工程技术最新文献

Climate change-driven single and combined abiotic stresses pose escalating threats to sustainable, climate-smart agriculture and global food security. Melatonin (MLT, a powerful plant biostimulant) has established noteworthy potential in improving stress tolerance by regulating diverse physiological, biochemical, and molecular responses. Therefore, this review delivers a comprehensive synopsis of MLT-enabled omics responses across genomics, transcriptomics, proteomics, metabolomics, miRNAomics, epigenomics, phenomics, ionomics, and microbiomics levels that collectively regulate plant adaptation to multiple abiotic stresses. We also highlight the crosstalk between these omics layers and the power of integrated multi-omics (panomics) approaches to harness the complex regulatory networks underlying MLT-enabled stress tolerance. Lastly, we argue for translating these omics insights into actionable strategies through advanced genetic engineering and synthetic biology platforms to develop MLT-enabled, stress-smart crop plants.

A crucial method for determining a person's mental health and assessing their degree of depression is depression detection. To identify depression through speech or conversation, a number of sophisticated methods and questionnaires have been created. The constraints of the current system are as follows: reduced effectiveness as a result of poor feature selection and extraction, problems with interpretability, and the difficulty of identifying depression in different languages. As a result, the proposed model is presented to offer improved accuracy and efficient performance. While adaptive threshold-based pre-processing (AdaT) is used to eliminate quiet and unnecessary information, the twinned Savitzky-Golay filter (TSaG) is used to minimize noise in the dataset. To turn the signal into an image, a Synchro-Squeezed Adaptive Wavelet Transform Algorithm (SSawT) is employed. The Singular Empirical Decomposition and Sparse Autoencoder (SiFE) model is used to extract linear and deep features. Input's deep, linear, and statistical properties are combined using the Weighted Soft Attention-based Fusion (WSAttF) model. From the fused features, the Chaotic Mud Ring Optimization algorithm (ChMR) chooses the best features. A Dilated Convolutional Neural Network (CNN) based Bidirectional-Long Short Term Memory-Bi-LSTM (DiCBiL) is used to detect different stages of depression, which lowers error rates and increases detection accuracy. The proposed method achieves 93.22% of F1-score, 93.11% precision, 93.12% recall, and 93.31% accuracy on the DAIC-WOZ original test set. During the testing time, two more datasets, namely AVEC 2019 and MELD, are used to validate the proposed performance, attaining an accuracy of 93.91% and 85.34% respectively.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-026-10411-9.

To extract information from the brain, the most cost-effective method is electroencephalography (EEG) signal acquisition. Therefore, many researchers have used EEG signals to capture brain activity. EEG signals are complex; hence, computer-aided models-especially machine learning (ML)-are generally employed to interpret them. The primary objective of this research is to demonstrate the feature-extraction capability of a new, novel method. The proposed feature-extraction approach employs a deterministic feature-engineering transformation, designed to restructure multi-strided signal representations through fixed linear operations. The resulting transformation graph exhibits a mountain-like structure; therefore, we term the model MountPat. To evaluate MountPat's performance, we present an explainable feature engineering (XFE) model with four main phases. In the first phase, we extract informative features using MountPat. In the second phase, we select the most informative features using cumulative weighted iterative neighborhood component analysis (CWNCA). In the third phase, we generate classification results by applying t-algorithm-based k-nearest neighbors (tkNN). In the fourth phase, we extract explainable insights from the EEG signals using the Directed Lobish (DLob) explainable artificial intelligence (XAI) method. To demonstrate the general classification ability of the MountPat-based XFE framework, we use six EEG datasets. Under rigorous subject-independent (LOSO) validation, the model achieves 76.36%-98.88% accuracy, demonstrating strong cross-subject generalization. Sample-wise tenfold CV results exceed 89% on all six datasets. Moreover, by deploying the DLob XAI method, we generate interpretable results for each dataset. These results clearly illustrate that the MountPat-based XFE framework is an effective feature-extraction approach for multichannel signal processing.

