工程技术最新文献

Zinc ions play a pivotal role in facilitating the development of cartilage in mice. Nevertheless, the precise underlying mechanism remains elusive. Our investigation was centered on elucidating the impact of zinc deficiency on cartilage maturation by modulating SUMO1 and UBC9 at both the protein and mRNA levels. We administered a regimen inducing zinc deficiency to gravid mice from E0.5 until euthanasia. Subsequently, we subjected the embryos to scrutiny employing HE, Safranin O staining and IHC. Primary chondrocytes were isolated from fetal mouse femoral condyles and utilized for Western blot analysis to discern the expression profiles of SUMO1, SUMO2/3, UBC9, SOX9, MMP13, Collagen II, RUNX2, and aggrecan. Furthermore, ATDC5 murine chondrocytes were subjected to treatment with ZnCl₂, followed by RT-PCR assessment to scrutinize the expression levels of MMP13, Collagen II, RUNX2, and aggrecan. Additionally, we conducted Co-IP assays on ZnCl₂-treated ATDC5 cells to explore the interaction between SOX9 and SUMO1. Our investigation unveiled that zinc deficiency led to a reduction in cartilage development, as evidenced by the HE results in fetal murine femur. Moreover, diminished expression levels of SUMO1 and UBC9 were observed in the IHC and Western blot results. Furthermore, Western blot and Co-IP assays revealed an augmented interaction between SOX9 and SUMO1, which was potentiated by ZnCl₂ treatment. Significantly, mutations at the SUMOylation site of SOX9 resulted in alterations in the expression patterns of crucial chondrogenesis factors. This research underscores how zinc ions promote cartilage development through the modification of SOX9 by SUMO1.

Fiber-photometric is a novel optogenetic method for recording neural activity in vivo, which allows the use of calcium indicators to observe and study the relationship between neural activity and behavior in free-ranging animals. Calcium indicators also convert changes in calcium concentration in cells or tissues into recordable fluorescent signals, which can then be observed using the system of fiber-photometric. To date, there is a paucity of relevant literature on the proper selection and application of fiber-photometric indicators. Therefore, this paper will detail how to correctly select and apply fiber-photometer indicators in four sections: the basic principle of optical fiber photometry, the selection of calcium fluorescent probes and viral vector systems, and the measurement of specific expression of fluorescent proteins in specific tissues. Therefore, the correct use of suitable fiber optic recording indicators will greatly assist researchers in exploring the link between neuronal activity and neuropsychiatric disorders.

Technology integration in modern education has transformed traditional teaching-learning methods, but maintaining student attentiveness during computer-aided activities remains challenging. Neuroimaging advancements provide valuable insights into cognitive processes. This study measures cognitive load during computer-aided education. We have collected functional near-infrared spectroscopy (fNIRS) brain signals while subjects perform mental tasks and rest. Three datasets have been considered to evaluate the performance of the proposed model. The first two datasets are open-access, and we prepare the third dataset by collecting fNIRS brain signals from 14 healthy subjects. Two feature extraction techniques are proposed: manual and automatic based on wavelet scattering transform (WST). A one dimensional convolutional neural network (1D CNN) is also proposed to automatically extract features through feature engineering and classification. For comparison, four machine learning classifiers, linear discriminant analysis (LDA), Naive Bayes (NB), k-nearest neighbors (KNN) and support vector machine (SVM), are also considered. Classification performance is evaluated using accuracy, precision, recall and F1-score across all datasets. Computational cost, i.e., the CPU time and memory utilization for extracting the features and testing the classifiers, is also evaluated. The results suggest that when considering four classifiers across three datasets and comparing among the manual and the WST-based feature extraction methods, the average performance of 1D CNN is superior in terms of classification accuracy (1.16 times higher), precision (1.10 times higher), recall (1.10 times higher) and F1-score (1.09 times higher). However, the CPU time and memory utilization for 1D CNN are significantly higher, 10.09 and 14.70 times, respectively. In comparison to four state-of-the-art deep learning models, the proposed 1D CNN also shows best classification accuracy (92.99%). The analysis of the results shows that identifying cognitive load, SVM with Gaussian kernel function on WST based methods, provides satisfactory classification performance with significantly less CPU time and memory utilization.

