Biology subjects

Smeets, P.

Publications and source records attributed to Smeets, P..

2 recordsLinked to original sources

bioRxiv2025

Statistical framework for assessing heterogeneous sensitivity of viruses to ultraviolet, ozone, and free chlorine

Disinfection is key to controlling the infection risk caused by viral contaminations. Recent disinfection guidelines often refer to a single virus resistant to the disinfectant of interest, despite a large variation in sensitivity to disinfectants between viruses or even strains within the same species. Here, we show a statistical framework to integrate multiple experimental datasets and measure the variation in sensitivity to disinfectants across different virus species by a parametric distribution, termed the disinfectant sensitivity distribution. To illustrate this framework, we collected 37, 9, and 28 species-dependent inactivation rate constants for ultraviolet (UV), ozone, and free chlorine from systematic reviews, and constructed the sensitivity distributions for those disinfectants. Our approach incorporated the uncertainty in individual inactivation rate constants using a Bayesian framework. The estimated sensitivity distributions suggested that 93.0% (95% CrI: 84.4-97.6%), 99.4% (95% CrI: 86.7-100%), and 90.3% (95% CrI: 78.2-96.6%) of the examined virus species for UV, ozone, and free chlorine are expected to be inactivated by 4-log if the disinfectant dose complies with the values recommended by USEPA Guidelines for 4-log inactivation, respectively. The proposed approach provides a reasonable extrapolation of observed inactivation kinetics to untested viruses and tools for more transparent risk assessments. Graphic for Table of Contents (TOC)/Abstract Art O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/633964v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1215b39org.highwire.dtl.DTLVardef@ec1b90org.highwire.dtl.DTLVardef@1059ee6org.highwire.dtl.DTLVardef@33aac_HPS_FORMAT_FIGEXP M_FIG C_FIG One sentence summaryWe propose a statistical framework to quantify the variation in sensitivity to common disinfectants across virus species synthesizing disinfection data.

microbiology↗

bioRxiv2017

Top-down expectation effects of food labels on motivation

When we buy our food, the information on the package informs us about the properties of the product, such as its taste and healthiness. These beliefs can influence the processing of food rewards and impact decision making beyond objective sensory properties. However, no studies, within or beyond the food domain, have assessed how written information, such as food labels, affect implicit motivation to obtain rewards, even though choices in daily life might be strongly driven by implicit motivational biases. We investigated how written information affects implicit motivation to obtain food rewards. We used food labels (high- and low-calorie), associated with an identical lemonade, to study motivation for food rewards during fMRI. In a joystick task, hungry participants (N=31) were instructed to make fast approach or avoid movements to earn the cued drinks. Behaviorally, we found a general approach bias, which was stronger for the drink that was most preferred during a subsequent choice test, i.e. the one labeled as low-calorie. This behavioral effect was accompanied by increased BOLD signal in the sensorimotor cortex during the response phase of the task for the preferred, low-calorie drink compared with the non-preferred, high-calorie drink. During the anticipation phase, the non-preferred, high-calorie drink label elicited stronger fMRI signal in the right ventral anterior insula, a region associated with aversion and taste intensity, than the preferred, low-calorie label. Together, these data suggest that high-calorie labeling can increase avoidance of drinks and reduce neural activity in brain regions associated with motor control. In conclusion, we show effects of food labeling on fMRI responses during anticipation and subsequent motivated action and on behavior, in the absence of objective taste differences, demonstrating the influence of written information on implicit biases. These findings contribute to our understanding of implicit biases in real-life eating behavior.

neuroscience↗