1.7: Nonexperimental Scientific Investigations - Biology

1.7: Nonexperimental Scientific Investigations - Biology

We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

Up in Smoke

You've probably seen this warning label dozens of times. It's been required on cigarette packs in the U.S. since 1965, one year after the U.S. Surgeon General first issued a report linking cigarette smoking with diseases such as lung cancer. The report was based on thousands of research articles, including important research results published by British scientists Richard Doll and Austin Bradford Hill. Starting in 1950, Doll and Hill conducted large-scale, long-term observational studies on smoking and lung cancer and demonstrated a strong correlation between the two.

Observational Studies

Many questions in human biology are investigated with observational as opposed to experimental studies. An observational study measures characteristics in a sample but does not attempt to manipulate variables of interest. A simple example of an observational study is a political poll. A sample of adults might be asked how old they are and which of two candidates they favor. The study provides a snapshot in time of potential voters' opinions and how they differ by age of the respondent. Whether the results of the study apply to the population as a whole depends mainly on how large and random the sample is.

How is an observational study different from an experiment — the gold standard of scientific research studies? The main difference is how subjects are treated. In an observational study, no attempt is made to influence the subjects in any way. In an experiment, in contrast, the researcher applies a treatment to a group of subjects and attempts to isolate the effects of the treatment on an outcome variable by comparing the experimental group with a control group. For example, in 1954, Jonas Salk did an experimental trial of his newly discovered polio vaccine by giving it to a very large sample of children. Children in an equally large control group were given a harmless injection of a saline solution but no vaccine. Salk then compared the two groups of children and determined that the vaccine was 80 to 90 percent effective in preventing polio.

Types of Observational Studies

There are three different types of observational studies: cross-sectional, case-control, and cohort studies. All three types have pros and cons.

Cross-sectional Studies

A cross-sectional study is a type of observational study that collects data from a sample of subjects just once at a certain point in time. The political poll described above is a simple example of a cross-sectional study. A possible link between smoking and lung cancer was also first suggested by cross-sectional studies. Researchers found a higher rate of lung cancer in people who smoked than in those who did not smoke at the time of the study. In other words, the two variables seemed to be associated.

Cross-sectional studies are relatively cheap and easy to do, but their results are weak, so they are rarely used alone. More often, a researcher uses a cross-sectional study to find variables that may be linked and then does a case-control or cohort study to further investigate a possible relationship between the two variables.

Case-Control Studies

A case-control study is a type of observational study that compares a group of subjects having a trait of interest (cases) with a group of similar subjects not having the trait (controls). This type of study is retrospective. Subjects are asked to report their behaviors in the past in an attempt to find correlations between specific past behaviors and current status. The retrospective nature of case-control studies is their main weakness. Subjects' responses may be inaccurate because they forget or are dishonest about past habits.

A classic example of a case-control study is the early research on smoking and lung cancer carried out by Doll and Hill (Figure (PageIndex{2})). In 1950, the two scientists interviewed 700 lung cancer patients (cases) and 700 people without lung cancer (controls). They gathered information on past smoking habits and other characteristics of people in the two groups. When they compared the two groups, they found a strong association between past smoking behavior and current lung cancer status.

Cohort Studies

A cohort study is an observational study in which a group of similar subjects (the cohort) is selected at the start of the study and then followed over time. This type of study is prospective. The researchers collect data on the cohort periodically for months or even years into the future. Because the researchers collect the information directly, the data are likely to be more accurate than the self-reported recall data in case-control studies. Prospective data also allow researchers to establish the sequence of progression of disease states or other conditions of interest. On the other hand, cohort studies are the most costly and difficult observational studies to undertake.

One of the largest-ever cohort studies was undertaken by Doll and Hill in 1951. It was based on their earlier case-control study and further investigated the link between smoking and lung cancer. The cohort that began the study included almost 50,000 British male physicians, and they were followed by the researchers over the next 50 years. Initial findings of the study were first reported in 1954, and then updated results were reported periodically after that. The last report was published in 2004, and it reflected on the previous 50 years of research findings. This study provided even stronger evidence for the correlation between smoking and lung cancer.

Numerous other research studies, including experimental studies, have shown conclusively that smoking causes lung cancer, among many other health problems. Figure (PageIndex{3}) shows some of the ill effects that have since been demonstrated to be caused by smoking.

Correlation vs. Causation in Observational Studies

Observational studies can generally establish correlation but not necessarily causation. Correlation is an association between two variables in which a change in one variable is associated with a change in the other variable. Correlation may be strong or weak. It can also be positive or negative.

  • If two variables are shown to have a positive correlation, both variables change in the same direction. For example, an observational study might find that more smoking is correlated with a higher risk of lung cancer. In other words, as smoking goes up, so does lung cancer.
  • If two variables are shown to have a negative correlation, they change in opposite directions. For example, an observational study might find that people who exercise more are less likely to develop lung cancer. In other words, as exercise increases, lung cancer decreases.

