Clear cell hepatocellular carcinoma: Gd-EOB-DTPA-enhanced MR imaging features and prognosis.

PURPOSE: To investigate imaging findings on gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging (Gd-EOB-DTPA-enhanced MRI) and prognosis of clear cell hepatocellular carcinoma (CCHCC) comparing with non-otherwise specified hepatocellular carcinoma (NOS-HCC). METHODS: The clinical, pathological and MR imaging features of 42 patients with CCHCC and 84 age-matched patients with NOS-HCC were retrospectively analyzed from January 2015 to October 2021. Univariate and multivariate logistic regression and Cox regression analyses were performed to identify independent diagnostic and prognostic factors for CCHCC. Disease-free survival (DFS) and overall survival (OS) were determined by Kaplan-Meier analysis. RESULTS: CCHCC showed fat content more frequently (P < 0.001) and relatively higher Edmondson tumor grade (P = 0.001) compared with NOS-HCC. The lesion-to-muscle ratio (LMR) and lesion-to-liver ratio (LLR) of CCHCC on pre-enhancement T1-weighted imaging (pre-T1WI) (P = 0.001, P = 0.003) and hepatobiliary phase (HBP) (P = 0.007, P = 0.048) were significantly higher than those of NOS-HCC. The area under the curve (AUC) for fat content, LLR on pre-T1WI and their combination with better diagnostic performance in predicting CCHCC were 0.678, 0.666, and 0.750, respectively. There was no statistically significant difference in clinical outcomes between CCHCC and NOS-HCC. Multivariate Cox analysis confirmed that tumor size > 2 cm and enhancing capsule were independent prognostic factors for DFS and OS among CCHCC patients. CONCLUSION: Fat content and adjusted lesion signal intensity on pre-T1WI and HBP could be used to differentiate CCHCC from NOS-HCC. CCHCC had similar prognosis with NOS-HCC.

stDiff: a diffusion model for imputing spatial transcriptomics through single-cell transcriptomics.

Spatial transcriptomics (ST) has become a powerful tool for exploring the spatial organization of gene expression in tissues. Imaging-based methods, though offering superior spatial resolutions at the single-cell level, are limited in either the number of imaged genes or the sensitivity of gene detection. Existing approaches for enhancing ST rely on the similarity between ST cells and reference single-cell RNA sequencing (scRNA-seq) cells. In contrast, we introduce stDiff, which leverages relationships between gene expression abundance in scRNA-seq data to enhance ST. stDiff employs a conditional diffusion model, capturing gene expression abundance relationships in scRNA-seq data through two Markov processes: one introducing noise to transcriptomics data and the other denoising to recover them. The missing portion of ST is predicted by incorporating the original ST data into the denoising process. In our comprehensive performance evaluation across 16 datasets, utilizing multiple clustering and similarity metrics, stDiff stands out for its exceptional ability to preserve topological structures among cells, positioning itself as a robust solution for cell population identification. Moreover, stDiff's enhancement outcomes closely mirror the actual ST data within the batch space. Across diverse spatial expression patterns, our model accurately reconstructs them, delineating distinct spatial boundaries. This highlights stDiff's capability to unify the observed and predicted segments of ST data for subsequent analysis. We anticipate that stDiff, with its innovative approach, will contribute to advancing ST imputation methodologies.

BiGATAE: a bipartite graph attention auto-encoder enhancing spatial domain identification from single-slice to multi-slices.

Recent advancements in spatial transcriptomics technology have revolutionized our ability to comprehensively characterize gene expression patterns within the tissue microenvironment, enabling us to grasp their functional significance in a spatial context. One key field of research in spatial transcriptomics is the identification of spatial domains, which refers to distinct regions within the tissue where specific gene expression patterns are observed. Diverse methodologies have been proposed, each with its unique characteristics. As the availability of spatial transcriptomics data continues to expand, there is a growing need for methods that can integrate information from multiple slices to discover spatial domains. To extend the applicability of existing single-slice analysis methods to multi-slice clustering, we introduce BiGATAE (Bipartite Graph Attention Auto Encoder) that leverages gene expression information from adjacent tissue slices to enhance spatial transcriptomics data. BiGATAE comprises two steps: aligning slices to generate an adjacency matrix for different spots in consecutive slices and constructing a bipartite graph. Subsequently, it utilizes a graph attention network to integrate information across different slices. Then it can seamlessly integrate with pre-existing techniques. To evaluate the performance of BiGATAE, we conducted benchmarking analyses on three different datasets. The experimental results demonstrate that for existing single-slice clustering methods, the integration of BiGATAE significantly enhances their performance. Moreover, single-slice clustering methods integrated with BiGATAE outperform methods specifically designed for multi-slice integration. These results underscore the proficiency of BiGATAE in facilitating information transfer across multiple slices and its capacity to broaden the applicability and sustainability of pre-existing methods.

Establishment of a Spermatogonial Stem Cell Line with Potential of Meiosis in a Hermaphroditic Fish, Epinephelus coioides.

