clusterBMA: Bayesian model averaging for clustering

Various methods have been developed to combine inference across multiple sets of results for unsupervised clustering, within the ensemble clustering literature. The approach of reporting results from one 'best' model out of several candidate clustering models generally ignores the uncertainty that arises from model selection, and results in inferences that are sensitive to the particular model and parameters chosen. Bayesian model averaging (BMA) is a popular approach for combining results across multiple models that offers some attractive benefits in this setting, including probabilistic interpretation of the combined cluster structure and quantification of model-based uncertainty. In this work we introduce clusterBMA, a method that enables weighted model averaging across results from multiple unsupervised clustering algorithms. We use clustering internal validation criteria to develop an approximation of the posterior model probability, used for weighting the results from each model. From a combined posterior similarity matrix representing a weighted average of the clustering solutions across models, we apply symmetric simplex matrix factorisation to calculate final probabilistic cluster allocations. In addition to outperforming other ensemble clustering methods on simulated data, clusterBMA offers unique features including probabilistic allocation to averaged clusters, combining allocation probabilities from 'hard' and 'soft' clustering algorithms, and measuring model-based uncertainty in averaged cluster allocation. This method is implemented in an accompanying R package of the same name. We use simulated datasets to explore the ability of the proposed technique to identify robust integrated clusters with varying levels of separation between subgroups, and with varying numbers of clusters between models. Benchmarking accuracy against four other ensemble methods previously demonstrated to be highly effective in the literature, clusterBMA matches or exceeds the performance of competing approaches under various conditions of dimensionality and cluster separation. clusterBMA substantially outperformed other ensemble methods for high dimensional simulated data with low cluster separation, with 1.16 to 7.12 times better performance as measured by the Adjusted Rand Index. We also explore the performance of this approach through a case study that aims to identify probabilistic clusters of individuals based on electroencephalography (EEG) data. In applied settings for clustering individuals based on health data, the features of probabilistic allocation and measurement of model-based uncertainty in averaged clusters are useful for clinical relevance and statistical communication.

[The role of ROS/TXNIP/NLRP3 pathway in the skin injury of trichloroethylene sensitized mice]

Objective: To explore the mechanism of reactive oxygen species/thioredoxin-interacting protein/nucleotide-binding oligomerization domain-like receptor 3 (ROS/TXNIP/NLRP3) pathway in the skin injury of trichloroethylene (TCE) sensitized mice. Methods: In August 2020, 40 female BALB/c mice were randomly divided into control group (n=5) , solvent control group (n=5) , TCE treatment group (n=15) and TCE+(2-(2, 2, 6, 6-Tetrameyhylpiperidin-1-oxyl-4-ylamino)-2-oxoethyl) triphenylphosphonium chloride (Mito TEMPO) treatment group (n=15) . The TCE sensitization model was established. Mice in the TCE treatment group and TCE+Mito TEMPO treatment group were divided into the sensitized positive group and the sensitized negative group according to the skin erythema and edema reactions on the back of the mice 24 h after the last stimulation. The mice were sacrificed 72 h after the last stimulation, the back skin of the mice was taken, and the skin lesions were observed. Immunohistochemistry (IHC) was used to detect the expression level of NLRP3, and the Western Blot was performed to detect the expression levels of NLRP3, apoptosis-associated speck-like protein containing a CARD (ASC) , cysteinyl aspartate specific proteinase 1 (Caspase 1) , Interleukin-1β (IL-1β) and TXNIP proteins in the skin of the mice, the reactive oxygen species (ROS) kit was used to detect the level of intracellular ROS in the back skin tissue. Results: The sensitization rates of TCE treatment group and TCE+Mito TEMPO treatment group were 40.0% (6/15) and 33.3% (5/15) , respectively, and there was no significant difference between the two groups (P>0.05) . The back skin of the mice in the TCE sensitized positive group was thickened and infiltrated by a large number of inflammatory cells. The number of mitochondria in the epidermis cells was significantly reduced, the mitochondrial crest disappeared and vacuolar degeneration occurred. TCE+Mito TEMPO sensitized positive group had less damage, more mitochondria and relatively normal cell structure. Compared with the solvent control group and corresponding sensitized negative groups, the expression levels of NLRP3, ASC, Caspase 1, IL-1β, TXNIP proteins and the content of ROS in the TCE sensitized positive group and TCE+Mito TEMPO sensitized positive group were significantly increased (P<0.05) . Compared with TCE sensitized positive group, the expression levels of NLRP3, ASC, Caspase 1, IL-1β, TXNIP proteins and the content of ROS in the TCE+Mito TEMPO sensitized positive group were significantly decreased (P<0.05) . Conclusion: ROS/TXNIP/NLRP3 pathway was activated and then encouraged the release of IL-1β, finally aggravated the TCE-induced skin injury.

