1,098,442 research outputs found

    Prion protein interacts with bace1 and differentially regulates its activity towards wild type and swedish mutant amyloid precursor protein

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    In Alzheimer disease amyloid-β (Aβ) peptides derived from the amyloid precursor protein (APP) accumulate in the brain. Cleavage of APP by the β-secretase BACE1 is the rate-limiting step in the production of Aβ. We have reported previously that the cellular prion protein (PrP(C)) inhibited the action of BACE1 toward human wild type APP (APP(WT)) in cellular models and that the levels of endogenous murine Aβ were significantly increased in PrP(C)-null mouse brain. Here we investigated the molecular and cellular mechanisms underlying this observation. PrP(C) interacted directly with the prodomain of the immature Golgi-localized form of BACE1. This interaction decreased BACE1 at the cell surface and in endosomes where it preferentially cleaves APP(WT) but increased it in the Golgi where it preferentially cleaves APP with the Swedish mutation (APP(Swe)). In transgenic mice expressing human APP with the Swedish and Indiana familial mutations (APP(Swe,Ind)), PrP(C) deletion had no influence on APP proteolytic processing, Aβ plaque deposition, or levels of soluble Aβ or Aβ oligomers. In cells, although PrP(C) inhibited the action of BACE1 on APP(WT), it did not inhibit BACE1 activity toward APP(Swe). The differential subcellular location of the BACE1 cleavage of APP(Swe) relative to APP(WT) provides an explanation for the failure of PrP(C) deletion to affect Aβ accumulation in APP(Swe,Ind) mice. Thus, although PrP(C) exerts no control on cleavage of APP(Swe) by BACE1, it has a profound influence on the cleavage of APP(WT), suggesting that PrP(C) may be a key protective player against sporadic Alzheimer disease

    Information Model of Cloud App Scaling with Variable Load Peaks

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    The information model of cloud app was done. It is a formal description of cloud app infrastructure and possible transitions between them, and cloud app current working state classification criterion. Cloud app current state classification criterion on the basis of Page-Hinckley method and calendar of events related to the cloud app working state considers the current state to one of three classes in order to improve the accuracy of prediction of cloud app workload.Proposed criterion was compared with standard offline criterion that analyzes information about the entire time series of cloud app through a considerable time after the events that lead to the load peak, and therefore can\u27t be used when grading in real time. It is shown that the classification of cloud app state is consistent in 92 % of cases.The resulting information model of cloud app scaling with variable load peaks can be used as a component of information technology for cloud app scaling with variable load peaks

    APP Expression in Primary Neuronal Cell Cultures fromP6 Mice during in vitro Differentiation

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    Primary neuronal cell cultures from P6 mice were investigated in order to study amyloid protein precursor (APP) gene expression in differentiating neurons. Cerebellar granule cells which strongly express APP 695 allowed the identification of three distinct isoforms of neuronal APP 695. The high-molecular-weight form of APP 695 is sialylated. The expression pattern of neuronal APP 695 changes during in vitro differentiation. Sialylated forms become more abundant upon longer cultivation time. The secreted forms of sialylated, neuronal APP 695 are shown to comigrate with APP isolated from cerebrospinal fluid. We suggest that the different sialylation states of APP 695 may reflect the modulation of cell-cell and cell-substrate interactions during in vitro differentiation and regeneration

    Ectodomain shedding of the amyloid precursor protein: Cellular control mechanisms and novel modifiers

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    Proteolytic cleavage in the ectodomain of the amyloid precursor protein (APP) is a key regulatory step in the generation of the Alzheimer's disease amyloid-beta (A beta) pepticle and occurs through two different protease activities termed alpha- and beta-secretase. Both proteases compete for APP cleavage, but have opposite effects on A beta generation. At present, little is known about the cellular pathways that control APP alpha- or beta-secretase cleavage and thus A beta generation. To explore the contributory pathways in more detail we have recently employed an expression cloning screen and identified several activators of APP cleavage by alpha- or beta-secretase. Among them were known activators of APP cleavage, for example protein kinase A, and novel activators, such as endophilin and the APP homolog amyloid precursor-like protein 1 (APLP1). Mechanistic analysis revealed that both endophilin and APLP1 reduce the rate of APP endocytosis and strongly increase APP cleavage by alpha-secretase. This review summarizes the results of the expression cloning screen in the context of recent developments in our understanding of the cellular regulation of APP alpha-secretase cleavage. Moreover, it highlights the particular importance of endocytic APP trafficking as a prime modulator of APP shedding. Copyright (c) 2006 S. Karger AG, Basel

