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Background The Wnt signaling pathway is core to the growth of bladder tumors. Epithelial-to-mesenchymal transition (EMT) is significant for bladder tumor metastasis. Nevertheless, the relationship between the Wnt signaling pathway, outcomes of bladder cancer (BLCA), and the specific mechanisms driving immune infiltration have not been studied. Methods We obtained Wnt pathway-related gene mRNA and clinicopathological data from the Cancer Genome Atlas (TCGA). We obtained 34 genes that were greatly correlated with outcome using univariate Cox regression analysis and conducted a completely randomized data t-test to perform clinical staging. According to the single-sample gene set enrichment analysis (ssGSEA), the weighted correlation network analysis (WGCNA) was applied to identify relevant biological functions. Various subtypes were identified using consensus cluster analysis. Univariate Cox regression and least absolute shrinkage sum selection operator–Cox regression algorithm analysis were conducted on TCGA and Gene Expression Omnibus data to identify risk characteristics. The Kaplan–Meier method and receiver running feature curves were adopted to calculate overall survival. Single-sample gene set enrichment analysis (ssGSEA) was adopted for the assessment of the degree of immune infiltration. Then, we demonstrated the relationship between PPP2CB and EMT function in two cell lines. Results Thirty-four Wnt signaling pathway-related genes were risk factors for BLCA outcome, and their expression levels differed by clinical stage. The co-expression of WGCNA showed the relationship between the Wnt signaling pathway and biological functions and was closely associated with EMT. We divided BLCA patients into two subtypes using consensus clustering. Survival curves and clinical analysis showed that the Wnt pathway enriched group had worse outcomes. The Wnt signature showed the significance of the outcome for MAPK10, PPP2CB, and RAC3. Based on these genes, the degree of immune infiltration was evaluated. Cell function experiments suggested that PPP2CB drives the proliferation and migration of BLCA cells. Conclusion We found that Wnt signaling pathway-related genes can be used as prognostic risk factors for BLCA, and the Wnt signaling pathway is a cancer-promoting signaling pathway associated with EMT. We identified three critical genes: MAPK10, RAC3, and PPP2CB. The genes in these three Wnt signaling pathways are associated with tumor cell EMT and immune cell infiltration. The most important finding was that these three genes were independent prognostic factors for BLCA.

This paper proposes a new multi-fidelity bi-level optimization (MFBLO) strategy for shape designs of underwater wings. Firstly, hydrodynamic analyses of the wing planform and sections are decoupled for constructing a bi-level shape optimization frame, which includes an upper-level task merely concerning the wing planform design and several lower-level tasks only related to the section designs. By doing this, the shape design optimization gets remarkable benefits from the reduction of dimension and computational costs. Secondly, the bridge function method combined with three multi-fidelity data fusion approaches CC1, CC2, and CC3 are proposed to conduct the bi-level optimization, respectively. After comparison analyses, CC2 shows higher computational efficiency and accuracy, which is more appropriate for the bi-level shape optimization frame. Finally, compared with the single-level optimization with the fixed planform or sections and the conventional high-dimensional optimization, the proposed MFBLO needs less computation budget and gets higher lift-drag ratio, showing its outstanding advantages in handling the shape optimization of underwater wings.

Autonomous region protection is a significant research area in multi-agent systems, aiming to empower defenders in preventing intruders from accessing specific regions. This paper presents a Multi-agent Region Protection Environment (MRPE) featuring fewer defenders, defender damages, and intruder evasion strategies targeting defenders. MRPE poses challenges for traditional protection methods due to its high nonstationarity and limited interception time window. To surmount these hurdles, we modify evolutionary reinforcement learning, giving rise to the corresponding multi-agent region protection method (MRPM). MRPM amalgamates the merits of evolutionary algorithms and deep reinforcement learning, specifically leveraging Differential Evolution (DE) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG). DE facilitates diverse sample exploration and overcomes sparse rewards, while MADDPG trains defenders and expedites the DE convergence process. Additionally, an elite selection strategy tailored for multi-agent systems is devised to enhance defender collaboration. The paper also presents ingenious designs for the fitness and reward functions to effectively drive policy optimizations. Finally, extensive numerical simulations are conducted to validate the effectiveness of MRPM.

