cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.9872681.v1
title: The daily, weekly, and seasonal cycles of body temperature analyzed at large scale

description: We performed large-scale analyses of circadian and infradian cycles of human body temperature, focusing on changes over the day, week, and year. Temperatures (<i>n</i>= 93,225) were collected using temporal artery thermometers from a Boston emergency department during 2009–2012 and were statistically analyzed using regression with cyclic splines. The overall mean body temperature was 36.7°C (98.1°F), with a 95% confidence interval of 36.7–36.7°C (98.1–98.1°F) and a standard deviation of 0.6°C (1.1°F). Over the day, mean body temperature followed a steady cycle, reaching its minimum at 6:00–8:00 and its maximum at 18:00–20:00. Across days of the week, this diurnal cycle was essentially unchanged, even though activities and sleeping hours change substantially during the weekly cycles of human behavior. Over the year, body temperatures were slightly colder in winter than summer (~0.2°C difference), consistent with most prior studies. We propose these seasonal differences might be due to ambient effects on body temperature that are not eliminated because they fall within the tolerance range of the thermoregulatory system. Over the year, bathyphase (daily time of minimum temperature) appeared to parallel sunrise times, as expected from sunrise’s zeitgeber role in circadian rhythms. However, orthophase (daily time of maximum temperature) and sunset times followed opposite seasonal patterns, with orthophase preceding nightfall in summer and following nightfall in winter. Throughout the year, bathyphase and orthophase remained separated by approximately 12 h, suggesting this interval might be conserved. Finally, although 37.0°C (98.6°F) is widely recognized as the mean or normal human body temperature, analysis showed mean temperature was &lt;37.0°C during all times of day, days of the week, and seasons of the year, supporting prior arguments that the 37.0°C standard has no scientific basis. Overall, this large study showed robust and consistent behavior of the human circadian cycle at the population level, providing a strong example of circadian homeostasis.
DOI: 10.6084/m9.figshare.9784805
title: Diurnal and circadian variations in intraocular pressure in goats exposed to different lighting conditions

description: The effect of constant light and constant darkness on intraocular pressure (IOP) in goats has not been investigated. We hypothesized that IOP variations would differ between goats kept under a cycle of 12 hours of light and 12 hours of darkness (LD), constant darkness (DD), and constant light (LL). To test this hypothesis, goats were exposed to these conditions for five days (LD, 30 goats; DD, 10 goats; LL, 10 goats). IOP was measured by applanation tonometry at 9 a.m. (beginning of photophase in LD) and 9 p.m. (beginning of scotophase in LD) on the fourth and fifth days of exposure. We found that changes in mean IOP from 9 a.m. to 9 p.m. differed significantly between groups (χ<sup>2</sup>(2) = 23.04, <i>p</i> &lt; .0001). Most goats in LD showed a regular pattern of higher IOP in the morning and lower IOP in the evening, whereas those in DD and LL did not follow this pattern. In LD conditions, mean IOP was 2.4 mm Hg lower at 9 p.m. than at 9 a.m. (95% confidence interval for the difference (CI): −2.8 to −1.9 mm Hg, <i>p</i> &lt; .0001). In DD conditions, mean IOP did not differ between 9 p.m. and 9 a.m. (CI: −0.9 to 0.8 mm Hg, <i>p</i> = .90). In LL conditions, it was 0.6 mm Hg lower at 9 p.m. (CI: −1.5 to 0.2 mm Hg, <i>p</i> = .12). Our results indicate that IOP in goats kept in LD is higher in the morning than in the evening, and that IOP variations are reduced in goats kept in DD and LL. These results suggest that exposure to alternating periods of light and darkness is important for maintaining rhythmic variations in IOP in this species.
DOI: 10.6084/m9.figshare.9872681
title: The daily, weekly, and seasonal cycles of body temperature analyzed at large scale

