Precise Modeling of the Protective Effects of Quercetin against Mycotoxin via System Identification with Neural Networks.

Cell cytotoxicity assays, such as cell viability and lactate dehydrogenase (LDH) activity assays, play an important role in toxicological studies of pharmaceutical compounds. However, precise modeling for cytotoxicity studies is essential for successful drug discovery. The aim of our study was to develop a computational modeling that is capable of performing precise prediction, processing, and data representation of cell cytotoxicity. For this, we investigated protective effect of quercetin against various mycotoxins (MTXs), including citrinin (CTN), patulin (PAT), and zearalenol (ZEAR) in four different human cancer cell lines (HeLa, PC-3, Hep G2, and SK-N-MC) in vitro. In addition, the protective effect of quercetin (QCT) against various MTXs was verified via modeling of their nonlinear protective functions using artificial neural networks. The protective model of QCT is built precisely via learning of sparsely measured experimental data by the artificial neural networks (ANNs). The neuromodel revealed that QCT pretreatment at doses of 7.5 to 20 µg/mL significantly attenuated MTX-induced alteration of the cell viability and the LDH activity on HeLa, PC-3, Hep G2, and SK-N-MC cell lines. It has shown that the neuromodel can be used to predict the protective effect of QCT against MTX-induced cytotoxicity for the measurement of percentage (%) of inhibition, cell viability, and LDH activity of MTXs.

Accurate Natural Trail Detection Using a Combination of a Deep Neural Network and Dynamic Programming.

This paper presents a vision sensor-based solution to the challenging problem of detecting and following trails in highly unstructured natural environments like forests, rural areas and mountains, using a combination of a deep neural network and dynamic programming. The deep neural network (DNN) concept has recently emerged as a very effective tool for processing vision sensor signals. A patch-based DNN is trained with supervised data to classify fixed-size image patches into "trail" and "non-trail" categories, and reshaped to a fully convolutional architecture to produce trail segmentation map for arbitrary-sized input images. As trail and non-trail patches do not exhibit clearly defined shapes or forms, the patch-based classifier is prone to misclassification, and produces sub-optimal trail segmentation maps. Dynamic programming is introduced to find an optimal trail on the sub-optimal DNN output map. Experimental results showing accurate trail detection for real-world trail datasets captured with a head mounted vision system are presented.

Linearized Programming of Memristors for Artificial Neuro-Sensor Signal Processing.

A linearized programming method of memristor-based neural weights is proposed. Memristor is known as an ideal element to implement a neural synapse due to its embedded functions of analog memory and analog multiplication. Its resistance variation with a voltage input is generally a nonlinear function of time. Linearization of memristance variation about time is very important for the easiness of memristor programming. In this paper, a method utilizing an anti-serial architecture for linear programming is proposed. The anti-serial architecture is composed of two memristors with opposite polarities. It linearizes the variation of memristance due to complimentary actions of two memristors. For programming a memristor, additional memristor with opposite polarity is employed. The linearization effect of weight programming of an anti-serial architecture is investigated and memristor bridge synapse which is built with two sets of anti-serial memristor architecture is taken as an application example of the proposed method. Simulations are performed with memristors of both linear drift model and nonlinear model.

A Circuit-Based Neural Network with Hybrid Learning of Backpropagation and Random Weight Change Algorithms.

A hybrid learning method of a software-based backpropagation learning and a hardware-based RWC learning is proposed for the development of circuit-based neural networks. The backpropagation is known as one of the most efficient learning algorithms. A weak point is that its hardware implementation is extremely difficult. The RWC algorithm, which is very easy to implement with respect to its hardware circuits, takes too many iterations for learning. The proposed learning algorithm is a hybrid one of these two. The main learning is performed with a software version of the BP algorithm, firstly, and then, learned weights are transplanted on a hardware version of a neural circuit. At the time of the weight transplantation, a significant amount of output error would occur due to the characteristic difference between the software and the hardware. In the proposed method, such error is reduced via a complementary learning of the RWC algorithm, which is implemented in a simple hardware. The usefulness of the proposed hybrid learning system is verified via simulations upon several classical learning problems.

Can Subsurface Oxygen Species in Oxides Participate in Catalytic Reactions? An 17O Solid-State Nuclear Magnetic Resonance Study.

