Nevin Manimala

Can J Public Health. 2021 May 14. doi: 10.17269/s41997-020-00471-7. Online ahead of print.

ABSTRACT

OBJECTIVES: Public health policies have been proposed to help address prevalent Canadian obesity rates. Along with the increase in obesity prevalence, explicit weight bias is also rampant in Western society. This paper aimed to assess the association between explicit weight bias attitudes and Canadian public support of these policy recommendations.

METHODS: Canadian adults (N = 903; 51% female; BMI = 27.3 ± 7.0 kg/m²) completed an online survey measuring explicit weight bias, using the three subscales of the Anti-Fat Attitudes Questionnaire: Willpower (belief in weight controllability), Fear of fat (fear of gaining weight), and Dislike (antipathy towards people with obesity). Whether these subscales were associated with policy support was assessed with logistic regression. Analyses were adjusted for age, race, gender, and income.

RESULTS: Public support of policy recommendations ranged from 53% to 90%. Explicit weight bias was primarily expressed through a fear of weight gain and the belief that weight gain was within the individual’s control based on willpower. Although the Dislike subscale was associated with lower support for several policies that enable or guide individual choice in behaviour change, the Willpower and Fear of fat subscales were associated with greater support for similar policies.

CONCLUSION: This study contributes to evidence-informed public health action by describing public support of public health policies and demonstrating an association between explicit weight bias and public support. A higher total explicit weight bias score increased the odds of supporting primarily less intrusive policies. However, dislike of individuals with obesity was associated with decreased odds of supporting many policies.

PMID:33990876 | DOI:10.17269/s41997-020-00471-7

Osteoporos Int. 2021 May 14. doi: 10.1007/s00198-021-05990-8. Online ahead of print.

ABSTRACT

This study analyzed characteristics of hip fracture patients who did not undergo surgery within 24 hours after hospitalization, as recommended by the Belgian quality standards. Reasons for delay were analyzed. Delay in surgery for hip fracture was related to the medical condition of the patients.

INTRODUCTION: To compare patients with optimal timing to patients with a delay in hip surgery, with respect to outcome (complications (postoperative) and mortality) and reasons for delay.

METHODS: A retrospective analysis of medical records compared patients operated on within 24h (Group A) to patients operated on more than 24h after admission (Group B). A follow-up period of 5 years after release or up to the time of data collection was used. Reasons for delay in relation with mortality were analyzed descriptively. Descriptive statistics were used for patient demographics and complications. Relationships causing a delayed surgery and mortality were analyzed using binary logistic regression. Additionally, a survival analysis was provided for overall mortality.

RESULTS: Respectively, 536 and 304 patients were included in Group A and B. The most prominent reason for delaying surgery was the patient not being medically fit (20.7%). Surgical delay was associated with more cardiovascular (p = 0.010), more pulmonary (p < 0.001), and less hematologic complications (p=0.037). Thirty-day mortality was higher with increasing age (p < 0.001), with hematologic (p < 0.001) or endocrine-metabolic complications (p = 0.001), and lower when no complications occurred (p = 0.004). Mortality at the end of data collection was higher for patients with delayed surgery (OR = 2.634, p < 0.001), an increased age (p = 0.006), male gender (p < 0.001), institutionalized patients (p = 0.009), pulmonary complication (p = 0.002), and having no endocrine-metabolic complications (p = 0.003). Survival analysis showed better survival for patients operated on within 24h (p < 0.001).

CONCLUSIONS: Delayed surgery for patients with hip fractures was associated with bad additional medical conditions. Survival was higher for patients operated on within 24h of admission.

PMID:33990873 | DOI:10.1007/s00198-021-05990-8

Environ Microbiol. 2021 May 14. doi: 10.1111/1462-2920.15595. Online ahead of print.

