ß-Glucuronidase-triggered reaction for fluorometric and colorimetric dual-mode assay based on the in situ formation of silicon nanoparticles.

BACKGROUND: ß-Glucuronidase (GUS) is considered as a promising biomarker for primary cancer. Thus, the reliable detection of GUS has great practical significance in the discovery and diagnosis of cancer. Compared with traditional organic probes, silicon nanoparticles (Si NPs) have emerged as robust optical nanomaterials due to their facile preparation, superior photobleaching resistance and excellent biocompatibility. However, most nanomaterials-based methods only output a single signal which is easily influenced by external factors in complex systems. Hence, developing nanomaterial-based multi-signal optical assays for highly sensitive GUS determination is still urgently desired. RESULTS: In this study, we developed a simple and efficient one-step method for the in situ preparation of yellow color and yellow-green fluorescent Si NPs. This was achieved by combining 3-[2-(2-aminoethylamino) ethylamino] propyl-trimethoxysilane with p-aminophenol (AP) in an aqueous solution. The obtained Si NPs showed yellow-green fluorescence at 535 nm when excited at 380 nm, while also exhibiting an absorption peak at a wavelength of 490 nm. Taking inspiration from the easy synthesis step regulated by AP, which is generated through the hydrolysis of 4-aminophenyl ß-D-glucuronide catalyzed by GUS, we constructed a direct fluorometric and colorimetric dual-mode method to measure GUS activity. The developed fluorometric and colorimetric sensing platform showed high sensitivity and accuracy with detection limits for GUS determination as low as 0.0093 and 0.081 U/L, respectively. SIGNIFICANCE: This study provides a facile dual-mode fluorometric and colorimetric approach for determination of GUS activity based on novel Si NPs for the first time. This designed sensing approach was successfully employed for the quantification of GUS in human serum samples and screening of GUS inhibitors, indicating the feasibility and potential applications in clinical cancer diagnosis and anti-cancer drug discovery.

Optimization of epilepsy detection method based on dynamic EEG channel screening.

To decrease the interference in the process of epileptic feature extraction caused by insufficient detection capability in partial channels of focal epilepsy, this paper proposes a novel epilepsy detection method based on dynamic electroencephalogram (EEG) channel screening. This method not only extracts more effective epilepsy features but also finds common features among different epilepsy subjects, providing an effective approach and theoretical support for across-subject epilepsy detection in clinical scenarios. Firstly, we use the Refine Composite Multiscale Dispersion Entropy (RCMDE) to measure the complexity of EEG signals between normal and seizure states and realize the dynamic EEG channel screening among different subjects, which can enhance the capability of feature extraction and the robustness of epilepsy detection. Subsequently, we discover common epilepsy features in 3-15 Hz among different subjects by the screened EEG channels. By this finding, we construct the Residual Convolutional Long Short-Term Memory (ResCon-LSTM) neural network to accomplish across-subject epilepsy detection. The experiment results on the CHB-MIT dataset indicate that the highest accuracy of epilepsy detection in the single-subject experiment is 98.523 %, improved by 5.298 % compared with non-channel screening. In the across-subject experiment, the average accuracy is 96.596 %. Therefore, this method could be effectively applied to different subjects by dynamically screening optimal channels and keep a good detection performance.

The effects of miRNA-27a-3p on human epidermal melanocytes.

OBJECTIVE: To characterize the effects of miRNA-27a-3p on the biological properties of human epidermal melanocytes (MCs). METHODS: MCs were obtained from human foreskins and transfected with miRNA-27a-3p mimic (induces the overexpression of miRNA-27a-3p), mimic-NC (the negative control group), miRNA-27a-3p inhibitor, or inhibitor-NC. After transfection, the proliferation of MCs in each group was evaluated by cell counting kit-8 (CCK-8) at 1, 3, 5, and 7 days. Twenty-four hours later, the MCs were transferred onto a living cell imaging platform and cultured for another 12 h to detect their trajectories and velocities. On days 3, 4, and 5 after transfection, the expression of melanogenesis-related mRNAs, protein levels, and melanin contents were measured using reverse transcription-polymerase chain reaction (RT-PCR), Western blotting, and NaOH solubilization, respectively. RESULTS: The RT-PCR results showed that miRNA-27a-3p was successfully transfected into MCs. The proliferation of MCs was restrained by miRNA-27a-3p. There were no significant differences in the movement trajectories of MCs in the four transfected groups, but the cell movement velocity in the mimic group was slightly lower; that is, the overexpression of miRNA-27a-3p inhibited the speed of MCs. The expression levels of melanogenesis-related mRNAs and proteins were decreased in the mimic group and were increased in the inhibitor group. Melanin content in the mimic group was lower than that in the other three groups. CONCLUSIONS: Overexpression of miRNA-27a-3p inhibits the expression of melanogenesis-related mRNAs and proteins, reduces the melanin content of human epidermal MCs, and slightly impacts their movement speed.

