Rapid Determination of Saponins in the Honey-Fried Processing of Rhizoma Cimicifugae by Near Infrared Diffuse Reflectance Spectroscopy.

ObjectiveA model of Near Infrared Diffuse Reflectance Spectroscopy (NIR-DRS) was established for the first time to determine the content of Shengmaxinside I in the honey-fried processing of Rhizoma Cimicifugae.MethodsShengmaxinside I content was determined by high-performance liquid chromatography (HPLC), and the data of the honey-fried processing of Rhizoma Cimicifugae samples from different batches of different origins by NIR-DRS were collected by TQ Analyst 8.0. Partial Least Squares (PLS) analysis was used to establish a near-infrared quantitative model.ResultsThe determination coefficient R² was 0.9878. The Cross-Validation Root Mean Square Error (RMSECV) was 0.0193%, validating the model with a validation set. The Root Mean Square Error of Prediction (RMSEP) was 0.1064%. The ratio of the standard deviation for the validation samples to the standard error of prediction (RPD) was 5.5130.ConclusionThis method is convenient and efficient, and the experimentally established model has good prediction ability, and can be used for the rapid determination of Shengmaxinside I content in the honey-fried processing of Rhizoma Cimicifugae

Self-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrations

Large language models (LMs) have exhibited superior in-context learning (ICL) ability to adopt to target tasks by prompting with a few input-output demonstrations. Towards better ICL, different methods are proposed to select representative demonstrations from existing training corpora. However, such a setting is not aligned with real-world practices, as end-users usually query LMs without accesses to demonstration pools. Inspired by evidence suggesting LMs' zero-shot capabilities are underrated, and the role of demonstrations are primarily for exposing models' intrinsic functionalities, we introduce Self-ICL, a simple framework for zero-shot ICL. Given a test input, Self-ICL first prompts the model to generate pseudo-inputs. Next, the model predicts pseudo-labels for the pseudo-inputs via zero-shot prompting. Finally, we construct pseudo-demonstrations from pseudo-input-label pairs, and perform ICL for the test input. Evaluation on BIG-Bench Hard shows Self-ICL steadily surpasses zero-shot and zero-shot chain-of-thought baselines on head-to-head and all-task average performance. Our findings suggest the possibility to bootstrap LMs' intrinsic capabilities towards better zero-shot performance.Comment: Work in progres

Large Language Models Perform Diagnostic Reasoning

We explore the extension of chain-of-thought (CoT) prompting to medical reasoning for the task of automatic diagnosis. Motivated by doctors' underlying reasoning process, we present Diagnostic-Reasoning CoT (DR-CoT). Empirical results demonstrate that by simply prompting large language models trained only on general text corpus with two DR-CoT exemplars, the diagnostic accuracy improves by 15% comparing to standard prompting. Moreover, the gap reaches a pronounced 18% in out-domain settings. Our findings suggest expert-knowledge reasoning in large language models can be elicited through proper promptings.Comment: Accepted as a Tiny Paper at ICLR 2023 (10 pages, 5 figures

Delayed Airway Obstruction after Internal Jugular Venous Catheterization in a Patient with Anticoagulant Therapy

Delayed onset of neck hematoma following central venous catheterization without arterial puncture is uncommon. Herein, we present a patient who developed a delayed neck hematoma after repeated attempts at right internal jugular venous puncture and subsequent enoxaparin administration. Progressive airway obstruction occurred on the third day after surgery. Ultrasound examination revealed diffuse hematoma of the right neck, and fibreoptic examination of the airway revealed pharyngeal edema. After emergent surgical removal of the hematoma, the patient was extubated uneventfully

Fidelity-Enriched Contrastive Search: Reconciling the Faithfulness-Diversity Trade-Off in Text Generation

In this paper, we address the hallucination problem commonly found in natural language generation tasks. Language models often generate fluent and convincing content but can lack consistency with the provided source, resulting in potential inaccuracies. We propose a new decoding method called Fidelity-Enriched Contrastive Search (FECS), which augments the contrastive search framework with context-aware regularization terms. FECS promotes tokens that are semantically similar to the provided source while penalizing repetitiveness in the generated text. We demonstrate its effectiveness across two tasks prone to hallucination: abstractive summarization and dialogue generation. Results show that FECS consistently enhances faithfulness across various language model sizes while maintaining output diversity comparable to well-performing decoding algorithms.Comment: Accepted as a short paper at EMNLP 202

