Notes

Panobinostat for the A number of Myeloma.
Neuron morphology reconstruction (tracing) in 3D volumetric images is critical for neuronal research. However, most existing neuron tracing methods are not applicable in challenging datasets where the neuron images are contaminated by noises or containing weak filament signals. In this paper, we present a two-stage 3D neuron segmentation approach via learning deep features and enhancing weak neuronal structures, to reduce the impact of image noise in the data and enhance the weak-signal neuronal structures. In the first stage, we train a voxel-wise multi-level fully convolutional network (FCN), which specializes in learning deep features, to obtain the 3D neuron image segmentation maps in an end-to-end manner. In the second stage, a ray-shooting model is employed to detect the discontinued segments in segmentation results of the first-stage, and the local neuron diameter of the broken point is estimated and direction of the filamentary fragment is detected by rayburst sampling algorithm. Then, a Hessian-repair model is built to repair the broken structures, by enhancing weak neuronal structures in a fibrous structure determined by the estimated local neuron diameter and the filamentary fragment direction. Experimental results demonstrate that our proposed segmentation approach achieves better segmentation performance than other state-of-the-art methods for 3D neuron segmentation. Compared with the neuron reconstruction results on the segmented images produced by other segmentation methods, the proposed approach gains 47.83% and 34.83% improvement in the average distance scores. The average Precision and Recall rates of the branch point detection with our proposed method are 38.74% and 22.53% higher than the detection results without segmentation.The Levenberg-Marquardt and Newton are two algorithms that use the Hessian for the artificial neural network learning. In this article, we propose a modified Levenberg-Marquardt algorithm for the artificial neural network learning containing the training and testing stages. The modified Levenberg-Marquardt algorithm is based on the Levenberg-Marquardt and Newton algorithms but with the following two differences to assure the error stability and weights boundedness 1) there is a singularity point in the learning rates of the Levenberg-Marquardt and Newton algorithms, while there is not a singularity point in the learning rate of the modified Levenberg-Marquardt algorithm and 2) the Levenberg-Marquardt and Newton algorithms have three different learning rates, while the modified Levenberg-Marquardt algorithm only has one learning rate. The error stability and weights boundedness of the modified Levenberg-Marquardt algorithm are assured based on the Lyapunov technique. We compare the artificial neural network learning with the modified Levenberg-Marquardt, Levenberg-Marquardt, Newton, and stable gradient algorithms for the learning of the electric and brain signals data set.This article focuses on the adaptive synchronization for a class of fractional-order coupled neural networks (FCNNs) with output coupling. The model is new for output coupling item in the FCNNs that treat FCNNs with state coupling as its particular case. Novel adaptive output controllers with logarithm quantization are designed to cope with the stability of the fractional-order error systems for the first attempt, which is also an effective way to synchronize fractional-order complex networks. Based on fractional-order Lyapunov functionals and linear matrix inequalities (LMIs) method, sufficient conditions rather than algebraic conditions are built to realize the synchronization of FCNNs with output coupling. A numerical simulation is put forward to substantiate the applicability of our results.Supernumerary Robotics Limbs, or SuperLimbs for short, are wearable extra limbs for augmenting the wearer. SuperLimbs are attached directly to a human and, thereby, transmit a force from the environment to the human body. This inherent haptic feedback allows the human to perceive the interaction between the robot and the environment, monitor its actions, and effectively control the robot. This paper addresses basic properties and the usefulness of the inherent haptic feedback from SuperLimbs in two exemplary cases. First, we show that the inherent haptic feedback allows the wearer to close the loop and manually regulate the force output of the SuperLimb. Second, we show that the inherent haptic feedback is sufficient for the wearer to supervise the autonomous actions of the SuperLimb. This ability is a critical requirement for safely and effectively performing multiple tasks simultaneously with the natural limbs and SuperLimbs. Together, these findings suggest the importance of designing SuperLimbs to take advantage of the inherent haptic feedback.Inference of disease-gene associations helps unravel the pathogenesis of diseases and contributes to the treatment. Although many machine learning-based methods have been developed to predict causative genes, accurate association inference remains challenging. One major reason is the inaccurate feature selection and accumulation of error brought by commonly used multi-stage training architecture. In addition, the existing methods do not incorporate cell-type-specific information, thus fail to study gene functions at a higher resolution. Therefore, we introduce single-cell transcriptome data and construct a context-aware network to unbiasedly integrate all data sources. Then we develop a graph convolution-based approach named CIPHER-SC to realize a complete end-to-end learning architecture. Our approach outperforms four state-of-the-art approaches in five-fold cross-validations on three distinct test sets with the best AUC of 0.9501, demonstrating its stable ability either to predict the novel genes or to predict with genetic basis. this website The ablation study shows that our complete end-to-end design and unbiased data integration boost the performance from 0.8727 to 0.9443 in AUC. The addition of single-cell data further improves the prediction accuracy and makes our results be enriched for cell-type-specific genes. These results confirm the ability of CIPHER-SC to discover reliable disease genes. Our implementation is available at http//github.com/YidingZhang117/CIPHER-SC.
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