Three-dimensional numerical study of flow characteristic and membrane fouling evolution in an enzymatic membrane reactor

In order to enhance the understanding of membrane fouling mechanism, the hydrodynamics of granular flow in a stirred enzymatic membrane reactor was numerically investigated in the present study. A three-dimensional Euler-Euler model, coupled with k-e mixture turbulence model and drag function for interphase momentum exchange, was applied to simulate the two-phase (fluid-solid) turbulent flow. Numerical simulations of single- or two-phase turbulent flow under various stirring speed were implemented. The numerical results coincide very well with some published experimental data. Results for the distributions of velocity, shear stress and turbulent kinetic energy were provided. Our results show that the increase of stirring speed could not only enlarge the circulation loops in the reactor, but it can also increase the shear stress on the membrane surface and accelerate the mixing process of granular materials. The time evolution of volumetric function of granular materials on the membrane surface has qualitatively explained the evolution of membrane fouling.Comment: 10 panges, 8 figure

Microcirculatory changes identified by photoacoustic microscopy in patients with complex regional pain syndrome type I after stellate ganglion blocks

Complex regional pain syndrome (CRPS) is a chronic pain syndrome that causes intractable pain, disability, and poor quality of life for patients. The etiology and pathophysiology of CRPS are still poorly understood. Due to a lack of proper diagnostic tools, the prognosis of CRPS is primarily based on clinical observation. The objective of this work is to evaluate a new imaging modality, photoacoustic microscopy (PAM), for assisting diagnoses and monitoring the progress and treatment outcome of CRPS. Blood vasculature and oxygen saturation (sO_2) were imaged by PAM from eight adult patients with CRPS-1. Patients’ hands and cuticles were imaged both before and after stellate ganglion block (SGB) for comparison. For all patients, both vascular structure and sO_2 could be assessed by PAM. In addition, more vessels and stronger signals were observed after SGB. The results show that PAM can help diagnose and monitor CRPS

A protocol specialized for microbial DNA extraction from living poplar wood

Microbial DNA extraction is a critical step in metagenomic research. High contents of chemical substances in wood tissues always cause low microbial DNA yield and quality. Up to date, almost no specialized methods involved in microbial DNA extraction from living wood were reported. In this study, an improved protocol (M1) concerning microbial DNA extraction from living poplar wood was developed. We compared microbial DNA yield and quality by M1 with those by other seven methods, including PowerSoil DNA isolation kit (M2), two soil microbial DNA extraction methods (M3 and M4), poplar genomic DNA extraction method from wood (M5), and microbial DNA extraction method from herb stems (M6), isolating bacteria (M7) and isolating fungus (M8). Results showed that M1 yielded much better quality and concentration of microbial DNA than the other methods (M2-M8) from both poplar wetwood and sapwood tissues. Following M1 protocol, 1 g of wetwood sample could yield 272.27 ng/ul (vol=50 ul) pure microbial DNA with the absorption ratios of 1.87 (A260/A230) and 1.66 (A260/A280). For 1 g of sapwood sample, these values were 361.83 ng/ul, 1.85 and 2.24, respectively. These DNA could be stably visualized by agarose gel electrophoresis and amplified by primer sets of bacteria (16S V3-V4, 16S-V4, 16S V4-V5) and fungus (ITS1, ITS2). While, the other seven methods only obtained less or contaminated microbial DNA, which could not be amplified stably by aforementioned primer sets. Our protocol provided an approach for microbial community study in living poplar wood in a more accurate way by molecular biology techniques

ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extraction

Large language models (LLMs), such as GPT-3 and ChatGPT, have demonstrated remarkable results in various natural language processing (NLP) tasks with in-context learning, which involves inference based on a few demonstration examples. Despite their successes in NLP tasks, no investigation has been conducted to assess the ability of LLMs to perform document information extraction (DIE) using in-context learning. Applying LLMs to DIE poses two challenges: the modality and task gap. To this end, we propose a simple but effective in-context learning framework called ICL-D3IE, which enables LLMs to perform DIE with different types of demonstration examples. Specifically, we extract the most difficult and distinct segments from hard training documents as hard demonstrations for benefiting all test instances. We design demonstrations describing relationships that enable LLMs to understand positional relationships. We introduce formatting demonstrations for easy answer extraction. Additionally, the framework improves diverse demonstrations by updating them iteratively. Our experiments on three widely used benchmark datasets demonstrate that the ICL-D3IE framework enables GPT-3/ChatGPT to achieve superior performance when compared to previous pre-trained methods fine-tuned with full training in both the in-distribution (ID) setting and in the out-of-distribution (OOD) setting

T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Large Language Model Signals for Science Question Answering

Large Language Models (LLMs) have recently demonstrated exceptional performance in various Natural Language Processing (NLP) tasks. They have also shown the ability to perform chain-of-thought (CoT) reasoning to solve complex problems. Recent studies have explored CoT reasoning in complex multimodal scenarios, such as the science question answering task, by fine-tuning multimodal models with high-quality human-annotated CoT rationales. However, collecting high-quality COT rationales is usually time-consuming and costly. Besides, the annotated rationales are hardly accurate due to the external essential information missed. To address these issues, we propose a novel method termed \emph{T-SciQ} that aims at teaching science question answering with LLM signals. The T-SciQ approach generates high-quality CoT rationales as teaching signals and is advanced to train much smaller models to perform CoT reasoning in complex modalities. Additionally, we introduce a novel data mixing strategy to produce more effective teaching data samples by policy for simple and complex science question answer problems. Extensive experimental results show that our T-SciQ method achieves a new state-of-the-art performance on the ScienceQA benchmark, with an accuracy of 96.18\%. Moreover, our approach outperforms the most powerful fine-tuned baseline by 4.5\%