12.4: Contexts- School and Work Settings in Early Adulthood
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Learning Objectives
By the end of this section, you will be able to:
- Describe the education and work decisions facing young adults after high school and the contexts that influence them
- Discuss the ways college pathways vary for young adults
- Describe work and career exploration during young adulthood
- Describe the early adult experience of finding work-life balance
Six years after graduating college, Sam finds his job as a design engineer very satisfying. He can apply his degree in mechanical engineering, works from home three days a week, and has the autonomy to implement creative ideas. However, his pathway to the job was challenging. His first job out of college, as a quality assurance engineer, required him to work from 8am to 5pm five days a week on the floor of a manufacturing plant. It was a noisy environment with low pay, but drawing on his community service experience volunteering for Habitat for Humanity, Sam impressed his supervisors with his adaptability, teamwork, and communication skills. Sam appreciates how far he has come during his early adult career and still looks for opportunities for continuing education and professional development to keep the doors open for future career mobility.
Sam’s story illustrates how the education and work trajectories of young adults can be both challenging and rewarding. In this section we will consider the influences on and outcomes of decisions regarding continuing education and employment.
Trajectories
Developmental scientists often refer to life’s developmental trajectories (Elder et al., 2015). A trajectory is a predicted pathway of development, and it is usually determined by our behaviors within our context. For example, if your community and family are able to support a college education—standards that define your context —and you earn good grades in high school and choose to go to college in early adulthood—which defines your behavior-- this interaction increases the likelihood that you will complete a college degree and earn a higher income, establishing your trajectory .
As the social clock of emerging and early adulthood has become more elongated and flexible in modern society, there is now space for many more possible developmental trajectories during this stage of life (Setterson & Ray, 2010; Eliason et al., 2015, p. 207) (Figure 12.18). Young adults perceive that personal accountability, independent decision-making, and financial independence are what make someone an adult, and the contexts from which they step into the adult world create a variety of ways to achieve these priorities (Sharon, 2016).
Post-High School Pathways
College enrollment rates are higher for the most recent cohorts of young adults than for previous generations, but since 2010 enrollment has remained relatively steady, with about 40 percent of 18- to 24-year-olds enrolling in a 2- or 4-year institution (National Center for Education Statistics, 2024). That means that for more than half of young adults, college is not the pathway. Other pathways include starting a family, undertaking vocational training, accepting full-time employment, enlisting in military service, and signing on for volunteer work or political activism (Figure 12.19).
Many young adults credit community service opportunities with sparking their interest in a particular field and providing a purpose-driven career pathway; however, most volunteerism and civic engagement among young adults occurs in sporadic episodes (Motulsky et al., 2020). Most young adults express interest in community service but do not regularly participate, citing a lack of time given the increasing responsibilities of early adulthood (Swartz et al., 2017; Wray-Lake et al., 2020). Female and Black young adults are most likely to volunteer their time for community service, but overall community service participation rates decline following high school. Political engagement and election participation increase, however, particularly among Black young adults (Wray-Lake et al., 2020). (Figure 12.20).
The Influence of Context
Many factors interact to influence the educational and vocational pathways young adults choose after high school, including family and community background, socioeconomic status, gender, and race. Those from rural areas often face hard choices, because they are less likely to find many education and employment opportunities close to home (Fenton et al., 2023). Half of all first-year college students at public institutions come from within 50 miles of campus, but education deserts , communities with no nearby college or university, are more common in rural areas (Stolzenberg et al., 2020; Hillman & Weichman, 2016).
Young adults from families with low socioeconomic status (SES) see the greatest earnings benefits from a college degree. But parents and community members who did not attend college may be less likely to see college as a realistic or valuable choice, and rural young adults are less likely to be encouraged by their families and teachers to prepare for or pursue a college education (Healy, 2017; Byun et al., 2012). Many young adults in rural communities choose instead to seek employment in local jobs that are more familiar, accessible, and close to family, rather than investing time and money in an advanced education or professional relocation that may not yield worthwhile occupational results (Corbett, 2016). Many also cite obligations to family or family property that keep them attached to their hometown (Wilson et al., 2017; Massey, 2020).
Young adults who remain in their hometown communities are more likely to experience earlier transitions to other adult roles such as full-time employment, marriage, and parenthood (Fenton, 2023). While these early transitions are predictive of decreased substance use, increased financial independence, and life satisfaction for these young adults, early marriage and parenthood decrease the likelihood of college attendance or degree completion and predict a lower household income (Maggs et al., 2012; Monaghan, 2020). Economic pressures have also made it difficult for many rural areas to provide living conditions of sufficient quality for young adults to stay or to return home after college (Motulsky et al., 2020). School closures, decreased property values, poverty, unemployment, low wages, and a scarcity of health care providers have resulted in a declining rural population in the United States, driven in part by the exodus of young people from those areas and toward urban or suburban areas that offer more opportunities (Economic Research Service, 2017; Massey, 2020). Those who do leave rural hometowns to advance their education or career are more likely to come from White families with higher SES and higher levels of education (Brand & Xie, 2010; Brooks & Redlin, 2009; Carr & Kefalas, 2011).
Race and ethnicity also predict different decisions regarding employment and education (Figure 12.21). Family care priorities, financial obstacles, and discrimination can dissuade young Black and Hispanic adults with high occupational aspirations from relocating to pursue those goals (Brooks & Redlin, 2009). Black young adults are also more likely to feel disconnected from settings that might provide developmental pathways, such as school, the workplace, and the military (Fenton et al, 2023), in part because they are more likely than White young adults to face interpersonal and institutional discrimination there (Hope et al., 2015). However, Black young adults are more likely than their White socioeconomic peers to complete a college degree (Karlson, 2019).
The intersection of race and gender identity also produces variation in trajectories for young adults. Partly because young men anticipate better prospects for stable well-paying jobs without a college education in stereotypically masculine vocations such as plumbing or construction, fewer men attend college than women. This gender gap is narrower among Black young adults (37 percent of men attend college vs. 44 percent of women) than among White (40 percent of men, 50 percent of women) and Hispanic young adults (33 percent of men, 44 percent of women) (Fry, 2023). White and minority young adults enlist in the military at comparable rates, but women are much less likely to do so than men (Barosso, 2019). White and Hispanic young adults are more likely to marry earlier than Black or Asian young adults, but Black women are more likely than White women to work, be the primary breadwinner in their household, and have caregiving responsibilities (Payne, 2019; Weller, 2019).
The transition into adulthood can be particularly challenging for young adults with disabilities. Interviews with young people with a range of disabilities, their caregivers, and service providers reveal several barriers to participating in typical adult life (Stewart et al., 2014; ALSO, 2023):
- Pace : Life for emerging adults is influenced not only by a social clock, but also by busy schedules and fast-paced interactions that can leave little room for those who need a flexible pace to accomplish their goals.
- Complexity and Literacy : Changing procedures and jargon-filled instructions can be confusing for those trying to accomplish even everyday tasks, such as reading a recipe or putting gas in the car.
- Stigma : In a culture that values independence in early adulthood, it can be difficult for a young adult with disabilities to ask for help and support. It is especially challenging to overcome “either-or” thinking that assumes someone either can do everything by themselves or needs constant supervision, with no gray area in between.
Many families are understandably reluctant to give their young adults more autonomy within such an uncertain landscape. Young adults with disabilities were often more willing than their families to pave their own way, despite the lack of clear directions or guaranteed outcomes, and providing opportunities for young adults with disabilities to exercise more independence can also reduce stigma within the community. The success of these young people requires early, careful, and customized planning that promotes collaboration among available disability services and the family, and that accommodates the strengths and limitations of the young adult within their desired future (Stewart et al., 2014). An individualized and holistic approach can help facilitate the development of life skills, opportunities, and support that will improve a young adult’s quality of life in domains such as independent living, employment, education, social life, and community involvement (ALSO, 2023).
College Considerations
College is not the only or best path for everyone, and for those who choose it, it not a one-size-fits-all experience. For first-generation college-students, the transition to college life can be a culture shock. While many such students begin college with pride and determination, the environment can feel uncomfortable or overwhelming as they navigate unfamiliar social networks and intimidating institutional policies and procedures (Motulsky et al., 2020). After high school, some students choose to do a gap year, to gain valuable personal and professional development before beginning college, through participation in immersive experiences such as community service work. Other students combine military service and college education by joining the Reserve Officers Training Corps (ROTC) (Figure 12.22). In exchange for an 8-year service commitment following graduation, the ROTC offers scholarships to adults interested in gaining military service training while enrolled as full-time college students (U.S. Army, n.d.). Next we look at pathways through college and outcomes for those who enroll.