Effective conservation of endangered species necessitates not only the preservation of core habitats but also the enhancement of landscape connectivity. As a critically endangered Crocodylia, the Chinese alligator (Alligator sinensis) strongly relies on the fragmented wetland habitat of the lower area of the Yangtze River. The integrity of its habitat needs evaluating, and the connectivity restoring plan needs designing. In this study, we estimated the suitability of the habitat in the lower area of the Yangtze River using a Maxent model. Then, the potential ecological corridors between each nature reserve were selected by the least‐cost path and circuit theory methods, and the landscape connectivity was analyzed. The results showed that the highly suitable habitat had a low integrity and was fragmented into small pieces by residential areas, farmland, and mountain areas. Four priority ecological corridors (i.e., Xiadu‐Hongxing, Changle‐Zhongqiao, Zhongqiao‐Shuangkeng, and Hongxing‐Shuangkeng) were selected. The land occupation of humans seriously impacts the integrity of the Chinese alligator, and the unsuitable forest and artificial landscapes along the corridors indicate the need for a massive habitat restoration project. The landscape connectivity of the habitat needs to be progressively restored to provide more possibilities for the dispersal of the Chinese alligator. The integrity of the Chinese alligator habitat was low. The man‐made landscape decreased the suitability of the habitat and limited the disposal of the Chinese alligator. Constructing the ecological corridors was difficult due to human activity, long distance, and slow diffusion speed of the alligator.

The collaboration of a multiple unmanned underwater vehicles (multi-UUVs) system has attracted widespread attention in recent years, as it can overcome the limitations of a single UUV and enhance mission completion efficiency. Oriented towards patrol and exploration missions with multiple waypoints, this paper proposes a balanced mission planning strategy, aiming to improve mission quality while reducing mission time for multi-UUVs. Firstly, due to the uneven performance of the two optimization objectives, a quick initialization screening method is employed specifically for mission quality to reduce the mission space. Secondly, to ensure mission load distribution and collaboration among multi-UUVs, and ease the difficulty in solving the issues of mission allocation and route planning, a balanced bi-level mission planning method based on regional segmentation is proposed. Finally, applicable weight evaluation criteria are utilized to evaluate the feasible solution set and determine the optimal solution. The efficacy of the balanced mission planning strategy is substantiated through comprehensive numerical simulations in a complex 2D marine environment.

The number and survival rate of juveniles play a key role in the recovery of Chinese alligators ( Alligator sinensis ). The differences between artificial and semi‐wild environments can directly affect the growth and development of juvenile alligators. This study analyzed the physical conditions (lengths and weights) and cloacal microbial communities of juvenile alligators in both artificial breeding (DJ, ZX) and semi‐wild (GJM) environments to reveal the significant effects of environmental pressure on their physiological state and microbiome. The results revealed that the body length (23.15 ± 1.06 cm) and weight (22.8 ± 3.08 g) of juvenile alligators in the GJM were significantly lower than those in the artificial environment (body length = 29.5 cm, weight = 68.6 g; p < 0.01). Moreover, the microbial α diversity (ACE, Shannon) of the GJM was significantly reduced, and the community structure was significantly separated (NMDS analysis), suggesting that the pressure of the semi‐wild environment inhibited growth. In terms of microbial composition, the relative abundance of Actinobacteria in the GJM group was significantly, increased, whereas that of Bacteroidetes was decreased, and Microbacteria and Cyanobacteria were unique; at the genus level, environmentally specific marker genera were identified (such as Limnohabitans and Pseudomonas in GJM and Fluviicola and Deinococcus in the artificial environments). LEfSe analysis further elucidated the differential marker microbiota (such as Actinobacteria/Cyanobacteria in GJM). In summary, stress (such as food shortages) in semi‐wild environments affects the growth and development of juvenile alligators by changing their microbial communities (such as enriched actinomycetes), This finding provides a microbial ecological basis for optimizing the conservation strategy of the Chinese alligator.

With the increasing complexity of ocean missions, using multiple unmanned underwater vehicles to collaborate in executing tasks has become an effective way to improve the overall efficiency of ocean operations. Current research on path planning for multiple unmanned underwater vehicles mainly focuses on the basis of particle models or fully known environmental information, while research directions mainly focus on single indicators such as completion time and energy consumption. This paper first constructs a UUV model and a task scenario with detection success rate as the objective function. Then, a parameterization method based on a spiral search path was proposed for designing variables. A hierarchical control strategy is designed to ensure handle formation constraints. A general optimization framework for task scenarios has been constructed and combined with algorithms to solve optimization problems. Finally, this study compared and analyzed the performance of different optimization algorithms under the optimization framework, evaluated the optimization results of different search strategies, and explored the impact of dynamic objectives on the detection success rate. The results showed that the optimized path had a search success rate that increased by more than 50% compared to the direct path and the cover search path, which verified the effectiveness of the proposed method and strategy.