[Clinicopathological and prognostic characteristics of clear cell papillary renal cell carcinoma]

Objective: To analyze the epidemiological, clinicopathological and prognostic characteristics of clear cell papillary renal cell carcinoma (CCPRCC) based on Chinese patient population. Method: Patients with renal cell carcinoma diagnosed at Peking University First Hospital from June 2016 to June 2020 were included in this study based on the inclusion and exclusion criteria. All cases were grouped according to CCPRCC, clear cell renal cell carcinoma (ccRCC), and papillary renal cell carcinoma (pRCC), and the general clinical, postoperative pathological and follow-up data of the patients were retrospectively analyzed. Result: A total of 18 CCPRCC patients were enrolled in this study, accounting for 0.44% (18/4 110) of the postoperative pathologically confirmed renal cell carcinoma cases in our hospital during this time period. The age range of the included patients was 28-86 years old, with a median age of 49.5 years old. There were 11/18 males and 7/18 females. All CCPRCC patients had no family history of renal malignant tumors. Among them, only one patient with CCPRCC had related clinical symptoms, that was intermittent waist and abdomen pain, while the other 17 cases were found by physical examination without any related symptoms. Compared with ccRCC and pRCC, there was no significant difference in their end stage renal disease history(χ²_ccRCC=0.291, χ²_pRCC=1.161,all P>0.05). The maximum diameter of CCPRCC tumor was smaller than pRCC (χ²=-2.280,P =0.027) but not significantly different from ccRCC (χ²=-0.579,P =0.565). The majority of patients with CCPRCC were in pT1, their pathological stage was earlier than the other two types, and their overall survival was better than ccRCC and pRCC (P<0.05). Conclusion: CCPRCC is a type of renal cell carcinoma with unique epidemiology, clinicopathology and prognostic characteristics. Patients with this subtype have an earlier clinical stage and a better prognosis than ccRCC and pRCC.

[Diagnosis of eco-environmental quality in the Keluke Lake basin, China]

In order to clarify the eco-environmental quality and its evolution characteristics of Keluke Lake basin, we selected 15 factors of physical geography, meteorology, land use/cover and social economy using comprehensive investigation, remote sensing interpretation and inversion, statistical analysis and other technical means, based on the relevant theories of environmental ecology. We used factor analysis and entropy method to calculate the index weight, constructed watershed soil quality model and ecological environment quality diagnosis model, and analyzed the changes of soil and eco-environmental quality in the Keluke Lake basin in 2000, 2005, 2010 and 2015. The results showed that the average eco-environmental quality in four periods was 21, 47, 54, and 72, showing a stable upward trend. The eco-environmental quality level changed from poor to good, while soil quality was at the middle level. Spatially, the eco-environmental quality of the northern mountainous area, the downstream wetland and the surrounding area of the river improved significantly. The change of eco-environmental quality was a result of human activities and natural factors. Soil quality and lake area were key factors indicating the eco-environment of the Keluke Lake basin. The minimum ecological water demand of the Keluke Lake was the basic guarantee to maintain the benign development of the eco-environment of the lake basin.

