Design of N-11-Azaartemisinins Potentially Active against Plasmodium falciparum by Combined Molecular Electrostatic Potential, Ligand-Receptor Interaction and Models Built with Supervised Machine Learning Methods

N-11-azaartemisinins potentially active against Plasmodium falciparum are designed by combining molecular electrostatic potential (MEP), ligand-receptor interaction, and models built with supervised machine learning methods (PCA, HCA, KNN, SIMCA, and SDA). The optimization of molecular structures was performed using the B3LYP/6-31G* approach. MEP maps and ligand-receptor interactions were used to investigate key structural features required for biological activities and likely interactions between N-11-azaartemisinins and heme, respectively. The supervised machine learning methods allowed the separation of the investigated compounds into two classes: cha and cla, with the properties εLUMO+1 (one level above lowest unoccupied molecular orbital energy), d(C6-C5) (distance between C6 and C5 atoms in ligands), and TSA (total surface area) responsible for the classification. The insights extracted from the investigation developed and the chemical intuition enabled the design of sixteen new N-11-azaartemisinins (prediction set), moreover, models built with supervised machine learning methods were applied to this prediction set. The result of this application showed twelve new promising N-11-azaartemisinins for synthesis and biological evaluation.

Complementary memtransistors for neuromorphic computing: How, what and why

Memtransistors in which the source-drain channel conductance can be nonvolatilely manipulated through the gate signals have emerged as promising components for implementing neuromorphic computing.On the other side,it is known that the complementary metal-oxide-semiconductor(CMOS)field effect transistors have played the fundamental role in the modern integrated circuit technology.Therefore,will complementary memtransistors(CMT)also play such a role in the future neuromorphic circuits and chips?In this review,various types of materials and physical mechanisms for constructing CMT(how)are inspected with their merits and need-to-address challenges discussed.Then the unique properties(what)and poten-tial applications of CMT in different learning algorithms/scenarios of spiking neural networks(why)are reviewed,including super-vised rule,reinforcement one,dynamic vision with in-sensor computing,etc.Through exploiting the complementary structure-related novel functions,significant reduction of hardware consuming,enhancement of energy/efficiency ratio and other advan-tages have been gained,illustrating the alluring prospect of design technology co-optimization(DTCO)of CMT towards neuro-morphic computing.

Extraction of Planting Information of Winter Wheat in a Province Based on GF-1/WFV Images

In order to explore the adaptability of domestic high-resolution GF-1 satellite images in the extraction of planting information of crops especially in a province, based on the 16-meter remote sensing images of a multi-spectral wide-spectrum camera （WFV） carried by the GF-1 satellite as well as land use type and field survey data of Shandong Province, the planting area and distribution regions of winter wheat in Shandong Province （the main producing area of winter wheat in China） in 2016 were extracted by decision tree classification method and supervised classification- maximum likelihood classification method, and the accuracy of the classification results was verified based on ground survey data and data published by the statistics bureau. The results showed that the method of taking the GF-1/WFV images as the main source of data, introducing multi-source information into the decision tree and supervised classification models, and then calculating the planting area of winter wheat in the province was feasible. The total accuracy of remote sensing interpretation of winter wheat in Shandong Province in 2016 reached 92.1 %, and Kappa coefficient was 0.806. The planting area of winter wheat extracted based on the remote sensing images in the province was slightly smaller than the area pro-vided by the statistics department, and the extraction accuracy of the area was 93.0%. Research indicates that GF-1/WFV images have great po-tential for development and application in remote sensing monitoring of planting information of crops in a province.

Best practices in managing, supervising, and assessing architectural graduation projects： A quantitative study

Graduation project courses refer to the culmination of the learning experiences of higher education. These courses consolidate the disciplinary knowledge gained during architectural education while they integrate most of the learning outcomes of a program, which are intended to prepare students for their transition to the profession of architecture. The educational methods of these courses require constant attention, regular review, and continuous development to remain consistent with the changing standards of the profession given the significance of these courses. The problem lies in the diversity and controversy of these methods, thereby implying inconsistency in the best practices. In this study, several questions are raised in terms of the nature of these courses, enrollment criteria, topic selection, learning experience, and assessment methods. This study aims to investigate the best practices of managing, supervising, and assessing architectural graduation projects to provide guidelines for establishing and/or developing these courses. An analytical deductive methodology is adopted. This methodology is supported by a survey of a sample of 105 worldwide academic architects and is structured into four sections, namely, the analysis of the components of graduation projects, the survey and its procedures, the quantitative findings of the survey, and a discussion of the issues and results. This study draws conclusions to its research questions, thereby extending its influence on the quality of architectural programs and the benefits for individuals who are concerned with their development.

SCGPred:A Score-based Method for Gene Structure Prediction by Combining Multiple Sources of Evidence

Predicting protein-coding genes still remains a significant challenge. Although a variety of computational programs that use commonly machine learning methods have emerged, the accuracy of predictions remains a low level when implementing in large genomic sequences. Moreover, computational gene finding in newly se- quenced genomes is especially a difficult task due to the absence of a training set of abundant validated genes. Here we present a new gene-finding program, SCGPred, to improve the accuracy of prediction by combining multiple sources of evidence. SCGPred can perform both supervised method in previously well-studied genomes and unsupervised one in novel genomes. By testing with datasets composed of large DNA sequences from human and a novel genome of Ustilago maydi, SCGPred gains a significant improvement in comparison to the popular ab initio gene predictors. We also demonstrate that SCGPred can significantly improve prediction in novel genomes by combining several foreign gene finders with similarity alignments, which is superior to other unsupervised methods. Therefore, SCGPred can serve as an alternative gene-finding tool for newly sequenced eukaryotic genomes. The program is freely available at http：//bio.scu.edu.cn/SCGPred/.