Circular RNA circZCCHC6 contributes to tumorigenesis by regulating LPCAT1 via miR-433-3p in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer-associated mortality worldwide. Circular RNA (circRNA) circZCCHC6 has been reported to be upregulated in the plasma from NSCLC patients. This study is designed to explore the role and mechanism of circZCCHC6 in NSCLC. CircZCCHC6, microRNA-433-3p (miR-433-3p), and lysophosphatidylcholine acyltransferase 1 (LPCAT1) level were determined by real-time quantitative polymerase chain reaction. Cell viability, cell cycle progression, migration, and invasion were assessed by 3-(4,5-dimethyl-2-thiazolyl)-2, 5-diphenyl-2-H-tetrazolium bromide (MTT), flow cytometry, wound healing, and transwell assays, severally. The binding relationship between miR-433-3p and circZCCHC6 or LPCAT1 was predicted by Circinteractome or Starbase, and then verified by a dual-luciferase reporter, RNA pull-down, or RNA Immunoprecipitation (RIP) assays. Protein levels of LPCAT1, Cyclin D1, E-cadherin, and Vimentin were examined by western blot assay. The biological role of circZCCHC6 on NSCLC tumor growth and epithelial-mesenchymal transition (EMT) was examined by the xenograft tumor model in vivo. CircZCCHC6 was highly expressed in NSCLC serum, tissues, and cells. Moreover, circZCCHC6 knockdown could repress cell viability, cell cycle progression, migration, invasion, and EMT in NSCLC cells in vitro. The mechanical analysis suggested that circZCCHC6 acted as a sponge of miR-433-3p to regulate LPCAT1 expression. CircZCCHC6 silencing hindered cell growth and EMT of NSCLC in vivo. CircZCCHC6 inhibited the progression of NSCLC cells partly by regulating the miR-433-3p/LPCAT1 axis, implying a promising therapeutic target for the NSCLC treatment.

Distributed Optimal and Self-Tuning Filters Based on Compressed Data for Networked Stochastic Uncertain Systems with Deception Attacks.

In this study, distributed security estimation problems for networked stochastic uncertain systems subject to stochastic deception attacks are investigated. In sensor networks, the measurement data of sensor nodes may be attacked maliciously in the process of data exchange between sensors. When the attack rates and noise variances for the stochastic deception attack signals are known, many measurement data received from neighbour nodes are compressed by a weighted measurement fusion algorithm based on the least-squares method at each sensor node. A distributed optimal filter in the linear minimum variance criterion is presented based on compressed measurement data. It has the same estimation accuracy as and lower computational cost than that based on uncompressed measurement data. When the attack rates and noise variances of the stochastic deception attack signals are unknown, a correlation function method is employed to identify them. Then, a distributed self-tuning filter is obtained by substituting the identified results into the distributed optimal filtering algorithm. The convergence of the presented algorithms is analyzed. A simulation example verifies the effectiveness of the proposed algorithms.

A dumbbell probe-based dual signal amplification strategy for sensitive detection of multiple DNA methyltransferases.

We demonstrate for the first time the integration of a dumbbell probe with dual signal amplification for the simultaneous detection of multiple DNA methyltransferases (MTases). This method is very sensitive with a detection limit of 2.15 × 10-5 U mL-1 for DNA adenine methyltransferase (Dam) and 3.23 × 10-6 U mL-1 for CpG Methyltransferase (M.SssI), and it can be used to screen the enzyme inhibitors and simultaneously measure Dam and M.SssI in complex biological samples.

Dephosphorylation-directed tricyclic DNA amplification cascades for sensitive detection of protein tyrosine phosphatase.

We develop a new fluorescence method for the sensitive detection of protein tyrosine phosphatase 1B (PTP1B) based on dephosphorylation-directed tricyclic DNA amplification cascades. This method exhibits good specificity and high sensitivity with a detection limit of 0.24 pM. Moreover, it can be applied for kinetics analysis, inhibitor screening, and the accurate detection of PTP1B in a variety of cancer cells.

Fano-like chiroptical response in plasmonic heterodimer nanostructures.

