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16.6: Summary Table - Biology

16.6: Summary Table - Biology


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Table (PageIndex{1}): Summary of the sources, transport mechanism, and major actions of the five main plant hormones

Hormone

Source(s)

Transport mechanism

Major actions

AuxinApical meristems, young leaves, and developing seedsPolar transport or nonpolar transport through the phloemTropisms, embryo and leaf development, root initiation, apical dominance, flowering and fruit development, prevention of abscission

Cytokinins

Root tips and other young tissues

Xylem

Gravitropism, cell division, germination, leaf formation, inhibition of apical dominance, inhibition of root initiation

Gibberellins

Young shoot tissues and seeds

Likely vascular tissue

Germination, shoot elongation, bolting, flowering, production of male flowers, fruit maturation

Ethylene

Stressed, wilting, or ripening tissues

Moves through the air as a gas

Abscission, senescence, fruit ripening, triple response, production of female flowers
Abscisic acidMature leaves and rootsVascular tissueSeed maturation and inhibition of germination, bud dormancy, stomatal closure

16.6: Summary Table - Biology

A neuron can receive input from other neurons and, if this input is strong enough, send the signal to downstream neurons. Transmission of a signal between neurons is generally carried by a chemical called a neurotransmitter. Transmission of a signal within a neuron (from dendrite to axon terminal) is carried by a brief reversal of the resting membrane potential called an action potential. When neurotransmitter molecules bind to receptors located on a neuron’s dendrites, ion channels open. At excitatory synapses, this opening allows positive ions to enter the neuron and results in depolarization of the membrane—a decrease in the difference in voltage between the inside and outside of the neuron. A stimulus from a sensory cell or another neuron depolarizes the target neuron to its threshold potential (−55 mV). Na + channels in the axon hillock open, allowing positive ions to enter the cell (Figure 1).

Once the sodium channels open, the neuron completely depolarizes to a membrane potential of about +40 mV. Action potentials are considered an “all-or nothing” event, in that, once the threshold potential is reached, the neuron always completely depolarizes. Once depolarization is complete, the cell must now “reset” its membrane voltage back to the resting potential. To accomplish this, the Na + channels close and cannot be opened. This begins the neuron’s refractory period, in which it cannot produce another action potential because its sodium channels will not open. At the same time, voltage-gated K + channels open, allowing K + to leave the cell. As K + ions leave the cell, the membrane potential once again becomes negative. The diffusion of K + out of the cell actually hyperpolarizes the cell, in that the membrane potential becomes more negative than the cell’s normal resting potential. At this point, the sodium channels will return to their resting state, meaning they are ready to open again if the membrane potential again exceeds the threshold potential. Eventually the extra K + ions diffuse out of the cell through the potassium leakage channels, bringing the cell from its hyperpolarized state, back to its resting membrane potential.

Practice Question

The formation of an action potential can be divided into five steps, which can be seen in Figure 1.

Figure 1. Action Potential

  1. A stimulus from a sensory cell or another neuron causes the target cell to depolarize toward the threshold potential.
  2. If the threshold of excitation is reached, all Na + channels open and the membrane depolarizes.
  3. At the peak action potential, K + channels open and K + begins to leave the cell. At the same time, Na + channels close.
  4. The membrane becomes hyperpolarized as K + ions continue to leave the cell. The hyperpolarized membrane is in a refractory period and cannot fire.
  5. The K + channels close and the Na + /K + transporter restores the resting potential.

Potassium channel blockers, such as amiodarone and procainamide, which are used to treat abnormal electrical activity in the heart, called cardiac dysrhythmia, impede the movement of K + through voltage-gated K + channels. Which part of the action potential would you expect potassium channels to affect?

Figure 2. The action potential is conducted down the axon as the axon membrane depolarizes, then repolarizes.


Find a Specialist Find a Specialist

If you need medical advice, you can look for doctors or other healthcare professionals who have experience with this disease. You may find these specialists through advocacy organizations, clinical trials, or articles published in medical journals. You may also want to contact a university or tertiary medical center in your area, because these centers tend to see more complex cases and have the latest technology and treatments.

If you can’t find a specialist in your local area, try contacting national or international specialists. They may be able to refer you to someone they know through conferences or research efforts. Some specialists may be willing to consult with you or your local doctors over the phone or by email if you can't travel to them for care.

