Cooperative Coevolution for Non-Separable Large-Scale Black-Box Optimization: Convergence Analyses and Distributed Accelerations

Given the ubiquity of non-separable optimization problems in real worlds, in this paper we analyze and extend the large-scale version of the well-known cooperative coevolution (CC), a divide-and-conquer optimization framework, on non-separable functions. First, we reveal empirical reasons of why decomposition-based methods are preferred or not in practice on some non-separable large-scale problems, which have not been clearly pointed out in many previous CC papers. Then, we formalize CC to a continuous game model via simplification, but without losing its essential property. Different from previous evolutionary game theory for CC, our new model provides a much simpler but useful viewpoint to analyze its convergence, since only the pure Nash equilibrium concept is needed and more general fitness landscapes can be explicitly considered. Based on convergence analyses, we propose a hierarchical decomposition strategy for better generalization, as for any decomposition there is a risk of getting trapped into a suboptimal Nash equilibrium. Finally, we use powerful distributed computing to accelerate it under the multi-level learning framework, which combines the fine-tuning ability from decomposition with the invariance property of CMA-ES. Experiments on a set of high-dimensional functions validate both its search performance and scalability (w.r.t. CPU cores) on a clustering computing platform with 400 CPU cores

Effects of cellular iron deficiency on the formation of vascular endothelial growth factor and angiogenesis. Iron deficiency and angiogenesis

<p>Abstract</p> <p>Background</p> <p>Young women diagnosed with breast cancer are known to have a higher mortality rate from the disease than older patients. Specific risk factors leading to this poorer outcome have not been identified. In the present study, we hypothesized that iron deficiency, a common ailment in young women, contributes to the poor outcome by promoting the hypoxia inducible factor-1α (HIF-1α and vascular endothelial growth factor (VEGF) formation. This hypothesis was tested in an <it>in vitro </it>cell culture model system.</p> <p>Results</p> <p>Human breast cancer MDA-MB-231 cells were transfected with transferrin receptor-1 (TfR1) shRNA to constitutively impair iron uptake. Cellular iron status was determined by a set of iron proteins and angiogenesis was evaluated by levels of VEGF in cells as well as by a mouse xenograft model. Significant decreases in ferritin with concomitant increases in VEGF were observed in TfR1 knockdown MDA-MB-231 cells when compared to the parental cells. TfR1 shRNA transfectants also evoked a stronger angiogenic response after the cells were injected subcutaneously into nude mice. The molecular mechanism appears that cellular iron deficiency elevates VEGF formation by stabilizing HIF-1α. This mechanism is also true in human breast cancer MCF-7 and liver cancer HepG2 cells.</p> <p>Conclusions</p> <p>Cellular iron deficiency increased HIF-1α, VEGF, and angiogenesis, suggesting that systemic iron deficiency might play an important part in the tumor angiogenesis and recurrence in this young age group of breast cancer patients.</p

Shear Stress Affects Biofilm Structure and Consequently Current Generation of Bioanode in Microbial Electrochemical Systems (MESs)

Shear stress is an important factor that affects the formation and structure of anode biofilms, which are strongly related to the extracellular electron transfer phenomena and bioelectric performance of bioanodes. Here, we show that using nitrogen sparging to induce shear stress during anode biofilm formation increases the linear sweep voltammetry peak current density of the mature anode biofilm from 2.37 ± 0.15 to 4.05 ± 0.25 A/m2. Electrochemical impedance spectroscopy results revealed that the shear-stress-enriched anode biofilm had a low charge transfer resistance of 46.34 Ω compared to that of the unperturbed enriched anode biofilm (72.2 Ω). Confocal laser scanning microscopy observations showed that the shear-stress-enriched biofilms were entirely viable, whereas the unperturbed enriched anode biofilm consisted of a live outer layer covering a dead inner-core layer. Based on biomass and community analyses, the shear-stress-enriched biofilm had four times the biofilm density (136.0 vs. 27.50 μg DNA/cm3) and twice the relative abundance of Geobacteraceae (over 80 vs. 40%) in comparison with those of the unperturbed enriched anode biofilm. These results show that applying high shear stress during anode biofilm enrichment can result in an entirely viable and dense biofilm with a high relative abundance of exoelectrogens and, consequently, better performance

Non-Fossil Origin Explains the Large Seasonal Variation of Highly Processed Organic Aerosol in the Northeastern Tibetan Plateau (3,200 m a.s.l.)

Carbonaceous aerosol plays an important role in climate, but its sources and atmospheric processes are least understood in the Tibetan Plateau (TP), a remote yet climatically sensitive region. This study presents the first seasonal cycle of radiocarbon and stable isotope 13C of organic and elemental carbon (OC and EC) in the atmosphere of the northeastern TP. Large seasonal variations of EC and OC concentrations were explained by non-fossil sources. Regardless of the season, fossil contribution to OC was strongly correlated with inverse OC concentrations. This allowed the separating a constant background source and a source responsible for OC variability that was mostly of non-fossil origin. The 13C signature of OC shows that OC was highly atmospherically processed and thus less volatile than OC found near sources or in urban areas. The 13C-depleted secondary sources contributed strongly to more volatile OC, whereas the 13C-enriched less volatile OC suggests the influence of atmospheric aging.</p

Improved stacking ensemble learning based on feature selection to accurately predict warfarin dose

