Search

Protein-protein interactions (PPIs) stabilization with molecular glues plays a crucial role in drug discovery, albeit with significant challenges. In this study, we propose a dual-site approach, targeting the PPI region and its dynamic surroundings. We conduct molecular dynamics simulations to identify critical sites on the PPI that stabilize the cyclin-dependent kinase 12 - DNA damage-binding protein 1 (CDK12-DDB1) complex, resulting in further cyclin K degradation. This exploration leads to the creation of LL-K12-18, a dual-site molecular glue, which enhances the glue properties to augment degradation kinetics and efficiency. Notably, LL-K12-18 demonstrates strong inhibition of gene transcription and anti-proliferative effects in tumor cells, showing significant potency improvements in MDA-MB-231 (88-fold) and MDA-MB-468 cells (307-fold) when compared to its precursor compound SR-4835. These findings underscore the potential of dual-site approaches in disrupting CDK12 function and offer a structural insight-based framework for the design of cyclin K molecular glues.Protein-protein interactions (PPIs) stabilization with molecular glues plays a crucial role in drug discovery. Here, the authors propose a dual-site approach for the design of clyclin K molecular glues, targeting the PPI region and its dynamic surroundings, and report a dual-site molecular glue for the cyclin-dependent kinase 12 - DNA damage-binding protein 1 complex, resulting in further cyclin K degradation. [ABSTRACT FROM AUTHOR]

As the evolutionary ancestor of Cas12 nuclease, the transposon (IS200/IS605)-encoded TnpB proteins act as compact RNA-guided DNA endonucleases. To explore their evolutionary diversity and potential as genome editors, we screened TnpBs from 64 annotated IS605 members and identified 25 active in Escherichia coli, of which three are active in human cells. Further characterization of these 25 TnpBs enables prediction of the transposon-associated motif (TAM) and the right-end element RNA (reRNA) directly from genomic sequences. We established a framework for annotating TnpB systems in prokaryotic genomes and applied it to identify 14 additional candidates. Among these, ISAam1 (369 amino acids (aa)) and ISYmu1 (382 aa) TnpBs demonstrated robust editing activity across dozens of genomic loci in human cells. Both RNA-guided genome editors demonstrated similar editing efficiency as SaCas9 (1,053 aa) while being substantially smaller. The enormous diversity of TnpBs holds potential for the discovery of additional valuable genome editors. Compact genome editors were discovered by mining a transposon library. [ABSTRACT FROM AUTHOR]

Phenotypic transformation of vascular smooth muscle cells (VSMCs) plays a crucial role in abdominal aortic aneurysm (AAA) formation. CARMN, a highly conserved, VSMC-enriched long noncoding RNA (lncRNA), is integral in orchestrating various vascular pathologies by modulating the phenotypic dynamics of VSMCs. The influence of CARMN on AAA formation, particularly its mechanisms, remains enigmatic. Our research, employing single-cell and bulk RNA sequencing, has uncovered a significant suppression of CARMN in AAA specimens, which correlates strongly with the contractile function of VSMCs. This reduced expression of CARMN was consistent in both 7- and 14-day porcine pancreatic elastase (PPE)-induced mouse models of AAA and in human clinical cases. Functional analyses disclosed that the diminution of CARMN exacerbated PPE-precipitated AAA formation, whereas its augmentation conferred protection against such formation. Mechanistically, we found CARMN's capacity to bind with SRF, thereby amplifying its role in driving the transcription of VSMC marker genes. In addition, our findings indicate an enhancement in CAMRN transcription, facilitated by the binding of NRF2 to its promoter region. Our study indicated that CARMN plays a protective role in preventing AAA formation and restrains the phenotypic transformation of VSMC through its interaction with SRF. Additionally, we observed that the expression of CARMN is augmented by NRF2 binding to its promoter region. These findings suggest the potential of CARMN as a viable therapeutic target in the treatment of AAA. [ABSTRACT FROM AUTHOR]

