IMGC-GNN: A multi-granularity coupled graph neural network recommendation method based on implicit relationships.

In the application recommendation field, collaborative filtering (CF) method is often considered to be one of the most effective methods. As the basis of CF-based recommendation methods, representation learning needs to learn two types of factors: attribute factors revealed by independent individuals (e.g., user attributes, application types) and interaction factors contained in collaborative signals (e.g., interactions influenced by others). However, existing CF-based methods fail to learn these two factors separately; therefore, it is difficult to understand the deeper motivation behind user behaviors, resulting in suboptimal performance. From this point of view, we propose a multi-granularity coupled graph neural network recommendation method based on implicit relationships (IMGC-GNN). Specifically, we introduce contextual information (time and space) into user-application interactions and construct a three-layer coupled graph. Then, the graph neural network approach is used to learn the attribute and interaction factors separately. For attribute representation learning, we decompose the coupled graph into three homogeneous graphs with users, applications, and contexts as nodes. Next, we use multilayer aggregation operations to learn features between users, between contexts, and between applications. For interaction representation learning, we construct a homogeneous graph with user-context-application interactions as nodes. Next, we use node similarity and structural similarity to learn the deep interaction features. Finally, according to the learned representations, IMGC-GNN makes accurate application recommendations to users in different contexts. To verify the validity of the proposed method, we conduct experiments on real-world interaction data from three cities and compare our model with seven baseline methods. The experimental results show that our method has the best performance in the top-k recommendation.

A rare case of Primary Central Nervous System Lymphoma initially diagnosed as demyelinating encephalopathy.

We report a case of histopathologically-confirmed primary central nervous system lymphoma who was initially diagnosed as demyelinating encephalopathy. A 58-year-old woman was admitted with confusion and left hemiparesis. Head MR showed abnormal flaky hypointense T1 and hyperintense T2 signals at right thalamus, splenium of corpus callosum, bilateral cerebral peduncle, pons, medulla oblongata, basal ganglia and right corona radiata. Her mental status improved a little and she was discharged from hospital after neuroprotective treatment. 10 days after her discharge, her confusion appeared again with hallucination and unsteady walking. Pathological examination revealed non-Hodgkin's lymphoma (WHO classification: DLBCL). The patient continued to deteriorate after the surgery and died 10 days later.

Targeted protein degradation in hematologic malignancies: latest updates from the 2023 ASH annual meeting.

Protein degraders, emerging as a novel class of therapeutic agents, have gained widespread attention due to their advantages. They have several advantages over traditional small molecule inhibitors, including high target selectivity and ability to target "undruggable" targets and overcome inhibitor drug resistance. Tremendous research and development efforts and massive investment have resulted in rapid advancement of protein degrader drug discovery in recent years. Here, we overview the latest clinical and preclinical updates on protein degraders presented at the 2023 ASH Annual Meeting.

CFDIL: a context-aware feature deep interaction learning for app recommendation.

The rapid development of mobile Internet has spawned various mobile applications (apps). A large number of apps make it difficult for users to choose apps conveniently, causing the app overload problem. As the most effective tool to solve the problem of app overload, app recommendation has attracted extensive attention of researchers. Traditional recommendation methods usually use historical usage data to explore users' preferences and then make recommendations. Although traditional methods have achieved certain success, the performance of app recommendation still needs to be improved due to the following two reasons. On the one hand, it is difficult to construct recommendation models when facing with the sparse user-app interaction data. On the other hand, contextual information has a large impact on users' preferences, which is often overlooked by traditional methods. To overcome the aforementioned problems, we proposed a context-aware feature deep interaction learning (CFDIL) method to explore users' preferences and then perform app recommendation by learning potential user-app relationships in different contexts. The novelty of CFDIL is as follows: (1) CFDIL incorporates contextual features into users' preferences modeling by constructing novel user and app feature portraits. (2) The problem of data sparsity is effectively solved by the use of dense user and app feature portraits, as well as the tensor operations for label sets. (3) CFDIL trains a new deep network structure, which can make accurate app recommendation using the contextual information and attribute information of users and apps. We applied CFDIL on three real datasets and conducted extensive experiments, which shows that CFDIL outperforms the benchmark methods.