Seasonal variations of the prevalence of metabolic syndrome and its markers using big-data of health check-ups

BACKGROUND@#It is crucial to understand the seasonal variation of Metabolic Syndrome (MetS) for the detection and management of MetS. Previous studies have demonstrated the seasonal variations in MetS prevalence and its markers, but their methods are not robust. To clarify the concrete seasonal variations in the MetS prevalence and its markers, we utilized a powerful method called Seasonal Trend Decomposition Procedure based on LOESS (STL) and a big dataset of health checkups.@*METHODS@#A total of 1,819,214 records of health checkups (759,839 records for men and 1,059,375 records for women) between April 2012 and December 2017 were included in this study. We examined the seasonal variations in the MetS prevalence and its markers using 5 years and 9 months health checkup data and STL analysis. MetS markers consisted of waist circumference (WC), systolic blood pressure (SBP), diastolic blood pressure (DBP), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), fasting plasma glucose (FPG).@*RESULTS@#We found that the MetS prevalence was high in winter and somewhat high in August. Among men, MetS prevalence was 2.64 ± 0.42 (mean ± SD) % higher in the highest month (January) than in the lowest month (June). Among women, MetS prevalence was 0.53 ± 0.24% higher in the highest month (January) than in the lowest month (June). Additionally, SBP, DBP, and HDL-C exhibited simple variations, being higher in winter and lower in summer, while WC, TG, and FPG displayed more complex variations.@*CONCLUSIONS@#This finding, complex seasonal variations of MetS prevalence, WC, TG, and FPG, could not be derived from previous studies using just the mean values in spring, summer, autumn and winter or the cosinor analysis. More attention should be paid to factors affecting seasonal variations of central obesity, dyslipidemia and insulin resistance.

Supercritical transesterification of vegetable oil and ethanol: temperature and residence time effects on phase formation and water content

Supercritical transesterification has emerged as a readily available alternative for biodiesel production since no catalyst is required, thereby generating fewer waste products. In this research, the supercritical transesterification of refined vegetable oil and aqueous ethanol was carried out at temperatures 400 to 480 °C and a 12:1 ethanol to oil molar ratio, to assess the effect of temperature and residence time in the formation of a homogeneous phase, effluent appearance and increased water content derived from glycerol etherification. The results showed that water was produced at temperatures higher than 400 °C, as expected from the occurrence of glycerol etherification, and that prolonged times resulted in gas and soot formation, indicating esters decomposition. Through water mass balances, it was possible to identify the set of operation conditions in which the water formed from glycerol etherification matched with the maximum expected according to the proposed reaction scheme.

La transesterificación supercrítica se ha propuesto como una alternativa para la producción de biodiesel ya que no requiere catalizador de esta manera se generan menos residuos. En esta investigación, la transesterificación supercrítica de aceite vegetal refinado y etanol acuoso se llevó a cabo a temperaturas en el rango 400 a 480 °C y relación molar etanol a aceite de 12:1, para evaluar el efecto de la temperatura y el tiempo de residencia en la formación de una fase homogénea, apariencia del efluente e incremento del contenido de agua resultado de las reacciones de eterificación del glicerol. Los resultados mostraron que se produjo agua a temperaturas mayores a 400°C, atribuida a la eterificación del glicerol, y que tiempos de residencia prolongados resultaron en formación de gas y hollín, indicativo de reacciones de descomposición de esteres. A través de balances de masa, fue posible identificar el conjunto de condiciones de operación a las cuales el agua formada por la eterificación del glicerol coincide con el valor máximo esperado de acuerdo con el esquema de reacción propuesto.

A transesterificação supercrítica foi proposta como uma alternativa para a produção de biodiesel porque não requer catalisador e, dessa forma, gera menos resíduos. Nesta pesquisa, a transesterificação supercrítica de aceite vegetal refinado e etanol acuoso foi conduzida a temperaturas entre 400 e 480 °C e uma relação molar de etanol e aceite de 12: 1, para avaliar o efeito da temperatura e do tempo de residência na formação de uma fase homogênea, apariência do efluente e aumento do conteúdo de água resultante das reações de eterificação do glicerol. Os resultados mostraram que se produziu água a temperaturas maiores que 400°C, atribuída à eterificação do glicerol, e que os tempos de residência prolongados resultaram na formação de gás e hollín, indicativo de reações de decomposição de ésteres. Por meio de balanças de massa, foi possível identificar o conjunto de condições de operação em que a água formada pela eterificação do glicerol coincide com o valor máximo esperado de acordo com o esquema de reação proposto.

