MicroRNA Regulation of Stem Cell Fate

MicroRNAs modulate target gene expression and are essential for normal development, but how does this pathway impact cell fate decisions? In this issue of Cell Stem Cell, Ivey et al. (2008) find that muscle-specific microRNAs repress nonmuscle genes to direct embryonic stem cell differentiation to mesoderm and muscle

Collaborative Evaluation: Exploring the Synergy of Large Language Models and Humans for Open-ended Generation Evaluation

Humans are widely involved in the evaluation of open-ended natural language generation tasks (NLG) that demand creativity, as automatic metrics often exhibit weak correlations with human judgments. Large language models (LLMs) recently have emerged as a scalable and cost-effective alternative to human evaluations. However, both humans and LLMs have limitations, i.e., inherent subjectivity and unreliable judgments, particularly for open-ended tasks that require adaptable metrics tailored to diverse task requirements. To explore the synergy between humans and LLM-based evaluators and address the challenges of existing inconsistent evaluation criteria in open-ended NLG tasks, we propose a Collaborative Evaluation pipeline CoEval, involving the design of a checklist of task-specific criteria and the detailed evaluation of texts, in which LLM generates initial ideation, and then humans engage in scrutiny. We conducted a series of experiments to investigate the mutual effects between LLMs and humans in CoEval. Results show that, by utilizing LLMs, CoEval effectively evaluates lengthy texts, saving significant time and reducing human evaluation outliers. Human scrutiny still plays a role, revising around 20% of LLM evaluation scores for ultimate reliability.Comment: We release our resources at \url{https://github.com/qtli/CoEval

Stock Announcement Effects of UK Cross-border and National M&As for 1998-2012

The recent trend of takeover bids is large deal and globalization. Cross-border transactions have increased rapidly these years. The expanded and diversified market offers more investment opportunities as well as more uncertain risks. The objective of this study is to show the impacts of different factors on shareholder wealth of bidders distinguishing national and cross-border transactions around the announcement date. The sample is of 400 mergers or acquisitions over the period of 1998-2012 by UK public firms who have successfully completed the transactions. Using the methodology of event study, this paper shows that national deals earn significantly more than cross-border deals, of 1 percent difference within 3 days event window, and of 0.7 percent within 5 days event window. To partition the two groups further, this paper also finds that industry diversification, payment method, relative size, target type differentiates the abnormal returns to the bidders

Towards Empathetic Dialogue Generation over Multi-type Knowledge

Enabling the machines with empathetic abilities to provide context-consistent responses is crucial on both semantic and emotional levels. The task of empathetic dialogue generation is proposed to address this problem. However, lacking external knowledge makes it difficult to perceive implicit emotions from limited dialogue history. To address the above challenges, we propose to leverage multi-type knowledge, i.e, the commonsense knowledge and emotional lexicon, to explicitly understand and express emotions in empathetic dialogue generation. We first enrich the dialogue history by jointly interacting with two-type knowledge and construct an emotional context graph. Then we introduce a multi-type knowledge-aware context encoder to learn emotional context representations and distill emotional signals, which are the prerequisites to predicate emotions expressed in responses. Finally, we propose an emotional cross-attention mechanism to exploit the emotional dependencies between the emotional context graph and the target empathetic response. Conducted on a benchmark dataset, extensive experimental results show that our proposed framework outperforms state-of-the-art baselines in terms of automatic metrics and human evaluations.Comment: arXiv admin note: text overlap with arXiv:1911.0869

Explanation Regeneration via Information Bottleneck

Explaining the black-box predictions of NLP models naturally and accurately is an important open problem in natural language generation. These free-text explanations are expected to contain sufficient and carefully-selected evidence to form supportive arguments for predictions. Due to the superior generative capacity of large pretrained language models, recent work built on prompt engineering enables explanation generation without specific training. However, explanation generated through single-pass prompting often lacks sufficiency and conciseness. To address this problem, we develop an information bottleneck method EIB to produce refined explanations that are sufficient and concise. Our approach regenerates the free-text explanation by polishing the single-pass output from the pretrained language model but retaining the information that supports the contents being explained. Experiments on two out-of-domain tasks verify the effectiveness of EIB through automatic evaluation and thoroughly-conducted human evaluation.Comment: Accepted in ACL2023 Finding

