The expression levels of chemotaxis-related molecules CXC chemokine receptor 1, interleukin-8, and pro-platelet basic protein in gingival tissues.

Background/purpose: Excessive host immune response is thought to be an important cause of periodontal tissue damage during periodontitis. The potent chemotaxis produced by locally released chemokines is the key signal to trigger this response. Here, we aimed to investigate the expression of CXC chemokine receptor 1 (CXCR1), and chemokines interleukin-8 (IL-8) and pro-platelet basic protein (PPBP) in human inflammatory gingival tissues compared with healthy tissues. Materials and methods: A total of 54 human gingival tissues, 27 healthy and 27 inflammatory samples, were collected. Fifteen specimens of each group were employed for quantitative reverse transcription polymerase chain reaction to determine the mRNA levels of CXCR1, IL-8, and PPBP. Six samples of each group were used for Western blotting to investigate the protein expression of CXCR1 and for enzyme-linked immunosorbent assay to evaluate the protein levels of IL-8 and PPBP, respectively. Results: The mRNA levels of chemokine receptor CXCR1, chemokine IL-8, and PPBP in inflammatory gingival tissues were significantly higher than those in healthy controls (P < 0.05). The protein levels of CXCR1, IL-8, and PPBP in inflammatory gingival tissues were also significantly higher than those in healthy gingival tissues (P < 0.05). Conclusion: When compared to healthy gingival tissues, the expression of CXCR1, IL-8, and PPBP in inflammatory gingival tissues is higher.

CNGBdb: China National GeneBank DataBase.

China National GeneBank DataBase (CNGBdb) is a data platform aiming to systematically archiving and sharing of multi-omics data in life science. As the service portal of Bio-informatics Data Center of the core structure, namely, "Three Banks and Two Platforms" of China National GeneBank (CNGB), CNGBdb has the advantages of rich sample resources, data resources, cooperation projects, powerful data computation and analysis capabilities. With the advent of high throughput sequencing technologies, research in life science has entered the big data era, which is in the need of closer international cooperation and data sharing. With the development of China's economy and the increase of investment in life science research, we need to establish a national public platform for data archiving and sharing in life science to promote the systematic management, application and industrial utilization. Currently, CNGBdb can provide genomic data archiving, information search engines, data management and data analysis services. The data schema of CNGBdb has covered projects, samples, experiments, runs, assemblies, variations and sequences. Until May 22, 2020, CNGBdb has archived 2176 research projects and more than 2221 TB sequencing data submitted by researchers globally. In the future, CNGBdb will continue to be dedicated to promoting data sharing in life science research and improving the service capability. CNGBdb website is: https://db.cngb.org/.

[The China National GeneBank─owned by all, completed by all and shared by all].

Genetic resources are important national strategic resources. Their preservation, protection and rational utilization form a solid foundation to guarantee national security and to build national competitiveness for the future. Due to a relatively late starting point, China is actively catching up with global peers in storing genetic samples and data. In view of this, in 2011 China approved a plan to build its first nation-level comprehensive gene bank, the China National GeneBank (CNGB), and entrusted BGI-Research to implement its construction and operation. It is China's first gene bank for "reading, writing and storing" bioresources. In this paper, we summarize the development of influential platforms at home and abroad, and focus on CNGB's position, mission, and its structure of "Three Banks and Two Platforms". CNGB launched its official operation in September 2016 and aims to develop a world-class, non-profit and strategic platform that supports science and technology development. It has built capacities to store tens of millions of traceable samples and to analyze handreds of thousanda of WGS each year. It has also set up China's first Pb-level digitalization platform and a high-efficient synthesis platform with a production rate of ten million bases per year. Based on such capacities, CNGB has established its open sharing mechanism for biological samples and data, provided public platform services for life science research, and achieved initial results in supporting innovation and development of the bio-industry.

Lagrange Programming Neural Network for Nondifferentiable Optimization Problems in Sparse Approximation.

