Late Cretaceous hydrothermal vent communities from the Troodos ophiolite, Cyprus: systematics and evolutionary significance

Modern hydrothermal vent communities are based on chemosynthesis by microbial primary producers. Molecular phylogenetic divergence estimates indicate that many of the dominant vent taxa arose during the Cenozoic and Cretaceous; however, the fossil record of vent communities from these time periods is poor. One occurrence of such Cretaceous vent communities pertains to six volcanogenic massive sulphide deposits in the Troodos ophiolite of Cyprus. These deposits represent hydrothermal activity on deep (2500–5000 m) arc-related spreading ridge(s) in the Neotethyan Ocean over several million years during the late Cenomanian and earliest Turonian. The Cyprus vent communities consist of worm tubes, representing possible vestimentiferans and serpulids, together with a moderate diversity of abyssochrysoid gastropods, belonging to eight new species (Desbruyeresia kinousaensis sp. nov., Desbruyeresia memiensis sp. nov., Desbruyeresia kambiaensis sp. nov., Hokkaidoconcha morisseaui sp. nov., Ascheria canni sp. nov., Cyprioconcha robertsoni gen. et sp. nov., Paskentana xenophontosi sp. nov. and Paskentana dixoni sp. nov.) in five genera and three families; none of the species is shared between vent sites. A single gaudryceratid ammonite from one of the vent sites most likely represents a water-logged shell that sank from surface waters. The gastropod fauna contains the first representatives of the genera Desbruyeresia, Hokkaidoconcha, Ascheria and Paskentana from hydrothermal vents, and also the youngest representative of the last-named genus in any environment. The Cypriot vent communities share tube worms with slightly older (Cenomanian) and younger (Turonian–Santonian) vent communities elsewhere in the western part of the Neotethyan Ocean

Women’s self-rated attraction to male faces does not correspond with physiological arousal

Data Availability Statement: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.Peer reviewedPublisher PD

Lime Cake as an Alternative Stabiliser for Loose Clayey Loams

Lime Cake (precipitated calcium carbonate PCC), a by-product of sugar production, is proposed as a stabiliser for improvement of loose silty clayey loams. Two inorganic pedogenic and organic precipitated calcium carbonate polymorphs are artificially synthesized into a base loosely compacted loamy soil. Formation, micromorphology, quality of cementing bonds, and physiochemical interactions in the interlayer are modelled at molecular level and verified by a suite of micro-analytical spectrometry techniques. Emphasis is put into determining the impacts of polysaccharides on soil strength and implications on soil pore anatomy. Erodibility, compressibility, volumetric change, and hydro-mechanical behaviour of base, and modified soils at yield and post-yield states are studied. Anomalies in suction-controlled post-yield stress–strain behaviour of modified soils are discussed and explained within the tenets of mechanics of composite soils with double porosity. PCC-reinforcement offers the closest possible packing at optimum water content. Desiccation cracking remains likely, but at relatively higher lower-bound water contents. Under low confinement levels and unsaturated state, strain-hardening prevails. Loss of shear strength on saturation is minimal. When saturated, PCC-reinforced soil develops substantially high levels of shear strength at all strain levels. Higher levels of confinement are needed for organic fibrous and onion-skin coating matters to effectively encrust the soil pore network; such high levels, however, leads to formation of an unwelcomed brittle, strain–softening stress–stress behaviour

Strengthening the Reporting of Genetic Risk Prediction Studies: The GRIPS Statement

Cecile Janssens and colleagues present the GRIPS Statement, a checklist to help strengthen the reporting of genetic risk prediction studies

Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study

Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. Methods: Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained. Results: Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches. Conclusion: The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Gender differences in health care use among the elderly population in areas of Norway and Finland. A cross-sectional analysis based on the HUNT study and the FINRISK Senior Survey

