Orientation and symmetries of Alexandrov spaces with applications in positive curvature

We develop two new tools for use in Alexandrov geometry: a theory of ramified orientable double covers and a particularly useful version of the Slice Theorem for actions of compact Lie groups. These tools are applied to the classification of compact, positively curved Alexandrov spaces with maximal symmetry rank.Comment: 34 pages. Simplified proofs throughout and a new proof of the Slice Theorem, correcting omissions in the previous versio

Simultaneous magnetic resonance diffusion and pseudo-diffusion tensor imaging

Purpose An emerging topic in diffusion magnetic resonance is imaging blood microcirculation alongside water diffusion using the intravoxel incoherent motion (IVIM) model. Recently, a combined IVIM diffusion tensor imaging (IVIM‐DTI) model was proposed, which accounts for both anisotropic pseudo‐diffusion due to blood microcirculation and anisotropic diffusion due to tissue microstructures. In this article, we propose a robust IVIM‐DTI approach for simultaneous diffusion and pseudo‐diffusion tensor imaging. Methods Conventional IVIM estimation methods can be broadly divided into two‐step (diffusion and pseudo‐diffusion estimated separately) and one‐step (diffusion and pseudo‐diffusion estimated simultaneously) methods. Here, both methods were applied on the IVIM‐DTI model. An improved one‐step method based on damped Gauss–Newton algorithm and a Gaussian prior for the model parameters was also introduced. The sensitivities of these methods to different parameter initializations were tested with realistic in silico simulations and experimental in vivo data. Results The one‐step damped Gauss–Newton method with a Gaussian prior was less sensitive to noise and the choice of initial parameters and delivered more accurate estimates of IVIM‐DTI parameters compared to the other methods. Conclusion One‐step estimation using damped Gauss–Newton and a Gaussian prior is a robust method for simultaneous diffusion and pseudo‐diffusion tensor imaging using IVIM‐DTI model

Molecular Characterization and Phylogenetic Study of Coxsackievirus A24v Causing Outbreaks of Acute Hemorrhagic Conjunctivitis (AHC) in Brazil

Coxsackievirus A24 variant (CA24v) is the most prevalent viral pathogen associated with acute hemorrhagic conjunctivitis (AHC) outbreaks. Sixteen years after its first outbreak in Brazil, this agent reemerged in 2003 in Brazil, spread to nearly all states and caused outbreaks until 2005. In 2009, a new outbreak occurred in the northeast region of the country. In this study, we performed a viral isolation in cell culture and characterized clinical samples collected from patients presenting symptoms during the outbreak of 2005 in Vitória, Espírito Santo State (ES) and the outbreak of 2009 in Recife, Pernambuco State (PE). We also performed a phylogenetic analysis of worldwide strains and all meaningful Brazilian isolates since 2003.Sterile cotton swabs were used to collect eye discharges, and all 210 clinical samples were used to inoculate cell cultures. Cytopathic effects in HEp-2 cells were seen in 58 of 180 (32%) samples from Vitória and 3 of 30 (10%) samples from Recife. Phylogenetic analysis based on a fragment of the VP1 and 3C gene revealed that the CA24v causing outbreaks in Brazil during the years 2003, 2004 and 2005 evolved from Asian isolates that had caused the South Korean outbreak of AHC during the summer of 2002. However, the 2009 outbreak of AHC in Pernambuco was originated from the reintroduction of a new CA24v strain that was circulating during 2007 in Asia, where CA24v outbreaks has been continuously reported since 1970.This study is the first phylogenetic analysis of AHC outbreaks caused by CA24v in Brazil. The results showed that Asian strains of CA24v were responsible for the outbreaks since 1987 and were independently introduced to Brazil in 2003 and 2009. Phylogenetic analysis of complete VP1 gene is a useful tool for studying the epidemiology of enteroviruses associated with outbreaks

Integrated Molecular-Morphologic Meningioma Classification: A Multicenter Retrospective Analysis, Retrospectively and Prospectively Validated

PURPOSE: Meningiomas are the most frequent primary intracranial tumors. Patient outcome varies widely from benign to highly aggressive, ultimately fatal courses. Reliable identification of risk of progression for individual patients is of pivotal importance. However, only biomarkers for highly aggressive tumors are established (CDKN2A/B and TERT), whereas no molecularly based stratification exists for the broad spectrum of patients with low- and intermediate-risk meningioma. METHODS: DNA methylation data and copy-number information were generated for 3,031 meningiomas (2,868 patients), and mutation data for 858 samples. DNA methylation subgroups, copy-number variations (CNVs), mutations, and WHO grading were analyzed. Prediction power for outcome was assessed in a retrospective cohort of 514 patients, validated on a retrospective cohort of 184, and on a prospective cohort of 287 multicenter cases. RESULTS: Both CNV- and methylation family-based subgrouping independently resulted in increased prediction accuracy of risk of recurrence compared with the WHO classification (c-indexes WHO 2016, CNV, and methylation family 0.699, 0.706, and 0.721, respectively). Merging all risk stratification approaches into an integrated molecular-morphologic score resulted in further substantial increase in accuracy (c-index 0.744). This integrated score consistently provided superior accuracy in all three cohorts, significantly outperforming WHO grading (c-index difference P = .005). Besides the overall stratification advantage, the integrated score separates more precisely for risk of progression at the diagnostically challenging interface of WHO grade 1 and grade 2 tumors (hazard ratio 4.34 [2.48-7.57] and 3.34 [1.28-8.72] retrospective and prospective validation cohorts, respectively). CONCLUSION: Merging these layers of histologic and molecular data into an integrated, three-tiered score significantly improves the precision in meningioma stratification. Implementation into diagnostic routine informs clinical decision making for patients with meningioma on the basis of robust outcome prediction

Environmental Predictors of Seasonal Influenza Epidemics across Temperate and Tropical Climates

Human influenza infections exhibit a strong seasonal cycle in temperate regions. Recent laboratory and epidemiological evidence suggests that low specific humidity conditions facilitate the airborne survival and transmission of the influenza virus in temperate regions, resulting in annual winter epidemics. However, this relationship is unlikely to account for the epidemiology of influenza in tropical and subtropical regions where epidemics often occur during the rainy season or transmit year-round without a well-defined season. We assessed the role of specific humidity and other local climatic variables on influenza virus seasonality by modeling epidemiological and climatic information from 78 study sites sampled globally. We substantiated that there are two types of environmental conditions associated with seasonal influenza epidemics: “cold-dry” and “humid-rainy”. For sites where monthly average specific humidity or temperature decreases below thresholds of approximately 11–12 g/kg and 18–21°C during the year, influenza activity peaks during the cold-dry season (i.e., winter) when specific humidity and temperature are at minimal levels. For sites where specific humidity and temperature do not decrease below these thresholds, seasonal influenza activity is more likely to peak in months when average precipitation totals are maximal and greater than 150 mm per month. These findings provide a simple climate-based model rooted in empirical data that accounts for the diversity of seasonal influenza patterns observed across temperate, subtropical and tropical climates