Link to Learning
A gap year can provide an emerging adult with time for reflection and exploration that is useful when choosing long-term education and vocational goals. Visit the Gap Year Association to learn about options for gap year activities and even find a gap year consultant.
Pathways
Statistics show significant differences in how and when young adults complete their undergraduate degrees (Monaghan, 2020). These differences often result from socioeconomic inequities that can present delays and interruptions for students with lower SES. Often driven by the need to find affordable, local, and part-time enrollment options, they are more likely to begin their college education as part-time students at a community college, a pathway associated with a lower likelihood of degree completion than full-time enrollment at a 4-year institution. Students with lower SES are also more likely to be employed full-time, get married, and become parents during emerging adulthood, which often necessitates part-time enrollment and lowers degree completion rates, especially for women.
Research on college enrollment patterns identified two primary groups of students, labeled rapid completers (38 percent) and marginal college goers (43 percent). Rapid completers enrolled and completed their degree before age 25. They were more likely to come from college-educated families with high SES. They also had the highest employment rates (part time during college and full time after graduation), with a median annual income of $94,000 during adulthood. Marginal college-goers attended part time and for short spells, with a 6 percent degree-completion rate during early adulthood. They were more likely to be employed full time and married during their college enrollment (Monaghan, 2020). Working part-time is associated with achieving better college outcomes, including greater campus engagement, leadership and skill development, and social networking, especially for first-generation students (Pike et al., 2008; Salisbury et al., 2012; Nuñez & Sansone, 2016). However, working more than 20 hours a week is associated with a lower GPA and lower college completion rate (Davis, 2023).
Outcomes
Young adults who enroll in college usually do so to get better job opportunities, including higher-earning and purpose-driven careers (Eagen et al., 2013; Johnson, 2023). If higher earnings are the goal, data suggest that completing an internship or study abroad experience during college is predictive of higher earnings in the workplace (Wolniak & Engberg, 2019). And while most graduates believe their education was a good investment, if they had to do it over, more than half say they would choose a different major to be better positioned to pursue their passions and better job opportunities (Johnson, 2023). Switching majors (as 30 percent of students do) can increase a student’s likelihood of graduating, especially when done within three years of starting and with the help of campus career resources (National Center for Education Statistics, 2017; Crawford Ciglar, 2020).
The benefits of choosing the “right” major continue after college: Adult job satisfaction and commitment have been found to be predicted by perceived alignment between college major and field of employment (Wolniak & Pascarella, 2005; Wolniack & Engberg, 2019), regardless of income. However, even if a graduate enters the workforce in a career exactly matching their major, chances are they won’t stay in that specific field over the course of their career (Roberts Wesleyan College, 2020; Fox, 2022).
Career Exploration
It is estimated that the average adult will change careers (not just jobs) 5 to 7 times in their life (Roberts Wesleyan College, 2020). In 2022 alone, 61 percent of young adults in the workforce who had quit their job within the last year said they wanted to change their field of work or occupation (Fox, 2022). In fact, some of the most satisfying jobs for emerging adults might not even exist yet. When most of today’s working adults were young, there was no such thing as social media, let alone a job as a social media manager. As technology continues to evolve, so will occupations in this and many other fields.
Career Preparation and Readiness
According to career expert Vicki Salemi, the key to professional readiness is to develop “transferable skills,” including communication, problem-solving, and collaboration, and to emphasize those skills on your resume and in interviews (Fox, 2022). Employers say they prefer applicants with “active and applied learning experiences,” which apprenticeships or internships can provide (Flaherty, 2021). Two-thirds of employers also say they want adaptable employees with good communication skills, which can be developed as a result of teamwork opportunities in military service or study-abroad experiences. If you are looking for a career that provides opportunity to learn new skills, face new challenges, and serve a purpose to others, completing community-service or civic engagement experiences is the best predictor of those outcomes. Such experiences also develop problem-solving skills, teamwork, and work ethic, among the top abilities employers seek (Gray, 2024).
Career Goals
A cross-cultural meta-analysis of the priorities influencing the career goals of young people found that they were influenced by extrinsic factors (such as the potential income or accessibility of the job), intrinsic motivations (such as personal interest or opportunities for professional development), and interpersonal influences (such as social responsibility or family influence). Those coming from families with more collectivistic cultural values, such as in India and South Korea, were more likely to be influenced by extrinsic factors and interpersonal influences. U.S. job seekers, on the other hand, were more likely to be guided by intrinsic motivations (Akosah-Twumasi et al., 2018).
Developing a vocational identity in early adulthood requires exploring the way our career goals relate to aspects of our self, such as interests, values, and abilities. Engaging in this exploration is predictive of job motivation, performance, and flexibility (Colquitt et al., 2000; Rowald, 2007; Kim et al., 2023). Research on college students shows a general increase in exploration and commitment during emerging adulthood (Kim et al., 2023), facilitated by aspects of personality such as conscientiousness, openness, internal locus of control, and self-efficacy (Lee et al., 2023). Young adults who are more satisfied with their career trajectories credit supportive mentorship from family, educators, and work colleagues, as well as their own proactive and strategic career exploration during school and in early job experiences (Swartz et al., 2017) (Figure 12.23).
Like other social developments, a young adult’s career trajectory reflects the interaction of context and behavior. According to Donald Super’s lifespan theory of career development, our career trajectory starts as early as childhood and evolves throughout adulthood as it interacts with other developmental roles and tasks (Super, 1990). In adolescence and emerging adulthood, the focus is exploration. However, in early to middle adulthood, the priority shifts to establishment, which includes committing to and advancing within a career in ways that consider the relative value of other roles such as family, peers, school, and the community.
For some young adults, the career establishment process may focus more on taking on more responsibility and advancing within successful or “good” jobs or careers. Young adults describe “good” jobs as full-time and stable positions that are personally rewarding, allow autonomy and responsibility at work, present opportunities to serve or mentor others, and offer generous employee compensation and benefits (such as work from home, health care, and vacation time). Those who find themselves in “bad” jobs may be more focused on changing jobs or careers, because their work offers insufficient compensation and benefits, inadequate training, little feeling of accomplishment or empowerment, and unsafe, unfair, or degrading conditions (Swartz et al., 2017).
As with other trajectories, the context from which people emerge into adulthood has a big influence on the likelihood of finding themselves in a good or bad job. Those in good jobs were more likely to come from privileged socioeconomic backgrounds, which provided support for advanced education and professional development experiences like study abroad or unpaid internships and safety nets to buffer them against setbacks. However, young adults also report behaviors that may have increased the likelihood of success in career establishment. For example, those in good jobs often approached their career with a more developmental and strategic mindset, using entry-level jobs to develop skills, expertise, and professional connections that helped them advance to more satisfying and challenging positions.
Young adults in bad jobs are more likely to develop a learned helplessness, which undermines a sense of direction or agency in their career development (Swartz et al., 2017). Often overwhelmed by the responsibilities of full-time job, education, and family, these adults dream of better work but are often unsure how to realize those dreams and lack available tools, support, or mentorship to guide them.
Link to Learning
Overwhelmed by the task of choosing a career direction? Explore this advice for organizing your career exploration and decision-making process from career development facilitator Dawn Rosenberg McCay.
Work-Life Balance
As young adults take on more responsibilities, they must balance obligations at work or school with other priorities. Finding this balance does not require giving equal attention to everything or “doing it all.” Rather, work-life balance is the arrangement that allows a person to spend the desired amount of time on each of the activities that matter to them, such as work, leisure, education, and caregiving (Dang et al., 2024). The amount of time allocated to various life activities may be unequal and change according to needs. Writer McAlary describes the value of seeing adult life balance as a scale that can tilt toward the activities that most need our attention, including as our priorities change over time (McAlary, 2017). College students report typically spending the most time per week (approximately 30 hours) on educational activities like attending class and studying. And while frequently exhibiting signs of burnout and exhaustion, young adults, including those who are caregivers for aging parents or younger siblings, report achieving a moderate level of balance between work and other priorities (Dang et al., 2024). Young adults with an internal locus of control are more likely to use active, problem-focused coping strategies to manage a broad range of developmental challenges, which can result in higher self-esteem, feelings of competence, and life satisfaction. (Kurtović et al., 2018; Singh & Choudhry, 2014). Some researchers have noted that more recently young adults are more willing to advocate for their work-life balance, screening jobs for or directly asking their employers to provide increased flexibility such as remote work, flexible hours, and time for self-care (Lee & Diller, 2023).