You can find more tips in our guide, How to Find a Disease Specialist. We also encourage you to explore the rest of this page to find resources that can help you find specialists.

Healthcare Resources

  • To find a medical professional who specializes in genetics, you can ask your doctor for a referral or you can search for one yourself. Online directories are provided by the American College of Medical Genetics and the National Society of Genetic Counselors. If you need additional help, contact a GARD Information Specialist. You can also learn more about genetic consultations from MedlinePlus Genetics.

Visual Studio 2019 version 16.11 Preview 1

.NET Hot Reload User Experienced for editing managed code at runtime

In this release we are excited to make available the first release of the new Hot Reload user experience when editing code files for applications such as WPF, Windows Forms, ASP.NET Core, Console, etc. With Hot Reload you can now modify your apps managed source code while the application is running with no need to pause execution or use a breakpoint. Instead, simply make a supported change and use the new “apply code changes” button in the toolbar to apply them immediately.

In this update of Visual Studio this new experience is available when running your application under the debugger (F5) and is powered by the Edit and Continue (EnC) mechanism. Therefore, anywhere that EnC is supported you can now also use Hot Reload alongside any other debugger features. .NET Hot Reload will also work alongside XAML Hot Reload, making it possible to make both UI and code-behind changes in your desktop applications such as WPF or WinUI.

Both EnC and Hot Reload also share the same limitations, so be aware that not every type of edit is currently supported. The complete list of what is or is not supported can be found in our documentation.

To learn more about Hot Reload and our long-term vision you can also read more details in our blog post.


Discussion

CRISPR-Cas9 technology has greatly facilitated the generation of mouse lines containing knockout or knockin alleles [26, 27]. However, the generation of conditional alleles remains a challenge using traditional ES cells and CRISPR-Cas9 gene-editing technologies. A previous report demonstrated 16% efficiency with two chimeric sgRNAs and two single-stranded oligonucleotides (referred here as two-donor floxing method) to produce conditional alleles in mice [11].

To evaluate the efficiency of the two-donor floxing method, we replicated the experiments described in the initial report on Mecp2 (10) at three laboratories using the same experimental approaches to generate the sgRNA and Cas9 and microinjected into mouse zygotes along with ssODN donors. Although we observed single LoxP site insertions and indels at the cleavage sites, the method was unsuccessful in inserting two LoxP site in cis. These results prompted us to conduct a survey on the experiences of the global transgenic research community in using this method for the routine generation of cKO models. Twenty transgenic core facilities or large-scale knockout mouse centers participated in the consortium contributing data for 56 loci and over 17,000 microinjected or electroporated zygotes. In contrast to the 16% efficiency observed in the first report [11], the large dataset from the consortium suggests that the method is < 1% efficient and the method generally produces a series of undesired editing events, which occur at a nearly 100-fold higher rate than the correct insertion of the two LoxP sites in cis. These results are comparable with previous reports demonstrating an important disparity in success rate varying from 0 to 7% of mice harboring two LoxP sites insertions in cis whether delivered by microinjection [22, 25, 28,29,30] or by electroporation [25]. We and others also noted the large number of deletions at the target sites following DNA cleavage [22].

What determines the success of the two-donor floxing method?

Because our dataset represented a “real-world situation” consisting of diverse experimental conditions, it actually provided an opportunity to investigate the effect of several different parameters on the method’s efficiency. The factors we analyzed included CRISPR-reagent formats reagent concentrations, whether the guides were pre-tested or not nucleotide composition at the target sites nature of the loci (lethal or non-lethal) distance between the two guide cleavage sites mouse strains used microinjection technician skills and laboratory/site factor. Our statistical and machine learning analyses suggested that none of these factors could explain the lower efficiency of this method. One plausible explanation is that the method relies on two recombination events leading to a successful insertion of two donors on the same chromosomal DNA, and the probability of such an event (among the multitude of other combination of events) becomes very low. We tested 11 loci (of the 48 failed projects with 2 ssODN donor approach) using one-donor DNA approaches (see the “What are the alternative approaches to the two-donor floxing method?” section for the list) with a 10- to 20-fold higher efficiency. This supports our hypothesis that the one recombination event that occurs when using one-donor DNA approach offers better efficiencies than two simultaneous recombination events needed when using the two-donor DNA approach. This raises a related question: will the efficiency be higher if LoxP insertions are far away from each other (e.g., several kilobases to 100 s of kilobases apart)? In this instance , because the two recombination events will be sufficiently far away from each other, will they negatively affect each other’s efficiency in a similar way as when they are close to each other? One study reported moderate efficiencies when they placed LoxP sites sufficiently far away, and the study included 6 loci [31]. The distances between the LoxP sites (and the efficiencies of insertions) were 361 kb (5%), 4 kb (2.5%), 205 kb (18%), 1.6 kb (5%), 348 kb (0%) ,and 7 kb (0%). We did not find evidence in our data to suggest that placing LoxP sites several kilobases apart will provide higher efficiencies, although our sample size is too small to formally rule out this hypothesis.

