A multi-population multi-stage adaptive weighted large-scale multi-objective optimization algorithm framework.

Weighted optimization framework (WOF) achieves variable dimensionality reduction by grouping variables and optimizing weights, playing an important role in large-scale multi-objective optimization problems. However, because of possible problems such as duplicate weight vectors in the selection process and loss of population diversity, the algorithm is susceptible to local optimization. Therefore, this paper develops an algorithm framework called multi-population multi-stage adaptive weighted optimization (MPSOF) to improve the performance of WOF in two aspects. First, the method of using multi-population is employed to address the issue of insufficient algorithmic diversity, while simultaneously reducing the likelihood of converging towards local optima. Secondly, a processing stage is incorporated into MPSOF, where a certain number of individuals are adaptively selected for updating based on the weight information and evolutionary status of different subpopulations, targeting different types of weights. This approach alleviates the impact of repetitive weights on the diversity of newly generated individuals, avoids the drawback of easily converging to local optima when using a single type of weight for updating, and effectively balances the diversity and convergence of subpopulations. Experiments of three types designed on several typical function sets demonstrate that MPSOF exceeds the comparison algorithms in the three metrics for Inverse Generation Distance, Hypervolume and Spacing.

Spatiotemporally continuous PM2.5 dataset in the Mekong River Basin from 2015 to 2022 using a stacking model.

With the potential to cause millions of deaths, PM2.5 pollution has become a global concern. In Southeast Asia, the Mekong River Basin (MRB) is experiencing heavy PM2.5 pollution and the existing PM2.5 studies in the MRB are limited in terms of accuracy and spatiotemporal coverage. To achieve high-accuracy and long-term PM2.5 monitoring of the MRB, fused aerosol optical depth (AOD) data and multi-source auxiliary data are fed into a stacking model to estimate PM2.5 concentrations. The proposed stacking model takes advantage of convolutional neural network (CNN) and Light Gradient Boosting Machine (LightGBM) models and can well represent the spatiotemporal heterogeneity of the PM2.5-AOD relationship. In the cross-validation (CV), comparison with CNN and LightGBM models shows that the stacking model can better suppress overfitting, with a higher coefficient of determination (R2) of 0.92, a lower root mean square error (RMSE) of 5.58 µg/m3, and a lower mean absolute error (MAE) of 3.44 µg/m3. For the first time, the high-accuracy PM2.5 dataset reveals spatially and temporally continuous PM2.5 pollution and variations in the MRB from 2015 to 2022. Moreover, the spatiotemporal variations of annual and monthly PM2.5 pollution are also investigated at the regional and national scales. The dataset will contribute to the analysis of the causes of PM2.5 pollution and the development of mitigation policies in the MRB.

A dual-salt fluorescent probe for specific recognition of mitochondrial NADH and potential cancer diagnosis.

Reduced nicotinamide adenine dinucleotide (NADH) is a kind of coenzyme and widely works as a biomarker in cancer cells. It plays a crucial role in many cellular metabolic processes, especially NADH in mitochondria is indispensable for the mitochondrial respiration chain that produces ATP. Herein, we designed a fluorescent probe Mito-FCC based on an ethylene-bridging dual-salt structure, in which benzo[e]indolium fluorophore was used as the mitochondria-targeting group and 1-methylquinolinium moiety as the NADH recognition unit. Mito-FCC exhibited high sensitivity and selectivity for NADH with a rapid "turn-on" fluorescence signal. The dual-salt structure endowed the probe with a reliable mitochondria-targeted ability even after the recognition unit was reduced by NADH. With the help of the probe, the fluctuations of endogenous NADH induced by glucose or pyruvate were imaged. Besides, Mito-FCC had a capability to make a distinction between cancer cells and normal cells due that the content of NADH in cancer cells was distinctly higher than that in normal ones. Notably, the visualization of tumor in vivo through monitoring NADH using Mito-FCC was realized successfully. These experimental results showed that Mito-FCC hold a great perspective in study of mitochondrial function and potential diagnosis of cancer diseases.

Prognostic risk factors for T1 thoracic esophageal cancer: a retrospective cohort study.

Background: Esophageal cancer has a poor overall prognosis and a high incidence of post-treatment complications. This study aimed to analyze the common surgical methods for treating T1 thoracic esophageal cancer and explore its prognostic risk factors to provide a basis for appropriate treatment selection. Methods: In this population-based retrospective cohort study, data of patients diagnosed with T1 thoracic esophageal cancer from 2010 to 2016 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were divided according to those who had surgery and those who had radiotherapy. Survival curves were generated using the Kaplan-Meier method and validated by the log-rank test. Cox's regression model was used to analyze the independent prognostic risk factors. Results: Overall, 2,027 eligible patients, including 824 and 1,203 patients in the surgical and non-surgical groups, respectively, were analyzed. There was no significant difference in survival between the surgical and non-surgical groups (P=0.79). In subgroup analysis, the Cox regression analysis showed that radiotherapy was a significant prognostic factor (P=0.00059). Conclusions: The impact of surgery on patients with T1 thoracic esophageal cancer was insignificant; however, radiotherapy was an independent prognostic risk factor. These results provide a reliable basis for clinical treatment of patients with T1 thoracic esophageal cancer.

