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Abstract Prior research demonstrates that news-related social media posts using negative language are re-posted more, rewarding users who produce negative content. We investigate whether negative material from external news sites is also introduced to social media through more user posts, offering comparable incentives for journalists to adopt a negative tone. Data from four US and UK news sites (95,282 articles) and two social media platforms (579,182,075 posts on Facebook and Twitter, now X) show social media users are 1.91 times more likely to share links to negative news articles. The impact of negativity varies by news site and social media platform and, for political articles, is moderated by topic focus, with users showing a greater inclination to share negative articles referring to opposing political groups. Additionally, negativity amplifies news dissemination on social media to a greater extent when accounting for the re-sharing of user posts containing article links. These findings suggest a higher prevalence of negatively toned articles on Facebook and Twitter compared to online news sites. Further, should journalists respond to the incentives created by the heightened sharing of negative articles to social media platforms, this could even increase negative news exposure for those who do not use social media.

Abstract Working life is associated with lifestyle, screening uptake, and occupational health risks that may explain differences in cancer onset. To better understand the association between working life and cancer risk, we need to account for the entire employment history. We investigated whether lifetime employment trajectories are associated with cancer risk. We used data from 6809 women and 5716 men, average age 70 years, from the Survey of Health, Ageing, and Retirement in Europe. Employment history from age 16 to 65 was collected retrospectively using a life calendar and trajectories were constructed using sequence analysis. Associations between employment trajectories and self-reported cancer were assessed using logistic regression. We identified eight employment trajectories for women and two for men. Among women, the risk of cancer was higher in the trajectories “Mainly full-time to home/family”, “Full-time or home/family to part-time”, “Mainly full-time”, and “Other” compared with the “Mainly home/family” trajectory. Among men, the risk of cancer was lower in the “Mainly self-employment” trajectory compared with “Mainly full-time”. We could show how employment trajectories were associated with cancer risk, underlining the potential of sequence analysis for life course epidemiology. More research is needed to understand these associations and determine if causal relationships exist.

Abstract Zika virus (ZIKV) infection was first reported in 2015 in Brazil as causing microcephaly and other developmental abnormalities in newborns, leading to the identification of Congenital Zika Syndrome (CZS). Viral infections have been considered an environmental risk factor for neurodevelopmental disorders outcome, such as Autism Spectrum Disorder (ASD). Moreover, not only the infection per se, but maternal immune system activation during pregnancy, has been linked to fetal neurodevelopmental disorders. To understand the impact of ZIKV vertical infection on brain development, we derived induced pluripotent stem cells (iPSC) from Brazilian children born with CZS, some of the patients also being diagnosed with ASD. Comparing iPSC-derived neurons from CZS with a control group, we found lower levels of pre- and postsynaptic proteins and reduced functional synapses by puncta co-localization. Furthermore, neurons and astrocytes derived from the CZS group showed decreased glutamate levels. Additionally, the CZS group exhibited elevated levels of cytokine production, one of which being IL-6, already associated with the ASD phenotype. These preliminary findings suggest that ZIKV vertical infection may cause long-lasting disruptions in brain development during fetal stages, even in the absence of the virus after birth. These disruptions could contribute to neurodevelopmental disorders manifestations such as ASD. Our study contributes with novel knowledge of the CZS outcomes and paves the way for clinical validation and the development of potential interventions to mitigate the impact of ZIKV vertical infection on neurodevelopment.

Abstract This work presents a novel approach to investigating the slip effect in nanofiber filter media. Electrospun nanofiber media with high efficiency and low pressure drop were produced at different concentrations and durations. The surface and cross-sectional morphology of nanofiber media were studied using FE-SEM. Fiber orientation and diameter distributions were also examined. The 3D virtual nanofiber media was modeled using this information along with the experimentally measured porosity and thickness of the media. The effect of the slip phenomenon in nanofiber media was studied numerically, and the results were compared to experimental data. Excellent agreements were found between the measured and simulation results. Additionally, filtration simulations considering aerosols injected with airflow through the nanofibrous filter media were conducted by considering the slip effect, and the effect of filter structure on filtration performance (removal efficiency and pressure drop) was investigated.