What are the alternative approaches to the two-donor floxing method?

During the previous 2–3 years, a few strategies have been reported that offer potential alternatives to the low-efficiency two-donor floxing method. These newer methods use one-DNA donor formats including long single-stranded DNAs, linear dsDNAs, or circular dsDNAs (plasmids). The first set of alternative methods utilize long single-stranded DNA as a donor a microinjection-based approach of this method was named Easi-CRISPR (efficient additions with ssDNA inserts-CRISPR) [23, 32], and an electroporation-based approach was named CLICK (CRISPR with lssDNA inducing cKO alleles) [17]. The efficiency of Easi-CRISPR for creating conditional alleles ranged from 8.5 to 18% with a median of 13% (in previous publications it ranged from 8.5 to 100% for seven different loci [23, 32]). The CLICK method was demonstrated using three loci (with four independent attempts) and had an efficiency ranging from 3.7 to 16.6% with a median of 11%. Along the lines of “one-donor DNA approaches,” the methods using two versions of double-stranded DNA donors have been reported, one each with linear and circular dsDNAs. A method termed Tild-CRISPR (targeted integration with linearized dsDNA-CRISPR) uses long-dsDNA as donors, which was demonstrated for two loci at 18.8% and 33.3% efficiency [33]. A second version of the dsDNA donor is a method utilizing circular dsDNA molecules (plasmids) to insert LoxP sites via pronuclear microinjection with efficiencies ranging from 1.5 to 5.9% for three loci [22], and 20% and 22% for 2 loci from our dataset.

We attempted seven of the loci that failed with the two-donor floxing method using the recently developed alternative methods such as the “second LoxP insertion in the next-generation method” where it uses the one-side LoxP inserted models of the first injection and re-injects the second side LoxP donor into zygotes derived from them. All of the projects produced successful conditional alleles at an average efficiency of 21%. The second method using the “sequential delivery of LoxP sites” introduces each of the guide RNA-ssODN sets into the same zygotes at 1-cell and 2-cell stages, respectively. We failed to generate cKO alleles for the three loci attempted, although the sample size was too small to provide any conclusion on the efficiency of this technique. Overall, our results suggest that the newer methods, particularly those that use the one-donor DNA approach, appear to be superior alternatives to the two-donor floxing method.

Based on these results, we make the following recommendations. Even though it is possible to obtain a cKO allele using the two-donor floxing method, because of its low efficiency, the method may not be suitable as the first choice for a routine generation of cKO mouse models. The newer methods, particularly those employing long DNA donors (ssDNA or dsDNA), provide superior efficiencies for the routine generation of cKO animal models.

Reproducibility of CRISPR-based research methods

Genome editing tools utilizing the CRISPR-Cas systems have transformed many biomedical research fields as they have contributed to a number of powerful research methods. While many published methods are reproducible (as evidenced by their wide usage), the research community often encounters issues in reproducing some published methods. This may be because the original “proof-of-concept” papers have used underpowered studies in demonstrating the method, the results of which could be an exception, rather than the rule. Our community effort drawing upon the expertise and wealth of data from a multi-center transgenic mouse core facilities and research laboratories has allowed for the evaluation of collective experiences with the previously published methods of generating cKO mouse alleles. Our conclusions and recommendation of reproducible and efficient methods of genome editing will reduce wastage of resources, including animal lives. Our work exemplifies the importance of critical re-evaluation of the methods impacting larger research communities. Studies like this, where larger community experiences about the published methods are gathered and the large datasets are critically analyzed to make recommendations of best practices, can be vital, especially as the application of CRISPR-Cas9 technology continues to grow in both basic research and eventually into the clinic.


