Analysis of the genome and transcriptome of Cryptococcus neoformans var. grubii reveals complex RNA expression and microevolution leading to virulence attenuation.

Cryptococcus neoformans is a pathogenic basidiomycetous yeast responsible for more than 600,000 deaths each year. It occurs as two serotypes (A and D) representing two varieties (i.e. grubii and neoformans, respectively). Here, we sequenced the genome and performed an RNA-Seq-based analysis of the C. neoformans var. grubii transcriptome structure. We determined the chromosomal locations, analyzed the sequence/structural features of the centromeres, and identified origins of replication. The genome was annotated based on automated and manual curation. More than 40,000 introns populating more than 99% of the expressed genes were identified. Although most of these introns are located in the coding DNA sequences (CDS), over 2,000 introns in the untranslated regions (UTRs) were also identified. Poly(A)-containing reads were employed to locate the polyadenylation sites of more than 80% of the genes. Examination of the sequences around these sites revealed a new poly(A)-site-associated motif (AUGHAH). In addition, 1,197 miscRNAs were identified. These miscRNAs can be spliced and/or polyadenylated, but do not appear to have obvious coding capacities. Finally, this genome sequence enabled a comparative analysis of strain H99 variants obtained after laboratory passage. The spectrum of mutations identified provides insights into the genetics underlying the micro-evolution of a laboratory strain, and identifies mutations involved in stress responses, mating efficiency, and virulence

The severity of pandemic H1N1 influenza in the United States, from April to July 2009: A Bayesian analysis

Background: Accurate measures of the severity of pandemic (H1N1) 2009 influenza (pH1N1) are needed to assess the likely impact of an anticipated resurgence in the autumn in the Northern Hemisphere. Severity has been difficult to measure because jurisdictions with large numbers of deaths and other severe outcomes have had too many cases to assess the total number with confidence. Also, detection of severe cases may be more likely, resulting in overestimation of the severity of an average case. We sought to estimate the probabilities that symptomatic infection would lead to hospitalization, ICU admission, and death by combining data from multiple sources. Methods and Findings: We used complementary data from two US cities: Milwaukee attempted to identify cases of medically attended infection whether or not they required hospitalization, while New York City focused on the identification of hospitalizations, intensive care admission or mechanical ventilation (hereafter, ICU), and deaths. New York data were used to estimate numerators for ICU and death, and two sources of data - medically attended cases in Milwaukee or self-reported influenza-like illness (ILI) in New York - were used to estimate ratios of symptomatic cases to hospitalizations. Combining these data with estimates of the fraction detected for each level of severity, we estimated the proportion of symptomatic patients who died (symptomatic case-fatality ratio, sCFR), required ICU (sCIR), and required hospitalization (sCHR), overall and by age category. Evidence, prior information, and associated uncertainty were analyzed in a Bayesian evidence synthesis framework. Using medically attended cases and estimates of the proportion of symptomatic cases medically attended, we estimated an sCFR of 0.048% (95% credible interval [CI] 0.026%-0.096%), sCIR of 0.239% (0.134%-0.458%), and sCHR of 1.44% (0.83%-2.64%). Using self-reported ILI, we obtained estimates approximately 7-96lower. sCFR and sCIR appear to be highest in persons aged 18 y and older, and lowest in children aged 5-17 y. sCHR appears to be lowest in persons aged 5-17; our data were too sparse to allow us to determine the group in which it was the highest. Conclusions: These estimates suggest that an autumn-winter pandemic wave of pH1N1 with comparable severity per case could lead to a number of deaths in the range from considerably below that associated with seasonal influenza to slightly higher, but with the greatest impact in children aged 0-4 and adults 18-64. These estimates of impact depend on assumptions about total incidence of infection and would be larger if incidence of symptomatic infection were higher or shifted toward adults, if viral virulence increased, or if suboptimal treatment resulted from stress on the health care system; numbers would decrease if the total proportion of the population symptomatically infected were lower than assumed.published_or_final_versio

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

Approximation of Stochastic Partial Differential Equations by a Kernel-based Collocation Method

