Older people consume less alcohol than any other adult age group. However, in recent years survey data on alcohol consumption in the United Kingdom have shown that while younger age groups have experienced a decline in alcohol consumption, drinking behaviours among the elderly have not reduced in the same way. This paper uses data from the English Longitudinal Study of Ageing to analyse both the frequency and quantity of older adult's alcohol consumption using a lifecourse approach over a ten-year period. Overall drinking declined over time and the analysis examined how socio-economic characteristics, partnership, employment and health statuses were associated with differences in drinking behaviours and how these changed over time. Higher wealth and level of education were associated with drinking more and drinking more frequently for men and women. Poorer self-rated health was associated with less frequent consumption and older people with poor and deteriorating health reported a steeper decline in the frequency of alcohol consumption over time. Men who were not in a partnership drank more than other men. For women, loss of a partner was associated with a steeper decline in drinking behaviours. These findings have implications for programmes to promote responsible drinking among older adults as they suggest that, for the most part, characteristics associated with sustaining wellbeing in later life are also linked to consuming more alcohol. Bingham see e.g. Griffin et al. et al. Older adults' alcohol consumption has come under increasing popular and academic scrutiny in recent years. In the United Kingdom (UK), for example, recent media coverage has drawn attention to the ‘hidden’ reality of alcohol misuse among older adults , and how this differs by gender.et al. The longitudinal analysis of drinking in late life was carried out using ELSA  and transitions between these two. For all the transition variables, more complex transitions that were not captured by the main categories were coded as ‘other’. These residual transitions accounted for approximately 2 per cent of partnership and 3 per cent of health transitions and, due to the small numbers, are not reported here. However, for employment, other transitions accounted for approximately 10 and 30 per cent of male and female respondents, respectively, and this category is reported. All other socio-economic variables were captured at wave 0  and include wealth quintiles, level of education and smoking behaviours. In the analysis we also considered variables for social support and social capital (as measured by membership of organisations) but these were not significant and we have not included these in the final models. Age at wave 0 was also included to control for age affects across the sample. We also checked if the relationship with age was linear by including age squared in the analysis, this was not significant and we have not included this variable in the final model.The analysis included various measures of socio-economic characteristics, lifecourse transition and other health behaviours, and the distribution of these variables is given in the Appendix. These include three lifecourse transition variables for partnership, employment status and health. There is considerable complexity in these transitions and in order to include these variables in the longitudinal model with sufficient cases for each transition status we have simplified these as follows. For partnership, we distinguish respondents who remained in a partnership over the study period, those who were not in a partnership in all waves and those who either formed or left a partnership between waves 0 and 5. One of the limitations of this variable is that we have not distinguished between the reasons for ending a partnership due to the small numbers involved (this could be widowhood or divorce/separation). Employment transitions focus on transitions into retirement and distinguish those who remained economically active from those who were always retired or who retired during the study period. Self-rated health was used as an indicator of health as it is a strong predictor of mortality  who were present in waves 0, 4 and 5 . Non-drinkers were not included in the analysis of weekly consumption as it was not possible to include a value of zero in the dependent variable. Thus, analysis of quantity of consumption was restricted to respondents who were drinkers in at least one wave .The models were built in a step-wise fashion to compare the impact of lifecourse events in more detail. The stepwise models were built up as follows: Model 1 included age and wave variables only; Model 2 included age, wave and partnership (including an interaction between partnership and wave); Model 3 included age, wave and employment (including an interaction between employment and wave); Model 4 included age, wave and health ; and Model 5  with all lifecourse transition variables, interactions between transition variables, and wave and other socio-economic characteristics included as covariates.The distribution of the frequency of drinking in last 12 months and mean weekly units consumed in waves 0 and 5 by gender are given in i.e. variables fitted without an interaction with wave), negative coefficients for a category indicate that individuals with this characteristic drank less frequently than the reference category at wave 0, positive coefficients indicate more frequent drinking. The coefficients for the interactions between the transition variables and wave can be interpreted to indicate whether the rate of change in frequency of drinking over time varies in comparison to the reference category . A negative coefficient for the interaction terms indicates a steeper decline in frequency over time for a particular sub-category in comparison to the reference group. In other words, as wave is negative in all models, a negative interaction term indicates that respondents in the sub-category in question experienced a steeper decline in frequency of drinking over the ten-year period, compared to the reference category. A positive coefficient suggests that individuals in the particular sub-category experienced a smaller decline in drinking frequency over time.The analysis of men's and women's frequency of drinking at baseline (wave 0) and over time  is summarised in p < 0.10). This group of men, after controlling for wealth, education and smoking (i.e. Model 5 only), reported drinking more frequently at wave 0 in comparison to men who remained in a partnership. The interaction with wave shows that men who were not in a partnership at each wave experienced a steeper decline in frequency of drinking over time compared to men who remained in a partnership. For women, any baseline associations between frequency and partnership status found in Model 2 were non-significant when other socio-economic variables were included in the full model. However, as for men, in comparison to those who remained in a partnership, women not in a partnership at all waves experienced a more pronounced decline in drinking frequency over time, and the same was also found for women whose partnership ended during the observation period. Retirement also had a limited impact on the frequency of drinking and there were no consistent relationships between employment transitions and drinking frequency for men and women. In the full models for both genders there were no significant coefficients (p < 0.05), though for women being retired all the time or those who experienced an alternative employment transition marginally increased the frequency of drinking at wave 0 (p < 0.10).The coefficients for wave and age in Model 1 demonstrate that over time and at older ages the frequency of drinking declined. The coefficient for wave was greater for women, indicating that over time women's frequency of alcohol consumption declined faster than men's. There were also important differences by gender and between the different lifecourse transition variables. Taking partnership status first, this