Structural controllability of general edge dynamics in complex network.

Dynamic processes that occur on the edge of complex networks are relevant to a variety of real-world systems, where states are defined on individual edges, and nodes are active components with information processing capabilities. In traditional studies of edge controllability, all adjacent edge states are assumed to be coupled. In this paper, we release this all-to-all coupling restriction and propose a general edge dynamics model. We give a theoretical framework to study the structural controllability of the general edge dynamics and find that the set of driver nodes for edge controllability is unique and determined by the local information of nodes. Applying our framework to a large number of model and real networks, we find that there exist lower and upper bounds of edge controllability, which are determined by the coupling density, where the coupling density is the proportion of adjacent edge states that are coupled. Then we investigate the proportion of effective coupling in edge controllability and find that homogeneous and relatively sparse networks have a higher proportion, and that the proportion is mainly determined by degree distribution. Finally, we analyze the role of edges in edge controllability and find that it is largely encoded by the coupling density and degree distribution, and are influenced by in- and out-degree correlation.

Two-dimensional ECG-based cardiac arrhythmia classification using DSE-ResNet.

Electrocardiogram (ECG) is mostly used for the clinical diagnosis of cardiac arrhythmia due to its simplicity, non-invasiveness, and reliability. Recently, many models based on the deep neural networks have been applied to the automatic classification of cardiac arrhythmia with great success. However, most models independently extract the internal features of each lead in the 12-lead ECG during the training phase, resulting in a lack of inter-lead features. Here, we propose a general model based on the two-dimensional ECG and ResNet with detached squeeze-and-excitation modules (DSE-ResNet) to realize the automatic classification of normal rhythm and 8 cardiac arrhythmias. The original 12-lead ECG is spliced into a two-dimensional plane like a grayscale picture. DSE-ResNet is used to simultaneously extract the internal and inter-lead features of the two-dimensional ECG. Furthermore, an orthogonal experiment method is used to optimize the hyper-parameters of DSE-ResNet and a multi-model voting strategy is used to improve classification performance. Experimental results based on the test set of China Physiological Signal Challenge 2018 (CPSC2018) show that our model has average [Formula: see text] for classifying normal rhythm and 8 cardiac arrhythmias. Meanwhile, compared with the state-of-art model in CPSC2018, our model achieved the best [Formula: see text] in 2 sub-abnormal types. This shows that the model based on the two-dimensional ECG and DSE-ResNet has advantage in detecting some cardiac arrhythmias and has the potential to be used as an auxiliary tool to help doctors perform cardiac arrhythmias analysis.

Deep Convolution Generative Adversarial Network-Based Electroencephalogram Data Augmentation for Post-Stroke Rehabilitation with Motor Imagery.

The motor imagery brain-computer interface (MI-BCI) system is currently one of the most advanced rehabilitation technologies, and it can be used to restore the motor function of stroke patients. The deep learning algorithms in the MI-BCI system require lots of training samples, but the electroencephalogram (EEG) data of stroke patients is quite scarce. Therefore, the expansion of EEG data has become an important part of stroke clinical rehabilitation research. In this paper, a deep convolution generative adversarial network (DCGAN) model is proposed to generate artificial EEG data and further expand the scale of the stroke dataset. First, multichannel one-dimensional EEG data is converted into a two-dimensional EEG spectrogram using EEG2Image based on the modified S-transform. Then, DCGAN is used to artificially generate EEG data based on MI. Finally, the validity of the generated artificial EEG data is proved. This paper preliminarily indicates that generating artificial stroke data is a promising strategy, which contributes to the further development of stroke clinical rehabilitation.

A framework for motor imagery with LSTM neural network.

BACKGROUND AND OBJECTIVE: How to learn robust representations from brain activities and to improve algorithm performance are the most significant issues for brain-computer interface systems. METHODS: This study introduces a long short-term memory recurrent neural network to decode the multichannel electroencephalogram or electrocorticogram for implementing an effective motor imagery-based brain-computer interface system. The unique information processing mechanism of the long short-term memory network characterizes spatio-temporal dynamics in time sequences. This study evaluates the proposed method using publically available electroencephalogram/electrocorticogram datasets. RESULTS: The decoded features coupled with a gradient boosting classifier could obtain high recognition accuracies of 99% for electroencephalogram and 100% for electrocorticogram, respectively. CONCLUSIONS: The results demonstrated that the proposed model can estimate robust spatial-temporal features and obtain significant performance improvement for motor imagery-based brain-computer interface systems. Further, the proposed method is of low computational complexity.

Controllability limit of edge dynamics in complex networks.

Edge dynamics is relevant to various real-world systems with complex network topological features. An edge dynamical system is controllable if it can be driven from any initial state to any desired state in finite time with appropriate control inputs. Here a framework is proposed to study the impact of correlation between in- and out-degrees on controlling the edge dynamics in complex networks. We use the maximum matching and direct acquisition methods to determine the controllability limit, i.e., the limit of acceptable change of the edge controllability by adjusting the degree correlation only. Applying the framework to plenty complex networks, we find that the controllability limits are ubiquitous in model and real networks. Arbitrary edge controllability in between the limits can be achieved by properly adjusting the degree correlation. Moreover, a nonsmooth phenomenon occurs in the upper limits, and exponential and power-law scaling behaviors are widespread in the approach or separation speed between the upper and lower limits.

Universal framework for edge controllability of complex networks.

