UNet retinal blood vessel segmentation algorithm based on improved pyramid pooling method and attention mechanism.

The segmentation results of retinal vessels have a significant impact on the automatic diagnosis of retinal diabetes, hypertension, cardiovascular and cerebrovascular diseases and other ophthalmic diseases. In order to improve the performance of blood vessels segmentation, a pyramid scene parseing U-Net segmentation algorithm based on attention mechanism was proposed. The modified PSP-Net pyramid pooling module is introduced on the basis of U-Net network, which aggregates the context information of different regions so as to improve the ability of obtaining global information. At the same time, attention mechanism was introduced in the skip connection part of U-Net network, which makes the integration of low-level features and high-level semantic features more efficient and reduces the loss of feature information through nonlinear connection mode. The sensitivity, specificity, accuracy and AUC of DRIVE and CHASE_DB1 data sets are 0.7814, 0.9810, 0.9556, 0.9780; 0.8195, 0.9727, 0.9590, 0.9784. Experimental results show that the PSP-UNet segmentation algorithm based on the attention mechanism enhances the detection ability of blood vessel pixels, suppresses the interference of irrelevant information and improves the network segmentation performance, which is superior to U-Net algorithm and some mainstream retinal vascular segmentation algorithms at present.

MicroRNA-126 Level Increases During Exercise Rehabilitation of Heart Failure with a Preserved Ejection Fraction.

OBJECTIVE: To evaluate the changes of plasma levels of miR-126 in heart failure with a preserved ejection fraction (HFpEF) patients undergoing an exercise rehabilitation intervention. METHODS: miR-126 levels in plasma were compared between 60HFpEF patients and 30 healthy volunteers. HFpEF patients underwent exercise rehabilitation for 12 weeks. Before and after rehabilitation, indicators of cardiac function, exercise tolerance, quality of life scores and miR-126 levels were measured and compared. Correlations between plasma levels of miR-126 and HFpEF were evaluated. RESULTS: The plasma levels of miR-126 in HFpEF patients were lower than those in healthy volunteers and increased significantly after exercise rehabilitation. HFpEF patients also showed significantly better cardiac function, exercise tolerance, and quality of life after rehabilitation. The results of Pearson correlation analysis and multiple linear regression showed that miR-126 levels were positively correlated with peak oxygen consumption (peak VO2) and metabolic equivalents (METs), and inversely associated with score on the Minnesota Living with Heart Failure Questionnaire (MLHF) as well as plasma N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels. CONCLUSION: miR-126 levels are low expressed in plasma among HFpEF patients. Effective exercise rehabilitation in HFpEF patients may positively impact the plasma level of miR-126, which is probably associated with the restoration of cardiac function, exercise tolerance and quality of life. miR-126 may be a potential biomarker for evaluating the efficacy of exercise rehabilitation for HFpEF patients.

Z-Shaped Transfer Functions for Binary Particle Swarm Optimization Algorithm.

Particle swarm optimization (PSO) algorithm is a swarm intelligent searching algorithm based on population that simulates the social behavior of birds, bees, or fish groups. The discrete binary particle swarm optimization (BPSO) algorithm maps the continuous search space to a binary space through a new transfer function, and the update process is designed to switch the position of the particles between 0 and 1 in the binary search space. Aiming at the existed BPSO algorithms which are easy to fall into the local optimum, a new Z-shaped probability transfer function is proposed to map the continuous search space to a binary space. By adopting nine typical benchmark functions, the proposed Z-probability transfer function and the V-shaped and S-shaped transfer functions are used to carry out the performance simulation experiments. The results show that the proposed Z-shaped probability transfer function improves the convergence speed and optimization accuracy of the BPSO algorithm.

Extrapyramidal side effect of donepezil hydrochloride in an elderly patient: A case report.

