In the toxicological regime, the toxicological endpoint and its dose-response relationship are two of the most prominent characters in conducting a risk assessment for chemical exposure. Systems biological methods have been used to comprehensively characterize the impact of toxicants on the biochemical pathways. However, the majority of the current studies are only based on single-dose, and limited

Antibiotic-resistant pathogens constitute an escalating public health concern. Hence a better understanding of the underlying processes responsible for this expansion is urgently needed. Co-selection of heavy metal/biocide and antibiotic resistance genes (ARGs) has been suggested as one potential mechanism promoting the proliferation of antimicrobial resistance (AMR). This paper aims to elucidate this

Anomaly detection is the process of identifying unexpected data samples in datasets. Automated anomaly detection is either performed using supervised machine learning models, which require a labelled dataset for their calibration, or unsupervised models, which do not require labels. While academic research has produced a vast array of tools and machine learning models for automated anomaly detection

Green pyrotechnics /firecrackers reported herein are driven by thermite reactions for self-contained and self-sustained exothermic chemical reactions to make heat and sound by the usage of minimal fuel (aluminium), oxidizer (potassium nitrate), and Sulfur. These firecrackers have the potential for generating less emissions (70%) compared to commercial firecracker-based counterparts due to the presence