Recent progress in mitochondria-targeted drug and drug-free agents for cancer therapy

The mitochondrion is a dynamic eukaryotic organelle that controls lethal and vital functions of the cell. Being a critical center of metabolic activities and involved in many diseases, mitochondria have been attracting attention as a potential target for therapeutics, especially for cancer treatment. Structural and functional differences between healthy and cancerous mitochondria, such as membrane potential, respiratory rate, energy production pathway, and gene mutations, could be employed for the design of selective targeting systems for cancer mitochondria. A number of mitochondria-targeting compounds, including mitochondria-directed conventional drugs, mitochondrial proteins/metabolism-inhibiting agents, and mitochondria-targeted photosensitizers, have been discussed. Recently, certain drug-free approaches have been introduced as an alternative to induce selective cancer mitochondria dysfunction, such as intramitochondrial aggregation, self-assembly, and biomineralization. In this review, we discuss the recent progress in mitochondria-targeted cancer therapy from the conventional approach of drug/cytotoxic agent conjugates to advanced drug-free approaches

FPANet: Frequency-based Video Demoireing using Frame-level Post Alignment

Interference between overlapping gird patterns creates moire patterns, degrading the visual quality of an image that captures a screen of a digital display device by an ordinary digital camera. Removing such moire patterns is challenging due to their complex patterns of diverse sizes and color distortions. Existing approaches mainly focus on filtering out in the spatial domain, failing to remove a large-scale moire pattern. In this paper, we propose a novel model called FPANet that learns filters in both frequency and spatial domains, improving the restoration quality by removing various sizes of moire patterns. To further enhance, our model takes multiple consecutive frames, learning to extract frame-invariant content features and outputting better quality temporally consistent images. We demonstrate the effectiveness of our proposed method with a publicly available large-scale dataset, observing that ours outperforms the state-of-the-art approaches, including ESDNet, VDmoire, MBCNN, WDNet, UNet, and DMCNN, in terms of the image and video quality metrics, such as PSNR, SSIM, LPIPS, FVD, and FSIM

Array-Based Protein Sensing Using an Aggregation-Induced Emission (AIE) Light-Up Probe

Protein detection and identification are important for the diagnosis of diseases; however, the development of facile sensing probes still remains challenging. Here, we present an array-based "turn on" protein-sensing platform capable of detecting and identifying proteins using aggregation-induced emission luminogens (AIEgens). The water-soluble AIEgens in which fluorescence was initially turned off showed strong fluorescence in the presence of nanomolar concentrations of proteins via restriction of the intramolecular rotation of the AIEgens. The binding affinities between the AIEgens and proteins were associated with various chemical functional groups on AIEgens, resulting in distinct fluorescent-signal outcomes for each protein. The combined fluorescence outputs provided sufficient information to detect and discriminate proteins of interest by linear discriminant analysis. Furthermore, the array-based sensor enabled classification of different concentrations of specific proteins. These results provide novel insight into the use of the AIEgens as a new type of sensing probe in array-based systems

LISA: Localized Image Stylization with Audio via Implicit Neural Representation

We present a novel framework, Localized Image Stylization with Audio (LISA) which performs audio-driven localized image stylization. Sound often provides information about the specific context of the scene and is closely related to a certain part of the scene or object. However, existing image stylization works have focused on stylizing the entire image using an image or text input. Stylizing a particular part of the image based on audio input is natural but challenging. In this work, we propose a framework that a user provides an audio input to localize the sound source in the input image and another for locally stylizing the target object or scene. LISA first produces a delicate localization map with an audio-visual localization network by leveraging CLIP embedding space. We then utilize implicit neural representation (INR) along with the predicted localization map to stylize the target object or scene based on sound information. The proposed INR can manipulate the localized pixel values to be semantically consistent with the provided audio input. Through a series of experiments, we show that the proposed framework outperforms the other audio-guided stylization methods. Moreover, LISA constructs concise localization maps and naturally manipulates the target object or scene in accordance with the given audio input

Event Fusion Photometric Stereo Network

We present a novel method to estimate the surface normal of an object in an ambient light environment using RGB and event cameras. Modern photometric stereo methods rely on an RGB camera, mainly in a dark room, to avoid ambient illumination. To alleviate the limitations of the darkroom environment and to use essential light information, we employ an event camera with a high dynamic range and low latency. This is the first study that uses an event camera for the photometric stereo task, which works on continuous light sources and ambient light environment. In this work, we also curate a novel photometric stereo dataset that is constructed by capturing objects with event and RGB cameras under numerous ambient lights environment. Additionally, we propose a novel framework named Event Fusion Photometric Stereo Network~(EFPS-Net), which estimates the surface normals of an object using both RGB frames and event signals. Our proposed method interpolates event observation maps that generate light information with sparse event signals to acquire fluent light information. Subsequently, the event-interpolated observation maps are fused with the RGB observation maps. Our numerous experiments showed that EFPS-Net outperforms state-of-the-art methods on a dataset captured in the real world where ambient lights exist. Consequently, we demonstrate that incorporating additional modalities with EFPS-Net alleviates the limitations that occurred from ambient illumination.Comment: 33 pages, 11 figure

ORA3D: Overlap Region Aware Multi-view 3D Object Detection

Current multi-view 3D object detection methods often fail to detect objects in the overlap region properly, and the networks' understanding of the scene is often limited to that of a monocular detection network. Moreover, objects in the overlap region are often largely occluded or suffer from deformation due to camera distortion, causing a domain shift. To mitigate this issue, we propose using the following two main modules: (1) Stereo Disparity Estimation for Weak Depth Supervision and (2) Adversarial Overlap Region Discriminator. The former utilizes the traditional stereo disparity estimation method to obtain reliable disparity information from the overlap region. Given the disparity estimates as supervision, we propose regularizing the network to fully utilize the geometric potential of binocular images and improve the overall detection accuracy accordingly. Further, the latter module minimizes the representational gap between non-overlap and overlapping regions. We demonstrate the effectiveness of the proposed method with the nuScenes large-scale multi-view 3D object detection data. Our experiments show that our proposed method outperforms current state-of-the-art models, i.e., DETR3D and BEVDet.Comment: BMVC202

The Power of Sound (TPoS): Audio Reactive Video Generation with Stable Diffusion

In recent years, video generation has become a prominent generative tool and has drawn significant attention. However, there is little consideration in audio-to-video generation, though audio contains unique qualities like temporal semantics and magnitude. Hence, we propose The Power of Sound (TPoS) model to incorporate audio input that includes both changeable temporal semantics and magnitude. To generate video frames, TPoS utilizes a latent stable diffusion model with textual semantic information, which is then guided by the sequential audio embedding from our pretrained Audio Encoder. As a result, this method produces audio reactive video contents. We demonstrate the effectiveness of TPoS across various tasks and compare its results with current state-of-the-art techniques in the field of audio-to-video generation. More examples are available at https://ku-vai.github.io/TPoS/Comment: ICCV202