The association of D-dimer with clinicopathological features of breast cancer and its usefulness in differential diagnosis: A systematic review and meta-analysis.

BACKGROUND: Studies have shown that D-dimer levels are significantly correlated with the differential diagnosis and clinicopathological features of breast cancer. However, the results are currently limited and controversial. Therefore, we performed this meta-analysis to evaluate the relationship between D-dimer levels and breast cancer. MATERIALS AND METHODS: The PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, Chinese Biomedical Literature, and Wanfang databases were searched to find studies that assessed the association of D-dimer with clinicopathological features of breast cancer and its usefulness in aiding with differential diagnosis. The standardized mean difference (SMD) was applied as the correlation measure. RESULTS: A total of 1244 patients with breast cancer from 15 eligible studies were included in the meta-analysis. D-dimer levels were higher in the breast cancer group than in the benign (SMD = 1.02; 95% confidence interval [CI] = 0.53-1.52) and healthy (SMD = 1.27; 95% CI = 0.85-1.68) control groups. In addition, elevated D-dimer levels were associated with progesterone receptor-negative tumors (SMD = -0.25; 95% CI = -0.44--0.05). Similarly, there was a significant correlation between D-dimer levels and tumor node metastasis staging (n = 11, SMD = 0.82; 95% CI = 0.57-1.06) and lymph node involvement (n = 8, SMD = 0.79; 95% CI = 0.50-1.09). In contrast, other clinicopathological factors, including estrogen receptor expression and human epidermal growth factor receptor 2 expression, were not associated with D-dimer levels. CONCLUSION: The results of this meta-analysis indicate that plasma D-dimer levels can be used as an important reference for the early identification and staging of breast cancer.

Synaptic modifications driven by spike-timing-dependent plasticity in weakly coupled bursting neurons.

In the course of development, sleep, or mental disorders, certain neurons in the brain display spontaneous spike-burst activity. The synaptic plasticity evoked by such activity is here studied in the presence of spike-timing-dependent plasticity (STDP). In two chemically coupled bursting model neurons, the spike-burst activity can translate the STDP related to pre- and postsynaptic spike activity into burst-timing-dependent plasticity (BTDP), based on the timing of bursts of pre- and postsynaptic neurons. The resulting BTDP exhibits exponential decays with the same time scales as those of STDP. In weakly coupled bursting neuron networks, the synaptic modification driven by the spike-burst activity obeys a power-law distribution. The model can also produce a power-law distribution of synaptic weights. Here, the considered bursting behavior is made of stereotypical groups of spikes, and bursting is evenly spaced by long intervals.

[Study on the Photoelectric Performance Indicators of Medical Electronic Endoscope].

OBJECTIVE: To establish a method for measuring the photoelectric performance index (luminance response characteristic and luminance tolerance) of medical electronic endoscope. METHODS: Based on the clinical application and product features of medical electronic endoscope, the umbrella grayscale test chart and the adjustable gray scale test chart are designed and made from two aspects of distribution and density differential. RESULTS: The influence of gray scale arrangement, background illumination intensity and illumination spectrum on photoelectric performance measurement of electronic endoscope is verified by test. CONCLUSIONS: It is a reference for the design analysis, evaluation and modification of electronic endoscope product photoelectric part, whether it is suitable for fast detection of umbels and more accurate gray scale test chart.

An Improved Teaching-Learning-Based Optimization with the Social Character of PSO for Global Optimization.

An improved teaching-learning-based optimization with combining of the social character of PSO (TLBO-PSO), which is considering the teacher's behavior influence on the students and the mean grade of the class, is proposed in the paper to find the global solutions of function optimization problems. In this method, the teacher phase of TLBO is modified; the new position of the individual is determined by the old position, the mean position, and the best position of current generation. The method overcomes disadvantage that the evolution of the original TLBO might stop when the mean position of students equals the position of the teacher. To decrease the computation cost of the algorithm, the process of removing the duplicate individual in original TLBO is not adopted in the improved algorithm. Moreover, the probability of local convergence of the improved method is decreased by the mutation operator. The effectiveness of the proposed method is tested on some benchmark functions, and the results are competitive with respect to some other methods.

Bare-bones teaching-learning-based optimization.

Teaching-learning-based optimization (TLBO) algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI) algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms.

Time series prediction with improved neuro-endocrine model.

The paper is focused on improving the performance of neuro-endocrine models with considering the interaction of glands. Comparing to conventional neuro-endocrine models, the concentration of hormone of one gland is modulated by those of others, and the weights of cells are modulated by the improved endocrine system. The interacted equation among all glands is designed and the parameters of them are chosen with theory analysis. Because all the parameters of the model are constants when the system reaches the equilibrium state, particle swarm optimization algorithm is utilized to search the optimal parameters of the model. The theory analysis indicates that the performance of neuro-endocrine model is better than or at least equal to that of corresponding artificial neural network. To indicate the effectiveness of the proposed model, some time series from different research fields, which are used in some literatures, are tested with the proposed model, the results indicate that the proposed model has some good performance.

