Fourteen gene GBM prognostic signature identifies association of immune response pathway and mesenchymal subtype with high risk group

Background: Recent research on glioblastoma (GBM) has focused on deducing gene signatures predicting prognosis. The present study evaluated the mRNA expression of selected genes and correlated with outcome to arrive at a prognostic gene signature. Methods: Patients with GBM (n = 123) were prospectively recruited, treated with a uniform protocol and followed up. Expression of 175 genes in GBM tissue was determined using qRT-PCR. A supervised principal component analysis followed by derivation of gene signature was performed. Independent validation of the signature was done using TCGA data. Gene Ontology and KEGG pathway analysis was carried out among patients from TCGA cohort. Results: A 14 gene signature was identified that predicted outcome in GBM. A weighted gene (WG) score was found to be an independent predictor of survival in multivariate analysis in the present cohort (HR = 2.507; B = 0.919; p < 0.001) and in TCGA cohort. Risk stratification by standardized WG score classified patients into low and high risk predicting survival both in our cohort (p = <0.001) and TCGA cohort (p = 0.001). Pathway analysis using the most differentially regulated genes (n = 76) between the low and high risk groups revealed association of activated inflammatory/immune response pathways and mesenchymal subtype in the high risk group. Conclusion: We have identified a 14 gene expression signature that can predict survival in GBM patients. A network analysis revealed activation of inflammatory response pathway specifically in high risk group. These findings may have implications in understanding of gliomagenesis, development of targeted therapies and selection of high risk cancer patients for alternate adjuvant therapies.

Joint pitch-analysis formant-synthesis framework for CS recovery of speech

A joint analysis-synthesis framework is developed for the compressive sensing (CS) recovery of speech signals. The signal is assumed to be sparse in the residual domain with the linear prediction filter used as the sparse transformation. Importantly this transform is not known apriori, since estimating the predictor filter requires the knowledge of the signal. Two prediction filters, one comb filter for pitch and another all pole formant filter are needed to induce maximum sparsity. An iterative method is proposed for the estimation of both the prediction filters and the signal itself. Formant prediction filter is used as the synthesis transform, while the pitch filter is used to model the periodicity in the residual excitation signal, in the analysis mode. Significant improvement in the LLR measure is seen over the previously reported formant filter estimation.

A DNA methylation prognostic signature of glioblastoma: identification of NPTX2-PTEN-NF-kappa B nexus

Glioblastoma (GBM) is the most common, malignant adult primary tumor with dismal patient survival, yet the molecular determinants of patient survival are poorly characterized. Global methylation profile of GBM samples (our cohort; n = 44) using high-resolution methylation microarrays was carried out. Cox regression analysis identified a 9-gene methylation signature that predicted survival in GBM patients. A risk-score derived from methylation signature predicted survival in univariate analysis in our and The Cancer Genome Atlas (TCGA) cohort. Multivariate analysis identified methylation risk score as an independent survival predictor in TCGA cohort. Methylation risk score stratified the patients into low-risk and high-risk groups with significant survival difference. Network analysis revealed an activated NF-kappa B pathway association with high-risk group. NF-kappa B inhibition reversed glioma chemoresistance, and RNA interference studies identified interleukin-6 and intercellular adhesion molecule-1 as key NF-kappa B targets in imparting chemoresistance. Promoter hypermethylation of neuronal pentraxin II (NPTX2), a risky methylated gene, was confirmed by bisulfite sequencing in GBMs. GBMs and glioma cell lines had low levels of NPTX2 transcripts, which could be reversed upon methylation inhibitor treatment. NPTX2 overexpression induced apoptosis, inhibited proliferation and anchorage-independent growth, and rendered glioma cells chemosensitive. Furthermore, NPTX2 repressed NF-kappa B activity by inhibiting AKT through a p53-PTEN-dependent pathway, thus explaining the hypermethylation and downregulation of NPTX2 in NF-kappa B-activated high-risk GBMs. Taken together, a 9-gene methylation signature was identified as an independent GBM prognosticator and could be used for GBM risk stratification. Prosurvival NF-kappa B pathway activation characterized high-risk patients with poor prognosis, indicating it to be a therapeutic target. (C) 2013 AACR.