In this paper we present the theoretical framework needed to justify the use of a kernel-based collocation method (meshfree approximation method) to estimate the solution of high-dimensional stochastic partial differential equations (SPDEs). Using an implicit time stepping scheme, we transform stochastic parabolic equations into stochastic elliptic equations. Our main attention is concentrated on the numerical solution of the elliptic equations at each time step. The estimator of the solution of the elliptic equations is given as a linear combination of reproducing kernels derived from the differential and boundary operators of the SPDE centered at collocation points to be chosen by the user. The random expansion coefficients are computed by solving a random system of linear equations. Numerical experiments demonstrate the feasibility of the method.Comment: Updated Version in International Journal of Computer Mathematics, Closed to Ye's Doctoral Thesi

Assessment of MRI issues at 3-Tesla for metallic surgical implants: findings applied to 61 additional skin closure staples and vessel ligation clips

<p>Abstract</p> <p>Purpose</p> <p>Metallic skin closure staples and vessel ligation clips should be tested at 3-Tesla to characterize MRI issues in order to ensure patient safety. Therefore, metallic surgical implants were assessed at 3-Tesla for magnetic field interactions, MRI-related heating, and artifacts.</p> <p>Methods</p> <p>A skin closure staple (Visistat Skin Stapler, staple, Polytetrafluoroethylene, PTFE, coated 316L/316LVM stainless steel; Teleflex Medical, Durham, NC) and a vessel ligation clip (Hemoclip Traditional, stainless steel; Teleflex Medical, Durham, NC) that represented the largest metallic sizes made from materials with the highest magnetic susceptibilities (i.e., based on material information) among 61 other surgical implants (52 metallic implants, 9 nonmetallic implants) underwent evaluation for magnetic field interactions, MRI-related heating, and artifacts using standardized techniques. MRI-related heating was assessed by placing each implant in a gelled-saline-filled phantom with MRI performed using a transmit/receive RF body coil at an MR system reported, whole body averaged SAR of 2.9-W/kg for 15-min. Artifacts were characterized using T1-weighted, SE and GRE pulse sequences.</p> <p>Results</p> <p>Each surgical implant showed minor magnetic field interactions (20- and 27-degrees, which is acceptable from a safety consideration). Heating was not substantial (highest temperature change, ≤ 1.6°C). Artifacts may create issues if the area of interest is in the same area or close to the respective surgical implant.</p> <p>Conclusions</p> <p>The results demonstrated that it would be acceptable for patients with these metallic surgical implants to undergo MRI at 3-Tesla or less. Because of the materials and dimensions of the surgical implants that underwent testing, these findings pertain to 61 additional similar implants.</p

Calorimetric Behavior of Phosphatidylcholine/Phosphatidylethanolamine Bilayers is Compatible with the Superlattice Model

Differential scanning calorimetry was used to study the phase behavior of binary lipid bilayers consisting of phosphatidylcholine (PC) and phosphatidylethanolamine (PE) of varying acyl chain length. A two-state transition model was used to resolve the individual transition components, and the two-state transition enthalpy, the relative enthalpy, and the transition temperature of each component were plotted as a function of composition. Intriguingly, abrupt changes in these thermodynamic parameters were observed at or close to many “critical” XPE values predicted by the superlattice model proposing that phospholipids with different headgroups tend to adopt regular rather than random lateral distributions. Statistical analysis indicated that the agreement between the observed and predicted “critical” compositions is highly significant. Accordingly, these data provide strong evidence that the molecules in PC/PE bilayers tend to adopt regular, superlattice-like lateral arrangements, which could be involved in the regulation of the lipid compositions of biological membranes

A Unifying Framework for Evaluating the Predictive Power of Genetic Variants Based on the Level of Heritability Explained

An increasing number of genetic variants have been identified for many complex diseases. However, it is controversial whether risk prediction based on genomic profiles will be useful clinically. Appropriate statistical measures to evaluate the performance of genetic risk prediction models are required. Previous studies have mainly focused on the use of the area under the receiver operating characteristic (ROC) curve, or AUC, to judge the predictive value of genetic tests. However, AUC has its limitations and should be complemented by other measures. In this study, we develop a novel unifying statistical framework that connects a large variety of predictive indices together. We showed that, given the overall disease probability and the level of variance in total liability (or heritability) explained by the genetic variants, we can estimate analytically a large variety of prediction metrics, for example the AUC, the mean risk difference between cases and non-cases, the net reclassification improvement (ability to reclassify people into high- and low-risk categories), the proportion of cases explained by a specific percentile of population at the highest risk, the variance of predicted risks, and the risk at any percentile. We also demonstrate how to construct graphs to visualize the performance of risk models, such as the ROC curve, the density of risks, and the predictiveness curve (disease risk plotted against risk percentile). The results from simulations match very well with our theoretical estimates. Finally we apply the methodology to nine complex diseases, evaluating the predictive power of genetic tests based on known susceptibility variants for each trait

