A Fast Algorithm to Compute Maximum k-Plexes in Social Network Analysis

A clique model is one of the most important techniques on the cohesive subgraph detection; however, its applications are rather limited due to restrictive conditions of the model. Hence much research resorts to k-plex — a graph in which any vertex is adjacent to all but at most k vertices — which is a relaxation model of the clique. In this paper, we study the maximum k-plex problem and propose a fast algorithm to compute maximum k-plexes by exploiting structural properties of the problem. In an n-vertex graph, the algorithm computes optimal solutions in cnnO(1) time for a constant c < 2 depending only on k. To the best of our knowledge, this is the first algorithm that breaks the trivial theoretical bound of 2n for each k ≥ 3. We also provide experimental results over multiple real-world social network instances in support

A method to prolong lithium-ion battery life during the full life cycle

Extended lifetime of lithium-ion batteries decreases economic costs and environmental burdens in achieving sustainable development. Cycle life tests are conducted on 18650-type commercial batteries, exhibiting nonlinear and inconsistent degradation. The accelerated fade dispersion is proposed to be triggered by the evolution of an additional potential of the anode during cycling as measured vs. Li$^+$/Li. A method to prolong the battery cycle lifetime is proposed, in which the lower cutoff voltage is raised to 3 V when the battery reaches a capacity degradation threshold. The results demonstrate a 38.1% increase in throughput at 70% of their beginning of life (BoL) capacity. The method is applied to two other types of lithium-ion batteries. A cycle lifetime extension of 16.7% and 33.7% is achieved at 70% of their BoL capacity, respectively. The proposed method enables lithium-ion batteries to provide long service time, cost savings, and environmental relief while facilitating suitable second-use applications

The potential role of the orexin system in premenstrual syndrome

Premenstrual syndrome (PMS) occurs recurrently during the luteal phase of a woman’s menstrual cycle and disappears after menstruation ends. It is characterized by abnormal changes in both the body and mood, and in certain cases, severe disruptions in daily life and even suicidal tendencies. Current drugs for treating PMS, such as selective serotonin reuptake inhibitors, do not yield satisfactory results. Orexin, a neuropeptide produced in the lateral hypothalamus, is garnering attention in the treatment of neurological disorders and is believed to modulate the symptoms of PMS. This paper reviews the advancements in research on sleep disturbances, mood changes, and cognitive impairment caused by PMS, and suggests potential pathways for orexin to address these symptoms. Furthermore, it delves into the role of orexin in the molecular mechanisms underlying PMS. Orexin regulates steroid hormones, and the cyclic fluctuations of estrogen and progesterone play a crucial role in the pathogenesis of PMS. Additionally, orexin also modulates the gamma-aminobutyric acid (GABA) system and the inflammatory response involved in coordinating the mechanism of PMS. Unraveling the role of orexin in the pathogenesis of PMS will not only aid in understanding the etiology of PMS but also hold implications for orexin as a novel target for treating PMS

The NLRP3 inflammasome is involved in resident intruder paradigm-induced aggressive behaviors in mice

Background: Aggressive behaviors are one of the most important negative behaviors that seriously endangers human health. Also, the central para-inflammation of microglia triggered by stress can affect neurological function, plasticity, and behavior. NLRP3 integrates stress-related signals and is a key driver of this neural para-inflammation. However, it is unclear whether the NLRP3 inflammasome is implicated in the development of aggressive behaviors.Methods: First, aggressive behavior model mice were established using the resident intruder paradigm. Then, aggressive behaviors were determined with open-field tests (OFT), elevated plus-maze (EPM), and aggressive behavior tests (AT). Moreover, the expression of P2X7R and NLRP3 inflammasome complexes were assessed by immunofluorescence and Western blot. The levels of NLRP3 and inflammatory cytokines were evaluated using enzyme-linked immunosorbent assay (ELISA) kits. Finally, nerve plasticity damage was observed by immunofluorescence, transmission electron microscope, and BrdU staining.Results: Overall, the resident intruder paradigm induced aggressive behaviors, activated the hippocampal P2X7R and NLRP3 inflammasome, and promoted the release of proinflammatory cytokines IL-1β in mice. Moreover, NLRP3 knockdown, administration of P2X7R antagonist (A804598), and IL-1β blocker (IL-1Ra) prevented NLRP3 inflammasome-driven inflammatory responses and ameliorated resident intruder paradigm-induced aggressive behaviors. Also, the resident intruder paradigm promoted the activation of mouse microglia, damaging synapses in the hippocampus, and suppressing hippocampal regeneration in mice. Besides, NLRP3 knockdown, administration of A804598, and IL-1Ra inhibited the activation of microglia, improved synaptic damage, and restored hippocampal regeneration.Conclusion: The NLRP3 inflammasome-driven inflammatory response contributed to resident intruder paradigm-induced aggressive behavior, which might be related to neuroplasticity. Therefore, the NLRP3 inflammasome can be a potential target to treat aggressive behavior-related mental illnesses