Physiological and subjective thermal response from Indians

A controlled laboratory experiment was carried out on forty Indian male college students for evaluating the effect of indoor thermal environment on occupants' response and thermal comfort. During experiment, indoor temperature varied from 21 degrees C to 33 degrees C, and the variables like relative humidity, airflow, air temperature and radiant temperature were recorded along with skin (T-sk) and oral temperature (T-core) from the subjects. From T-sk and T-c, body temperature (T-b) was evaluated. Subjective Thermal Sensation Vote (TSV) was recorded using ASHRAE 7-point scale. In PMV model, Fanger's T-sk equation was used to accommodate adaptive response. Stepwise regression analysis result showed T-b was better predictor of TSV than T-sk and T-core. Regional skin temperature response, lower sweat threshold temperature with no dipping sweat and higher cutaneous sweating threshold temperature were observed as thermal adaptive responses. Using PMV model, thermal comfort zone was evaluated as (22.46-25.41) degrees C with neutral temperature of 23.91 degrees C, whereas using TSV response, wider comfort zone was estimated as (23.25-2632) degrees C with neutral temperature at 24.83 degrees C. It was observed that PMV-model overestimated the actual thermal response. Interestingly, these subjects were found to be less sensitive to hot but more sensitive to cold. A new TSV-PPD relation (PPDnew) was obtained with an asymmetric distribution of hot-cold thermal sensation response in Indians. (C) 2013 Elsevier Ltd. All rights reserved.

PMV model is insufficient to capture subjective thermal response from Indians

A controlled laboratory experiment was carried out on forty Indian male college students for evaluating the effect of indoor thermal environment on occupants' response and thermal comfort. During experiment, indoor temperature varied from 21 degrees C to 33 degrees C, and the variables like relative humidity, airflow, air temperature and radiant temperature were recorded along with subject's physiological parameters (skin (T-sk) and oral temperature (T-c)) and subjective thermal sensation responses (TSV). From T-sk and T-c, body temperature (T-b) was evaluated. Subjective Thermal Sensation Vote (TSV) was recorded using ASHRAE 7-point scale. In PMV model, Fanger's T-sk equation was used to accommodate adaptive response. Step-wise regression analysis result showed T-b was better predictor of TSV than T-sk and T-c. Regional skin temperature response, suppressed sweating without dipping, lower sweating threshold temperature and higher cutaneous threshold for sweating were observed as thermal adaptive responses. These adaptive responses cannot be considered in PMV model. To incorporate subjective adaptive response, mean skin temperature (T-sk) is considered in dry heat loss calculation. Along with these, PMV-model and other two methodologies are adopted to calculate PMV values and results are compared. However, recent literature is limited to measure the sweat rate in Indians and consideration of constant Ersw in PMV model needs to be corrected. Using measured T-sk in PMV model (Method(1)), thermal comfort zone corresponding to 0.5 <= PMV <= 0.5 was evaluated as (22.46-25.41) degrees C with neutral temperature of 23.91 degrees C, similarly while using TSV response, wider comfort zone was estimated as (23.25-26.32) degrees C with neutral temperature at 24.83 degrees C, which was further increased to with TSV-PPDnew, relation. It was observed that PMV-model overestimated the actual thermal response. Interestingly, these subjects were found to be less sensitive to hot but more sensitive to cold. A new TSV-PPD relation (PPDnew) was obtained from the population distribution of TSV response with an asymmetric distribution of hot-cold thermal sensation response from Indians. The calculations of human thermal stress according to steady state energy balance models used on PMV model seem to be inadequate to evaluate human thermal sensation of Indians. Relevance to industry: The purpose of this paper is to estimate thermal comfort zone and optimum temperature for Indians. It also highlights that PMV model seems to be inadequate to evaluate subjective thermal perception in Indians. These results can be used in feedback control of HVAC systems in residential and industrial buildings. (C) 2014 Elsevier B.V. All rights reserved.

