Explore the vast range of titles available.

In view of the network security risks caused by the integration of the Industrial Internet of Things (IIoT) in the construction of smart cities, this research proposes a digital security identification model (DL-DSIM) based on deep learning, which aims to improve the data transmission efficiency and system security in the smart city environment. With the widespread application of advanced technologies in smart cities, the rapid development of IIoT has become an important force in promoting its evolution, but at the same time, the expansion of the attack surface has also brought many security risks. In order to deal with these problems, this paper designs a flexible three-layer architecture framework, and introduces a new intrusion detection feature selection method that combines flock optimization (CSO) and genetic algorithm (GA) to reduce the complexity of feature selection and enhance the detection and processing power of security vulnerabilities through deep neural networks (DNN). The simulation experiment results show that DL-DSIM achieved 99.13% accuracy, 98.5% recall rate, 98.39% F value, 99.16% accuracy and 95.62% specificity in the training phase, and also achieved 96.1% accuracy, 95.48% recall rate, 96.38% F value, 95.89% accuracy and 93% specificity in the test phase. These achievements fully reflect the efficiency of DL-DSIM in resisting network security threats, provide a reliable security mechanism for IIoT systems in smart cities, and further promote the sustainable development of smart cities and the construction of digital security.

A large number of sensors are typically installed in industrial plants to collect real-time operational data. These sensors monitor data with time series correlation and spatial correlation over time. In previous studies, GNN has built many successful models to deal with time series data, but most of these models have fixed perspectives and struggle to capture the dynamic correlations in time and space simultaneously. Therefore, this paper constructs a multi-scale dynamic graph neural network (MSDG) for anomaly detection in industrial sensor data. First, a multi-scale sliding window mechanism is proposed to input different scale sensor data into the corresponding network. Then, a dynamic graph neural network is constructed to capture the spatial–temporal dependencies of multivariate sensor data. Finally, the model comprehensively considers the extracted features for sequence reconstruction and utilizes the reconstruction errors for anomaly detection. Experiments have been conducted on three real public datasets, and the results show that the proposed method outperforms the mainstream methods.

Recent years have seen an explosion of interest in the evolution of neural circuits. Comparison of animals from different families, orders, and phyla reveals fascinating variation in brain morphology, circuit structure, and neural cell types. However, it can be difficult to connect the complex changes that occur across long evolutionary distances to behavior. Luckily, these changes accumulate through processes that should also be observable in recent time, making more tractable comparisons of closely related species relevant and complementary. Here, we review several decades of research on the evolution of insect olfactory circuits across short evolutionary time scales. We describe two well-studied systems, Drosophila sechellia flies and Heliothis moths, in detailed case studies. We then move through key types of circuit evolution, cataloging examples from other insects and looking for general patterns. The literature is dominated by changes in sensory neuron number and tuning at the periphery—often enhancing neural response to odorants with new ecological or social relevance. However, changes in the way olfactory information is processed by central circuits is clearly important in a few cases, and we suspect the development of genetic tools in non-model species will reveal a broad role for central circuit evolution. Moving forward, such tools should also be used to rigorously test causal links between brain evolution and behavior.

Type-B aflatoxins (AFB1 and AFB2) frequently contaminate food, especially nuts and fried figs, and seriously threaten human health; hence, it is necessary for the newly rapid and sensitive detection methods to prevent the consumption of potentially contaminated food. Here, a lateral flow aptasensor for the detection of type-B aflatoxins was developed. It is based on the use of fluorescent dye Cy5 as a label for the aptamer, and on the competition between type-B aflatoxins and the complementary DNA of the aptamer. This is the first time that the complementary strand of the aptamer has been used as the test line (T-line) to detect type-B aflatoxins. In addition, the truncated aptamer was used to improve the affinity with type-B aflatoxins in our study. Therefore, the lengths of aptamer and cDNA probe were optimized as key parameters for higher sensitivity. In addition, binding buffer and organic solvent were investigated. The results showed that the best pair for achieving improved sensitivity and accuracy in detecting AFB1 was formed by a shorter aptamer (32 bases) coupled with the probe complementary to the AFB1 binding region of the aptamer. Under the optimal experimental conditions, the test strip showed an excellent linear relationship in the range from 0.2 to 20 ng/mL with a limit of detection of 0.16 ng/mL. This aptamer-based strip was successfully applied to the determination of type-B aflatoxins in spiked and commercial peanuts, almonds, and dried figs, and the recoveries of the spiked samples were from 93.3%−112.0%. The aptamer-complementary strand-based lateral flow test strip is a potential alternative tool for the rapid and sensitive detection of type-B aflatoxins in nuts and dried figs. It is of help for monitoring aflatoxins to avoid the consumption of unsafe food.

