Explainable Topic-Enhanced Argument Mining from Heterogeneous Sources

Given a controversial target such as ``nuclear energy'', argument mining aims to identify the argumentative text from heterogeneous sources. Current approaches focus on exploring better ways of integrating the target-associated semantic information with the argumentative text. Despite their empirical successes, two issues remain unsolved: (i) a target is represented by a word or a phrase, which is insufficient to cover a diverse set of target-related subtopics; (ii) the sentence-level topic information within an argument, which we believe is crucial for argument mining, is ignored. To tackle the above issues, we propose a novel explainable topic-enhanced argument mining approach. Specifically, with the use of the neural topic model and the language model, the target information is augmented by explainable topic representations. Moreover, the sentence-level topic information within the argument is captured by minimizing the distance between its latent topic distribution and its semantic representation through mutual learning. Experiments have been conducted on the benchmark dataset in both the in-target setting and the cross-target setting. Results demonstrate the superiority of the proposed model against the state-of-the-art baselines.Comment: 10 pages, 3 figure

Let-7b expression determines response to chemotherapy through the regulation of Cyclin D1 in Glioblastoma

BACKGROUND: Glioblastoma is the most common type of primary brain tumors. Cisplatin is a commonly used chemotherapeutic agent for Glioblastoma patients. Despite a consistent rate of initial responses, cisplatin treatment often develops chemoresistance, leading to therapeutic failure. Cellular resistance to cisplatin is of great concern and understanding the molecular mechanisms is an utter need. METHODS: Glioblastoma cell line U251 cells were exposed to increasing doses of cisplatin for 6 months to establish cisplatin-resistant cell line U251R. The differential miRNA expression profiles in U251 and U251R cell lines were identified by microarray analysis and confirmed by Q-PCR. MiRNA mimics were transfected into U251R cells, and cellular response to cisplatin-induced apoptosis and cell cycle distribution were examined by FACS analysis. RESULTS: U251R cells showed 3.1-fold increase in cisplatin resistance compared to its parental U251 cells. Microarray analysis identified Let-7b and other miRNAs significantly down-regulated in U251R cells compared to U251 cells. Transfection of Let-7b mimics greatly re-sensitized U251R cells to cisplatin, while transfection of other miRNAs has no effect or slightly effect. Cyclin D1 is predicted as a target of Let-7b through bioinformatics analysis. Over-expression of Let-7b mimics suppressed cyclin D1 protein expression and inhibited cyclin D1-3’-UTR luciferase activity. Knockdown of cyclin D1 expression significantly increased cisplatin-induced G1 arrest and apoptosis. CONCLUSIONS: Collectively, our results indicated that cisplatin treatment leads to Let-7b suppression, which in turn up-regulates cyclin D1 expression. Let-7b may serve as a marker of cisplatin resistance, and can enhance the therapeutic benefit of cisplatin in glioblastoma cells

EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification

Automatic multi-hop fact verification task has gained significant attention in recent years. Despite impressive results, these well-designed models perform poorly on out-of-domain data. One possible solution is to augment the training data with counterfactuals, which are generated by minimally altering the causal features of the original data. However, current counterfactual data augmentation techniques fail to handle multi-hop fact verification due to their incapability to preserve the complex logical relationships within multiple correlated texts. In this paper, we overcome this limitation by developing a rationale-sensitive method to generate linguistically diverse and label-flipping counterfactuals while preserving logical relationships. In specific, the diverse and fluent counterfactuals are generated via an Explain-Edit-Generate architecture. Moreover, the checking and filtering modules are proposed to regularize the counterfactual data with logical relations and flipped labels. Experimental results show that the proposed approach outperforms the SOTA baselines and can generate linguistically diverse counterfactual data without disrupting their logical relationships.Comment: Accepted by EMNLP2023 Main Conferenc

AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling

We introduce AnyGPT, an any-to-any multimodal language model that utilizes discrete representations for the unified processing of various modalities, including speech, text, images, and music. AnyGPT can be trained stably without any alterations to the current large language model (LLM) architecture or training paradigms. Instead, it relies exclusively on data-level preprocessing, facilitating the seamless integration of new modalities into LLMs, akin to the incorporation of new languages. We build a multimodal text-centric dataset for multimodal alignment pre-training. Utilizing generative models, we synthesize the first large-scale any-to-any multimodal instruction dataset. It consists of 108k samples of multi-turn conversations that intricately interweave various modalities, thus equipping the model to handle arbitrary combinations of multimodal inputs and outputs. Experimental results demonstrate that AnyGPT is capable of facilitating any-to-any multimodal conversation while achieving performance comparable to specialized models across all modalities, proving that discrete representations can effectively and conveniently unify multiple modalities within a language model. Demos are shown in https://junzhan2000.github.io/AnyGPT.github.io/Comment: 28 pages, 16 figures, under review, work in progres

