In this paper, we present a multimodal emotion recognition framework called EmotionMeter that combines brain waves and eye movements. To increase the feasibility and wearability of EmotionMeter in ...real-world applications, we design a six-electrode placement above the ears to collect electroencephalography (EEG) signals. We combine EEG and eye movements for integrating the internal cognitive states and external subconscious behaviors of users to improve the recognition accuracy of EmotionMeter. The experimental results demonstrate that modality fusion with multimodal deep neural networks can significantly enhance the performance compared with a single modality, and the best mean accuracy of 85.11% is achieved for four emotions (happy, sad, fear, and neutral). We explore the complementary characteristics of EEG and eye movements for their representational capacities and identify that EEG has the advantage of classifying happy emotion, whereas eye movements outperform EEG in recognizing fear emotion. To investigate the stability of EmotionMeter over time, each subject performs the experiments three times on different days. EmotionMeter obtains a mean recognition accuracy of 72.39% across sessions with the six-electrode EEG and eye movement features. These experimental results demonstrate the effectiveness of EmotionMeter within and between sessions.
Herein, an anionic metal-organic framework, formulated as {Zn
3
(OH)(bmipia)(H
2
O)
3
4
·Zn(H
2
O)6.5
2
}
n
(
FCS-3
), was prepared from zinc ions and semi-rigid carboxylate ligands of 5-
N
,
N
...-bis(5-methylisophthalic acid)amion isophthalic acid (H
6
bmipia) and was employed as a unique fluorescence turn-on chemical sensor for the ultra-sensitive detection of various antibiotics in the aqueous phase.
Fabrication and engineering of an anionic metal-organic framework as a unique turn-on fluorescent chemical sensor for the ultra-sensitive detection of antibiotics.
Twist is a critical epithelial-mesenchymal transition (EMT)-inducing transcription factor that increases expression of vimentin. How Twist1 regulates this expression remains unclear. Here, we report ...that Twist1 regulates Cullin2 (Cul2) circular RNA to increase expression of vimentin in EMT. Twist1 bound the Cul2 promoter to activate its transcription and to selectively promote expression of Cul2 circular RNA (circ-10720), but not mRNA. circ-10720 positively correlated with Twist1, tumor malignance, and poor prognosis in hepatocellular carcinoma (HCC). Twist1 promoted vimentin expression by increasing levels of circ-10720, which can absorb miRNAs that target vimentin. circ-10720 knockdown counteracted the tumor-promoting activity of Twist1
and in patient-derived xenograft and diethylnitrosamine-induced TetOn-Twist1 transgenic mouse HCC models. These data unveil a mechanism by which Twist1 regulates vimentin during EMT. They also provide potential therapeutic targets for HCC treatment and provide new insight for circular RNA (circRNA)-based diagnostic and therapeutic strategies.
A circRNA-based mechanism drives Twist1-mediated regulation of vimentin during EMT and provides potential therapeutic targets for treatment of HCC.
http://cancerres.aacrjournals.org/content/canres/78/15/4150/F1.large.jpg
.
To investigate critical frequency bands and channels, this paper introduces deep belief networks (DBNs) to constructing EEG-based emotion recognition models for three emotions: positive, neutral and ...negative. We develop an EEG dataset acquired from 15 subjects. Each subject performs the experiments twice at the interval of a few days. DBNs are trained with differential entropy features extracted from multichannel EEG data. We examine the weights of the trained DBNs and investigate the critical frequency bands and channels. Four different profiles of 4, 6, 9, and 12 channels are selected. The recognition accuracies of these four profiles are relatively stable with the best accuracy of 86.65%, which is even better than that of the original 62 channels. The critical frequency bands and channels determined by using the weights of trained DBNs are consistent with the existing observations. In addition, our experiment results show that neural signatures associated with different emotions do exist and they share commonality across sessions and individuals. We compare the performance of deep models with shallow models. The average accuracies of DBN, SVM, LR, and KNN are 86.08%, 83.99%, 82.70%, and 72.60%, respectively.
In this paper, we investigate stable patterns of electroencephalogram (EEG) over time for emotion recognition using a machine learning approach. Up to now, various findings of activated patterns ...associated with different emotions have been reported. However, their stability over time has not been fully investigated yet. In this paper, we focus on identifying EEG stability in emotion recognition. We systematically evaluate the performance of various popular feature extraction, feature selection, feature smoothing and pattern classification methods with the DEAP dataset and a newly developed dataset called SEED for this study. Discriminative Graph regularized Extreme Learning Machine with differential entropy features achieves the best average accuracies of 69.67 and 91.07 percent on the DEAP and SEED datasets, respectively. The experimental results indicate that stable patterns exhibit consistency across sessions; the lateral temporal areas activate more for positive emotions than negative emotions in beta and gamma bands; the neural patterns of neutral emotions have higher alpha responses at parietal and occipital sites; and for negative emotions, the neural patterns have significant higher delta responses at parietal and occipital sites and higher gamma responses at prefrontal sites. The performance of our emotion recognition models shows that the neural patterns are relatively stable within and between sessions.