Multi-site downscaling of maximum and minimum daily temperature using support vector machine

Climate change impact assessment studies involve downscaling large-scale atmospheric predictor variables (LSAPVs) simulated by general circulation models (GCMs) to site-scale meteorological variables. This article presents a least-square support vector machine (LS-SVM)-based methodology for multi-site downscaling of maximum and minimum daily temperature series. The methodology involves (1) delineation of sites in the study area into clusters based on correlation structure of predictands, (2) downscaling LSAPVs to monthly time series of predictands at a representative site identified in each of the clusters, (3) translation of the downscaled information in each cluster from the representative site to that at other sites using LS-SVM inter-site regression relationships, and (4) disaggregation of the information at each site from monthly to daily time scale using k-nearest neighbour disaggregation methodology. Effectiveness of the methodology is demonstrated by application to data pertaining to four sites in the catchment of Beas river basin, India. Simulations of Canadian coupled global climate model (CGCM3.1/T63) for four IPCC SRES scenarios namely A1B, A2, B1 and COMMIT were downscaled to future projections of the predictands in the study area. Comparison of results with those based on recently proposed multivariate multiple linear regression (MMLR) based downscaling method and multi-site multivariate statistical downscaling (MMSD) method indicate that the proposed method is promising and it can be considered as a feasible choice in statistical downscaling studies. The performance of the method in downscaling daily minimum temperature was found to be better when compared with that in downscaling daily maximum temperature. Results indicate an increase in annual average maximum and minimum temperatures at all the sites for A1B, A2 and B1 scenarios. The projected increment is high for A2 scenario, and it is followed by that for A1B, B1 and COMMIT scenarios. Projections, in general, indicated an increase in mean monthly maximum and minimum temperatures during January to February and October to December.

Development and Evaluation of Statistical Downscaling Models for Monthly Precipitation

Several statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs). This paper presents and compares different statistical downscaling models that use multiple linear regression (MLR), positive coefficient regression (PCR), stepwise regression (SR), and support vector machine (SVM) techniques for estimating monthly rainfall amounts in the state of Florida. Mean sea level pressure, air temperature, geopotential height, specific humidity, U wind, and V wind are used as the explanatory variables/predictors in the downscaling models. Data for these variables are obtained from the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis dataset and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model, version 3 (CGCM3) GCM simulations. The principal component analysis (PCA) and fuzzy c-means clustering method (FCM) are used as part of downscaling model to reduce the dimensionality of the dataset and identify the clusters in the data, respectively. Evaluation of the performances of the models using different error and statistical measures indicates that the SVM-based model performed better than all the other models in reproducing most monthly rainfall statistics at 18 sites. Output from the third-generation CGCM3 GCM for the A1B scenario was used for future projections. For the projection period 2001-10, MLR was used to relate variables at the GCM and NCEP grid scales. Use of MLR in linking the predictor variables at the GCM and NCEP grid scales yielded better reproduction of monthly rainfall statistics at most of the stations (12 out of 18) compared to those by spatial interpolation technique used in earlier studies.

LURR's Twenty Years and Its Perspective

Seismogenic process is a nonlinear and irreversible one, so that the response to loading of a seismogenic zone is different from the unloading one. This difference reflects quantitatively the process of an earthquake preparation. A physics-based new parameter-Load/Unload Response Ratio (LURR) was proposed to measure quantitatively the proximity to a strong earthquake and then used to be an earthquake predictor. In the present paper, a brief history of LURR is recalled; inspection of real earthquake cases, numerical simulations and laboratory studies of LURR, prediction efforts in terms of LURR, probability problem of LURR and its prospect are also expatiated.

