Markov chain Monte Carlo methodoloy for inference with generalised linear spatial models

Many real world phenomena are described through models that include an unobserved process which is usually characterised by a continuous distribution. Such models are widely used in geostatistics where a continuous spatial phenomenon is modelled through an underlying latent Gaussian process. If the observed data are also Gaussian then inference for the underlying process and the model parameters is relatively straightforward. In many applications though the assumption of normally distributed data is not sensible and the assumption of Poisson or binomial data is more suitable. These models, with non-Gaussian data, are known as generalised linear spatial models (GLSM). In such cases, inference requires more sophisticated techniques and a common approach is the use of Markov chain Monte Carlo methods (MCMC). However, the correlation between the components of the latent process and the correlation between the latent process and the model parameters generally hinders the performance of any MCMC scheme which updates the latent process and the parameters sequentially. In this thesis we focus on the Poisson GLSM and elaborate on the problem of the correlation within the latent process. In particular, our aim is to construct an efficient proposal distribution for sampling from the posterior distribution of the latent process conditionally on the other parameters. Initially, we investigate the idea of constructing a global normal approximation to the conditional posterior distribution of the latent process and use it as the proposal distribution in a simple and fast MCMC scheme. For this purpose, we initially employ various transformations of the data and find that some of the constructed schemes perform well in certain low dimensional scenarios. Subsequently, we construct one dimensional proposals for each component of the latent process through an approximation to each univariate marginal posterior conditional on a few principal components. The suggested MCMC scheme updates each component of the process separately and then proceeds by updating the few important principal components. As suggested by our results, this method has a stable and efficient performance in a variety of scenarios and dimensions

Tyrosine Kinase Inhibitors for Non-Small Cell Lung Cancer and Eye Metastasis: Disease Relapse or a New Entity?

Lung cancer is still diagnosed during the advanced stage of the disease and most patients do not have the opportunity for surgical treatment, despite the new diagnostic equipment that has been made available in recent years, such as the radial and linear endobronchial ultrasound (EBUS) and electromagnetic fiberoptic bronchoscopy. However, novel targeted therapies with second generation tyrosine kinase inhibitors and immunotherapy are available. In this commentary, we will focus on eye metastasis after initiation of tyrosine kinase inhibitors due to epidermal growth factor mutation of lung cancer adenocarcinoma.Â

Tyrosine Kinase Inhibitors for Non-Small Cell Lung Cancer and Eye Metastasis: Disease Relapse or a New Entity?

Lung cancer is still diagnosed during the advanced stage of the disease and most patients do not have the opportunity for surgical treatment, despite the new diagnostic equipment that has been made available in recent years, such as the radial and linear endobronchial ultrasound (EBUS) and electromagnetic fiberoptic bronchoscopy. However, novel targeted therapies with second generation tyrosine kinase inhibitors and immunotherapy are available. In this commentary, we will focus on eye metastasis after initiation of tyrosine kinase inhibitors due to epidermal growth factor mutation of lung cancer adenocarcinoma.

Lung Cancer and Eye Metastases

It has been observed that lung cancer either non-small cell or small cell is responsible for eye metastases. This form of metastases in several cases was the first manifestation of the disease and further investigation led to the diagnosis of the underlying malignancy. Both types of lung cancer are equally responsible for this demonstration. Furthermore; both chemotherapy and tyrosine kinase inhibitors have shown equal positive results in treating the exophalmos manifestation. Up to date information will be presented in our current work

IL-33 expression in response to SARS-CoV-2 correlates with seropositivity in COVID-19 convalescent individuals

Our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still developing. We perform an observational study to investigate seroprevalence and immune responses in subjects professionally exposed to SARS-CoV-2 and their family members (155 individuals; ages 5-79 years). Seropositivity for SARS-CoV-2 Spike glycoprotein aligns with PCR results that confirm the previous infection. Anti-Spike IgG/IgM titers remain high 60 days post-infection and do not strongly associate with symptoms, except for fever. We analyze PBMCs from a subset of seropositive and seronegative adults. TLR7 agonist-activation reveals an increased population of IL-6+TNF-IL-1β+ monocytes, while SARS-CoV-2 peptide stimulation elicits IL-33, IL-6, IFNa2, and IL-23 expression in seropositive individuals. IL-33 correlates with CD4+ T cell activation in PBMCs from convalescent subjects and is likely due to T cell-mediated effects on IL-33-producing cells. IL-33 is associated with pulmonary infection and chronic diseases like asthma and COPD, but its role in COVID-19 is unknown. Analysis of published scRNAseq data of bronchoalveolar lavage fluid (BALF) from patients with mild to severe COVID-19 reveals a population of IL-33-producing cells that increases with the disease. Together these findings show that IL-33 production is linked to SARS-CoV-2 infection and warrant further investigation of IL-33 in COVID-19 pathogenesis and immunity

Machine learning on Crays to optimise petrophysical workflows in oil and gas exploration

