Estimation of total landfill surface methane emissions using geospatial approach combined with measured surface ambient air methane concentrations.

The concentration of surface air methane (CH4) measured in parts per million by volume (ppmv) near the soil/atmosphere interface should, in theory, have a positive correlation with surface methane emissions fluxes, measured in grams per square meter per day (gm-2d-1). Some researchers suggest that CH4 flux can be reasonably inferred from simple measurements of CH4 concentrations near the landfill surface. Ground-based and drone-based surface emissions monitoring (SEMs) were performed at several municipal solid waste landfills as tracer correlation method (TCM) testing was being used to measure total methane emissions from the same landfills. The TCM data and SEM data were used to establish a new simple correlation to convert surface methane concentrations in ppmv to localized surface methane emission flux in gm-2d-1.The SEM data obtained from ten ground and drone monitoring campaigns were log-transformed and geospatially treated using inverse distance weighting to the power of 2 to predict methane surface concentrations in the entire footprint of the SEM measurements area. The developed new correlation equation was then used to convert every predicted surface methane concentration to an emissions flux. The total estimate of surface emissions from the entire landfill was obtained by integrating the predicted fluxes over the area of the footprint of the SEM measurement area. The use of the new developed correlation resulted in higher total emissions estimates than other correlations reported in the literature and should be considered more conservative. Not including other factors, the proposed approach provides estimate of total methane emissions with a coefficient of variation of 20%. This study introduces a novel approach that utilizes a developed correlation between surface methane concentrations and surface emissions fluxes to estimate total methane emissions from municipal solid waste landfills or from a specified area. This study provides an additional use of the quarterly SEM data.Implications: The proposed approach provides an occasion for additional use of the easily obtainable quarterly SEMs data that can be performed by most landfills. The SEMs data are the most abundant landfill methane concentrations data. This approach gives them more benefit for the user. It is intended to convert ambient air concentrations to some estimates of surface emissions that can help landfill owners with decision making such as remediation activities or adjustments of their gas collection a systems.

Comparing estimates of fugitive landfill methane emissions using inverse plume modeling obtained with Surface Emission Monitoring (SEM), Drone Emission Monitoring (DEM), and Downwind Plume Emission Monitoring (DWPEM).

As part of the global effort to quantify and manage anthropogenic greenhouse gas emissions, there is considerable interest in quantifying methane emissions in municipal solid waste landfills. A variety of analytical and experimental methods are currently in use for this task. In this paper, an optimization-based estimation method is employed to assess fugitive landfill methane emissions. The method combines inverse plume modeling with ambient air methane concentration measurements. Three different measurement approaches are tested and compared. The method is combined with surface emission monitoring (SEM), above ground drone emission monitoring (DEM), and downwind plume emission monitoring (DWPEM). The methodology is first trialed and validated using synthetic datasets in a hand-generated case study. A field study is also presented where SEM, DEM and DWPEM are tested and compared. Methane flux during two-days measurement campaign was estimated to be between 228 and 350 g/s depending on the type of measurements used. Compared to SEM, using unmanned aerial systems (UAS) allows for a rapid and comprehensive coverage of the site. However, as showed through this work, advancement of DEM-based methane sampling is governed by the advances that could be made in UAS-compatible measurement instrumentations. Downwind plume emission monitoring led to a smaller estimated flux compared with SEM and DEM without information about positions of major leak points in the landfill. Even though, the method is simple and rapid for landfill methane screening. Finally, the optimization-based methodology originally developed for SEM, shows promising results when it is combined with the drone-based collected data and downwind concentration measurements. The studied cases also discovered the limitations of the studied sampling strategies which is exploited to identify improvement strategies and recommendations for a more efficient assessment of fugitive landfill methane emissions.Implications: Fugitive landfill methane emission estimation is tackled in the present study. An optimization-based method combined with inverse plume modeling is employed to treat data from surface emission monitoring, drone-based emission monitoring and downwind plume emission monitoring. The study helped revealing the advantages and the limitations of the studied sampling strategies. Recommendations for an efficient assessment of landfill methane emissions are formulated. The method trialed in this study for fugitive landfill methane emission could also be appropriate for rapid screening of analogous greenhouse gas emission hotspots.

Stable isotopic determination of methane oxidation: When smaller scales are better.

