Voltage‐Driven Reversible Insertion into and Leaving from a Lipid Bilayer: Tuning Transmembrane Transport of Artificial Channels

Three new artificial transmembrane channel molecules have been designed and synthesized by attaching positively charged Arg‐incorporated tripeptide chains to pillar[5]arene. Fluorescent and patch‐clamp experiments revealed that voltage can drive the molecules to insert into and leave from a lipid bilayer and thus switch on and off the transport of K⁺ ions. One of the molecules was found to display antimicrobial activity toward Bacillus subtilis with half maximal inhibitory concentration (IC₅₀) of 10 μM which is comparable to that of natural channel‐forming peptide alamethicin.     

羟基磷灰石/聚乙烯醇/壳聚糖水凝胶在模拟体液中的力学性能

从仿生学角度出发,将自制的人工角膜支架材料羟基磷灰石/聚乙烯醇/壳聚糖(n-HA/PVA/CS)浸泡在模拟体液中,对材料的含水率及力学性能进行了测试,并利用扫描电镜、X射线衍射仪、电感耦合等离子体原子发射光谱仪及热重分析仪研究了材料在模拟体液中的形貌、晶体结构、元素组成及热稳定性.结果表明,在模拟体液中,n-HA/PVA/CS复合水凝胶的含水率为80%~86%,具有较高的拉伸强度,能承受正常眼压,且热稳定性较好.在浸泡后期,n-HA/CS/PVA复合材料对Ca2+的吸附和释放达到动态平衡;而其表面含有微量的纳米羟基磷灰石沉积,有利于纤维细胞的长入.     

Discrete Hf18 Metal-oxo Cluster as a Heterogeneous Nanozyme for Site-Specific Proteolysis

The selective hydrolysis of proteins by non-enzymatic catalysis is difficult to achieve, yet it is crucial for applications in biotechnology and proteomics. Herein, we report that discrete hafnium metal-oxo cluster [Hf₁₈O₁₀(OH)₂₆(SO₄)₁₃⋅(H₂O)₃₃] ( Hf₁₈ ), which is centred by the same hexamer motif found in many MOFs, acts as a heterogeneous catalyst for the efficient hydrolysis of horse heart myoglobin (HHM) in low buffer concentrations. Among 154 amino acids present in the sequence of HHM, strictly selective cleavage at only 6 solvent accessible aspartate residues was observed. Mechanistic experiments suggest that the hydrolytic activity is likely derived from the actuation of Hf^IV Lewis acidic sites and the Brønsted acidic surface of Hf₁₈ . X-ray scattering and ESI-MS revealed that Hf₁₈ is completely insoluble in these conditions, confirming the HHM hydrolysis is caused by a heterogeneous reaction of the solid Hf₁₈ cluster, and not from smaller, soluble Hf species that could leach into solution.     

Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning

Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental times. We present a proof‐of‐concept of the application of deep learning and neural networks for high‐quality, reliable, and very fast NMR spectra reconstruction from limited experimental data. We show that the neural network training can be achieved using solely synthetic NMR signals, which lifts the prohibiting demand for a large volume of realistic training data usually required for a deep learning approach.     

Artificial intelligence–based models for the qualitative and quantitative prediction of a phytochemical compound using HPLC method

Isoquercitrin is a flavonoid chemical compound that can be extracted from different plant species such as Mangifera indica (mango), Rheum nobile , Annona squamosal , Camellia sinensis (tea), and coriander ( Coriandrum sativum L.). It possesses various biological activities such as the prevention of thromboembolism and has anticancer, antiinflammatory, and antifatigue activities. Therefore, there is a critical need to elucidate and predict the qualitative and quantitative properties of this phytochemical compound using the high performance liquid chromatography (HPLC) technique. In this paper, three different nonlinear models including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM),in addition to a classical linear model [multilinear regression analysis (MLR)], were used for the prediction of the retention time (tR) and peak area (PA) for isoquercitrin using HPLC. The simulation uses concentration of the standard, composition of the mobile phases (MP-A and MP-B), and pH as the corresponding input variables. The performance efficiency of the models was evaluated using relative mean square error (RMSE), mean square error (MSE), determination coefficient (DC), and correlation coefficient (CC). The obtained results demonstrated that all four models are capable of predicting the qualitative and quantitative properties of the bioactive compound. A predictive comparison of the models showed that M3 had the highest prediction accuracy among the three models. Further evaluation of the results showed that ANFIS–M3 outperformed the other models and serves as the best model for the prediction of PA. On the other hand, ANN–M3proved its merit and emerged as the best model for tR simulation. The overall predictive accuracy of the best models showed them to be reliable tools for both qualitative and quantitative determination.     

