Backward Reasoning in Large Language Models for Verification

Chain-of-Though (CoT) prompting has shown promising performance in various reasoning tasks. Recently, Self-Consistency \citep{wang2023selfconsistency} proposes to sample a diverse set of reasoning chains which may lead to different answers while the answer that receives the most votes is selected. In this paper, we propose a novel method to use backward reasoning in verifying candidate answers. We mask a token in the question by ${\bf x}$ and ask the LLM to predict the masked token when a candidate answer is provided by \textit{a simple template}, i.e., ``\textit{\textbf{If we know the answer of the above question is \{a candidate answer\}, what is the value of unknown variable ${\bf x}$?}}'' Intuitively, the LLM is expected to predict the masked token successfully if the provided candidate answer is correct. We further propose FOBAR to combine forward and backward reasoning for estimating the probability of candidate answers. We conduct extensive experiments on six data sets and three LLMs. Experimental results demonstrate that FOBAR achieves state-of-the-art performance on various reasoning benchmarks.Comment: Preprin

MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models

Large language models (LLMs) have pushed the limits of natural language understanding and exhibited excellent problem-solving ability. Despite the great success, most existing open-source LLMs (e.g., LLaMA-2) are still far away from satisfactory for solving mathematical problem due to the complex reasoning procedures. To bridge this gap, we propose MetaMath, a fine-tuned language model that specializes in mathematical reasoning. Specifically, we start by bootstrapping mathematical questions by rewriting the question from multiple perspectives without extra knowledge, which results in a new dataset called MetaMathQA. Then we fine-tune the LLaMA-2 models on MetaMathQA. Experimental results on two popular benchmarks (i.e., GSM8K and MATH) for mathematical reasoning demonstrate that MetaMath outperforms a suite of open-source LLMs by a significant margin. Our MetaMath-7B model achieves 66.4% on GSM8K and 19.4% on MATH, exceeding the state-of-the-art models of the same size by 11.5% and 8.7%. Particularly, MetaMath-70B achieves an accuracy of 82.3% on GSM8K, slightly better than GPT-3.5-Turbo. We release all the MetaMathQA dataset, the MetaMath models with different model sizes and the training code for public use.Comment: Technical Report, Work in Progress. Project Page: https://meta-math.github.io

Low-cost flexible plasmonic nanobump metasurfaces for label-free sensing of serum tumor marker

Abstract(#br)The use of plasmonic metasurface for sensing has great potential on label-free detection of human tumor markers, which could benefit clinical examination. In this work, we adopt nanoimprint and plasma etching to optimize the nanofabrication for low-cost flexible plasmonic metasurface sensors with gold nanobump arrays, which enable facile surface bio-functionality, high sensitivity and simple optical measurement in the visible range. A high bulk refractive index sensitivity of 454.4 nm/RIU is achieved for the prototype plasmonic metasurface sensors at the wavelengths from 620 nm to 720 nm. The rapid quantitative tumor marker sensing of carcinoembryonic antigen in human serum samples from less than 10 ng/mL to more than 87 ng/mL is achieved, which demonstrates good agreement with the conventional chemiluminescence immunoassay system and sufficiently covers the threshold tumor marker concentration of 20 ng/mL for early cancer prediction. Our method is capable of low-cost high-throughput manufacturing for flexible lightweight plasmonic metasurface sensors, which will facilitate wide applications on portable biomedical sensing devices for future point-of-care diagnosis and mobile healthcare

Low-cost flexible plasmonic nanobump metasurfaces for label-free sensing of serum tumor marker.

The use of plasmonic metasurface for sensing has great potential on label-free detection of human tumor markers, which could benefit clinical examination. In this work, we adopt nanoimprint and plasma etching to optimize the nanofabrication for low-cost flexible plasmonic metasurface sensors with gold nanobump arrays, which enable facile surface bio-functionality, high sensitivity and simple optical measurement in the visible range. A high bulk refractive index sensitivity of 454.4 nm/RIU is achieved for the prototype plasmonic metasurface sensors at the wavelengths from 620 nm to 720 nm. The rapid quantitative tumor marker sensing of carcinoembryonic antigen in human serum samples from less than 10 ng/mL to more than 87 ng/mL is achieved, which demonstrates good agreement with the conventional chemiluminescence immunoassay system and sufficiently covers the threshold tumor marker concentration of 20 ng/mL for early cancer prediction. Our method is capable of low-cost high-throughput manufacturing for flexible lightweight plasmonic metasurface sensors, which will facilitate wide applications on portable biomedical sensing devices for future point-of-care diagnosis and mobile healthcare

Oncostatin M Protects Rod and Cone Photoreceptors and Promotes Regeneration of Cone Outer Segment in a Rat Model of Retinal Degeneration