Background Hepatocellular carcinoma (HCC) is the leading cause of cancer-related mortality worldwide owing to the lack of effective and early diagnostic tools and therapeutic approaches. DNAJC7, a member of the DnaJ heat shock family, is crucial in protein folding and stability; however, its specific functions and mechanisms in HCC remain unclear. Objective This study aimed to explore the role of DNAJC7 in HCC progression and evaluate its potential clinical significance as a prognostic marker. Methods Public databases (TCGA, ICGC, GEO, and TIMER) were used to assess DNAJC7 expression, correlations with clinical parameters, and related signaling pathways. Proliferation, migration, invasion, and cell cycle assays were performed to evaluate the function of DNAJC7 in HCC. Immune infiltration and associations with checkpoint proteins were analyzed using TIMER, and a Gene Set Enrichment Analysis (GSEA) was used to explore enriched pathways. Results DNAJC7 expression was higher in HCC tissues than in adjacent normal tissues and was associated with advanced malignancy and poor prognosis, including a lower overall survival, progression-free survival, and disease-free survival. DNAJC7 knockdown resulted in reduced malignant behavior of HCC cells, leading to S-phase cell cycle arrest. Increased DNAJC7 expression was associated with immune cell infiltration and the presence of immunological checkpoint molecules, including CTLA4 and PD-1. GSEA highlighted the activation of key pathways, including WNT signaling and cell cycle regulation. Conclusion DNAJC7 regulates tumor cell proliferation, migration, invasion, and immune evasion by acting as an oncogene in HCC. It can serve as a diagnostic and prognostic biomarker and potential treatment target for HCC.

Background Influenza A virus (IAV) can cause severe and life-threatening illness in humans and animals. Therefore, it is important to search for host antiviral proteins and elucidate their antiviral mechanisms for the development of potential treatments. As a part of human innate immunity, host restriction factors can inhibit the replication of viruses, among which SAM and HD domain containing deoxynucleoside triphosphate triphosphohydrolase 1 (SAMHD1) can restrict the replication of viruses, such as HIV and enterovirus EV71. Viruses also developed countermeasures in the arms race with their hosts. There are few reports about whether SAMHD1 has a restriction effect on IAV. Methods To investigate the impact of IAV infection on SAMHD1 expression in A549 cells, we infected A549 cells with a varying multiplicity of infection (MOI) of IAV and collected cell samples at different time points for WB and RT-qPCR analysis to detect viral protein and SAMHD1 levels. The virus replication level in the cell culture supernatant was determined using TCID50 assay. Luciferase assay was used to reveal that H5N1 virus polymerase acidic protein (PA) affected the activity of the SAMHD1 promoter. To assess the antiviral capacity of SAMHD1, we generated a knockdown and overexpressed cell line for detecting H5N1 replication. Results In this study, we observed that SAMHD1 can restrict the intracellular replication of H5N1 and that the H5N1 viral protein PA can downregulate the expression of SAMHD1 by affecting SAMHD1 transcriptional promoter activity. We also found that SAMHD1's ability to restrict H5N1 is related to phosphorylation at 592-tyrosine. Conclusions In conclusion, we found that SAMHD1 may affect the replication of IAVs as a host restriction factor and be countered by PA. Furthermore, SAMHD1 may be a potential target for developing antiviral drugs.

