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Ensemble clustering has become a widely used technique for improving robustness and accuracy by combining multiple clustering results. However, traditional ensemble clustering methods often fail to provide fair treatment between groups defined by sensitive attributes. Central to many ensemble methods is the symmetric co-association matrix, which captures pairwise similarity between data points based on their co-occurrence across base clusterings. This paper introduces a fair ensemble clustering method based on the symmetric co-association matrix. The proposed method integrates fairness constraints into the objective function of the ensemble process, using the results from base clusterings that lack fairness considerations as input. The optimization is performed iteratively, and the final clustering results are represented directly by a label matrix obtained efficiently using a coordinate descent approach. By integrating fairness into the clustering process, the method avoids the need for post-processing to achieve fair results. Comprehensive experiments on both real-world and synthetic datasets validate the effectiveness and practicality of the proposed method.

This paper investigates the application of cooperative relaying systems with non-orthogonal multiple access (NOMA) in low-altitude intelligent networking-enabled medical Internet of Things (IoT) and analyzes their transmission performance. First, to enhance the communication quality of remote base stations, we deploy a relaying unmanned aerial vehicle (UAV). A two-slot NOMA cooperative transmission mechanism is proposed accordingly. Next, for the NOMA-enhanced UAV-relayed smart healthcare system under Rician fading channels, an exact closed-form expression for the achievable rate is derived using the incomplete Gamma function. Then, to improve computational efficiency, a low-complexity approximation method based on Gauss–Chebyshev quadrature is designed, overcoming the high complexity of the exact expression. Finally, the simulation results validate a close match between the proposed approximation and the exact values (average approximation error below 6.17%), and demonstrate superior achievable rate performance compared to three state-of-the-art schemes.

The Aeromonas salmonicida is responsible for causing furunculosis in various fish species. Furunculosis is a ubiquitous disease that affects the aquaculture industry and causes the mass mortality of turbot. Vibrio vulnificus is a pathogen that causes skin ulcers and hemorrhagic septicemia in fish, resulting in significant mortality in aquaculture. In this study, we have established a bivalent inactivated vaccine against A. salmonicida and V. vulnificus with Montanide™ ISA 763 AVG as an adjuvant. This bivalent inactivated vaccine was used to immunize turbot by intraperitoneal injection, and the relevant immune indexes were detected. The results demonstrate that the bivalent inactivated vaccine exhibited a relative percent survival (RPS) of 77% following A. salmonicida and V. vulnificus intraperitoneal challenge. The vaccinated group exhibited higher levels of acid phosphatase activity and lysozyme activity compared to the control group. ELISA results showed a significant increase in serum antibody levels in immunized turbot, which was positively correlated with immunity. In the kidney tissue, related immune genes (TLR5, CD4, MHCI and MHCII) were up-regulated significantly, showing that the vaccine can induce cellular and humoral immune responses in turbot. In conclusion, the bivalent inactivated vaccine against A. salmonicida and V. vulnificus was immunogenic, efficiently preventing turbot from infection, which has the potential to be applied in aquaculture.

Research on the fairness of spectral clustering has gradually increased attention. Normally, existing methods of fair spectral clustering add a fairness constraint to the original objective function so that fairness is guaranteed. However, similar to the solver of traditional spectral clustering, that of fairness spectral clustering has to relax a discrete value condition into an arbitrary one, which leads to the deterioration of both fairness and clustering quality. Moreover, the eigen-problem is inevitable in the solver, which takes O(n3) time complexity and is not available for large-scale data. In this paper, we propose a fair spectral clustering algorithm by employing the coordinate descent method to find the solution. As the relaxation of the discreteness condition is discarded, the fairness is improved. Furthermore, we refine the process of coordinate descent by avoiding redundant calculations, and as a result, the time complexity is reduced from O(n3) to O(n2). Additionally, the importance of clustering quality and fairness is symmetric; hence, we achieve a trade-off between them by adjusting the parameters. The experimental findings, obtained from both real-world and synthetic datasets, clearly illustrate that our proposal delivers superior fairness and clustering quality with the best BAL compared to other fair clustering methods. In addition, our method is more efficient than existing fair spectral clustering algorithms.