had a limited impact on the frequency of drinking for men and women. Partnership status was not significant for the frequency of drinking at baseline for men, though there was a marginally significant finding for men not in a partnership  were larger in comparison to the other transitions variables and were, mostly, significant. These coefficients were also consistent for men and women, though they were greater for the latter. At wave 0 respondents with poor self-rated health drank less frequently; as measured by the coefficients for those in continual poor and improving self-rated health as both groups had poor self-rated health at wave 0. The interaction with wave shows that those in continual poor self-rated health experienced a sharper decline in drinking frequency compared to those who remained in good health, and the same effect is observed for those whose health deteriorated between waves. For women, this latter group had a more pronounced decline in the frequency of drinking than those in permanent poor health.In contrast to partnership and employment, self-rated health was more strongly associated with the frequency of drinking at the baseline and over time. The coefficients for both baseline and slope  were drinking more than men in a partnership at the baseline. These baseline coefficients for men not in a partnership were greater than coefficients for all other variables except for smoking, which demonstrates the importance of partnership status for men's baseline drinking. For women, partnership status had no significant association with baseline weekly alcohol consumption. However, women whose partnership ended during the study period experienced a steeper decline of weekly units over time compared to women who remained in a partnership and this trend replicates the same finding for frequency. It would appear that partnership was protective for men regarding how much they drank at the baseline, but not how often. While for women the end of a partnership led to a decline in both the frequency and quantity of alcohol consumption over time.p < 0.10). Both men and women with deteriorating self-rated health reported a steeper decline in weekly consumption of alcohol over time compared to older adults who remained in good health. This suggests that worsening self-rated health was associated with a reduction in the amount of alcohol consumed over time.Retirement was not important for men's baseline drinking, which was similar to the results for the frequency model, though men who retired over the period of observation had a steeper decline in drinking than men who remained in employment. Retired women, those who retired between the waves and women who experienced another employment transition all drank less at wave 0 than employed women. For health, at wave 0 men with poor self-rated health whose health subsequently improved drank less, while women whose health deteriorated drank more, though these coefficients were only marginally significant , the numbers were too small to provide detail on the risks of alcohol misuse in later life and our analysis has not considered these risks. Moreover, due to the number of different permutations we have had to simplify the lifecourse transition variables, and in particular have not distinguished between reasons for end of partnership.The analyses of lifecourse transitions and alcohol consumption confirm the importance of declining health in influencing how often and how much older adults drink. These results suggest that among older adults, those who drink more often have better health, and regular alcohol consumption in later life is indicative of good health. Yet the findings for lifecourse transitions relating to partnership and employment are less consistent and vary by gender. In particular, the differences between men's and women's partnership status and the amount that they drink suggest that the social context of alcohol consumption is gendered. Lifecourse transitions that impact on older people's social connectivity may lead to less drinking , but the results for partnership status for men hint at the possibility that some forms of social disconnection are associated with drinking more. Drinking in later life is just as much a social activity as it is at younger ages and one that older adults do because they enjoy a drink and derive some pleasure out of drinking with others. Our analysis has shown that as social relationships change in later life, these may impact on drinking behaviours, though the precise direction of this change varies according to the lifecourse event in question  and by gender. These findings have implications for policies to support responsible drinking in later life, as they show that characteristics associated with improved wellbeing in later life  are also linked with drinking more and drinking more often. This highlights the importance of situating alcohol consumption within social and economic contexts and with reference to individual circumstances, as well as the possibility that policies to improve older people's wellbeing may have unintended consequences for alcohol consumption.


Legionella pneumophila. Given their impact on human health, Legionella species and the mechanisms responsible for their replication within host cells are often studied in alveolar macrophages, the primary human cell type associated with disease. Despite the potential severity of individual cases of disease, Legionella are not spread from person-to-person. Thus, from the pathogen's perspective, interactions with human cells are accidents of time and space—evolutionary dead ends with no impact on Legionella's long-term survival or pathogenic trajectory. To understand Legionella as a pathogen is to understand its interaction with its natural hosts: the polyphyletic protozoa, a group of unicellular eukaryotes with a staggering amount of evolutionary diversity. While much remains to be understood about these enigmatic hosts, we summarize the current state of knowledge concerning Legionella's natural host range, the diversity of Legionella-protozoa interactions, the factors influencing these interactions, the importance of avoiding the generalization of protozoan-bacterial interactions based on a limited number of model hosts and the central role of protozoa to the biology, evolution, and persistence of Legionella in the environment.The 1976 outbreak of Legionnaires' disease led to the discovery of the intracellular bacterial pathogen  Legionella, in particular Legionella pneumophila, which will be the major focus of this review.In the environment, bacteria are targets of predation by grazing protozoa  to Percolozoa (excavates) to Ciliophora (ciliated protozoa)  are also efficient at killing L. pneumophila  might resemble the first evolutionary step toward becoming an intracellular pathogen.While Legionella are defined by two main techniques: co-culture and co-isolation. When combined with microscopy, co-culture techniques allow for the direct visualization of Legionella within host cells, and by analyzing infected cells over time, bacterial replication within a particular host provides direct experimental evidence of Legionella survival and replication. When combined with plating assays to monitor bacterial numbers, co-culture methods allow bacterial growth rates, maximum growth and the impact of bacterial dose and various external conditions on the interaction to be analyzed. However, while Legionella may be able to replicate in a given host under specific laboratory conditions, the experimental system may not reflect conditions encountered in the environment and thus, biologically relevant interactions that commonly occur in nature. Co-isolation studies attempt to address this issue by examining the co-existence of protozoa and Legionella in environmental samples. In rare cases, protozoa harboring Legionella have been isolated from environmental samples providing direct evidence of their interaction in the environment  or the morphological appearance of trophozoites , Hartmannella  