Local Moment Instability of Os in Honeycomb Li2.15Os0.85O3.

Compounds with honeycomb structures occupied by strong spin orbit coupled (SOC) moments are considered to be candidate Kitaev quantum spin liquids. Here we present the first example of Os on a honeycomb structure, Li2.15(3)Os0.85(3)O3 (C2/c, a = 5.09 Å, b = 8.81 Å, c = 9.83 Å, β = 99.3°). Neutron diffraction shows large site disorder in the honeycomb layer and X-ray absorption spectroscopy indicates a valence state of Os (4.7 ± 0.2), consistent with the nominal concentration. We observe a transport band gap of Δ = 243 ± 23 meV, a large van Vleck susceptibility, and an effective moment of 0.85 μB, much lower than expected from 70% Os(+5). No evidence of long range order is found above 0.10 K but a spin glass-like peak in ac-susceptibility is observed at 0.5 K. The specific heat displays an impurity spin contribution in addition to a power law ∝T(0.63±0.06). Applied density functional theory (DFT) leads to a reduced moment, suggesting incipient itineracy of the valence electrons, and finding evidence that Li over stoichiometry leads to Os(4+)-Os(5+) mixed valence. This local picture is discussed in light of the site disorder and a possible underlying quantum spin liquid state

Femoral Stem Displacement in a Patient Suffering Recurrent Dislocations After Hip Hemiarthroplasty: Case Report

Displacement of the femoral component during attempt to closed reduction of a dislocated hip arthroplasty is an exceptionally rare, catastrophic event, which renders operative management obligatory. We report the proximal migration of a femoral stem during attempt to closed reduction in a patient with recurrent postoperative dislocations after hip hemiarthroplasty, and describe successful management by conversion to a standard total hip arthroplasty, retaining the same stem in the existing cement mantle. This illustrative case is reported not only as an extremely rare event, but also to highlight and discuss pitfalls and efficient measures in the management of this complex issue

Nulliparity enhances the risk of second primary malignancy of the breast in a cohort of women treated for thyroid cancer

<p>Abstract</p> <p>Background</p> <p>Many studies have reported an increased risk of developing a second primary malignancy (SPM) of the breast in women treated for thyroid cancer. In this study, we investigated several potential risk factors for this association. The aim of this retrospective cohort study was to identify a subgroup of women surgically treated for papillary thyroid cancer that may benefit from more careful breast cancer screening.</p> <p>Methods</p> <p>A total of 101 women surgically treated for papillary thyroid cancer from 1996 to 2009 with subsequent follow-up were interviewed by phone regarding personal risk factors and lifestyle habits. Only 75 questionnaires could be evaluated due to a 25.7% rate of patients not retrieved or refusing the interview. Data analysis was performed using a multivariate logistic model.</p> <p>Results</p> <p>The standardised incidence ratio (SIR) for breast cancer was 3.58 (95% IC 1.14 - 8.37). Our data suggest a protective effect of multiparity on the development of a SPM of the breast (O.R. 0.15; 95% IC 0.25 - 0.86). Significant associations were not found with other known risk factors including Body Mass Index (BMI), age at first tumour, concurrent metabolic diseases, smoking, physical activity and familiarity.</p> <p>Conclusions</p> <p>This study confirms that a higher incidence of SPM of the breast is observed in women treated for papillary thyroid cancer. Additionally, this risk is increased by nulliparity, thus a strict breast screening program for nulliparous women treated for thyroid cancer may be advisable.</p

attract: A Method for Identifying Core Pathways That Define Cellular Phenotypes

attract is a knowledge-driven analytical approach for identifying and annotating the gene-sets that best discriminate between cell phenotypes. attract finds distinguishing patterns within pathways, decomposes pathways into meta-genes representative of these patterns, and then generates synexpression groups of highly correlated genes from the entire transcriptome dataset. attract can be applied to a wide range of biological systems and is freely available as a Bioconductor package and has been incorporated into the MeV software system

Risk Factors for Nonsynchronous Second Primary Malignancy and Related Death in Patients with Differentiated Thyroid Carcinoma