One of the main differences between observational studies and experiments is the issue of correlation vs. causation. Because observational studies do not control all variables, any correlations they show between variables cannot be interpreted as one variable causes another. In experiments, in contrast, all possible variables are controlled, making it safer to conclude that changes in one variable cause changes in another. Unfortunately, when observational studies are reported in the news media, this distinction is not often made. Instead, a variable that is correlated with another in an observational study may be reported incorrectly as causing changes in the other variable.

In observational studies, it is always possible that some other variable affects both of the variables of interest and explains the correlation. An example of the confusion of correlation and causation in observational studies is the case of the health effects of coffee. Many early observational studies of coffee consumption and health found a positive correlation between drinking coffee and health problems such as heart disease and cancer. Does this mean that drinking coffee causes these health problems? Not necessarily, although news media have reported this conclusion. Looking more deeply into the issue reveals that coffee drinking is also associated with a less health-conscious lifestyle. People who drink coffee tend to practice other behaviors that may negatively impact their health, such as smoking cigarettes or drinking alcohol. Larger observational studies in which such lifestyle differences were taken into account have found no correlation between coffee consumption and health problems. In fact, they have found that moderate coffee consumption may actually have some health benefits.

Rationale for Observational Studies

If observational studies cannot establish causation, why are they done? Why aren't all research questions investigated experimentally? There are several important reasons to do observational studies:

  • An observational study may be the only type of study that is feasible for certain research questions because experiments are impossible, impractical, or unethical to undertake. For example, it would be unethical to do an experiment on smoking and health in which subjects in the smoking sample are deliberately exposed to tobacco smoke and then observed to see if they develop lung cancer.
  • An observational study is generally cheaper and easier to conduct than an experimental study.
  • An observational study usually can study more subjects and obtain a larger set of data than an experimental study.


Another way to gain scientific knowledge without experimentation is with modeling. A model is a representation of part of the real world. Did you ever build a model car or airplane? Scientific models are something like that. They represent the real world but are simpler. This is one reason that models are especially useful for investigating complex systems. By studying a much simpler model, it is easier to learn how the real system works.

As a hypothesis, a model must be evaluated. It is assessed by criteria such as how well it represents the real world, what limitations it has, and how useful it is. The usefulness of a model depends on how well its predictions match observations of the real world. Keep in mind that even when a model's predictions match real-world observations, it doesn't prove that the model is correct or that it is the only model that works.

Modeling Biological Systems

Many phenomena in biology occur as part of a complex system, whether the system is a cell, a human organ such as the brain, or an entire ecosystem. Models of biological systems can range from simple two-dimensional diagrams to complex computer simulations. Figure (PageIndex{3}) depicts a model of nicotine's effect on cells in the nervous system.

Model Organisms

Using other organisms as models of the human body is another way models are used in human biology research. A model organism is a nonhuman species that is extensively studied to understand particular biological phenomena. The expectation is that discoveries made in the model organism will provide insights into the workings of the human organism. In researching human diseases, for example, model organisms allow for a better understanding of the disease process without the added risk of harming actual human beings. The model species chosen should react to the disease or its treatment in a way that resembles human physiology. Although biological activity in a model organism does not ensure the same effect in humans, many drugs, treatments, and cures for human diseases are developed in part with the guidance of model organisms.

Model organisms that have been used in human biology research range from bacteria such as E. coli to nonhuman primates such as chimpanzees. The mouse Mus musculus, pictured below, is a commonly used model organism in human medical research. For example, it has been widely used to study diet-induced obesity and related health problems. In fact, the mouse model of diet-induced obesity has become one of the most important tools for understanding the interplay of high-fat Western diets and the development of obesity.

Feature: Reliable Sources

You may get most of your news from the Internet. You probably also research personal questions and term paper topics online. Unlike the information in newspapers and most television news broadcasts, information on the Internet is not regulated for quality or accuracy. Almost anybody can publish almost anything they wish on the web. The responsibility is on the user to evaluate Internet resources. How do you know if the resources you find online are reliable? The questions below will help you assess their reliability.

  1. How did you find the web page? If you just "googled" a topic or question, the search results may or may not be reliable. More likely to be trustworthy are web pages recommended by a faculty member, cited in an academic source, or linked with a reputable website.
  2. What is the website's domain? If its URL includes .edu, it is affiliated with a college or university. If it includes .gov, it is affiliated with the federal government, and if it includes .org it is affiliated with a nonprofit organization. Such websites are generally more trustworthy sources of information than .com websites, which are commercial or business websites.
  3. Who is the author of the web page? Is the author affiliated with a recognized organization or institution? Are the author's credentials listed, and are they relevant to the information on the page? Is current contact information for the author provided?
  4. Is the information trustworthy? Are sources cited for facts and figures? Is a bibliography provided? Does there seem to be a particular bias or point of view presented, or does the information seem fair and balanced? Does the page contain advertising that might impact the content of information that is included?
  5. Is the information current? When was the page created and last updated? Are the links on the page current and functional?