ABSTRACT

Some Serratia entomophila isolates have been successfully exploited in biopesticides due to their ability to cause amber disease in larvae of the Aotearoa (New Zealand) endemic pasture pest, Costelytra giveni. Anti-feeding prophage and ABC toxin complex virulence determinants are encoded by a 153-kb single-copy conjugative plasmid (pADAP; amber disease-associated plasmid). Despite growing understanding of the S. entomophila pADAP model plasmid, little is known about the wider plasmid family. Here, we sequence and analyze mega-plasmids from 50 Serratia isolates that induce variable disease phenotypes in the C. giveni insect host. Mega-plasmids are highly conserved within S. entomophila, but show considerable divergence in Serratia proteamaculans with other variants in S. liquefaciens and S. marcescens, likely reflecting niche adaption. In this study to reconstruct ancestral relationships for a complex mega-plasmid system, strong co-evolution between Serratia species and their plasmids were found. We identify twelve distinct mega-plasmid genotypes, all sharing a conserved gene backbone, but encoding highly variable accessory regions including virulence factors, secondary metabolite biosynthesis, Nitrogen fixation genes and toxin-antitoxin systems. We show that the variable pathogenicity of Serratia isolates is largely caused by presence/absence of virulence clusters on the mega-plasmids, but notably, is augmented by external chromosomally encoded factors. This article is protected by copyright. All rights reserved.

PMID:33989447 | DOI:10.1111/1462-2920.15595

Syst Biol. 2021 May 13:syab037. doi: 10.1093/sysbio/syab037. Online ahead of print.

ABSTRACT

In Bayesian phylogenetics, the coalescent process provides an informative framework for inferring changes in the effective size of a population from a phylogeny (or tree) of sequences sampled from that population. Popular coalescent inference approaches such as the Bayesian Skyline Plot, Skyride and Skygrid all model these population size changes with a discontinuous, piecewise-constant function but then apply a smoothing prior to ensure that their posterior population size estimates transition gradually with time. These prior distributions implicitly encode extra population size information that is not available from the observed coalescent data i.e., the tree. Here we present a novel statistic, Ω, to quantify and disaggregate the relative contributions of the coalescent data and prior assumptions to the resulting posterior estimate precision. Our statistic also measures the additional mutual information introduced by such priors. Using Ω we show that, because it is surprisingly easy to over-parametrise piecewise-constant population models, common smoothing priors can lead to overconfident and potentially misleading inference, even under robust experimental designs. We propose Ω as a useful tool for detecting when effective population size estimates are overly reliant on prior assumptions and for improving quantification of the uncertainty in those estimates.

PMID:33989428 | DOI:10.1093/sysbio/syab037

J Exp Bot. 2021 May 14:erab214. doi: 10.1093/jxb/erab214. Online ahead of print.

ABSTRACT

By responding to the variable soil environments in which rice crops are grown, roots are likely to contribute to yield stability across a range of soil moistures, nutrient levels, and establishment methods. In this study, we explored different approaches to quantification of root plasticity and characterization of its relationship with yield stability. Using four different statistical approaches (plasticity index, slope, AMMI, and factor analytic) on a set of 17 genotypes including several recently-developed breeding lines targeted to dry-direct seeding, we identified only very few direct relationships between root plasticity and yield stability. However, genotypes identified as having combined yield stability and root plasticity showed higher grain yields across trials. Furthermore, root plasticity was expressed to a greater degree in puddled transplanted trials rather than under dry direct seeding. Significant interactions between nitrogen and water resulted in contrasting relationships between nitrogen use efficiency and biomass stability between puddled transplanted and direct seeded conditions. These results reflect the complex interaction between nitrogen, drought, and even different types of drought (as a result of the establishment method) on rice root growth, and suggest that although rice root plasticity may confer stable yield across a range of environments it may be necessary to more narrowly define the targeted environments to which it will be most beneficial.

PMID:33989419 | DOI:10.1093/jxb/erab214

Neurosurgery. 2021 May 14:nyab164. doi: 10.1093/neuros/nyab164. Online ahead of print.

ABSTRACT

BACKGROUND: Currently, different postoperative predictors of chordoma recurrence have been identified. Tumor growth rate (TGR) is an image-based calculation that provides quantitative information of tumor’s volume changing over time and has been shown to predict progression-free survival (PFS) in other tumor types.

