Multifunctional Plasmonic Grating Based on the Phase Modulation of Excitation Light

Multifunctional optical devices are desirable at all times due to their features of flexibility and high efficiency. Based on the principle that the phase of excitation light can be transferred to the generated surface plasmon polaritons (SPPs), a plasmonic grating with three functions is proposed and numerically demonstrated. The Cherenkov SPPs wake or nondiffracting SPPs Bessel beam or focusing SPPs field can be correspondingly excited for the excitation light, which is modulated by a linear gradient phase or a symmetrical phase or a spherical phase, respectively. Moreover, the features of these functions such as the propagation direction of SPPs wake, the size and direction of the SPPs Bessel beam, and the position of SPPs focus can be dynamically manipulated. In consideration of the fact that no extra fabrication is required to obtain the different SPPs fields, the proposed approach can effectively reduce the cost in practical applications.

Application of Linear Gradient Solvent System in Centrifugal Partition Chromatography Facilitating Bioassay-Guided Fractionation of Yongdamsagan-Tang, Traditional Oriental Decoction

As important pharmaceutical resources, traditional herbal medicines retain continuous attention. To do that, isolation and identification of bioactive molecules from traditional herbal decoction are important. However, conventional fractionation through octadecyl silica column faces irreversible sample adsorption that causes a bias in bioactivity assessment. However, liquid-liquid chromatographic system suffers tedious K value calculation as well as insufficient capacity in separation power when crude extract composed of widely ranging polarities. Here, we developed a comprehensive linear gradient solvent system for centrifugal partition chromatography (CPC) to aid bioassay-guided isolation. The lower aqueous phase of the n-hexane-acetonitrile-water (10:2:8, v/v) was used as the stationary, whereas its upper organic phase followed by the upper phase of ethyl acetate-acetonitrile-water and water-saturated n-butanol-acetonitrile-water in the same ratio were eluted in a linear gradient mode, thereby increasing polarity in the mobile phase. The HPLC profiling of CPC fraction showed that proposed gradient CPC was suitable to separate metabolites from Yongdamsagan-Tang, a traditional medicinal decoction made of ten herbal plants. Exhibiting a high recovery yield of 98.3%, antioxidant response element (ARE) luciferase-inducing assay in HepG2 cells indicated that the fractions composed of baicalein and wogonin, the marker natural products of Scutellaria baicalensis, were to be the most effective molecules from Yongdamsagan-Tang. The presented results demonstrated that bioassay-guided separation that assisted with a linear gradient CPC is an incomparable alternative to HPLC and biphasic CPC in terms of higher yield rate and redundant K value calculation, respectively, which led to an unbiased/time-saving separation and identification of bioactive molecules from the complex crude extract of natural products.

A Simple and Efficient Two-Dimensional High-Speed Counter-Current Chromatography Linear Gradient and Isocratic Elution Modes for the Preparative Separation of Coumarins from Roots of Toddalia asiatica (Linn.) Lam.

Toddalia asiatica (L.) Lam. (Rutaceae) has shown a broad spectrum of biological properties, such as anti-inflammatory, antioxidant, antimicrobial, anti-HIV, and anticancer properties. The present study is concerned with the separation of the main components with broad partition coefficients (KD values) from T. asiatica, using linear gradient high-speed counter-current chromatography (LGCCC) combined with an off-line two-dimensional (2D) mode. Similar to the binary gradient HPLC, the LGCCC mode is operated by the adjustment of the proportion between the mobile phase of 5:5:1:9 (v/v) (pump A) and 5:5:4.5:5.5 (v/v) (pump B) in an n-hexane/ethyl acetate/methanol/water solvent system. The off-line 2D-CCC mode was used in this study for the secondary separation of two similar KD value compounds with n-hexane/ethyl acetate/methanol/water (5:5:4:6, v/v). Notably, six coumarins, namely, tomentin (1), toddalolactone (2), 5,7,8-trimethoxycoumarin (3), mexoticin (4), isopimpinellin (5), and toddanone (6), were efficiently separated. The structures of the pure compounds were elucidated by spectral techniques and compared with the literature.

Developing an Ensembled Machine Learning Prediction Model for Marine Fish and Aquaculture Production

The fishing industry is identified as a strategic sector to raise domestic protein production and supply in Malaysia. Global changes in climatic variables have impacted and continue to impact marine fish and aquaculture production, where machine learning (ML) methods are yet to be extensively used to study aquatic systems in Malaysia. ML-based algorithms could be paired with feature importance, i.e., (features that have the most predictive power) to achieve better prediction accuracy and can provide new insights on fish production. This research aims to develop an ML-based prediction of marine fish and aquaculture production. Based on the feature importance scores, we select the group of climatic variables for three different ML models: linear, gradient boosting, and random forest regression. The past 20 years (2000–2019) of climatic variables and fish production data were used to train and test the ML models. Finally, an ensemble approach named voting regression combines those three ML models. Performance matrices are generated and the results showed that the ensembled ML model obtains R2 values of 0.75, 0.81, and 0.55 for marine water, freshwater, and brackish water, respectively, which outperforms the single ML model in predicting all three types of fish production (in tons) in Malaysia.

An Attempt to Boost Molecular Descriptors with Quantum-Derived Features in Prediction of Maximum Emission Wavelengths of Chromophores

The following research assesses the capability of machine learning in predicting maximum emission wavelength of organic compounds. The predictions are based on structure descriptors and fingerprints widely applied in cheminformatics. In an attempt to further improve accuracy, developed machine learning models were enriched with quantum mechanics derived features. Multi linear, gradient boosting and random forest regressions were applied. Computers were trained and tested with database of experimental data of optical properties.

An Attempt to Boost Molecular Descriptors with Quantum-Derived Features in Prediction of Maximum Emission Wavelengths of Chromophores

The following research assesses the capability of machine learning in predicting maximum emission wavelength of organic compounds. The predictions are based on structure descriptors and fingerprints widely applied in cheminformatics. In an attempt to further improve accuracy, developed machine learning models were enriched with quantum mechanics derived features. Multi linear, gradient boosting and random forest regressions were applied. Computers were trained and tested with database of experimental data of optical properties.