Put this advice into practice. Go online and find several web pages that provide information on the topic of smoking and lung cancer. Which websites do you think provide the most reliable information? Why?


  1. Explain why observational studies cannot establish causation. Describe an example to illustrate your explanation.
  2. Compare and contrast the three types of observational studies described above.
  3. Identify three possible reasons for doing an observational study.
  4. Why are models commonly used in human biology research?
  5. Multiple answers: What kind of a study involves the recall of variables that occurred in the past? What kind involves the observation of variables from the beginning?
    1. positive correlation; negative correlation
    2. negative correlation; positive correlation
    3. retrospective; prospective
    4. prospective; retrospective
  6. True or False. A positive correlation means there are health benefits to the variable under investigation.
  7. True or False. A cohort is a group of subjects of different ages, weights, genders, and health statuses.
  8. A study is done to investigate whether soda consumption influences the development of diabetes. The subjects are individuals recently diagnosed with diabetes compared to controls who do not have diabetes. All of the respondents are asked how many times a week they drank soda over the last two years. Answer the following questions about this scientific investigation.
    1. What type of observational study is this?
    2. The subjects with diabetes are “matched” to the controls, meaning that the researchers tried to minimize the effect of other variables outside of the variable of interest (i.e. soda consumption). What do you think some of those other variables could be?
    3. Do you think the data about soda consumption will be accurate? Why or why not?
    4. How could you change the study to get more accurate data on whether there is a relationship between soda consumption and diabetes? Explain why your new study would be more accurate.
  9. Do you think that computer simulation models of biological systems can be accurate without observations or experiments on actual living organisms or tissues?
  10. Explain why both observational and experimental investigations are useful in science.

Explore More

Learn more about the Blue Brain Project by watching this TED talk.

Visceral and somatic pain modalities reveal Na V 1.7-independent visceral nociceptive pathways

Key points: Voltage-gated sodium channels play a fundamental role in determining neuronal excitability. Specifically, voltage-gated sodium channel subtype NaV 1.7 is required for sensing acute and inflammatory somatic pain in mice and humans but its significance in pain originating from the viscera is unknown. Using comparative behavioural models evoking somatic and visceral pain pathways, we identify the requirement for NaV 1.7 in regulating somatic (noxious heat pain threshold) but not in visceral pain signalling. These results enable us to better understand the mechanisms underlying the transduction of noxious stimuli from the viscera, suggest that the investigation of pain pathways should be undertaken in a modality-specific manner and help to direct drug discovery efforts towards novel visceral analgesics.

Abstract: Voltage-gated sodium channel NaV 1.7 is required for acute and inflammatory pain in mice and humans but its significance for visceral pain is unknown. Here we examine the role of NaV 1.7 in visceral pain processing and the development of referred hyperalgesia using a conditional nociceptor-specific NaV 1.7 knockout mouse (NaV 1.7 Nav1.8 ) and selective small-molecule NaV 1.7 antagonist PF-5198007. NaV 1.7 Nav1.8 mice showed normal nociceptive behaviours in response to intracolonic application of either capsaicin or mustard oil, stimuli known to evoke sustained nociceptor activity and sensitization following tissue damage, respectively. Normal responses following induction of cystitis by cyclophosphamide were also observed in both NaV 1.7 Nav1.8 and littermate controls. Loss, or blockade, of NaV 1.7 did not affect afferent responses to noxious mechanical and chemical stimuli in nerve-gut preparations in mouse, or following antagonism of NaV 1.7 in resected human appendix stimulated by noxious distending pressures. However, expression analysis of voltage-gated sodium channel α subunits revealed NaV 1.7 mRNA transcripts in nearly all retrogradely labelled colonic neurons, suggesting redundancy in function. By contrast, using comparative somatic behavioural models we identify that genetic deletion of NaV 1.7 (in NaV 1.8-expressing neurons) regulates noxious heat pain threshold and that this can be recapitulated by the selective NaV 1.7 antagonist PF-5198007. Our data demonstrate that NaV 1.7 (in NaV 1.8-expressing neurons) contributes to defined pain pathways in a modality-dependent manner, modulating somatic noxious heat pain, but is not required for visceral pain processing, and advocate that pharmacological block of NaV 1.7 alone in the viscera may be insufficient in targeting chronic visceral pain.

Keywords: NaV1.7 colorectal heat pain visceral nociception visceral pain voltage gated sodium channel.

© 2017 The Authors. The Journal of Physiology © 2017 The Physiological Society.