The major limitation of the Lagrange programming neural network (LPNN) approach is that the objective function and the constraints should be twice differentiable. Since sparse approximation involves nondifferentiable functions, the original LPNN approach is not suitable for recovering sparse signals. This paper proposes a new formulation of the LPNN approach based on the concept of the locally competitive algorithm (LCA). Unlike the classical LCA approach which is able to solve unconstrained optimization problems only, the proposed LPNN approach is able to solve the constrained optimization problems. Two problems in sparse approximation are considered. They are basis pursuit (BP) and constrained BP denoise (CBPDN). We propose two LPNN models, namely, BP-LPNN and CBPDN-LPNN, to solve these two problems. For these two models, we show that the equilibrium points of the models are the optimal solutions of the two problems, and that the optimal solutions of the two problems are the equilibrium points of the two models. Besides, the equilibrium points are stable. Simulations are carried out to verify the effectiveness of these two LPNN models.

TRC4, an improved triptolide derivative, specifically targets to truncated form of retinoid X receptor-alpha in cancer cells.

The nuclear retinoid X receptor-α (RXRα) plays critical roles in cell homeostasis and in many physiological processes mainly through its transcriptional function. However, an N-terminal truncated form of RXRα, tRXRα, was frequently described in various cancer cells and tumor tissues, thus representing a new promising drug target. We recently demonstrated that triptolide (TR01) could target to the oncogenic activity of tRXRα. To improve its tumor selectivity, we developed several TR01 derivatives by introducing different amine ester groups on C-14-hydroxyl site. Interestingly, C-14 modification could differently affect the expression of tRXRα without interfering the level of its full length RXRα. Among the derivatives, TRC4 could strongly reduce tRXRα expression, while TRC5-7 increased it. The capability of inhibiting tRXRα expression was shown to be closely associated with its inactivation of AKT and induction of apoptosis in various cancer cells. Conversely, treatment of cancer cells with the tRXRα-stabilizing compounds TRC5-7 resulted in enhanced AKT activity and apoptosis-resistance. However, although TR01 could strongly reduce tRXRα expression and AKT activity, it also strongly inhibited the expression and transcriptional activity of RXRα in normal cells. Importantly, the tRXRα-selective TRC4 that did not significantly inhibit RXRα transcriptional function retained the most potency of the anticancer effect of TR01 and had no significant effect on the viability of normal cells. In conclusion, our results demonstrated that tRXRα-selective TRC4 will have potential clinical application in terms of drug target and side effects. Our findings will offer new strategies to develop improved triptolide analogs for cancer therapy.

Sparse-representation algorithms for blind estimation of acoustic-multipath channels.

Acoustic channel estimation is an important problem in various applications. Unlike many existing channel estimation techniques that need known probe or training signals, this paper develops a blind multipath channel identification algorithm. The proposed approach is based on the single-input multiple-output model and exploits the sparse multichannel structure. Three sparse representation algorithms, namely, matching pursuit, orthogonal matching pursuit, and basis pursuit, are applied to the blind sparse identification problem. Compared with the classical least squares approach to blind multichannel estimation, the proposed scheme does not require that the channel order be exactly determined and it is robust to channel order selection. Moreover, the ill-conditioning induced by the large delay spread is overcome by the sparse constraint. Simulation results for deconvolution of both underwater and room acoustic channels confirm the effectiveness of the proposed approach.

Time reversal communication over doubly spread channels.

Conventional time reversal can mitigate multipath delay dispersion by temporal focusing. But it is not applicable to time-varying channels with a Doppler spread. Although recently time reversal communication has been adapted to time-variant channels, the modified technique requires frequent channel updates to track channel variations and cannot handle large Doppler spread, which means that it cannot achieve frequency focusing. In this paper, two time reversal receivers for underwater acoustic communications over doubly spread channels are proposed. The proposed approach, which can be interpreted as time-frequency channel matching, is based on the channel spreading function rather than impulse response adopted by the existing techniques; this leads to much less frequent channel updates. Unlike existing methods that only correct a single Doppler shift, the proposed approach uses a rake-like structure to compensate for multiple Doppler shifts and hence can eliminate severe Doppler spread induced by temporal channel variations. Simulation results verify the effectiveness of the proposed approach, indicating that it can simultaneously counteract delay and Doppler spreads, achieving both temporal and frequency focusing.

Fast estimation of sparse doubly spread acoustic channels.

