Hirudo Medicinalis and the plastic surgeon

Medicinal leech therapy is an ancient craft that dates back to ancient Egypt and the beginnings of civilisation. The popularity of Hirudo Medicinalis has varied throughout history, reaching such a peak in Europe in the early 19th century that supplies were exhausted. During the latter half of the 19th century, their use fell out of favour, as they did not fit in with the emerging concepts of modern medicine. Leeches have enjoyed a renaissance in the world of reconstructive microsurgery during recent years, and their first reported use in alleviating venous engorgement following flap surgery was reported in this journal [M Derganc, F Zdravic, Venous congestion of flaps treated by application of leeches, Br J Plast Surg 13 (1960) 187]. Contemporary plastic and reconstructive surgeons in units throughout the United Kingdom and Ireland continue to use leeches to aid salvage of failing flaps. We carried out a survey of all 62 plastic surgery units in the United Kingdom and the Republic of Ireland to assess the current extent of use, and to investigate current practice. We have shown that the majority of plastic surgery units in the UK and Ireland use leeches post-operatively and that the average number of patients requiring leech therapy was 10 cases per unit per year. Almost all units use antibiotic prophylaxis, but the type of antibiotic and combination used is variable. We outline current practice and suggest a protocol for the use of leeches. Whilst the use of leeches is widespread, the plastic surgery community has progressed little in defining indications for their use or in achieving an accepted protocol for their application in units throughout the UK and Irelan

A Bayesian mixture modelling approach for spatial proteomics.

Analysis of the spatial sub-cellular distribution of proteins is of vital importance to fully understand context specific protein function. Some proteins can be found with a single location within a cell, but up to half of proteins may reside in multiple locations, can dynamically re-localise, or reside within an unknown functional compartment. These considerations lead to uncertainty in associating a protein to a single location. Currently, mass spectrometry (MS) based spatial proteomics relies on supervised machine learning algorithms to assign proteins to sub-cellular locations based on common gradient profiles. However, such methods fail to quantify uncertainty associated with sub-cellular class assignment. Here we reformulate the framework on which we perform statistical analysis. We propose a Bayesian generative classifier based on Gaussian mixture models to assign proteins probabilistically to sub-cellular niches, thus proteins have a probability distribution over sub-cellular locations, with Bayesian computation performed using the expectation-maximisation (EM) algorithm, as well as Markov-chain Monte-Carlo (MCMC). Our methodology allows proteome-wide uncertainty quantification, thus adding a further layer to the analysis of spatial proteomics. Our framework is flexible, allowing many different systems to be analysed and reveals new modelling opportunities for spatial proteomics. We find our methods perform competitively with current state-of-the art machine learning methods, whilst simultaneously providing more information. We highlight several examples where classification based on the support vector machine is unable to make any conclusions, while uncertainty quantification using our approach provides biologically intriguing results. To our knowledge this is the first Bayesian model of MS-based spatial proteomics data.LG was supported by the BBSRC Strategic Longer and Larger grant (Award BB/L002817/1) and the Wellcome Trust Senior Investigator Award 110170/Z/15/Z awarded to KSL. PDWK was supported by the MRC (project reference MC_UP_0801/1). CMM was supported by a Wellcome Trust Technology Development Grant (Grant number 108467/Z/15/Z). OMC is a Wellcome Trust Mathematical Genomics and Medicine student supported financially by the School of Clinical Medicine, University of Cambridge. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Atom probe tomography characterisation of a laser diode structure grown by molecular beam epitaxy