Stable isotope measurements are an effective tool for evaluating methane (CH4) consumption in landfill soils. However, determining the extent of CH4 oxidation in soils using this approach can be inherently biased, depending on characteristics of the study site and the sampling strategy that is employed. In this study, we establish the unusual case that sampling at smaller scales captures a better representation of the degree of oxidation occurring in landfill cover soils. We did this by comparing three techniques (Plume, Probe, and Transect) that vary in the location of sampling within a site and in the areal footprint of each sample. The Plume method yielded estimates of CH4 oxidation that were 13-16% lower than the Transect and Probe methods, respectively. The Probe and Transect methods, two relatively small-scale and high resolution methods, the latter of which has not been previously described, are best suited to quantify CH4 oxidation in landfill soils as they demonstrably overcome the tendency of stable isotope methods to underestimate CH4 oxidation at the landfill scale. We recommend the use of these two sampling methods for monitoring the efficacy of landfill CH4 reduction strategies that are desired to help meet the goals of the Paris Agreement.

Estimation of fugitive landfill methane emissions using surface emission monitoring and Genetic Algorithms optimization.

As municipal solid waste (MSW) landfills can generate significant amounts of methane, there is considerable interest in quantifying fugitive methane emissions at such facilities. A variety of methods exist for the estimation of methane emissions from landfills. These methods are either based on analytical emission models or on measurements. This paper presents a method to estimate methane emissions using ambient air methane measurements obtained on the surface of a landfill. Genetic Algorithms based optimization combined with the standard Gaussian dispersion model is employed to identify locations as well as emission rates of potential emission sources throughout a municipal solid waste landfill. Four case studies are employed in order to evaluate the performance of the proposed methodology. It is shown that the proposed approach enables estimation of landfill methane emissions and localization of major emission hotspots in the studied landfills. The proposed source-locating-scheme could be seen as a cost effective method assisting landfill operators to reasonably estimate and locate major methane emissions.

Comparison of Field Measurements to Methane Emissions Models at a New Landfill.

Estimates of methane emissions from landfills rely primarily on models due to both technical and economic limitations. While models are easy to implement, there is uncertainty due to the use of parameters that are difficult to validate. The objective of this research was to compare modeled emissions using several greenhouse gas (GHG) emissions reporting protocols including: (1) Intergovernmental Panel on Climate Change (IPCC); (2) U.S. Environmental Protection Agency Greenhouse Gas Reporting Program (EPA GHGRP); (3) California Air Resources Board (CARB); and (4) Solid Waste Industry for Climate Solutions (SWICS), with measured emissions data collected over three calendar years from a young landfill with no gas collection system. By working with whole landfill measurements of fugitive methane emissions and methane oxidation, the collection efficiency could be set to zero, thus eliminating one source of parameter uncertainty. The models consistently overestimated annual methane emissions by a factor ranging from 4-31. Varying input parameters over reasonable ranges reduced this range to 1.3-8. Waste age at the studied landfill was less than four years and the results suggest the need for measurements at additional landfills to evaluate the accuracy of the tested models to young landfills.

Dental avulsions: Review and recommendations.

Tooth avulsion injuries are common. This article will review causes of tooth avulsion and provide management guidelines. Recommendations for NP education and practice will be reviewed.

Promoter methylation of ITF2, but not APC, is associated with microsatellite instability in two populations of colorectal cancer patients.

BACKGROUND: Aberrant Wnt signaling activation occurs commonly in colorectal carcinogenesis, leading to upregulation of many target genes. APC (adenomatous polyposis coli) is an important component of the ß-catenin destruction complex, which regulates Wnt signaling, and is often mutated in colorectal cancer (CRC). In addition to mutational events, epigenetic changes arise frequently in CRC, specifically, promoter hypermethylation which silences tumor suppressor genes. APC and the Wnt signaling target gene ITF2 (immunoglobulin transcription factor 2) incur hypermethylation in various cancers, however, methylation-dependent regulation of these genes in CRC has not been studied in large, well-characterized patient cohorts. The microsatellite instability (MSI) subtype of CRC, featuring DNA mismatch repair deficiency and often promoter hypermethylation of MutL homolog 1 (MLH1), has a favorable outcome and is characterized by different chemotherapeutic responses than microsatellite stable (MSS) tumors. Other epigenetic events distinguishing these subtypes have not yet been fully elucidated. METHODS: Here, we quantify promoter methylation of ITF2 and APC by MethyLight in two case-case studies nested in population-based CRC cohorts from the Ontario Familial Colorectal Cancer Registry (n = 330) and the Newfoundland Familial Colorectal Cancer Registry (n = 102) comparing MSI status groups. RESULTS: ITF2 and APC methylation are significantly associated with tumor versus normal state (both P < 1.0 × 10(-6)). ITF2 is methylated in 45.8% of MSI cases and 26.9% of MSS cases and is significantly associated with MSI in Ontario (P = 0.002) and Newfoundland (P = 0.005) as well as the MSI-associated feature of MLH1 promoter hypermethylation (P = 6.72 × 10(-4)). APC methylation, although tumor-specific, does not show a significant association with tumor subtype, age, gender, or stage, indicating it is a general tumor-specific CRC biomarker. CONCLUSIONS: This study demonstrates, for the first time, MSI-associated ITF2 methylation, and further reveals the subtype-specific epigenetic events modulating Wnt signaling in CRC.

