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Polyimide films with low dielectric properties and high thermal stability have been widely used in advanced integrate circuit industry. Herein, this article briefly summarized the recent achievements in reducing the dielectric properties of polyimide films via a variety of methods or strategies, and the mechanical properties, thermal stability or optical properties were also summarized. Achieving the synergy of low dielectric properties, high thermal stability and superior mechanical properties is the research directions and priories of future development for polyimide films. Graphical Abstract Highlights Reducing the molecular polarizability and enhancing the fractional free volume are the main strategies to decrease k of PI films. Introducing porous structures is the most efficient approach to decrease the k of PI films. Achieving the synergy of low k , high thermal stability and superior mechanical properties are the research priories for PI films.

Depression is a major psychological disorder with a growing impact worldwide. Traditional methods for detecting the risk of depression, predominantly reliant on psychiatric evaluations and self-assessment questionnaires, are often criticized for their inefficiency and lack of objectivity. Advancements in deep learning have paved the way for innovations in depression risk detection methods that fuse multimodal data. This paper introduces a novel framework, the Audio, Video, and Text Fusion-Three Branch Network (AVTF-TBN), designed to amalgamate auditory, visual, and textual cues for a comprehensive analysis of depression risk. Our approach encompasses three dedicated branches—Audio Branch, Video Branch, and Text Branch—each responsible for extracting salient features from the corresponding modality. These features are subsequently fused through a multimodal fusion (MMF) module, yielding a robust feature vector that feeds into a predictive modeling layer. To further our research, we devised an emotion elicitation paradigm based on two distinct tasks—reading and interviewing—implemented to gather a rich, sensor-based depression risk detection dataset. The sensory equipment, such as cameras, captures subtle facial expressions and vocal characteristics essential for our analysis. The research thoroughly investigates the data generated by varying emotional stimuli and evaluates the contribution of different tasks to emotion evocation. During the experiment, the AVTF-TBN model has the best performance when the data from the two tasks are simultaneously used for detection, where the F1 Score is 0.78, Precision is 0.76, and Recall is 0.81. Our experimental results confirm the validity of the paradigm and demonstrate the efficacy of the AVTF-TBN model in detecting depression risk, showcasing the crucial role of sensor-based data in mental health detection.

•Happiness and sadness have discrete neural representations in terms of FC profiles.•Emotions are represented in distributed networks rather than a single network.•VN and DMN contribute to distinct representation of sustained emotional experience.•Temporal accumulative emotional experiences are reflected in neural representations. Determining and decoding emotional brain processes under ecologically valid conditions remains a key challenge in affective neuroscience. The current functional Magnetic Resonance Imaging (fMRI) based emotion decoding studies are mainly based on brief and isolated episodes of emotion induction, while sustained emotional experience in naturalistic environments that mirror daily life experiences are scarce. Here we used 12 different 10-minute movie clips as ecologically valid emotion-evoking procedures in n = 52 individuals to explore emotion-specific fMRI functional connectivity (FC) profiles on the whole-brain level at high spatial resolution (432 parcellations including cortical and subcortical structures). Employing machine-learning based decoding and cross validation procedures allowed to investigate FC profiles contributing to classification that can accurately distinguish sustained happiness and sadness and that generalize across subjects, movie clips, and parcellations. Both functional brain network-based and subnetwork-based emotion classification results suggested that emotion manifests as distributed representation of multiple networks, rather than a single functional network or subnetwork. Further, the results showed that the Visual Network (VN) and Default Mode Network (DMN) associated functional networks, especially VN-DMN, exhibited a strong contribution to emotion classification. To further estimate the temporal accumulative effect of naturalistic long-term movie-based video-evoking emotions, we divided the 10-min episode into three stages: early stimulation (1∼200 s), middle stimulation (201∼400 s), and late stimulation (401∼600 s) and examined the emotion classification performance at different stimulation stages. We found that the late stimulation contributes most to the classification (accuracy=85.32%, F1-score=85.62%) compared to early and middle stimulation stages, implying that continuous exposure to emotional stimulation can lead to more intense emotions and further enhance emotion-specific distinguishable representations. The present work demonstrated that sustained happiness and sadness under naturalistic conditions are presented in emotion-specific network profiles and these expressions may play different roles in the generation and modulation of emotions. These findings elucidated the importance of network level adaptations for sustained emotional experiences during naturalistic contexts and open new venues for imaging network level contributions under naturalistic conditions.

