BL-Hi-C reveals the 3D genome structure of Brassica crops with high sensitivity.

High-throughput Chromatin Conformation Capture (Hi-C) technologies can be used to investigate the three-dimensional genomic structure of plants. However, the practical utility of these technologies is impeded by significant background noise, hindering their capability in detecting fine 3D genomic structures. In this study, we optimized the Bridge Linker Hi-C technology (BL-Hi-C) to comprehensively investigate the 3D chromatin landscape of Brassica rapa and Brassica oleracea. The Bouquet configuration of both B. rapa and B. oleracea was elucidated through the construction of a 3D genome simulation. The optimized BL-Hi-C exhibited lower background noise compared to conventional Hi-C methods. Taking this advantage, we used BL-Hi-C to identify FLC gene loops in Arabidopsis, B. rapa, and B. oleracea. We observed that gene loops of FLC2 exhibited conservation across Arabidopsis, B. rapa, and B. oleracea. While gene loops of syntenic FLCs exhibited conservation across B. rapa and B. oleracea, variations in gene loops were evident among multiple paralogs FLCs within the same species. Collectively, our findings highlight the high sensitivity of optimized BL-Hi-C as a powerful tool for investigating the fine 3D genomic organization.

A novel hybrid algorithm based on Harris Hawks for tumor feature gene selection.

Background: Gene expression data are often used to classify cancer genes. In such high-dimensional datasets, however, only a few feature genes are closely related to tumors. Therefore, it is important to accurately select a subset of feature genes with high contributions to cancer classification. Methods: In this article, a new three-stage hybrid gene selection method is proposed that combines a variance filter, extremely randomized tree and Harris Hawks (VEH). In the first stage, we evaluated each gene in the dataset through the variance filter and selected the feature genes that meet the variance threshold. In the second stage, we use extremely randomized tree to further eliminate irrelevant genes. Finally, we used the Harris Hawks algorithm to select the gene subset from the previous two stages to obtain the optimal feature gene subset. Results: We evaluated the proposed method using three different classifiers on eight published microarray gene expression datasets. The results showed a 100% classification accuracy for VEH in gastric cancer, acute lymphoblastic leukemia and ovarian cancer, and an average classification accuracy of 95.33% across a variety of other cancers. Compared with other advanced feature selection algorithms, VEH has obvious advantages when measured by many evaluation criteria.

A new hybrid algorithm for three-stage gene selection based on whale optimization.

In biomedical data mining, the gene dimension is often much larger than the sample size. To solve this problem, we need to use a feature selection algorithm to select feature gene subsets with a strong correlation with phenotype to ensure the accuracy of subsequent analysis. This paper presents a new three-stage hybrid feature gene selection method, that combines a variance filter, extremely randomized tree, and whale optimization algorithm. First, a variance filter is used to reduce the dimension of the feature gene space, and an extremely randomized tree is used to further reduce the feature gene set. Finally, the whale optimization algorithm is used to select the optimal feature gene subset. We evaluate the proposed method with three different classifiers in seven published gene expression profile datasets and compare it with other advanced feature selection algorithms. The results show that the proposed method has significant advantages in a variety of evaluation indicators.

Conserved noncoding sequences correlate with distant gene contacts in Arabidopsis and Brassica.

Physical contact between genes distant on chromosomes is a potentially important way for genes to coordinate their expressions. To investigate the potential importance of distant contacts, we performed high-throughput chromatin conformation capture (Hi-C) experiments on leaf nuclei isolated from Brassica rapa and Brassica oleracea. We then combined our results with published Hi-C data from Arabidopsis thaliana. We found that distant genes come into physical contact and do so preferentially between the proximal promoter of one gene and the downstream region of another gene. Genes with higher numbers of conserved noncoding sequences (CNSs) nearby were more likely to have contact with distant genes. With more CNSs came higher numbers of transcription factor binding sites and more histone modifications associated with the activity. In addition, for the genes we studied, distant contacting genes with CNSs were more likely to be transcriptionally coordinated. These observations suggest that CNSs may enrich active histone modifications and recruit transcription factors, correlating with distant contacts to ensure coordinated expression. This study advances our knowledge of gene contacts and provides insights into the relationship between CNSs and distant gene contacts in plants.