Kernelized Sparse Bayesian Matrix Factorization

Extracting low-rank and/or sparse structures using matrix factorization techniques has been extensively studied in the machine learning community. Kernelized matrix factorization (KMF) is a powerful tool to incorporate side information into the low-rank approximation model, which has been applied to solve the problems of data mining, recommender systems, image restoration, and machine vision. However, most existing KMF models rely on specifying the rows and columns of the data matrix through a Gaussian process prior and have to tune manually the rank. There are also computational issues of existing models based on regularization or the Markov chain Monte Carlo. In this article, we develop a hierarchical kernelized sparse Bayesian matrix factorization (KSBMF) model to integrate side information. The KSBMF automatically infers the parameters and latent variables including the reduced rank using the variational Bayesian inference. In addition, the model simultaneously achieves low-rankness through sparse Bayesian learning and columnwise sparsity through an enforced constraint on latent factor matrices. We further connect the KSBMF with the nonlocal image processing framework to develop two algorithms for image denoising and inpainting. Experimental results demonstrate that KSBMF outperforms the state-of-the-art approaches for these image-restoration tasks under various levels of corruption.

A pilot-scale three-dimensional electrochemical reactor combined with anaerobic-anoxic-oxic system for advanced treatment of coking wastewater

Coking wastewater is highly concentrated and extremely toxic, greatly challenging the treatment technologies. Conventional biological technology such as anaerobic-anoxic-oxic (A²O) system is inefficient, since various biological reactions are inhibited by toxicants in coking wastewater. In this work, a pilot-scale three-dimensional electrochemical reactor (3DER) is integrated into the A²O system as a pretreatment unit to improve the treatment efficiency of coking wastewater. The results indicate that 3DER pretreatment increased the biodegradability of coking wastewater, promoting the degradation of coking wastewater in A²O system. The integrated 3DER-A²O system can remove 94.4% of COD and 76.2% of TN from coking wastewater, and the energy consumption was only 0.22 kWh/kg COD and 4.69 kWh/kg TN. The components of coking wastewater were significantly simplified and the acute toxicity was reduced from 99% to 12% after the treatment. The integrated 3DER-A²O system provides a new solution for coking wastewater treatment, showing a promising application potential.

High-speed robust polarization modulation for quantum key distribution

Polarization modulation plays a key role in polarization-encoding quantum key distribution (QKD). Here, we report a new, to the best of our knowledge, polarization modulation scheme based on an inherently stable Sagnac interferometer. The presented scheme is free of polarization mode dispersion and calibration as well as insensitive to environmental influences. Successful experiments at a repetition frequency of 1.25 GHz have been conducted to demonstrate the feasibility and stability of the scheme. The measured average quantum bit-error rate of the four polarization states is as low as 0.27% for 80 consecutive minutes without any adjustment. This high-speed intrinsically stable polarization modulation can be widely applied to many polarization-encoding QKD systems, such as BB84, MDI, etc.

Image Denoising Based on Nonlocal Bayesian Singular Value Thresholding and Stein's Unbiased Risk Estimator

Singular value thresholding (SVT)- or nuclear norm minimization (NNM)-based nonlocal image denoising methods often rely on the precise estimation of the noise variance. However, most existing methods either assume that the noise variance is known or require an extra step to estimate it. Under the iterative regularization framework, the error in the noise variance estimate propagates and accumulates with each iteration, ultimately degrading the overall denoising performance. In addition, the essence of these methods is still least squares estimation, which can cause a very high mean-squared error (MSE) and is inadequate for handling missing data or outliers. In order to address these deficiencies, we present a hybrid denoising model based on variational Bayesian inference and Stein's unbiased risk estimator (SURE), which consists of two complementary steps. In the first step, the variational Bayesian SVT performs a low-rank approximation of the nonlocal image patch matrix to simultaneously remove the noise and estimate the noise variance. In the second step, we modify the conventional SURE full-rank SVT and its divergence formulas for rank-reduced eigen-triplets to remove the residual artifacts. The proposed hybrid BSSVT method achieves better performance in recovering the true image compared with state-of-the-art methods.

Optically injected intensity-stable pulse source for secure quantum key distribution

The security of decoy-state quantum key distribution (QKD) highly depends on the accurate control of multiple intensity states. Although several theoretical studies on the QKD with loosely controlled source intensities have been proposed, there is still a large gap between the experimental realization and the theoretical analysis. In this paper, we adopt the gain-switching method to generate short optical pulses, and the corresponding intensity stabilities are quantitatively measured. The method via optical injection is proposed to make effective reductions of the intensity fluctuations from 6.47%∼1.59% to 1.95%∼1.15% at different optical powers. QKD performance adopting the experimental results is also analyzed and discussed. For a typical 40 dB high-attenuation QKD system, the relative increase on the secure key rates reaches 51.89% for the corresponding intensity fluctuations of 1.15% with optical injection and 1.59% without optical injection. The presented intensity-stable optical pulse source can find wide applications in different QKD protocols, such as BB84, DPS, COW, etc.