Pleural fluid prealbumin and C-reactive protein in the differential diagnosis of infectious and malignant pleural effusions.

Clinical history and physical examination are helpful in indicating the potential causes of pleural effusions (PEs). However, the accurate diagnosis and establishment of the causes of PE is an ongoing challenge in daily clinical practice. The primary aim of this study was to distinguish between infectious PE and malignant PE (MPE) by measuring two major acute phase response biomarkers: prealbumin (PA) and C-reactive protein (CRP). The study was a prospective trial involving 151 patients who were diagnosed with infectious PE or MPE. Patients with infectious PE were divided into two subgroups: tuberculous PE (TBPE) and parapneumonic PE (PNPE). A further 58 patients with PEs that showed no evidence of MPE, TBPE or PNPE were classified as the chronic non-specific PE (NSPE) group. Demographic characteristics and pleural fluids of the subjects were collected consecutively. The discriminative properties of pleural fluid routine biochemistries, and PA and CRP were evaluated. PA, CRP and classical fluid parameters were also applied to classify patients with infectious PE and MPE. Receiver operating characteristics (ROC) analysis established the cutoffs of PA and CRP for discriminating between groups. Pleural fluid PA levels were significantly higher in the MPE group (n=47) than in the infectious PE group (n=104). Pleural fluid CRP levels were significantly higher in the infectious PE group than in the MPE group. Pleural fluid PA levels were identified to be moderately negatively correlated with CRP levels in the MPE group, with a statistically significant correlation coefficient of -0.352. The ROC curve showed that the sensitivity and specificity of PA for the diagnosis of MPE were 0.851 and 0.548, respectively, at the cutoff of 28.3 mg/l. The area under the curve (AUC) was 0.784 (95% CI, 0.707-0.861). Using CRP as a diagnostic parameter resulted in an comparable AUC of 0.810 (95% CI, 0.736-0.885), at the cutoff of 35.2 mg/l. Combinations of PA and CRP resulted in incrementally discriminating values for MPE, with a sensitivity of 0.617 and a specificity of 0.903. The measurement of PA and CRP levels in pleural fluid may be a useful adjunctive test in PE, as a potential differentiator between infectious PE and MPE.

Spontaneous scale-free structure in adaptive networks with synchronously dynamical linking.

Inspired by the anti-Hebbian learning rule in neural systems, we study how the feedback from dynamical synchronization shapes network structure by adding new links. Through extensive numerical simulations, we find that an adaptive network spontaneously forms scale-free structure, as confirmed in many real systems. Moreover, the adaptive process produces two nontrivial power-law behaviors of deviation strength from mean activity of the network and negative degree correlation, which exists widely in technological and biological networks. Importantly, these scalings are robust to variation of the adaptive network parameters, which may have meaningful implications in the scale-free formation and manipulation of dynamical networks. Our study thus suggests an alternative adaptive mechanism for the formation of scale-free structure with negative degree correlation, which means that nodes of high degree tend to connect, on average, with others of low degree and vice versa. The relevance of the results to structure formation and dynamical property in neural networks is briefly discussed as well.

[Diagnostic value of serum angiopoietin-2 level in pancreatic cancer].

OBJECTIVE: To evaluate the diagnostic value of serum angiopoietin-2 (Ang-2) level in pancreatic cancer patients. METHODS: Serum Ang-2 level was measured by enzyme-linked immunosorbent assays (ELISA) in samples from 116 patients with pancreatic cancer, 50 patients with chronic pancreatitis, and 50 normal control subjects. RESULTS: The serum Ang-2 level in patients with pancreatic cancer [(1539.0 ± 449.3) ng/L] was significantly higher than those in patients with pancreatitis [(1044.6 ± 246.1) ng/L, P < 0.01] and normal control subjects [(1075.6 ± 228.2) ng/L, P < 0.01]. There was no significant difference of serum Ang-2 levels between patients with pancreatitis and normal controls. Pancreatic caner patients with lymph node metastasis had a significantly higher serum Ang-2 level [(1890.1 ± 354.9) ng/L] than those without metastasis [(1212.1 ± 224.2) ng/L, P < 0.01]. The area under ROC curve of serum Ang-2 level for diagnosis of pancreatic cancer was 0.819. CONCLUSION: The serum Ang-2 level can be a useful indicator for diagnosis of pancreatic cancer.

[A study on retrieval of atmospheric aerosol parameters and trace gases based on differential optical absorption spectroscopy].

Due to heavy air pollutants and aerosols in our country, the model for simultaneous evaluation of aerosol parameters and trace gases based on differential optical absorption spectroscopy is developed in the present paper. The spectra were used to get the concentrations of many trace gases and mean geometrical diameter, total number concentration and total volume of aerosol simultaneously over the same air volume. Retrieval of aerosol parameters was performed by the "table look-up" method based on total extinction coefficient. Experimental results show that trace gases and aerosol parameters can be successfully retrieved using this model.