MolE8: finding DFT potential energy surface minima values from force-field optimised organic molecules with new machine learning representations

The use of machine learning techniques in computational chemistry has gained significant momentum since large molecular databases are now readily available. Predictions of molecular properties using machine learning have advantages over the traditional quantum mechanics calculations because they can be cheaper computationally without losing the accuracy. We present a new extrapolatable and explainable molecular representation based on bonds, angles and dihedrals that can be used to train machine learning models. The trained models can accurately predict the electronic energy and the free energy of small organic molecules with atom types C, H N and O, with a mean absolute error of 1.2 kcal mol⁻¹. The models can be extrapolated to larger organic molecules with an average error of less than 3.7 kcal mol⁻¹ for 10 or fewer heavy atoms, which represent a chemical space two orders of magnitude larger. The rapid energy predictions of multiple molecules, up to 7 times faster than previous ML models of similar accuracy, has been achieved by sampling geometries around the potential energy surface minima. Therefore, the input geometries do not have to be located precisely on the minima and we show that accurate density functional theory energy predictions can be made from force-field optimised geometries with a mean absolute error 2.5 kcal mol⁻¹.

New representations and machine learning calculate DFT minima from force field geometries.     

Reaction dynamics studied via femtosecond X-ray liquidography at X-ray free-electron lasers

X-ray free-electron lasers (XFELs) provide femtosecond X-ray pulses suitable for pump–probe time-resolved studies with a femtosecond time resolution. Since the advent of the first XFEL in 2009, recent years have witnessed a great number of applications with various pump–probe techniques at XFELs. Among these, time-resolved X-ray liquidography (TRXL) is a powerful method for visualizing structural dynamics in the liquid solution phase. Here, we classify various chemical and biological molecular systems studied via femtosecond TRXL (fs-TRXL) at XFELs, depending on the focus of the studied process, into (i) bond cleavage and formation, (ii) charge distribution and electron transfer, (iii) orientational dynamics, (iv) solvation dynamics, (v) coherent nuclear wavepacket dynamics, and (vi) protein structural dynamics, and provide a brief review on each category. We also lay out a plausible roadmap for future fs-TRXL studies for areas that have not been explored yet.

Femtosecond X-ray liquidography using X-ray free-electron lasers (XFELs) visualizes various aspects of reaction dynamics.     

Mesoporous sodium four-coordinate aluminosilicate nanoparticles modulate dendritic cell pyroptosis and activate innate and adaptive immunity

Pyroptosis is a programmed cell death widely studied in cancer cells for tumour inhibition, but rarely in dendritic cell (DC) activation for vaccine development. Here, we report the synthesis of sodium stabilized mesoporous aluminosilicate nanoparticles as DC pyroptosis modulators and antigen carriers. By surface modification of sodium-stabilized four-coordinate aluminium species on dendritic mesoporous silica nanoparticles, the resultant Na-^IVAl-DMSN significantly activated DC through caspase-1 dependent pyroptosis via pH responsive intracellular ion exchange. The released proinflammatory cellular contents further mediated DC hyperactivation with prolonged cytokine release. In vivo studies showed that Na-^IVAl-DMSN induced enhanced cellular immunity mediated by natural killer (NK) cells, cytotoxic T cells, and memory T cells as well as humoral immune response. Our results provide a new principle for the design of next-generation nanoadjuvants for vaccine applications.

Na-^IVAl-DMSN acts as both antigen carriers and modulators to “hyperactivate” dendritic cells (DCs) via potassium (K⁺) efflux dependent pyroptosis, eventually leading to enhanced adaptive and innate immunity.     

Development of a Quantitative Method for Detection of Multiclass Veterinary Drugs in Feed Using Modified QuPPe Extraction and LC–MS/MS

A method was developed for the rapid and quantitative analysis of 30 veterinary drugs belonging to 17 classes (amphenicols (1), anthelmintics (1), cephalosporins (4), coccidiostats (1), lincosamides (1), macrolide (1), nitroimidazole (1), penicillins (3), phenylhydrazines (1), polypeptides (1), pyrethrins (1), quinolones (5), sulfonamides (3), tetracycline (3), neuroleptic agents (1), triazene trypanocidal agents (1), other. (1)) in feeds. The proposed method with a modified Quick Polar Pesticides (QuPPe) sample preparation was validated for the determination of 30 veterinary drugs in feed samples by liquid chromatography triple-quadrupole mass spectrometry (LC–MS/MS). The sample was extracted with methanol containing 1% acetic acid and purified by dispersive solid-phase extraction (d-SPE) with C18. Good linearity (r² ≥ 0.98) was observed, and the LOQ values ranged from 10 to 200 µg/kg. Average recoveries ranged from 70.8 to 118.4%, and the relative standard deviation was ≤ 18.7%. This validated method was used in the determination of 30 veterinary drugs in 142 feed samples obtained from South Korea. The results show that lincomycin was present in only one of the tested feed samples, although it was detected at a value lower than the LOQ. In conclusion, this multi-residue method can be used for screening through the detection and quantitation of residual multiclass veterinary drugs in feed samples.     