Spermatogonial stem cells (SSCs) are unique adult stem cells capable of self-renewal and differentiation into sperm. Grouper is a protogynous hermaphroditic fish farmed widely in the tropical and subtropical seas. In this study, we established an SSC line derived from adult testis of orange-spotted grouper, Epinephelus coioides. In the presence of basic fibroblast growth factor (bFGF) and leukemia inhibitory factor (LIF), the cells could be maintained with proliferation and self-renewal over 20 months and 120 passages under in vitro culture conditions. The cells exhibited strong alkaline phosphatase activity and the characteristics of SSCs with the expression of germ cell markers, including Vasa, Dazl, and Plzf, as well as the stem cell markers Nanog, Oct4, and Ssea1. Furthermore, the cultured cells could be induced by 11-ketotestosterone treatment to highly express the meiotic markers Rec8, Sycp3, and Dmc1, and produce some spherical cells, and even sperm-like cells with a tail. The findings of this study suggested that the cultured grouper SSC line would serve as an excellent tool to study the molecular mechanisms behind SSCs self-renewal and differentiation, meiosis during spermatogenesis, and sex reversal in hermaphroditic vertebrates. Moreover, this SSC line has great application value in grouper fish aquaculture, such as germ cell transplantation, genetic manipulation, and disease research.

Fruchterman-Reingold Hexagon Empowered Node Deployment in Wireless Sensor Network Application.

Internet of Things (IoT) and Big Data technologies are becoming increasingly significant parts of national defense and the military, as well as in the civilian usage. The proper deployment of large-scale wireless sensor network (WSN) provides the foundation for these advanced technologies. Based on the Fruchterman-Reingold graph layout, we propose the Fruchterman-Reingold Hexagon (FR-HEX) algorithm for the deployment of WSNs. By allocating edges of hexagonal topology to sensor nodes, the network forms hexagonal network topology. A comprehensive evaluation of 50 simulations is conducted, which utilizes three evaluation metrics: average moving distance, pair correlation diversion (PCD), and system coverage rate. The FR-HEX algorithm performs consistently, the WSN topologies are properly regulated, the PCD values are below 0.05, and the WSN system coverage rate reaches 94%. Simulations involving obstacles and failed nodes are carried out to explore the practical applicability of the FR-HEX algorithm. In general, the FR-HEX algorithm can take full advantage of sensors' hardware capabilities in the deployment. It may be a viable option for some IoT and Big Data applications in the near future.

A Novel 3D Node Deployment Inspired by Dusty Plasma Crystallization in UAV-Assisted Wireless Sensor Network Applications.

With the rapid progress of hardware and software, a wireless sensor network has been widely used in many applications in various fields. However, most discussions for the WSN node deployment mainly concentrated on the two-dimensional plane. In such a case, some large scale applications, such as information detection in deep space or deep sea, will require a good three dimensional (3D) sensor deployment scenario and also attract most scientists' interests. Excellent deployment algorithms enable sensors to be quickly deployed in designated areas with the help of unmanned aerial vehicles (UAVs). In this paper, for the first time, we present a three dimensional network deployment algorithm inspired by physical dusty plasma crystallization theory in large-scale WSN applications. Four kinds of performance evaluation methods in 3D space, such as the moving distance, the spatial distribution diversion, system coverage rate, and the system utilization are introduced and have been carefully tested.Furthermore, in order to improve the performance of the final deployment, we integrated the system coverage rate and the system utilization to analyze the parameter effects of the Debye length and the node sensing radius. This criterion attempts to find the optimal sensing radius with a fixed Debye length to maximize the sensing range of the sensor network while reducing the system redundancy. The results suggest that our 3D algorithm can quickly complete an overall 3D network deployment and then dynamically adjust parameters to achieve a better distribution. In practical applications, engineers may choose appropriate parameters based on the sensor's hardware capabilities to achieve a better 3D sensor network deployment. It may be significantly used in some large-scale 3D WSN applications in the near future.

Pou5f1 and Nanog Are Reliable Germ Cell-Specific Genes in Gonad of a Protogynous Hermaphroditic Fish, Orange-Spotted Grouper (Epinephelus coioides).

Pluripotency markers Pou5f1 and Nanog are core transcription factors regulating early embryonic development and maintaining the pluripotency and self-renewal of stem cells. Pou5f1 and Nanog also play important roles in germ cell development and gametogenesis. In this study, Pou5f1 (EcPou5f1) and Nanog (EcNanog) were cloned from orange-spotted grouper, Epinephelus coioides. The full-length cDNAs of EcPou5f1 and EcNanog were 2790 and 1820 bp, and encoded 475 and 432 amino acids, respectively. EcPou5f1 exhibited a specific expression in gonads, whereas EcNanog was expressed highly in gonads and weakly in some somatic tissues. In situ hybridization analyses showed that the mRNA signals of EcNanog and EcPou5f1 were exclusively restricted to germ cells in gonads. Likewise, immunohistofluorescence staining revealed that EcNanog protein was limited to germ cells. Moreover, both EcPou5f1 and EcNanog mRNAs were discovered to be co-localized with Vasa mRNA, a well-known germ cell maker, in male and female germ cells. These results implied that EcPou5f1 and EcNanog could be also regarded as reliable germ cell marker genes. Therefore, the findings of this study would pave the way for elucidating the mechanism whereby EcPou5f1 and EcNanog regulate germ cell development and gametogenesis in grouper fish, and even in other protogynous hermaphroditic species.