Evaluation of Hypoglycemic and Antioxidant Activities of Soybean Meal Products Fermented by Lactobacillus plantarum FPS 2520 and Bacillus subtilis N1 in Rats Fed with High-Fat Diet.

The hypoglycemic and antioxidant activities of Lactobacillus plantarum FPS 2520 and/or Bacillus subtilis N1 fermented soybean meal (SBM) in rats fed a high-fat diet (HFD) were investigated by assessing plasma glucose levels, insulin resistance, and oxidative stress-induced organ damage. Supplementation with FPS 2520- and/or N1-fermented SBM (500 and 1000 mg/kg of body weight per day) to HFD-induced obese rats for 6 weeks significantly down-regulated the concentration of plasma glucose during the oral glucose tolerance test (OGTT), as well as the concentration of fasting plasma glucose, insulin, and the value of the homeostasis model assessment of insulin resistance (HOMA-IR). In addition, plasma and hepatic levels of malondialdehyde (MDA) were alleviated in rats fed fermented SBM, especially SBM fermented by mixed strains. Moreover, fermented SBM treatment reduced HFD-exacerbated increases in plasma aspartate aminotransferase (AST), alanine aminotransferase (ALT), creatinine, and uric acid levels. Based on these results, we clearly demonstrate the effect of fermented SBM on improving insulin resistance and oxidation-induced organ damage. Therefore, it is suggested that fermented SBM has the potential to be developed as functional foods for the management of obesity-induced hyperglycemia and organ damage.

Marinacci anastomosis (reverse Martin-Gruber anastomosis): A case report.

RATIONALE: The incidence of Martin-Gruber anastomosis ranges from 5% to 34%, which is characterized by crossing over from the median to the ulnar nerve and innervating the first dorsal interosseous, thenar or hypothenar muscles. However, the reverse Martin-Gruber anastomosis, or Marinacci anastomosis, is far less discussed and appears in recent literature. PATIENT CONCERNS: A 56-year-old man presented to the clinic of a university hospital because of left neck soreness with numbness radiating to the left lateral shoulder. The neck discomfort was aggravated while the neck rotated or tilted to the right. DIAGNOSIS: Higher compound muscle action potential over the abductor pollicis brevis on elbow stimulation than on the wrist was found during upper limb nerve conduction velocity study. Ulnar to median anastomosis was identified. INTERVENTION: We performed cervical spine X-ray and electrophysiological examinations and monitored the patient. OUTCOMES: We identified that this patient had left C5 and C6 subacute radiculopathy with active denervation and left subclinical ulnar sensory neuropathy, and verified the existence of ulnar-to-median anastomosis. LESSONS: We demonstrated a pure motor ulnar-to-median anastomosis without sensory correspondence and higher CMAP over the abductor pollicis brevis on elbow stimulation of the ulnar nerve than on the wrist. The prevalence might be underestimated in a Chinese population-based published study.

Evaluation of Antiobesity Activity of Soybean Meal Products Fermented by Lactobacillus plantarum FPS 2520 and Bacillus subtilis N1 in Rats Fed with High-Fat Diet.

Single strain or mixed strains of Lactobacillus plantarum FPS 2520 and Bacillus subtilis N1 were used to ferment soybean meal (SBM), and the antiobesity activity of the fermented SBM product was investigated in rats fed with high-fat diet (HFD). After fermentation, free amino nitrogen, total peptide, and isoflavone contents were markedly raised, and genistein and daidzein were the major isoflavones in the fermented SBM. After fed with HFD for 10 weeks, obese Sprague-Dawley rats were orally treated with various fermented products for 6 weeks. The body weight gains, as well as weights of abdominal fat and epididymis fat, of rats fed with fermented SBM products were significantly downregulated. The treatment with the mixed-strains fermented SBM product significantly decreased plasma levels of triglyceride (TG), total cholesterol (TC), and low-density lipoprotein-cholesterol, but increased the level of high-density lipoprotein-cholesterol. Moreover, the levels of TG, TC, fatty acid synthase, and acetyl-CoA carboxylase (ACC) in liver were diminished, and the activities of hormone-sensitive lipase and lipoprotein lipase in adipose tissue were augmented. Taken together, these data demonstrated the antiobesity activity of fermented SBM products, among which the product fermented by the mixed strains being the most effective one. Therefore, these fermented SBM products are potential to be developed as functional foods or additives for treatment of obesity and prevention against obesity-induced complications.