Abstract Predicting physical properties of complex multi-scale systems is a common challenge and demands analysis of various temporal and spatial scales. However, physics alone is often not sufficient due to lack of knowledge on certain details of the system. With sufficient data, however, machine learning techniques may aid. If data are yet relatively cumbersome to obtain, hybrid methods may come to the rescue. We focus in this report on using various types of neural networks (NN) including NN’s into which physics information is encoded (PeNN’s) and also studied effects of NN’s hyperparameters. We apply the networks to predict the viscosity of an emulsion as a function of shear rate. We show that using various network performance metrics as the mean squared error and the coefficient of determination ( $$R^2$$ R 2 ) that the PeNN’s always perform better than the NN’s, as also confirmed by a Friedman test with a p-value smaller than 0.0002. The PeNN’s capture extrapolation and interpolation very well, contrary to the NN’s. In addition, we have found that the NN’s hyperparameters including network complexity and optimization methods do not have any effect on the above conclusions. We suggest that encoding NN’s with any disciplinary system based information yields promise to better predict properties of complex systems than NN’s alone, which will be in particular advantageous for small numbers of data. Such encoding would also be scalable, allowing different properties to be combined, without repetitive training of the NN’s.

Abstract Advances in tagging technologies are expanding opportunities to estimate survival of fish and wildlife populations. Yet, capture and handling effects could impact survival outcomes and bias inference about natural mortality processes. We developed a multistage time-to-event model that can partition the survival process into sequential phases that reflect the tagged animal experience, including handling and release mortality, post-release recovery mortality, and subsequently, natural mortality. We demonstrate performance of multistage survival models through simulation testing and through fish and bird telemetry case studies. Models are implemented in a Bayesian framework and can accommodate left, right, and interval censorship events. Our results indicate that accurate survival estimates can be achieved with reasonable sample sizes ( $$n\approx 100+)$$ n ≈ 100 + ) and that multimodel inference can inform hypotheses about the configuration and length of survival stages needed to adequately describe mortality processes for tracked specimens. While we focus on survival estimation for tagged fish and wildlife populations, multistage time-to-event models could be used to understand other phenomena of interest such as migration, reproduction, or disease events across a range of taxa including plants and insects.

Abstract Although misinformation exposure takes place within a social context, significant conclusions have been drawn about misinformation susceptibility through studies that largely examine judgements in a social vacuum. Bridging the gap between social influence research and the cognitive science of misinformation, we examine the mechanisms through which social context impacts misinformation susceptibility across 5 experiments (N = 20,477). We find that social cues only impact individual judgements when they influence perceptions of wider social consensus, and that source similarity only biases news consumers when the source is high in credibility. Specifically, high and low engagement cues (‘likes’) reduced misinformation susceptibility relative to a control, and endorsement cues increased susceptibility, but discrediting cues had no impact. Furthermore, political ingroup sources increased susceptibility if the source was high in credibility, but political outgroup sources had no effect relative to a control. This work highlights the importance of studying cognitive processes within a social context, as judgements of (mis)information change when embedded in the social world. These findings further underscore the need for multifaceted interventions that take account of the social context in which false information is processed to effectively mitigate the impact of misinformation on the public.

Abstract Mechanisms through which most known Alzheimer’s disease (AD) loci operate to increase AD risk remain unclear. Although Apolipoprotein E (APOE) is known to regulate lipid homeostasis, the effects of broader AD genetic liability on non-lipid metabolites remain unknown, and the earliest ages at which metabolic perturbations occur and how these change over time are yet to be elucidated. We examined the effects of AD genetic liability on the plasma metabolome across the life course. Using a reverse Mendelian randomization framework in two population-based cohorts [Avon Longitudinal Study of Parents and Children (ALSPAC, n = 5648) and UK Biobank (n ≤ 118,466)], we estimated the effects of genetic liability to AD on 229 plasma metabolites, at seven different life stages, spanning 8 to 73 years. We also compared the specific effects of APOE ε4 and APOE ε2 carriage on metabolites. In ALSPAC, AD genetic liability demonstrated the strongest positive associations with cholesterol-related traits, with similar magnitudes of association observed across all age groups including in childhood. In UK Biobank, the effect of AD liability on several lipid traits decreased with age. Fatty acid metabolites demonstrated positive associations with AD liability in both cohorts, though with smaller magnitudes than lipid traits. Sensitivity analyses indicated that observed effects are largely driven by the strongest AD instrument, APOE, with many contrasting effects observed on lipids and fatty acids for both ε4 and ε2 carriage. Our findings indicate pronounced effects of the ε4 and ε2 genetic variants on both pro- and anti-atherogenic lipid traits and sphingomyelins, which begin in childhood and either persist into later life or appear to change dynamically.

