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Inspired by our previous efforts on the modifications of diarylpyrimidines as HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTI) and reported crystallography study, novel diarylnicotinamide derivatives were designed with a "triazole tail" occupying the entrance channel in the NNRTI binding pocket of the reverse transcriptase to afford additional interactions. The newly designed compounds were then synthesized and evaluated for their anti-HIV activities in MT-4 cells. All the compounds showed excellent to good activity against wild-type HIV-1 strain with EC of 0.02-1.77 μM. Evaluations of selected compounds against more drug-resistant strains showed these compounds had advantage of inhibiting E138K mutant virus which is a key drug-resistant mutant to the new generation of NNRTIs. Among this series, propionitrile (3b2, EC = 0.020 μM, EC = 0.015 μM, CC = 40.15 μM), pyrrolidin-1-ylmethanone (3b8, EC = 0.020 μM, EC = 0.014 μM, CC = 58.09 μM) and morpholinomethanone (3b9, EC = 0.020 μM, EC = 0.027 μM, CC = 180.90 μM) derivatives are the three most promising compounds which are equally potent to the marketed drug Etravirine against E138K mutant strain but with much lower cytotoxicity. Furthermore, detailed SAR, inhibitory activity against RT and docking study of the representative compounds are also discussed.

Angiogenesis is the formation of new blood vessels from the existing vasculature, which is involved in multiple biological processes, including atherosclerosis, ischemic heart disease, and cancer. Ginsenoside-Rb1 (Rb1), the most abundant ginsenoside isolated form Panax ginseng, has been identified as a promising anti-angiogenic agent via the up-regulation of PEDF. However, the underlying molecular mechanisms still unknown. In the present study, human umbilical vein endothelial cells (HUVECs) were selected to perform in vitro assays. Rb1 (0–20 nM) treatment induced pigment epithelial-derived factor (PEDF) protein expression in concentration and time-dependent manners. Interestingly, it was also demonstrated that the exposure of Rb1 (10 nM) could increase PEDF protein expression without any alteration on mRNA level, suggesting the involvement of posttranscriptional regulation. Furthermore, bioinformatics predictions indicated the regulation of miR-33a on PEDF mRNA 3′-UTR, which was further confirmed by luciferase reporter gene assay and real-time PCR. Over-expression of pre-miR-33a was found to regress partly Rb1-mediated PEDF increment and anti-angiogenic effect in HUVECs. Additionally, Rb1-reduced miR-33a and increased PEDF expression was prevented by pre-incubation with peroxisome proliferator-activated receptor-γ (PPAR-γ) antagonist (GW9662) or transfection with PPAR-γ siRNA in HUVECs. Taken together, our findings demonstrated that Rb1 exerted anti-angiogenic effects through PPAR-γ signaling pathway via modulating miR-33a and PEDF expressions. Thus, Rb1 may have the potential of being developed as an anti-angiogenic agent, however, further appropriate studies are warranted to evaluate the effect in vivo.

The goal of standard compressive sensing is to estimate an unknown vector from linear measurements under the assumption of sparsity in some basis. Recently, it has been shown that significantly fewer measurements may be required if the sparsity assumption is replaced by the assumption that the unknown vector lies near the range of a suitably-chosen generative model. In particular, in (Bora et al., 2017) it was shown that roughly O(k log L) random Gaussian measurements suffice for accurate recovery when the k-input generative model is bounded and L-Lipschitz, and that O(kd log w) measurements suffice for k-input ReLU networks with depth d and width w. In this paper, we establish corresponding algorithm-independent lower bounds on the sample complexity using tools from minimax statistical analysis. In accordance with the above upper bounds, our results are summarized as follows: (i) We construct an L-Lipschitz generative model capable of generating group-sparse signals, and show that the resulting necessary number of measurements is Ω(k log L); (ii) Using similar ideas, we construct two-layer ReLU networks of high width requiring Ω(k log w) measurements, as well as lower-width deep ReLU networks requiring Ω(kd) measurements. As a result, we establish that the scaling laws derived in (Bora et al., 2017) are optimal or near-optimal in the absence of further assumptions. I. INTRODUCTIONOver the past 1-2 decades, tremendous research effort has been placed on theoretical and algorithmic studies of high-dimensional linear inverse problems [1], [2]. The prevailing approach has been to model low-dimensional structure via assumptions such as sparsity or low rankness, and numerous algorithmic approaches have been shown to be successful, including convex relaxations [3], [4], greedy methods [5], [6], and more. The problem of sparse estimation via linear measurements (commonly referred to as compressive sensing) is particularly well-understood, with theoretical developments including sharp performance bounds for both practical algorithms [4], [6]-[8] and (potentially intractable) information-theoretically optimal algorithms [9]-[12]. Following the tremendous success of deep generative models in a variety of applications [13], a new perspective on compressive sensing was recently introduced, in which the sparsity assumption is replaced by the assumption of the underlying signal being well-modeled by a generative model (typically corresponding to a deep neural network) [14]. This approach was seen to exhibit impressive performance in experiments, with reductions in the number of measurements by large factors such as 5 to 10 compared to sparsity-based methods. 2 In addition, [14] provided theoretical guarantees on their proposed algorithm, essentially showing that an L-Lipschitz generative model with bounded k-dimensional inputs leads to reliable recovery with m = O(k log L)random Gaussian measurements (see Section II for a precise statement). Moreover, for a ReLU network generative model from R k to R n with width w and depth d...