Patient-derived xenografts or organoids in the discovery of traditional and self-assembled drug for tumor immunotherapy

In addition to the rapid development of immune checkpoint inhibitors, there has also been a surge in the development of self-assembly immunotherapy drugs. Based on the immune target, traditional tumor immunotherapy drugs are classified into five categories, namely immune checkpoint inhibitors, direct immune modulators, adoptive cell therapy, oncolytic viruses, and cancer vaccines. Additionally, the emergence of self-assembled drugs with improved precision and environmental sensitivity offers a promising innovation approach to tumor immunotherapy. Despite rapid advances in tumor immunotherapy drug development, all candidate drugs require preclinical evaluation for safety and efficacy, and conventional evaluations are primarily conducted using two-dimensional cell lines and animal models, an approach that may be unsuitable for immunotherapy drugs. The patient-derived xenograft and organoids models, however, maintain the heterogeneity and immunity of the pathological tumor heterogeneity

The Weight Hierarchies of Linear Codes from Simplicial Complexes

The study of the generalized Hamming weight of linear codes is a significant research topic in coding theory as it conveys the structural information of the codes and determines their performance in various applications. However, determining the generalized Hamming weights of linear codes, especially the weight hierarchy, is generally challenging. In this paper, we investigate the generalized Hamming weights of a class of linear code \C over \bF_q, which is constructed from defining sets. These defining sets are either special simplicial complexes or their complements in \bF_q^m. We determine the complete weight hierarchies of these codes by analyzing the maximum or minimum intersection of certain simplicial complexes and all $r$-dimensional subspaces of \bF_q^m, where 1\leq r\leq {\rm dim}_{\bF_q}(\C)

Two classes of reducible cyclic codes with large minimum symbol-pair distances

The high-density data storage technology aims to design high-capacity storage at a relatively low cost. In order to achieve this goal, symbol-pair codes were proposed by Cassuto and Blaum \cite{CB10,CB11} to handle channels that output pairs of overlapping symbols. Such a channel is called symbol-pair read channel, which introduce new concept called symbol-pair weight and minimum symbol-pair distance. In this paper, we consider the parameters of two classes of reducible cyclic codes under the symbol-pair metric. Based on the theory of cyclotomic numbers and Gaussian period over finite fields, we show the possible symbol-pair weights of these codes. Their minimum symbol-pair distances are twice the minimum Hamming distances under some conditions. Moreover, we obtain some three symbol-pair weight codes and determine their symbol-pair weight distribution. A class of MDS symbol-pair codes is also established. Among other results, we determine the values of some generalized cyclotomic numbers

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

User response prediction, which models the user preference w.r.t. the presented items, plays a key role in online services. With two-decade rapid development, nowadays the cumulated user behavior sequences on mature Internet service platforms have become extremely long since the user's first registration. Each user not only has intrinsic tastes, but also keeps changing her personal interests during lifetime. Hence, it is challenging to handle such lifelong sequential modeling for each individual user. Existing methodologies for sequential modeling are only capable of dealing with relatively recent user behaviors, which leaves huge space for modeling long-term especially lifelong sequential patterns to facilitate user modeling. Moreover, one user's behavior may be accounted for various previous behaviors within her whole online activity history, i.e., long-term dependency with multi-scale sequential patterns. In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user. The model also adopts a hierarchical and periodical updating mechanism to capture multi-scale sequential patterns of user interests while supporting the evolving user behavior logs. The experimental results over three large-scale real-world datasets have demonstrated the advantages of our proposed model with significant improvement in user response prediction performance against the state-of-the-arts.Comment: SIGIR 2019. Reproducible codes and datasets: https://github.com/alimamarankgroup/HPM