With the increased concern over environment protection, cellulose acetate (CA) has drawn great interests as an alternative for packaging material due to its biodegradability and abundant resources; whereas, the poor antistatic property and thermal conductivity restrict its application in packaging. In this work, we proposed a simple but effective strategy to produce high performance graphene nanoplatelet (GNP)/CA composite films via the consecutive homogenization and solvent casting processes. Relying on the spontaneous absorption of CA during homogenization, the GNP/CA produced shows an excellent dispersibility in the N,N-Dimethylformamide (DMF) solution and many fewer structural defects compared with GNPs alone. As a result, the composite films obtained exhibit simultaneously and significantly enhanced antistatic, heat dissipative and mechanical properties compared with CA. Specifically, the GNP/CA composite with the optimal formula has promising overall performances (namely, surface resistivity of 3.33 × 107 Ω/sq, in-plane thermal conductivity of 5.359 W (m · K) , out-of-plane thermal conductivity of 0.785 W (m · K) , and tensile strength of 37.1 MPa). Featured by its promising overall properties, simple production processes and biodegradability, the as-prepared GNP/CA composite film shows a great potential for application in packaging. [ABSTRACT FROM AUTHOR]

Background: This study aimed to develop and validate a deep learning (DL) model to identify atelectasis and attic retraction pocket in cases of otitis media with effusion (OME) using multi-center otoscopic images. Method: A total of 6393 OME otoscopic images from three centers were used to develop and validate a DL model for detecting atelectasis and attic retraction pocket. A threefold random cross-validation procedure was adopted to divide the dataset into training validation sets on a patient level. A team of otologists was assigned to diagnose and characterize atelectasis and attic retraction pocket in otoscopic images. Receiver operating characteristic (ROC) curves, including area under the ROC curve (AUC), accuracy, sensitivity, and specificity were used to assess the performance of the DL model. Class Activation Mapping (CAM) illustrated the discriminative regions in the otoscopic images. Results: Among all OME otoscopic images, 3564 (55.74%) were identified with attic retraction pocket, and 2460 (38.48%) with atelectasis. The diagnostic DL model of attic retraction pocket and atelectasis achieved a threefold cross-validation accuracy of 89% and 79%, AUC of 0.89 and 0.87, a sensitivity of 0.93 and 0.71, and a specificity of 0.62 and 0.84, respectively. Larger and deeper cases of atelectasis and attic retraction pocket showed greater weight, based on the red color depicted in the heat map of CAM. Conclusion: The DL algorithm could be employed to identify atelectasis and attic retraction pocket in otoscopic images of OME, and as a tool to assist in the accurate diagnosis of OME. [ABSTRACT FROM AUTHOR]

Conductive polymer composites based strain sensors have been widely investigated in recent years. However, the integration of a wide strain detection range and high sensitivity is still challenging. In this work, a double-percolation structured fiber strain sensor based on a carbon nanotube (CNT)/styrene butadiene styrene (SBS) conductive phase and a thermoplastic polyurethane (TPU) insulating phase (CNT/SBS@TPU) was fabricated using a simple melt mixing and fused filament fabrication strategy. The effect of SBS: TPU ratios on the electrical and sensing properties of samples was investigated, at 1 wt% CNT loading. It was shown that the SBS: TPU ratios significantly affected the performance of sensors. The highest conductivity of 1.08 × 10–3 S/m was obtained for SBS/5-TPU/5 (SBS: TPU = 5: 5). Due to the squeezing and interruptive effects of the insulating phase, SBS/5-TPU/5 showed a high sensitivity of 2,645,866, wide detection range (0.2%-92% strain), high linearity (R2 = 0.95 at 0–10% strain) and excellent stability (2000 cycles). Additionally, the SBS/5-TPU/5 sensor could effectively monitor varying frequencies within 0.05–1 Hz and showed prospective applications in human motion monitoring and tactile recognition. This work provides a route for manufacturing high-performance fiber strain sensors used for wearable intelligent devices and human-computer interaction. [ABSTRACT FROM AUTHOR]

Background: Pulmonary hypertension is common in chronic kidney disease (CKD) patients. However, the prognostic value of pulmonary hypertension in Chinese predialytic CKD patients is rarely reported. We evaluated the relevant factors and prognostic value of pulmonary hypertension in CKD patients. Methods: This retrospective cohort study enrolled 1092 predialytic patients from The Third Affiliated Hospital of Sun Yat-Sen University from May 1st, 2011, to December 31st, 2016. Data of interest were retrieved from electronic medical records. Pulmonary hypertension was defined as pulmonary arterial systolic pressure (PASP) ≥ 35 mmHg by echocardiology. All participants were followed from the date of the first echocardiography examination. The primary endpoints were all-cause mortality and cardiovascular mortality. The secondary endpoint was end-stage renal disease (ESRD) defined as starting renal replacement therapy. Results: The prevalence of pulmonary hypertension was 15.9% in the study population. For CKD stage 1, 2, 3a, 3b, 4 and 5, the prevalence was 6.0%, 9.6%, 17.2%, 13.3%, 20.7% and 26.6%, respectively. Older age, lower left ventricular ejection fraction, anemia and higher pulse pressure were independently associated with pulmonary hypertension in CKD patients. In multivariate Cox regression analysis, pulmonary hypertension was the independent risk factor for cardiovascular mortality, but not of all-cause mortality and ESRD. Conclusions: Pulmonary hypertension is not rare in early CKD patients. Patients with older age, anemia, higher pulse pressure and compromised heart function were more likely to comorbid pulmonary hypertension. Pulmonary hypertension maybe a sign of worse cardiovascular outcome in CKD patients. [ABSTRACT FROM AUTHOR]

