scholarly journals The Impact of Improved Thermistor Calibration on the Expendable Bathythermograph Profile Data

2017 ◽  
Vol 34 (9) ◽  
pp. 1947-1961 ◽  
Author(s):  
Marlos Goes ◽  
Elizabeth Babcock ◽  
Francis Bringas ◽  
Peter Ortner ◽  
Gustavo Goni
Keyword(s):  
Small Sample Size ◽  
Heat Content ◽  
Small Sample ◽  
Profile Data ◽  
Expendable Bathythermograph ◽  
Different Types ◽  
Long Term Trends ◽  
The Mean ◽  
The Impact

AbstractExpendable bathythermograph (XBT) data provide one of the longest available records of upper-ocean temperature. However, temperature and depth biases in XBT data adversely affect estimates of long-term trends of ocean heat content and, to a lesser extent, estimates of volume and heat transport in the ocean. Several corrections have been proposed to overcome historical biases in XBT data, which rely on constantly monitoring these biases. This paper provides an analysis of data collected during three recent hydrographic cruises that utilized different types of probes, and examines methods to reduce temperature and depth biases by improving the thermistor calibration and reducing the mass variability of the XBT probes.The results obtained show that the use of individual thermistor calibration in XBT probes is the most effective calibration to decrease the thermal bias, improving the mean thermal bias to less than 0.02°C and its tolerance from 0.1° to 0.03°C. The temperature variance of probes with screened thermistors is significantly reduced by approximately 60% in comparison to standard probes. On the other hand, probes with a tighter weight tolerance did not show statistically significant reductions in the spread of depth biases, possibly because of the small sample size or the sensitivity of the depth accuracy to other causes affecting the analysis.

scholarly journals Congenital Micropenis: Aetiology And Management

2021 ◽  
Author(s):  
Marianna Rita Stancampiano ◽  
Kentaro Suzuki ◽  
Stuart O’Toole ◽  
Gianni Russo ◽  
Gen Yamada ◽  
...  
Keyword(s):  
Racial Differences ◽  
Small Sample Size ◽  
Small Sample ◽  
Long Term Outcomes ◽  
Penile Length ◽  
Appearance Quality ◽  
Wide Range ◽  
The Impact

Abstract In the newborn, penile length is determined by a number of androgen dependent and independent factors. The current literature suggests that there are inter-racial differences in stretched penile length in the newborn and although congenital micropenis should be defined as a stretched penile length of less than 2.5SDS of the mean for the corresponding population and gestation, a pragmatic approach would be to evaluate all boys with a stretched penile length below 2 cm, as congenital micropenis can be a marker for a wide range of endocrine conditions. However, it remains unclear as to whether the state of micropenis, itself, is associated with any long-term consequences. There is a lack of systematic studies comparing the impact of different therapeutic options on long-term outcomes, in terms of genital appearance, quality of life and sexual satisfaction. To date, research has been hampered by a small sample size and inclusion of a wide range of heterogeneous diagnoses; for these reasons, condition specific outcomes have been difficult to compare between studies. Lastly, there is a need for a greater collaborative effort in collecting standardized data so that all real-world or experimental interventions performed at an early age can be studied systematically into adulthood.

scholarly journals Anthropogenic and Natural Factors Affecting Trends in Atmospheric Methane in Barrow, Alaska

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 187
Author(s):  
Christopher Lawrence ◽  
Huiting Mao
Keyword(s):  
Small Sample Size ◽  
Strong Support ◽  
Warm Season ◽  
Growth Period ◽  
Complex Problem ◽  
Small Sample ◽  
Oil Field ◽  
Atmospheric Methane ◽  
Long Term Trends

This study examined the long-term trends in Arctic ambient methane (CH4) mixing ratios over 1986–2014 and investigated their potential causes. Significant correlations between carbon monoxide (CO) and CH4 in Barrow, Alaska (r = −0.59, p = 0.007) and Alert, Canada (r = −0.62, p =0.004) with the strongest correlations occurring in April (r = −0.81, p = 0.000, and r = −0.80, p = 0.000) suggest local to global anthropogenic contributions to ambient CH4 during the cold months. Backward trajectories indicate a significant influence (27% of total trajectories) of local emissions from the Prudhoe Bay Oil Field on ambient CH4 in Barrow in winter, and this influence was dominated by other factors in summer. The mean CH4 wetland emission flux in Barrow over 1986–2014 was estimated to be 0.008 ± 0.002 µg m−2 s−1 while in Tiksi, Russia it was 0.010 µg m−2 s−1 over 2012–2016, which is comparable to the lower end of measurements in the literature. Note that in Barrow, there was a decrease in wetland flux from 0.0083 ± 0.002 µg m−2 s−1 over 1986–1998 to 0.0077 ± 0.002 µg m−2 s−1 from 1999–2006 followed by an increase to 0.0081 ± 0.002 µg m−2 s−1 over 2007–2014. Although the difference between the three values is not statistically significant due to small sample size, it is indicative of possible warm season wetland emissions contributing to the zero-growth period. Strong support for this hypothesis is that these changes are consistent with a concurrent drop in summertime temperature possibly causing a decrease in wetland emissions over 1998–2006 based on the statistically significant correlations between temperature and CH4 during August through November (r~0.36–0.56, p= ≤ 0.05). In a warming climate, permafrost thawing can increase CH4 wetland emissions and also decrease wetlands making it a complex problem, and, hence, further study is needed to better understand the mechanisms driving long-term trends in Arctic CH4.