In recent years, a new type of Spiroplasma has been found that can cause “tremor disease” of the Chinese mitten crab Eriocheir sinensis. The outbreak of epidemic tremor disease has caused a serious setback in the Chinese mitten crab farming industry, with an incidence rate of more than 30% and mortality rates of 80–100%. Therefore, finding a sensitive method to detect tremor disease in E. sinensis has become a current research focus. In this research, a loop-mediated isothermal amplification detection method coupled with hydroxynaphthol blue dye (LAMP-HNB) was developed and used to rapidly detect Spiroplasma eriocheiris. First, we designed and synthesized specific outer primers, inner primers and loop primers based on the 16S ribosomal RNA gene of S. eriocheiris. Second, the LAMP-HNB detection method for S. eriocheiris was successfully established by screening the primers, adjusting the temperature and time of the reaction, and optimizing the concentrations of Mg2+ and dNTPs. In the specific tests, only samples infected with S. eriocheiris showed positive results, and other infections caused by bacteria and parasites tested negative, proving that the test has high specificity. Moreover, the detection limit was 2.5 × 10–6 ng/µL, indicating high sensitivity. This method for detecting S. eriocheiris provides rapid visual output based on LAMP-HNB detection and is a simple, fast, sensitive, and inexpensive method that can be applied to a wide range of field investigations.

Different subtypes of breast cancer pose great challenges for precision therapy, especially triple-negative breast cancer (TNBC), because it lacks effective therapeutic targets and is highly resistant to chemotherapy. In this study, the transmembrane protein ITM2A was systematically identified as a novel prognostic biomarker and potential therapeutic target for TNBC. ITM2A was found to be significantly under expressed in TNBC tissues, as revealed by differential expression profiling. Furthermore, patients exhibiting low ITM2A expression demonstrated worse overall survival (OS), recurrence-free survival (RFS), and distant metastasis-free survival (DMFS). A combined multi-omics analysis revealed a significant association between low ITM2A expression and immunosuppressive microenvironmental features. It is noteworthy that the ITM2A high-expression group exhibited substantial clinical benefits in anti-PD-L1 treatment (AUC=0.982) and CAR-T treatment (AUC=0.827). Gene Ontology functional annotation and KEGG pathway enrichment analysis indicated that ITM2A may coordinate anti-tumor immune responses by regulating copper ion metabolic reprogramming and immune checkpoint networks. Pharmacogenomic analysis further confirmed that the expression level of ITM2A was negatively correlated with the sensitivity of etoposide. By establishing the 'immunometabolism-therapeutic response' regulatory axis of ITM2A, this study hopes to provide an innovative theoretical basis for the targeted treatment of TNBC and the precise stratification of immunotherapy.

Additive manufacturing has thrived over the past decade due to its prominent potential of fast prototyping and complex components fabrication. Unlike conventional manufacturing that subtracts the desired products from the raw materials, 3D printing builds components upon layers, which may result in some degrees of uncertain shifts in terms of the material properties. While enhancing in-situ monitoring provides improving quality assurance during the process, reliable nondestructive methods are on-demand to provide the feedback of end products' elastic properties. A line-focus transducer system that utilizes ultrasound for material characterization is presented. The main hardware is a large aperture lens-less line-focus transducer with a theoretical central frequency of 10MHz, while a time-resolved method is adopted to avoid any mechanical scanning. This testing method is based on the propagation of surface and bulk acoustic waves and their relationship with mediums' elastic properties. The system is first validated by measuring conventionally produced metal bars and then applied to Stainless Steel 316L samples that are manufactured from Direct Metal Laser Sintering 3D printer. Sufficient signal processing and data analysis are conducted to predict samples' elastic properties' actual values, indicating a good match with the information on the official datasheet. This research aims to 1) introduce the fundamental of the piezoelectric effect, nondestructive testing methods, and additive manufacturing methods, 2) establish a solid understanding of the relationship between acoustic wave propagation and the stiffness constants. 3) build up a reliable line-focus transducer system with neat experiment and analysis procedure, 4) conclude the result and future expectations.