Reinforcement learning with appropriately designed reward signal could be used to solve many sequential learning problems. However, in practice, the reinforcement learning algorithms could be broken in unexpected, counterintuitive ways. One of the failure modes is reward hacking which usually happens when a reward function makes the agent obtain high return in an unexpected way. This unexpected way may subvert the designer's intentions and lead to accidents during training. In this paper, a new multi-step state-action value algorithm is proposed to solve the problem of reward hacking. Unlike traditional algorithms, the proposed method uses a new return function, which alters the discount of future rewards and no longer stresses the immediate reward as the main influence when selecting the current state action. The performance of the proposed method is evaluated on two games, Mappy and Mountain Car. The empirical results demonstrate that the proposed method can alleviate the negative impact of reward hacking and greatly improve the performance of reinforcement learning algorithm. Moreover, the results illustrate that the proposed method could also be applied to the continuous state space problem successfully. [ABSTRACT FROM AUTHOR]

Visual fixation is an item in the visual function subscale of the Coma Recovery Scale-Revised (CRS-R). Sometimes clinicians using the behavioral scales find it difficult to detect because of the motor impairment in patients with disorders of consciousness (DOCs). Brain-computer interface (BCI) can be used to improve clinical assessment because it directly detects the brain response to an external stimulus in the absence of behavioral expression. In this study, we designed a BCI system to assist the visual fixation assessment of DOC patients. The results from 15 patients indicated that three showed visual fixation in both CRS-R and BCI assessments and one did not show such behavior in the CRS-R assessment but achieved significant online accuracy in the BCI assessment. The results revealed that electroencephalography-based BCI can detect the brain response for visual fixation. Therefore, the proposed BCI may provide a promising method for assisting behavioral assessment using the CRS-R. [ABSTRACT FROM AUTHOR]

Sleep spindles are thought to be related to some sleep diseases and play an important role in memory consolidation. They were traditionally identified by physiology experts based on rules and recently detected by automatic algorithms. However, many automatic approaches were validated on the different electroencephalogram (EEG) using various assessment methods, making it difficult to appraised a method objectively and fairly. In this paper, we proposed a sliding window-based probability estimation (SWPE) method for sleep spindle detection. We performed a continuous wavelet transform with Mexican hat wavelet function, following by a sliding window to find out the candidate spindle points corresponding to the large wavelet coefficients at the frequencies of spindles and estimated their probabilities. To enhance the results, we used the envelope of the rectified signal to reject some false sleep spindle candidates. This was an enhanced method and we called it SWPE-E in this paper. Finally, we compared our approaches with four approaches on the same public available EEG database, and the result showed the significative improvement of our proposed approaches. [ABSTRACT FROM AUTHOR]

Bit faults induced by single-event upsets in instruction may not cause a system to experience an error. The instruction vulnerability factor (IVF) is first defined to quantify the effect of non-effective upsets on program reliability in this paper; and the mean time to failure (MTTF) model of program memory is then derived based on IVF. Further analysis of MTTF model concludes that the MTTF of program memory using error correcting code (ECC) and scrubbing is not always better than unhardened program memory. The constraints that should be met upon utilizing ECC and scrubbing in program memory are presented for the first time, to the best of authors' knowledge. Additionally, the proposed models and conclusions are validated by Monte Carlo simulations in MATLAB. These results show that the proposed models have a good accuracy and their margin of error is less than 3 % compared with MATLAB simulation results. It should be highlighted that our conclusions may be used to contribute to selecting the optimal fault-tolerant technique to harden the program memory. [ABSTRACT FROM AUTHOR]