RNA-seq and ATAC-seq analyses of multilineage differentiating stress enduring cells: Comparison with dermal fibroblasts.

The aim of this study is to characterize the molecular properties of multilineage differentiating stress-enduring (Muse) cells compared with dermal fibroblasts (FBs) and to characterize differences in their transcriptomes and open chromatin regions that are involved in cellular plasticity. Assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) and RNA sequencing (RNA-seq) analyses was then performed on FBs and Muse cells. Subsequently, cell type-selective gene regulatory regions were identified by coalition analysis. Expression patterns of transcription factors (TFs) and signaling pathways intermediates were verified using quantitative real-time polymerase chain reaction and Western blot analyses. RNA-seq identified 2355 significantly differentially expressed genes (DEGs) that regulate the transcriptome, including 1222 upregulated and 1133 downregulated DEGs. The general panorama of RNA-seq and ATAC-seq analyses confirmed the differences in TFs and open chromatin regions between FBs and Muse cells. ATAC-seq analysis showed that Muse cells had more reproducible and meaningful peaks than FBs, and the peak signals were concentrated near promoter-transcription start site areas. In genomic regions that can be preferentially accessed in FBs and Muse cells, more than 200 TFs had binding motif sequences. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and coalition analyses identified differences in factors involved in the cell cycle and the protein kinase B (AKT) signaling pathway of FBs and Muse cells. The results of RNA-seq and ATAC-seq analyses clarified the genetic basis of the different biological properties of Muse cells and FBs. These results suggest that the cell cycle transition and the AKT signaling pathway may affect the morphology and biological characteristics of Muse cells.

Evaluating the metabolic functional profiles of the microbial community and alfalfa (Medicago sativa) traits affected by the presence of carbon nanotubes and antimony in drained and waterlogged sediments.

Antimony (Sb) is the ubiquitous re-emerging contaminant greatly accumulated in sediments which has been revealed risky to ecological environment. However, the impacts of Sb (III/V) on microbes and plants in sediments, under different water management with presence of engineering materials are poorly understood. This study conducted sequential incubation of sediments (flooding, draining and planting) with presence of multiwall carbon nanotubes (MWCNTs) and Sb to explore the influence on microbial functional diversity, Sb accumulation and alfalfa traits. Results showed that water management and planting led to greater impacts of sediment enzyme activities and microbial community metabolic function and bioavailable Sb fractions (defined as sum of acid-soluble fraction and reducible fraction, F1 + F2). Available fractions of Sb (V) showed higher correlation to microbial metabolism (r = 0.933) than that of Sb (III) (r = -0.480) in planting stage. MWCNTs with increasing concentrations (0.011%, w/w) positively correlated to microbial community metabolic function in planting stage whereas resulted in decreasing of Sb (III/V) concentrations in alfalfa, although 0.01% MWCNT led to increase of Sb (V) and decrease of Sb (V) by 50.97% and 32.68% respectively. This study provided information for investigating combined ecological impacts of heavy metal and engineering materials under different water managing sediments.

Deep Learning with Skip Connection Attention for Choroid Layer Segmentation in OCT Images.

Since the thickness and shape of the choroid layer are indicators for the diagnosis of several ophthalmic diseases, the choroid layer segmentation is an important task. There exist many challenges in segmentation of the choroid layer. In this paper, in view of the lack of context information due to the ambiguous boundaries, and the subsequent inconsistent predictions of the same category targets ascribed to the lack of context information or the large regions, a novel Skip Connection Attention (SCA) module which is integrated into the U-Shape architecture is proposed to improve the precision of choroid layer segmentation in Optical Coherence Tomography (OCT) images. The main function of the SCA module is to capture the global context in the highest level to provide the decoder with stage-by-stage guidance, to extract more context information and generate more consistent predictions for the same class targets. By integrating the SCA module into the U-Net and CE-Net, we show that the module improves the accuracy of the choroid layer segmentation.

Improve the Classification Efficiency of High-Frequency Phase-Tagged SSVEP by a Recursive Bayesian-Based Approach.

Among the Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), the phase-tagged SSVEP (p-SSVEP) has been proved a reliable paradigm to extend the number of available targets, especially for high-frequency SSVEP-based BCIs. However, the recognition efficiency of the high-frequency p-SSVEP still remains relatively low. A longer data segment may achieve a higher classification accuracy, but the time consumption of computation leads to the decrease of information transfer rate. This paper presents a recursive Bayesian-based approach to improve the high-frequency p-SSVEP classification efficiency. In each signal processing period, the classification result is generated by the current scores, the condition probability and a recursive prior probability (dynamic prior probability). The experiment displays the SSVEP stimuli with 20 Hz and 30 Hz respectively, and each frequency contains six phases. This paper compared three classification approaches and the recursive Bayesian-based approach could obtain the highest classification accuracy and practical bit rate under the same data length. The mean accuracy and practical bit rate were 89.7% and 37.8 bits/min with 20Hz, and 89.0% and 36.5 bits/min with 30Hz, respectively Furthermore, the recursive Bayesian-based approach could reduce the individual differences among different subjects. Therefore, the recursive Bayesian-based approach can lead to high classification efficiency in high-frequency p-SSVEP.