Prevalence of latent tuberculosis infection in BCG-vaccinated healthcare workers by using an interferon-gamma release assay and the tuberculin skin test in an intermediate tuberculosis burden country

BackgroundThe risk of healthcare workers (HCWs) acquiring tuberculosis (TB) infection is high. We determined the prevalence of latent TB infection (LTBI) in HCWs with a high Bacille Calmette-Guérin (BCG) vaccine coverage in an intermediate TB burden country by using an interferon-gamma release assay [QuantiFERON-TB Gold (QFT-G)] and by using the tuberculin skin test (TST). Risk factors associated with a positive test were determined.MethodsThis prospective cross-sectional study enrolled HCWs from a medical center in Taiwan. Participants were grouped into workers without exposure (Group 1) and workers who self-reported a history of TB exposure (Group 2). All participants completed a questionnaire to collect demographic information and risk factors for acquiring TB. The QFT-G test and the TST were administered and risk factors for a positive test were analyzed.ResultsWe recruited 193 HCWs [149 (77.2%) female workers] with a mean age of 35.6 years. All were BCG-vaccinated. The prevalence of LTBI was 88.8% (based on the TST) and 14.5% (based on the QFT-G test). There was no difference between HCWs with and without known exposure to TB. Agreement between the tests was poor (i.e., the kappa value was less than 0.05). Multivariable logistic regression showed that only the QFT-G test was associated with age (35 years or greater) (adjusted OR, 2.53; p = 0.03).ConclusionBy using the QFT-G test or TST, this study found a similar prevalence of LTBI in HCWs with and without known exposure to TB. This suggests that in intermediate TB burden countries exposure to TB may occur within the hospital and within the community. Compared to the TST, the QFT-G test was correlated better with age, which is a known risk factor for latent TB infection

ZARA: Improving Few-Shot Self-Rationalization for Small Language Models

Language models (LMs) that jointly generate end-task answers as well as free-text rationales are known as self-rationalization models. Recent works demonstrate great performance gain for self-rationalization by few-shot prompting LMs with rationale-augmented exemplars. However, the ability to benefit from explanations only emerges with large-scale LMs, which have poor accessibility. In this work, we explore the less-studied setting of leveraging explanations for small LMs to improve few-shot self-rationalization. We first revisit the relationship between rationales and answers. Inspired by the implicit mental process of how human beings assess explanations, we present a novel approach, Zero-shot Augmentation of Rationale-Answer pairs (ZARA), to automatically construct pseudo-parallel data for self-training by reducing the problem of plausibility judgement to natural language inference. Experimental results show ZARA achieves SOTA performance on the FEB benchmark, for both the task accuracy and the explanation metric. In addition, we conduct human and quantitative evaluation validating ZARA's ability to automatically identify plausible and accurate rationale-answer pairs.Comment: Accepted as a long paper at EMNLP Findings 202

Aedes albopictus salivary proteins adenosine deaminase and 34k2 interact with human mast cell specific proteases tryptase and chymase

When mosquitoes probe to feed blood, they inoculate a mixture of salivary molecules into vertebrate hosts' skin causing acute inflammatory reactions where mast cell-derived mediators are involved. Mosquito saliva contains many proteins with largely unknown biological functions. Here, two Aedes albopictus salivary proteins - adenosine deaminase (alADA) and al34k2 - were investigated for their immunological impact on mast cells and two mast cell-specific proteases, the tryptase and the chymase. Mouse bone marrow-derived mast cells were challenged with increased concentrations of recombinant alADA or al34k2 for 1, 3, and 6 h, and to measure mast cell activation, the activity levels of beta-hexosaminidase and tryptase and secretion of IL-6 were evaluated. In addition, a direct interaction between alADA or al34k2 with tryptase or chymase was investigated. Results show that bone marrow-derived mast cells challenged with 10 mu g/ml of alADA secreted significant levels of beta-hexosaminidase, tryptase, and IL-6. Furthermore, both al34k2 and alADA are cut by human tryptase and chymase. Interestingly, al34k2 dose-dependently enhance enzymatic activity of both tryptase and chymase. In contrast, while alADA enhances the enzymatic activity of tryptase, chymase activity was inhibited. Our finding suggests that alADA and al34k2 via interaction with mast cell-specific proteases tryptase and chymase modulate mast cell-driven immune response in the local skin microenvironment. alADA- and al34k2-mediated modulation of tryptase and chymase may also recruit more inflammatory cells and induce vascular leakage, which may contribute to the inflammatory responses at the mosquito bite site