Machine learning and advanced statistical modeling can identify key quality management practices that affect post-pasteurization contamination of fluid milk

Spoilage of HTST- (high-temperature, short-time) and vat- pasteurized fluid milk due to introduction of Gram-negative bacteria post-pasteurization remains a challenge for the dairy industry. While processing facility level practices (e.g., sanitation practices) are known to impact the frequency of post-pasteurization contamination (PPC), the relative importance of different practices is not well defined, affecting the ability of facilities to select intervention targets that reduce PPC and provide the greatest return on investment. Thus, the goal of this study was to use an existing longitudinal dataset of bacterial spoilage indicators obtained for pasteurized fluid milk samples collected from 23 processing facilities between July 2015 and November 2017 (with 3 to 5 samplings per facility) and data from a survey on fluid milk quality management practices, to identify factors associated with PPC and rank their relative importance, using two separate approaches: (i) multimodel inference and (ii) conditional random forest. Data pre-processing for multimodel inference analysis showed (i) nearly all factors were significantly associated with PPC when assessed individually using univariable logistic regression and (ii) numerous pairs of factors were strongly associated with each other (Cramer’s V ³0.80). Multimodel inference and conditional random forest analyses identified similar drivers associated with PPC; factors identified as most important based on these analyses included cleaning and sanitation practices, activities related to good manufacturing practices, container type (which is a proxy for different filling equipment), in-house finished product testing, and designation of a quality department, indicating potential targets for reducing PPC. In addition, this study illustrates how machine learning approaches can be used with highly correlated and unbalanced data, as typical for food safety and quality, to facilitate improved data analyses and decision-making.

Prestige and content biases together shape the cultural transmission of narratives

Context-based cultural transmission biases such as prestige are thought to have been a primary driver in shaping the dynamics of human cultural evolution. However, few empirical studies have measured the importance of prestige relative to other effects, such as the content biases present within transmitted information. Here, we report the findings of an experimental transmission study designed to compare the simultaneous effects of a high- or low-prestige model with the presence of content containing social, survival, emotional, moral, rational, or counterintuitive information. Results from multimodel inference reveal that prestige is a significant factor in determining salience and recall, but that several content biases, specifically social, survival, negative emotional, and biological counterintuitive information, are significantly more influential. Further, we find evidence that prestige serves as a conditional learning strategy when no content cues are available. Our results demonstrate that content biases serve a vital and underappreciated role in cultural transmission.

Titi monkeys combine alarm calls to create probabilistic meaning

Previous work suggested that titi monkeys Callicebus nigrifrons combine two alarm calls, the A- and B-calls, to communicate about predator type and location. To explore how listeners process these sequences, we recorded alarm call sequences of six free-ranging groups exposed to terrestrial and aerial predator models, placed on the ground or in the canopy, and used multimodel inference to assess the information encoded in the sequences. We then carried out playback experiments to identify the features used by listeners to react to the available information. Results indicated that information about predator type and location were encoded by the proportion of B-call pairs relative to all call pairs of the sequence (i.e., proportion of BB-grams). The results suggest that the meaning of the sequence is not conveyed in a categorical but probabilistic manner. We discuss the implications of these findings for current theories of animal communication and language evolution.