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Anti-microbial potentials associated with Helicteres isora silver precious metal nanoparticles against substantially drug-resistant (XDR) medical isolates of Pseudomonas aeruginosa.
In vitro, across 15 short-axis measurements with a wide variety of Doppler angles, errors in the flow estimates were below 10% and in vivo, the average velocities in systole obtained from longitudinal (v=69.1 cm/s) and cross-sectional (v=66.5 cm/s) acquisitions were in agreement. Further research is required to validate these results on a larger population.GOAL Low-intensity focused ultrasound stimulation (LIFUS) has the potential to noninvasively penetrate the intact skull and to modulate neural activity in the cortex and deep brain nuclei. selleckchem The midbrain periaqueductal gray (PAG) is associated with the generation of defensive behaviors. The aim of this study was to examine whether LIFUS of the PAG induced defensive behaviors in mice. METHODS A 3.8 MHz head-mounted ultrasound transducer with a small focus size (0.5 mm × 0.5 mm) was fabricated in house to precisely stimulate the free-moving mice. The corresponding behaviors were recorded in real time. Avoidance, flight, and freezing were used to assess ultrasound-induced defensive responses. The safety of LIFUS was examined via Hematoxylin and Eosin (H&E) staining and Nissl staining. RESULTS Ultrasound stimulation of the PAG induced multiple defensive behaviors, including location-specific passive avoidance behavior, flight, and freezing. In addition, H&E and Nissl staining verified that LIFUS did not cause injury to the brain tissue. CONCLUSION These findings demonstrate that LIFUS may have neuromodulatory effects on innate defensive behaviors in mice. SIGNIFICANCE LIFUS may be used as a novel neuromodulatory tool for the treatment of psychological diseases associated with defensive behaviors.Acute coronary syndromes and strokes are mainly caused by atherosclerotic plaque rupture. Abnormal increase of vasa vasorum is reported as a key evidence of plaque progression and vulnerability. However, due to their tiny size, it is still challenging to noninvasively identify vasa vasorum (VV) near the major vessels. Ultrasound super-resolution (USR), a technique that provides high spatial resolution beyond the acoustic diffraction limit, demonstrated an adequate spatial resolution for VV detection in early studies. However, a thorough validation of this technology in the plaque model is particularly needed in order to continue further extended preclinical studies. In this letter, we present an experiment protocol that verifies the USR technology for VV identification with subsequent histology and ex vivo micro-computed tomography (lCT). Deconvolution-based USR imaging was applied on two rabbits to identify the VV near the atherosclerotic plaque in the femoral artery. Histology and ex-vivo lCT imaging were performed on excised femoral tissue to validate the USR technique both pathologically and morphologically. This established validation protocol could facilitate future extended preclinical studies towards the clinical translation of USR imaging for VV identification.The overall goal of this study is to employ quantitative magnetic resonance imaging (MRI) data to constrain a patient-specific, computational fluid dynamics (CFD) model of blood flow and interstitial transport in breast cancer. We develop image processing methodologies to generate tumor-related vasculatureinterstitium geometry and realistic material properties, using dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted MRI (DW-MRI) data. These data are used to constrain CFD simulations for determining the tumorassociated blood supply and interstitial transport characteristics unique to each patient. We then perform a proof-of-principle statistical comparison between these hemodynamic characteristics in 11 malignant and 5 benign lesions from 12 patients. Significant differences between groups (i.e., malignant versus benign) were observed for the median of tumor-associated interstitial flow velocity (P = 0.028), and the ranges of tumor-associated blood pressure (P = 0.016) and vascular extraction rate (P = 0.040). The implication is that malignant lesions tend to have larger magnitude of interstitial flow velocity, and higher heterogeneity in blood pressure and vascular extraction rate. Multivariable logistic models based on combinations of these hemodynamic data achieved excellent differentiation between malignant and benign lesions with an area under the receiver operator characteristic curve of 1.0, sensitivity of 1.0, and specificity of 1.0. This imagebased model system is a fundamentally new way to map flow and pressure fields related to breast tumors using only non-invasive, clinically available imaging data and established laws of fluid mechanics. Furthermore, the results provide preliminary evidence for this methodology's utility for the quantitative characterization of breast cancer.Magnetic resonance imaging (MRI) is a widely used neuroimaging technique that can provide images of different contrasts (i.e., modalities). Fusing this multi-modal data has proven particularly effective for boosting model performance in many tasks. However, due to poor data quality and frequent patient dropout, collecting all modalities for every patient remains a challenge. Medical image synthesis has been proposed as an effective solution, where any missing modalities are synthesized from the existing ones. In this paper, we propose a novel Hybrid-fusion Network (Hi-Net) for multi-modal MR image synthesis, which learns a mapping from multi-modal source images (i.e., existing modalities) to target images (i.e., missing modalities). In our Hi-Net, a modality-specific network is utilized to learn representations for each individual modality, and a fusion network is employed to learn the common latent representation