Electroencephalography (EEG) can objectively reflect an individual's emotional state. However, due to significant inter-subject differences, existing methods exhibit low generalization performance in emotion recognition across different individuals. Therefore, an EEG emotion classification framework based on deep feature aggregation and multi-source domain adaptation is proposed by us. First, we design a deep feature aggregation module that introduces a novel approach for extracting EEG hemisphere asymmetry features and integrates these features with the frequency and spatiotemporal characteristics of the EEG signals. Additionally, a multi-source domain adaptation strategy is proposed, where multiple independent feature extraction sub-networks are employed to process each domain separately, extracting discriminative features and thereby alleviating the feature shift problem between domains. Then, a domain adaptation strategy is employed to align multiple source domains with the target domain, thereby reducing inter-domain distribution discrepancies and facilitating effective cross-domain knowledge transfer. Simultaneously, to enhance the learning ability of target samples near the decision boundary, pseudo-labels are dynamically generated for the unlabeled samples in the target domain. By leveraging predictions from multiple classifiers, we calculate the average confidence of each pseudo-label group and select the pseudo-label set with the highest confidence as the final label for the target sample. Finally, the mean of the outputs from multiple classifiers is used as the model's final prediction. A comprehensive set of experiments was performed using the publicly available SEED and SEED-IV datasets. The findings indicate that the method we proposed outperforms alternative methods.

Rheumatoid arthritis (RA) is a chronic autoimmune disorder characterized by synovial hyperplasia, inflammatory cell infiltration, and joint destruction. This study investigates the inhibitory effects and metabolic mechanisms of Eucalrobusone C (EC), a novel formyl-phloroglucinol meroterpenoid derivative isolated from Eucalyptus robusta, on Tumour Necrosis Factor-α (TNF-α)-induced rheumatoid arthritis fibroblast-like synoviocytes (RA-FLSs). EC was extracted and purified, with purity confirmed using ¹H Nuclear Magnetic Resonance Spectrum (NMR) at 400 MHz. RA-FLSs were exposed to varying concentrations of EC, followed by comprehensive assessment including CCK8 assay for cell proliferation, flow cytometry for cell death, and Transwell assay for migration and invasion capacity. Metabolomic profiling employed Ultra-High Performance Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry (UHPLC-Q-TOF MS), integrated with multivariate statistical analysis and bioinformatics tools to identify metabolic alterations. Results indicated that EC suppressed RA-FLS proliferation in a time- and concentration-dependent manner, significantly enhanced apoptosis, and inhibited cell migration and invasion. Metabolomics analysis detected 898 metabolites, with 112 upregulated and 67 downregulated in EC-treated groups compared to TNF-α-induced controls. Key differentially expressed metabolites were enriched in pathways including ABC transporters, neuroactive ligand-receptor interactions, protein digestion and absorption, and cAMP signalling. These findings suggest that EC exerts anti-rheumatic effects by modulating these metabolic pathways, offering potential as a therapeutic agent for RA management.

A five-dimensional multi-scroll chaotic system is presented by introducing two memristive elements into a three-dimensional chaotic system. The resulting model generates multi-scroll attractors whose scroll count can be regulated by tuning the memristors' internal parameters. We analyze the equilibria and then quantify the dynamic behaviors using phase portraits, Poincaré sections, bifurcation diagrams, and Lyapunov exponents. Coexisting multi-scroll attractors are observed, and their attraction basins are mapped to visualize the corresponding spatial domains. Parameter-driven adjustment of local amplitude is also demonstrated, enabling flexible modulation of the system output. A DSP-based implementation is further provided to validate the realizability of the proposed design. The study advances memristor-assisted multi-scroll construction and supports engineering-oriented hardware realization of high-dimensional chaotic systems.

Alzheimer's disease (AD) pathology begins years before symptoms appear, and dynamic flexibility of the medial temporal lobe (MTL) may serve as an early functional biomarker. Using data from 656 older adults in the Rutgers Aging and Brain Health Alliance study, we evaluated whether cognitive, genetic, biochemical, and demographic predictors could estimate MTL dynamic flexibility, despite substantial missingness (1,866 missing values; 25.86%). Only 42 participants (6.40%) had complete data; therefore, we compared case deletion with five imputation strategies (MICE, GAIN, MissForest, MIWAE, ReMasker) and eight regression models, assessing prediction accuracy using repeated 5-fold cross-validation. Complete-case analysis yielded limited performance (average [Formula: see text], [Formula: see text]). After imputation, all methods improved accuracy, with MissForest paired with Bagging Trees or Random Forest achieving the lowest prediction error ([Formula: see text]). The greatest improvement in concordance occurred when GAIN was combined with Bagging Trees/Random Forest ([Formula: see text]), representing a  57% gain over the best complete-case model. A Scheirer-Ray-Hare ANOVA confirmed significant differences across imputation strategies ([Formula: see text]). Runtime analyses showed GAIN and MissForest to be both accurate and computationally efficient, while deep generative imputers were slower. These findings demonstrate that robust imputation is essential for maximizing data utility and predictive reliability in high-missingness neuroimaging studies and highlight the potential of ensemble tree models combined with advanced imputation techniques for estimating MTL dynamic flexibility in aging populations.

