Figure 6. The “About” section
Figure 6. NLP link extraction to Wikipedia The extraction of links to Wikipedia is also considered as interesting, but not as interesting as the “sister content”. A total of 58% reported a score of 4 or 5. Figure 7: NLP auto-tagging content with terms from vocabulary Auto-tagging content with terms from vocabularies are regarded as interesting to 65% of the respondents and very interesting to 15%. This may indicate that there is a possible market potential for this kind of knowledge/software/service.
Figure 6. Log risk ratios comparing risks of women who either had a D-CST for both visits (red), transitioned from a D-CST to a L-CST between visits (green), or transitioned from L-CST to D-CST between visits (blue) to women who had a L-CST for both visits. .................................................................................................................................57 Figure 7: Lactobacillus proportion between 8-14 and 24-30 weeks gestational age by prenatal antibiotic use 58 Background/ Literature Review Minority populations suffer from a disproportionately high burden of adverse health outcomes across the globe. In the United States, African American race is one of the major risk factors for morbidity and mortality (1). Ever since the Centers for Disease Control and Prevention (CDC) began collecting data on maternal and infant morbidity and mortality in 1981, the data has consistently shown large racial divides in the health of pregnant mothers and their newborns. In particular, African Americans experience far higher rates of maternal and infant mortality, preterm birth, low birth weight, and cesarean section than other races (2). Differential health care access and treatment, chronic stress from racial discrimination, and poor education have been proposed as dominant drivers of the higher rates of adverse birth outcomes in African Americans. However, despite hundreds of studies showing significant associations between socioeconomic factors and racial differences in birth outcomes, there is little understanding of the biological mechanisms underlying these associations. This review will cover the details of racial disparities in adverse birth outcomes in the United States and discuss several variables that could partially explain the disparities. It will focus ultimately on the association between race and the genera composition of the vaginal microbiome, which is an important factor potentially underpinning the racial dissimilarities in pregnancy outcomes. Finally, there will be a discussion on the need for epidemiologic and geospatial modeling to describe how racially-correlated variables are connected to an vaginal dysbiosis and its consequences on birth outcomes.
Examples of Figure 6 in a sentence
Figure 6 – continued Spectral energy distributions for the 24 objects from Table 1 identified as galaxies.
Coordination of GFM and DC voltage controlOne option for converter station control modes and their designation in a multi-terminal HVDC system without GFM functionality for large-scale offshore wind power integration would be as shown in Figure 6.
The peak harbour porpoise density across the SAC is estimated to be >3 per km2 (Figure 6) (Heinänen and Skov 2015).
This is a robust active power control chain, as active power imbalances occurring due to variations in wind power generation get transferred and distributed to the two onshore areas purely by local controls and without communication between subsystems.v/f control PQ control AC/DC #1=~=Vdc droop controlAC/DC #3=~===~~==v/f control PQ control AC/DC #2=~=Vdc droop controlAC/DC #4=~===~~== Figure 6.
Figure 6 actu- ally models the behaviour of the system but no single faulty event is present (any event that can happen is normal).Figure 7 presents a complex behaviour that one might want to diagnose within the system of Figure 6.
More Definitions of Figure 6
Figure 6. Log risk ratios comparing risks of women who either had a D-CST for both visits (red), transitioned from a D-CST to a L-CST between visits (green), or transitioned from L- CST to D-CST between visits (blue) to women who had a L-CST for both visits. 95% confidence intervals not crossing the y-axis indicate a significantly higher risk of the outcome compared to women who had a L-CST for both visits. Outcomes include all births before 38 weeks gestational age (All Early), all births before 36 weeks gestational age (All PTB), spontaneous births before 38 weeks gestational age (Sp. Early), and spontaneous births before 36 weeks gestational age (Sp. PTB). Lactobacillus Proportion Prior to V1 (n=41) Between V1 and V2 (n=32) Post V2 (n=22) No antibiotic use (n=87) Time Figure 7: Lactobacillus proportion between 8-14 and 24-30 weeks gestational age by prenatal antibiotic use.