Plasmonic chirality has attracted more and more attention recently due to the enhanced chiroptical response and its potential applications in biosensing. Plasmonic Fano resonance arises from the interference between a dark narrow resonance and a bright broad resonance, and it provides a new paradigm to control the plasmon mode interactions. Even though a strong circular dichroism (CD) effect has been predicted in chiral nanostructures with a Fano resonance, there are few experimental studies, and the correlation between the two effects is unclear. In this research, we investigate these two effects in plasmonic heterodimer nanorods in the same spectral range. We find that the heterodimer nanostructure exhibits a Fano-like resonance and Fano-like chiroptical response, both of which are correlated with the coupling between a super-radiant electric dipole and a sub-radiant magnetic dipole mode. Due to the interference nature of the Fano resonance, the Fano-like chiroptical response exhibits distinctively sharp features in a narrow spectral range. This Fano-like chiroptical response can be explained by a modified chiral molecule theory and a simplified coupled electric-magnetic dipole model. This research may provide new insight into the physics picture of plasmonic chirality and paves the way for the development of sensitive plasmonic sensors.

Self-Tuning Distributed Fusion Filter for Multi-Sensor Networked Systems with Unknown Packet Receiving Rates, Noise Variances, and Model Parameters.

In this study, we researched the problem of self-tuning (ST) distributed fusion state estimation for multi-sensor networked stochastic linear discrete-time systems with unknown packet receiving rates, noise variances (NVs), and model parameters (MPs). Packet dropouts may occur when sensor data are sent to a local processor. A Bernoulli distributed stochastic variable is adopted to depict phenomena of packet dropouts. By model transformation, the identification problem of packet receiving rates is transformed into that of unknown MPs for a new augmented system. The recursive extended least squares (RELS) algorithm is used to simultaneously identify packet receiving rates and MPs in the original system. Then, a correlation function method is used to identify unknown NVs. Further, a ST distributed fusion state filter is achieved by applying identified packet receiving rates, NVs, and MPs to the corresponding optimal estimation algorithms. It is strictly proven that ST algorithms converge to optimal algorithms under the condition that the identifiers for parameters are consistent. Two examples verify the effectiveness of the proposed algorithms.

Expression profile of microRNAs in bronchoalveolar lavage fluid of rats as predictors for smoke inhalation injury.

Smoke inhalation injury (SII) is an independent risk factor for morbidity and mortality in patients with severe burns, however, the underlying mechanisms of SII are still not fully understood. In our study, we established an advanced rat model of SII based on the previous work, and explored the dynamic changes of pathophysiology and inflammatory factors during 28days post SII. We also measured the different expressions of miRNAs in bronchoalveolar lavage fluid (BALF) between SII and normal control rats by miRNA microarray. At 1day after smoke inhalation, the histopathological results exhibited inflammatory exudates in the lung tissue with significant edema. As time went on, the lung injuries gradually appeared at alveolar septum thickening and alveolar collapse, which suggested that it further induced damage to lung parenchyma by smoke inhalation. Particularly, the collagen deposition indicating pulmonary fibrosis happened at 28days post-injury. Plasma IL-6 and TNF-a were significantly increased after 1day of smoke inhalation. Plasma IL-10, BALF TNF-α and IL-10 were significantly increased after 2days of smoke inhalation. By extending the observation time, the levels of plasma IL-6, BALF TNF-a and IL-10 appeared a second peak again after 14days of injury. Compared with the normal control group, there were 23 upregulated miRNAs and 2 downregulated miRNAs in BALF of SII group at 1day post-injury. RT-qPCR validation assay confirmed that the changes of miR-34c-5p, miR-92b-3p, miR-205, miR-34b-3p, miR-92a-3p, let-7b-5p, let-7c-5p in BALF were consistent with the conclusion of the miRNA microarray. In summary, we showed the dynamic changes of pathologic changes and inflammatory factors in rats with SII, and a subset of seven miRNAs changed in BALF after SII which may be used for diagnosis and potential therapeutic targets.

Advances in Multi-Sensor Information Fusion: Theory and Applications 2017.

The information fusion technique can integrate a large amount of data and knowledge representing the same real-world object and obtain a consistent, accurate, and useful representation of that object. The data may be independent or redundant, and can be obtained by different sensors at the same time or at different times. A suitable combination of investigative methods can substantially increase the profit of information in comparison with that from a single sensor. Multi-sensor information fusion has been a key issue in sensor research since the 1970s, and it has been applied in many fields. For example, manufacturing and process control industries can generate a lot of data, which have real, actionable business value. The fusion of these data can greatly improve productivity through digitization. The goal of this special issue is to report innovative ideas and solutions for multi-sensor information fusion in the emerging applications era, focusing on development, adoption, and applications.