Comparison of Prediction Models for Lynch Syndrome Among Individuals With Colorectal Cancer.

BACKGROUND: Recent guidelines recommend the Lynch Syndrome prediction models MMRPredict, MMRPro, and PREMM1,2,6 for the identification of MMR gene mutation carriers. We compared the predictive performance and clinical usefulness of these prediction models to identify mutation carriers. METHODS: Pedigree data from CRC patients in 11 North American, European, and Australian cohorts (6 clinic- and 5 population-based sites) were used to calculate predicted probabilities of pathogenic MLH1, MSH2, or MSH6 gene mutations by each model and gene-specific predictions by MMRPro and PREMM1,2,6. We examined discrimination with area under the receiver operating characteristic curve (AUC), calibration with observed to expected (O/E) ratio, and clinical usefulness using decision curve analysis to select patients for further evaluation. All statistical tests were two-sided. RESULTS: Mutations were detected in 539 of 2304 (23%) individuals from the clinic-based cohorts (237 MLH1, 251 MSH2, 51 MSH6) and 150 of 3451 (4.4%) individuals from the population-based cohorts (47 MLH1, 71 MSH2, 32 MSH6). Discrimination was similar for clinic- and population-based cohorts: AUCs of 0.76 vs 0.77 for MMRPredict, 0.82 vs 0.85 for MMRPro, and 0.85 vs 0.88 for PREMM1,2,6. For clinic- and population-based cohorts, O/E deviated from 1 for MMRPredict (0.38 and 0.31, respectively) and MMRPro (0.62 and 0.36) but were more satisfactory for PREMM1,2,6 (1.0 and 0.70). MMRPro or PREMM1,2,6 predictions were clinically useful at thresholds of 5% or greater and in particular at greater than 15%. CONCLUSIONS: MMRPro and PREMM1,2,6 can well be used to select CRC patients from genetics clinics or population-based settings for tumor and/or germline testing at a 5% or higher risk. If no MMR deficiency is detected and risk exceeds 15%, we suggest considering additional genetic etiologies for the cause of cancer in the family.

Genetic structure of the Newfoundland and Labrador population: founder effects modulate variability.

The population of the province of Newfoundland and Labrador (NL) has been a resource for genetic studies because of its historical isolation and increased prevalence of several monogenic disorders. Controversy remains regarding the genetic substructure and the extent of genetic homogeneity, which have implications for disease gene mapping. Population substructure has been reported from other isolated populations such as Iceland, Finland and Sardinia. We undertook this study to further our understanding of the genetic architecture of the NL population. We enrolled 494 individuals randomly selected from NL. Genome-wide SNP data were analyzed together with that from 14 other populations including HapMap3, Ireland, Britain and Native American samples from the Human Genome Diversity Project. Using multidimensional scaling and admixture analysis, we observed that the genetic structure of the NL population resembles that of the British population but can be divided into three clusters that correspond to religious/ethnic origins: Protestant English, Roman Catholic Irish and North American aboriginals. We observed reduced heterozygosity and an increased inbreeding coefficient (mean=0.005), which corresponds to that expected in the offspring of third-cousin marriages. We also found that the NL population has a significantly higher number of runs of homozygosity (ROH) and longer lengths of ROH segments. These results are consistent with our understanding of the population history and indicate that the NL population may be ideal for identifying recessive variants for complex diseases that affect populations of European origin.