EEG signals exhibit commonality and variability across subjects, sessions, and tasks. But most existing EEG studies focus on mean group effects (commonality) by averaging signals over trials and subjects. The substantial intra- and inter-subject variability of EEG have often been overlooked. The recently significant technological advances in machine learning, especially deep learning, have brought technological innovations to EEG signal application in many aspects, but there are still great challenges in cross-session, cross-task, and cross-subject EEG decoding. In this work, an EEG-based biometric competition based on a large-scale M3CV (A Multi-subject, Multi-session, and Multi-task Database for investigation of EEG Commonality and Variability) database was launched to better characterize and harness the intra- and inter-subject variability and promote the development of machine learning algorithm in this field. In the M3CV database, EEG signals were recorded from 106 subjects, of which 95 subjects repeated two sessions of the experiments on different days. The whole experiment consisted of 6 paradigms, including resting-state, transient-state sensory, steady-state sensory, cognitive oddball, motor execution, and steady-state sensory with selective attention with 14 types of EEG signals, 120000 epochs. Two learning tasks (identification and verification), performance metrics, and baseline methods were introduced in the competition. In general, the proposed M3CV dataset and the EEG-based biometric competition aim to provide the opportunity to develop advanced machine learning algorithms for achieving an in-depth understanding of the commonality and variability of EEG signals across subjects, sessions, and tasks.

Previous functional magnetic resonance imaging (fMRI) studies have shown that brain responses to nociceptive pain, non‐nociceptive somatosensory, visual, and auditory stimuli are extremely similar. Actually, perception of external sensory stimulation requires complex interactions among distributed cortical and subcortical brain regions. However, the interactions among these regions elicited by nociceptive pain remain unclear, which limits our understanding of mechanisms of pain from a brain network perspective. Task fMRI data were collected with a random sequence of intermixed stimuli of four sensory modalities in 80 healthy subjects. Whole‐brain psychophysiological interaction analysis was performed to identify task‐modulated functional connectivity (FC) patterns for each modality. Task‐modulated FC strength and graph‐theoretical‐based network properties were compared among the four modalities. Lastly, we performed across‐sensory‐modality prediction analysis based on the whole‐brain task‐modulated FC patterns to confirm the specific relationship between brain patterns and sensory modalities. For each sensory modality, task‐modulated FC patterns were distributed over widespread brain regions beyond those typically activated or deactivated during the stimulation. As compared with the other three sensory modalities, nociceptive stimulation exhibited significantly different patterns (more widespread and stronger FC within the cingulo‐opercular network, between cingulo‐opercular and sensorimotor networks, between cingulo‐opercular and emotional networks, and between default mode and emotional networks) and global property (smaller modularity). Further, a cross‐sensory‐modality prediction analysis found that task‐modulated FC patterns could predict sensory modality at the subject level successfully. Collectively, these results demonstrated that the whole‐brain task‐modulated FC is preferentially modulated by pain, thus providing new insights into the neural mechanisms of pain processing. This study aimed to reveal whether nociceptive pain could elicit different task‐modulated functional connectivity (FC) patterns from other sensory modalities (non‐nociceptive somatosensory, visual, and auditory). According to the results, nociceptive stimulation exhibited significantly different regional and global task‐modulated FC features from those of the other three sensory modalities, and the task‐modulated FC patterns were predictive of sensory modality at the subject level. These results could provide new insights from the perceptive of task‐modulated brain network into the neural mechanisms of pain processing.

The dominant approach in investigating the individual reliability for event-related potentials (ERPs) is to extract peak-related features at electrodes showing the strongest group effects. Such a peak-based approach implicitly assumes ERP components showing a stronger group effect are also more reliable, but this assumption has not been substantially validated and few studies have investigated the reliability of ERPs beyond peaks. In this study, we performed a rigorous evaluation of the test-retest reliability of ERPs collected in a multisensory and cognitive experiment from 82 healthy adolescents, each having two sessions. By comparing group effects and individual reliability, we found that a stronger group-level response in ERPs did not guarantee higher reliability. A perspective of neural oscillation should be adopted for the analysis of reliability. Further, by simulating ERPs with an oscillation-based computational model, we found that the consistency between group-level ERP responses and individual reliability was modulated by inter-subject latency jitter and inter-trial variability. The current findings suggest that the conventional peak-based approach may underestimate the individual reliability in ERPs and a neural oscillation perspective on ERP reliability should be considered. Hence, a comprehensive evaluation of the reliability of ERP measurements should be considered in individual-level neurophysiological trait evaluation and psychiatric disorder diagnosis.

