Numerical and empirical modeling of peak deceleration and stress analysis of polyurethane elastomer under impact loading test

In this work, we present the modeling of the peak deceleration (PD) using data of the experimental drop test. Specimens with different thicknesses and areas tested in the drop test device which has adaptable height and weight. In the empirical modeling of the PD, the thickness, area, drop mass and drop height considered as separable functions. An analytical model and Neural Network (NN) was used as the empirical models. Further, the stress on the material was calculated using differential equations and the Finite Element Method (FEM). The Obtained PD from the experimental test, analytical and NN models was converted to the stress on the material using a derived differential equation. Finally, the best model for analyzing the PD and Stress on the material was presented.     

The physicochemical properties role of a functionalized alkyl-peptide in nanofibre formation and neural progenitor cells viability and survival

Amphiphilic alkyl-peptides as novel biomaterials form 3D scaffolds that are applicable in tissue engineering. Here, the nanofibre formation capability of a distinct alkyl-peptide was investigated using coarse-grained molecular dynamics simulation (CGMD) and experimental methods. The alkyl-peptide (Ace-FAQRVPPEEEGGGAAAAK-Nhe(C16)) was functionalized with a peptide epitope (FAQRVPPP) which can help to maintenance and differentiation of neural stem cells. Two alkyl-peptide systems were investigated: the all-functionalized system (with only bioactive alkyl-peptides) and the distributed system (a combination of bioactive and non-bioactive alkyl-peptides with ratio 1:2). The CGMD and TEM results confirm elongated nanofibres for all-functionalized system and cylindrical nanofibres for the distributed one. Furthermore, PC12 cells show a reliable growth on both 2D alkyl-peptides coated surfaces. Because of the nanofibres negative surface charges, the cell morphologies show clustered form in the distributed system and rounded shape in the all-functionalized one. Since the stem cell state preserves in cluster form, the physicochemical property of these nanofibres allows a potential advantage in stem cell long-time maintains.     

基于上转换纳米粒子的低损伤神经界面技术

Optogenetics is a neuromodulation technology that combines light control technology with genetic technology, thus allowing the selective activation and inhibition of the electrical activity in specific types of neurons with millisecond time resolution. Over the past several years, optogenetics has become a powerful tool for understanding the organization and functions of neural circuits, and it holds great promise to treat neurological disorders. To date, the excitation wavelengths of commonly employed opsins in optogenetics are located in the visible spectrum. This poses a serious limitation for neural activity regulation because the intense absorption and scattering of visible light by tissues lead to the loss of excitation light energy and also cause tissue heating. To regulate the activity of neurons in deep brain regions, it is necessary to implant optical fibers or optoelectronic devices into target brain areas, which however can induce severe tissue damage. Non- or minimally-invasive remote control technologies that can manipulate neural activity have been highly desirable in neuroscience research. Upconversion nanoparticles (UCNPs) can emit light with a short wavelength and high frequency upon excitation by light with a long wavelength and low frequency. Therefore, UCNPs can convert low-frequency near-infrared (NIR) light into high-frequency visible light for the activation of light-sensitive proteins, thus indirectly realizing the NIR optogenetic system. Because NIR light has a large tissue penetration depth, UCNP-mediated optogenetics has attracted significant interest for deep-tissue neuromodulation. However, in UCNP-mediated in vivo optogenetic experiments, as the up-conversion efficiency of UCNPs is low, it is generally necessary to apply high-power NIR light to obtain up-converted fluorescence with energy high enough to activate a photosensitive protein. High-power NIR light can cause thermal damage to tissues, which seriously restricts the applications of UCNPs in optogenetic technology. Therefore, the exploration of strategies to increase the up-conversion efficiency, fluorescence intensity, and biocompatibility of UCNPs is of great significance to their wide applications in optogenetic systems. This review summarizes recent developments and challenges in UCNP-mediated optogenetics for deep-brain neuromodulation. We firstly discuss the correspondence between the parameters of UCNPs and employed opsins in optogenetic experiments, which mainly include excitation wavelengths, emission wavelengths, and luminescent lifetimes. Thereafter, we introduce the methods to enhance the conversion efficiency of UCNPs, including optimizing the structure of UCNPs and modifying the organic dyes in UCNPs. In addition, we also discuss the future opportunities in combining UCNP-mediated optogenetics with flexible microelectrode technology for the long-term detection and regulation of neural activity in the case of minimal injury.     

面向脑机接口的犹他神经电极技术

A human brain is composed of a large number of interconnected neurons forming a neural network. To study the functional mechanism of the neural network, it is necessary to record the activity of individual neurons over a large area simultaneously. Brain-computer interface (BCI) refers to the connection established between the human/animal brain and computers/other electronic devices, which enables direct interaction between the brain and external devices. It plays an important role in understanding, protecting, and simulating the brain, especially in helping patients with neurological disorders to restore their impaired motor and sensory functions. Neural electrodes are electrophysiological devices that form the core of BCI, which convert neuronal electrical signals (carried by ions) into general electrical signals (carried by electrons). They can record or interfere with the state of neural activity. The Utah Electrode Array (UEA) designed by the University of Utah is a mainstream neural electrode fabricated by bulk micromachining. Its unique three-dimensional needle-like structure enables each electrode to obtain high spatiotemporal resolution and good insulation between each other. After implantation, the tip of each electrode affects only a small group of neurons around it even allowing to record the action potential of a single neuron. The availability of a large number of electrodes, high quality of signals, and long service life has made UEA the first choice for collecting neuronal signals. Moreover, UEA is the only implantable neural electrode that can record signals in the human cerebral cortex. This article mainly serves as an introduction to the construction, manufacturing process, and functioning of UEA, with a focus on the research progress in fabricating high-density electrode arrays, wireless neural interfaces, and optrode arrays using silicon, glass, and metal as that material of construction. We also discuss the surface modification techniques that can be used to reduce the electrode impedance, minimize the rejection by brain tissue, and improve the corrosion resistance of the electrode. In addition, we summarize the clinical applications where patients can control external devices and get sensory feedback by implanting UEA. Furthermore, we discuss the challenges faced by existing electrodes such as the difficulty in increasing electrode density, poor response of integrated wireless neural interface, and the problems of biocompatibility. To achieve stability and durability of the electrode, advancements in both material science and manufacturing technology are required. We hope that this review can broaden the scope of ideas for the development of UEA. The realization of a fully implantable neural microsystem can contribute to an improved understanding of the functional mechanisms of the neural network and treatment of neurological diseases.     

