Book Review : The Library at the End of the World: Natural Science and its Illustrators Edited by Anita Hansen and Margaret Davies

The Royal Society of Tasmania (RST), the oldest scientific society in Australia and New Zealand, was founded in 1843 and quickly established its own library. In 1845 the Lieutenant-Governor of Van Diemen’s Land wrote to the Royal, the Antiquarian and the Linnean societies in London, of which he was a member, seeking books for the RST’s library. By 1849 the collection held 250 volumes. As the Society grew, the library acquired books more purposefully, emphasising Tasmanian natural history but keeping abreast of national and international publications. In its first century the library was the major public source of natural history knowledge in and about this remote intellectual outpost. The Library at the End of the World reviews and illustrates some of the library’s treasure

Robust Conditional Independence maps of single-voxel Magnetic Resonance Spectra to elucidate associations between brain tumours and metabolites.

The aim of the paper is two-fold. First, we show that structure finding with the PC algorithm can be inherently unstable and requires further operational constraints in order to consistently obtain models that are faithful to the data. We propose a methodology to stabilise the structure finding process, minimising both false positive and false negative error rates. This is demonstrated with synthetic data. Second, to apply the proposed structure finding methodology to a data set comprising single-voxel Magnetic Resonance Spectra of normal brain and three classes of brain tumours, to elucidate the associations between brain tumour types and a range of observed metabolites that are known to be relevant for their characterisation. The data set is bootstrapped in order to maximise the robustness of feature selection for nominated target variables. Specifically, Conditional Independence maps (CI-maps) built from the data and their derived Bayesian networks have been used. A Directed Acyclic Graph (DAG) is built from CI-maps, being a major challenge the minimization of errors in the graph structure. This work presents empirical evidence on how to reduce false positive errors via the False Discovery Rate, and how to identify appropriate parameter settings to improve the False Negative Reduction. In addition, several node ordering policies are investigated that transform the graph into a DAG. The obtained results show that ordering nodes by strength of mutual information can recover a representative DAG in a reasonable time, although a more accurate graph can be recovered using a random order of samples at the expense of increasing the computation time

111In-labelled polymeric nanoparticles incorporating a ruthenium-based radiosensitizer for EGFR-targeted combination therapy in oesophageal cancer cells

Radiolabelled, drug-loaded nanoparticles may combine the theranostic properties of radionuclides, the controlled release of chemotherapy and cancer cell targeting. Here, we report the preparation of poly(lactic-co-glycolic acid) (PLGA) nanoparticles surface conjugated to DTPA-hEGF (DTPA = diethylenetriaminepentaacetic acid, hEGF = human epidermal growth factor) and encapsulating the ruthenium-based DNA replication inhibitor and radiosensitizer Ru(phen)2(tpphz)2+ (phen = 1,10-phenanthroline, tpphz = tetrapyridophenazine) Ru1. The functionalized PLGA surface incorporates the metal ion chelator DTPA for radiolabelling and the targeting ligand for EGF receptor (EGFR). Nanoparticles radiolabelled with 111In are taken up preferentially by EGFR-overexpressing oesophageal cancer cells, where they exhibit radiotoxicity through the generation of cellular DNA damage. Moreover, nanoparticle co-delivery of Ru1 alongside 111In results in decreased cell survival compared to single-agent formulations; an effect that occurs through DNA damage enhancement and an additive relationship between 111In and Ru1. Substantially decreased uptake and radiotoxicity of nanoparticles towards normal human fibroblasts and oesophageal cancer cells with normal EGFR levels is observed. This work demonstrates nanoparticle co-delivery of a therapeutic radionuclide plus a ruthenium-based radiosensitizer can achieve combinational and targeted therapeutic effects in cancer cells that overexpress EGFR

Quantum clustering in non-spherical data distributions: Finding a suitable number of clusters