Analyzing functional magnetic resonance imaging (fMRI) data from the encoding perspective provides a powerful tool to explore human vision. Using voxel-wise encoding models, previous studies predicted the brain activity evoked by external stimuli successfully. However, these models constructed a regularized regression model for each single voxel separately, which overlooked the intrinsic spatial property of fMRI data. In this work, we proposed a multi-target regression model that predicts the activities of adjacent voxels simultaneously. Different from the previous models, the spatial constraint is considered in our model. The effectiveness of the proposed model is demonstrated by comparing it with two state-of-the-art voxel-wise models on a publicly available dataset. Results indicate that the proposed method can predict voxel responses more accurately than the competing methods. [ABSTRACT FROM AUTHOR]

A novel SRAM cell tolerant to single-event upsets (SEUs) is presented in this paper. By adding four more transistors inside, the proposed circuit can obtain higher critical charge at each internal node compared to the conventional 6-transistor (6T) cell. Arrays of 2k-bit capacitance of these two designs were implemented in a 65 nm CMOS bulk technology for comparison purpose. Radiation experiments showed that, at the nominal 1.0 V supply voltage, the proposed cell achieves 47.1 % and 49.3 % reduction in alpha and proton soft error rates (SER) with an area overhead of 37 %. [ABSTRACT FROM AUTHOR]

A built-in single event upsets (SEUs) detector is presented in this paper. This detector utilizes charge sharing to detect an SEU in a sequential cell, and the detection process is analyzed through Accuro simulations in a 65 nm technology. The normal operation of this detector would not induce obvious performance degradation of the target circuit. Through using this detector, error correction can be achieved based on dual modular redundancy (DMR) while the related power is about 20.4 % lower than that induced by triple modular redundancy (TMR). [ABSTRACT FROM AUTHOR]

A radiation hardened by design (RHBD) latch and its temporally hardened version to tolerate double node upsets are proposed in this paper. C-Elements are used to construct structures for fault correction. The temporally hardened version can further tolerate some single-event transients (SETs) at input port and clock line. Compared with Quintuple Modular Redundancy (QMR), the proposed non-temporally and temporally hardened latches are more area and power efficient with improved propagation delays. Compared with several previously reported temporally hardened latches, the proposed temporally hardened latch may introduce lower performance loss induced as setup time increase. Several multi-node upset tolerant latches are also compared with these two designs in terms of area, power, and delay. A cell level soft error analysis (TFIT) shows that the upset threshold LETs of the proposed latches in 180 nm process are higher than 16 MeV-cm/mg. [ABSTRACT FROM AUTHOR]

In this paper, a new layout for SRAM 6T bitcell is presented. The new layout is a simple modification over the traditional 6T layout, but it has demonstrated better soft error tolerance over the traditional layout in radiation experiments. The area of the new layout is 31 % larger than the traditional layout. In TCAD simulation, it demonstrates over 2× smaller error cross section than the traditional layout. In alpha particle and proton experiments, its soft error rate can be reduced up to 73 % compared to the traditional layout. [ABSTRACT FROM AUTHOR]

The aim of this study was to observe the symptom improvement and clinical benefit in elderly patients with advanced non-small cell lung cancer (NSCLC) stratified on the basis of CGA findings after treatment with a combination of traditional Chinese medicine and Western medicine. Twenty-four elderly advanced NSCLC patients with a mean age of 73.0 ± 5.3 (65-83) years were categorized into three stratifications according to CGA results, namely function independent, mildly function impaired, and function dependent. They received standardized therapy, individualized therapy, and best supportive care, respectively. The patients receiving standardized therapy and individualized therapy were randomized into two groups, with or without traditional Chinese medicine for symptom control, while for all the patients receiving best supportive care, traditional Chinese medicine was administered. Nine non-elderly NSCLC patients (<65 years old) were enrolled as control and treated in accordance with NCCN NSCLC treatment guidelines. EORTC QLQ-C30 core scale, LC13 scale, and MDASI-TCM scale were used to assess relevant symptoms before and after treatment. After treatment for 3 weeks, it was shown by QLQ-C30+LC13 scales, for function-dependent patients, that the physical and role performances and the global health status were improved and the symptoms of fatigue and cough were alleviated; by MDASI-TCM scale, the symptoms of fatigue, cough, and expectoration were improved. In function-independent and mildly function-impaired elderly patients, there were no significant changes in functional status and symptoms. But in non-elderly patients, the physical and social performances were lowered, and the symptoms of fatigue, constipation, and poor appetite were aggravated. The elderly patients with advanced NSCLC were categorized on the basis of CGA findings, and traditional Chinese medicine may be beneficial to symptom control of function-dependent patients. [ABSTRACT FROM AUTHOR]