Life Hacks: Developing Work-Life Balance Early in Your Career
The American Psychological Association offers these active coping strategies for developing and maintaining work-life balance during early adulthood (DeAngelis, 2017):
- Plan your tasks at home and at work in advance. That includes daily, weekly, and even long-term career planning.
- Communicate . Talk with your family, friends, romantic partners, and work colleagues. Don’t expect others to intuitively know what you need and want. Ask for and offer help.
- Set boundaries . Know when to (politely) say no so that you are able to give adequate time to the tasks already on your plate and take care of your priorities and needs.
- Don’t give 110 percent . It’s too early to get burned out! Give yourself (and others) the grace to sometimes fail or be “good enough.”
Life balance in early adulthood relates not only to whether a person is able to satisfactorily divide their time among adult responsibilities, but also to the way they prioritize the sequence of developmental challenges. So far in this chapter, we have addressed the early adulthood challenges to develop the self, identity, relationships with friends and family, and education and vocational pathways. In the last section, we will discuss how young adults’ romantic relationships grow within the context of these other changes.
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Wolniak, G. C., & Engberg, M. E. (2019). Do “high-impact” college experiences affect early career outcomes? Review of Higher Education , 42 (3), 825–858. https://doi.org/10.1353/rhe.2019.0021
Wolniak, G. C., & Pascarella, E. T. (2005). The effects of college major and job field congruence on job satisfaction. Journal of Vocational Behavior, 67 (2) , 233–251. https://doi.org/10.1016/j.jvb.2004.08.010
Wray-Lake, L., Arruda, E. H., & Schulenberg, J. E. (2020). Civic development across the transition to adulthood in a national U.S. sample: Variations by race/ethnicity, parent education, and gender. Developmental Psychology, 56 (10), 1948–1967. doi.org/10.1037/dev0001101


6.2: Analysis of regression
Single line
Analysis of correlation allows to determine if variables are dependent and calculate the strength and sign of the dependence. However, if the goal is to understand the other features of dependence (like direction), and, even more important, predict (extrapolate) results (Figure \(\PageIndex{1}\)) we need another kind of analysis, the analysis of regression .
It gives much more information on the relationship, but requires us to assign variables beforehand to one of two categories: influence (predictor) or response . This approach is rooted in the nature of the data: for example, we may use air temperature to predict ice cream sales, but hardly the other way around.
The most well-known example is a simple linear regression :
\(\mbox{response} = \mbox{intercept} + \mbox{slope}\times\mbox{influence}\)
or, in R formula language, even simpler:
response ~ influence
That model estimates the average value of response if the value of influence is known (note that both effect and influence are measurement variables). The differences between observed and predicted values are model errors (or, better, residuals ). The goal is to minimize residuals (Figure \(\PageIndex{3}\)); since residuals could be both positive and negative, it is typically done via squared values, this method is called least squares .
Ideally, residuals should have the normal distribution with zero mean and constant variance which is not dependent on effect and influence. In that case, residuals are homogeneous. In other cases, residuals could show heterogeneity. And if there is the dependence between residuals and influence, then most likely the overall model should be non-linear and therefore requires the other kind of analysis.
Linear regression model is based on the several assumptions:
- Linearity of the relationship. It means that for a unit change in influence, there should always be a corresponding change in effect. Units of change in response variable should retain the same size and sign throughout the range of influence.
- Normality of residuals . Please note that normality of data is not an assumption! However, if you want to get rid of most other assumptions, you might want to use other regression methods like LOESS.
- Homoscedasticity of residuals. Variability within residuals should remain constant across the whole range of influence, or else we could not predict the effect reliably.
The null hypothesis states that nothing in the variability of response is explained by the model. Numerically, R-squared coefficient is the the degree to which the variability of response is explained by the model, therefore null hypothesis is that R-squared equals zero , this approach uses F-statistics (Fisher’s statistics), like in ANOVA. There are also checks of additional null hypotheses that both intercept and slope are zeros . If all three p-values are smaller than the level of significance (0.05), the whole model is statistically significant.
Here is an example. The embedded women data contains observations on the height and weight of 15 women. We will try to understand the dependence between weight and height, graphically at first (Figure \(\PageIndex{2}\)):
Code \(\PageIndex{1}\) (R):
(Here we used function Cladd() which adds confidence bands to the plot\(^{[1]}\).)
Let us visualize residuals better (Figure \(\PageIndex{3}\)):
Code \(\PageIndex{2}\) (R):
To look on the results of model analysis, we can employ summary() :
Code \(\PageIndex{3}\) (R):
This long output is better to read from bottom to the top. We can say that:
- The significance of relation (reflected with R-squared) is high from the statistical point of view: F-statistics is \(1433\) with overall p-value: 1.091e-14 .
-
The R-squared (use
Adjusted R-squared
because this is better suited for the model) is really big, \(R^2 = 0.9903\). This means that almost all variation in response variable (weight) is explained by predictor (height).
R-squared is related with the coefficient of correlation and might be used as the measure of effect size . Since it is squared, high values start from 0.25:
Code \(\PageIndex{4}\) (R):
-
Both coefficients are statistically different from zero, this might be seen via “stars” (like
***
), and also via actual p-values
Pr(>|t|): 1.71e-09
for intercept, and
1.09e-14
for
height
, which represents the slope.
To calculate slope in degrees, one might run:
Code \(\PageIndex{5}\) (R):
- Overall, our model is:
Weight (estimated) = -87.51667 + 3.45 * Height ,
so if the height grows by 4 inches, the weight will grow on approximately 14 pounds.
- The maximal positive residual is \(3.1167\) lb, maximal negative is \(-1.7333\) lb.
- Half of residuals are quite close to the median (within approximately \(\pm1\) interval).
On the first glance, the model summary looks fine. However, before making any conclusions, we must also check assumptions of the model. The command plot(women.lm) returns four consecutive plots:
- First plot, residuals vs. fitted values , is most important. Ideally, it should show no structure (uniform variation and no trend); this satisfies both linearity and homoscedascicity assumptions.
- Unfortunately, women.lm model has an obvious trend which indicates non-linearity. Residuals are positive when fitted values are small, negative for fitted values in the mid-range, and positive again for large fitted values. Clearly, the first assumption of the linear regression analysis is violated.
Code \(\PageIndex{6}\) (R):
- To understand residuals vs . fitted plots better, please run the following code yourself and look on the resulted plots:
- On the the next plot, standardized residuals do not follow the normal line perfectly (see the explanation of the QQ plot in the previous chapter), but they are “good enough”. To review different variants of these plots, run the following code yourself:
Code \(\PageIndex{7}\) (R):
- Test for the normality should also work:
Code \(\PageIndex{8}\) (R):
- The third, Scale-Location plot, is similar to the residuals vs . fitted, but instead of “raw” residuals it uses the square roots of their standardized values. It is also used to reveal trends in the magnitudes of residuals. In a good model, these values should be more or less randomly distributed.
- Finally, the last plot demonstrates which values exert most influence over the final shape of the model. Here the two values with most leverage are the first and the last measurements, those, in fact, that stay furtherest away from linearity.
(If you need to know more about summary and plotting of linear models, check help pages with commands ?summary.lm and ?plot.lm . By the way, as ANOVA has many similarities to the linear model analysis, in R you can run same diagnostic plots for any ANOVA model.)
Now it is clear that our first linear model does not work well for our data which is likely non-linear . While there are many non-linear regression methods, let us modify it first in a more simple way to introduce non-linearity. One of simple ways is to add the cubed term , because weight relates with volume, and volume is a cube of linear sizes:
Code \(\PageIndex{9}\) (R):
(Function I() was used to tell R that height^3 is arithmetical operation and not the part of model formula.)
The quick look on the residuals vs . fitted plot (Figure \(\PageIndex{4}\)) shows that this second model fits much better! Confidence bands and predicted line are also look more appropriate :
You may want also to see the confidence intervals for linear model parameters. For that purpose, use confint(women.lm) .
Another example is from egg data studied graphically in the second chapter (Figure 2.9.1). Does the length of the egg linearly relate with with of the egg?