description: We performed large-scale analyses of circadian and infradian cycles of human body temperature, focusing on changes over the day, week, and year. Temperatures (<i>n</i>= 93,225) were collected using temporal artery thermometers from a Boston emergency department during 2009–2012 and were statistically analyzed using regression with cyclic splines. The overall mean body temperature was 36.7°C (98.1°F), with a 95% confidence interval of 36.7–36.7°C (98.1–98.1°F) and a standard deviation of 0.6°C (1.1°F). Over the day, mean body temperature followed a steady cycle, reaching its minimum at 6:00–8:00 and its maximum at 18:00–20:00. Across days of the week, this diurnal cycle was essentially unchanged, even though activities and sleeping hours change substantially during the weekly cycles of human behavior. Over the year, body temperatures were slightly colder in winter than summer (~0.2°C difference), consistent with most prior studies. We propose these seasonal differences might be due to ambient effects on body temperature that are not eliminated because they fall within the tolerance range of the thermoregulatory system. Over the year, bathyphase (daily time of minimum temperature) appeared to parallel sunrise times, as expected from sunrise’s zeitgeber role in circadian rhythms. However, orthophase (daily time of maximum temperature) and sunset times followed opposite seasonal patterns, with orthophase preceding nightfall in summer and following nightfall in winter. Throughout the year, bathyphase and orthophase remained separated by approximately 12 h, suggesting this interval might be conserved. Finally, although 37.0°C (98.6°F) is widely recognized as the mean or normal human body temperature, analysis showed mean temperature was &lt;37.0°C during all times of day, days of the week, and seasons of the year, supporting prior arguments that the 37.0°C standard has no scientific basis. Overall, this large study showed robust and consistent behavior of the human circadian cycle at the population level, providing a strong example of circadian homeostasis.
DOI: 10.6084/m9.figshare.10032857
title: Psoriatic and psoriatic arthritis patients with and without jet-lag: does it matter for disease severity scores? Insights and implications from a pilot, prospective study

description: <b>Background</b>: Jet-lag may affect air-travelers crossing at least two time-zones and has several health-care implications. It occurs when the human biological rhythms are out of synch with respect to the day-night cycle at the country destination. Its effect in psoriasis is missing. We aimed to evaluate the effect of Jet-lag in psoriatic patients’ management. <b>Methods</b>: This is a prospective observational study that enrolled psoriatic patients that underwent a flight: patients who experienced jet-lag were compared to patients who did not experience jet-lag. Before the flight, a dermatologist recorded clinical and demographical data with particular attention to Psoriasis Area Severity Index (PASI) and Disease Activity in Psoriatic Arthritis (DAPSA). Patients performed Self-Administered Psoriasis Area Severity Index (SAPASI), the Dermatology Life Quality Index (DLQI) and the pruritus Visual Analog Scale (VAS) scores. After the flight, patients completed the SAPASI, DLQI and pruritus-VAS scores. <b>Results</b>: The sample recruited comprised of 70 psoriatic patients aged 42.4 ± 9.7 years (median 42.5 years). Thirty (42.9%) were males, mean BMI was 25.5 ± 2.2 kg/m<sup>2</sup>. Average disease duration was 15.2 ± 7.1 years, and 20 (28.6%) subjects had developed PsA. Average hours of flight were 5.4 ± 3.5 (median 3.5 h), with 34 (48.6%) subjects reporting jet-lag. At the multivariate regression analysis, the change in the SAPASI score resulted correlated with jet-lag (regression coefficient 1.63, <i>p</i> = .0092), as well the change in the DLQI score (regression coefficient = 1.73, <i>p</i> = .0009), but no change on the pruritus VAS scale was found. <b>Conclusions</b>: The present study suggests that jet-lag may influence disease severity and DLQI scores, but not itch in psoriatic patients.
DOI: 10.25582/data.2020-09.2062736/2012940
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2063340/2012941
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2060842/2012942
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2062790/2012943
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2061029/2012944
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2062954/2012945
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2062657/2012946
title: Data for EMSL Project 51126 from September 2020
DOI: 10.6084/m9.figshare.8153231
title: Decision-Making in the Arms of a Dependent Relationship: Explaining Shifts in Importer Acquisition Patterns of Major Weapon Systems, 1955–2007