OBJECTIVE: To explore the usefulness of TGR as a new preoperative radiological marker for chordoma recurrence.

METHODS: A retrospective single-institution study was carried out including patients reflecting these criteria: confirmed diagnosis of chordoma on pathological analysis, no history of previous radiation, and at least 2 preoperative thin-slice magnetic resonance images available to measure TGR. TGR was calculated for all patients, showing the percentage change in tumor size over 1 mo.

RESULTS: A total of 32 patients were retained for analysis. Patients with a TGR ≥ 10.12%/m had a statistically significantly lower mean PFS (P < .0001). TGR ≥ 10.12%/m (odds ratio = 26, P = .001) was observed more frequently in recurrent chordoma. In a subgroup analysis, we found that the association of Ki-67 labeling index ≥ 6% and TGR ≥ 10.12%/m was correlated with recurrence (P = .0008).

CONCLUSION: TGR may be considered as a preoperative radiological indicator of tumor proliferation and seems to preoperatively identify more aggressive tumors with a higher tendency to recur. Our findings suggest that the therapeutic strategy and clinical-radiological follow-up of patients with chordoma can be adapted also according to this new parameter.

PMID:33989415 | DOI:10.1093/neuros/nyab164

Bioinformatics. 2021 May 14:btab378. doi: 10.1093/bioinformatics/btab378. Online ahead of print.

ABSTRACT

SUMMARY: The sparse allele vectors (SAV) file format is an efficient storage format for large-scale DNA variation data and is designed for high throughput association analysis by leveraging techniques for fast deserialization of data into computer memory. A command line interface has been developed to complement the storage format and supports basic features like importing, exporting and subsetting. Additionally, a C ++ programming API is available allowing for easy integration into analysis software.

AVAILABILITY AND IMPLEMENTATION: https://github.com/statgen/savvy.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:33989384 | DOI:10.1093/bioinformatics/btab378

Phys Chem Chem Phys. 2021 May 14. doi: 10.1039/d1cp00418b. Online ahead of print.

ABSTRACT

We report the electronic, magnetic, structural, vibrational, and X-ray absorption spectroscopy of the inverse full-Heusler Fe2IrSi alloy. We employed state-of-the-art first-principles computational techniques. Our ab initio calculations revealed a ferromagnetic half-metallicity with a magnetic moment of ∼5.01 μB, which follows the Slater Pauling rule. We show rich magnetic behavior due to spin-orbit coupling through the entanglement of the Fe-3d/Ir-5d orbitals. The large extension of the Ir-5d orbital and the itinerant Fe-3d states enhanced spin-orbit and electron-electron interactions, respectively. The analyses of our results reveal that electron-electron interactions are essential for the proper description of the electronic properties while spin-orbit coupling effects are vital to accurately characterize the X-ray absorption and X-ray magnetic circular dichroism spectra. We estimate the strength of the spin-orbit coupling by comparing the intensity of the white-line features at the L3 and L2 absorption edges. This led to a branching ratio that deviates strongly from the statistical ratio of 2, indicative of strong spin-orbit coupling effects in the inverse full-Heusler Fe2IrSi alloy.

PMID:33989367 | DOI:10.1039/d1cp00418b

PLoS One. 2021 May 14;16(5):e0251621. doi: 10.1371/journal.pone.0251621. eCollection 2021.

ABSTRACT

BACKGROUND: Acute respiratory infections are rising in developing countries including Ethiopia. Lack of evidence for the prevalence and associated factors of acute respiratory infection among street sweepers and door-to-door waste collectors in Dessie City, Ethiopia is a challenge for the implementation of appropriate measures to control acute respiratory infection. Thus, this study was designed to address the gaps.