A network-based pharmacology study of active compounds and targets of Fritillaria thunbergii against influenza

Seasonal and pandemic influenza infections are serious threats to public health and the global economy. Since antigenic drift reduces the effectiveness of conventional therapies against the virus, herbal medicine has been proposed as an alternative. Fritillaria thunbergii (FT) have been traditionally used to treat airway inflammatory diseases such as coughs, bronchitis, pneumonia, and fever-based illnesses. Herein, we used a network pharmacology-based strategy to predict potential compounds from Fritillaria thunbergii (FT), target genes, and cellular pathways to better combat influenza and influenza-associated diseases. We identified five compounds, and 47 target genes using a compound-target network (C-T). Two compounds (beta-sitosterol and pelargonidin) and nine target genes (BCL2, CASP3, HSP90AA1, ICAM1, JUN, NOS2, PPARG, PTGS1, PTGS2) were identified using a compound-influenza disease target network (C-D). Protein-protein interaction (PPI) network was constructed and we identified eight proteins from nine target genes formed a network. The compound-disease-pathway network (C-D-P) revealed three classes of pathways linked to influenza: cancer, viral diseases, and inflammation. Taken together, our systems biology data from C-T, C-D, PPI and C-D-P networks predicted potent compounds from FT and new therapeutic targets and pathways involved in influenza.     

Simultaneous Determination of Multicomponents in Air Toxic Organic Compounds Using Artificial Neural Networks in Ftir Spectroscopy

The application of Artificial Neural Networks (ANNs) for nonlinear multivariate calibration using simulated FTIR data was demonstrated in this paper. Neural networks consisting of three layers of nodes were trained by using the back-propagation learning rule. Since parameters affect the performance of the network greatly, simulated data were used to train the network in order to get a satisfactory combination of all parameters. The mixtures of four air toxic organic compounds whose FTIR spectra are overlapped were chosen to evaluate the calibration and prediction ability of the network. The relative standard error (RSD%), the percent standard error of prediction samples (%SEP) and the percent standard error of calibration samples (%SEC) are used for evaluating the ability of the neural network.     

Optimal flow conditions of a tracheobronchial model to reengineer lung structures

The high demand for lung transplants cannot be matched by an adequate number of lungs from donors. Since fully ex-novo lungs are far from being feasible, tissue engi-neering is actively considering implantation of engineered lungs where the devitalized structure of a donor is used as scaffold to be repopulated by stem cells of the receiv-ing patient. A decellularized donated lung is treated inside a bioreactor where transport through the tracheobronchial tree (TBT) will allow for both deposition of stem cells and nour-ishment for their subsequent growth, thus developing new lung tissue. The key concern is to set optimally the boundary conditions to utilize in the bioreactor. We propose a pre-dictive model of slow liquid ventilation, which combines a one-dimensional (1-D) mathematical model of the TBT and a solute deposition model strongly dependent on fluid velocity across the tree. With it, we were able to track and drive the concentration of a generic solute across the airways, look-ing for its optimal distribution. This was given by properly adjusting the pumps' regime serving the bioreactor. A feed-back system, created by coupling the two models, allowed us to derive the optimal pattern. The TBT model can be easily invertible, thus yielding a straightforward flow/pressure law at the inlet to optimize the efficiency of the bioreactor.     

Entangled network and quantum communication

A theoretical scheme is introduced to generate entangled network via Dzyaloshinskii-Moriya (DM) interaction. The dynamics of entanglement between different nodes, which is generated by direct or indirect interaction, is investigated. It is shown that, the direction of (DM) interaction and the locations of the nodes have a sensational effect on the degree of entanglement. The minimum entanglement generated between all the nodes is quantified. The upper and lower bounds of the entanglement depend on the direction of DM interaction, and the repetition of the behavior depends on the strength of DM. The generated entangled nodes are used as quantum channel to perform quantum teleportation, where it is shown that the fidelity of teleporting unknown information between the network members depends on the locations of the members.     

An efficient statistics-based method for the automated detection of sperm whale clicks

In this paper, we present a new method for the automated detection of sperm whale regular clicks and creaks based on statistical computations. In the first stage, a spectrogram is computed from the input waveform, followed by a noise normalisation process. A frequency domain filter is then applied, and the energy accumulated in each time frame is calculated. Two-second time-windows are then classified as containing either regular clicks, creaks, or noise based on statistical parameters using a neural network classifier. Finally, previously obtained statistical parameters are used to implement an energy-based detection criterion for the classified time-windows. Individual regular clicks and creaks are isolated by linking contiguous detected time frames. The proposed method was tested on five recordings of sperm whale sounds. Comparison of the detection performances to hand-labelled regular clicks and creaks revealed that this method outperforms two recently reported waveform-based methods when working with the same recordings files. An average percentage of detection of 86.97% was attained for the set of files. This method consumes also little computation time.