China is the second-largest maize producer and consumer globally. During maize production, Fusarium spp. often gets infected, and mycotoxins like fumonisin contaminate it. Fumonisin has become the most widely polluted mycotoxin type in China. Planting genetically - modified maize is an economical and effective approach to reducing fumonisin pollution in products. This study aimed to evaluate the effectiveness of two transgenic events from China, Bt-Cry1Ab-Ma CM8101 and Bt-Cry1Ab, Cry2Ab, G10evo Ruifeng 8, in reducing fumonisin pollution in maize under the stress of natural and Lepidopteran pests (Ostrinia furnacalis, Mythimna separate, Helicoverpa armigera) in two Chinese sites from 2018-2019. The results showed that under the stress of Lepidoptera insects (O. furnacalis and H. armigera), the total amount of fumonisin in Bt maize decreased significantly. Maize with two insect-resistant transgenic events reduced fumonisin by over 70%. In years with serious fumonisin pollution, the effects of CM8101 and Ruifeng 8 on reducing pollution were more significant. Bt maize can provide area-wide pest management and thus contribute to a progressive phase-down of chemical pesticide use. Genetically-modified insecticidal crops can ensure food and nutrition security, contribute to the sustainable intensification of China's agriculture, and reduce the environmental footprint of food systems.

Fatigue-induced incidents in transportation, aerospace, military, and other areas have been on the rise, posing a threat to human life and safety. The determination of fatigue states holds significant importance, especially through reliable and conveniently available physiological indicators. Here, a portable custom-built fNIRS system was used to monitor the fatigue state caused by nap deprivation. fNIRS signals in ten channels at the prefrontal cortex were collected, changes in blood oxygen concentration were analyzed, followed by a deep learning model to classify fatigue states. For the high-dimensionality and multi-channel characteristics of the fNIRS signal data, a novel 1D revised CNN-ResNet network was proposed based on the double-layer channel attenuation residual block. The results showed a 97.78% accuracy in fatigue state classification, significantly superior than several conventional methods. Furthermore, a fatigue-arousal experiment was designed to explore the feasibility of forced arousal of fatigued subjects through exercise stimulation. The fNIRS results showed a significant increase in brain activity with the conduction of exercise. The proposed method serves as a reliable tool for the evaluation of fatigue states, potentially reducing fatigue-induced harms and risks.

Enhancing the accuracy of emotion recognition models through multimodal learning is a common approach. However, challenges such as insufficient modal feature learning in multimodal inference and scarcity of sample data continue to pose obstacles that need to be overcome. Therefore, we propose a novel adaptive lightweight multimodal efficient feature inference network (ALME-FIN). We introduce a time-domain lightweight adaptive network (TDLAN) and a two-dimensional dynamic focusing network (TDDFN) for multimodal feature learning. The TDLAN incorporates the denoising process as an integral part of network training, achieving adaptive denoising for each sample through the continuous optimization of the trainable filtering threshold. Simultaneously, it incorporates an interactive convolutional sampling module, enabling lightweight multi-scale feature extraction in the time domain. TDDFN effectively extracts core image features while filtering out redundancies. During the training process, the Multi-network dynamic gradient adjustment framework (MDGAF) dynamically monitors the feature learning efficacy across different modalities. It timely adjusts the training gradients of networks to allocate additional optimization time for under-optimized modalities, thereby maximizing the utilization of multimodal feature information. Moreover, the introduction of a Multi-class relationship interaction module prior to the classifier aids the model in clearly understanding the relationships among different category samples. This approach enables the model to achieve relatively accurate emotion recognition even in scenarios of limited sample availability. Compared to existing multimodal learning techniques, ALME-FIN exhibits a more efficient multimodal feature inference method that can achieve satisfactory emotional recognition performance even with a limited number of samples.

