Ciproxifan, an H~3~ Receptor Antagonist, Improves Learning and Memory in the APP Mouse Model of Alzheimer's Disease

Mice that express the mutant form of the human amyloid precursor gene associated with early-onset familial Alzheimer's disease demonstrate memory deficits and amyloid plaques. We show here that ciproxifan, a prototypical antagonist of H~3~-type histamine receptors, alleviates two types of learning and memory impairments in such mice. These data support the idea that modulation of H~3~ receptors represents a viable therapeutic strategy in the treatment of Alzheimer's disease

Social Network and Content Analysis of the North American Carbon Program as a Scientific Community of Practice

The North American Carbon Program (NACP) was formed to further the scientific understanding of sources, sinks, and stocks of carbon in Earth's environment. Carbon cycle science integrates multidisciplinary research, providing decision-support information for managing climate and carbon-related change across multiple sectors of society. This investigation uses the conceptual framework of com-munities of practice (CoP) to explore the role that the NACP has played in connecting researchers into a carbon cycle knowledge network, and in enabling them to conduct physical science that includes ideas from social science. A CoP describes the communities formed when people consistently engage in shared communication and activities toward a common passion or learning goal. We apply the CoP model by using keyword analysis of abstracts from scientific publications to analyze the research outputs of the NACP in terms of its knowledge domain. We also construct a co-authorship network from the publications of core NACP members, describe the structure and social pathways within the community. Results of the content analysis indicate that the NACP community of practice has substantially expanded its research on human and social impacts on the carbon cycle, contributing to a better understanding of how human and physical processes interact with one another. Results of the co-authorship social network analysis demonstrate that the NACP has formed a tightly connected community with many social pathways through which knowledge may flow, and that it has also expanded its network of institutions involved in carbon cycle research over the past seven years

NASA’s carbon monitoring system and arctic-boreal vulnerability experiment (ABoVE) social network and community of practice

The NASA Carbon Monitoring System (CMS) and Arctic-Boreal Vulnerability Experiment (ABoVE) have been planned and funded by the NASA Earth Science Division. Both programs have a focus on engaging stakeholders and developing science useful for decision making. The resulting programs have funded significant scientific output and advancements in understanding how satellite remote sensing observations can be used to not just study how the Earth is changing, but also create data products that are of high utility to stakeholders and decisions makers. In this paper we focus on documenting thematic diversity of research themes and methods used, and how the CMS and ABoVE themes are related. We do this through developing a Correlated Topic Model on the 521 papers produced by the two programs and plotting the results in a network diagram. Through analysis of the themes in these papers, we document the relationships between researchers and institutions participating in CMS and ABoVE programs and the benefits from sustained engagement with stakeholders due to recurring funding. We note an absence of policy engagement in the papers and conclude that funded researchers need to be more ambitious and explicit in drawing the connection between their research and carbon policy implications in order to meet the stated goals of the CMS and ABoVE programs

Accelerating the Translation of Nanomaterials in Biomedicine

Due to their size and tailorable physicochemical properties, nanomaterials are an emerging class of structures utilized in biomedical applications. There are now many prominent examples of nanomaterials being used to improve human health, in areas ranging from imaging and diagnostics to therapeutics and regenerative medicine. An overview of these examples reveals several common areas of synergy and future challenges. This Nano Focus discusses the current status and future potential of promising nanomaterials and their translation from the laboratory to the clinic, by highlighting a handful of successful examples

