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As we’ve just seen, perhaps the easiest way to determine if a eukaryotic organism is a protist is to first determine if it’s an animal, a plant, or a fungus. Unlike the other kingdoms, which are grouped together based on shared characteristics, protists are grouped together out of convenience.

At the beginning of this chapter, we talked about the potential to use micro-algae, a type of protist, as a sustainable energy source. However, as we’ve learned, not all protists are benign.

Researchers at the Agricultural Research Service (ARS) have assembled and maintained collections that allow them to explore the diversity, evolution, and distribution of parasites and pathogens. This collection was established in 1892 and is among the largest parasite collections in the world. It holds more than 20 million catalogued specimens representing nematodes, tapeworms, flukes, protists, and some parasitic arthropods, such as fleas, ticks, and lice. Such archives provide a foundation to identify shifting geographic and host ranges for parasites and diseases that may emerge with accelerated global climate change.

At the Center for Medical, Agricultural and Veterinary Entomology (CMAVE), in Gainesville, Fla., researchers are using a collection of microsporidia to act as soldiers of biological warfare at the tiniest level against red imported fire ants. CMAVE entomologist David Oi is using species of spore-producing insect pathogens, such as Kneallhazia solenopsae, to bring about declines in red imported fire ant (Solenopsis invicta) populations. In Argentina, these infectious soldiers are associated with localized declines of 53 percent to 100 percent in fire ant populations, according to Oi.

In addition, Oi and CMAVE colleagues Sanford Porter and Steven Valles were able to get K. solenopsae to infect phorid flies without harming them. This is important because phorid flies may serve as vectors to infect red imported fire ants with the microsporidia—perhaps facilitating the spread of infection to other colonies.

Adapting Author

Sally Walters earned a PhD in evolutionary psychology from Simon Fraser University in 1998. Since then, she has taught numerous introductory psychology classes in various formats — face-to-face, online, and mixed-mode — at Capilano University. She also teaches a variety of upper level courses and has developed a new course on Psychology and the Internet. Since 2012, she has been an Open Learning Faculty Member at Thompson Rivers University, Open Learning. She enjoys interacting with students in a variety of online courses. At TRU, Dr. Walters has been responsible for designing online courses and revising existing ones. She enjoys the challenge of making psychology interesting and accessible to students from diverse backgrounds. In her spare time, she enjoys reading, genealogy, listening to podcasts while walking, creating art, and an excellent cup of coffee.

What is Segmental Analysis?

Body composition analysis is a method of describing what your body is made of, including fat, muscle, protein, minerals, and body water. In conventional BIA body composition analyzers , the entire body is analyzed as just one section or cylinder. This single-cylinder method results in only one impedance value, which is used to determine the body composition data for a user.

However, because each body part has different volumes, the single-cylinder method results in very skewed data. Segmental Analysis provides body composition data in segments in addition to the usual full body analysis.

For example, the InBody technology divides the body into five segments or “cylinders”: the two arms, two legs, and the trunk (the area between the neck and legs.)

Anyone can theoretically be underdeveloped/overdeveloped (depending on your body goals) for certain body segments. The good news is that segmental analysis allows you to identify and compare these segments.

In the InBody result sheet, the top bar shows Lean Body Mass (in pounds) is in a given segment. The top bar of the Segmental Lean Analysis compares the pounds of Lean Body Mass in proportion to your height and gender. This top bar can also be used for comparison between segments. An uneven weight distribution between the right and left legs may be a sign of overtraining or injury. Later on, you will see how strength and conditioning coaches use segmental analysis to train their athletes.

The number shown at the bottom bar is the percentage relating the lean mass in the segment that is analyzed to the overall body weight. This shows whether the amount of Lean Body Mass you have in a segment in proportion to your total body weight is sufficient. The 100% = sufficient.

It’s worth noting that the Lean Body Mass being referred to in the results sheet doesn’t refer to how much “muscle” (also known as Skeletal Muscle Mass) you have in each segment. So it would be wrong to call Segmental Lean Analysis a muscle analysis chart. While it’s a given that skeletal gains in a body segment will be reflected as gains in the Segmental Lean Analysis chart, not every gain in Lean Body Mass can be explained by muscle gain. How come? Because Lean Body Mass also accounts for body water. This makes Segmental Analysis useful not just for tracking muscle, but also for certain injury and disease states (which will be discussed in detail below).

In hindsight, your segmental distribution could indicate that you have maintained, developed, or lost muscle/fat mass proportionately. While it’s true that you can’t spot-reduce fat, you can develop or maintain certain muscles in the body by using them more, whether through exercise or your day-to-day activities. Putting It Together- Protists - Biology

4 - 2 DNA Structure and Replication

4 - 3 Errors can occur in DNA replication that create potential mutations

4 - 4 Errors in DNA can also occur outside of replication

4 - 5 Transcription involves the copying of DNA into RNA

4 - 6 The level of mRNA is a common regulatory point in prokaryotes

4 - 7 Translation is the conversion of mRNA into protein at the ribosome

4 - 8 The moving polymerase problem

5 - Microbial Nutrition

5 - 2 The cell is made up of a few common elements

5 - 3 Microbes can be classified based upon their nutritional requirements

5 - 5 Sterilization of media

6 - Microbial Growth

6 - 2 Describing bacterial growth and quantifying it

6 - 3 Measuring bacterial growth

6 - 4 Growth in laboratory culture

6 - 5 The environment greatly affects the growth of microbes

7 - Control of Microbes

7 - 2 Temperature is a common physical method for controlling microbes

7 - 3 Other physical forms of treatment

7 - 4 Chemical treatments act on microbes to prevent their growth

7 - 5 Antimicrobial activity is measured using standard tests

8 - Metabolism

8 - 2 Important foundations in metabolism

8 - 3 Enzymes are biological catalysts

8 - 4 Fermentation, energy generation without using a membrane

8 - 5 Respiration involves donation of electrons to an inorganic terminal electron acceptor

8 - 6 High-energy electrons are converted into ATP using a membrane

8 - 7 Many microbes are capable of anaerobic respiration

8 - 8 Some microbes can grow completely on inorganic sources of carbon, energy and electrons

