The utilization of paper-level classification system on the evaluation of journal impact

CAS Journal Ranking, a ranking system of journals based on the bibliometric indicator of citation impact, has been widely used in meso and macro-scale research evaluation in China since its first release in 2004. The ranking's coverage is journals which contained in the Clarivate's Journal Citation Reports (JCR). This paper will mainly introduce the upgraded version of the 2019 CAS journal ranking. Aiming at limitations around the indicator and classification system utilized in earlier editions, also the problem of journals' interdisciplinarity or multidisciplinarity, we will discuss the improvements in the 2019 upgraded version of CAS journal ranking (1) the CWTS paper-level classification system, a more fine-grained system, has been utilized, (2) a new indicator, Field Normalized Citation Success Index (FNCSI), which ia robust against not only extremely highly cited publications, but also the wrongly assigned document type, has been used, and (3) the calculation of the indicator is from a paper-level. In addition, this paper will present a small part of ranking results and an interpretation of the robustness of the new FNCSI indicator. By exploring more sophisticated methods and indicators, like the CWTS paper-level classification system and the new FNCSI indicator, CAS Journal Ranking will continue its original purpose for responsible research evaluation

Targeting the Epigenetic Lesion in MLL-Rearranged Leukemia

It has become increasingly apparent that the misregulation of histone modification actively contributes to cancer. The histone H3 lysine 79 (H3K79) methyltransferase Dot1l has been implicated in the development of leukemias bearing translocations of the Mixed Lineage Leukemia (MLL) gene. We studied the global epigenetic profile for H3K79 dimethylation and found abnormal H3K79 dimethylation profiles exist not only in leukemias driven by MLL-fusion proteins with nuclear partners like AF9, but also in leukemia with MLL-fusions containing cytoplasmic partners like AF6. Genetic inactivation of Dot1l led to downregulation of fusion target genes and impaired both in vitro bone marrow transformation and in vivo leukemia development by MLL-AF10, CALM-AF10 as well as MLL-AF6, suggesting that aberrant H3K79 methylation by DOT1L sustains fusion-target gene expression in MLL rearranged leukemias and CALM-AF10 rearranged leukemias. Pharmacological inhibition of DOT1L selectively killed MLL-AF10 and MLL-AF6 transformed cells but not Hox9/Meis1 transformed cells, pointing to DOT1L as a potential therapeutic target in MLL-rearranged leukemia. We further characterized the DOT1L complex under physiological conditions from human leukemia cells and identified AF10 as a key DOT1L complex component. Given the importance of H3K79 methylation in MLL-rearranged leukemia, we sought to study the role of DOT1L complex component AF10 in H3K79 methylation and MLL leukemia. We generated conditional knockout mice in which the Dot1l-interacting octapeptide-motif leucine-zipper (OM-LZ) domain of Af10 was flanked by LoxP sites. Cre induced deletion of $Af10^{OM-LZ}$ is predicted to abrogate the Af10-Dot1l interaction. Our histone mass spectrometry data demonstrated that deletion of the endogenous $Af10^{OM-LZ}$ domain abrogated global H3K79 dimethylation but retained H3K79 monomethylation. Interestingly, bone marrow transformation by MLLAF6 and MLL-AF9 is abrogated by induced deletion of endogenous $Af10^{OM-LZ}$, while bone marrow transformation by MLL-AF10 and CALM-AF10 is not affected by deletion of endogenous $Af10^{OM-LZ}$, confirming the importance of Af10-Dot1l interaction in MLL- or CALM fusion-leukemias. Moreover, we showed $Af10^{OM-LZ}$ deletion prolonged survival of MLL-AF9 leukemia in vivo and led to chromotin compaction and downregulation of MLL fusion targets in MLL-AF9 leukemia. Therefore our results demonstrate a role for Af10 in the conversion of H3K79 monomethylation to dimethylation and reveal the AF10-DOT1L interaction as an attractive therapeutic target in MLL-rearranged leukemias

Exploring the Potential of Large Language Models in Computational Argumentation

Computational argumentation has become an essential tool in various fields, including artificial intelligence, law, and public policy. It is an emerging research field in natural language processing (NLP) that attracts increasing attention. Research on computational argumentation mainly involves two types of tasks: argument mining and argument generation. As large language models (LLMs) have demonstrated strong abilities in understanding context and generating natural language, it is worthwhile to evaluate the performance of LLMs on various computational argumentation tasks. This work aims to embark on an assessment of LLMs, such as ChatGPT, Flan models and LLaMA2 models, under zero-shot and few-shot settings within the realm of computational argumentation. We organize existing tasks into 6 main classes and standardise the format of 14 open-sourced datasets. In addition, we present a new benchmark dataset on counter speech generation, that aims to holistically evaluate the end-to-end performance of LLMs on argument mining and argument generation. Extensive experiments show that LLMs exhibit commendable performance across most of these datasets, demonstrating their capabilities in the field of argumentation. We also highlight the limitations in evaluating computational argumentation and provide suggestions for future research directions in this field