Figure 1. Spontaneous visceral‐pain related behaviours in…

Figure 1. Spontaneous visceral‐pain related behaviours in Na V 1.7 Nav1.8 and littermate mice following…

Figure 2. Visceral pain related behaviours evoked…

Figure 2. Visceral pain related behaviours evoked by cyclophosphamide‐induced cystitis in Na V 1.7 Nav1.8…

Figure 3. Visceral afferent responses to noxious…

Figure 3. Visceral afferent responses to noxious distension of the distal colon in Na V…

Figure 4. Effect of capsaicin and mustard…

Figure 4. Effect of capsaicin and mustard oil on visceral afferent responses

Figure 5. Expression of voltage‐gated sodium channel…

Figure 5. Expression of voltage‐gated sodium channel mRNA transcripts in mouse colonic sensory neurons by…

Figure 6. Somatic pain behaviours and tibial…

Figure 6. Somatic pain behaviours and tibial nerve activity to noxious thermal stimulation in Na…

Figure 7. Effect of selective small‐molecule antagonism…

Figure 7. Effect of selective small‐molecule antagonism of Na V 1.7 in resected human appendices…

When to Use Non-Experimental Research

As we saw in the last chapter , experimental research is appropriate when the researcher has a specific research question or hypothesis about a causal relationship between two variables—and it is possible, feasible, and ethical to manipulate the independent variable. It stands to reason, therefore, that non-experimental research is appropriate—even necessary—when these conditions are not met. There are many times in which non-experimental research is preferred, including when:

  • the research question or hypothesis relates to a single variable rather than a statistical relationship between two variables (e.g., How accurate are people’s first impressions?).
  • the research question pertains to a non-causal statistical relationship between variables (e.g., is there a correlation between verbal intelligence and mathematical intelligence?).
  • the research question is about a causal relationship, but the independent variable cannot be manipulated or participants cannot be randomly assigned to conditions or orders of conditions for practical or ethical reasons (e.g., does damage to a person’s hippocampus impair the formation of long-term memory traces?).
  • the research question is broad and exploratory, or is about what it is like to have a particular experience (e.g., what is it like to be a working mother diagnosed with depression?).

Again, the choice between the experimental and non-experimental approaches is generally dictated by the nature of the research question. Recall the three goals of science are to describe, to predict, and to explain. If the goal is to explain and the research question pertains to causal relationships, then the experimental approach is typically preferred. If the goal is to describe or to predict, a non-experimental approach will suffice. But the two approaches can also be used to address the same research question in complementary ways. For example, Similarly, after his original study, Milgram conducted experiments to explore the factors that affect obedience. He manipulated several independent variables, such as the distance between the experimenter and the participant, the participant and the confederate, and the location of the study (Milgram, 1974) [1] .

Design of Specific Primer Set for Detection of B.1.1.7 SARS-CoV-2 Variant using Deep Learning

The SARS-CoV-2 variant B.1.1.7 lineage, also known as clade GR from Global Initiative on Sharing All Influenza Data (GISAID), Nextstrain clade 20B, or Variant Under Investigation in December 2020 (VUI – 202012/01), appears to have an increased transmissability in comparison to other variants. Thus, to contain and study this variant of the SARS-CoV-2 virus, it is necessary to develop a specific molecular test to uniquely identify it. Using a completely automated pipeline involving deep learning techniques, we designed a primer set which is specific to SARS-CoV-2 variant B.1.1.7 with >99% accuracy, starting from 8,923 sequences from GISAID. The resulting primer set is in the region of the synonymous mutation C16176T in the ORF1ab gene, using the canonical sequence of the variant B.1.1.7 as a reference. Further in-silico testing shows that the primer set’s sequences do not appear in different viruses, using 20,571 virus samples from the National Center for Biotechnology Information (NCBI), nor in other coronaviruses, using 487 samples from National Genomics Data Center (NGDC). In conclusion, the presented primer set can be exploited as part of a multiplexed approach in the initial diagnosis of Covid-19 patients, or used as a second step of diagnosis in cases already positive to Covid-19, to identify individuals carrying the B.1.1.7 variant.

In silico investigation of the new UK (B.1.1.7) and South African (501Y.V2) SARS-CoV-2 variants with a focus at the ACE2-Spike RBD interface

SARS-CoV-2 exploits angiotensin-converting enzyme 2 (ACE2) as a receptor to invade cells. It has been reported that the UK and South African strains may have higher transmission capabilities, eventually due to amino acid substitutions on the SARS-CoV-2 Spike protein. The pathogenicity seems modified but is still under investigation. Here we used the experimental structure of the Spike RBD domain co-crystallized with part of the ACE2 receptor and several in silico methods to analyze the possible impacts of three amino acid replacements (Spike K417N, E484K, N501Y) with regard to ACE2 binding. We found that the N501Y replacement in this region of the interface (present in both UK and South African strains) should be favorable for the interaction with ACE2 while the K417N and E484K substitutions (South African) would seem unfavorable. It is unclear if the N501Y substitution in the South African strain could counterbalance the predicted less favorable (regarding binding) K417N and E484K Spike replacements. Our finding suggests that, if indeed the South African strain has a high transmission level, this could be due to the N501Y replacement and/or to substitutions in regions outside the direct Spike-ACE2 interface.