and Naegleria  as well as Tetrahymena pyrofomis, Echinamoeba exudans, and Tetramitus jugosus (formerly Vahlkampfia jugosus) , collectively it represented three different phyla Amoebozoa, Ciliophora, and Percolozoa and amongst them, four distantly related classes of protozoa, Discosea (Acanthamoebae), Tubulinea (Echinamoeba and Hartmannella), Heterolobosea (Naegleria and Tetramitus), and Oligohymenophorea (Tetrahymena)  Figure .L. pneumophila pathogenesis have progressively expanded the list of protozoan hosts of this bacterium (Table Acanthamoeba (Acanthamoeba lenticulata and Acanthamoeba royreba) and Naegleria (Naegleria fowleri) as well as more distantly related genera from their respective phyla such as Dictyostelium discoideum   , Oxytricha bifaria, Stylonychia mytilus, Paramecium caudatum and a member of the Ciliophrya genus, and in one case L. pneumophila replication (Tetrahymena thermophila), greatly expanding representation from this group , A. astronyxis, and Cashia limocoides (Amoebozoa) and Solumitrus palustris (Percolozoa) , Cercozoa (Euglypha sp.), Euglenozoa (Bodonidae sp.), and Opsithokonta (Rhinosporidium sp.) were identified. Two additional classes of protozoa from previously identified phyla are also represented, Variosea  and Oligohymenophorea with representatives encompassing four different families spanning three orders within this group. For those classes of protozoa already identified as hosts by co-culture experiments, three additional orders, Thecamoebida (Stenamoeba limacina), Arcellinida (Centropyxis sp.), and Sporadotricina (Aspidiscidae family) and five genera  were identified. Finally, of the known hosts of L. pneumophila from co-culture experiments, additional species of Acanthamoeba (A. jacobsi), Naegleria , Tetramitus (Tetramius enterica), and Vahlkampfia  were also isolated. Combined, co-isolation and co-culture experiments represent 7 of the 8 phyla of the protozoa kingdom, 12 of the 41 classes within these phyla and 21 of the 82 defined orders, demonstrating the tremendous diversity amongst L. pneumophila hosts.Co-isolation studies predict a number of additional phyla and classes of protozoa may support th Table . In addiL. pneumophila in environmental reservoirs may indicate that they are more likely to be true hosts of the bacterium. While the Acanthamoeba spp., Naegleria spp., Vahlkampfia spp., and Hartmannella spp. (including Vermamoeba vermifomis) are commonly found in multiple sources  and a correlation between the levels of Amoebozoa present in the water and the abundance of Legionella isolates  many protozoa remain poorly characterized, difficult to culture, and/or unsequenced; (2) the shear diversity of protozoa and complexity of natural interactions makes experimental analysis of phenotypes under “physiologically relevant” conditions extremely daunting ; and (3) how can non-binary interactions with mixed bacterial and host populations be examined in a reproducible and informative fashion? Given the importance of protozoa to L. pneumophila biology , we strongly advocate efforts for the sequencing and detailed study of these organisms. While it is enticing to retreat to the comfort of studying Legionella-host interactions in mammalian macrophages and perhaps one or two model protozoa, an exciting, informative, frustrating, and messy reality remains largely unexplored. Perhaps once the diversity of bacterial-protozoan behaviors is better understood, a panel of model hosts could be chosen not based on ease of culture, but instead to capture the greatest breadth of this diversity.A critical challenge in understanding the molecular mechanisms of TO, DB, AE, and GZ wrote the manuscript. GZ and AE generated the phylogenetic tree.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


To evaluate short-term effects of publishing revised lower risk national drinking guidelines on related awareness and knowledge. To examine where drinkers heard about guidelines over the same period.Trend analysis of the Alcohol Toolkit Study, a monthly repeat cross-sectional national survey.England, November 2015 to May 2016.A total of 11 845 adults (18+) living in private households in England.Publication of revised national drinking guidelines in January 2016 which reduced the male guideline by approximately one-third to 14 units per week.Whether drinkers (1) had heard of drinking guidelines (awareness), (2) stated the guideline was above, exactly or below 14 units (knowledge) and (3) reported seeing the stated guideline number of units in the last month in each of 11 locations (exposure). Sociodemographics: sex, age , social grade . Alcohol consumption derived from graduated frequency questions: low risk (<14 units/week), increasing/high risk (14+ units/week).Following publication of the guidelines, the proportion of drinkers aware of guidelines did not increase from its baseline level of 85.1% (CI 82.7% to 87.1%). However, the proportion of male drinkers saying the guideline was 14 units or less increased from 22.6% (CI 18.9% to 26.7%) in December to 43.3% (CI 38.9% to 47.8%) in January and was at 35.6% (CI 31.6% to 39.9%) in May. Last month exposure to the guidelines was below 25% in all locations except television/radio where exposure increased from 33% (CI 28.8% to 36.2%) in December to 65% (CI 61.2% to 68.3%) in January. Awareness and knowledge of guidelines was lowest in social grade DE and this gap remained after publication.Publication of new or revised lower risk drinking guidelines can improve drinkers’ knowledge of these guidelines within all sociodemographic groups; however, in the absence of sustained promotional activity, positive effects may not be maintained and social inequalities in awareness and knowledge of guidelines are likely to persist. Low-risk drinking guidelines are published by governments or health authorities in most high-income countries but they are rarely evaluated and little robust evidence is available evaluating their effects on outcomes of interest.To the authors’ knowledge, this study is the first internationally to use prospective, high-frequency survey data to examine the short-term effects of publishing new or revised drinking guidelines.Monthly data allowed for examination of how short-term effects emerge and decay after a major component of public health information is announced and from what sources the public heard about this information at different time points.In January 2016, the UK's Chief Medical Officers published proposed revisions to the country's lower risk drinking guidelines.3The new guidelines were developed between 2013 and 2016 by an expert committee who reviewed existing evidence and commissioned new epidemiological modelling and studies of public attitudes.8Although drinking guidelines are published in at least 37 countries,13–20To date, there has been no official large-scale promotional campaign for the new UK guidelines. However, the announcement was a lead story for national news outlets and attracted substantial commentary in subsequent weeks, some of which was highly critical. Many health websites and other promotional materials which mention the guidelines have been updated but alcoholic drink labels remain unchanged, even though ∼80% of alcoholic product labels in the UK include the drinking guidelines.21This study uses monthly cross-sectional survey data to assess the size and duration of short-term effects of announcing new UK lower risk drinking guidelines on drinkers' guideline-related awareness and knowledge. In addition, it investigates trends in drinkers' sources of information about the guidelines and variation in changes in awareness and knowledge by age, sex, socioeconomic status and alcohol consumption level.Data come from the Alcohol Toolkit Study (ATS), a monthly repeat cross-sectional survey which began in March 2014 and collects data from nationally representative samples of ∼1600 adults each month living in private households in England. Since November 2015, the ATS has included questions relating to the drinking guidelines, and this analysis uses data from the 7 months between November 2015 and May 2016 (the most recently available month). January data were collected in the week after publication of the revised guidelines.The full ATS methods are described elsewhere.Analyses here focused on drinkers who were identified via the AUDIT questionnaire, a widely used screening instrument for problem drinking which has good validity, high internal consistency and good test–retest reliability across gender, age and cultures.Awareness of guidelines among drinkers was measured using the question: ‘Before this interview, have you ever heard of there being a recommended maximum number of alcohol units people should drink in a day or a week? This is sometimes known as a “drinking guideline”’. Responses were dichotomised as yes or no. The concept of units was explained during the AUDIT questionnaire which was administered earlier in the survey.Knowledge of the guideline among those who had heard of the concept was measured using the question: ‘How many units per day or per week is the drinking guideline for males/females?’