Notes

Shaoyao decoction attenuates DSS-induced ulcerative colitis, macrophage as well as NLRP3 inflammasome service over the MKP1/NF-κB process.
In this article, pinning control has been applied to stabilize a kind of stochastic network with finite states called probabilistic logical networks (PLNs). First, the solvability of pinning controllers, including the selection of pinning nodes and the corresponding control design, is addressed to guarantee the stabilization of PLNs. Then, based on the complete matrices set, one necessary and sufficient condition is obtained for PLNs to be stabilized by exact p nodes. In addition, an algorithm is presented for obtaining the minimal number of pinning nodes and how exact they are. As an application, our algorithm is used to calculate the minimal number of pinning nodes for the stabilization of logical networks (LNs). Examples are given to illustrate the process of choosing pinning nodes and show the efficiency of the obtained results.This article proposes three new methods to enlarge the feasible region for guaranteeing stability for generalized neural networks having time-varying delays based on the Lyapunov method. First, two new zero equalities in which three states are augmented are proposed and inserted into the results of the time derivative of the constructed Lyapunov-Krasovskii functionals for the first time. selleck chemicals Second, inspired by the Wirtinger-based integral inequality, new Lyapunov-Krasovskii functionals are introduced. Finally, by utilizing the relationship among the augmented vectors and from the original equation, newly augmented zero equalities are established and Finsler's lemma are applied. Through three numerical examples, it is verified that the proposed methods can contribute to enhance the allowable region of maximum delay bounds.Process mining is a suitable method for knowledge extraction from patient pathways. Structured in event logs, medical events are complex, often described using various medical codes. An efficient labeling of these events before applying process mining analysis is challenging. This paper presents an innovative methodology to handle the complexity of events in medical event logs. Based on autoencoding, accurate labels are created by clustering similar events in latent space. Moreover, the explanation of created labels is provided by the decoding of its corresponding events. Tested on synthetic events, the method is able to find hidden clusters on sparse binary data, as well as accurately explain created labels. A case study on real healthcare data is performed. Results confirm the suitability of the method to extract knowledge from complex event logs representing patient pathways.We propose a new generic type of artificial neurons called q-neurons. A q-neuron is a stochastic neuron with its activation function relying on Jackson's discrete q-derivative for a stochastic parameter q. We show how to generalize neural network architectures with q-neurons and demonstrate the scalability and ease of implementation of q-neurons into legacy deep learning frameworks. We report experimental results that consistently improve performance over state-of-the-art standard activation functions, both on training and test loss functions.Non-coding RNAs (ncRNAs) play an important role in various biological processes and are associated with diseases. Distinguishing between coding RNAs and ncRNAs, also known as predicting coding potential of RNA sequences, is critical for downstream biological function analysis. Many machine learning-based methods have been proposed for predicting coding potential of RNA sequences. Recent studies reveal that most existing methods have poor performance on RNA sequences with short Open Reading Frames (sORF, ORF length less then 303nt). In this work, we analyze the distribution of ORF length of RNA sequences, and observe that the number of coding RNAs with sORF is inadequate and coding RNAs with sORF are much less than ncRNAs with sORF. Thus, there exists the problem of local data imbalance in RNA sequences with sORF. We propose a coding potential prediction method CPE-SLDI, which uses data oversampling techniques to augment samples for coding RNAs with sORF so as to alleviate local data imbalance. Compared with existing methods, CPE-SLDI produces the better performances, and studies reveal that the data augmentation by various data oversampling techniques can enhance the performance of coding potential prediction, especially for RNA sequences with sORF. The implementation of the proposed method is available at https//github.com/chenxgscuec/CPESLDI.In this work, we present a paradigm bridging electromagnetic (EM) and molecular communication through a stimuli-responsive intra-body model. It has been established that protein molecules, which play a key role in governing cell behavior, can be selectively stimulated using Terahertz (THz) band frequencies. By triggering protein vibrational modes using THz waves, we induce changes in protein conformation, resulting in the activation of a controlled cascade of biochemical and biomechanical events. To analyze such an interaction, we formulate a communication system composed of a nanoantenna transmitter and a protein receiver. We adopt a Markov chain model to account for protein stochasticity with transition rates governed by the nanoantenna force. Both two-state and multi-state protein models are presented to depict different biological configurations. Closed form expressions for the mutual information of each scenario is derived and maximized to find the capacity between the input nanoantenna force and the protein state. The results we obtain indicate that controlled protein signaling provides a communication platform for information transmission between the nanoantenna and the protein with a clear physical significance. The analysis reported in this work should further research into the EM-based control of protein networks.We studied the performance of a robotic orthosis designed to assist the paretic hand after stroke. It is wearable and fully user-controlled, serving two possible roles as a therapeutic tool that facilitates device-mediated hand exercises to recover neuromuscular function or as an assistive device for use in everyday activities to aid functional use of the hand. We present the clinical outcomes of a pilot study designed as a feasibility test for these hypotheses. 