Simulation of the Load-Unload Response Ratio and Critical Sensitivity in the Lattice Solid Model

The Load-Unload Response Ratio (LURR) method is an intermediate-term earthquake prediction approach that has shown considerable promise. It involves calculating the ratio of a specified energy release measure during loading and unloading where loading and unloading periods are determined from the earth tide induced perturbations in the Coulomb Failure Stress on optimally oriented faults. In the lead-up to large earthquakes, high LURR values are frequently observed a few months or years prior to the event. These signals may have a similar origin to the observed accelerating seismic moment release (AMR) prior to many large earthquakes or may be due to critical sensitivity of the crust when a large earthquake is imminent. As a first step towards studying the underlying physical mechanism for the LURR observations, numerical studies are conducted using the particle based lattice solid model (LSM) to determine whether LURR observations can be reproduced. The model is initialized as a heterogeneous 2-D block made up of random-sized particles bonded by elastic-brittle links. The system is subjected to uniaxial compression from rigid driving plates on the upper and lower edges of the model. Experiments are conducted using both strain and stress control to load the plates. A sinusoidal stress perturbation is added to the gradual compressional loading to simulate loading and unloading cycles and LURR is calculated. The results reproduce signals similar to those observed in earthquake prediction practice with a high LURR value followed by a sudden drop prior to macroscopic failure of the sample. The results suggest that LURR provides a good predictor for catastrophic failure in elastic-brittle systems and motivate further research to study the underlying physical mechanisms and statistical properties of high LURR values. The results provide encouragement for earthquake prediction research and the use of advanced simulation models to probe the physics of earthquakes.

Vertical Differentiation and Entry Deterrence: A Reconsideration

In this work we emphasize why market coverage should be considered endogenous for a correct analysis of entry deterrence in vertical differentiation models and discuss the implications of this endogeneity for that analysis. We consider contexts without quality costs and also contexts with convex fixed quality costs.

Stability of the factorial structure of metabolic syndrome from childhood to adolescence : a 6-year follow-up study

Background: Metabolic syndrome (MS) is a clustering of cardiometabolic risk factors that is considered a predictor of cardiovascular disease, type 2 diabetes and mortality. There is no consistent evidence on whether the MS construct works in the same way in different populations and at different stages in life. Methods: We used confirmatory factor analysis to examine if a single-factor-model including waist circumference, triglycerides/HDL-c, insulin and mean arterial pressure underlies metabolic syndrome from the childhood to adolescence in a 6-years follow-up study in 174 Swedish and 460 Estonian children aged 9 years at baseline. Indeed, we analyze the tracking of a previously validated MS index over this 6-years period. Results: The estimates of goodness-of-fit for the single-factor-model underlying MS were acceptable both in children and adolescents. The construct stability of a new model including the differences from baseline to the end of the follow-up in the components of the proposed model displayed good fit indexes for the change, supporting the hypothesis of a single factor underlying MS component trends. Conclusions: A single-factor-model underlying MS is stable across the puberty in both Estonian and Swedish young people. The MS index tracks acceptably from childhood to adolescence.

Efecto de la formación en el trabajo sobre la movilidad laboral

[ES] En este trabajo se analiza empíricamente la relación existente entre la formación dada por la empresa a sus trabajadores y la posterior salida de la empresa de dichos trabajadores. La principal aportación a la literatura existente en este campo se encuentra en la metodología empírica que se utiliza. En concreto, se propone un modelo de estimación que específicamente tiene en cuenta la posible endogeneidad existente entre la variable de formación y la de movilidad laboral.