The impacts of subsurface species of catalysts on reaction processes are still under debate, largely due to a lack of characterization methods for distinguishing these species from the surface species and the bulk. By using 17O solid-state nuclear magnetic resonance (NMR) spectroscopy, which can distinguish subsurface oxygen ions in CeO2 (111) nanorods, we explore the effects of subsurface species of oxides in CO oxidation reactions. The intensities of the 17O NMR signals due to surface and subsurface oxygen ions decrease after the introduction of CO into CeO2 nanorods, with a more significant decrease observed for the latter, confirming the participation of subsurface oxygen species. Density functional theory calculations show that the reaction involves subsurface oxygen ions filling the surface oxygen vacancies created by the direct contact of surface oxygen with CO. This new approach can be extended to the study of the role of oxygen species in other catalytic reactions.

Prediction of protein content in paddy rice (Oryza sativa L.) combining near-infrared spectroscopy and deep-learning algorithm.

Rice is a staple crop in Asia, with more than 400 million tons consumed annually worldwide. The protein content of rice is a major determinant of its unique structural, physical, and nutritional properties. Chemical analysis, a traditional method for measuring rice's protein content, demands considerable manpower, time, and costs, including preprocessing such as removing the rice husk. Therefore, of the technology is needed to rapidly and nondestructively measure the protein content of paddy rice during harvest and storage stages. In this study, the nondestructive technique for predicting the protein content of rice with husks (paddy rice) was developed using near-infrared spectroscopy and deep learning techniques. The protein content prediction model based on partial least square regression, support vector regression, and deep neural network (DNN) were developed using the near-infrared spectrum in the range of 950 to 2200 nm. 1800 spectra of the paddy rice and 1200 spectra from the brown rice were obtained, and these were used for model development and performance evaluation of the developed model. Various spectral preprocessing techniques was applied. The DNN model showed the best results among three types of rice protein content prediction models. The optimal DNN model for paddy rice was the model with first-order derivative preprocessing and the accuracy was a coefficient of determination for prediction, Rp 2 = 0.972 and root mean squared error for prediction, RMSEP = 0.048%. The optimal DNN model for brown rice was the model applied first-order derivative preprocessing with Rp 2 = 0.987 and RMSEP = 0.033%. These results demonstrate the commercial feasibility of using near-infrared spectroscopy for the non-destructive prediction of protein content in both husked rice seeds and paddy rice.

Fine control for the preparation of ceria nanorods (111).

The morphologies and exposed surfaces of ceria nanocrystals are important factors in determining their performance. In order to establish a structure-property relationship for ceria nanomaterials, it is essential to have materials with well-defined morphologies and specific exposed facets. This is also crucial for acquiring high resolution 17O solid-state NMR spectra. In this study, we explore the synthesis conditions for preparing CeO2 nanorods with exposed (111) facets. The effects of alkali concentration, hydrothermal temperature and time, cerium source and oxidation agent are investigated and optimal synthesis conditions are found. The resulting CeO2 nanorods show very narrow 17O NMR peaks for the oxygen ions in the first, second and third layers, providing a foundation for future research on mechanisms involving ceria materials using 17O solid-state NMR spectroscopy.

A voltage mode memristor bridge synaptic circuit with memristor emulators.

A memristor bridge neural circuit which is able to perform signed synaptic weighting was proposed in our previous study, where the synaptic operation was verified via software simulation of the mathematical model of the HP memristor. This study is an extension of the previous work advancing toward the circuit implementation where the architecture of the memristor bridge synapse is built with memristor emulator circuits. In addition, a simple neural network which performs both synaptic weighting and summation is built by combining memristor emulators-based synapses and differential amplifier circuits. The feasibility of the memristor bridge neural circuit is verified via SPICE simulations.

Insights into memory effect mechanisms of layered double hydroxides with solid-state NMR spectroscopy.

