SSM are more responsive than IFM to FGF-2-triggered protection from calcium-induced mPTP, by a mitochondrial Cx43 channel-mediated pathway, associated with mitochondrial Cx43 phosphorylation at PKCε target sites.

The recent discovery of several myogenic cardiac progenitor cells in the post-natal heart suggests that some myocardial cells may remain undifferentiated during embryonic development. In this study, we examined the subcellular characteristics of the embryonic (E) mouse ventricular myocardial cells using transmission electron microscopy (TEM). At the ultrastructural level, we identified three different cell populations within the myocardial layer of the E11.5 heart. These cells were designated as undifferentiated cells (43 ± 6%), moderately differentiated cells (43 ± 2%) and mature cardiomyocytes (14 ± 4%). Undifferentiated cells contained a large nucleus and sparse cytoplasm with no myofibrillar bundles. Moderately differentiated cells contained randomly arranged myofilaments in the cytoplasm. In contrast, mature cardiomyocytes contained well-developed sarcomere structures. We also confirmed the presence of similar undifferentiated cells albeit at low levels in the E16.5 (∼20%) and E18.5 (∼7%) myocardium. Further we used immunogold labeling technique to test whether these distinct cell populations were also positive for markers such as Nkx2.5, ISL1 and ANF. A preponderance of anti-Nkx2.5 label was found in the undifferentiated and moderately differentiated cell types. Anti-ANF label was found only in the cytoplasmic compartment of moderately differentiated and mature myocardial cells. All of the undifferentiated cells were negative for anti-ANF labeling. We did not find immuno-gold labeling with ISL1 in any of the three myocardial cell types. Based on these results, we suggest that embryonic myocardial cell differentiation is a gradual process and undifferentiated cells expressing Nkx2.5 in post-chamber myocardium may represent a progenitor cell population while cells expressing Nkx2.5 and ANF represent differentiating myocytes.

The rapid growth of server virtualization has ignited a wide adoption of software-based virtual switches, with significant interest in speeding up their performance. In a similar trend, software-defined networking (SDN), with its strong reliance on rule-based flow classification, has also created renewed interest in multi-dimensional packet classification. However, despite these recent advances, the performance of current software-based packet classifiers is still limited, mostly by the low parallelism of general-purpose CPUs. In this paper, we explore how to accelerate packet classification using the high parallelism and latency-hiding capabilities of graphic processing units (GPUs). We implement GPU-accelerated versions for both linear and tuple search, currently deployed in virtual switches, and also introduce a novel algorithm called Bloom search. These algorithms are integrated with high-speed packet I/O to build GSwitch, a GPU-accelerated software switch. Our experimental evaluation shows that GSwitch is at least 7x faster than an equally-priced CPU classifier and is able to reach 10 Gbps with minimum-sized packets and a rule set containing 128K OpenFlow entries with 512 different wildcard patterns.

The prevalence of mobile devices especially smartphones has attracted research on mobile content delivery techniques. In this paper, we propose to take advantage of the storage available at wireless access points to bring content closer to mobile devices, hence improving the downloading performance. Specifically, we propose to have a separate popularity based cache and a prefetch buffer at the network edge to capture both longterm and short-term content access patterns. Further, we point out that it is insufficient to rely on a device's past history to predict when and where to prefetch, especially in urban settings; instead, we propose to derive a prediction model based on the aggregated network-level statistics.We discuss the proposed mobile content caching/prefetching method in the context of the MobilityFirst future Internet architecture. In MobilityFirst, when mobile clients move between network attachment points (e.g., Wi-Fi access points), their network association records are logged by the network, which then naturally facilitates the network-level mobility prediction. Through detailed simulations with real taxi mobility traces, we show that such a strategy is more effective than earlier schemes in satisfying content requests at the edge (higher cache hit ratios), leading to shorter content download latencies. Specifically, the fraction of requests satisfied at the edge increases by a factor of 2.9 compared to a caching only approach, and by 45% compared to individual user-based prediction and prefetching.

Content-centric networking (CCN) adopts a receiver-driven, hop-by-hop transport approach that facilitates in-network caching, which in turn leads to multiple sources and multiple paths for transferring content. In such a case, keeping a single round trip time (RTT) estimator for a multi-path flow is insufficient as each path may experience different round trip times. To solve this problem, it has been proposed to use multiple RTT estimators to predict network condition.In this paper, we examine an alternative approach to this problem, CHoPCoP, which utilizes explicit congestion control to cope with the multiple-source, multiple-path situation. Protocol design innovations of CHoPCoP include a random early marking (REM) scheme that explicitly signals network congestion, and a per-hop fair share Interest shaping algorithm (FISP) and a receiver Interest control method (RIC) that regulate the Interest rates at routers and the receiver respectively. We have implemented CHoPCoP on the ORBIT testbed and conducted experiments under various network and traffic settings. The evaluation shows that CHoPCoP is a viable approach that can effectively deal with congestion in the multipath environment.