Multiview clustering algorithms have attracted intensive attention and achieved superior performance in various fields recently. Despite the great success of multiview clustering methods in realistic applications, we observe that most of them are difficult to apply to large-scale datasets due to their cubic complexity. Moreover, they usually use a two-stage scheme to obtain the discrete clustering labels, which inevitably causes a suboptimal solution. In light of this, an efficient and effective one-step multiview clustering (E2OMVC) method is proposed to directly obtain clustering indicators with a small-time burden. Specifically, according to the anchor graphs, the smaller similarity graph of each view is constructed, from which the low-dimensional latent features are generated to form the latent partition representation. By introducing a label discretization mechanism, the binary indicator matrix can be directly obtained from the unified partition representation which is formed by fusing all latent partition representations from different views. In addition, by coupling the fusion of all latent information and the clustering task into a joint framework, the two processes can help each other and obtain a better clustering result. Extensive experimental results demonstrate that the proposed method can achieve comparable or better performance than the state-of-the-art methods. The demo code of this work is publicly available at https://github.com/WangJun2023/EEOMVC.

In this article, we consider the cooperative tracking problem for a class of nonlinear multiagent systems (MASs) with unknown dynamics under denial-of-service (DoS) attacks. To solve such a problem, a hierarchical cooperative resilient learning method, which involves a distributed resilient observer and a decentralized learning controller, is introduced in this article. Due to the existence of communication layers in the hierarchical control architecture, it may lead to communication delays and DoS attacks. Motivated by this consideration, a resilient model-free adaptive control (MFAC) method is developed to withstand the influence of communication delays and DoS attacks. First, a virtual reference signal is designed for each agent to estimate the time-varying reference signal under DoS attacks. To facilitate the tracking of each agent, the virtual reference signal is discretized. Then, a decentralized MFAC algorithm is designed for each agent such that each agent can track the reference signal by only using the obtained local information. Finally, a simulation example is proposed to verify the effectiveness of the developed method.

Estrogen-related receptor gamma (ERRγ), one of three members of the ERR family, is an inducible transcription factor. ERRγ has dual functions in different tissues. The decreased expression of ERRγ in the brain, stomach, prostate, and fat cells can cause neuropsychological dysfunction, gastric cancer, prostate cancer, and obesity. However, when ERRγ is present in the liver, pancreas, and thyroid follicular cells, ERRγ overexpression is related to liver cancer, type II diabetes, oxidative liver injury, and anaplastic thyroid carcinoma. Signaling pathway studies have confirmed that ERRγ agonists or inverse agonists can regulate ERRγ expression to treat related diseases. The collision between residue Phe435 and the modulator is a key factor determining the activation or inhibition of ERRγ. Although more than 20 agonists and inverse agonists of ERRγ have been reported, no clinical studies have been found in the literature. This review summarizes the important relationship between ERRγ-related signaling pathways and diseases, research progress, and the structure-activity relationship of modulators. These findings provide guidance for further study on new ERRγ modulators.

Deep clustering, which can elegantly exploit data representation to seek a partition of the samples, has attracted intensive attention. Recently, combining auto-encoder (AE) with graph neural networks (GNNs) has accomplished excellent performance by introducing structural information implied among data in clustering tasks. However, we observe that there are some limitations of most existing works: 1) in practical graph datasets, there exist some noisy or inaccurate connections among nodes, which would confuse network learning and cause biased representations, thus leading to unsatisfied clustering performance; 2) lacking dynamic information fusion module to carefully combine and refine the node attributes and the graph structural information to learn more consistent representations; and 3) failing to exploit the two separated views' information for generating a more robust target distribution. To solve these problems, we propose a novel method termed deep fusion clustering network with reliable structure preservation (DFCN-RSP). Specifically, the random walk mechanism is introduced to boost the reliability of the original graph structure by measuring localized structure similarities among nodes. It can simultaneously filter out noisy connections and supplement reliable connections in the original graph. Moreover, we provide a transformer-based graph auto-encoder (TGAE) that can use a self-attention mechanism with the localized structure similarity information to fine-tune the fused topology structure among nodes layer by layer. Furthermore, we provide a dynamic cross-modality fusion strategy to combine the representations learned from both TGAE and AE. Also, we design a triplet self-supervision strategy and a target distribution generation measure to explore the cross-modality information. The experimental results on five public benchmark datasets reflect that DFCN-RSP is more competitive than the state-of-the-art deep clustering algorithms. The corresponding code is available at https://github.com/gongleii/DFCN-RSP.