9 - Photosynthesis

9 - 2 Photosynthetic microbes have several common characteristics

9 - 3 Light is collected by protein complexes containing photopigments

9 - 4 Purple bacteria, one class of anoxygenic photosynthetic bacteria

9 - 5 The green bacteria are anoxygenic photosynthetics that form a chlorosome

9 - 6 The cyanobacteria perform oxygenic photosynthesis

10 - Anabolism

10 - 2 Assimilation of carbon

10 - 3 Nitrogen and Sulfur assimilation

10 - 4 Assimiliation of other elements

10 - 5 Amino acids and simple synthesis

10 - 6 The synthesis of some amino acids share common steps

10 - 7 Nucleotide and lipid biosynthesis involved complex pathways

10 - 8 Monomers are assembled to form polymers

11 - Regulation of Metabolism

11 - 2 Regulation is a way to respond to a changing environment

11 - 3 The different types of regulation

11 - 4 Expression of the <i>lac</i> operon requires the presence of lactose and the absence of glucose

11 - 5 The tryptophan operon is controlled by repression, attenuation and feedback inhibition

11 - 6 Sporulation in <i>Bacillus subtilis</i> is directed by sigma factors and turned on by a phosphorelay system

11 - 7 <i>Vibrio fischeri</i> senses cell density using a small diffusible molecule that binds to an activator

11 - 8 Heat-shock gene expression is controlled by sigma factors, mRNA secondary structure, and protein stability

11 - 9 Nitrogen fixation can be controlled by a positive activator, mRNA stability, and enzyme modification

12 - Genomics and Genetics

12 - 2 Sequencing and what it tells us

12 - 3 What are the applications of the information gained through genomics?

12 - 4 An introduction to genetics and genetic engineering

12 - 5 How to find a needle in a hay stack

12 - 6 Generation of random mutations

12 - 7 Gene Transfer Systems

12 - 8 Genetic mapping, complementation and gene fusions

12 - 9 Suppressors are second-site mutations that change the phenotype of a mutant to be more like that of the wild type

13 - Basic Virology and Prokaryotic Viruses

13 - 2 Viral challenges and structures

13 - 3 The viral life cycle, early events

13 - 4 The viral life cycle, late events

13 - 5 Lambda phage is a lysogenic virus with double-stranded DNA.

13 - 6 T4 is a large, lytic phage with a large double-stranded DNA genome

13 - 7 P22 is a lysogenic, double-stranded DNA phage that was important in the development of bacterial genetics

13 - 8 P1 is a double-stranded DNA phage with an unusual ability to infect different hosts

13 - 9 Q&beta is a small, single-stranded RNA virus

13 - 10 M13 has a genome composed of a single-stranded, circular DNA molecule

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Participant Demographics

In total, 32 life science graduate students from 25 different institutions across the continental United States were interviewed. The majority (69%) of the participants attended highest research activity (R1) universities, with the remainder from higher research institutions (R2, 19%), moderate research institutions (R3, 9%), and special focus institutions (3% Carnegie Classification of Institutions of Higher Education [Indiana University Center for Postsecondary Research, 2017]). Participants ranged in age from 23 to 40 years old (mean = 28.6 years, SD = 3.5). The majority of the participants identified as female (59%) 75% as white/Caucasian, 13% Asian American, 9% Latina/o, and 3% identified as Indian (South Asian). There were no significant differences (chi-square goodness-of-fit, all p > 0.05) between our sample’s reported demographics (gender, race/ethnicity, and university type) and those reported in the NSF Survey of Earned Doctorates and the Survey of Graduate Students and Postdoctorates in Science and Engineering (NSF, 2016a,b).

Graduate Student Status and Professional Goals

Overall, 97% of our participants were PhD students, and all participants were at least in the second year of their graduate programs (mean year in program = 4.3, SD = 1.3). Participants were conducting graduate research on topics that spanned subdisciplines of biology: 37.5% molecular or cellular biology, genetics, or immunology 34% ecology 16% evolutionary biology and 12.5% biology education research (BER). Additionally, 9% of the students who had a non-BER research focus self-­reported participating to some extent in an education research project in addition to their primary research projects. We considered that graduate students who had participated in BER may have a biased awareness of EBT strategies that would not be representative of life science graduate students in general. Upon reflection and discussion of the interview transcripts and based on statistical tests for differences among BER students and/or those who had participated in education research, the research team determined that their experiences did not differ from those of their peers who had not been involved in education research. Therefore, these data include graduate students studying both basic biology research and BER.

Participants reported being interested in pursuing a varied set of professional goals: 28% hoped to obtain primarily research positions in academia 31% explicitly stated they want to obtain an academic position that would allow them to balance both research and teaching responsibilities and 19% were interested in primarily teaching positions. The remaining 22% described plans to leave academia for careers in government, industry, or science communication and outreach.

Graduate Students Receive Little Support for Instructional Training

To address our first research question, we report on our participants’ experiences with teaching, mandatory TA training, and their perceptions regarding their program’s support for their instructional training. Our participants had diverse experiences in their roles as TAs. The majority were experienced TAs—19% had one term of TA experience, 44% had between two and five terms experience, and 31% had between six and 14 terms of experience as a TA. Only 6% of the participants had never been a TA before. Most of the participants had experience teaching lab sections (72%) and/or recitation sections (63%) however, 19% had experience as the instructor of record for a course. A few participants did not provide a specific count of the number of terms of TA experience they had thus, the reported terms of TA experience are conservative estimates based on the information provided. For example, one participant explained,

I’ve taught a lot of different classes. I’ve taught Plant Ecology, Introductory Biology, Genetics, and right now I’m teaching a Botany class.—Male, third-year ecology PhD student

This student did not specify whether he had taught multiple iterations of any of the four classes listed therefore, we recorded that he only had four terms of TA experience.

Most Graduate Students Participate in Some Form of Mandatory TA Training.

We felt it was important to understand what man datory training our participants had received from their universities with regard to their teaching responsibilities, and whether their training had included information about EBT strategies. Only 28% of our participants described taking a required TA training course that lasted a full term, while 47% described participating in a boot camp–style TA training either before or concurrently with their first term as a teaching assistant (Figure 1A).

