An automatic measurement system of distal femur morphological parameters using 3D slicer software.

In the field of joint surgery, the computer-aided design of knee prostheses suitable for the Chinese population requires a large quantity of anatomical knee data. In this study, we propose a new method that uses 3D Slicer software to automatically measure the morphological parameters of the distal femur. First, 141 femur samples were segmented from CT data to establish the femoral shape library. Next, balanced iterative reducing and clustering using hierarchies (BIRCH) combined with iterative closest point (ICP) and generalised procrustes analysis (GPA) were used to achieve fast registration of the femur samples. The statistical model was automatically calculated from the registered femur samples, and an orthopaedic surgeon marked the points on the statistical model. Finally, we developed an automatic measurement system using 3D Slicer software, and a deformable model matching method was applied to establish the point correspondence between the statistical model and the other samples. By matching points on the statistical model to corresponding points in other samples, we measured all other samples. We marked six points and measured eight parameters. We evaluated the performance of automatic matching by comparing the points marked manually with those matched automatically and verified the accuracy of the system by comparing the manual and automatic measurement results. The results indicated that the average error of the automatic matching points was 1.03 mm, and the average length error and average angle error measured automatically by the system were 0.37 mm and 0.63°, respectively. These errors were smaller than the intra-rater and inter-rater errors measured manually by two different surgeons, which showed that the accuracy of our automatic method was high. Taken together, this study established an accurate and automatic measurement system for the distal femur based on the secondary development of 3D Slicer software to assist orthopaedic surgeons in completing the measurements of big data and further promote the improved design of Chinese-specific knee prostheses.

Selecting transferrable neurophysiological features for inter-individual emotion recognition via a shared-subspace feature elimination approach.

The interplay between human emotions, personality, and motivation results in individual specificity in neurophysiological data distributions for the same emotional category. To address this issue for building an emotion recognition system based on electroencephalogram (EEG) features, we propose a shared-subspace feature elimination (SSFE) approach to identify EEG variables with common characteristics across multiple individuals. In the SSFE framework, a low-dimensional space defined by a selected number of EEG features is created to represent the inter-emotion discriminant for different pairs of subjects evaluated based on the interclass margin. Using two public databases-DEAP and MAHNOB-HCI-the performance of the SSFE is validated according to the leave-one-subject-out paradigm. The performance of the proposed framework is compared with five other feature-selection methods. The effectiveness and computational cost of the SSFE is investigated across six machine learning models based on their optimal hyperparameters. In the end, the competitive binary classification accuracy from the SSFE of arousal and valence recognitions are determined to be 0.6521 and 0.6635, respectively, for DEAP, and 0.6520 and 0.6537, respectively for MAHNOB-HCI.

Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders.

To estimate the reliability and cognitive states of operator performance in a human-machine collaborative environment, we propose a novel human mental workload (MW) recognizer based on deep learning principles and utilizing the features of the electroencephalogram (EEG). To determine personalized properties in high dimensional EEG indicators, we introduce a feature mapping layer in stacked denoising autoencoder (SDAE) that is capable of preserving the local information in EEG dynamics. The ensemble classifier is then built via the subject-specific integrated deep learning committee, and adapts to the cognitive properties of a specific human operator and alleviates inter-subject feature variations. We validate our algorithms and the ensemble SDAE classifier with local information preservation (denoted by EL-SDAE) on an EEG database collected during the execution of complex human-machine tasks. The classification performance indicates that the EL-SDAE outperforms several classical MW estimators when its optimal network architecture has been identified.

Rhythmic expression of circadian clock genes in the preovulatory ovarian follicles of the laying hen.

The circadian clock is reported to play a role in the ovaries in a variety of vertebrate species, including the domestic hen. However, the ovary is an organ that changes daily, and the laying hen maintains a strict follicular hierarchy. The aim of this study was to examine the spatial-temporal expression of several known canonical clock genes in the granulosa and theca layers of six hierarchy follicles. We demonstrated that the granulosa cells (GCs) of the F1-F3 follicles harbored intrinsic oscillatory mechanisms in vivo. In addition, cultured granulosa cells (GCs) from F1 follicles exposed to luteinizing hormone (LH) synchronization displayed Per2 mRNA oscillations, whereas, the less mature GCs (F5 plus F6) displayed no circadian change in Per2 mRNA levels. Cultures containing follicle-stimulating hormone (FSH) combined with LH expressed levels of Per2 mRNA that were 2.5-fold higher than those in cultures with LH or FSH alone. These results show that there is spatial specificity in the localization of clock cells in hen preovulatory follicles. In addition, our results support the hypothesis that gonadotropins provide a cue for the development of the functional cellular clock in immature GCs.