scholarly journals Average deviation for measuring variation in data in small samples (n < 5)

2017 ◽  
Author(s):  
Wenfa Ng
Keyword(s):  
Sample Size ◽  
Small Sample Size ◽  
Small Sample ◽  
Standard Normal Distribution ◽  
Average Deviation ◽  
Standard Normal ◽  
The Mean ◽  
The Impact ◽  
Outlier Measurement ◽  
Calculated Average

Good control of experiment variability is critical to experiment success, and many methods are available for quantifying variation in data. Popular methods for measuring variability in data typically uses a statistical distribution such as a standard normal distribution, but these distributions are designed for large sample size with n > 30. However, experiments typically generate less than 5 replicates (n < 5). Thus, the key requirement for the use of standard normal distribution is not satisfied, which bring forth the need for the development of alternative ways of quantifying the variation in collected data for small sample size. This abstract describes a new statistic, average deviation, that aims to quantify the variation of repeated measurements of a variable. By taking an average of the sum of the differences between the mean and all measurements, average deviation provides a better representation of the variation in data around a mean, while capturing the impact of significant deviation from the mean by individual measurement. However, division of the sum of deviation of all measurements from the mean by the sample size meant that the presence of outlier measurement may not be fully represented by the calculated average deviation. Thus, the new statistic is better used with a small sample size of less than 5, which helps reduce the extent in which an outlier’s influence on the average deviation would be diluted. In summary, for small sample size, average deviation better represents the deviation between each measurement and the mean compared to statistical distribution-based approaches such as standard error. However, desire to not dilute the impact of outlier measurement on the calculated average deviation meant that the new statistic is only suitable for sample size less than 5.

A Randomized, Placebo-Controlled Trial of the Discontinuation of Long-Term Antipsychotics in Dementia

2002 ◽  
Vol 14 (2) ◽  
pp. 197-210 ◽  
Author(s):  
Robert van Reekum ◽  
Diana Clarke ◽  
David Conn ◽  
Nathan Herrmann ◽  
Goran Eryavec ◽  
...  
Keyword(s):  
Placebo Group ◽  
Behavioral Problems ◽  
Small Sample Size ◽  
Controlled Trial ◽  
Active Treatment Group ◽  
Small Sample ◽  
Clinical Predictors ◽  
Safe Discontinuation ◽  
The Impact

The objectives of this randomized clinical trial were to investigate the impact of the discontinuation of long-term antipsychotics in residents with dementia in chronic care institutions and to identify clinical predictors of safe discontinuation. Subjects included 34 residents with dementia who were on antipsychotics for more than 6 months and whose behavior was currently stable. Subjects were randomized to either continue receiving their regular dosage of antipsychotics or to receive placebo for 6 months. Early withdrawal from the study was not statistically different between the groups (relative risk [RR] = 1.57, 95% confidence interval [CI] 0.76–3.26), and though not significantly different, subjects in the placebo group were more likely to be withdrawn from the study because of worsening behavior (RR = 1.25, 95% CI 0.33–4.76). Three subjects in the placebo group were withdrawn from the study due to worsening of extrapyramidal symptoms. The active treatment group had more behavioral problems (e.g., physical aggression towards others, p < .05) compared to the placebo group. The placebo group developed more apathy, but balancing this outcome was a relative improvement in congitive functioning. Baseline antipsychotic dose was predictive of behavioral worsening upon discontinuation of long-term antipsychotic drugs. The primary limitation of the study was the small sample size. In conclusion, a trial of discontinuation of antipsychotics should be considered in this population.

scholarly journals Average deviation for measuring variation in data in small samples (n < 5)

2017 ◽  
Author(s):  
Wenfa Ng
Keyword(s):  
Sample Size ◽  
Small Sample Size ◽  
Small Sample ◽  
Standard Normal Distribution ◽  
Average Deviation ◽  
Standard Normal ◽  
The Mean ◽  
The Impact ◽  
Outlier Measurement ◽  
Calculated Average