SMAD3 Is Upregulated in Human Osteoarthritic Cartilage Independent of the Promoter DNA Methylation.

OBJECTIVE: To compare SMAD3 gene expression between human osteoarthritic and healthy cartilage and to examine whether expression is regulated by the promoter DNA methylation of the gene. METHODS: Human cartilage samples were collected from patients undergoing total hip/knee joint replacement surgery due to primary osteoarthritis (OA), and from patients with hip fractures as controls. DNA/RNA was extracted from the cartilage tissues. Real-time quantitative PCR was performed to measure gene expression, and Sequenom EpiTyper was used to assay DNA methylation. Mann-Whitney test was used to compare the methylation and expression levels between OA cases and controls. Spearman rank correlation coefficient was calculated to examine the association between the methylation and gene expression. RESULTS: A total of 58 patients with OA (36 women, 22 men; mean age 64 ± 9 yrs) and 55 controls (43 women, 12 men; mean age 79 ± 10 yrs) were studied. SMAD3 expression was on average 83% higher in OA cartilage than in controls (p = 0.0005). No difference was observed for DNA methylation levels in the SMAD3 promoter region between OA cases and controls. No correlation was found between SMAD3 expression and promoter DNA methylation. CONCLUSION: Our study demonstrates that SMAD3 is significantly overexpressed in OA. This overexpression cannot be explained by DNA methylation in the promoter region. The results suggest that the transforming growth factor-ß/SMAD3 pathway may be overactivated in OA cartilage and has potential in developing targeted therapies for OA.

Genome-wide DNA methylation study of hip and knee cartilage reveals embryonic organ and skeletal system morphogenesis as major pathways involved in osteoarthritis.

BACKGROUND: Evidence suggests that epigenetics plays a role in osteoarthrits (OA). The aim of the study was to describe the genome wide DNA methylation changes in hip and knee OA and identify novel genes and pathways involved in OA by comparing the DNA methylome of the hip and knee osteoarthritic cartilage tissues with those of OA-free individuals. METHODS: Cartilage samples were collected from hip or knee joint replacement patients either due to primary OA or hip fractures as controls. DNA was extracted from the collected cartilage and assayed by Illumina Infinium HumanMethylation450 BeadChip array, which allows for the analysis of >480,000 CpG sites. Student T-test was conducted for each CpG site and those sites with at least 10 % methylation difference and a p value <0.0005 were defined as differentially methylated regions (DMRs) for OA. A sub-analysis was also done for hip and knee OA separately. DAVID v6.7 was used for the functional annotation clustering of the DMR genes. Clustering analysis was done using multiple dimensional scaling and hierarchical clustering methods. RESULTS: The study included 5 patients with hip OA, 6 patients with knee OA and 7 hip cartilage samples from OA-free individuals. The comparisons of hip, knee and combined hip/knee OA patients with controls resulted in 26, 72, and 103 DMRs, respectively. The comparison between hip and knee OA revealed 67 DMRs. The overall number of the sites after considering the overlaps was 239, among which 151 sites were annotated to 145 genes. One-fifth of these genes were reported in previous studies. The functional annotation clustering of the identified genes revealed clusters significantly enriched in skeletal system morphogenesis and development. The analysis revealed significant difference among OA and OA-free cartilage, but less different between hip OA and knee OA. CONCLUSIONS: We found that a number of CpG sites and genes across the genome were differentially methylated in OA patients, a remarkable portion of which seem to be involved in potential etiologic mechanisms of OA. Genes involved in skeletal developmental pathways and embryonic organ morphogenesis may be a potential area for further OA studies.

Overexpression of MMP13 in human osteoarthritic cartilage is associated with the SMAD-independent TGF-ß signalling pathway.