Atom probe tomography (APT) has been used to achieve three-dimensional characterization of a III-nitride laser diode (LD) structure grown by molecular beam epitaxy (MBE). Four APT data sets have been obtained, with fields of view up to 400 nm in depth and 120 nm in diameter. These data sets contain material from the InGaN quantum well (QW) active region, as well as the surrounding p- and n-doped waveguide and cladding layers, enabling comprehensive study of the structure and composition of the LD structure. Two regions of the same sample, with different average indium contents (18% and 16%) in the QW region, were studied. The APT data are shown to provide easy access to the p-type dopant levels, and the composition of a thin AlGaN barrier layer. Next, the distribution of indium within the InGaN QW was analyzed, to assess any possible inhomogeneity of the distribution of indium (“indium clustering”). No evidence for a statistically significant deviation from a random distribution was found, indicating that these MBE-grown InGaN QWs do not require indium clusters for carrier localization. However, the APT data show steps in the QW interfaces, leading to well-width fluctuations, which may act to localize carriers. Additionally, the unexpected presence of a small amount (x = 0.005) of indium in a layer grown intentionally as GaN was revealed. Finally, the same statistical method applied to the QW was used to show that the indium distribution within a thick InGaN waveguide layer in the n-doped region did not show any deviation from randomness

Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics.

The Dirichlet Process (DP) mixture model has become a popular choice for model-based clustering, largely because it allows the number of clusters to be inferred. The sequential updating and greedy search (SUGS) algorithm (Wang & Dunson, 2011) was proposed as a fast method for performing approximate Bayesian inference in DP mixture models, by posing clustering as a Bayesian model selection (BMS) problem and avoiding the use of computationally costly Markov chain Monte Carlo methods. Here we consider how this approach may be extended to permit variable selection for clustering, and also demonstrate the benefits of Bayesian model averaging (BMA) in place of BMS. Through an array of simulation examples and well-studied examples from cancer transcriptomics, we show that our method performs competitively with the current state-of-the-art, while also offering computational benefits. We apply our approach to reverse-phase protein array (RPPA) data from The Cancer Genome Atlas (TCGA) in order to perform a pan-cancer proteomic characterisation of 5157 tumour samples. We have implemented our approach, together with the original SUGS algorithm, in an open-source R package named sugsvarsel, which accelerates analysis by performing intensive computations in C++ and provides automated parallel processing. The R package is freely available from: https://github.com/ococrook/sugsvarsel.Medical Research Council, Funder Id: http://dx.doi.org/10.13039/501100000265, Wellcome Trust Mathematical Genomics and Medicine student supported financially by the School of Clinical Medicine, University of Cambridge. Grant Number: MC_UU_00002/10, MC_UU_00002/13

Dating of the oldest continental sediments from the Himalayan foreland basin

A detailed knowledge of Himalayan development is important for our wider understanding of several global processes, ranging from models of plateau uplift to changes in oceanic chemistry and climate(1-4). Continental sediments 55 Myr old found in a foreland basin in Pakistan(5) are, by more than 20 Myr, the oldest deposits thought to have been eroded from the Himalayan metamorphic mountain belt. This constraint on when erosion began has influenced models of the timing and diachrony of the India-Eurasia collision(6-8), timing and mechanisms of exhumation(9,10) and uplift(11), as well as our general understanding of foreland basin dynamics(12). But the depositional age of these basin sediments was based on biostratigraphy from four intercalated marl units(5). Here we present dates of 257 detrital grains of white mica from this succession, using the Ar-40-(39) Ar method, and find that the largest concentration of ages are at 36-40 Myr. These dates are incompatible with the biostratigraphy unless the mineral ages have been reset, a possibility that we reject on the basis of a number of lines of evidence. A more detailed mapping of this formation suggests that the marl units are structurally intercalated with the continental sediments and accordingly that biostratigraphy cannot be used to date the clastic succession. The oldest continental foreland basin sediments containing metamorphic detritus eroded from the Himalaya orogeny therefore seem to be at least 15-20 Myr younger than previously believed, and models based on the older age must be re-evaluated

Use of attribute association error probability estimates to evaluate quality of medical record geocodes