Dynamic Tracking Human Mesenchymal Stem Cells Tropism following Smoke Inhalation Injury in NOD/SCID Mice.

Multiple preclinical evidences have supported the potential value of mesenchymal stem cells (MSCs) for treatment of acute lung injury (ALI). However, few studies focus on the dynamic tropism of MSCs in animals with acute lung injury. In this study, we track systemically transplanted human bone marrow-derived mesenchymal stem cells (hBMSCs) in NOD/SCID mice with smoke inhalation injury (SII) through bioluminescence imaging (BLI). The results showed that hBMSCs systemically delivered into healthy NOD/SCID mouse initially reside in the lungs and then partially translocate to the abdomen after 24 h. Compared with the uninjured control group treated with hBMSCs, higher numbers of hBMSCs were found in the lungs of the SII NOD/SCID mice. In both the uninjured and SII mice, the BLI signals in the lungs steadily decreased over time and disappeared by 5 days after treatment. hBMSCs significantly attenuated lung injury, elevated the levels of KGF, decreased the levels of TNF-α in BALF, and inhibited inflammatory cell infiltration in the mice with SII. In conclusion, our findings demonstrated that more systemically infused hBMSCs localized to the lungs in mice with SII. hBMSC xenografts repaired smoke inhalation-induced lung injury in mice. This repair was maybe due to the effect of anti-inflammatory and secreting KGF of hMSCs but not associated with the differentiation of the hBMSCs into alveolar epithelial cells.

In Vivo Tracking of Systemically Administered Allogeneic Bone Marrow Mesenchymal Stem Cells in Normal Rats through Bioluminescence Imaging.

Recently, mesenchymal stem cells (MSCs) are increasingly used as a panacea for multiple types of disease short of effective treatment. Dozens of clinical trials published demonstrated strikingly positive therapeutic effects of MSCs. However, as a specific agent, little research has focused on the dynamic distribution of MSCs after in vivo administration. In this study, we track systemically transplanted allogeneic bone marrow mesenchymal stem cells (BMSCs) in normal rats through bioluminescence imaging (BLI) in real time. Ex vivo organ imaging, immunohistochemistry (IHC), and RT-PCR were conducted to verify the histological distribution of BMSCs. Our results showed that BMSCs home to the dorsal skin apart from the lungs and kidneys after tail vein injection and could not be detected 14 days later. Allogeneic BMSCs mainly appeared not at the parenchymatous organs but at the subepidermal connective tissue and adipose tissue in healthy rats. There were no significant MSCs-related adverse effects except for transient decrease in neutrophils. These findings will provide experimental evidences for a better understanding of the biocharacteristics of BMSCs.

State Estimation for a Class of Non-Uniform Sampling Systems with Missing Measurements.

This paper is concerned with the state estimation problem for a class of non-uniform sampling systems with missing measurements where the state is updated uniformly and the measurements are sampled randomly. A new state model is developed to depict the dynamics at the measurement sampling points within a state update period. A non-augmented state estimator dependent on the missing rate is presented by applying an innovation analysis approach. It can provide the state estimates at the state update points and at the measurement sampling points within a state update period. Compared with the augmented method, the proposed algorithm can reduce the computational burden with the increase of the number of measurement samples within a state update period. It can deal with the optimal estimation problem for single and multi-sensor systems in a unified way. To improve the reliability, a distributed suboptimal fusion estimator at the state update points is also given for multi-sensor systems by using the covariance intersection fusion algorithm. The simulation research verifies the effectiveness of the proposed algorithms.

Evaluating the nucleus effect on the dynamic indentation behavior of cells.