A Brain-Robot Interaction System by Fusing Human and Machine Intelligence.

This paper presents a new brain-robot interaction system by fusing human and machine intelligence to improve the real-time control performance. This system consists of a hybrid P300 and steady-state visual evoked potential (SSVEP) mode conveying a human being's intention, and the machine intelligence combining a fuzzy-logic-based image processing algorithm with multi-sensor fusion technology. A subject selects an object of interest via P300, and the classification algorithm transfers the corresponding parameters to an improved fuzzy color extractor for object extraction. A central vision tracking strategy automatically guides the NAO humanoid robot to the destination selected by the subject intentions represented by brainwaves. During this process, human supervises the system at high level, while machine intelligence assists the robot in accomplishing tasks by analyzing image feeding back from the camera, distance monitoring using out-of-gauge alarms from sonars, and collision detecting from bumper sensors. In this scenario, the SSVEP takes over the situations in which the machine intelligence cannot make decisions. The experimental results show that the subjects can control the robot to a destination of interest, with fewer commands than only using a brain-robot interface. Therefore, the fusion of human and machine intelligence greatly alleviates the brain load and enhances the robot executive efficiency of a brain-robot interaction system.

Object Extraction in Cluttered Environments via a P300-Based IFCE.

One of the fundamental issues for robot navigation is to extract an object of interest from an image. The biggest challenges for extracting objects of interest are how to use a machine to model the objects in which a human is interested and extract them quickly and reliably under varying illumination conditions. This article develops a novel method for segmenting an object of interest in a cluttered environment by combining a P300-based brain computer interface (BCI) and an improved fuzzy color extractor (IFCE). The induced P300 potential identifies the corresponding region of interest and obtains the target of interest for the IFCE. The classification results not only represent the human mind but also deliver the associated seed pixel and fuzzy parameters to extract the specific objects in which the human is interested. Then, the IFCE is used to extract the corresponding objects. The results show that the IFCE delivers better performance than the BP network or the traditional FCE. The use of a P300-based IFCE provides a reliable solution for assisting a computer in identifying an object of interest within images taken under varying illumination intensities.

Progress in EEG-Based Brain Robot Interaction Systems.

The most popular noninvasive Brain Robot Interaction (BRI) technology uses the electroencephalogram- (EEG-) based Brain Computer Interface (BCI), to serve as an additional communication channel, for robot control via brainwaves. This technology is promising for elderly or disabled patient assistance with daily life. The key issue of a BRI system is to identify human mental activities, by decoding brainwaves, acquired with an EEG device. Compared with other BCI applications, such as word speller, the development of these applications may be more challenging since control of robot systems via brainwaves must consider surrounding environment feedback in real-time, robot mechanical kinematics, and dynamics, as well as robot control architecture and behavior. This article reviews the major techniques needed for developing BRI systems. In this review article, we first briefly introduce the background and development of mind-controlled robot technologies. Second, we discuss the EEG-based brain signal models with respect to generating principles, evoking mechanisms, and experimental paradigms. Subsequently, we review in detail commonly used methods for decoding brain signals, namely, preprocessing, feature extraction, and feature classification, and summarize several typical application examples. Next, we describe a few BRI applications, including wheelchairs, manipulators, drones, and humanoid robots with respect to synchronous and asynchronous BCI-based techniques. Finally, we address some existing problems and challenges with future BRI techniques.

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots.

Brain-Robot Interaction (BRI), which provides an innovative communication pathway between human and a robotic device via brain signals, is prospective in helping the disabled in their daily lives. The overall goal of our method is to establish an SSVEP-based experimental procedure by integrating multiple software programs, such as OpenViBE, Choregraph, and Central software as well as user developed programs written in C++ and MATLAB, to enable the study of brain-robot interaction with humanoid robots. This is achieved by first placing EEG electrodes on a human subject to measure the brain responses through an EEG data acquisition system. A user interface is used to elicit SSVEP responses and to display video feedback in the closed-loop control experiments. The second step is to record the EEG signals of first-time subjects, to analyze their SSVEP features offline, and to train the classifier for each subject. Next, the Online Signal Processor and the Robot Controller are configured for the online control of a humanoid robot. As the final step, the subject completes three specific closed-loop control experiments within different environments to evaluate the brain-robot interaction performance. The advantage of this approach is its reliability and flexibility because it is developed by integrating multiple software programs. The results show that using this approach, the subject is capable of interacting with the humanoid robot via brain signals. This allows the mind-controlled humanoid robot to perform typical tasks that are popular in robotic research and are helpful in assisting the disabled.