FIGURE 1. Most participants had some type of formal teaching training, although few of those with formal training had been trained in instructional strategies. (A) Types of teaching training that graduate students report receiving to date in their training programs. (B) Reported amount of training in instructional strategies for those who participated in mandatory formal training courses or boot camp.

While we were encouraged that 75% of our participants had received some formal mandatory training through a course or boot camp, 46% of those who had received formal training reported that they were not given any instruction in the use of any teaching strategies (Figure 1B). An additional 29% of those with formal training reported receiving very little training in instructional strategies—described by one participant through the following statement:

It’s mostly not really about teaching strategies but mostly, how to identify sexual harassment and those sorts of things. They do tell you some of the strategies out there, but they don’t really emphasize them that much.—Male, fifth-year ecology PhD student

Only 12.5% of graduate students reported that they had received substantial training in the use of various instructional strategies in their formal mandatory training, for example,

We also had an opportunity to present for 5 minutes to practice teaching and then also a period later on where it was 15 minutes practice teaching … It’s kind of neat to see other people teach. We also talked about some teaching strategies and active-learning strategies.—Female, second-year cellular biology PhD student

Graduate Students Perceive a Lack of Support to Develop Instructional Skills.

In total, 72% of our participants discussed the various deficits in their opportunities to develop their instructional skills within their programs. Some graduate students (28%) additionally highlighted the disparity between the lack of these opportunities and their departments’ proclaimed value for teaching (Table 1).

TABLE 1. Participant perceptions regarding lack of support for teaching from their graduate training programs

The most commonly described deficit of instructional development was limited instructional training (44%). Although some of these participants explained that they did not have access to any instructional training, many who perceived limited instructional training simply felt that the training they did receive was insufficient. Others who perceived limited instructional training at their institution were aware of optional training, but described barriers that prevented them from taking advantage of these opportunities—they had no incentives to attend, or even felt pressure from peers or advisors to not spend time on instructional training at the cost of forfeiting time that should be spent on research. For example,

I’m not sure how many students actually take those optional (teaching) courses but perhaps (the department should) advertise those a little bit more. I personally don’t know anybody who’s actually taken those courses yet.—Male, second-year ecology PhD student

Similarly, participants who expressed that they had limited opportunities to teach (34%) both described logistical limitations (primarily limited teaching opportunities at their institutions) or a lack of support from peers and advisors toward pursuing teaching opportunities simply for the sake of gaining experience as an instructor, rather than the necessity of receiving financial support from a TA-ship:

I really wanted to do more teaching and basically everybody told me to stop doing that … it would be nice if there was a little more support for people who wanted to teach more.—Female, fourth-year evolutionary biology PhD student

One-third of the participants (all who had at least some opportunities to teach) perceived a deficit of instructional professional development, reporting they had limited opportunities to expand their teaching role (34%). A couple of these participants repeatedly taught the same class and felt that the challenge of teaching a different type of course (i.e., course content, a majors vs. nonmajors class, or anything other than a lab section) would further develop their instructional skills. Other participants in this group expressed that a standard TA-ship, in which they were provided with materials and constrained expectations for what needs to happen in their classroom, is insufficient for fully preparing them as instructors:

For me a huge (challenge) is going to be actually teaching a full course … I really need to be able to put all the pieces together. Including the teaching strategies, developing lesson plans, doing the assessments, because that I’ve never done before, putting it all together.—Female, fifth-year molecular biology PhD student

These graduate students desired the opportunities to develop teaching materials, to experience giving large lectures, or to fully design and teach an undergraduate course.

A smaller but compelling group of graduate students described situations in which they perceived that their institution provided lip service toward valuing teaching (28%), explaining or giving examples in which their institution attempted to give the appearance of valuing teaching, but in practice did not sufficiently support graduate students in learning how to teach. For example, some students described that their institutions technically provided institutional training, but that it was a highly insufficient effort to actually develop their instructional skills. Some of these students expressed incredulity that their programs expected them to develop instructional skills in their training, due to either the lack of informative instructional skills emphasized in the training, or the minimal nature of the training (one as short as 15 minutes: “I think there was [training] … It was like a 15-minute, couple of slides at our grad student orientation. That was it” [Female, fifth-year ecology PhD student]). Other participants perceived negative attitudes from their peers and faculty within their departments toward the instructional opportunities offered and explained that many in their department considered instructional training activities were “blow-off” or “useless” pursuits.

Graduate Students Are Aware of the Academic Culture Shift Favoring EBT

Perhaps surprisingly, in investigating our second research question, we found that our participants exhibited a high level of awareness and appreciation for EBT strategies (Table 2). In total, 84% of our sample conveyed that they value EBT strategies. Many of these participants demonstrated their value of EBT strategies both by explaining why they find evidence-based strategies to be more effective through their experiences either as a student or an instructor and by simply describing the active-learning strategies that they preferred over didactic lecture.

TABLE 2. Participant perceptions related to EBT

“Your undergrad degree should be focused on you learning how to learn … you can’t just passively receive this information.”—Female, third-year biology education PhD student

“Different topics come up reflecting backgrounds of each student, what they have learned or what they have experienced, and I think that gives the opportunity for us to kind of dig the topic a little bit deeper.”—Female, fourth-year molecular/cellular biology PhD student

“Because I went out of my way, I got to learn about active learning and technology in the classroom and all that, but at least in my experience, it’s not something you learn unless you actively try and go learn it.”—Male, fifth-year ecology PhD student

“I think people who love teaching and are excited about teaching don’t want to feel like they’re doing a mediocre job. We have to take it upon ourselves to seek out training. Those resources are totally there. It has to be driven by graduate students.”—Male, fifth-year ecology PhD student

“I know there has been a push toward that sort of active learning, because it’s supposed to get students a little bit more engaged than they would otherwise be just sitting in a lecture room, listening to the professor.”—Male, third-year ecology PhD student

“I think you’re going to have to have professors who want to be there and are thinking about how to structure a class instead of finding someone who’s really good at their field and being like ‘Well you know a lot about this, tell people about it.’”—Female, sixth-year molecular/cellular biology PhD student

“I’m trying to get away from the traditional lecture format. Instead of spewing information at the students, really taking students’ needs into account, thinking about pedagogy and active learning … My undergrad was more of just show up, get lectured at for 50 minutes, and then take the test.”—Male, fifth-year ecology PhD student

“We started assessing our students more and kind of test them in what they have learned and we’ve realized that it doesn’t correlate with what we want them to learn. There’s this big disconnect in what we’re doing and what they’re actually getting out of it.”—Female, third-year evolutionary biology master’s student

Demonstrating their interest in and commitment to gaining instructional experience, 59% of participants sought out nonmandatory teaching opportunities. These participants found opportunities to attend teaching-centric workshops or classes, to give guest lectures, and to teach extra classes or develop course materials for the purpose of gaining instructional experience. Many of these participants described these nonmandatory opportunities as the experiences that allowed them to further learn and practice implementation of EBT strategies.

