Autonomic arousal and attentional orienting to visual threat are predicted by awareness

The rapid detection and evaluation of threat is of fundamental importance for survival. Theories suggest that this evolutionary pressure has driven functional adaptations in a specialized visual pathway that evaluates threat independently of conscious awareness. This is supported by evidence that threat-relevant stimuli rendered invisible by backward masking can induce physiological fear responses and modulate spatial attention. The validity of these findings has since been questioned by research using stringent, objective measures of awareness. Here, we use a modified continuous flash suppression paradigm to ask whether threatening images induce adaptive changes in autonomic arousal, attention, or perception when presented outside of awareness. In trials where stimuli broke suppression to become visible, threatening stimuli induced a significantly larger skin conductance response than nonthreatening stimuli and attracted spatial attention over scrambled images. However, these effects were eliminated in trials where observers were unaware of the stimuli. In addition, concurrent behavioral data provided no evidence that threatening images gained prioritized access to awareness. Taken together, our data suggest that the evaluation and spatial detection of visual threat are predicted by awareness

Fearful faces have a sensory advantage in the competition for awareness

Only a subset of visual signals give rise to a conscious percept. Threat signals, such as fearful faces, are particularly salient to human vision. Research suggests that fearful faces are evaluated without awareness and preferentially promoted to conscious perception. This agrees with evolutionary theories that posit a dedicated pathway specialized in processing threat-relevant signals. We propose an alternative explanation for this "fear advantage." Using psychophysical data from continuous flash suppression (CFS) and masking experiments, we demonstrate that awareness of facial expressions is predicted by effective contrast: the relationship between their Fourier spectrum and the contrast sensitivity function. Fearful faces have higher effective contrast than neutral expressions and this, not threat content, predicts their enhanced access to awareness. Importantly, our findings do not support the existence of a specialized mechanism that promotes threatening stimuli to awareness. Rather, our data suggest that evolutionary or learned adaptations have molded the fearful expression to exploit our general-purpose sensory mechanisms

Designing Interactive Displays to Promote Effective use of Evidence

Interactive displays are increasing being used to convey information, and are a significant factor in promoting statistical literacy (and illiteracy). Durham University and the House of Commons Library are collaborating to create data visualisations (DV) which will be accessible to politicians, researchers and journalists. The focus of this paper is a DV designed to be useful in the run-up to the 2015 general election. The aim was to assemble a rich resource from multiple sources, and to make it easy for target groups to manipulate data and draw conclusions. We identify important changes to the DV as it evolved over 13 iterations, and draw conclusions about appropriate design processes and validation

Lockdown, bottoms up? Changes in adolescent substance use across the COVID-19 pandemic

The COVID-19 pandemic notably altered adolescent substance use during the initial stage (Spring 2020) of the pandemic. The purpose of this longitudinal study is to examine trajectories of adolescent substance use across the pandemic and subsequent periods of stay-at-home orders and re-opening efforts. We further examined differences as a function of current high school student versus graduate status. Adolescents (n = 1068, 14–18 years, Mage = 16.95 years and 76.7% female at T1) completed 4 different self-report surveys, starting during the first stay-at-home order and ending approximately 14 months later. Negative binomial hurdle models predicted: (1) the likelihood of no substance use and (2) frequency of days of substance use. As hypothesized, results demonstrated significant increases in adolescents’ likelihood of alcohol use, binge drinking, and cannabis use once initial stay-at-home orders were lifted, yet few changes occurred as a result of a second stay-at-home order, with rates never lowering again to that of the first lockdown. Further, graduates (and particularly those who transitioned out of high school during the study) demonstrated a greater likelihood and frequency of substance use and were more stable in their trajectories across periods of stay-at-home orders than current high school students. Unexpectedly, however, there was a strong increase in current high school students’ likelihood of e-cigarette use and a significant linear increase in participants’ frequency of e-cigarette use over the study. Results suggest adolescent substance use, and in particular, e-cigarette use among current high school students, may be of increasing concern as the pandemic evolves

Evaluating the spatial transferability and temporal repeatability of remote sensing-based lake water quality retrieval algorithms at the European scale:a meta-analysis approach

Many studies have shown the considerable potential for the application of remote-sensing-based methods for deriving estimates of lake water quality. However, the reliable application of these methods across time and space is complicated by the diversity of lake types, sensor configuration, and the multitude of different algorithms proposed. This study tested one operational and 46 empirical algorithms sourced from the peer-reviewed literature that have individually shown potential for estimating lake water quality properties in the form of chlorophyll-a (algal biomass) and Secchi disc depth (SDD) (water transparency) in independent studies. Nearly half (19) of the algorithms were unsuitable for use with the remote-sensing data available for this study. The remaining 28 were assessed using the Terra/Aqua satellite archive to identify the best performing algorithms in terms of accuracy and transferability within the period 2001–2004 in four test lakes, namely Vänern, Vättern, Geneva, and Balaton. These lakes represent the broad continuum of large European lake types, varying in terms of eco-region (latitude/longitude and altitude), morphology, mixing regime, and trophic status. All algorithms were tested for each lake separately and combined to assess the degree of their applicability in ecologically different sites. None of the algorithms assessed in this study exhibited promise when all four lakes were combined into a single data set and most algorithms performed poorly even for specific lake types. A chlorophyll-a retrieval algorithm originally developed for eutrophic lakes showed the most promising results (R2 = 0.59) in oligotrophic lakes. Two SDD retrieval algorithms, one originally developed for turbid lakes and the other for lakes with various characteristics, exhibited promising results in relatively less turbid lakes (R2 = 0.62 and 0.76, respectively). The results presented here highlight the complexity associated with remotely sensed lake water quality estimates and the high degree of uncertainty due to various limitations, including the lake water optical properties and the choice of methods

