Cross-modal credibility modelling for EEG-based multimodal emotion recognition.

Objective.The study of emotion recognition through electroencephalography (EEG) has garnered significant attention recently. Integrating EEG with other peripheral physiological signals may greatly enhance performance in emotion recognition. Nonetheless, existing approaches still suffer from two predominant challenges: modality heterogeneity, stemming from the diverse mechanisms across modalities, and fusion credibility, which arises when one or multiple modalities fail to provide highly credible signals.Approach.In this paper, we introduce a novel multimodal physiological signal fusion model that incorporates both intra-inter modality reconstruction and sequential pattern consistency, thereby ensuring a computable and credible EEG-based multimodal emotion recognition. For the modality heterogeneity issue, we first implement a local self-attention transformer to obtain intra-modal features for each respective modality. Subsequently, we devise a pairwise cross-attention transformer to reveal the inter-modal correlations among different modalities, thereby rendering different modalities compatible and diminishing the heterogeneity concern. For the fusion credibility issue, we introduce the concept of sequential pattern consistency to measure whether different modalities evolve in a consistent way. Specifically, we propose to measure the varying trends of different modalities, and compute the inter-modality consistency scores to ascertain fusion credibility.Main results.We conduct extensive experiments on two benchmarked datasets (DEAP and MAHNOB-HCI) with the subject-dependent paradigm. For the DEAP dataset, our method improves the accuracy by 4.58%, and the F1 score by 0.63%, compared to the state-of-the-art baseline. Similarly, for the MAHNOB-HCI dataset, our method improves the accuracy by 3.97%, and the F1 score by 4.21%. In addition, we gain much insight into the proposed framework through significance test, ablation experiments, confusion matrices and hyperparameter analysis. Consequently, we demonstrate the effectiveness of the proposed credibility modelling through statistical analysis and carefully designed experiments.Significance.All experimental results demonstrate the effectiveness of our proposed architecture and indicate that credibility modelling is essential for multimodal emotion recognition.

Focused ultrasound-mediated brain genome editing.

Gene editing in the brain has been challenging because of the restricted transport imposed by the blood-brain barrier (BBB). Current approaches mainly rely on local injection to bypass the BBB. However, such administration is highly invasive and not amenable to treating certain delicate regions of the brain. We demonstrate a safe and effective gene editing technique by using focused ultrasound (FUS) to transiently open the BBB for the transport of intravenously delivered CRISPR/Cas9 machinery to the brain.

Value-Based Subgoal Discovery and Path Planning for Reaching Long-Horizon Goals.

Learning to reach long-horizon goals in spatial traversal tasks is a significant challenge for autonomous agents. Recent subgoal graph-based planning methods address this challenge by decomposing a goal into a sequence of shorter-horizon subgoals. These methods, however, use arbitrary heuristics for sampling or discovering subgoals, which may not conform to the cumulative reward distribution. Moreover, they are prone to learning erroneous connections (edges) between subgoals, especially those lying across obstacles. To address these issues, this article proposes a novel subgoal graph-based planning method called learning subgoal graph using value-based subgoal discovery and automatic pruning (LSGVP). The proposed method uses a subgoal discovery heuristic that is based on a cumulative reward (value) measure and yields sparse subgoals, including those lying on the higher cumulative reward paths. Moreover, LSGVP guides the agent to automatically prune the learned subgoal graph to remove the erroneous edges. The combination of these novel features helps the LSGVP agent to achieve higher cumulative positive rewards than other subgoal sampling or discovery heuristics, as well as higher goal-reaching success rates than other state-of-the-art subgoal graph-based planning methods.

Phenylboronic Acid-Functionalized Polyplexes Tailored to Oral CRISPR Delivery.

