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To address the complex requirements of network intrusion detection in IoT environments, this study proposes a hybrid intelligent framework that integrates the Whale Optimization Algorithm (WOA) and the Grey Wolf Optimization (GWO) algorithm—referred to as WOA-GWO. This framework leverages a cooperative mechanism to balance global exploration and local exploitation capabilities. WOA’s spiral bubble-net search strategy endows the model with efficient global optimization in large-scale feature spaces, while GWO’s hunting behavior, based on a social hierarchy, enhances fine-tuned optimization in key feature regions. The complementary design of the two algorithms effectively overcomes the limitations of single-algorithm approaches, such as susceptibility to local optima and slow convergence speed. Compared with traditional models like the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) and Support Vector Machine (SVM), the proposed framework significantly improves the sensitivity and generalization ability for detecting various types of attacks through dynamic feature selection and parameter optimization. Experimental results demonstrate that the hybrid algorithm exhibits superior real-time responsiveness in binary classification tasks, thanks to its lightweight design that reduces dependency on computational resources. In multi-class attack identification scenarios, the framework mitigates feature confusion between rare attacks (e.g., user-to-root attacks) and normal traffic through adaptive feature weight allocation. This study further validates the potential of swarm intelligence algorithms in the field of IoT security, offering a novel methodological foundation for efficient threat detection in resource-constrained environments.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease characterized by the production of a diverse array of autoantibodies and the dysfunctional activation of the complement system. The specific association between the complement component C3a (C3a) protein and antibodies specific for double-stranded DNA (anti-dsDNA), however, has not been studied in detail to date. This study was thus designed to more fully explore circulating C3a levels in SLE patients. In total, 13 SLE patients were enrolled in this study after having been diagnosed in accordance with the SLICC classification criteria, with 7 and 6 patients respectively exhibiting positivity for anti-dsDNA and anti-Sm autoantibodies. Serum complement component C1q (C1q) and C3a levels in samples from these patients were detected via Western blotting, while other serological, biochemical, and clinical parkers associated with disease activity were detected using standard laboratory techniques. The levels of serum C3a in anti-dsDNA+ patients were significantly elevated as compared to those in anti-Sm+ patients ( P  < 0.01), and a positive correlation between serum C3a levels and SLE Disease Activity Index scores was detected ( P  < 0.05, r  = 0.6134). C3a levels are correlated with the degree of SLE disease activity and other clinically relevant readouts in SLE patients. C3a levels may also enable the differentiation between inactive and active SLE, while also offering value as an advantageous biomarker for thrombophilia monitoring in SLE patients.

Background It is reported that growth hormone (GH) can alleviate oxidative stress (OS) induced apoptosis in some types of cells by activating the PI3K/Akt signaling pathway. This study investigated the role and underlying mechanism of GH in OS and apoptosis in granulosa cells (GCs) of patients with polycystic ovary syndrome (PCOS). Methods Primary GCs were collected from patients with and without PCOS (controls, n  = 32) during oocyte retrieval. The patients with PCOS were randomly assigned to take GH treatment (PCOS-GH, n  = 30) or without GH treatment (PCOS-C, n  = 31). Reactive oxygen species (ROS) level was determined by spectrophotometry and fluorescence microscopy. GC apoptosis and mitochondrial membrane potential (MMP) were detected by Annexin V-FITC/PI double-staining and JC-1 staining, respectively (flow cytometry). The expression of apoptosis-related genes and proteins involved in PI3K/Akt signaling was determined by quantitative reverse-transcription polymerase chain reaction and western blotting, while active caspase-9 and caspase-3 levels of GCs were determined by enzyme-linked immunosorbent assay. Results Our study found that in GCs of the PCOS-GH group, the ROS levels and apoptotic rates were significantly decreased, whereas MMP was significantly increased when compared to those in the PCOS-C group ( P  < 0.05). The mRNA levels of FOXO1 , Bax , caspase-9 , and caspase-3 were significantly decreased, whereas Bcl-2 was increased in GCs of the PCOS-GH group than those in the PCOS-C group ( P  < 0.05). The protein levels of FOXO1, Bax, cleaved caspase-9/caspase-9 and cleaved caspase-3/caspase-3 were decreased, whereas p-PI3K/PI3K, p-Akt/Akt, p-FOXO1 and Bcl-2 were increased in GCs of the PCOS-GH group, compared with those in the PCOS-C group ( P  < 0.05). Conclusion OS induced apoptosis and downregulated the PI3K/Akt signaling pathway in patients with PCOS. GH could alleviate apoptosis and activate the PI3K/Akt signaling pathway. Clinical trial registration number Chinese Clinical Trial Registry. ChiCTR1800019437 . Prospectively registered on October 20, 2018.

