Characterization of vascular endothelial progenitor cells from chicken bone marrow

BACKGROUND: Endothelial progenitor cells (EPC) are a type of stem cell used in the treatment of atherosclerosis, vascular injury and regeneration. At present, most of the EPCs studied are from human and mouse, whereas the study of poultry-derived EPCs has rarely been reported. In the present study, chicken bone marrow-derived EPCs were isolated and studied at the cellular level using immunofluorescence and RT-PCR. RESULTS: We found that the majority of chicken EPCs were spindle shaped. The growth-curves of chicken EPCs at passages (P) 1, -5 and -9 were typically “S”-shaped. The viability of chicken EPCs, before and after cryopreservation was 92.2% and 81.1%, respectively. Thus, cryopreservation had no obvious effects on the viability of chicken EPCs. Dil-ac-LDL and FITC-UAE-1 uptake assays and immunofluorescent detection of the cell surface markers CD34, CD133, VEGFR-2 confirmed that the cells obtained in vitro were EPCs. Observation of endothelial-specific Weibel-Palade bodies using transmission electron microscopy further confirmed that the cells were of endothelial lineage. In addition, chicken EPCs differentiated into endothelial cells and smooth muscle cells upon induction with VEGF and PDGF-BB, respectively, suggesting that the chicken EPCs retained multipotency in vitro. CONCLUSIONS: These results suggest that chicken EPCs not only have strong self-renewal capacity, but also the potential to differentiate into endothelial and smooth muscle cells. This research provides theoretical basis and experimental evidence for potential therapeutic application of endothelial progenitor cells in the treatment of atherosclerosis, vascular injury and diabetic complications

Large-Scale Analysis of Framework-Specific Exceptions in Android Apps

Mobile apps have become ubiquitous. For app developers, it is a key priority to ensure their apps' correctness and reliability. However, many apps still suffer from occasional to frequent crashes, weakening their competitive edge. Large-scale, deep analyses of the characteristics of real-world app crashes can provide useful insights to guide developers, or help improve testing and analysis tools. However, such studies do not exist -- this paper fills this gap. Over a four-month long effort, we have collected 16,245 unique exception traces from 2,486 open-source Android apps, and observed that framework-specific exceptions account for the majority of these crashes. We then extensively investigated the 8,243 framework-specific exceptions (which took six person-months): (1) identifying their characteristics (e.g., manifestation locations, common fault categories), (2) evaluating their manifestation via state-of-the-art bug detection techniques, and (3) reviewing their fixes. Besides the insights they provide, these findings motivate and enable follow-up research on mobile apps, such as bug detection, fault localization and patch generation. In addition, to demonstrate the utility of our findings, we have optimized Stoat, a dynamic testing tool, and implemented ExLocator, an exception localization tool, for Android apps. Stoat is able to quickly uncover three previously-unknown, confirmed/fixed crashes in Gmail and Google+; ExLocator is capable of precisely locating the root causes of identified exceptions in real-world apps. Our substantial dataset is made publicly available to share with and benefit the community.Comment: ICSE'18: the 40th International Conference on Software Engineerin

Why does Prediction Accuracy Decrease over Time? Uncertain Positive Learning for Cloud Failure Prediction

With the rapid growth of cloud computing, a variety of software services have been deployed in the cloud. To ensure the reliability of cloud services, prior studies focus on failure instance (disk, node, and switch, etc.) prediction. Once the output of prediction is positive, mitigation actions are taken to rapidly resolve the underlying failure. According to our real-world practice in Microsoft Azure, we find that the prediction accuracy may decrease by about 9% after retraining the models. Considering that the mitigation actions may result in uncertain positive instances since they cannot be verified after mitigation, which may introduce more noise while updating the prediction model. To the best of our knowledge, we are the first to identify this Uncertain Positive Learning (UPLearning) issue in the real-world cloud failure prediction scenario. To tackle this problem, we design an Uncertain Positive Learning Risk Estimator (Uptake) approach. Using two real-world datasets of disk failure prediction and conducting node prediction experiments in Microsoft Azure, which is a top-tier cloud provider that serves millions of users, we demonstrate Uptake can significantly improve the failure prediction accuracy by 5% on average

Sestrin2 protects against hypoxic nerve injury by regulating mitophagy through SESN2/AMPK pathway