description: Arms dependency is typically framed as a security issue that states seek to avoid. Dependency creates an opportunity for an exporter to attempt to exert influence over the importer’s foreign and domestic policy. However, the arms trade is a trade and influence attempts to create economic costs for exporters by damaging relationships with current and potential customers. Thus, heavily dependent states do not necessarily need to change suppliers to avoid the threat. Additionally, as arms transfers are a signal of political support, dependency may be a sign of a mutually beneficial relationship rather than one that is potentially dangerous. This article evaluates these arguments using logistic regression models to evaluate changes in suppliers of major weapons systems. It finds that the relationship between dependence and arms transfers is more complex than previously argued where the nature of the relationship depends both on the type of exporter and the type of arm being exported.
DOI: 10.6084/m9.figshare.8153231.v1
title: Decision-Making in the Arms of a Dependent Relationship: Explaining Shifts in Importer Acquisition Patterns of Major Weapon Systems, 1955–2007

description: Arms dependency is typically framed as a security issue that states seek to avoid. Dependency creates an opportunity for an exporter to attempt to exert influence over the importer’s foreign and domestic policy. However, the arms trade is a trade and influence attempts to create economic costs for exporters by damaging relationships with current and potential customers. Thus, heavily dependent states do not necessarily need to change suppliers to avoid the threat. Additionally, as arms transfers are a signal of political support, dependency may be a sign of a mutually beneficial relationship rather than one that is potentially dangerous. This article evaluates these arguments using logistic regression models to evaluate changes in suppliers of major weapons systems. It finds that the relationship between dependence and arms transfers is more complex than previously argued where the nature of the relationship depends both on the type of exporter and the type of arm being exported.
DOI: 10.25582/data.2020-09.2062716/2012947
title: Data for EMSL Project 51126 from September 2020
DOI: 10.6084/m9.figshare.11298530
title: Time of day difference in postprandial glucose and insulin responses: Systematic review and meta-analysis of acute postprandial studies

description: Current dietary trends show that humans consume up to 40% of their energy intake during the night. Those who habitually eat during the night are observed to have an increased risk of metabolic conditions such as type-2 diabetes and cardiovascular disease. Increasing evidence suggest that a biological consequence of eating during the night is a larger postprandial glucose response, compared to meals eaten earlier in the day. However, findings from individual acute postprandial studies have been inconsistent, due to variations in protocols. Therefore, this review aimed to systematically summarize findings from acute postprandial studies and investigate whether postprandial glucose and insulin response at night differs to during the day in healthy adults. This would indicate a possible physiological mechanism linking habitual nighttime eating and increased risk of metabolic conditions. Seven electronic databases were searched in February 2018. Included studies met the following criteria: had a day-time test between 0700 – 1600h, a nighttime test between 2000 and 0400h, the test meals were identical and consumed by the same participant at both day and night time points, preceded by a 3-h fast (minimum). Primary outcome measures were postprandial glucose and insulin incremental area under the curve (iAUC) or area under the curve (AUC). Studies that reported numerical data were included in the meta-analyses, conducted using Stata statistical software (version 13.0, StataCorp, College Station, TX, USA). For eligible studies that did not report numerical data, their authors’ conclusions on the effect of time of day on the primary outcome measures were summarized qualitatively. Full text of 172 articles were assessed for eligibility. Fifteen studies met the eligibility criteria, ten of which were included in the meta-analyses. Meta-analysis for glucose showed a lower postprandial glucose response in the day compared to during the night, after an identical meal (SMD = −1.66; 95% CI, −1.97 to −1.36; <i>p</i> &lt; .001). This was supported by the findings from included studies ineligible for meta-analysis. Meta-analysis also showed a lower postprandial insulin response in the day compared to during the night (SMD = −0.35; 95% CI, −0.63 to −0.06; <i>p</i> = .016). However, findings from included studies ineligible for meta-analysis were inconsistent. Our results suggest poor glucose tolerance at night compared to the day. This may be a contributing factor to the increased risk of metabolic diseases observed in those who habitually eat during the night, such as shift workers.
DOI: 10.6084/m9.figshare.11298530.v1
title: Time of day difference in postprandial glucose and insulin responses: Systematic review and meta-analysis of acute postprandial studies