Aberrant activation of the epidermal growth factor receptor (EGFR/ERBB1) by erythroblastic leukemia viral oncogene homolog (ERBB) ligands contributes to various tumor malignancies, including lung cancer and colorectal cancer (CRC). Epiregulin (EREG) is one of the EGFR ligands and is low expressed in most normal tissues. Elevated EREG in various cancers mainly activates EGFR signaling pathways and promotes cancer progression. Notably, a higher EREG expression level in CRC with wild-type Kirsten rat sarcoma viral oncogene homolog (KRAS) is related to better efficacy of therapeutic treatment. By contrast, the resistance of anti-EGFR therapy in CRC was driven by low EREG expression, aberrant genetic mutation and signal pathway alterations. Additionally, EREG overexpression in non-small cell lung cancer (NSCLC) is anticipated to be a therapeutic target for EGFR-tyrosine kinase inhibitor (EGFR-TKI). However, recent findings indicate that EREG derived from macrophages promotes NSCLC cell resistance to EGFR-TKI treatment. The emerging events of EREG-mediated tumor promotion signals are generated by autocrine and paracrine loops that arise from tumor epithelial cells, fibroblasts, and macrophages in the tumor microenvironment (TME). The TME is a crucial element for the development of various cancer types and drug resistance. The regulation of EREG/EGFR pathways depends on distinct oncogenic driver mutations and cell contexts that allows specific pharmacological targeting alone or combinational treatment for tailored therapy. Novel strategies targeting EREG/EGFR, tumor-associated macrophages, and alternative activation oncoproteins are under development or undergoing clinical trials. In this review, we summarize the clinical outcomes of EREG expression and the interaction of this ligand in the TME. The EREG/EGFR pathway may be a potential target and may be combined with other driver mutation targets to combat specific cancers.

Association of Coagulation factor V (F5) polymorphisms with the occurrence of many types of cancers has been widely reported, but whether it is of prognostic relevance in some cancers remain to be resolved. The RNA-sequencing dataset was downloaded from The Cancer Genome Atlas (TCGA). The potential of F5 genes to predict the survival time of gastric cancer (GC) patients was investigated using univariate and multivariate survival analysis whereas “Kaplan-Meier plotter” (KM-plotter) online tools were employed to validate the outcomes. TCGA data revealed that F5 mRNA levels were significantly upregulated in gastric cancer samples. Survival analysis confirmed that high levels of F5 mRNA correlated with short overall survival (OS) in gastric cancer patients, and the area under the curve (AUC) values of 1-, 2-, and 5-year OS rate were 0.554, 0.593, and 0.603, respectively. Survival analysis by KM-plotter indicated that the high expression of F5 mRNA was significantly associated with a shorter OS compared with the low expression level in all patients with GC, and this was also the case for patients in stage III (hazard ratio HR=1.78, P=0.017). These findings suggest that the F5 gene is significantly upregulated in GC tumour tissues, and may be a potential prognostic biomarker for GC.

Decades of theory and empirical studies have demonstrated links between biodiversity and ecosystem functioning, yet the putative processes that underlie these patterns remain elusive. This is especially true for forest ecosystems, where the functional traits of plant species are challenging to quantify. We analyzed 74,563 forest inventory plots that span 35 ecoregions in the contiguous USA and found that in ~77% of the ecoregions mixed mycorrhizal plots were more productive than plots where either arbuscular or ectomycorrhizal fungal-associated tree species were dominant. Moreover, the positive effects of mixing mycorrhizal strategies on forest productivity were more pronounced at low than high tree species richness. We conclude that at low richness different mycorrhizal strategies may allow tree species to partition nutrient uptake and thus can increase community productivity, whereas at high richness other dimensions of functional diversity can enhance resource partitioning and community productivity. Our findings highlight the importance of mixed mycorrhizal strategies, in addition to that of taxonomic diversity in general, for maintaining ecosystem functioning in forests. Trees often associate with mycorrhizal fungi, arbuscular mycorrhizal (AM) or ectomycorrhizal (ECM) fungi. Luo et al. analyze 74,563 forest plots across the contiguous USA, showing that forests with mixed AM and ECM tree species are more productive than when dominated by AM or ECM tree species.