INTRODUCTION: In vitro and animal model of osteoarthritis (OA) studies suggest that TGF-ß signalling is involved in OA, but human data is limited. We undertook this study to elucidate the role of TGF-ß signalling pathway in OA by comparing the expression levels of TGFB1 and BMP2 as ligands, SMAD3 as an intracellular mediator, and MMP13 as a targeted gene between human osteoarthritic and healthy cartilage. METHODS: Human cartilage samples were collected from patients undergoing total hip/knee joint replacement surgery due to primary OA or hip fractures as controls. RNA was extracted from the cartilage tissues. Real-time quantitative PCR was performed to measure gene expression. Mann-Whitney test was utilized to compare the expression levels of TGFB1, BMP2, SMAD3 and MMP13 in human cartilage between OA cases and controls. Spearman's rank correlation coefficient (rho) was calculated to examine the correlation between the expression levels of the four genes studied and non-parametric regression was used to adjust for covariates. RESULTS: A total of 32 OA cases (25 hip OA and 7 knee OA) and 21 healthy controls were included. The expression of TGFB1, SMAD3, and MMP13 were on average 70%, 46%, and 355% higher, respectively, whereas the expression of BMP2 was 88% lower, in OA-affected cartilage than that of controls (all p < 0.03), but no difference was observed between hip and knee OA (all p > 0.4). The expression of TGFB1 was correlated with the expression of SMAD3 (rho = 0.50, p = 0.003) and MMP13 (rho = 0.46, p = 0.007) in OA-affected cartilage and the significance became stronger after adjustment for age, sex, and BMI. The expression of BMP2 was negatively correlated with both TGFB1 (rho = -0.50, p = 0.02) and MMP13 (rho = -0.48, p = 0.02) in healthy cartilage, but the significance was altered after adjustment for the covariates. There was no correlation between the expression of SMAD3 and MMP13. CONCLUSIONS: Our results demonstrate that MMP13 expression is associated with an increased expression of TGFB1 in OA-affected cartilage, possibly through SMAD-independent TGF-ß pathway. Furthermore, TGF-ß/SMAD3 is overactivated in OA cartilage; yet, the consequence of this overactivation remains to be established.

Mitochondrial DNA polymorphisms, its copy number change and outcome in colorectal cancer.

BACKGROUND: Mitochondrion is a small organelle inside the eukaryotic cells. It has its own genome (mtDNA) and encodes for proteins that are critical for energy production and cellular metabolism. Mitochondrial dysfunctions have been implicated in cancer progression and may be related to poor prognosis in cancer patients. In this study we hypothesized that genetic variations in mtDNA are associated with clinical outcome in colorectal cancer patients. METHODS: We tested the associations of six mtDNA polymorphisms [MitoT479C, MitoT491C, MitoT10035C, MitoA13781G, 10398 (A/G), and 16189 (T/C)] and the mtDNA copy number change with overall survival (OS) and disease-free survival (DFS) times. Two mtDNA polymorphisms were genotyped using the TaqMan(®) SNP genotyping technique and the genotypes for the remaining four mtDNA polymorphisms were obtained by the Illumina(®) HumanOmni1-Quad genome wide SNP genotyping platform in 536 patients. The mtDNA copy number change (in tumor tissues with respect to non-tumor tissues) was estimated using the quantitative real time polymerase chain reaction for 274 patients. Associations of these mtDNA variations with OS and DFS were tested using the Cox regression method. RESULTS: In both univariate and multivariable analyses, none of the six mtDNA polymorphisms were associated with OS or DFS. 39.6 and 60.4% of the patients had increased and decreased mtDNA copy number in their tumor tissues when compared to their non-tumor rectum or colon tissues, respectively. However, in contrast to previous findings, the change in the mtDNA copy number was associated with neither OS nor DFS in our patient cohort. CONCLUSIONS: Our results suggest that the mitochondrial genetic markers investigated in this study are not associated with outcome in colorectal cancer.

A Survival Association Study of 102 Polymorphisms Previously Associated with Survival Outcomes in Colorectal Cancer.

Several published studies identified associations of a number of polymorphisms with a variety of survival outcomes in colorectal cancer. In this study, we aimed to explore 102 previously reported common genetic polymorphisms and their associations with overall survival (OS) and disease-free survival (DFS) in a colorectal cancer patient cohort from Newfoundland (n = 505). Genotypes were obtained using a genomewide SNP genotyping platform. For each polymorphism, the best possible genetic model was estimated for both overall survival and disease-free survival using a previously published approach. These SNPs were then analyzed under their genetic models by Cox regression method. Correction for multiple comparisons was performed by the False Discovery Rate (FDR) method. Univariate analysis results showed that RRM1-rs12806698, IFNGR1-rs1327474, DDX20-rs197412, and PTGS2-rs5275 polymorphisms were nominally associated with OS or DFS (p < 0.01). In stage-adjusted analysis, the nominal associations of DDX20-rs197412, PTGS2-rs5275, and HSPA5-rs391957 with DFS were detected. However, after FDR correction none of these polymorphisms remained significantly associated with the survival outcomes. We conclude that polymorphisms investigated in this study are not associated with OS or DFS in our colorectal cancer patient cohort.