Cooperation of luteinizing hormone signaling pathways in preovulatory avian follicles regulates circadian clock expression in granulosa cell.

Ovulation in birds is triggered by a surge of luteinizing hormone (LH), and the ovulatory cycle is affected by the circadian rhythms of clock genes transcription levels in follicles. The influence of LH signaling cascades action on circadian clock genes was investigated using granulosa cells of preovulatory follicles from Roman hens cultured in a serum-free system. The expression of core oscillators (Bmal1, Clock, Cry1, Per2, and Rev-erbß), clock-controlled gene (Star), Egr-1 and LHr was measured by quantitative real-time PCR. Significant changes in clock genes transcription levels were observed in control groups over 24 h, indicating that cell-autonomous rhythms exist in granulosa cells. Intriguingly, the transcript levels of clock genes increased with LH treatment during 24 h of culture; they peaked 4 h in advance of controls and second but weaker oscillations were also observed. It appeared that LH changed the cell-autonomous rhythm and cycle time of clock genes. To further investigate the LH signaling cascades, inhibitors of cyclic adenosine monophosphate (cAMP), p38 mitogen-activated protein kinases (p38MAPK) and extracellular signal-regulated kinases 1 and 2 (ERK1/2) pathways were used. The transcript levels of clock genes were suppressed by blocking cAMP, but increased with similar expression patterns by blocking the p38MPAK and ERK1/2 pathways over 24 h. Thus, the influence of LH signaling cascades in chicken ovulation is mediated by the cAMP pathway and also involves the p38MAPK and ERK1/2 pathways.

APOBEC2 mRNA and protein is predominantly expressed in skeletal and cardiac muscles of chickens.

Apolipoprotein B mRNA-editing enzyme catalytic subunit 2 (APOBEC2) plays an important role in regulating and maintaining muscle development in mammals. In this study, we evaluated APOBEC2 mRNA abundance and protein expression and the results indicated that APOBEC2 mRNA was most abundant in skeletal and cardiac muscle, with relatively low expression in the gonads, gizzard and subcutaneous fat tissues of chickens. Immunoreactive APOBEC2 was localized to the cell nucleus of developing myocardium and skeletal myofibers. There were significant differences in mRNA and protein abundance among ages, tissues, and between males and females. In conclusion, APOBEC2 was expressed as the greatest in skeletal muscle and cardiac muscle where it localized to the nucleus. Thus, APOBEC2 may play an important role in muscle development in chickens.

MUSTN1 mRNA Abundance and Protein Localization is Greatest in Muscle Tissues of Chinese Meat-Quality Chickens.

The Mustang, Musculoskeletal Temporally Activated Novel-1 Gene (MUSTN1) plays an important role in regulating musculoskeletal development in mammals. We evaluated the developmental and tissue-specific regulation of MUSTN1 mRNA and protein abundance in Erlang Mountainous (EM) chickens. Results indicated that MUSTN1 mRNA/protein was expressed in most tissues with especially high expression in heart and skeletal muscle. The MUSTN1 protein localized to the nucleus in myocardium and skeletal muscle fibers. There were significant differences in mRNA and protein abundance among tissues, ages and between males and females. In conclusion, MUSTN1 was expressed the greatest in skeletal muscle where it localized to the nucleus. Thus, in chickens MUSTN1 may play a vital role in muscle development.

Auto-tuning of cascade control systems.

In this paper, a novel auto-tuning method for a cascade control system is proposed. By employing a simple relay feedback test, both inner and outer loop model parameters can be simultaneously identified. Consequently, well-established proportional-integral-derivative (PID) tuning rules can be applied to tune both loops. Compared with existing methods, the new method is simpler and yet more effective. It can be directly integrated into commercially available industrial auto-tuning systems. Some examples are given to illustrate the effectiveness and robustness of the proposed method.