Characteristics of Motoneurons Controlling Single Finger Movement in Different Individuals / 医用生物力学

Objective To analyze characteristics of motoneurons controlling the extension of a single finger in different individuals, and obtain the similarity and difference of micro-motoneurons characteristics in different individuals. Methods The motoneurons were decomposed by blind source separation algorithm. The two dimensional (2D) features of the neurons were quantified, and the fingers were classified by the features of the neurons decomposed by different individuals. In addition, the proportion of shared motor neurons was used to study characteristics of motoneurons innervating the coordinated movement of different fingers between individuals. Results There were significant differences in spatial distribution of motoneurons between the index finger and the middle finger for different individuals, but the activation area was similar. Using data from different people as training sets and testing sets, the average accuracy of finger classification was 86. 99% , and it was significantly improved to 90. 07% after using transfer component analysis (TCA) calibration. Through analysis on the proportion of shared neurons in different individuals, it was found that the proportion of shared neurons between index finger and other three fingers (middle finger, ring finger and little finger) was relatively low, while that between ring finger and little finger was high. Conclusions The spatial discharge characteristics of motoneurons controlling different fingers in different individuals are similar and have small individual differences. This study reveals the internal neural mechanism of different individuals during finger movement, and provides references for clinical neural mechanism analysis of patients with finger movement disorders and the related engineering applications

Deep learning-based dose prediction in radiotherapy planning for head and neck cancer / 南方医科大学学报

OBJECTIVE@#To propose an deep learning-based algorithm for automatic prediction of dose distribution in radiotherapy planning for head and neck cancer.@*METHODS@#We propose a novel beam dose decomposition learning (BDDL) method designed on a cascade network. The delivery matter of beam through the planning target volume (PTV) was fitted with the pre-defined beam angles, which served as an input to the convolution neural network (CNN). The output of the network was decomposed into multiple sub-fractions of dose distribution along the beam directions to carry out a complex task by performing multiple simpler sub-tasks, thus allowing the model more focused on extracting the local features. The subfractions of dose distribution map were merged into a distribution map using the proposed multi-voting mechanism. We also introduced dose distribution features of the regions-of-interest (ROIs) and boundary map as the loss function during the training phase to serve as constraining factors of the network when extracting features of the ROIs and areas of dose boundary. Public datasets of radiotherapy planning for head and neck cancer were used for obtaining the accuracy of dose distribution of the BDDL method and for implementing the ablation study of the proposed method.@*RESULTS@#The BDDL method achieved a Dose score of 2.166 and a DVH score of 1.178 (P < 0.05), demonstrating its superior prediction accuracy to that of current state-ofthe-art (SOTA) methods. Compared with the C3D method, which was in the first place in OpenKBP-2020 Challenge, the BDDL method improved the Dose score and DVH score by 26.3% and 30%, respectively. The results of the ablation study also demonstrated the effectiveness of each key component of the BDDL method.@*CONCLUSION@#The BDDL method utilizes the prior knowledge of the delivery matter of beam and dose distribution in the ROIs to establish a dose prediction model. Compared with the existing methods, the proposed method is interpretable and reliable and can be potentially applied in clinical radiotherapy.

Multiresolution discrete optimization registration method of ultrasound and magnetic resonance images based on key points / 生物医学工程学杂志

The registration of preoperative magnetic resonance (MR) images and intraoperative ultrasound (US) images is very important in the planning of brain tumor surgery and during surgery. Considering that the two-modality images have different intensity range and resolution, and the US images are degraded by lots of speckle noises, a self-similarity context (SSC) descriptor based on local neighborhood information was adopted to define the similarity measure. The ultrasound images were considered as the reference, the corners were extracted as the key points using three-dimensional differential operators, and the dense displacement sampling discrete optimization algorithm was adopted for registration. The whole registration process was divided into two stages including the affine registration and the elastic registration. In the affine registration stage, the image was decomposed using multi-resolution scheme, and in the elastic registration stage, the displacement vectors of key points were regularized using the minimum convolution and mean field reasoning strategies. The registration experiment was performed on the preoperative MR images and intraoperative US images of 22 patients. The overall error after affine registration was (1.57 ± 0.30) mm, and the average computation time of each pair of images was only 1.36 s; while the overall error after elastic registration was further reduced to (1.40 ± 0.28) mm, and the average registration time was 1.53 s. The experimental results show that the proposed method has prominent registration accuracy and high computational efficiency.

