Explore the vast range of titles available.

The retrieval of hit/lead compounds with novel scaffolds during early drug development is an important but challenging task. Various generative models have been proposed to create drug-like molecules. However, the capacity of these generative models to design wet-lab-validated and target-specific molecules with novel scaffolds has hardly been verified. We herein propose a generative deep learning (GDL) model, a distribution-learning conditional recurrent neural network (cRNN), to generate tailor-made virtual compound libraries for given biological targets. The GDL model is then applied to RIPK1. Virtual screening against the generated tailor-made compound library and subsequent bioactivity evaluation lead to the discovery of a potent and selective RIPK1 inhibitor with a previously unreported scaffold, RI-962. This compound displays potent in vitro activity in protecting cells from necroptosis, and good in vivo efficacy in two inflammatory models. Collectively, the findings prove the capacity of our GDL model in generating hit/lead compounds with unreported scaffolds, highlighting a great potential of deep learning in drug discovery. Retrieval of a new starting active compound with a novel scaffold during early drug development is an important but challenging task. Here, the authors propose a generative deep learning model and by applying this model they discover a potent and highly selective RIPK1 inhibitor with a previously unreported scaffold.

Emerging SARS-CoV-2 variants, particularly the Omicron variant and its sublineages, continually threaten the global public health. Small molecule antivirals are an effective treatment strategy to fight against the virus. However, the first-generation antivirals either show limited clinical efficacy and/or have some defects in pharmacokinetic (PK) properties. Moreover, with increased use of these drugs across the globe, they face great pressure of drug resistance. We herein present the discovery and characterization of a new generation antiviral drug candidate (SY110), which is a potent and selective inhibitor of SARS-CoV-2 main protease (M pro ). This compound displayed potent in vitro antiviral activity against not only the predominant SARS-CoV-2 Omicron sublineage BA.5, but also other highly pathogenic human coronaviruses including SARS-CoV-1 and MERS-CoV. In the Omicron-infected K18-hACE2 mouse model, oral treatment with SY110 significantly lowered the viral burdens in lung and alleviated the virus-induced pathology. Importantly, SY110 possesses favorable PK properties with high oral drug exposure and oral bioavailability, and also an outstanding safety profile. Furthermore, SY110 exhibited sensitivity to several drug-resistance M pro mutations. Collectively, this investigation provides a promising new drug candidate against Omicron and other variants of SARS-CoV-2.

Nitrate (NO3−) reduction in water has been receiving increasing attention in water treatment due to its carcinogenic and endocrine-disrupting properties. This study employs a novel advanced reduction process, the UV/oxalic acid/ferric iron systems (UV/C2O42−/Fe3+ systems), in reducing NO3− due to its high reduction efficiency, excellent selectivity, and low treatment cost. The UV/C2O42−/Fe3+ process reduced NO3− with pseudo-first-order reaction rate constants of 0.0150 ± 0.0013 min−1, minimizing 91.4% of 60 mg/L NO3− and reaching 84.2% of selectivity for gaseous nitrogen after 180 min at pHini. 7.0 and 0.5 mg/L dissolved oxygen (DO). Carbon dioxide radical anion (CO2•−) played a predominant role in reducing NO3−. Gaseous nitrogen and NH4+, as well as CO2, were the main nitrogen- and carbon-containing products, respectively, and reduction pathways were proposed accordingly. A suitable level of oxalic acids (3 mM) and NO3− (60 mg/L) was recommended; increasing initial iron concentrations and UV intensity increased NO3− reduction. Instead, increasing the solution pH decreased the reduction, and 0.5–8.0 mg/L DO negligibly affected the process. Moreover, UV/C2O42−/Fe3+ systems were not retarded by 0.1–10 mM SO42− or Cl− or 0.1–1.0 mM HCO3− but were prohibited by 10 mM HCO3− and 30 mg-C/L humic acids. There was a lower reduction of NO3− in simulated groundwater (72.8%) than deionized water after 180 min at pHini. 7.0 and 0.5 mg/L DO, which meets the drinking water standard (<10 mg/L N-NO3−). Therefore, UV/C2O42−/Fe3+ systems are promising approaches to selectively and efficiently reduce NO3− in drinking water.

