Deciphering male influence in gynogenetic Pengze crucian carp (Carassius auratus var. pengsenensis): insights from Nanopore sequencing of structural variations

In this study, we investigate gynogenetic reproduction in Pengze Crucian Carp (Carassius auratus var. pengsenensis) using third-generation Nanopore sequencing to uncover structural variations (SVs) in offspring. Our objective was to understand the role of male genetic material in gynogenesis by examining the genomes of both parents and their offspring. We discovered a notable number of male-specific structural variations (MSSVs): 1,195 to 1,709 MSSVs in homologous offspring, accounting for approximately 0.52%–0.60% of their detected SVs, and 236 to 350 MSSVs in heterologous offspring, making up about 0.10%–0.13%. These results highlight the significant influence of male genetic material on the genetic composition of offspring, particularly in homologous pairs, challenging the traditional view of asexual reproduction. The gene annotation of MSSVs revealed their presence in critical gene regions, indicating potential functional impacts. Specifically, we found 5 MSSVs in the exonic regions of protein-coding genes in homologous offspring, suggesting possible direct effects on protein structure and function. Validation of an MSSV in the exonic region of the polyunsaturated fatty acid 5-lipoxygenase gene confirmed male genetic material transmission in some offspring. This study underscores the importance of further research on the genetic diversity and gynogenesis mechanisms, providing valuable insights for reproductive biology, aquaculture, and fostering innovation in biological research and aquaculture practices

A Comparative Study of Frequent Pattern Mining with Trajectory Data

Sequential pattern mining (SPM) is a major class of data mining topics with a wide range of applications. The continuity and uncertain nature of trajectory data make it distinctively different from typical transactional data, which requires additional data transformation to prepare for SPM. However, little research focuses on comparing the performance of SPM algorithms and their applications in the context of trajectory data. This study selected some representative sequential pattern mining algorithms and evaluated them with various parameters to understand the effect of the involved parameters on their performances. We studied the resultant sequential patterns, runtime, and RAM consumption in the context of the taxi trajectory dataset, the T-drive dataset. It was demonstrated in this work that a method to discretize trajectory data and different SPM algorithms were performed on trajectory databases. The results were visualized on actual Beijing road maps, reflecting traffic congestion conditions. Results demonstrated contiguous constraint-based algorithms could provide a concise representation of output sequences and functions at low min_sup with balanced RAM consumption and execution time. This study can be used as a guide for academics and professionals when determining the most suitable SPM algorithm for applications that involve trajectory data

Study on the linkage between macro policy and market effectiveness in China's stock market: Based on run test of China's stock market index.

The macro policy of the stock market is an important market information. The implementation goal of the macro policy of the stock market is mainly to improve the effectiveness of the stock market. However, whether this effectiveness has achieved the goal is worth verifying through empirical data. The exertion of this information utility is closely related to the effectiveness of the stock market. Use the run test method in statistics to collect and sort out the daily data of stock price index in recent 30 years, the linkage between 75 macro policy events and 35 trading days of market efficiencies before and after the macro event are tested since 1992 to 2022. The results show that 50.66% of the macro policies are positively linked to the effectiveness of the stock market, while 49.34% of the macro policies have reduced the effectiveness of the market operation. This shows that the effectiveness of China's stock market is not high, and the nonlinear characteristics are obvious, so the policy formulation of the stock market needs further improvement

Deep learning for differentiation of osteolytic osteosarcoma and giant cell tumor around the knee joint on radiographs: a multicenter study

Abstract Objectives To develop a deep learning (DL) model for differentiating between osteolytic osteosarcoma (OS) and giant cell tumor (GCT) on radiographs. Methods Patients with osteolytic OS and GCT proven by postoperative pathology were retrospectively recruited from four centers (center A, training and internal testing; centers B, C, and D, external testing). Sixteen radiologists with different experiences in musculoskeletal imaging diagnosis were divided into three groups and participated with or without the DL model’s assistance. DL model was generated using EfficientNet-B6 architecture, and the clinical model was trained using clinical variables. The performance of various models was compared using McNemar’s test. Results Three hundred thirty-three patients were included (mean age, 27 years ± 12 [SD]; 186 men). Compared to the clinical model, the DL model achieved a higher area under the curve (AUC) in both the internal (0.97 vs. 0.77, p = 0.008) and external test set (0.97 vs. 0.64, p < 0.001). In the total test set (including the internal and external test sets), the DL model achieved higher accuracy than the junior expert committee (93.1% vs. 72.4%; p < 0.001) and was comparable to the intermediate and senior expert committee (93.1% vs. 88.8%, p = 0.25; 87.1%, p = 0.35). With DL model assistance, the accuracy of the junior expert committee was improved from 72.4% to 91.4% (p = 0.051). Conclusion The DL model accurately distinguished osteolytic OS and GCT with better performance than the junior radiologists, whose own diagnostic performances were significantly improved with the aid of the model, indicating the potential for the differential diagnosis of the two bone tumors on radiographs. Critical relevance statement The deep learning model can accurately distinguish osteolytic osteosarcoma and giant cell tumor on radiographs, which may help radiologists improve the diagnostic accuracy of two types of tumors. Key points • The DL model shows robust performance in distinguishing osteolytic osteosarcoma and giant cell tumor. • The diagnosis performance of the DL model is better than junior radiologists’. • The DL model shows potential for differentiating osteolytic osteosarcoma and giant cell tumor. Graphical Abstrac

Promoting Charge Separation in <i>g</i>‑C₃N₄/Graphene/MoS₂ Photocatalysts by Two-Dimensional Nanojunction for Enhanced Photocatalytic H₂ Production

Graphitic carbon nitride (<i>g</i>-C₃N₄) is a promising photocatalyst for solar H₂ generation, but the practical application of <i>g</i>-C₃N₄ is still limited by the low separation efficiency of photogenerated charge carriers. Herein, we report the construction of ternary <i>g</i>-C₃N₄/graphene/MoS₂ two-dimensional nanojunction photocatalysts for enhanced visible light photocatalytic H₂ production from water. As demonstrated by photoluminescence and transient photocurrent studies, the intimate two-dimensional nanojuction can efficiently accelerate the charge transfer, resulting in the high photocatalytic activity. The <i>g</i>-C₃N₄/graphene/MoS₂ composite with 0.5% graphene and 1.2% MoS₂ achieves a high H₂ evolution rate of 317 μmol h^–1 g^–1, and the apparent quantum yield reaches 3.4% at 420 nm. More importantly, the ternary <i>g</i>-C₃N₄/graphene/MoS₂ two-dimensional nanojunction photocatalyst exhibits much higher photocatalytic activity than the optimized Pt-loaded <i>g</i>-C₃N₄ photocatalyst