Branch-length estimation bias misleads molecular dating for a vertebrate mitochondrial phylogeny

Despite recent methodological advances in inferring the time-scale of biological evolution from molecular data, the fundamental question of whether our substitution models are sufficiently well specified to accurately estimate branch-lengths has received little attention. I examine this implicit assumption of all molecular dating methods, on a vertebrate mitochondrial protein-coding dataset. Comparison with analyses in which the data are RY-coded (AG → R; CT → Y) suggests that even rates-across-sites maximum likelihood greatly under-compensates for multiple substitutions among the standard (ACGT) NT-coded data, which has been subject to greater phylogenetic signal erosion. Accordingly, the fossil record indicates that branch-lengths inferred from the NT-coded data translate into divergence time overestimates when calibrated from deeper in the tree. Intriguingly, RY-coding led to the opposite result. The underlying NT and RY substitution model misspecifications likely relate respectively to “hidden” rate heterogeneity and changes in substitution processes across the tree, for which I provide simulated examples. Given the magnitude of the inferred molecular dating errors, branch-length estimation biases may partly explain current conflicts with some palaeontological dating estimates.

Malnutrition and its impact on cost of hospitalization, length of stay, readmission and 3-year mortality

Background & aims: The confounding effect of disease on the outcomes of malnutrition using diagnosis-related groups (DRG) has never been studied in a multidisciplinary setting. This study aims to determine the prevalence of malnutrition in a tertiary hospital in Singapore and its impact on hospitalization outcomes and costs, controlling for DRG. Methods: This prospective cohort study included a matched case control study. Subjective Global Assessment was used to assess the nutritional status on admission of 818 adults. Hospitalization outcomes over 3 years were adjusted for gender, age, ethnicity, and matched for DRG. Results: Malnourished patients (29%) had longer hospital stays (6.9 ± 7.3 days vs. 4.6 ± 5.6 days, p < 0.001) and were more likely to be readmitted within 15 days (adjusted relative risk = 1.9, 95%CI 1.1–3.2, p = 0.025). Within a DRG, the mean difference between actual cost of hospitalization and the average cost for malnourished patients was greater than well-nourished patients (p = 0.014). Mortality was higher in malnourished patients at 1 year (34% vs. 4.1 %), 2 years (42.6% vs. 6.7%) and 3 years (48.5% vs. 9.9%); p < 0.001 for all. Overall, malnutrition was a significant predictor of mortality (adjusted hazard ratio = 4.4, 95% CI 3.3-6.0, p < 0.001). Conclusions: Malnutrition was evident in up to one third of the inpatients and led to poor hospitalization outcomes and survival as well as increased costs of care, even after matching for DRG. Strategies to prevent and treat malnutrition in the hospital and post-discharge are needed.

Root cause analysis with enriched process logs

In the field of process mining, the use of event logs for the purpose of root cause analysis is increasingly studied. In such an analysis, the availability of attributes/features that may explain the root cause of some phenomena is crucial. Currently, the process of obtaining these attributes from raw event logs is performed more or less on a case-by-case basis: there is still a lack of generalized systematic approach that captures this process. This paper proposes a systematic approach to enrich and transform event logs in order to obtain the required attributes for root cause analysis using classical data mining techniques, the classification techniques. This approach is formalized and its applicability has been validated using both self-generated and publicly-available logs.

Shortening cemented femoral implants. An in vitro investigation to quantify exeter femoral implant rotational stability vs simulated implant length

The Exeter stems vary in length from 90 to 150 mm. The shorter stems generally have lower offsets. The purpose of this study was to determine if length of stem, with fixed offset, affected rotational stability. Mechanical testing was carried out on 10 implant-cement constructs with 2 loading profiles, rising from chair and stair climbing, at different simulated implant lengths using purpose-built apparatus. This paper presents a mechanism for clinically observed rotational stability and explains the mechanical characteristics required for rotational stability in Exeter femoral stems. © 2012.

Strontium-containing mesoporous bioactive glass scaffolds with improved osteogenic/cementogenic differentiation of periodontal ligament cells for periodontal tissue engineering

To achieve the ultimate goal of periodontal tissue engineering, it is of great importance to develop bioactive scaffolds which could stimulate the osteogenic/cementogenic differentiation of periodontal ligament cells (PDLCs) for the favorable regeneration of alveolar bone, root cementum, and periodontal ligament. Strontium (Sr) and Sr-containing biomaterials have been found to induce osteoblast activity. However, there is no systematic report about the interaction between Sr or Sr-containing biomaterials and PDLCs for periodontal tissue engineering. The aims of this study were to prepare Sr-containing mesoporous bioactive glass (Sr-MBG) scaffolds and investigate whether the addition of Sr could stimulate the osteogenic/cementogenic differentiation of PDLCs in tissue engineering scaffold system. The composition, microstructure and mesopore properties (specific surface area, nano-pore volume and nano-pore distribution) of Sr-MBG scaffolds were characterized. The proliferation, alkaline phosphatase (ALP) activity and osteogenesis/cementogenesis-related gene expression (ALP, Runx2, Col I, OPN and CEMP1) of PDLCs on different kinds of Sr-MBG scaffolds were systematically investigated. The results show that Sr plays an important role in influencing the mesoporous structure of MBG scaffolds in which high contents of Sr decreased the well-ordered mesopores as well as their surface area/pore volume. Sr2+ ions could be released from Sr-MBG scaffolds in a controlled way. The incorporation of Sr into MBG scaffolds has significantly stimulated ALP activity and osteogenesis/cementogenesis-related gene expression of PDLCs. Furthermore, Sr-MBG scaffolds in simulated body fluids environment still maintained excellent apatite-mineralization ability. The study suggests that the incorporation of Sr into MBG scaffolds is a viable way to stimulate the biological response of PDLCs. Sr-MBG scaffolds are a promising bioactive material for periodontal tissue engineering application.

A mass spectrometric assay for the quantification of neuropeptide PYY in plasma

A multiple reaction monitoring mass spectrometric assay for the quantification of PYY in human plasma has been developed. A two stage sample preparation protocol was employed in which plasma containing the full length neuropeptide was first digested using trypsin, followed by solid-phase extraction to extract the digested peptide from the complex plasma matrix. The peptide extracts were analysed by LC-MS using multiple reaction monitoring to detect and quantify PYY. The method has been validated for plasma samples, yielding linear responses over the range 5–1,000 ng mL−1. The method is rapid, robust and specific for plasma PYY detection.

Learning domain-specific sentiment lexicons for predicting product sales

Generic sentiment lexicons have been widely used for sentiment analysis these days. However, manually constructing sentiment lexicons is very time-consuming and it may not be feasible for certain application domains where annotation expertise is not available. One contribution of this paper is the development of a statistical learning based computational method for the automatic construction of domain-specific sentiment lexicons to enhance cross-domain sentiment analysis. Our initial experiments show that the proposed methodology can automatically generate domain-specific sentiment lexicons which contribute to improve the effectiveness of opinion retrieval at the document level. Another contribution of our work is that we show the feasibility of applying the sentiment metric derived based on the automatically constructed sentiment lexicons to predict product sales of certain product categories. Our research contributes to the development of more effective sentiment analysis system to extract business intelligence from numerous opinionated expressions posted to the Web