Notes

[Validation study involving conjecture types of hepatitis B virus-related hepatocellular carcinoma].
The proposed tracker SiamRTU performs favorably against state-of-the-art approaches.In order to effectively and flexibly control acoustic pattern, an efficient optimization design method of acoustic liquid lens (ALL) is developed by the frame of particle swarm optimization (PSO) algorithm. The ALL is composed of ethanol and dimethicone, and its parameters include ethanol concentration (EC), volume fraction of dimethicone (VFD) and total volume (TV). Based on the established finite element model and orthogonal design method, the data of acoustic pattern and ALL can be obtained by using COMSOL Multiphysics. Based on the simulation data, the neural network models are constructed to characterize the relationship between the parameters of ALL and the performance of acoustic pattern. The optimization design criteria of ALL are constructed based on the performance parameters of acoustic pattern, including focal distance (FD), transverse resolution (TR) and longitudinal resolution (LR). Based on the optimization criteria, the modified PSO algorithm is utilized to optimize the design parameters of ALL in the developed method. According to desired FD, TR and LR of acoustic pattern (20, 1 and 17 mm), the optimized EC, VFD and TV of ALL are about 0.838, 0.165 and 164.4 μL. The performance parameters of acoustic pattern verified by simulation and experiments agree with the desired ones. In addition, using 6 MHz ultrasonic transducer with the optimized ALL, the ultrasonic imaging of tungsten wires and porcine eyeball further demonstrates the effectiveness and feasibility of the developed method.This paper proposes a mixed low-rank approximation and second-order tensor-based total variation (LRSOTTV) approach for the super-resolution and denoising of retinal optical coherence tomography (OCT) images through effective utilization of nonlocal spatial correlations and local smoothness properties. OCT imaging relies on interferometry, which explains why OCT images suffer from a high level of noise. In addition, data subsampling is conducted during OCT A-scan and B-scan acquisition. Therefore, using effective super-resolution algorithms is necessary for reconstructing high-resolution clean OCT images. In this paper, a low-rank regularization approach is proposed for exploiting nonlocal self-similarity prior to OCT image reconstruction. To benefit from the advantages of the redundancy of multi-slice OCT data, we construct a third-order tensor by extracting the nonlocal similar three-dimensional blocks and grouping them by applying the k-nearest-neighbor method. Next, the nuclear norm is used as a regularization term to shrink the singular values of the constructed tensor in the non-local correlation direction. Further, the regularization approaches of the first-order tensor-based total variation (FOTTV) and SOTTV are proposed for better preservation of retinal layers and suppression of artifacts in OCT images. The alternative direction method of multipliers (ADMM) technique is then used to solve the resulting optimization problem. Our experiments show that integrating SOTTV instead of FOTTV into a low-rank approximation model can achieve noticeably improved results. Our experimental results on the denoising and super-resolution of OCT images demonstrate that the proposed model can provide images whose numerical and visual qualities are higher than those obtained by using state-of-the-art methods.Dynamic optical imaging of retinal hemodynamics is a rapidly evolving technique in vision and eye-disease research. Video-recording, which may be readily accessible and affordable, captures several distinct functional phenomena such as the spontaneous venous pulsations (SVP) of central vein or local arterial blood supply etc. These phenomena display specific dynamic patterns that have been detected using manual or semi-automated methods. We propose a pioneering concept in retina video-imaging using blind source separation (BSS) serving as an automated localizer of distinct areas with temporally synchronized hemodynamics. The feasibility of BSS techniques (such as spatial principal component analysis and spatial independent component analysis) and K-means based post-processing method were successfully tested on the monocular and binocular video-ophthalmoscopic (VO) recordings of optic nerve head (ONH) in healthy subjects. BSSs automatically detected three spatially distinct reproducible areas, i.e. SVP, optic cup pulsations (OCP) that included areas of larger vessels in the nasal part of ONH, and "other" pulsations (OP). The K-means post-processing reduced a spike noise from the patterns' dynamics while high linear dependence between the non-filtered and post-processed signals was preserved. Although the dynamics of all patterns were heart rate related, the morphology analysis demonstrated significant phase shifts between SVP and OCP, and between SVP and OP. In addition, we detected low frequency oscillations that may represent respiratory-induced effects in time-courses of the VO recordings.The performance of most the clustering methods hinges on the used pairwise affinity, which is usually denoted by a similarity matrix. However, the pairwise similarity is notoriously known for its venerability of noise contamination or the imbalance in samples or features, and thus hinders accurate clustering. To tackle this issue, we propose to use information among samples to boost the clustering performance. We proved that a simplified similarity for pairs, denoted by a fourth order tensor, equals to the Kronecker product of pairwise similarity matrices under decomposable assumption, or provide complementary information for which the pairwise similarity missed under indecomposable assumption. Then a high order similarity matrix is obtained from the tensor similarity via eigenvalue decomposition. The high order similarity capturing spatial information serves as a robust complement for the pairwise similarity. It is further integrated with the popular pairwise similarity, named by IPS2, to boost the clustering performance. Extensive experiments demonstrated that the proposed IPS2 significantly outperformed previous similarity-based methods on real-world datasets and it was capable of handling the clustering task over under-sampled and noisy datasets.We introduce a novel network, called CO-attention Siamese Network (COSNet), to address the zero-shot video object segmentation task in a holistic fashion. We exploit the inherent correlation among video frames and incorporate a global co-attention mechanism to further improve the state-of-the-art deep learning based solutions that primarily focus on learning discriminative foreground representations over appearance and motion in short-term temporal segments. The co-attention layers in COSNet provide efficient and competent stages for capturing global correlations and scene context by jointly computing and appending co-attention responses into a joint feature space. Cucurbitacin I ic50 COSNet is a unified and end-to-end trainable framework where different co-attention variants can be derived for capturing diverse properties of the learned joint feature space. We train COSNet with pairs (or groups) of video frames, and this naturally augments training data and allows increased learning capacity. During the segmentation stage, the co-attention model encodes useful information by processing multiple reference frames together, which is leveraged to infer the frequently reappearing and salient foreground objects better. Our extensive experiments over three large benchmarks demonstrate that COSNet outperforms the current alternatives by a large margin. Our algorithm implementations have been made publicly available at https//github.com/carrierlxk/COSNet.
Magnetoencephalography (MEG) signals typically reflect a mixture of neuromagnetic fields, subject-related artifacts, external interference and sensor noise. Even inside a magnetically shielded room, external interference can be significantly stronger than brain signals. Methods such as signal-space projection (SSP) and signal-space separation (SSS) have been developed to suppress this residual interference, but their performance might not be sufficient in cases of strong interference or when the sources of interference change over time.