Public education and outreach leads to a better informed public on Puget Sound and watershed issues. Using beach life and spawning salmon as a way to share knowledge and start the conservation conversation, the Beach Naturalist and Cedar River Salmon Journey programs have been educating Puget Sound residents for over 15 years. These programs benefit two audiences: the volunteers who serve in the program and the public who participate. Volunteers are provided in-depth information about Puget Sound life, watersheds, salmon and conservation strategies. These passionate volunteers translate this information and share it with the public they engage in the environments we hope to protect: at local beaches in the nearshore, the Chittenden Locks along salmonid migratory routes and at salmon spawning locations along the Cedar River. By providing opportunities for the public to learn more and create personal connections with the animals and habitat we share, we suggest choices people make in their daily lives that can help protect the watershed

Machine learning on Crays to optimise petrophysical workflows in oil and gas exploration

The oil and gas industry is awash with sub-surface data, which is used to characterize the rock and fluid properties beneath the seabed. This in turn drives commercial decision making and exploration, but the industry currently relies upon highly manual workflows when processing data. A key question is whether this can be improved using machine learning to complement the activities of petrophysicists searching for hydrocarbons. In this paper we present work done, in collaboration with Rock Solid Images (RSI), using supervised machine learning on a Cray XC30 to train models that streamline the manual data interpretation process. With a general aim of decreasing the petrophysical interpretation time down from over 7 days to 7 minutes, in this paper we describe the use of mathematical models that have been trained using raw well log data, for completing each of the four stages of a petrophysical interpretation workflow, along with initial data cleaning. We explore how the predictions from these models compare against the interpretations of human petrophysicists, along with numerous options and techniques that were used to optimise the prediction of our models. The power provided by modern supercomputers such as Cray machines is crucial here, but some popular machine learning framework are unable to take full advantage of modern HPC machines. As such we will also explore the suitability of the machine learning tools we have used, and describe steps we took to work round their limitations. The result of this work is the ability, for the first time, to use machine learning for the entire petrophysical workflow. Whilst there are numerous challenges, limitations and caveats, we demonstrate that machine learning has an important role to play in the processing of sub-surface data.Comment: This is the pre-peer reviewed version of the following article: Machine learning on Crays to optimise petrophysical workflows in oil and gas exploration, which has been published in final form at https://doi.org/10.1002/cpe.565

Prognostic stratification of patients with advanced renal cell carcinoma treated with sunitinib: comparison with the Memorial Sloan-Kettering prognostic factors model

<p>Abstract</p> <p>Background</p> <p>The treatment paradigm in advanced renal cell carcinoma (RCC) has changed in the recent years. Sunitinib has been established as a new standard for first-line therapy. We studied the prognostic significance of baseline characteristics and we compared the risk stratification with the established Memorial Sloan Kettering Cancer Center (MSKCC) model.</p> <p>Methods</p> <p>This is a retrospective analysis of patients treated in six Greek Oncology Units of HECOG. Inclusion criteria were: advanced renal cell carcinoma not amenable to surgery and treatment with Sunitinib. Previous cytokine therapy but no targeted agents were allowed. Overall survival (OS) was the major end point. Significance of prognostic factors was evaluated with multivariate cox regression analysis. A model was developed to stratify patients according to risk.</p> <p>Results</p> <p>One hundred and nine patients were included. Median follow up has been 15.8 months and median OS 17.1 months (95% CI: 13.7-20.6). Time from diagnosis to the start of Sunitinib (<= 12 months vs. >12 months, p = 0.001), number of metastatic sites (1 vs. >1, p = 0.003) and performance status (PS) (<= 1 vs >1, p = 0.001) were independently associated with OS. Stratification in two risk groups ("low" risk: 0 or 1 risk factors; "high" risk: 2 or 3 risk factors) resulted in distinctly different OS (median not reached [NR] vs. 10.8 [95% confidence interval (CI): 8.3-13.3], p < 0.001). The application of the MSKCC risk criteria resulted in stratification into 3 groups (low and intermediate and poor risk) with distinctly different prognosis underlying its validity. Nevertheless, MSKCC model did not show an improved prognostic performance over the model developed by this analysis.</p> <p>Conclusions</p> <p>Studies on risk stratification of patients with advanced RCC treated with targeted therapies are warranted. Our results suggest that a simpler than the MSKCC model can be developed. Such models should be further validated.</p

The regulation of oncogenic Ras/ERK signalling by dual-specificitymitogen activated protein kinase phosphatases (MKPs)

AbstractDual-specificity MAP kinase (MAPK) phosphatases (MKPs or DUSPs) are well-established negative regulators of MAPK signalling in mammalian cells and tissues. By virtue of their differential subcellular localisation and ability to specifically recognise, dephosphorylate and inactivate different MAPK isoforms, they are key spatiotemporal regulators of pathway activity. Furthermore, as they are transcriptionally regulated as downstream targets of MAPK signalling they can either act as classical negative feedback regulators or mediate cross talk between distinct MAPK pathways. Because MAPKs and particularly Ras/ERK signalling are implicated in cancer initiation and development, the observation that MKPs are abnormally regulated in human tumours has been interpreted as evidence that these enzymes can either suppress or promote carcinogenesis. However, definitive evidence of such roles has been lacking. Here we review recent work based on the use of mouse models, biochemical studies and clinical data that demonstrate key roles for MKPs in modulating the oncogenic potential of Ras/ERK signalling and also indicate that these enzymes may play a role in the response of tumours to certain anticancer drugs. Overall, this work reinforces the importance of negative regulatory mechanisms in modulating the activity of oncogenic MAPK signalling and indicates that MKPs may provide novel targets for therapeutic intervention in cancer