Error bars in experimental biology

Error bars commonly appear in figures in publications, but experimental biologists are often unsure how they should be used and interpreted. In this article we illustrate some basic features of error bars and explain how they can help communicate data and assist correct interpretation. Error bars may show confidence intervals, standard errors, standard deviations, or other quantities. Different types of error bars give quite different information, and so figure legends must make clear what error bars represent. We suggest eight simple rules to assist with effective use and interpretation of error bars.

What are error bars for?

Journals that publish science—knowledge gained through repeated observation or experiment𠅍on't just present new conclusions, they also present evidence so readers can verify that the authors' reasoning is correct. Figures with error bars can, if used properly (1𠄶), give information describing the data (descriptive statistics), or information about what conclusions, or inferences, are justified (inferential statistics). These two basic categories of error bars are depicted in exactly the same way, but are actually fundamentally different. Our aim is to illustrate basic properties of figures with any of the common error bars, as summarized in Table I , and to explain how they should be used.

Table I.

Common error bars

What do error bars tell you?

Descriptive error bars.

Range and standard deviation (SD) are used for descriptive error bars because they show how the data are spread ( Fig. 1 ). Range error bars encompass the lowest and highest values. SD is calculated by the formula

where X refers to the individual data points, M is the mean, and Σ (sigma) means add to find the sum, for all the n data points. SD is, roughly, the average or typical difference between the data points and their mean, M. About two thirds of the data points will lie within the region of mean ± 1 SD, and �% of the data points will be within 2 SD of the mean.

It is highly desirable to use larger n, to achieve narrower inferential error bars and more precise estimates of true population values.

Descriptive error bars. Means with error bars for three cases: n = 3, n = 10, and n = 30. The small black dots are data points, and the column denotes the data mean M. The bars on the left of each column show range, and the bars on the right show standard deviation (SD). M and SD are the same for every case, but notice how much the range increases with n. Note also that although the range error bars encompass all of the experimental results, they do not necessarily cover all the results that could possibly occur. SD error bars include about two thirds of the sample, and 2 x SD error bars would encompass roughly 95% of the sample.

Descriptive error bars can also be used to see whether a single result fits within the normal range. For example, if you wished to see if a red blood cell count was normal, you could see whether it was within 2 SD of the mean of the population as a whole. Less than 5% of all red blood cell counts are more than 2 SD from the mean, so if the count in question is more than 2 SD from the mean, you might consider it to be abnormal.

As you increase the size of your sample, or repeat the experiment more times, the mean of your results (M) will tend to get closer and closer to the true mean, or the mean of the whole population, μ. We can use M as our best estimate of the unknown μ. Similarly, as you repeat an experiment more and more times, the SD of your results will tend to more and more closely approximate the true standard deviation (σ) that you would get if the experiment was performed an infinite number of times, or on the whole population. However, the SD of the experimental results will approximate to σ, whether n is large or small. Like M, SD does not change systematically as n changes, and we can use SD as our best estimate of the unknown σ, whatever the value of n.

Inferential error bars.

In experimental biology it is more common to be interested in comparing samples from two groups, to see if they are different. For example, you might be comparing wild-type mice with mutant mice, or drug with placebo, or experimental results with controls. To make inferences from the data (i.e., to make a judgment whether the groups are significantly different, or whether the differences might just be due to random fluctuation or chance), a different type of error bar can be used. These are standard error (SE) bars and confidence intervals (CIs). The mean of the data, M, with SE or CI error bars, gives an indication of the region where you can expect the mean of the whole possible set of results, or the whole population, μ, to lie ( Fig. 2 ). The interval defines the values that are most plausible for μ.

Confidence intervals. Means and 95% CIs for 20 independent sets of results, each of size n = 10, from a population with mean μ = 40 (marked by the dotted line). In the long run we expect 95% of such CIs to capture μ here 18 do so (large black dots) and 2 do not (open circles). Successive CIs vary considerably, not only in position relative to μ, but also in length. The variation from CI to CI would be less for larger sets of results, for example n = 30 or more, but variation in position and in CI length would be even greater for smaller samples, for example n = 3.