Effective delivery of the CRISPR-Cas9 components is crucial to realizing the therapeutic potential. Although many delivery approaches have been developed for this application, oral delivery has not been explored due to the degradative nature of the gastrointestinal tract. For this issue, we developed a series of novel phenylboronic acid (PBA)-functionalized chitosan-polyethylenimine (CS-PEI) polymers for oral CRISPR delivery. PBA functionalization equipped the polyplex with higher stability, smooth transport across the mucus, and efficient endosomal escape and cytosolic unpackaging in the cells. From a library of 12 PBA-functionalized CS-PEI polyplexes, we identified a formulation that showed the most effective penetration in the intestinal mucosa after oral gavage to mice. The optimized formulation performed feasible CRISPR-mediated downregulation of the target protein and reduction in the downstream cholesterol. As the first oral CRISPR carrier, this study suggests the potential of addressing the needs of both local and systemic editing in a patient-compliant manner.

Focused ultrasound-mediated brain genome editing.

Gene editing in the mammalian brain has been challenging because of the restricted transport imposed by the blood-brain barrier (BBB). Current approaches rely on local injection to bypass the BBB. However, such administration is highly invasive and not amenable to treating certain delicate regions of the brain. We demonstrate a safe and effective gene editing technique by using focused ultrasound (FUS) to transiently open the BBB for the transport of intravenously delivered CRISPR/Cas9 machinery to the brain.

Targeting Proinflammatory Molecules Using Multifunctional MnO Nanoparticles to Inhibit Breast Cancer Recurrence and Metastasis.

Photothermal therapy (PTT) is an effective treatment modality that is highly selective for tumor suppression and is a hopeful alternative to traditional cancer therapy. However, PTT-induced inflammatory responses may result in undesirable side effects including increased risks of tumor recurrence and metastasis. Here we developed multifunctional MnO nanoparticles as scavengers of proinflammatory molecules to alleviate the PTT-induced inflammatory response. The MnO nanoparticles improve the PTT therapy by (1) binding and scavenging proinflammatory molecules to inhibit the proinflammatory molecule-induced Toll-like receptors (TLR) activation and nuclear factor kappa B (NF-κB) signaling; (2) inhibiting activated macrophage-induced macrophage recruitment; and (3) inhibiting tumor cell migration and invasion. In vivo experimental results showed that further treatment with MnO nanoparticles after laser therapy not only inhibited the PTT-induced inflammatory response and primary tumor recurrence but also significantly reduced tumor metastasis due to the scavenging activity. These findings suggest that MnO nanoparticles hold the potential for mitigating the therapy-induced severe inflammatory response and inhibiting tumor recurrence and metastasis.

Nanoparticulate cell-free DNA scavenger for treating inflammatory bone loss in periodontitis.

Periodontitis is a common type of inflammatory bone loss and a risk factor for systemic diseases. The pathogenesis of periodontitis involves inflammatory dysregulation, which represents a target for new therapeutic strategies to treat periodontitis. After establishing the correlation of cell-free DNA (cfDNA) level with periodontitis in patient samples, we test the hypothesis that the cfDNA-scavenging approach will benefit periodontitis treatment. We create a nanoparticulate cfDNA scavenger specific for periodontitis by coating selenium-doped hydroxyapatite nanoparticles (SeHANs) with cationic polyamidoamine dendrimers (PAMAM-G3), namely G3@SeHANs, and compare the activities of G3@SeHANs with those of soluble PAMAM-G3 polymer. Both G3@SeHANs and PAMAM-G3 inhibit periodontitis-related proinflammation in vitro by scavenging cfDNA and alleviate inflammatory bone loss in a mouse model of ligature-induced periodontitis. G3@SeHANs also regulate the mononuclear phagocyte system in a periodontitis environment, promoting the M2 over the M1 macrophage phenotype. G3@SeHANs show greater therapeutic effects than PAMAM-G3 in reducing proinflammation and alveolar bone loss in vivo. Our findings demonstrate the importance of cfDNA in periodontitis and the potential for using hydroxyapatite-based nanoparticulate cfDNA scavengers to ameliorate periodontitis.