Abstract Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.

Abstract Behavioral traits in dogs are assessed for a wide range of purposes such as determining selection for breeding, chance of being adopted or prediction of working aptitude. Most methods for assessing behavioral traits are questionnaire or observation-based, requiring significant amounts of time, effort and expertise. In addition, these methods might be also susceptible to subjectivity and bias, negatively impacting their reliability. In this study, we proposed an automated computational approach that may provide a more objective, robust and resource-efficient alternative to current solutions. Using part of a ‘Stranger Test’ protocol, we tested n = 53 dogs for their response to the presence and neutral actions of a stranger. Dog coping styles were scored by three dog behavior experts. Moreover, data were collected from their owners/trainers using the Canine Behavioral Assessment and Research Questionnaire (C-BARQ). An unsupervised clustering of the dogs’ trajectories revealed two main clusters showing a significant difference in the stranger-directed fear C-BARQ category, as well as a good separation between (sufficiently) relaxed dogs and dogs with excessive behaviors towards strangers based on expert scoring. Based on the clustering, we obtained a machine learning classifier for expert scoring of coping styles towards strangers, which reached an accuracy of 78%. We also obtained a regression model predicting C-BARQ scores with varying performance, the best being Owner-Directed Aggression (with a mean average error of 0.108) and Excitability (with a mean square error of 0.032). This case study demonstrates a novel paradigm of ‘machine-based’ dog behavioral assessment, highlighting the value and great promise of AI in this context.

Abstract Building misinformation resilience at scale continues to pose a challenge. Gamified “inoculation” interventions have shown promise in improving people’s ability to recognize manipulation techniques commonly used in misinformation, but so far few interventions exist that tackle multimodal misinformation (e.g., videos, images). We developed a game called Cat Park, in which players learn about five manipulation techniques (trolling, emotional manipulation, amplification, polarization, and conspiracism), and how misinformation can spread through images. To test the game’s efficacy, we conducted a conceptual replication (N = 380) of Roozenbeek and van der Linden’s 2020 study about Harmony Square, with the same study design, item set, and hypotheses. Like the original study, we find that people who play Cat Park find misinformation significantly less reliable post-gameplay (d = 0.95, p

Abstract Huntington’s disease (HD) is a neurodegenerative disorder caused by expanded (≥ 40) glutamine-encoding CAG repeats in the huntingtin gene, which leads to dysfunction and death of predominantly striatal and cortical neurons. While the genetic profile and clinical signs and symptoms of the disease are better known, changes in the functional architecture of the brain, especially before the clinical expression becomes apparent, are not fully and consistently characterized. In this study, we sought to uncover functional changes in the brain in the heterozygous (HET) zQ175 delta-neo (DN) mouse model at 3, 6, and 10 months of age, using resting-state functional magnetic resonance imaging (RS-fMRI). This mouse model shows molecular, cellular and circuitry alterations that worsen through age. Motor function disturbances are manifested in this model at 6 and 10 months of age. Specifically, we investigated, longitudinally, changes in co-activation patterns (CAPs) that are the transient states of brain activity constituting the resting-state networks (RSNs). Most robust changes in the temporal properties of CAPs occurred at the 10-months time point; the durations of two anti-correlated CAPs, characterized by simultaneous co-activation of default-mode like network (DMLN) and co-deactivation of lateral-cortical network (LCN) and vice-versa, were reduced in the zQ175 DN HET animals compared to the wild-type mice. Changes in the spatial properties, measured in terms of activation levels of different brain regions, during CAPs were found at all three ages and became progressively more pronounced at 6-, and 10 months of age. We then assessed the cross-validated predictive power of CAP metrics to distinguish HET animals from controls. Spatial properties of CAPs performed significantly better than the chance level at all three ages with 80% classification accuracy at 6 and 10 months of age.