Mesh Convolutional Restricted Boltzmann Machines for Unsupervised Learning of Features With Structure Preservation on 3-D Meshes.

Discriminative features of 3-D meshes are significant to many 3-D shape analysis tasks. However, handcrafted descriptors and traditional unsupervised 3-D feature learning methods suffer from several significant weaknesses: 1) the extensive human intervention is involved; 2) the local and global structure information of 3-D meshes cannot be preserved, which is in fact an important source of discriminability; 3) the irregular vertex topology and arbitrary resolution of 3-D meshes do not allow the direct application of the popular deep learning models; 4) the orientation is ambiguous on the mesh surface; and 5) the effect of rigid and nonrigid transformations on 3-D meshes cannot be eliminated. As a remedy, we propose a deep learning model with a novel irregular model structure, called mesh convolutional restricted Boltzmann machines (MCRBMs). MCRBM aims to simultaneously learn structure-preserving local and global features from a novel raw representation, local function energy distribution. In addition, multiple MCRBMs can be stacked into a deeper model, called mesh convolutional deep belief networks (MCDBNs). MCDBN employs a novel local structure preserving convolution (LSPC) strategy to convolve the geometry and the local structure learned by the lower MCRBM to the upper MCRBM. LSPC facilitates resolving the challenging issue of the orientation ambiguity on the mesh surface in MCDBN. Experiments using the proposed MCRBM and MCDBN were conducted on three common aspects: global shape retrieval, partial shape retrieval, and shape correspondence. Results show that the features learned by the proposed methods outperform the other state-of-the-art 3-D shape features.

Asymmetric Actuator Backlash Compensation in Quantized Adaptive Control of Uncertain Networked Nonlinear Systems.

This paper mainly aims at the problem of adaptive quantized control for a class of uncertain nonlinear systems preceded by asymmetric actuator backlash. One challenging problem that blocks the construction of our control scheme is that the real control signal is wrapped in the coupling of quantization effect and nonsmooth backlash nonlinearity. To resolve this challenge, this paper presents a two-stage separation approach established on two new technical components, which are the approximate asymmetric backlash model and the nonlinear decomposition of quantizer, respectively. Then the real control is successfully separated from the coupling dynamics. Furthermore, by employing the neural networks and adaptation method in control design, a quantized controller is developed to guarantee the asymptotic convergence of tracking error to an adjustable region of zero and uniform ultimate boundedness of all closed-loop signals. Eventually, simulations are conducted to support our theoretical results.

Relevance of nerve conduction velocity in the assessment of balance performance in older adults with diabetes mellitus.