The effect of the nucleus on the cell mechanical behavior was investigated based on the dynamic indentation response of cells under a spherical tip. A "two-component" cell model (including cytoplasm and nucleus) is used, and the dynamic indentation behavior is studied by a semiempirical method, which is established based on fitting the numerical simulation results of the quasi-static indentation response of cells. We found that the "routine analysis" (based on the Hertz's contact solution of homogeneous model) significantly overestimated the nucleus effect on the overall cell indentation response due to the effects of the Hertz contact radius and the substrate stiffening. These effects are significantly stronger in the "two-component" cell model than in the homogeneous model. The inaccuracy created by the "routine analysis" slightly increases with the modulus ratio of nucleus to cytoplasm and the volume fraction of nucleus. Finally, the error sensitivity to the geometrical parameters used in the model is discussed, which shows the indentation analysis is not very sensitive to these parameters, and the reasonable assumptions for these parameters are effective. This systematic analysis can provide a useful guideline to understanding the mechanical behavior of cells and nuclei.

Multisensor optimal information fusion input white noise deconvolution estimators.

The unified multisensor optimal information fusion criterion weighted by matrices is rederived in the linear minimum variance sense, where the assumption of normal distribution is avoided. Based on this fusion criterion, the optimal information fusion input white noise deconvolution estimators are presented for discrete time-varying linear stochastic control system with multiple sensors and correlated noises, which can be applied to seismic data processing in oil exploration. A three-layer fusion structure with fault tolerant property and reliability is given. The first fusion layer and the second fusion layer both have netted parallel structures to determine the first-step prediction error cross-covariance for the state and the estimation error cross-covariance for the input white noise between any two sensors at each time step, respectively. The third fusion layer is the fusion center to determine the optimal matrix weights and obtain the optimal fusion input white noise estimators. The simulation results for Bernoulli-Gaussian input white noise deconvolution estimators show the effectiveness.

[The three-dimensional nonlinear finite element analysis of force system of the "rocking-chair archwire"].

OBJECTIVE: The purpose of this study was to qualitatively and quantitatively analyze the initial force system of "rocking-chair archwire" on every tooth. METHODS: Three-dimensional finite element model of "rocking-chair archwire" was set up, and nonlinear method was used to analyze the force system. RESULTS: The archwire exerted intruding force, labial force, lingual-root torque and mesial-labial moment on incisors; Extruding force, lingual-root torque and mesial-labial moment were applied on canines and premolars; The archwire also created intruding force, buccal force, buccal-root torque and mesial-labial moment on molars. CONCLUSIONS: "rocking-chair archwire" produced a complicated force system. While intruding incisors, molars and extruding premolars. It has a tendency to induce tooth rotation and tipping.

[The short-term and long-term treatment outcomes in patients with pulmonary tuberculosis positive for drug-resistant and sensitive strains].

OBJECTIVES: To analyze the treatment outcomes in patients with smear positive tuberculosis, and to compare the difference in treatment response among patients infected with drug-sensitive and drug-resistant strains. METHODS: From 1998 to 2000, seven hundred and seventy-seven patients with primary smear-positive tuberculosis, which were from 30 surveillance sites, were followed for two years to monitor their treatment outcomes. RESULTS: At the completion of the 6 months' therapy, the overall rate of treatment failure was 1.8%, 2.6% for the drug-resistant cases and 1.6% for the drug-sensitive cases. Six-month follow-up showed a positive conversion rate of 2.7% in all the cases, 8.5% and 1.2% (P < 0.005) in the drug-resistant and the drug-sensitive cases respectively. One year follow-up showed that the positive conversion rate was 2.6% in all the cases, 6.9% and 1.6% (P < 0.005) in the drug-resistant and the drug-sensitive cases, respectively. Two-year follow-up showed an overall positive conversion rate of 1.3%, 1.0% and 1.3% in the drug-resistant and the drug-sensitive cases, respectively. Of the 152 drug-resistant cases, the rate of treatment failure was 2.6% at the completion of 6 months' therapy, but in cases with MDR-TB the rate was 10.3%. Six-month follow-up showed an overall positive conversion rate of 8.5%, but the rate reached 37.0% in cases with MDR-TB. One-year and two-year follow-up showed that the positive conversion rates were 6.9% and 1.0% respectively in all the drug-resistant cases, but 6.3% and 6.7% respectively in the MDR cases. CONCLUSIONS: Under the guidelines of the National Tuberculosis Program (NTP), the 2H(3)R(3)S(3)Z(3)/4H(3)R(3) regimen for primary smear-positive pulmonary TB was effective. But the cure rate was lower and the positive conversion rate higher in patients with MDR-TB.