BACKGROUND: Differentiated thyroid cancer (DTC) survivors are at increased risk of developing nonsynchronous second primary malignancy (NSPM). This study aims to examine possible risk factors leading to occurrence of NSPM as well as risk factors leading to NSPM-related death in patients with DTC. METHODS: Of the 1,106 patients with DTC managed at our institution, 92 (8.3%) patients developed NSPM and 40 (3.6%) patients died of NSPM. All causes of death were confirmed by medical record, autopsy report or death certificate. Clinicopathological variables were compared between those without NSPM and with NSPM as well as between those who died of NSPM and did not die of NSPM. Significant variables on univariate analysis were entered into a Cox proportional hazards model. RESULTS: The median latency period from diagnosis of DTC to NSPM was 142.7 (range 16.8-511.0) months. For occurrence of NSPM, age at DTC diagnosis >/=50 years old [relative risk (RR) = 2.35], cumulative radioactive iodine (RAI) activity 3.0-8.9 GBq (RR = 2.38), and external local radiotherapy (ERT) (RR = 1.95) were significant risk factors. For NSPM-related death, age at DTC diagnosis >/=50 years old (RR = 3.32) and nonbreast cancer (RR = 5.76) were significant risk factors. CONCLUSIONS: NSPM accounted for 18.7% of all deaths in DTC, but mortality was high (43.5%). Age at DTC diagnosis >/=50 years old, cumulative RAI activity 3.0-8.9 GBq, and ERT were significant risk factors for occurrence of NSPM, whereas age at DTC diagnosis >/=50 years old and the diagnosis of nonbreast cancer were significant risk factors for NSPM-related death.published_or_final_versionSpringer Open Choice, 21 Feb 201

NEAT: An efficient network enrichment analysis test

Background: Network enrichment analysis is a powerful method, which allows to integrate gene enrichment analysis with the information on relationships between genes that is provided by gene networks. Existing tests for network enrichment analysis deal only with undirected networks, they can be computationally slow and are based on normality assumptions. Results: We propose NEAT, a test for network enrichment analysis. The test is based on the hypergeometric distribution, which naturally arises as the null distribution in this context. NEAT can be applied not only to undirected, but to directed and partially directed networks as well. Our simulations indicate that NEAT is considerably faster than alternative resampling-based methods, and that its capacity to detect enrichments is at least as good as the one of alternative tests. We discuss applications of NEAT to network analyses in yeast by testing for enrichment of the Environmental Stress Response target gene set with GO Slim and KEGG functional gene sets, and also by inspecting associations between functional sets themselves. Conclusions: NEAT is a flexible and efficient test for network enrichment analysis that aims to overcome some limitations of existing resampling-based tests. The method is implemented in the R package neat, which can be freely downloaded from CRAN ( https://cran.r-project.org/package=neat )

A comparative study on gene-set analysis methods for assessing differential expression associated with the survival phenotype

Abstract Background Many gene-set analysis methods have been previously proposed and compared through simulation studies and analysis of real datasets for binary phenotypes. We focused on the survival phenotype and compared the performances of Gene Set Enrichment Analysis (GSEA), Global Test (GT), Wald-type Test (WT) and Global Boost Test (GBST) methods in a simulation study and on two ovarian cancer data sets. We considered two versions of GSEA by allowing different weights: GSEA1 uses equal weights, yielding results similar to the Kolmogorov-Smirnov test; while GSEA2's weights are based on the correlation between genes and the phenotype. Results We compared GSEA1, GSEA2, GT, WT and GBST in a simulation study with various settings for the correlation structure of the genes and the association parameter between the survival outcome and the genes. Simulation results indicated that GT, WT and GBST consistently have higher power than GSEA1 and GSEA2 across all scenarios. However, the power of the five tests depends on the combination of correlation structure and association parameter. For the ovarian cancer data set, using the FDR threshold of q Conclusion Simulation studies and a real data example indicate that GT, WT and GBST tend to have high power, whereas GSEA1 and GSEA2 have lower power. We also found that the power of the five tests is much higher when genes are correlated than when genes are independent, when survival is positively associated with genes. It seems that there is a synergistic effect in detecting significant gene sets when significant genes have within-class correlation and the association between survival and genes is positive or negative (i.e., one-direction correlation).</p

Integration of gene expression data with prior knowledge for network analysis and validation