Symplectic geometry spectrum regression for prediction of noisy time series

We present the symplectic geometry spectrum regression (SGSR) technique as well as a regularized method based on SGSR for prediction of nonlinear time series. The main tool of analysis is the symplectic geometry spectrum analysis, which decomposes a time series into the sum of a small number of independent and interpretable components. The key to successful regularization is to damp higher order symplectic geometry spectrum components. The effectiveness of SGSR and its superiority over local approximation using ordinary least squares are demonstrated through prediction of two noisy synthetic chaotic time series (Lorenz and Rössler series), and then tested for prediction of three real-world data sets (Mississippi River flow data and electromyographic and mechanomyographic signal recorded from human body).

Muscle networks: Connectivity analysis of EMG activity during postural control

Understanding the mechanisms that reduce the many degrees of freedom in the musculoskeletal system remains an outstanding challenge. Muscle synergies reduce the dimensionality and hence simplify the control problem. How this is achieved is not yet known. Here we use network theory to assess the coordination between multiple muscles and to elucidate the neural implementation of muscle synergies. We performed connectivity analysis of surface EMG from ten leg muscles to extract the muscle networks while human participants were standing upright in four different conditions. We observed widespread connectivity between muscles at multiple distinct frequency bands. The network topology differed significantly between frequencies and between conditions. These findings demonstrate how muscle networks can be used to investigate the neural circuitry of motor coordination. The presence of disparate muscle networks across frequencies suggests that the neuromuscular system is organized into a multiplex network allowing for parallel and hierarchical control structures.

Symplectic geometry spectrum analysis of nonlinear time series

Various time-series decomposition techniques, including wavelet transform, singular spectrum analysis, empirical mode decomposition and independent component analysis, have been developed for non-linear dynamic system analysis. In this paper, we describe a symplectic geometry spectrum analysis (SGSA) method to decompose a time series into a set of independent additive components. SGSA is performed in four steps: embedding, symplectic QR decomposition, grouping and diagonal averaging. The obtained components can be used for de-noising, prediction, control and synchronization. We demonstrate the effectiveness of SGSA in reconstructing and predicting two noisy benchmark nonlinear dynamic systems: the Lorenz and Mackey-Glass attractors. Examples of prediction of a decadal average sunspot number time series and a mechanomyographic signal recorded from human skeletal muscle further demonstrate the applicability of the SGSA method in real-life applications.

Resveratrol induces antioxidant and heat shock protein mRNA expression in response to heat stress in black-boned chickens

This study investigated the effects of dietary resveratrol at 0, 200, 400, or 600 mg/kg of diet on the performance, immune organ growth index, serum parameters, and expression levels of heat shock protein (Hsp) 27, Hsp70, and Hsp90 mRNA in the bursa of Fabricius, thymus, and spleen of 42-d-old female black-boned chickens exposed to heat stress at 37 ± 2°C for 15 d. The results showed that heat stress reduced daily feed intake and BW gain; decreased serum glutathione (GSH), growth hormone, and insulin-like growth factor-1 levels; and inhibited GSH peroxidase (GSH-Px), superoxide dismutase (SOD), and catalase (CAT) activities compared with birds subjected to thermo-neutral circumstances. Chickens that were fed diets supplemented with resveratrol exhibited a linear increase in feed intake and BW gain (P < 0.001); serum GSH, growth hormone, and insulin-like growth factor-1 levels (P ≤ 0.01); and GSH-Px, SOD, and CAT activities (P < 0.001) compared with chickens that were fed diets without resveratrol during heat stress. In contrast, serum malonaldehyde concentrations were decreased (P < 0.001) in the chickens fed a resveratrol-supplemented diet. Heat stress also reduced (P < 0.05) the growth index of the bursa of Fabricus and spleen; however, it had no effect on the growth index of the thymus. The growth index of the bursa of Fabricius and spleen increased (P < 0.05) upon heat stress and coincided with an increase in supplemental resveratrol levels. The expression of Hsp27, Hsp70, and Hsp90 mRNA in the bursa of Fabricius and spleen were increased (P < 0.01), but those of Hsp27 and Hsp90 mRNA in thymus were decreased (P < 0.01) under heat stress compared with no heat stress. Resveratrol attenuated the heat stress-induced overexpression of Hsp27, Hsp70, and Hsp90 mRNA in the bursa of Fabricius and spleen and increased the low expression of Hsp27 and Hsp90 mRNA in thymus upon heat stress. The results suggest that supplemental resveratrol improves growth performance and reduces oxidative stress in heat-stressed black-boned chickens by increasing serum growth hormone concentrations and modulating the expression of heat shock genes in organs of the immune system.