Good control of experiment variability is critical to experiment success, and many methods are available for quantifying variation in data. Popular methods for measuring variability in data typically uses a statistical distribution such as a standard normal distribution, but these distributions are designed for large sample size with n > 30. However, experiments typically generate less than 5 replicates (n < 5). Thus, the key requirement for the use of standard normal distribution is not satisfied, which bring forth the need for the development of alternative ways of quantifying the variation in collected data for small sample size. This abstract describes a new statistic, average deviation, that aims to quantify the variation of repeated measurements of a variable. By taking an average of the sum of the differences between the mean and all measurements, average deviation provides a better representation of the variation in data around a mean, while capturing the impact of significant deviation from the mean by individual measurement. However, division of the sum of deviation of all measurements from the mean by the sample size meant that the presence of outlier measurement may not be fully represented by the calculated average deviation. Thus, the new statistic is better used with a small sample size of less than 5, which helps reduce the extent in which an outlier’s influence on the average deviation would be diluted. In summary, for small sample size, average deviation better represents the deviation between each measurement and the mean compared to statistical distribution-based approaches such as standard error. However, desire to not dilute the impact of outlier measurement on the calculated average deviation meant that the new statistic is only suitable for sample size less than 5.

An influence of social isolation on the level of physical activity as well as on well-being and mental state of people during the coronavirus COVID-19 pandemic

2019 ◽  
Vol 129 (4) ◽  
pp. 127-131
Author(s):  
Agnieszka Parfin ◽  
Krystian Wdowiak ◽  
Marzena Furtak-Niczyporuk ◽  
Jolanta Herda
Keyword(s):  
Physical Activity ◽  
Social Isolation ◽  
Small Sample Size ◽  
Statistical Tests ◽  
Population Group ◽  
Well Being ◽  
Small Sample ◽  
Subjective Well Being ◽  
Tract Infections ◽  
The Impact

AbstractIntroduction. The COVID-19 is the name of an infectious disease caused by a new strain of coronavirus SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). It was first diagnosed in December 2019 in patients in Wuhan City, Hubei Province, China. The symptoms are dominated by features of respiratory tract infections, in some patients with a very severe course leading to respiratory failure and, in extreme cases to death. Due to the spread of the infection worldwide, the WHO declared a pandemic in March 2020.Aim. An investigation of the impact of social isolation introduced due to the coronavirus pandemic on selected aspects of life. The researchers focused on observing changes in habits related to physical activity and their connections with people’s subjective well-being and emotional state.Material and methods. The study was carried out within the international project of the group „IRG on COVID and exercise”. The research tool was a standardized questionnaire.Results. Based on the data collected and the analysis of the percentage results, it can be observed that the overwhelming majority of people taking up physical activity reported a better mood during the pandemic. However, statistical tests do not confirm these relationships due to the small sample size.Conclusions. Isolation favours physical activity. Future, in-depth studies, by enlarging the population group, are necessary to confirm the above observations.

scholarly journals The Role of Dopamine Receptor D2 in Bridging the Intention-Behavior Gap in Sport Participation

2021 ◽  
Vol 18 (5) ◽  
pp. 2379
Author(s):  
Seiyeong Park ◽  
Junhye Kwon ◽  
Chiyoung Ahn ◽  
Hae-Sung Cho ◽  
Hyo Youl Moon ◽  
...  
Keyword(s):  
University Students ◽  
Dopamine Receptor ◽  
Sport Participation ◽  
Small Sample Size ◽  
Behavioral Model ◽  
Small Sample ◽  
Male Students ◽  
Future Research ◽  
Dopamine Receptor D2 ◽  
The Impact

Previous studies have identified that a behavior can occur through the strongest predictor intention, but there is a gap between intention and behavior. Dopamine receptor D2 (DRD2) is known to account for a variance in sporting behaviors in human and animal subjects. However, the relationship between DRD2 and sport participation has been poorly studied, and the limited available reports are inconsistent. The present study was performed to examine the impact of DRD2 on sport participation among Korean university students based on the integrated behavioral model (IBM). Data were collected from enrolled university students in Seoul (N = 45). Participants answered survey questions first, and then they gave investigators their hair to provide DNA information (i.e., the A1 allele of DRD2). DRD2 had a significant effect on sport participation, but only in male students. Male students who carried the A1 allele of DRD2 significantly participated in 105.10 min more sporting activities than male students who did not. Moreover, the effect of intention on sport participation was significantly decreased when considering DRD2. Despite the small sample size, the results of this study could be a preliminary case for a larger study and indicate the direction of future research. Our results suggest that DRD2 may have played an important role as the “actual skill” shown in the IBM.