Graduate students were also aware of the increasing value that universities and education research places on EBT, which we describe as participants perceiving the changing landscape of academia in teaching (78%). Graduate students who perceived this shift in academia described observing a trend in increased use of EBT and perceived that universities are increasingly expecting EBT to be used in their classrooms:

It seems like even at larger state schools, there’s a greater focus on student-centered learning, active-learning, nontraditional classrooms, group work in a more transformative way. It’s become much more important at a variety of institutions.—Male, fifth-year ecology PhD student

A smaller subset of this group (47% of participants) fell into a group that explicitly exhibited self-awareness of their own role in this shift toward valuing EBT strategies (part of the changing landscape of academia). These participants repeatedly used first-person language that conveyed personal accountability for promoting attitude shifts and adoption in favor of EBT strategies within their departments and fields. Further, these participants often described the specific changes they had made (or planned to make) to their own teaching to advance the use of EBT within their discipline or described specific interactions with their peers and/or actions they had taken within their departments in support and promotion of EBT adoption.

Graduate Students Are Interested in Adopting EBT Strategies

To address our third research question, we mapped the progress of graduate students in adopting EBT strategies using the DOI model. As we used our codebook to identify the major themes present in these interviews, we also were able to discern that certain themes and holistic trends correlated to groups of graduate students who were in different stages of the process of incorporating EBT strategies into their teaching philosophies. For each stage in the model, we mapped the proportion of the 32 participants who successfully “continue” through each stage and the proportion who fall out of the adoption process (Figure 2). Here, we describe characteristics of groups of participants who arrived at each stage of the model. For clarity, we will continue to use percentages to describe proportions of our total participants who fall into the different DOI stages, but proportions of small subgroups presenting specific characteristics within each DOI stage will be described numerically.

FIGURE 2. Path of graduate students through the DOI model toward adoption of EBT. The number of participants who demonstrated progression to each stage in the model are depicted above the x-axis (in green), while the number of participants who drop out at each stage in the model are depicted below the x-axis (in red). Some participants neither “drop out” or progress to the subsequent stage in the model—for example, while five of the 12 participants who had used EBT strategies progressed to the Confirmation stage, the remaining seven simply did not demonstrate significant reflection to either positively or negatively confirm their use of EBT strategies.

Stage 1. Knowledge: Most Graduate Students Know About EBT.

Knowledge of an innovation is the stage when an individual learns of the existence of the innovation, which can be impacted by the individual’s socioeconomic status, personality, communication behavior, and access to relevant communication channels (Rogers, 2003). For graduate students, communication channels that lead to knowledge of EBT strategies could include professional development events and courses, their research advisors, instructors and lab managers for the courses they TA in, and peers. Graduate students in our study exhibited a wide range in their level of knowledge of EBT strategies and were accustomed to an assortment of different terminology to describe EBT. We specifically asked students about their familiarity with student-centered teaching practices versus instructor-centered teaching practices (Supplemental Material), and for those who asked for a definition of student-centered teaching practices, we described the contrast between didactic lecturing versus putting more responsibility for learning on students through active-learning strategies. We considered participants who exhibited understanding of evidence-based strategies throughout their interviews to have Knowledge about EBT, for example,

Student-centered learning is the idea is that the students are taking a much more active role in their own education … stuff like doing hands-on activities or doing the research on a particular topic or leading a discussion.—Female, fifth-year genetics PhD student

Participants who were unfamiliar with EBT strategies, even with the help of an explanation, stopped progressing toward adoption of EBT strategies at the Knowledge stage.

Most of our participants (87%) had an accurate working definition of student-centered teaching (or active learning) and were, at minimum, familiar with at least one or two specific strategies. Nearly all of these participants who have knowledge of EBTs moved on to the second stage in the model, and only one participant remained at this stage in the model—that student was aware of EBTs, but held an ambivalent attitude toward them.

Participants who dropped out at the Knowledge stage (12.5%) lacked a clear conception of EBT strategies, even when prompted with definitions and/or examples, which prevented them from truly beginning the process of adopting EBT. Intriguingly, participants in this group did express some interest in the concept of engaging students beyond what would be expected in a purely didactic classroom. For example, one participant (male, third-year ecology PhD student) indicated a desire to design an “interactive” class but could not communicate how he would facilitate that:

Participant: With Introductory Biology, it’s really much more of a lecture type setting, but I would try to make it to where it was a little bit interactive, when you were asking students questions.

Interviewer: Do you have ideas how you might facilitate that interaction?

Participant: I don’t think I do specifically. For labs, I’ll ask questions, and then it’s … Labs are always very much obviously interactive. I don’t think I have so much of an idea for a classroom setting.

While their lack of awareness about EBT strategies prevented them from progressing through the model, it is encouraging that this group appears to be open to the idea of learning about EBT.

Stage 2. Persuasion: Most Graduate Students Have Positive Attitudes Toward EBT.

