CAR: Conceptualization-Augmented Reasoner for Zero-Shot Commonsense Question Answering

The task of zero-shot commonsense question answering evaluates models on their capacity to reason about general scenarios beyond those presented in specific datasets. Existing approaches for tackling this task leverage external knowledge from CommonSense Knowledge Bases (CSKBs) by pretraining the model on synthetic QA pairs constructed from CSKBs. In these approaches, negative examples (distractors) are formulated by randomly sampling from CSKBs using fairly primitive keyword constraints. However, two bottlenecks limit these approaches: the inherent incompleteness of CSKBs limits the semantic coverage of synthetic QA pairs, and the lack of human annotations makes the sampled negative examples potentially uninformative and contradictory. To tackle these limitations above, we propose Conceptualization-Augmented Reasoner (CAR), a zero-shot commonsense question-answering framework that fully leverages the power of conceptualization. Specifically, CAR abstracts a commonsense knowledge triple to many higher-level instances, which increases the coverage of CSKB and expands the ground-truth answer space, reducing the likelihood of selecting false-negative distractors. Extensive experiments demonstrate that CAR more robustly generalizes to answering questions about zero-shot commonsense scenarios than existing methods, including large language models, such as GPT3.5 and ChatGPT. Our codes, data, and model checkpoints are available at https://github.com/HKUST-KnowComp/CAR

TILFA: A Unified Framework for Text, Image, and Layout Fusion in Argument Mining

A main goal of Argument Mining (AM) is to analyze an author's stance. Unlike previous AM datasets focusing only on text, the shared task at the 10th Workshop on Argument Mining introduces a dataset including both text and images. Importantly, these images contain both visual elements and optical characters. Our new framework, TILFA (A Unified Framework for Text, Image, and Layout Fusion in Argument Mining), is designed to handle this mixed data. It excels at not only understanding text but also detecting optical characters and recognizing layout details in images. Our model significantly outperforms existing baselines, earning our team, KnowComp, the 1st place in the leaderboard of Argumentative Stance Classification subtask in this shared task.Comment: Accepted to the 10th Workshop on Argument Mining, co-located with EMNLP 202

Correlation between long-term aspirin use and F-fluorodeoxyglucose uptake in colorectal cancer measured by PET/CT.

The aim of this study was to evaluate the relationship between long-term aspirin use with pretreatment 18 Fluorodeoxyglucose (FDG) uptake of primary lesions of Colorectal cancer (CRC) and evaluate their clinical significance.We enrolled 84 patients with CRC who underwent 18F-FDG PET/CT scanning before surgery between 1st July 2008 and 1st March 2013 and followed up until 1st March 2014. Maximum standardized uptake value (SUVmax) of the primary tumor was measured by 18F-FDG PET/CT. The history of aspirin taken and other clinicopathogical factors were also obtained and their relationships were examined by Mann-Whitney or χ2 tests. Progression-free survival (PFS) was determined by standard Kaplan-Meier survival analysis. Cox proportional hazards regression was performed to determine whether history of aspirin taken, pretreatment SUVmax, age, gender, TNM stage, tumor sizes and differentiation influenced outcomes.CRC Patients with long-term history of aspirin use had lower SUVmax of primary lesions than control group (9.74±2.62 vs. 13.91±6.18) and showed a trend towards improved PFS after curative surgery. However, pretreatment of SUVmax showed no prognostic value in patients with CRC.Long-term aspirin use is associated with lower pretreatment SUVmax of CRC and is a promising prognostic factor for predicting PFS in patients with CRC

¹⁸F-fluoro-deoxyglucose positron emission tomography/computed tomography scan findings in Rosai-Dorfman disease with IgG4-positive plasma cell infiltration mimicking breast malignancy: a case report and literature review

Abstract Introduction Rosai-Dorfman disease, also known as sinus histiocytosis with massive lymphadenopathy, is a rare benign disorder characterized histologically by lymphatic sinus dilatation due to histiocyte proliferation. Rosai-Dorfman disease accompanied by IgG4+ plasma cell infiltration is an even rarer situation. To the best of our knowledge, no imaging report of fluoro-deoxyglucose positron emission tomography/computed tomography findings of Rosai-Dorfman disease with IgG4+ plasma cell infiltration has been published, although a series of pathological research has focused on this phenomenon. Case presentation We reviewed the 18F-fluoro-deoxyglucose positron emission tomography/computed tomography scan of a 78-year-old Chinese woman with a solid mass that was found in her right breast during a health checkup. 18F-fluoro-deoxyglucose positron emission tomography/computed tomography showed a hypermetabolic nodule in her right breast and slightly heterogeneous increased fluoro-deoxyglucose uptake of the pulmonary nodules, which were histologically proven to be mammary Rosai-Dorfman disease with IgG4+ plasma cell infiltration and pulmonary amyloidosis, respectively. A literature review was performed to gather information on this rare disease process. Conclusions Although distinguishing benign lymphoplasmacytic proliferation from malignancy may be difficult with 18F-fluoro-deoxyglucose positron emission tomography/computed tomography in light of the pattern and intensity of fluoro-deoxyglucose uptake, our case highlights that whole-body positron emission tomography/computed tomography imaging not only can display the extent of the disease to help complete staging but also can provide functional information about disease activity to guide biopsy.</p

Evaluation of a Wobbling Method Applied to Correcting Defective Pixels of CZT Detectors in SPECT Imaging

In this paper, we propose a wobbling method to correct bad pixels in cadmium zinc telluride (CZT) detectors, using information of related images. We build up an automated device that realizes the wobbling correction for small animal Single Photon Emission Computed Tomography (SPECT) imaging. The wobbling correction method is applied to various constellations of defective pixels. The corrected images are compared with the results of conventional interpolation method, and the correction effectiveness is evaluated quantitatively using the factor of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). In summary, the proposed wobbling method, equipped with the automatic mechanical system, provides a better image quality for correcting defective pixels, which could be used for all pixelated detectors for molecular imaging

Univariate and multivariate analysis of prognostic factors of PFS.

<p>Univariate and multivariate analysis of prognostic factors of PFS.</p

Patient demographics and clinical characteristics.

<p>Patient demographics and clinical characteristics.</p

Clinicopathological implication of Pretreament SUVmax combination status.

<p>Clinicopathological implication of Pretreament SUVmax combination status.</p

Brain Network Alterations in Alzheimer’s Disease Identified by Early-Phase PIB-PET

The aim of this study was to identify the brain networks from early-phase 11C-PIB (perfusion PIB, pPIB) data and to compare the brain networks of patients with differentiating Alzheimer’s disease (AD) with cognitively normal subjects (CN) and of mild cognitively impaired patients (MCI) with CN. Forty participants (14 CN, 12 MCI, and 14 AD) underwent 11C-PIB and 18F-FDG PET/CT scans. Parallel independent component analysis (pICA) was used to identify correlated brain networks from the 11C-pPIB and 18F-FDG data, and a two-sample t-test was used to evaluate group differences in the corrected brain networks between AD and CN, and between MCI and CN. Our study identified a brain network of perfusion (early-phase 11C-PIB) that highly correlated with a glucose metabolism (18F-FDG) brain network and colocalized with the default mode network (DMN) in an AD-specific neurodegenerative cohort. Particularly, decreased 18F-FDG uptake correlated with a decreased regional cerebral blood flow in the frontal, parietal, and temporal regions of the DMN. The group comparisons revealed similar spatial patterns of the brain networks derived from the 11C-pPIB and 18F-FDG data. Our findings indicate that 11C-pPIB derived from the early-phase 11C-PIB could provide complementary information for 18F-FDG examination in AD