Modeling Population Growth in R with the biogrowth Package

The growth of populations is of interest in a broad variety of fields, such as epidemiology, economics or biology. Although a large variety of growth models are available in the scientific literature, their application usually requires advanced knowledge of mathematical programming and statistical inference, especially when modelling growth under dynamic environmental conditions. This article presents the biogrowth package for R, which implements functions for modelling the growth of populations. It can predict growth under static or dynamic environments, considering the effect of an arbitrary number of environmental factors. Moreover, it can be used to fit growth models to data gathered under static or dynamic environmental conditions. The package allows the user to fix any model parameter prior to the fit, an approach that can mitigate identifiability issues associated to growth models. The package includes common S3 methods for visualization and statistical analysis (summary of the fit, predictions, . . . ), easing result interpretation. It also includes functions for model comparison/selection. We illustrate the functions in biogrowth using examples from food science and economy

Early parental death and psychosocial risk factors for dementia: A case-control study in Europe

Objectives: To assess the association between early parental death and the risk of dementia in adult life, and to examine the risk factors associated with early parental death in people with and without dementia. Methods / Design: A population-based case-control study of a sample of 65,997 participants from the Survey of Health, Ageing and Retirement in Europe study. Early parental death was operationalized as parental death at the age of ≤ 16 years. Main analyses were conducted using bivariate and multivariate logistic regression analyses. Results: The odds ratio (OR) for dementia in individuals who experienced early parental death (father or mother) at the age of ≤ 16 years was 1.83 (95%CI 1.61-2.09) and 1.54 (95%CI 1.35-1.76) adjusted for age, gender, and education. In the multivariate logistic regression analysis carried out with the whole sample, early parental death increased the risk of dementia (OR = 1.50, 95%CI 1.31-1.72), along with older age (OR = 5.92, 95%CI 4.86-7.17), neuroticism (OR = 2.94,95%CI 2.61-3.31), low education level (OR = 1.84, 95%CI 1.64- 2.05) and low income (OR = 1.49, 95%CI 1.34-1.67). Discussion: Early parental death (< 16 years) was associated with an increased risk of dementia. We discuss the neurobiological markers associated with adverse childhood experiences (ACEs) and dementia as well as interventions to counteract the negative health effects on adults

Transcriptomic analysis reveals an association of FCGBP with Parkinson’s disease

Transcriptomics in Parkinson’s disease (PD) offers new insights into the molecular mechanism of PD pathogenesis. Several pathways, such as inflammation and protein degradation, have been identified by differential gene expression analysis. Our aim was to identify gene expression differences underlying the disease etiology and the discovery of pre-symptomatic risk biomarkers for PD from a multicenter study in the context of the PROPAG-AGEING project. We performed RNA sequencing from 47 patients with de novo PD, 10 centenarians, and 65 healthy controls. Using identified differentially expressed genes, functional annotations were assigned using gene ontology to unveil significant enriched biological processes. The expression of 16 selected genes was validated using OpenArray® assays and samples from independent cohorts of 201 patients with advanced PD, 340 healthy siblings of PD patients, and 177 healthy controls. Differential gene expression analysis identified higher FCGBP expression in patients with de novo PD compared with healthy controls and compared with centenarians. Furthermore, FCGBP showed no differences in terms of population origin or aging process. The increased FCGBP expression was validated in patients with advanced PD and their siblings. Thus, we provided evidence for an upregulation of FCGBP mRNA levels not only in patients with PD but also in individuals at putative higher risk of PD, suggesting that it could be important in gut–brain PD interaction, mediating the connection between microbiota and intestinal inflammatory processes, as well as neuroinflammation and neurodegeneration

Expectations for the high-energy neutrino detection from starburst galaxies with KM3NeT/ARCA

Star-forming galaxies (SFGs) and starburst galaxies (SBGs) are extragalactic sources which could produce high-energy neutrinos. In principle, they could play a rather important role for explaining at least a sizeable part of IceCube’s observations of astophysical neutrino. Using a recent theoretical model which implemented a blending of spectral indeces, we present the KM3NeT/ARCA sensitivities for such a diffuse flux from the startburst galaxies. In particular, we provide the 5-year differential sensitivity for the two building blocks of ARCA. We make use only of the track-like events in the range of 100 GeV - 10 PeV differentiate in 11 bins of energy. We show how the upcoming neutrino telescope could observe the diffuse SFG and SBG within 5 years of data taking. We found the minimum of the sensitivity at around 100 TeV, which is also the energy where the SBG contribution is expected to peak. This would not only constrain the multi-component fit of the observed astrophysical neutrino flux at that energy (100 TeV), but would also provide us a direct link between the star-forming activity in the reservoir environments and the hadronic emissions.Postprint (published version