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Out of the 676 possible pairs of regional indicator symbols (26 26), only 270 are considered valid Unicode region codes.These are a subset of the region sequences in the Common Locale Data Repository (CLDR):[6][7][8]

The accepted solution was to add 26 characters for letters used for the representation of regional indicators, which used in pairs would represent the ten national flags and possible future extensions.[2]Per the Unicode Standard "the main purpose of such [regional indicator symbol] pairs is to provide unambiguous roundtrip mappings to certain characters used in the emoji core sets"[21]specifically the ten national flags:[22] ??, ??, ??, ??, ??, ??, ??, ??, ??, and ??.

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Accumulating evidence suggests that, independent of physical activity levels, sedentary behaviours are associated with increased risk of cardio-metabolic disease, all-cause mortality, and a variety of physiological and psychological problems. Therefore, the purpose of this systematic review is to determine the relationship between sedentary behaviour and health indicators in school-aged children and youth aged 5-17 years. Online databases (MEDLINE, EMBASE and PsycINFO), personal libraries and government documents were searched for relevant studies examining time spent engaging in sedentary behaviours and six specific health indicators (body composition, fitness, metabolic syndrome and cardiovascular disease, self-esteem, pro-social behaviour and academic achievement). 232 studies including 983,840 participants met inclusion criteria and were included in the review. Television (TV) watching was the most common measure of sedentary behaviour and body composition was the most common outcome measure. Qualitative analysis of all studies revealed a dose-response relation between increased sedentary behaviour and unfavourable health outcomes. Watching TV for more than 2 hours per day was associated with unfavourable body composition, decreased fitness, lowered scores for self-esteem and pro-social behaviour and decreased academic achievement. Meta-analysis was completed for randomized controlled studies that aimed to reduce sedentary time and reported change in body mass index (BMI) as their primary outcome. In this regard, a meta-analysis revealed an overall significant effect of -0.81 (95% CI of -1.44 to -0.17, p = 0.01) indicating an overall decrease in mean BMI associated with the interventions. There is a large body of evidence from all study designs which suggests that decreasing any type of sedentary time is associated with lower health risk in youth aged 5-17 years. In particular, the evidence suggests that daily TV viewing in excess of 2 hours is associated with reduced physical and psychosocial health, and that lowering sedentary time leads to reductions in BMI.

Studies were included only if there was a specific measure of sedentary behaviour. Eligible exposures of sedentary behaviours included those obtained via direct (e.g., measurements of sitting, or low activity measured by accelerometer) and self-reported (e.g., questionnaires asking about TV watching, video gaming, non-school computer use, and screen time - composite measures of TV, video games, computers) methods. Sedentary behaviour was often measured as a composite measure of all time engaging in sedentary behaviours including screen time outside of school hours. Six health indicators were chosen based on the literature, expert input, and a desire to have relevant measures from a range of holistic health indicators (i.e. not only physical health, but also emotional, mental and intellectual health). The six eligible indicators in this review were:

Strengths of this review included a comprehensive search strategy, a-priori inclusion and exclusion criteria and analyses, and inclusion of non-English language articles. We included direct and indirect measures of sedentary behaviour and focused on 6 diverse health indicators in children and youth. Although efforts were made to include grey literature (e.g. by contacting key informants and reviewing government documents), we did not include conference proceedings and other types of grey literature because it was impractical and unfeasible to sift through all unpublished work, and also because of limitations in the quality of reporting in conference abstracts [267, 268]. We do not anticipate that additional, unpublished work would change the results.

Our study has limitations, including the types of outcome measurements and analyses reported in the primary studies and primary study quality. The scope of this review was large and included a great deal of health indicators and measurement tools. A more detailed meta-analysis would have allowed us to estimate the overall effect sizes for each outcome. However, due to the heterogeneity of the data, it was impossible to complete such analysis. Furthermore, some studies had missing information on participant characteristics making it impossible to determine if basic demographics act as a confounder for the relationship between sedentary behaviour and health. Many studies also grouped their variables into tertiles, or groups that also took into account physical activity level. Although it was still possible to ascertain information regarding the association between level of sedentary behaviour and health indicators, it made it very difficult to compare the information across studies. Similarly, very few studies measured time spent being sedentary directly (i.e. with direct observation or accelerometry). Previous work [269, 270] has shown significant differences between direct and indirect measures of physical activity; similar work needs to be completed with respect to sedentary behaviour to gain a better understanding of possible biases in previous studies. Indirect measurements of sedentary behaviour often lead to grouping for analyses. This may lead to bias in the results of the systematic review as many studies arbitrarily grouped their participants as ''high users" if they watched more than 2 hours of television per day. This could perhaps be falsely leading us to conclude that 2 hours is the critical cut-point or threshold. Further work using direct (i.e. accelerometer) measures of sedentary behaviour and screen time as continuous variables will help to clarify if a cut-point of 2 hours is in fact biased.

