交叉学科基础课教学实践——以化学生物学为例

化学生物学是20世纪后期发展起来的一门前沿科学,在分子水平上探索生命现象背后的化学本质,以研究和调控生命现象为目标。化学生物学课程内容涉及生物科学、化学、医学、药学等多学科,具有显著的新兴多学科交叉特色。教学团队紧密围绕我国深化教育教学改革、建设世界一流大学和世界一流学科等系列国家战略,提出了“培养复合创新型人才,探索交叉学科基础课程建设之路”的改革目标。在实践中,坚持“多元交叉融合”的改革理念,落实“一导向、一引领、三保障”的改革思路,践行以“探索未知、科技报国”为主线的思政教育,为前沿交叉学科基础课程建设、为国家复合创新型人才培养探索一条可推广复制的破壁之路。

通过英语课外阅读培养初中生自主学习能力的方法探究

随着教育教学改革的不断深入,教师教育观念的更新,教学不再单纯是传授知识的过程,更是引导学生在面对信息发达,纷繁多变的社会时学会如何自主有效地学习,不断提高自身能力的过程。在初中英语学习过程中,课外阅读不仅是休闲放松、改善心理状态的方式,更是培养学生自主学习能力,提高思想境界的手段。学生阅读能力的培养就是自主学习能力培养,二者相辅相成,相互促进,学生的阅读能力逐步提高,自主学习能力也随之提高。

DDA Simulations of Large Flume Tests and Large Landslides Triggered by the Wenchuan Earthquake

This paper presents some numerical simulations using Discontinuous Deformation Analysis (DDA). First, DDA was applied to reproduce a series of granular flows released in a large flume. The comparison between simulated velocity and that measured in the tests demonstrates the effectiveness of DDA on description of kinematic behavior of blocky assembly. Simulated results were highly sensitive to the shape and angularity of blocky elements. Employing unrealistic block might result in different behavior from real situation. Second, three large landslides triggered by the Wenchuan earthquake were simulated. A well agreement with field data was obtained if apparent friction coefficient determined by post-analysis was adopted.

The Cyclization Reactions of Lawesson's Reagent with Bifunctional Substrates

The cyclizaion reactions of Lawesson's reagent(LR) with the substrates containing two functional groups such as hydroxy groups,amino groups, has been applied to prepare the O-P-O,N-P-N type 5-membered and 6-membered phosphorus heterocycles.The heterocyclizaional possibility of LR with bifunctional compounds was also discussed.

Selenium-engineered bottom-up-synthesized lanthanide coordination nanoframeworks as efficiency X-ray-responsive radiosensitizers

Radiotherapy is one of the main therapeutic methods for cancers;however,nonselective killing of normal cells and tumor cells by X-ray inevitably results in toxicity and side effects.Developing low-toxicity and high-efficiency radiosensitizers to reduce the practical dose of X-ray is a promising approach to overcoming these side effects.Here,we report the use of carboxylatecontaining organic ligands to construct one-dimensional high-Z lanthanide chains for efficient response to X-ray.The onedimensional lanthanide chains are stacked through weak interactions to form coordination nanoframeworks in the presence of polyethylenimine(PEI).The morphology and activity of the synthesized nanoframeworks can be regulated through selenium atom engineering.This study presents a promising approach for effective radiotherapy through selenium-engineering stable lanthanide nanoframeworks with precise coordination structures as radiosensitizers to mitigate X-ray side effects.

High-dimensional zinc porphyrin nanoframeworks as efficient radiosensitizers for cervical cancer

Radiotherapy is widely used clinically, but the toxic and side effects of nonselective killing of high-energy radiation limit its application. Finding biocompatible materials to assemble radiotherapy sensitizers and studying their sensitization patterns are of great significance for the clinical application. Here, biocompatible zinc porphyrin was chosen as sub-unit to construct various dimensional coordination frameworks. By employing top-down approach, suitable nanoframeworks with various dimensional zinc porphyrin were synthesized as radiosensitizers. The experimental data showed that high-dimensional zinc porphyrin nanoframeworks exhibit higher X-ray response performance.

Self-supervised graph learning with target-adaptive masking for session-based recommendation

Session-based recommendation aims to predict the next item based on a user’s limited interactions within a short period.Existing approaches use mainly recurrent neural networks(RNNs)or graph neural networks(GNNs)to model the sequential patterns or the transition relationships between items.However,such models either ignore the over-smoothing issue of GNNs,or directly use cross-entropy loss with a softmax layer for model optimization,which easily results in the over-fitting problem.To tackle the above issues,we propose a self-supervised graph learning with target-adaptive masking(SGL-TM)method.Specifically,we first construct a global graph based on all involved sessions and subsequently capture the self-supervised signals from the global connections between items,which helps supervise the model in generating accurate representations of items in the ongoing session.After that,we calculate the main supervised loss by comparing the ground truth with the predicted scores of items adjusted by our designed target-adaptive masking module.Finally,we combine the main supervised component with the auxiliary self-supervision module to obtain the final loss for optimizing the model parameters.Extensive experimental results from two benchmark datasets,Gowalla and Diginetica,indicate that SGL-TM can outperform state-of-the-art baselines in terms of Recall@20 and MRR@20,especially in short sessions.

Atom engineering-regulated in situ transition of Cu(Ⅰ)-Cu(Ⅱ)for efficiently overcoming cancer drug resistance

The search of highly efficient drugs for overcoming cancer drug resistance continues to be a challenge for scientists.Constructing a metal drug based in situ oxidation-state transition system to disturb the redox balance in cancer cells is a promising approach for overcoming cancer drug resistance.Inspired by natural redox-active copper enzyme centers,we developed a Cu(Ⅰ)-Cu(Ⅱ) in situ transition system in this work.Through atom engineering,we fine-tuned the thermodynamic stability of this system to investigate its anticancer activity.The results indicated that the synthetic Cu(Ⅰ)-Cu(Ⅱ) system could under-go in situ transition in vitro and in vivo,to disrupt the intracellular redox balance and trigger mitochondrial dysfunction and G2/M arrest,leading to apoptosis and overcoming cancer drug resistance.This study presents a feasible way to overcome cancer drug resistance by designing an in situ oxidation-state transition metal drug system.

Personalized query suggestion diversification in information retrieval

Query suggestions help users refine their queries after they input an initial query.Previous work on query suggestion has mainly concentrated on approaches that are similarity-based or context-based,developing models that either focus on adapting to a specific user(personalization)or on diversifying query aspects in order to maximize the probability of the user being satisfied(diversification).We consider the task of generating query suggestions that are both personalized and diversified.We propose a personalized query suggestion diversification(PQSD)model,where a user's long-term search behavior is injected into a basic greedy query suggestion diversification model that considers a user's search context in their current session.Query aspects are identified through clicked documents based on the open directory project(ODP)with a latent dirichlet allocation(LDA)topic model.We quantify the improvement of our proposed PQSD model against a state-of-the-art baseline using the public america online(AOL)query log and show that it beats the baseline in terms of metrics used in query suggestion ranking and diversification.The experimental results show that PQSD achieves its best performance when only queries with clicked documents are taken as search context rather than all queries,especially when more query suggestions are returned in the list.

China Family Development Report(2015)

Introduction The family is the most basic unit of society and its development provides important support and impetus to social advances.China has the largest population in the world and the greatest number of families,whose sizes,structures and types are becoming increasingly diverse and complex,keeping pace with ever-changing external circumstances.