Figure 6. Second Vehicle Testing Station that was visited
Figure 6. The safe replication service key functionality is the inter-working of iRODS and EPIC
Figure 6. The components of National Official Register of the Territorial Division of the Country (TERYT system).TERC - Identifiers and names of units of territorial division of the country, SIMC - Identifiers and names of localities (SIMC), BREC - Statistical regions and census enumeration areas (BREC), NOBC - Identification of addresses of streets, real estates, buildings and dwellings and ULIC - Central Catalogue of Streets (ULIC) In TERYT all addresses are geocoded and can be placed within national geographic administration areas. Each address is identified by hierarchical territorial identifier (TERH ID) that place each address in voivodship, counties and municipality (Figure 7) the last digit of the TERC ID reflect the unit type A B
Figure 6. Methodology to model multiple households in GEM-E3-FIT The Bottom Up model (BU) is first calibrated using the aggregate consumption, wage and non-wage income, population and labour demand and supply of the Top down (TD) CGE model and the distribution by decile (that is described below). Then, the TD model projects total income, sectoral production, unit production costs, end-user prices and demand for skills until 2050. Total wage and non-wage income, transfers, prices of goods and services, wages by skill and skill requirements are passed on to the BU model in order to compute income and consumption for income deciles, and estimate the impacts on different households including changes in labour income and skill requirements. The next step is the aggregation of individual consumption to the single representative household and plug this in to the TD model where a new set of prices and wages will be computed, ensuring that the economy is in an equilibrium, where demand is covered by supply. This loop continues until the change in prices and demand for products is below a certain threshold ensuring general equilibrium. In the BU model households are aggregated to 10 income classes with different consumption and saving patterns and different sources of income. It is assumed that the equivalized household size by decile and the type of labour skills supplied by each decile remain constant over time. The link between skills, sectoral activity and income deciles depends on the skills acquired by each household in the base year and the evolution of sectoral production. As explained in Section 2, the analysis of distributional impacts such as energy poverty may require a finer resolution of individual households which is not captured by the decile representation. To do so ex-post calculations can be implemented using truncated normal distributions of households within each decile. Estimating income inequality and energy poverty indicators in GEM-E3-FIT
Figure 6. SysML modelling using the second approach Henceforth we can define the mapping as follows:
Figure 6. Participants’ level of agreement with the statement “I am able to recognise what disrespectful relationships look, sound or feel like” (N=529) Particpants level of agreement with statement "I am able to recognise what disrespectful relationships look, sound or feel like" 300 250 200 150 100 50 0 Strongly disagree Disagree Neutral Agree Strongly agree Knowledge of youth support services and how to access them Effects of sexual violence and support and help seeking Students were asked to rate their level of agreement with the statements “I understand the impact of child sexual abuse” and “I know why people may not tell other that they have experience child sexual abuse” Figure 7 shows that 88% of participants agreed or strongly agreed with the statement “I understand the impact of child sexual abuse”. Figure 7: participants’ level of agreement with the statement “I understand the impact of child sexual abuse” (N=529) Particpants level of agreement with statement "I understand the impact of child sexual abuse" 300 250 200 150 100 50 0 Strongly disagree Disagree Neutral Agree Strongly agree Figure 8 shows that 88% of participants agreed or strongly agreed with the statement “I know why some people may not tell others they have experienced child sexual abuse”. Figure 8: participants level of agreement with the statement “I know why some people may not tell others they have experienced child sexual abuse” (N=529) Particpants level of agreement with statement "I know why some people may not tell others they have experienced child sexual abuse" 300 250 200 150 100 50 0 Strongly disagree Disagree Neutral Agree Strongly agree Participants were also asked to select support services inside of outside of their school that they would feel comfortable seeking support from. Figure 9 shows that 68% of participants identified their friends as a support service that they would access within their school. 53% of participants identified their school counsellor, 26% of students identified their xxxx, 21% of students identified their Peer Sexuality Support People, 19% of participants identified their form teacher as well as other support services not specified,17% identified their subject teacher, 14% identified their school nurse, 12% identified their school social worker and 11% identified their school doctor. Figure 9: Participants level of comfort accessing specific services within their school (N=529) Participants level of comfort accessing specific services within their sc...