Hybrid soft computing systems for electromyographic signals analysis: a review

Electromyographic (EMG) is a bio-signal collected on human skeletal muscle. Analysis of EMG signals has been widely used to detect human movement intent, control various human-machine interfaces, diagnose neuromuscular diseases, and model neuromusculoskeletal system. With the advances of artificial intelligence and soft computing, many sophisticated techniques have been proposed for such purpose. Hybrid soft computing system (HSCS), the integration of these different techniques, aims to further improve the effectiveness, efficiency, and accuracy of EMG analysis. This paper reviews and compares key combinations of neural network, support vector machine, fuzzy logic, evolutionary computing, and swarm intelligence for EMG analysis. Our suggestions on the possible future development of HSCS in EMG analysis are also given in terms of basic soft computing techniques, further combination of these techniques, and their other applications in EMG analysis.

A hybrid symplectic principal component analysis and central tendency measure method for detection of determinism in noisy time series with application to mechanomyography

We present a hybrid symplectic geometry and central tendency measure (CTM) method for detection of determinism in noisy time series. CTM is effective for detecting determinism in short time series and has been applied in many areas of nonlinear analysis. However, its performance significantly degrades in the presence of strong noise. In order to circumvent this difficulty, we propose to use symplectic principal component analysis (SPCA), a new chaotic signal de-noising method, as the first step to recover the system dynamics. CTM is then applied to determine whether the time series arises from a stochastic process or has a deterministic component. Results from numerical experiments, ranging from six benchmark deterministic models to 1/f noise, suggest that the hybrid method can significantly improve detection of determinism in noisy time series by about 20 dB when the data are contaminated by Gaussian noise. Furthermore, we apply our algorithm to study the mechanomyographic (MMG) signals arising from contraction of human skeletal muscle. Results obtained from the hybrid symplectic principal component analysis and central tendency measure demonstrate that the skeletal muscle motor unit dynamics can indeed be deterministic, in agreement with previous studies. However, the conventional CTM method was not able to definitely detect the underlying deterministic dynamics. This result on MMG signal analysis is helpful in understanding neuromuscular control mechanisms and developing MMG-based engineering control applications.

Molecular dynamics investigation of Cl(-) transport through the closed and open states of the 2α12β2γ2 GABA(A) receptor

The α1β2γ2 gamma-aminobutyric type A receptor (GABA(A)R) is one of the most widely expressed GABA(A)R subtypes in the mammalian brain. GABA(A)Rsbelonging to the Cys-loop superfamily of ligand-gated ion channels have been identified as key targets for many clinical drugs, and the motions that govern the gating mechanism are still not well understood. In this study, an open-state GABA(A)R was constructed using the structure of the glutamate-gated chloride channel (GluCl), which has a high sequence identity to GABA(A)R. A closed-state model was constructed using the structure of the nicotinic acetylcholine receptor (nAChR). Molecular dynamics simulations of the open-state and closed-state GABA(A)R were performed. We calculated the electrostatic potential of the two conformations, the pore radius of the two ion channels and the root-mean-square fluctuation. We observed the presence of two positively charged girdles around the ion channel and found flexible regions in the GABA(A)R. Then, the free-energy of chloride ion permeations through the closed-state and open-state G GABA(A)R has been estimated using adaptive biasing force (ABF) simulation. For the closed-state G GABA(A)R, we observed two major energy barriers for chloride ion translocation in the transmembrane domain (TMD). For the open-state GABA(A)R, there was only one energy barrier formed by two Thr261 (α1), two Thr255 (β2) and one Thr271 (γ2). By using ABF simulation, the overall free-energy profile is obtained for Cl(-) transporting through GABA(A)R, which gives a complete map of the ion channel of Cl(-) permeation.

