GPT-4 Vision on Medical Image Classification -- A Case Study on COVID-19 Dataset

This technical report delves into the application of GPT-4 Vision (GPT-4V) in the nuanced realm of COVID-19 image classification, leveraging the transformative potential of in-context learning to enhance diagnostic processes

Elucidating the Synergic Effect in Nanoscale MoS\u3csub\u3e2\u3c/sub\u3e/TiO\u3csub\u3e2\u3c/sub\u3e Heterointerface for Na-Ion Storage

Interface engineering in electrode materials is an attractive strategy for enhancing charge storage, enabling fast kinetics, and improving cycling stability for energy storage systems. Nevertheless, the performance improvement is usually ambiguously ascribed to the “synergetic effect”, the fundamental understanding toward the effect of the interface at molecular level in composite materials remains elusive. In this work, a well-defined nanoscale MoS2/TiO2 interface is rationally designed by immobilizing TiO2 nanocrystals on MoS2 nanosheets. The role of heterostructure interface between TiO2 and MoS2 by operando synchrotron X-ray diffraction (sXRD), solid-state nuclear magnetic resonance, and density functional theory calculations is investigated. It is found that the existence of a hetero-interfacial electric field can promote charge transfer kinetics. Based on operando sXRD, it is revealed that the heterostructure follows a solid-solution reaction mechanism with small volume changes during cycling. As such, the electrode demonstrates ultrafast Na+ ions storage of 300 mAh g−1 at 10 A g−1 and excellent reversible capacity of 540 mAh g−1 at 0.2 A g−1. This work provides significant insights into understanding of heterostructure interface at molecular level, which suggests new strategies for creating unconventional nanocomposite electrode materials for energy storage systems

TSegFormer: 3D Tooth Segmentation in Intraoral Scans with Geometry Guided Transformer

Optical Intraoral Scanners (IOS) are widely used in digital dentistry to provide detailed 3D information of dental crowns and the gingiva. Accurate 3D tooth segmentation in IOSs is critical for various dental applications, while previous methods are error-prone at complicated boundaries and exhibit unsatisfactory results across patients. In this paper, we propose TSegFormer which captures both local and global dependencies among different teeth and the gingiva in the IOS point clouds with a multi-task 3D transformer architecture. Moreover, we design a geometry-guided loss based on a novel point curvature to refine boundaries in an end-to-end manner, avoiding time-consuming post-processing to reach clinically applicable segmentation. In addition, we create a dataset with 16,000 IOSs, the largest ever IOS dataset to the best of our knowledge. The experimental results demonstrate that our TSegFormer consistently surpasses existing state-of-the-art baselines. The superiority of TSegFormer is corroborated by extensive analysis, visualizations and real-world clinical applicability tests. Our code is available at https://github.com/huiminxiong/TSegFormer.Comment: MICCAI 2023, STAR(Student Travel) award. 11 pages, 3 figures, 5 tables. arXiv admin note: text overlap with arXiv:2210.1662

Lytic cocktail:An effective method to alleviate severe burn induced hyper-metabolism through regulating white adipose tissue browning

Background: Browning of white adipose tissue is associated with elevated resting metabolic rates and is considered to be one of the indispensable causes of hypermetabolism in burn patients. Hypermetabolism means increased resting energy expenditure, raised body temperature and acute-phase proteins. Persistently elevated levels of circulating stress hormones have been reported to induce browning of subcutaneous white adipose tissue. The lytic cocktail is a combination of medicines pethidine, chlorpromazine, and promethazine that has been used clinically in sedation for the management of patients. As reported this sedative treatment can reduce the expression of catecholamines in major burn rats. Thus, in this paper we focused on the effects of lytic cocktail in the regulation of white adipose tissue browning and hypermetabolism and we further investigated the underlying mechanism.Methods: A 30% total body surface area (TBSA) III degree scald rat model was used for this study. The rats were randomly divided into a sham scald group, a scalding with immediate resuscitation group, and a group of scalding with immediate resuscitation and lytic cocktail treatment. The levels of norepinephrine and epinephrine in plasma were dynamically detected. Changes of the rat body weight and food intake were recorded and compared as indexes of metabolism responses after post-scalding. For the study of white adipose tissue browning, inguinal adipose tissue was used. Metabolic changes, while indicatives of white fat browning were measured by PET/CT. The expression of white adipose browning related proteins and the changes of mitochondria number were used to assess browning of inguinal adipose.Results: The level of plasma catecholamines norepinephrine and epinephrine in the lytic cocktail-treated group was significantly lower than the other two groups. Morphology and PET/CT showed that the inguinal white adipose browning was inhibited in the lytic cocktail treated group, whereas scalding with immediate resuscitation group showed browning of white adipose. The number of mitochondria, the expressions of white adipose browning related proteins in the lytic cocktail group were also significantly lower than that of the group of scalding with immediate resuscitation.Conclusion: By reducing expression of heat-related proteins, the application of lytic cocktail medicines inhibits the white adipose tissue browning, which suppresses hypermetabolism in scalded rats. The mechanism might be related to decreased expression levels of stress hormones induced by lytic cocktail. This research suggests that lytic cocktails may be an effective treatment for hypermetabolism after severe burn injury

Fast Model Debias with Machine Unlearning

Recent discoveries have revealed that deep neural networks might behave in a biased manner in many real-world scenarios. For instance, deep networks trained on a large-scale face recognition dataset CelebA tend to predict blonde hair for females and black hair for males. Such biases not only jeopardize the robustness of models but also perpetuate and amplify social biases, which is especially concerning for automated decision-making processes in healthcare, recruitment, etc., as they could exacerbate unfair economic and social inequalities among different groups. Existing debiasing methods suffer from high costs in bias labeling or model re-training, while also exhibiting a deficiency in terms of elucidating the origins of biases within the model. To this respect, we propose a fast model debiasing framework (FMD) which offers an efficient approach to identify, evaluate and remove biases inherent in trained models. The FMD identifies biased attributes through an explicit counterfactual concept and quantifies the influence of data samples with influence functions. Moreover, we design a machine unlearning-based strategy to efficiently and effectively remove the bias in a trained model with a small counterfactual dataset. Experiments on the Colored MNIST, CelebA, and Adult Income datasets along with experiments with large language models demonstrate that our method achieves superior or competing accuracies compared with state-of-the-art methods while attaining significantly fewer biases and requiring much less debiasing cost. Notably, our method requires only a small external dataset and updating a minimal amount of model parameters, without the requirement of access to training data that may be too large or unavailable in practice

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data