Rethinking ME/CFS Diagnostic Reference Intervals via Machine Learning, and the Utility of Activin B for Defining Symptom Severity

Biomarker discovery applied to myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), a disabling disease of inconclusive aetiology, has identified several cytokines to potentially fulfil a role as a quantitative blood/serum marker for laboratory diagnosis, with activin B a recent addition. We explored further the potential of serum activin B as a ME/CFS biomarker, alone and in combination with a range of routine test results obtained from pathology laboratories. Previous pilot study results showed that activin B was significantly elevated for the ME/CFS participants compared to healthy (control) participants. All the participants were recruited via CFS Discovery and assessed via the Canadian/International Consensus Criteria. A significant difference for serum activin B was also detected for ME/CFS and control cohorts recruited for this study, but median levels were significantly lower for the ME/CFS cohort. Random Forest (RF) modelling identified five routine pathology blood test markers that collectively predicted ME/CFS at ≥62% when compared via weighted standing time (WST) severity classes. A closer analysis revealed that the inclusion of activin B to the panel of pathology markers improved the prediction of mild to moderate ME/CFS cases. Applying correct WST class prediction from RFA modelling, new reference intervals were calculated for activin B and associated pathology markers, where 24-h urinary creatinine clearance, serum urea and serum activin B showed the best potential as diagnostic markers. While the serum activin B results remained statistically significant for the new participant cohorts, activin B was found to also have utility in enhancing the prediction of symptom severity, as represented by WST class.This research was funded by the Judith. J. Mason and Harold S. Williams Memorial Foundation (The Mason Foundation), grant number CT23141–23142

Influence of hydraulics on the dowsntream migratory route of Atlantic salmon (Salmo salar)

The interaction between hydrodynamics and the upstream migration of fish has been investigated in several studies over the past few years, but there has been a relative lack of investigation into the effects of hydraulics on the downstream migration of fish. In Norway, mortality associated with downstream migration is strongly related to hydropower production. However, there is a poor understanding of how fish choose their migratory routes, which limits the development of measures to mitigate the impact of anthropogenic structures on fish migration. The current study aimed to analyze the effect of hydraulics on the choice of downstream migration route of Atlantic salmon smolts (Salmo salar) in the proximity of an intake to a hydropower plant in Norway. We combined computational fluid dynamic modeling with 3-Dimensional acoustic telemetry positioning of a group of tagged smolts to investigate the interaction between fish and the flow dynamics, and to determine the influence of this interaction on fish migratory routes. A Hidden Markov Model was used to predict migratory routes based on the relations found between the hydraulic variables and fish behaviour. Results showed relationships between fish migratory routes and the different components of flow velocity. The knowledge acquired in this study is expected to inform opportunities and decision making in aquatic resources management and engineering design to increase fish survival past hydropower intakes and other anthropogenic structures

Inter-rater reliability of functional MRI data quality control assessments: a standardised protocol and practical guide using pyfMRIqc

Quality control is a critical step in the processing and analysis of functional magnetic resonance imaging data. Its purpose is to remove problematic data that could otherwise lead to downstream errors in the analysis and reporting of results. The manual inspection of data can be a laborious and error-prone process that is susceptible to human error. The development of automated tools aims to mitigate these issues. One such tool is pyfMRIqc, which we previously developed as a user-friendly method for assessing data quality. Yet, these methods still generate output that requires subjective interpretations about whether the quality of a given dataset meets an acceptable standard for further analysis. Here we present a quality control protocol using pyfMRIqc and assess the inter-rater reliability of four independent raters using this protocol for data from the fMRI Open QC project (https://osf.io/qaesm/). Data were classified by raters as either “include,” “uncertain,” or “exclude.” There was moderate to substantial agreement between raters for “include” and “exclude,” but little to no agreement for “uncertain.” In most cases only a single rater used the “uncertain” classification for a given participant’s data, with the remaining raters showing agreement for “include”/“exclude” decisions in all but one case. We suggest several approaches to increase rater agreement and reduce disagreement for “uncertain” cases, aiding classification consistency

Redefining the "carrier" state for foot-and-mouth disease from the dynamics of virus persistence in endemically affected cattle populations

The foot-and-mouth disease virus (FMDV) “carrier” state was defined by van Bekkum in 1959. It was based on the recovery of infectious virus 28 days or more post infection and has been a useful construct for experimental studies. Using historic data from 1,107 cattle, collected as part of a population based study of endemic FMD in 2000, we developed a mixed effects logistic regression model to predict the probability of recovering viable FMDV by probang and culture, conditional on the animal’s age and time since last reported outbreak. We constructed a second set of models to predict the probability of an animal being probang positive given its antibody response in three common non-structural protein (NSP) ELISAs and its age. We argue that, in natural ecological settings, the current definition of a ”carrier” fails to capture the dynamics of either persistence of the virus (as measured by recovery using probangs) or the uncertainty in transmission from such animals that the term implies. In these respects it is not particularly useful. We therefore propose the first predictive statistical models for identifying persistently infected cattle in an endemic setting that captures some of the dynamics of the probability of persistence. Furthermore, we provide a set of predictive tools to use alongside NSP ELISAs to help target persistently infected cattle

The Role of Agriculture in the UN Climate Talks

Agriculture, and consequently food security and livelihoods, is already being affected by climate change, according to latest science from the IPCC. The various strands of work already underway on agriculture within the UNFCCC process can be strengthened and made more coherent. A 2015 climate agreement should reference food production and provide the financial, technical and capacity building support for countries to devise ambitious actions for the agricultural sector. A new climate agreement should be consistent with the Sustainable Development Goal (SDG) proces