Abstract Metabolic dysfunction underlies several chronic diseases. Dietary interventions can reverse metabolic declines and slow aging but remaining compliant is difficult. 17α-estradiol (17α-E2) treatment improves metabolic parameters and slows aging in male mice without inducing significant feminization. We recently reported that estrogen receptor α is required for the majority of 17α-E2-mediated benefits in male mice, but that 17α-E2 also attenuates fibrogenesis in liver, which is regulated by estrogen receptor β (ERβ)-expressing hepatic stellate cells (HSC). The current studies sought to determine if 17α-E2-mediated benefits on systemic and hepatic metabolism are ERβ-dependent. We found that 17α-E2 treatment reversed obesity and related systemic metabolic sequela in both male and female mice, but this was partially blocked in female, but not male, ERβKO mice. ERβ ablation in male mice attenuated 17α-E2-mediated benefits on hepatic stearoyl-coenyzme A desaturase 1 (SCD1) and transforming growth factor β1 (TGF-β1) production, which play critical roles in HSC activation and liver fibrosis. We also found that 17α-E2 treatment suppresses SCD1 production in cultured hepatocytes and hepatic stellate cells, indicating that 17α-E2 directly signals in both cell-types to suppress drivers of steatosis and fibrosis. We conclude that ERβ partially controls 17α-E2-mediated benefits on systemic metabolic regulation in female, but not male, mice, and that 17α-E2 likely signals through ERβ in HSCs to attenuate pro-fibrotic mechanisms.

Abstract Pathogens with persistent environmental stages can have devastating effects on wildlife communities. White-nose syndrome (WNS), caused by the fungus Pseudogymnoascus destructans, has caused widespread declines in bat populations of North America. In 2009, during the early stages of the WNS investigation and before molecular techniques had been developed to readily detect P. destructans in environmental samples, we initiated this study to assess whether P. destructans can persist in the hibernaculum environment in the absence of its conclusive bat host and cause infections in naive bats. We transferred little brown bats (Myotis lucifugus) from an unaffected winter colony in northwest Wisconsin to two P. destructans contaminated hibernacula in Vermont where native bats had been excluded. Infection with P. destructans was apparent on some bats within 8 weeks following the introduction of unexposed bats to these environments, and mortality from WNS was confirmed by histopathology at both sites 14 weeks following introduction. These results indicate that environmental exposure to P. destructans is sufficient to cause the infection and mortality associated with WNS in naive bats, which increases the probability of winter colony extirpation and complicates conservation efforts.

Abstract The COVID-19 pandemic has exposed the vulnerability of ethnic minorities again. Health inequity within ethnic minorities has been explained by factors such as higher prevalence of underlying disease, restricted access to care, and lower vaccination rates. In this study, we investigated the effect of cultural tailoring of communicators and media outlets, respectively, on vaccine willingness in an influenza vaccination campaign in the Netherlands. A total of 1226 participants were recruited from two culturally non-tailored media outlets (Dutch newspaper and Facebook), and one media outlet tailored to a large community in the Netherlands with Indian ancestry. The participants from all three media outlets were randomly exposed to a vaccination awareness video delivered by a physician with an Indian or Dutch background, followed by an online survey. Cultural tailoring compared to cultural non-tailoring of communicators showed no difference in improvement of vaccine willingness (13.9% vs. 20.7% increment, respectively, p = 0.083). However, the media outlet tailored to the community with Indian ancestry, resulted in a higher improvement of vaccine willingness compared to non-tailored media outlets (46.7% vs. 14.7% increment, respectively, p

Abstract Psychological flow is a state of full task immersion. The present study was conducted to test the hypothesis that psychological flow is positively related to activity of the phasic locus coeruleus-norepinephrine (LC-NE) system, which supports decisions on whether to engage in or disengage from the current activity. Subjective flow was assessed among 36 participants who engaged in a gamified version of the n-back task with various difficulty levels (0, 1, 2, and 3 back). During the tasks, continuous pupil diameter and EEG were recorded. We found that psychological flow and two presumed indicators of the phasic LC-NE activity (pupil dilation and EEG P300 amplitude) fit inverted U-shapes with increasing subjective task difficulty. Moreover, a positive linear relationship between psychological flow and pupil dilation (not with P300) was found. In conclusion, this study indicates the involvement of the LC-NE system in the peak experience of flow.