Efficacy and safety of stereotactic radiotherapy on elderly patients with stage I-II central non-small cell lung cancer

BackgroundMany studies demonstrated the safety and efficacy of SBRT in the treatment of elderly patients with early-stage non-small cell lung cancer (NSCLC). However, those studies focused on patients with peripheral lung cancer. This study aimed to evaluate the clinical efficacy and toxicity of SBRT in elderly patients with stage I-II central NSCLC in single institution.MethodsFrom April 2009 to January 2020, a retrospective study was conducted on patients ≥ 65 years old with stage I-II NSCLC that was centrally localized and treated with SBRT at a single institution. Absolute C-reactive protein (CRP)/albumin ratio (CAR) and body mass index (BMI) recorded at pretreatment were analyzed. Endpoints included overall survival (OS), progression-free survival (PFS), cancer-specific death, noncancer-specific death, local progression (LP) and distant progression (DP).ResultsStereotactic body radiation treatment (SBRT) was administered to a total of 44 patients. The most common dose fractionation schedule was 60 Gy given in 5 fractions. The median PFS of the cohort was 31 months (95% CI, 19.47–42.53 months). The median OS of all patients was 69 months (95% CI, 33.8–104.2 months). The median time to noncancer-specific death was 54.5 months. The median time to cancer-specific death was 36 months. The cumulative incidences of cancer-specific death at 1 year, 5 years, and 10 years were 11.63% (95%CI, 4.2–23.23%), 42.99% (95%CI, 27.56–57.53%), and 65.94% (95%CI, 45.76–80.1%), respectively. pre-SBRT BMI of ≤ 22.77 (HR 4.60, 95% CI 1.84–11.51, P=0.001) and pre-SBRT CAR of ≤0.91 (HR 5.19, 95% CI 2.15–12.52, P<0.000) were significant predictors of higher OS on multivariable analysis. The median times to LP and DP were 10 months and 11 months, respectively. In terms of acute toxicity, grade 1 including cough (38.64%), radiation pneumonitis (29.55%), anemia (25%), and fatigue (20.45%) was often observed. There was no evidence of grade 4 or 5 acute toxicity. In terms of late toxicity, 2 patients developed grade 1 pulmonary fibrosis during follow-up.ConclusionThis study showed that SBRT can effectively control local tumor progression, and have acceptable toxicity for elderly patients with centrally located stage I-II NSCLC. Lower pre-SBRT BMI and lower pre-SBRT CAR were associated with a decreased risk of cancer-specific death

Noema formIng Cluster survEy (NICE): Discovery of a starbursting galaxy group with a radio-luminous core at z=3.95

The study of distant galaxy groups and clusters at the peak epoch of star formation is limited by the lack of a statistically and homogeneously selected and spectroscopically confirmed sample. Recent discoveries of concentrated starburst activities in cluster cores have opened a new window to hunt for these structures based on their integrated IR luminosities. Hereby we carry out the large NOEMA (NOrthern Extended Millimeter Array) program targeting a statistical sample of infrared-luminous sources associated with overdensities of massive galaxies at z>2, the Noema formIng Cluster survEy (NICE). We present the first result from the ongoing NICE survey, a compact group at z=3.95 in the Lockman Hole field (LH-SBC3), confirmed via four massive (M_star>10^10.5M_sun) galaxies detected in CO(4-3) and [CI](1-0) lines. The four CO-detected members of LH-SBC3 are distributed over a 180 kpc physical scale, and the entire structure has an estimated halo mass of ~10^13Msun and total star formation rate (SFR) of ~4000Msun/yr. In addition, the most massive galaxy hosts a radio-loud AGN with L_1.4GHz, rest = 3.0*10^25W/Hz. The discovery of LH-SBC3 demonstrates the feasibility of our method to efficiently identify high-z compact groups or forming cluster cores. The existence of these starbursting cluster cores up to z~4 provides critical insights into the mass assembly history of the central massive galaxies in clusters.Comment: 7 pages, 7 figures, submitted to A&