Finally, as described above, the vast majority of the current evidence has been based on self-report questionnaires focused on TV viewing and body composition. It is now clear that these two variables are related. Future work needs to move beyond this relationship and focus on other modes of sedentarism (e.g., prolonged sitting, passive transport) and other associated health indicators. To do this, objective measures of the time, type and context of sedentary pursuits will be needed in combination with robust and standardized measures of health indicators.

This systematic review summarizes the current evidence examining the relationship between sedentary behaviours and a series of health indicators. It was determined that increased sedentary time was associated with negative health outcomes in both boys and girls; this was true across all study designs with the majority of studies (85.8%) reporting similar relationships. The majority of current work has focused on television viewing and body composition and suggests that children and youth should watch less than 2 hours of TV per day during their discretionary time. Furthermore, children and youth should try to minimize the time they spend engaging in other sedentary pursuits throughout the day (e.g. playing video games, using the computer for non-school work or prolonged sitting). This work can be used to inform the development of evidence-based sedentary behaviour recommendations for children and youth.

I can be running a race where a car bumps into me, and it is not a clean run, even though I was not impeding. The game needs to be more clear on a "Clean Run" and have an indicator letting us know that the run will no longer be clean.

Methods: We genotyped 300 adult COVID-19 Egyptian patients for TLR-4 (Asp299Gly and Thr399Ile) SNPs using PCR-restriction fragment length polymorphism (PCR-RFLP). We also measured interleukin (IL)-6 levels by enzyme-linked immunosorbent assay (ELISA) as an indicator of the cytokine storm.

The Cyber Threat Indicator and Defensive Measures Submission System provides a secure, web-enabled method of sharing cyber threat indicators and defensive measures with DHS. This system helps analysts to process cyber threat indicators and defensive measures for further sharing with Federal Government and private sector entities. + More Detail

We encourage you to share any information that falls within the definitions above of a cyber threat indicator or defensive measure. All cyber threat indicators and defensive measures submitted through this system by a non-federal entity as defined in CISA are deemed submitted under section 104(c) and 105(c)(1)(B) of CISA. Please do not submit personal information not directly related to a cybersecurity threat using this form. For more information about sharing cyber threat indicators and defensive measures with the Federal Government under CISA, see

To submit a cyber threat indicator or defensive measure by email, please send an encrypted email to the DHS Indicator Submission Inbox (cisaservicedesk@cisa.dhs.gov). The PGP/GPG key is available at -cert.gov/contact-us

The aim is to develop and validate a cognitive impairment indicator that summarizes cognitive performance across a neuropsychological battery. Prospective cohort design, but cross-sectional analyses for the current project. The setting is community-based.

Abstract: The quality of life, which is a complex characteristic of human existence, its level and conditions, in the research practice is measured by statistical and sociological methods. This characteristic reflects the degree of satisfaction with different needs and subjective perception of life and its individual aspects. In this work the statistical method is chosen to describe the quality of life. It aims to the indicators' analysis, which are connected with food consumption (using Kemerovo region since 2010 to 2014 as an example) and differentiated into two parts: standard of living and living conditions. The analyzed level (households' expenses share for food in overall consumer spending structure, food consumption structure, its nutrition and energy value) and conditions indicators (food prices, consumer price indexes, a minimum food set cost dynamics and its ratio with the average income, retail food trade turnover, its share in total turnover of the region, public catering turnover) have shown low life quality in the region in comparison with Russia in general, and also its decrease for the last one or two years, which is confirmed by traditional indicators of living standard and quality. ff782bc1db