METHODS: A comparative cross-sectional study was conducted among 84 door-to-door waste collectors and 84 street sweepers from March to May 2018. A simple random sampling technique was used to select study participants. Data were collected by trained data collectors using a pretested structured questionnaire and on-the-spot direct observation checklist. Data were analyzed using three different binary logistic regression models at 95% confidence interval (CI): the first model (Model I) was used to identify factors associated with acute respiratory infection among street sweepers, whereas the second model (Model II) was used to identify factors associated with acute respiratory infection among door-to-door waste collectors, and the third model (Model III) was used for pooled analysis to identify factors associated with acute respiratory infection among both street sweepers and door-to-door waste collectors. From each model multivariable logistic regression, variables with a p-value <0.05 were taken as factors significantly associated with acute respiratory infection.

RESULTS: The overall prevalence of acute respiratory infection among studied population was 42.85% with 95% CI (35.1, 50.0%). The prevalence of acute respiratory infection among street sweepers was 48.80% (95% CI: 37.3, 64.8%) and among door-to-door waste collectors was 36.90% (95% CI: 27.4, 46.4%). There was no statistically significant difference between the prevalence of acute respiratory infection among the two groups due to the overlapping of the 95% CI. Among the street sweepers, we found that factors significantly associated with acute respiratory infection were not cleaning personal protective equipment after use (adjusted odds ratio [AOR]: 2.40; 95% CI: 1.15, 5.51) and use of coal/wood for cooking (AOR: 3.95; 95% CI: 1.52, 7.89), whereas among door-to-door waste collectors, were not using a nose/mouth mask while on duty (AOR: 5.57; 95% CI: 1.39, 9.32) and not receiving health and safety training (AOR: 3.82; 95% CI: 1.14-7.03) were factors significantly associated with acute respiratory infection among door-to-door-waste collectors. From the pooled analysis, we found that not using a nose/mouth mask while on duty (AOR: 2.19; 95% CI: 1.16, 4.53) and using coal/wood for cooking (AOR: 2.74; 95% CI: 1.18, 6.95) were factors significantly associated with acute respiratory infection for both street sweepers and door-to-door waste collectors.

CONCLUSION: The prevalence of acute respiratory infection among street sweepers and door-to-door waste collectors has no statistically significant difference. For both groups, not using a nose/mouth mask while on duty and using coal/wood for cooking fuel factors associated with acute respiratory infection. The municipality should motivate and monitor workers use of personal protective equipment including masks and gloves. Workers should use a nose/mouth mask while on duty and should choose a clean energy source for cooking at home.

PMID:33989364 | DOI:10.1371/journal.pone.0251621

PLoS One. 2021 May 14;16(5):e0251818. doi: 10.1371/journal.pone.0251818. eCollection 2021.

ABSTRACT

As species distribution models, and similar techniques, have emerged in marine ecology, a vast array of predictor variables have been created and diverse methodologies have been applied. Marine fish are vital food resources worldwide, yet identifying the most suitable methodology and predictors to characterize spatial habitat associations, and the subsequent distributions, often remains ambiguous. Our objectives were to identify knowledge gaps in fish guilds, identify research themes, and to determine how data sources, statistics, and predictor variables differ among fish guilds. Data were obtained from an international literature search of peer-reviewed articles (2007-2018; n = 225) and research themes were determined based on abstracts. We tested for differences in data sources and modeling techniques using multinomial regressions and used a linear discriminant analysis to distinguish differences in predictors among fish guilds. Our results show predictive studies increased over time, but studies of forage fish, sharks, coral reef fish, and other fish guilds remain sparse. Research themes emphasized habitat suitability and distribution shifts, but also addressed abundance, occurrence, stock assessment, and biomass. Methodologies differed by fish guilds based on data limitations and research theme. The most frequent predictors overall were depth and temperature, but most fish guilds were distinguished by their own set of predictors that focused on their specific life history and ecology. A one-size-fits-all approach is not suitable for predicting marine fish distributions. However, given the paucity of studies for some fish guilds, researchers would benefit from utilizing predictors and methods derived from more commonly studied fish when similar habitat requirements are expected. Overall, the findings provide a guide for determining predictor variables to test and identifies novel opportunities to apply non-spatial knowledge and mechanisms to models.

PMID:33989361 | DOI:10.1371/journal.pone.0251818