Code \(\PageIndex{10}\) (R):
We can analyze the assumptions first:
Code \(\PageIndex{11}\) (R):
The most important, residuals vs. fitted is not perfect but could be considered as “good enough” (please check it yourself): there is no obvious trend, and residuals seem to be more or less equally spread (homoscedasticity is fulfilled). Distribution of residuals is close to normal. Now we can interpret the model summary:
Code \(\PageIndex{12}\) (R):
Significance of the slope means that the line is definitely slanted (this is actually what is called “relation” in common language). However, intercept is not significantly different from zero:
Code \(\PageIndex{13}\) (R):
(Confidence interval for intercept includes zero.)
To check the magnitude of effect size, one can use:
Code \(\PageIndex{14}\) (R):
This is a really large effect.
Third example is based on a simple idea to check if the success in multiple choice test depends on time spent with it. Data presents in
exams.txt
file which contains results of two multiple choice tests in a large class:
Code \(\PageIndex{15}\) (R):
First variable is the number of test, two others are order of finishing the work, and resulted number of points (out of 50). We assume here that the order reflects the time spent on test. Select one of two exams:
Code \(\PageIndex{16}\) (R):
... and plot it first (please check this plot yourself):
Code \(\PageIndex{17}\) (R):
Well, no visible relation occurs. Now we approach it inferentially:
Code \(\PageIndex{18}\) (R):
As usual, this output is read from bottom to the top. First, statistical significance of the relation is absent, and relation (adjusted R-squared) itself is almost zero. Even if intercept is significant, slope is not and therefore could easily be zero. There is no relation between time spent and result of the test.
To double check if the linear model approach was at all applicable in this case, run diagnostic plots yourself:
Code \(\PageIndex{19}\) (R):
And as the final touch, try the regression line and confidence bands:
Code \(\PageIndex{20}\) (R):
Almost horizontal—no relation. It is also interesting to check if the other exam went the same way. Please find out yourself.
). Please find which morphological measurement characters are most correlated, and check the linear model of their relationships.
) plant. Please find which pair of morphological characters is most correlated and analyze the linear model which includes these characters. Also, check if length of leaf is different between the three biggest populations of sundew.
As the linear models and ANOVA have many in common, there is no problem in the analysis of multiple groups with the default linear regression methods. Consider our ANOVA data:
Code \(\PageIndex{21}\) (R):
This example shows few additional “tricks”. First, this is how to analyze several response variables at once. This is applicable also to aov() — try it yourself.
Next, it shows how to re-level factor putting one of proximal levels first. That helps to compare coefficients. In our case, it shows that blonds do not differ from browns by weight. Note that “intercepts” here have no clear relation with plotting linear relationships.
It is also easy to calculate the effect size because R-squared is the effect size .
Last but not least, please check assumptions of the linear model with plot(lm(...)) . At the moment in R , this works only for singular response.
Is there the linear relation between the weight and height in our ANOVA hwc data?
Many lines
Sometimes, there is a need to analyze not just linear relationships between variables, but to answer second order question: compare several regression lines .
In formula language, this is described as
response ~ influence * factor
where factor is a categorical variable responsible for the distinction between regression lines, and star ( * ) indicates that we are simultaneously checking (1) response from influence (predictor), (2) response from factor and (3) response from interaction between influence and factor.
This kind of analysis is frequently called ANCOVA, “ANalysis of COVAriation”. The ANCOVA will check if there is any difference between intercept and slope of the first regression line and intercepts and slopes of all other regression lines where each line corresponds with one factor level.
Let us start from the example borrowed from M.J. Crawley’s “R Book”. 40 plants were treated in two groups: grazed (in first two weeks of the cultivation) and not grazed. Rootstock diameter was also measured. At the end of season, dry fruit production was measured from both groups. First, we analyze the data graphically:
Code \(\PageIndex{22}\) (R):
As it is seen on the plot (Figure \(\PageIndex{5}\)), regression lines for grazed and non-grazed plants are likely different. Now to the ANCOVA model:
Code \(\PageIndex{23}\) (R):
Model output is similar to the linear model but one more term is present. This term indicated interaction which labeled with colon. Since Grazing factor has two level arranged alphabetically, first level ( Grazed ) used as default and therefore (Intercept) belongs to grazed plants group. The intercept of non-grazed group is labeled as GrazingUngrazed . In fact, this is not even an intercept but difference between intercept of non-grazed group and intercept of grazed group. Analogously, slope for grazed is labeled as Root , and difference between slopes of non-grazed and grazed labeled as Root:GrazingUngrazed . This difference is interaction, or how grazing affects the shape of relation between rootstock size and fruit weight. To convert this output into regression formulas, some calculation will be needed:
Fruit = -125.174 + 23.24 * Root (grazed) Fruit = (-125.174 + 30.806) + (23.24 + 0.756) * Root (non-grazed)
Note that difference between slopes is not significant. Therefore, interaction could be ignored. Let us check if this is true:
Code \(\PageIndex{24}\) (R):
First, we updated our first model by removing the interaction term. This is the
additive
model. Then
summary()
told us that all coefficients are now significant (check its output yourself). This is definitely better. Finally, we employed AIC (Akaike’s Information Criterion). AIC came from the theory of information and typically reflects the entropy, in other words, adequacy of the model. The smaller is AIC, the better is a model. Then the second model is the unmistakable winner.
By the way, we could specify the same additive model using plus sign instead of star in the model formula.
What will the AIC tell about our previous example, women data models?
Code \(\PageIndex{25}\) (R):
Again, the second model (with the cubed term) is better.
It is well known that in the analysis of voting results, dependence between attendance and the number of people voted for the particular candidate, plays a great role. It is possible, for example, to elucidate if elections were falsified. Here we will use the elections.txt data file containing voting results for three different Russian parties in more than 100 districts:
Code \(\PageIndex{26}\) (R):
To simplify typing, we will attach() the data frame (if you do the same, do not forget to detach() it at the end) and calculate proportions of voters and the overall attendance:
Code \(\PageIndex{27}\) (R):
Now we will look on the dependence between attendance and voting graphically (Figure \(\PageIndex{6}\)):
Code \(\PageIndex{28}\) (R):
So the third party had a voting process which was suspiciously different from voting processes for two other parties. It was clear even from the graphical analysis but we might want to test it inferentially, using ANCOVA:
Code \(\PageIndex{29}\) (R):
(Here we created and checked the new data frame. In elections2 , all variables are now stack() ’ed in two columns, and the third column contains the party code.)
Code \(\PageIndex{30}\) (R):
Here (Intercept) belongs specifically to the model for first party. Its p-value indicates if it differs significantly from zero. Second coefficient, atten , belongs to the continuous predictor, attendance. It is not an intercept but slope of a regression. It is also compared to zero.
Next four rows represent differences from the first party, two for intercepts and two for slopes (this is the traditional way to structure output in R ). Last two items represent interactions. We were most interested if there is an interaction between attendance and voting for the third party, this interaction is common in case of falsifications and our results support this idea.
(Figure \(\PageIndex{7}\), phenomenon when in each population there are mostly two types of plants: with flowers bearing long stamens and short style, and with flowers bearing long style and short stamens. It was proved that heterostyly helps in pollination. Please check if the linear dependencies between lengths of style and stamens are different in these two species. Find also which model is better, full (multiplicative, with interactions) or reduced (additive, without interactions).
Figure \(\PageIndex{7}\) Heterostyly in primroses: flowers from the different plants of one population.
More then one way, again
Armed with the knowledge about AIC, multiplicative and additive models, we can return now to the ANOVA two-way layouts, briefly mentioned before. Consider the following example:
Code \(\PageIndex{31}\) (R):
(To start, we converted dose into factor. Otherwise, our model will be ANCOVA instead of ANOVA.)
Assumptions met, now the core analysis:
Code \(\PageIndex{32}\) (R):
Now we see what was already visible on the interaction plot (Figure 5.3.4: model with interactions is better, and significant are all three terms : dose, supplement, and interaction between them.
Effect size is really high:
Code \(\PageIndex{33}\) (R):
Post hoc tests are typically more dangerous in two-way analysis, simply because there are much more comparisons. However, it is possible to run TukeyHSD() :
Code \(\PageIndex{34}\) (R):
The rest of comparisons is here omitted, but TukeyHSD() has plotting method allowing to plot the single or last element (Figure 6.3.1):
Code \(\PageIndex{35}\) (R):
References
1. Function Cladd() is applicable only to simple linear models. If you want confidence bands in more complex cases, check the Cladd() code to see what it does exactly.


