Fibulin-1 promotes intimal hyperplasia after venous stent implantation through ACE mediated angiotensin II signaling.

Objective: Stent intimal hyperplasia leads to in stent restenosis and thrombosis. This study determined whether Fibulin-1 activity in smooth muscle cells (SMCs) contributes to stent restenosis or thrombosis. Methods: Stent implantation was conducted in a pig model. Target vessel samples were stained and analyzed by protein mass spectrometry. Cell experiments and Fibulin-1 SMC specific knockout mice (Fbln1SMKO) were used to investigate the mechanism of Fibulin-1 induced SMC proliferation and thrombosis. Results: SMC proliferation and phenotypic transition are the main pathological changes of intimal hyperplasia in venous stents. Protein mass spectrometry analysis revealed a total of 67 upregulated proteins and 39 downregulated proteins in intimal hyperplasia after stent implantation compared with normal iliac vein tissues. Among them, Fibulin-1 ranked among the top proteins altered. Fibulin-1 overexpressing human SMCs (Fibulin-1-hSMCs) showed increased migration and phenotypic switching from contractile to secretory type and Fibulin-1 inhibition decreased the activity of SMCs. Mechanistically, Fibulin-1-hSMCs displayed increased levels of angiotensin converting enzyme (ACE) expression and angiotensin II signaling. Inhibition of ACE or angiotensin II signaling alleviated the migration of Fibulin-1-hSMCs. Using Fibulin-1 SMC specific knockout mice (Fbln1SMKO) and venous thrombosis model, we demonstrated that Fibulin-1 deletion attenuated intimal SMCs proliferation and thrombosis. Further, Fibulin-1 concentration was high in iliac vein compression syndrome (IVCS) patients treated with stent and was an independent predictor of venous insufficiency. Conclusions: Fibulin-1 promotes SMC proliferation partially through ACE secretion and angiotensin II signaling after stent implantation. Fibulin-1 plays a role in venous insufficiency syndrome, implicating the protein in the detection and treatment of IVCS.

Distributed Iterative Learning Control of Nonlinear Multiagent Systems Using Controller-Based Dynamic Linearization Method.

When applied to the consensus tracking of repetitive leader-follower multiagent systems (MASs), most of existing distributed iterative learning control (DILC) methods assume that the dynamics of agents are exactly known or up to the affine form. In this article, we study a more general case where the dynamics of agents are unknown, nonlinear, nonaffine, and heterogeneous, and the communication topologies can be iteration-varying. More specifically, we first apply the controller-based dynamic linearization method in the iteration domain to obtain a parametric learning controller using only the local input-output data collected from neighboring agents in a directed graph, and then propose a data-driven distributed adaptive iterative learning control (DAILC) method through the parameter-adaptive learning methods. We show that for each time instant, the tracking error is ultimately bounded in the iteration domain for both of the cases with iteration-invariant and iteration-varying communication topologies. The simulation results show that the proposed DAILC method has faster convergence speed, higher tracking accuracy, and more robust learning and tracking in comparison with a typical DAILC method.

Rethinking the Importance of Quantization Bias, Toward Full Low-Bit Training.

Quantization is a promising technique to reduce the computation and storage costs of DNNs. Low-bit ( ≤ 8 bits) precision training remains an open problem due to the difficulty of gradient quantization. In this paper, we find two long-standing misunderstandings of the bias of gradient quantization noise. First, the large bias of gradient quantization noise, instead of the variance, is the key factor of training accuracy loss. Second, the widely used stochastic rounding cannot solve the training crash problem caused by the gradient quantization bias in practice. Moreover, we find that the asymmetric distribution of gradients causes a large bias of gradient quantization noise. Based on our findings, we propose a novel adaptive piecewise quantization method to effectively limit the bias of gradient quantization noise. Accordingly, we propose a new data format, Piecewise Fixed Point (PWF), to present data after quantization. We apply our method to different applications including image classification, machine translation, optical character recognition, and text classification. We achieve approximately 1.9 âˆ¼ 3.5× speedup compared with full precision training with an accuracy loss of less than 0.5%. To the best of our knowledge, this is the first work to quantize gradients of all layers to 8 bits in both large-scale CNN and RNN training with negligible accuracy loss.

