Physiological Correlates of Volunteering

We review research on physiological correlates of volunteering, a neglected but promising research field. Some of these correlates seem to be causal factors influencing volunteering. Volunteers tend to have better physical health, both self-reported and expert-assessed, better mental health, and perform better on cognitive tasks. Research thus far has rarely examined neurological, neurochemical, hormonal, and genetic correlates of volunteering to any significant extent, especially controlling for other factors as potential confounds. Evolutionary theory and behavioral genetic research suggest the importance of such physiological factors in humans. Basically, many aspects of social relationships and social activities have effects on health (e.g., Newman and Roberts 2013; Uchino 2004), as the widely used biopsychosocial (BPS) model suggests (Institute of Medicine 2001). Studies of formal volunteering (FV), charitable giving, and altruistic behavior suggest that physiological characteristics are related to volunteering, including specific genes (such as oxytocin receptor [OXTR] genes, Arginine vasopressin receptor [AVPR] genes, dopamine D4 receptor [DRD4] genes, and 5-HTTLPR). We recommend that future research on physiological factors be extended to non-Western populations, focusing specifically on volunteering, and differentiating between different forms and types of volunteering and civic participation

The impact of women's social position on fertility in developing countries

This paper examines ideas about possible ways in which the extent of women's autonomy, women's economic dependency, and other aspects of their position vis-à-vis men influence fertility in Third World populations. Women's position or “status” seems likely to be related to the supply of children because of its links with age at marriage. Women's position may also affect the demand for children and the costs of fertility regulation, though some connections suggested in the literature are implausible. The paper ends with suggestions for future research.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45660/1/11206_2005_Article_BF01124382.pd

The carbon dioxide system in the Arabian Sea

In 1995 the WHOI (C. Goyet) and MIAMI (F.J. Millero) groups participated on a number of research cruises in the Arabian Sea as part of the U.S. Joint Global Ocean Flux Study (JGOFS) sponsored by the National Science Foundation (NSF). This paper gives the results of our total inorganic carbon dioxide (TCO 2), total alkalinity (TA) and potentiometric pH measurements made on Arabian Sea water samples during these cruises. Measurements made on Certified Reference Material (CRM) indicate that the reproducibility of the measurements was ±0.007 in pH, ±3.2 μmol kg -1 in TA, and ±1.2 μmol kg -1 in TCO 2 (N=180). The surface measurements (0–30 m) of pH and normalized TCO 2 and TA were quite uniform throughout the year (pH=8.1±0.05, NTCO 2=1950±20 μmol kg -1 and NTA=2290±5 μmol kg -1). The larger variations in NTCO 2 in the surface waters are related to changes in primary production and upwelling in the coastal waters. The depth profiles of pH, pCO 2, TA, and TCO 2 were similar to those in the Equatorial Pacific Ocean. The components of the carbonate system (CO 2, HCO - 3, CO 2- 3) and the saturation state (Ω) for calcite and aragonite were determined from the measurements of TA and TCO 2. The waters below 600 and 3400 m in the Arabian Sea were undersaturated (Ω<1.0) for aragonite and calcite, respectively. The CO 2 measurements have been combined with the nutrient data to examine the stoichiometric ratios of C/N, C/P, C/O 2, and C/SiO 2 of the waters. Marked differences were found for the waters above and below the oxygen minimum zone. The surface water results have been used to develop the following stoichiometry for phytoplankton in the Arabian Sea (CH 2O) 125(NH 3) 14(H 3PO 4)(SiO 2) 13. The oxidation of this material is due to reactions with O 2 (77%) and NO 3 (23%) with the resultant formation of N 2 and N 2O. The maximum amount of organic carbon oxidized has been estimated to be 3.1 μmol kg -1 in the deep waters with as much as 0.9 μmol kg -1 in the oxygen minimum zone with NO 3. The maximum amount of CaCO 3 dissolved in the deep waters is 116 μmol kg -1. These results, together with the organic material collected from the sediment traps, should be useful in characterizing the formation and degradation of plant material in the Arabian Sea

Automatic detection of rapid eye movements (REMs): A machine learning approach

BACKGROUND: Rapid eye movements (REMs) are a defining feature of REM sleep. The number of discrete REMs over time, or REM density, has been investigated as a marker of clinical psychopathology and memory consolidation. However, human detection of REMs is a time-consuming and subjective process. Therefore, reliable, automated REM detection software is a valuable research tool. NEW METHOD: We developed an automatic REM detection algorithm combining a novel set of extracted features and the ‘AdaBoost’ classification algorithm to detect the presence of REMs in Electrooculogram data collected from the right and left outer canthi (ROC/LOC). Algorithm performance measures of Recall (percentage of REMs detected) and Precision (percentage of REMs detected that are true REMs) were calculated and compared to the gold standard of human detection by three expert sleep scorers. REM detection by four non-experts were also investigated and compared to expert raters and the algorithm. RESULTS: The algorithm performance (78.1% Recall, 82.6% Precision) surpassed that of the average (expert & non-expert) single human detection performance (76% Recall, 83% Precision). Agreement between non-experts (Cronbach Alpha = 0.65) is markedly lower than experts (Cronbach Alpha = 0.80). COMPARISON WITH EXISTING METHOD(S): By following reported methods, we implemented all previously published LOC and ROC based detection algorithms on our dataset. Our algorithm performance exceeded all others. CONCLUSIONS: The automatic detection algorithm presented is a viable and efficient method of REM detection as it reliably matches the performance of human scorers and outperforms all other known LOC- and ROC-based detection algorithms