Expression changes and clinical significance of serum neuron-specific enolase and squamous cell carcinoma antigen in lung cancer patients after radiotherapy

Objective: To explore the changes and clinical significance of serum Neuron-Specific Enolase (NSE) and Squamous Cell Carcinoma antigen (SCC) in patients with lung cancer before and after radiotherapy. Methods: 82 patients with lung cancer were treated with radiotherapy, and effective clinical intervention was given during the radiotherapy process. The patients were followed up for 1 year after radiotherapy and were divided into a recurrence and metastasis group (n = 28) and a non-recurrence and metastasis group (n = 54) according to their prognosis. Another 54 healthy volunteers examined in the present study's hospital during the same period were selected as the control group. To compare the changes of NSE and SCC levels in serum in patients with lung cancer at admission and after radiotherapy, and to explore their clinical significance. Results: After intervention, NSE and SCC levels in the serum of the two groups of patients were significantly lower than those before intervention, and the levels of CD4+ and CD4+/CD8+ were significantly higher than those before intervention (p < 0.05); the level of CD8+ was not significantly different from that before intervention (p > 0.05). And NSE and SCC levels in the intervention group were significantly lower than those in the routine group, the levels of CD4+, CD4+/CD8+ were significantly higher than those in the routine group (p < 0.05). Conclusion: NSE and SCC in serum can preliminarily evaluate the effect of radiotherapy in patients with lung cancer and have a certain predictive effect on prognosis

On Stability and Consensus of Signed Networks: A Self-loop Compensation Perspective

Positive semidefinite is not an inherent property of signed Laplacians, which renders the stability and consensus of multi-agent system on undirected signed networks intricate. Inspired by the correlation between diagonal dominance and spectrum of signed Laplacians, this paper proposes a self-loop compensation mechanism in the design of interaction protocol amongst agents and examines the stability/consensus of the compensated signed networks. It turns out that self-loop compensation acts as exerting a virtual leader on these agents that are incident to negative edges, steering whom towards origin. Analytical connections between self-loop compensation and the collective behavior of the compensated signed network are established. Necessary and/or sufficient conditions for predictable cluster consensus of signed networks via self-loop compensation are provided. The optimality of self-loop compensation is discussed. Furthermore, we extend our results to directed signed networks where the symmetry of signed Laplacian is not free. Simulation examples are provided to demonstrate the theoretical results

Dynamic Event-Triggered Consensus of Multi-agent Systems on Matrix-weighted Networks

This paper examines event-triggered consensus of multi-agent systems on matrix-weighted networks, where the interdependencies among higher-dimensional states of neighboring agents are characterized by matrix-weighted edges in the network. Specifically, a distributed dynamic event-triggered coordination strategy is proposed for this category of generalized networks, in which an auxiliary system is employed for each agent to dynamically adjust the trigger threshold, which plays an essential role in guaranteeing that the triggering time sequence does not exhibit Zeno behavior. Distributed event-triggered control protocols are proposed to guarantee leaderless and leader-follower consensus for multi-agent systems on matrix-weighted networks, respectively. It is shown that that the spectral properties of matrix-valued weights are crucial in event-triggered mechanism design for matrix-weighted networks. Finally, simulation examples are provided to demonstrate the theoretical results

Vector-valued Privacy-Preserving Average Consensus

Achieving average consensus without disclosing sensitive information can be a critical concern for multi-agent coordination. This paper examines privacy-preserving average consensus (PPAC) for vector-valued multi-agent networks. In particular, a set of agents with vector-valued states aim to collaboratively reach an exact average consensus of their initial states, while each agent's initial state cannot be disclosed to other agents. We show that the vector-valued PPAC problem can be solved via associated matrix-weighted networks with the higher-dimensional agent state. Specifically, a novel distributed vector-valued PPAC algorithm is proposed by lifting the agent-state to higher-dimensional space and designing the associated matrix-weighted network with dynamic, low-rank, positive semi-definite coupling matrices to both conceal the vector-valued agent state and guarantee that the multi-agent network asymptotically converges to the average consensus. Essentially, the convergence analysis can be transformed into the average consensus problem on switching matrix-weighted networks. We show that the exact average consensus can be guaranteed and the initial agents' states can be kept private if each agent has at least one "legitimate" neighbor. The algorithm, involving only basic matrix operations, is computationally more efficient than cryptography-based approaches and can be implemented in a fully distributed manner without relying on a third party. Numerical simulation is provided to illustrate the effectiveness of the proposed algorithm