The estimation of doubly spread underwater acoustic channels is addressed. By exploiting the sparsity in the delay-Doppler domain, this paper proposes a fast projected gradient method (FPGM) that can handle complex-valued data for estimating the delay-Doppler spread function of a time-varying channel. The proposed FPGM formulates the sparse channel estimation as a complex-valued convex optimization using an [script-l](1)-norm constraint. Conventional approaches to complex-valued optimization split the complex variables into their real and imaginary parts; this doubles the dimension compared with the original problem and may break the special data structure. Unlike the conventional methods, the proposed method directly handles the complex variables as a whole without splitting them into real numbers; hence the dimension will not increase. By exploiting the block Toeplitz-like structure of the coefficient matrix, the computational complexity of the FPGM is reduced to O(LNlogN), where L is the dimension of the Doppler shift and N is the signal length. Simulation results verify the accuracy and efficiency of the FPGM, indicating that is robust to parameter selection and is orders-of-magnitude faster than standard convex optimization algorithms. The Kauai experimental data processing results are also provided to demonstrate the performance of the proposed algorithm.

Time delay and Doppler estimation for wideband acoustic signals in multipath environments.

Estimation of the parameters of a multipath underwater acoustic channel is of great interest for a variety of applications. This paper proposes a high-resolution method for jointly estimating the multipath time delays, Doppler scales, and attenuation amplitudes of a time-varying acoustical channel. The proposed method formulates the estimation of channel parameters into a sparse representation problem. With the [script-l](1)-norm as the measure of sparsity, the proposed method makes use of the basis pursuit (BP) criterion to find the sparse solution. The ill-conditioning can be effectively reduced by the [script-l](1)-norm regularization. Unlike many existing methods that are only applicable to narrowband signals, the proposed method can handle both narrowband and wideband signals. Simulation results are provided to verify the performance and effectiveness of the proposed algorithm, indicating that it has a super-resolution in both delay and Doppler domain, and it is robust to noise.

Deconvolution of sparse underwater acoustic multipath channel with a large time-delay spread.

The deconvolution of multipath underwater acoustic channel with a large time-delay spread is investigated. The channel deconvolution involves estimating the multipath time-delays and attenuation factors from a noisy received signal consisting of multiple overlapped signals. Similar to conventional deconvolution methods, the proposed method estimates channel impulse response based on least-squares criterion. However, the proposed method harnesses the sparse structure of an underwater acoustic channel, and [script-l](1)-norm of the channel impulse response is adopted as the cost function to be minimized. In addition, the available a priori knowledge of support constraint and attenuation factor constraint are imposed and channel deconvolution problem is converted to a convex optimization problem. Instead of employing the existing standard algorithms, which require huge storage space and high computational complexity, a simple iterative algorithm for solving the optimization problem with fast convergence rate and low complexity is developed. The computational complexity of the proposed algorithm is O(N log(2)(N)) per iteration with N being the length of the received signal. Simulation results confirm that the proposed method provides better performance in terms of temporal resolution and robustness to noise compared with other extant multipath channel deconvolution techniques.

Immunophenotypes, apoptosis, and expression of Fas and Bcl-2 from peripheral blood lymphocytes in patients with secondary early syphilis.

OBJECTIVES: Whether syphilis influences lymphocyte apoptosis has not been reported so far. GOAL: The goals of this study were to determine whether syphilis influences lymphocyte apoptosis and to investigate the molecular mechanism of apoptosis. STUDY DESIGN: Peripheral blood lymphocyte (PBL) immunophenotypes, apoptosis, and expression of Fas (CD95) and Bcl-2 were detected by flow cytometry in 33 patients with secondary early syphilis and 30 healthy subjects. RESULTS: Compared with the control subjects, the percentage of CD4 T cells and the ratio of CD4:CD8 were significantly decreased, and that of CD8 T cells and the apoptotic rates of PBLs and CD4 T cells were obviously increased in syphilitic patients. Fas overexpression and Bcl-2 downexpression in the syphilitic group were observed in PBLs and CD4 cells but not in CD8 and CD19 cells. CONCLUSIONS: The increased apoptosis of PBLs and CD4 T cells by Fas-mediated death pathway and downexpression of Bcl-2 protein could account for immune dysfunction in secondary early syphilis, which is responsible for the incomplete clearance of Treponema pallidum from the lesions and the chronic infection.