Mortality and years of life lost of lung cancer among residents in Pudong New Area of Shanghai from 1995 to 2021 / 上海预防医学

ObjectiveTo investigate the epidemiological traits and potential years of life lost associated with lung cancer mortality among inhabitants of Shanghai's Pudong New Area from 1995 to 2021， in order to serve as a reference for developing intervention approaches. MethodsThe death surveillance system was used to gather statistics on lung cancer deaths. Crude mortality rate （CMR）， standardized mortality rate （SMR）， potential years of life lost （PYLL）， average years of life lost （AYLL）， annual percent change （APC） of the lung cancer deaths were analyzed. The impact of age-structural and non-age-structural factors on changes in lung cancer mortality was quantified using difference decomposition. ResultsThe CMR and SMR of lung cancer among residents in Pudong New Area between 1995 and 2021 were 58.21/105 and 26.75/105， respectively. The CMR of lung cancer increased over the years （APC=1.91%， 95%CI=1.60%‒2.30%； Z=11.487， P<0.001）， and the SMR of lung cancer declined over the years （APC=-1.50%， 95%CI=-1.80%‒-1.20%； Z=-9.006， P<0.001）. Age structure factors and gender factors contributed to the increase of lung cancer mortality， while non-population age structure factors overall appeared to play a protective role which might be related to the improvements in factors such as tobacco control and environmental management. The PYLL of lung cancer was 160 296 person years， the PYLL rate was 2.24‰， and the AYLL was 3.86 years per person. ConclusionAge structure factors are a significant contributor to the disease burden and result in the increase in the crude lung cancer mortality rate of Pudong residents of shanghai. Comprehensive monitoring， preventive， and control methods should be implemented.

Fetal electrocardiogram signal extraction and analysis method combining fast independent component analysis algorithm and convolutional neural network / 生物医学工程学杂志

Fetal electrocardiogram (ECG) signals provide important clinical information for early diagnosis and intervention of fetal abnormalities. In this paper, we propose a new method for fetal ECG signal extraction and analysis. Firstly, an improved fast independent component analysis method and singular value decomposition algorithm are combined to extract high-quality fetal ECG signals and solve the waveform missing problem. Secondly, a novel convolutional neural network model is applied to identify the QRS complex waves of fetal ECG signals and effectively solve the waveform overlap problem. Finally, high quality extraction of fetal ECG signals and intelligent recognition of fetal QRS complex waves are achieved. The method proposed in this paper was validated with the data from the PhysioNet computing in cardiology challenge 2013 database of the Complex Physiological Signals Research Resource Network. The results show that the average sensitivity and positive prediction values of the extraction algorithm are 98.21% and 99.52%, respectively, and the average sensitivity and positive prediction values of the QRS complex waves recognition algorithm are 94.14% and 95.80%, respectively, which are better than those of other research results. In conclusion, the algorithm and model proposed in this paper have some practical significance and may provide a theoretical basis for clinical medical decision making in the future.

A multiscale feature extraction algorithm for dysarthric speech recognition / 生物医学工程学杂志

In this paper, we propose a multi-scale mel domain feature map extraction algorithm to solve the problem that the speech recognition rate of dysarthria is difficult to improve. We used the empirical mode decomposition method to decompose speech signals and extracted Fbank features and their first-order differences for each of the three effective components to construct a new feature map, which could capture details in the frequency domain. Secondly, due to the problems of effective feature loss and high computational complexity in the training process of single channel neural network, we proposed a speech recognition network model in this paper. Finally, training and decoding were performed on the public UA-Speech dataset. The experimental results showed that the accuracy of the speech recognition model of this method reached 92.77%. Therefore, the algorithm proposed in this paper can effectively improve the speech recognition rate of dysarthria.

