BackgroundBacteria inhabiting the human body have important roles in a number of physiological processes and are known to be shared amongst genetically-related individuals. Far less is known about viruses inhabiting the human body, but their ecology suggests they may be shared between close contacts.ResultsHere, we report the ecology of viruses in the guts and mouths of a cohort and demonstrate that substantial numbers of gut and oral viruses were shared amongst genetically unrelated, cohabitating individuals. Most of these viruses were bacteriophages, and each individual had distinct oral and gut viral ecology from their housemates despite the fact that some of their bacteriophages were shared. The distribution of bacteriophages over time within households indicated that they were frequently transmitted between the microbiomes of household contacts.ConclusionsBecause bacteriophages may shape human oral and gut bacterial ecology, their transmission to household contacts suggests they could have substantial roles in shaping the microbiota within a household.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-016-0212-z) contains supplementary material, which is available to authorized users.

Here we provide an integrative review of basic control circuits, and introduce techniques by which their regulation can be quantitatively measured using human neuroimaging. We illustrate the utility of the control systems approach using four human neuroimaging threat detection studies (N = 226), to which we applied circuit-wide analyses in order to identify the key mechanism underlying individual variation. In so doing, we build upon the canonical prefrontal-limbic control system to integrate circuit-wide influence from the inferior frontal gyrus (IFG). These were incorporated into a computational control systems model constrained by neuroanatomy and designed to replicate our experimental data. In this model, the IFG acts as an informational set point, gating signals between the primary prefrontal-limbic negative feedback loop and its cortical information-gathering loop. Along the cortical route, if the sensory cortex provides sufficient information to make a threat assessment, the signal passes to the ventromedial prefrontal cortex (vmPFC), whose threat-detection threshold subsequently modulates amygdala outputs. However, if signal outputs from the sensory cortex do not provide sufficient information during the first pass, the signal loops back to the sensory cortex, with each cycle providing increasingly fine-grained processing of sensory data. Simulations replicate IFG (chaotic) dynamics experimentally observed at both ends at the threat-detection spectrum. As such, they identify distinct types of IFG disconnection from the circuit, with associated clinical outcomes. If IFG thresholds are too high, the IFG and sensory cortex cycle for too long; in the meantime the coarse-grained (excitatory) pathway will dominate, biasing ambiguous stimuli as false positives. On the other hand, if cortical IFG thresholds are too low, the inhibitory pathway will suppress the amygdala without cycling back to the sensory cortex for much-needed fine-grained sensory cortical data, biasing ambiguous stimuli as false negatives. Thus, the control systems model provides a consistent mechanism for IFG regulation, capable of producing results consistent with our data for the full spectrum of threat-detection: from fearful to optimal to reckless. More generally, it illustrates how quantitative characterization of circuit dynamics can be used to unify a fundamental dimension across psychiatric affective symptoms, with implications for populations that range from anxiety disorders to addiction.

Background COVID-19 is an infectious disease caused by the SARS-CoV-2 virus that has caused substantial impact on population health, healthcare, and social and economic systems around the world. Several vaccines have been developed to control the pandemic with varying effectiveness and safety profiles. One of the biggest obstacles to implementing successful vaccination programmes is vaccine hesitancy stemming from concerns about effectiveness and safety. This review aims to identify the factors influencing COVID-19 vaccine hesitancy and acceptance and to organize the factors using the social ecological framework. Methods We adopted the five-stage methodological framework developed by Arksey and O’Malley to guide this scoping review. Selection criteria was based on the PICo (Population, Phenomenon of interest and Context) framework. Factors associated with acceptance and hesitancy were grouped into the following: intrapersonal, interpersonal, institutional, community, and public policy factors using the social ecological framework. Results Fifty-one studies fulfilled this review’s inclusion criteria. Most studies were conducted in Europe and North America, followed by Asia and the Middle East. COVID-19 vaccine acceptance and hesitancy rates varied across countries. Some common demographic factors associated with hesitancy were younger age, being female, having lower than college education, and having a lower income level. Most of the barriers and facilitators to acceptance of the COVID-19 vaccines were intrapersonal factors, such as personal characteristics and preferences, concerns with COVID-19 vaccines, history/perception of general vaccination, and knowledge of COVID-19 and health. The remaining interpersonal, institution, community, and public policy factors were grouped into factors identified as barriers and facilitators. Conclusion Our review identified barriers and facilitators of vaccine acceptance and hesitancy and organised them using the social ecological framework. While some barriers and facilitators such as vaccine safety are universal, differentiated barriers might exist for different target groups, which need to be understood if they are to be addressed to maximize vaccine acceptance.

Background Proximal sesamoid bone (PSB) fractures are the most common fatal musculoskeletal injury in North American racehorses. Computed tomography has the potential to detect morphological changes in bone structure but can be challenging to analyse reliably and quantitatively. Objectives To develop a radiomics platform that allows the comparison of features from micro‐CTs (µCT) of PSBs in horses that sustained catastrophic fractures with horses that did not. To compare features calculated with a radiomics approach with features calculated from a previously published study that used quantitative µCT in the same specimens. Study design Retrospective study of cadaver specimens of µCT images of PSBs using prospectively applied radiomics. Methods Radiomics features were computed on standardised CT datasets to benchmark the software. Features from µCT images of PSBs from eight horses that sustained PSB fracture and eight controls were computed using the contralateral, intact forelimb from horses sustaining PSB fracture (cases, n = 19) and all available forelimbs for controls (n = 30). Two‐hundred and fifteen radiomic features were calculated, and similar or comparable features were compared with those reported in a previous study that used the same specimens. Results Morphologic features computed with the radiomics approach, such as volume, minor axis dimensions and anisotropy were highly correlated with previously published data. A high number of imperceptible radiomic features, such as entropy, coarseness and histogram features were also found to be significantly different (P < .01). The extent of the differences in image features for the cases and controls PSBs depends on radiomic calculation settings. Main limitations Only datasets obtained from cadaver specimens were included in the study. Conclusions A radiomics approach for analysing µCT images of PSBs was able to identify and reproduce differences in image features in cases and controls. Furthermore, radiomics revealed many more imperceptible image features between cases and control PSBs.

Interruptions are thought to be significantly associated with medication administration errors. Researchers have tried to reduce medication errors by decreasing or eliminating interruptions. In this paper, we argue that interventions are often (perhaps unreflectively) based on one particular model of risk reduction-that of barriers placed between the source of risk and the object-to-be-protected. Well-intentioned interventions can lead to unanticipated effects because the assumptions created by the risk model are not critically examined. In this paper we review the barrier model and the assumptions it makes about risk and risk reduction/prevention, as well as the model's incompatibility with work in healthcare. We consider how these problems lead to interruptions interventions with unintended negative consequences. Then we examine possible alternatives, viz. organising work for high reliability, preventing safety drift, and engineering resilience into the work activity. These all approach risk in different ways, and as such, propose interruptions interventions that are vastly different from interventions based on the barrier model. The purpose of this paper is to encourage a different approach for designing interruptions interventions. Such reflection may help healthcare communities innovate beyond old, ineffective, and often counter-productive interventions to handle interruptions.