On variability of total ozone derived from TOVS data over peninsular Indian sub-continent and adjoining oceanic area

The total ozone derived from TOVS data from NOAA 12 satellite through one step physical retrieval algorithm of International TOVS Processing Package (ITPP) version 5.0 has been used to identify its diurnal, monthly, latitudinal and longitudinal variability during 1998 over the domain Equator to 26° N / 60-100° E. The linkage of maximum total ozone with warmer tropopause and lower stratosphere has been re-established. The colder upper tropospheric temperature which is normally associated with maximum ozone concentration throughout the year elsewhere in the world has also been identified in this study but the relationship gets reversed during southwest monsoon months(June-September) over the domain considered. The moisture available in abundance in the lower troposphere gets precipitated due to the convective instability prevailing in the atmosphere during monsoon season and very little moisture is only available for vertical transport into the upper troposphere atop 500 hPa. The latent heat released by the precipitation processes warms up the middle and upper atmosphere. The warm and dry upper troposphere could be the reason for less depletion of ozone in the upper troposphere during monsoonal months and this is supported by the positive correlation coefficient prevailing in monsoon season between total ozone and upper tropospheric (aloft 300 hPa) temperature. The warmness in middle and upper troposphere which is associated with less depletion and/or production of more ozone in the upper troposphere may perhaps contribute for the higher total ozone during monsoon months than in other seasons over peninsular Indian region. The minimum concentration is observed during January (226 DU) over 6° N and the maximum (283DU) over 18° N during August. Longitudinal variability is less pronounced than the latitudinal variability.

Non-zonal structures of the midlatitude mesosphere/lower thermosphere dynamics studied by using atmospheric models and radar observations.

<p class="western" align="left">Radar observations from two SKiYMET radars at Collm (51°N, 13°E) and Kazan (56°N, 49°E) during 2016-2017 are used to investigate the longitudinal variability of the mesosphere/lower thermosphere (MLT) wind regime over western and eastern Europe. Both of the meteor radars have similar setups and apply the same analysis procedures to correctly compare MLT parameters and validate the simulated winds. The radar observations confirm the established seasonal variability of the wind distribution, but this distribution is not identical for the two stations. The results show good qualitative agreement with global circulations model predictions by the Middle and Upper Atmosphere Model (MUAM) and the Upper Atmosphere ICOsahedral Non-hydrostatic model (UA-ICON). The MUAM and UA-ICON models well reproduce the main dynamical features, namely the vertical and temporal distributions of the winds observed throughout the year. However, there are also some differences in the longitudinal wind variability of the models and radar observations. Numerical experiments with modified parameterization settings have also been carried out to study the response of the MLT wind circulation to the gravity waves originating from the lower atmosphere. The MUAM model results show that a decrease/increase in the gravity wave intensity at the lower atmosphere leads to an increase/decrease of the mesospheric zonal wind jet extension and the zonal wind reversal.</p>

Stability of healthy subgingival microbiome across space and time

The subgingival microbiome is one of the most stable microbial ecosystems in the human body. Alterations in the subgingival microbiome have been associated with periodontal disease, but their variations over time and between different subgingival sites in periodontally healthy individuals have not been well described. We performed extensive, longitudinal sampling of the subgingival microbiome from five periodontally healthy individuals to define baseline spatial and temporal variations. A total of 251 subgingival samples from 5 subjects were collected over 6–12 months and deep sequenced. The overall microbial diversity and composition differed significantly between individuals. Within each individual, we observed considerable differences in microbiome composition between different subgingival sites. However, for a given site, the microbiome was remarkably stable over time, and this stability was associated with increased microbial diversity but was inversely correlated with the enrichment of putative periodontal pathogens. In contrast to microbiome composition, the predicted functional metagenome was similar across space and time, suggesting that periodontal health is associated with shared gene functions encoded by different microbiome consortia that are individualized. To our knowledge, this is one of the most detailed longitudinal analysis of the healthy subgingival microbiome to date that examined the longitudinal variability of different subgingival sites within individuals. These results suggest that a single measurement of the healthy subgingival microbiome at a given site can provide long term information of the microbial composition and functional potential, but sampling of each site is necessary to define the composition and community structure at individual subgingival sites.

