Epigenetic Effects and Molecular Mechanisms of Tumorigenesis Induced by Cigarette Smoke: An Overview

Cigarette smoking is one of the major causes of carcinogenesis. Direct genotoxicity induced by cigarette smoke leads to initiation of carcinogenesis. Nongenotoxic (epigenetic) effects of cigarette smoke also act as modulators altering cellular functions. These two effects underlie the mechanisms of tumor promotion and progression. While there is no lack of general reviews on the genotoxic and carcinogenic potentials of cigarette smoke in lung carcinogenesis, updated review on the epigenetic effects and molecular mechanisms of cigarette smoke and carcinogenesis, not limited to lung, is lacking. We are presenting a comprehensive review of recent investigations on cigarette smoke, with special attentions to nicotine, NNK, and PAHs. The current understanding on their molecular mechanisms include (1) receptors, (2) cell cycle regulators, (3) signaling pathways, (4) apoptosis mediators, (5) angiogenic factors, and (6) invasive and metastasis mediators. This review highlighted the complexity biological responses to cigarette smoke components and their involvements in tumorigenesis

Hybrid intelligence model based on image features for the prediction of flotation concentrate grade

In flotation processes, concentrate grade is the key production index but is difficult to be measured online. The mechanism models reflect the basic tendency of concentrate grade changes but cannot provide adequate prediction precision. The data-driven models based on froth image features provide accurate prediction within well-sampled space but rely heavily on sample data with less generalization capability. So, a hybrid intelligent model combining the two kinds of model is proposed in this paper. Since the information of image features is enormous, and the relationship between image features and concentrate grade is nonlinear, a B-spline partial least squares (BS-PLS) method is adopted to construct the data-driven model for concentrate grade prediction. In order to gain better generalization capability and prediction accuracy, information entropy is introduced to integrate the mechanism model and the BS-PLS model together and modify the model output online through an output deviation compensation strategy. Moreover, a slide window scheme is employed to update the hybrid model in order to improve its adaptability. The industrial practical data testing results show that the performance of the hybrid model is better than either of the two single models and it satisfies the accuracy and stability requirements in industrial applications

A hybrid multiobjective differential evolution algorithm and its application to the optimization of grinding and classification

The grinding-classification is the prerequisite process for full recovery of the nonrenewable minerals with both production quality and quantity objectives concerned. Its natural formulation is a constrained multiobjective optimization problem of complex expression since the process is composed of one grinding machine and two classification machines. In this paper, a hybrid differential evolution (DE) algorithm with multi-population is proposed. Some infeasible solutions with better performance are allowed to be saved, and they participate randomly in the evolution. In order to exploit the meaningful infeasible solutions, a functionally partitioned multi-population mechanism is designed to find an optimal solution from all possible directions. Meanwhile, a simplex method for local search is inserted into the evolution process to enhance the searching strategy in the optimization process. Simulation results from the test of some benchmark problems indicate that the proposed algorithm tends to converge quickly and effectively to the Pareto frontier with better distribution. Finally, the proposed algorithm is applied to solve a multiobjective optimization model of a grinding and classification process. Based on the technique for order performance by similarity to ideal solution (TOPSIS), the satisfactory solution is obtained by using a decision-making method for multiple attributes

The emerging oilseed crop Sesamum indicum enters the “Omics” era

Sesame (Sesamum indicum L.) is one of the oldest oilseed crops widely grown in Africa and Asia for its high-quality nutritional seeds. It is well adapted to harsh environments and constitutes an alternative cash crop for smallholders in developing countries. Despite its economic and nutritional importance, sesame is considered as an orphan crop because it has received very little attention from science. As a consequence, it lags behind the other major oil crops as far as genetic improvement is concerned. In recent years, the scenario has considerably changed with the decoding of the sesame nuclear genome leading to the development of various genomic resources including molecular markers, comprehensive genetic maps, high-quality transcriptome assemblies, web-based functional databases and diverse daft genome sequences. The availability of these tools in association with the discovery of candidate genes and quantitative trait locis for key agronomic traits including high oil content and quality, waterlogging and drought tolerance, disease resistance, cytoplasmic male sterility, high yield, pave the way to the development of some new strategies for sesame genetic improvement. As a result, sesame has graduated from an “orphan crop” to a “genomic resource-rich crop.” With the limited research teams working on sesame worldwide, more synergic efforts are needed to integrate these resources in sesame breeding for productivity upsurge, ensuring food security and improved livelihood in developing countries. This review retraces the evolution of sesame research by highlighting the recent advances in the “Omics” area and also critically discusses the future prospects for a further genetic improvement and a better expansion of this crop. (Résumé d'auteur