Fluorination of the donor and/or acceptor blocks of photoactive semiconducting polymers is a leading strategy to enhance organic solar cell (OSC) performance. Here, the effects are investigated in ...OSCs using fluorine‐free (TPD‐3) and fluorinated (TPD‐3F) donor polymers, paired with the nonfullerene acceptor Y6. Interestingly and unexpectedly, fluorination negatively affects performance, and fluorine‐free TPD‐3:Y6 OSCs exhibit a far higher power conversion efficiency (PCE = 14.5%) than in the fluorine‐containing TPD‐3F:Y6 blends (PCE = 11.5%). Transmission electron microscopy (TEM) analysis indicates that the TPD‐3F:Y6 blends have larger phase domain sizes than TPD‐3:Y6, which reduces exciton dissociation efficiency to 81% for TPD‐3F:Y6 versus 93% for TPD‐3:Y6. Additionally, grazing incidence wide‐angle X‐ray scattering (GIWAXS) reveals that the TPD‐3F:Y6 blends are less textured than those of TPD‐3:Y6, while space‐charge limited currents reveal lower and unbalanced hole/electron mobility in TPD‐3F:Y6 versus TPD‐3:Y6 blends. Charge recombination dynamic, transient absorption, and donor–acceptor miscibility assays additionally support this picture. Furthermore, conventional architecture TPD‐3:Y6 OSCs deliver a PCE of 15.2%, among the highest to date for halogen‐free polymer donor OSCs. Finally, a large‐area (20.4 cm2) TPD‐3:Y6 blend module exhibits an outstanding PCE of 9.31%, one of the highest to date for modules of area >20 cm2.
A high power conversion efficiency (PCE) of 15.2% is achieved by via a halogen‐free, polymer donor in TPD‐3:Y6‐based organic solar cells, which is far higher than that of its fluorined counterpart, TPD‐3F (11.4%). Comparative characterization including, transmission electron microscopy, grazing incidence wide‐angle X‐ray scattering, transient absorption, miscibility, measurements explain this result. Additionally, a PCE of 9.31% is achieved by TPD‐3:Y6‐based 20.4 cm2 modules.
In this note, we show a wave-breaking criterion for the Constantin–Lannes equation in a simple and direct way, which suggests that the wave breaking for the equation may occur even with small slope ...of the initial value. Our result improves some related works obtained recently.
Herein, a general strategy for chemo‐ and regioselective 1,2‐reduction of chromium‐bound arenes was developed, thus providing rapid access to 1,3‐cyclohexadienes. Selective arene activation via ...π‐complexation along with the use of mild hydride Ph3SiH can overcome the inherently low reactivity of arene π‐bonds while tolerating various reduction‐sensitive functional groups. Its versatility further enables a regiodivergent deuteration. Using different sequences of (non)deuterated hydride and acid reagents, the deuterated positions as well as the degrees of deuterium incorporation can be controlled precisely, which leads to a large and previously inaccessible chemical space for 1,3‐cyclohexadiene isotopologues. A reasonable mechanism was proposed based on intermediate capture and control experiments. The synthetic value of this selective 1,2‐reduction was demonstrated in the formal total synthesis of (±)‐galanthamine and (±)‐lycoramine.
A general and efficient protocol for chemo‐ and regioselective 1,2‐reduction of arenes via η6‐coordination to chromium has been reported for the first time. The versatility of this strategy further leads to a regiodivergent reductive deuteration that enables precise control of the deuteration sites as well as the degrees of deuterium incorporation.
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Bioprinting offers a highly-automated and advanced manufacturing platform that facilitates the deposition of bio-inks (living cells, biomaterials and growth factors) in a scalable and ...reproducible manner, a process that is lacking in conventional tissue engineering approaches. Significant improvements in the field of bioprinting have occurred over the last two decades. This reviews provides an in-depth analysis of recent improvements in the bioprinting techniques, progress in bio-ink development, implementation of new bioprinting and tissue maturation strategies. Special attention is givent to the role of polymer science and how it complements 3D bioprinting to overcome some of the major impediments in the field of organ printing. A concise overview of the anatomy and physiology of different tissues/organs is provided, followed by important design considerations to better facilitate the fabrication of biomimetic tissues/organs for tissue engineering and regenerative medicine (TERM). Last, a realistic overview of current status in organ bioprinting is presented, including recent accomplishments in bioprinting tissue-engineered constructs, the limitations and challenges, as well as opportunities for future research. We strongly believe that with the advances in polymer sciences, it will be an impending reality for on-demand bioprinting of patient-specific tissues/organs.
The goal of early action recognition is to predict action label when the sequence is partially observed. The existing methods treat the early action recognition task as sequential classification ...problems on different observation ratios of an action sequence. Since these models are trained by differentiating positive category from all negative classes, the diverse information of different negative categories is ignored, which we believe can be collected to help improve the recognition performance. In this paper, we step towards to a new direction by introducing category exclusion to early action recognition. We model the exclusion as a mask operation on the classification probability output of a pre-trained early action recognition classifier. Specifically, we use policy-based reinforcement learning to train an agent. The agent generates a series of binary masks to exclude interfering negative categories during action execution and hence help improve the recognition accuracy. The proposed method is evaluated on three benchmark recognition datasets, NTU-RGBD, First-Person Hand Action, as well as UCF-101. The proposed method enhances the recognition accuracy consistently over all different observation ratios on the three datasets, where the accuracy improvements on the early stages are especially significant.
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