SummaryWe generated mice that overexpress the sirtuin, SIRT1. Transgenic mice have been generated by knocking in SIRT1 cDNA into the β β β β -actin locus. Mice that are hemizygous for this transgene express normal levels of β β β β -actin and higher levels of SIRT1 protein in several tissues. Transgenic mice display some phenotypes similar to mice on a calorierestricted diet: they are leaner than littermate controls; are more metabolically active; display reductions in blood cholesterol, adipokines, insulin and fasted glucose; and are more glucose tolerant. Furthermore, transgenic mice perform better on a rotarod challenge and also show a delay in reproduction. Our findings suggest that increased expression of SIRT1 in mice elicits beneficial phenotypes that may be relevant to human health and longevity.

Multi-class image segmentation has made significant advances in recent years through the combination of local and global features. One important type of global feature is that of inter-class spatial relationships. For example, identifying "tree" pixels indicates that pixels above and to the sides are more likely to be "sky" whereas pixels below are more likely to be "grass." Incorporating such global information across the entire image and between all classes is a computational challenge as it is image-dependent, and hence, cannot be precomputed.In this work we propose a method for capturing global information from inter-class spatial relationships and encoding it as a local feature. We employ a two-stage classification process to label all image pixels. First, we generate predictions which are used to compute a local relative location feature from learned relative location maps. In the second stage, we combine this with appearance-based features to provide a final segmentation. We compare our results to recent published results on several multiclass image segmentation databases and show that the incorporation of relative location information allows us to significantly outperform the current state-of-the-art.

Sirt1, an NAD-dependent protein deacetylase has emerged as important regulator of mammalian transcription in response to cellular metabolic status and stress1. Here we demonstrate that Sirt1 plays a neuroprotective role in models of Huntington’s disease (HD), an inherited neurodegenerative disorder caused by a glutamine repeat expansion in huntingtin protein2. Brain-specific knockout of Sirt1 results in exacerbation of brain pathology in HD mice, whereas overexpression of Sirt1 improves survival, neuropathology and BDNF expression in HD mice. We show that Sirt1 deacetylase activity directly targets neurons to mediate neuroprotection from mutant huntingtin. The neuroprotective effect of Sirt1 requires the presence of TORC1, a brain-specific modulator of CREB activity3. We show that under normal conditions Sirt1 deacetylates and activates TORC1 by promoting its dephoshorylation and interaction with CREB. We identified BDNF as an important target of Sirt1 and TORC1 transcriptional activity in normal and HD neurons. Mutant huntingtin interferes with the TORC1-CREB interaction to repress BDNF transcription and Sirt1 rescues this defect in vitro and in vivo. These studies suggest a key role of Sirt1 in transcriptional networks in normal and HD brain and offer an opportunity for therapeutic development.

We propose a method for detecting and describing features of faces using deformable templates. The feature of interest, an eye for example, is described by a parameterized template. An energy function is defined which links edges, peaks, and valleys in the image intensity to corresponding properties of the template. The template then interacts dynamically with the image by altering its parameter values to minimize the energy function, thereby deforming itself to find the best fit. The final parameter values can be used as descriptors for the feature. We illustrate this method by showing deformable templates detecting eyes and mouths in real images. We demonstrate their ability for tracking features.

Summary A hallmark of Alzheimer's disease (AD) is the accumulation of plaques of Aβ 1-40 and 1-42 peptides, which result from the sequential cleavage of APP by the β and γ-secretases. The production of Aβ peptides is avoided by alternate cleavage of APP by the α and γ-secretases. Here we show that production of β-amyloid and plaques in a mouse model of AD are reduced by overexpressing the NAD-dependent deacetylase SIRT1 in brain, and are increased by knocking out SIRT1 in brain. SIRT1 directly activates the transcription of the gene encoding the α-secretase, ADAM10. SIRT1 deacetylates and coactivates the retinoic acid receptor β, a known regulator of ADAM10 transcription. ADAM10 activation by SIRT1 also induces the Notch pathway, which is known to repair neuronal damage in the brain. Our findings indicate SIRT1 activation is a viable strategy to combat AD, and perhaps other neurodegenerative diseases.