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Nervous system Hemangioblastoma inside a Child Affected person Related to Von Hippel-Lindau Ailment: An incident Document and Novels Evaluate.
This study reviews recent ECG clustering techniques with the focus on machine learning and deep learning algorithms. We critically review and compare these techniques, discuss their applications and limitations, and provide future research directions. This review provides further insights into ECG clustering and presents the necessary information required to adopt the appropriate algorithm for a specific application.A sub-1GHz transmitter (TX) integrated chip (IC) with ultra-low power consumption and moderately high adjacent channel power rejection (ACPR) is presented for in-body bio-sensing applications. The 400 MHz 12-phase digital power amplifier (DPA) is implemented with the proposed 16QAM modulation scheme to improve the energy efficiency. The TX IC also contains a 900 MHz FSK TX realized with a symmetrical edge-combiner, which can be used in the low accuracy mode. A fully digital modulator with band shaping is integrated on the chip for the improvement of ACPR performance. Fabricated in 65-nm CMOS process, the chip occupies an active area of 0.75 mm2. Under 0.5 V supply voltage, the TX consumes less than 0.66 mW power consumption while delivering -15 dBm of output power when operating at both bands. The presented TX has an energy efficiency performance comparable to the state-of-the-arts low power designs, with the measured average energy consumption of 64.5/220 pJ/bit, and the measured figure-of-merit (FoM) of 2.04/6.98 nJ/(bit · mW) for the two bands. Compared with the state-of-the-arts sub-1mW designs in literatures, the ACPR is improved by at least 13 dB.Molecular Communication is an emerging technology enabling communications in nano-networks. Ca2+ signal is one promising option of MC due to the important role in bio-metabolisms and the available characteristics in communication engineering. So far, scientists analyze Ca2+ signaling via bio-experiments and simulations. Current researches lack a mathematical model for quantitative analysis of Ca2+ signal propagation on the network scale. In this work, we investigate the propagation patterns of Ca2+ signals in bio-cellular network. Firstly, we propose an improved Ca2+ dynamics model to describe Ca2+ signals considering movements of cells and attenuation of Ca2+ concentration. Then, we perform multi-modal analysis through the waveform characteristics, and classify cells according to their states. Moreover, a mathematical model is put forward to analyze the propagation of calcium signals based on typical epidemic model. The proposed model fully considers the similarity between 1) epidemic disease propagates among mobile individuals; 2) Ca2+ signal propagates among mobile cells. The proposed model is amended to fit the case considering unique characters of Ca2+ signal. Finally, simulation results show that the proposed Ca2+ propagation model is coincident with Monte Carlo simulation results, indicating that the model is helpful for understanding how far and how fast Ca2+ signal can propagate.There are many types of retinal disease, and accurately detecting these diseases is crucial for proper diagnosis. Convolutional neural networks (CNNs) typically perform well on detection tasks, and the attention module of CNNs can generate heatmaps as visual explanations of the model. However, the generated heatmap can only detect the most discriminative part, which is problematic because many object regions may exist in the region beside the heatmap in an area known as a complementary heatmap. In this study, we developed a method specifically designed multi-retinal diseases detection from fundus images with the complementary heatmap. The proposed CAM-based method is designed for 2D color images of the retina, rather than MRI images or other forms of data. Moreover, unlike other visual images for disease detection, fundus images of multiple retinal diseases have features such as distinguishable lesion region boundaries, overlapped lesion regions between diseases, and specific pathological structures (e.g. scattered blood spots) that lead to mis-classifications. Based on these considerations, we designed two new loss functions, attention-explore loss and attention-refine loss, to generate accurate heatmaps. We select both "bad" and "good" heatmaps based on the prediction score of ground truth and train them with the two loss functions. When the detection accuracy increases, the classification performance of the model is also improved. Experiments on a dataset consisting of five diseases showed that our approach improved both the detection accuracy and the classification accuracy, and the improved heatmaps were closer to the lesion regions than those of current state-of-the-art methods.Few-shot learning deals with the fundamental and challenging problem of learning from a few annotated samples, while being able to generalize well on new tasks. The crux of few-shot learning is to extract prior knowledge from related tasks to enable fast adaptation to a new task with a limited amount of data. In this paper, we propose meta-learning kernels with random Fourier features for few-shot learning, we call MetaKernel. Specically, we propose learning variational random features in a data-driven manner to obtain task-specic kernels by leveraging the shared knowledge provided by related tasks in a meta-learning setting. We treat the random feature basis as the latent variable, which is estimated by variational inference. The shared knowledge from related tasks is incorporated into a context inference of the posterior, which we achieve via a long-short term memory module. To establish more expressive kernels, we deploy conditional normalizing ows based on coupling layers to achieve a richer posterior distribution over random Fourier bases. see more The resultant kernels are more informative and discriminative, which further improves the few-shot learning. We conduct experiments on both few-shot image classication and regression tasks. The results on fourteen datasets demonstrate MetaKernel consistently better performance than state-of-the-art alternatives.
Relying on the idea that functional connectivity provides important insights on the underlying dynamic of neuronal interactions, we propose a novel framework that combines functional connectivity estimators and covariance-based pipelines to improve the classification of mental states, such as motor imagery.