scholarly journals Different long-term trends of the oxygen red 630.0 nm line nightglow intensity as the result of lowering the ionosphere F2 layer

2008 ◽  
Vol 26 (8) ◽  
pp. 2069-2080 ◽  
Author(s):  
N. B. Gudadze ◽  
G. G. Didebulidze ◽  
L. N. Lomidze ◽  
G. Sh. Javakhishvili ◽  
M. A. Marsagishvili ◽  
...  
Keyword(s):  
Electron Density ◽  
Line Intensity ◽  
Peak Height ◽  
Height Distribution ◽  
Theoretical Simulation ◽  
Type Layer ◽  
Long Term Trends ◽  
The Mean ◽  
Long Term Trend

Abstract. Long-term observations of total nightglow intensity of the atomic oxygen red 630.0 nm line at Abastumani (41.75° N, 42.82° E) in 1957–1993 and measurements of the ionosphere F2 layer parameters from the Tbilisi ionosphere station (41.65° N, 44.75° E) in 1963–1986 have been analyzed. It is shown that a decrease in the long-term trend of the mean annual red 630.0 nm line intensity from the pre-midnight value (+0.770±1.045 R/year) to its minimum negative value (−1.080±0.670 R/year) at the midnight/after midnight is a possible result of the observed lowering of the peak height of the ionosphere F2 layer electron density hmF2 (−0.455±0.343 km/year). A theoretical simulation is carried out using a simple Chapman-type layer (damping in time) for the height distribution of the F2 layer electron density. The estimated values of the lowering in the hmF2, the increase in the red line intensity at pre-midnight and its decrease at midnight/after midnight are close to their observational ones, when a negative trend in the total neutral density of the upper atmosphere and an increase in the mean northward wind (or its possible consequence – a decrease in the southward one) are assumed.

Estimating Global Ocean Heat Content Changes in the Upper 1800 m since 1950 and the Influence of Climatology Choice*

2014 ◽  
Vol 27 (5) ◽  
pp. 1945-1957 ◽  
Author(s):  
John M. Lyman ◽  
Gregory C. Johnson
Keyword(s):  
Heat Content ◽  
Ocean Heat Content ◽  
Global Ocean ◽  
Ocean Temperature ◽  
Vertical Separation ◽  
Ocean Heat Uptake ◽  
Expendable Bathythermograph ◽  
Heat Uptake ◽  
The Mean

Abstract Ocean heat content anomalies are analyzed from 1950 to 2011 in five distinct depth layers (0–100, 100–300, 300–700, 700–900, and 900–1800 m). These layers correspond to historic increases in common maximum sampling depths of ocean temperature measurements with time, as different instruments—mechanical bathythermograph (MBT), shallow expendable bathythermograph (XBT), deep XBT, early sometimes shallower Argo profiling floats, and recent Argo floats capable of worldwide sampling to 2000 m—have come into widespread use. This vertical separation of maps allows computation of annual ocean heat content anomalies and their sampling uncertainties back to 1950 while taking account of in situ sampling advances and changing sampling patterns. The 0–100-m layer is measured over 50% of the globe annually starting in 1956, the 100–300-m layer starting in 1967, the 300–700-m layer starting in 1983, and the deepest two layers considered here starting in 2003 and 2004, during the implementation of Argo. Furthermore, global ocean heat uptake estimates since 1950 depend strongly on assumptions made concerning changes in undersampled or unsampled ocean regions. If unsampled areas are assumed to have zero anomalies and are included in the global integrals, the choice of climatological reference from which anomalies are estimated can strongly influence the global integral values and their trend: the sparser the sampling and the bigger the mean difference between climatological and actual values, the larger the influence.

Stillbirth Rate and Weight at Birth of Quintuplets in Japan

1989 ◽  
Vol 38 (1-2) ◽  
pp. 65-69 ◽  
Author(s):  
Yoko Imaizumi
Keyword(s):  
Sample Size ◽  
Birth Order ◽  
Gestational Age ◽  
Size Effects ◽  
Maternal Age ◽  
Small Sample Size ◽  
Small Sample ◽  
Stillbirth Rate ◽  
The Mean

AbstractNation-wide data in Japan on births and prenatal deaths of 16 sets of quintuplets during 1974-1985 were analysed. Among the 16 sets, 3 sets were liveborn, 8 were stillborn, and 5 were mixed, with a stillbirth rate of 0.64 (51/80). Effects of sex, maternal age and birth order on the stillbirth rate were not considered because of the small sample size. Effects of gestational age and birthweight on stillbirth rate were also examined. The mean weight of the 40 quintuplet individuals was 1,048 g.

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