Association between body mass index and mortality for colorectal cancer survivors: overall and by tumor molecular phenotype.

BACKGROUND: Microsatellite instability (MSI) and BRAF mutation status are associated with colorectal cancer survival, whereas the role of body mass index (BMI) is less clear. We evaluated the association between BMI and colorectal cancer survival, overall and by strata of MSI, BRAF mutation, sex, and other factors. METHODS: This study included 5,615 men and women diagnosed with invasive colorectal cancer who were followed for mortality (maximum: 14.7 years; mean: 5.9 years). Prediagnosis BMI was derived from self-reported weight approximately one year before diagnosis and height. Tumor MSI and BRAF mutation status were available for 4,131 and 4,414 persons, respectively. Multivariable hazard ratios (HR) and 95% confidence intervals (CI) were estimated from delayed-entry Cox proportional hazards models. RESULTS: In multivariable models, high prediagnosis BMI was associated with higher risk of all-cause mortality in both sexes (per 5-kg/m(2); HR, 1.10; 95% CI, 1.06-1.15), with similar associations stratified by sex (Pinteraction: 0.41), colon versus rectum (Pinteraction: 0.86), MSI status (Pinteraction: 0.84), and BRAF mutation status (Pinteraction: 0.28). In joint models, with MS-stable/MSI-low and normal BMI as the reference group, risk of death was higher for MS-stable/MSI-low and obese BMI (HR, 1.32; P value: 0.0002), not statistically significantly lower for MSI-high and normal BMI (HR, 0.86; P value: 0.29), and approximately the same for MSI-high and obese BMI (HR, 1.00; P value: 0.98). CONCLUSIONS: High prediagnosis BMI was associated with increased mortality; this association was consistent across participant subgroups, including strata of tumor molecular phenotype. IMPACT: High BMI may attenuate the survival benefit otherwise observed with MSI-high tumors.

A genome wide association study on Newfoundland colorectal cancer patients' survival outcomes.

BACKGROUND: In this study we performed genome-wide association studies to identify candidate SNPs that may predict the risk of disease outcome in colorectal cancer. METHODS: Patient cohort consisted of 505 unrelated patients with Caucasian ancestry. Germline DNA samples were genotyped using the Illumina® human Omni-1quad SNP chip. Associations of SNPs with overall and disease free survivals were examined primarily for 431 patients with microsatellite instability-low (MSI-L) or stable (MSS) colorectal tumors using Cox proportional hazards method adjusting for clinical covariates. Bootstrap method was applied for internal validation of results. As exploratory analyses, association analyses for the colon (n = 334) and rectal (n = 171) cancer patients were also performed. RESULTS: As a result, there was no SNP that reached the genomewide significance levels (p < 5x10(-8)) in any of the analyses. A small number of genetic markers (n = 10) showed nominal associations (p <10(-6)) for MSS/MSI-L, colon, or rectal cancer patient groups. These markers were located in two non-coding RNA genes or intergenic regions and none were amino acid substituting polymorphisms. Bootstrap analysis for the MSS/MSI-L cohort data suggested the robustness of the observed nominal associations. CONCLUSIONS: Likely due to small number of patients, our study did not identify an acceptable level of association of SNPs with outcome in MSS/MSI-L, colon, or rectal cancer patients. A number of SNPs with sub-optimal p-values were, however, identified; these loci may be promising and examined in other larger-sized patient cohorts.

Relationship between blood plasma and synovial fluid metabolite concentrations in patients with osteoarthritis.