Transmission of the UK and possibly South African SARS-CoV-2 strains appears substantially increased compared to other variants

This could be due, in part, to increased affinity between the variant Spike proteins and ACE2

We investigated in silico the 3D structure of the Spike-ACE2 complex with a focus on Spike K417N, E484K and N501Y

The N501Y substitution is predicted to increase the affinity toward ACE2 (UK strain) with subsequent enhanced transmissibility and possibly pathogenicity

Additional substitutions at positions 417 and 484 (South African strain) may pertub the interaction with ACE2 raising questions about transmissibility and pathogenicity

Mechanistic hypotheses for the rapid spread

To understand possible biological mechanisms for the faster spread of VOC 202012/01 relative to preexisting variants, we extended an age-structured and regionally structured mathematical model of SARS-CoV-2 transmission (10, 15) to consider two co-circulating variants (fig. S8 and tables S2 and S3). The model uses Google mobility data (11), validated by social contact surveys (10), to capture changes in contact patterns over time for each region of England. We created five versions of the model, each including one alternative parameter capturing a potential mechanism.

The hypotheses we tested are as follows. First, observations of lower cycle threshold (Ct) values (1618)—that is, higher viral load—support the idea that VOC may be more transmissible per contact with an infectious person than preexisting variants (hypothesis 1). Second, longitudinal testing data (17) suggest that VOC may be associated with a longer period of viral shedding and hence a potentially longer infectious period (hypothesis 2). Third, the ∆H69/∆V70 deletion in spike contributed to immune escape in an immunocompromised patient (7), which suggests that immunity to preexisting variants may afford reduced protection against infection with VOC (hypothesis 3). Fourth, the initial spread of VOC during the November 2020 lockdown in England, during which schools were open, suggests that children may be more susceptible to infection with VOC than with preexisting variants (hypothesis 4). Children are typically less susceptible to SARS-CoV-2 infection than adults (19, 20), possibly because of immune cross-protection due to other human coronaviruses (21), which could be less protective against VOC. Finally, VOC could have a shorter generation time than preexisting variants (hypothesis 5). A shorter generation time could account for an increased growth rate without requiring a higher reproduction number, which would make control of VOC 202012/01 through social distancing measures relatively easier to achieve.

We fit each model to time series of COVID-19 deaths, hospital admissions, hospital and ICU bed occupancy, polymerase chain reaction (PCR) prevalence, seroprevalence, and the proportion of community SARS-CoV-2 tests with S gene target failure across the three most heavily affected NHS England regions, over the period 1 March to 24 December 2020 (Fig. 3 and figs. S9 to S14). We assessed models using deviance information criteria (DIC) and compared model predictions to observed data for the 14 days after the fitting period (i.e., 25 December 2020–7 January 2021). Of the five hypotheses assessed, hypothesis 1 (increased transmissibility) had the lowest (i.e., best) combined DIC and predictive deviance. Hypothesis 2 (longer infectious period) and hypothesis 4 (increased susceptibility in children) also fitted the data well, although hypothesis 4 is not well supported by household secondary attack rate data (fig. S15) or by age-specific patterns of S gene target failure in the community (fig. S16), neither of which identify a substantial increase in susceptibility among children. Hypothesis 3 (immune escape) and hypothesis 5 (shorter generation time) fit poorly (Fig. 3A and table S4). In particular, hypothesis 5 predicted that the relative frequency of VOC 202012/01 should have dropped during stringent restrictions in late December 2020, because when two variants have the same effective reproduction number Rt < 1 but different generation times, infections decline faster for the variant with the shorter generation time.

Each row shows a different assumed mechanism. (A) Relative frequency of VOC 202012/01 (black line and ribbon respectively denote observed S gene target failure frequency with 95% binomial credible interval purple line and ribbon respectively denote mean and 95% credible interval from model fit). (B) Posterior estimates (mean and 95% credible intervals) for relative odds of hospitalization (severe illness), relative odds of ICU admission (critical illness), relative odds of death (fatal illness), growth rate as a multiplicative factor per week [i.e., exp(7·∆r)], and the parameter that defines the hypothesized mechanism all parameters are relative to those estimated for preexisting variants. (C to E) Illustrative model fits for the South East NHS England region: (C) fitted two-strain increased transmissibility model with VOC 202012/01 included (D) fitted two-strain increased transmissibility model with VOC 202012/01 removed (E) fitted single-strain model without emergence of VOC 202012/01. Black lines denote observed data error bars denote the date range and 95% credible intervals for observed PCR prevalence and seroprevalence colored lines and ribbons denote median and 95% credible intervals from model fit.