Lettuce (Lactuca sativa) is rich in vitamins, minerals, and bioactive components, serving as an important source of selenium (Se) intake for humans. This study investigated the effects of Se treatment on lettuce using different concentrations of sodium selenite (Na2SeO3), focusing on biomass, physiological indicators, nutritional composition, and physiological changes during storage. Through correlation analysis of the transcriptome and Se species, the absorption and conversion mechanisms of Se in lettuce were revealed. The results showed that Se treatment initially increased the chlorophyll content in lettuce, followed by a decrease. Soluble sugar, soluble protein, total phenols, and anthocyanins increased at low Se concentrations but decreased at high concentrations. Flavonoid content decreased only at 1 mg/L Se, while other treatments were higher than the control group. GSH content and superoxide dismutase, catalase, and peroxidase activities initially increased and then decreased, while malondialdehyde (MDA) content first decreased and then increased. Five Se species, including Se (IV), Se (VI), selenocysteine (SeCys2), selenomethionine (SeMet), and methylselenocysteine (MeSeCys), were detected in lettuce leaves after Se treatment, with SeMet being the most abundant. During storage, Se-treated lettuce exhibited lower weight loss, a*, b*, browning index, and color difference (ΔE) values compared to the control group. CAT and POD activities and GSH content also followed a trend of initial increase followed by a decrease. Transcriptome data analysis revealed that genes such as MYB1, RPK1, PTR44, NTRC, WRKY7, and CSLD3 were associated with the stress response of Se-treated lettuce.

The purpose is to explore the brain’s structural difference in local morphology and between-region networks between two types of peripheral neuropathic pain (PNP): postherpetic neuralgia (PHN) and lower back pain (LBP). A total of 54 participants including 38 LBP and 16 PHN patients were enrolled. The average pain scores were 7.6 and 7.5 for LBP and PHN. High-resolution structural T1 weighted images were obtained. Both grey matter volume (GMV) and morphological connectivity (MC) were extracted. An independent two-sample t-test with false discovery rate (FDR) correction was used to identify the brain regions where LBP and PHN patients showed significant GMV difference. Next, we explored the differences of MC network between LBP and PHN patients and detected the group differences in network properties by using the two-sample t-test and FDR correction. Compared with PHN, LBP patients had significantly larger GMV in temporal gyrus, insula and fusiform gyrus ( p  < 0.05). The LBP cohort had significantly stronger MC in the connection between right precuneus and left opercular part of inferior frontal gyrus ( p  < 0.05). LBP patients had significantly stronger degree in left anterior cingulate gyrus and left rectus gyrus ( p  < 0.05) while had significantly weaker degree than PHN patients in left orbital part of middle frontal gyrus, left supplementary motor area and left superior parietal lobule ( p  < 0.05). LBP and PHN patients had significant differences in the brain’s GMV, MC, and network properties, which implies that different PNPs have different neural mechanisms concerning pain modulation.

The exceptional physicochemical properties of selenium nanoparticles (SeNPs) have led to their widespread development. The function of SeNPs is significantly influenced by their shape and particle size, which are in turn determined by the applied synthesis methods. This work presents a critical and comparative analysis of physical, chemical, and biosynthetic methods. The key point is to elaborate on how different methods precisely regulate the particle size, morphology, and stability that are crucial to their functional efficacy. This work emphasizes the importance of creating standardized protocols for characterizing SeNPs in order to make meaningful comparisons between the effectiveness of various studies. We further elucidate the underlying mechanisms of SeNPs’ anti-tumor, antioxidant, and antibacterial activities. A key novelty of this work lies in its systematic construction of a bridge between the synthesis, properties, functions, applications, and translational potential and its provision of a critical assessment. Finally, the review identifies and summarizes the principal challenges hindering clinical and commercial translation, including the imperative for standardized toxicological evaluation, scalable synthesis, and regulatory alignment.

Selenium (Se) is an indispensable trace element for humans and other animals. Various studies have demonstrated the beneficial effects of Se on plants, including the promotion of growth, accumulation of secondary metabolites, and enhancement of antioxidant capacity, thereby improving plant stress resistance. Consequently, Se biofortification has emerged as an effective strategy to elevate Se content and nutritional quality in plants, attracting widespread attention. The mechanism of selenium (Se) at the plant secondary metabolic level has not yet been fully elucidated, and it remains an unanswered question as to how selenium affects plant secondary metabolic pathways and how these metabolic pathways respond to selenium biofortification. Although it has been shown that selenium can affect the antioxidant system and defense mechanisms in plants, detailed mechanisms of selenium’s action on plant secondary metabolic pathways, including its effects on specific metabolic enzymes and regulatory genes, still need to be revealed by further in-depth studies. The present study aims to elucidate the mechanisms of Se absorption, transport, and metabolism in plants under Se-rich conditions and to investigate the impact of various Se biofortification methods on the content of plant secondary metabolites. By integrating existing research progress, this paper will delve into the potential molecular regulatory mechanisms of Se on plant secondary metabolism, aiming to unravel the interplay between Se and plant secondary metabolism. This study provides a novel perspective and direction for future research on plant secondary metabolism and the biological utilization of Se.