Physicochemical Properties and Volatile Organic Compounds of Dairy Beef Round Subjected to Various Cooking Methods.

To evaluate the effect of different cooking methods on the physicochemical quality and volatile organic compounds (VOC) of dairy beef round, twelve beef round pieces were divided into four groups: raw, boiling, microwave, and sous-vide. The sous-vide group had a higher pH than the boiling or microwave groups. The boiling group exhibited the highest shear force and CIE L*, followed by the microwave and sous-vide groups (p<0.05). The sous-vide group received higher taste and tenderness scores from panelists (p<0.05) and showed significantly higher levels of aspartic and glutamic acids than the other groups. The sous-vide and microwave groups had the highest oleic acid and polyunsaturated fatty acid levels, respectively. The sous-vide group had significantly higher hypoxanthine and inosine levels than the other groups. However, the microwave group had higher inosine monophosphate levels than the other groups. The sous-vide group had a higher alcohol content, including 1-octen-3-ol, than the other groups. Octanal and nonanal were the most abundant aldehydes in all groups. (R)-(-)-14-methyl-8-hexadecyn-1-ol, p-cresol, and 1-tridecyne were used to distinguish the VOC for each group in the multivariate analysis. Sous-vide could be effective in increasing meat tenderness as well as taste-related free amino acid (aspartic acid and glutamic acid) and fatty acid (oleic acid) levels. Furthermore, specific VOC, including 1-octen-3-ol, 2-ethylhexanal ethylene glycol acetal, and 2-octen-1-ol, (E)-, could be potential markers for distinguishing sous-vide from other cooking methods. Further studies are required to understand the mechanisms underlying the predominant association of these VOC with the sous-vide cooking method.

Pan-cancer integrative histology-genomic analysis via multimodal deep learning.

The rapidly emerging field of computational pathology has demonstrated promise in developing objective prognostic models from histology images. However, most prognostic models are either based on histology or genomics alone and do not address how these data sources can be integrated to develop joint image-omic prognostic models. Additionally, identifying explainable morphological and molecular descriptors from these models that govern such prognosis is of interest. We use multimodal deep learning to jointly examine pathology whole-slide images and molecular profile data from 14 cancer types. Our weakly supervised, multimodal deep-learning algorithm is able to fuse these heterogeneous modalities to predict outcomes and discover prognostic features that correlate with poor and favorable outcomes. We present all analyses for morphological and molecular correlates of patient prognosis across the 14 cancer types at both a disease and a patient level in an interactive open-access database to allow for further exploration, biomarker discovery, and feature assessment.