At the Persuasion stage, graduate students formed a positive or negative attitude regarding the use of EBT strategies. All participants who had formed positive attitudes toward EBT strategies (75%) progressed to the Decision stage of the DOI model. For example,

One of the shortcomings I see in our current way we do higher education in the sciences is so much of it is just canned stuff, where it’s come in, do this lab, listen to this. Getting more active inquiry, working through things, working through problems, and actually seeing the process of science in action, I think would be a good thing for the field as a whole.—Male, fifth-year ecology PhD student

A few participants who were aware of EBT strategies had a negative attitude toward them (9%), therefore dropping out of the process of adopting EBT strategies at the Persuasion stage (Figure 2). These students felt that there were opportunities within their departments to develop their teaching skills, but they were not interested in pursuing them:

I would say that I’m more prepared to be a research faculty member. I could do the teaching as well, but considering I’ve personally prepared myself to be a researcher, that’s where it is. If I wanted to prepare myself to be a better teaching faculty member, I could have said to my advisor, “I want to TA every semester,” which would have increased my experiences. I would have had that opportunity if I wanted to.—Male, fourth-year molecular biology PhD student

Unsurprisingly, participants with negative attitudes toward EBT strategies also unanimously did not think there would be much of a benefit toward learning about EBT:

I have those things that I took away from undergrad that I enjoyed, and the things I didn’t enjoy. I feel like between a mesh of all that, I wouldn’t change too much.—Male, second-year evolutionary biology PhD student

Stage 3. Decision: Graduate Students with Positive Attitudes Toward EBT Plan to Implement EBT.

Graduate students who progressed through the Decision stage toward EBT adoption described specific EBT strategies that they plan to use if they ever design their own undergraduate biology class:

I’ve at least heard about [EBT strategies] and I think what I really want to do now is actually implement them.—Female, fifth-year genetics PhD student

Because all graduate students who had a positive attitude toward EBT strategies had decided to implement EBT strategies (75% of total), no students dropped out of the model at this stage.

Stage 4. Implementation: Most Graduate Students Have Not Implemented EBT.

Graduate students who reached the Implementation stage described specific experiences in which they had chosen to implement one or more EBT strategies as an instructor. Of the 75% of graduate students who had decided to implement EBT strategies, half actually found opportunities to do so, while the other half had not yet implemented EBT, thereby dropping out of the model at this stage (Figure 2). For example,

I’ve unfortunately only after being a teaching assistant received instruction in evidence-based active-learning instruction. Just being aware of that, and of some of the instructors who use such methods has really changed my opinion about how a classroom should be run.—Female, fourth-year immunology PhD student

Because graduate students have variable access to TA-ships, and sometimes little control of the curriculum, it is inescapable that some graduate students do not have the opportunity to progress through the Implementation stage. Presumably for this reason, many of the participants who did not implement EBT seemed to have similar attitudes and perceptions as those who had actually implemented EBT. For example, both groups identified the potential benefits of EBT for undergraduate students, and they were aware of the changing landscape of academia (Table 2) that increasingly values effective undergraduate teaching.

Stage 5. Confirmation: Few Graduate Students Complete the Process of EBT Adoption.

Not all graduate students who have implemented EBT have had opportunities and/or adequate guidance to reflect on their EBT experience to the extent to which they can confidently confirm that they are using strategies they would like to adopt into their permanent teaching repertoire. Despite this potentially unequal access to the Confirmation stage, we identified that 16% of our participants had reached this stage (Figure 2). The reflections of those who reached this stage positively affirmed their use of EBT strategies:

Personally, my most successful student-centered learning strategies usually revolve around class discussion, usually in sort of a think–pair–share, jigsaw sort of format and, then, taking that back out into a broader overall class discussion with me and with the students more or less leading it … I think that it helps them develop, cognitively, beyond the early stages for their earlier years and up, their undergraduate experience. I would say that’s probably my favorite tool, actually, Socratic method.—Male, sixth-year ecology PhD student

In addition to the reflective statements that defined the participants who were placed in the Confirmation stage, participants at this stage were highly metacognitive of their own role in the academic attitude shift toward teaching (part of the changing landscape of academia Table 2).

We informally observed some trends in our collected data among groups of participants at different stages in the DOI model. Participants in all stages of the DOI model described limited instructional professional development opportunities (lack of TA training, opportunities to teach, or ability to increase their autonomy in the classroom Table 1). However, four of the 12 students who had not implemented EBT had the perception that EBT was not possible in large classes, while only one of the participants who actually implemented EBT expressed this perception. None of the participants who dropped out of the DOI model in the early stages (Knowledge and Persuasion) had sought out nonmandatory instructional training or teaching experiences (seeks out teaching opportunities Table 2). In contrast, participants who reached the Decision, Implementation, and Confirmation stages often did seek out nonmandatory teaching or training experiences. In a similar pattern, an increasingly higher proportion of participants in the Decision, Implementation, and Confirmation stages of the DOI model were aware of their role as part of the changing landscape of academia (Table 2). This suggests that whether or not graduate students use EBT may not be entirely controlled by their TA assignments and the circumstances of their programs, but also by the drive of the individual students to build those experiences for themselves.

TA Experience, Time in Program, and Career Goals Do Not Appear to Be Important Factors in Adoption of EBT Strategies

We sought to identify whether there were trends in experiences among participants who stopped or continued progressing toward EBT adoption at particular stages in the DOI model. Those who had progressed further toward adopting EBT tended to have been in their programs for longer and had more TA experience (Table 3), but low sample sizes and high standard deviations for these numbers suggest that these are supporting rather than defining factors of EBT adoption. There was no indication that participation in a mandatory TA training had a positive impact on adoption of EBT—in fact, very few of the participants who progressed to the final stages of the model had taken a mandatory TA training course (Table 3). We also examined whether experience with BER (either as the primary focus of their PhD or supplemental to their primary research focus), correlated with progression toward EBT adoption. While all seven participants with BER experience had decided to implement EBT strategies, only one reached the Confirmation stage, indicating that participation in BER was not necessarily a factor facilitating progression through the DOI model.

TABLE 3. Training experiences of participants at different stages in the DOI model

There was no indication that having an interest in EBT corresponded to specific career goals, although participants who indicated that they would seek teaching-only academic positions all knew about EBT and had at least decided to use EBT strategies in the future (Figure 3). Graduate students who reached the Implementation and Confirmation stages were not strictly focused on a career in teaching—several were interested in primarily research positions or in leaving academia. Only one participant who indicated interest in a position that balanced both research and teaching responsibilities did not have knowledge of EBT strategies (Figure 3).

FIGURE 3. Participants at different stages in the DOI model had varied career goals, though all participants who were primarily interested in teaching reached the Implementation stage. (A) The career goals of participants who are in the process of progressing through the model are represented in the top graph (green). (B) Career goals of participants who have dropped out and stopped progressing through the DOI model are in the lower graph (red).