Quantum Clustering (QC) provides an alternative approach to clustering algorithms, several of which are based on geometric relationships between data points. Instead, QC makes use of quantum mechanics concepts to find structures (clusters) in data sets by finding the minima of a quantum potential. The starting point of QC is a Parzen estimator with a fixed length scale, which significantly affects the final cluster allocation. This dependence on an adjustable parameter is common to other methods. We propose a framework to find suitable values of the length parameter σ by optimising twin measures of cluster separation and consistency for a given cluster number. This is an extension of the Separation and Concordance framework previously introduced for K-means clustering. Experimental results on two synthetic data sets and three challenging real-world data sets show that optimisation of cluster separation identifies QC solutions with consistently high Jaccard score measured against true-cluster labels while optimisation of cluster consistency provides insights into hierarchical cluster structure. © 2017 Elsevier B.V

Measuring and explaining mortality in Dutch hospitals; The Hospital Standardized Mortality Rate between 2003 and 2005

Background. Indicators of hospital quality, such as hospital standardized mortality ratios (HSMR), have been used increasingly to assess and improve hospital quality. Our aim has been to describe and explain variation in new HSMRs for the Netherlands. Methods. HSMRs were estimated using data from the complete population of discharged patients during 2003 to 2005. We used binary logistic regression to indirectly standardize for differences in case-mix. Out of a total of 101 hospitals 89 hospitals remained in our explanatory analysis. In this analysis we explored the association between HSMRs and determinants that can and cannot be influenced by hospitals. For this analysis we used a two-level hierarchical linear regression model to explain variation in yearly HSMRs. Results. The average HSMR decreased yearly with more than eight

KrillDB: A de novo transcriptome database for the Antarctic krill (Euphausia superba)

© 2017 Sales et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Antarctic krill (Euphausia superba) is a key species in the Southern Ocean with an estimated biomass between 100 and 500 million tonnes. Changes in krill population viability would have catastrophic effect on the Antarctic ecosystem. One looming threat due to elevated levels of anthropogenic atmospheric carbon dioxide (CO2) is ocean acidification (lowering of sea water pH by CO2 dissolving into the oceans). The genetics of Antarctic krill has long been of scientific interest for both for the analysis of population structure and analysis of functional genetics. However, the genetic resources available for the species are relatively modest. We have developed the most advanced genetic database on Euphausia superba, KrillDB, which includes comprehensive data sets of former and present transcriptome projects. In particular, we have built a de novo transcriptome assembly using more than 360 million Illumina sequence reads generated from larval krill including individuals subjected to different CO2levels. The database gives access to: 1) the full list of assembled genes and transcripts; 2) their level of similarity to transcripts and proteins from other species; 3) the predicted protein domains contained within each transcript; 4) their predicted GO terms; 5) the level of expression of each transcript in the different larval stages and CO2treatments. All references to external entities (sequences, domains, GO terms) are equipped with a link to the appropriate source database. Moreover, the software implements a full-text search engine that makes it possible to submit free-form queries. KrillDB represents the first largescale attempt at classifying and annotating the full krill transcriptome. For this reason, we believe it will constitute a cornerstone of future approaches devoted to physiological and molecular study of this key species in the Southern Ocean food web

Research activity and the association with mortality.