Dynamical processes occurring on the edges in complex networks are relevant to a variety of real-world situations. Despite recent advances, a framework for edge controllability is still required for complex networks of arbitrary structure and interaction strength. Generalizing a previously introduced class of processes for edge dynamics, the switchboard dynamics, and exploit- ing the exact controllability theory, we develop a universal framework in which the controllability of any node is exclusively determined by its local weighted structure. This framework enables us to identify a unique set of critical nodes for control, to derive analytic formulas and articulate efficient algorithms to determine the exact upper and lower controllability bounds, and to evaluate strongly structural controllability of any given network. Applying our framework to a large number of model and real-world networks, we find that the interaction strength plays a more significant role in edge controllability than the network structure does, due to a vast range between the bounds determined mainly by the interaction strength. Moreover, transcriptional regulatory networks and electronic circuits are much more strongly structurally controllable (SSC) than other types of real-world networks, directed networks are more SSC than undirected networks, and sparse networks are typically more SSC than dense networks.

Robustness of controlling edge dynamics in complex networks against node failure.

The robustness of controlling complex networks is significant in network science. In this paper, we focus on evaluating and analyzing the robustness of controlling edge dynamics in complex networks against node failure. Using three categories of all nodes to quantify the robustness, we find that the percentages of the three types of nodes are mainly related to the degree distribution of networks. The simulation results of model networks and analytic calculations show that the sparse inhomogeneous networks, which emerge in many real complex networks, have strong control robustness from the point of the number of ordinary nodes, but the strong positive correlation between in and out degrees reduces the control robustness. Evaluation of real-world networks indicates that most of them have few or no critical nodes, that is, they do not need to increase driver nodes to maintain control for most of node failures. Then an adding circuit-link strategy is proposed to optimize the robustness of edge controllability.

Layer by layer self-assembly of poly[2-(methacryloyloxy) ethyl phosphorylcholine] multilayer via the ionic complexation with zirconium.

Zirconium-phosphonate (Zr-P) ionic complexation chemistry is explored as a new approach to fabricate poly[2-(methacryloyloxy) ethyl phosphorylcholine] (PMPC) multilayer film by layer-by-layer self-assembly method. Quartz crystal microbalance with dissipation (QCM-D) and optical ellipsometry measurements demonstrated that PMPC layer can be fully absorbed on each Zr(4+) layer. The thickness of the multilayer film with a good linear relationship was followed by the ellipsometry in situ adlayer characterization. The influence of pH of the PMPC and Zr(4+) solutions on the multilayer deposition were investigated by optical ellipsometry. QCM-D results indicated that the multilayer film is stable in a PBS flowing chamber at a high flow rate of 5.2×10(-3)m/s. The ellipsometry data demonstrated that 67.2% of the film still remained on the silicon wafer after being strong shaken in PBS at 80 rpm for 12h. The adsorption of bovine serum albumin (BSA) and fetal bovine serum (FBS) on the PMPC surface was monitored by the QCM-D and spectroscopic ellipsometry, and the results showed the multilayer film have excellent protein resistance.

Aggregation-induced emission of tetraphenylethene derivative as a fluorescence method for probing the assembling/disassembling of amphiphilic molecules.

The aggregation-induced emission (AIE) of a 1,2-diphenyl-1,2-di(p-tolyl)ethene (TPE) was explored as a novel fluorescence method for probing the assembling/disassembling of amphiphilic molecules. The fluorescence intensity was able to monitor the formation of micelles and determine the critical micelle concentration (CMC) of surfactants. The temperature-dependent micellization of the pharmaceutically important PEO-PPO-PEO copolymer, Pluronic F127, was further studied by using the TPE fluorescence spectrum intensity. Our results showed good agreement with those reported in the literature by using other methods. The special advantage of the AIE probe method was further explored to determine the assembling/disassembling process of the colored amphiphilic molecule, 1-[4-(3-phenylazophenoxy)butyl]triethylamine bromide (AzoC4), whose CMC value has not previously been described. Since the TPE fluorescence signal mainly comes from the aqueous phase, not from the inside of hydrophobic core, it provides a possible platform to study the CMC of those colored surfactants. Based on the novel fluorescence properties of TPE in the aggregated and dispersed states, one can conclude that the TPE method is a promising method for the determination of the CMC and critical micellization temperature (CMT), particularly having a special advantage to determine the assembling/disassembling process of colored amphiphilic molecules.

Biocompatible micelles based on comb-like PEG derivates: formation, characterization, and photo-responsiveness.

A novel comb-like derivative CPEG-g-DNQ was prepared by incorporating light responsive 2-diazo-1,2-naphthoquinone (DNQ) groups into the structure of comb-like poly(ethylene glycol) (CPEG). DLS and TEM results showed that CPEG-g-DNQ self-assembled into spherical micelles with an average size of about 135 nm in water. Upon exposure to light, the micelles could be disrupted because of the conversion of hydrophobic DNQ to hydrophilic 3-indenecarboylic acid. Additionally, hydrophobic coumarin 102 was successfully loaded into the micelles and photo-induced ON-OFF release was demonstrated by fluorescence spectroscopy. MTT assay revealed that the micelles are biocompatible. These photo-responsive micelles might have great potential for controlled release of hydrophobic drugs.

Fluorescence detection of alkaline phosphatase activity with ß-cyclodextrin-modified quantum dots.

An alkaline phosphatase activity detection system was constructed based on the different quenching effect of the enzyme substrate and product on the ß-CD-functionalized CdTe QDs.