Frontiers of machine learning in smart food safety.

Integration of machine learning (ML) technologies into the realm of smart food safety represents a rapidly evolving field with significant potential to transform the management and assurance of food quality and safety. This chapter will discuss the capabilities of ML across different segments of the food supply chain, encompassing pre-harvest agricultural activities to post-harvest processes and delivery to the consumers. Three specific examples of applying cutting-edge ML to advance food science are detailed in this chapter, including its use to improve beer flavor, using natural language processing to predict food safety incidents, and leveraging social media to detect foodborne disease outbreaks.

Confrontation of endolymphatic hydrops diagnosis on 3-Tesla MRI to clinical and audiovestibular findings in Meniere's disease.

Objective: The aim of this study was to compare different MRI diagnostic criteria for endolymphatic hydrops (EH) and to investigate the relation between audiovestibular and MRI findings in Meniere's disease (MD).

Materials And Methods: Prospective cross-sectional cohort study in 2 referral centers included 76 patients with unilateral ( = 62) or bilateral ( = 14) MD. All patients underwent inner ear 3T-MRI 4 h ( = 52) or >24H ( = 24) following audiovestibular tests.

Fireflies produce ultrasonic clicks during flight as a potential aposematic anti-bat signal.

Fireflies are known for emitting light signals for intraspecific communication. However, in doing so, they reveal themselves to many potential nocturnal predators from a large distance. Therefore, many fireflies evolved unpalatable compounds and probably use their light signals as anti-predator aposematic signals.