11 chronic stroke (>2 years) patients with moderate muscle tone (Modified Ashworth Scale ≤ 2 in upper extremity) engaged in a month-long training protocol using the orthosis. Individuals were evaluated using standardized outcome measures, both with and without orthosis assistance. Fugl-Meyer post intervention scores without robotic assistance showed improvement focused specifically at the distal joints of the upper limb, suggesting the use of the orthosis as a rehabilitative device for the hand. Action Research Arm Test scores post intervention with robotic assistance showed that the device may serve an assistive role in grasping tasks. These results highlight the potential for wearable and user-driven robotic hand orthoses to extend the use and training of the affected upper limb after stroke.Lossy compression brings artifacts into the compressed image and degrades the visual quality. In recent years, many compression artifacts removal methods based on convolutional neural network (CNN) have been developed with great success. However, these methods usually train a model based on one specific value or a small range of quality factors. Obviously, if the test images quality factor does not match to the assumed value range, then degraded performance will be resulted. With this motivation and further consideration of practical usage, a highly robust compression artifacts removal network is proposed in this paper. Our proposed network is a single model approach that can be trained for handling a wide range of quality factors while consistently delivering superior or comparable image artifacts removal performance. To demonstrate, we focus on the JPEG compression with quality factors, ranging from 1 to 60. Note that a turnkey success of our proposed network lies in the novel utilization of the quantization tables as part of the training data. Furthermore, it has two branches in parallel-i.e., the restoration branch and the global branch. The former effectively removes the local artifacts, such as ringing artifacts removal. On the other hand, the latter extracts the global features of the entire image that provides highly instrumental image quality improvement, especially effective on dealing with the global artifacts, such as blocking, color shifting. Extensive experimental results performed on color and grayscale images have clearly demonstrated the effectiveness and efficacy of our proposed single-model approach on the removal of compression artifacts from the decoded image.Tissue harmonic imaging is often the preferred ultrasound imaging modality due to its ability to suppress reverberations. The method requires good control of the transmit stage of the ultrasound scanner, as harmonics in the transmitted ultrasound pulses will interfere with the harmonics generated in the tissue during nonlinear propagation, degrading image quality. In this study, a medical ultrasound probe used in tissue harmonic imaging was experimentally characterized for transmitted second-harmonic distortion to identify and compare the sources of nonlinear distortion in the probe and transmit electronics. The system was tested up to amplitudes above what is found during conventional operation, pushing the system to the limits in order to investigate the phenomenon. Under these conditions, second-harmonic levels up to -20 dB relative to the fundamental frequency were found in the ultrasound pulses transmitted from the probe. The transmit stage consists of high-voltage transmit electronics, cable, tuning inductors, and the acoustic stack. The contribution from the different stages in the ultrasound transmit chain was quantified by separating and measuring at different positions. Nonlinearities in the acoustic transducer stack were identified as the dominating source for second harmonics in the transmitted ultrasound pulses. Contribution from other components, e.g., transmit electronics and cable and tuning circuitry, were found to be negligible compared with that from the acoustic stack. Investigation of the stack's electrical impedance at different driving voltages revealed that the impedance changes significantly as a function of excitation voltage. The second-harmonic peak in the transmitted pulses can be explained by this nonlinear electrical impedance distorting the driving voltage and current.The Wave Controlled Aliasing In Parallel Imaging (Wave-CAIPI) technique manifests great potential to highly accelerate three-dimensional (3D) balanced steady-state free precession (bSSFP) through substantially reducing the geometric factor (g-factor) and aliasing artifacts of image reconstruction. However, severe banding artifacts appear in bSSFP imaging due to unbalanced gradients with nonzero 0th moment applied by the conventional Wave-CAIPI technique. In this study, we propose a 3D Wave-bSSFP scheme that adopts truncated wave gradients with zero 0th moment to avoid introducing additional banding artifacts and to maintain the advantages of wave encoding. The simulation results indicate that the number of wave cycles that are truncated and different options of applying wave gradients affect both the g-factor reduction and image quality, but the influence is limited. In phantom experiments, the proposed technique shows similar acceleration performance as the conventional Wave-CAIPI technique and effectively eliminates its introduced banding artifacts.
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