Who are the 'silent spreaders'?: contact tracing in spatio-temporal memory models.

The COVID-19 epidemic has swept the world for over two years. However, a large number of infectious asymptomatic COVID-19 cases (ACCs) are still making the breaking up of the transmission chains very difficult. Efforts by epidemiological researchers in many countries have thrown light on the clinical features of ACCs, but there is still a lack of practical approaches to detect ACCs so as to help contain the pandemic. To address the issue of ACCs, this paper presents a neural network model called Spatio-Temporal Episodic Memory for COVID-19 (STEM-COVID) to identify ACCs from contact tracing data. Based on the fusion Adaptive Resonance Theory (ART), the model encodes a collective spatio-temporal episodic memory of individuals and incorporates an effective mechanism of parallel searches for ACCs. Specifically, the episodic traces of the identified positive cases are used to map out the episodic traces of suspected ACCs using a weighted evidence pooling method. To evaluate the efficacy of STEM-COVID, a realistic agent-based simulation model for COVID-19 spreading is implemented based on the recent epidemiological findings on ACCs. The experiments based on rigorous simulation scenarios, manifesting the current situation of COVID-19 spread, show that the STEM-COVID model with weighted evidence pooling has a higher level of accuracy and efficiency for identifying ACCs when compared with several baselines. Moreover, the model displays strong robustness against noisy data and different ACC proportions, which partially reflects the effect of breakthrough infections after vaccination on the virus transmission.

End-to-End Hierarchical Reinforcement Learning With Integrated Subgoal Discovery.

Hierarchical reinforcement learning (HRL) is a promising approach to perform long-horizon goal-reaching tasks by decomposing the goals into subgoals. In a holistic HRL paradigm, an agent must autonomously discover such subgoals and also learn a hierarchy of policies that uses them to reach the goals. Recently introduced end-to-end HRL methods accomplish this by using the higher-level policy in the hierarchy to directly search the useful subgoals in a continuous subgoal space. However, learning such a policy may be challenging when the subgoal space is large. We propose integrated discovery of salient subgoals (LIDOSS), an end-to-end HRL method with an integrated subgoal discovery heuristic that reduces the search space of the higher-level policy, by explicitly focusing on the subgoals that have a greater probability of occurrence on various state-transition trajectories leading to the goal. We evaluate LIDOSS on a set of continuous control tasks in the MuJoCo domain against hierarchical actor critic (HAC), a state-of-the-art end-to-end HRL method. The results show that LIDOSS attains better goal achievement rates than HAC in most of the tasks.

A Cationic Metal-Organic Framework to Scavenge Cell-Free DNA for Severe Sepsis Management.

Circulating cell-free DNA (cfDNA) released by damaged cells causes inflammation and has been associated with the progression of sepsis. One proposed strategy to treat sepsis is to scavenge this inflammatory circulating cfDNA. Here, we develop a cfDNA-scavenging nanoparticle (NP) that consists of cationic polyethylenimine (PEI) of different molecular weight grafted to zeolitic imidazolate framework-8 (PEI-g-ZIF) in a simple one-pot process. PEI-g-ZIF NPs fabricated using PEI 1800 and PEI 25k but not PEI 600 suppressed cfDNA-induced TLR activation and subsequent nuclear factor kappa B pathway activity. PEI 1800-g-ZIF NPs showed greater inhibition of cfDNA-associated inflammation and multiple organ injury than naked PEI 1800 (lacking ZIF), and had greater therapeutic efficacy in treating sepsis. These results indicate that PEI-g-ZIF NPs acts as a "nanotrap" that improves upon naked PEI in scavenging circulating cfDNA, reducing inflammation, and reversing the progression of sepsis, thus providing a novel strategy for sepsis treatment.

CRISPR/Cas9-mediated mutagenesis to validate the synergy between PARP1 inhibition and chemotherapy in BRCA1-mutated breast cancer cells.

