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Abstract Plant-sucking insects have intricate associations with a diverse array of microorganisms to facilitate their adaptation to specific ecological niches. The midgut of phytophagous true bugs is generally structured into four distinct compartments to accommodate their microbiota. Nevertheless, there is limited understanding regarding the origins of these gut microbiomes, the mechanisms behind microbial community assembly, and the interactions between gut microbiomes and their insect hosts. In this study, we conducted a comprehensive survey of microbial communities within the midgut compartments of a bean bug Riptortus pedestris, soybean plant, and bulk soil across 12 distinct geographical fields in China, utilizing high-throughput sequencing of the 16 S rRNA gene. Our findings illuminated that gut microbiota of the plant-sucking insects predominantly originated from the surrounding soil environment, and plants also play a subordinate role in mediating microbial acquisition for the insects. Furthermore, our investigation suggested that the composition of the insect gut microbiome was probably shaped by host selection and/or microbe-microbe interactions at the gut compartment level, with marginal influence from soil and geographical factors. Additionally, we had unveiled a noteworthy dynamic in the acquisition of core bacterial taxa, particularly Burkholderia, which were initially sourced from the environment and subsequently enriched within the insect midgut compartments. This bacterial enrichment played a significant role in enhancing insect host reproduction. These findings contribute to our evolving understanding of microbiomes within the insect-plant-soil ecosystem, shedding additional light on the intricate interactions between insects and their microbiomes that underpin the ecological significance of microbial partnerships in host adaptation.

Quasi-static axial compression experiments were conducted on the composite thin-walled C-channels with different flange widths. The axial compression failure modes and failure mechanisms were analyzed through CT scanning，and the effects of different flange widths on the axial compression energy-absorption characteristics of C-channels were evaluated by using the energy-absorption indexes. The single-layer shell model and multi-layer shell model of composite C-channel were established，and the finite element models were verified by comparing the failure modes，load-displacement curves and energy-absorption indexes. The results show that the flange width has a significant effect on the axial compression failure modes and energy-absorption characteristics of C-channels. The C-channels with flange widths of 25 mm and 30 mm are failed in the stable and asymptotic manner under the axial compression loads and have good energy-absorption characteristics. The deviation of mean crushing force，energy absorption and specific energy absorption obtained by the multi-layer shell model is within 5%，while that obtained by the single-layer shell model is within 8%，which indicating that the multi-layer shell model of C-channel has the higher simulation accuracy.

The construction of cell differentiation trajectories from single-cell transcriptomic data or proteomic data by single-cell trajectory inference methods can help to understand the developmental process of normal tissues or provide pathologically relevant information. However, the accuracy and robustness of current single-cell trajectory inference algorithms are still a challenge, one of the reasons is the noise caused by the detection of a large number of unrelated genes in single-cell sequencing. In order to solve this problem, a trajectory inference method iterTIPD (iterative trajectory inference based on probability distribution) based on iterative feature selection is proposed. Its innovation lies in iteratively applying feature selection methods widely used for screening differentially expressed genes to linear or branching single-cell RNA sequencing data, and improving the accuracy and robustness of cell pseudotime ordering by selecting the gene subset that contributes the most to the constructed differentiation trajectory. Experimental results on four scRNA-seq datasets show that iterTIPD can effectively improve the accuracy and robustness of the single-cell trajectory inference algorithm. IterTIPD also improves the performance of other trajectory inference algorithms, proving generalization of iterTIPD. The differentiation trajectory of neural stem cells is reconstructed by iterTIPD algorithm, and the comparison shows that the differentiation trajectory is highly consistent with the known neural stem cell differentiation trajectory. Meanwhile, Top2a and Gja1 may be novel markers defining activated neural stem cell subpopulations.

Differential network analysis has become an important approach in identifying driver genes in development and disease. However, most studies capture only local features of the underlying gene-regulatory network topology. These approaches are vulnerable to noise and other changes which mask driver-gene activity. Therefore, methods are urgently needed which can separate the impact of true regulatory elements from stochastic changes and downstream effects. We propose the differential network flow (DNF) method to identify key regulators of progression in development or disease. Given the network representation of consecutive biological states, DNF quantifies the essentiality of each node by differences in the distribution of network flow, which are capable of capturing comprehensive topological differences from local to global feature domains. DNF achieves more accurate driver-gene identification than other state-of-the-art methods when applied to four human datasets from The Cancer Genome Atlas and three single-cell RNA-seq datasets of murine neural and hematopoietic differentiation. Furthermore, we predict key regulators of crosstalk between separate networks underlying both neuronal differentiation and the progression of neurodegenerative disease, among which APP is predicted as a driver gene of neural stem cell differentiation. Our method is a new approach for quantifying the essentiality of genes across networks of different biological states.

Glioblastoma (GBM) is a fast-growing type of malignant primary brain tumor. To explore the mechanisms in GBM, complex biological networks are used to reveal crucial changes among different biological states, which reflect on the development of living organisms. It is critical to discover the kernel differential subgraph (KDS) that leads to drastic changes. However, identifying the KDS is similar to the Steiner Tree problem that is an NP-hard problem. In this paper, we developed a criterion to explore the KDS (CKDS), which considered the connectivity and scale of KDS, the topological difference of nodes and function relevance between genes in the KDS. The CKDS algorithm was applied to simulated datasets and three single-cell RNA sequencing (scRNA-seq) datasets including GBM, fetal human cortical neurons (FHCN) and neural differentiation. Then we performed the network topology and functional enrichment analyses on the extracted KDSs. Compared with the state-of-art methods, the CKDS algorithm outperformed on simulated datasets to discover the KDSs. In the GBM and FHCN, seventeen genes (one biomarker, nine regulatory genes, one driver genes, six therapeutic targets) and KEGG pathways in KDSs were strongly supported by literature mining that they were highly interrelated with GBM. Moreover, focused on GBM, there were fifteen genes (including ten regulatory genes, three driver genes, one biomarkers, one therapeutic target) and KEGG pathways found in the KDS of neural differentiation process from activated neural stem cells (aNSC) to neural progenitor cells (NPC), while few genes and no pathway were found in the period from NPC to astrocytes (Ast). These experiments indicated that the process from aNSC to NPC is a key differentiation period affecting the development of GBM. Therefore, the CKDS algorithm provides a unique perspective in identifying cell-type-specific genes and KDSs.