An Autonomous Chemical Robot Discovers the Rules of Inorganic Coordination Chemistry without Prior Knowledge

We present a chemical discovery robot for the efficient and reliable discovery of supramolecular architectures through the exploration of a huge reaction space exceeding ten billion combinations. The system was designed to search for areas of reactivity found through autonomous selection of the reagent types, amounts, and reaction conditions aiming for combinations that are reactive. The process consists of two parts where reagents are mixed together, choosing from one type of aldehyde, one amine and one azide (from a possible family of two amines, two aldehydes and four azides) with different volumes, ratios, reaction times, and temperatures, whereby the reagents are passed through a copper coil reactor. Next, either cobalt or iron is added, again from a large number of possible quantities. The reactivity was determined by evaluating differences in pH, UV‐Vis, and mass spectra before and after the search was started. The algorithm was focused on the exploration of interesting regions, as defined by the outputs from the sensors, and this led to the discovery of a range of 1‐benzyl‐(1,2,3‐triazol‐4‐yl)‐N‐alkyl‐(2‐pyridinemethanimine) ligands and new complexes: [Fe(L¹)₂](ClO₄)₂ ( 1 ); [Fe(L²)₂](ClO₄)₂ ( 2 ); [Co₂(L³)₂](ClO₄)₄ ( 3 ); [Fe₂(L³)₂](ClO₄)₄ ( 4 ), which were crystallised and their structure confirmed by single‐crystal X‐ray diffraction determination, as well as a range of new supramolecular clusters discovered in solution using high‐resolution mass spectrometry.     

基于DIGNET网络的数据融合方法

针对数据融合和目标识别的特点,提出了基于DIGNET自组织聚类人工神经网络的数据融合方法。考虑到多传感器系统测量多个参量的特点,用并行的子网络结构代替中间隐层,实现了基于决策层的信息融合目标识别。利用仿真数据对基于DIGNET的数据融合方法进行了实验研究。实验结果表明,该方法具有数据正确分类率高和抗噪能力强等优点,有效地实现了融合识别。将该方法应用于前视红外和可见光双传感器目标跟踪系统的数据融合识别是可行的。     

平面波输入下海水-海床-结构动力相互作用分析

海洋工程结构的地震反应分析是保证海洋工程结构地震安全的重要环节.由于其所处的复杂环境, 该问题涉及到流固耦合和土-结相互作用.本文基于海水、饱和海床、基岩流固耦合统一计算框架,采用Davidenkov模型和修正的Masing准则考虑饱和海床的非线性,在脉冲SV波垂直入射下, 进行了海域场地和海洋工程结构的动力响应分析. 首先,对比分析了线性自由场和非线性自由场输入情形的海域场地非线性反应,结果表明线性自由场输入时反应不合理,自由场分析和场地分析应该采用相一致的本构模型. 然后,对比分析了海床分别为线性和非线性情形时,海域场地以及海水-海床-结构体系的反应特征. 与线性海床情形相比,非线性对海床反应的影响主要由如下两方面因素控制: 一方面,非线性导致饱和海床模量减小, 饱和海床与基岩间的波阻抗比减小,由基岩到饱和海床间的反射系数和透射系数增加, 导致反应增大; 另一方面,非线性导致阻尼加大, 使海床反应减小. 对于本文算例而言,阻尼对非线性海床结果的影响占主导作用.      

核电结构土-结相互作用分析分区混合计算方法

土-结构相互作用分析是核电结构进行抗震设计和安全评估的重要环节.在核电结构的土-结相互作用分析中,阻尼和非线性是影响结构反应的重要因素. 若采用频域分析,可以方便考虑阻尼,但需通过等效线性化来考虑非线性,不适合于强震作用下的土体非线性.若采用时域分析的逐步积分方法,适合于考虑非线性,但材料阻尼一般采用瑞利阻尼模型,除了紧靠指定阻尼比的少数几个振型外,其他振型的反应将受到瑞利阻尼模型所确定的大阻尼所抑制,造成地震反应与真实情形有较大差异.若采用时域分析的模态叠加法,可合理计入阻尼效应,但模态叠加法不能考虑非线性.因此,如何合理考虑阻尼和非线性是核电结构土-结相互作用分析需要关注的问题.基于此,本文提出一种模态叠加和时步积分结合的土-结相互作用分区算法.其中,出于安全性考虑,地震作用下核电主体结构一般不允许进入非线性,因此结构可采用模态叠加方法,以便合理考虑结构阻尼;土体和基础采用显式时步积分法,可考虑土体非线性;通过人工边界条件考虑无限域的影响 (辐射阻尼).通过简单算例对该方法进行了验证,并用于CAP1400核电结构的土-结相互作用分析中,对比分析了采用模态阻尼和瑞利阻尼时核电结构和场地反应的差异,结果表明结构阻尼模型对场地的反应影响不大,但对结构反应影响明显,在实际工程中应合理选取阻尼模型.