4: Cell Structure
Your body has many kinds of cells, each specialized for a specific purpose. Just as a home is made from a variety of building materials, the human body is constructed from many cell types. For example, epithelial cells protect the surface of the body and cover the organs and body cavities within. Bone cells help to support and protect the body. Cells of the immune system fight invading bacteria. Additionally, blood and blood cells carry nutrients and oxygen throughout the body while removing carbon dioxide. Each of these cell types plays a vital role during the growth, development, and day-to-day maintenance of the body. In spite of their enormous variety, however, cells from all organisms—even ones as diverse as bacteria, onion, and human—share certain fundamental characteristics.
-
- 4.1: Introduction to Cell Structure
- Your body has many kinds of cells, each specialized for a specific purpose. Just as a home is made from a variety of building materials, the human body is constructed from many cell types. For example, epithelial cells protect the surface of the body and cover the organs and body cavities within. Bone cells help to support and protect the body. Cells of the immune system fight invading bacteria. Additionally, blood and blood cells carry nutrients and oxygen throughout the body while removing CO2
-
- 4.2: Studying Cells
- A cell is the smallest unit of a living thing. A living thing, whether made of one cell (like bacteria) or many cells (like a human), is called an organism.  There are many types of cells, all grouped into one of two broad categories: prokaryotic and eukaryotic. For example, both animal and plant cells are classified as eukaryotic cells, whereas bacterial cells are classified as prokaryotic.
-
- 4.3: Prokaryotic Cells
- Cells fall into one of two broad categories: prokaryotic and eukaryotic. Only the predominantly single-celled organisms of the domains Bacteria and Archaea are classified as prokaryotes (pro- = “before”; -kary- = “nucleus”). Cells of animals, plants, fungi, and protists are all eukaryotes (ceu- = “true”) and are made up of eukaryotic cells.
-
- 4.4: Eukaryotic Cells
- Our natural world also utilizes the principle of form following function, especially in cell biology, and this will become clear as we explore eukaryotic cells. Unlike prokaryotic cells, eukaryotic cells have: 1) a membrane-bound nucleus; 2) numerous membrane-bound organelles such as the endoplasmic reticulum, Golgi apparatus, chloroplasts, mitochondria, and others; and 3) several, rod-shaped chromosomes. Because a eukaryotic cell’s nucleus is surrounded by a membrane, it is has “true nucleus.”
-
- 4.5: The Endomembrane System and Proteins
- The endomembrane system is a group of membranes and organelles in eukaryotic cells that works together to modify, package, and transport lipids and proteins. It includes the nuclear envelope, lysosomes, and vesicles, which we’ve already mentioned, and the endoplasmic reticulum and Golgi apparatus. Although not technically within the cell, the plasma membrane is included in the endomembrane system because, as you will see, it interacts with the other endomembranous organelles.
-
- 4.6: The Cytoskeleton
- Within the cytoplasm, there are ions and organic molecules, plus a network of protein fibers that help maintain the shape of the cell, secure some organelles in specific positions, allow cytoplasm and vesicles to move within the cell, and enable cells within multicellular organisms to move. Collectively, this network of protein fibers is known as the cytoskeleton. There are three types of fibers within the cytoskeleton: microfilaments, intermediate filaments, and microtubules.
-
- 4.7: Connections between Cells and Cellular Activities
- You already know that a group of similar cells working together is called a tissue. As you might expect, if cells are to work together, they must communicate with each other, just as you need to communicate with others if you work on a group project. Let’s take a look at how cells communicate with each other.
Contributors and Attributions
-
Connie Rye (East Mississippi Community College), Robert Wise (University of Wisconsin, Oshkosh), Vladimir Jurukovski (Suffolk County Community College), Jean DeSaix (University of North Carolina at Chapel Hill), Jung Choi (Georgia Institute of Technology), Yael Avissar (Rhode Island College) among other contributing authors. Original content by OpenStax (CC BY 4.0; Download for free at<EMAIL_ADDRESS>).


10.6: Assignment, Delegation, and Third Party Beneficiaries
Contracts are by law assignable and delegable. This means that the rights conveyed by the contract may be transferred to another party by assignment , unless an express restriction on assignment exists within the contract, or unless an assignment violates public policy. Likewise, the duties imposed on a party may be transferred to another party by delegation , unless the contract expressly restricts delegation, there is a substantial interest in personal performance by the original party to the contract, or if delegation would violate public policy.
As a general rule, a party may assign contract rights without the consent of the other party. This is common in the construction industry where a general contractor may assign rights and delegate duties to subcontractors for specific work that needs to be performed under the main contract. For example, the general contractor may delegate the duty to perform electrical work to an electrician, as well as assign the right to be paid for the work performed.
In delegation and assignment, the original contracting party is not “off the hook” if it transfers its duties or rights to another party. For instance, a subtenant assumed the rights and duties imposed on the original tenant in a lease. If the subletting tenant does not pay the rent, the original tenant is still liable.
The way to excuse oneself from legal liability under a contract is through novation. Novation is essentially a new contract that transfers all rights and duties to a new party to the contract and releases the previous party from any further obligation. It is the procedure in which one party is dismissed completely from the contract because a third party is substituted. In this situation, the dismissed party no longer has any liability under the original contract. To be effective, all parties must agree to the novation.
Third Party Beneficiaries
Assignment and delegation under a contract should not be confused with rights of third party beneficiaries. A third party beneficiary is someone who is not a party to the contract but stands to benefit from it. Life insurance policies are a classic example of contracts with third party beneficiaries. The insurance company and the insured are parties to the contract. But the person who receives payment upon the death of the insured is the third party beneficiary.
Third party beneficiaries can either be intended or incidental. An intended beneficiary is someone who the parties intend to receive the benefit of the contract. For example, the named beneficiary of a life insurance policy. The beneficiary does not need to know about the contract to have his or her rights vest.
An incidental beneficiary is someone who benefits from a contract but was not intended by the parties to benefit. For example, if a business pays for a professional to landscape its property, the neighbors are incidental beneficiaries to the landscaping contract. They benefit from the improved appearance and property values, but the business did not enter the contract with an intent to benefit them. Incidental beneficiaries do not have a legally enforceable interest in the contract.


19.5: Disruption of the Cell Cycle Checkpoints Can Cause Cancer
If a checkpoint fails or if a cell suffers physical damage to chromosomes during cell division, or if it suffers a debilitating somatic mutation in a prior S phase, it may selfdestruct in response to a consequent biochemical anomaly. This is another example of apoptosis . On the other hand, when cells die from external injury, they undergo necrosis , an accidental rather than a programmed death. In the cells shown below, apoptosis or necrosis were chemically induced, followed and identified as apoptotic or necrotic using fluorescent markers (propidium iodide, green; acridine orange, orange).
Only green-fluorescing (apoptotic) cells eventually formed apoptotic bodies. In contrast, necrotic (orange-fluorescing) cells lose their plasma membranes, do not form such ‘bodies’ and will eventually disintegrate. (400x magnification). Differences in ultrastructure between necrosis and apoptosis are also seen in electron micrographs of cone and rod cells (left and right, respectively) below. An asterisk indicates the cytoplasmic swelling characteristic of necrotic cone. White arrows point to nuclei characteristic of apoptosis of in rod cells.
As we’ve noted, cycling cells continue to divide until they attain G 0 in the terminally differentiated state. Most terminally differentiated cells are cleared by apoptosis when they reach the end of their effective lives, to be replaced by stem cells. We also noted that accidental signaling can bring cells out of G 0 , leading to renewed cell proliferation. While these cells are obviously abnormal, they are not detected by apoptotic defense mechanisms. Thus, they undergo uncontrolled cell divisions, becoming cancer cells. Likewise, physically damaged or mutated cells may sometimes escape apoptotic clearance. When they do, they may also become cancer cells. Apoptotic clearance and uncontrolled cancer cell proliferation are compared below
346 Apoptosis (Programmed Cell Death) vs. Necrosis
A. P53 Protein Mediates Normal Cell Cycle Control
Cancerous growth could result if a normal dividing cell should suffer a somatic mutation that disrupts normal cell cycle control. Think an over-expression of cdk for example. Alternatively, imagine cyclin levels in daughter cells that never drop; such cells would never stop cycling.