Avances en algoritmos de estimación de distribuciones. Alternativas en el aprendizaje y representación de problemas

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任意阶隐式指数时程差分多步法及其在非线性系统中的应用

对非线性系统提出了任意阶隐式指数时程差分多步法 ,实现了任意阶次指数时程差分预测 校正算法 .发展完善了指数时程差分法 .将新算法应用于非线性系统 ,取得了较好的效果 .数值结果表明隐式指数时程差分多步法很好地修正了显式指数时程差分多步法 ,隐式指数时程差分多步法是一种高精度、高效率的方法

Protected species aerial survey data collection and analysis in waters underlying the R-5306A airspace: final report submitted to US Marine Corps, MCAS Cherry Point

To be in compliance with the Endangered Species Act and the Marine Mammal Protection Act, the United States Department of the Navy is required to assess the potential environmental impacts of conducting at-sea training operations on sea turtles and marine mammals. Limited recent and area-specific density data of sea turtles and dolphins exist for many of the Navy’s operations areas (OPAREAs), including the Marine Corps Air Station (MCAS) Cherry Point OPAREA, which encompasses portions of Core and Pamlico Sounds, North Carolina. Aerial surveys were conducted to document the seasonal distribution and estimated density of sea turtles and dolphins within Core Sound and portions of Pamlico Sound, and coastal waters extending one mile offshore. Sea Surface Temperature (SST) data for each survey were extracted from 1.4 km/pixel resolution Advanced Very High Resolution Radiometer remote images. A total of 92 turtles and 1,625 dolphins were sighted during 41 aerial surveys, conducted from July 2004 to April 2006. In the spring (March – May; 7.9°C to 21.7°C mean SST), the majority of turtles sighted were along the coast, mainly from the northern Core Banks northward to Cape Hatteras. By the summer (June – Aug.; 25.2°C to 30.8°C mean SST), turtles were fairly evenly dispersed along the entire survey range of the coast and Pamlico Sound, with only a few sightings in Core Sound. In the autumn (Sept. – Nov.; 9.6°C to 29.6°C mean SST), the majority of turtles sighted were along the coast and in eastern Pamlico Sound; however, fewer turtles were observed along the coast than in the summer. No turtles were seen during the winter surveys (Dec. – Feb.; 7.6°C to 11.2°C mean SST). The estimated mean surface density of turtles was highest along the coast in the summer of 2005 (0.615 turtles/km², SE = 0.220). In Core and Pamlico Sounds the highest mean surface density occurred during the autumn of 2005 (0.016 turtles/km², SE = 0.009). The mean seasonal abundance estimates were always highest in the coastal region, except in the winter when turtles were not sighted in either region. For Pamlico Sound, surface densities were always greater in the eastern than western section. The range of mean temperatures at which turtles were sighted was 9.68°C to 30.82°C. The majority of turtles sighted were within water ≥ 11°C. Dolphins were observed within estuarine waters and along the coast year-round; however, there were some general seasonal movements. In particular, during the summer sightings decreased along the coast and dolphins were distributed throughout Core and Pamlico Sounds, while in the winter the majority of dolphins were located along the coast and in southeastern Pamlico Sound. Although relative numbers changed seasonally between these areas, the estimated mean surface density of dolphins was highest along the coast in the spring of 2006 (9.564 dolphins/km², SE = 5.571). In Core and Pamlico Sounds the highest mean surface density occurred during the autumn of 2004 (0.192 dolphins/km², SE = 0.066). The estimated mean surface density of dolphins was lowest along the coast in the summer of 2004 (0.461 dolphins/km², SE = 0.294). The estimated mean surface density of dolphins was lowest in Core and Pamlico Sounds in the summer of 2005 (0.024 dolphins/km², SE = 0.011). In Pamlico Sound, estimated surface densities were greater in the eastern section except in the autumn. Dolphins were sighted throughout the entire range of mean SST (7.60°C to 30.82°C), with a tendency towards fewer dolphins sighted as water temperatures increased. Based on the findings of this study, sea turtles are most likely to be encountered within the OPAREAs when SST is ≥ 11°C. Since sea turtle distributions are generally limited by water temperature, knowing the SST of a given area is a useful predictor of sea turtle presence. Since dolphins were observed within estuarine waters year-round and throughout the entire range of mean SST’s, they likely could be encountered in the OPAREAs any time of the year. Although our findings indicated the greatest number of dolphins to be present in the winter and the least in the summer, their movements also may be related to other factors such as the availability of prey. (PDF contains 28 pages)