Association of RNA-modification "writer" genes with prognosis and response to immunotherapy in patients with low-grade glioma.

RNA modification is a key regulatory mechanism involved in tumorigenesis, tumor progression, and the immune response. However, the potential role of RNA modification "writer" genes in the immune microenvironment of gliomas and their effect on the response to immunotherapy remains unclear. The purpose of this study was to evaluate the role of RNA modification "writer" gene in the prognosis and immunotherapy response of low-grade glioma (LGG). The consensus non-negative matrix factorization (CNMF) method was used to identify different RNA modification subtypes. We used a novel eigengene screening method, the variable neighborhood learning Harris Hawks optimizer (VNLHHO), to screen for eigengenes among the RNA modification subtypes. We constructed a principal components analysis score(PCA_score)-based prognostic prediction model and validated it using an independent cohort. We also analyzed the association between PCA_score and the immune and molecular features of LGG. The results suggested that LGG can be divided into two different RNA modification-based subtypes with distinct prognostic and molecular features. High PCA_score was significantly associated with a poor prognosis in LGG and was an independent prognostic factor. A nomogram containing PCA_score and clinical features was constructed, and it showed a significant predictive value. PCA_score was negatively correlated with tumor purity and the abundance of CD4+ T cells in LGG patients. LGG patients with high PCA_score had lower Tumor Immune Dysfunction and Exclusion scores and showed an immunotherapy response. In conclusion, we report a novel RNA modification-based prognostic model for LGG that lays the foundation for evaluating LGG prognosis and developing more effective therapeutic strategies for these tumors.

A novel prognostic model for cutaneous melanoma based on an immune-related gene signature and clinical variables.

Abundant evidence has indicated that the prognosis of cutaneous melanoma (CM) patients is highly complicated by the tumour immune microenvironment. We retrieved the clinical data and gene expression data of CM patients in The Cancer Genome Atlas (TCGA) database for modelling and validation analysis. Based on single-sample gene set enrichment analysis (ssGSEA) and consensus clustering analysis, CM patients were classified into three immune level groups, and the differences in the tumour immune microenvironment and clinical characteristics were evaluated. Seven immune-related CM prognostic molecules, including three mRNAs (SUCO, BTN3A1 and TBC1D2), three lncRNAs (HLA-DQB1-AS1, C9orf139 and C22orf34) and one miRNA (hsa-miR-17-5p), were screened by differential expression analysis, ceRNA network analysis, LASSO Cox regression analysis and univariate Cox regression analysis. Their biological functions were mainly concentrated in the phospholipid metabolic process, transcription regulator complex, protein serine/threonine kinase activity and MAPK signalling pathway. We established a novel prognostic model for CM integrating clinical variables and immune molecules that showed promising predictive performance demonstrated by receiver operating characteristic curves (AUC ≥ 0.74), providing a scientific basis for predicting the prognosis and improving the clinical outcomes of CM patients.

Depression and Quality of Life in Patients with Gliomas: A Narrative Review.

In patients with gliomas, depression is a common complication that may cause severe psychological barriers and deteriorate the patient's quality of life (QoL). Currently, the Hospital Anxiety and Depression Scale (HADS) is the most commonly used tool to diagnose depression in patients with gliomas. Female sex, unmarried status, low education level, high tumor grade, and a history of mental illness may increase the risks of depression and depressive symptoms in patients with gliomas. The QoL of patients with gliomas can be directly reduced by depression. Therefore, the evaluation and intervention of mood disorders could improve the overall QoL of patients with gliomas. Antidepressant use has become a treatment strategy for patients with gliomas and comorbid depression. This narrative review summarizes the current issues related to depression in patients with gliomas, including the prevalence, risk factors, and diagnostic criteria of depression as well as changes in QoL caused by comorbid depression and antidepressant use. The purpose of this review is to guide clinicians to assess the psychological status of patients with gliomas and to provide clinicians and oncologists with a new treatment strategy to improve the prognosis of such patients.