Other possibilities include a cell in G 0 that is stimulated to begin cycling again by an inappropriate encounter with a hormone or other signal. If undetected, these anomalies can transform cells to cancer cells. The p53 protein (illustrated below) is a DNA-binding, gene-regulatory protein that detects some of these anomalies and enables dividing cells to repair the damage before proceeding through cell cycle check points…, or failing that, will lead to apoptosis of the cell.
Not surprisingly, mutations in the gene for the P53 protein (called TP53 in humans) are associated with many human cancers (pancreatic, lung, renal cell, breast, etc.). As many as half of human cancers are associated with mutated p53 genes. Thus, p53 is one of a class of tumor suppressor proteins . Studies of humans with a condition known as LFS ( Li-Fraumeni syndrome ) have at least one mutated p53 allele. The mutation leads to a ~100% lifetime risk of cancer, beginning in childhood. In cultured cells, mutagenized p53 genes exhibit key characteristics of cancer cells, including unregulated cell proliferation and suppression of apoptosis.
1. How p53 Works
The p53 protein is normally bound to an active Mdm2 protein. To enable cell cycle checkpoints, p53-Mdm2 must separate and be kept separate to allow p53 time to act. In dividing cells, physical stress or chemical stress such as DNA damage during cell growth can activate an ATM kinase . ATM kinase in turn, phosphorylates Mdm2 , causing it to dissociate from p53. The same kinase also phosphorylates another kinase, Chk2 , as well as the now ‘free’ p53 . ATM kinase-initiated events are further detailed below.
Each of the proteins and enzymes phosphorylated by the ATM kinase has a role in cell cycle checkpoint function and cell cycle arrest while errors are corrected:
- Now separated from Mdm2, Phospho-p53 actively up-regulates several genes, including the p21 gene.
- The P21 protein binds to cdks ; cyclins can’t bind P21-cdks .
- Active Phospho-Chk2 catalyzes cyclin phosphorylation; phospho-cyclins can’t bind to p21-cdks .
- The inability of cyclins to bind cdks specifically blocks the cell cycle between the G 1 and S , and the G 2 -to- M phases.
These kinase-mediated events at cell cycle checkpoints are illustrated below.
The cell cycle is remains arrested while the cell attempts to finish essential biochemical activities necessary to correct stress-induced or other physical or chemical aberrations before moving on to the next phase of the cycle. If DNA repairs or other corrections are successful, the cell can progress to the next phase.
If not, proteasomes target the Chk2-cyclin complex for degradation. Likewise, any P53 remaining bound to unphosphorylated Mdm2 is also targeted for proteasome destruction. The result is that any cell unable to correct effects of stress or chemical damage, or to repair DNA damage, is target for apoptosis .
The levels and activity of p53 as well as the other proteins discussed above, control both the amount of p53 protein available to respond to cell cycling anomalies, and the responses themselves. Phosphorylation (activation) of p53 not only leads to a rapid arrest of the cell cycle, but also to the activation of genes encoding proteins required for DNA repair and of proteins required for apoptosis (in the event that repair efforts fail). The interactions of p53 with different proteins leading to alternate cell fates are summarized below.
To sum up, p53 suppresses malignant tumor growth either by
- allowing DNA or other cellular repair before resumption of normal cell cycling, preventing unregulated cell divisions; after repair, p53 and other proteins are inactivated and/or destroyed and the cell cycle can resume.
- The inability to repair/correct cell cycling problems sets in motion events leading to apoptosis, thereby also blocking tumorigenesis by killing off damaged cells.
It should be clear now why a mutant p53 that reduces or eliminates p21 protein production or blocks essential DNA repair protein production, will allow damaged cells to enter S and keep them replicating and dividing, transforming such them into cancer cells. In an interesting twist, it seems that compared to humans, few whales or elephants die from cancer, despite having thousands of times more cells than humans. The reason seems to be that, at least for elephants, they have as many as 20 copies (40 alleles) of their p53 genes! Thus a mutation in one allele of one of them may have little effect, while the tumor-repressing effects of the remaining p53 genes prevails. Read about this recent research at Whales and Elephants Don't Get Cancer!
2. The Centrality of p53 Action in Cell Cycle Regulation
Because of its multiple roles in regulating and promoting DNA repair, and in controlling cell cycle checkpoints, p53 has been called “ the Guardian of the Genome ”! Here is further evidence of this central role.
a) 'Oncogenic Viruses'
Cancer causing viruses include Human Papilloma Virus ( HPV ), Epstein Barr Virus ( EBV ), human immunodeficiency virus ( HIV ), Hepatitis B and C viruses ( HBV, HCV ), Human herpes virus 8 ( HHV-8 ) and simian virus 40 ( SV40 ).
There is a demonstrated link between SV40, p53 and cancer. SV40 is a viral contaminant of polio vaccines that were used in the 1960s. The virus is tumorigenic in mammals, though an association of SV40 and cancer in humans is unproven. In infected cells, SV40 DNA enters the nucleus where it can integrate into the host cell genome. SV40 infections are usually latent, (i.e., they cause no harm). However, activation can lead to cellular transformation and the growth of malignant sarcomas in muscles as well as tumors in other organs. The RNA polymerase II in infected cells transcribes the SV40 genes, producing proteins that replicate and encapsulate the viral DNA in a membrane to make new viral particles. However, the relatively small SV40 genome does not encode all of the enzymes and factors need for viral DNA replication. The infected cells themselves provide these factors, producing them only during the S phase. At that time, the SV40 large T antigen (made soon after infection) enters the host cell nucleus where it regulates transcription of genes essential to viral replication and viral particle formation. The large T antigen also binds to p53 , interfering with transcription of proteins whose genes are regulated by p53 . Unable to exercise checkpoint functions, the host cell divides uncontrollably, forming cancerous tumors. Deregulation of the cell cycle by large T antigen ensures progress to the S phase and unregulated co-replication of viral and host cell DNA.
b) p53 and Signal Transduction
Stress can activate signal transduction pathways. For example, mutations affecting the MAPK (MAP kinase) signaling pathway can lead to tumorigenesis. This can be explained by the observation that when activated, the MAPK pathway leads to amplified production of a kinase that phosphorylates p53 . Active phospho-p53 in turn augments activation of the MAPK signal transduction pathway. You may recall that MAPK signal transduction typically ends with a mitogenic response.
Another example of p53 interaction is with FAK ( focal adhesion kinase ) proteins. FAK activity is increased by integrin -mediated signal transduction. Recall that membrane integrins bind fibronectin , contributing to formation of the extracellular matrix, or ECM . Elevated FAK activity participates in the regulation of cell-cell and cell-ECM adhesion at focal adhesion points . Another role for FAK is to bind directly to inactive p53 and increase p53-Mdm2 binding. As we have just seen, persistent p53-Mdm2 is targeted for ubiquitination… and ultimate destruction! In fact, abnormally high levels of FAK are associated with many different tumor cell lines (colon, breast, thyroid, ovarian, melanoma, sarcoma…). These result when p53 is unable properly to activate cell cycle checkpoints.
While the interactions implied here are complex and under active study, these p53 activities certainly confirm its central role as both guardian of the genome and as guardian of cell division .
B. Growth and Behavior of Cancer Cells
Different cancer cell types have different growth and other behavioral properties. You may have heard of slow growing and fast growing cancers. Colon cancers are typically slow growing. Periodic colonoscopies that detect and remove colorectal tumors in middle-age or older people can prevent the disease (although the risks of disease and the procedure itself must be balanced). Pancreatic cancers are fast growing and usually go undetected until they reach an advanced stage. The twin goals of medical research are to detect the different cancers early enough for successful intervention, and of course, to find effective treatments.
A single mutated cell in a tissue can become the growth point of a tumor , essentially a mass of cells cloned from the original mutated one. Benign tumors or growths (for example breast and uterine fibroids in women, or common moles in any of us) stop growing and are not life threatening. They are often surgically removed for the comfort of the patient (or because cells in some otherwise benign tumors may have a potential to become cancerous).
Malignant tumors (also called malignant neoplasms ) are cancerous and can grow beyond the boundaries of the tumor itself. When tumor cells are shed they may enter the bloodstream and travel to other parts of the body, the phenomenon called metastasis . Cancer cells that metastasize can become the focal point of new tumor formation in many different tissues. Because cancer cells continue to cycle and replicate their DNA, they can undergo yet more somatic mutations. These further changes can facilitate metastasis and cancer cell growth in different locations in the body.