INTRODUCTION: Alzheimer disease (AD) is a neurodegenerative disease characterized by progressive cognitive dysfunction, which is mainly manifested as memory impairment and a reduced ability to self-care, often accompanied by neuropsychiatric and behavioral disorders. Donepezil is the second drug to be approved by the US FDA for the treatment of AD. Of the five FDA-approved drugs for AD treatment, donepezil is currently the most widely used. Here, we report an extrapyramidal adverse reaction to donepezil in an elderly patient with AD. PATIENT CONCERNS: An 87-year-old woman presented with a 1-year history of forgetfulness that was aggravated since the past 2 months. She had a long-term history of multiple major conditions, including hypertension, diabetes, osteoporosis, and arterial plaques. Brain imaging showed age-related changes, and her Mini Mental State Examination score was 20. Other tests revealed no abnormalities apart from multiple thyroid nodules on ultrasonography. DIAGNOSIS: She was diagnosed with AD, hypertension, type 2 diabetes mellitus, diabetic neuropathy, osteoporosis, carotid and lower-extremity arterial plaques, thyroid nodules. INTERVENTIONS: She was treated with donepezil (5 mg/day), amlodipine besylate (5 mg/day), glimepiride (4 mg/day), methylcobalamin (1.5 mg/day), calcium carbonate D3 (600 mg/day), simvastatin (20 mg/day) and enteric-coated aspirin (100 mg/day). OUTCOMES: Four days later, she experienced fatigue, panic, sweating, and one episode of vomiting. On the 5th day, she developed increased muscle tension, speech difficulty, and involuntary tremors. Imaging and blood tests revealed no obvious abnormality, and the patient was not receiving psychotropic drugs. An extrapyramidal adverse reaction to donepezil was considered, and the drug was discontinued, after which the symptoms gradually disappeared. CONCLUSION: Serious adverse reactions to donepezil can occur in elderly patients, who typically require multiple medications for a variety of comorbidities. In particular, extrapyramidal reactions have occurred when donepezil is administered in combination with psychotropic drugs. However, in our patient, an extrapyramidal adverse reaction occurred in the absence of psychotropic drugs. Thus, clinicians must be aware of inter-individual differences in drug actions and possible serious adverse reactions, and carefully monitor these patients to ensure the timely detection of adverse events and their safe treatment.

Improved Bat Algorithm Based on Multipopulation Strategy of Island Model for Solving Global Function Optimization Problem.

The bat algorithm (BA) is a heuristic algorithm that globally optimizes by simulating the bat echolocation behavior. In order to improve the search performance and further improve the convergence speed and optimization precision of the bat algorithm, an improved algorithm based on chaotic map is introduced, and the improved bat algorithm of Levy flight search strategy and contraction factor is proposed. The optimal chaotic map operator is selected based on the simulation experiments results. Then, a multipopulation parallel bat algorithm based on the island model is proposed. Finally, the typical test functions are used to carry out the simulation experiments. The simulation results show that the proposed improved algorithm can effectively improve the convergence speed and optimization accuracy.

An Improved Grey Wolf Optimizer Based on Differential Evolution and Elimination Mechanism.

The grey wolf optimizer (GWO) is a novel type of swarm intelligence optimization algorithm. An improved grey wolf optimizer (IGWO) with evolution and elimination mechanism was proposed so as to achieve the proper compromise between exploration and exploitation, further accelerate the convergence and increase the optimization accuracy of GWO. The biological evolution and the "survival of the fittest" (SOF) principle of biological updating of nature are added to the basic wolf algorithm. The differential evolution (DE) is adopted as the evolutionary pattern of wolves. The wolf pack is updated according to the SOF principle so as to make the algorithm not fall into the local optimum. That is, after each iteration of the algorithm sort the fitness value that corresponds to each wolf by ascending order, and then eliminate R wolves with worst fitness value, meanwhile randomly generate wolves equal to the number of eliminated wolves. Finally, 12 typical benchmark functions are used to carry out simulation experiments with GWO with differential evolution (DGWO), GWO algorithm with SOF mechanism (SGWO), IGWO, DE algorithm, particle swarm algorithm (PSO), artificial bee colony (ABC) algorithm and cuckoo search (CS) algorithm. Experimental results show that IGWO obtains the better convergence velocity and optimization accuracy.

Effect of the Infrared Absorption Properties of Sodium Nitrate with Photonic Crystal.