INTRODUCTION: The aims of this study were to describe the key features of acute NHS Trusts with different levels of research activity and to investigate associations between research activity and clinical outcomes. METHODS: National Institute for Health Research (NIHR) Comprehensive Clinical Research Network (CCRN) funding and number of patients recruited to NIHR Clinical Research Network (CRN) portfolio studies for each NHS Trusts were used as markers of research activity. Patient-level data for adult non-elective admissions were extracted from the English Hospital Episode Statistics (2005-10). Risk-adjusted mortality associations between Trust structures, research activity and, clinical outcomes were investigated. RESULTS: Low mortality Trusts received greater levels of funding and recruited more patients adjusted for size of Trust (n = 35, 2,349 £/bed [95% CI 1,855-2,843], 5.9 patients/bed [2.7-9.0]) than Trusts with expected (n = 63, 1,110 £/bed, [864-1,357] p<0.0001, 2.6 patients/bed [1.7-3.5] p<0.0169) or, high (n = 42, 930 £/bed [683-1,177] p = 0.0001, 1.8 patients/bed [1.4-2.1] p<0.0005) mortality rates. The most research active Trusts were those with more doctors, nurses, critical care beds, operating theatres and, made greater use of radiology. Multifactorial analysis demonstrated better survival in the top funding and patient recruitment tertiles (lowest vs. highest (odds ratio & 95% CI: funding 1.050 [1.033-1.068] p<0.0001, recruitment 1.069 [1.052-1.086] p<0.0001), middle vs. highest (funding 1.040 [1.024-1.055] p<0.0001, recruitment 1.085 [1.070-1.100] p<0.0001). CONCLUSIONS: Research active Trusts appear to have key differences in composition than less research active Trusts. Research active Trusts had lower risk-adjusted mortality for acute admissions, which persisted after adjustment for staffing and other structural factors

Indicators of breast cancer severity and appropriateness of surgery based on hospital administrative data in the Lazio Region, Italy

BACKGROUND: Administrative data can serve as an easily available source for epidemiological and evaluation studies. The aim of this study is to evaluate the use of hospital administrative data to determine breast cancer severity and the appropriateness of surgical treatment. METHODS: the study population consisted of 398 patients randomly selected from a cohort of women hospitalized for first-time breast cancer surgery in the Lazio Region, Italy. Tumor severity was defined in three different ways: 1) tumor size; 2) clinical stage (TNM); 3) severity indicator based on HIS data (SI). Sensitivity, specificity, and positive predictive value (PPV) of the severity indicator in evaluating appropriateness of surgery were calculated. The accuracy of HIS data was measured using Kappa statistic. RESULTS: Most of 387 cases were classified as T1 and T2 (tumor size), more than 70% were in stage I or II and the SI classified 60% of cases in medium-low category. Variation from guidelines indications identified under and over treatments. The accuracy of the SI to predict under-treatment was relatively good (58% of all procedures classified as under-treatment using pT where also classified as such using SI), and even greater predicting over-treatment (88.2% of all procedures classified as over treatment using pT where also classified as such using SI). Agreement between clinical chart and hospital discharge reports was K = 0.35. CONCLUSION: Our findings suggest that administrative data need to be used with caution when evaluating surgical appropriateness, mainly because of the limited ability of SI to predict tumor size and the questionable quality of HIS data as observed in other studies

Assessing the determinants of stillbirths and early neonatal deaths using routinely collected data in an inner city area

BACKGROUND: Within the UK there is considerable variation in the perinatal mortality rate. The objective of this study was to assess the factors associated with stillbirths and early neonatal deaths (ENND) and the suitability of the available databases in a health authority with one of the highest rates in the country. METHODS: Two case-control studies were carried out in three hospital trusts in the Lambeth, Southwark and Lewisham Health Authority, London, using routinely collected information. In one study, 342 stillbirths and 1,368 controls were included, and in the other study, 205 ENND and 820 controls were included. In the two studies cases and controls were matched for hospital trust. RESULTS: A birthweight below 1.5 kg was found in 54% and 48% of the stillbirths and ENND, respectively. More than 50% of the cases, stillbirths and ENND, had a length of gestation below 32 weeks. Length of gestation, birthweight, emergency caesarean section and age of the mother were associated with stillbirths. Birthweight and Apgar score at 1 minute as a categorical variable were associated with ENND. There was no direct evidence of an effect of social deprivation on the outcomes of interest. CONCLUSION: Birthweight and length of gestation are the most influential factors on an unfavourable outcome. Conception at an older age has a serious impact on stillbirth rates. In our health authority social disadvantage did not have a direct impact on stillbirth and ENND. Maternity information systems should collect routine data on fewer variables, but their quality in terms of value, standardization and completion rates must improve