Rapid determination of melamine in milk using water-soluble CdTe quantum dots as fluorescence probes

Water-soluble CdTe quantum dots of different sizes capped with thioglycolic acid (TGA-CdTe QDs) were synthesised via a microwave-assisted method. It was found that melamine could quench the fluorescence emission of TGA-CdTe QDs in aqueous solution. Based on this, a novel method for the determination of melamine has been developed. Under optimum conditions, the fluorescence intensity of TGA-CdTe QDs versus melamine concentrations gave a linear response according to the Stern-Volmer equation. The proposed method has been successfully used to detect melamine in liquid milk with a detection limit of 0.04mg L-1, and the whole process including sample pre-treatment could be accomplished within 30 min. The obvious merits provided by this method, such as simplicity, rapidity, low cost and high sensitivity would make it promising for on-site screening of melamine adulterant in milk products. The possible mechanism involved in the interaction of melamine with TGA-CdTe QDs is discussed

StreamAD: A cloud platform metrics-oriented benchmark for unsupervised online anomaly detection

Cloud platforms, serving as fundamental infrastructure, play a significant role in developing modern applications. In recent years, there has been growing interest among researchers in utilizing machine learning algorithms to rapidly detect and diagnose faults within complex cloud platforms, aiming to improve the quality of service and optimize system performance. There is a need for online anomaly detection on cloud platform metrics to provide timely fault alerts. To assist Site Reliability Engineers (SREs) in selecting suitable anomaly detection algorithms based on specific use cases, we introduce a benchmark called StreamAD. This benchmark offers three-fold contributions: (1) it encompasses eleven unsupervised algorithms with open-source code; (2) it abstracts various common operators for online anomaly detection which enhances the efficiency of algorithm development; (3) it provides extensive comparisons of various algorithms using different evaluation methods; With StreamAD, researchers can efficiently conduct comprehensive evaluations for new algorithms, which can further facilitate research in this area. The code of StreamAD is published at https://github.com/Fengrui-Liu/StreamAD

Zinc–Carnosine Metallodrug Network as Dual Metabolism Inhibitor Overcoming Metabolic Reprogramming for Efficient Cancer Therapy

The targeting of tumor metabolism as a novel strategy for cancer therapy has attracted tremendous attention. Herein, we develop a dual metabolism inhibitor, Zn–carnosine metallodrug network nanoparticles (Zn-Car MNs), which exhibits good Cu-depletion and Cu-responsive drug release, causing potent inhibition of both OXPHOS and glycolysis. Notably, Zn-Car MNs can decrease the activity of cytochrome c oxidase and the content of NAD+, so as to reduce ATP production in cancer cells. Thereby, energy deprivation, together with the depolarized mitochondrial membrane potential and increased oxidative stress, results in apoptosis of cancer cells. In result, Zn-Car MNs exerted more efficient metabolism-targeted therapy than the classic copper chelator, tetrathiomolybdate (TM), in both breast cancer (sensitive to copper depletion) and colon cancer (less sensitive to copper depletion) models. The efficacy and therapy of Zn-Car MNs suggest the possibility to overcome the drug resistance caused by metabolic reprogramming in tumors and has potential clinical relevance