Improving feature selection performance for classification of gene expression data using Harris Hawks optimizer with variable neighborhood learning.

Gene expression profiling has played a significant role in the identification and classification of tumor molecules. In gene expression data, only a few feature genes are closely related to tumors. It is a challenging task to select highly discriminative feature genes, and existing methods fail to deal with this problem efficiently. This article proposes a novel metaheuristic approach for gene feature extraction, called variable neighborhood learning Harris Hawks optimizer (VNLHHO). First, the F-score is used for a primary selection of the genes in gene expression data to narrow down the selection range of the feature genes. Subsequently, a variable neighborhood learning strategy is constructed to balance the global exploration and local exploitation of the Harris Hawks optimization. Finally, mutation operations are employed to increase the diversity of the population, so as to prevent the algorithm from falling into a local optimum. In addition, a novel activation function is used to convert the continuous solution of the VNLHHO into binary values, and a naive Bayesian classifier is utilized as a fitness function to select feature genes that can help classify biological tissues of binary and multi-class cancers. An experiment is conducted on gene expression profile data of eight types of tumors. The results show that the classification accuracy of the VNLHHO is greater than 96.128% for tumors in the colon, nervous system and lungs and 100% for the rest. We compare seven other algorithms and demonstrate the superiority of the VNLHHO in terms of the classification accuracy, fitness value and AUC value in feature selection for gene expression data.

Series-Spatial Transcriptome Profiling of Leafy Head Reveals the Key Transition Leaves for Head Formation in Chinese Cabbage.

Chinese cabbage is an important leaf heading vegetable crop. At the heading stage, its leaves across inner to outer show significant morphological differentiation. However, the genetic control of this complex leaf morphological differentiation remains unclear. Here, we reported the transcriptome profiling of Chinese cabbage plant at the heading stage using 24 spatially dissected tissues representing different regions of the inner to outer leaves. Genome-wide transcriptome analysis clearly separated the inner leaf tissues from the outer leaf tissues. In particular, we identified the key transition leaf by the spatial expression analysis of key genes for leaf development and sugar metabolism. We observed that the key transition leaves were the first inwardly curved ones. Surprisingly, most of the heading candidate genes identified by domestication selection analysis obviously showed a corresponding expression transition, supporting that key transition leaves are related to leafy head formation. The key transition leaves were controlled by a complex signal network, including not only internal hormones and protein kinases but also external light and other stimuli. Our findings provide new insights and the rich resource to unravel the genetic control of heading traits.

Linkage Mapping and Comparative Transcriptome Analysis of Firmness in Watermelon (Citrullus lanatus).

