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Improved Functionality associated with Higher Areal Thickness Indirect Push Implosions in the National Key Facility employing a Four-Shock Adiabat Molded Travel.
Finally, the proposed method was demonstrated to show resistance to sequence length-dependent performance deterioration.It is well-known that the major reason for the rapid proliferation of cancer cells are the hypomethylation of the whole cancer genome and the hypermethylation of the promoter of particular tumor suppressor genes. Locating 5-methylcytosine (5mC) sites in promoters is therefore a crucial step in further understanding of the relationship be-tween promoter methylation and the regulation of mRNA gene expression. High throughput identification of DNA 5mC in wet lab is still time-consuming and labor-extensive. Thus, finding the 5mC site of genome-wide DNA pro-moters is still an important task. We compared the effectiveness of the most popular and strong machine learning techniques namely XGBoost, Random Forest, Deep Forest, and Deep Feedforward Neural Network in predicting the 5mC sites of genome-wide DNA promoters. A feature extraction method based on k-mers embeddings learned from a language model were also applied. Overall, the performance of all the surveyed models surpassed deep learning models of the latest studies on the same dataset employing other encoding scheme. Furthermore, the best model achieved AUC scores of 0.962 on both cross-validation and independent test data. We concluded that our approach was efficient for identifying 5mC sites of promoters with high performance.Numerous microbes have been found to have vital impacts on human health through affecting biological processes. Therefore, exploring potential associations between microbes and diseases will promote the understanding and diagnosis of diseases. In this study, we present a novel computational model, named MSLINE, to infer potential microbe-disease associations by integrating Multiple Similarities and Large-scale Information Network Embedding (LINE) based on known associations. Specifically, on the basis of known microbe-disease associations from the Human Microbe-Disease Association Database, we first increase the known associations by collecting proven associations from existing literatures. We then construct a microbe-disease heterogeneous network (MDHN) by integrating known associations and multiple similarities (including Gaussian interaction profile kernel similarity, microbe function similarity, disease semantic similarity and disease-symptom similarity). After that, we implement random walk and LINE algorithm on MDHN to learn its structure information. Finally, we score the microbe-disease associations according to the structure information for every nodes. In the Leave-one-out cross validation and 5-fold cross validation, MSLINE performs better compared to other existing methods. Moreover, case studies of different diseases proved that MSLINE could predict the potential microbe-disease associations efficiently.Using "human-in-the-loop" (HIL) optimization can obtain suitable exoskeleton assistance patterns to improve walking economy. However, there are differences in these patterns under different gait conditions, and currently most HIL optimizations use metabolic cost, which requires long periods to be estimated for each control law, as the physiological objective to minimize. We aimed to construct a muscle-activity-based cost function and to find the appropriate initial assistance patterns in HIL optimization of multi-gait ankle exoskeleton assistance. One healthy subject walked assisted by an ankle exoskeleton under nine gait conditions and each condition was the combination of different walking speeds, ground slopes and load weights. Ten assistance patterns were provided for the subject under each gait condition. Then we constructed a cost function based on surface electromyography signals of four lower leg muscles and select the muscle weight combination by using particle swarm optimization algorithm to compose the cost function with maximum differences between different assistance patterns. The mean weights of medial gastrocnemius, lateral gastrocnemius, soleus and tibialis anterior activity under all gait conditions are 0.153, 0.104, 0.953 and 0.145, respectively. Then we verified the effectiveness of this cost function by optimization and validation experiments conducted on four subjects. Our results are expected to guide the selection of muscle-activity-based cost functions and improve the time efficiency of HIL optimization.Bioelectric medicine treatments target disorders of the nervous system unresponsive to pharmacological methods. While current stimulation paradigms effectively treat many disorders, the underlying mechanisms are relatively unknown, and current neuroscience recording electrodes are often limited in their specificity to gross averages across many neurons or axons. Here, we develop a novel, durable carbon fiber electrode array adaptable to many neural structures for precise neural recording. Carbon fibers ( [Formula see text] diameter) were sharpened using a reproducible blowtorchmethod that uses the reflection of fibers against the surface of a water bath. The arrays were developed by partially embedding carbon fibers in medical-grade silicone to improve durability. We recorded acute spontaneous electrophysiology from the rat cervical vagus nerve (CVN), feline dorsal root ganglia (DRG), and rat brain. Blowtorching resulted in fibers of 72.3 ± 33.5-degree tip angle with [Formula see text] exposed carbon. Observable neural clusters were recorded using sharpened carbon fiber electrodes fromrat CVN ( [Formula see text]), feline DRG ( [Formula see text]), and rat brain ( [Formula see text]). Recordings