Association between variants of the leptin receptor gene (LEPR) and overweight: a systematic review and an analysis of the CoLaus study

Background Three non-synonymous single nucleotide polymorphisms (Q223R, K109R and K656N) of the leptin receptor gene (LEPR) have been tested for association with obesity-related outcomes in multiple studies, showing inconclusive results. We performed a systematic review and meta-analysis on the association of the three LEPR variants with BMI. In addition, we analysed 15 SNPs within the LEPR gene in the CoLaus study, assessing the interaction of the variants with sex. Methodology/Principal Findings We searched electronic databases, including population-based studies that investigated the association between LEPR variants Q223R, K109R and K656N and obesity- related phenotypes in healthy, unrelated subjects. We furthermore performed meta-analyses of the genotype and allele frequencies in case-control studies. Results were stratified by SNP and by potential effect modifiers. CoLaus data were analysed by logistic and linear regressions and tested for interaction with sex. The meta-analysis of published data did not show an overall association between any of the tested LEPR variants and overweight. However, the choice of a BMI cut-off value to distinguish cases from controls was crucial to explain heterogeneity in Q223R. Differences in allele frequencies across ethnic groups are compatible with natural selection of derived alleles in Q223R and K109R and of the ancient allele in K656N in Asians. In CoLaus, the rs10128072, rs3790438 and rs3790437 variants showed interaction with sex for their association with overweight, waist circumference and fat mass in linear regressions. Conclusions Our systematic review and analysis of primary data from the CoLaus study did not show an overall association between LEPR SNPs and overweight. Most studies were underpowered to detect small effect sizes. A potential effect modification by sex, population stratification, as well as the role of natural selection should be addressed in future genetic association studies.

Geophysical Analysis of Seasonal Montandon Gravel Ridge Water Table Fluctuation and Moisture Gradient Variation due to Storm Events

The purpose of this research project is to continue exploring the Montandon Long-Term Hydrologic Research Site(LTHR) by using multiple geophysical methods to obtain more accurate and precise information regarding subsurface hydrologic properties of a local gravel ridge,which are important to both the health of surrounding ecosystems and local agriculture. Through using non-invasive geophysical methods such as seismic refraction, Direct Current resistivity and ground penetrating radar (GPR) instead of invasive methods such as boreholedrilling which displace sediment and may alter water flow, data collection is less likely to bias the data itself. In addition to imaging the gravel ridge subsurface, another important researchpurpose is to observe how both water table elevation and the moisture gradient (moisture content of the unsaturated zone) change over a seasonal time period and directly after storm events. The combination of three types of data collection allows the strengths of each method combine together and provide a relatively strongly supported conclusions compared to previous research. Precipitation and geophysical data suggest that an overall increase in precipitation during the summer months causes a sharp decrease in subsurface resistivity within the unsaturated zone. GPR velocity data indicate significant immediate increase in moisture content within the shallow vadose zone (< 1m), suggesting that rain water was infiltrating into the shallow subsurface. Furthermore, the combination of resistivity and GPR results suggest that the decreased resistivity within the shallow layers is due to increased ion content within groundwater. This is unexpected as rainwater is assumed to have a DC resistivity value of 3.33*105 ohm-m. These results may suggest that ions within the sediment must beincorporated into the infiltrating water.

Replicative phenotyping adds value to genotypic resistance testing in heavily pre-treated HIV-infected individuals--the Swiss HIV Cohort Study