Recently, sparse representation has received a great deal of attention in voxel selection and decoding analysis of functional magnetic resonance imaging (fMRI) data. Due to contamination of large noise in fMRI data, the conventional sparse representation methods may not get stable results. Moreover, the selected activated brain regions may lose clustering effects and are less biologically interpretable. In order to overcome the above mentioned problems, we exploit the error-tolerant formulation of sparse representation and introduce multi-dimensional derivative constraints (smoothness constraints) in optimization. Two new methods are proposed in this paper. One is robust voxel selection with multi-dimensional constraint (RVSMDC). With the error-tolerant formulation and smoothness constraints on regression coefficients, RVSMDC is robust against noise/error and achieves clustering effects. To directly decode neural activities from fMRI data, we also proposed robust sparse decoding with multi-dimensional constraints (RSDMDC), which minimize the regression error of fMRI data to the task function with sparsity and smoothness constraints on regression coefficients. Numerical results validate the effectiveness of the two proposed methods. [ABSTRACT FROM AUTHOR]

Wheelchair control requires multiple degrees of freedom and fast intention detection, which makes electroencephalography (EEG)-based wheelchair control a big challenge. In our previous study, we have achieved direction (turning left and right) and speed (acceleration and deceleration) control of a wheelchair using a hybrid brain-computer interface (BCI) combining motor imagery and P300 potentials. In this paper, we proposed hybrid EEG-EOG BCI, which combines motor imagery, P300 potentials, and eye blinking to implement forward, backward, and stop control of a wheelchair. By performing relevant activities, users (e.g., those with amyotrophic lateral sclerosis and locked-in syndrome) can navigate the wheelchair with seven steering behaviors. Experimental results on four healthy subjects not only demonstrate the efficiency and robustness of our brain-controlled wheelchair system but also indicate that all the four subjects could control the wheelchair spontaneously and efficiently without any other assistance (e.g., an automatic navigation system). [ABSTRACT FROM AUTHOR]

In this paper, we present a new model-based approach for human gait recognition in the sagittal plane via deterministic learning (DL) theory. Side silhouette lower limb joint angles characterize the gait system dynamics and are selected as the gait feature. Locally accurate identification of the gait system dynamics is achieved by using radial basis function (RBF) neural networks through DL. The obtained knowledge of the approximated gait system dynamics is stored in constant RBF networks. A gait signature is then derived from the extracted gait system dynamics along the phase portrait of joint angles. A bank of estimators is constructed using constant RBF networks to represent the training gait patterns. By comparing the set of estimators with a test gait pattern, a set of recognition errors are generated, and the average L norms of the errors are taken as the similarity measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern. Therefore, the test gait pattern can be rapidly recognized according to the smallest error principle. In contrast to other existing approaches, the main focus of this paper is on obtaining and reusing the knowledge of the gait system dynamics. Finally, experiments are carried out on the CASIA-A and CASIA-B gait databases to benchmark the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, a comparison of two existing P300 spellers is conducted. In the first speller, the visual stimuli of characters are presented in a single character (SC) paradigm and each button corresponding to a character flashes individually in a random order. The second speller is based on a region-based (RB) paradigm. In the first level, all characters are grouped and each button corresponding to a group flashes individually in a random order. Once a group is selected, the characters in it will appear on the flashing buttons of the second level for the selection of desired character. In a spelling experiment involving 12 subjects, higher online accuracy was obtained on the RB paradigm-based P300 speller than the SC paradigm-based P300 speller. Furthermore, we analyzed P300 detection performance, the P300 waveforms and Fisher ratios using the data collected by the two spellers. It was found that the stimuli display paradigm of the RB speller enhances P300 potential and is more suitable for P300 detection. [ABSTRACT FROM AUTHOR]

There are often demands for region target detection such as air pollution detection and oil spill monitoring, even though small target detection has gained much attention in the field of hyperspectral detection. In this paper, we present a long-tail distribution based multiscale-multivariate autoregressive hyperspectral detector to handle such region targets. We establish a multiscale-multiband autoregresive model (MMAM) to characterize the inter-band, the spatial and the band-spatial correlation in hyperspectral data simultaneously and have the corresponding multiscale-multiband likelihood ratio (MMLR) test. Due to the long tail property of MMAM noise, we treat the statistical characteristics of MMAM noise as multivariate t distribution. Then, alternating projection involving fixed-point iteration and gradient based searching (APFPGS) are utilized to fit this statistical distribution. Experimental results on the real hyperspectral imagery recorded with A series of Environmental Probe System (EPS-A) show that our approach has better performance in hyperspectral region target detection than the other four detectors. [ABSTRACT FROM AUTHOR]

