d staple foods. This trial was registered at clinicaltrials.gov as NCT03586245. Copyright © The Author(s) 2020.INTRODUCTION Major depression is a leading cause of morbidity in military personnel and an important impediment to operational readiness in military organizations. Although treatment options are available, a large proportion of individuals with depression do not access mental health services. Quantifying and closing this treatment gap is a public health priority. However, the scientific literature on the major depression treatment gap in military organizations has never been systematically reviewed. METHODS We systematically searched the EMBASE, MEDLINE, and PsychINFO databases for studies measuring recent mental health service use in personnel serving in the armed forces of a Five-Eye country (Australia, Canada, New Zealand, the United Kingdom, or the United States). We excluded studies conducted with retired veterans. Because of the substantial heterogeneity in included studies, we did not pool their results.  https://www.selleckchem.com/products/Sunitinib-Malate-(Sutent).html  Instead, we computed median period prevalence of mental health service use. RESULTS Twenty-eight st treatment gaps estimated treatment gaps were larger when depressed patients were identified by screening tools instead of diagnostic interviews. Researchers should be wary of overestimating the mental health treatment gap when using screening tools in future studies. © Crown copyright 2020.ABSTRACT Listeria monocytogenes is a ubiquitous, intracellular foodborne pathogen that is responsible for invasive listeriosis. The ability of L. monocytogenes to cause disease has some correlation with the serotypes of a specific lineage group, making the identification of lineage groups important for epidemiological analysis. The development of typing methods to link the strains of L. monocytogenes to an outbreak of listeriosis would help minimize the spread of the disease. The aim of this study was to design a PCR-restriction fragment length polymorphism (RFLP) method to differentiate between the lineage groups of L. monocytogenes. PCR-amplified fragments of the hly gene for 12 serotypes of L. monocytogenes were sequenced, aligned, and analyzed with the BioEdit program, and single nucleotide polymorphisms (SNPs) within regions of this gene were identified. Because of the difficulty in acquiring a serotype 4ab reference strain, this serotype was not included in this study. We tested the specificity and accuracy of the PCR-RFLP method on these L. monocytogenes reference strains and validated the method with 172 L. monocytogenes strains recovered from humans, food, and the food processing environment in 2000 to 2002 and 2008 to 2010 from regions within South Africa. PCR-RFLP analysis applied in this study placed L. monocytogenes serotypes into one of three lineage groups based on the sequence differences and SNPs within each lineage group. The SNPs were conserved in a region where RFLP analysis could be applied for a distinction between L. monocytogenes lineage groups. HIGHLIGHTS Copyright ©, International Association for Food Protection.ABSTRACT The aim of this study was to load liposomes with Barije (Ferula gummosa) essential oil (EO) and to evaluate their physical and antibacterial properties. Liposomes were produced with specific ratios of lecithin/cholesterol by thin-film hydration and sonication. The chemical composition of the EO was analyzed by gas chromatography and mass spectroscopy. The physical properties of the liposomes (particle size, polydispersity index, zeta potential, and encapsulation efficiency) were evaluated. The antimicrobial effects of these liposomes against Escherichia coli O157H7 were determined based on the MIC and disk diffusion results. The effect of subinhibitory concentrations (sub-MICs) of EO against the growth of the bacterium over 24 h was evaluated before and after encapsulation. The major components of EO were β-pinene (60.84%) and α-pinene (9.14%). The mean liposome radius of EO-loaded liposomes was 74.27 to 99.93 nm, which was significantly different from that of the empty liposomes (138.76 nm) (P less then 0.05). Addition of cholesterol to the lecithin bilayer increased the particle size and reduced the encapsulation efficiency (P less then 0.05). The electrostatic stability of the empty liposomes was improved by adding cholesterol, but when the EO was replaced in the liposomes, there was no significant change in electrostatic stability of liposomes with cholesterol (P less then 0.05). MICs were 14.5 μg/mL for the EO-loaded nanoliposomes containing 30 mg of lecithin and 30 mg of cholesterol and 10 μg/mL for nonencapsulated EO. This trend was confirmed by measuring the inhibition zone diameter. Sub-MICs of liposomal EO (containing 60 mg of lecithin) decreased bacterial levels to a greater degree than did free EO, especially at 50 and 75% of the MIC. HIGHLIGHTS Copyright ©, International Association for Food Protection.MOTIVATION Single-cell RNA sequencing (scRNA-seq) technology provides a powerful tool for investigating cell heterogeneity and cell subpopulations by allowing the quantification of gene expression at single cell level. However, scRNA-seq data analysis remains challenging because of various technical noises such as dropout events (i.e., excessive zero counts in the expression matrix). RESULTS By taking consideration of the association among cells and genes, we propose a novel collaborative matrix factorization-based method called CMF-Impute to impute the dropout entries of a given scRNA-seq expression matrix. We test CMF-Impute and compare it with the other five state-of-the-art methods on six popular real scRNA-seq datasets of various sizes and three simulated datasets. For simulated datasets, CMF-Impute outperforms other methods in imputing the closest dropouts to the original expression values as evaluated by both the sum of squared error (SSE) and Pearson correlation coefficient (PCC). For real datasets, CMF-Impute achieves the most accurate cell classification results in spite of the choice of different clustering methods like SC3 or t-SNE followed by K-means as evaluated by both adjusted rand index (ARI) and normalized mutual information (NMI). Finally, we demonstrate that CMF-Impute is powerful in reconstructing cell-to-cell and gene-to-gene correlation, and in inferring cell lineage trajectories. AVAILABILITY CMF-Impute is written as a Matlab package which is available at (https//github.com/xujunlin123/CMFImpute.git)Supplementary information Supplementary data are available at Bioinformatics online. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.