Abstract Although patients with advanced cancer often experience multiple symptoms simultaneously, clinicians usually focus on symptoms that are volunteered by patients during regular history-taking. We aimed to evaluate the feasibility of a Bayesian network (BN) model to predict the presence of simultaneous symptoms, based on the presence of other symptoms. Our goal is to help clinicians prioritize which symptoms to assess. Patient-reported severity of 11 symptoms (scale 0–10) was measured using an adapted Edmonton Symptom Assessment Scale (ESAS) in a national cross-sectional survey among advanced cancer patients. Scores were dichotomized ( 10% difference was considered inaccurate. Symptom scores of 532 patients were collected. A symptom score ≥ 4 was most prevalent for fatigue (64.7%). AUCs varied between 0.60 and 0.78, with satisfactory AUCs for 8/11 symptoms. Calibration was accurate for 101/110 predicted conditional probabilities. Whether a patient experienced fatigue was directly associated with experiencing 7 other symptoms. For example, in the absence or presence of fatigue, the model predicted a 8.6% and 33.1% probability of experiencing anxiety, respectively. It is feasible to use BN development for prioritizing symptom assessment. Fatigue seems most eligble to serve as a starting symptom for predicting the probability of experiencing simultaneous symptoms.

Abstract Aerosols can transmit infectious diseases including SARS-CoV-2, influenza and norovirus. Flushed toilets emit aerosols that spread pathogens contained in feces, but little is known about the spatiotemporal evolution of these plumes or the velocity fields that transport them. Using laser light to illuminate ejected aerosols we quantify the kinematics of plumes emanating from a commercial flushometer-type toilet, and use the motion of aerosol particles to compute velocity fields of the associated flow. The toilet flush produces a strong chaotic jet with velocities exceeding 2 m/s; this jet transports aerosols to heights reaching 1.5 m within 8 seconds of initiating a flush. Quantifying toilet plumes and associated flow velocities provides a foundation for future design strategies to mitigate plume formation or to disinfect pathogens within it.

Abstract A prolonged period of cognitive performance often leads to mental fatigue, a psychobiological state that increases the risk of injury and accidents. Previous studies have trained machine learning algorithms on Heart Rate Variability (HRV) data to detect fatigue in order to prevent its consequences. However, the results of these studies cannot be generalised because of various methodological issues including the use of only one type of cognitive task to induce fatigue which makes any predictions task-specific. In this study, we combined the datasets of three experiments each of which applied different cognitive tasks for fatigue induction and trained algorithms that detect fatigue and predict its severity. We also tested different time window lengths and compared algorithms trained on resting and task related data. We found that classification performance was best when the support vector classifier was trained on task related HRV calculated for a 5-min time window (AUC = 0.843, accuracy = 0.761). For the prediction of fatigue severity, CatBoost regression showed the best performance when trained on 3-min HRV data and self-reported measures (R2 = 0.248, RMSE = 17.058). These results indicate that both the detection and prediction of fatigue based on HRV are effective when machine learning models are trained on heterogeneous, multi-task datasets.

Abstract The number of studies published on postnatal microcephaly in children with Congenital Zika Syndrome is small, clinical presentations vary and aspects of the evolution of these children remain unclarified. The present case series examined clinical characteristics and assessed the growth velocity of the head circumference, weight and height Z-scores in 23 children who developed postnatal microcephaly during follow-up in the Microcephaly Epidemic Research Group Pediatric Cohort. To estimate the change in the head circumference, weight and height Z-scores over time and compare the mean difference between sexes, we used multilevel mixed-effects linear regressions with child-specific random effects. Among these children, 60.9% (n = 14/23) presented with craniofacial disproportion, 60.9% (n = 14/23) with strabismus, 47.8% (n = 11/23) with early onset seizures, 47.8% (n = 11/23) with dysphagia and 43.5% (n = 10/23) with arthrogryposis. Of the 82.7% (n = 19/23) children who underwent neuroimaging, 78.9% (n = 15/19) presented with alterations in the central nervous system. Monthly growth velocity, expressed in Z-scores, of the head circumference was − 0.098 (95% CI % − 0.117 to − 0.080), of weight was: − 0.010 (95%-CI − 0.033 to 0.014) and of height was: − 0.023 (95%-CI − 0.046 to 0.0001). Postnatal microcephaly occurred mainly in children who had already presented with signs of severe brain damage at birth; there was variability in weight and height development, with no set pattern.