description: Current dietary trends show that humans consume up to 40% of their energy intake during the night. Those who habitually eat during the night are observed to have an increased risk of metabolic conditions such as type-2 diabetes and cardiovascular disease. Increasing evidence suggest that a biological consequence of eating during the night is a larger postprandial glucose response, compared to meals eaten earlier in the day. However, findings from individual acute postprandial studies have been inconsistent, due to variations in protocols. Therefore, this review aimed to systematically summarize findings from acute postprandial studies and investigate whether postprandial glucose and insulin response at night differs to during the day in healthy adults. This would indicate a possible physiological mechanism linking habitual nighttime eating and increased risk of metabolic conditions. Seven electronic databases were searched in February 2018. Included studies met the following criteria: had a day-time test between 0700 – 1600h, a nighttime test between 2000 and 0400h, the test meals were identical and consumed by the same participant at both day and night time points, preceded by a 3-h fast (minimum). Primary outcome measures were postprandial glucose and insulin incremental area under the curve (iAUC) or area under the curve (AUC). Studies that reported numerical data were included in the meta-analyses, conducted using Stata statistical software (version 13.0, StataCorp, College Station, TX, USA). For eligible studies that did not report numerical data, their authors’ conclusions on the effect of time of day on the primary outcome measures were summarized qualitatively. Full text of 172 articles were assessed for eligibility. Fifteen studies met the eligibility criteria, ten of which were included in the meta-analyses. Meta-analysis for glucose showed a lower postprandial glucose response in the day compared to during the night, after an identical meal (SMD = −1.66; 95% CI, −1.97 to −1.36; <i>p</i> &lt; .001). This was supported by the findings from included studies ineligible for meta-analysis. Meta-analysis also showed a lower postprandial insulin response in the day compared to during the night (SMD = −0.35; 95% CI, −0.63 to −0.06; <i>p</i> = .016). However, findings from included studies ineligible for meta-analysis were inconsistent. Our results suggest poor glucose tolerance at night compared to the day. This may be a contributing factor to the increased risk of metabolic diseases observed in those who habitually eat during the night, such as shift workers.
DOI: 10.25582/data.2020-09.2063168/2012948
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2060996/2012949
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2062874/2012950
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2065120/2012951
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2061092/2012952
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2062808/2012953
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2063019/2012954
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2063047/2012955
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2063252/2012956
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2062869/2012957
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2063106/2012958
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2063322/2012959
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2063041/2012960
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2062680/2012961
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2065058/2012962
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-10.2072066/2012963
title: Data for EMSL Project 51126 from October 2020
DOI: 10.25582/data.2020-09.2062821/2012964
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2062938/2012965
title: Data for EMSL Project 51126 from September 2020
DOI: 10.6084/m9.figshare.21913143.v1
title: Rapid compensatory plasticity revealed by dynamic correlated activity in vivo

description: Neural data used to generate al lthe figures in our manuscript
DOI: 10.25582/data.2020-09.2060988/2012966
title: Data for EMSL Project 51126 from September 2020
DOI: 10.6084/m9.figshare.21913143
title: Rapid compensatory plasticity revealed by dynamic correlated activity in vivo

description: Neural data used to generate al lthe figures in our manuscript
DOI: 10.25582/data.2020-09.2065062/2012967
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2063076/2012968
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2062765/2012969
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2061003/2012970
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2062815/2012971
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2061103/2012972
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2060909/2012973
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2062811/2012974
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2060971/2012975
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2063275/2012976
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2060938/2012977
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2063014/2012978
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2062750/2012979
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2062772/2012980
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2061062/2012981
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2065102/2012982
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2063243/2012983
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2062803/2012984
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2065060/2012985
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2060920/2012986
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2060947/2012987
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-10.2072081/2012988
title: Data for EMSL Project 51126 from October 2020
DOI: 10.25582/data.2020-09.2060927/2012989
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2060955/2012990
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2062703/2012991
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2063048/2012992
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2063064/2012993
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2061117/2012994
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2060916/2012995
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2062780/2012996
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2060906/2012997
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2061082/2012998
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2062668/2012999
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2063063/2013000
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2065053/2013001
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2063167/2013002
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2060903/2013003
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2061110/2013004
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2060902/2013005
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2063217/2013006
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2062741/2013007
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2065087/2013008
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2060991/2013009
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2065066/2013010
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2065114/2013011
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-10.2072084/2013012
title: Data for EMSL Project 51126 from October 2020
DOI: 10.25582/data.2020-09.2062704/2013013
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2063207/2013014
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2062877/2013015
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2061094/2013016
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2063029/2013017
title: Data for EMSL Project 51126 from September 2020
DOI: 10.25582/data.2020-09.2063330/2013018
title: Data for EMSL Project 51126 from September 2020