ChemReco: automated recognition of hand-drawn carbon-hydrogen-oxygen structures using deep learning.

Chemical molecular structures are a direct and convenient means of expressing chemical knowledge, playing a vital role in academic communication. In chemistry, hand drawing is a common task for students and researchers. If we can convert hand-drawn chemical molecular structures into machine-readable formats, like SMILES encoding, computers can efficiently process and analyze these structures, significantly enhancing the efficiency of chemical research.

Subnano-Fe (Co, Ni) clusters anchored on halloysite nanotubes: an efficient Fenton-like catalyst for the degradation of tetracycline.

Iron-based catalysts are environmentally friendly, and iron minerals are abundant in the earth's crust, with great potential advantages for PMS-based advanced oxidation process applications. However, homogeneous Fe/PMS systems suffer from side reactions and are challenging to reuse. Therefore, developing catalysts with improved stability and activity is a long-term goal for practical Fe-based catalyst applications.

Development of high-resolution multiple-SNP arrays for genetic analyses and molecular breeding through genotyping by target sequencing and liquid chip.

Genotyping platforms, as critical supports for genomics, genetics, and molecular breeding, have been well implemented at national institutions/universities in developed countries and multinational seed companies that possess high-throughput, automatic, large-scale, and shared facilities. In this study, we integrated an improved genotyping by target sequencing (GBTS) system with capture-in-solution (liquid chip) technology to develop a multiple single-nucleotide polymorphism (mSNP) approach in which mSNPs can be captured from a single amplicon. From one 40K maize mSNP panel, we developed three types of markers (40K mSNPs, 251K SNPs, and 690K haplotypes), and generated multiple panels with various marker densities (1K-40K mSNPs) by sequencing at different depths.

The high diversity of SARS-CoV-2-related coronaviruses in pangolins alerts potential ecological risks.

Understanding the zoonotic origin and evolution history of SARS-CoV-2 will provide critical insights for alerting and preventing future outbreaks. A significant gap remains for the possible role of pangolins as a reservoir of SARS-CoV-2 related coronaviruses (SC2r-CoVs). Here, we screened SC2r-CoVs in 172 samples from 163 pangolin individuals of four species, and detected positive signals in muscles of four and, for the first time, one .

The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN.

To address the problem of unstable training and poor accuracy in image classification algorithms based on generative adversarial networks (GAN), a novel sensor network structure for classification processing using auxiliary classifier generative adversarial networks (ACGAN) is proposed in this paper. Firstly, the real/fake discrimination of sensor samples in the network has been canceled at the output layer of the discriminative network and only the posterior probability estimation of the sample tag is outputted. Secondly, by regarding the real sensor samples as supervised data and the generative sensor samples as labeled fake data, we have reconstructed the loss function of the generator and discriminator by using the real/fake attributes of sensor samples and the cross-entropy loss function of the label.