Emerging SARS-CoV-2 variants, particularly the Omicron variant and its sublineages, continually threaten the global public health. Small molecule antivirals are an effective treatment strategy to fight against the virus. However, the first-generation antivirals either show limited clinical efficacy and/or have some defects in pharmacokinetic (PK) properties. Moreover, with increased use of these drugs across the globe, they face great pressure of drug resistance. We herein present the discovery and characterization of a new generation antiviral drug candidate (SY110), which is a potent and selective inhibitor of SARS-CoV-2 main protease (M ). This compound displayed potent in vitro antiviral activity against not only the predominant SARS-CoV-2 Omicron sublineage BA.5, but also other highly pathogenic human coronaviruses including SARS-CoV-1 and MERS-CoV. In the Omicron-infected K18-hACE2 mouse model, oral treatment with SY110 significantly lowered the viral burdens in lung and alleviated the virus-induced pathology. Importantly, SY110 possesses favorable PK properties with high oral drug exposure and oral bioavailability, and also an outstanding safety profile. Furthermore, SY110 exhibited sensitivity to several drug-resistance M mutations. Collectively, this investigation provides a promising new drug candidate against Omicron and other variants of SARS-CoV-2.

Wastewater containing organic dyes has become one of the important challenges in water treatment due to its high salt content and resistance to natural degradation. In this work, a novelty adsorbent, PEI-SMA, was prepared by grafting polyethyleneimine (PEI) onto styrene-maleic anhydride copolymer (SMA) through an amidation reaction. The various factors, such as pH, adsorbent dosage, contact time, dye concentration, and temperature, which may affect the adsorption of PEI-SMA for Reactive Black 5 (RB5), were systematically investigated by static adsorption experiments. The adsorption process of PEI-SMA for RB5 was more consistent with the Langmuir isotherm model and the pseudo-second-order model, suggesting a single-layer chemisorption. PEI-SMA exhibits excellent adsorption performance for RB5 dye, with a maximum adsorption capacity of 1749.19 mg g–1 at pH = 2. Additionally, PEI-SMA exhibited highly efficient RB5 competitive adsorption against coexisting Cl− and SO42− ions and cationic dyes. The adsorption mechanism was explored, and it can be explained as the synergistic effect of electrostatic interaction, hydrogen bonding and π–π interaction. This study demonstrates that PEI-SMA could act as a high performance and promising candidate for the effective adsorption of anionic dyes from aqueous solutions.

This study investigates finite-time energy-to-peak control for pure-time-delay Markov jump systems. The main objective is to obtain some theorems such that the corresponding pure-time-delay Markov jump systems are finite time energy-to-peak stable or stabilizable. First, based on mathematical transformation, the pure-time-delay Markov jump systems are described in a model description that includes the current system state and several distributed time-delay items. Second, according to linear matrix inequality (LMI) theory, a positive energy functional is constructed, which includes a triple integral item. Then, after some mathematical operations, some sufficient conditions are obtained for Markov jump systems to be finite-time energy-to-peak stable or stabilizable. The obtained results are expressed in LMIs, which can be conveniently solved by computers. Finally, examples are given to show the usefulness of the obtained theorems.

As a renewable and degradable biomass material, cellulose diacetate (CDA) has significant development potential and has gained widespread interest from researchers. However, its poor thermal stability at high temperatures limits its practical use in the extrusion process and restricts its applications in other fields, such as high-heat airflow filters, battery separators and special textile materials. To enhance the thermal stability of CDA, three heat-resistance additives, i.e., polyphenylene sulfide (PPS), polycarbonate (PC) and polyimide (PI), were introduced to synthesize PPS/CDA, PC/CDA and PI/CDA composite materials through melt extrusion. The incorporation of three heat-resistant additives increased the glass transition temperature (Tg), initial melting temperature (Tmi) and final melting temperature (Tmf) of the composites, and it reduced the heat loss at 195 °C. After conducting the isothermal thermogravimetry test for 3 h at 215 °C in an air atmosphere, the weight loss of PPS/CDA, PC/CDA and PI/CDA composites was 4.6%, 4.1% and 3.7%, respectively, which was 5.1% lower than that of pure CDA. Morphology characterization tests using a 3D digital microscope and a field emission scanning electron microscope (FESEM) revealed the compatibility order with CDA as the following: PC > PPS > PI. Additionally, Fourier transform infrared spectroscopy (FT–IR) disclosed that hydrogen bonds were formed between heat-resistant additives and CDA molecules, and the carbonyl groups in CDA showed conjugation and hyperconjugation effects with the benzene rings in the additives. Therefore, the enhanced thermal stability of CDA composites can be attributed to the molecular entanglement and crosslinking between additives and CDA molecules.

