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Piperazine hexahydrate (PI) and diethylenetriamine penta(methylene phosphonic acid) (DTPMP) are used to synthesize flame-retardant PI-DTPMP through supramolecular reaction. The incorporation of PI-DTPMP simultaneously improves the mechanical and flame-retardant properties of epoxy resin (EP). With only 5 wt% loading of PI-DTPMP, EP composites show excellent flame retardancy with a limiting oxygen index (LOI) of 36.4% and V-0 rating in the UL-94 test. The cone calorimetric test results demonstrates that the peak heat release rate, total heat release, and total smoke release of EP/5%PI-DTPMP are reduced by 44.9%, 34.7%, and 37.7% compared with that of neat EP, respectively, owing to the formation of compact and insulating char layer during combustion. Moreover, EP/PI-DTPMP composites show enhanced mechanical properties with the improved tensile and impact strength, as well as higher storage modulus, crosslink densities, and glass-transition temperatures in dynamic mechanical analysis. [ABSTRACT FROM AUTHOR]

Usually, it is difficult to achieve satisfactory fire retardancy of epoxy resin (EP) at low addition by adding inorganic nano-fillers alone. Herein, sepiolite nanofibers loaded with layered double metal hydroxide (a-SEP@LDH) and phosphorus/nitrogen-containing flame retardant nanosheets (PAMA) were prepared via hydrothermal method, respectively. The UL-94 V-0 rating and an limiting oxygen index (LOI) value of 31.6% were achieved for EP by loading 1 wt% a-SEP@LDH and 2 wt% PAMA. Compared to adding sole a-SEP@LDH, the thermal stability of EP/a-SEP@LDH/PAMA3 was improved significantly, the total heat release (THR) and peak heat release rate (pHRR) decreased by 10.2 and 28.8%, respectively. Additionally, the total CO2 production decreased by 12.5% and the char residue yield increased to 19.9 wt%. Moreover, the tensile strength and impact strength of the EP composites were remarkably improved owing to the incorporation of PAMA. To sum up, this work provides an environmentally benign, low-cost and efficient way for EP to achieve outstanding fire retardance, thermal stability and high mechanical properties at a low addition. [ABSTRACT FROM AUTHOR]

BACKGROUND: The incidence of liver tumors is among the top three in China. The treatments of benign and malignant tumors are different. Accurate diagnosis plays an important role in guiding the treatment of tumors. OBJECTIVE: The aim of this study is to solve the following: (1) blurred boundary between the liver tumor and other organs causes incorrect segmentation of liver tumor boundaries; (2) large difference in tumor size and the diversity in texture and grayscale are major challenges in liver tumor classification tasks. METHODS: Firstly, the liver tumor is segmented from the original CT images by a tumor segmentation network, UNet++ with fusion loss and atrous spatial pyramid pooling (FLAS-UNet++). The proposed segmentation method can solve the problem of tumor edge segmentation error by learning the tumor edge information. Secondly they are adaptively cropped according to the tumor volume to reduce the over-fitting and over-sensitivity of the deep network. Thirdly an improved Dense Block is designed to pay more attention to the changes in grayscale and texture between benign and malignant tumors. Finally, the features extracted from the network combined with tumor volume, patient's sex and age, are sent to a classifier for diagnosis. RESULT: Liver tumor segmentation results show that the dice, HD95 reached 71.9%, 12.1 mm, respectively. The classification results show that the accuracy, specificity, sensitivity and area under curve reached 82.4%, 79.8%, 84.4%, 87.5%, respectively. The segmentation and classification results are both better than other's methods and mainstream networks. CONCLUSIONS: In order to solve existing problems of liver tumor CT image classification methods, our method realizes the accurate segmentation and classification of liver tumors in CT images and has important clinical application value. [ABSTRACT FROM AUTHOR]

Acute myocardial infarction (AMI) is a severe cardiovascular disease. This study aimed to identify crucial microRNAs (miRNAs) and mRNAs in AMI by establishing a miRNA-mRNA network. The microarray datasets GSE31568, GSE148153, and GSE66360 were downloaded from the Gene Expression Omnibus (GEO) database. We identified differentially expressed miRNAs (DE-miRNAs) and mRNAs (DE-mRNAs) in AMI samples compared with normal control samples. The consistently changing miRNAs in both GSE31568 and GSE148153 datasets were selected as candidate DE-miRNAs. The interactions between the candidate DE-miRNAs and DEmRNAs were analyzed, and a miRNA-mRNA network and a protein-protein interaction network were constructed, along with functional enrichment and pathway analyses. A total of 209 DE-miRNAs in the GSE31568 dataset, 857 DE-miRNAs in the GSE148153 dataset, and 351 DE-mRNAs in the GSE66360 dataset were identified. Eighteen candidate DE-miRNAs were selected from both the GSE31568 and GSE148153 datasets. Furthermore, miR-646, miR-127-5p, miR-509-5p, miR-509-3-5p, and miR-767-5p were shown to have a higher degree in the miRNA-mRNA network. THBS-1 as well as FOS was a hub gene in the miRNA-mRNA network and the protein-protein interaction (PPI) network, respectively. CDKN1A was important in both miRNAmRNA network and PPI network. We established a miRNA-mRNA network in AMI and identified five miRNAs and three genes, which might be used as biomarkers and potential therapeutic targets for patients with AMI. [ABSTRACT FROM AUTHOR]

In this paper, we propose Device Authentication Code (DAC), a novel method for authenticating IoT devices with wireless interface, by exploiting their radio frequency (RF) signatures. The proposed DAC is based on RF fingerprinting, an information-theoretic method, feature learning, and the discriminatory power of deep learning. Specifically, an autoencoder is used to automatically extract features from the RF traces and the reconstruction error is used as the DAC, and this DAC is unique to each individual device. Then Kolmogorov-Smirnov (K-S) test is used to match the distribution of the reconstruction error generated by the receiver and the DAC in the received message, and the result will determine whether the device of interest is an intruder. We validate this concept on two experimentally collected RF traces from six ZigBee devices and five universal software defined radio peripheral devices, respectively. The traces span a range of Signal-to-Noise Ratio by varying locations, mobility of the devices, channel interference, and noise to ensure robustness of the model. Experimental results demonstrate that DAC is able to prevent device impersonation by extracting salient features that are unique to each wireless device of interest and can be used to identify radio frequency devices. Furthermore, the proposed method does not need the RF traces of the intruder during model training to be able to identify devices not seen during training, which makes it practical. [ABSTRACT FROM AUTHOR]

Background/Aims: Cardiac fibrosis is a pathological change leading to cardiac remodeling during the progression of myocardial ischemic diseases, and its therapeutic strategy remains to be explored. S100A4, a calcium-binding protein, participates in fibrotic diseases with an unclear mechanism. This study aimed to investigate the role of S100A4 in cardiac fibrosis. Methods: Cardiac fibroblasts from neonatal C57BL/6 mouse hearts were isolated and cultured. Myocardial infarction was induced by ligating the left anterior descending coronary artery (LAD). The ligation was not performed in the sham group. A volume of 5×105pfu/g adenovirus or 5 µM/g ICG-001 was intramyocardially injected into five parts bordering the infarction zone or normal region. We used Western blotting, quantitative RT-PCR, immunofluorescence, immunohistochemistry and Masson's trichrome staining to explore the function of S100A4. Results: We found significant increases of S100A4 level and cardiac fibrosis markers, and ß-catenin signaling activation in vitro and in vivo. In addition, knockdown of S100A4 significantly reduced cardiac fibrosis and ß-catenin levels. Moreover, the expression of S100A4 decreased after ICG-001 inhibited ß-catenin signal pathway. Conclusion: Downregulation of S100A4 alleviates cardiac fibrosis via Wnt/ß-catenin pathway in mice. S100A4 may be a therapeutic target of cardiac fibrosis. [ABSTRACT FROM AUTHOR]

ABSTRACT A phosphorus/silicon flame retardant, MVC-DOPO, was synthesized from 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO) and 2,4,6,8-tetra-methyl-2,4,6,8-tetra-vinyl-cyclo-tetrasiloxane (MVC) via addition reaction. Its flame-retardant effect on polycarbonate (PC) was investigated. The phosphorus/silicon flame retardant increased the limited oxygen index and UL-94 rating and reduced the heat release rate and total heat release of DOPO-MVC/PC composites during combustion, indicating the excellent flame-retardant effect of MVC-DOPO on PC. MVC-DOPO inhibited the burning intensity of PC material in the gaseous phase and promoted the formation of a more viscous residue in the condensed phase. Through releasing phosphorus-containing pieces and phenoxy radicals from the phosphaphenanthrene group, MVC-DOPO quenched the combustion chain reaction in the gaseous phase; through promoting formation of a more viscous residue and a dense char layer from the main actions of the cyclotetrasiloxane group, MVC-DOPO reduced fuel release and generated a barrier effect in the condensed phase. Hence, MVC-DOPO effectively exerted a flame-retardant effect on PC material in both the gaseous and condensed phases during combustion. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018, 135, 45815. [ABSTRACT FROM AUTHOR]

Situational assessment (SA) is one of the key parts for the application of intelligent alternative-energy vehicles (IAVs) in the sustainable transportation. It helps IAVs understand and comprehend traffic environments better. In SA, it is crucial to be aware of uncertainty-risks, such as sensor failure or communication loss. The objective of this study is to assess traffic situations considering uncertainty-risks, including environment predicting uncertainty. According to the stochastic environment model, collision probabilities between multiple vehicles are estimated based on integrated trajectory prediction under uncertainty, which combines the physics- and maneuver-based trajectory prediction models for accurate prediction results in the long term. The SA method considers the probabilities of collision at different predicting points, the masses, and relative speeds between the possible colliding objects. In addition, risks beyond the prediction horizon are considered with the proposition of infinite risk assessments (IRAs). This method is applied and proved to assess risks regarding unexpected obstacles in traffic, sensor failure or communication loss, and imperfect detections with different sensing accuracies of the environment. The results indicate that the SA method could evaluate traffic risks under uncertainty in the dynamic traffic environment. This could help IAVs’ plan motion trajectories and make high-level decisions in uncertain environments. [ABSTRACT FROM AUTHOR]

A hyperbranched derivative of trazine group (EA) and ammonium polyphosphate (APP) are incorporated into polylactide (PLA) in a constant amount. The component ratio effect of APP/EA on the flammability and anti-dripping property of PLA are investigated using the limiting oxygen index, vertical burning, and cone calorimetry tests. Results indicate that composite has the highest LOI value (41.2%) and the lowest peak heat release rate (139 kW/m2) when the mass ratio of APP/EA is 15/5. Meanwhile, UL 94 V-0 rating is achieved and no flammable dripping is observed. Residues are also investigated by scanning electron microscopy, Fouriertransform infrared, and X-ray photoelectron spectroscopy. Results demonstrate that a complete cross-linking reaction has occurred in PLA/15%APP/5%EA composite, which improves the melt strength and locks more chemical constituents containing carbon and nitrogen in the residues. Therefore, a dense, compact and intumescent char layer is formed. [ABSTRACT FROM AUTHOR]

Motivated by the frustration of translation of research advances in the molecular and cellular biology of cancer into treatment, this study calls for cross-disciplinary efforts and proposes a methodology of incorporating drug pharmacology information into drug therapeutic response modeling using a computational systems biology approach. The objectives are two fold. The first one is to involve effective mathematical modeling in the drug development stage to incorporate preclinical and clinical data in order to decrease costs of drug development and increase pipeline productivity, since it is extremely expensive and difficult to get the optimal compromise of dosage and schedule through empirical testing. The second objective is to provide valuable suggestions to adjust individual drug dosing regimens to improve therapeutic effects considering most anticancer agents have wide inter-individual pharmacokinetic variability and a narrow therapeutic index. A dynamic hybrid systems model is proposed to study drug antitumor effect from the perspective of tumor growth dynamics, specifically the dosing and schedule of the periodic drug intake, and a drug's pharmacokinetics and pharmacodynamics information are linked together in the proposed model using a state-space approach. It is proved analytically that there exists an optimal drug dosage and interval administration point, and demonstrated through simulation study. [ABSTRACT FROM AUTHOR]

Determining liver segmentation accurately from MRIs is the primary and crucial step for any automated liver perfusion analysis, which provides important information about the blood supply to the liver. Although implicit contour extraction methods, such as level set methods (LSMs) and active contours, are often used to segment livers, the results are not always satisfactory due to the presence of artifacts and low-gradient response on the liver boundary. In this paper, we propose a multiple-initialization, multiple-step LSM to overcome the leakage and over-segmentation problems. The multiple-initialization curves are first evolved separately using the fast marching methods and LSMs, which are then combined with a convex hull algorithm to obtain a rough liver contour. Finally, the contour is evolved again using global level set smoothing to determine a precise liver boundary. Experimental results on 12 abdominal MRI series showed that the proposed approach obtained better liver segmentation results, so that a refined liver perfusion curve without respiration affection can be obtained by using a modified chamfer matching algorithm and the perfusion curve is evaluated by radiologists. [ABSTRACT FROM AUTHOR]

A key issue in genomic signal processing is the inference of gene regulatory networks. These are used both to understand the role of biological regulation in phenotypic determination and to derive therapeutic strategies for genetic-based diseases. In this paper, gene regulatory networks are inferred via evolutionary modeling based on time-series microarray measurements. A non-linear differential equation model is adopted. It includes random noise parameters for intrinsic noise arising from stochasticity in transcription and translation and for external noise arising from factors such as the amount of RNA polymerase, levels of regulatory proteins, and the effects of mRNA and protein degradation. An iterative algorithm is proposed for model identification. Genetic programming is applied to identify the structure of the model and Kalman filtering is used to estimate the parameters in each iteration. Both standard and robust Kalman filtering are considered. The effectiveness of the proposed scheme is demonstrated by using synthetic data and by using microarray measurements pertaining to yeast protein synthesis. [ABSTRACT FROM AUTHOR]

A joint power control and maximally disjoint routing algorithm is proposed for multihop CDMA wireless ad hoc networks. A framework of power control with QoS constraints in CDMA wireless ad hoc networks is introduced and the feasibility condition of the power control problem is identified. Both centralized solution and distributed implementations are derived to calculate the transmission power given the required throughput and the set of transmitting nodes. Then, a joint power control and maximally disjoint routing scheme is proposed for routing data traffic with minimum rate constraint while maintaining high energy efficiency and prolonged network lifetime. Furthermore, in order to provide reliable end-to-end data delivery, the proposed joint power control and maximally disjoint routing scheme is augmented by a dynamic traffic switching mechanism to mitigate the effect of node mobility or node failure. Simulation results demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, the uplink power control problem is considered for CDMA cellular systems, where stochastic SIR measurements are performed at base stations. A distributed stochastic power control algorithm is proposed assuming SIR measurements contain white noise. The proposed scheme minimize the sum of variances of mobile's transmission power and signal-to-interference error. The algorithm derived is fully distributed in the sense that each user only needs to know its own signal-to-interference measurement and channel variation. Uncertainties of wireless channels are accommodated by using a robust estimator. Simulation results indicate that the proposed power control scheme has very fast convergence. In addition, it also works fine with 4-bit quantization. [ABSTRACT FROM PUBLISHER]