. Participants were asked about their own sex only and allowed to respond in units per week or per day. For this analysis, responses were either trichotomised as more than, exactly or below 14 units per week or 2 units a day or dichotomised as above versus exactly or below 14 units per week or 2 units per day (hereafter 14 units per week or 14 units for brevity). These classifications were used to test whether announcing the new guidelines increased the proportion of people saying the guideline was 14 units per week.To assess where people see or hear about the guideline figure they gave, those who gave a figure were asked which of a list of places they had seen, read or heard about it in the last month . ResponsThe following characteristics were assessed: sex, age  and social grade which is an occupation-based measure of socioeconomic status, trichotomised here as AB , C1C2  and DE (semiskilled or unskilled occupations and unemployed).Alcohol consumption was measured via a graduated frequency approach.Descriptive analyses are used to examine change in outcome measures compared with December 2015, the last month before new guidelines were announced. Variation between subgroups in exposure to guidelines for the whole time period is also examined descriptively. All analyses are based on weighted survey data and cases are not excluded if they have missing data on some variables. Further analyses presented in the 10.1136/bmjopen-2016-013804.supp1Supplementary appendixInformed consent is given verbally by ATS participants after interviewers explain the study and give assurance that it is being conducted in line with the Market Research Society Code of Conduct.In December, 87% of drinkers said they had heard of drinking guidelines . DespiteIn contrast, there was a change in knowledge of the guideline following the announcement. In December, 33% of drinkers thought the guideline was above 14 units per week and this fell significantly to 22% in January. Conversely, the proportion of drinkers saying the guideline was exactly 14 units increased significantly from 21% to 29%. In the absence of sustained promotional activity, these effects on drinkers' knowledge were not sustained and the proportion of drinkers stating the guideline was 14 units per week fell to 27% in February and 24% in March. There was some evidence of a secondary increase in knowledge emerging gradually from March onwards, but further data points are required to confirm this.Among drinkers who gave a figure for the guidelines, 32% reported no exposure to this figure in December but this dropped to 7% in January and remained low at 15% in May . TV and When comparing awareness across sociodemographic groups, a majority of drinkers in all groups were aware of guidelines at all time points; however, there were significant differences in awareness by social grade with only 70% of those in social grade DE aware of guidelines in December compared with 89% in grade C1C2 and 93% in grade AB . This siThose in grade DE were also significantly less likely to say the guideline was 14 units or less than those in grade AB (29% vs 46%). This gap was still present in January despite both groups registering the change in guidelines (42% vs 56%) and persisted in May after knowledge decreased again . Although the new guidelines of 14 units a week were a change from daily to weekly guidance, only the male guideline was actually reduced and the proportion of male drinkers saying the guideline was 14 units per week or less increased following the announcement, although only to 43%. This figure declined again after January, although there was some evidence of an emerging secondary gradual increase in knowledge. As with awareness, drinkers in social grade DE had lower levels of knowledge before, during and after January than those in higher social grades. The lack of large-scale promotional activity beyond news coverage meant that television and radio were the main media through which drinkers were exposed to the drinking guidelines and this was particularly the case after the announcement in January. Less than a quarter of drinkers reported hearing about the guidelines through any other medium in January.This study is the first to the authors' knowledge to use prospective high-frequency survey data to examine the emergence and decay of short-term effects of promoting new or revised drinking guidelines. Further strengths include the use of consistent data collection methods and measures over survey waves, the inclusion of preintervention and postintervention data, the nationally representative sample and the examination of multiple outcomes including awareness, knowledge and place of exposure. Limitations include the short preintervention period and the potential for the traditionally heavy and light drinking months of December and January to confound intervention effects. With regard to exposure, the accessibility of television clips and newspaper reports through social media and websites means that the source of some exposure may be difficult for respondents to classify. Finally, self-reporting biases are common to all studies on alcohol use and lead to underestimation of alcohol consumption.Overall, the findings broadly align with previous studies by suggesting that announcing revisions to drinking guidelines can lead to modest improvements in drinkers' knowledge of the guidelines.To date, the UK Government has not announced a major promotional campaign for the new drinking guidelines. This is likely to limit their impact as routine promotional activity appears to go largely unnoticed. Only a small minority of drinkers reported recent exposure to the guidelines from sources not linked to news coverage. In particular, only around a fifth of drinkers noticed the guidelines on product labels despite alcohol producers and retailers ensuring around 80% of products include the drinking guidelines on their label as part of the Public Health Responsibility Deal (PHRD).29et alFurther research is required to evaluate how revision and promotion of the UK's lower risk drinking guidelines affects alcohol consumption and alcohol-related harm. The results above suggest a rapidly decaying short-term effect on knowledge but also indicate a secondary effect may be emerging and research will be required to characterise the trajectory of any effects in the medium and long term. Qualitative evidence is also required regarding how drinkers accommodate the new guidelines within their existing understanding about alcohol-related risks and apply that broader understanding to their own and others' alcohol consumption. Lovatt Publication of new or revised lower risk drinking guidelines can improve drinkers' knowledge of these guidelines within all sociodemographic groups; however, in the absence of sustained promotional activity, positive effects may be short-lived and social inequalities in awareness and knowledge of guidelines are likely to persist.