In this paper, we address the important problem of feature selection for a P300-based brain computer interface (BCI) speller system in several aspects. Firstly, time segment selection and electroencephalogram channel selection are jointly performed for better discriminability of P300 and background signals. Secondly, in view of the situation that training data with labels are insufficient, we propose an iterative semi-supervised support vector machine for joint spatio-temporal feature selection as well as classification, in which both labeled training data and unlabeled test data are utilized. More importantly, the semi-supervised learning enables the adaptivity of the system. The performance of our algorithm has been evaluated through the analysis of a P300 dataset provided by BCI Competition 2005 and another dataset collected from an in-house P300 speller system. The results show that our algorithm for joint feature selection and classification achieves satisfactory performance, meanwhile it can significantly reduce the training effort of the system. Furthermore, this algorithm is implemented online and the corresponding results demonstrate that our algorithm can improve the adaptiveness of the P300-based BCI speller. [ABSTRACT FROM AUTHOR]

Parameter setting plays an important role for improving the performance of a brain computer interface (BCI). Currently, parameters (e.g. channels and frequency band) are often manually selected. It is time-consuming and not easy to obtain an optimal combination of parameters for a BCI. In this paper, motor imagery-based BCIs are considered, in which channels and frequency band are key parameters. First, a semi-supervised support vector machine algorithm is proposed for automatically selecting a set of channels with given frequency band. Next, this algorithm is extended for joint channel-frequency selection. In this approach, both training data with labels and test data without labels are used for training a classifier. Hence it can be used in small training data case. Finally, our algorithms are applied to a BCI competition data set. Our data analysis results show that these algorithms are effective for selection of frequency band and channels when the training data set is small. [ABSTRACT FROM AUTHOR]

In this paper, the general class of singular systems with delay and linear constant coefficient singular systems with delay are discussed. First, several definitions of stability are presented for singular systems with delay, and general sufficient stability conditions and instability conditions are obtained. Second, stability and instability are analyzed for linear constant coefficient singular systems with delay. [ABSTRACT FROM AUTHOR]

Existing evidence suggests that the default-mode network (DMN) and fronto-pariatal network (FPN) play an important role in altered states of consciousness. However, the brain mechanisms underlying impaired consciousness and the specific network interactions involved are not well understood. We studied the topological properties of brain functional networks using resting-state functional MRI data acquired from 18 patients (11 vegetative state/unresponsive wakefulness syndrome, VS/UWS, and 7 minimally conscious state, MCS) and compared these properties with those of healthy controls. We identified that the topological properties in DMN and FPN are anti-correlated which comes, in part, from the contribution of interactions between dorsolateral prefrontal cortex of the FPN and precuneus of the DMN. Notably, altered nodal connectivity strength was distance-dependent, with most disruptions appearing in long-distance connections within the FPN but in short-distance connections within the DMN. A multivariate pattern-classification analysis revealed that combination of topological patterns between the FPN and DMN could predict conscious state more effectively than connectivity within either network. Taken together, our results imply distinct interactions between the FPN and DMN, which may mediate conscious state. [ABSTRACT FROM AUTHOR]

Currently, detecting awareness in patients with disorders of consciousness (DOC) is a challenging task, which is commonly addressed through behavioral observation scales such as the JFK Coma Recovery Scale-Revised. Brain-computer interfaces (BCIs) provide an alternative approach to detect awareness in patients with DOC. However, these patients have a much lower capability of using BCIs compared to healthy individuals. This study proposed a novel BCI using temporally, spatially, and semantically congruent audiovisual stimuli involving numbers (i.e., visual and spoken numbers). Subjects were instructed to selectively attend to the target stimuli cued by instruction. Ten healthy subjects first participated in the experiment to evaluate the system. The results indicated that the audiovisual BCI system outperformed auditory-only and visual-only systems. Through event-related potential analysis, we observed audiovisual integration effects for target stimuli, which enhanced the discriminability between brain responses for target and nontarget stimuli and thus improved the performance of the audiovisual BCI. This system was then applied to detect the awareness of seven DOC patients, five of whom exhibited command following as well as number recognition. Thus, this audiovisual BCI system may be used as a supportive bedside tool for awareness detection in patients with DOC. [ABSTRACT FROM AUTHOR]