Teasing apart the joint effect of demography and natural selection in the birth of a contact zone

Vast population movements induced by recurrent climatic cycles have shaped the genetic structure of plant species. This is especially true in Scandinavia that was repeatedly glaciated. During glacial periods trees were confined to refugia, south and east of the ice sheet, from which they recolonized Scandinavia as the ice melted away. This multi-pronged recolonization led to large contact zones in most species. We leverage large genomic data from 5000 trees to reconstruct the demographic history of Norway spruce (Picea abies) and test for the presence of natural selection during the recolonization process and the establishment of the contact zone. Sweden is today made up of two large genetic clusters, a southern one originating from the Baltics and a Northern one originating from Northern Russia. The contact zone delineating these two clusters closely matches the limit between two major climatic regions. This suggests that natural selection contributed to the establishment and the maintenance of the contact zone. To test this hypothesis we first used Approximate Bayesian Computation; an Isolation-with migration model with genomewide linked selection fits the data better than a purely neutral one. Secondly, we identified loci characterized by both extreme allele frequency differences between geographic regions and association to the variables defining the climatic zones. These loci, many of which are related to phenology, form clusters present on all linkage groups. Altogether, the current genetic structure reflects the joint effect of climatic cycles, recolonization and selection on the establishment of strong local adaptation and con-tact zones.

Do partnership dissolutions and living alone affect systemic chronic inflammation? A cohort study of Danish adults

BackgroundPartnership breakups and living alone are associated with several adverse health outcomes. The aim of this study, carried out in Denmark, is to investigate whether accumulated numbers of divorces/partnership breakups or years lived alone across 26 years of adult life are associated with levels of inflammation, and if vulnerability with regards to gender or educational level can be identified.Methods4835 participants from the Copenhagen Aging and Midlife Biobank (CAMB) aged 48–62 years were included. Data on accumulated numbers of partnership breakups and years living alone were retrieved from a national standardised annual register. Inflammatory markers interleukin 6 (IL-6) and high sensitivity C-reactive protein (hsCRP) were measured in blood samples. Multivariate linear regression analyses were adjusted for age, educational level, early major life events, body mass index, chronic diseases, medicinal intake affecting inflammation, acute inflammation and personality scores.ResultsFor men, an association was found between an increasing number of partnership breakups or number of years living alone and higher levels of inflammatory markers. No such association was found for women, and no evidence of partnership breakups and educational level having a joint effect was found for either gender.ConclusionThe findings suggest a strong association between years lived alone or accumulated number of partnership breakups and low-grade inflammation for middle-aged men, but not for women. Among those of either sex with a lower level of education, no specific vulnerability to accumulated years lived alone or number of breakups was identified.

Joint effect of particulate matter and cigarette smoke on women’s sex hormones

Background Although relationships between exposure to air pollution and reproductive health are broadly studied, mechanisms behind these phenomena are still unknown. The aim of the study was to assess whether exposure to particulate matter (PM10) and tobacco smoking have an impact on menstrual profiles of 17β-estradiol (E2) and progesterone (P) and the E2/P ratio. Methods Levels of sex hormones were measured daily in saliva during the entire menstrual cycle among 132 healthy, urban women. Exposure to smoking (active or passive) was assessed by questionnaire, whilst exposure to PM10 with municipal monitoring data. Results During the early luteal phase, profiles of E2 were elevated among women with higher versus lower exposure to PM10 (p = 0.02, post-hoc tests). Among those who were exposed versus unexposed to tobacco smoking, the levels of mean E2 measured during the entire cycle were higher (p = 0.02). The difference in mean E2 levels between the group of joint exposure (i.e. to high PM10 and passive or active smoking) versus the reference group (low PM10, no smoking) was statistically significant at p = 0.03 (18.4 vs. 12.4 pmol/l, respectively). The E2/P ratios were higher among women with higher versus lower exposure to PM10 and this difference was seen only in the early luteal phase (p = 0.01, exploratory post-hoc tests). Conclusions We found that PM10 and tobacco smoking affect ovarian hormones independently and do not interact with each other. Both exposures appear to have estrogenic effects even though women's susceptibility to these effects differs across the menstrual cycle. We propose that the hormonal mechanisms are involved in observed relationships between air pollution and smoking with women’s reproductive health.

An information integration account for causal generalizations in uncertain causal networks

In reasoning about common cause networks, given that a cause generates an effect, people often need to infer how likely the cause generate another effect. This causal generalization question has not systematically been investigated in previous research. We propose the information integration account for causal generalizations in uncertain casual networks with dichotomized continuous variables. It predicts that causal generalization is the joint function of conditional probabilities of causal links and cause strength indicated by the proportion of present collateral effects. Two experiments investigated causal generalizations in uncertain causal networks with and without probability distributions, respectively. It was found that in the presence of probability distributions there was the joint effect of conditional probability and cause strength on causal generalization; in the absence of probability distributions causal generalization depend only on cause strength. The overall response pattern favors the information integration account over the other alternative accounts.