OBJECTIVE: To investigate the relationship between plasma and synovial fluid (SF) metabolite concentrations in patients with osteoarthritis (OA). METHODS: Blood plasma and SF samples were collected from patients with primary knee OA undergoing total knee arthroplasty. Metabolic profiling was performed by electrospray ionization tandem mass spectrometry using the AbsoluteIDQ kit. The profiling yielded 168 metabolite concentrations. Correlation analysis between SF and plasma metabolite concentrations was done on absolute concentrations as well as metabolite concentration ratios using Spearman's rank correlation (ρ) method. RESULTS: A total of 69 patients with knee OA were included, 30 men and 39 women, with an average age of 66 ± 8 years. For the absolute metabolite concentrations, the average ρ was 0.23 ± 0.13. Only 8 out of 168 metabolite concentrations had a ρ ≥ 0.45, with a p value ≤ 2.98 × 10(-4), statistically significant after correcting multiple testing with the Bonferroni method. For the metabolite ratios (n = 28,056), the average ρ was 0.29 ± 0.20. There were 4018 metabolite ratios with a ρ ≥ 0.52 and a p value ≤ 1.78 × 10(-6), significant after correcting multiple testing. Sex-separate analyses found no difference in ρ between men and women. Similarly, there was no difference in ρ between people younger and older than 65 years. CONCLUSION: Correlation between blood plasma and SF metabolite concentrations are modest. Metabolite ratios, which are considered proxies for enzymatic reaction rates and have higher correlations, should be considered when using blood plasma as a surrogate of SF in OA biomarker identification.

Nurse Practitioner Mental Health Care in the Primary Context: A Californian Case Study.

In America, mental health needs surpass the availability of specialized providers. This vulnerable population also has other obstacles for comprehensive care including gaps in medical coverage, stigma, economic barriers, and a geographical mal­distribution of qualified mental health professionals. A wide availability of primary care providers, including primary care and family nurse practitioners, are well-positioned to deliver integrated mental and physical health care. A case study from a Southern California Coachella Valley primary care clinic with integrated services is used to demonstrate the much-needed approach of care to address health disparities that face low­income immigrants, migrant workers, and others without access to specialized care centers and providers. It is argued that mental health care should be part of all holistic treatment provided by primary care and family nurse practitioners. This has implications for curricula and practice development.

Underwater optical wireless communications: depth-dependent beam refraction.

Global refractive gradients in seawater cause pointing problems for optical wireless communications. A refractive index depth profile of the Pacific Ocean was calculated from measured salinity, temperature, and pressure, determining the end points of a refracted and nonrefracted 200 m communication link. Numerical ray tracing was used with a point source for angles between 10° and 80° and transmission wavelengths of 500-650 nm; the maximum end-point difference found was 0.23 m. A 500 nm laser with a 0.57° full-angle FOV was traced; the nonrefracted receiver location was outside the FOV for all links angled >15° to the vertical. However, most pointing issues underwater are unlikely to be significant with suitable FOV choice and natural scattering of the source.

Classification of osteoarthritis phenotypes by metabolomics analysis.

OBJECTIVES: To identify metabolic markers that can classify patients with osteoarthritis (OA) into subgroups. DESIGN: A case-only study design was utilised. PARTICIPANTS: Patients were recruited from those who underwent total knee or hip replacement surgery due to primary OA between November 2011 and December 2013 in St. Clare's Mercy Hospital and Health Science Centre General Hospital in St. John's, capital of Newfoundland and Labrador (NL), Canada. 38 men and 42 women were included in the study. The mean age was 65.2±8.7 years. OUTCOME MEASURES: Synovial fluid samples were collected at the time of their joint surgeries. Metabolic profiling was performed on the synovial fluid samples by the targeted metabolomics approach, and various analytic methods were utilised to identify metabolic markers for classifying subgroups of patients with OA. Potential confounders such as age, sex, body mass index (BMI) and comorbidities were considered in the analysis. RESULTS: Two distinct patient groups, A and B, were clearly identified in the 80 patients with OA. Patients in group A had a significantly higher concentration on 37 of 39 acylcarnitines, but the free carnitine was significantly lower in their synovial fluids than in those of patients in group B. The latter group was further subdivided into two subgroups, that is, B1 and B2. The corresponding metabolites that contributed to the grouping were 86 metabolites including 75 glycerophospholipids (6 lysophosphatidylcholines, 69 phosphatidylcholines), 9 sphingolipids, 1 biogenic amine and 1 acylcarnitine. The grouping was not associated with any known confounders including age, sex, BMI and comorbidities. The possible biological processes involved in these clusters are carnitine, lipid and collagen metabolism, respectively. CONCLUSIONS: The study demonstrated that OA consists of metabolically distinct subgroups. Identification of these distinct subgroups will help to unravel the pathogenesis and develop targeted therapies for OA.