We fitted a combined model incorporating the five hypotheses above, but it was not able to identify a single consistent mechanism across NHS England regions hence, a wide range of parameter values are compatible with the observed growth rate of VOC 202012/01 (fig. S14). On the basis of our analysis, we identify increased transmissibility as the most parsimonious model, but we emphasize that the five mechanisms explored here are not mutually exclusive and may be operating in concert.

The increased transmissibility model does not identify a clear increase or decrease in the severity of disease associated with VOC 202012/01, finding similar odds of hospitalization given infection [odds ratio, 0.92 95% credible interval (CrI), 0.77 to 1.10], critical illness [odds ratio, 0.90 (CrI, 0.58 to 1.40)], and death [odds ratio, 0.90 (0.68 to 1.20)] when the model was fitted to the three most heavily affected NHS England regions (Fig. 3B). These estimates should be treated with caution, as we would not expect to identify a clear signal of severity when fitting to data up to 24 December 2020, given delays between infection and hospitalization or death. However, the fitted model finds strong evidence of higher relative transmissibility, estimated at 65% (CrI, 39 to 93%) higher than preexisting variants for the three most heavily affected NHS England regions, or 82% (CrI, 43 to 130%) when estimated across all seven NHS England regions (Table 1, model 5a). These estimates of increased transmissibility are consistent with our statistical estimates and with a previous estimate of a 70% increased reproduction number for VOC 202012/01 (16). This model reproduces observed epidemiological dynamics for VOC 202012/01 (Fig. 3C and fig. S17). Without the introduction of a new variant with a higher growth rate, the model is unable to reproduce observed dynamics (Fig. 3, D and E, and figs. S17 to S19) these findings lend further support to the idea that changing contact patterns do not explain the spread of VOC 202012/01.

1.7: Nonexperimental Scientific Investigations - Biology

In the phylogenetic tree shown, which organism is most distantly related to 2? In the diagram shown which is the most recent common ancestor of 1 and 3?
  1. E. coli is a eukaryote and share similarities with most of the living organisms.
  2. E. coli is a prokaryote. The various metabolic processes and core functions in E. coli share homology with higher organisms.
  3. E. coli contains a nucleus and membrane bound cell organelles that are shared by all the living organisms.
  4. E. coli is a prokaryote and reproduces through binary fission which is common to most of the living organisms.
  1. Archeopteryx is the connecting link between birds and reptiles which shows that birds and reptiles are related.
  2. Birds have scales, having the same origin as that of reptiles.
  3. Birds and reptiles have the same circulatory and excretory systems and both are egg laying animals.
  4. Birds and reptiles have similar anatomical and morphological features.
  • You are here:  
  • Home
  • Andover Biology Department Textbooks
  • Openstax Biology for AP Courses (textbook for Bio58x sequence)
  • Bio581
  • Chapter 1 The Study of Life
  • 1.7 Test Prep for AP Courses

This text is based on Openstax Biology for AP Courses, Senior Contributing Authors Julianne Zedalis, The Bishop's School in La Jolla, CA, John Eggebrecht, Cornell University Contributing Authors Yael Avissar, Rhode Island College, Jung Choi, Georgia Institute of Technology, Jean DeSaix, University of North Carolina at Chapel Hill, Vladimir Jurukovski, Suffolk County Community College, Connie Rye, East Mississippi Community College, Robert Wise, University of Wisconsin, Oshkosh

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 Unported License, with no additional restrictions

Study evaluates potential causes of increased transmission in SARS-CoV-2 variants

Washington, D.C. - June 20, 2021 - Although two SARS-CoV-2 variants are associated with higher transmission, patients with these variants show no evidence of higher viral loads in their upper respiratory tracts compared to the control group, a Johns Hopkins School of Medicine study found.

The emergence and higher transmission of the evolving variants of SARS-CoV-2, the virus that causes COVID-19, has been concerning. The researchers investigated B.1.1.7, the variant first identified in the UK, and B.1.351, the variant first identified in South Africa, to evaluate if patients showed higher viral loads, and consequently increased shedding and transmissibility.

Variants were identified using whole genome sequencing. Researchers used a large cohort of samples to show that the UK variant constituted 75% of the circulating viruses by April 2021. The researchers compared 134 variant samples to 126 control samples and with access to the patients' clinical information, were able to correlate the genomics data with the clinical disease and outcomes. All samples underwent additional testing to determine their viral load. The information was associated with the stage of the disease by looking at the days after the start of symptoms which added clarity in comparing viral shedding between groups.