We present a fascinating model that has lately caught attention among physicists working in complexity related fields. Though it originated from mathematics and later from economics, the model is very enlightening in many aspects that we shall highlight in this review. It is called The Stable Marriage Problem (though the marriage metaphor can be generalized to many other contexts), and it consists of matching men and women, considering preference-lists where individuals express their preference over the members of the opposite gender. This problem appeared for the first time in 1962 in the seminal paper of Gale and Shapley and has aroused interest in many fields of science, including economics, game theory, computer science, etc. Recently it has also attracted many physicists who, using the powerful tools of statistical mechanics, have also approached it as an optimization problem. Here we present a complete overview of the Stable Marriage Problem emphasizing its multidisciplinary aspect, and reviewing the key results in the disciplines that it has influenced most. We focus, in particular, in the old and recent results achieved by physicists, finally introducing two new promising models inspired by the philosophy of the Stable Marriage Problem. Moreover, we present an innovative reinterpretation of the problem, useful to highlight the revolutionary role of information in the contemporary economy.


A considerable proportion of physiological, pharmacological and disease processes involves the interaction between proteins (i.e. peptides, polypeptides, or their complexes) across distinct subcellular, tissue and anatomical compartments. In particular, such protein interactions play a key role in mediating communication between cells that participate in juxtacrine (e.g. Notch signaling [1]), paracrine (e.g. IGF𠄁 [2]), endocrine (e.g. thyroid hormone action [3]) and exocrine (e.g. immunological factors passed on via lactation [4]) processes.

Direct protein interactions are realised by processes involving two or more proteins that bind directly with one another. Two key prerequistes for such an occurrence are that: 1) interacting proteins are spatially co‐located in the same portion of a compartment, and 2) the molecular constituents of that site are well mixed. For non co‐located cells to communicate, therefore, at least one of the interacting proteins produced by one of the cells must translocate to the site of location of its binding partner produced by the other cell. This requirement for translocation mechanisms is fulfilled by physiological processes that include transport modalities such as diffusion, advection and convection. Such mechanisms take place along a route, in a succession of sites, via a series of these distinct physiological transport modalities.

A simple example of a short communication route is that taken by a protein diffusing from the bloodstream in the capillaries of a given organ (e.g. coronary microcirculation) to the extracellular tissue fluid compartment of that organ (e.g. tissue fluid in the left ventricular wall). In this case, the anatomical route starts inside a capillary and ends in the extracellular tissue fluid with an intermediate step in the endothelial intercellular space during the crossing of the capillary wall by the protein as it is filtrated by the capillary. This example is simplified, of course, because in general the capillaries are not the production sites of their filtrate. Moreover, even such a simple example shows the potential complexity of giving an account of such phenomena in that: 1) sites (i.e. portions of blood in the capillaries of an organ, as well as portions of tissue fluid of that same organ) need to be identified and 2) transport modalities need to be taken into account relative to the translocating objects and their physical and chemical characteristics.

The latter point is crucial for the representation of communication processes involving translocation mechanisms, since prevailing biophysical conditions may facilitate or impede particular mechanisms. For example, vascular insufficiency may reduce the rate of translocation between capillaries and tissue fluids connected to each other. Conversely, a state of inflammation may increase the rate of translocation as the overall endothelial intercellular gap space is increased. These examples draw attention to the complexity of barrier crossing mechanisms and the regulation of accessibility between regions for different kinds of translocated proteins. These aspects are pervasive, multiscale and arise in greater number as routes of communication become more complex.

It is not straightforward to find anatomical translocation routes for specific pairs of interacting proteins—or for the translocation of any other kind of small molecule. Protein‐protein interactions data𠅊s well as a wealth of data of varied sorts�n be found in a number of databases which are maintained at great expense by the biomedical community. These databases can be very large and also very specialised. Naturally, they do not contain all relevant anatomical or physiological knowledge. Increasingly, however, these databases include controlled vocabulary and pointers to ontologies making them potentially connected to knowledge representations in their domains such pointers are, sometimes, to relevant anatomical locations. There is no shortage of anatomical knowledge and yet it is not always readily available in database format or not always to a realistic degree of detail and completion. Furthermore, should such data become available, the finding of translocation routes would remain a challenge indeed, the task would involve processing data and inferring implicit facts.

Knowledge representation and reasoning (see [5] for a short overview and [6] for a more thorough treatment) become relevant to bridge the gaps described in the foregoing and to combine and articulate data in distinct, specialised biological domains. This paper demonstrates how the combination of data (in the form of ground facts about proteins and their interactions, on the one hand, and body compartment connectivity, on the other hand) and knowledge (in the form of a formal theory of physiological communication between body compartments) can be achieved using knowledge representation and reasoning techniques. The paper describes a reusable and reimplementable elaboration of a knowledge base in the logic�sed knowledge representation tradition (see [7]). This elaboration results in an expert system for answering physiological connectivity queries on top of a knowledge base of anatomical connections and in combination with knowledge about translocating objects (here, proteins).

A direct contribution of the paper is to specify (parts of) a formal theory of physiological communication across anatomical compartments that may be used to query biomedical data. A method is contributed too, albeit through a specific illustrative case study, and points toward the construction and maintenance of tools and resources for using biomedical data (here, protein interaction data). The work is, however, prototypical and the achievement of code release and resource deployment still requires further development. An indirect contribution of the paper is the exemplification provided by the discussion of how a knowledge base system can be used in connection to other pieces of software (e.g. fast implementations of graph traversal algorithms) so as to make use of data curated in biomedical databases.

The paper presents knowledge representation requirements for the elicitation of routes of communication addressing the above complexity and the core of a theory addressing these requirements. To this end, the paper uses a scenario from endocrine physiology of manageable complexity for the purpose of discussion. In this scenario, a protein hormone is secreted by a cell and released into the bloodstream. This hormone then reaches an anatomically distinct site where it binds to its receptor deployed at the surface of another cell (in another tissue). The present treatment is primarily concerned with translocation pathways for molecules at a physiological level. This scenario assumes a process of protein synthesis that ends with the placement of the final product in certain subcellular sites defining the boundaries of communication routes. Furthermore, each protein is assumed to be located in one or more of three partitioning subcellular compartments, namely: i) cytoplasm, ii) plasma membrane, iii) extracellular space. Our purpose is to identify ways in which sites in these compartments may be linked to allow translocation processes to occur𠅋y extension our approach applies to other substances than hormones.