Hallucination Machine. It comprises a novel combination of two powerful technologies: deep convolutional neural networks (DCNNs) and panoramic videos of natural scenes, viewed immersively through a head-mounted display (panoramic VR). By doing this, we are able to simulate visual hallucinatory experiences in a biologically plausible and ecologically valid way. Two experiments illustrate potential applications of the Hallucination Machine. First, we show that the system induces visual phenomenology qualitatively similar to classical psychedelics. In a second experiment, we find that simulated hallucinations do not evoke the temporal distortion commonly associated with altered states. Overall, the Hallucination Machine offers a valuable new technique for simulating altered phenomenology without directly altering the underlying neurophysiology.Altered states of consciousness, such as psychotic or pharmacologically-induced hallucinations, provide a unique opportunity to examine the mechanisms underlying conscious perception. However, the phenomenological properties of these states are difficult to isolate experimentally from other, more general physiological and cognitive effects of psychoactive substances or psychopathological conditions. Thus, simulating phenomenological aspects of altered states in the absence of these other more general effects provides an important experimental tool for consciousness science and psychiatry. Here we describe such a tool, which we call the  Altered states are defined as a qualitative alteration in the overall pattern of mental functioning, such that the experiencer feels their consciousness is radically different from “normal”3, and are typically considered distinct from common global alterations of consciousness such as dreaming. ASC are not defined by any particular content of consciousness, but cover a wide range of qualitative properties including temporal distortion, disruptions of the self, ego-dissolution, visual distortions and hallucinations, among others7. Causes of ASC include psychedelic drugs  as well as pathological or psychiatric conditions such as epilepsy or psychosis10. In recent years, there has been a resurgence in research investigating altered states induced by psychedelic drugs. These studies attempt to understand the neural underpinnings that cause altered conscious experience13 as well as investigating the potential psychotherapeutic applications of these drugs14. However, psychedelic compounds have many systemic physiological effects, not all of which are likely relevant to the generation of altered perceptual phenomenology. It is difficult, using pharmacological manipulations alone, to distinguish the primary causes of altered phenomenology from the secondary effects of other more general aspects of neurophysiology and basic sensory processing. Understanding the specific nature of altered phenomenology in the psychedelic state therefore stands as an important experimental challenge.There is a long history of studying altered states of consciousness (ASC) in order to better understand phenomenological properties of conscious perception16. For example, deep convolutional neural networks (DCNNs) have been particularly successful in the difficult task of object recognition in photographs of natural scenes18.Here, we address this challenge by combining virtual reality and machine learning to isolate and simulate one specific aspect of psychedelic phenomenology: visual hallucinations. In machine learning, deep neural networks (DNNs) developed for machine vision have now improved to a level comparable to that achieved by humans21. For example, the neural responses induced by a visual stimulus in the human inferior temporal (IT) cortex, widely implicated in object recognition, have been shown to be similar to the activity pattern of higher (deeper) layers of the DCNN23. Features selectively detected by lower layers of the same DCNN bear striking similarities to the low-level features processed by the early visual cortices such as V1 and V4. These findings demonstrate that even though DCNNs were not explicitly designed to model the visual system, after training for challenging object recognition tasks they show marked similarities to the functional and hierarchical structure of human visual cortices.Studies comparing the internal representational structure of trained DCNNs with primate and human brains performing similar object recognition tasks, have revealed surprising similarities in the representational spaces between these two distinct systemsDeep Dream was developed for this purpose25. Deep Dream works by clamping the activity of nodes at a user-defined layer in the DCNN and then inverting the information flow, so that an input image is changed until the network settles into a stable state . Intuitively, this means changing the image rather than changing the network in order to match the features of the image with what is represented in the target layer – so that the resulting image is shaped by what the network ‘expects’ to see, at the level of detail determined by the clamped layer. More precisely, the algorithm modifies natural images to reflect the categorical features learnt by the network25, with the nature of the modification depending on which layer of the network is clamped . While previous studies have used computer-generated imagery (CGI) in VR that demonstrate some qualitative similarity to visual hallucinations29, we aimed to generate highly naturalistic and dynamic simulated hallucinations. To do so, we presented 360-degree (panoramic) videos of pre-recorded natural scenes within a head-mounted display (HMD), which had been modified using the Deep Dream algorithm. The presentation of panoramic video using a HMD equipped with head-tracking (panoramic VR) allows the individual’s actions  to change the viewpoint in the video in a naturalistic manner. This congruency between visual and bodily motion allows participants to experience naturalistic simulated hallucinations in a fully immersive way, which would be impossible to achieve using a standard computer display or conventional CGI VR. We call this combination of techniques the Hallucination Machine.We set out to simulate the visual hallucinatory aspects of the psychedelic state using Hallucination Machine is able to simulate natural visual hallucinations, we conducted two proof-of-concept experiments. The first experiment investigated the ecological validity of experiences produced by the Hallucination Machine. We compared the simulated experiences produced by the Hallucination Machine to unaltered control videos  and by pharmacologically induced psychedelic states31 (across studies). Twelve participants took part in Experiment 1. The results are shown in Fig. Hallucination Machine are qualitatively distinct from the control videos , also revealed evidence supporting the alternative hypothesis for the following dimension ‘intensity’, with weaker evidence for ‘pattern’ and ‘strange’, suggesting that there are some qualitative differences between Hallucination Machine and psilocybin experiences  showed the strongest evidence for an effect of target interval only , 2 s (M = 2.41 s SE = 0.11 s) and 4 s (M = 4.38 s SE = 0.16 s)). A model including only video type showed evidence in favour of the null