Hua Han

Real noisy-clean pairs on a large scale are costly and difficult to obtain. Meanwhile, supervised denoisers trained on synthetic data perform poorly in practice. Self-supervised denoisers, which learn only from single noisy images, solve the data collection problem. However, self-supervised denoising methods, especially blindspot-driven ones, suffer sizable information loss during input or network design. The absence of valuable information dramatically reduces the upper bound of denoising performance. In this paper, we propose a simple yet efficient approach called Blind2Unblind to overcome the information loss in blindspot-driven denoising methods. First, we introduce a global-aware mask mapper that enables global perception and accelerates training. The mask mapper samples all pixels at blind spots on denoised volumes and maps them to the same channel, allowing the loss function to optimize all blind spots at once. Second, we propose a revisible loss to train the denoising network and make blind spots visible. The denoiser can learn directly from raw noise images without losing information or being trapped in identity mapping. We also theoretically analyze the convergence of the revisible loss. Extensive experiments on synthetic and real-world datasets demonstrate the superior performance of our approach compared to previous work. Code is available at https://github.com/demonsjin/Blind2Unblind.

The arousal state of the brain covaries with the motor state of the animal. How these state changes are coordinated remains unclear. We discovered that sleep-wake brain states and motor behaviors are coregulated by shared neurons in the substantia nigra pars reticulata (SNr). Analysis of mouse home-cage behavior identified four states with different levels of brain arousal and motor activity: locomotion, nonlocomotor movement, quiet wakefulness, and sleep; transitions occurred not randomly but primarily between neighboring states. The glutamic acid decarboxylase 2 but not the parvalbumin subset of SNr γ-aminobutyric acid (GABA)-releasing (GABAergic) neurons was preferentially active in states of low motor activity and arousal. Their activation or inactivation biased the direction of natural behavioral transitions and promoted or suppressed sleep, respectively. These GABAergic neurons integrate wide-ranging inputs and innervate multiple arousal-promoting and motor-control circuits through extensive collateral projections.

A major obstacle in Alzheimer's disease (AD) research is the lack of predictive and translatable animal models that reflect disease progression and drug efficacy. Transgenic mice overexpressing amyloid precursor protein (App) gene manifest non-physiological and ectopic expression of APP and its fragments in the brain, which is not observed in AD patients. The App knock-in mice circumvented some of these problems, but they do not exhibit tau pathology and neuronal death. We have generated a rat model, with three familiar App mutations and humanized Aβ sequence knocked into the rat App gene. Without altering the levels of full-length APP and other APP fragments, this model exhibits pathologies and disease progression resembling those in human patients: deposit of Aβ plaques in relevant brain regions, microglia activation and gliosis, progressive synaptic degeneration and AD-relevant cognitive deficits. Interestingly, we have observed tau pathology, neuronal apoptosis and necroptosis and brain atrophy, phenotypes rarely seen in other APP models. This App knock-in rat model may serve as a useful tool for AD research, identifying new drug targets and biomarkers, and testing therapeutics.