Activation of GABA type A receptor is involved in the anti-insomnia effect of Huanglian Wendan Decoction

Huanglian Wendan Decoction (HWD) is a traditional Chinese medicine (TCM) prescribed to patients diagnosed with insomnia, which can achieve excellent therapeutic outcomes. As positively modulating the γ-aminobutyric acid (GABA) type A receptors (GABAARs) is the most effective strategy to manage insomnia, this study aimed to investigate whether the activation of GABAARs is involved in the anti-insomnia effect of HWD. We assessed the metabolites of HWD using LC/MS and the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and tested the pharmacological activity in vitro and in vivo using whole-cell patch clamp and insomnia zebrafish model. In HEK293 cells expressing α1β3γ2L GABAARs, HWD effectively increased the GABA-induced currents and could induce GABAAR-mediated currents independent of the application of GABA. In the LC-MS (QToF) assay, 31 metabolites were discovered in negative ion modes and 37 metabolites were found in positive ion modes, but neither three selected active metabolites, Danshensu, Coptisine, or Dihydromyricetin, showed potentiating effects on GABA currents. 62 active metabolites of the seven botanical drugs were collected based on the TCMSP database and 19 of them were selected for patch-clamp verification according to the virtual docking simulations and other parameters. At a concentration of 100 μM, GABA-induced currents were increased by (+)-Cuparene (278.80% ± 19.13%), Ethyl glucoside (225.40% ± 21.77%), and β-Caryophyllene (290.11% ± 17.71%). In addition, (+)-Cuparene, Ethyl glucoside, and β-Caryophyllene could also serve as positive allosteric modulators (PAMs) and shifted the GABA dose-response curve (DRC) leftward significantly. In the PCPA-induced zebrafish model, Ethyl glucoside showed anti-insomnia effects at concentrations of 100 μM. In this research, we demonstrated that the activation of GABAARs was involved in the anti-insomnia effect of HWD, and Ethyl glucoside might be a key metabolite in treating insomnia

Identification and Validation of Immune-Related Gene Prognostic Signature for Hepatocellular Carcinoma

Immune-related genes (IRGs) have been identified as critical drivers of the initiation and progression of hepatocellular carcinoma (HCC). This study is aimed at constructing an IRG signature for HCC and validating its prognostic value in clinical application. The prognostic signature was developed by integrating multiple IRG expression data sets from TCGA and GEO databases. The IRGs were then combined with clinical features to validate the robustness of the prognostic signature through bioinformatics tools. A total of 1039 IRGs were identified in the 657 HCC samples. Subsequently, the IRGs were subjected to univariate Cox regression and LASSO Cox regression analyses in the training set to construct an IRG signature comprising nine immune-related gene pairs (IRGPs). Functional analyses revealed that the nine IRGPs were associated with tumor immune mechanisms, including cell proliferation, cell-mediated immunity, and tumorigenesis signal pathway. Concerning the overall survival rate, the IRGPs distinctly grouped the HCC samples into the high- and low-risk groups. Also, we found that the risk score based on nine IRGPs was related to clinical and pathologic factors and remained a valid independent prognostic signature after adjusting for tumor TNM, grade, and grade in multivariate Cox regression analyses. The prognostic value of the nine IRGPs was further validated by forest and nomogram plots, which revealed that it was superior to the tumor TNM, grade, and stage. Our findings suggest that the nine-IRGP signature can be effective in determining the disease outcomes of HCC patients

Anti-colorectal cancer of Ardisia gigantifolia Stapf. and targets prediction via network pharmacology and molecular docking study