For patients carrying BRCA1 mutations, at least one-third develop triple negative breast cancer (TNBC). Not only is TNBC difficult to treat due to the lack of molecular target receptors, but BRCA1 mutations (BRCA1m) also result in chemotherapeutic resistance, making disease recurrence more likely. Although BRCA1m are highly heterogeneous and therefore difficult to target, BRCA1 gene's synthetic lethal pair, PARP1, is conserved in BRCA1m cancer cells. Therefore, we hypothesize that targeting PARP1 might be a fruitful direction to sensitize BRCA1m cancer cells to chemotherapy. We used CRISPR/Cas9 technology to generate PARP1 deficiency in two TNBC cell lines, MDA-MB-231 (BRCA1 wild-type) and MDA-MB-436 (BRCA1m). We explored whether this PARP1 disruption (PARP1m) could significantly lower the chemotherapeutic dose necessary to achieve therapeutic efficacy in both a 2D and 3D tumor-on-a-chip model. With both BRCA1m and PARP1m, the TNBC cells were more sensitive to three representative chemotherapeutic breast cancer drugs, doxorubicin, gemcitabine and docetaxel, compared with the PARP1 wild-type counterpart in the 2D culture environment. However, PARP1m did not result in this synergy in the 3D tumor-on-a-chip model, suggesting that drug dosing in the tumor microenvironment may influence the synergy. Taken together, our results highlight a discrepancy in the efficacy of the combination of PARP1 inhibition and chemotherapy for TNBC treatment, which should be clarified to justify further clinical testing.

Assessment of the Efficacy of EEG-Based MI-BCI With Visual Feedback and EEG Correlates of Mental Fatigue for Upper-Limb Stroke Rehabilitation.

OBJECTIVE: This single-arm multisite trial investigates the efficacy of the neurostyle brain exercise therapy towards enhanced recovery (nBETTER) system, an electroencephalogram (EEG)-based motor imagery brain-computer interface (MI-BCI) employing visual feedback for upper-limb stroke rehabilitation, and the presence of EEG correlates of mental fatigue during BCI usage. METHODS: A total of 13 recruited stroke patients underwent thrice-weekly nBETTER therapy coupled with standard arm therapy over six weeks. Upper-extremity Fugl-Meyer motor assessment (FMA) scores were measured at baseline (week 0), post-intervention (week 6), and follow-ups (weeks 12 and 24). In total, 11/13 patients (mean age 55.2 years old, mean post-stroke duration 333.7 days, mean baseline FMA 35.5) completed the study. RESULTS: Significant FMA gains relative to baseline were observed at weeks 6 and 24. Retrospectively comparing to the standard arm therapy (SAT) control group and BCI with haptic knob (BCI-HK) intervention group from a previous similar study, the SAT group had no significant gains, whereas the BCI-HK group had significant gains at weeks 6, 12, and 24. EEG analysis revealed significant positive correlations between relative beta power and BCI performance in the frontal and central brain regions, suggesting that mental fatigue may contribute to poorer BCI performance. CONCLUSION: nBETTER, an EEG-based MI-BCI employing only visual feedback, helps stroke survivors sustain short-term FMA improvement. Analysis of EEG relative beta power indicates that mental fatigue may be present. SIGNIFICANCE: This study adds nBETTER to the growing literature of safe and effective stroke rehabilitation MI-BCI, and suggests an additional fatigue-monitoring role in future such BCI.

An iterative cross-subject negative-unlabeled learning algorithm for quantifying passive fatigue.