Because error bars can be descriptive or inferential, and could be any of the bars listed in Table I or even something else, they are meaningless, or misleading, if the figure legend does not state what kind they are. This leads to the first rule. Rule 1: when showing error bars, always describe in the figure legends what they are.

Statistical significance tests and P values

If you carry out a statistical significance test, the result is a P value, where P is the probability that, if there really is no difference, you would get, by chance, a difference as large as the one you observed, or even larger. Other things (e.g., sample size, variation) being equal, a larger difference in results gives a lower P value, which makes you suspect there is a true difference. By convention, if P < 0.05 you say the result is statistically significant, and if P < 0.01 you say the result is highly significant and you can be more confident you have found a true effect. As always with statistical inference, you may be wrong! Perhaps there really is no effect, and you had the bad luck to get one of the 5% (if P < 0.05) or 1% (if P < 0.01) of sets of results that suggests a difference where there is none. Of course, even if results are statistically highly significant, it does not mean they are necessarily biologically important. It is also essential to note that if P > 0.05, and you therefore cannot conclude there is a statistically significant effect, you may not conclude that the effect is zero. There may be a real effect, but it is small, or you may not have repeated your experiment often enough to reveal it. It is a common and serious error to conclude “no effect exists” just because P is greater than 0.05. If you measured the heights of three male and three female Biddelonian basketball players, and did not see a significant difference, you could not conclude that sex has no relationship with height, as a larger sample size might reveal one. A big advantage of inferential error bars is that their length gives a graphic signal of how much uncertainty there is in the data: The true value of the mean μ we are estimating could plausibly be anywhere in the 95% CI. Wide inferential bars indicate large error short inferential bars indicate high precision.

Replicates or independent samples—what is n?

Science typically copes with the wide variation that occurs in nature by measuring a number (n) of independently sampled individuals, independently conducted experiments, or independent observations.

Rule 2: the value of n (i.e., the sample size, or the number of independently performed experiments) must be stated in the figure legend.

It is essential that n (the number of independent results) is carefully distinguished from the number of replicates, which refers to repetition of measurement on one individual in a single condition, or multiple measurements of the same or identical samples. Consider trying to determine whether deletion of a gene in mice affects tail length. We could choose one mutant mouse and one wild type, and perform 20 replicate measurements of each of their tails. We could calculate the means, SDs, and SEs of the replicate measurements, but these would not permit us to answer the central question of whether gene deletion affects tail length, because n would equal 1 for each genotype, no matter how often each tail was measured. To address the question successfully we must distinguish the possible effect of gene deletion from natural animal-to-animal variation, and to do this we need to measure the tail lengths of a number of mice, including several mutants and several wild types, with n > 1 for each type.

Similarly, a number of replicate cell cultures can be made by pipetting the same volume of cells from the same stock culture into adjacent wells of a tissue culture plate, and subsequently treating them identically. Although it would be possible to assay the plate and determine the means and errors of the replicate wells, the errors would reflect the accuracy of pipetting, not the reproduciblity of the differences between the experimental cells and the control cells. For replicates, n = 1, and it is therefore inappropriate to show error bars or statistics.

If an experiment involves triplicate cultures, and is repeated four independent times, then n = 4, not 3 or 12. The variation within each set of triplicates is related to the fidelity with which the replicates were created, and is irrelevant to the hypothesis being tested.

To identify the appropriate value for n, think of what entire population is being sampled, or what the entire set of experiments would be if all possible ones of that type were performed. Conclusions can be drawn only about that population, so make sure it is appropriate to the question the research is intended to answer.

In the example of replicate cultures from the one stock of cells, the population being sampled is the stock cell culture. For n to be greater than 1, the experiment would have to be performed using separate stock cultures, or separate cell clones of the same type. Again, consider the population you wish to make inferences about—it is unlikely to be just a single stock culture. Whenever you see a figure with very small error bars (such as Fig. 3 ), you should ask yourself whether the very small variation implied by the error bars is due to analysis of replicates rather than independent samples. If so, the bars are useless for making the inference you are considering.

Inappropriate use of error bars. Enzyme activity for MEFs showing mean + SD from duplicate samples from one of three representative experiments. Values for wild-type vs. −/− MEFs were significant for enzyme activity at the 3-h timepoint (P < 0.0005). This figure and its legend are typical, but illustrate inappropriate and misleading use of statistics because n = 1. The very low variation of the duplicate samples implies consistency of pipetting, but says nothing about whether the differences between the wild-type and −/− MEFs are reproducible. In this case, the means and errors of the three experiments should have been shown.

