Drug-drug interaction(DDI)refers to the interaction between two or more drugs in the body,altering their efficacy or pharmacokinetics.Fully considering and accurately predicting DDI has become an indispensable part of...Drug-drug interaction(DDI)refers to the interaction between two or more drugs in the body,altering their efficacy or pharmacokinetics.Fully considering and accurately predicting DDI has become an indispensable part of ensuring safe medication for patients.In recent years,many deep learning-based methods have been proposed to predict DDI.However,most existing computational models tend to oversimplify the fusion of drug structural and topological information,often relying on methods such as splicing or weighted summation,which fail to adequately capture the potential complementarity between structural and topological features.This loss of information may lead to models that do not fully leverage these features,thus limiting their performance in DDI prediction.To address these challenges,we propose a relation-aware cross adversarial network for predicting DDI,named RCAN-DDI,which combines a relationship-aware structure feature learning module and a topological feature learning module based on DDI networks to capture multimodal features of drugs.To explore the correlations and complementarities among different information sources,the cross-adversarial network is introduced to fully integrate features from various modalities,enhancing the predictive performance of the model.The experimental results demonstrate that the RCAN-DDI method outperforms other methods.Even in cases of labelled DDI scarcity,the method exhibits good robustness in the DDI prediction task.Furthermore,the effectiveness of the cross-adversarial module is validated through ablation experiments,demonstrating its superiority in learning multimodal complementary information.展开更多
By double beam and double wave interferomatric (DDI) method, the optical constants of thin films, i.e. refractive index, extinction coefficient and thickness may be determined in infrared (3.39 μm) and in visible (...By double beam and double wave interferomatric (DDI) method, the optical constants of thin films, i.e. refractive index, extinction coefficient and thickness may be determined in infrared (3.39 μm) and in visible (0.633 μm) wavelengths in the same optical path with a tunable double wave He Ne laser designed by ourselves. The measuring principle and the device are describod.展开更多
采用傅里叶变换红外(FT-IR)光谱法研究了二聚脂肪酸二异氰酸酯(DDI)/端羟基聚丁二烯(HTPB)体系的固化反应动力学,并与异佛尔酮二异氰酸酯(IPDI)/HTPB体系进行了比较。初步探索了DDI在HTPB推进剂中的应用。结果表明,DDI/HTPB体系的固化...采用傅里叶变换红外(FT-IR)光谱法研究了二聚脂肪酸二异氰酸酯(DDI)/端羟基聚丁二烯(HTPB)体系的固化反应动力学,并与异佛尔酮二异氰酸酯(IPDI)/HTPB体系进行了比较。初步探索了DDI在HTPB推进剂中的应用。结果表明,DDI/HTPB体系的固化反应为二级反应,表观活化能为37.02 k J·mol-1,相比IPDI/HTPB体系降低了3.5 k J·mol-1,说明DDI的反应活性稍高于IPDI,反应活性适中,可作为低毒固化剂应用于HTPB推进剂中。DDI/HTPB体系推进剂具有较好的常温力学性能,抗拉强度为0.85 MPa时,最大伸长率为44.1%,可基本满足推进剂的常温力学性能要求。展开更多
基金supported by the Natural Science Foundation of Shandong Province(Grant No.:ZR2023MF053)the National Natural Science Foundation of China(Grant No.:61902430).
文摘Drug-drug interaction(DDI)refers to the interaction between two or more drugs in the body,altering their efficacy or pharmacokinetics.Fully considering and accurately predicting DDI has become an indispensable part of ensuring safe medication for patients.In recent years,many deep learning-based methods have been proposed to predict DDI.However,most existing computational models tend to oversimplify the fusion of drug structural and topological information,often relying on methods such as splicing or weighted summation,which fail to adequately capture the potential complementarity between structural and topological features.This loss of information may lead to models that do not fully leverage these features,thus limiting their performance in DDI prediction.To address these challenges,we propose a relation-aware cross adversarial network for predicting DDI,named RCAN-DDI,which combines a relationship-aware structure feature learning module and a topological feature learning module based on DDI networks to capture multimodal features of drugs.To explore the correlations and complementarities among different information sources,the cross-adversarial network is introduced to fully integrate features from various modalities,enhancing the predictive performance of the model.The experimental results demonstrate that the RCAN-DDI method outperforms other methods.Even in cases of labelled DDI scarcity,the method exhibits good robustness in the DDI prediction task.Furthermore,the effectiveness of the cross-adversarial module is validated through ablation experiments,demonstrating its superiority in learning multimodal complementary information.
文摘By double beam and double wave interferomatric (DDI) method, the optical constants of thin films, i.e. refractive index, extinction coefficient and thickness may be determined in infrared (3.39 μm) and in visible (0.633 μm) wavelengths in the same optical path with a tunable double wave He Ne laser designed by ourselves. The measuring principle and the device are describod.
文摘供者来源性感染（donor derived infection,DDI）是指在器官捐献后,捐献者体内存在的病原体通过器官移植过程使受者罹患相同的感染。产生DDI的客观原因是绝大部分捐献者在原发疾病救治过程中可能经历重大手术、持续气管插管或切开行机械通气,留置深静脉导管、导尿管等各种导管,时常需要血液透析、人工肝、体外膜肺氧合等治疗,都曾入住重症监护病房（intensive care unit,ICU）,
文摘采用傅里叶变换红外(FT-IR)光谱法研究了二聚脂肪酸二异氰酸酯(DDI)/端羟基聚丁二烯(HTPB)体系的固化反应动力学,并与异佛尔酮二异氰酸酯(IPDI)/HTPB体系进行了比较。初步探索了DDI在HTPB推进剂中的应用。结果表明,DDI/HTPB体系的固化反应为二级反应,表观活化能为37.02 k J·mol-1,相比IPDI/HTPB体系降低了3.5 k J·mol-1,说明DDI的反应活性稍高于IPDI,反应活性适中,可作为低毒固化剂应用于HTPB推进剂中。DDI/HTPB体系推进剂具有较好的常温力学性能,抗拉强度为0.85 MPa时,最大伸长率为44.1%,可基本满足推进剂的常温力学性能要求。