Towards the application of one-dimensional sonomyography for powered upper-limb prosthetic control using machine learning models

BACKGROUND

The inherent properties of surface electromyography limit its potential for multi-degrees of freedom control. Our previous studies demonstrated that wrist angle could be predicted by muscle thickness measured from B-mode ultrasound, and hence, it could be an alternative signal for prosthetic control. However, an ultrasound imaging machine is too bulky and expensive.

OBJECTIVE

We aim to utilize a portable A-mode ultrasound system to examine the feasibility of using one-dimensional sonomyography (i.e. muscle thickness signals detected by A-mode ultrasound) to predict wrist angle with three different machine learning models - (1) support vector machine (SVM), (2) radial basis function artificial neural network (RBF ANN), and (3) back-propagation artificial neural network (BP ANN).

STUDY DESIGN

Feasibility study using nine healthy subjects.

METHODS

Each subject performed wrist extension guided at 15, 22.5, and 30 cycles/minute, respectively. Data obtained from 22.5 cycles/minute trials was used to train the models and the remaining trials were used for cross-validation. Prediction accuracy was quantified by relative root mean square error (RMSE) and correlation coefficients (CC).

RESULTS

Excellent prediction was noted using SVM (RMSE = 13%, CC = 0.975), which outperformed the other methods.

CONCLUSION

It appears that one-dimensional sonomyography could be an alternative signal for prosthetic control. Clinical relevance Surface electromyography has inherent limitations that prohibit its full functional use for prosthetic control. Research that explores alternative signals to improve prosthetic control (such as the one-dimensional sonomyography signals evaluated in this study) may revolutionize powered prosthesis design and ultimately benefit amputee patients.

Fuzzy central tendency measure for time series variability analysis with application to fatigue electromyography signals

A new method, namely fuzzy central tendency measure (fCTM) analysis, that could enable measurement of the variability of a time series, is presented in this study. Tests on simulated data sets show that fCTM is superior to the conventional central tendency measure (CTM) in several respects, including improved relative consistency and robustness to noise. The proposed fCTM method was applied to electromyograph (EMG) signals recorded during sustained isometric contraction for tracking local muscle fatigue. The results showed that the fCTM increased significantly during the development of muscle fatigue, and it was more sensitive to the fatigue phenomenon than mean frequency (MNF), the most commonly-used muscle fatigue indicator.

[Early diagnosis and treatment of compartment syndrome caused by landslides:a report of 20 cases]

OBJECTIVE

To summarize early diagnosis and treatment methods of 20 patients with compartment syndrome caused by landslides during coal mine accidents in order to improve the level of diagnosis and treatment of compartment syndrome and reduce disability.

METHODS

From September 2006 to April 2010,20 patients with compartment syndrome were treated with the methods of early decompression, systemic support. All the patients were male with an average age of 42 years (ranged, 23 to 54). All the patients with high tension limb swelling, pain, referred pain passive positive; 5 extremities feeling diminish or disappear and the distal blood vessel beat were normal or weakened or disappeared; myoglobinuria, hyperkalemia, serum urea nitrogen and creatinine increased in 5 cases and oliguria in occurred 1 case. The function of affected limbs was observed according to disability ratings.

RESULTS

Three cases complicated with infection of affected limb and 6 cases occurred with renal function insufficiency. Total recovery was in 16 cases, basically recovery in 3, amputation in 1 case. All patients were followed up for 6-15 months with an average of 12 months. The ability to work according to national standard identification--Employee work-related injuries and occupational disability rating classification (GB/T16180-2006) to assess, grade 5 was in 1 case, grade 8 in 2 cases, grade 10 in 1 case, no grade in 16 cases.