Sometimes a figure shows only the data for a representative experiment, implying that several other similar experiments were also conducted. If a representative experiment is shown, then n = 1, and no error bars or P values should be shown. Instead, the means and errors of all the independent experiments should be given, where n is the number of experiments performed.

Rule 3: error bars and statistics should only be shown for independently repeated experiments, and never for replicates. If a “representative” experiment is shown, it should not have error bars or P values, because in such an experiment, n = 1 ( Fig. 3 shows what not to do).

What type of error bar should be used?

Rule 4: because experimental biologists are usually trying to compare experimental results with controls, it is usually appropriate to show inferential error bars, such as SE or CI, rather than SD. However, if n is very small (for example n = 3), rather than showing error bars and statistics, it is better to simply plot the individual data points.

What is the difference between SE bars and CIs?

Standard error (SE).

Suppose three experiments gave measurements of 28.7, 38.7, and 52.6, which are the data points in the n = 3 case at the left in Fig. 1 . The mean of the data is M = 40.0, and the SD = 12.0, which is the length of each arm of the SD bars. M (in this case 40.0) is the best estimate of the true mean μ that we would like to know. But how accurate an estimate is it? This can be shown by inferential error bars such as standard error (SE, sometimes referred to as the standard error of the mean, SEM) or a confidence interval (CI). SE is defined as SE = SD/√n. In Fig. 4 , the large dots mark the means of the same three samples as in Fig. 1 . For the n = 3 case, SE = 12.0/𢆣 = 6.93, and this is the length of each arm of the SE bars shown.

Inferential error bars. Means with SE and 95% CI error bars for three cases, ranging in size from n = 3 to n = 30, with descriptive SD bars shown for comparison. The small black dots are data points, and the large dots indicate the data mean M. For each case the error bars on the left show SD, those in the middle show 95% CI, and those on the right show SE. Note that SD does not change, whereas the SE bars and CI both decrease as n gets larger. The ratio of CI to SE is the t statistic for that n, and changes with n. Values of t are shown at the bottom. For each case, we can be 95% confident that the 95% CI includes μ, the true mean. The likelihood that the SE bars capture μ varies depending on n, and is lower for n = 3 (for such low values of n, it is better to simply plot the data points rather than showing error bars, as we have done here for illustrative purposes).

The SE varies inversely with the square root of n, so the more often an experiment is repeated, or the more samples are measured, the smaller the SE becomes ( Fig. 4 ). This allows more and more accurate estimates of the true mean, μ, by the mean of the experimental results, M.

We illustrate and give rules for n = 3 not because we recommend using such a small n, but because researchers currently often use such small n values and it is necessary to be able to interpret their papers. It is highly desirable to use larger n, to achieve narrower inferential error bars and more precise estimates of true population values.

Confidence interval (CI).

Fig. 2 illustrates what happens if, hypothetically, 20 different labs performed the same experiments, with n = 10 in each case. The 95% CI error bars are approximately M ± 2xSE, and they vary in position because of course M varies from lab to lab, and they also vary in width because SE varies. Such error bars capture the true mean μ on �% of occasions—in Fig. 2 , the results from 18 out of the 20 labs happen to include μ. The trouble is in real life we don't know μ, and we never know if our error bar interval is in the 95% majority and includes μ, or by bad luck is one of the 5% of cases that just misses μ.

The error bars in Fig. 2 are only approximately M ± 2xSE. They are in fact 95% CIs, which are designed by statisticians so in the long run exactly 95% will capture μ. To achieve this, the interval needs to be M ± t (n𠄱) ×SE, where t (n𠄱) is a critical value from tables of the t statistic. This critical value varies with n. For n = 10 or more it is 𢏂, but for small n it increases, and for n = 3 it is 𢏄. Therefore M ± 2xSE intervals are quite good approximations to 95% CIs when n is 10 or more, but not for small n. CIs can be thought of as SE bars that have been adjusted by a factor (t) so they can be interpreted the same way, regardless of n.

This relation means you can easily swap in your mind's eye between SE bars and 95% CIs. If a figure shows SE bars you can mentally double them in width, to get approximate 95% CIs, as long as n is 10 or more. However, if n = 3, you need to multiply the SE bars by 4.