Persistence of Excitation for Identifying Switched Linear Systems.

This paper investigates the uniqueness of parameters via persistence of excitation for switched linear systems. The main contribution is a much weaker sufficient condition on the regressors to be persistently exciting that guarantees the uniqueness of the parameter sets and also provides new insights in understanding the relation among different subsystems. It is found that for uniquely determining the parameters of switched linear systems, the needed minimum number of samples derived from our sufficient condition is much smaller than that reported in the literature.

HBx-related long non-coding RNA 01152 promotes cell proliferation and survival by IL-23 in hepatocellular carcinoma.

Accumulating evidence suggests that long-noncoding RNA (lncRNA) plays important roles in hepatitis B virus (HBV) infections. However, the mechanism underlying how lncRNA regulate hepatocellular carcinoma process remains largely unknown. In this study we found that the expression of LINC01152 was significantly increased in HBV positive HCC tissues and cells and was induced by HBx in vitro. The overexpression of LINC01152 could increases HCC cell proliferation and promotes tumor formation in nude mice. Mechanistically, HBx could increase the transcription of LINC01152. Elevated LINC01152 binds to the promoter region of IL-23, promoting its transcriptional activity and upregulating the levels of Stat3 and p-Stat3. Our findings suggest that LINC01152 plays an important role in HBV-related hepatocellular carcinoma development and may serve as a therapeutic marker for hepatocellular carcinoma.

Motion estimation without integer-pel search.

The typical motion estimation (ME) consists of three main steps, including spatial-temporal prediction, integer-pel search, and fractional-pel search. The integer-pel search, which seeks the best matched integer-pel position within a search window, is considered to be crucial for video encoding. It occupies over 50% of the overall encoding time (when adopting the full search scheme) for software encoders, and introduces remarkable area cost, memory traffic, and power consumption to hardware encoders. In this paper, we find that video sequences (especially high-resolution videos) can often be encoded effectively and efficiently even without integer-pel search. Such counter-intuitive phenomenon is not only because that spatial-temporal prediction and fractional-pel search are accurate enough for the ME of many blocks. In fact, we observe that when the predicted motion vector is biased from the optimal motion vector (mainly for boundary blocks of irregularly moving objects), it is also hard for integer-pel search to reduce the final rate-distortion cost: the deviation of reference position could be alleviated with the fractional-pel interpolation and rate-distortion optimization techniques (e.g., adaptive macroblock mode). Considering the decreasing proportion of boundary blocks caused by the increasing resolution of videos, integer-pel search may be rather cost-ineffective in the era of high-resolution. Experimental results on 36 typical sequences of different resolutions encoded with x264, which is a widely-used video encoder, comply with our analysis well. For 1080p sequences, removing the integer-pel search saves 57.9% of the overall H.264 encoding time on average (compared to the original x264 with full integer-pel search using default parameters), while the resultant performance loss is negligible: the bit-rate is increased by only 0.18%, while the peak signal-to-noise ratio is decreased by only 0.01 dB per frame averagely.

A new approach for analyzing average time complexity of population-based evolutionary algorithms on unimodal problems.

In the past decades, many theoretical results related to the time complexity of evolutionary algorithms (EAs) on different problems are obtained. However, there is not any general and easy-to-apply approach designed particularly for population-based EAs on unimodal problems. In this paper, we first generalize the concept of the takeover time to EAs with mutation, then we utilize the generalized takeover time to obtain the mean first hitting time of EAs and, thus, propose a general approach for analyzing EAs on unimodal problems. As examples, we consider the so-called (N + N) EAs and we show that, on two well-known unimodal problems, leadingones and onemax , the EAs with the bitwise mutation and two commonly used selection schemes both need O(n ln n + n(2)/N) and O(n ln ln n + n ln n/N) generations to find the global optimum, respectively. Except for the new results above, our approach can also be applied directly for obtaining results for some population-based EAs on some other unimodal problems. Moreover, we also discuss when the general approach is valid to provide us tight bounds of the mean first hitting times and when our approach should be combined with problem-specific knowledge to get the tight bounds. It is the first time a general idea for analyzing population-based EAs on unimodal problems is discussed theoretically.