Giant electron-electron scattering in the Fermi-liquid state of Na_0.7CoO_2

The in-plane resistivity, rho, and thermal conductivity, kappa, of a single crystal of Na_0.7CoO_2 were measured down to 40 mK. Verification of the Wiedemann-Franz law, kappa/T = L_0/rho as T -> 0, and observation of a T^2 dependence of rho at low temperature, rho = rho_0 + AT^2, establish the existence of a well-defined Fermi-liquid state. The measured value of coefficient A reveals enormous electron-electron scattering, characterized by the largest Kadowaki-Woods ratio, A/gamma^2, encountered in any material. The rapid suppression of A with magnetic field suggests a possible proximity to a magnetic quantum critical point. We also speculate on the possible role of magnetic frustration and proximity to a Mott insulator.Comment: 4 pages, 4 figures; replaced with published version; added references and supporting dat

Synchronization in small-world systems

We quantify the dynamical implications of the small-world phenomenon. We consider the generic synchronization of oscillator networks of arbitrary topology, and link the linear stability of the synchronous state to an algebraic condition of the Laplacian of the graph. We show numerically that the addition of random shortcuts produces improved network synchronizability. Further, we use a perturbation analysis to place the synchronization threshold in relation to the boundaries of the small-world region. Our results also show that small-worlds synchronize as efficiently as random graphs and hypercubes, and more so than standard constructive graphs

Self-assembly, modularity and physical complexity

We present a quantitative measure of physical complexity, based on the amount of information required to build a given physical structure through self-assembly. Our procedure can be adapted to any given geometry, and thus to any given type of physical system. We illustrate our approach using self-assembling polyominoes, and demonstrate the breadth of its potential applications by quantifying the physical complexity of molecules and protein complexes. This measure is particularly well suited for the detection of symmetry and modularity in the underlying structure, and allows for a quantitative definition of structural modularity. Furthermore we use our approach to show that symmetric and modular structures are favoured in biological self-assembly, for example of protein complexes. Lastly, we also introduce the notions of joint, mutual and conditional complexity, which provide a useful distance measure between physical structures.Comment: 9 pages, submitted for publicatio

Prognostic value of cardiac tests in potential kidney transplant recipients: a systematic review

Background: Whether abnormal myocardial perfusion scintigraphy (MPS), dobutamine stress echocardiography (DSE) or coronary angiography, performed during preoperative evaluation for potential kidney transplant recipients, predicts future cardiovascular morbidity is unclear. We assessed test performance for predicting all-cause mortality, cardiovascular mortality and major adverse cardiac events (MACE). Methods: We searched MEDLINE and EMBASE (to February 2014), appraised studies, and calculated risk differences and relative risk ratios (RRR) with 95% confidence intervals (95% CI) using random effects meta-analysis. Results: Fifty-two studies (7401 participants) contributed data to the meta-analysis. Among the different tests, similar numbers of patients experienced MACE after an abnormal test result compared with a normal result (risk difference: MPS 20 per 100 patients tested [95% CI, 0.11-0.29], DSE 24 [95% CI, 0.10-0.38], and coronary angiography 20 [95% CI, 0.08-0.32; P = 0.91]). Although there was some evidence that coronary angiography was better at predicting all-cause mortality than MPS (RRR, 0.69; 95% CI, 0.49-0.96; P = 0.03) and DSE (RRR, 0.72; 95% CI, 0.50-1.02; P = 0.06), noninvasive tests were as good as coronary angiography at predicting cardiovascular mortality (RRR, MPS, 0.89; 95% CI, 0.38-2.10; P = 0.78; DSE, 1.09; 95% CI, 0.12-10.05; P = 0.93), and MACE (RRR: MPS, 1.09; 95% CI, 0.64-1.86; P = 0.74; DSE, 1.56; 95% CI, 0.71-3.45; P = 0.25). Conclusions: Noninvasive tests are as good as coronary angiography at predicting future adverse cardiovascular events in advanced chronic kidney disease. However, a substantial number of people with negative test results go on to experience adverse cardiac events

Structures of Darunavir-Resistant HIV‑1 Protease Mutant Reveal Atypical Binding of Darunavir to Wide Open Flaps

The molecular basis for high resistance to clinical inhibitors of HIV-1 protease (PR) was examined for the variant designated PRP51 that was selected for resistance to darunavir (DRV). High resolution crystal structures of PRP51 with the active site D25N mutation revealed a ligand-free form and an inhibitor-bound form showing a unique binding site and orientation for DRV. This inactivating mutation is known to increase the dimer dissociation constant and decrease DRV affinity of PR. The PRP51‑D25N dimers were in the open conformation with widely separated flaps, as reported for other highly resistant variants. PRP51‑D25N dimer bound two DRV molecules and showed larger separation of 8.7 Å between the closest atoms of the two flaps compared with 4.4 Å for the ligand-free structure of this mutant. The ligand-free structure, however, lacked van der Waals contacts between Ile50 and Pro81′ from the other subunit in the dimer, unlike the majority of PR structures. DRV is bound inside the active site cavity; however, the inhibitor is oriented almost perpendicular to its typical position and exhibits only 2 direct hydrogen bond and two water-mediated interactions with atoms of PRP51‑D25N compared with 11 hydrogen bond interactions seen for DRV bound in the typical position in wild-type enzyme. The atypical location of DRV may provide opportunities for design of novel inhibitors targeting the open conformation of PR drug-resistant mutants