Production of solid acid catalyst using waste cigarette filters for esterification.

Cigarette filters were utilized as carbon source for the production of solid carbon acid catalysts. In this study, the process of carbonization and simultaneous sulfonation via hydrothermal treatment was employed. The catalysts were prepared by mixing cigarette filters and sulfuric acid at temperatures of 100, 150, and 190 °C for durations ranging from 2 to 8 h. It was observed that the highest conversion of oleic acid occurred when the catalyst was synthesized at 190 °C for 4 h. The optimized conditions for the esterification reaction using this catalyst included an oleic acid to methanol molar ratio of 1:12, a catalyst loading of 5 wt%, and a temperature of 100 °C for 1 h. Additionally, the catalyst was successfully reused four times without significantly impacting the reaction yield. These findings highlight a promising approach for the utilization of waste materials, with immediate implications for waste management practices and positive environmental impacts.

Merged Affinity Network Association Clustering: Joint multi-omic/clinical clustering to identify disease endotypes.

Although clinical and laboratory data have long been used to guide medical practice, this information is rarely integrated with multi-omic data to identify endotypes. We present Merged Affinity Network Association Clustering (MANAclust), a coding-free, automated pipeline enabling integration of categorical and numeric data spanning clinical and multi-omic profiles for unsupervised clustering to identify disease subsets. Using simulations and real-world data from The Cancer Genome Atlas, we demonstrate that MANAclust's feature selection algorithms are accurate and outperform competitors. We also apply MANAclust to a clinically and multi-omically phenotyped asthma cohort. MANAclust identifies clinically and molecularly distinct clusters, including heterogeneous groups of "healthy controls" and viral and allergy-driven subsets of asthmatic subjects. We also find that subjects with similar clinical presentations have disparate molecular profiles, highlighting the need for additional testing to uncover asthma endotypes. This work facilitates data-driven personalized medicine through integration of clinical parameters with multi-omics. MANAclust is freely available at https://bitbucket.org/scottyler892/manaclust/src/master/.