Prediction of protein content in paddy rice () 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.

Can Subsurface Oxygen Species in Oxides Participate in Catalytic Reactions? An O 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 O solid-state nuclear magnetic resonance (NMR) spectroscopy, which can distinguish subsurface oxygen ions in CeO (111) nanorods, we explore the effects of subsurface species of oxides in CO oxidation reactions. The intensities of the O NMR signals due to surface and subsurface oxygen ions decrease after the introduction of CO into CeO nanorods, with a more significant decrease observed for the latter, confirming the participation of subsurface oxygen species.

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 O solid-state NMR spectra.

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.

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.

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.

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.

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.

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.

[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.

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.

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.

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.

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.

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.