted as well as analyzed representative spectral reflectance curves under 14 stress modes. The canopy spectral profile characteristics, vegetation index, and principal component analysis demonstrated the differences in rice under different nutrient stresses. A transformer-based deep learning network SHCFTT (SuperPCA-HybridSN-CBAM-Feature tokenization transformer) was established for identifying nutrient stress patterns from hyperspectral images while being compared with classic support vector machines, 1D-CNN (1D-Convolutional Neural Network), and 3D-CNN. The total accuracy of the SHCFTT model under different modeling strategies and different years ranged from 93.92% to 100%, indicating the positive effect of the proposed method on improving the accuracy of identifying nutrient stress in rice.

Product innovation is a robust approach for enterprises to acquire and maintain a competitive edge. In order to support the development of enterprise product innovation, it is essential to establish an innovation pathway. Grounded in a modular perspective and considering the product innovation process, this study segments the process into product demand analysis, product module partitioning, product innovation opportunity recognition, and product innovation design. Consequently, an integrated product innovation pathway is constructed by incorporating relevant innovation theories and methodologies, encompassing the innovation process, theory, and methods. The feasibility and efficacy of this research are validated through a case study of a large aircraft assembly line, indicating that this approach effectively bolsters product innovation development and holds practical significance.