Unexplained longitudinal variability in COVID-19 antibody status by Lateral Flow Immuno-Antibody testing

BackgroundCOVID-19 antibody testing allows population studies to classify participants by previous SARS-CoV-2 infection status. Home lateral flow immune-antibody testing devices offer a very convenient way of doing this, but relatively little is known about how measurement and antibody variability will affect consistency in results over time. We examined consistency by looking at the outcome of two tests three months apart while COVID-19 infection rates were low (summer 2020 in the UK).MethodsThe KCL-Coronavirus Health and Experiences in Colleagues at King’s is an occupational cohort of staff and postgraduate research students. Lateral flow immune-antibody testing kits were sent to participant’s homes in late June 2020 and late September 2020. Participants also completed regular surveys that included asking about COVID-19 symptoms and whether they thought they had been infected.ResultsWe studied 1489 participants returned valid results in both June and September (59% of those sent kits). Lateral flow immune-antibody test was positive for 7.2% in June and 5.9% in September, with 3.9% positive in both. Being more symptomatic or suspecting infection increased the probability of ever being positive. Of those positive in June, 46% (49/107) were negative in September (seroreversion), and this was similar regardless of symptom characteristics, suspicion, and timing of possible infection. A possible outlier was those aged over 55 years, where only 3 of 13 (23%) had seroreversion.DiscussionThese results do not follow the pattern reported from studies specifically designed to monitor seropositivity, which have found greater consistency over time and the influence of presence, timing and severity of symptoms on seroreversion. We suggest several factors that may have contributed to this difference: our low bar in defining initial seropositivity (single test); a non-quantitative test known to have relatively low sensitivity; participants carrying out testing. We would encourage other studies to use these real-world performance characteristics alongside those from laboratory studies to plan and analyse any antibody testing.

Longitudinal variability in mortality predicts COVID-19 deaths

Within Europe, death rates due to COVID-19 vary greatly, with some countries being severely hit while others to date are almost unaffected. This has created a heated debate in particular regarding how effective the different measures applied by the governments are in limiting the spread of the disease and ultimately deaths. It would be of considerable interest to pinpoint the factors that determine a country’s susceptibility to a pandemic such as COVID-19. Here we present data demonstrating that mortality due to COVID-19 in a given country could have been predicted to some extent even before the pandemic hit Europe, simply by looking at longitudinal variability of death rates in the years preceding the current outbreak. The variability in death rates during the winter influenza seasons of 2015–2019 correlates to excess mortality in 2020 during the COVID-19 outbreak (Spearman’s ρ = 0.68, 95 % CI = 0.40–0.84, p < 0.001). In contrast, there was no correlation with age, population density, latitude, GNP, governmental health spending, number of intensive care beds, degree of urbanization, or rates of influenza vaccination. These data suggest an intrinsic susceptibility in certain countries to excess mortality associated with viral respiratory diseases including COVID-19.

Visit-to-visit variability of serum uric acid measurements and the risk of all-cause mortality in the general population