The two-dimensional photonic crystal (Ni mesh with square lattice of 10 µm) was prepared and Ni film was thickened to 3 µm by electroplating. Its optical properties and its influence on the infrared absorption of sodium nitrate were investigated. The results show that there is a transmission peak centered at 1 450 cm-1 under normal incidence, and the peak covered the frequencies ranging from 1 300 to 1 500 cm-1, the antisymmetric stretching vibration of nitrate. Moreover, the change of absorption intensities of nitrate's antisymmetric stretching vibration is essentially consisting with the transmittance of photonic crystal. It indicates that the absorption intensity of sodium nitrate is improved by the modulated infrared light.

Feed-Forward Neural Network Soft-Sensor Modeling of Flotation Process Based on Particle Swarm Optimization and Gravitational Search Algorithm.

For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, a feed-forward neural network (FNN) based soft-sensor model optimized by the hybrid algorithm combining particle swarm optimization (PSO) algorithm and gravitational search algorithm (GSA) is proposed. Although GSA has better optimization capability, it has slow convergence velocity and is easy to fall into local optimum. So in this paper, the velocity vector and position vector of GSA are adjusted by PSO algorithm in order to improve its convergence speed and prediction accuracy. Finally, the proposed hybrid algorithm is adopted to optimize the parameters of FNN soft-sensor model. Simulation results show that the model has better generalization and prediction accuracy for the concentrate grade and tailings recovery rate to meet the online soft-sensor requirements of the real-time control in the flotation process.

Cuckoo Search Algorithm Based on Repeat-Cycle Asymptotic Self-Learning and Self-Evolving Disturbance for Function Optimization.

In order to improve convergence velocity and optimization accuracy of the cuckoo search (CS) algorithm for solving the function optimization problems, a new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird's nests location. In order to select a reasonable repeat-cycled disturbance number, a further study on the choice of disturbance times is made. Finally, six typical test functions are adopted to carry out simulation experiments, meanwhile, compare algorithms of this paper with two typical swarm intelligence algorithms particle swarm optimization (PSO) algorithm and artificial bee colony (ABC) algorithm. The results show that the improved cuckoo search algorithm has better convergence velocity and optimization accuracy.

SOM neural network fault diagnosis method of polymerization kettle equipment optimized by improved PSO algorithm.

For meeting the real-time fault diagnosis and the optimization monitoring requirements of the polymerization kettle in the polyvinyl chloride resin (PVC) production process, a fault diagnosis strategy based on the self-organizing map (SOM) neural network is proposed. Firstly, a mapping between the polymerization process data and the fault pattern is established by analyzing the production technology of polymerization kettle equipment. The particle swarm optimization (PSO) algorithm with a new dynamical adjustment method of inertial weights is adopted to optimize the structural parameters of SOM neural network. The fault pattern classification of the polymerization kettle equipment is to realize the nonlinear mapping from symptom set to fault set according to the given symptom set. Finally, the simulation experiments of fault diagnosis are conducted by combining with the industrial on-site historical data of the polymerization kettle and the simulation results show that the proposed PSO-SOM fault diagnosis strategy is effective.

Features extraction of flotation froth images and BP neural network soft-sensor model of concentrate grade optimized by shuffled cuckoo searching algorithm.

For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy.

Improved GSO optimized ESN soft-sensor model of flotation process based on multisource heterogeneous information fusion.

For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, an echo state network (ESN) based fusion soft-sensor model optimized by the improved glowworm swarm optimization (GSO) algorithm is proposed. Firstly, the color feature (saturation and brightness) and texture features (angular second moment, sum entropy, inertia moment, etc.) based on grey-level co-occurrence matrix (GLCM) are adopted to describe the visual characteristics of the flotation froth image. Then the kernel principal component analysis (KPCA) method is used to reduce the dimensionality of the high-dimensional input vector composed by the flotation froth image characteristics and process datum and extracts the nonlinear principal components in order to reduce the ESN dimension and network complex. The ESN soft-sensor model of flotation process is optimized by the GSO algorithm with congestion factor. Simulation results show that the model has better generalization and prediction accuracy to meet the online soft-sensor requirements of the real-time control in the flotation process.