Purpose This study investigated the relationship between peripheral nerve conduction velocity (NCV) and balance performance in older adults with diabetes. Methods Twenty older adults with diabetes were recruited to evaluate the NCV of their lower limbs and balance performance. The balance assessments comprised the timed up and go (TUG) test, Berg balance scale (BBS), unipedal stance test (UST), multidirectional reach test (MDRT), maximum step length (MSL) test and quiet standing with eyes open and closed. The relationship between NCV and balance performance was evaluated by Pearson's correlation coefficients, and the balance performances of the diabetic patients with and without peripheral neuropathy were compared by using Mann-Whitney U tests. Results The NCV in the lower limbs exhibited a moderate to strong correlation with most of the balance tests including the TUG (r = -0.435 to -0.520, p < 0.05), BBS (r = 0.406-0.554, p < 0.05), UST (r = 0.409-0.647, p < 0.05) and MSL (r = 0.399-0.585, P < 0.05). In addition, patients with diabetic peripheral neuropathy had a poorer TUG (p < 0.05), BBS (p < 0.01), UST (p < 0.05) and MSL performance (p < 0.05) compared with those without peripheral neuropathy (p < 0.05). Conclusion Our findings revealed that a decline in peripheral nerve conduction in the lower limb is not only an indication of nerve dysfunction, but may also be related to the impairment of balance performance in patients with diabetes. Implications for Rehabilitation Nerve conduction velocity in the lower limbs of diabetic older adults showed moderate to strong correlations with most of the results of balance tests, which are commonly used in clinics. Decline in nerve conduction velocity of the lower limbs may be related to the impairment of balance control in patients with diabetes. Diabetic older adults with peripheral neuropathy exhibited greater postural instability than those without peripheral neuropathy.

Landslide Displacement Prediction With Uncertainty Based on Neural Networks With Random Hidden Weights.

In this paper, we propose a new approach to establish a landslide displacement forecasting model based on artificial neural networks (ANNs) with random hidden weights. To quantify the uncertainty associated with the predictions, a framework for probabilistic forecasting of landslide displacement is developed. The aim of this paper is to construct prediction intervals (PIs) instead of deterministic forecasting. A lower-upper bound estimation (LUBE) method is adopted to construct ANN-based PIs, while a new single hidden layer feedforward ANN with random hidden weights for LUBE is proposed. Unlike the original implementation of LUBE, the input weights and hidden biases of the ANN are randomly chosen, and only the output weights need to be adjusted. Combining particle swarm optimization (PSO) and gravitational search algorithm (GSA), a hybrid evolutionary algorithm, PSOGSA, is utilized to optimize the output weights. Furthermore, a new ANN objective function, which combines a modified combinational coverage width-based criterion with one-norm regularization, is proposed. Two benchmark data sets and two real-world landslide data sets are presented to illustrate the capability and merit of our method. Experimental results reveal that the proposed method can construct high-quality PIs.

Multiobjective Optimization of Linear Cooperative Spectrum Sensing: Pareto Solutions and Refinement.

In linear cooperative spectrum sensing, the weights of secondary users and detection threshold should be optimally chosen to minimize missed detection probability and to maximize secondary network throughput. Since these two objectives are not completely compatible, we study this problem from the viewpoint of multiple-objective optimization. We aim to obtain a set of evenly distributed Pareto solutions. To this end, here, we introduce the normal constraint (NC) method to transform the problem into a set of single-objective optimization (SOO) problems. Each SOO problem usually results in a Pareto solution. However, NC does not provide any solution method to these SOO problems, nor any indication on the optimal number of Pareto solutions. Furthermore, NC has no preference over all Pareto solutions, while a designer may be only interested in some of them. In this paper, we employ a stochastic global optimization algorithm to solve the SOO problems, and then propose a simple method to determine the optimal number of Pareto solutions under a computational complexity constraint. In addition, we extend NC to refine the Pareto solutions and select the ones of interest. Finally, we verify the effectiveness and efficiency of the proposed methods through computer simulations.

Continuous passive motion and its effects on knee flexion after total knee arthroplasty in patients with knee osteoarthritis.