Tensor decomposition: new strategy for deciphering mechanism of precision medicine for same treatment of different diseases / 中国中药杂志

The aging society has led to a substantial increase in the number of clinical comorbidities. To meet the needs of comorbidity treatment, polypharmacy is widely used in clinical practice. However, polypharmacy has drawbacks such as treatment conflict. Same treatment of different diseases refers to treating different diseases with same treatment. Therefore, the principle of same treatment of different diseases can alleviate the problems caused by polypharmacy. Under the research background of precision medicine, it becomes possible to explore the mechanism of same treatment of different diseases and achieve its clinical application. However, drugs successfully developed in the past have revealed shortcomings in clinical use. To better interpret the mechanism of precision medicine for same treatment of different diseases, under the multi-dimensional attributes including dynamic space and time, omics was performed, and a new strategy of tensor decomposition was proposed. With the characteristics of complete data, tensor decomposition is advantageous in data mining and can fully grasp the connotation of precision treatment of different diseases with same treatment under dynamic spatiotemporal changes. This method is used for drug repositioning in some biocomputations. By taking advantage of the dimensionality reduction of tensor decomposition and integrating the dual influences of time and space, this study achieved accurate target prediction of same treatment of different diseases at each stage, and discovered the mechanism of precision medicine of same treatment for different diseases, providing scientific support for precision prescription and treatment of different diseases with same treatment in clinical practice. This study thus conducted preliminary exploration of the pharmacological mechanism of precision Chinese medicine treatment.

A semi-supervised material quantitative intelligent imaging algorithm for spectral CT based on prior information perception learning / 南方医科大学学报

OBJECTIVE@#To propose a semi-supervised material quantitative intelligent imaging algorithm based on prior information perception learning (SLMD-Net) to improve the quality and precision of spectral CT imaging.@*METHODS@#The algorithm includes a supervised and a self- supervised submodule. In the supervised submodule, the mapping relationship between low and high signal-to-noise ratio (SNR) data was constructed through mean square error loss function learning based on a small labeled dataset. In the self- supervised sub-module, an image recovery model was utilized to construct the loss function incorporating the prior information from a large unlabeled low SNR basic material image dataset, and the total variation (TV) model was used to to characterize the prior information of the images. The two submodules were combined to form the SLMD-Net method, and pre-clinical simulation data were used to validate the feasibility and effectiveness of the algorithm.@*RESULTS@#Compared with the traditional model-driven quantitative imaging methods (FBP-DI, PWLS-PCG, and E3DTV), data-driven supervised-learning-based quantitative imaging methods (SUMD-Net and BFCNN), a material quantitative imaging method based on unsupervised learning (UNTV-Net) and semi-supervised learning-based cycle consistent generative adversarial network (Semi-CycleGAN), the proposed SLMD-Net method had better performance in both visual and quantitative assessments. For quantitative imaging of water and bone materials, the SLMD-Net method had the highest PSNR index (31.82 and 29.06), the highest FSIM index (0.95 and 0.90), and the lowest RMSE index (0.03 and 0.02), respectively) and achieved significantly higher image quality scores than the other 7 material decomposition methods (P < 0.05). The material quantitative imaging performance of SLMD-Net was close to that of the supervised network SUMD-Net trained with labeled data with a doubled size.@*CONCLUSIONS@#A small labeled dataset and a large unlabeled low SNR material image dataset can be fully used to suppress noise amplification and artifacts in basic material decomposition in spectral CT and reduce the dependence on labeled data-driven network, which considers more realistic scenario in clinics.

Effects of nutrient (N, P, K) fertilization on the dynamics of fine roots in tropical rain forests with different soil texture in the Colombian Pacific region / Efectos de la fertilización con nutrientes (N, P, K) sobre la dinámica de raíces finas en bosques húmedos tropicales con diferente textura del suelo en la región del Pacífico colombiano

Abstract Introduction: Fine root dynamics include production, turnover and decomposition; they are crucial to forest health, affect the entire biogeochemical complex of the ecosystem, and consequently, they substantially affect carbon balance. However, the influence of environmental factors and soil nutrient limitation on fine roots presents considerable uncertainties and has not been studied in tropical forests with more than 7 000 mm annual rainfall. Objective: To measure the effect of fertilization on fine roots in the high precipitation Chocó forest. Methods: We worked in two sites of the Chocó region, Colombia (August 2014-May 2015), where rainfall exceeds 10 000 mm per year. We applied five fertilization treatments (N, P, K, NPK and Control) to two soil type plots. Soil cylinders were removed at pre-established intervals to measure roots. Results: Phosphorus applications increased fined roots; and more fine roots were produced in sandy than in loam soil. The effects of fertilization were related, but not clearly determined by edaphic conditions. Conclusions: In this Chocó forest, the production of fine roots was higher in sandy and nutrient-rich soils but belowground net primary productivity was limited by the content of edaphic Phosphorus.

