The studies showed that green tea and/or some constituents can offer some protection against UV-induced DNA damage in human cell cultures and also in human peripheral blood samples taken post-tea ingestion.

This small case series demonstrates that mTHPC-PDT is a useful initial treatment for VIN III. It is relatively selective, shows good cosmesis and conserves form and function. This is a major advantage over surgery. Repeat treatments are also possible, which is important in a condition such as VIN, which tends to be multifocal. Systemic mTHPC-PDT appears to have an advantage over topical 5-aminolaevulinic acid-PDT as the photosensitizer is distributed widely in areas of disease and consequently identifies foci which may not be apparent clinically but become evident when illuminated.

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When exposed to UVR, MRC5 fibroblasts incubated with mercuric chloride (0-15 microM) for 1 hour show increased DNA damage (as measured by the comet assay) compared to control cells (UVR irradiated but no mercuric chloride). This demonstrates that mercuric chloride and UVR in combination increase DNA damage in a synergistic manner. This may have implications to those exposed to mercury as it suggests that exposure to mercury in the environment may increase sensitivity to sunlight-induced carcinogenesis.

Early backdoor attacks against machine learning set off an arms race in attack and defence development. Defences have since appeared demonstrating some ability to detect backdoors in models or even remove them. These defences work by inspecting the training data, the model, or the integrity of the training procedure. In this work, we show that backdoors can be added during compilation, circumventing any safeguards in the data preparation and model training stages. As an illustration, the attacker can insert weight-based backdoors during the hardware compilation step that will not be detected by any training or data-preparation process. Next, we demonstrate that some backdoors, such as ImpNet, can only be reliably detected at the stage where they are inserted and removing them anywhere else presents a significant challenge. We conclude that machine-learning model security requires assurance of provenance along the entire technical pipeline, including the data, model architecture, compiler, and hardware specification.

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Labeling articulated objects in unconstrained settings have a wide variety of applications including entertainment, neuroscience, psychology, ethology, and many fields of medicine. Large offline labeled datasets do not exist for all but the most common articulated object categories (e.g., humans). Hand labeling these landmarks within a video sequence is a laborious task. Learned landmark detectors can help, but can be error-prone when trained from only a few examples. Multi-camera systems that train fine-grained detectors have shown significant promise in detecting such errors, allowing for self-supervised solutions that only need a small percentage of the video sequence to be hand-labeled. The approach, however, is based on calibrated cameras and rigid geometry, making it expensive, difficult to manage, and impractical in real-world scenarios. In this paper, we address these bottlenecks by combining a non-rigid 3D neural prior with deep flow to obtain high-fidelity landmark estimates from videos with only two or three uncalibrated, handheld cameras. With just a few annotations (representing 1-2% of the frames), we are able to produce 2D results comparable to state-of-the-art fully supervised methods, along with 3D reconstructions that are impossible with other existing approaches. Our Multi-view Bootstrapping in the Wild (MBW) approach demonstrates impressive results on standard human datasets, as well as tigers, cheetahs, fish, colobus monkeys, chimpanzees, and flamingos from videos captured casually in a zoo. We release the codebase for MBW as well as this challenging zoo dataset consisting image frames of tail-end distribution categories with their corresponding 2D, 3D labels generated from minimal human intervention. * indicates the authors advised equally 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks.