Numerical Investigation on Anti-Explosion Performance of Non-Metallic Annular Protective Structures.

Explosive shock wave protection is an important issue that urgently needs to be solved in the current military and public security safety fields. Non-metallic protective structures have the characteristics of being lightweight and having low secondary damage, making them an important research object in the field of equivalent protection. In this paper, the numerical simulation was performed to investigate the dynamic mechanical response of non-metallic annular protective structures under the internal blast, which were made by the continuous winding of PE fibers. The impact of various charges, the number of fiber layers, and polyurethane foam on the damage to protective structures was analyzed. The numerical results showed that 120 PE fiber layers could protect 50 g TNT equivalent explosives. However, solely increasing the thickness of fiber layers cannot effectively enhance the protection efficiency. By adding polyurethane foam in the inner layer, the stress acting on the fiber could be effectively reduced. A 30 mm thick polyurethane layer can reduce the equivalent stress of the fiber layer by 41.6%. This paper can provide some reference for the numerical simulations of non-metallic explosion protection structures.

GraphTS: Graph-represented time series for subsequence anomaly detection.

Automatic detection of subsequence anomalies (i.e., an abnormal waveform denoted by a sequence of data points) in time series is critical in a wide variety of domains. However, most existing methods for subsequence anomaly detection often require knowing the length and the total number of anomalies in time series. Some methods fail to capture recurrent subsequence anomalies due to using only local or neighborhood information for anomaly detection. To address these limitations, in this paper, we propose a novel graph-represented time series (GraphTS) method for discovering subsequence anomalies. In GraphTS, we provide a new concept of time series graph representation model, which represents both recurrent and rare patterns in a time series. Particularly, in GraphTS, we develop a new 2D time series visualization (2Dviz) method, which compacts all 1D time series patterns into a 2D spatial temporal space. The 2Dviz method transfers time series patterns into a higher-resolution plot for easier sequence anomaly recognition (or detecting subsequence anomalies). Then, a Graph is constructed based on the 2D spatial temporal space of time series to capture recurrent and rare subsequence patterns effectively. The represented Graph also can be used to discover single and recurrent subsequence anomalies with arbitrary lengths. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in terms of accuracy and efficiency.

Digestion and utilization of plant-based diets by transgenic pigs secreting ß-glucanase, xylanase, and phytase in their salivary glands.

Novel transgenic (TG) pigs co-expressing three microbial enzymes, ß-glucanase, xylanase, and phytase, in their salivary glands were previously generated, which exhibited reduced phosphorus and nitrogen emissions and improved growth performances. In the present study, we attempted to explore the age-related change of the TG enzymic activity, the residual activity of the enzymes in the simulated gastrointestinal tract, and the effect of the transgenes on the digestion of nitrogen and phosphorus content in the fiber-rich, plant-based diets. Results showed that all the three enzymes were stably expressed over the growing and finishing periods in the F2 generation TG pigs. In simulated gastric juice, all the three enzymes exhibited excellent gastrointestinal environment adaptability. The apparent total tract digestibility of phosphorus was increased by 69.05% and 499.64%, while fecal phosphate outputs were decreased by 56.66% and 37.32%, in the TG pigs compared with the wild-type littermates fed with low non-starch polysaccharides diets and high fiber diets, respectively. Over half of available phosphorus and water-soluble phosphorus in fecal phosphorus were reduced. We also found the performance of phosphorus, calcium, and nitrogen retention rates were significantly improved, resulting in faster growth performance in TG pigs. The results indicate that TG pigs can effectively digest the high-fiber diets and exhibit good growth performance compared with wild type pigs.

In Situ Active Switching of Bipolar Current Rectification in 2D Semiconductor Vertical Diodes.

Two-dimensional semiconducting transition-metal dichalcogenides (TMDCs) have attracted extensive attention as building blocks of miniaturized electronic and optical devices. However, as the characteristics of TMDC devices are predominately determined by their device structures, the function of TMDC devices is fixed once fabricated, leaving the reconfigurable active device and circuit a challenge. Here, we have demonstrated the current rectification switching in TMDC vertical diodes using a liquid metal (EGaIn) top electrode with a reconfigurable contact area. The rectification switching is closely related to the ultrathin gallium oxide layer on the surface of EGaIn. Under the small contact, with the existence of gallium oxide, photocurrent dominates the electrical transport showing a negative rectification, while as the contact increases, the broken gallium oxide leads to rectification switching to the positive bias direction. Such rectification switching applies to thin TMDC flakes down to 3 nm, benefitting from the soft electrical contact between the TMDC and the EGaIn electrode. Our work shows the new possibility of actively reconfigurable TMDC vertical diodes enabled by the liquid metal electrode and will promote promising applications of flexible and tunable TMDC-based nanoelectronic devices.

Bi-dimensional principal gene feature selection from big gene expression data.

Gene expression sample data, which usually contains massive expression profiles of genes, is commonly used for disease related gene analysis. The selection of relevant genes from huge amount of genes is always a fundamental process in applications of gene expression data. As more and more genes have been detected, the size of gene expression data becomes larger and larger; this challenges the computing efficiency for extracting the relevant and important genes from gene expression data. In this paper, we provide a novel Bi-dimensional Principal Feature Selection (BPFS) method for efficiently extracting critical genes from big gene expression data. It applies the principal component analysis (PCA) method on sample and gene domains successively, aiming at extracting the relevant gene features and reducing redundancies while losing less information. The experimental results on four real-world cancer gene expression datasets show that the proposed BPFS method greatly reduces the data size and achieves a nearly double processing speed compared to the counterpart methods, while maintaining better accuracy and effectiveness.

Epsilon-near-zero substrate-enabled strong coupling between molecular vibrations and mid-infrared plasmons.

As the strong light-matter interaction between molecular vibrations and mid-infrared optical resonant modes, vibrational strong coupling (VSC) has the potential to modify the intrinsic chemistry of molecules, leading to the control of ground-state chemical reactions. Here, by using quartz as an epsilon-near-zero (ENZ) substrate, we have realized VSC between organic molecular vibrations and mid-infrared plasmons on metallic antennas. The ENZ substrate enables sharp mid-infrared plasmonic resonances (Q factor ∼50) which efficiently couple to the molecular vibrations of polymethyl methacrylate (PMMA) molecules with prominent mode splitting. The coupling strength is proportional to the square root of the thickness of the PMMA layer and reaches the VSC regime with a thickness of ∼300 nm. The coupling strength also depends on the polarization of the incident light, illustrating an additional way to control the molecule-plasmon coupling. Our findings provide a new, to the best of our knowledge, possibility to realize VSC with metallic antennas and pave the way to increase the sensitivity of molecular vibrational spectroscopy.

Elevating Surface-Enhanced Infrared Absorption with Quantum Mechanical Effects of Plasmonic Nanocavities.

Plasmonic nanocavities, with the ability to localize and concentrate light into nanometer-scale dimensions, have been widely used for ultrasensitive spectroscopy, biosensing, and photodetection. However, as the nanocavity gap approaches the subnanometer length scale, plasmonic enhancement, together with plasmonic enhanced optical processes, turns to quenching because of quantum mechanical effects. Here, instead of quenching, we show that quantum mechanical effects of plasmonic nanocavities can elevate surface-enhanced infrared absorption (SEIRA) of molecular moieties. The plasmonic nanocavities, nanojunctions of gold and cadmium oxide nanoparticles, support prominent mid-infrared plasmonic resonances and enable SEIRA of an alkanethiol monolayer (CH3(CH2)n-1SH, n = 3-16). With a subnanometer cavity gap (n < 6), plasmonic resonances turn to blue shift and the SEIRA signal starts a pronounced increase, benefiting from the quantum tunneling effect across the plasmonic nanocavities. Our findings demonstrate the new possibility of optimizing the field enhancement and SEIRA sensitivity of mid-infrared plasmonic nanocavities.

Vibrational Strong Coupling between Surface Phonon Polaritons and Organic Molecules via Single Quartz Micropillars.

Vibrational strong coupling (VSC), the strong coupling between optical resonances and the dipolar absorption of molecular vibrations at mid-infrared frequencies, holds the great potential for the development of ultrasensitive infrared spectroscopy, the modification of chemical properties of molecules, and the control of chemical reactions. In the realm of ultracompact VSC, there is a need to scale down the size of mid-infrared optical resonators and to elevate their optical field strength. Herein, by using single quartz micropillars as mid-infrared optical resonators, the strong coupling is demonstrated between surface phonon polariton (SPhP) resonances and molecular vibrations from far-field observation. The single quartz micropillars support sharp SPhP resonances with an ultrasmall mode volume, which strongly couples with the molecular vibrations of 4-nitrobenzyl alcohol (C7 H7 NO3 ) molecules featuring pronounced mode splitting and anticrossing dispersion. The coupling strength depends on the molecular concentration and reaches the strong coupling regime with only 7300 molecules. The findings pave the way for promoting the VSC sensitivity, miniaturing the VSC devices, and will boost the development of ultracompact mid-infrared spectroscopy and chemical reaction control devices.

Adaptation of Gut Microbiome to Transgenic Pigs Secreting ß-Glucanase, Xylanase, and Phytase.

We previously generated transgenic pigs with enhanced growth rate and reduced nutrient loss. However, the composition of their gut microbiome is unknown. In this study, we successfully generated EGFP marker-free transgenic (MF-TG) pigs with high expression levels of microbial ß-glucanase, xylanase, and phytase in the parotid gland. We collected intestinal contents from the ileum, cecum and colon of five MF-TG and five wild-type (WT) sows and investigated the gut microbiome of the transgenic pigs via metagenomic analysis. Results showed that the levels of probiotics, such as Lactobacillus reuteri and Streptococcus, were more abundant in the cecum of the MF-TG pigs and higher than those of WT pigs. By contrast, the levels of harmful microorganisms, such as Campylobacter, Chlamydia trachomatis, and Campylobacter fetus, and various unidentified viruses, were higher in the cecum of the WT pigs than those of the MF-TG pigs. By comparing unigenes and the eggNOG database, we found that the microorganisms in the colon of the MF-TG pigs had high fractional abundance in DNA (cytosine-5)-methyltransferase 1 and serine-type D-Ala-D-Ala carboxypeptidase, whereas the aspartate carbamoyltransferase regulatory subunit and outer membrane protein pathways were enriched in the WT pigs. Moreover, the microorganisms in the cecum of the MF-TG pigs were active in GlycosylTransferase Family 8 (GT8), Glycoside Hydrolase Family 13 (GH13), and Glycoside Hydrolase Family 32 (GH32). Furthermore, the levels of numerous carbohydrases, such as glucan 1,3-beta-glucosidase, xylan 1,4-beta-xylosidase and exo-1,3-1,4-glucanase, were higher in the cecum of the MF-TG pigs than those of the WT pigs. The results indicated that intestinal microbes can change adaptively to the secretion of transgenic enzymes, thereby forming a benign cooperation with their host. This cooperation could be beneficial for improving feed efficiency.

[Knockdown the expression of ku70 and lig4 in HEK293T cells by CRISPR/Cas13 system].

Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated proteins (Cas) system is a hotspot of gene editing and gene expression research, in which CRISPR/Cas13 system provides a new direction for RNA interference and editing. In this study, we designed and synthesized the corresponding gRNAs of CRISPR/Cas13a and CRISPR/Cas13b systems in non-homologous end joining (NHEJ) pathway, such as Ku70 and Lig4, and then detected the expression of ku70 and lig4 in HEK293T cells. The CRISPR/Cas13a system could efficiently knockdown the mRNA expression of ku70 and lig4 more than 50%, and CRISPR/Cas13b system also suppressed ku70 and lig4 about 92% and 76%, respectively. Also, CRISPR/Cas13a, b systems could down-regulate Ku70 and Lig4 proteins level to 68% and 53%, respectively. The study demonstrates that the CRISPR/Cas13 system could effectively knockdown the expression of RNA and protein in HEK293T cells, providing a new strategy for gene function and regulation research.

Exploring Douglas-Peucker Algorithm in the Detection of Epileptic Seizure from Multicategory EEG Signals.

Discovering the concealed patterns of Electroencephalogram (EEG) signals is a crucial part in efficient detection of epileptic seizures. This study develops a new scheme based on Douglas-Peucker algorithm (DP) and principal component analysis (PCA) for extraction of representative and discriminatory information from epileptic EEG data. As the multichannel EEG signals are highly correlated and are in large volumes, the DP algorithm is applied to extract the most representative samples from EEG data. The PCA is utilised to produce uncorrelated variables and to reduce the dimensionality of the DP samples for better recognition. To verify the robustness of the proposed method, four machine learning techniques, random forest classifier (RF), k-nearest neighbour algorithm (k-NN), support vector machine (SVM), and decision tree classifier (DT), are employed on the obtained features. Furthermore, we assess the performance of the proposed methods by comparing it with some recently reported algorithms. The experimental results show that the DP technique effectively extracts the representative samples from EEG signals compressing up to over 47% sample points of EEG signals. The results also indicate that the proposed feature method with the RF classifier achieves the best performance and yields 99.85% of the overall classification accuracy (OCA). The proposed method outperforms the most recently reported methods in terms of OCA in the same epileptic EEG database.

A Review on Automatic Facial Expression Recognition Systems Assisted by Multimodal Sensor Data.

Facial Expression Recognition (FER) can be widely applied to various research areas, such as mental diseases diagnosis and human social/physiological interaction detection. With the emerging advanced technologies in hardware and sensors, FER systems have been developed to support real-world application scenes, instead of laboratory environments. Although the laboratory-controlled FER systems achieve very high accuracy, around 97%, the technical transferring from the laboratory to real-world applications faces a great barrier of very low accuracy, approximately 50%. In this survey, we comprehensively discuss three significant challenges in the unconstrained real-world environments, such as illumination variation, head pose, and subject-dependence, which may not be resolved by only analysing images/videos in the FER system. We focus on those sensors that may provide extra information and help the FER systems to detect emotion in both static images and video sequences. We introduce three categories of sensors that may help improve the accuracy and reliability of an expression recognition system by tackling the challenges mentioned above in pure image/video processing. The first group is detailed-face sensors, which detect a small dynamic change of a face component, such as eye-trackers, which may help differentiate the background noise and the feature of faces. The second is non-visual sensors, such as audio, depth, and EEG sensors, which provide extra information in addition to visual dimension and improve the recognition reliability for example in illumination variation and position shift situation. The last is target-focused sensors, such as infrared thermal sensors, which can facilitate the FER systems to filter useless visual contents and may help resist illumination variation. Also, we discuss the methods of fusing different inputs obtained from multimodal sensors in an emotion system. We comparatively review the most prominent multimodal emotional expression recognition approaches and point out their advantages and limitations. We briefly introduce the benchmark data sets related to FER systems for each category of sensors and extend our survey to the open challenges and issues. Meanwhile, we design a framework of an expression recognition system, which uses multimodal sensor data (provided by the three categories of sensors) to provide complete information about emotions to assist the pure face image/video analysis. We theoretically analyse the feasibility and achievability of our new expression recognition system, especially for the use in the wild environment, and point out the future directions to design an efficient, emotional expression recognition system.

Rapid screening of explosives in ambient environment by aerodynamic assisted thermo desorption mass spectrometry.

Rapid, direct, and trace detection of explosives in an open environment is of particular need in homeland and/or transportation security. In this work, an aerodynamic assisted thermo desorption mass spectrometry method was developed for the direct quantitative analyses of explosives from a distance. Remote non-volatile explosive sensing was achieved for 2, 4, 6-trinitrotoluene, trinitrohexahydro-1, 3, 5-triazine, 8701 (main ingredient: RDX 98.5%), and C4 (a type of plastic explosive) with a distance of 0.65 m. Furthermore, a close to 324 cm2 effective sampling area could be achieved, and the limits of detection are in the ng range. This device can be deployed in airports and subway stations for high-throughput and automatic luggage/personnel screening of prohibited articles, such as explosives and illicit drugs. Copyright © 2016 John Wiley & Sons, Ltd.

An aerodynamic assisted miniature mass spectrometer for enhanced volatile sample analysis.

Previously, we have reported the development of a miniature mass spectrometer with a continuous atmospheric pressure interface (CAPI), and the use of it for non-volatile sample analysis, such as drugs, peptides and proteins. However due to the diffusion effects in the CAPI, especially stronger for light ions, the instrument shows low detection sensitivities for volatile samples when coupling with an atmosphere pressure chemical ionization (APCI) source (>ppmv). In this study, an in-vacuum plasma ionization source was designed and integrated into the system. By performing ionization in the first vacuum stage, ion transfer loss through the CAPI was minimized and tens of ppbv level detection sensitivities were achieved for volatile samples. Due to its improved sensitivity, chemical source tracing was demonstrated in an indoor environment as a simple proof-of-concept example. Furthermore, an aerodynamic sampling method was developed to facilitate directional sample transfer towards the miniature mass spectrometer in an open environment. By coupling this aerodynamic method with the miniature mass spectrometer, remote chemical source sensing could be achieved at a distance of more than two meters. This aerodynamic sampling method could also be applied to other mass spectrometry instruments for enhanced sample sampling in open environments.

Static and Dynamic Properties of Semi-Crystalline Polyethylene.

Properties of extruded polymers are strongly affected by molecular structure. For two different semi-crystalline polymers, low-density polyethylene (LDPE) and ultra-high molecular weight polyethylene (UHMWPE), this investigation measures the elastic modulus, plastic flow stress and strain-rate dependence of yield stress. Also, it examines the effect of molecular structure on post-necking tensile fracture. The static and dynamic material tests reveal that extruded UHMWPE has a somewhat larger yield stress and much larger strain to failure than LDPE. For both types of polyethylene, the strain at tensile failure decreases with increasing strain-rate. For strain-rates 0.001⁻3400 s-1, the yield stress variation is accurately represented by the Cowper⁻Symonds equation. These results indicate that, at high strain rates, UHMWPE is more energy absorbent than LDPE as a result of its long chain molecular structure with few branches.

A Hyper-Viscoelastic Constitutive Model for Polyurea under Uniaxial Compressive Loading.

A hyper-viscoelastic constitutive model for polyurea by separating hyperelastic and viscoelastic behaviors has been put forward. Hyperelasticity represents the rate-independent responses at low strain rates, described by a three-parameter Mooney-Rivlin model and a third Ogden model. By fitting the quasi-static experimental data, the Ogden model is more appropriate to describe the hyperelastic behaviors for its better agreement at strain over 0.3. Meanwhile, viscoelasticity represents the rate-dependent responses at high strain rates, described by the Standard Linear Solids (SLS) model and the K-BKZ model. By fitting the experimental data of split Hopkinson pressure bar (SHPB), the SLS model is more appropriate to describe the viscoelastic behaviors at strain rates below 1600 s-1, but the K-BKZ model performs better at strain rates over 2100 s-1 because of the substantial increase of Young's modulus and the state of polyurea transforming from rubbery to glassy. The K-BKZ model is chosen to describe the viscoelastic behavior, for its low Root Mean Square Error (RMSE) at strain rates below 1600 s-1. From the discussion above, the hyper-viscoelastic constitutive model is chosen to be the combination of the Ogden model and the K-BKZ model.

Wireless sensor networks for heritage object deformation detection and tracking algorithm.

Deformation is the direct cause of heritage object collapse. It is significant to monitor and signal the early warnings of the deformation of heritage objects. However, traditional heritage object monitoring methods only roughly monitor a simple-shaped heritage object as a whole, but cannot monitor complicated heritage objects, which may have a large number of surfaces inside and outside. Wireless sensor networks, comprising many small-sized, low-cost, low-power intelligent sensor nodes, are more useful to detect the deformation of every small part of the heritage objects. Wireless sensor networks need an effective mechanism to reduce both the communication costs and energy consumption in order to monitor the heritage objects in real time. In this paper, we provide an effective heritage object deformation detection and tracking method using wireless sensor networks (EffeHDDT). In EffeHDDT, we discover a connected core set of sensor nodes to reduce the communication cost for transmitting and collecting the data of the sensor networks. Particularly, we propose a heritage object boundary detecting and tracking mechanism. Both theoretical analysis and experimental results demonstrate that our EffeHDDT method outperforms the existing methods in terms of network traffic and the precision of the deformation detection.

Downregulation of L-type amino acid transporter 1 expression inhibits the growth, migration and invasion of gastric cancer cells.

Gastric cancer is the second leading cause of cancer-related mortality worldwide. Identifying the molecules that play critical roles in the development of gastric cancer, and clarifying their mechanisms, will contribute to the development of novel molecularly targeted therapeutic drugs. Recently, the large (L)-type amino acid transporter 1 (LAT1), a glycoprotein that transports amino acids through the cell membrane when associated with CD98hc, has been demonstrated to be overexpressed in various types of cancer, and to regulate multiple biological process, including cell growth, migration and invasion. However, the involvement of LAT1 in gastric cancer remains unclear. In the present study, stable gastric cancer cell lines with a LAT1 knockdown were established by transfection of constructs with inserted short (sh) RNAs, in order to clarify the role of LAT1 in gastric caner. A significant decrease in LAT1 expression was observed in the established LAT1-silenced SGC7901 cells compared with the corresponding control cells; however, the expression levels of its partner, CD98hc, were not altered. Furthermore, downregulation of LAT1 expression inhibited the proliferation, migration and invasion of gastric cancer cells. In addition, the decreased expression of LAT1 induced cell cycle arrest in the G1/M phase. These findings suggested that LAT1 may be significant in the progression and metastasis of gastric cancer, and may be developed as a therapeutic target for cancer therapy.