BACKGROUND: The utility of patient attributes associated with the spatiotemporal analysis of medical records lies not just in their values but also the strength of association between them. Estimating the extent to which a hierarchy of conditional probability exists between patient attribute associations such as patient identifying fields, patient and date of diagnosis, and patient and address at diagnosis is fundamental to estimating the strength of association between patient and geocode, and patient and enumeration area. We propose a hierarchy for the attribute associations within medical records that enable spatiotemporal relationships. We also present a set of metrics that store attribute association error probability (AAEP), to estimate error probability for all attribute associations upon which certainty in a patient geocode depends. METHODS: A series of experiments were undertaken to understand how error estimation could be operationalized within health data and what levels of AAEP in real data reveal themselves using these methods. Specifically, the goals of this evaluation were to (1) assess if the concept of our error assessment techniques could be implemented by a population-based cancer registry; (2) apply the techniques to real data from a large health data agency and characterize the observed levels of AAEP; and (3) demonstrate how detected AAEP might impact spatiotemporal health research. RESULTS: We present an evaluation of AAEP metrics generated for cancer cases in a North Carolina county. We show examples of how we estimated AAEP for selected attribute associations and circumstances. We demonstrate the distribution of AAEP in our case sample across attribute associations, and demonstrate ways in which disease registry specific operations influence the prevalence of AAEP estimates for specific attribute associations. CONCLUSIONS: The effort to detect and store estimates of AAEP is worthwhile because of the increase in confidence fostered by the attribute association level approach to the assessment of uncertainty in patient geocodes, relative to existing geocoding related uncertainty metrics

Precision farming and archaeology

With a significant growth in the agricultural technology industry a vast amount of agricultural data is now being collected on farms throughout the world. Farmers aim to utilise these technologies to regularly record and manage the variation of crops and soils within their fields, to reduce inputs, increase yields and enhance environmental sustainability. In this paper we aim to highlight the variety of different data types and methodological processes involved in modern precision farming and explore how potentially interconnected these systems are with the archaeological communit

Geochemistry, faunal composition and trophic structure in reducing sediments on the southwest South Georgia margin

Despite a number of studies in areas of focused methane seepage, the extent of transitional sediments of more diffuse methane seepage, and their influence upon biological communities is poorly understood. We investigated an area of reducing sediments with elevated levels of methane on the South Georgia margin around 250 m depth and report data from a series of geochemical and biological analyses. Here, the geochemical signatures were consistent with weak methane seepage and the role of sub-surface methane consumption was clearly very important, preventing gas emissions into bottom waters. As a result, the contribution of methane-derived carbon to the microbial and metazoan food webs was very limited, although sulfur isotopic signatures indicated a wider range of dietary contributions than was apparent from carbon isotope ratios. Macrofaunal assemblages had high dominance and were indicative of reducing sediments, with many taxa common to other similar environments and no seep-endemic fauna, indicating transitional assemblages. Also similar to other cold seep areas, there were samples of authigenic carbonate, but rather than occurring as pavements or sedimentary concretions, these carbonates were restricted to patches on the shells of Axinulus antarcticus (Bivalvia, Thyasiridae), which is suggestive of microbe–metazoan interactions

The time course of auditory and language-specific mechanisms in compensation for sibilant assimilation

Models of spoken-word recognition differ on whether compensation for assimilation is language-specific or depends on general auditory processing. English and French participants were taught words that began or ended with the sibilants /s/ and /∫/. Both languages exhibit some assimilation in sibilant sequences (e.g., /s/ becomes like [∫] in dress shop and classe chargée), but they differ in the strength and predominance of anticipatory versus carryover assimilation. After training, participants were presented with novel words embedded in sentences, some of which contained an assimilatory context either preceding or following. A continuum of target sounds ranging from [s] to [∫] was spliced into the novel words, representing a range of possible assimilation strengths. Listeners' perceptions were examined using a visual-world eyetracking paradigm in which the listener clicked on pictures matching the novel words. We found two distinct language-general context effects: a contrastive effect when the assimilating context preceded the target, and flattening of the sibilant categorization function (increased ambiguity) when the assimilating context followed. Furthermore, we found that English but not French listeners were able to resolve the ambiguity created by the following assimilatory context, consistent with their greater experience with assimilation in this context. The combination of these mechanisms allows listeners to deal flexibly with variability in speech forms