from the feline DRG included physiologically relevant signals from increased bladder pressure and cutaneous brushing. These results suggest that this carbon fiber array is a uniquely durable and adaptable neural recordingdevice. In the future, this device may be useful as a bioelectric medicine tool for diagnosis and closed-loop neural control of therapeutic treatments and monitoring systems.Minimal paths are regarded as a powerful and efficient tool for boundary detection and image segmentation due to its global optimality and the well-established numerical solutions such as fast marching method. In this paper, we introduce a flexible interactive image segmentation model based on the Eikonal partial differential equation (PDE) framework in conjunction with region-based homogeneity enhancement. A key ingredient in the introduced model is the construction of local geodesic metrics, which are capable of integrating anisotropic and asymmetric edge features, implicit region-based homogeneity features and/or curvature regularization. The incorporation of the region-based homogeneity features into the metrics considered relies on an implicit representation of these features, which is one of the contributions of this work. Moreover, we also introduce a way to build simple closed contours as the concatenation of two disjoint open curves. Experimental results prove that the proposed model indeed outperforms state-of-the-art minimal paths-based image segmentation approaches.In this paper, the impact of demosaicing on gradient extraction is studied and a gradient-based feature extraction pipeline based on raw Bayer pattern images is proposed. It is shown both theoretically and experimentally that the Bayer pattern images are applicable to the central difference gradient-based feature extraction algorithms with negligible performance degradation, as long as the arrangement of color filter array (CFA) patterns matches the gradient operators. The color difference constancy assumption, which is widely used in various demosaicing algorithms, is applied in the proposed Bayer pattern image-based gradient extraction pipeline. Experimental results show that the gradients extracted from Bayer pattern images are robust enough to be used in histogram of oriented gradients (HOG)-based pedestrian detection algorithms and shift-invariant feature transform (SIFT)-based matching algorithms. By skipping most of the steps in the image signal processing (ISP) pipeline, the computational complexity and power consumption of a computer vision system can be reduced significantly.Previous work on interactive 3D labeling focused on improving user experience based on virtual/augmented reality and, thereby, speeding-up the labeling of scenes. In this article, we present a novel interactive, collaborative VR-based 3D labeling system for live-captured scenes by multiple remotely connected users based on sparse multi-user input with automatic label propagation and completion. FDI-6 Hence, our system is particularly beneficial in the case of multiple users that are able to label different scene parts from the respectively adequate views in parallel. Our proposed system relies on 1) the RGB-D capture of an environment by a user, 2) a reconstruction client that integrates this stream into a 3D model, 3) a server that gets scene updates and manages the global 3D scene model as well as client requests and the integration/propagation of labels, 4) labeling clients that allow an independent VR-based scene exploration and labeling for each user, and 5) remotely connected users that provide a sparse 3D labeling used to control the label propagation over objects and the label prediction to other scene parts. Our evaluation demonstrates the intuitive collaborative 3D labeling experience as well as its capability to meet the efficiency constraints regarding reconstruction speed, data streaming, visualization, and labeling.
Our goal was to analyze the electrophysiological response to direct electrical stimulation (DES) systematically applied at a wide range of parameters and anatomical sites, with particular focus on neural activities associated with memory and cognition.

We used a large set of intracranial EEG (iEEG) recordings with DES from 45 subjects with electrodes implanted both subdurally on the cortical surface and subcortically into the brain parenchyma. Subjects were stimulated in blocks of alternating frequency and amplitude parameters during quiet wakefulness.

Stimulating at different frequencies and amplitudes of electric current revealed a persistent pattern of response in the slow and the fast neural activities. In particular, amplification of the theta (47 Hz) and attenuation of the gamma (2952 Hz) power-in-band was observed with increasing the stimulation parameters. This opposite effect on the low and high frequency bands was found across a network of selected local and distal sites proportionally to the proximity and magnitude of the electric current. Power increase in the theta and decrease in the gamma band was driven by the total electric charge delivered with either increasing the frequency or amplitude of the stimulation current. This inverse effect on the theta and gamma activities was consistently observed in response to different stimulation frequencies and amplitudes.

Our results suggest a uniform DES effect of amplifying theta and suppressing gamma neural activities in the human brain.

These findings reveal the utility of simple power-in-band features for understanding and optimizing the effects of electrical stimulation on brain functions.
These findings reveal the utility of simple power-in-band features for understanding and optimizing the effects of electrical stimulation on brain functions.
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