工程技术最新文献

Deep brain stimulation (DBS) is a well-established treatment for both neurological and psychiatric disorders. Directional DBS has the potential to minimize stimulation-induced side effects and maximize clinical benefits. Many new directional leads, stimulation patterns and programming strategies have been developed in recent years. Therefore, it is necessary to review new progress in directional DBS. This paper summarizes progress for directional DBS from the perspective of directional DBS leads, stimulation patterns, and programming strategies which are three key elements of DBS systems. Directional DBS leads are reviewed in electrode design and volume of tissue activated visualization strategies. Stimulation patterns are reviewed in stimulation parameters and advances in stimulation patterns. Programming strategies are reviewed in computational modeling, monopolar review, direction indicators and adaptive DBS. This review will provide a comprehensive overview of primary directional DBS leads, stimulation patterns and programming strategies, making it helpful for those who are developing DBS systems.

Emotion recognition plays a crucial role in brain-computer interfaces (BCI) which helps to identify and classify human emotions as positive, negative, and neutral. Emotion analysis in BCI maintains a substantial perspective in distinct fields such as healthcare, education, gaming, and human-computer interaction. In healthcare, emotion analysis based on electroencephalography (EEG) signals is deployed to provide personalized support for patients with autism or mood disorders. Recently, several deep learning (DL) based approaches have been developed for accurate emotion recognition tasks. Yet, previous works often struggle with poor recognition accuracy, high dimensionality, and high computational time. This research work designed an innovative framework named Proximity-conserving Auto-encoder (PCAE) for accurate emotion recognition based on EEG signals and resolves challenges faced by traditional emotion analysis techniques. For preserving local structures among the EEG data and reducing dimensionality, the proposed PCAE approach is introduced and it captures the essential features related to emotional states. The EEG data are collected from the EEG Brainwave dataset using a Muse EEG headband and applying preprocessing steps to enhance signal quality. The proposed PCAE model incorporates multiple convolution and deconvolution layers for encoding and decoding and deploys a Local Proximity Preservation Layer for preserving local correlations in the latent space. In addition, it develops a Proximity-conserving Squeeze-and-Excitation Auto-encoder (PC-SEAE) model to further improve the feature extraction ability of the PCAE technique. The proposed PCAE technique utilizes Maximum Mean Discrepancy (MMD) regularization to decrease the distribution discrepancy between input data and the extracted features. Moreover, the proposed model designs an ensemble model for emotion categorization that incorporates a one-versus-support vector machine (SVM), random forest (RF), and Long Short-Term Memory (LSTM) networks by utilizing each classifier's strength to enhance classification accuracy. Further, the performance of the proposed PCAE model is evaluated using diverse performance measures and the model attains outstanding results including accuracy, precision, and Kappa coefficient of 98.87%, 98.69%, and 0.983 respectively. This experimental validation proves that the proposed PCAE framework provides a significant contribution to accurate emotion recognition and classification systems.

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.

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.

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.

In light of the fact that climate change has emerged as one of the difficulties confronting the global food system, researchers are obligated to work toward developing fundamental crops, particularly wheat, to combat environmental stress, including drought and salt. In the present study, genetic engineering was used to transfer the Arabidopsis MDAR1 gene, which controls the buildup of ascorbic acid (AsA) to make bread wheat less likely to be sensitive to salt stress. The biolistic bombardment was used to transfer cDNA from the Arabidopsis thaliana plant that encodes MDAR1 into Bobwhite 56 cultivar wheat plants. A molecular investigation was performed on six different transgenic lines to confirm the integration of the transgene, the copy number, and the expression of the transgene. There were one to three copies of the transgene, and there was no association found between the number of copies of the transgene and All the data generated or analyzed during this study are included in this published article [and its supplementary information files].the presence of its expression. Compared to plants that were not transgenic, the amount of ascorbic acid (AsA) that accumulated in the transgenic plants was twice as high. ROS concentrations are significantly lower in transgenic plants compared to non-transgenic plants under both control and salt stress conditions, effectively reducing oxidative stress. By cultivating transgenic T2 plants in a greenhouse, we were able to determine whether they were able to tolerate the potentially damaging effects of salt stress (200 mm). The study concluded that transgenic wheat plants that consistently expressed the MDAR1 gene become tolerant to salt stress with improvement in growth characteristics.

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.

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.

Tremor is a rhythmic, involuntary oscillatory movement that severely affects some aspects of a patient's daily life. The use of wearable tremor-suppressing orthoses has become an effective, noninvasive treatment method for controlling tremors. This article summarizes recent developments in upper limb tremor suppression orthoses, aiming to provide a foundation for future research. By analyzing the working mechanisms, degrees-of-freedom (DOFs), weight, and tremor suppression effectiveness of various types of orthoses, the following conclusions are drawn: We found that differences in the working mechanism and the number of suppression directions are related to the weight of the device; weight, in turn, is a major factor affecting the comfort of the orthoses; and the combination of the number and weight of the damping direction affects the effect of the damping equipment. Balancing these three factors should be a key focus of future research. Moreover, researchers are placing greater emphasis on the comfort of the wearer during the development of these orthoses.