C. Cancer Treatment Strategies
There are many different kinds of cancers originating in different tissues of the body. They all share the property of uncontrolled cell division, albeit for different molecular and not always well-understood reasons. The two major cancer treatment strategies developed in the 20th century all aim at disrupting replication in some way.
- Radiation therapy relies on the fact that most cells in our bodies do not divide, aiming mutagenic radiation at tumors in the hope that replicating DNA will be mutated at so many sites (i.e., genes) that the tumor cells can no longer survive or replicate properly.
- Chemotherapy is used to attack tumors that do not respond well to radiation or that are not easily be reached by radiation technologies, and to fight cancers that do not even form focused tumors (such as lymphomas and leukemias involving lymph and blood cells). These chemotherapies also aim to derange replication or mitotic activities. For example, recall cordycepin (dideoxyadenosine triphosphate, or ddATP). When present during replication, ddATP is incorporated into a growing DNA chain, after which no additional nucleotides can be added to the DNA strand. That makes ddATP a potent chemotherapeutic disruptor of replication. Taxol is another chemo drug that acts in this case, not by inhibiting S phase replication, but by blocking spindle fiber microtubules from depolymerizing, thus blocking mitotic anaphase and telophase in the latter part of the M and C phases of the cycle. Colchicine (a plant alkaloid) attacks cancer (and other dividing) cells by blocking microtubule formation and therefore preventing spindle fiber formation in mitotic prophase.
These therapies are not effective against all cancers, and of course, they don’t target specific kinds of cancer cells. Their success relies simply on the fact that cancer cells proliferate rapidly and constantly while other cell types do not. Many if not all of the side effects of radiation and chemotherapies result from the damage done to normal dividing cells (e.g., hair follicle cells accounting for hair loss among many cancer patients, depletion of blood cells that fail to be replaced by stem cells in bone marrow).
Much research now is focused on mobilizing the body’s own immune system to create more specific, targeted cancer treatments. In a fascinating bit of history, more than 100 years ago, Dr. William B. Coley injected a terminal cancer patient with streptococcal bacteria, who then emerged tumor-free upon his recovery from the infection (for details, check out The Earliest Cancer Immunotherapy Trials ). The phenomenon of “Dr. Coley’s Toxins” was initially thought to be an anti-tumor effect of the bacteria. But by 1948 it was widely attributed to the immune response activated by the infection. In the 1990s, scientists revisited the immune response to cancer, and by the turn of the 21st century, studies of cancer immunotherapy picked up steam (and more substantial research funding!).
Recent animal immunotherapy experiments and human clinical trials are promising. A few immunotherapies have already been approved by the U.S. FDA (Food and Drug Administration). Cancer immunotherapy strategies capitalize on the fact that your body sometimes recognizes cancer cell markers (e.g., cell surface molecules) as foreign, thus mounting an immune defense against those cells. But that response is sometimes not powerful enough to clear new, rapidly dividing cancer cells. Cancer apparently results when the immune response is weak. There are different, sometimes overlapping approaches to cancer immunotherapy. All are based on the fact that cancer cells that have mutated in some way and are producing aberrant proteins that the immune system can see as foreign enough to elicit an immune response, however slight. Some immunotherapies seek to boost that immune response. Others seek isolate or generate unique cancer cell antigens that will immunize a patient when injected with these cancer antigens. Some immunotherapies are summarized in the table on the next page. As you can see from the table, immuno-targeting cancer cells has already proven to be highly effective. In some cases the therapy is an example of personalized medicine , in which treatments are uniquely tailored to you as a patient. Issues with immunotherapies are that
- they are time and labor intensive, and costly to produce.
- while they may ‘cure’ you, they likely won’t not work on someone else.
- like radiation and chemotherapy, immunotherapies come with their own unpleasant and sometimes severe side effects.
A more detailed discussion of cancer immunotherapies is on the cancer.gov website at Cancer Treatment Immunotherpay .
NOTE: The term checkpoint inhibitor in the context of immunotherapies is different than the term checkpoints describing portals to progress through the eukaryotic cell cycle.


10.6: Translation and the Genetic Code
- describe how a protein is synthesized from mRNA.
- describe the characteristics of the genetic code.
Gene is a segment of deoxyribonucleic acid (DNA) carrying the code for a specific polypeptide. Each molecule of messenger RNA (mRNA) is a transcribed copy of a gene that is used by a cell for synthesizing a polypeptide chain. If a protein contains two or more different polypeptide chains, each chain is coded by a different gene. We turn now to the question of how the sequence of nucleotides in a molecule of ribonucleic acid (RNA) is translated into an amino acid sequence.
Genetic Code
How can a molecule containing just 4 different nucleotides specify the sequence of the 20 amino acids that occur in proteins? If each nucleotide coded for 1 amino acid, then obviously the nucleic acids could code for only 4 amino acids. What if amino acids were coded for by groups of 2 nucleotides? There are 4 2 , or 16, different combinations of 2 nucleotides (AA, AU , AC , AG , UU , and so on). Such a code is more extensive but still not adequate to code for 20 amino acids. However, if the nucleotides are arranged in groups of 3, the number of different possible combinations is 4 3 , or 64. Here we have a code that is extensive enough to direct the synthesis of the primary structure of a protein molecule.
Video : NDSU Virtual Cell Animations project animation "Translation". For more information, see VCell, NDSU(opens in new window) [vcell.ndsu.nodak.edu]
The genetic code can therefore be described as the identification of each group of three nucleotides and its particular amino acid. The sequence of these triplet groups in the mRNA dictates the sequence of the amino acids in the protein. Each individual three-nucleotide coding unit, as we have seen, is called a codon .
The Codon Chart
Early experimenters were faced with the task of determining which of the 64 possible codons stood for each of the 20 amino acids. The cracking of the genetic code was the joint accomplishment of several well-known geneticists—notably Har Khorana, Marshall Nirenberg, Philip Leder, and Severo Ochoa—from 1961 to 1964. The genetic dictionary they compiled, summarized in Figure \(\PageIndex{1}\), shows that out of 64 codons, 61 codons code for amino acids, and 3 codons serve as signals for the termination of polypeptide synthesis (much like the period at the end of a sentence). Notice that only methionine (AUG) and tryptophan (UGG) have single codons. All other amino acids have two or more codons.
Translation
Protein synthesis is accomplished by orderly interactions between mRNA and the other ribonucleic acids (transfer RNA [tRNA] and ribosomal RNA [rRNA]), the ribosome, and more than 100 enzymes. The mRNA formed in the nucleus during transcription is transported across the nuclear membrane into the cytoplasm to the ribosomes—carrying with it the genetic instructions. The process in which the information encoded in the mRNA is used to direct the sequencing of amino acids and thus ultimately to synthesize a protein is referred to as translation.
Before an amino acid can be incorporated into a polypeptide chain, it must be attached to its unique tRNA. This crucial process requires an enzyme known as aminoacyl-tRNA synthetase (Figure \(\PageIndex{2}\)). There is a specific aminoacyl-tRNA synthetase for each amino acid. This high degree of specificity is vital to the incorporation of the correct amino acid into a protein. After the amino acid molecule has been bound to its tRNA carrier, protein synthesis can take place. Figure \(\PageIndex{3}\) depicts a schematic stepwise representation of this all-important process.
A portion of an mRNA molecule has the sequence 5′‑AUGCCACGAGUUGAC‑3′. What amino acid sequence does this code for?
Solution
Use Figure \(\PageIndex{1}\) to determine what amino acid each set of three nucleotides (codon) codes for. Remember that the sequence is read starting from the 5′ end and that a protein is synthesized starting with the N-terminal amino acid.
The sequence 5′‑AUGCCACGAGUUGAC‑3′ codes for met-pro-arg-val-asp.
A portion of an RNA molecule has the sequence 5′‑AUGCUGAAUUGCGUAGGA‑3′. What amino acid sequence does this code for? What process is this called?
- Answer
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The sequence 5′‑AUGCUGAAUUGCGUAGGA‑3′ codes for met-leu-asn-cys-val-gly. The process is translation.
Further experimentation threw much light on the nature of the genetic code, as follows:
- The code is virtually universal; animal, plant, and bacterial cells use the same codons to specify each amino acid (with a few exceptions).
- The code is “degenerate”; in all but two cases (methionine and tryptophan). Degenerate means more than one triplet codes for a given amino acid.