Abstractive Opinion Tagging

In e-commerce, opinion tags refer to a ranked list of tags provided by the e-commerce platform that reflect characteristics of reviews of an item. To assist consumers to quickly grasp a large number of reviews about an item, opinion tags are increasingly being applied by e-commerce platforms. Current mechanisms for generating opinion tags rely on either manual labelling or heuristic methods, which is time-consuming and ineffective. In this paper, we propose the abstractive opinion tagging task, where systems have to automatically generate a ranked list of opinion tags that are based on, but need not occur in, a given set of user-generated reviews. The abstractive opinion tagging task comes with three main challenges: (1) the noisy nature of reviews; (2) the formal nature of opinion tags vs. the colloquial language usage in reviews; and (3) the need to distinguish between different items with very similar aspects. To address these challenges, we propose an abstractive opinion tagging framework, named AOT-Net, to generate a ranked list of opinion tags given a large number of reviews. First, a sentence-level salience estimation component estimates each review's salience score. Next, a review clustering and ranking component ranks reviews in two steps: first, reviews are grouped into clusters and ranked by cluster size; then, reviews within each cluster are ranked by their distance to the cluster center. Finally, given the ranked reviews, a rank-aware opinion tagging component incorporates an alignment feature and alignment loss to generate a ranked list of opinion tags. To facilitate the study of this task, we create and release a large-scale dataset, called eComTag, crawled from real-world e-commerce websites. Extensive experiments conducted on the eComTag dataset verify the effectiveness of the proposed AOT-Net in terms of various evaluation metrics.Comment: Accepted by WSDM 202

PARP inhibitor maintenance treatment for newly diagnosed ovarian cancer patients: a real-world study from China

PurposeThis study evaluated the efficacy and safety in a real-world population of epithelial ovarian cancer (EOC) treated with poly (ADP-ribose) polymerase inhibitor (PARPi) as first-line maintenance therapy in the largest gynecologic oncology center in Western China.MethodsThis study included patients newly diagnosed EOC who received PARPi as first-line maintenance therapy in West China Second University Hospital from August 1, 2018 to September 31, 2022. The primary endpoints were progression-free survival (PFS) and safety evaluated by Common Terminology Criteria for Adverse Events Version 5.0(CTCAE 5.0). The secondary endpoints were overall survival (OS) and prognostic factors influencing the PFS of patients in real world.ResultsAmong the eligible 164 patients, 104 patients received olaparib and 60 patients received niraparib. 100 patients (61.0%) had mutations in breast cancer susceptibility gene (BRCA). 87 patients (53.0%) received primary debulking surgery (PDS) while 77 patients (47.0%) received interval debulking surgery (IDS). 94 patients (94/164, 57.3%) achieved R0 and 39 patients (23.8%) achieved R1 after PDS/IDS. 112 (68.3%) achieved complete response (CR) after first-line chemotherapy, while 49 (29.9%) achieved partial response (PR). The median follow-up time was 17.0 months (95% CI 15.6-18.4), and the median PFS has not been reached yet. Multivariate analysis demonstrated that BRCA mutations and CR/PR after platinum-based chemotherapy were independent factors associated with prolonged PFS. Hematologic toxicity was the most common grade≥3 AE. There were no incidence of myelodysplastic syndromes/acute myelogenous leukemia (MDS/AML).ConclusionFocusing on PARPi as first-line maintenance therapy for patients with EOC, this study represented the largest single-center real-world study in China to date. Two independent factors were identified to prolong the PFS of patients: BRCA mutated type and CR/PR after primary treatment, which should be further confirmed with long-term follow-up and large sample sizes

HEXIM1 is a promiscuous double-stranded RNA-binding protein and interacts with RNAs in addition to 7SK in cultured cells

P-TEFb regulates eukaryotic gene expression at the level of transcription elongation, and is itself controlled by the reversible association of 7SK RNA and an RNA-binding protein HEXIM1 or HEXIM2. In an effort to determine the minimal region of 7SK needed to interact with HEXIM1 in vitro, we found that an oligo comprised of nucleotides 10–48 sufficed. A bid to further narrow down the minimal region of 7SK led to a surprising finding that HEXIM1 binds to double-stranded RNA in a sequence-independent manner. Both dsRNA and 7SK (10–48), but not dsDNA, competed efficiently with full-length 7SK for HEXIM1 binding in vitro. Upon binding dsRNA, a large conformational change was observed in HEXIM1 that allowed the recruitment and inhibition of P-TEFb. Both subcellular fractionation and immunofluorescence demonstrated that, while most HEXIM1 is found in the nucleus, a significant fraction is found in the cytoplasm. Immunoprecipitation experiments demonstrated that both nuclear and cytoplasmic HEXIM1 is associated with RNA. Interestingly, the one microRNA examined (mir-16) was found in HEXIM1 immunoprecipitates, while the small nuclear RNAs, U6 and U2, were not. Our study illuminates novel properties of HEXIM1 both in vitro and in vivo, and suggests that HEXIM1 may be involved in other nuclear and cytoplasmic processes besides controlling P-TEFb