Anomaly Detection in Paleoclimate Records using Permutation Entropy

Permutation entropy techniques can be useful in identifying anomalies in paleoclimate data records, including noise, outliers, and post-processing issues. We demonstrate this using weighted and unweighted permutation entropy of water-isotope records in a deep polar ice core. In one region of these isotope records, our previous calculations revealed an abrupt change in the complexity of the traces: specifically, in the amount of new information that appeared at every time step. We conjectured that this effect was due to noise introduced by an older laboratory instrument. In this paper, we validate that conjecture by re-analyzing a section of the ice core using a more-advanced version of the laboratory instrument. The anomalous noise levels are absent from the permutation entropy traces of the new data. In other sections of the core, we show that permutation entropy techniques can be used to identify anomalies in the raw data that are not associated with climatic or glaciological processes, but rather effects occurring during field work, laboratory analysis, or data post-processing. These examples make it clear that permutation entropy is a useful forensic tool for identifying sections of data that require targeted re-analysis---and can even be useful in guiding that analysis.Comment: 15 pages, 7 figure

Biological treatment of the knee with platelet-rich plasma or bone marrow aspirate concentrates

ABSTRACT — Knee pathologies including focal cartilage injuries, osteoarthritis (OA), and ligament injuries are common. The poor regeneration and healing potential of cartilage has led to the search for other treatment modalities with improved healing capacity. Furthermore, with an increasing elderly population that desires to remain active, the burden of knee pathologies is expected to increase. Increased sports participation and the desire to return to activities faster is also demanding more effective and minimally invasive treatment options. Thus, the use of biologic agents in the treatment of knee pathologies has emerged as a potential option. Despite the increasing use of biologic agents for knee pathology, there are conflicting results on the efficacy of these products. Furthermore, strong data supporting the optimal preparation methods and composition for widely used biologic agents, such as platelet-rich plasma (PRP) and bone marrow aspirate concentrate (BMAC), largely remain absent from the literature. This review presents the literature on the most commonly employed biologic agents for the different knee pathologies

Novel food-safe spin-lattice relaxation time calibration samples for use in magnetic resonance sensor development

Magnetic Resonance (MR) sensors are an area of increasing interest for the measurement and monitoring of material properties. There are two relaxation times associated with samples that can be measured with MR sensors: The spin-lattice and spin-spin relaxations. When developing new sensors, it is desirable to have a series of standards by which instruments can be assessed. The standard calibration materials available typically comprise different concentrations of Nickel Sulphate, which is carcinogenic and toxic. In this work, we report the use of solutions containing full fat milk powder as a safe and inexpensive material that shortens the longitudinal relaxation time of water over a wide range of values. We demonstrate that concentrations in distilled water from 5% W/V to 64% W/V give T1 values from 1.7 s down to 469 ms respectively in a 1.5T clinical MRI, while within an MR sensor, these times were from 1.6 s down to 431 ms. In addition, both systems have the same exponential coefficient (-0.022*concentration) that demonstrates the effectiveness of the NMR sensor in comparison to the clinical MRI

Planning a method for covariate adjustment in individually randomised trials: a practical guide

Background: It has long been advised to account for baseline covariates in the analysis of confirmatory randomised trials, with the main statistical justifications being that this increases power and, when a randomisation scheme balanced covariates, permits a valid estimate of experimental error. There are various methods available to account for covariates but it is not clear how to choose among them. // Methods: Taking the perspective of writing a statistical analysis plan, we consider how to choose between the three most promising broad approaches: direct adjustment, standardisation and inverse-probability-of-treatment weighting. // Results: The three approaches are similar in being asymptotically efficient, in losing efficiency with mis-specified covariate functions and in handling designed balance. If a marginal estimand is targeted (for example, a risk difference or survival difference), then direct adjustment should be avoided because it involves fitting non-standard models that are subject to convergence issues. Convergence is most likely with IPTW. Robust standard errors used by IPTW are anti-conservative at small sample sizes. All approaches can use similar methods to handle missing covariate data. With missing outcome data, each method has its own way to estimate a treatment effect in the all-randomised population. We illustrate some issues in a reanalysis of GetTested, a randomised trial designed to assess the effectiveness of an electonic sexually transmitted infection testing and results service. // Conclusions: No single approach is always best: the choice will depend on the trial context. We encourage trialists to consider all three methods more routinely

Expression of RUNX1 correlates with poor patient prognosis in triple negative breast cancer

The RUNX1 transcription factor is widely recognised for its tumour suppressor effects in leukaemia. Recently a putative link to breast cancer has started to emerge, however the function of RUNX1 in breast cancer is still unknown. To investigate if RUNX1 expression was important to clinical outcome in primary breast tumours a tissue microarray (TMA) containing biopsies from 483 patients with primary operable invasive ductal breast cancer was stained by immunohistochemistry. RUNX1 was associated with progesterone receptor (PR)-positive tumours (P<0.05), more tumour CD4+(P<0.05) and CD8+(P<0.01) T-lymphocytic infiltrate, increased tumour CD138+plasma cell (P<0.01) and more CD68+macrophage infiltrate (P<0.001). RUNX1 expression did not influence outcome of oestrogen receptor (ER)-positive or HER2-positive disease, however on univariate analysis a high RUNX1 protein was significantly associated with poorer cancer-specific survival in patients with ER-negative (P<0.05) and with triple negative (TN) invasive breast cancer (P<0.05). Furthermore, multivariate Cox regression analysis of cancer-specific survival showed a trend towards significance in ER-negative patients (P<0.1) and was significant in triple negative patients (P<0.05). Of relevance, triple negative breast cancer currently lacks good biomarkers and patients with this subtype do not benefit from the option of targeted therapy unlike patients with ER-positive or HER2-positive disease. Using multivariate analysis RUNX1 was identified as an independent prognostic marker in the triple negative subgroup. Overall, our study identifies RUNX1 as a new prognostic indicator correlating with poor prognosis specifically in the triple negative subtype of human breast cancer