OBJECTIVE: This paper proposes an iterative negative-unlabeled (NU) learning algorithm for cross-subject detection of passive fatigue from labelled alert (negative) and unlabeled driving EEG data. APPROACH: Unlike other studies which used manual labeling of the fatigue state, the proposed algorithm (PA) first iteratively uses 29 subjects' alert data and unlabeled driving data to identify the most fatigued block of EEG data in each subject in a cross-subject manner. Subsequently, the PA computes subjects' driving fatigue score. Repeated measures correlations of the score to EEG band powers are then performed. MAIN RESULTS: The PA yields an averaged accuracy of 93.77% ± 8.15% across subjects in detecting fatigue, which is significantly better than the various baselines. The fatigue scores obtained are also significantly positively correlated with theta band power and negatively correlated with beta band power that are known to respectively increase and decrease in presence of passive fatigue. There is a strong negative correlation with alpha band power as well. SIGNIFICANCE: The proposed iterative NU learning algorithm is capable of labelling and quantifying the most fatigued block in a cross-subject manner despite the lack of ground truth in the fatigue levels of unlabeled driving EEG data. Together with the significant correlations with theta, alpha and beta band power, the results show promise in the application of the proposed algorithm to detect fatigue from EEG.

Engineered Mesenchymal Stem Cell/Nanomedicine Spheroid as an Active Drug Delivery Platform for Combinational Glioblastoma Therapy.

Mesenchymal stem cell (MSC) has been increasingly applied to cancer therapy because of its tumor-tropic capability. However, short retention at target tissue and limited payload option hinder the progress of MSC-based cancer therapy. Herein, we proposed a hybrid spheroid/nanomedicine system, comprising MSC spheroid entrapping drug-loaded nanocomposite, to address these limitations. Spheroid formulation enhanced MSC's tumor tropism and facilitated loading of different types of therapeutic payloads. This system acted as an active drug delivery platform seeking and specifically targeting glioblastoma cells. It enabled effective delivery of combinational protein and chemotherapeutic drugs by engineered MSC and nanocomposite, respectively. In an in vivo migration model, the hybrid spheroid showed higher nanocomposite retention in the tumor tissue compared with the single MSC approach, leading to enhanced tumor inhibition in a heterotopic glioblastoma murine model. Taken together, this system integrates the merits of cell- and nanoparticle- mediated drug delivery with the tumor-homing characteristics of MSC to advance targeted combinational cancer therapy.

MuSCoWERT: multi-scale consistence of weighted edge Radon transform for horizon detection in maritime images.

This paper addresses the problem of horizon detection, a fundamental process in numerous object detection algorithms, in a maritime environment. The maritime environment is characterized by the absence of fixed features, the presence of numerous linear features in dynamically changing objects and background and constantly varying illumination, rendering the typically simple problem of detecting the horizon a challenging one. We present a novel method called multi-scale consistence of weighted edge Radon transform, abbreviated as MuSCoWERT. It detects the long linear features consistent over multiple scales using multi-scale median filtering of the image followed by Radon transform on a weighted edge map and computing the histogram of the detected linear features. We show that MuSCoWERT has excellent performance, better than seven other contemporary methods, for 84 challenging maritime videos, containing over 33,000 frames, and captured using visible range and near-infrared range sensors mounted onboard, onshore, or on floating buoys. It has a median error of about 2 pixels (less than 0.2%) from the center of the actual horizon and a median angular error of less than 0.4 deg. We are also sharing a new challenging horizon detection dataset of 65 videos of visible, infrared cameras for onshore and onboard ship camera placement.

Neural network modelling of the influence of channelopathies on reflex visual attention.

This paper introduces a model of Emergent Visual Attention in presence of calcium channelopathy (EVAC). By modelling channelopathy, EVAC constitutes an effort towards identifying the possible causes of autism. The network structure embodies the dual pathways model of cortical processing of visual input, with reflex attention as an emergent property of neural interactions. EVAC extends existing work by introducing attention shift in a larger-scale network and applying a phenomenological model of channelopathy. In presence of a distractor, the channelopathic network's rate of failure to shift attention is lower than the control network's, but overall, the control network exhibits a lower classification error rate. The simulation results also show differences in task-relative reaction times between control and channelopathic networks. The attention shift timings inferred from the model are consistent with studies of attention shift in autistic children.