Layered double oxides (LDOs) can restore the parent layered double hydroxides (LDHs) structure under hydrous conditions, and this "memory effect" plays a critical role in the applications of LDHs, yet the detailed mechanism is still under debate. Here, we apply a strategy based on ex situ and in situ solid-state NMR spectroscopy to monitor the Mg/Al-LDO structure changes during recovery at the atomic scale. Despite the common belief that aqueous solution is required, we discover that the structure recovery can occur in a virtually solid-state process. Local structural information obtained with NMR spectroscopy shows that the recovery in aqueous solution follows dissolution-recrystallization mechanism, while the solid-state recovery is retro-topotactic, indicating a true "memory effect". The amount of water is key in determining the interactions of water with oxides, thus the memory effect mechanism. The results also provide a more environmentally friendly and economically feasible LDHs preparation route.

Memristive Imitation of Synaptic Transmission and Plasticity.

In this paper, a memristive artificial neural circuit imitating the excitatory chemical synaptic transmission of biological synapse is designed. The proposed memristor-based neural circuit exhibits synaptic plasticity, one of the important neurochemical foundations for learning and memory, which is demonstrated via the efficient imitation of short-term facilitation and long-term potentiation. Moreover, the memristive artificial circuit also mimics the distinct biological attributes of strong stimulation and deficient synthesis of neurotransmitters. The proposed artificial neural model is designed in SPICE, and the biological functionalities are demonstrated via various simulations. The simulation results obtained with the proposed artificial synapse are similar to the biological features of chemical synaptic transmission and synaptic plasticity.

Suspended particulate variations and mass size distributions of incense burning at Tzu Yun Yen temple in Taiwan, Taichung.

Ambient suspended particulate concentrations were measured at Tzu Yun Yen temple in this study. This is characteristic place of incense burning and indoor air pollution sampling site. A universal sampler, micro-orifice uniform deposited impactor (MOUDI) sampler and dry deposition plate were used to measure particulate concentrations. Tzu Yun Yen temple is a typical famous Buddhist-Taoist combined temple, where many pilgrims come from different areas and various belief systems indicating the eclecticism of the temple. The average number of people visiting this temple is above 5000 per day. The PM2.5/PM10 ratios ranged between 31.2 and 87.4% and averaged 69.6+/-12.3% during the incense-burning period, respectively. The results also demonstrated that the fine particulates concentrations (PM2.5) constituted the majority of indoor-suspended particulates at Tzu Yun Yen temple. PM10 concentration was 110.1 microg m(-3) for Zhong Yuan Jie (A festival on the seventh full moon in a lunar year, otherwise known as a summer lantern festival and (or) the commemoration of the dead. Almost all temples have maximum pilgrims for the commemoration of the dead on this day.) and the 1st or 15th of nong li (Nong li is a Chinese lunar calendar system in which 1 year is divided into fixed periods, and the beginning and end of a year is determined. The new moon and full moon are the 1st and 15th, respectively of each month in the Chinese lunar calendar. Many pilgrims appeared at each temple for blessing and good luck for themselves on the 1st and 15th of each Chinese lunar month.) with numbers higher than non-Zhong Yuan Jie and non-1st or -15th day of nong li (average = 85.5 microg m(-3)). In general, the average dry deposition flux (49.4 mg m(-2) day(-1)) in the indoor environment is lower than those measured in the outdoor environment (184.0 mg m(-2) day(-1)) in this study. The mean dry deposition flux of indoor/outdoor ratio was 46.2%. The average mass size distributions were bimodal with the major peaks within 0.56-1 microm and 5.6-10 microm, respectively during non-Zhong Yuan Jie and non-1st or -15th days of each month (Chinese lunar calendar). The average mass size distributions were bimodal with the major peaks within 0.18-0.32 microm and 5.6-10 microm, respectively during Zhong Yuan Jie and the 1st or 15th of nong li of each month (Chinese lunar calendar) at Tzu Yun Yen temple.

[Exposure degree of important non-target arthropods to Cry2Aa in Bt rice fields].

