Markov Chain Monte Carlo and the Application to Geodetic Time Series Analysis

The time evolution of geophysical phenomena can be characterised by stochastic time series. The stochastic nature of the signal stems from the geophysical phenomena involved and any noise, which may be due to, e.g., un-modelled effects or measurement errors. Until the 1990's, it was usually assumed that white noise could fully characterise this noise. However, this was demonstrated to be not the case and it was proven that this assumption leads to underestimated uncertainties of the geophysical parameters inferred from the geodetic time series. Therefore, in order to fully quantify all the uncertainties as robustly as possible, it is imperative to estimate not only the deterministic but also the stochastic parameters of the time series. In this regard, the Markov Chain Monte Carlo (MCMC) method can provide a sample of the distribution function of all parameters, including those regarding the noise, e.g., spectral index and amplitudes. After presenting the MCMC method and its implementation in our MCMC software we apply it to synthetic and real time series and perform a cross-evaluation using Maximum Likelihood Estimation (MLE) as implemented in the CATS software. Several examples as to how the MCMC method performs as a parameter estimation method for geodetic time series are given in this chapter. These include the applications to GPS position time series, superconducting gravity time series and monthly mean sea level (MSL) records, which all show very different stochastic properties. The impact of the estimated parameter uncertainties on sub-sequentially derived products is briefly demonstrated for the case of plate motion models. Finally, the MCMC results for weekly downsampled versions of the benchmark synthetic GNSS time series as provided in Chapter 2 are presented separately in an appendix

Correction to: First results on survival from a large Phase 3 clinical trial of an autologous dendritic cell vaccine in newly diagnosed glioblastoma

Following publication of the original article [1], the authors reported an error in the spelling of one of the author names. In this Correction the incorrect and correct author names are indicated and the author name has been updated in the original publication. The authors also reported an error in the Methods section of the original article. In this Correction the incorrect and correct versions of the affected sentence are indicated. The original article has not been updated with regards to the error in the Methods section.https://deepblue.lib.umich.edu/bitstream/2027.42/144529/1/12967_2018_Article_1552.pd

SAFA: Semi-automated footprinting analysis software for high-throughput quantification of nucleic acid footprinting experiments

Footprinting is a powerful and widely used tool for characterizing the structure, thermodynamics, and kinetics of nucleic acid folding and ligand binding reactions. However, quantitative analysis of the gel images produced by footprinting experiments is tedious and time-consuming, due to the absence of informatics tools specifically designed for footprinting analysis. We have developed SAFA, a semi-automated footprinting analysis software package that achieves accurate gel quantification while reducing the time to analyze a gel from several hours to 15 min or less. The increase in analysis speed is achieved through a graphical user interface that implements a novel methodology for lane and band assignment, called “gel rectification,” and an optimized band deconvolution algorithm. The SAFA software yields results that are consistent with published methodologies and reduces the investigator-dependent variability compared to less automated methods. These software developments simplify the analysis procedure for a footprinting gel and can therefore facilitate the use of quantitative footprinting techniques in nucleic acid laboratories that otherwise might not have considered their use. Further, the increased throughput provided by SAFA may allow a more comprehensive understanding of molecular interactions. The software and documentation are freely available for download at http://safa.stanford.edu

A Catastrophic Biodiversity Loss in the Environment Is Being Replicated on the Skin Microbiome: Is This a Major Contributor to the Chronic Disease Epidemic?

There has been a catastrophic loss of biodiversity in ecosystems across the world. A similar crisis has been observed in the human gut microbiome, which has been linked to “all human diseases affecting westernized countries”. This is of great importance because chronic diseases are the leading cause of death worldwide and make up 90% of America’s healthcare costs. Disease development is complex and multifactorial, but there is one part of the body’s interlinked ecosystem that is often overlooked in discussions about whole-body health, and that is the skin microbiome. This is despite it being a crucial part of the immune, endocrine, and nervous systems and being continuously exposed to environmental stressors. Here we show that a parallel biodiversity loss of 30–84% has occurred on the skin of people in the developed world compared to our ancestors. Research has shown that dysbiosis of the skin microbiome has been linked to many common skin diseases and, more recently, that it could even play an active role in the development of a growing number of whole-body health problems, such as food allergies, asthma, cardiovascular diseases, and Parkinson’s, traditionally thought unrelated to the skin. Damaged skin is now known to induce systemic inflammation, which is involved in many chronic diseases. We highlight that biodiversity loss is not only a common finding in dysbiotic ecosystems but also a type of dysbiosis. As a result, we make the case that biodiversity loss in the skin microbiome is a major contributor to the chronic disease epidemic. The link between biodiversity loss and dysbiosis forms the basis of this paper’s focus on the subject. The key to understanding why biodiversity loss creates an unhealthy system could be highlighted by complex physics. We introduce entropy to help understand why biodiversity has been linked with ecosystem health and stability. Meanwhile, we also introduce ecosystems as being governed by “non-linear physics” principles—including chaos theory—which suggests that every individual part of any system is intrinsically linked and implies any disruption to a small part of the system (skin) could have a significant and unknown effect on overall system health (whole-body health). Recognizing the link between ecosystem health and human health allows us to understand how crucial it could be to maintain biodiversity across systems everywhere, from the macro-environment we inhabit right down to our body’s microbiome. Further, in-depth research is needed so we can aid in the treatment of chronic diseases and potentially change how we think about our health. With millions of people currently suffering, research to help mitigate the crisis is of vital importance