Retinitis pigmentosa (RP) is a group of photoreceptor degenerative disorders that lead to loss of vision. Typically, rod photoreceptors degenerate first, resulting in loss of night and peripheral vision. Secondary cone degeneration eventually affects central vision, leading to total blindness. Previous studies have shown that photoreceptors could be protected from degeneration by exogenous neurotrophic factors, including ciliary neurotrophic factor (CNTF), a member of the IL-6 family of cytokines. Using a transgenic rat model of retinal degeneration (the S334-ter rat), we investigated the effects of Oncostatin M (OSM), another member of the IL-6 family of cytokines, on photoreceptor protection. We found that exogenous OSM protects both rod and cone photoreceptors. In addition, OSM promotes regeneration of cone outer segments in early stages of cone degeneration. Further investigation showed that OSM treatment induces STAT3 phosphorylation in Müller cells but not in photoreceptors, suggesting that OSM not directly acts on photoreceptors and that the protective effects of OSM on photoreceptors are mediated by Müller cells. These findings support the therapeutic strategy using members of IL-6 family of cytokines for retinal degenerative disorders. They also provide evidence that activation of the STAT3 pathway in Müller cells promotes photoreceptor survival. Our work highlights the importance of Müller cell-photoreceptor interaction in the retina, which may serve as a model of glia-neuron interaction in general

Difficulty in central venous catheter placement due to congenital partial anomalous pulmonary venous return: A case report

Abstract Congenital partial anomalous pulmonary venous return (PAPVR) is rare and present in 0.04%–0.7% of the population. This may pose a significant problem for clinicians performing internal jugular venous catheter placement. This report depicts an abnormal internal jugular venous catheter placement due to a PAPVR to help physicians recognize and deal with variant pulmonary veins

Quality Assessment for Comparing Image Enhancement Algorithms

As the image enhancement algorithms developed in recent years, how to compare the performances of different image enhancement algorithms becomes a novel task. In this paper, we propose a framework to do quality assessment for comparing image enhancement algorithms. Not like traditional image quality assessment approaches, we focus on the relative quality ranking between enhanced images rather than giving an absolute quality score for a single enhanced image. We construct a dataset which contains source images in bad visibility and their enhanced images processed by different enhancement algorithms, and then do subjective assessment in a pair-wise way to get the relative ranking of these enhanced images. A rank function is trained to fit the subjective assessment results

Doping Ag in ZnO Nanorods to Improve the Performance of Related Enzymatic Glucose Sensors

In this paper, the performance of a zinc oxide (ZnO) nanorod-based enzymatic glucose sensor was enhanced with silver (Ag)-doped ZnO (ZnO-Ag) nanorods. The effect of the doped Ag on the surface morphologies, wettability, and electron transfer capability of the ZnO-Ag nanorods, as well as the catalytic character of glucose oxidase (GOx) and the performance of the glucose sensor was investigated. The results indicate that the doped Ag slightly weakens the surface roughness and hydrophilicity of the ZnO-Ag nanorods, but remarkably increases their electron transfer ability and enhances the catalytic character of GOx. Consequently, the combined effects of the above influencing factors lead to a notable improvement of the performance of the glucose sensor, that is, the sensitivity increases and the detection limit decreases. The optimal amount of the doped Ag is determined to be 2 mM, and the corresponding glucose sensor exhibits a sensitivity of 3.85 μA/(mM·cm2), detection limit of 1.5 μM, linear range of 1.5 × 10−3–6.5 mM, and Michaelis-Menten constant of 3.87 mM. Moreover, the glucose sensor shows excellent selectivity to urea, ascorbic acid, and uric acid, in addition to displaying good storage stability. These results demonstrate that ZnO-Ag nanorods are promising matrix materials for the construction of other enzymatic biosensors

The Combination of Cell Cultured Technology and In Silico Model to Inform the Drug Development

Human-derived in vitro models can provide high-throughput efficacy and toxicity data without a species gap in drug development. Challenges are still encountered regarding the full utilisation of massive data in clinical settings. The lack of translated methods hinders the reliable prediction of clinical outcomes. Therefore, in this study, in silico models were proposed to tackle these obstacles from in vitro to in vivo translation, and the current major cell culture methods were introduced, such as human-induced pluripotent stem cells (hiPSCs), 3D cells, organoids, and microphysiological systems (MPS). Furthermore, the role and applications of several in silico models were summarised, including the physiologically based pharmacokinetic model (PBPK), pharmacokinetic/pharmacodynamic model (PK/PD), quantitative systems pharmacology model (QSP), and virtual clinical trials. These credible translation cases will provide templates for subsequent in vitro to in vivo translation. We believe that synergising high-quality in vitro data with existing models can better guide drug development and clinical use