Here we suggest a new method, extended signal-space separation (eSSS), which combines a physical model of the magnetic fields (as in SSS) with a statistical description of the interference (as in SSP). We demonstrate the performance of this method via simulations and experimental MEG data.

The eSSS method clearly outperforms SSS and SSP in interference suppression regardless of the extent of a priori information available on the interference sources. We also show that the method does not cause location or amplitude bias in dipole modeling.

Our eSSS method provides better data quality than SSP or SSS and can be readily combined with other SSS-based methods, such as tSSS or head movement compensation. Thus, eSSS extends and complements the interference suppression techniques currently available for MEG.

Due to its ability to suppress external interference to the level of sensor noise, eSSS can facilitate single-trial data analysis, exemplified in automated analysis of epileptic data. Such an enhanced suppression performance is especially important in environments with large interference fields.
Due to its ability to suppress external interference to the level of sensor noise, eSSS can facilitate single-trial data analysis, exemplified in automated analysis of epileptic data. Such an enhanced suppression performance is especially important in environments with large interference fields.Chemoresistance causes tumor recurrence and metastasis, resulting in poor clinical outcomes and low survival, and has been considered an obstacle to tumor therapy. The development of novel therapeutic approaches that can effectively kill chemoresistant tumor cells (CRTCs) is therefore critical to overcoming these obstacles.
Here, we introduce an emerging physical feature-based therapeutic approach based on nanosecond pulsed electric fields (nsPEFs). The goal of this study is to investigate the effect of nsPEFs on CRTCs.

The cell viability, ablation effects on a 3D-cultured scaffold, and lethal thresholds of nsPEFs were evaluated according to fluorescence staining assays.

nsPEF treatment preferentially affected chemoresistant cells (A549/CDDP) with a higher cell viability inhibition ability/cell death rate, larger ablation area, and lower ablation threshold compared to their respective homologous tumor cells (A549). The experimental and theoretical studies suggested that nsPEFs displayed selective behavior toward intracellular structures. With this selective character, nsPEFs can induce higher electroporation effects (e.g., higher pore number, larger electroporation area, and faster fluorescence dissipation on the nuclear envelope) on CRTCs due to their larger nuclear size and cell membrane capacitance.

These findings demonstrated that nsPEFs induced preferential ablation of CRTCs over their respective homologous tumor cells.

This study provides an experimental and theoretical basis for the study of killing CRTCs by electrical treatments and suggests potential applications in the optimization of novel anti-chemoresistance methods.
This study provides an experimental and theoretical basis for the study of killing CRTCs by electrical treatments and suggests potential applications in the optimization of novel anti-chemoresistance methods.
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