hypothesis (BF10 = 0.194), indicating that video type did not affect interval production  without the interaction  = 0.935, p < 0.401, η2 = 0.045). However, the main effect of video type did not reach significance  = 0.476, p = 0.498, η2 = 0.023).One participant was excluded from the analysis due to producing intervals in the experimental session that were temporally inverted compared to the target durations. A two-way Bayesian repeated measures ANOVA consisting of factors target interval  and video type  = 3.705, p = 0.007, Cohen’s d = 0.808; and ‘attention’, “How focused were you on the time production task”, BF10 = 4.830, t(20) = 2.822, p = 0.077, Cohen’s d = 0.616 were reduced during the Hallucination Machine. Responses regarding Duration (BF10 = 0.278) and Speed (BF10 = 0.281) both revealed evidence for no difference between Hallucination Machine and control video. Other comparisons failed to reach an evidentiary threshold in both Bayesian and normal t-tests.Post-hoc standard and Bayesian see Fig. . These rHallucination Machine, which provides a novel method for investigating  hallucinogenic phenomenology. It combines two technologies: Panoramic video of natural scenes presented using VR, allowing the video to be experienced in a fully immersive environment, and an application of deep convolutional neural networks (DCNNs), Deep Dream, which when suitably adapted can transform panoramic video to mimic hallucinatory phenomenology in a biologically plausible manner. The Hallucination Machine enables systematic and parameterizable manipulation of distinct aspects of altered states of consciousness (ASCs), specifically visual hallucinations, without involving the widespread systemic effects caused by pharmacological manipulations.We have described the implementation of the Hallucination Machine with those elicited by both  control videos (within subjects) and by pharmacologically induced psychedelic states (across studies). Comparisons between control and Hallucination Machine with natural scenes revealed significant differences in perceptual and imagination dimensions  as well as the overall intensity and emotional arousal of the experience. Notably, these specific dimensions were also reported as being increased after pharmacological administration of psilocybin31. Experiment 1 therefore showed that hallucination-like panoramic video presented within an immersive VR environment gave rise to subjective experiences that displayed marked similarities across multiple dimensions to actual psychedelic states31. Although we were not able to directly compare the Hallucination Machine experiences to pharmacologically induced psychedelic experiences in the same subjects, the pattern of findings in Experiment 1 support the conclusion that the Hallucination Machine successfully simulates many aspects of ASC induced by psychedelic drugs.In two experiments we evaluated the effectiveness of this system. Experiment 1 compared subjective experiences evoked by the 6. In contrast to these earlier findings, neither objective measures  nor subjective ratings  in order to maximize the chances of creating dramatic, vivid, and complex simulated hallucinations. Future extensions could ‘close the loop’ by allowing participants  to adjust the Hallucination Machine parameters in order to more closely match their previous experiences. This approach would substantially extend phenomenological analysis based on verbal report, and may potentially allow individual ASCs to be related in a highly specific manner to altered neuronal computations in perceptual hierarchies.A crucial feature of the Hallucination Machine is the use of highly immersive panoramic video of natural scenes presented in virtual reality (VR). Conventional CGI-based VR applications have been developed for analysis or simulation of atypical conscious states including psychosis, sensory hypersensitivity, and visual hallucinations35. However, these previous applications all use of CGI imagery, which while sometimes impressively realistic, is always noticeably distinct from real-world visual input and is therefore suboptimal for investigations of altered visual phenomenology. Our setup, by contrast, utilises panoramic recording of real world environments thereby providing a more immersive naturalistic visual experience enabling a much closer approximation to altered states of visual phenomenology. In the present study, these advantages outweigh the drawbacks of current VR systems that utilise real world environments, notably the inability to freely move around or interact with the environment (except via head-movements).Another key feature of the Hallucination Machine may shed new light on the neural mechanisms underlying physiologically-induced hallucinogenic states. This potential rests on the close functional mappings between the architecture of DCNNs like those used here and the functional architecture of the primate visual system36, as well as the equivalences between the ‘top-down’ functional flow (back propagation in Deep Dream) of the Deep Dream algorithm and the role of top-down signalling in Bayesian or ‘predictive processing’ theories of perception37.Besides having potential for non-pharmacological simulation of hallucinogenic phenomenology, the Deep Dream algorithm is the use of backpropagation to alter the input image in order to minimize categorization errors. This process bears intuitive similarities to the influence of perceptual predictions within predictive processing accounts of perception. In predictive processing theories of visual perception, perceptual content is determined by the reciprocal exchange of (top-down) perceptual predictions and (bottom-up) perceptual predictions errors. The minimisation of perceptual prediction error, across multiple hierarchical layers, approximates a process of Bayesian inference such that perceptual content corresponds to the brain’s “best guess” of the causes of its sensory input. In this framework, hallucinations can be viewed as resulting from imbalances between top-down perceptual predictions (prior expectations or ‘beliefs’) and bottom-up sensory signals. Specifically, excessively strong relative weighting of perceptual priors ) may overwhelm sensory (prediction error) signals leading to hallucinatory perceptions43.A defining feature of the 36. Broadly, the responses of ‘shallow’ layers of a DCNN correspond to the activity of early stages of visual processing, while the responses of ‘deep’ layers of DCNN correspond to the activity of later stages of visual processing. These findings support the idea that feedforward processing through a DCNN recapitulates at least part of the processing relevant to the formation of visual percepts in human brains. Critically, although the DCNN architecture (at least as used in this study) is purely feedforward, the application of the Deep Dream algorithm approximates, at least informally, some aspects of the top-down signalling that is central to predictive processing accounts of perception. Specifically, instead of updating network weights via backpropagation to reduce classification error (as in DCNN training), Deep Dream alters the input image (again via backpropagation) while clamping the activity of a pre-selected DCNN layer. The network itself is not altered in this process. Therefore, the result