Background Replicative phenotypic HIV resistance testing (rPRT) uses recombinant infectious virus to measure viral replication in the presence of antiretroviral drugs. Due to its high sensitivity of detection of viral minorities and its dissecting power for complex viral resistance patterns and mixed virus populations rPRT might help to improve HIV resistance diagnostics, particularly for patients with multiple drug failures. The aim was to investigate whether the addition of rPRT to genotypic resistance testing (GRT) compared to GRT alone is beneficial for obtaining a virological response in heavily pre-treated HIV-infected patients. Methods Patients with resistance tests between 2002 and 2006 were followed within the Swiss HIV Cohort Study (SHCS). We assessed patients' virological success after their antiretroviral therapy was switched following resistance testing. Multilevel logistic regression models with SHCS centre as a random effect were used to investigate the association between the type of resistance test and virological response (HIV-1 RNA <50 copies/mL or ≥1.5log reduction). Results Of 1158 individuals with resistance tests 221 with GRT+rPRT and 937 with GRT were eligible for analysis. Overall virological response rates were 85.1% for GRT+rPRT and 81.4% for GRT. In the subgroup of patients with >2 previous failures, the odds ratio (OR) for virological response of GRT+rPRT compared to GRT was 1.45 (95% CI 1.00-2.09). Multivariate analyses indicate a significant improvement with GRT+rPRT compared to GRT alone (OR 1.68, 95% CI 1.31-2.15). Conclusions In heavily pre-treated patients rPRT-based resistance information adds benefit, contributing to a higher rate of treatment success.

Investigating human audio-visual object perception with a combination of hypothesis-generating and hypothesis-testing fMRI analysis tools

Primate multisensory object perception involves distributed brain regions. To investigate the network character of these regions of the human brain, we applied data-driven group spatial independent component analysis (ICA) to a functional magnetic resonance imaging (fMRI) data set acquired during a passive audio-visual (AV) experiment with common object stimuli. We labeled three group-level independent component (IC) maps as auditory (A), visual (V), and AV, based on their spatial layouts and activation time courses. The overlap between these IC maps served as definition of a distributed network of multisensory candidate regions including superior temporal, ventral occipito-temporal, posterior parietal and prefrontal regions. During an independent second fMRI experiment, we explicitly tested their involvement in AV integration. Activations in nine out of these twelve regions met the max-criterion (A < AV > V) for multisensory integration. Comparison of this approach with a general linear model-based region-of-interest definition revealed its complementary value for multisensory neuroimaging. In conclusion, we estimated functional networks of uni- and multisensory functional connectivity from one dataset and validated their functional roles in an independent dataset. These findings demonstrate the particular value of ICA for multisensory neuroimaging research and using independent datasets to test hypotheses generated from a data-driven analysis.

Analysis Methodology for Flow-level Evaluation of a Hybrid Mobile-Sensor Network

Our society uses a large diversity of co-existing wired and wireless networks in order to satisfy its communication needs. A cooper- ation between these networks can benefit performance, service availabil- ity and deployment ease, and leads to the emergence of hybrid networks. This position paper focuses on a hybrid mobile-sensor network identify- ing potential advantages and challenges of its use and defining feasible applications. The main value of the paper, however, is in the proposed analysis approach to evaluate the performance at the mobile network side given the mixed mobile-sensor traffic. The approach combines packet- level analysis with modelling of flow-level behaviour and can be applied for the study of various application scenarios. In this paper we consider two applications with distinct traffic models namely multimedia traffic and best-effort traffic.

Analysis of 3D Soft Tissue Changes After 1- and 2-Jaw Orthognathic Surgery in Mandibular Prognathism Patients

Purpose Orthognathic surgery has the objective of altering facial balance to achieve esthetic results in patients who have severe disharmony of the jaws. The purpose was to quantify the soft tissue changes after orthognathic surgery, as well as to assess the differences in 3D soft tissue changes in the middle and lower third of the face between the 1- and 2-jaw surgery groups, in mandibular prognathism patients. Materials and Methods We assessed soft tissue changes of patients who have been diagnosed with mandibular prognathism and received either isolated mandibular surgery or bimaxillary surgery. The quantitative surface displacement was assessed by superimposing preoperative and postoperative volumetric images. An observer measured a surface-distance value that is shown as a contour line. Differences between the groups were determined by the Mann-Whitney U test. The Spearman correlation coefficient was used to evaluate a potential correlation between patients' surgical and cephalometric variables and soft tissue changes after orthognathic surgery in each group. Results There were significant differences in the middle third of the face between the 1- and 2-jaw surgery groups. Soft tissues in the lower third of the face changed in both surgery groups, but not significantly. The correlation patterns were more evident in the lower third of the face. Conclusion The overall soft tissue changes of the midfacial area were more evident in the 2-jaw surgery group. In 2-jaw surgery, significant changes would be expected in the midfacial area, but caution should be exercised in patients who have a wide alar base.

