Fabrication and properties of pressure-sintered reaction-bonded Si3N4 ceramics with addition of Eu2O3–MgO–Y2O3

Dense pressure-sintered reaction-bonded Si₃N₄ (PSRBSN) ceramics were obtained by a hot-press sintering method. Precursor Si powders were prepared with Eu₂O₃–MgO–Y₂O₃ sintering additive. The addition of Eu₂O₃–MgO–Y₂O₃ was shown to promote full nitridation of the Si powder. The nitrided Si₃N₄ particles had an equiaxial morphology, without whisker formation, after the Si powders doped with Eu₂O₃–MgO–Y₂O₃ were nitrided at 1400 °C for 2 h. After hot pressing, the relative density, Vickers hardness, flexural strength, and fracture toughness of the PSRBSN ceramics, with 5 wt% Eu₂O₃ doping, were 98.3 ± 0.2%, 17.8 ± 0.8 GPa, 697.0 ± 67.0 MPa, and 7.3 ± 0.3 MPa m^1/2, respectively. The thermal conductivity was 73.6 ± 0.2 W m^?1 K^?1, significantly higher than the counterpart without Eu₂O₃ doping, or with ZrO₂ doping by conventional methods.     

药物相互作用研究在新药研发和审评决策中的应用

药物相互作用改变了剂量效应关系，可能会降低疗效或增加毒性，是临床应用中合并用药治疗时重要的考虑因素。预测具有临床意义的药物相互作用是药物研发过程中获益风险评估的重要环节。本文概述了药物研发过程中药物相互作用研究的目的和意义，体内和体外研究的主要内容；梳理分析了2020年国家药品监督管理局（National Medical Products Administration, NMPA）和美国食品药品监督管理局（Food and Drug Administration, FDA）批准上市的新药药物相互作用研究情况，旨在为我国药物研发过程中药物相互作用研究及其监管审评提供参考。     

Structural Aspects and Prediction of Calmodulin-Binding Proteins

Calmodulin (CaM) is an important intracellular protein that binds Ca²⁺ and functions as a critical second messenger involved in numerous biological activities through extensive interactions with proteins and peptides. CaM’s ability to adapt to binding targets with different structures is related to the flexible central helix separating the N- and C-terminal lobes, which allows for conformational changes between extended and collapsed forms of the protein. CaM-binding targets are most often identified using prediction algorithms that utilize sequence and structural data to predict regions of peptides and proteins that can interact with CaM. In this review, we provide an overview of different CaM-binding proteins, the motifs through which they interact with CaM, and shared properties that make them good binding partners for CaM. Additionally, we discuss the historical and current methods for predicting CaM binding, and the similarities and differences between these methods and their relative success at prediction. As new CaM-binding proteins are identified and classified, we will gain a broader understanding of the biological processes regulated through changes in Ca²⁺ concentration through interactions with CaM.     

Scanning Protein Surfaces with DNA-Encoded Libraries

Understanding the ligandability of a target protein, defined as the capability of a protein to bind drug-like compounds on any site, can give important stimuli to drug-development projects. For instance, inhibition of protein–protein interactions usually depends on the identification of protein surface binders. DNA-encoded chemical libraries (DELs) allow scanning of protein surfaces with large chemical space. Encoded library selection screens uncovered several protein–protein interaction inhibitors and compounds binding to the surface of G protein-coupled receptors (GPCRs) and kinases. The protein surface-binding chemotypes from DELs are predominantly chemically modified and cyclized peptides, and functional small-molecule peptidomimetics. Peptoid libraries and structural peptidomimetics have been less studied in the DEL field, hinting at hitherto less populated chemical space and suggesting alternative library designs. Roughly a third of bioactive molecules evolved from smaller, target-focused libraries. They showcase the potential of encoded libraries to identify more potent molecules from weak, for example, fragment-like, starting points.     

In Silico Analysis of the Molecular-Level Impact of SMPD1 Variants on Niemann-Pick Disease Severity

Sphingomyelin phosphodiesterase (SMPD1) is a key enzyme in the sphingolipid metabolism. Genetic SMPD1 variants have been related to the Niemann-Pick lysosomal storage disorder, which has different degrees of phenotypic severity ranging from severe symptomatology involving the central nervous system (type A) to milder ones (type B). They have also been linked to neurodegenerative disorders such as Parkinson and Alzheimer. In this paper, we leveraged structural, evolutionary and stability information on SMPD1 to predict and analyze the impact of variants at the molecular level. We developed the SMPD1-ZooM algorithm, which is able to predict with good accuracy whether variants cause Niemann-Pick disease and its phenotypic severity; the predictor is freely available for download. We performed a large-scale analysis of all possible SMPD1 variants, which led us to identify protein regions that are either robust or fragile with respect to amino acid variations, and show the importance of aromatic-involving interactions in SMPD1 function and stability. Our study also revealed a good correlation between SMPD1-ZooM scores and in vitro loss of SMPD1 activity. The understanding of the molecular effects of SMPD1 variants is of crucial importance to improve genetic screening of SMPD1-related disorders and to develop personalized treatments that restore SMPD1 functionality.     

川中地区侏罗系沙溪庙组致密气处理和解释关键技术与应用

四川盆地蕴含丰富的致密砂岩气资源，近期利用高精度三维资料开展侏罗系沙溪庙组河道砂体勘探取得突出成效。沙溪庙组河道砂体具有横向变化快、储层非均质性强的特点，因此提高河道砂体的边界识别及其含气性预测精度是致密气地震勘探的关键。通过开展AVO特征低频保护的“六分法”（分类、分频、分时、分域、分步和分区）高保真叠前去噪、近地表Q补偿和OVT域叠前时间偏移等技术攻关，形成了一套针对川中地区侏罗系沙溪庙组致密气藏的“双高”（高保真、高分辨率）地震处理技术，并创新应用“双亮点”属性及多波、多分量砂体含气性地震预测等解释技术，提高了含气砂体预测精度。该技术系列在川中沙溪庙组致密气预测应用成果显著，地震数据频带得到了拓宽，低频信息更加丰富，资料信噪比明显提升，河道边界及其含气性预测的精度大幅提高，钻井成功率超过83%，应用成果有力地支撑了该地区沙溪庙组致密气的增储上产。     

NaWuReT-Workshop: Forschung in der Reaktionstechnik für und mit der Gesellschaft

In this article, the results of the NaWuReT (Early Career Reaction Engineers) workshop on the topic “Are we doing relevant science?” are presented. The topics “(In)surmountable hurdles for Citizen Scientists in reaction engineering?” and “Circular Economy in reaction engineering with/for society?” were discussed. Therefrom, a variety of ideas and suggestions were extracted.     

FE-SEA方法在平台支持船噪声预报中的应用

平台支持船由于作业需要通常配备有动力定位系统,其在侧推工况下舱室噪声超标较为严重。针对这个问题采用计算流体力学(CFD)方法,得到侧推螺旋桨作用在导管上的脉动压力,并将时域计算结果转换成噪声计算的激励条件。采用有限元(FE)与统计能量分析(SEA)混合方法建立船体中频段FE-SEA耦合模型并建立船体高频段SEA模型,对某65 m AHTS船侧推工况下全频段(63 Hz~8000 Hz)舱室噪声进行预报,分析该船噪声分布规律及主要影响因素。并建立起全船的SEA模型,在中频段对比SEA与FE-SEA两种方法得到的舱室声压级频谱曲线,验证了使用混合模型的必要性。