Background Evidence on longitudinal variability of serum uric acid (SUA) and risk of all-cause mortality in the general population is limited, as many prior studies focused on a single measurement of SUA. Methods A total of 53,956 participants in the Kailuan study who underwent three health examinations during 2006 to 2010 were enrolled. Variability of SUA was measured using the coefficient of variation (primary index), standard deviation, average real variability, and variability independent of the mean. Cox proportional hazard regressions were used to calculate the hazard ratio (HR) and 95% confidence interval (CI) for the association of variability of SUA with subsequent risk of all-cause mortality, considering its magnitude and the direction and across different baseline SUA categories. Results Over a median follow-up of 7.04 years, 2728 participants died. The highest variability of SUA was associated with an increased risk of all-cause mortality, the HR was 1.33 (95% CI, 1.20–1.49) compared with the lowest variability. In this group, both a large fall (HR, 1.28; 95% CI, 1.14–1.44) and rise (HR, 1.18; 95% 1.05–1.32) in SUA were related to risk of all-cause mortality. These associations were similar across different baseline SUA categories. Consistent results were observed in alternative measures of SUA variability. Moreover, individuals with higher variability in SUA were more related to common risk factors than those with stable SUA. Conclusions Higher variability in SUA was independently associated with the risk of all-cause mortality irrespective of baseline SUA and direction of variability in the general population.

Longitudinal Variability in Mortality Predicts Covid-19 Deaths

Within Europe, death rates due to covid-19 vary greatly, with some countries being hardly hit while others to date are almost unaffected. This has created a very heated debate in particular regarding how effective the different measures applied by the governments are in limiting the spread of the disease and ultimately deaths. It would be of considerable interest to pinpoint the factors that determine a country’s susceptibility to a pandemic such as covid-19. Here we present data demonstrating that mortality due to covid-19 in a given country could have been predicted even before the pandemic hit Europe, simply by looking at longitudinal variability of death rates in the years preceding the current outbreak. The variability in death rates during the winter influenza seasons of 2015-2019 correlate strongly to excess mortality caused by covid-19 in 2020 (R2=0.48, p<0,0001). In contrast, there was no correlation with age, population density, latitude, GNP, governmental health spending, degree of urbanization, or rates of influenza vaccination. These data suggest an intrinsic susceptibility in certain countries to excess mortality associated with viral respiratory diseases including covid-19.

Comparison of EEJ Longitudinal Variation from Satellite and Ground Measurements over Different Solar Activity Levels

The longitudinal variability and local time of equatorial electrojet (EEJ) current using simultaneous data recorded by ground and satellite magnetometers at different levels of solar activity were investigated. In this study, we used data from the CHAMP and Swarm satellites to obtain EEJ current measurements around the globe. The ground data were provided by the MAGDAS, INTERMAGNET, and IIG networks. The ground observation was carried out by analyzing magnetometer data in four different sectors: the South American, Indian, African, and Southeast Asian sectors. These ground data were normalized to the dip equator to overcome the latitudinal variation of each station. The analysis for both measurements was performed using quiet day data. Both the ground and satellite data were categorized according to solar activity level; low, moderate, and high. The results revealed that, during the low solar activity, there was a good agreement between the longitudinal profiles of the EEJ measured using the satellite and the ground data. In general, strong correlations were obtained in most of the sectors where ground data were available between 11 and 13 local time (LT). Besides that, our analysis revealed that the different times of maximum EEJ appearances were seasonally dependent only at certain longitude sectors.

Longitudinal variability in mortality predicts Covid-19 deaths

Within Europe, death rates due to covid-19 vary greatly, with some countries being hardly hit while others to date are almost unaffected. It would be of interest to pinpoint the factors that determine a country’s susceptibility to a pandemic such as covid-19.Here we present data demonstrating that mortality due to covid-19 in a given country could have been largely predicted even before the pandemic hit Europe, simply by looking at longitudinal variability of all-cause mortality rates in the years preceding the current outbreak. The variability in death rates during the influenza seasons of 2015-2019 correlate to excess mortality caused by covid-19 in 2020 (R2=0.48, p<0.0001). In contrast, we found no correlation between such excess mortality and age, population density, degree of urbanization, latitude, GNP, governmental health spendings or rates of influenza vaccinations.These data may be of some relevance when discussing the effectiveness of acute measures in order to limit the spread of the disease and ultimately deaths. They suggest that in some European countries there is an intrinsic susceptibility to fatal respiratory viral disease including covid-19; a susceptibility that was evident long before the arrival of the current pandemic.