A Riemannian classifier is trained for each estimator and an ensemble classifier combines the decisions in each feature space. A thorough assessment of the functional connectivity estimators is provided and the best performing pipeline among those tested, called FUCONE, is evaluated on different conditions and datasets.

Using a meta-analysis to aggregate results across datasets, FUCONE performed significantly better than all state-of-the-art methods.

The performance gain is mostly imputable to the improved diversity of the feature spaces, increasing the robustness of the ensemble classifier with respect to the inter- and intra-subject variability.

Our results offer new insights into the need to consider functional connectivity-based methods to improve the BCI performance.
Our results offer new insights into the need to consider functional connectivity-based methods to improve the BCI performance.
Contact irreversible electroporation (IRE) is a method for ablating cells by applying electric pulses via surface electrodes in contact with a target tissue. To facilitate the application of the contact IRE to superficial lesion treatment, this study further extended the ablation depth, which had been limited to a 400-μm depth in our previous study, by using concentric electrodes.

A prototype device of concentric electrodes was manufactured using a Teflon-coated copper wire inserted in a copper tube. The ablation area was experimentally determined using a tissue phantom comprising 3D cultured fibroblasts and compared with the electric field distribution obtained using numerical analyses.

Experiments showed that cells 540 μm from the surface of the tissue phantom were necrotized by the application of 150 pulses at 100 V. The outline of the ablation area agreed well with the contour line of 0.4 kV/cm acquired by the analyses. The ablation depth predicted for the concentric electrode using this critical electric field was 1.4 times deeper than that for the parallel electrode. For the actual application of treatment, a multiple-electrode device that bundles several pairs of concentric electrodes was developed, and confirmed that to be effective for treating wide areas with a single treatment.

The electric field estimated by the analyses with the experimentally determined threshold confirmed that concentric electrodes could attain a deeper ablation than parallel electrodes.

Using the concentric electrodes, we were able to localize ablation to specific target cells with much less damage to neighboring cells.
Using the concentric electrodes, we were able to localize ablation to specific target cells with much less damage to neighboring cells.
Two-dimensional (2D) photoacoustic (PA) imaging based on array transducers provide high spatial resolution in the lateral direction by adopting receive dynamic focusing. However, the quality of PA image is often deteriorated by poor elevational resolution which is achieved by an acoustic lens. To overcome this limitation, we present a three-dimensional (3D) image reconstruction method using a commercial one-dimensional (1D) array transducer.

In the method, the elevational resolution is improved by applying synthetic aperture focusing (SAF) technique along the elevational direction. For this, a commercially available 1D array transducer with an acoustic lens is modeled and appropriate synthetic focusing delay that can minimize the effect of the acoustic lens is derived by mathematical analysis.

From the simulation and experiment results, it was demonstrated that the proposed method can enhance the image quality of PA imaging, i.e., elevational resolution and signal-to-noise ratio (SNR).

3D PA images with improved elevational resolution were achieved using a clinical 1D array transducer.

The presented method may be useful for clinical application such as detecting microcalcification, imaging of tumor vasculature and guidance of biopsy in real time.
The presented method may be useful for clinical application such as detecting microcalcification, imaging of tumor vasculature and guidance of biopsy in real time.The arterivirus porcine reproductive and respiratory syndrome virus (PRRSV) causes significant economic losses to the swine industry worldwide. Here we apply ribosome profiling (RiboSeq) and parallel RNA sequencing (RNASeq) to characterise the transcriptome and translatome of both species of PRRSV and to analyse the host response to infection. We calculated programmed ribosomal frameshift (PRF) efficiency at both sites on the viral genome. This revealed the nsp2 PRF site as the second known example where temporally regulated frameshifting occurs, with increasing -2 PRF efficiency likely facilitated by accumulation of the PRF-stimulatory viral protein, nsp1β. Surprisingly, we find that PRF efficiency at the canonical ORF1ab frameshift site also increases over time, in contradiction of the common assumption that RNA structure-directed frameshift sites operate at a fixed efficiency. This has potential implications for the numerous other viruses with canonical PRF sites. Furthermore, we discovered several highly translated additional viral ORFs, the translation of which may be facilitated by multiple novel viral transcripts.
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