Hypoxia induced by high altitude can lead to severe neurological dysfunction. Mitophagy is known to play a crucial role in hypoxic nerve injury. However, the regulatory mechanism of mitophagy during this injury remains unclear. Recent studies have highlighted the role of Sestrin2 (SESN2), an evolutionarily conserved stress-inducible protein against acute hypoxia. Our study demonstrated that hypoxia treatment increased SESN2 expression and activated mitophagy in PC12 cells. Furthermore, the knock-out of Sesn2 gene led to a significant increase in mitochondrial membrane potential and ATP concentrations, which protected the PC12 cells from hypoxic injury. Although the AMPK/mTOR pathway was significantly altered under hypoxia, it does not seem to participate in mitophagy regulation. Instead, our data suggest that the mitophagy receptor FUNDC1 plays a vital role in hypoxia-induced mitophagy. Moreover, SESN2 may function through synergistic regulation with other pathways, such as SESN2/AMPK, to mediate cellular adaptation to hypoxia, including the regulation of mitophagy in neuron cells. Therefore, SESN2 plays a critical role in regulating neural cell response to hypoxia. These findings offer valuable insights into the underlying molecular mechanisms governing the regulation of mitophagy under hypoxia and further highlight the potential of SESN2 as a promising therapeutic target for hypoxic nerve injury

Comparative proteome analysis revealed the differences in response to both Mycobacterium tuberculosis and Mycobacterium bovis infection of bovine alveolar macrophages

Tuberculosis (TB), attributed to the Mycobacterium tuberculosis complex, is one of the most serious zoonotic diseases worldwide. Nevertheless, the host mechanisms preferentially leveraged by Mycobacterium remain unclear. After infection, both Mycobacterium tuberculosis (MTB) and Mycobacterium bovis (MB) bacteria exhibit intimate interactions with host alveolar macrophages; however, the specific mechanisms underlying these macrophage responses remain ambiguous. In our study, we performed a comparative proteomic analysis of bovine alveolar macrophages (BAMs) infected with MTB or MB to elucidate the differential responses of BAMs to each pathogen at the protein level. Our findings revealed heightened TB infection susceptibility of BAMs that had been previously infected with MTB or MB. Moreover, we observed that both types of mycobacteria triggered significant changes in BAM energy metabolism. A variety of proteins and signalling pathways associated with autophagy and inflammation-related progression were highly activated in BAMs following MB infection. Additionally, proteins linked to energy metabolism were highly expressed in BAMs following MTB infection. In summary, we propose that BAMs may resist MTB and MB infections via different mechanisms. Our findings provide critical insights into TB pathogenesis, unveiling potential biomarkers to facilitate more effective TB treatment strategies. Additionally, our data lend support to the hypothesis that MTB may be transmitted via cross-species infection

Joint Source/Channel Coding For JPEG2000

In today's world, demands of digital multimedia services are growing tremendously, together with the development of new communication technologies and investigation of new transmission media. Two common problems encountered in multimedia services are unreliable transmission channels and limited resources. This dissertation investigates advanced source coding and error control techniques, and is dedicated to designing joint source-channel coding schemes for robust image/video transmission. Error resilience properties of JPEG2000 codestreams are investigated first, and an LDPC-based joint iterative decoding scheme is proposed. Next, a progressive decoding method is presented for still and motion image transmission. The underlying channel codes are created using a Plotkin construction and offer the novel ability of using one long channel codeword to protect an entire image, yet still allowing progressive decoding. Progressive quality improvements occur in two ways: the first is the usual progressive refinement, where image quality is improved as more data are received; the second is that residual error rates of earlier received data are reduced as more data are received. Finally, multichannel systems are studied and an optimal rate allocation algorithm is proposed for parallel transmission of scalable images in multichannel systems. The proposed algorithm selects a subchannel as well as a channel code rate for each packet, based on the signal-to-noise ratios (SNR) of the subchannels. The resulting scheme provides unequal error protection of source bits and significant gains are obtained over equal error protection (EEP) schemes. An application of the proposed algorithm to JPEG2000 transmission shows the advantages of exploiting differences in SNRs between subchannels. Multiplexing of multiple sources is also considered, and additional gains are achieved by exploiting information diversity among the sources