To deploy Electroencephalogram (EEG) based Mental Workload Recognition (MWR) systems in the real world, it is crucial to develop general models that can be applied across subjects. Previous studies have utilized domain adaptation to mitigate inter-subject discrepancies in EEG data distributions. However, they have focused on reducing global domain discrepancy, while neglecting local workload-categorical domain divergence. This degrades the workload-discriminating ability of subject-invariant features. To deal with this problem, we propose a novel joint category-wise and domain-wise alignment Domain Adaptation (cdaDA) algorithm, using bi-classifier learning and domain discriminative adversarial learning. The bi-classifier learning approach is adopted to address the similarities and differences between categories, helping to align EEG data within the same mental workload categories. Additionally, the domain discriminative adversarial learning technique is adopted to consider global domain information by minimizing global domain discrepancy. By integrating both local category information and global domain information, the cdaDA model performs a coarse-to-fine alignment and achieves promising cross-subject MWR results.

Epilepsy is a neurological disorder in which complex electrophysiological processes are closely linked to inherent nonlinear kinetic properties. This study investigates the effects of sinusoidal sensory stimulation bias and time-delay on the dynamics of epileptic seizures within a corticothalamic neural network model. The results indicate that an increase in sensory stimulation bias can prematurely terminate seizures, and high-frequency stimulation can induce a phenomenon of frequency resonance. Meanwhile, discharge states transitions are associated with the emergence of bifurcation points. Time-delay exerts a significant regulatory influence on pathways with delay embedding (I2-PY), whereas its impact on pathways without delay embedding (I1-I1 and thalamic relay nucleus (TC)-I2) is negligible. Under sinusoidal sensory stimulation, the responses of three pathways (I1-I1, I1-PY, and I2-PY) associated with inhibitory interneurons reveal that the inhibitory properties of interneurons can suppress seizures; however, an excessively strong inhibitory effect may also precipitate seizures and facilitate state transitions. These findings contribute to a deeper understanding of seizure dynamics and may guide future research in the transmission and evolution of seizures.

Chitosan nanoparticles have been extensively utilised as polymeric drug carriers in nanoparticles formulations due to their potential to enhance drug delivery, efficacy, and safety. Numerous toxicity studies have been previously conducted to assess the safety profile of chitosan-based nanoparticles. These toxicity studies employed various methodologies, including test animals, interventions, and different routes of administration. This review aims to summarise research on the safety profile of chitosan-based nanoparticles in drug delivery, with a focus on general toxicity tests to determine LD50 and NOAEL values. It can serve as a repository and reference for chitosan-based nanoparticles, facilitating future research and further development of drugs delivery system using chitosan nanoparticles. Publications from 2014 to 2024 were obtained from PubMed, Scopus, Google Scholar, and ScienceDirect, in accordance with the inclusion and exclusion criteria.The ARRIVE 2.0 guidelines were employed to evaluate the quality and risk-of-bias in the in vivo toxicity studies. The results demonstrated favourable toxicity profiles, often exhibiting reduced toxicity compared to free drugs or substances. Acute toxicity studies consistently reported high LD50 values, frequently exceeding 5000 mg/kg body weight, while subacute studies typically revealed no significant adverse effects. Various routes of administration varied, including oral, intravenous, intraperitoneal, inhalation, and topical, each demonstrating promising safety profiles.

Rumination thinking is a type of negative repetitive thinking, a tendency to constantly focus on the causes, consequences and other aspects of negative events, which has implications for a variety of psychiatric disorders. Previous studies have confirmed a strong association between rumination thinking and poor sleep or insomnia, but the direction of causality between the two is not entirely clear. This study examined the relationship between rumination thinking and sleep quality using a longitudinal approach and resting-state functional MRI data. Participants were 373 university students (males: n = 84, 18.67 ± 0.76 years old) who completed questionnaires at two time points (T1 and T2) and had resting-state MRI data collected. The results of the cross-lagged model analysis revealed a bidirectional causal relationship between rumination thinking and sleep quality. Additionally, the functional connectivity (FC) of the precuneus and lingual gyrus was found to be negatively correlated with rumination thinking and sleep quality. Furthermore, mediation analysis showed that rumination thinking at T1 fully mediated the relationship between FC of the precuneus-lingual and sleep quality at T2. These findings suggest that rumination thinking and sleep quality are causally related in a bidirectional manner and that the FC of the precuneus and lingual gyrus may serve as the neural basis for rumination thinking to predict sleep quality. Overall, this study provides new insights for enhancing sleep quality and promoting overall health.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10223-3.