Quantifying blue carbon stocks and the role of protected areas to conserve coastal wetlands.

Vegetated coastal ecosystems, in particular mangroves, tidal marshes and seagrasses are highly efficient at sequestering and storing carbon, making them valuable assets for climate change mitigation and adaptation. The state of Queensland, in northeastern Australia, contains almost half of the total area of these blue carbon ecosystems in the country, yet there are few detailed regional or state-wide assessments of their total sedimentary organic carbon (SOC) stocks. We compiled existing SOC data and used boosted regression tree models to evaluate the influence of environmental variables in explaining the variability in SOC stocks, and to produce spatially explicit blue carbon estimates. The final models explained 75 % (for mangroves and tidal marshes) and 65 % (for seagrasses) of the variability in SOC stocks. Total SOC stocks in the state of Queensland were estimated at 569 ± 98 Tg C (173 ± 32 Tg C, 232 ± 50 Tg C, and 164 ± 16 Tg C from mangroves, tidal marshes and seagrasses, respectively). Regional predictions for each of Queensland's eleven Natural Resource Management regions revealed that 60 % of the state's SOC stocks occurred within three regions (Cape York, Torres Strait and Southern Gulf Natural Resource Management regions) due to a combination of high values of SOC stocks and large areas of coastal wetlands. Protected areas in Queensland play an important role in conserving SOC assets in Queensland's coastal wetlands. For example, ~19 Tg C within terrestrial protected areas, ~27 Tg C within marine protected areas and ~ 40 Tg C within areas of matters of State Environmental Significance. Using multi-decadal (1987-2020) mapped distributions of mangroves in Queensland; we found that mangrove area increased by approximately 30,000 ha from 1987 to 2020, which led to temporal fluctuations in mangrove plant and SOC stocks. We estimated that plant stocks decreased from ~45 Tg C in 1987 to ~34.2 Tg C in 2020, while SOC stocks remained relatively constant from ~107.9 Tg C in 1987 to 108.0 Tg C in 2020. Considering the level of current protection, emissions from mangrove deforestation are potentially very low; therefore, representing minor opportunities for mangrove blue carbon projects in the region. Our study provides much needed information on current trends in carbon stocks and their conservation in Queensland's coastal wetlands, while also contributing to guide future management actions, including blue carbon restoration projects.

Performance evaluation of a non-invasive one-step multiplex RT-qPCR assay for detection of SARS-CoV-2 direct from saliva.

Polymerase chain reaction (PCR) has proven to be the gold-standard for SARS-CoV-2 detection in clinical settings. The most common approaches rely on nasopharyngeal specimens obtained from swabs, followed by RNA extraction, reverse transcription and quantitative PCR. Although swab-based PCR is sensitive, swabbing is invasive and unpleasant to administer, reducing patient compliance for regular testing and resulting in an increased risk of improper sampling. To overcome these obstacles, we developed a non-invasive one-step RT-qPCR assay performed directly on saliva specimens. The University of Nottingham Asymptomatic Testing Service protocol simplifies sample collection and bypasses the need for RNA extraction, or additives, thus helping to encourage more regular testing and reducing processing time and costs. We have evaluated the assay against the performance criteria specified by the UK regulatory bodies and attained accreditation (BS EN ISO/IEC 17,025:2017) for SARS-CoV-2 diagnostic testing by the United Kingdom Accreditation Service. We observed a sensitivity of 1 viral copy per microlitre of saliva, and demonstrated a concordance of > 99.4% between our results and those of other accredited testing facilities. We concluded that saliva is a stable medium that allows for a highly precise, repeatable, and robust testing method.

Direct RT-qPCR Assay for the Detection of SARS-CoV-2 in Saliva Samples.

Since mid-2020 there have been complexities and difficulties in the standardisation and administration of nasopharyngeal swabs. Coupled with the variable and/or poor accuracy of lateral flow devices, this has led to increased societal 'testing fatigue' and reduced confidence in test results. Consequently, asymptomatic individuals have developed reluctance towards repeat testing, which remains the best way to monitor COVID-19 cases in the wider population. On the other hand, saliva-based PCR, a non-invasive, highly sensitive, and accurate test suitable for everyone, is gaining momentum as a straightforward and reliable means of detecting SARS-CoV-2 in symptomatic and asymptomatic individuals. Here, we provide an itemised list of the equipment and reagents involved in the process of sample submission, inactivation and analysis, as well as a detailed description of how each of these steps is performed.