Abstract Background Ardisia gigantifolia Stapf. (AGS), a Chinese folk medicine widely grows in the south of China and several studies reported that AGS could inhibit the proliferation of breast cancer, liver cancer, and bladder cancer cell lines. However, little is known about its anti-colorectal cancer (CRC) efficiency. Methods In the present study, a combination of MTT assay, network pharmacological analysis, bioinformatics, molecular docking, and molecular dynamics simulation study was used to investigate the active ingredients, and targets of AGS against CRC, as well as the potential mechanism. Results MTT assay showed that three kinds of fractions from AGS, including the n-butanol extract (NBAGS), ethyl acetate fraction (EAAGS), and petroleum ether fraction (PEAGS) significantly inhibited the proliferation of CRC cells, with the IC50 values of 197.24, 264.85, 15.45 µg/mL on HCT116 cells, and 523.6, 323.59, 150.31 µg/mL on SW620 cells, respectively. Eleven active ingredients, including, 11-O-galloylbergenin, 11-O-protocatechuoylbergenin, 11-O-syringylbergenin, ardisiacrispin B, bergenin, epicatechin-3-gallate, gallic acid, quercetin, stigmasterol, stigmasterol-3-o-β-D-glucopyranoside were identified. A total of 173 targets related to the bioactive components and 21,572 targets related to CRC were picked out through database searching. Based on the crossover targets of AGS and CRC, a protein-protein interaction network was built up by the String database, from which it was concluded that the core targets would be SRC, MAPK1, ESR1, HSP90AA1, MAPK8. Besides, GO analysis showed that the numbers of biological process, cellular component, and molecular function of AGS against CRC were 1079, 44, and 132, respectively, and KEGG pathway enrichment indicated that 96 signaling pathways in all would probably be involved in AGS against CRC, among which MAPK signaling pathway, lipid, and atherosclerosis, proteoglycans in cancer, prostate cancer, adherens junction would probably be the major pathways. The docking study verified that AGS had multiple ingredients and multiple targets against CRC. Molecular dynamics (MD) simulation analysis showed that the binding would be stable via forming hydrogen bonds. Conclusion Our study showed that AGS had good anti-CRC potency with the characteristics of multi-ingredients, -targets, and -signaling pathways

Complete Technical Scheme for Automatic Biological Dose Estimation Platform

To establish a complete technical solution for the automatic radiation biological dose estimation platform for biological dose estimation and classification of the wounded in large-scale radiation accidents, the “dose–effect curve by dicentric chromosome (DIC) automatic analysis” was established and its accuracy was verified. The effects of analyzed cell number and the special treatment of the culture on dose estimation by DIC automatic analysis were studied. Besides, sample processing capabilities of the special equipments were tested. The fitted “dose–effect curve by DIC automatic analysis” was presented as follows: Y = (0.01806 ± 0.00032) D 2 + (0.01279 ± 0.00084) D + (0.0004891 ± 0.0001358) ( R 2 = 0.961). Three-gradient scanning method, culture refrigeration method, and interprofessional collaboration under extreme conditions were proposed to improve the detection speed, prolong the sample processing time window, and reduce the equipment investment. In addition, the optimized device allocation ratio for the automatic biological dose estimation laboratory was proposed to eliminate the efficiency bottleneck. The complete set of technical solutions for the high-throughput automatic biological dose estimation laboratory proposed in this study can meet the requirements of early classification and rapid biological dose assessment of the wounded during the large-scale nuclear radiation events, and it is worthy of further promotion

A Novel Mis-Seg-Focus Loss Function Based on a Two-Stage nnU-Net Framework for Accurate Brain Tissue Segmentation

Brain tissue segmentation plays a critical role in the diagnosis, treatment, and study of brain diseases. Accurately identifying these boundaries is essential for improving segmentation accuracy. However, distinguishing boundaries between different brain tissues can be challenging, as they often overlap. Existing deep learning methods primarily calculate the overall segmentation results without adequately addressing local regions, leading to error propagation and mis-segmentation along boundaries. In this study, we propose a novel mis-segmentation-focused loss function based on a two-stage nnU-Net framework. Our approach aims to enhance the model’s ability to handle ambiguous boundaries and overlapping anatomical structures, thereby achieving more accurate brain tissue segmentation results. Specifically, the first stage targets the identification of mis-segmentation regions using a global loss function, while the second stage involves defining a mis-segmentation loss function to adaptively adjust the model, thus improving its capability to handle ambiguous boundaries and overlapping anatomical structures. Experimental evaluations on two datasets demonstrate that our proposed method outperforms existing approaches both quantitatively and qualitatively