Characterisation of tumour microenvironment remodelling following oncogene inhibition in preclinical studies with imaging mass cytometry.

Mouse models are critical in pre-clinical studies of cancer therapy, allowing dissection of mechanisms through chemical and genetic manipulations that are not feasible in the clinical setting. In studies of the tumour microenvironment (TME), multiplexed imaging methods can provide a rich source of information. However, the application of such technologies in mouse tissues is still in its infancy. Here we present a workflow for studying the TME using imaging mass cytometry with a panel of 27 antibodies on frozen mouse tissues. We optimise and validate image segmentation strategies and automate the process in a Nextflow-based pipeline (imcyto) that is scalable and portable, allowing for parallelised segmentation of large multi-image datasets. With these methods we interrogate the remodelling of the TME induced by a KRAS G12C inhibitor in an immune competent mouse orthotopic lung cancer model, highlighting the infiltration and activation of antigen presenting cells and effector cells

Characterisation of tumour microenvironment remodelling following oncogene inhibition in preclinical studies with imaging mass cytometry

Mouse models are critical in pre-clinical studies of cancer therapy, allowing dissection of mechanisms through chemical and genetic manipulations that are not feasible in the clinical setting. In studies of the tumour microenvironment (TME), multiplexed imaging methods can provide a rich source of information. However, the application of such technologies in mouse tissues is still in its infancy. Here we present a workflow for studying the TME using imaging mass cytometry with a panel of 27 antibodies on frozen mouse tissues. We optimise and validate image segmentation strategies and automate the process in a Nextflow-based pipeline (imcyto) that is scalable and portable, allowing for parallelised segmentation of large multi-image datasets. With these methods we interrogate the remodelling of the TME induced by a KRAS G12C inhibitor in an immune competent mouse orthotopic lung cancer model, highlighting the infiltration and activation of antigen presenting cells and effector cells

Association between Air Pollution and Intrauterine Mortality in Sao Paulo, Brazil

The associations among daily counts of intrauterine mortality and pollutant concentrations (NO2, SO2, CO, O3, and particulate matter (3/4)10 microm) were investigated for the period ranging from January 1991 to December 1992 in the city of São Paulo, Brazil. We used Poisson regression techniques, adjusted for season and weather. The association between intrauterine mortality and air pollution was strong for NO2 (coefficient = 0.0013/ microg/m3; p<0.01) but lesser for SO2 (coefficient = 0.0005/ microg/m3; p<0.10) and CO (coefficient = 0.0223/ppm; p<0.10). A significant association was observed when an index that combined these three pollutants was considered in the models instead of considering each pollutant individually (p<0.01). These associations exhibited a short time lag, not over 5 days. In addition, some evidence of fetal exposure to air pollution was obtained by disclosing a significant association between the levels of carboxyhemoglobin of blood sampled from the umbilical cord and ambient CO levels in children delivered by nonsmoking pregnant women in the period from May to July 1995. Our results suggest that air pollution in São Paulo may promote adverse health effects on fetuses.ImagesFigure 1Figure 2Figure 3Figure 4Figure 5Figure 6Figure 7Figure

Stimulus-dependent maximum entropy models of neural population codes

Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. To be able to infer a model for this distribution from large-scale neural recordings, we introduce a stimulus-dependent maximum entropy (SDME) model---a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. The model is able to capture the single-cell response properties as well as the correlations in neural spiking due to shared stimulus and due to effective neuron-to-neuron connections. Here we show that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. As a result, the SDME model gives a more accurate account of single cell responses and in particular outperforms uncoupled models in reproducing the distributions of codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like surprise and information transmission in a neural population.Comment: 11 pages, 7 figure

From Spiking Neuron Models to Linear-Nonlinear Models

Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates

Acaricide Residues in Laying Hens Naturally Infested by Red Mite Dermanyssus gallinae

In the poultry industry, control of the red mite D. gallinae primarily relies worldwide on acaricides registered for use in agriculture or for livestock, and those most widely used are carbamates, followed by amidines, pyrethroids and organophosphates. Due to the repeated use of acaricides - sometimes in high concentrations - to control infestation, red mites may become resistant, and acaricides may accumulate in chicken organs and tissues, and also in eggs. To highlight some situations of misuse/abuse of chemicals and of risk to human health, we investigated laying hens, destined to the slaughterhouse, for the presence of acaricide residues in their organs and tissues. We used 45 hens from which we collected a total of 225 samples from the following tissues and organs: skin, fat, liver, muscle, hearth, and kidney. In these samples we analyzed the residual contents of carbaryl and permethrin by LC-MS/MS

Global energy governance : a review and research agenda

Over the past few years, global energy governance (GEG) has emerged as a major new field of enquiry in international studies. Scholars engaged in this field seek to understand how the energy sector is governed at the global level, by whom and with what consequences. By focusing on governance, they broaden and enrich the geopolitical and hard-nosed security perspectives that have long been, and still are, the dominant perspectives through which energy is analysed. Though still a nascent field, the literature on GEG is thriving and continues to attract the attention of a growing number of researchers. This article reviews the GEG literature as it has developed over the past 10 years. Our aim is to highlight both the progress and limitations of the field, and to identify some opportunities for future research. The article proceeds as follows. First, it traces the origins of the GEG literature (section “Origins and roots of GEG research”). The subsequent sections deal with the two topics that have received the most attention in the GEG literature: Why does energy need global governance (section “The goals and rationale of global energy governance”)? And, who governs energy (section “Mapping the global energy architecture”)? We then address a third question that has received far less attention: How well or poor is energy governed (section “Evaluating global energy governance”)? In our conclusions (section “Conclusions and outlook”), we reflect on the current state of GEG, review recent trends and innovations, and identify some questions that warrant future consideration by scholars. This article is published as part of a thematic collection on global governance

Representation of Dynamical Stimuli in Populations of Threshold Neurons

Many sensory or cognitive events are associated with dynamic current modulations in cortical neurons. This raises an urgent demand for tractable model approaches addressing the merits and limits of potential encoding strategies. Yet, current theoretical approaches addressing the response to mean- and variance-encoded stimuli rarely provide complete response functions for both modes of encoding in the presence of correlated noise. Here, we investigate the neuronal population response to dynamical modifications of the mean or variance of the synaptic bombardment using an alternative threshold model framework. In the variance and mean channel, we provide explicit expressions for the linear and non-linear frequency response functions in the presence of correlated noise and use them to derive population rate response to step-like stimuli. For mean-encoded signals, we find that the complete response function depends only on the temporal width of the input correlation function, but not on other functional specifics. Furthermore, we show that both mean- and variance-encoded signals can relay high-frequency inputs, and in both schemes step-like changes can be detected instantaneously. Finally, we obtain the pairwise spike correlation function and the spike triggered average from the linear mean-evoked response function. These results provide a maximally tractable limiting case that complements and extends previous results obtained in the integrate and fire framework