Water Status and Predictive Models of Moisture Content during Drying of Soybean Dregs Based on LF-NMR

To explore the drying characteristics of soybean dregs and a nondestructive moisture content test method, in this study, soybean dregs were dried with hot air (80 °C), the moisture content was measured using the drying method, water status was analyzed using low-field nuclear magnetic resonance (LF-NMR) and the moisture content prediction models were built and validated. The results revealed that the moisture contents of the soybean dregs were 0.57 and 0.01 g/g(w.b.), respectively, after drying for 5 and 7 h. The effective moisture diffusivity increased with the decrease in moisture content; it ranged from 5.27 × 10⁻⁹ to 6.96 × 10⁻⁸ m²·s⁻¹. Soybean dregs contained bound water (T₂₁), immobilized water (T₂₂) and free water (T₂₃ and T₂₃’). With the proceeding of drying, all of the relaxation peaks shifted left until a new peak (T₂₃’) appeared; then, the structure of soybean dregs changed, and the relaxation peaks reformed, and the peak shifted left again. The peak area may predict the moisture content of soybean dregs, and the gray values of images predict the moisture contents mainly composed of free water or immobilized water. The results may provide a reference for drying of soybean dregs and a new moisture detection method.     

The Influence of Tyrosol-Enriched Rhodiola sachalinensis Extracts Bioconverted by the Mycelium of Bovista plumbe on Scopolamine-Induced Cognitive,Behavioral, and Physiological Responses in Mice

Alzheimer’s disease (AD) is an age-related neurodegenerative disorder characterized by cognitive deficits, which are accompanied by memory loss and cognitive disruption. Rhodiola sachalinensis (RSE) is a medicinal plant that has been used in northeastern Asia for various pharmacological activities. We attempted to carry out the bioconversion of RSE (Bio-RSE) using the mycelium of Bovista plumbe to obtain tyrosol-enriched Bio-RSE. The objective of this study was to investigate the effects of Bio-RSE on the activation of the cholinergic system and the inhibition of oxidative stress in mice with scopolamine (Sco)-induced memory impairment. Sco (1 mg/kg body weight, i.p.) impaired the mice’s performance on the Y-maze test, passive avoidance test, and water maze test. However, the number of abnormal behaviors was reduced in the groups supplemented with Bio-RSE. Bio-RSE treatment improved working memory and avoidance times against electronic shock, increased step-through latency, and reduced the time to reach the escape zone in the water maze test. Bio-RSE dramatically improved the cholinergic system by decreasing acetylcholinesterase activity and regulated oxidative stress by increasing antioxidant enzymes (superoxide dismutase (SOD) and catalase (CAT)). The reduction in nuclear factor erythroid 2-related factor 2 (Nrf2)/heme oxygenase-1 (HO-1) signaling in the brain tissue due to scopolamine was restored by the administration of Bio-RSE. Bio-RSE also significantly decreased amyloid-beta 1–42 (Aβ1–42) and amyloid precursor protein (APP) expression. Moreover, the increased malondialdehyde (MDA) level and low total antioxidant capacity in Sco-treated mouse brains were reversed by Bio-RSE, and an increase in Nrf2 and HO-1 was also observed. In conclusion, Bio-RSE protected against Sco-induced cognitive impairment by activating Nrf2/HO-1 signaling and may be developed as a potential beneficial material for AD.     

高频地波雷达下的多目标跟踪数据处理

根据高频地波雷达监测海上低速移动目标的要求及该类雷达的特点,提出了一种与高频地波雷达目标跟踪相适应的多目标跟踪数据处理方案。在该方案中对BTC(bayes tracking confirm)方法与BTT(bayes tracking termination)方法进行了改进,解决了上述两种方法在高频地波雷达实际应用中存在的问题。提出了贝叶斯准则与次数判定相结合的跟踪起始和终结方法。模拟结果表明,该方案能够稳定、有效地实现高频地波雷达密集回波下的多目标跟踪。     

高效凹面聚光集热海水蒸馏淡化及供暖混合装置

在便携的高效凹面聚光集热装置基础上,通过综合利用所获得的太阳热辐射能实现海水淡化蒸馏、温差发电以及温箱供暖,从而达到太阳能高效综合利用的目的.为岛礁等特殊环境下的海水淡化与供暖需求提供一种途径.     

几何参数对喷射器性能的影响分析

运用计算流体动力学软件Fluent对喷射器进行数值模拟,研究了工作喷嘴喉部直径、等截面混合室直径和长度等几何参数对喷射器性能的影响,并对数值模拟结果进行了分析。研究表明:在一定的工况下,工作喷嘴及等截面混合室对喷射器性能有极大的影响,存在最佳的喉部和混合室直径使得喷射系数最大;随着混合室长度的增加,喷射器的喷射系数将逐渐降低。     

$lt;i$gt;γ$lt;/i$gt;射线总剂量辐照效应对应变Sip型金属氧化物半导体场效应晶体管阈值电压与跨导的影响研究

分析了双轴应变Si p型金属氧化物半导体场效应晶体管(PMOSFET)在γ射线辐照下载流子的微观输运过程, 揭示了γ射线的作用机理及器件电学特性随辐照总剂量的演化规律, 建立了总剂量辐照条件下的双轴应变Si PMOSFET 阈值电压与跨导等电学特性模型, 并对其进行了模拟仿真. 由仿真结果可知, 阈值电压的绝对值会随着辐照总剂量的积累而增加, 辐照总剂量较低时阈值电压的变化与总剂量基本呈线性关系, 高剂量时趋于饱和; 辐照产生的陷阱电荷增加了沟道区载流子之间的碰撞概率, 导致了沟道载流子迁移率的退化以及跨导的降低. 在此基础上, 进行实验验证, 测试结果表明实验数据与仿真结果基本相符, 为双轴应变Si PMOSFET辐照可靠性的研究和应变集成电路的应用与推广提供了理论依据和实践基础. 关键词： 应变Sip型金属氧化物半导体场效应晶体管 总剂量辐照 阈值电压 跨导