Feature selection for the high-dimensional Cox proportional hazards model (Cox model) is very important in many microarray genetic studies. In this paper, we propose a sequential feature selection procedure for this model. We define a novel partial profile score to assess the impact of unselected features conditional on the current model, significant features are thereby added into the model sequentially, and the Extended Bayesian Information Criteria (EBIC) is adopted as a stopping rule. Under mild conditions, we show that this procedure is selection consistent. Extensive simulation studies and two real data applications are conducted to demonstrate the advantage of our proposed procedure over several representative approaches.

We consider variable selection in high-dimensional sparse multiresponse linear regression models, in which a q-dimensional response vector has a linear relationship with a p-dimensional covariate vector through a sparse coefficient matrix B∈Rp×q. We propose a consistent procedure for the purpose of identifying the nonzeros in B. The procedure consists of two major steps, where the first step focuses on the detection of all the nonzero rows in B, the latter aims to further discover its individual nonzero cells. The first step is an extension of Orthogonal Matching Pursuit (OMP) and the second step adopts the bootstrap strategy. The theoretical property of our proposed procedure is established. Extensive numerical studies are presented to compare its performances with available representatives.

Nuclear receptors (NRs) are a class of transcription factors that play a pivotal role in carcinogenesis, but their function in colorectal cancer (CRC) remains unclear. Here, we investigate the role NRs play in CRC pathogenesis. We found that hepatocyte nuclear factor 4 gamma (HNF4G; NR2A2), hepatocyte nuclear factor 4α (HNF4A; NR2A1), and retinoid-related orphan receptor γ (RORC; NR1F3) were significantly upregulated in CRC tissues analyzed by GEPIA bioinformatics tool. The expression of HNF4G was examined in CRC samples and cell lines by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry. Increased expression of HNF4G was strongly associated with high tumor-node-metastasis stage and poor prognosis. Moreover, overexpression of HNF4G significantly promoted the proliferation of CRC cells in vitro. Next, we found that HNF4G promoted CRC proliferation via the PI3K/AKT pathway through targeting of GNG12 and PTK2. In addition, HNF4G was verified as a direct target of microRNA-766-3p (miR-766-3p). miR-766-3p inhibited the proliferation of CRC cells by targeting HNF4G in vitro and in vivo. Collectively, our study indicates that miR-766-3p reduces the proliferation of CRC cells by targeting HNF4G expression and thus inhibits the PI3K/AKT pathway. Therefore, development of therapies which target the miR-766-3p/HNF4G axis may aid in the treatment of CRC.

Tactile sensing is important for robots to perceive the world as it captures the physical surface properties of the object with which it is in contact and is robust to illumination and colour variances. However, due to the limited sensing area and the resistance of their fixed surface when they are applied with relative motions to the object, current tactile sensors have to tap the tactile sensor on the target object a great number of times when assessing a large surface, i.e., pressing, lifting up, and shifting to another region. This process is ineffective and time-consuming. It is also undesirable to drag such sensors as this often damages the sensitive membrane of the sensor or the object. To address these problems, we propose a roller-based optical tactile sensor named TouchRoller, which can roll around its centre axis. It maintains being in contact with the assessed surface throughout the entire motion, allowing for efficient and continuous measurement. Extensive experiments showed that the TouchRoller sensor can cover a textured surface of 8 cm × 11 cm in a short time of 10 s, much more effectively than a flat optical tactile sensor (in 196 s). The reconstructed map of the texture from the collected tactile images has a high Structural Similarity Index (SSIM) of 0.31 on average when compared with the visual texture. In addition, the contacts on the sensor can be localised with a low localisation error, 2.63 mm in the centre regions and 7.66 mm on average. The proposed sensor will enable the fast assessment of large surfaces with high-resolution tactile sensing and the effective collection of tactile images.