BackgroundWith the rapid development of artificial intelligence, prediction of warfarin dose via machine learning has received more and more attention. Since the dose prediction involve both linear and nonlinear problems, traditional machine learning algorithms are ineffective to solve such problems at one time.ObjectiveBased on the characteristics of clinical data of Chinese warfarin patients, an improved stacking ensemble learning can achieve higher prediction accuracy.MethodsInformation of 641 patients from southern China who had reached a steady state on warfarin was collected, including demographic information, medical history, genotype, and co-medication status. The dataset was randomly divided into a training set (90%) and a test set (10%). The predictive capability is evaluated on a new test set generated by stacking ensemble learning. Additional factors associated with warfarin dose were discovered by feature selection methods.ResultsA newly proposed heuristic-stacking ensemble learning performs better than traditional-stacking ensemble learning in key metrics such as accuracy of ideal dose (73.44%, 71.88%), mean absolute errors (0.11 mg/day, 0.13 mg/day), root mean square errors (0.18 mg/day, 0.20 mg/day) and R2 (0.87, 0.82).ConclusionsThe developed heuristic-stacking ensemble learning can satisfactorily predict warfarin dose with high accuracy. A relationship between hypertension, a history of severe preoperative embolism, and warfarin dose is found, which provides a useful reference for the warfarin dose administration in the future

Soluble Urokinase Plasminogen Activator Receptor and the Risk of Coronary Artery Disease in Young Chinese Patients

Background. Soluble urokinase plasminogen activator receptor (suPAR) is a novel marker of chronic inflammation and is considered to be a risk factor for coronary artery disease (CAD) in Caucasians. This study investigated the role of suPAR in young Chinese patients with CAD. Methods. The study involved a total of 196 consecutive young (age ≤ 55 years) patients with angiographically proven CAD and 188 age-matched non-CAD individuals as controls. Traditional risk factors were evaluated using conventional assays, and levels of suPAR were measured by sandwich enzyme-linked immunosorbent assays. Results. Levels of suPAR were significantly correlated with age (r=0.20, P=0.04), smoking (r=0.33, P=0.008), body mass index (r=0.21, P=0.03), and high-sensitivity C-reactive protein (hs-CRP; r=0.31, P=0.01). Multivariate logistic regression analysis showed that male sex (odds ratio (OR) = 3.12; 95% confidence interval (CI) = 1.18–8.25, P=0.02), smoking (OR = 3.41, 95% CI = 1.55–7.50, P=0.002), triglyceride (OR = 1.89, 95% CI = 1.10–3.25, P=0.02), high-sensitivity C-reactive protein (OR = 1.24, 95% CI = 1.02–0.03, P=0.03), and suPAR (OR = 1.37, 95% CI = 1.09–1.72, P=0.007) were independently associated with CAD risk in young patients. Conclusions. SuPAR is a novel independent risk factor for CAD in young Chinese patients. Further studies evaluating the effect of anti-inflammatory treatment on the suPAR levels and the risk of CAD are needed

Characterization of Giant Magnetostrictive Materials Using Three Complex Material Parameters by Particle Swarm Optimization

Complex material parameters that can represent the losses of giant magnetostrictive materials (GMMs) are the key parameters for high-power transducer design and performance analysis. Since the GMMs work under pre-stress conditions and their performance is highly sensitive to pre-stress, the complex parameters of a GMM are preferably characterized in a specific pre-stress condition. In this study, an optimized characterization method for GMMs is proposed using three complex material parameters. Firstly, a lumped parameter model is improved for a longitudinal transducer by incorporating three material losses. Then, the structural damping and contact damping are experimentally measured and applied to confine the parametric variance ranges. Using the improved lumped parameter model, the real parts of the three key material parameters are characterized by fitting the experimental impedance data while the imaginary parts are separately extracted by the phase data. The global sensitivity analysis that accounts for the interaction effects of the multiple parameter variances shows that the proposed method outperforms the classical method as the sensitivities of all the six key parameters to both impedance and phase fitness functions are all high, which implies that the extracted material complex parameters are credible. In addition, the stability and credibility of the proposed parameter characterization is further corroborated by the results of ten random characterizations

Sensitivity to Oxygen in Microbial Electrochemical Systems Biofilms

Summary: The formation and bioelectric performance of anode biofilms in microbial electrochemical systems (MESs) are sensitive to oxygen. Investigating the temporal-spatial structure of anode biofilms will help elucidate the interfaces between oxygen and bacteria, thereby facilitating the applications of MESs in wastewater treatment and energy recovery. Here, use of optical coherence tomography, frozen sections, and a microsensor revealed that the aerobic biofilms exhibited a multilayered sandwich structure with a sparse gap between the aerobe- and amphimicrobe-enriched outer layer and the dense exoelectrogen-enriched inner layer, whereas the anaerobic biofilm consisted of only a single dense layer. Our results showed that the inner layer of aerobic anode biofilms performed electricity generation, whereas the outer layer only consumed oxygen. In this case, electron donor diffusion through the outer layer became the limiting factor in electricity generation by the bioanode. Consequently, as the anode biofilms matured, current generation decreased. : Chemical Engineering; Bio-Electrochemistry; Materials Science Subject Areas: Chemical Engineering, Bio-Electrochemistry, Materials Scienc

Dynamic Lane Tracking Control of the Commercial Vehicle Based on RMPC Algorithm Considering the State of Preceding Vehicle

In order to improve the adaptability of the lane keeping control system to complex environments, a dynamic lane tracking control strategy of the commercial vehicle based on the robust model predictive control (RMPC) algorithm is proposed considering the state of the preceding vehicle. An RMPC controller is designed with path deviation and control increment as the objective function. The model predictive control problem is transformed into a min–max optimization problem. The linear matrix inequality (LMI) is used for the optimal solution to obtain the optimal control quantity. The strategy to improve the safety and comfort dynamically in the process of lane keeping is designed by adjusting the weight coefficient matrix of RMPC based on fuzzy theory. The results of the simulation and HiL test show that the RMPC controller can meet the requirement of adjusting the lane tracking process dynamically according to the state of the preceding vehicle, which keeps the balance between safety and comfort