The specific endocrine process studied in this work is the translocation of Atrial Natriuretic Peptide (ANP) hormone from the wall of the cardiac atria to the extracellular tissue fluid in kidneys, where ANP binds to the cell‐surface receptor ANPr. The motivation for our choice is three𠄏old: 1) experimental research on ANP endocrinology is well established [8], 2) this endocrine process is linked to a number of common disease scenarios (e.g. [9]‐[11]), and 3) mathematical modelling of this process (e.g. as part of the Guyton model of circulation [12]) provides a quantitative framework explicitly relating rates of hormone secretion with the cardiovascular effect on the kidneys.

The ANP work in this paper is illustrative of the proposed method which consists in logically defining and constructing anatomical paths as ordered series of segments in a graph capturing transport modalities between sites in the body. In the context of the above endocrine scenario, the method and the theory presented are applied to a knowledge base of human anatomical connectivity statements in order to elicit candidate routes that link the cardiac location of ANP production to the renal site of ANPr.

The next section presents the knowledge representation requirements and a theory which addresses them. The section after the next presents a prototype system in the context of the ANP use case. This is followed by a discussion section before finishing the paper with a section containing conclusive remarks.

The Nature of Code

At ITP, I teach a course entitled Introduction to Computational Media. In this course, the students learn the basics of programming (variables, conditionals, loops, objects, arrays) as well as a survey of applications related to making interactive projects (images, pixels, computer vision, networking, data, 3D). The course mostly follows the material found in my intro book Learning Processing in many ways, The Nature of Code serves as a follow-up. Once you’ve learned the basics and seen an array of applications, your next step might be to delve deeply into a particular area. For example, you could focus on computer vision (and read a book like Greg Borenstein’s Making Things See). In the most basic sense, this book is one possible next step in a world of many. It picks up exactly where Learning Processing leaves off, demonstrating more advanced programming techniques with Processing that focus on algorithms and simulation.

The goal of this book is simple. We want to take a look at something that naturally occurs in our physical world, then determine how we can write code to simulate that occurrence.

So then what is this book exactly? Is it a science book? The answer is a resounding no. True, we might examine topics that come from physics or biology, but it won’t be our job to investigate these topics with a particularly high level of academic rigor. Instead, we’re going to glance at scientific concepts and grab the parts that we need in the service of building a particular software example.

Is this an art or design book? I would also say no after all, we are going to focus on algorithms and their affiliated programming techniques. Sure, the results will all be visual in nature (manifested as animated Processing sketches), but they will exist more as demonstrations of the algorithms and programming techniques themselves, drawn only with simple shapes and grayscale. It is my hope, however, that designers and artists can incorporate all of the material here into their practice to make new, engaging work.

In the end, if this book is anything, it is really just a good old-fashioned programming book. While a scientific topic may seed a chapter (Newtonian physics, cellular growth, evolution) or the results might inspire an artistic project, the content itself will always boil down to the code implementation, with a particular focus on object-oriented programming.

P.2 A word about Processing

I am using Processing in this book for a number of reasons. For one, it’s the language and environment with which I am most comfortable, and it’s what I enjoy using for my personal work. Two, it’s free, open-source, and well suited to beginners. There is an active, energetic community of people who program with Processing for many, it’s the first programming language they’ve learned. In this sense, I hope that I can reach a wide audience and demonstrate the concepts in a friendly manner by using Processing.

All that said, there is nothing that ties what we are doing in this book strictly to Processing. This book could have been written using ActionScript, JavaScript, Java (without Processing), or any number of other open-source “creative coding” environments like openFrameworks, Cinder, or the newly released pocode. It is my hope that after I’ve completed this book, I’ll be able to release versions of the examples that run in other environments. If anyone is interested in helping to port the examples, please feel free to contact me ([email protected]).

All of the examples in this book have been tested with Processing 2.0b6, but for the most part, they should also work with earlier versions of Processing. I’ll be keeping them up-to-date with whatever the latest version is. The most recent code can always be found on GitHub.

P.3 What do you need to know?

The prerequisite for understanding the material in this book could be stated as: “one semester of programming instruction with Processing (including familiarity with object-oriented programming).” That said, there’s no reason why you couldn’t read this book having learned programming using a different language or development environment. The key here is that you have experience with programming.

If you’ve never written any code before, you are going to struggle, because this book assumes knowledge of all the basics. I would suggest picking up an introductory book on Processing, a number of which are listed on the Processing website.

If you are an experienced programmer, but haven’t worked with Processing, you can probably pick it up by downloading Processing, poking through the examples, and reading through the Getting Started page.

I should also point out that experience with object-oriented programming is crucial. We’ll review some of the basics in the book’s introduction, but I would suggest reading the Processing tutorial on objects first.

P.4 What are you using to read this book?

Are you reading this book on a Kindle? Printed paper? On your laptop in PDF form? On a tablet showing an animated HTML5 version? Are you strapped to a chair, absorbing the content directly into your brain via a series of electrodes, tubes, and cartridges?

The book you are reading right now was generated with the Magic Book project. The Magic Book is an open-source framework for self-publishing developed at ITP. The idea here is that you only need to write the book once as a simple text file. Once you’ve written your content, you press a magic button, and out comes your book in a variety of formats—PDF, HTML5, printed hardcopy, Kindle, etc. Everything is designed and styled using CSS. As of the first release, the only versions available will be digital PDF, printed hardcopy, and HTML5 (which will include animated versions of the examples using Processing.js). Hopefully over the course of the next year, the book will be available in additional formats. If you’d like to help with this, please contact me ([email protected]).

P.5 The “story” of this book

If you glance over the book’s table of contents, you’ll notice there are ten chapters, each one covering a different topic. And in one sense, this book is just that—a survey of ten concepts and associated code examples. Nevertheless, in putting together the material, I had always imagined something of a linear narrative. Before you begin reading the chapters, I’d like to walk you through this story.

