Comparison of the virucidal efficacy of essential oils (cinnamon, clove, and thyme) against hepatitis A virus in suspension and on food-contact surfaces.

Essential oils (EOs) have been used for centuries as flavor enhancers in foods, and owing to their antimicrobial properties, they have potential as natural food preservatives. However, their effect on food-borne viruses is unknown. Therefore, in this study, the virucidal effects of three EOs (cinnamon, clove, and thyme) on the infectivity of the hepatitis A virus (HAV) were investigated. Different concentrations of each EO (0.05, 0.1, 0.5, and 1%) were mixed with viral suspensions in accordance with ASTM E1052-11:2011 and incubated for 1 h at room temperature. The EOs exhibited a concentration-dependent effect in the suspension tests, and HAV titers decreased by approximately 1.60 log PFU/mL when treated with EOs at the highest concentration of 1%. The antiviral effect of EOs treated at 1% for 1 h was also evidenced in surface disinfection tests according to the OECD:2013, as approximately 2 log PFU/mL reduction on hard food-contact surfaces (stainless steel and polypropylene) and approximately 2 and 1.4 log PFU/mL reduction on low-density polyethylene and kraft (soft food-contact surfaces), respectively. Moreover, RT-qPCR results revealed that HAV genome copies were negligibly reduced until treated with a high concentration (1%) in suspension and carrier tests. Overall, our findings highlighted the potential of cinnamon, clove, and thyme EOs as natural disinfectants capable of limiting HAV (cross-) contamination conveyed by food-contact surfaces. These findings advance our knowledge of EOs as antimicrobials and their potential in the food sector as alternative natural components to reduce viral contamination and improve food safety.

A deep learning approach for staging embryonic tissue isolates with small data.

Machine learning approaches are becoming increasingly widespread and are now present in most areas of research. Their recent surge can be explained in part due to our ability to generate and store enormous amounts of data with which to train these models. The requirement for large training sets is also responsible for limiting further potential applications of machine learning, particularly in fields where data tend to be scarce such as developmental biology. However, recent research seems to indicate that machine learning and Big Data can sometimes be decoupled to train models with modest amounts of data. In this work we set out to train a CNN-based classifier to stage zebrafish tail buds at four different stages of development using small information-rich data sets. Our results show that two and three dimensional convolutional neural networks can be trained to stage developing zebrafish tail buds based on both morphological and gene expression confocal microscopy images, achieving in each case up to 100% test accuracy scores. Importantly, we show that high accuracy can be achieved with data set sizes of under 100 images, much smaller than the typical training set size for a convolutional neural net. Furthermore, our classifier shows that it is possible to stage isolated embryonic structures without the need to refer to classic developmental landmarks in the whole embryo, which will be particularly useful to stage 3D culture in vitro systems such as organoids. We hope that this work will provide a proof of principle that will help dispel the myth that large data set sizes are always required to train CNNs, and encourage researchers in fields where data are scarce to also apply ML approaches.

Convolutional neural network model to predict causal risk factors that share complex regulatory features.

Major progress in disease genetics has been made through genome-wide association studies (GWASs). One of the key tasks for post-GWAS analyses is to identify causal noncoding variants with regulatory function. Here, on the basis of >2000 functional features, we developed a convolutional neural network framework for combinatorial, nonlinear modeling of complex patterns shared by risk variants scattered among multiple associated loci. When applied for major psychiatric disorders and autoimmune diseases, neural and immune features, respectively, exhibited high explanatory power while reflecting the pathophysiology of the relevant disease. The predicted causal variants were concentrated in active regulatory regions of relevant cell types and tended to be in physical contact with transcription factors while residing in evolutionarily conserved regions and resulting in expression changes of genes related to the given disease. We demonstrate some examples of novel candidate causal variants and associated genes. Our method is expected to contribute to the identification and functional interpretation of potential causal noncoding variants in post-GWAS analyses.