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Alzheimer's Disease (AD) is a neurodegenerative disease characterized by the accumulation of beta amyloid plaques and the formation of neurofibrillary tau tangles. These plaques and tangles lead to the learning and memory deficits characteristic of AD. Estrogen and estrogen‐like compounds have been shown to slow the development of plaques and tangles. Though estrogen receptors are found in the male hippocampus, it is unclear how estrogen affects learning and memory in the male hippocampus. Male triple‐transgenic (3×Tg‐AD) mice mimic the pathology and learning and memory deficits of AD in humans. To examine the potential effects on the male hippocampus, 3×Tg‐AD mice were either fed a dose of 4–5 mg/kg of genistein, a phytoestrogen found in soy or a placebo and tested behaviorally in the Morris Water Maze and Olfactory Habituation‐Dishabituation tasks. The 3×Tg‐AD mice had significantly longer swim paths to find a hidden platform as well as a lack of selective search in the platform location when the platform was removed. There was no significant difference between the 3×Tg‐AD and wild type mice during the olfactory task of habituation‐dishabituation. As the pathology of the AD develops in the 3×Tg‐AD mice, the mice will be retested to examine the differences in learning and memory. Presently, the 3×Tg‐AD were impaired in successfully learning the location of a hidden platform but were not impaired in their olfactory abilities.Support or Funding InformationRonald Troyer Research Fellowship, Drake UniversityHarris Research Endowment, Drake UniversityDrake Undergraduate Science Collaborative Institute, Drake University

As the importance of data in today’s research increases, the effective management of research data is of central interest for reproducibility. Research is often conducted in large interdisciplinary consortia that collaboratively collect and analyse such data. This raises the need of intra-consortia data sharing. In this article, we propose the use of data management platforms to facilitate this exchange among research partners. Based on the experiences of a large research project, we customized the CKAN software to satisfy these needs for intra-consortia data sharing.

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We present a study of crash-consistency bugs in persistentmemory (PM) file systems and analyze their implications for file-system design and testing crash consistency. We develop FLYTRAP, a framework to test PM file systems for crash-consistency bugs. FLYTRAP discovered 18 new bugs across four PM file systems; the bugs have been confirmed by developers and many have been already fixed. The discovered bugs have serious consequences such as breaking the atomicity of rename or making the file system unmountable. We present a detailed study of the bugs we found and discuss some important lessons from these observations. For instance, one of our findings is that many of the bugs are due to logic errors, rather than errors in using flushes or fences; this has important applications for future work on testing PM file systems. Another key finding is that many bugs arise from attempts to improve efficiency by performing metadata updates in-place and that recovery code that deals with rebuilding in-DRAM state is a significant source of bugs. These observations have important implications for designing and testing PM file systems. Our code is available at https://github.com/utsaslab/flytrap.