IDH/MGMT-driven molecular classification of low-grade glioma is a strong predictor for long-term survival

BACKGROUND Low-grade gliomas (LGGs) are rare brain neoplasms, with survival spanning up to a few decades. Thus, accurate evaluations on how biomarkers impact survival among patients with LGG require long-term studies on samples prospectively collected over a long period. METHODS The 210 adult LGGs collected in our databank were screened for IDH1 and IDH2 mutations (IDHmut), MGMT gene promoter methylation (MGMTmet), 1p/19q loss of heterozygosity (1p19qloh), and nuclear TP53 immunopositivity (TP53pos). Multivariate survival analyses with multiple imputation of missing data were performed using either histopathology or molecular markers. Both models were compared using Akaike's information criterion (AIC). The molecular model was reduced by stepwise model selection to filter out the most critical predictors. A third model was generated to assess for various marker combinations. RESULTS Molecular parameters were better survival predictors than histology (ΔAIC = 12.5, P< .001). Forty-five percent of studied patients died. MGMTmet was positively associated with IDHmut (P< .001). In the molecular model with marker combinations, IDHmut/MGMTmet combined status had a favorable impact on overall survival, compared with IDHwt (hazard ratio [HR] = 0.33, P< .01), and even more so the triple combination, IDHmut/MGMTmet/1p19qloh (HR = 0.18, P< .001). Furthermore, IDHmut/MGMTmet/TP53pos triple combination was a significant risk factor for malignant transformation (HR = 2.75, P< .05). CONCLUSION By integrating networks of activated molecular glioma pathways, the model based on genotype better predicts prognosis than histology and, therefore, provides a more reliable tool for standardizing future treatment strategies.

Reanalysis-driven climate simulation over CORDEX North America domain using the Canadian Regional Climate Model, version 5: model performance evaluation

The performance of reanalysis-driven Canadian Regional Climate Model, version 5 (CRCM5) in reproducing the present climate over the North American COordinated Regional climate Downscaling EXperiment domain for the 1989–2008 period has been assessed in comparison with several observation-based datasets. The model reproduces satisfactorily the near-surface temperature and precipitation characteristics over most part of North America. Coastal and mountainous zones remain problematic: a cold bias (2–6 °C) prevails over Rocky Mountains in summertime and all year-round over Mexico; winter precipitation in mountainous coastal regions is overestimated. The precipitation patterns related to the North American Monsoon are well reproduced, except on its northern limit. The spatial and temporal structure of the Great Plains Low-Level Jet is well reproduced by the model; however, the night-time precipitation maximum in the jet area is underestimated. The performance of CRCM5 was assessed against earlier CRCM versions and other RCMs. CRCM5 is shown to have been substantially improved compared to CRCM3 and CRCM4 in terms of seasonal mean statistics, and to be comparable to other modern RCMs.

MiniCASCC - A battery driven fog collector for ecosystem research

We developed a small version of the Caltech active strand cloud water collector (CASCC) for biogeochemical investigations in ecological applications. The device is battery powered and thus allows operation at locations where mains power is not available. The collector is designed for sampling periods of up to one week, depending on fog frequency. Our new device is equipped with standard sensors for air temperature, relative humidity, wind, and horizontal visibility for fog detection with a low-cost optical sensor. In mountain areas and during times when clouds are thin the installation of the visibility sensor became a key issue, which limits the potential to estimate liquid water content of the sampled fog. Field tests with 5 devices at three different sites in the Swiss Alps (Niesen) and the Jura Mountains (Lägeren, Switzerland) during two extended summer seasons in 2006 and 2007 showed that in almost all cases it was possible to obtain sample volumes which were large enough for the examination of basic inorganic chemistry of the collected cloud water. Collection rates varied typically from 12 to 30 mL h− 1. The fog droplet cutoff diameter is ≈ 6 μm, which is low enough to include all droplet sizes that are relevant for the liquid water content of typical fog types in the collected samples. From theoretical assumptions of the collection efficiency and theoretical droplet spectra it is possible to estimate the liquid water content of the sampled fog or cloud. Our new fog collector can be constructed and operated at relatively low costs. In combination with chemical and isotopic analyses of the sampled water, this allows to quantify nutrient and pollutant fluxes as is typically needed in ecosystem biogeochemistry studies.

