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Insomnia affects millions of people worldwide, and non-pharmacological treatment options are limited. A bed excited with multiple vibration sources was used to explore beat frequency vibration (BFV) as a non-pharmacological treatment for insomnia. A repeated measures design pilot study of 14 participants with mild-moderate insomnia symptom severity (self-reported on the Insomnia Severity Index) was conducted to determine the effects of BFV, and traditional standing wave vibration (SWV) on sleep latency and sleep electrocortical activity. Participants were monitored using high-density electroencephalography (HD-EEG). [https://www.selleckchem.com/products/nsc-23766.html NSC 23766] Sleep latency was compared between treatment conditions. A trend of decreasing sleep latency due to BFV was found for unequivocal sleep latency (p ≤ 0.068). Neural complexity during wake, N1, and N2 stages were compared using Multi-Scale Sample Entropy (MSE), which demonstrated significantly lower MSE between wake and N2 stages (p ≤ 0.002). During N2 sleep, BFV showed lower MSE than the control session in the left frontoparietal region. As a measure of information integration, reduced entropy may indicate that BFV decreases conscious awareness during deeper stages of sleep. SWV caused reduced alpha activity and increased delta activity during wake. BFV caused increased delta activity during N2 sleep. These preliminary results suggest that BFV may help decrease sleep latency, reduce conscious awareness, and increase sleep drive expression during deeper stages of sleep. SWV may be beneficial for decreasing expression of arousal and increasing expression of sleep drive during wake, implying that beat frequency vibration may be beneficial to sleep.Restoring the clean background from the superimposed images containing a noisy layer is the common crux of a classical category of tasks on image restoration such as image reflection removal, image deraining and image dehazing. These tasks are typically formulated and tackled individually due to diverse and complicated appearance patterns of noise layers within the image. In this work we present the Deep-Masking Generative Network (DMGN), which is a unified framework for background restoration from the superimposed images and is able to cope with different types of noise. Our proposed DMGN follows a coarse-to-fine generative process a coarse background image and a noise image are first generated in parallel, then the noise image is further leveraged to refine the background image to achieve a higher-quality background image. In particular, we design the novel Residual Deep-Masking Cell as the core operating unit for our DMGN to enhance the effective information and suppress the negative information during image generation via learning a gating mask to control the information flow. By iteratively employing this Residual Deep-Masking Cell, our proposed DMGN is able to generate both high-quality background image and noisy image progressively. Furthermore, we propose a two-pronged strategy to effectively leverage the generated noise image as contrasting cues to facilitate the refinement of the background image. Extensive experiments across three typical tasks for image background restoration, including image reflection removal, image rain steak removal and image dehazing, show that our DMGN consistently outperforms state-of-the-art methods specifically designed for each single task.Vision-language research has become very popular, which focuses on understanding of visual contents, language semantics and relationships between them. Video question answering (Video QA) is one of the typical tasks. Recently, several BERT style pre-training methods have been proposed and shown effectiveness on various vision-language tasks. In this work, we leverage the successful vision-language transformer structure to solve the Video QA problem. However, we do not pre-train it with any video data, because video pre-training requires massive computing resources and is hard to perform with only a few GPUs. Instead, our work aims to leverage image-language pre-training to help with video-language modeling, by sharing a common module design. We further introduce an adaptive spatio-temporal graph to enhance the vision-language representation learning. That is, we adaptively refine the spatio-temporal tubes of salient objects according to their spatio-temporal relations learned through a hierarchical graph convolution process. Finally, we can obtain a number of fine-grained tube-level video object representations, as the visual inputs of the vision-language transformer module. Experiments on three widely used Video QA datasets show that our model achieves the new state-of-the-art results.Urinary catheters often become contaminated with biofilms, resulting in catheter-associated urinary tract infections (CAUTIs) that adversely affect patient outcomes. Histotripsy is a non-invasive focused ultrasound therapy previously developed for the non-invasive ablation of cancerous tumors and soft tissues. Histotripsy has also previously shown the ability to treat biofilms on glass slides and surgical meshes. Here, we investigate the potential of histotripsy for the treatment of CAUTIs for the first time in vitro. Clinically relevant catheter materials (Tygon, Silicone, and latex catheter mimics) and commonly used clinical catheters were tested to determine the feasibility of producing luminal histotripsy bubble clouds. A Pseudomonas aeruginosa (strain PA14) biofilm model was developed and tested to produce luminal biofilms in an in vitro Tygon catheter mimic. This model was treated with histotripsy to determine the ability to remove a luminal biofilm. Finally, the bactericidal effects of histotripsy were tested by treating PA14 suspended inside the Tygon catheter mimic. Results showed that histotripsy produced precise luminal cavitation within all tested catheter mimics and clinical catheters. Histotripsy treatment of a PA14 biofilm with histotripsy reduced luminal biofilm OD590 signal down to background levels. Further, the treatment of suspended PA14 in LB showed a 3.45±0.11 log10 reduction in CFU/mL after 6 histotripsy scans across the catheter mimics. Overall, the results of this study demonstrate the potential of histotripsy to provide a new modality for removing bacterial biofilms from catheter-based medical devices and suggest that additional work is warranted to investigate histotripsy for the treatment of CAUTIs and other biomaterial-associated infections.