Part I: Inanimate objects

A soccer ball lies in the grass. A kick launches it into the air. Gravity pulls it back down. A heavy gust of wind keeps it afloat a moment longer until it falls and bounces off the head of a jumping player. The soccer ball is not alive it makes no choices as to how it will move throughout the world. Rather, it is an inanimate object waiting to be pushed and pulled by the forces of its environment.

How would we model a soccer ball moving in Processing? If you’ve ever programmed a circle moving across a window, then you’ve probably written the following line of code.

You draw some shape at location x . With each frame of animation, you increment the value of x , redraw the shape and voila—the illusion of motion! Maybe you took it a step or two further, and included a y location, as well as variables for speed along the x and y axes.

Part I of this story will take us one step further. We’re going to take these variables xspeed and yspeed and learn how together they form a vector (Chapter 1), the building block of motion. We won’t get any new functionality out of this, but it will build a solid foundation for the rest of the book.

Once we know a little something about vectors, we’re going to quickly realize that a force (Chapter 2) is a vector. Kick a soccer ball and you are applying a force. What does a force cause an object to do? According to Isaac Newton, force equals mass times acceleration. That force causes an object to accelerate. Modeling forces will allow us to create systems with dynamic motion where objects move according to a variety of rules.

Now, that soccer ball to which you applied a force might have also been spinning. If an object moves according to its acceleration, it can spin according to its angular acceleration (Chapter 3). Understanding the basics of angles and trigonometry will allow us to model rotating objects as well as grasp the principles behind oscillating motion, like a pendulum swinging or a spring bouncing.

Once we’ve tackled the basics of motion and forces for an individual inanimate object, we’ll learn how to make thousands upon thousands of those objects and manage them in a single system called a particle system (Chapter 4). Particle systems will allow us to look at some advanced features of object-oriented programming, namely inheritance and polymorphism.

In Chapters 1 through 4, all of the examples will be written from “scratch”—meaning the code for the algorithms driving the motion of the objects will be written directly in Processing. We’re certainly not the first programmers ever to consider the idea of simulating physics in animation, so next we’ll examine how physics libraries (Chapter 5) can be used to model more advanced and sophisticated behaviors. We’ll look at Box2D and toxiclibs' Verlet Physics package.

Part II: It’s alive!

What does it mean to model life? Not an easy question to answer, but we can begin by building objects that have an ability to perceive their environment. Let’s think about this for a moment. A block that falls off a table moves according to forces, as does a dolphin swimming through the water. But there is a key difference. The block cannot decide to leap off that table. The dolphin can decide to leap out of the water. The dolphin can have dreams and desires. It can feel hunger or fear, and those feelings can inform its movements. By examining techniques behind modeling autonomous agents (Chapter 6), we will breathe life into our inanimate objects, allowing them to make decisions about their movements according to their understanding of their environment.

Through combining the concept of autonomous agents with what we learned about modeling systems in Chapter 4, we’ll look at models of group behavior that exhibit the properties of complexity. A complex system is typically defined as a system that is “more than the sum of its parts.” While the individual elements of the system may be incredibly simple and easily understood, the behavior of the system as a whole can be highly complex, intelligent, and difficult to predict. This will lead us away from thinking purely about modeling motion and into the realm of rule-based systems. What can we model with cellular automata (Chapter 7), a system of cells living on a grid? What types of patterns can we generate with fractals (Chapter 8), the geometry of nature?

Part III: Intelligence

We made things move. Then we gave those things hopes and dreams and fears, along with rules to live by. The last step in this book will be to make our creations even smarter. Can we apply the biological process of evolution to computational systems (Chapter 9) in order to evolve our objects? Taking inspiration from the human brain, can we program an artificial neural network (Chapter 10) that can learn from its mistakes and allow our objects to adapt to their environment?

P.6 This book as a syllabus

While the content in this book certainly makes for an intense and highly compressed semester, I have designed it to fit into a fourteen-week course. Nevertheless, it’s worth mentioning that I find that the book chapters sometimes work better expanded across multiple weeks. For example, the syllabus for my course generally works out as follows:

Introduction and Vectors (Chapter 1)

Particle Systems (Chapter 4)

Physics Libraries Part I (Chapter 5)

Physics Libraries Part II & Steering (Chapters 5-6)

Present midterm projects about motion

Complex Systems: Flocking and 1D Cellular Automata (Chapters 6-7)

Complex Systems: 2D Cellular Automata and Fractals (Chapters 7-8)

Genetic Algorithms (Chapter 9)

Neural Networks (Chapter 10)

Final project presentation

If you are considering using this text for a course or workshop, please feel free to contact me. I hope to eventually release a companion set of videos and slide presentations as supplementary educational materials.

P.7 The Ecosystem Project

As much as I’d like to pretend you could learn everything by curling up in a comfy chair and reading some prose about programming, to learn programming, you’re really going to have to do some programming. You might find it helpful to keep in mind a project idea (or two) to develop as a set of exercises while going from chapter to chapter. In fact, when teaching the Nature of Code course at ITP, I have often found that students enjoy building a single project, step by step, week by week, over the course of a semester.

At the end of each chapter, you’ll find a series of exercises for one such project—exercises that build on each other, one topic at a time. Consider the following scenario. You’ve been asked by a science museum to develop the software for a new exhibit—The Digital Ecosystem, a world of animated, procedural creatures that live on a projection screen for visitors to enjoy as they enter the museum. I don’t mean to suggest that this is a particularly innovative or creative concept. Rather, we’ll use this example project idea as a literal representation of the content in the book, demonstrating how the elements fit together in a single software project. I encourage you to develop your own idea, one that is more abstract and creative in its thinking.

P.8 Where do I find the code online and submit feedback?

For all things book-related, please visit the Nature of Code website. The raw source text of the book and all of the illustrations are on GitHub. Please leave feedback and submit corrections using GitHub issues.

The source code for all of the examples (and exercises) is also available on GitHub. The chapters themselves include code snippets in-line with the text. However, I want to mention that in many cases, I have shortened or simplified the code snippets in order to illustrate a specific point. In all cases, the full code with comments can be found via GitHub.

If you have questions about the code itself, I would suggest posting them on the Processing forum.

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