of the Deep Dream process can be intuitively understood as the imposition of a strong perceptual prior on incoming sensory data, establishing a functional  parallel with the predictive processing account of perceptual hallucinations given above.Close functional and more informal structural correspondences between DCNNs and the primate visual system have been previously noted44, to more well-defined recognizable forms such as faces, objects, and scenes 46. As already mentioned, the output images of Deep Dream are dramatically altered depending on which layer of the network is clamped during the image-alteration process. Fixing higher layers tends to produce output similar to more complex hallucinations ons Fig. , while fons Fig.  and S3. ons Fig. , simple ers Fig. . Conversers Fig. .Figure 5Hallucination Machine combines panoramic video and audio presented within a head-mounted display, with a modified version of ‘Deep Dream’ algorithm, which is used to visualize the activity and selectivity of layers within DCNNs trained for complex visual classification tasks. In two experiments we found that the subjective experiences induced by the Hallucination Machine differed significantly from control  videos, while bearing phenomenological similarities to the psychedelic state (following administration of psilocybin). The immersive nature of our paradigm, the close correspondence in representational levels between layers of DCNNs and the primate visual hierarchy along with the informal similarities between DCNNs and biological visual systems, together suggest that the Hallucination Machine is capable of simulating biologically plausible and ecologically valid visual hallucinations. In addition, the method carries promise for isolating the network basis of specific altered visual phenomenological states, such as the differences between simple and complex visual hallucinations. Overall, the Hallucination Machine provides a powerful new tool to complement the resurgence of research into altered states of consciousness.We have described a method for simulating altered visual phenomenology similar to visual hallucinations reported in the psychedelic state. Our Hallucination Machine was created by applying the Deep Dream algorithm to each frame of a pre-recorded panoramic video presented using a HMD . The frame rate of the video was 16 fps at a resolution of 4096 × 2048. All video footage was presented using a head mounted display  using in-house software developed using Unity3D.32. During this training procedure, features in all layers are learned via backpropagation (with various modifications) to associate a set of training images to distinct categories. Consequently, the trained network implements a mapping from the pixels of the input image to the categories, represented as activation of specific units of the top layer of the network. Given this network, to create the panoramic video we applied the Deep Dream algorithm frame-by-frame to the raw video footage.The DCNN – a deeply layered feedforward neural network – used in this study had been pre-trained on a thousand categories of natural photographs used in the Large Scale Visual Recognition Challenge 2010 (ILSVRC2010)Deep Dream algorithm also uses error backpropagation, but instead of updating the weights between nodes in the DCNN, it fixes the weights between nodes across the entire network and then iteratively updates the input image itself to minimize categorization errors via gradient descent. Over multiple iterations this process alters the input image, whatever it might be , so that it encompasses features that the layer of the DCNN has been trained to select . When applied while fixing a relatively low level of the network, the result is an image emphasizing local geometric features of the input. When applied while fixing relatively high levels of the network, the result is an image that imposes object-like features on the input, resembling a complex hallucination. Examples of the output of Deep Dream used in Experiments 1 and 2 are shown in Fig. The Deep Dream program was intended to process a single static image , others have developed implementations of this algorithm that process image sequences in order to make videos by blending the hallucinatory content of the previous frame with the current frame . The principle here is to take a user defined proportion from 0–1 (blending ratio) of the previous frame’s hallucinatory patterns  and integrate it into the current frame. In this way, each frame inherits some of the hallucinatory content of the previous frame, as opposed to Deep Dream starting from scratch for each frame. This frame-to-frame inheritance enables the hallucinatory patterns to remain relatively constant as the video unfolds. We extended one such implementation47 to optimise the hallucinogenic properties of the video. In our extension, the optical flow of each frame is calculated by comparing the difference in the movement of all pixels between the current and previous frame. The hallucinatory patterns from areas where the optical flow was detected is merged to the current  frame based on the weighting provided by the blending ratio. The Deep Dream algorithm is then applied to this merged frame. We also optimised the blending ratio between each pair of frames, setting different blending ratios in areas of the image with high  or low  optical flow. This was done to avoid saturation of areas of the image with low optical flows by the higher blending ratios used for areas with high optical flow. The details of our implementation of Deep Dream are provided in the supplemental material. Our software for creating the Deep Dream video can be found on GitHub48. The Deep Dream video used throughout the reported experiments was generated by selecting a higher DCNN layer, which responds selectively to higher-level categorical features .Although the original Twelve participants completed Experiment 1 . Participants provided informed consent before taking part and received £10 or course credits as compensation for their time. All methods were carried out in accordance with approved guidelines provided by the University of Sussex, Research Ethics Committee.Hallucination Machine or the original unaltered (control) video footage. Each video presentation lasted 3 minutes and was repeated twice, with a 180-degree direction flip of the initial orientation between the two presentations (presenting the part of the scene that would have been directly behind their viewpoint in the first presentation) to help ensure that participants experienced the majority of the panoramically-recorded scene. Participants were encouraged to freely investigate the scene in a naturalistic manner. While sitting on a stool they could explore the video footage with 3-degrees of freedom rotational movement. While the video footage is spherical, there is a bind spot of approximately 33-degrees located at the bottom of the sphere due to the field of view of the camera. After each video, participants were asked to rate their experiences for each question via an ASC questionnaire which used a visual analog scale for each question . We quantified how close to the null (no difference between results), or to the alternative hypothesis (difference in results), each result was using JASP49 with a default Cauchy