"The reason why these variants show higher transmissibility is not yet clear," said Adannaya Amadi, lead author on the study. "However, our findings did show that the patients infected with these variants are less likely to be asymptomatic compared to the control group. Although those infected with the variants were not at higher risk for death or intensive care admission, they were more likely to be hospitalized."

This study was performed at Dr. Heba Mostafa's research laboratory at Johns Hopkins School of Medicine, which has been performing large scale whole genome sequencing of SARS-CoV-2 for the State of Maryland and contributing data to the national publicly available surveillance figures.

Alex Luo, C. Paul Morris, Matthew Schwartz, Eili Y. Klein and Heba H. Mostafa also contributed to this work. The study was funded by NIH, the Johns Hopkins Department of Pathology, The Johns Hopkins University and the Maryland Department of Health.

This abstract will be presented at the World Microbe Forum online from June 20-24 live from Baltimore, Maryland. World Microbe Forum is a collaboration between the American Society for Microbiology (ASM), the Federation of European Microbiological Societies (FEMS), and several other societies, which is breaking barriers to share science and address the most pressing challenges facing humankind today.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

Uveal melanoma: From diagnosis to treatment and the science in between

Melanomas of the choroid, ciliary body, and iris of the eye are collectively known as uveal melanomas. These cancers represent 5% of all melanoma diagnoses in the United States, and their age-adjusted risk is 5 per 1 million population. These less frequent melanomas are dissimilar to their more common cutaneous melanoma relative, with differing risk factors, primary treatment, anatomic spread, molecular changes, and responses to systemic therapy. Once uveal melanoma becomes metastatic, therapy options are limited and are often extrapolated from cutaneous melanoma therapies despite the routine exclusion of patients with uveal melanoma from clinical trials. Clinical trials directed at uveal melanoma have been completed or are in progress, and data from these well designed investigations will help guide future directions in this orphan disease. Cancer 2016122:2299-2312. © 2016 American Cancer Society.

Keywords: breast cancer 1-associated protein 1 (BAP1) choroidal melanoma diagnosis guanine nucleotide binding protein α11 (GNA11) guanine nucleotide-binding protein Q polypeptide (GNAQ) ocular melanoma review science treatment uveal melanoma.

© 2016 American Cancer Society.

Conflict of interest statement

Conflict of Interest: BE and SW are scientific committee members for the Uveal Melanoma TCGA. SP received research funding to conduct clinical trials and perform translational research in uveal melanoma. All other authors endorse no conflict of interest with the subject of this manuscript.

Materials and methods


United States.

The Institutional Review Board (IRB) from the Yale University Human Research Protection Program determined that the RT-qPCR testing and sequencing of de-identified remnant COVID-19 clinical samples conducted in this study is not research involving human patients (IRB Protocol ID: 2000028599).


Sample collection and genetic characterization were approved under the Brazilian National IRB (CONEP) CAAE 30101720.1.0000.0068.

South Africa.

We used de-identified remnant nasopharyngeal and oropharyngeal swab samples from patients testing positive for SARS-CoV-2 by RT-qPCR from public health and private medical diagnostics laboratories in South Africa. The project was approved by University of KwaZulu-Natal Biomedical Research Ethics Committee (protocol reference no. BREC/00001195/2020 project title: COVID-19 transmission and natural history in KwaZulu-Natal, South Africa: epidemiological investigation to guide prevention and clinical care). Individual participant consent was not required for the genomic surveillance. This requirement was waived by the research ethics committees.

The sample IDs displayed in S2 Data are not known outside the research groups and cannot be used to reidentify any subject.

Analysis of public SARS-CoV-2 genomes

All available SARS-CoV-2 data (402,899 genomes) were downloaded on January 22, 2021 from GISAID and evaluated for the presence of ORF1a Δ3675–3677 and spike Δ69–70. Phylogenetic analysis of a subset of 4,046 SARS-CoV-2 genomes was performed using Nextstrain [14], downsampled as shown using the “global build” on January 22, 2021 ( A list of SARS-CoV-2 genomes used in the analysis is available in S1 Data.

Multiplex RT-qPCR with probes

A detailed protocol of our multiplexed RT-qPCR to screen for SARS-COV-2 B.1.1.7, B.1.351, and P.1 VOC can be found on [17]. In brief, our multiplex RT-qPCR assay consists of the CDC N1 [15] and the newly designed ORF1a Δ3675–3677 and spike Δ69–70 primer–probe sets (S2 Table). We used the NEB Luna universal probe 1-Step RT-qPCR kit with 200 nM of N1 primers, 100 nM of N1 probe, 400 nM of the ORF1a and spike primers, 200 nM of ORF1a and spike probes, and 5 μL of nucleic acid in a total reaction volume of 20 μL. Thermocycler conditions were reverse transcription for 10 minutes at 55°C, initial denaturation for 1 minute at 95°C, followed by 40 cycles of 10 seconds at 95°C and 30 seconds at 55°C. During initial validation, we ran the PCR for 45 cycles. Differentiation between VOC is based on target failure of the ORF1a and/or spike primer–probe sets (S3 Table).