Covalent-linked porphyrin/single-walled carbon nanotube nanohybrids: synthesis and influence of porphyrin substituents on nonlinear optical performance

Electron-withdrawing 4-cyanophenyl-, electronically innocent phenyl-, and electron-donating 4-dimethylaminophenyl-functionalized porphyrin/single-walled carbon nanotube (SWCNT) nanohybrids have been synthesized and characterized by ultraviolet–visible absorption, steady-state fluorescence, Fourier transform infrared, and Raman spectroscopies, X-ray photoelectron spectroscopy, scanning electron microscopy, transmission electron microscopy and thermogravimetric analysis. Nonlinear optical (NLO) studies using the Z-scan technique revealed that both the cyano (CN) and the dimethylamino (DMA) substituents have a positive effect in optimizing the optical limiting performance of the SWCNT–porphyrin nanohybrids, owing to increased reverse saturable absorption (RSA) of the porphyrin moieties after functionalization by CN or DMA. In comparison with CN, the DMA group has a more positive influence on the porphyrin excited states and thereby the RSA and NLO activity.This research was financially supported by the National Natural Science Foundation of China (Nos. 51432006 and 51172100), the Ministry of Education and the State Administration of Foreign Experts Affairs for the 111 Project (No. B13025), the Ministry of Education of China for the Changjiang Innovation Research Team (No. IRT14R23), 100 Talents Program of CAS, and the Ministry of Science and Technology of China for International Science Linkages Program (2011DFG52970). M.G.H. and C.Z. thank the Australian Research Council for support

A Non-Destructive Distinctive Method for Discrimination of Automobile Lubricant Variety by Visible and Short-Wave Infrared Spectroscopy

A novel method which is a combination of wavelet packet transform (WPT), uninformative variable elimination by partial least squares (UVE-PLS) and simulated annealing (SA) to extract best variance information among different varieties of lubricants is presented. A total of 180 samples (60 for each variety) were characterized on the basis of visible and short-wave infrared spectroscopy (VIS-SWNIR), and 90 samples (30 for each variety) were randomly selected for the calibration set, whereas, the remaining 90 samples (30 for each variety) were used for the validation set. The spectral data was split into different frequency bands by WPT, and different frequency bands were obtained. SA was employed to look for the best variance band (BVB) among different varieties of lubricants. In order to improve prediction precision further, BVB was processed by UVE-PLS and the optimal cutoff threshold of UVE was found by SA. Finally, five variables were mined, and were set as inputs for a least square-support vector machine (LS-SVM) to build the recognition model. An optimal model with a correlation coefficient (R) of 0.9850 and root mean square error of prediction (RMSEP) of 0.0827 was obtained. The overall results indicated that the method of combining WPT, UVE-PLS and SA was a powerful way to select diagnostic information for discrimination among different varieties of lubricating oil, furthermore, a more parsimonious and efficient LS-SVM model could be obtained

An Attempt to Understand Kidney's Protein Handling Function by Comparing Plasma and Urine Proteomes

With the help of proteomics technology, the human plasma and urine proteomes, which closely represent the protein compositions of the input and output of the kidney, respectively, have been profiled in much greater detail by different research teams. Many datasets have been accumulated to form “reference profiles” of the plasma and urine proteomes. Comparing these two proteomes may help us understand the protein handling aspect of kidney function in a way, however, which has been unavailable until the recent advances in proteomics technology.After removing secreted proteins downstream of the kidney, 2611 proteins in plasma and 1522 in urine were identified with high confidence and compared based on available proteomic data to generate three subproteomes, the plasma-only subproteome, the plasma-and-urine subproteome, and the urine-only subproteome, and they correspond to three groups of proteins that are handled in three different ways by the kidney. The available experimental molecular weights of the proteins in the three subproteomes were collected and analyzed. Since the functions of the overrepresented proteins in the plasma-and-urine subproteome are probably the major functions that can be routinely regulated by excretion from the kidney in physiological conditions, Gene Ontology term enrichment in the plasma-and-urine subproteome versus the whole plasma proteome was analyzed. Protease activity, calcium and growth factor binding proteins, and coagulation and immune response-related proteins were found to be enriched.The comparison method described in this paper provides an illustration of a new approach for studying organ functions with a proteomics methodology. Because of its distinctive input (plasma) and output (urine), it is reasonable to predict that the kidney will be the first organ whose functions are further elucidated by proteomic methods in the near future. It can also be anticipated that there will be more applications for proteomics in organ function research