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Hudson Bell
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Colorado Tick Fever is a rare viral disease spread by the bite of an infected Rocky Mountain wood tick (Dermacentor andersoni). These ticks are found between 4,000 and 10,000 feet above sea level. Most cases occur during spring and summer months, this is when ticks are most active. The virus does not spread from person to person, it is only from the bite of a tick. There are no vaccines to prevent or medicines to treat this disease. Common symptoms include fever, chills, headache, body aches and fatigue and can occur 1 to 14 days after being infected. Many people will have several days of fever followed by several days of relief and then a recurrent shorter period of illness and fever. Ways to reduce your risk of infection include using EPA insect repellent, wear longs sleeve shirts and pants while outside, avoid wooded and busy areas with high grass, and perform tick checks as soon as possible after spending time outdoors. The risk of Colorado Tick Fever increases with the time a tick is attached to you. If you or your loved ones have exposure and symptoms please reach out to your health care provider.




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To help address this need, today Microsoft is launching a global skills initiative aimed at bringing more digital skills to 25 million people worldwide by the end of the year. This initiative will bring together every part of our company, combining existing and new resources from LinkedIn, GitHub, and Microsoft. It will be grounded in three areas of activity:


First, we will offer low-cost access to industry-recognized Microsoft Certifications based on exams that demonstrate proficiency in Microsoft technologies. We are making exams for these Microsoft Certifications available at a significantly discounted fee of $15 available to those who self-attest that their employment has been impacted by COVID-19. This represents a large discount on the price of exams that typically cost more than $100. We are committed to supporting the integrity of certifications by enabling proctoring safely in an online setting that is accessible from anywhere. The $15 fee will be paid to and will enable third parties to scale to meet the potential surge in examination resources and will support the integrity of the certification by enabling proctoring via a safe, online setting that is accessible from anywhere. We will also work with governments, nonprofits, foundations, and other private sector partners if they wish to absorb this third-party cost.


We believe the strength of these resources is their comprehensive nature. To help people find and navigate all of our offerings, we have made all of these resources accessible from a single location: opportunity.linkedin.com. A job seeker or anyone looking to develop these on-demand skills can start here and will be guided through the learning paths based on the roles in which they are interested.


In part this will enable nonprofits to translate these resources into additional languages and to localize and tailor the learning content. These groups will also provide and support teachers and facilitators to help learners complete learning pathways and certification, and provide connections to wrap-around supports, coaching, and mentoring. We expect these grants will enable the nonprofits to reach 5 million unemployed workers, with a focus on particularly vulnerable groups. This includes people with disabilities, people from low-income communities, and people from diverse backgrounds that are underrepresented in tech, including women and underrepresented minorities.


This needs to change. Therefore, as a part of this skills initiative, Microsoft will dedicate support to community-based nonprofit organizations working to increase skill development and economic opportunities for communities of color, especially Black and African American communities. We will provide $5 million in cash grants to community-based nonprofit organizations that are led by and serve communities of color in the United States. This summer, we will publish additional information on this opportunity and will select organizations for this funding by fall of this year. We recognize that this is but a small part of the long overdue investment needed to address historical racial inequities in our society. We look forward to partnering with communities and other like-minded individuals and organizations to use our voice and resources to advocate for change to support communities of color.


Expert judges evaluate each of the items to determine whether they represent the domain of interest. These expert judges should be independent of those who developed the item pool. Expert judgment can be done systematically to avoid bias in the assessment of items. Multiple judges have been used (typically ranging from 5 to 7) (25). Their assessments have been quantified using formalized scaling and statistical procedures such as the content validity ratio for quantifying consensus (43), content validity index for measuring proportional agreement (44), or Cohen's coefficient kappa (k) for measuring inter-rater or expert agreement (45). Among the three procedures, we recommend Cohen's coefficient kappa, which has been found to be most efficient (46). Additionally, an increase in the number of experts has been found to increase the robustness of the ratings (25, 44).


The item discrimination index has been found to improve test items in at least three ways. First, non-discriminating items, which fail to discriminate between respondents because they may be too easy, too hard, or ambiguous, should be removed (71). Second, items which negatively discriminate, e.g., items which fail to differentiate rightly between medically diagnosed depressed and non-depressed respondents on a happiness scale, should be reexamined and modified (70, 71). Third, items that positively discriminate should be retained, e.g., items that are correctly affirmed by a greater proportion of respondents who are medically free of depression, with very low affirmation by respondents diagnosed to be medically depressed (71). In some cases, it has been recommended that such positively discriminating items be considered for revision (70) as the differences could be due to the level of difficulty of the item.


Concurrent criterion validity is the extent to which test scores have a stronger relationship with criterion (gold standard) measurement made at the time of test administration or shortly afterward (2). This can be estimated using Pearson product-moment correlation or latent variable modeling. The work of Greca and Stone on the psychometric evaluation of the revised version of a social anxiety scale for children (SASC-R) provides a good example for the evaluation of concurrent validity (140). In this study, the authors collected data on an earlier validated version of the SASC scale consisting of 10 items, as well as the revised version, SASC-R, which had additional 16 items making a 26-item scale. The SASC consisted of two sub scales [fear of negative evaluation (FNE), social avoidance and distress (SAD)] and the SASC-R produced three new subscales (FNE, SAD-New, and SAD-General). Using a Pearson product-moment correlation, the authors examined the inter-correlations between the common subscales for FNE, and between SAD and SAD-New. With a validity coefficient of 0.94 and 0.88, respectively, the authors found evidence of concurrent validity.


Differentiation or comparison between known groups examines the distribution of a newly developed scale score over known binary items (126). This is premised on previous theoretical and empirical knowledge of the performance of the binary groups. An example of best practice is seen in the work of Boateng et al. on the validation of a household water insecurity scale in Kenya. In this study, we compared the mean household water insecurity scores over households with or without E. coli present in their drinking water. Consistent with what we knew from the extant literature, we found households with E. coli present in their drinking water had higher mean water insecurity scores than households that had no E. coli in drinking water. This suggested our scale could discriminate between particular known groups.


Although correlational analysis is frequently used by several scholars, bivariate regression analysis is preferred to correlational analysis for quantifying validity (127, 128). Regression analysis between scale scores and an indicator of the domain examined has a number of important advantages over correlational analysis. First, regression analysis quantifies the association in meaningful units, facilitating judgment of validity. Second, regression analysis avoids confounding validity with the underlying variation in the sample and therefore the results from one sample are more applicable to other samples in which the underlying variation may differ. Third, regression analysis is preferred because the regression model can be used to examine discriminant validity by adding potential alternative measures. In addition to regression analysis, alternative techniques such as analysis of standard deviations of the differences between scores and the examination of intraclass correlation coefficients (ICC) have been recommended as viable options (128).


When examining cell-type-specific enrichment using partitioned heritability, we show that the greatest level of enrichment for cell-type-specific groupings comes from the brain and central nervous system. This is indicated by the fact that the 24 cell types that were significantly enriched using the gene expression data set were all cell types that are found within the brain and the rest of the central nervous system (Fig. 5c and Supplementary Data 10). In addition, using the chromatin-based sets, 32 of the 34 cell groupings that were significantly enriched were drawn from the brain and the central nervous system (Fig. 5d and Supplementary Data 11).


Polygenic risk scores (PGRSs) were derived using the summary statistics from our GWAS of household income and GS:SFHS. When examining the PGRSs within each of the five income groups in GS:SFHS, we found that those in category 5 (those earning more than 70,000) had the highest PGRSs (Fig. 7a). The predicted income for the PGRSs was lower in each subsequent level of household income in GS:SFHS. 041b061a72


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