of multi-modal data. Then, a multi-modal synthesis network is designed to densely combine the latent representation with hierarchical features from each modality, acting as a generator to synthesize the target images. Moreover, a layer-wise multi-modal fusion strategy effectively exploits the correlations among multiple modalities, where a Mixed Fusion Block (MFB) is proposed to adaptively weight different fusion strategies. Extensive experiments demonstrate the proposed model outperforms other state-of-the-art medical image synthesis methods.Magnetic resonance imaging (MRI) is widely used for screening, diagnosis, image-guided therapy, and scientific research. A significant advantage of MRI over other imaging modalities such as computed tomography (CT) and nuclear imaging is that it clearly shows soft tissues in multi-contrasts. Compared with other medical image super-resolution methods that are in a single contrast, multi-contrast super-resolution studies can synergize multiple contrast images to achieve better super-resolution results. In this paper, we propose a one-level nonprogressive neural network for low up-sampling multi-contrast super-resolution and a two-level progressive network for high upsampling multi-contrast super-resolution. The proposed networks integrate multi-contrast information in a high-level feature space and optimize the imaging performance by minimizing a composite loss function, which includes mean-squared-error, adversarial loss, perceptual loss, and textural loss. Our experimental results demonstrate that 1) the proposed networks can produce MRI super-resolution images with good image quality and outperform other multi-contrast super-resolution methods in terms of structural similarity and peak signal-to-noise ratio; 2) combining multi-contrast information in a high-level feature space leads to a signicantly improved result than a combination in the lowlevel pixel space; and 3) the progressive network produces a better super-resolution image quality than the non-progressive network, even if the original low-resolution images were highly down-sampled.In in-utero MRI, motion correction for fetal body and placenta poses a particular challenge due to the presence of local non-rigid transformations of organs caused by bending and stretching. The existing slice-to-volume registration (SVR) reconstruction methods are widely employed for motion correction of fetal brain that undergoes only rigid transformation. However, for reconstruction of fetal body and placenta, rigid registration cannot resolve the issue of misregistrations due to deformable motion, resulting in degradation of features in the reconstructed volume. link2 We propose a Deformable SVR (DSVR), a novel approach for non-rigid motion correction of fetal MRI based on a hierarchical deformable SVR scheme to allow high resolution reconstruction of the fetal body and placenta. Additionally, a robust scheme for structure-based rejection of outliers minimises the impact of registration errors. The improved performance of DSVR in comparison to SVR and patch-to-volume registration (PVR) methods is quantitatively demonstrated in simulated experiments and 20 fetal MRI datasets from 28-31 weeks gestational age (GA) range with varying degree of motion corruption. In addition, we present qualitative evaluation of 100 fetal body cases from 20-34 weeks GA range.The classification to materials of oracle bone is one of the most basic aspects for oracle bone morphology. However, the classification method depending on experts' experience requires long-term learning and accumulation for professional knowledge. This paper presents a multi-regional convolutional neural network to classify the rubbings of oracle bones. Firstly, we detected the "shield pattern" and "tooth pattern" on the oracle bone rubbings, then complete the division of multiple areas on an image of oracle bone. Secondly, the convolutional neural network is used to extract the features of each region and we complete the fusion of multiple local features. Finally, the classification to tortoise shell and animal bone was realized. Utilizing the image of oracle bone provided by experts, we did experiment, the result show our method has better classification accuracy. It has made contributions to the progress of the study of oracle bone morphology.Compared with global average pooling in existing deep convolutional neural networks (CNNs), global covariance pooling can capture richer statistics of deep features, having potential for improving representation and generalization abilities of deep CNNs. However, integration of global covariance pooling into deep CNNs brings two challenges (1) robust covariance estimation given deep features of high dimension and small sample size; (2) appropriate usage of geometry of covariances. To address these challenges, we propose a global Matrix Power Normalized COVariance (MPN-COV) Pooling. Our MPN-COV conforms to a robust covariance estimator, very suitable for scenario of high dimension and small sample size. It can also be regarded as power-Euclidean metric between covariances, effectively exploiting their geometry. Furthermore, a global Gaussian embedding network is proposed to incorporate first-order statistics into MPN-COV. link3 For fast training of MPN-COV networks, we implement an iterative matrix square root normalization, avoiding GPU unfriendly eigen-decomposition inherent in MPN-COV. Additionally, progressive 1x1 convolutions and group convolution are introduced to compress covariance representations. The proposed methods are highly modular, readily plugged into existing deep CNNs. Extensive experiments are conducted on large-scale object classification, scene categorization, fine-grained visual recognition and texture classification, showing our methods outperform the counterparts and obtain state-of-the-art performance.
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