To assess water quality (WQ) online for assuring the safety of drinking water, a novel early warning system integrating a high-frequency monitoring system (HFMS) and data quality control (QC) was developed at Lake Qiandao. The HFMS was designed for monitoring water quality, nutrient inputs by main tributaries, water currents and meteorology at different sites at Lake Qiandao. The EWS focused on data availability, a QC method, a statistical analysis method and data applications instead of technological aspects for sondes, wireless data transfer and interface software development. QC was implemented before use to delete the abnormal values of outliers, to detect change points, to analyse the change trend, to interpolate discrete missing measurements, and find continuous missing or wrong observations caused by technical problems with the sonde. For demonstrating advantages and data availability, surface and profiling measurements at two sites were plotted. The plots show obvious seasonal and diel variations, demonstrating the success of integration of the system with advanced automated technology and good QC. This successfully developed system is now not only giving early warning signals, but also providing critical WQ information for the security of drinking water diverted to Hangzhou city through a tunnel of 110 km length. The automatic monitoring data with QC is also being used to produce initial conditions for WQ prediction based on a three dimensional hydrodynamic-ecosystem model.

To assess water quality (WQ) online for assuring the safety of drinking water, a novel early warning system integrating a high-frequency monitoring system (HFMS) and data quality control (QC) was developed at Lake Qiandao. The HFMS was designed for monitoring water quality, nutrient inputs by main tributaries, water currents and meteorology at different sites at Lake Qiandao. The EWS focused on data availability, a QC method, a statistical analysis method and data applications instead of technological aspects for sondes, wireless data transfer and interface software development. QC was implemented before use to delete the abnormal values of outliers, to detect change points, to analyse the change trend, to interpolate discrete missing measurements, and find continuous missing or wrong observations caused by technical problems with the sonde. For demonstrating advantages and data availability, surface and profiling measurements at two sites were plotted. The plots show obvious seasonal and diel variations, demonstrating the success of integration of the system with advanced automated technology and good QC. This successfully developed system is now not only giving early warning signals, but also providing critical WQ information for the security of drinking water diverted to Hangzhou city through a tunnel of 110 km length. The automatic monitoring data with QC is also being used to produce initial conditions for WQ prediction based on a three dimensional hydrodynamic-ecosystem model.

为准确识别烤烟种植分布特征，基于2020年玉溪烤烟大田生长期不同时相的11景Sentinel-1卫星影像，结合野外实地调研，分析不同土地利用类型合成孔径雷达（SAR）极化信息的可区分度，并结合70%样地训练随机森林分类方法提取不同土地利用的分布范围，最后用30%调研样地验证烤烟种植的提取精度。结果表明，垂直发射垂直接收（VV）相较于垂直发射水平接收（VH）更能区分不同土地利用类型，VH、VV、VV+VH和VV+VH+VV/VH四种极化信息组合方式中，地物分类精度最高的为VV+VH组合，总体分类精度为87.6%，Kappa系数为0.847，其中，烤烟种植识别的制图精度为96.3%，用户精度为89.4%。在玉溪典型烟区采用多时相SAR识别烤烟种植分布，识别准确率基本能满足区域烤烟种植识别的精度要求。To reveal the spatial distribution of flue-cured tobacco, 11 Sentinel-1 images obtained in the growing season of flue-cured tobacco in Yuxi City, Yunnan Province in 2020 were utilized in combination with field survey data. First, the polarization of the synthetic aperture radar（SAR）with different land use types were compared. Next, the random forest model was trained with 70% field survey data to make a land-use map. Finally, the identification accuracy of the flue-cured tobacco was validated with 30% field survey data. The results indicated that vertical transmit-vertical receive（VV）polarization distinguished land-use types better than the vertical transmit-horiz