Based on the principle of "risk = hazard x exposure", the selected representative nontarget organisms in the assessment of the potential effects of insect-resistant genetically modified (GM) crops on non-target arthropods in laboratory are generally the arthropod species highly exposed to the insecticidal proteins expressed by the GM crops in farmland ecosystem. In order to understand the exposure degree of the important arthropod species to Cry proteins in Bt rice fields, and to select the appropriate non-target arthropods in the risk assessment of insect-resistant GM crops, the enzyme-linked immunosorbent assay (ELISA) was conducted to measure the Cry2Aa protein concentration in the arthropods collected from the cry2Aa rice fields at different rice growth stages. The results showed that there was a significant difference in the Cry2Aa content protein concentration in different arthropod species. Some species did not contain Cry2Aa protein, while some species contained larger amounts of Cry2Aa protein. Relative to the arthropods colleted after rice anthesis, the arthropods colleted in rice anthesis contained relative higher concentrations of Cry2Aa protein, especially for the predacious arthropods. No Cry proteins were detected in parasitic arthropods. This study provided references for the laboratory assessment of the effects of GM rice on nontarget arthropods.

Memristor bridge synapse-based neural network and its learning.

Analog hardware architecture of a memristor bridge synapse-based multilayer neural network and its learning scheme is proposed. The use of memristor bridge synapse in the proposed architecture solves one of the major problems, regarding nonvolatile weight storage in analog neural network implementations. To compensate for the spatial nonuniformity and nonideal response of the memristor bridge synapse, a modified chip-in-the-loop learning scheme suitable for the proposed neural network architecture is also proposed. In the proposed method, the initial learning is conducted in software, and the behavior of the software-trained network is learned by the hardware network by learning each of the single-layered neurons of the network independently. The forward calculation of the single-layered neuron learning is implemented on circuit hardware, and followed by a weight updating phase assisted by a host computer. Unlike conventional chip-in-the-loop learning, the need for the readout of synaptic weights for calculating weight updates in each epoch is eliminated by virtue of the memristor bridge synapse and the proposed learning scheme. The hardware architecture along with the successful implementation of proposed learning on a three-bit parity network, and on a car detection network is also presented.

Supercritical fluid CO2 extraction of Acorus calamus L. (Arales: Araceae) and its contact toxicity to Sitophilus zeamais Motschusky (Coleoptera: Curculionidae).

Contact toxicities of Acorus calamus L. (Arales: Araceae) extracts obtained from four published extraction methods: soakage, soxhlet, ultrasonic and supercritical fluid CO2 (SFE-CO2), were compared in this study. Under the given extraction conditions, SFE-CO2 extract exhibited the highest contact toxicity against S. zeamais of the four methods. With the SFE-CO2 method, extraction temperature, pressure, time and the amount of EtOH (the extraction solvent) were identified as having a significant effect on the extract. Orthogonal experiments showed that the optimal extraction parameters were: temperature--55°C, pressure--35 MPa, time--40 min and EtOH--150 mL per 200 g of dry powder. Under these conditions, the yield was 4.12% and the LD50 of the extract against S. zeamais after 96 h of treatment was 27.26 µg cm⁻². ß-asarone was the dominant component of the extract derived from the SFE-CO2 method, accounting for 24.39% of the extract. These results may contribute to the designing of large-scale production processes for obtaining A. calamus extract, which proves to be an effective alternative for the control of stored product insect pests.

The measurements of ambient particulates (TSP, PM2.5, PM2.5-10), chemical component concentration variation, and mutagenicity study during 1998-2001 in central Taiwan.

During June 1998 and February 2001, the experiments of this study were conducted at four sampling sites (THUPB, THUC, HKIT and CCRT) with different characters (suburban, rural and traffic). The chemical components (Cl-, Na+, K+, Mg2+, Ca2+, Fe, Zn, Pb, Ni) in suspended particle were also analyzed simultaneously. The particulate mass concentrations are higher in the traffic site (CCRT) than the other sampling sites in this study. This is because that high traffic density flow characterized CCRT sampling site. Besides, the fine particle (PM2.5) concentration was the dominant species out of the total suspended particles in central Taiwan, Taichung. The same phenomenon is found in most of cities around the world. Moreover, chloride, nitrate sulfate and ammonium are higher in Taiwan than other sampling sites in the world. The results also indicated that the control of acidic and secondary aerosol pollutants have become an important issue in Taiwan. In addition, mutagenic assays on the organic extracts of airborne particulates at different sampling sites were also conducted in central Taiwan. The data obtained here also reflected that the mutagenicity of the suspended particulates are significantly higher in winter period than it occurred in summer period.