Host galaxy identification for supernova surveys

Host galaxy identification is a crucial step for modern supernova (SN) surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope (LSST), which will discover SNe by the thousands. Spectroscopic resources are limited, so in the absence of real-time SN spectra these surveys must rely on host galaxy spectra to obtain accurate redshifts for the Hubble diagram and to improve photometric classification of SNe. In addition, SN luminosities are known to correlate with host-galaxy properties. Therefore, reliable identification of host galaxies is essential for cosmology and SN science. We simulate SN events and their locations within their host galaxies to develop and test methods for matching SNe to their hosts. We use both real and simulated galaxy catalog data from the Advanced Camera for Surveys General Catalog and MICECATv2.0, respectively. We also incorporate "hostless" SNe residing in undetected faint hosts into our analysis, with an assumed hostless rate of 5%. Our fully automated algorithm is run on catalog data and matches SNe to their hosts with 91% accuracy. We find that including a machine learning component, run after the initial matching algorithm, improves the accuracy (purity) of the matching to 97% with a 2% cost in efficiency (true positive rate). Although the exact results are dependent on the details of the survey and the galaxy catalogs used, the method of identifying host galaxies we outline here can be applied to any transient survey

A Machine Learning Approach for Identifying Novel Cell Type–Specific Transcriptional Regulators of Myogenesis

Transcriptional enhancers integrate the contributions of multiple classes of transcription factors (TFs) to orchestrate the myriad spatio-temporal gene expression programs that occur during development. A molecular understanding of enhancers with similar activities requires the identification of both their unique and their shared sequence features. To address this problem, we combined phylogenetic profiling with a DNA–based enhancer sequence classifier that analyzes the TF binding sites (TFBSs) governing the transcription of a co-expressed gene set. We first assembled a small number of enhancers that are active in Drosophila melanogaster muscle founder cells (FCs) and other mesodermal cell types. Using phylogenetic profiling, we increased the number of enhancers by incorporating orthologous but divergent sequences from other Drosophila species. Functional assays revealed that the diverged enhancer orthologs were active in largely similar patterns as their D. melanogaster counterparts, although there was extensive evolutionary shuffling of known TFBSs. We then built and trained a classifier using this enhancer set and identified additional related enhancers based on the presence or absence of known and putative TFBSs. Predicted FC enhancers were over-represented in proximity to known FC genes; and many of the TFBSs learned by the classifier were found to be critical for enhancer activity, including POU homeodomain, Myb, Ets, Forkhead, and T-box motifs. Empirical testing also revealed that the T-box TF encoded by org-1 is a previously uncharacterized regulator of muscle cell identity. Finally, we found extensive diversity in the composition of TFBSs within known FC enhancers, suggesting that motif combinatorics plays an essential role in the cellular specificity exhibited by such enhancers. In summary, machine learning combined with evolutionary sequence analysis is useful for recognizing novel TFBSs and for facilitating the identification of cognate TFs that coordinate cell type–specific developmental gene expression patterns

Identifying New Treatments For Memory Disorders: From Mice To Men

The population of older Americans will expand greatly in the next 20 years and, as a consequence, disorders of aging, such as Alzheimer’s disease, will become more prevalent. Drug treatments for Alzheimer’s disease currently exist, however they are either ineffective for some people or cause significant side effects. These drugs were developed to correct imbalances in brain chemistry, which may or may not exist early in the disease. However, a brain abnormality that clearly appears early in the course of Alzheimer’s disease is neuronal injury and/or loss in the hippocampus and related medial temporal lobe structures of the brain. The purpose of our research has been to experimentally produce a similar condition of neuronal loss in laboratory animals and to use these animals to test the efficacy of potential new treatments for Alzheimer’s disease. Animals (rats and mice) with neuronal loss in the hippocampus exhibit changes in activity that may be relevant to the agitation observed in Alzheimer’s disease. Moreover, such animals demonstrate profound memory deficits, especially in the area of spatial memory. Our research to date has shown that drugs that are currently used in the treatment of Alzheimer’s disease are ineffective in improving memory in animals with hippocampal neuronal loss. However, some antipsychotic drugs that are prescribed for agitation in Alzheimer’s disease also seem to slightly improve memory in animals with hippocampal neuronal loss. This research should enhance our understanding of the biological basic of memory and offer new insights into improving treatment for memory disorders