A tutorial on data-driven methods for statistically assessing ERP topographies

Dynamic changes in ERP topographies can be conveniently analyzed by means of microstates, the so-called "atoms of thoughts", that represent brief periods of quasi-stable synchronized network activation. Comparing temporal microstate features such as on- and offset or duration between groups and conditions therefore allows a precise assessment of the timing of cognitive processes. So far, this has been achieved by assigning the individual time-varying ERP maps to spatially defined microstate templates obtained from clustering the grand mean data into predetermined numbers of topographies (microstate prototypes). Features obtained from these individual assignments were then statistically compared. This has the problem that the individual noise dilutes the match between individual topographies and templates leading to lower statistical power. We therefore propose a randomization-based procedure that works without assigning grand-mean microstate prototypes to individual data. In addition, we propose a new criterion to select the optimal number of microstate prototypes based on cross-validation across subjects. After a formal introduction, the method is applied to a sample data set of an N400 experiment and to simulated data with varying signal-to-noise ratios, and the results are compared to existing methods. In a first comparison with previously employed statistical procedures, the new method showed an increased robustness to noise, and a higher sensitivity for more subtle effects of microstate timing. We conclude that the proposed method is well-suited for the assessment of timing differences in cognitive processes. The increased statistical power allows identifying more subtle effects, which is particularly important in small and scarce patient populations.

Improving Management of Distributed Services Using Correlations and Predictions in SLA-Driven Cloud Computing Systems

Recent advancements in cloud computing have enabled the proliferation of distributed applications, which require management and control of multiple services. However, without an efficient mechanism for scaling services in response to changing environmental conditions and number of users, application performance might suffer, leading to Service Level Agreement (SLA) violations and inefficient use of hardware resources. We introduce a system for controlling the complexity of scaling applications composed of multiple services using mechanisms based on fulfillment of SLAs. We present how service monitoring information can be used in conjunction with service level objectives, predictions, and correlations between performance indicators for optimizing the allocation of services belonging to distributed applications. We validate our models using experiments and simulations involving a distributed enterprise information system. We show how discovering correlations between application performance indicators can be used as a basis for creating refined service level objectives, which can then be used for scaling the application and improving the overall application's performance under similar conditions.

SLA-driven predictive orchestration for distributed cloud-based mobile services

We describe a system for performing SLA-driven management and orchestration of distributed infrastructures composed of services supporting mobile computing use cases. In particular, we focus on a Follow-Me Cloud scenario in which we consider mobile users accessing cloud-enable services. We combine a SLA-driven approach to infrastructure optimization, with forecast-based performance degradation preventive actions and pattern detection for supporting mobile cloud infrastructure management. We present our system's information model and architecture including the algorithmic support and the proposed scenarios for system evaluation.

Clinically-driven need for secondary interventions after endovascular revascularization of tibial arteries in patients with critical limb ischemia

PURPOSE To assess the need for clinically-driven secondary revascularization in critical limb ischemia (CLI) patients subsequent to tibial angioplasty during a 2-year follow-up. METHODS Between 2008 and 2010, a total of 128 consecutive CLI patients (80 men; mean age 76.5±9.8 years) underwent tibial angioplasty in 139 limbs. Rutherford categories, ankle-brachial index measurements, and lower limb oscillometries were prospectively assessed. All patients were followed at 3, 6, 12 months, and annually thereafter. Rates of death, primary and secondary sustained clinical improvement, target lesion (TLR) and target extremity revascularization (TER), as well as major amputation, were analyzed retrospectively. Primary clinical improvement was defined as improvement in Rutherford category to a level of intermittent claudication without unplanned amputation or TLR. RESULTS All-cause mortality was 8.6%, 14.8%, 22.9%, and 29.1% at 3, 6, 12, and 24 months. At the same intervals, rates of primary sustained clinical improvement were 74.5%, 53.0%, 42.7%, and 37.1%; for secondary improvement, the rates were 89.1%, 76.0%, 68.4%, and 65.0%. Clinically-driven TLR rates were 14.6%, 29.1%, 41.6%, 46.2%; the rates for TER were 3.0%, 13.6%, 17.2%, and 27.6% in corresponding intervals, while the rates of major amputation were 1.5%, 5.5%, 10.1%, and 10.1%. CONCLUSION Clinically-driven TLR is frequently required to maintain favorable functional clinical outcomes in CLI patients following tibial angioplasty. Dedicated technologies addressing tibial arterial restenosis warrant further academic scrutiny.