Algae control in oligotrophic surface water under the joint effect of nutritional competition and microbial algae-lytic substance

Harmful algal abnormal proliferation presents the most severe threat to the quality of oligotrophic surface water even in source water such as South-to-North Water Diversion Project in China. A novel...

Individual and joint effects of trehalose and glutamate on diabetic retinopathy: a propensity score-matched case-control study

Although previous studies demonstrate that trehalose can help maintain glucose homeostasis in healthy humans, its role and joint effect with glutamate on diabetic retinopathy (DR) remain unclear. We aimed to comprehensively quantify the associations of trehalose and glutamate with DR. This study included 69 pairs of DR and matched type 2 diabetic (T2D) patients. Serum trehalose and glutamate were determined via ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry system. Covariates were collected by a standardized questionnaire, clinical examinations and laboratory assessments. Individual and joint association of trehalose and glutamate with DR were quantified by multiple conditional logistic regression models. The adjusted odds of DR averagely decreased by 86% [odds ratio (OR): 0.14; 95% confidence interval (CI): 0.06,0.33] with per interquartile range increase of trehalose. Comparing with the lowest quartile, adjusted OR (95% CI) were 0.20 (0.05,0.83), 0.14 (0.03,0.63) and 0.01 (<0.01,0.05) for participants in the 2nd, 3rd and 4th quartiles of trehalose, respectively. In addition, as compared to their counterparts, T2D patients with lower trehalose (<median) and higher glutamate (≥ median) had the highest odds of DR (OR: 36.81; 95% CI: 6.75, 200.61). Apparent super-multiplicative effect of trehalose and glutamate on DR was observed, whereas relative excess risk due to interaction (RERI) was not significant. The study suggests that trehalose is beneficial to inhibit the occurrence of DR and synergistically decreases the risk of DR with reduced glutamate. Our findings also provide new insights into the mechanisms of DR and further longitudinal studies are required to confirm these findings.

DOES CORRUPTION SAND THE WHEELS OF FINANCIAL SECTOR DEVELOPMENT? EVIDENCE FROM GLOBAL PANEL DATA

This study examines the effects of corruption and political instability and violence on the financial sector development. We estimate the impact for a panel of countries classified by income groups and regulatory quality. The study considers the period from 1996 to 2015 for analysis. The empirical models of this study test the linear as well as nonlinear relationships between corruption and financial sector development. Our analysis utilizes a dynamic panel data model and takes care of the potential endogeneity problem in estimation. The results show that corruption has a negative effect on financial sector development for all as well as different income-group countries. Our results further show that the effects of corruption are nonlinear in nature and indicate that corruption is more financial development-reducing when its level is very high. We also test the joint effect of corruption and political instability and violence on financial development. It largely shows that their combined effect is positive, implying that widespread corruption can positively affect financial development if a country is suffering from an unstable political institution.

Spatial Patterns of the Urban Agglomeration of the Yellow River Ji-Shaped Bend Based on Space of Multiple Flows

Previous studies on the space of flows have mainly focused on a single flow element and have given less consideration to the joint effect of complex correlations among multiple flows. To fill this gap, in the context of the coordinated development of the urban agglomeration of the Yellow River Ji-shaped bend (UAYB), in this study, three flows, namely population flow, logistics flow, and information flow, are selected to research the spatial patterns of the UAYB. The results show the following: (1) The information flow among cities in the UAYB is the strongest, followed by logistics flow and population flow. (2) Hohhot, Ordos, Yinchuan, and Taiyuan are the core cities and have attracted more flows to converge there; different flows have formed different central features. (3) Regarding the correlation of the three flows, information flow has a strong correlation with the other two flows. (4) From the perspective of the joint effects of the three flows considered, the hierarchy of UAYB is dominated by Hohhot and Taiyuan and sub-dominated by Yinchuan and Ordos; four prominent city groups have been formed with these four cities as the center. On the basis of our results, we put forward some recommendations on the integrated development of cities at various levels within the UAYB to provide a reference for its spatial optimization strategy.

Climatic and soil factors explain the two-dimensional spectrum of global plant trait variation

Plant functional traits can predict community assembly and ecosystem functioning and are thus widely used in global models of vegetation dynamics and land–climate feedbacks. Still, we lack a global understanding of how land and climate affect plant traits. A previous global analysis of six traits observed two main axes of variation: (1) size variation at the organ and plant level and (2) leaf economics balancing leaf persistence against plant growth potential. The orthogonality of these two axes suggests they are differently influenced by environmental drivers. We find that these axes persist in a global dataset of 17 traits across more than 20,000 species. We find a dominant joint effect of climate and soil on trait variation. Additional independent climate effects are also observed across most traits, whereas independent soil effects are almost exclusively observed for economics traits. Variation in size traits correlates well with a latitudinal gradient related to water or energy limitation. In contrast, variation in economics traits is better explained by interactions of climate with soil fertility. These findings have the potential to improve our understanding of biodiversity patterns and our predictions of climate change impacts on biogeochemical cycles.