CONCLUSION

Arteriopalmus of dorsalis pedis weaken and vanished can not be regard as an evidence in early diagnosis of compartment syndrome. Early diagnosis and decompression, systemic support and treatment is the key in reducing disability.

Ant colony optimization-based feature selection method for surface electromyography signals classification

This paper presented a new ant colony optimization (ACO) feature selection method to classify hand motion surface electromyography (sEMG) signals. The multiple channels of sEMG recordings make the dimensionality of sEMG feature grow dramatically. It is known that the informative feature subset with small size is a precondition for the accurate and computationally efficient classification strategy. Therefore, this study proposed an ACO based feature selection scheme using the heuristic information measured by the minimum redundancy maximum relevance criterion (ACO-mRMR). The experiments were conducted on ten subjects with eight upper limb motions. Two feature sets, i.e., time domain features combined with autoregressive model coefficients (TDAR) and wavelet transform (WT) features, were extracted from the recorded sEMG signals. The average classification accuracies of using ACO reduced TDAR and WT features were 95.45±2.2% and 96.08±3.3%, respectively. The principal component analysis (PCA) was also conducted on the same data sets for comparison. The average classification accuracies of using PCA reduced TDAR and WT features were 91.51±4.9% and 89.87±4.4%, respectively. The results demonstrated that the proposed ACO-mRMR based feature selection method can achieve considerably high classification rates in sEMG motion classification task and be applicable to other biomedical signals pattern analysis.

Detection of chaos in human fatigue mechanomyogarphy signals

We undertake the study of the chaotic nature of mechanomygraphy (MMG) signal by recourse to the recent developments in the field of nonlinear dynamics. The MMG signals were measured from biceps brachii muscle of 5 subjects during fatigue of isometric contraction at 80% maximal voluntary contraction (MVC) level. Deterministic chaotic character was detected in all data by using the Volterra-Wiener-Korenberg model and noise titration approach. The noise limit (NL), which is a power indicator of chaos of fatigue MMG signals, is 22.2000 + or - 8.7293. Furthermore, we studied the nonlinear dynamic features of MMG signals by computing their correlation dimension D(2), which is 3.3524 + or - 0.3645 across all the subjects. These results indicate that MMG is a high-dimensional chaotic signal and support the use of the theory of nonlinear dynamics for the analysis and modeling the MMG signals.

Evaluation of sonomyography (SMG) for control compared with electromyography (EMG) in a discrete target tracking task

Most of the commercial upper-limb externally powered prosthetic devices are controlled by electromyography (EMG) signals. We previously proposed using the real-time change of muscle thickness detected using ultrasound, namely sonomyography (SMG), for the control of prostheses. In this study, we compared the performance of subjects using 1-D SMG signal and surface EMG signal, using a discrete target tracking protocol involving a series of letter cancellation tasks. Each task involved using grip force, EMG or SMG from a wrist extensor muscle to move a cursor to one of 5 locations on a computer screen, at the first four of which were located a letter and last of which was a word of "NEXT". The target was defined by the location showing the letter "E" and, once the subject reached this target, they were instructed to "cancel" the E from the screen, using a button operated by the contralateral hand. A paired t-test revealed that the percentage of letters correctly cancelled with force/angle and SMG signal in isometric force control, and with SMG in wrist extension were significantly higher than with EMG (P<0.05) for both isometric control and wrist extension. The results suggest that SMG signal has great potential as an alternative to EMG for prosthetic control.

A comparative study of pattern synchronization detection between neural signals using different cross-entropy measures

Cross-approximate entropy (X-ApEn) and cross-sample entropy (X-SampEn) have been employed as bivariate pattern synchronization measures for characterizing interdependencies between neural signals. In this study, we proposed a new measure, cross-fuzzy entropy (X-FuzzyEn), to describe the synchronicity of patterns. The performances of three statistics were first quantitatively tested using five different coupled systems including both deterministic and stochastic models, i.e., coupled broadband noises, Lorenz-Lorenz, Rossler-Rossler, Rossler-Lorenz, and neural mass model. All the measures were compared with each other with respect to their ability to distinguish between different levels of coupling and their robustness against noise. The three measures were then applied to a real-life problem, pattern synchronization analysis of left and right hemisphere rat electroencephalographic (EEG) signals. Both simulated and real EEG data analysis results showed that the X-FuzzyEn provided an improved evaluation of bivariate series pattern synchronization and could be more conveniently and powerfully applied to different neural dynamical systems contaminated by noise.