DEB: definite error bounded tangent estimator for digital curves.

We propose a simple and fast method for tangent estimation of digital curves. This geometric-based method uses a small local region for tangent estimation and has a definite upper bound error for continuous as well as digital conics, i.e., circles, ellipses, parabolas, and hyperbolas. Explicit expressions of the upper bounds for continuous and digitized curves are derived, which can also be applied to nonconic curves. Our approach is benchmarked against 72 contemporary tangent estimation methods and demonstrates good performance for conic, nonconic, and noisy curves. In addition, we demonstrate a good multigrid and isotropic performance and low computational complexity of O(1) and better performance than most methods in terms of maximum and average errors in tangent computation for a large variety of digital curves.

ERNN: a biologically inspired feedforward neural network to discriminate emotion from EEG signal.

Emotions play an important role in human cognition, perception, decision making, and interaction. This paper presents a six-layer biologically inspired feedforward neural network to discriminate human emotions from EEG. The neural network comprises a shift register memory after spectral filtering for the input layer, and the estimation of coherence between each pair of input signals for the hidden layer. EEG data are collected from 57 healthy participants from eight locations while subjected to audio-visual stimuli. Discrimination of emotions from EEG is investigated based on valence and arousal levels. The accuracy of the proposed neural network is compared with various feature extraction methods and feedforward learning algorithms. The results showed that the highest accuracy is achieved when using the proposed neural network with a type of radial basis function.

EEG data space adaptation to reduce intersession nonstationarity in brain-computer interface.

A major challenge in EEG-based brain-computer interfaces (BCIs) is the intersession nonstationarity in the EEG data that often leads to deteriorated BCI performances. To address this issue, this letter proposes a novel data space adaptation technique, EEG data space adaptation (EEG-DSA), to linearly transform the EEG data from the target space (evaluation session), such that the distribution difference to the source space (training session) is minimized. Using the Kullback-Leibler (KL) divergence criterion, we propose two versions of the EEG-DSA algorithm: the supervised version, when labeled data are available in the evaluation session, and the unsupervised version, when labeled data are not available. The performance of the proposed EEG-DSA algorithm is evaluated on the publicly available BCI Competition IV data set IIa and a data set recorded from 16 subjects performing motor imagery tasks on different days. The results show that the proposed EEG-DSA algorithm in both the supervised and unsupervised versions significantly outperforms the results without adaptation in terms of classification accuracy. The results also show that for subjects with poor BCI performances when no adaptation is applied, the proposed EEG-DSA algorithm in both the supervised and unsupervised versions significantly outperforms the unsupervised bias adaptation algorithm (PMean).

Optimizing spatial filters by minimizing within-class dissimilarities in electroencephalogram-based brain-computer interface.

A major challenge in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is the inherent nonstationarities in the EEG data. Variations of the signal properties from intra and inter sessions often lead to deteriorated BCI performances, as features extracted by methods such as common spatial patterns (CSP) are not invariant against the changes. To extract features that are robust and invariant, this paper proposes a novel spatial filtering algorithm called Kullback-Leibler (KL) CSP. The CSP algorithm only considers the discrimination between the means of the classes, but does not consider within-class scatters information. In contrast, the proposed KLCSP algorithm simultaneously maximizes the discrimination between the class means, and minimizes the within-class dissimilarities measured by a loss function based on the KL divergence. The performance of the proposed KLCSP algorithm is compared against two existing algorithms, CSP and stationary CSP (sCSP), using the publicly available BCI competition III dataset IVa and a large dataset from stroke patients performing neuro-rehabilitation. The results show that the proposed KLCSP algorithm significantly outperforms both the CSP and the sCSP algorithms, in terms of classification accuracy, by reducing within-class variations. This results in more compact and separable features.