Colorectal cancer is one of the common malignant tumors with poor prognosis, which is partly due to the lack of an effective biomarker. The purpose of this study is to explore the impact of membrane palmitoylated protein (MPP2) on the prognosis of colorectal cancer patients. We obtained transcriptome data and DNA methylation data of 380 colorectal cancer patients from the Cancer Genome Atlas (TCGA). Then we used a series of bioinformatics analysis methods to reveal the relationship between MPP2 expression, DNA methylation sites, prognosis, immune checkpoint and clinical characteristics of patients. Finally, in vitro experiment and the meta-analysis of thousands of patients further confirmed the impact of MPP2 on the prognosis of colorectal cancer patients and cell migration and proliferation. The expression level of MPP2 is negatively regulated by its DNA methylation sites, which leads to its low expression in colorectal cancer. High expression of MPP2 is an independent prognostic risk factor, which may be a biomarker for colorectal cancer. Moreover, the expression of MPP2 shows a close relationship with immune checkpoint or immune cells infiltration, especially CD4 + T cells. In addition, meta-analysis involving 1584 patients from four different data further confirmed that MPP2 was a risk factor for colorectal cancer. Finally, knockdown of MPP2 could significantly inhibit the proliferation of SW480 cells via mTOR signaling pathway. This study was the first to suggest that MPP2 may become a promising biomarker, and has an important role in immune checkpoint or immune cell infiltration in colorectal cancer.

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a severe global health crisis; its structural protein envelope (E) is critical for viral entry, budding, production, and induction of pathology which makes it a potential target for therapeutics against COVID-19. Here, we find that the E3 ligase RNF5 interacts with and catalyzes ubiquitination of E on the 63rd lysine, leading to its degradation by the ubiquitin-proteasome system (UPS). Importantly, RNF5-induced degradation of E inhibits SARS-CoV-2 replication and the RNF5 pharmacological activator Analog-1 alleviates disease development in a mouse infection model. We also found that RNF5 is distinctively expressed in different age groups and in patients displaying different disease severity, which may be exploited as a prognostic marker for COVID-19. Furthermore, RNF5 recognized the E protein from various SARS-CoV-2 strains and SARS-CoV, suggesting that targeting RNF5 is a broad-spectrum antiviral strategy. Our findings provide novel insights into the role of UPS in antagonizing SARS-CoV-2 replication, which opens new avenues for therapeutic intervention to combat the COVID-19 pandemic.

The auditory steady-state response (ASSR) has been used to detect auditory processing deficits in patients with psychiatric disorders. However, the methodology of ASSR recording from the brain surface has not been standardized in preclinical studies, limiting its use as a translational biomarker. The sites of maximal ASSR in humans are the vertex and/or middle frontal area, although it has been suggested that the auditory cortex is the source of the ASSR. We constructed and validated novel methods for ASSR recording using a switchable pedestal which allows ASSR recording alternatively from temporal or parietal cortex with a wide range of frequencies in freely moving rats. We further evaluated ASSR as a translational tool by assessing the effect of ketamine. The ASSR measured at parietal cortex did not show clear event-related spectral perturbation (ERSP) or inter-trial coherence (ITC) in any frequency bands or a change with ketamine. In contrast, the ASSR at temporal cortex showed clear ERSP and ITC where 40 Hz was maximal in both gamma-band frequencies. Ketamine exerted a biphasic effect in ERSP at gamma bands. These findings suggest that temporal cortex recording with a wide frequency range is a robust methodology to detect ASSR, potentially enabling application as a translational biomarker in psychiatric and developmental disorders.

In this work, we quantified goat milk powder adulteration by adding urea, melamine, and starch individually and simultaneously, with the utilization of near infrared (NIR) spectroscopy coupled with chemometrics. For single-adulterant samples, the successive projections algorithm (SPA) selected three, three, and four optimal wavelengths for urea, melamine, and starch, respectively. Models were built based on partial least squares regression (PLS) and the selected wavelengths, exhibiting good predictive ability with an Rp2 above 0.987 and an RMSEP below 0.403%. For multiple-adulterants samples, PLS2 and multivariate curve resolution alternating least squares (MCR-ALS) were adopted to build the models to quantify the three adulterants simultaneously. The PLS2 results showed adequate precision and results better than those of MCR-ALS. Except for urea, MCR-ALS models presented good predictive results for milk, melamine, and starch concentrations. MCR-ALS allowed detection of adulteration with new and unknown substitutes as well as the development of models without the need for the usage of a large data set.