Detection of synchrony in biosignals using cross fuzzy entropy

A new method, namely Cross fuzzy entropy (C-FuzzyEn) analysis, that could enable the measurement of the synchrony or similarity of patterns between two distinct signals, was presented in this study. Tests on simulated data sets showed that C-FuzzyEn was superior to the conventional cross sample entropy (C-SampEn) in several aspects, including giving entropy definition in case of small parameters, better relative consistency, and less dependence on record length. The proposed C-FuzzyEn was then applied for the analysis of simultaneously recorded electromyography (EMG) and mechanomyography (MMG) signals during sustained isometric contraction for monitoring local muscle fatigue. The results showed that the C-FuzzyEn of EMG-MMG decreased significantly during the development of muscle fatigue. The time-decrease trend of C-FuzzyEn is similar to the mean frequency (MNF) of EMG, the commonly used muscle fatigue indicator.

Platelet phospholipid n-3 PUFA negatively associated with plasma homocysteine in middle-aged and geriatric hyperlipaemia patients

Studies showed that increased dietary intake of n-3 polyunsaturated fatty acids (PUFA) has a cardiovascular beneficial effect. Increased plasma phospholipid (PL) docosahexaenoic acid (22:6n-3) is associated with decreased plasma homocysteine (Hcy). The aim of this study was to investigate the relationship between platelet PL fatty acid and plasma Hcy in middle-aged and geriatric hyperlipaemia patients (50 males, 31 females) and 65 healthy subjects (43 males, 22 females) in Hangzhou, China. Plasma Hcy demonstrated significant positive correlation with adrenic acid (22:4n-6) (r = 0.188, P = 0.018) and negative correlation with 22:6n-3 (r = -0.277, P = 0.001) and the ratio of n-3/n-6 (r = -0.231, P = 0.003) in sex-, age- and BMI-controlled partial correlation analysis. The present results suggest that increased ratio of n-3/n-6 PUFA in platelet PL is associated with decreased thrombotic risks such as plasma Hcy in middle-aged and geriatric hyperlipaemia patients in Hangzhou.

[Imaging features of lung carcinoma, pulmonary tuberculoma, and inflammatory pseudotumor on helical incremental dynamic CT scan--a report of 44 cases]

BACKGROUND & OBJECTIVE

Lung carcinoma, pulmonary tuberculoma, and inflammatory pesudotumor are the most common solitary pulmonary nodules (SPN). Computed tomography (CT) is the major diagnostic examination of them. Most recent reports focused on their morphologic features on CT. The differential diagnoses are still difficult. This study was to explore their imaging features on helical incremental dynamic CT scan according to the differences of their hematal supply, and get more information for the diagnosis and differential diagnosis.

METHODS

Incremental dynamic CT scan was performed on 115 cases of solitary pulmonary nodule. Of the 44 cases proved by postoperative pathology, 30 were lung carcinoma, 7 were pulmonary tuberculoma, and 7 were inflammatory pesudotumor.

RESULTS

Of the 30 cases of lung carcinoma, 7 were mildly enhanced, 14 were moderately enhanced, 5 were highly enhanced, 2 were remarkably enhanced, and 7 were not enhanced; 9 had peak enhancement began at 18-28 s after injection, 17 began at 30-39 s, and 4 began at 40-44 s, with an average time of 32 s. Of the 7 cases of tuberculoma, 5 were not enhanced, and 2 were cricoid enhanced. All the 7 cases of inflammatory pesudotumor were remarkably enhanced, with the peak enhancement value of over 100 Hu in all cases; the peak enhancements began at 26 s, 28 s, 30 s, 32 s, 33 s, 110 s, and 122 s, respectively, after injection.

CONCLUSION

Helical incremental dynamic CT is helpful in differential diagnoses of lung carcinoma, pulmonary tuberculoma, and inflammatory pesudotumor.