<p>Abstract</p> <p>Background</p> <p>Reconstruction of protein-protein interaction or metabolic networks based on expression data often involves in silico predictions, while on the other hand, there are unspecific networks of in vivo interactions derived from knowledge bases.</p> <p>We analyze networks designed to come as close as possible to data measured in vivo, both with respect to the set of nodes which were taken to be expressed in experiment as well as with respect to the interactions between them which were taken from manually curated databases</p> <p>Results</p> <p>A signaling network derived from the TRANSPATH database and a metabolic network derived from KEGG LIGAND are each filtered onto expression data from breast cancer (SAGE) considering different levels of restrictiveness in edge and vertex selection.</p> <p>We perform several validation steps, in particular we define pathway over-representation tests based on refined null models to recover functional modules. The prominent role of the spindle checkpoint-related pathways in breast cancer is exhibited. High-ranking key nodes cluster in functional groups retrieved from literature. Results are consistent between several functional and topological analyses and between signaling and metabolic aspects.</p> <p>Conclusions</p> <p>This construction involved as a crucial step the passage to a mammalian protein identifier format as well as to a reaction-based semantics of metabolism. This yielded good connectivity but also led to the need to perform benchmark tests to exclude loss of essential information. Such validation, albeit tedious due to limitations of existing methods, turned out to be informative, and in particular provided biological insights as well as information on the degrees of coherence of the networks despite fragmentation of experimental data.</p> <p>Key node analysis exploited the networks for potentially interesting proteins in view of drug target prediction.</p

Mathematical modelling of lymphatic filariasis elimination programmes in India: Required duration of mass drug administration and post-treatment level of infection indicators

Background: India has made great progress towards the elimination of lymphatic filariasis. By 2015, most endemic districts had completed at least five annual rounds of mass drug administration (MDA). The next challenge is to determine when MDA can be stopped. We performed a simulation study with the individual-based model LYMFASIM to help clarify this. Methods: We used a model-variant for Indian settings. We considered different hypotheses on detectability of antigenaemia (Ag) in relation to underlying adult worm burden, choosing the most likely hypothesis by comparing the model predicted association between community-level microfilaraemia (Mf) and antigenaemia (Ag) prevalence levels to observed data (collated from literature). Next, we estimated how long MDA must be continued in order to achieve elimination in different transmission settings and what Mf and Ag prevalence may still remain 1 year after the last required MDA round. The robustness of key-outcomes was assessed in a sensitivity analysis. Results: Our model matched observed data qualitatively well when we assumed an Ag detection rate of 50 % for single worm infections, which increases with the number of adult worms (modelled by relating detection to the presence of female worms). The required duration of annual MDA increased with higher baseline endemicity and lower coverage (varying between 2 and 12 rounds), while the remaining residual infection 1 year after the last required treatment declined with transmission intensity. For low and high transmission settings, the median residual infection levels were 1.0 % and 0.4 % (Mf prevalence in the 5+ population), and 3.5 % and 2.0 % (Ag prevalence in 6-7 year-old children). Conclusion: To achieve elimination in high transmission settings, MDA must be continued longer and infection levels must be reduced to lower levels than in low-endemic communities. Although our simulations were for Indian settings, qualitatively similar patterns are also expected in other areas. This should be taken into account in decision algorithms to define whether MDA can be interrupted. Transmission assessment surveys should ideally be targeted to communities with the highest pre-control transmission levels, to minimize the risk of programme failure

Genome-wide association and HLA fine-mapping studies identify risk loci and genetic pathways underlying allergic rhinitis

Allergic rhinitis is the most common clinical presentation of allergy, affecting 400 million people worldwide, with increasing incidence in westernized countries1,2. To elucidate the genetic architecture and understand the underlying disease mechanisms, we carried out a meta-analysis of allergic rhinitis in 59,762 cases and 152,358 controls of European ancestry and identified a total of 41 risk loci for allergic rhinitis, including 20 loci not previously associated with allergic rhinitis, which were confirmed in a replication phase of 60,720 cases and 618,527 controls. Functional annotation implicated genes involved in various immune pathways, and fine mapping of the HLA region suggested amino acid variants important for antigen binding. We further performed genome-wide association study (GWAS) analyses of allergic sensitization against inhalant allergens and nonallergic rhinitis, which suggested shared genetic mechanisms across rhinitis-related traits. Future studies of the identified loci and genes might identify novel targets for treatment and prevention of allergic rhinitis