Resumen Introducción: La dinámica de las raíces finas incluye producción, rotación y descomposición; son cruciales para la salud de los bosques, afectan todo el complejo biogeoquímico del ecosistema y, en consecuencia, afectan sustancialmente el balance de carbono. Sin embargo, la influencia de los factores ambientales y la limitación de nutrientes del suelo en las raíces finas presenta incertidumbres considerables y no se ha estudiado en bosques tropicales con más de 7 000 mm de precipitación anual. Objetivo: Medir el efecto de la fertilización en las raíces finas en el bosque chocoano de alta precipitación. Métodos: Se trabajó en dos sitios de la región del Chocó, Colombia (agosto 2014-mayo 2015), donde las precipitaciones superan los 10 000 mm anuales. Se aplicaron cinco tratamientos de fertilización (N, P, K, NPK y Control) a dos parcelas por tipo de suelo. Los cilindros de suelo se retiraron a intervalos preestablecidos para medir las raíces. Resultados: Las aplicaciones de fósforo aumentaron las raíces finas; y se produjeron más raíces finas en suelos arenosos que en francos. Los efectos de la fertilización estuvieron relacionados, pero no claramente determinados por las condiciones edáficas. Conclusiones: En este bosque chocoano, la producción de raíces finas fue mayor en suelos arenosos y ricos en nutrientes, pero la productividad primaria neta subterránea estuvo limitada por el contenido de fósforo edáfico.

Estimation of Time Since Death of Bodies Above Soil Surface in A Guinea Forest-Savannah Vegetation of Nigeria Using Visible Post Mortem Changes

Background: Forensic taphonomy is the use of decomposition timeline estimation to unravel mystery behind time of death confirmation in homicide cases involving the law court. The Guinea forest-savannah vegetation is one of the vegetations in Nigeria characterized by short trees, grassland, very hot temperatures almost round the year, speedy wind, etc. It has two distinct seasons – rainy and dry seasons. This study aimed at investigating the visible post mortem changes of domestic pigs (Sus scrofa domestica) in a Guinea forest-savannah vegetation of Nigeria so that it can be used to estimate time since death of bodies on the soil surface. Methods: A stratified random sampling technique was used to select two male and two female matured domestic pigs from a private pig farm located close to the research facility. The visible post mortem changes were observed daily (morning, afternoon and evening) for 49 days. Results: Four stages of decomposition were identified namely fresh, bloat, active decay, and advanced decay stages. Mummification process started at the sixth day post mortem which slowed the rate of decomposition, and prevented the animals to completely skeletonize within the study period. Extreme atmospheric temperature was the major factor that aided the mummification of the animals. Conclusions: Decomposition of domestic pigs in this region accelerates at the early hours of post mortem, and subsequently slows down due to extreme climatic conditions. In addition, it takes carcasses on the soil surface more than 49 days to completely skeletonize due to its vegetative factors. This implies that most crime investigations carried out in this region must take into account the climatic conditions before estimating the time of death.

Analysis of causal mediation effect with odds ratio and excess relative risk as evaluation indexes / 四川精神卫生

The purpose of this paper was to introduce how to set the options of variable levels and multimodal covariates， and to demonstrate the causal mediation effect analysis method with odds ratio （OR） and excess relative risk （ERR） as evaluation indicators through examples. For treatment variables， mediator variables and covariates， the variable-level options of them could be set through the evaluate statement. For categorical variables and their interaction terms， they could be treated as multimodal covariates， and the variable levels could also be set for them by using the evaluate statement. Through an example， this paper used SAS to realize the causal mediation effect analysis and the decomposition of effect components with OR and ERR as the evaluation indicators.