prior of 0.707 half-width at half-maximum50. A BF10 > 3.0 is interpreted as evidence for accepting the alternative hypothesis (i.e. there is a difference), whereas BF10 < 1/3 is interpreted as evidence for accepting the null hypothesis (i.e. there is no difference)51. Standard paired t-test Bonferroni corrected for multiple comparisons were also conducted.Bayesian paired Mage = 23.9, SD = 6.71, 13 female). Participants provided informed consent before taking part and received £10 or course credits as compensation for their time. All methods were carried out in accordance with approved guidelines provided by the University of Sussex, Research Ethics Committee.A new group of Twenty-two participants that did not participate in Experiment 1 completed Experiment 2  Low, 6.54 (SD = 1.61) Middle, and 6.33 (SD = 2.18) High. Participants had to repeat the practice session if the Pearson’s correlation between the target and produced intervals was less than 0.5.The experiment began with a practice session of a standard temporal production task. In each of 20 trials, participants heard one of three tones, each of a different pitch . On each trial the pitch was randomly selected. Participants were asked to produce specific time intervals for each tone Hallucination Machine (6 blocks), and similar to Experiment 1, participants were instructed to explore the scene freely in a naturalistic manner. The order of the videos was counter-balanced across participants. Each block lasted 3 minutes, leading to a total exposure of 18 minutes for each video type. While participants explored the immersive video, low, middle or high pitch tones were presented in a random order (the average numbers of tones per block were 6.17 (SD = 2.02) Low, 6.16 (SD = 2.00) Middle, 6.21 (SD = 1.94) High for Hallucination Machine, and 6.62 (SD = 2.44) Low, 6.63 (SD = 2.42) Middle, and 6.61 (SD = 2.55) High for the control video). Immediately after hearing the tone, participants had to produce the interval relating to the tone   or the  blocks o SD = 1.9 High forHallucination Machine) was used to investigate the effect of video type on interval production. A standard two-factorial repeated measures ANOVA using the same factors as above was also conducted.A Bayesian two-factorial repeated measures ANOVA consisting of the factors interval production  and video type  was used to investigate the effect of video type on interval production. Similar to Experiment 1, for cases in which standard statistics did not reveal a significant difference, we quantified how close to the null (no difference between results) or alternative hypothesis (difference in results) each result was by an additional two-way Bayesian ANOVA using the same factors as above. In a similar fashion, for cases in which standard t-tests did not reveal significant differences in subjective ratings between video type we used additional Bayesian t-tests.A two-factorial repeated measures ANOVA consisting of the factors interval production  and video type (control/Video materials used in the study are available in the supplemental material. The datasets generated in Experiment 1 and 2 are available from the corresponding author upon request.Video S1Video S2Video S3Supplementary Material


Is radiation as dangerous as they say? John Cameron, Medical Physics Publishing, Madison, WI 2002.PACS number(s): 87.50.Gi, 87.52.Px, 87.52.Tr Although this is not a book, this video is sold by Medical Physics Publishing and appears in their 2002 Spring‐Summer Catalog. It is a video lecture that was prepared for a Medical Effects of Ionizing Radiation (MEIR) Course at the Armed Forces Radiobiology Research Institute (AFRRI) in August 2001. Along with the video there is a 12‐page supplement. This consists of a hard copy of the slides shown in the video, a three‐page article by Cameron, “Is radiation an essential trace energy?” and a short abstract, “Promoting Understanding of Radiation in the Radiology Clinic,” both unpublished, but each with a short reference list. These articles cover most of the material in the video.It would have been helpful to know the subject matter of the other lectures and the context in which they were presented. Cameron's thesis is that a little radiation rather than being harmful is, in fact, of benefit, especially to older people. This is opposite to contemporary wisdom so that this video needs to be shown at the same time that that the opposite view point is given and with a moderator to facilitate discussion of questions raised by the contrast between the two. One can only hope that this was done when the video was used for its original purpose. Unfortunately the Medical Physics Publishing catalog seems to promise more than was contained in the video. The catalog says, “… he (Cameron) challenges the generally accepted linear no threshold (LNT) hypothesis and presents the results of several studies to back his argument.” However, the linear no threshold theory is not mentioned or alluded to once in the video, nor as far as I could see, in the supplementary material. This is not a balanced presentation giving the strengths and weakness of both viewpoints.This is the presentation of an evangelist preaching his gospel of safe radiation. Certainly Cameron has the right to his viewpoint and the right to present it and indeed there might be some validity to what he says, but there are too many flaws in this presentation to come to any other conclusion than to say that it is an interesting idea.et al., DOE DE‐AC02–79 EV10095).Cameron presents the results of several studies to back up his argument. Only one of these studies has been published in a peer reviewed scientific publication. That is the study of the mortality of British radiologist from 1900 to 1980 by Smith and Doll published in the British Journal of Radiology in 1981. This data is interesting and perhaps it is surprising that no one, as far as I know, has seriously followed this up. It does on the surface seem to support Cameron's viewpoint. So does the study of nuclear shipyard workers in which Cameron participated. Again this is a long‐term study of workers exposed to radiation versus those who were not but it has never been published in a peer reviewed scientific journal, although the report is apparently available from the Department of Energy , nor I expect will it be done in the lifetime of most of the readers of this journal.I found the lecture entertaining and interesting, although I wished that the audio had been a little clearer. Also it was not possible to see the details in some of the slides both on the screen and in the handout. Although the video cannot be changed it should be possible to make sure that the copies of the slides in the handout are sharp and readable.Having pointed out some of the flaws in Cameron's presentation I still think that this video has a place in teaching institutions. It could be used in any lecture series dealing with radiation protection, regulatory issues, radiation effects, radiobiology, etc., but only as part of a series of lectures that presents all sides of the issue and hopefully with an instructor that can bring a balanced approach to this subject. It could also be used as a starting point in a discussion with the students on the issues raised by this review of the video presentation itself.