The effects of prostacyclin (PGI2) on gastric emptying and secretion were studied in five unanesthetized chair-adapted rhesus monkeys. A dye dilution technique was used to determine simultaneously ...gastric fractional emptying, hydrogen ion (H+) output, fluid output, and H+ concentration of the gastric juice. A continuous intravenous infusion of either sodium carbonate buffer (control) or PGI2 (125, 175, or 250 ng/kg/min) was given during a 40-min basal period and following the intragastric administration of an 80-ml water load. During the basal period, 175 and 250 ng/kg/min significantly decreased H+ output compared to control. Only 250 ng/kg/min, however, significantly reduced basal fluid output compared to control. As a result, the H+ concentration of the gastric juice was lowered by both 175 and 250 ng/kg/min. The effects of PGI2 on H+ output, fluid output, and H+ concentration of the secreted juice after the water load were similar to those observed during the basal period. In addition to its anti-secretory action, PGI2 inhibited significantly postload, but not basal, gastric fractional emptying. Because a fall in intragastric H+ concentration is known to enhance fractional emptying, it appears that the retarding effect of PGI2 on fractional emptying is not mediated by its ability to suppress H+ output.
Supervised classification of land cover across space and time is a long-standing goal of the Earth Science community. Although most past and current analyses focus on detecting changes between two or ...more times, the opening of the USGS Landsat archive in 2009 has enabled exploration of methods for higher-frequency, time-serial monitoring of land cover dynamics. Modifying the protocols used to develop the 2001 National Land Cover Database (NLCD 2001), we fit a single classifier to a spatio-temporally distributed reference sample and applied the model to 55 Landsat-5 images covering a section of the North Carolina Piedmont Plateau from 1984 to 2007. A generalized classification scheme, multi-temporal sampling design, supervised classification based on intra-annual spectral indices, and design-based accuracy assessment yielded a time-series of 16 land cover maps from 1985 to 2006 with a spatial extent of 1.7×106ha, minimum mapping unit of 1ha, and mean temporal interval of <2years. Comparable to accuracy of the NLCD 2001 Land Cover Layer for the region, overall accuracy for a spatio-temporally independent test sample was 75%, with κ=0.7. When weighted by class proportions, percent correctly classified and kappa rose to 88% and 0.84, respectively. The resulting map series shows spatially and temporally complex changes in water, urban, forest, and herbaceous cover resulting from natural and anthropogenic processes that would not be observable in either uni- or bi-temporal maps. Agricultural crop area dropped from ~45% in the 1980s to ~36% in the 1990s and then rose slightly to ~38% at the end of the period. Forest area increased to a maximum of ~55% in the 1990s and then dropped to ~53% in 2005. Urban growth appeared to be most rapid in the 1980s and 1990s and slowed thereafter. With continued focus on the semantics, causation, sampling, and uncertainty underlying spectral land cover classification, long-term series of Landsat images will provide increasingly robust, reliable records for a growing scientific user community. These multi-temporal datasets will be indispensable for understanding past land cover dynamics and predicting the implications of future change on the provision and management of ecosystem services.
► Long-term, time-serial land cover maps derived retrieved from the Landsat archive. ► Derivation of area-weighting correction for sampling bias in validation metrics. ► 75% correctly classified with 0.7 Kappa statistic before area-weighting. ► 88% correctly classified with 0.84 Kappa statistic after area-weighting. ► Time-serial monitoring resolves greater temporal complexity than change detection.
Magnetic resonance imaging (MRI) has transformed our understanding of the human brain through well-replicated mapping of abilities to specific structures (for example, lesion studies) and functions
...(for example, task functional MRI (fMRI)). Mental health research and care have yet to realize similar advances from MRI. A primary challenge has been replicating associations between inter-individual differences in brain structure or function and complex cognitive or mental health phenotypes (brain-wide association studies (BWAS)). Such BWAS have typically relied on sample sizes appropriate for classical brain mapping
(the median neuroimaging study sample size is about 25), but potentially too small for capturing reproducible brain-behavioural phenotype associations
. Here we used three of the largest neuroimaging datasets currently available-with a total sample size of around 50,000 individuals-to quantify BWAS effect sizes and reproducibility as a function of sample size. BWAS associations were smaller than previously thought, resulting in statistically underpowered studies, inflated effect sizes and replication failures at typical sample sizes. As sample sizes grew into the thousands, replication rates began to improve and effect size inflation decreased. More robust BWAS effects were detected for functional MRI (versus structural), cognitive tests (versus mental health questionnaires) and multivariate methods (versus univariate). Smaller than expected brain-phenotype associations and variability across population subsamples can explain widespread BWAS replication failures. In contrast to non-BWAS approaches with larger effects (for example, lesions, interventions and within-person), BWAS reproducibility requires samples with thousands of individuals.
Proactive corporate social responsibility (CSR) involves business practices adopted voluntarily by firms that go beyond regulatory requirements in order to actively support sustainable economic, ...social and environmental development, and thereby contribute broadly and positively to society. This empirical study examines the role of the economic, social and environmental dimensions of proactive CSR on the association between three specific capabilities—shared vision, stakeholder management and strategic proactivity—and financial performance in small and medium enterprises (SMEs). Using quantitative data collected from a sample of 171 Australian SMEs in the machinery and equipment manufacturing sector and employing structural equation modelling, we find that the adoption of practices in each CSR dimension by SMEs is influenced slightly differently by each capability, and affects financial performance differentially. The study also demonstrates the importance of the interaction between the three dimensions of proactive CSR in positively moderating the deployment of each individual CSR dimension to generate financial performance. Paying primary attention to the economic dimension of proactive CSR and selectively focusing on social and environmental elements of proactive CSR that drive and support the economic dimension are of key importance to sustainable long-term financial success for SMEs.
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