Background Current guidelines support different management of cryptococcosis between severely immunodeficient and immunocompetent populations. However, few studies have focused on cryptococcosis patients with mild-to-moderate immunodeficiency. We performed this study to determine the clinical features of pulmonary (PC) and extrapulmonary cryptococcosis (EPC) and compared them among populations with different immune statuses to support appropriate clinical management of this public health threat. Methods All cases were reported by 14 tertiary teaching hospitals in Jiangsu Province, China from January 2013 to December 2018. The trends in incidence, demographic data, medical history, clinical symptoms, laboratory test indicators, imaging characteristics and diagnostic method of these patients were then stratified by immune status, namely immunocompetent (IC, patients with no recognized underlying disease or those with an underlying disease that does not influence immunity, such as hypertension), mild-to-moderate immunodeficiency (MID, patients with diabetes mellitus, end-stage liver or kidney disease, autoimmune diseases treated with low-dose glucocorticoid therapy, and cancer treated with chemotherapy) and severe immunodeficiency (SID, patients with acquired immunodeficiency syndrome, haematologic malignancies, solid organ transplantation or haematologic stem cell transplantation, idiopathic CD4 lymphocytosis, agranulocytosis, aggressive glucocorticoid or immunosuppressive therapy and other conditions or treatments that result in severe immunosuppression). Results The clinical data of 255 cryptococcosis patients were collected. In total, 66.3% of patients (169) were IC, 16.9% (43) had MID, and 16.9% (43) had SID. 10.1% of the patients (17) with IC were EPC, 18.6% of the patients (8) with MID were EPC, and 74.4% of patients (32) were EPC (IC/MID vs. SID, p < 0.001). Fever was more common in the SID group than in the IC and MID groups (69.8% vs. 14.8% vs. 37.2%, p < 0.001). Of chest CT scan, most lesions were distributed under the pleura (72.7%), presenting as nodules/lumps (90.3%) or consolidations (10.7%). Pleural effusion was more common in SID group compared to IC group (33.3% vs. 2.4%, p < 0.001). Positivity rate on the serum capsular polysaccharide antigen detection (CrAg) test was higher in the SID group than in the other two groups [100.0% vs. 84.4% (MID) vs. 78.2% (IC), p = 0.013]. Positivity rate on the serum CrAg test was also higher in cryptococcal meningitis patients than in PC patients (100.0% vs. 79.5%, p = 0.015). Conclusions The clinical presentation of MID patients is intermediate between SID and IC patients and is similar to that of IC patients. The serum CrAg test is more sensitive for the identification of SID or EPC patients.

Purpose To assess the therapeutic value of biomarker-guided chemotherapy in patients with advanced non-small cell lung cancer (NSCLC). Methods Eighty-five NSCLC patients at stage IIIb or IV were divided into two groups based on the feasibility of biomarker analysis. Group A included patients with biomarker data ( n  = 41); Group B were patients without biomarker results ( n  = 44). Tumor samples obtained by fiberoptic bronchoscopy and computerized tomography-guided needle biopsy were analyzed by immunohistochemistry for intratumoral level of excision repair cross-complementing gene 1 (ERCC1), ribonucleotide reductase M1 (RRM1), and β-tubulin III. Chemotherapy regimens in Group A were determined according to the status of molecular signatures, whereas a standard gemcitabine plus cisplatin regimen was used for Group B. Tumor response, patient survival, and adverse effects were monitored for both groups. Results The overall response rate, defined as complete response plus partial response, was 56.1 % for Group A, significantly higher than that in Group B (31.8 %; P  = 0.024). The median progression-free survival (PFS) time was 5.2 months for Group A, significantly longer than that of Group B (4.1 months; P  = 0.026). The 1-year survival rate of Group A was 65.9 %, significantly higher than that of Group B (40.9 %; P  = 0.021), whereas the median overall survival times were 13.5 versus 12.5 months for Groups A and B, respectively ( P  = 0.483). The adverse effects in the two groups were essentially the same. Conclusions Biomarker-tailored chemotherapy based on ERCC1, RRM1, and β-tubulin III expression showed significantly increased response rate, median PFS time, and 1-year survival rate in patients with NSCLC.

This paper presents a new global optimization algorithm named MGOSIC to solve unconstrained expensive black-box optimization problems. In MGOSIC, three surrogate models Kriging, Radial Basis Function (RBF), and Quadratic Response Surfaces (QRS) are dynamically constructed, respectively. Additionally, a multi-point infill criterion is proposed to obtain new points in each cycle, where a score-based strategy is presented to mark cheap points generated by Latin hypercube sampling. According to their predictive values from the three surrogate models, the promising cheap points are assigned with different scores. In order to obtain the samples with diversity, a Max-Min approach is proposed to select promising sample points from the cheap point sets with higher scores. Simultaneously, the best solutions predicted by Kriging, RBF, and QRS are also recorded as supplementary samples, respectively. Once MGOSIC gets stuck in a local valley, the estimated mean square error of Kriging will be maximized to explore the sparsely sampled regions. Moreover, the whole optimization algorithm is carried out alternately in the global space and a reduced space. In summary, MGOSIC not only brings a new idea for multi-point sampling, but also builds a reasonable balance between exploitation and exploration. Finally, 19 mathematical benchmark cases and an engineering application of hydrofoil optimization are used to test MGOSIC. Furthermore, seven existing global optimization algorithms are also tested as contrast. The final results show that MGOSIC has high efficiency, strong stability, and better multi-point sampling capability in dealing with expensive black-box optimization problems.