工程技术最新文献

Tetramethylpyrazine (TMP) has been confirmed to suppress inflammation in endometriosis (EMs). Herein, this study investigated whether and how TMP affected NLRP3 inflammasomes and oxidative stress in EMs. After establishment of an EMs rat model, rats were treated with different concentrations of TMP. The size of endometriotic lesions and the latency and frequency of torsion in rats were recorded, followed by the measurement of relevant indicators (TNF-α, IL-6, IL-2, IL-10, MDA, SOD, GSH, CAT, ROS, NLRP3, ASC, GSDMD, caspase-1, Nrf2, and HO-1). The study experimentally determined that TMP treatment markedly decreased the size of endometriotic lesions and improved torsion in rats with EMs. The levels of inflammatory proteins, oxidative stress markers, NLRP3 inflammasome, and pyroptotic proteins were elevated in rats with EMs, all of which were reversed upon TMP treatment. Additionally, the activities of SOD, GSH, and CAT were lowered in rats with EMs, which were partly abrogated by TMP treatment. Furthermore, the downregulation of Nrf2 and HO-1 was counteracted by TMP treatment. To sum up, TMP represses excessive oxidative stress, NLRP3 inflammasome activation, and pyroptosis in rats with EMs. Additionally, TMP may activate the Nrf2/HO-1 pathway.

A phenomenological model for aesthetic appraisal is proposed in terms of pragmatic information for a dynamic update semantics over belief states of an aesthetic appreciator. The model qualitatively correlates with aesthetic pleasure ratings in an experimental study on cadential effects in Western tonal music, conducted by Cheung et al. (Curr Biol 29(23):4084-4092.e4, 2019). Finally, related computational and neurodynamical accounts are discussed.

The rise of antibiotic-resistant bacteria demands new antimicrobial strategies. Glyceryl monolaurate (GML) shows antibacterial activity against Gram-positive bacteria like S. aureus but is ineffective against Gram-negative E. coli due to its outer membrane. GML's limited solubility and susceptibility to bacterial lipases hinder its direct use. This study developed glyceryl monooleate (GMO) lipid liquid crystalline nanoparticles (LLCNPs) incorporating GML to enhance its stability and efficacy. Using a central composite design (CCD), an optimal GMO:GML:F127 mass ratio of 26.5:3.5:1.5 was achieved. Characterization via dynamic light scattering (DLS), small angle X-ray scattering (SAXS), and cryo-transmission electron microscopy (cryo-TEM) confirmed the formation of bicontinuous cubic phase nanoparticles (Pn3m space group) with hydrophobic, hydrophilic, and amphiphilic regions, enabling the incorporation of diverse agents and the presence of sponge-like nanoparticles. The optimized LLCNPs inhibited S. aureus growth at concentrations ≥10 µg/mL by disrupting its membrane potential but showed no activity against E. coli. Cytotoxicity studies indicated that GML incorporation did not significantly affect cell viability compared to pure GMO LLCNPs. This nanoparticle system offers a biocompatible solution for treating Gram-positive bacterial infections and may synergize with existing antibiotics, warranting further investigation into its mechanisms and therapeutic potential.

Face masks became a part of everyday life during the SARS-CoV-2 pandemic. Previous studies showed that the face cognition mechanism involves holistic face processing, and the absence of face features could lower the cognition ability. This is opposed to the experience during the pandemic, when people could correctly recognize faces, although the mask covered a part of the face. This paper clarifies the partial face cognition mechanism of the full and partial faces based on the electroencephalogram (EEG) and eye-tracking data. We observed two event-related potentials, P3a in the frontal lobe and P3b in the parietal lobe, as subcomponents of P300. The amplitude of both P3a and P3b were lowered when the eyes were invisible, and the amplitude of P3a evoked by the nose covered was larger than the full face. The eye-tracking data showed that 16 out of 18 participants focused on the eyes associated with the EEG results. Our results demonstrate that the eyes are the most crucial feature of facial cognition. Moreover, the face with the nose covered might enhance cognition ability due to the visual working memory capacity. Our experiment also shows the possibility of people recognizing faces using both holistic and structural face processing. In addition, we calculated canonical correlation using the P300 and the total fixation duration of the eye-tracking data. The results show high correlation in the cognition of the full face and the face and nose covered ( $R_c=0.93$ ) which resembles the masked face. The finding suggests that people can recognize the masked face as well as the full face in similar cognition patterns.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10231-3.

Several genetically modified (GM) potatoes have been developed by introducing endogenous genes derived from potatoes, such as asparagine synthetase-1 (Asn1) and polyphenol oxidase 5 (Ppo5), to improve quality. Therefore, it is difficult to distinguish between GM and non-GM potatoes. In this study, we developed a sequence-specific polymerase chain reaction (PCR) detection method to identify innate and inserted genes. We designed four Asn1 gene-specific primers and eight construct-specific detection primers to evaluate GM potatoes (E12, X17, and Y9) and non-GM crops. Consequently, PCR products corresponding to the original endogenous potato genes and the inserted genes were clearly distinguished and simultaneously identified. In addition, the PCR method demonstrated sufficient sensitivity to identify GM content at levels as low as 0.5%. Thus, this study offers an effective detection method for monitoring or screening approved and unapproved GM potato events using Asn1 and Ppo5 transgenes in foods and feeds.