    Use of a Mobile Application to Increase Patient Compliance to a Prescribed Home Exercise Program and Improve Outcomes

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    Methods: The creator of the app offered free use of their app to a physical therapy clinic. As the app is only compatible with Apple products, the clinic used the app with any patient that had an iPhone. Retrospective review was conducted to determine if differences in patient outcomes were observed. Patients who had access to an iPad or iPhone were considered part of the “app group” and used the mobile app to reference and report PT HEP compliance. Patients without access to an iPad or iPhone were considered part of the “non-app group” and received traditional PT HEP prescription and monitoring. Patient data was extracted from patient medical records, de-identified, and sent to University researchers. An independent t-test was used to analyze age and compliance of the app group and the non-app group. Mann-Whitney U tests were used to analyze number of exercises assigned, global rating of change, functional index score, and pain rating. (See pdf for complete abstract

    Smartphone-Based Prenatal Education for Parents with Preterm Birth Risk Factors

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    Objective To develop an educational mobile application (app) for expectant parents diagnosed with risk factors for premature birth. Methods Parent and medical advisory panels delineated the vision for the app. The app helps prepare for preterm birth. For pilot testing, obstetricians offered the app between 18–22 weeks gestational age to English speaking parents with risk factors for preterm birth. After 4 weeks of use, each participant completed a questionnaire. The software tracked topics accessed and duration of use. Results For pilot testing, 31 participants were recruited and 28 completed the questionnaire. After app utilization, participants reported heightened awareness of preterm birth (93%), more discussion of pregnancy or prematurity issues with partner (86%), increased questions at clinic visits (43%), and increased anxiety (21%). Participants reported receiving more prematurity information from the app than from their healthcare providers. The 15 participants for whom tracking data was available accessed the app for an average of 8 h. Conclusion Parents with increased risk for preterm birth may benefit from this mobile app educational program. Practice implications If the pregnancy results in preterm birth hospitalization, parents would have built a foundation of knowledge to make informed medical care choices

    QuizPower: a mobile app with app inventor and XAMPP service integration

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    This paper details the development of a mobile app for the Android operating system using MIT App Inventor language and development platform. The app, Quiz Power, provides students a way to study course material in an engaging and effective manner. At its current stage the app is intended strictly for use in a mobile app with App Inventor course, although it provides the facility to be adapted for other courses by simply changing the web data store. Development occurred during the spring semester of 2013. Students in the course played a vital role in providing feedback on course material, which would be the basis for the structure of the quiz as well as the questions. The significance of the project is the integration of the MIT App Inventor service with a web service implemented and managed by the department

    apk2vec: Semi-supervised multi-view representation learning for profiling Android applications

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    Building behavior profiles of Android applications (apps) with holistic, rich and multi-view information (e.g., incorporating several semantic views of an app such as API sequences, system calls, etc.) would help catering downstream analytics tasks such as app categorization, recommendation and malware analysis significantly better. Towards this goal, we design a semi-supervised Representation Learning (RL) framework named apk2vec to automatically generate a compact representation (aka profile/embedding) for a given app. More specifically, apk2vec has the three following unique characteristics which make it an excellent choice for largescale app profiling: (1) it encompasses information from multiple semantic views such as API sequences, permissions, etc., (2) being a semi-supervised embedding technique, it can make use of labels associated with apps (e.g., malware family or app category labels) to build high quality app profiles, and (3) it combines RL and feature hashing which allows it to efficiently build profiles of apps that stream over time (i.e., online learning). The resulting semi-supervised multi-view hash embeddings of apps could then be used for a wide variety of downstream tasks such as the ones mentioned above. Our extensive evaluations with more than 42,000 apps demonstrate that apk2vec's app profiles could significantly outperform state-of-the-art techniques in four app analytics tasks namely, malware detection, familial clustering, app clone detection and app recommendation.Comment: International Conference on Data Mining, 201
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