Investigation of the effects of temperature and ions on the interaction between ECG and BSA by the fluorescence quenching method

The effects of temperature and common ions on binding (-)-epicatechin gallate (ECG) to bovine serum albumin (BSA) are investigated. The binding constants (Ka) between ECG and BSA are 1.20 Ч 106 (17°C), 1.38 Ч 106 (27°C), and 5.69 x 106 L mol-1 (37°C), and the number of binding sites (n) were 1.14, 1.15, and 1.26, respectively. These results showed that the increasing temperature improves the stability of the ECG-BSA system, which results in a higher binding constant and the number of binding sites of the ECG-BSA system. The presence of Co2+ and Zn2+ ions decreased the binding constants (Ka) and the number of binding sites (n) of ECG-BSA complex. However, the presence of Cu2+ and Ni2+ increased the affinity of ECG for BSA largely. The positive ΔH and positive ΔS indicated that hydrophobic forces might play a major role in the binding between ECG and BSA

毛发宏扫描全息分析系统

The existing disease diagnosis techniques are more or less harmful to the human body, so a simple and non-invasive disease screening technology is urgently needed. This paper introduces a hair scanning technique for the diagnosis of diseases

毛发宏扫描全息分析系统

The existing disease diagnosis techniques are more or less harmful to the human body, so a simple and non-invasive disease screening technology is urgently needed. This paper introduces a hair scanning technique for the diagnosis of diseases

Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agent

Large Language Models (LLMs) have demonstrated a remarkable ability to generalize zero-shot to various language-related tasks. This paper focuses on the study of exploring generative LLMs such as ChatGPT and GPT-4 for relevance ranking in Information Retrieval (IR). Surprisingly, our experiments reveal that properly instructed ChatGPT and GPT-4 can deliver competitive, even superior results than supervised methods on popular IR benchmarks. Notably, GPT-4 outperforms the fully fine-tuned monoT5-3B on MS MARCO by an average of 2.7 nDCG on TREC datasets, an average of 2.3 nDCG on eight BEIR datasets, and an average of 2.7 nDCG on ten low-resource languages Mr.TyDi. Subsequently, we delve into the potential for distilling the ranking capabilities of ChatGPT into a specialized model. Our small specialized model that trained on 10K ChatGPT generated data outperforms monoT5 trained on 400K annotated MS MARCO data on BEIR. The code to reproduce our results is available at www.github.com/sunnweiwei/RankGP

SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objects

Monocular 3D detectors achieve remarkable performance on cars and smaller objects. However, their performance drops on larger objects, leading to fatal accidents. Some attribute the failures to training data scarcity or their receptive field requirements of large objects. In this paper, we highlight this understudied problem of generalization to large objects. We find that modern frontal detectors struggle to generalize to large objects even on nearly balanced datasets. We argue that the cause of failure is the sensitivity of depth regression losses to noise of larger objects. To bridge this gap, we comprehensively investigate regression and dice losses, examining their robustness under varying error levels and object sizes. We mathematically prove that the dice loss leads to superior noise-robustness and model convergence for large objects compared to regression losses for a simplified case. Leveraging our theoretical insights, we propose SeaBird (Segmentation in Bird's View) as the first step towards generalizing to large objects. SeaBird effectively integrates BEV segmentation on foreground objects for 3D detection, with the segmentation head trained with the dice loss. SeaBird achieves SoTA results on the KITTI-360 leaderboard and improves existing detectors on the nuScenes leaderboard, particularly for large objects. Code and models at https://github.com/abhi1kumar/SeaBirdComment: CVPR 202

Serial Dependence in Dermatological Judgments

This research was funded by the National Institutes of Health (NIH) grant number R01CA236793.Peer reviewedPublisher PD