Individuals with high autistic traits (AT) encounter challenges in social interaction, similar to autistic persons. Precise screening and focused interventions positively contribute to improving this situation. Functional connectivity analyses can measure information transmission and integration between brain regions, providing neurophysiological insights into these challenges. This study aimed to investigate the patterns of brain networks in high AT individuals to offer theoretical support for screening and intervention decisions. EEG data were collected during a 4-min resting state session with eyes open and closed from 48 participants. Using the Autism Spectrum Quotient (AQ) scale, participants were categorized into the high AT group (HAT, n = 15) and low AT groups (LAT, n = 15). We computed the interhemispheric and intrahemispheric alpha coherence in two groups. The correlation between physiological indices and AQ scores was also examined. Results revealed that HAT exhibited significantly lower alpha coherence in the homologous hemispheres of the occipital cortex compared to LAT during the eyes-closed resting state. Additionally, significant negative correlations were observed between the degree of AT (AQ scores) and the alpha coherence in the occipital cortex, as well as in the right frontal and left occipital regions. The findings indicated that high AT individuals exhibit decreased connectivity in the occipital region, potentially resulting in diminished ability to process social information from visual inputs. Our discovery contributes to a deeper comprehension of the neural underpinnings of social challenges in high AT individuals, providing neurophysiological signatures for screening and intervention strategies for this population.

As a longstanding and indispensable part of developing countries, small farmers face challenges brought by the dissemination of GM technology. Despite governments' efforts to promote collective cultivation of GM crops through top-down policies aimed at enhancing small farmers' production efficiency and market competitiveness, actual participation rates among small farmers in many developing countries remain low. This reflects a gap and mismatch between policy design and the actual needs of small farmers. Based on a survey and empirical analysis of 964 small farmers in Guangdong and Xinjiang, China, this study finds that small farmers' acceptance of GM technology is influenced not only by expected profitability but also by factors such as their independence and risk assessment of the technology. The findings reveal that, first, small farmers' expected profitability from GM technology and their perception of independent market adaptability positively influence their willingness to participate in collective GM crop farming. Independent market adaptability acts as a partial mediator in this relationship and is moderated by small farmers' risk assessments of GM technology. Variables such as gender, age, education level, and farming experience do not show significant effects. This study enriches the theoretical frameworks related to technology acceptance, innovation and diffusion, livelihood strategies, and collective transformation among small farmers in developing countries. It provides scientific evidence for policymakers to design more effective and aligned policies concerning GM crops.

Variations in information processing patterns induced by operational directives under varying fatigue conditions within the cerebral cortex can be identified and analyzed through electroencephalogram (EEG) signals. The inherent complexity of EEG signals poses significant challenges in the effective detection of driver fatigue across diverse task scenarios. Recent advancements in deep learning, particularly the Transformer architecture, have shown substantial benefits in the retrieval and integration of multi-dimensional information. Nevertheless, the majority of current research primarily focuses on the application of Transformers for temporal information extraction, often overlooking other dimensions of EEG data. In response to this gap, the present study introduces a Multidimensional Adaptive Transformer Recognition Network specifically tailored for the identification of driving fatigue states. This network features a multidimensional Transformer architecture for feature extraction that adaptively assigns weights to various information dimensions, thereby facilitating feature compression and the effective extraction of structural information. This methodology ultimately enhances the model's accuracy and generalization capabilities. The experimental results indicate that the proposed methodology outperforms existing research methods when utilized with the SEED-VIG and SFDE datasets. Additionally, the analysis of multidimensional and frequency band features highlights the ability of the proposed network framework to elucidate differences in various multidimensional features during the identification of fatigue states.

Rhizosphere bacterial community studies offer valuable insights into the environmental implications of genetically modified (GM) crops. This study compared the effects of a non-GM maize cultivar, namely Hi-IIA, with those of a herbicide-resistant maize cultivar containing the phosphinothricin N-acetyltransferase gene on the rhizosphere bacterial community across growth stages. 16s rRNA amplicon sequencing and data analysis tools revealed no significant differences in bacterial community composition or diversity between the cultivars. Principal component analysis revealed that differences in community structure were driven by plant growth stages rather than plant type. Polymerase chain reaction analysis was conducted to examine the potential horizontal transfer of the introduced gene from the GM maize to rhizosphere microorganisms; however, the introduced gene was not detected in the soil genomic DNA. Overall, the environmental impact of GM maize, particularly on soil microorganisms, is negligible, and the cultivation of GM maize does not alter significantly the rhizosphere bacterial community.

Ageing significantly contributes to osteoarthritis (OA) and metabolic syndrome (MetS) pathogenesis, yet the underlying mechanisms remain unknown. This study aimed to identify ageing-related biomarkers in OA patients with MetS. OA and MetS datasets and ageing-related genes (ARGs) were retrieved from public databases. The limma package was used to identify differentially expressed genes (DEGs), and weighted gene coexpression network analysis (WGCNA) screened gene modules, and machine learning algorithms, such as random forest (RF), support vector machine (SVM), generalised linear model (GLM), and extreme gradient boosting (XGB), were employed. The nomogram and receiver operating characteristic (ROC) curve assess the diagnostic value, and CIBERSORT analysed immune cell infiltration. We identified 20 intersecting genes among DEGs of OA, key module genes of MetS, and ARGs. By comparing the accuracy of the four machine learning models for disease prediction, the SVM model, which includes CEBPB, PTEN, ARPC1B, PIK3R1, and CDC42, was selected. These hub ARGs not only demonstrated strong diagnostic values based on nomogram data but also exhibited a significant correlation with immune cell infiltration. Building on these findings, we have identified five hub ARGs that are associated with immune cell infiltration and have constructed a nomogram aimed at early diagnosing OA patients with MetS.