PURPOSE: This study evaluated the effects of continuous passive motion (CPM) on accelerated flexion after total knee arthroplasty (TKA) and whether CPM application measures (i.e. initial angle and daily increment) are associated with functional outcomes. METHODS: A retrospective investigation was conducted at the rehabilitation centre of a university-based teaching hospital. Patients who received CPM therapy immediately after TKA surgery were categorized into rapid-, normal-, and slow-progress groups according to their response to CPM during their acute inpatient stay. Knee pain, passive knee flexion, and knee function-measured using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC)-were assessed preoperatively at discharge and at 3- and 6-month outpatient follow-up visits. RESULTS: A total of 354 patients were followed for 6 months after inpatient-stay discharge. The patients in the rapid-progress group (n = 119) exhibited significantly greater knee flexions than those in the slow-progress group did (n = 103) at the 3-month follow-up [mean difference (MD) = 10.3°, 95 % confidence interval (CI) 4.3°-16.3°, p < 0.001] and 6-month follow-up (MD = 10.9°, 95 % CI 6.3°-15.6°, p < 0.001). Significant WOMAC score differences between the rapid- and slow-progress groups were observed at the 3-month follow-up (MD = 7.2, 95 % CI 5.4-9.1, p < 0.001) and 6-month follow-up (MD = 16.1, 95 % CI 13.4-18.7, p < 0.001). CPM initial angles and rapid progress significantly predicted short- and long-term outcomes in knee flexion and WOMAC scores (p < 0.001). CONCLUSION: When CPM is used, early application with initial high flexion and rapid progress benefits knee function up to 6 months after TKA. LEVEL OF EVIDENCE: II.

Weighted Couple Sparse Representation With Classified Regularization for Impulse Noise Removal.

Many impulse noise (IN) reduction methods suffer from two obstacles, the improper noise detectors and imperfect filters they used. To address such issue, in this paper, a weighted couple sparse representation model is presented to remove IN. In the proposed model, the complicated relationships between the reconstructed and the noisy images are exploited to make the coding coefficients more appropriate to recover the noise-free image. Moreover, the image pixels are classified into clear, slightly corrupted, and heavily corrupted ones. Different data-fidelity regularizations are then accordingly applied to different pixels to further improve the denoising performance. In our proposed method, the dictionary is directly trained on the noisy raw data by addressing a weighted rank-one minimization problem, which can capture more features of the original data. Experimental results demonstrate that the proposed method is superior to several state-of-the-art denoising methods.

Design and evaluation of potentiometric principles for bladder volume monitoring: a preliminary study.

Recent advances in microelectronics and wireless transmission technology have led to the development of various implantable sensors for real-time monitoring of bladder conditions. Although various sensing approaches for monitoring bladder conditions were reported, most such sensors have remained at the laboratory stage due to the existence of vital drawbacks. In the present study, we explored a new concept for monitoring the bladder capacity on the basis of potentiometric principles. A prototype of a potentiometer module was designed and fabricated and integrated with a commercial wireless transmission module and power unit. A series of in vitro pig bladder experiments was conducted to determine the best design parameters for implementing the prototype potentiometric device and to prove its feasibility. We successfully implemented the potentiometric module in a pig bladder model in vitro, and the error of the accuracy of bladder volume detection was <±3%. Although the proposed potentiometric device was built using a commercial wireless module, the design principles and animal experience gathered from this research can serve as a basis for developing new implantable bladder sensors in the future.

Robust Nonnegative Patch Alignment for Dimensionality Reduction.

Dimensionality reduction is an important method to analyze high-dimensional data and has many applications in pattern recognition and computer vision. In this paper, we propose a robust nonnegative patch alignment for dimensionality reduction, which includes a reconstruction error term and a whole alignment term. We use correntropy-induced metric to measure the reconstruction error, in which the weight is learned adaptively for each entry. For the whole alignment, we propose locality-preserving robust nonnegative patch alignment (LP-RNA) and sparsity-preserviing robust nonnegative patch alignment (SP-RNA), which are unsupervised and supervised, respectively. In the LP-RNA, we propose a locally sparse graph to encode the local geometric structure of the manifold embedded in high-dimensional space. In particular, we select large p -nearest neighbors for each sample, then obtain the sparse representation with respect to these neighbors. The sparse representation is used to build a graph, which simultaneously enjoys locality, sparseness, and robustness. In the SP-RNA, we simultaneously use local geometric structure and discriminative information, in which the sparse reconstruction coefficient is used to characterize the local geometric structure and weighted distance is used to measure the separability of different classes. For the induced nonconvex objective function, we formulate it into a weighted nonnegative matrix factorization based on half-quadratic optimization. We propose a multiplicative update rule to solve this function and show that the objective function converges to a local optimum. Several experimental results on synthetic and real data sets demonstrate that the learned representation is more discriminative and robust than most existing dimensionality reduction methods.

