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.

Maize is a major food crop in China, and drought is one of the major abiotic stresses that threaten the growth and development of the crop, seriously affecting the crop yield. 4-coumaric acid coenzyme A ligase (4CL) is a key enzyme in the phenylpropane metabolic pathway, which can regulate the lignin content of the plant and play an important role in the plant's resistance to drought stress, plays an important role in plant resistance to drought stress. In the present study, we screened the differentially expressed up-regulated gene Zm4CL-like9 under drought stress by pre-transcriptome sequencing data (PRJNA793522) in the laboratory, and analyzed the significant up-regulation of Zm4CL-like9 gene in roots under drought stress by qRT-PCR(Real-Time Quantitative Reverse Transcription PCR). The results of prokaryotic expression experiments showed that the protein encoded by the Zm4CL-like9 gene was able to be expressed in prokaryotic cells and could effectively improve the drought tolerance of E. coli. Phenotypic analysis of transgenic Arabidopsis plants under drought stress revealed that seed germination rate, root length, and plant survival after drought rehydration were significantly higher in transgenic Zm4CL-like9 Arabidopsis compared with wild-type Arabidopsis; physiological and biochemical indexes revealed that peroxidase activity, proline (Pro) content, and chlorophyll content were significantly higher in transgenic Arabidopsis compared with wild-type Arabidopsis. Under drought stress, the expression of drought-related genes was significantly up-regulated in transgenic Arabidopsis compared with wild-type Arabidopsis. Taken together, the Zm4CL-like9 gene enhances plant resistance to drought stress by reducing reactive oxygen species accumulation in plants.

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.

Gastric cancer remains one of the deadliest cancers globally due to delayed detection and limited treatment options, underscoring the critical need for innovative prognostic methods. Disulfidptosis, a recently discovered programmed cell death triggered by disulphide stress, presents a fresh avenue for therapeutic exploration. This research examines disulfidptosis-related long noncoding RNAs (DRLs) in gastric cancer, with the goal of leveraging these lncRNAs as potential markers to enhance patient outcomes and treatment approaches. Comprehensive genomic and clinical data from stomach adenocarcinoma (STAD) were obtained from The Cancer Genome Atlas (TCGA). Employing least absolute shrinkage and selection operator (LASSO) regression analysis, a prognostic model was devised incorporating five key DRLs to forecast survival rates. The effectiveness of this model was validated using Kaplan-Meier survival plots, receiver operating characteristic (ROC) curves, and extensive functional enrichment studies. The importance of select lncRNAs and the expression variability of genes tied to disulfidptosis were validated via quantitative real-time PCR (qRT-PCR) and Western blot tests, establishing a solid foundation for their prognostic utility. Analyses of functional enrichment and tumour mutation burden highlighted the biological importance of these DRLs, connecting them to critical cancer pathways and immune responses. These discoveries broaden our comprehension of the molecular framework of gastric cancer and bolster the development of tailored treatment plans, highlighting the substantial role of DRLs in clinical prognosis and therapeutic intervention.

The correlations of several fundamental properties of human brain connections are investigated in a consensus connectome, constructed from 1064 braingraphs, each on 1015 vertices, corresponding to 1015 anatomical brain areas. The properties examined include the edge length, the fiber count, or edge width, meaning the number of discovered axon bundles forming the edge and the occurrence number of the edge, meaning the number of individual braingraphs where the edge exists. By using our previously published robust braingraphs at https://braingraph.org, we have prepared a single consensus graph from the data and compared the statistical similarity of the edge occurrence numbers, edge lengths, and fiber counts of the edges. We have found a strong positive Spearman correlation between the edge occurrence numbers and the fiber count numbers, showing that statistically, the most frequent cerebral connections have the largest widths, i.e., the fiber count. We have found a negative Spearman correlation between the fiber lengths and fiber counts, showing that, typically, the shortest edges are the widest or strongest by their fiber counts. We have also found a negative Spearman correlation between the occurrence numbers and the edge lengths: it shows that typically, the long edges are infrequent, and the frequent edges are short.