Performance of Detecting IgM Antibodies against Enterovirus 71 for Early Diagnosis

Enterovirus 71 (EV71) infection is more likely to induce severe complications and mortality than other enteroviruses. Methods for detection of IgM antibody against EV71 had been established for years, however, the performance of the methods in the very early diagnosis of EV71 infection had not been fully evaluated, which is especially meaningful because of the short incubation period of EV71 infection. In this report, the performance of an IgM anti-EV71 assay was evaluated using acute sera collected from 165 EV71 infected patients, 165 patients infected with other enteroviruses, and more than 2,000 sera from healthy children or children with other infected diseases. The results showed a 90% sensitivity in 20 patients who were in their first illness day, and similar sensitivity remained till 4 days after onset. After then the sensitivity increased to 95% to 100% for more than one month. The specificity of the assay in non-HFMD children is 99.1% (95% CI: 98.6–99.4), similar as the 99.9% specificity in healthy adults. The cross-reaction rate in patients infected with other non-EV71 enteroviruses was 11.4%. In conclusion, the data here presented show that the detection of IgM anti-EV71 by ELISA affords a reliable, convenient, and prompt diagnosis of EV71 infection

Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data

BACKGROUND: Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data. RESULTS: We developed a recursive support vector machine (R-SVM) algorithm to select important genes/biomarkers for the classification of noisy data. We compared its performance to a similar, state-of-the-art method (SVM recursive feature elimination or SVM-RFE), paying special attention to the ability of recovering the true informative genes/biomarkers and the robustness to outliers in the data. Simulation experiments show that a 5 %-~20 % improvement over SVM-RFE can be achieved regard to these properties. The SVM-based methods are also compared with a conventional univariate method and their respective strengths and weaknesses are discussed. R-SVM was applied to two sets of SELDI-TOF-MS proteomics data, one from a human breast cancer study and the other from a study on rat liver cirrhosis. Important biomarkers found by the algorithm were validated by follow-up biological experiments. CONCLUSION: The proposed R-SVM method is suitable for analyzing noisy high-throughput proteomics and microarray data and it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features. The multivariate SVM-based method outperforms the univariate method in the classification performance, but univariate methods can reveal more of the differentially expressed features especially when there are correlations between the features

Clustered ChIP-Seq-defined transcription factor binding sites and histone modifications map distinct classes of regulatory elements

<p>Abstract</p> <p>Background</p> <p>Transcription factor binding to DNA requires both an appropriate binding element and suitably open chromatin, which together help to define regulatory elements within the genome. Current methods of identifying regulatory elements, such as promoters or enhancers, typically rely on sequence conservation, existing gene annotations or specific marks, such as histone modifications and p300 binding methods, each of which has its own biases.</p> <p>Results</p> <p>Herein we show that an approach based on clustering of transcription factor peaks from high-throughput sequencing coupled with chromatin immunoprecipitation (Chip-Seq) can be used to evaluate markers for regulatory elements. We used 67 data sets for 54 unique transcription factors distributed over two cell lines to create regulatory element clusters. By integrating the clusters from our approach with histone modifications and data for open chromatin, we identified general methylation of lysine 4 on histone H3 (H3K4me) as the most specific marker for transcription factor clusters. Clusters mapping to annotated genes showed distinct patterns in cluster composition related to gene expression and histone modifications. Clusters mapping to intergenic regions fall into two groups either directly involved in transcription, including miRNAs and long noncoding RNAs, or facilitating transcription by long-range interactions. The latter clusters were specifically enriched with H3K4me1, but less with acetylation of lysine 27 on histone 3 or p300 binding.</p> <p>Conclusion</p> <p>By integrating genomewide data of transcription factor binding and chromatin structure and using our data-driven approach, we pinpointed the chromatin marks that best explain transcription factor association with different regulatory elements. Our results also indicate that a modest selection of transcription factors may be sufficient to map most regulatory elements in the human genome.</p