- The first two bases of each codon are most significant; the third base often varies. This suggests that a change in the third base by a mutation may still permit the correct incorporation of a given amino acid into a protein. The third base is sometimes called the “wobble” base.
- The code is continuous and nonoverlapping; there are no nucleotides between codons, and adjacent codons do not overlap.
- The three termination codons are read by special proteins called release factors, which signal the end of the translation process.
- The codon AUG codes for methionine and is also the initiation codon. Thus methionine is the first amino acid in each newly synthesized polypeptide. This first amino acid is usually removed enzymatically before the polypeptide chain is completed; the vast majority of polypeptides do not begin with methionine.
After the protein is made it is processed to become active with post-translational modification . There is removal of Met at the N-terminus, trimming and cutting of the polypeptide chain, folding of the protein into its proper three-dimensional shape, addition of prosthetic groups and metal ions, formation of the disulfide bridges, and association with other polypeptide chains to form the quaternary structure.
Summary
In translation, the information in mRNA directs the order of amino acids in protein synthesis. A set of three nucleotides (codon) codes for a specific amino acid.


6.2: Composite Rules
We now use our elementary formulas obtained for (6.2) to perform the integral given by \((6.1)\)
6.2.1. Trapezoidal rule
We suppose that the function \(f(x)\) is known at the \(n+1\) points labeled as \(x_{0}, x_{1}, \ldots, x_{n}\) , with the endpoints given by \(x_{0}=a\) and \(x_{n}=b\) . Define
\[f_{i}=f\left(x_{i}\right), \quad h_{i}=x_{i+1}-x_{i} \nonumber \]
Then the integral of (6.1) may be decomposed as
\[\begin{aligned} \int_{a}^{b} f(x) d x &=\sum_{i=0}^{n-1} \int_{x_{i}}^{x_{i+1}} f(x) d x \\ &=\sum_{i=0}^{n-1} \int_{0}^{h_{i}} f\left(x_{i}+s\right) d s \end{aligned} \nonumber \]
where the last equality arises from the change-of-variables \(s=x-x_{i}\) . Applying the trapezoidal rule to the integral, we have
\[\int_{a}^{b} f(x) d x=\sum_{i=0}^{n-1} \frac{h_{i}}{2}\left(f_{i}+f_{i+1}\right) \nonumber \]
If the points are not evenly spaced, say because the data are experimental values, then the \(h_{i}\) may differ for each value of \(i\) and (6.6) is to be used directly.
However, if the points are evenly spaced, say because \(f(x)\) can be computed, we have \(h_{i}=h\) , independent of \(i\) . We can then define
\[x_{i}=a+i h, \quad i=0,1, \ldots, n \nonumber \]
and since the end point \(b\) satisfies \(b=a+n h\) , we have
\[h=\frac{b-a}{n} \nonumber \]
The composite trapezoidal rule for evenly space points then becomes
\[\begin{aligned} \int_{a}^{b} f(x) d x &=\frac{h}{2} \sum_{i=0}^{n-1}\left(f_{i}+f_{i+1}\right) \\ &=\frac{h}{2}\left(f_{0}+2 f_{1}+\cdots+2 f_{n-1}+f_{n}\right) \end{aligned} \nonumber \]
The first and last terms have a multiple of one; all other terms have a multiple of two; and the entire sum is multiplied by \(h / 2\) .
6.2.2. Simpson’s rule
We here consider the composite Simpson’s rule for evenly space points. We apply Simpson’s rule over intervals of \(2 h\) , starting from \(a\) and ending at \(b\) :
\[\begin{aligned} \int_{a}^{b} f(x) d x=\frac{h}{3}\left(f_{0}+4 f_{1}+f_{2}\right)+\frac{h}{3}\left(f_{2}+4 f_{3}+f_{4}\right)+& \ldots \\ &+\frac{h}{3}\left(f_{n-2}+4 f_{n-1}+f_{n}\right) \end{aligned} \nonumber \]
Note that \(n\) must be even for this scheme to work. Combining terms, we have
\[\int_{a}^{b} f(x) d x=\frac{h}{3}\left(f_{0}+4 f_{1}+2 f_{2}+4 f_{3}+2 f_{4}+\cdots+4 f_{n-1}+f_{n}\right) \nonumber \]
The first and last terms have a multiple of one; the even indexed terms have a multiple of \(2 ;\) the odd indexed terms have a multiple of \(4 ;\) and the entire sum is multiplied by \(h / 3\) .


1.4: Waves of Feminism
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The first wave of feminism took place in the late 19th and early 20th centuries, emerging out of urban industrialism and liberal, socialist politics. While the major goal of this movement was to win women the right to vote, these women (and men) were addressing numerous dimensions of gender inequality, it just became popularized when these activists fought the right to vote. By gaining political power with the right to vote, the leaders of this movement realized they could then gain political momentum concerning issues such as sexual, reproductive, and economic matters. Women had been systematically excluded from history-making because men had the power to define what history was and what (and whom) was important. While often taken for granted, this first wave is largely credited for fueling the feminist fire. 4 The first wave of feminism is credited with the development of the feminist consciousness : a recognition by women that they were treated unequally as a group and that their subordination is socially created and maintained by a system that can be replaced through collective action.
In 1920 women won the right to vote. However, once the vote was won, women did not turn out to the polls as often as men, and when they went, they often voted similarly to men (maybe as they had been told to by men). Many women withdrew from the movement, believing that once the vote was won, there was no more work to do. Young women especially neglected to see the necessity of the movement by depicting feminists as lonely, unmarried women who unnecessarily antagonized and provoked men.
Coming off the heels of World War II, the second wave of feminism focused on the workplace, sexuality, family, and reproductive rights. 5 Betty Freidan’s book The Feminine Mystique is credited with stoking the fire for the second wave. Freidan exposed a voice of unhappiness and boredom of white, educated, middle-class housewives. She even referred to the suburbs as “comfortable concentration camps.” She named the depression, loneliness, and empty feeling experienced by so many housewives as “the problem that has no name.” Freidan reveled that this was not an individual problem, but rather it was a social problem. Freidan and 27 others founded the National Organization for Women (NOW) in 1966. It has since become the largest feminist grassroots organization in the U.S., with hundreds of chapters in all 50 states and hundreds of thousands of contributing members and supporters, focusing on a broad range of women’s rights issues, including economic justice, pay equity, racial discrimination, women’s health and body image, women with disabilities, reproductive rights and justice, family law, marriage and family formation rights of same-sex couples, representation of women in the media, and global feminist issues.
The second wave began in the 1960s and continued into the 90s. Prior to the spark of the second wave of feminism, it was largely perceived that women had met their equality goals. This wave unfolded in the context of the anti-war and civil rights movements and the growing self-consciousness of a variety of minority groups. Much of the movement was focused on passing the Equal Rights Amendment to the Constitution guaranteeing social equality regardless of sex. While the first wave of feminism was largely driven by middle class, Western, cisgender, white women, the second wave of feminism drew in women of color and developing nations, seeking solidarity, claiming, “Women's struggle is class struggle.” However, the feminists group at that time attracted mainly women who felt a personal sting of gender discrimination, including many lesbians. An eventual split between homo- and heterosexual activists resulted after the heterosexual feminists largely felt the lesbian presence would hurt the movement by devaluing or delegitimizing it.
The third wave of feminism began in the mid-90's, and in this phase many constructs were destabilized, including the notions of body, gender, sexuality, and heteronormativity. 6 There are three major themes in the third wave of feminism:
- there is a greater focus on women’s issues in less developed nations;
- criticizing values that dominate work and society, such as challenging competition, toughness, and independence as ideal qualities (traditionally thought of as “male” qualities) and arguing to replace them with cooperation, connection, and interdependence as being ideal qualities; and
- there has been an emphasis placed on women’s sexual pleasure.
There has been debate whether or not we are experiencing a fourth wave of feminism. Some contest the fourth wave can be seen in terms of participants’ rising concern with intersectionality, whereby women’s suppression can only fully be understood in a context of the marginalization of other groups and genders. In other words, feminism is part of a larger consciousness of oppression along with racism, ageism, classism, abelism, and sexual orientation.
4
Bailey, C. (1997). Making waves and drawing lines: The politics of defining the vicissitudes of feminism.
Hypatia
, 12(3), 17-28.
5
Ibid
6
Snyder, R. (2008). What Is Third‐Wave Feminism? A New Directions Essay.
Signs
, 34(1), 175-196. doi:10.1086/588436