Aflatoxin B1 (AFB1) is a common mycotoxin that is of significant global concern due to its impact on food safety. Herein, we innovatively develop a sensing platform to detect AFB1 based on evaporation of surfactant solutions on the hydrophobic surface, resulting in dried patterns with varied sizes. The surfactant CTAB solution produces a relatively large dried pattern due to the surface wetting. However, the reduction in the dried pattern size is found when the mixture of CTAB and AFB1 aptamer is tested, because the formation of CTAB/aptamer complex. Moreover, the dried pattern size of the mixture of CTAB, aptamer, and AFB1 increases due to the specific binding of AFB1 to its aptamer. Using this innovative strategy, the AFB1 detection can be fulfilled with a detection limit of 0.77 pg/mL. As a simple, convenient, inexpensive, and label-free method, the surfactant-mediated surface droplet evaporation-based biosensor is very promising for various potential applications.

Histone acetylation modifications in filamentous fungi play a crucial role in epigenetic gene regulation and are closely linked to the transcription of secondary metabolite (SM) biosynthetic gene clusters (BGCs). Histone deacetylases (HDACs) play a pivotal role in determining the extent of histone acetylation modifications and act as triggers for the expression activity of target BGCs. The genus Chaetomium is widely recognized as a rich source of novel and bioactive SMs. Deletion of a class I HDAC gene of Chaetomium olivaceum SD-80A, g7489, induces a substantial pleiotropic effect on the expression of SM BGCs. The C. olivaceum SD-80A ∆g7489 strain exhibited significant changes in morphology, sporulation ability, and secondary metabolic profile, resulting in the emergence of new compound peaks. Notably, three polyketides (A1–A3) and one asterriquinone (A4) were isolated from this mutant strain. Furthermore, our study explored the BGCs of A1–A4, confirming the function of two polyketide synthases (PKSs). Collectively, our findings highlight the promising potential of molecular epigenetic approaches for the elucidation of novel active compounds and their biosynthetic elements in Chaetomium species. This finding holds great significance for the exploration and utilization of Chaetomium resources.Key points• Deletion of a class I histone deacetylase activated secondary metabolite gene clusters.• Three polyketides and one asterriquinone were isolated from HDAC deleted strain.• Two different PKSs were reported in C. olivaceum SD-80A.

BackgroundPanax quinquefolius L, widely recognized for its valuable contributions to medicine, has aroused considerable attention globally. Different from the extensive research has been dedicated to the root of P. quinquefolius, its berry has received relatively scant focus. Given its promising medicinal properties, this study was focused on the structural characterizations and anti-inflammatory potential of acidic polysaccharides from the P. quinquefolius berry. Materials and methodsP. quinquefolius berry was extracted with hot water, precipitated by alcohol, separated by DEAE-52-cellulose column to give a series of fractions. One of these fractions was further purified via Sephadex G-200 column to give three fractions. Then, the main fraction named as AGBP-A3 was characterized by methylation analysis, NMR spectroscopy, etc. Its anti-inflammatory activity was assessed by RAW 264.7 cell model, zebrafish model and molecular docking. ResultsThe main chain comprised of α-L-Rhap, α-D-GalAp and β-D-Galp, while the branch consisted mainly of α-L-Araf, β-D-Glcp, α-D-GalAp, β-D-Galp. The RAW264.7 cell assay results showed that the inhibition rates against IL-6 and IL-1β secretion at the concentration of 625 ng/mL were 24.83 %, 11.84 %, while the inhibition rate against IL-10 secretion was 70.17 % at the concentration of 312 ng/mL. In the zebrafish assay, the migrating neutrophils were significantly reduced in number, and their migration to inflammatory tissues was inhibited. Molecular docking predictions correlated well with the results of the anti-inflammatory assay. ConclusionThe present study demonstrated the structure of acidic polysaccharides of P. quinquefolius berry and their effect on inflammation, providing a reference for screening anti-inflammatory drugs.

Shewanella oneidensis (S. oneidensis) bacteria have bioremediation capabilities for various heavy metals, however, their repertoire of mercury-removal proteins is limited, restricting their effectiveness in mercury remediation. In this study, the integration of an exogenous mer operon into S. oneidensis expanded the range of mercury-removal proteins, significantly boosting both mercury tolerance and removal efficiency. The engineering bacteria (MR-mer) exhibited growth in a 180 mg/L Hg2+ solution, showing a 40% increase in tolerance over S. oneidensis. In a liquid medium with 50 mg/L Hg2+, the MR-mer strain achieved 73% mercury removal efficiency, outperforming the original strain by 50%, and facilitated Hg0 formation on the cell membrane. Extensive analysis of transmembrane structures and Fourier analysis revealed that mer operon proteins might target the cell membrane, modifying its functional group contents to enhance mercury adsorption. Additionally, the absence of the key protein OmcA in the electron transport chain led to a 30% reduction in Hg2+ removal capacity. This suggests that S. oneidensis transfers electrons to the cell surface through this chain, providing electrons for Hg2+ reduction. In conclusion, the mer operon, targeting the cell membrane in MR-mer strain, synergizes with the electron transport chain to markedly elevate Hg2+ removal capability. This approach not only augments Shewanella's mercury biodegradation capacity, but also offers novel perspectives for microbial heavy metal pollutant removal.