Abstract Multimorbidity is more than just the addition of individual illnesses, and its diagnosis and treatment poses special problems. General practitioners play an important role in looking after multimorbid patients. The aim of this study is to assess the prevalence and pattern of acute and chronic multimorbidity in primary care patients, regardless of body system and age group. A convenience sample of 2099 patients treated by 40 general practitioners was assessed using the Burvill scale. This measure of multimorbidity differentiates according to organ system and covers both acute and chronic illnesses. It also allows severity ratings to be assessed for both acute and chronic conditions, and thus patients’ actual need for general practice care. Patients reported an average of 3.5 (SD = 2.0) acute and/or chronically affected body systems. Overall, 12.7% of patients reported only one health problem, 83.0% at least two, 65.8% at least three, 46.1% at least four, and 29.7% five or more. The most frequent problems were musculoskeletal (62.5%) and psychological (56.6%). Some morbidities were interrelated, while others co-occurred despite being medically independent. In primary care, multimorbidity is the rule rather than the exception. Acute and chronic morbidity both contribute to the burden of illness. Body systems reflect treatment needs. Instead of specialist treatment for individual illnesses, an integrative treatment approach is needed. This is the specialty of general practitioners.

Little is known about the causes of meningioma. Obesity and obesity-related traits have been reported in several epidemiological observational studies to be risk factors for meningioma. We performed an analysis of genetic variants associated with obesity-related traits to assess the relationship with meningioma risk using Mendelian randomization (MR), an approach unaffected by biases from temporal variability and reverse causation that might have affected earlier investigations. We considered 11 obesity-related traits, identified genetic instruments for these factors, and assessed their association with meningioma risk using data from a genome-wide association study comprising 1,606 meningioma patients and 9,823 controls. To evaluate the causal relationship between the obesity-related traits and meningioma risk, we consider the estimated odds ratio (OR) of meningioma for each genetic instrument. We identified positive associations between body mass index (odds ratio [ORSD] = 1.27, 95% confidence interval [CI] = 1.03-1.56, P = 0.028) and body fat percentage (ORSD = 1.28, 95% CI = 1.01-1.63, P = 0.042) with meningioma risk, albeit non-significant after correction for multiple testing. Associations for basal metabolic rate, diastolic blood pressure, fasting glucose, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, systolic blood pressure, total cholesterol, triglycerides and waist circumference with risk of meningioma were non-significant. Our analysis provides additional support for obesity being associated with an increased risk of meningioma.

Abstract Four tailspike proteins (TSP1-4) of Escherichia coli O157:H7 bacteriophage CBA120 enable infection of multiple hosts. They form a branched complex that attaches to the tail baseplate. Each TSP recognizes a different lipopolysaccharide on the membrane of a different bacterial host. The 335 N-terminal residues of TSP4 promote the assembly of the TSP complex and anchor it to the tail baseplate. The crystal structure of TSP4-N335 reveals a trimeric protein comprising four domains. The baseplate anchor domain (AD) contains an intertwined triple-stranded β-helix. The ensuing XD1, XD2 and XD3 β-sheet containing domains mediate the binding of TSP1-3 to TSP4. Each of the XD domains adopts the same fold as the respective XD domains of bacteriophage T4 gp10 baseplate protein, known to engage in protein–protein interactions via its XD2 and XD3 domains. The structural similarity suggests that XD2 and XD3 of TSP4 also function in protein–protein interactions. Analytical ultracentrifugation analyses of TSP4-N335 and of domain deletion proteins showed how TSP4-N335 promotes the formation of the TSP quaternary complex. TSP1 and TSP2 bind directly to TSP4 whereas TSP3 binding requires a pre-formed TSP4-N335:TSP2 complex. A 3-dimensional model of the bacteriophage CBA120 TSP complex has been developed based on the structural and ultracentrifuge information.