Co-transmission, the release of multiple neurotransmitters from a single neuron, is an increasingly recognized phenomenon in the nervous system. A particularly interesting combination of neurotransmitters exhibiting co-transmission is glutamate and GABA, which, when co-released from neurons, demonstrate complex biphasic activity patterns that vary depending on the time or amplitude differences from the excitatory (AMPA) or inhibitory (GABA_A) signals. Naively, the outcome signal produced by these differences can be functionally interpreted as simple mechanisms that only add or remove spikes by excitation or inhibition. However, the complex interaction of multiple time-scales and amplitudes may deliver a more complex temporal coding, which is experimentally difficult to access and interpret. In this work, we employ an extensive computational approach to distinguish these postsynaptic co-transmission patterns and how they interact with dendritic filtering and ionic currents. We specifically focus on modeling the summation patterns and their flexible dynamics that arise from the many combinations of temporal and amplitude co-transmission differences. Our results indicate a number of summation patterns that excite, inhibit, and act transiently, which have been previously attributed to the interplay between the intrinsic active and passive electrical properties of the postsynaptic dendritic membrane. Our computational framework provides an insight into the complex interplay that arises between co-transmission and dendritic filtering, allowing for a mechanistic understanding underlying the integration and processing of co-transmitted signals in neural circuits.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10383-2.

As healthcare text data becomes increasingly complex, it is vital for sentiment analysis to capture local patterns and global contextual dependencies. In this paper, we propose a hybrid Swin Transformer-BiLSTM-Spatial MLP (Swin-MLP) model that leverages hierarchical attention, shifted-window mechanisms, and spatial MLP layers to extract features from domain-specific healthcare text better. The framework is tested on domain-specific datasets for Drug Review and Medical Text, and performance is assessed against baseline models (BERT, LSTM, and GRU). Our findings show that the Swin-MLP model performs significantly better overall, achieving superior metrics (accuracy, precision, recall, F1-score, and AUC) and improving mean accuracy by 1-2% over BERT. Statistical tests to assess significance (McNemar's test and paired t-test) indicate that improvements are statistically significant (p < 0.05), suggesting the efficacy of the architectural innovations. The results' implications indicate that the model is robust, efficiently converges to classification, and is potentially helpful for a wide range of domain-specific sentiment analyses in healthcare. We will examine future research directions into exploring lightweight attention mechanisms, cross-domain multimodal sentiment analysis, federated learning to protect privacy, and hardware implications for rapid training and inference.

An open-level, single-arm, phase-4 clinical trial was carried out to assess the safety and potential benefits of micronized coated ferric pyrophosphate (MEFP) in patients with iron deficiency anaemia (IDA). For 12 weeks, 60 patients between the ages of 18 and 60 with moderate IDA were randomly received MEFP by PO daily. The efficacy endpoints as haemoglobin levels, mean corpuscular haemoglobin (MCH), mean cell haemoglobin concentration (MCHC), packed cell volume (PCV), red blood cell count (RBC), serum ferritin and transferrin saturation (%) were measured. Adverse event reports and physical examinations were performed as a measure of safety assessment. The results revealed that haemoglobin, MCV, MCHC, serum ferritin, transferrin saturation (%), PCV and RBC increased significantly from baseline. Fewer occurrences were observed in a few patients, and their adverse events were minimal. There was no adverse effect on liver or renal functions. Few minor improvements were noticed at the completion of the study. In conclusion, MEFP appears to be effective in IDA and well tolerated, with a favourable safety profile. MEFP is an effective, safe therapeutic alternative in IDA subjects for increasing haemoglobin concentration and iron stores along with improvement of symptoms related to anaemia.