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  • Author: Yuanyuan Feng x
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In today’s data-centric economy, data flows are increasingly diverse and complex. This is best exemplified by mobile apps, which are given access to an increasing number of sensitive APIs. Mobile operating systems have attempted to balance the introduction of sensitive APIs with a growing collection of permission settings, which users can grant or deny. The challenge is that the number of settings has become unmanageable. Yet research also shows that existing settings continue to fall short when it comes to accurately capturing people’s privacy preferences. An example is the inability to control mobile app permissions based on the purpose for which an app is requesting access to sensitive data. In short, while users are already overwhelmed, accurately capturing their privacy preferences would require the introduction of an even greater number of settings. A promising approach to mitigating this trade-off lies in using machine learning to generate setting recommendations or bundle some settings. This article is the first of its kind to offer a quantitative assessment of how machine learning can help mitigate this trade-off, focusing on mobile app permissions. Results suggest that it is indeed possible to more accurately capture people’s privacy preferences while also reducing user burden.


Introduction: The functions and mechanisms of prion proteins (PrPC) are currently unknown, but most experts believe that deformed or pathogenic prion proteins (PrPSc) originate from PrPC, and that there may be plural main sites for the conversion of normal PrPC into PrPSc. In order to better understand the mechanism of PrPC transformation to PrPSc, the most important step is to determine the replacement or substitution site.

Material and Methods: BALB/c mice were challenged with prion RML strain and from 90 days post-challenge (dpc) mice were sacrificed weekly until all of them had been at 160 dpc. The ultra-structure and pathological changes of the brain of experimental mice were observed and recorded by transmission electron microscopy.

Results: There were a large number of pathogen-like particles aggregated in the myelin sheath of the brain nerves, followed by delamination, hyperplasia, swelling, disintegration, phagocytic vacuolation, and other pathological lesions in the myelin sheath. The aggregated particles did not overflow from the myelin in unstained samples. The phenomenon of particle aggregation persisted all through the disease course, and was the earliest observed pathological change.

Conclusion: It was deduced that the myelin sheath and lipid rafts in brain nerves, including axons and dendrites, were the main sites for the conversion of PrPC to PrPSc, and the PrPSc should be formed directly by the conversion of protein conformation without the involvement of nucleic acids.