BACKGROUND: The aim of the study was to examine gender differences in the self-reported use of health care services by the elderly in rural and metropolitan areas of two Nordic countries with slightly different health care systems: Finland and Norway. METHODS: Population based, cross-sectional surveys conducted in Nord-Tröndelag Norway (1995–97) and in rural and metropolitan areas of Finland (1997) were employed. In the Norwegian data, a total of 7,919 individuals, aged 65–74 years old were included, and the Finnish data included 1,500 individuals. The outcome variables comprised whether participants had visited a general practitioner or a specialist, or had received hospital care or physiotherapy during the past 12 months. Gender differences in the use of health care services were analysed by multiple logistic regression, controlling for health status and socio-demographic characteristics. RESULTS: In Norway, elderly women visited a specialist or were hospitalised less often than men. In Finland, elderly women used all health care services except hospital care more often than men. In Norway, less frequent use of specialist care by women was not associated with self-reported health or chronic diseases. CONCLUSION: The findings revealed differences in self-reported use of secondary care among different genders in areas of Norway and Finland

Environmental Influences on Mate Preferences as Assessed by a Scenario Manipulation Experiment

Many evolutionary psychology studies have addressed the topic of mate preferences, focusing particularly on gender and cultural differences. However, the extent to which situational and environmental variables might affect mate preferences has been comparatively neglected. We tested 288 participants in order to investigate the perceived relative importance of six traits of an ideal partner (wealth, dominance, intelligence, height, kindness, attractiveness) under four different hypothetical scenarios (status quo/nowadays, violence/post-nuclear, poverty/resource exhaustion, prosperity/global well-being). An equal number of participants (36 women, 36 men) was allotted to each scenario; each was asked to allocate 120 points across the six traits according to their perceived value. Overall, intelligence was the trait to which participants assigned most importance, followed by kindness and attractiveness, and then by wealth, dominance and height. Men appraised attractiveness as more valuable than women. Scenario strongly influenced the relative importance attributed to traits, the main finding being that wealth and dominance were more valued in the poverty and post-nuclear scenarios, respectively, compared to the other scenarios. Scenario manipulation generally had similar effects in both sexes, but women appeared particularly prone to trade off other traits for dominance in the violence scenario, and men particularly prone to trade off other traits for wealth in the poverty scenario. Our results are in line with other correlational studies of situational variables and mate preferences, and represent strong evidence of a causal relationship of environmental factors on specific mate preferences, corroborating the notion of an evolved plasticity to current ecological conditions. A control experiment seems to suggest that our scenarios can be considered as realistic descriptions of the intended ecological conditions

Environmental Influences on Mate Preferences as Assessed by a Scenario Manipulation Experiment

Many evolutionary psychology studies have addressed the topic of mate preferences, focusing particularly on gender and cultural differences. However, the extent to which situational and environmental variables might affect mate preferences has been comparatively neglected. We tested 288 participants in order to investigate the perceived relative importance of six traits of an ideal partner (wealth, dominance, intelligence, height, kindness, attractiveness) under four different hypothetical scenarios (status quo/nowadays, violence/post-nuclear, poverty/resource exhaustion, prosperity/global well-being). An equal number of participants (36 women, 36 men) was allotted to each scenario; each was asked to allocate 120 points across the six traits according to their perceived value. Overall, intelligence was the trait to which participants assigned most importance, followed by kindness and attractiveness, and then by wealth, dominance and height. Men appraised attractiveness as more valuable than women. Scenario strongly influenced the relative importance attributed to traits, the main finding being that wealth and dominance were more valued in the poverty and post-nuclear scenarios, respectively, compared to the other scenarios. Scenario manipulation generally had similar effects in both sexes, but women appeared particularly prone to trade off other traits for dominance in the violence scenario, and men particularly prone to trade off other traits for wealth in the poverty scenario. Our results are in line with other correlational studies of situational variables and mate preferences, and represent strong evidence of a causal relationship of environmental factors on specific mate preferences, corroborating the notion of an evolved plasticity to current ecological conditions. A control experiment seems to suggest that our scenarios can be considered as realistic descriptions of the intended ecological conditions

Why Moral Expertise Needs Moral Theory

Discussions of the nature or possibility of moral expertise have largely proceeded in atheoretical terms, with little attention paid to whether moral expertise depends on theoretical knowledge of morality. Here I argue that moral expertise is more theory-dependent than is commonly recognized: Moral expertise consists, at least in part, in knowledge of the correct or best moral theory, and second, that knowledge of moral theory is essential to moral experts dispensing expert counsel to non-experts. Moral experts would not be moral experts absent knowledge of moral theory, nor could they play the testimonial role we would expect them to play in moral inquiry and deliberation absent such knowledg