Key technology and multi-directional decomposition method of the causal mediation effect analysis / 四川精神卫生

The purpose of this paper was to introduce five key techniques and the multi-directional decomposition methods of effect components in the analysis of causal mediation effects. The contents of the five key technologies were as follows： ① identification of causal mediation effect； ② regression method of causal mediation effect analysis； ③ maximum likelihood estimation； ④ estimation of total effect and various component effects； ⑤ estimation by bootstrap method. The multi-directional decomposition methods included 3 bidirectional decompositions， 2 three-directional decompositions and 1 four-directional decomposition. Through an example， a causal mediation effect analysis model including covariates and interaction terms was constructed with the help of SAS， bidirectional decomposition， three-directional decomposition and four-directional decomposition were carried out for the total effect in the causal mediation effect analysis， and the output results were explained.

Quantitative analysis of the effect of contact pressure on the reflection characteristics of radial pressure wave / 生物医学工程学杂志

The radial artery pulse wave contains a wealth of physiological and pathological information about the human body, and non-invasive studies of the radial artery pulse wave can assess arterial vascular elasticity in different age groups.The piezoelectric pulse wave transducers were used to non-invasively acquire radial artery pulse waves at different contact pressures in young and middle-aged and elderly populations. The radial artery waveforms were decomposed using a triangular blood flow model fitting method to obtain forward and reflected waves and calculate reflection parameters. Finally a correlation analysis and regression analysis of the contact pressure Psensor with the reflection parameters was carried out. The results showed that the reflection parameters RM, RI and Rd had a strong negative correlation with Psensor in both types of subjects, and the correlation coefficients and slopes of the regression curves were significantly different between the two types of subjects (P<0.05). Based on the results of this study, excessive contact pressure on the transducer should be avoided when detecting radial artery reflection waves in clinical practice. The results also show that the magnitude of the slope of the regression curve between the reflection parameters and the transducer contact pressure may be a potentially useful indicator for quantifying the elastic properties of the vessel.

Two decomposition algorithms of dual-energy cone beam CT and their dependence on the phantom sizes / 中华放射医学与防护杂志

Objective:To analyze the effects of two decomposition algorithms of dual-energy cone beam CT (DECBCT) (direct decomposition and iterative decomposition) on the image quality and material decomposition accuracy of different sizes of phantoms.Methods:Different sizes of imaging parts of patients were simulated using the combination of CatPhan604 phantoms and customized annuluses. CBCT with high energy of 140 kVp and low energy of 100 kVp were acquired using the Varian Edge CBCT system. Then the material decomposition of DECBCT images was performed using the two algorithms. The electron density (ED) and contrast-to-noise ratio (CNR) of each material in the CTP682 module were calculated. They were used to assess the decomposition accuracy and image quality of the two algorithms.Results:Based on the values in the Catphan604 manual, both algorithms have high ED accuracy. Only the ED accuracy of four materials of the smallest sized phantom showed statistical difference ( z = -4.21, 4.30, 2.87, 5.45, P < 0.05), but the average relative error was less than 1%. The CNR of the iterative decomposition algorithm was significantly higher than that of the direct decomposition, increasing by 51.8%-703.47%. The increase in the phantom size significantly reduced the accuracy of ED, and the increased amplitude of the relative error was up to a maximum of 2.52%. The large phantom size also reduced the image quality of iterative decomposition, and the decreased amplitude of CNR was up to a maximum of 39.71. Conclusions:Compared with the direct decomposition, the iterative decomposition algorithm can significantly reduce the image noise and improve the contrast without losing the accuracy of electron density in the DECBCT construction of different sizes of phantoms.

Motor imagery electroencephalogram classification based on sparse spatiotemporal decomposition and channel attention / 生物医学工程学杂志

Motor imagery electroencephalogram (EEG) signals are non-stationary time series with a low signal-to-noise ratio. Therefore, the single-channel EEG analysis method is difficult to effectively describe the interaction characteristics between multi-channel signals. This paper proposed a deep learning network model based on the multi-channel attention mechanism. First, we performed time-frequency sparse decomposition on the pre-processed data, which enhanced the difference of time-frequency characteristics of EEG signals. Then we used the attention module to map the data in time and space so that the model could make full use of the data characteristics of different channels of EEG signals. Finally, the improved time-convolution network (TCN) was used for feature fusion and classification. The BCI competition IV-2a data set was used to verify the proposed algorithm. The experimental results showed that the proposed algorithm could effectively improve the classification accuracy of motor imagination EEG signals, which achieved an average accuracy of 83.03% for 9 subjects. Compared with the existing methods, the classification accuracy of EEG signals was improved. With the enhanced difference features between different motor imagery EEG data, the proposed method is important for the study of improving classifier performance.