Limit of detection

We used Twist synthetic SARS-CoV-2 RNA controls 2 (GenBank ID: MN908947.3 GISAID ID: Wuhan-Hu-1) and control 14 (GenBank ID: EPI_ISL_710528 GISAID ID: England/205041766/2020) to determine the limit of detection of the screening RT-qPCR assay. We tested a 2-fold dilution series from 100 copies/μL to 1 copy/μL for both RNA controls in triplicate and confirmed that the lowest concentration that was detected in all 3 replicates by 20 additional replicates.

Validation and sequence confirmation

United States.

We validated our approach using known SARS-CoV-2 positive clinical samples. Briefly, we extracted nucleic acid from 300 μL viral transport medium from nasopharyngeal swabs and eluted in 75 μL using the MagMAX viral/pathogen nucleic acid isolation kit (Thermo Fisher Scientific, Waltham, MA, United States). Extracted nucleic acid was tested by our multiplexed RT-qPCR assay and then sequenced using the Illumina COVIDSeq Test RUO version for the NovaSeq (paired-end 150), or using a slightly modified ARTIC Network nCoV-2019 sequencing protocol for the Oxford Nanopore MinION [20,21]. These modifications include extending incubation periods of ligation reactions and including a bead-based clean-up step following dA-tailing. MinION sequencing runs were monitored using RAMPART [22]. Consensus sequences were generated using the ARTIC Network bioinformatics pipeline and lineages were assigned using Pangolin v.2.0 [23,24]. GISAID accession numbers for all SARS-CoV-2 genomes used to validate our approach are listed in S2 Data.


To validate the detection of the P.1 lineage, we selected 23 samples (16 P.1 and 7 others) that had been sequenced using the ARTIC protocol on the MinION sequencing platform, as previously described [12,25]. In brief, viral RNA was isolated from RT-PCR positive samples using QIAamp Viral RNA Mini kit (Qiagen, Hilden, Germany), following the manufacturer’s instructions. cDNA was synthesized with random hexamers and the Protoscript II First Strand cDNA synthesis Kit (New England Biolabs, Ipswich, MA, United States). Whole genome multiplex-PCR amplification was then conducted using the ARTIC network SARS-CoV-2 V3 primer scheme with the Q5 High-Fidelity DNA polymerase (New England Biolabs). Multiplex-PCR products were purified by using AmpureXP beads (Beckman Coulter, Brea, CA, United States), and quantification was carried out using the Qubit dsDNA High Sensitivity assay on the Qubit 3.0 (Life Technologies, Carlsbad, CA, United States). Samples were then normalized in an equimolar proportion of 10 ng per sample. After end repair and dA tailing, DNA fragments were barcoded using the EXP-NBD104 (1–12) and EXP-NBD114 (13–24) Native Barcoding Kits (Oxford Nanopore Technologies, Oxford, United Kingdom). Barcoded samples were pooled together and sequencing adapter ligation was performed using the SQK-LSK 109 Kit (Oxford Nanopore Technologies). Sequencing libraries were loaded onto an R9.4.1 flow-cell (Oxford NanoporeTechnologies) and sequenced using MinKNOW version 20.10.3 (Oxford Nanopore Technologies). We tested RNA from sequenced samples with the multiplex RT-qPCR as described above using the Applied Biosystems 7500 real-time PCR machine (Thermo Fisher Scientific) and a lowered threshold of Ct 30.

South Africa.

We extracted nucleic acid using the Chemagic 360 (PerkinElmer, Waltham, MA, United States). Briefly, 200 μl of viral transport medium from each swab sample was extracted and eluted in 100 μl using the Viral NA/gDNA kit. Complementary DNA (cDNA) synthesis, PCR, whole genome sequencing, and genome assembly was done as previously described in detail using the ARCTIC protocol [11,20]. Out of the sequenced samples, we selected 24 B.1.351 samples and 24 samples belonging to other lineages to validate the RT-qPCR assay. We adapted the protocol by using the TaqPath 1-Step multiplex master mix (Thermo Fisher Scientific) with 5 μl of extracted nucleic acid in a total reaction volume of 20 μl. Samples were amplified using the QuantStudio 7 Flex Real-Time PCR System using the following PCR conditions: Uracil-N-glycosylase (UNG) incubation for 2 minutes at 25°C, reverse transcription for 15 minutes at 50°C, polymerase activation for 2 minutes at 95°C, followed by 40 cycles of amplification at 95°C for 3 seconds and 55°C for 30 seconds.

Watch the video: Research Questions Hypothesis and Variables (May 2022).