Favourable prognostic value of antibodies anti-HSP20 in patients with cystic echinococcosis: a differential immunoproteomic approach

Seeking biomarkers reflecting disease development in cystic echinococcosis (CE), we used a proteomic approach linked to immunological characterisation for the identification of respective antigens. Two-dimensional gel electrophoresis (2-DE) of sheep hydatid fluid, followed by immunoblot analysis (IB) with sera from patients with distinct phases of disease, enabled us to identify by mass spectrometry heat shock protein 20 (HSP20) as a potential marker of active CE. Using IB, antibodies specific to the 34 kDa band of HSP20 were detected in sera from 61/95 (64%) patients with CE, but not in sera from healthy subjects. IB revealed anti-HSP20 antibodies in a higher percentage of sera from patients with active disease than in sera from patients with inactive disease (81 vs. 24%; P = 10(-4)). These primary results were confirmed in a long-term follow-up study after pharmacological and surgical treatment. Herewith anti-HSP20 antibody levels significantly decreased over the course of treatment in sera from patients with cured disease, relative to sera from patients with progressive disease (P = 0.017). Thus, during CE, a comprehensive strategy of proteomic identification combined with immunological validation represents a promising approach for the identification of biomarkers useful for the prognostic assessment of treatment of CE patients.

Characterization of CDKN2A(p16) methylation and impact in colorectal cancer: systematic analysis using pyrosequencing

Background The aim of this study is to analyse CDKN2A methylation using pyrosequencing on a large cohort of colorectal cancers and corresponding non-neoplastic tissues. In a second step, the effect of methylation on clinical outcome is addressed. Methods Primary colorectal cancers and matched non-neoplastic tissues from 432 patients underwent CDKN2A methylation analysis by pyrosequencing (PyroMarkQ96). Methylation was then related to clinical outcome, microsatellite instability (MSI), and BRAF and KRAS mutation. Different amplification conditions (35 to 50 PCR cycles) using a range of 0-100% methylated DNA were tested. Results Background methylation was at most 10% with ≥35 PCR cycles. Correlation of observed and expected values was high, even at low methylation levels (0.02%, 0.6%, 2%). Accuracy of detection was optimal with 45 PCR cycles. Methylation in normal mucosa ranged from 0 to >90% in some cases. Based on the maximum value of 10% background, positivity was defined as a ≥20% difference in methylation between tumor and normal tissue, which occurred in 87 cases. CDKN2A methylation positivity was associated with MSI (p = 0.025), BRAF mutation (p < 0.0001), higher tumor grade (p < 0.0001), mucinous histology (p = 0.0209) but not with KRAS mutation. CDKN2A methylation had an independent adverse effect (p = 0.0058) on prognosis. Conclusion The non-negligible CDKN2A methylation of normal colorectal mucosa may confound the assessment of tumor-specific hypermethylation, suggesting that corresponding non-neoplastic tissue should be used as a control. CDKN2A methylation is robustly detected by pyrosequencing, even at low levels, suggesting that this unfavorable prognostic biomarker warrants investigation in prospective studies.

Bivariate meta-analysis of predictive values of diagnostic tests can be an alternative to bivariate meta-analysis of sensitivity and specificity

Meta-analysis of predictive values is usually discouraged because these values are directly affected by disease prevalence, but sensitivity and specificity sometimes show substantial heterogeneity as well. We propose a bivariate random-effects logitnormal model for the meta-analysis of the positive predictive value (PPV) and negative predictive value (NPV) of diagnostic tests.