IL-21 restricts virus-driven Treg cell expansion in chronic LCMV infection

Foxp3+ regulatory T (Treg) cells are essential for the maintenance of immune homeostasis and tolerance. During viral infections, Treg cells can limit the immunopathology resulting from excessive inflammation, yet potentially inhibit effective antiviral T cell responses and promote virus persistence. We report here that the fast-replicating LCMV strain Docile triggers a massive expansion of the Treg population that directly correlates with the size of the virus inoculum and its tendency to establish a chronic, persistent infection. This Treg cell proliferation was greatly enhanced in IL-21R-/- mice and depletion of Treg cells partially rescued defective CD8+ T cell cytokine responses and improved viral clearance in some but not all organs. Notably, IL-21 inhibited Treg cell expansion in a cell intrinsic manner. Moreover, experimental augmentation of Treg cells driven by injection of IL-2/anti-IL-2 immune complexes drastically impaired the functionality of the antiviral T cell response and impeded virus clearance. As a consequence, mice became highly susceptible to chronic infection following exposure to low virus doses. These findings reveal virus-driven Treg cell proliferation as potential evasion strategy that facilitates T cell exhaustion and virus persistence. Furthermore, they suggest that besides its primary function as a direct survival signal for antiviral CD8+ T cells during chronic infections, IL-21 may also indirectly promote CD8+ T cell poly-functionality by restricting the suppressive activity of infection-induced Treg cells.

Immune recognition of self nucleic acids driven by endogenous antimicrobial peptides: role in autoimmunity

Innate immune recognition of extracellular host-derived self-DNA and self-RNA is prevented by endosomal seclusion of the Toll-like receptors (TLRs) in the dendritic cells (DCs). However, in psoriasis plasmacytoid dendritic cells have been found to be able to sense self-DNA molecules in complex with the endogenous cationic antimicrobial peptide LL37, which are internalized into the endosomal compartments and thus can access TLR9. We investigated whether this endogenous peptide can also interact with extracellular self-RNA and lead to DC activation. We found that LL37 binds self-RNA as well as self-DNA going into an electrostatic interaction; forms micro-aggregates of nano-scale particles protected from enzymatic degradation and transport it into the endosomal compartments of both plasmacytoid and myeloid dendritic cells. In the plasmacytoid DCs, the self-RNA-LL37 complexes activate TLR7 and like the self-DNA-LL37 complexes, trigger the production of IFN-α in the absence of induction of maturation or production of IL-6 and TNF-α. In contrast to the self-DNA-LL37 complexes, the self-RNA-LL37 complexes are also internalized into the endosomal compartments of myeloid dendritic cells and trigger activation through TLR8, leading to the production of TNF-α and IL-6, and the maturation of the myeloid DCs. Furthermore, we found that these self nucleic acid-LL37 complexes can be found in vivo in the skin lesions of the cutaneous autoimmune disease psoriasis, where they are associated with mature mDCs in situ. On the other hand, in the systemic autoimmune disease systemic lupus erythematosus, self-DNA-LL37 complexes were found to be a constituent of the circulating immune complexes isolated from patient sera. This interaction between the endogenous peptide with the self nucleic acid molecules present in the immune complexes was found to be electrostatic and it confers resistance to enzymatic degradation of the nucleic acid molecules in the immune complexes. Moreover, autoantibodies to these endogenous peptides were found to trigger neutrophil activation and release of neutrophil extracellular traps composed of DNA, which are potential sources of the self nucleic acid-LL37 complexes present in SLE immune complexes. Our results demonstrate that the cationic antimicrobial peptide LL37 drives the innate immune recognition of self nucleic acid molecules through toll-like receptors in human dendritic cells, thus elucidating a pathway for innate sensing of host cell death. This pathway of autoreactivity was found to be pathologically relevant in human autoimmune diseases psoriasis and SLE, and thus this study provides new insights into the mechanisms autoimmune diseases.

Ontology driven integration platform for clinical and translational research

Semantic Web technologies offer a promising framework for integration of disparate biomedical data. In this paper we present the semantic information integration platform under development at the Center for Clinical and Translational Sciences (CCTS) at the University of Texas Health Science Center at Houston (UTHSC-H) as part of our Clinical and Translational Science Award (CTSA) program. We utilize the Semantic Web technologies not only for integrating, repurposing and classification of multi-source clinical data, but also to construct a distributed environment for information sharing, and collaboration online. Service Oriented Architecture (SOA) is used to modularize and distribute reusable services in a dynamic and distributed environment. Components of the semantic solution and its overall architecture are described.