Zika Virus and the Risk for Renter Households.

Recent research on family income indicates that a lack of economic stability can affect healthy housing. Those with limited resources experience higher rates of inadequate and unstable housing many times forcing them to live in undesirable communities in which there can be several community-level health-related issues. One community-level health-related factor of concern has been the reemergence of Zika virus. Some research has indicated that a higher risk of catching Zika virus may exist in neighborhoods and areas with unhealthy housing. Therefore, this study sought to explore the existence of a relationship between rental housing and the Zika virus. Our findings indicated a significant correlation existed between renter occupied household units and the presence of Zika virus. This finding is notable as it indicates that renters have a higher chance of contracting Zika virus than non-renters. Future research should further examine the demographic and housing situation in other communities reporting cases of the Zika virus.

Collaborating to Create Healthier Communities.

The Montgomery County United Way and Sam Houston State University explored existing public health and community-based issues and associated assets and gaps within Montgomery County, Texas, through the integration of quantitative geospatial data, demographic information, and the application of geographical information systems. The intent of the initial results was to use maps to visually compare the magnitude of significant emerging health issues in Montgomery County with other counties across Texas in an effort to better educate and increase the awareness of the general public within Montgomery County. The second objective was to present an example of the visual effect and impact of the overall spatial analysis and mapping process with a focus on a specific community issue that could likely be addressed and potentially solved in a relatively short amount of time. This second and more spatially detailed aspect of the evaluation resulted in the creation of an asset map that presented high concentrations or densities of payday loan services in Montgomery County. Recognizing the potential risks of payday loans, Montgomery County United Way presented the asset maps to the effected community leaders. Upon review, leaders discovered there were dense clusters of payday loan sites in areas with high rates of poverty. Prior to the creation of this aggregated payday loan business location map, an individual in a community typically only saw one, or two at the most, payday loan business during a daily commute. Within a very short period and through a grassroots effort to improve community well-being, there were significant formal and positive changes made by the community.

Locally dispersing populations in heterogeneous dynamic landscapes with spatiotemporal correlations. II. Habitat driven by voter dynamics.

We examine a spatially explicit population model on a dynamic landscape with suitable and unsuitable habitat driven by voter or contagion dynamics. We consider four cases, consisting of all combinations of local and global interactions for both population dispersal and habitat dynamics. For both local and global population dispersal, using local habitat dynamics always increases population density relative to the case with global habitat dynamics, due to the resulting segregation of habitat turnover, decrease in effective habitat turnover rate, and presence of stable habitat corridors. With global habitat dynamics, a population using local dispersal exhibits lower density than one with global dispersal due to local crowding as well as frequent disturbance due to habitat transitions. On the other hand, with local habitat dynamics, a population using local dispersal can exploit suitable habitat patches and use dynamic corridors to colonize new regions. The latter effect is not seen with static landscapes, where clustered habitat can lead to the isolation of suitable patches due to surrounding unsuitable habitat.

Investigating the existence of quantum metabolic values in non-Hodgkin's lymphoma by 2-deoxy-2-[F-18]fluoro-D-glucose positron emission tomography.

OBJECTIVES: To investigate the existence of quantum metabolic values in various subtypes of non-Hodgkin's lymphoma (NHL). METHODS: Fifty-eight patients with newly diagnosed NHL and positron emission tomography (PET) performed within three months of biopsy were included. The standardized uptake value (SUV) from PET over the area of biopsy and serum glucose [Glc] were recorded. The group glucose sensitivity(G) for indolent and aggressive NHL was obtained by linear regression with ln(SUV) = G x ln[Glc] + C, where C is a constant for the group. Finally, the individual's glucose sensitivity (g) was obtained by g = {ln(SUV)-C}/ln[Glc], along with their means in various subtypes of NHL. To further investigate the influence of extreme [Glc] conditions, the SUVs corrected by the individually calculated g at various glucose levels, [Glc'] using SUV' =SUV x {[Glc']/[Glc]}(g), were compared to the original SUVs for both indolent and aggressive NHL. RESULTS: The averaged g (=G) for aggressive was significant different from that for indolent NHL (-0.94 +/- 0.51 vs. +0.13 +/- 0.10, respectively, p < 0.00005). There were significant differences in SUV for [Glc] < 80 or >110 mg/dl for both types of NHL. Unlike overlap among SUVs between NHL subtypes, the g value clearly categorized them into two distinct groups with positive (near-zero) and negative g values (around -1) for the indolent and aggressive NHLs, respectively. CONCLUSIONS: Distinct quantum metabolic values of -1 and 0 were noted in NHL. Aggressive NHL has a more negative value (or higher glucose sensitivity) than that of indolent and, thus, is more susceptible to extreme glucose variation.