Analysis and Prediction of Transient Opacity Spikes Using Dimensional Modeling

Dimensional modeling, GT-Power in particular, has been used for two related purposes-to quantify and understand the inaccuracies of transient engine flow estimates that cause transient smoke spikes and to improve empirical models of opacity or particulate matter used for engine calibration. It has been proposed by dimensional modeling that exhaust gas recirculation flow rate was significantly underestimated and volumetric efficiency was overestimated by the electronic control module during the turbocharger lag period of an electronically controlled heavy duty diesel engine. Factoring in cylinder-to-cylinder variation, it has been shown that the electronic control module estimated fuel-Oxygen ratio was lower than actual by up to 35% during the turbocharger lag period but within 2% of actual elsewhere, thus hindering fuel-Oxygen ratio limit-based smoke control. The dimensional modeling of transient flow was enabled with a new method of simulating transient data in which the manifold pressures and exhaust gas recirculation system flow resistance, characterized as a function of exhaust gas recirculation valve position at each measured transient data point, were replicated by quasi-static or transient simulation to predict engine flows. Dimensional modeling was also used to transform the engine operating parameter model input space to a more fundamental lower dimensional space so that a nearest neighbor approach could be used to predict smoke emissions. This new approach, intended for engine calibration and control modeling, was termed the "nonparametric reduced dimensionality" approach. It was used to predict federal test procedure cumulative particulate matter within 7% of measured value, based solely on steady-state training data. Very little correlation between the model inputs in the transformed space was observed as compared to the engine operating parameter space. This more uniform, smaller, shrunken model input space might explain how the nonparametric reduced dimensionality approach model could successfully predict federal test procedure emissions when roughly 40% of all transient points were classified as outliers as per the steady-state training data.

A Natural Gas Severance Tax-An Economic Analysis

This study estimates the economic effects of a severance tax on the market for natural gas produced from shale sources using non-conventional extraction methods, such as horizontal drilling and fracking. Results suggest that a severance tax of 5% would increase the price of natural gas by as much as 3.82% and decrease gas extraction by an estimated 1.16% to a value of 9.52%. If applied to the Commonwealth of Pennsylvania in the United States, a 5% severance tax is estimated to raise between US$443 and $486 million per year in public revenue. The marginal deadweight loss associated with a 5% severance tax is estimated between 1.27% and 12.85% of the last dollar earned. The burden of this tax falls on both producers and consumers and depends upon the underlying assumptions made regarding the price responsiveness of consumers and producers. Under plausible assumptions, a family consuming 1000 MMcfs (approximate to 2.8 x 10(4) m(3)) per year of natural gas is estimated to pay an additional $100 per year after the implementation of a 5% severance tax.

Safe and early discharge after colorectal surgery due to C-reactive protein: a diagnostic meta-analysis of 1832 patients

To assess the predictive value of C-reactive protein (CRP) level for postoperative infectious complications after colorectal surgery.

Prognostic impact of lymph node ratio outperforms positive lymph nodes and lymph nodes harvested: a time-dependent analysis in mismatch repair-proficient and –deficient colrectal cancers

Objective: We compare the prognostic strength of the lymph node ratio (LNR), positive lymph nodes (+LNs) and collected lymph nodes (LNcoll) using a time-dependent analysis in colorectal cancer patients stratified by mismatch repair (MMR) status. Method: 580 stage III-IV patients were included. Multivariable Cox regression analysis and time-dependent receiver operating characteristic (tROC) curve analysis were performed. The Area under the Curve (AUC) over time was compared for the three features. Results were validated on a second cohort of 105 stage III-IV patients. Results: The AUC for the LNR was 0.71 and outperformed + LNs and LNcoll by 10–15 % in both MMR-proficient and deficient cancers. LNR and + LNs were both significant (p<0.0001) in multivariable analysis but the effect was considerably stronger for the LNR [LNR: HR=5.18 (95 % CI: 3.5–7.6); +LNs=1.06 (95 % CI: 1.04–1.08)]. Similar results were obtained for patients with >12 LNcoll. An optimal cut off score for LNR=0.231 was validated on the second cohort (p<0.001). Conclusion: The LNR outperforms the + LNs and LNcoll even in patients with >12 LNcoll. Its clinical value is not confounded by MMR status. A cut-of score of 0.231 may best stratify patients into prognostic subgroups and could be a basis for the future prospective analysis of the LNR.