Watermelon fruit texture and quality are determined by flesh firmness. As a quality trait, flesh firmness is controlled by multigenes. Defining the key regulatory factors of watermelon flesh firmness is of great significance for watermelon genetic breeding. In this study, the hard-flesh egusi seed watermelon PI186490 was used as the male parent, the soft-flesh cultivated watermelon W1-1 was used as the female parent, and 175 F2 generations were obtained from selfing F1. Primary mapping of the major genes controlling center flesh firmness was achieved by bulked-segregant analysis (BSA)-Seq analysis and molecular marker technology. Finally, major genes were delimited in the physical interval between 6,210,787 and 7,742,559 bp on chromosome 2 and between 207,553 and 403,137 bp on chromosome 8. The content of each cell wall component and hormone was measured, and comparative transcriptome analysis was performed during fruit development in watermelon. The protopectin, cellulose, hemicellulose, indole-3-acetic acid (IAA) and abscisic acid (ABA) contents were measured, and paraffin sections were made during the three fruit developmental stages. The results revealed that protopectin, celluloses, and hemicelluloses exhibited similar trends for flesh firmness, while the IAA and ABA concentrations continued to decrease with fruit ripening. Paraffin sections showed that PI186490 cells were more numerous, were more tightly packed, had clearer cell wall edges and had thicker cell walls than W1-1 cells at every developmental stage. Comparative transcriptome analysis was conducted on RNA samples of flesh during fruit development and ripening in W1-1 and PI186490. The results from the localization interval transcriptome analysis showed that Cla016033 (DUF579 family member), which may influence the cell wall component contents to adjust the flesh firmness in watermelon fruit, was different in W1-1 and PI186490 and that Cla012507 (MADS-box transcription factor) may be involved in the regulation of fruit ripening and affect the hardness of watermelon fruit.

Evacuate or Stay? A Typhoon Evacuation Decision Model in China Based on the Evolutionary Game Theory in Complex Networks.

The Chinese Government has played an important role in organizing the evacuation of typhoon disasters, and in-depth analysis of individual behavioral decisions is a prerequisite for adopting an effective emergency organization plan. Existing evacuation plans only consider how the Government issues the early warning and organizes the mandatory evacuation, but does not formulate effective policies to improve the efficiency of self-evacuation of evacuees and lacks the understanding of individual evacuation decision-making. Using game-based theory in a small-world network context, we build an evolutionary game model of evacuation decision diffusion between evacuees in the context of a complex network. The model simulates the effects of guaranteeing the evacuation order and providing material supplies on the evacuation decision diffusion in a small-world network in China. The results showed that various levels of policy-implementation led to different rates of evacuation. As the cost-reduction of the evacuation process increased, the evacuation response rate in the social system increased. In contrast, as the rate of reducing the non-evacuation cost decreased or the cost-reduction rate of non-evacuation increased, the evacuation response rate in the social system decreased. The study findings provided insights on emergency planning and the effectiveness of their implementation in social networks, which can be used to improve evacuation policy.

Electrospun Flexible Cellulose Acetate-Based Separators for Sodium-Ion Batteries with Ultralong Cycle Stability and Excellent Wettability: The Role of Interface Chemical Groups.

Na-ion batteries are one of the best technologies for large-scale applications depending on almost infinite and widespread sodium resources. However, the state-of-the-art separators cannot meet the engineering needs of large-scale sodium-ion batteries to match the intensively investigated electrode materials. Here, a kind of flexible modified cellulose acetate separator (MCA) for sodium-ion batteries was synthesized via the electrospinning process and subsequently optimizing the interface chemical groups by changing acetyl to hydroxyl partly. Upon the rational design, the flexible MCA separator exhibits high chemical stability and excellent wettability (contact angles nearly 0°) in electrolytes (EC/PC, EC/DMC, diglyme, and triglyme). Moreover, the flexible MCA separator shows high onset temperature of degradation (over 250 °C) and excellent thermal stability (no shrinkage at 220 °C). Electrochemical measurements, importantly, show that the Na-ion batteries with flexible MCA separator exhibit ultralong cycle life (93.78%, 10 000 cycles) and high rate capacity (100.1 mAh g-1 at 10 C) in the Na/Na3V2(PO4)3 (NVP) half cell (2.5-4.0 V) and good cycle performance (98.59%, 100 cycles) in the Na/SnS2 half cell (0.01-3 V), respectively. Moreover, the full cell (SnS2/NVP) with flexible MCA separator displays the capacity of 98 mAh g-1 and almost no reduction after 40 cycles at 0.118 A g-1. Thus, this work provides a kind of flexible modified cellulose acetate separator for Na-ion batteries with great potential for practical large-scale applications.