A rigorous statistical framework for spatio-temporal pollution prediction and estimation of its long-term impact on health

In the United Kingdom, air pollution is linked to around 40000 premature deaths each year, but estimating its health effects is challenging in a spatio-temporal study. The challenges include spatial misalignment between the pollution and disease data; uncertainty in the estimated pollution surface; and complex residual spatio-temporal autocorrelation in the disease data. This article develops a two-stage model that addresses these issues. The first stage is a spatio-temporal fusion model linking modeled and measured pollution data, while the second stage links these predictions to the disease data. The methodology is motivated by a new five-year study investigating the effects of multiple pollutants on respiratory hospitalizations in England between 2007 and 2011, using pollution and disease data relating to local and unitary authorities on a monthly time scale

A Bayesian localized conditional autoregressive model for estimating the health effects of air pollution

Estimation of the long-term health effects of air pollution is a challenging task, especially when modeling spatial small-area disease incidence data in an ecological study design. The challenge comes from the unobserved underlying spatial autocorrelation structure in these data, which is accounted for using random effects modeled by a globally smooth conditional autoregressive model. These smooth random effects confound the effects of air pollution, which are also globally smooth. To avoid this collinearity a Bayesian localized conditional autoregressive model is developed for the random effects. This localized model is flexible spatially, in the sense that it is not only able to model areas of spatial smoothness, but also it is able to capture step changes in the random effects surface. This methodological development allows us to improve the estimation performance of the covariate effects, compared to using traditional conditional auto-regressive models. These results are established using a simulation study, and are then illustrated with our motivating study on air pollution and respiratory ill health in Greater Glasgow, Scotland in 2011. The model shows substantial health effects of particulate matter air pollution and nitrogen dioxide, whose effects have been consistently attenuated by the currently available globally smooth models

Neural activity tracking identity and confidence in social information

Humans learn about the environment either directly by interacting with it or indirectly by seeking information about it from social sources such as conspecifics. The degree of confidence in the information obtained through either route should determine the impact that it has on adapting and changing behaviour. We examined whether and how behavioural and neural computations differ during non-social learning as opposed to learning from social sources. Trial-wise confidence judgements about non-social and social information sources offered a window into this learning process. Despite matching exactly the statistical features of social and non-social conditions, confidence judgements were more accurate and less changeable when they were made about social as opposed to non-social information sources. In addition to subjective reports of confidence, differences were also apparent in the Bayesian estimates of participants' subjective beliefs. Univariate activity in dorsomedial prefrontal cortex and posterior temporoparietal junction more closely tracked confidence about social as opposed to non-social information sources. In addition, the multivariate patterns of activity in the same areas encoded identities of social information sources compared to non-social information sources

NRF2-driven miR-125B1 and miR-29B1 transcriptional regulation controls a novel anti-apoptotic miRNA regulatory network for AML survival

Transcription factor NRF2 is an important regulator of oxidative stress. It is involved in cancer progression, and has abnormal constitutive expression in acute myeloid leukaemia (AML). Posttranscriptional regulation by microRNAs (miRNAs) can affect the malignant phenotype of AML cells. In this study, we identified and characterised NRF2-regulated miRNAs in AML. An miRNA array identified miRNA expression level changes in response to NRF2 knockdown in AML cells. Further analysis of miRNAs concomitantly regulated by knockdown of the NRF2 inhibitor KEAP1 revealed the major candidate NRF2-mediated miRNAs in AML. We identified miR-125B to be upregulated and miR-29B to be downregulated by NRF2 in AML. Subsequent bioinformatic analysis identified putative NRF2 binding sites upstream of the miR-125B1 coding region and downstream of the mir-29B1 coding region. Chromatin immunoprecipitation analyses showed that NRF2 binds to these antioxidant response elements (AREs) located in the 5′ untranslated regions of miR-125B and miR-29B. Finally, primary AML samples transfected with anti-miR-125B antagomiR or miR-29B mimic showed increased cell death responsiveness either alone or co-treated with standard AML chemotherapy. In summary, we find that NRF2 regulation of miR-125B and miR-29B acts to promote leukaemic cell survival, and their manipulation enhances AML responsiveness towards cytotoxic chemotherapeutics