The Aeromonas salmonicida is responsible for causing furunculosis in various fish species. Furunculosis is a ubiquitous disease that affects the aquaculture industry and causes the mass mortality of turbot. Vibrio vulnificus is a pathogen that causes skin ulcers and hemorrhagic septicemia in fish, resulting in significant mortality in aquaculture. In this study, we have established a bivalent inactivated vaccine against A. salmonicida and V. vulnificus with Montanide™ ISA 763 AVG as an adjuvant. This bivalent inactivated vaccine was used to immunize turbot by intraperitoneal injection, and the relevant immune indexes were detected. The results demonstrate that the bivalent inactivated vaccine exhibited a relative percent survival (RPS) of 77% following A. salmonicida and V. vulnificus intraperitoneal challenge. The vaccinated group exhibited higher levels of acid phosphatase activity and lysozyme activity compared to the control group. ELISA results showed a significant increase in serum antibody levels in immunized turbot, which was positively correlated with immunity. In the kidney tissue, related immune genes (TLR5, CD4, MHCI and MHCII) were up-regulated significantly, showing that the vaccine can induce cellular and humoral immune responses in turbot. In conclusion, the bivalent inactivated vaccine against A. salmonicida and V. vulnificus was immunogenic, efficiently preventing turbot from infection, which has the potential to be applied in aquaculture.

Background Chinese cabbage (Brassica rapa ssp. pekinensis) experienced a whole-genome triplication event and thus has three subgenomes: least fractioned, medium fractioned, and most fractioned subgenome. Environmental changes affect leaf development, which in turn influence the yield. To improve the yield and resistance to different climate scenarios, a comprehensive understanding of leaf development is required including insights into the full diversity of cell types and transcriptional networks underlying their specificity. Results Here, we generate the transcriptional landscape of Chinese cabbage leaf at single-cell resolution by performing single-cell RNA sequencing of 30,000 individual cells. We characterize seven major cell types with 19 transcriptionally distinct cell clusters based on the expression of the reported marker genes. We find that genes in the least fractioned subgenome are predominantly expressed compared with those in the medium and most fractioned subgenomes in different cell types. Moreover, we generate a single-cell transcriptional map of leaves in response to high temperature. We find that heat stress not only affects gene expression in a cell type-specific manner but also impacts subgenome dominance. Conclusions Our study highlights the transcriptional networks in different cell types and provides a better understanding of transcriptional regulation during leaf development and transcriptional response to heat stress in Chinese cabbage.

Understanding the driving mechanisms of vegetation development is critical for maintaining terrestrial ecosystem function in mountain areas, especially under the background of climate change. The Qinba Mountains (QBM), a critical north–south transition zone in China, is an environmentally fragile area that is vulnerable to climate change. It is essential to characterize how its ecological environment has changed. Currently, such a characterization remains unclear in the spatiotemporal patterns of the nonlinear effects and interactions between environmental factors and vegetation changes in the QBM. Here, we utilized the Normalized Difference Vegetation Index (NDVI), obtained from Google Earth Engine (GEE) platform, as an indicator of terrestrial ecosystem conditions. Then, we measured the spatiotemporal heterogeneity for vegetation variation in the QBM from 2003 to 2018. Specifically, the Geodetector method, a new geographically statistical method without linear assumptions, was employed to detect the interaction between vegetation and environmental driving factors. The results indicated that there is a trend of a general increase in vegetation growth amplitude (the average NDVI increased from 0.810 to 0.858). The areas with an NDVI greater than 0.8 are mainly distributed in the Qinling Mountains and the Daba Mountains, which account for more than 76.39% of the QBM area. For the entire region, the global Moran’s index of the NDVI is greater than 0.95, indicating that vegetation is highly concentrated in the spatial domain. The Geodetector identified that landform type was the primary factor in controlling vegetation changes, contributing 24.19% to the total variation, while the explanatory powers of the aridity index and the wetness index for vegetation changes were 22.49% and 21.47%, respectively. Furthermore, the interaction effects between any two factors outperformed the influence of a single environmental variable. The interaction between air temperature and the aridity index was the most significant element, contributing to 47.10% of the vegetation variation. These findings can not only improve our understanding in the interactive effects of environmental forces on vegetation change, but also be a valuable reference for ecosystem management in the QBM area, such as ecological conservation planning and the assessment of ecosystem functions.