Abstract Peat accumulation in high latitude wetlands represents a natural long-term carbon sink, resulting from the cumulative excess of growing season net ecosystem production over non-growing season (NGS) net mineralization in soils. With high latitudes experiencing warming at a faster pace than the global average, especially during the NGS, a major concern is that enhanced mineralization of soil organic carbon will steadily increase CO2 emissions from northern peatlands. In this study, we conducted laboratory incubations with soils from boreal and temperate peatlands across Canada. Peat soils were pretreated for different soil moisture levels, and CO2 production rates were measured at 12 sequential temperatures, covering a range from − 10 to + 35 °C including one freeze–thaw event. On average, the CO2 production rates in the boreal peat samples increased more sharply with temperature than in the temperate peat samples. For same temperature, optimum soil moisture levels for CO2 production were higher in the peat samples from more flooded sites. However, standard reaction kinetics (e.g., Q 10 temperature coefficient and Arrhenius equation) failed to account for the apparent lack of temperature dependence of CO2 production rates measured below 0 °C, and a sudden increase after a freezing event. Thus, we caution against using the simple kinetic expressions to represent the CO2 emissions from northern peatlands, especially regarding the long NGS period with multiple soil freeze and thaw events.

Abstract Prospective momentary psychological and biological measures of real-time daily life stress experiences have been examined in several psychiatric disorders, but not in adults with an autism spectrum disorder (ASD). The current electronic self-monitoring study examined associations between momentary daily life stressors and (i) negative affect (NA; emotional stress reactivity) and (ii) cortisol levels (biological stress reactivity) in males and females with ASD (N = 50) and without ASD (N = 51). The Experience Sampling Method, including saliva sampling, was used to measure three types of daily life stress (activity-related, event-related, and social stress), NA, and cortisol. Multilevel regression analyses demonstrated significant interactions between group and stress (i.e., activity-related and event-related stress) in the model of NA, indicating stronger emotional stress reactivity in the ASD than in the control group. In the model of cortisol, none of the group × stress interactions were significant. Male/female sex had no moderating effect on either emotional or biological stress reactivity. In conclusion, adults with ASD showed a stronger emotional stress (but not cortisol) reactivity in response to unpleasant daily life events and activities. The findings highlight the feasibility of electronic self-monitoring in individuals with ASD, which may contribute to the development of more personalized stress-management approaches.

Abstract Inadequate sensitivity has been the primary limitation for implementing high-throughput sequencing for studies of tick-borne agents. Here we describe the development of TBDCapSeq, a sequencing assay that uses hybridization capture probes that cover the complete genomes of the eleven most common tick-borne agents found in the United States. The probes are used for solution-based capture and enrichment of pathogen nucleic acid followed by high-throughput sequencing. We evaluated the performance of TBDCapSeq to surveil samples that included human whole blood, mouse tissues, and field-collected ticks. For Borrelia burgdorferi and Babesia microti, the sensitivity of TBDCapSeq was comparable and occasionally exceeded the performance of agent-specific quantitative PCR and resulted in 25 to > 10,000-fold increase in pathogen reads when compared to standard unbiased sequencing. TBDCapSeq also enabled genome analyses directly within vertebrate and tick hosts. The implementation of TBDCapSeq could have major impact in studies of tick-borne pathogens by improving detection and facilitating genomic research that was previously unachievable with standard sequencing approaches.

Abstract Behavioural studies revealed that the dog–human relationship resembles the human mother–child bond, but the underlying mechanisms remain unclear. Here, we report the results of a multi-method approach combining fMRI (N = 17), eye-tracking (N = 15), and behavioural preference tests (N = 24) to explore the engagement of an attachment-like system in dogs seeing human faces. We presented morph videos of the caregiver, a familiar person, and a stranger showing either happy or angry facial expressions. Regardless of emotion, viewing the caregiver activated brain regions associated with emotion and attachment processing in humans. In contrast, the stranger elicited activation mainly in brain regions related to visual and motor processing, and the familiar person relatively weak activations overall. While the majority of happy stimuli led to increased activation of the caudate nucleus associated with reward processing, angry stimuli led to activations in limbic regions. Both the eye-tracking and preference test data supported the superior role of the caregiver’s face and were in line with the findings from the fMRI experiment. While preliminary, these findings indicate that cutting across different levels, from brain to behaviour, can provide novel and converging insights into the engagement of the putative attachment system when dogs interact with humans.