A statistical investigation of normal regional intra-subject heterogeneity of brain metabolism and perfusion by F-18 FDG and O-15 H2O PET imaging.

BACKGROUND: The definite evaluation of the regional cerebral heterogeneity using perfusion and metabolism by a single modality of PET imaging has not been well addressed. Thus a statistical analysis of voxel variables from identical brain regions on metabolic and perfusion PET images was carried out to determine characteristics of the regional heterogeneity of F-18 FDG and O-15 H2O cerebral uptake in normal subjects. METHODS: Fourteen normal subjects with normal CT and/or MRI and physical examination including MMSE were scanned by both F-18 FDG and O-15 H2O PET within same day with head-holder and facemask. The images were co-registered and each individual voxel counts (Q) were normalized by the global maximal voxel counts (M) as R = Q/M. The voxel counts were also converted to z-score map by z = (Q - mean)/SD. Twelve pairs of ROIs (24 total) were systematically placed on the z-score map at cortical locations 15-degree apart and identically for metabolism and perfusion. Inter- and intra-subject correlation coefficients (r) were computed, both globally and hemispherically, from metabolism and perfusion: between regions for the same tracer and between tracers for the same region. Moments of means and histograms were computed globally along with asymmetric indices as their hemispherical differences. RESULTS: Statistical investigations verified with data showed that, for a given scan, correlation analyses are expectedly alike regardless of variables (Q, R, z) used. The varieties of correlation (r's) of normal subjects, showing symmetry, were mostly around 0.8 and with coefficient of variations near 10%. Analyses of histograms showed non-Gaussian behavior (skew = -0.3 and kurtosis = 0.4) of metabolism on average, in contrast to near Gaussian perfusion. CONCLUSION: The co-registered cerebral metabolism and perfusion z maps demonstrated regional heterogeneity but with attractively low coefficient of variations in the correlation markers.

Glucose-normalized standardized uptake value from (18)F-FDG PET in classifying lymphomas.

UNLABELLED: Our objective was to derive the best glucose sensitivity factor (g-value) and the most discriminating standardized uptake value (SUV) normalized to glucose for classifying indolent and aggressive lymphomas. METHODS: The maximum SUV obtained from (18)F-FDG PET over the area of biopsy in 102 patients was normalized by serum glucose ([Glc]) to a standard of 100 mg/dL. Discriminant analysis was performed by using each SUV(100) (SUV x {100/[Glc]}(g), calculated using various g-values ranging from -3.0 to 0, one at a time) as a variable against the lymphoma grades, and plotting the percentage of correct classifications against g (g-plot) to search for the best g-value in normalizing SUV(100) for classifying grades. To address the influence of the extreme glucose conditions, we repeated the same analyses in 12 patients with [Glc] < or = 70 mg/dL or [Glc] > or = 110 mg/dL. RESULTS: SUV(100) correctly classified lymphoma grades ranging from 62% to 73% (P < 0.0005), depending on the g-value, with a maximum at a g-value of -0.5. For the subgroup with extreme glucose values, the g-plot also revealed higher and more optimal discrimination at a g-value of -0.5 (92%) than at a g-value of 0 (83%) (P = 0.03). The discrimination deteriorated at g < -1 in both analyses. The box plot for all cases using a g-value of -0.5 showed little overlap in classifying lymphoma grades. For a visually selected threshold SUV(100) of 7.25, the sensitivity, specificity, and accuracy of identifying aggressive grades were 82%, 79%, and 81%, respectively. CONCLUSION: The results suggest that metabolic discrimination between lymphoma grades using a glucose-normalized SUV from (18)F-FDG PET is improved by introducing g-value as an extra degree of freedom.