Adaptive Neural Output Feedback Control of Output-Constrained Nonlinear Systems With Unknown Output Nonlinearity.

This paper addresses the problem of adaptive neural output-feedback control for a class of special nonlinear systems with the hysteretic output mechanism and the unmeasured states. A modified Bouc-Wen model is first employed to capture the output hysteresis phenomenon in the design procedure. For its fusion with the neural networks and the Nussbaum-type function, two key lemmas are established using some extended properties of this model. To avoid the bad system performance caused by the output nonlinearity, a barrier Lyapunov function technique is introduced to guarantee the prescribed constraint of the tracking error. In addition, a robust filtering method is designed to cancel the restriction that all the system states require to be measured. Based on the Lyapunov synthesis, a new neural adaptive controller is constructed to guarantee the prescribed convergence of the tracking error and the semiglobal uniform ultimate boundedness of all the signals in the closed-loop system. Simulations are implemented to evaluate the performance of the proposed neural control algorithm in this paper.

Adaptive neural control for a class of nonlinear time-varying delay systems with unknown hysteresis.

This paper investigates the fusion of unknown direction hysteresis model with adaptive neural control techniques in face of time-delayed continuous time nonlinear systems without strict-feedback form. Compared with previous works on the hysteresis phenomenon, the direction of the modified Bouc-Wen hysteresis model investigated in the literature is unknown. To reduce the computation burden in adaptation mechanism, an optimized adaptation method is successfully applied to the control design. Based on the Lyapunov-Krasovskii method, two neural-network-based adaptive control algorithms are constructed to guarantee that all the system states and adaptive parameters remain bounded, and the tracking error converges to an adjustable neighborhood of the origin. In final, some numerical examples are provided to validate the effectiveness of the proposed control methods.

A multiple-feature and multiple-kernel scene segmentation algorithm for humanoid robot.

This technical correspondence presents a multiple-feature and multiple-kernel support vector machine (MFMK-SVM) methodology to achieve a more reliable and robust segmentation performance for humanoid robot. The pixel wise intensity, gradient, and C1 SMF features are extracted via the local homogeneity model and Gabor filter, which would be used as inputs of MFMK-SVM model. It may provide multiple features of the samples for easier implementation and efficient computation of MFMK-SVM model. A new clustering method, which is called feature validity-interval type-2 fuzzy C-means (FV-IT2FCM) clustering algorithm, is proposed by integrating a type-2 fuzzy criterion in the clustering optimization process to improve the robustness and reliability of clustering results by the iterative optimization. Furthermore, the clustering validity is employed to select the training samples for the learning of the MFMK-SVM model. The MFMK-SVM scene segmentation method is able to fully take advantage of the multiple features of scene image and the ability of multiple kernels. Experiments on the BSDS dataset and real natural scene images demonstrate the superior performance of our proposed method.

Energy-efficient SVM learning control system for biped walking robots.

An energy-efficient support vector machine (EE-SVM) learning control system considering the energy cost of each training sample of biped dynamic is proposed to realize energy-efficient biped walking. Energy costs of the biped walking samples are calculated. Then the samples are weighed with the inverses of the energy costs. An EE-SVM objective function with energy-related slack variables is proposed, which follows the principle that the sample with the lowest energy consumption is treated as the most important one in the training. That means the samples with lower energy consumption contribute more to the EE-SVM regression function learning, which highly increases the energy efficiency of the biped walking. Simulation results demonstrate the effectiveness of the proposed method.