Evaluation of transcriptional cyclin dependent kinase inhibitors as potential cancer therapeutics

Cancer cells depend heavily on sustained expression of anti-apoptotic proteins. Targeting transcription to suppress these anti-apoptotic proteins seems a promising strategy for anti-cancer therapy. Cyclin-dependant kinase 9 (CDK9) regulates transcription elongation by phosphorylating Ser2 on the C-terminal domain of RNA polymerase II, while CDK7 phosphorylates Ser5 during transcription initiation. A screening cascade comprised of an MTT assay, a caspase-3 activation assay, a p53 stabilization assay and a mitotic index assay was developed to classify compounds and identify lead transcriptional CDK inhibitors from a novel class of 2,4,5-trisubstituted pyrimidines. Compounds S3-41 and CDKI-71 are the most potent CDK9 inhibitors identified by the screening cascade. They showed potent anti-proliferative activity in the MTT assay and induce both caspase-3 activity and p53 protein level at the GI50 concentration. In addition, no significant effect on mitotic index was observed. The detailed mechanism of action of CDKI-71 was further investigated and compared with the clinical compound, flavopiridol. Like flavopiridol, CDKI-71 displayed potent cytotoxicity and caspase-dependent apoptosis that were closely associated with the inhibition of RNAPII phosphorylation at Ser2. This indicated effective targeting of cyclinT-CDK9 and the downstream inhibition of anti-apoptotic proteins such as Mcl-l in cells. Similar to flavopiridol, CDKI-71 down-regulated a large range of genes, including Mcl-l and Bcl-2. No correlation between apoptosis and inhibition of cell cycle CDKs 1 and 2 was observed. Non-transformed lung fibroblast cell lines showed resistance to CDKI-71 treatment. In contrast, flavopiridol showed little selectivity between cancer and normal cells. Flavopiridol also induced genotoxic stress through the induction of DNA double-strand breakage. These results suggest that CDKI-71 has a great potential to be developed as an anti-cancer agent. Another study focused on in vitro anti-tumour mechanism of CDKI-83, a dual inhibitor of CDK9 and CDKI, was performed in A2780 ovarian cancer cells. CDKI-71 presented potent anti-proliferation activity and induced apoptosis in A2780 cells. By inhibiting cellular CDK1 and CDK9 activities, CDKI-83 arrested cells in G2 phase and reduced anti-apoptotic proteins at both mRNA and protein levels, respectively. This study suggests that the combination of CDK9 and CDK1 inhibition results in effective induction of apoptosis in cancer cells

Syntax Tree Constrained Graph Network for Visual Question Answering

Visual Question Answering (VQA) aims to automatically answer natural language questions related to given image content. Existing VQA methods integrate vision modeling and language understanding to explore the deep semantics of the question. However, these methods ignore the significant syntax information of the question, which plays a vital role in understanding the essential semantics of the question and guiding the visual feature refinement. To fill the gap, we suggested a novel Syntax Tree Constrained Graph Network (STCGN) for VQA based on entity message passing and syntax tree. This model is able to extract a syntax tree from questions and obtain more precise syntax information. Specifically, we parse questions and obtain the question syntax tree using the Stanford syntax parsing tool. From the word level and phrase level, syntactic phrase features and question features are extracted using a hierarchical tree convolutional network. We then design a message-passing mechanism for phrase-aware visual entities and capture entity features according to a given visual context. Extensive experiments on VQA2.0 datasets demonstrate the superiority of our proposed model

LiSum: Open Source Software License Summarization with Multi-Task Learning

Open source software (OSS) licenses regulate the conditions under which users can reuse, modify, and distribute the software legally. However, there exist various OSS licenses in the community, written in a formal language, which are typically long and complicated to understand. In this paper, we conducted a 661-participants online survey to investigate the perspectives and practices of developers towards OSS licenses. The user study revealed an indeed need for an automated tool to facilitate license understanding. Motivated by the user study and the fast growth of licenses in the community, we propose the first study towards automated license summarization. Specifically, we released the first high quality text summarization dataset and designed two tasks, i.e., license text summarization (LTS), aiming at generating a relatively short summary for an arbitrary license, and license term classification (LTC), focusing on the attitude inference towards a predefined set of key license terms (e.g., Distribute). Aiming at the two tasks, we present LiSum, a multi-task learning method to help developers overcome the obstacles of understanding OSS licenses. Comprehensive experiments demonstrated that the proposed jointly training objective boosted the performance on both tasks, surpassing state-of-the-art baselines with gains of at least 5 points w.r.t. F1 scores of four summarization metrics and achieving 95.13% micro average F1 score for classification simultaneously. We released all the datasets, the replication package, and the questionnaires for the community