Targeting BTK for the treatment of FLT3-ITD mutated acute myeloid leukemia

Approximately 20% of patients with acute myeloid leukaemia (AML) have a mutation in FMS-like-tyrosine-kinase-3 (FLT3). FLT3 is a trans-membrane receptor with a tyrosine kinase domain which, when activated, initiates a cascade of phosphorylated proteins including the SRC family of kinases. Recently our group and others have shown that pharmacologic inhibition and genetic knockdown of Bruton's tyrosine kinase (BTK) blocks AML blast proliferation, leukaemic cell adhesion to bone marrow stromal cells as well as migration of AML blasts. The anti-proliferative effects of BTK inhibition in human AML are mediated via inhibition of downstream NF-κB pro-survival signalling however the upstream drivers of BTK activation in human AML have yet to be fully characterised. Here we place the FLT3-ITD upstream of BTK in AML and show that the BTK inhibitor ibrutinib inhibits the survival and proliferation of FLT3-ITD primary AML blasts and AML cell lines. Furthermore ibrutinib inhibits the activation of downstream kinases including MAPK, AKT and STAT5. In addition we show that BTK RNAi inhibits proliferation of FLT3-ITD AML cells. Finally we report that ibrutinib reverses the cyto-protective role of BMSC on FLT3-ITD AML survival. These results argue for the evaluation of ibrutinib in patients with FLT3-ITD mutated AML

Imagining the future self through thought experiments

The ability of the mind to conceptualize what is not present is essential. It allows us to reason counterfactually about what might have happened had events unfolded differently or had another course of action been taken. It allows us to think about what might happen – to perform 'Gedankenexperimente' (thought experiments) – before we act. However, the cognitive and neural mechanisms mediating this ability are poorly understood. We suggest that the frontopolar cortex (FPC) keeps track of and evaluates alternative choices (what we might have done), whereas the anterior lateral prefrontal cortex (alPFC) compares simulations of possible future scenarios (what we might do) and evaluates their reward values. Together, these brain regions support the construction of suppositional scenarios

The representation of abstract task rules in the human prefrontal cortex

We have previously reported sustained activation in the ventral prefrontal cortex while participants prepared to perform 1 of 2 tasks as instructed. But there are studies that have reported activation reflecting task rules elsewhere in prefrontal cortex, and this is true in particular when it was left to the participants to decide which rule to obey. The aim of the present experiment was to use functional magnetic resonance imaging (fMRI) to find whether there was activation in common, irrespective of the way that the task rules were established. On each trial, we presented a word after a variable delay, and participants had to decide either whether the word was abstract or concrete or whether it had 2 syllables. The participants either decided before the delay which task they would perform or were instructed by written cues. Comparing the self-generated with the instructed trials, there was early task set activation during the delay in the middle frontal gyrus. On the other hand, a conjunction analysis revealed sustained activation in the ventral prefrontal and polar cortex for both conditions. We argue that the ventral prefrontal cortex is specialized for handling conditional rules regardless of how the task rules were established

Identification and disruption of a neural mechanism for accumulating prospective metacognitive information prior to decision-making

More than one type of probability must be considered when making decisions. It is as necessary to know one's chance of performing choices correctly as it is to know the chances that desired outcomes will follow choices. We refer to these two choice contingencies as internal and external probability. Neural activity across many frontal and parietal areas reflected internal and external probabilities in a similar manner during decision-making. However, neural recording and manipulation approaches suggest that one area, the anterior lateral prefrontal cortex (alPFC), is highly specialized for making prospective, metacognitive judgments on the basis of internal probability; it is essential for knowing which decisions to tackle, given its assessment of how well they will be performed. Its activity predicted prospective metacognitive judgments, and individual variation in activity predicted individual variation in metacognitive judgments. Its disruption altered metacognitive judgments, leading participants to tackle perceptual decisions they were likely to fail