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

Non-Volatile Memory Array Based Quantization- and Noise-Resilient LSTM Neural Networks

In cloud and edge computing models, it is important that compute devices at the edge be as power efficient as possible. Long short-term memory (LSTM) neural networks have been widely used for natural language processing, time series prediction and many other sequential data tasks. Thus, for these applications there is increasing need for low-power accelerators for LSTM model inference at the edge. In order to reduce power dissipation due to data transfers within inference devices, there has been significant interest in accelerating vector-matrix multiplication (VMM) operations using non-volatile memory (NVM) weight arrays. In NVM array-based hardware, reduced bit-widths also significantly increases the power efficiency. In this paper, we focus on the application of quantization-aware training algorithm to LSTM models, and the benefits these models bring in terms of resilience against both quantization error and analog device noise. We have shown that only 4-bit NVM weights and 4-bit ADC/DACs are needed to produce equivalent LSTM network performance as floating-point baseline. Reasonable levels of ADC quantization noise and weight noise can be naturally tolerated within our NVMbased quantized LSTM network. Benchmark analysis of our proposed LSTM accelerator for inference has shown at least 2.4x better computing efficiency and 40x higher area efficiency than traditional digital approaches (GPU, FPGA, and ASIC). Some other novel approaches based on NVM promise to deliver higher computing efficiency (up to 4.7x) but require larger arrays with potential higher error rates.Comment: Published in: 2019 IEEE International Conference on Rebooting Computing (ICRC

The Lottery Ticket Hypothesis for Vision Transformers

The conventional lottery ticket hypothesis (LTH) claims that there exists a sparse subnetwork within a dense neural network and a proper random initialization method, called the winning ticket, such that it can be trained from scratch to almost as good as the dense counterpart. Meanwhile, the research of LTH in vision transformers (ViTs) is scarcely evaluated. In this paper, we first show that the conventional winning ticket is hard to find at weight level of ViTs by existing methods. Then, we generalize the LTH for ViTs to input images consisting of image patches inspired by the input dependence of ViTs. That is, there exists a subset of input image patches such that a ViT can be trained from scratch by using only this subset of patches and achieve similar accuracy to the ViTs trained by using all image patches. We call this subset of input patches the winning tickets, which represent a significant amount of information in the input. Furthermore, we present a simple yet effective method to find the winning tickets in input patches for various types of ViT, including DeiT, LV-ViT, and Swin Transformers. More specifically, we use a ticket selector to generate the winning tickets based on the informativeness of patches. Meanwhile, we build another randomly selected subset of patches for comparison, and the experiments show that there is clear difference between the performance of models trained with winning tickets and randomly selected subsets

Peeling the Onion: Hierarchical Reduction of Data Redundancy for Efficient Vision Transformer Training

Vision transformers (ViTs) have recently obtained success in many applications, but their intensive computation and heavy memory usage at both training and inference time limit their generalization. Previous compression algorithms usually start from the pre-trained dense models and only focus on efficient inference, while time-consuming training is still unavoidable. In contrast, this paper points out that the million-scale training data is redundant, which is the fundamental reason for the tedious training. To address the issue, this paper aims to introduce sparsity into data and proposes an end-to-end efficient training framework from three sparse perspectives, dubbed Tri-Level E-ViT. Specifically, we leverage a hierarchical data redundancy reduction scheme, by exploring the sparsity under three levels: number of training examples in the dataset, number of patches (tokens) in each example, and number of connections between tokens that lie in attention weights. With extensive experiments, we demonstrate that our proposed technique can noticeably accelerate training for various ViT architectures while maintaining accuracy. Remarkably, under certain ratios, we are able to improve the ViT accuracy rather than compromising it. For example, we can achieve 15.2% speedup with 72.6% (+0.4) Top-1 accuracy on Deit-T, and 15.7% speedup with 79.9% (+0.1) Top-1 accuracy on Deit-S. This proves the existence of data redundancy in ViT.Comment: AAAI 202

PDGF induced microRNA alterations in cancer cells

Platelet derived growth factor (PDGF) regulates gene transcription by binding to specific receptors. PDGF plays a critical role in oncogenesis in brain and other tumors, regulates angiogenesis, and remodels the stroma in physiologic conditions. Here, we show by using microRNA (miR) arrays that PDGFs regulate the expression and function of miRs in glioblastoma and ovarian cancer cells. The two PDGF ligands AA and BB affect expression of several miRs in ligand-specific manner; the most robust changes consisting of let-7d repression by PDGF-AA and miR-146b induction by PDGF-BB. Induction of miR-146b by PDGF-BB is modulated via MAPK-dependent induction of c-fos. We demonstrate that PDGF regulates expression of some of its known targets (e.g. cyclin D1) through miR alterations and identify the epidermal growth factor receptor (EGFR) as a new PDGF-BB target. We show that its expression and function are repressed by PDGF-induced miR-146b and that mir-146b and EGFR correlate inversely in human glioblastomas. We propose that PDGF-regulated gene transcription involves alterations in non-coding RNAs and provide evidence for a miR-dependent feedback mechanism balancing growth factor receptor signaling in cancer cells

Radioactive Wastewater Treatment Technologies: A Review

With the wide application of nuclear energy, the problem of radioactive pollution has attracted worldwide attention, and the research on the treatment of radioactive wastewater is imminent. How to treat radioactive wastewater deeply and efficiently has become the most critical issue in the development of nuclear energy technology. The radioactive wastewater produced after using nuclear technology has the characteristics of many kinds, high concentration, and large quantity. Therefore, it is of great significance to study the treatment technology of radioactive wastewater in reprocessing plants. The process flow and waste liquid types of the post-treatment plant are reviewed. The commonly used evaporation concentration, adsorption, precipitation, ion exchange, biotechnology, membrane separation, and photocatalysis are summarized. The basic principles and technological characteristics of them are introduced. The advantages and disadvantages of different single and combined processes are compared, and the development trend of future processing technology is prospected

Effects of Processing on Starch Structure, Textural, and Digestive Property of “Horisenbada”, a Traditional Mongolian Food

Horisenbada, prepared by the soaking, steaming, and baking of millets, is a traditional Mongolian food and is characterized by its long shelf life, convenience, and nutrition. In this study, the effect of processing on the starch structure, textural, and digestive property of millets was investigated. Compared to the soaking treatment, steaming and baking significantly reduced the molecular size and crystallinity of the millet starch, while baking increased the proportion of long amylose chains, partially destroyed starch granules, and formed a closely packed granular structure. Soaking and steaming significantly reduced the hardness of the millets, while the hardness of baked millets is comparable to that of raw millet grains. By fitting digestive curves with a first-order model and logarithm of the slope (LOS) plot, it showed that the baking treatment significantly reduced the digestibility of millets, the steaming treatment increased the digestibility of millets, while the soaked millets displayed a similar digestive property with raw millets, in terms of both digestion rate and digestion degree. This study could improve the understanding of the effects of processing on the palatability and health benefits of Horisenbada

Characterization of vascular endothelial progenitor cells from chicken bone marrow

Abstract 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.</p