Assessing network structure with practical sampling methods

Using data from an enumerated network of worldwide flight connections between airports, we examine how sampling designs and sample size influence network metrics. Specifically, we apply three types of sampling designs: simple random sampling, nonrandom strategic sampling (i.e., selection of the largest airports), and a variation of snowball sampling. For the latter sampling method, we design what we refer to as a controlled snowball sampling design, which selects nodes in a manner analogous to a respondent-driven sampling design. For each design, we evaluate five commonly used measures of network structure and examine the percentage of total air traffic accounted for by each design. The empirical application shows that (1) the random and controlled snowball sampling designs give rise to more efficient estimates of the true underlying structure, and (2) the strategic sampling method can account for a greater proportion of the total number of passenger movements occurring in the network. Document type: Repor

Creation of the first national linked colorectal cancer dataset in Scotland:prospects for future research and a reflection on lessons learned

Introduction: Current understanding of cancer patients, their treatment pathways and outcomes relies mainly on information from clinical trials and prospective research studies representing a selected sub-set of the patient population. Whole-population analysis is necessary if we are to assess the true impact of new interventions or policy in a real-world setting. Accurate measurement of geographic variation in healthcare use and outcomes also relies on population-level data. Routine access to such data offers efficiency in research resource allocation and a basis for policy that addresses inequalities in care provision. Objective: Acknowledging these benefits, the objective of this project was to create a population level dataset in Scotland of patients with a diagnosis of colorectal cancer (CRC). Methods: This paper describes the process of creating a novel, national dataset in Scotland. Results: In total, thirty two separate healthcare administrative datasets have been linked to provide a comprehensive resource to investigate the management pathways and outcomes for patients with CRC in Scotland, as well as the costs of providing CRC treatment. This is the first time that chemotherapy prescribing and national audit datasets have been linked with the Scottish Cancer Registry on a national scale. Conclusions: We describe how the acquired dataset can be used as a research resource and reflect on the data access challenges relating to its creation. Lessons learned from this process and the policy implications for future studies using administrative cancer data are highlighted

Alteration of the Canine Metabolome After a 3-Week Supplementation of Cannabidiol (CBD) Containing Treats: An Exploratory Study of Healthy Animals

Despite the increased interest and widespread use of cannabidiol (CBD) in humans and companion animals, much remains to be learned about its effects on health and physiology. Metabolomics is a useful tool to evaluate changes in the health status of animals and to analyze metabolic alterations caused by diet, disease, or other factors. Thus, the purpose of this investigation was to evaluate the impact of CBD supplementation on the canine plasma metabolome. Sixteen dogs (18.2 ± 3.4 kg BW) were utilized in a completely randomized design with treatments consisting of control and 4.5 mg CBD/kg BW/d. After 21 d of treatment, blood was collected ~2 h after treat consumption. Plasma collected from samples was analyzed using CIL/LC-MS-based untargeted metabolomics to analyze amine/phenol- and carbonyl-containing metabolites. Metabolites that differed — fold change (FC) ≥ 1.2 or ≤ 0.83 and false discovery ratio (FDR) ≤ 0.05 — between the two treatments were identified using a volcano plot. Biomarker analysis based on receiver operating characteristic (ROC) curves was performed to identify biomarker candidates (area under ROC ≥ 0.90) of the effects of CBD supplementation. Volcano plot analysis revealed that 32 amine/phenol-containing metabolites and five carbonyl-containing metabolites were differentially altered (FC ≥ 1.2 or ≤ 0.83, FDR ≤ 0.05) by CBD; these metabolites are involved in the metabolism of amino acids, glucose, vitamins, nucleotides, and hydroxycinnamic acid derivatives. Biomarker analysis identified 24 amine/phenol-containing metabolites and 1 carbonyl-containing metabolite as candidate biomarkers of the effects of CBD (area under ROC ≥ 0.90; P \u3c 0.01). Results of this study indicate that 3 weeks of 4.5 mg CBD/kg BW/d supplementation altered the canine metabolome. Additional work is warranted to investigate the physiological relevance of these changes

Loss of Insulin Receptor in Osteoprogenitor Cells Impairs Structural Strength of Bone

Type 1 diabetes mellitus (T1D) is associated with decreased bone mineral density, a deficit in bone structure, and subsequently an increased risk of fragility fracture. These clinical observations, paralleled by animal models of T1D, suggest that the insulinopenia of T1D has a deleterious effect on bone. To further examine the action of insulin signaling on bone development, we generated mice with an osteoprogenitor-selective (osterix-Cre) ablation of the insulin receptor (IR), designated OIRKO. OIRKO mice exhibited an 80% decrease in IR in osteoblasts. Prenatal elimination of IR did not affect fetal survival or gross morphology. However, loss of IR in mouse osteoblasts resulted in a postnatal growth-constricted phenotype. By 10–12 weeks of age, femurs of OIRKO mice were more slender, with a thinner diaphyseal cortex and, consequently, a decrease in whole bone strength when subjected to bending. In male mice alone, decreased metaphyseal trabecular bone, with thinner and more rodlike trabeculae, was also observed. OIRKO mice did not, however, exhibit abnormal glucose tolerance. The skeletal phenotype of the OIRKO mouse appeared more severe than that of previously reported bone-specific IR knockdown models, and confirms that insulin receptor expression in osteoblasts is critically important for proper bone development and maintenance of structural integrity