Effects of Layered Clothing on the Post-mortem Decomposition of Rat Carcasses on Grass Bushes and Dead Leaves (Kesan Pakaian Berlapis Terhadap Penguraian Postmortem Bangkai Tikus di atas Dedaun Rumput yang Telah Mati) / Jurnal Sains Kesihatan Malaysia

@#A post-mortem decomposition is defined by the evaluation of the physical and chemical changes of a cadaver or a carcass in order to estimate the cause of death and the time of death. The purpose of this study is not to replicate a real crime scene but to enhance knowledge of the effects of clothing in a decomposition process, mainly, the effects of layered cotton clothing on the post-mortem decomposition of adult female rat carcasses. Fifteen rats were divided into three groups: control, one-layered and two-layered clothed. The control subjects were unclothed and used to compare the post-mortem changes with the clothed subjects, one-layered and two-layered rat carcasses. All subjects were placed outdoor, 2.5 meters away from each other on grass bushes and dead leaves. Ambient temperature and humidity were recorded to observe if it associates with the post-mortem changes of the rats. Insect activity on each subject was observed. Post-mortem changes were measured using the Total Body Score system. The results showed that the control group underwent a faster decomposition compared to the clothed groups. The different layers of clothing did not show a vast difference in post-mortem changes. The ambient temperature of 28°C influences the post-mortem decomposition. The decomposition was rapid under the dominance of maggots compared to ants as ant colonies delayed the decomposition process. The study gave the knowledge of the effect of clothing in decomposition of female rats in forensic science.

A heart sound classification method based on complete ensemble empirical modal decomposition with adaptive noise permutation entropy and support vector machine / 生物医学工程学杂志

Heart sound signal is a kind of physiological signal with nonlinear and nonstationary features. In order to improve the accuracy and efficiency of the phonocardiogram (PCG) classification, a new method was proposed by means of support vector machine (SVM) in which the complete ensemble empirical modal decomposition with adaptive noise (CEEMDAN) permutation entropy was as the eigenvector of heart sound signal. Firstly, the PCG was decomposed by CEEMDAN into a number of intrinsic mode functions (IMFs) from high to low frequency. Secondly, the IMFs were sifted according to the correlation coefficient, energy factor and signal-to-noise ratio. Then the instantaneous frequency was extracted by Hilbert transform, and its permutation entropy was constituted into eigenvector. Finally, the accuracy of the method was verified by using a hundred PCG samples selected from the 2016 PhysioNet/CinC Challenge. The results showed that the accuracy rate of the proposed method could reach up to 87%. In comparison with the traditional EMD and EEMD permutation entropy methods, the accuracy rate was increased by 18%-24%, which demonstrates the efficiency of the proposed method.

A nonlocal spectral similarity-induced material decomposition method for noise reduction of dual-energy CT images / 南方医科大学学报

OBJECTIVE@#To propose a nonlocal spectral similarity-induced material decomposition network (NSSD-Net) to reduce the correlation noise in the low-dose spectral CT decomposed images.@*METHODS@#We first built a model-driven iterative decomposition model for dual-energy CT, optimized the objective function solving process using the iterative shrinking threshold algorithm (ISTA), and cast the ISTA decomposition model into the deep learning network. We then developed a novel cost function based on the nonlocal spectral similarity to constrain the training process. To validate the decomposition performance, we established a material decomposition dataset by real patient dual-energy CT data. The NSSD-Net was compared with two traditional model-driven material decomposition methods, one data-based material decomposition method and one data-model coupling-driven material decomposition supervised learning method.@*RESULTS@#The quantitative results showed that compared with the two traditional methods, the NSSD-Net method obtained the highest PNSR values (31.383 and 31.444) and SSIM values (0.970 and 0.963) and the lowest RMSE values (2.901 and 1.633). Compared with the datamodel coupling-driven supervised decomposition method, the NSSD-Net method obtained the highest SSIM values on water and bone decomposed results. The results of subjective image quality assessment by clinical experts showed that the NSSD-Net achieved the highest image quality assessment scores on water and bone basis material (8.625 and 8.250), showing significant differences from the other 4 decomposition methods (P < 0.001).@*CONCLUSION@#The proposed method can achieve high-precision material decomposition and avoid training data quality issues and model unexplainable issues.