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Osteoarthritis (OA), the most prevalent aging-related joint disease, is characterized by insufficient extracellular matrix synthesis and articular cartilage degradation, mediated by several proteinases, including Adamts-5. miR-140 is one of a very limited number of noncoding microRNAs (miRNAs) specifically expressed in cartilage; however, its role in development and/or tissue maintenance is largely uncharacterized. To examine miR-140 function in tissue development and homeostasis, we generated a mouse line through a targeted deletion of miR-140. miR-140−/− mice manifested a mild skeletal phenotype with a short stature, although the structure of the articular joint cartilage appeared grossly normal in 1-mo-old miR-140−/− mice. Interestingly, miR-140−/− mice showed age-related OA-like changes characterized by proteoglycan loss and fibrillation of articular cartilage. Conversely, transgenic (TG) mice overexpressing miR-140 in cartilage were resistant to antigen-induced arthritis. OA-like changes in miR-140-deficient mice can be attributed, in part, to elevated Adamts-5 expression, regulated directly by miR-140. We show that miR-140 regulates cartilage development and homeostasis, and its loss contributes to the development of age-related OA-like changes.

It is widely known that significant in-sample evidence of predictability does not guarantee significant out-of-sample predictability. This is often interpreted as an indication that in-sample evidence is likely to be spurious and should be discounted. In this paper, we question this interpretation. Our analysis shows that neither data mining nor dynamic misspecification of the model under the null nor unmodelled structural change under the null are plausible explanations of the observed tendency of in-sample tests to reject the no-predictability null more often than out-of-sample tests. We provide an alternative explanation based on the higher power of in-sample tests of predictability in many situations. We conclude that results of in-sample tests of predictability will typically be more credible than results of out-of-sample tests.Predictive ability, Spurious inference, Data mining, Model instability,

No abstract

Osteoarthritis (OA) is a chronic and highly prevalent degenerative joint disease. Approximately 40 million Americans are currently affected, and this number is predicted to increase to 60 million within the next 20 years as a result of population aging and an increase in life expectancy (1,2). Current treatment is limited to pain management, and disease-modifying therapies are not available in the late phase of the disease process, at which point joint replacement surgery is often indicated. OA has been associated with age-related loss of the homeostatic balance between degradation and repair mechanisms. Cartilage cellularity in OA is reduced by chondrocyte death, and remaining chondrocytes are activated by cytokines and growth factors to a catabolic and abnormal differentiation that leads to degradation