MRI estimations of cerebral blood volume (CBV), useful in mapping brain dysfunction, typically require intravenous (IV) injections of contrast agents. Transgenically engineered mice have emerged as the dominant animal model with which to investigate disorders of the brain and novel therapeutic agents. The difficulty in gaining IV access in mice prohibits repeated administration of contrast in the same animal, limiting the ability to map CBV changes over time. Here we address this limitation by first optimizing an approach for estimating CBV that relies on intraperitoneal (IP) rather than IV injections of the contrast agent gadodiamide. Next, we show that CBV maps generated with IP or IV injections are quantitatively comparable. Finally, we show that CBV maps generated with IP gadodiamide can be acquired repeatedly, reliably and safely over time. Although this approach has certain limitations, estimating CBV with IP injections is well-suited for mapping the spatiotemporal pattern of brain dysfunction in mice models of disease, and for testing pharmacological agents.
Although reductions in the expression of the calcium-buffering proteins calbindin D-28K (CB) and parvalbumin (PV) have been observed in the aging brain, it is unknown whether these changes contribute to age-related hippocampal dysfunction. To address this issue, we measured basal hippocampal metabolism and hippocampal structure across the lifespan of C57BL/6J, calbindin D-28k knockout (CBKO) and parvalbumin knockout (PVKO) mice. Basal metabolism was estimated using steady state relative cerebral blood volume (rCBV), which is a variant of fMRI that provides the highest spatial resolution, optimal for the analysis of individual subregions of the hippocampal formation. We found that like primates, normal aging in C57BL/6J mice is characterized by an age-dependent decline in rCBV-estimated dentate gyrus metabolism. Although abnormal hippocampal fMRI signals were observed in CBKO and PVKO mice, only CBKO mice showed accelerated age-dependent decline of rCBV-estimated metabolism in the dentate gyrus. We also found age-independent structural changes in CBKO mice, which included an enlarged hippocampus and neocortex as well as global brain hypertrophy. These metabolic and structural changes in CBKO mice correlated with a deficit in hippocampus-dependent learning in the active place avoidance task. Our results suggest that the decrease in CB that occurs during normal aging is involved in age-related hippocampal metabolic decline. Our findings also illustrate the value of using multiple MRI techniques in transgenic mice to investigate mechanisms involved in the functional and structural changes that occur during aging.
A recent paper on benchmarking eventual consistency showed that when a constant workload is applied against Cassandra, the staleness of values returned by read operations exhibits interesting but unexplained variations when plotted against time. In this paper we reproduce this phenomenon and investigate in greater depth the low-level mechanisms that give rise to stale reads. We show that the staleness spikes exhibited by Cassandra are strongly correlated with garbage collection, particularly the "stop-the-world" phase which pauses all application threads in a Java virtual machine. We show experimentally that the staleness spikes can be virtually eliminated by delaying read operations artificially at servers immediately after a garbage collection pause. In our experiments this yields more than a 98% reduction in the number of consistency anomalies that exceed 5ms, and has negligible impact on throughput and latency.
Since John von Neumann suggested utilizing Logistic map as a random number generator in 1947, a great number of encryption schemes based on Logistic map and/or its variants have been proposed. This paper re-evaluates the security of an image cipher based on transformed logistic maps and proves that the image cipher can be deciphered efficiently under two different conditions: 1) two pairs of known plain-images and the corresponding cipher-images with computational complexity of O(2 18 + L); 2) two pairs of chosen plain-images and the corresponding cipher-images with computational complexity of O(L), where L is the number of pixels in the plain-image. In contrast, the required condition in the previous deciphering method is eightyseven pairs of chosen plain-images and the corresponding cipher-images with computational complexity of O(2 7 + L). In addition, three other security flaws existing in most Logistic-mapbased ciphers are also reported.
When a fault occurs in a section or a component of a given power system, the malfunctioning of protective relays (PRs) and circuit breakers (CBs), and the false and missing alarms, may manifestly complicate the fault diagnosis procedure. It is necessary to develop a methodologically appropriate framework for this application. As a branch of stochastic programming, the well-developed chance-constrained programming approach provides an efficient way to solve programming problems fraught with uncertainties. In this work, a novel fault diagnosis analytic model is developed with the ability of accommodating the malfunctioning of PRs and CBs, as well as the false and/or missing alarms. The genetic algorithm combined with Monte Carlo simulations are then employed to solve the optimization model. The feasibility and efficiency of the developed model and method are verified by a real fault scenario in an actual power system. In addition, it is demonstrated by simulation results that the computation speed of the developed method meets the requirements for the on-line fault diagnosis of actual power systems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.