Rule 5: 95% CIs capture μ on 95% of occasions, so you can be 95% confident your interval includes μ. SE bars can be doubled in width to get the approximate 95% CI, provided n is 10 or more. If n = 3, SE bars must be multiplied by 4 to get the approximate 95% CI.

Determining CIs requires slightly more calculating by the authors of a paper, but for people reading it, CIs make things easier to understand, as they mean the same thing regardless of n. For this reason, in medicine, CIs have been recommended for more than 20 years, and are required by many journals (7).

Fig. 4 illustrates the relation between SD, SE, and 95% CI. The data points are shown as dots to emphasize the different values of n (from 3 to 30). The leftmost error bars show SD, the same in each case. The middle error bars show 95% CIs, and the bars on the right show SE bars𠅋oth these types of bars vary greatly with n, and are especially wide for small n. The ratio of CI/SE bar width is t (n𠄱) the values are shown at the bottom of the figure. Note also that, whatever error bars are shown, it can be helpful to the reader to show the individual data points, especially for small n, as in Figs. 1 and ​ and4, 4 , and rule 4.

Using inferential intervals to compare groups

When comparing two sets of results, e.g., from n knock-out mice and n wild-type mice, you can compare the SE bars or the 95% CIs on the two means (6). The smaller the overlap of bars, or the larger the gap between bars, the smaller the P value and the stronger the evidence for a true difference. As well as noting whether the figure shows SE bars or 95% CIs, it is vital to note n, because the rules giving approximate P are different for n = 3 and for n ≥ 10.

Fig. 5 illustrates the rules for SE bars. The panels on the right show what is needed when n ≥ 10: a gap equal to SE indicates P ≈ 0.05 and a gap of 2SE indicates P ≈ 0.01. To assess the gap, use the average SE for the two groups, meaning the average of one arm of the group C bars and one arm of the E bars. However, if n = 3 (the number beloved of joke tellers, Snark hunters (8), and experimental biologists), the P value has to be estimated differently. In this case, P ≈ 0.05 if double the SE bars just touch, meaning a gap of 2 SE.

Estimating statistical significance using the overlap rule for SE bars. Here, SE bars are shown on two separate means, for control results C and experimental results E, when n is 3 (left) or n is 10 or more (right). “Gap” refers to the number of error bar arms that would fit between the bottom of the error bars on the controls and the top of the bars on the experimental results i.e., a gap of 2 means the distance between the C and E error bars is equal to twice the average of the SEs for the two samples. When n = 3, and double the length of the SE error bars just touch (i.e., the gap is 2 SEs), P is 𢏀.05 (we don't recommend using error bars where n = 3 or some other very small value, but we include rules to help the reader interpret such figures, which are common in experimental biology).

Rule 6: when n = 3, and double the SE bars don't overlap, P < 0.05, and if double the SE bars just touch, P is close to 0.05 ( Fig. 5 , leftmost panel). If n is 10 or more, a gap of SE indicates P ≈ 0.05 and a gap of 2 SE indicates P ≈ 0.01 ( Fig. 5 , right panels).

Rule 5 states how SE bars relate to 95% CIs. Combining that relation with rule 6 for SE bars gives the rules for 95% CIs, which are illustrated in Fig. 6 . When n ≥ 10 (right panels), overlap of half of one arm indicates P ≈ 0.05, and just touching means P ≈ 0.01. To assess overlap, use the average of one arm of the group C interval and one arm of the E interval. If n = 3 (left panels), P ≈ 0.05 when two arms entirely overlap so each mean is about lined up with the end of the other CI. If the overlap is 0.5, P ≈ 0.01.

Estimating statistical significance using the overlap rule for 95% CI bars. Here, 95% CI bars are shown on two separate means, for control results C and experimental results E, when n is 3 (left) or n is 10 or more (right). “Overlap” refers to the fraction of the average CI error bar arm, i.e., the average of the control (C) and experimental (E) arms. When n ≥ 10, if CI error bars overlap by half the average arm length, P ≈ 0.05. If the tips of the error bars just touch, P ≈ 0.01.

Rule 7: with 95% CIs and n = 3, overlap of one full arm indicates P ≈ 0.05, and overlap of half an arm indicates P ≈ 0.01 ( Fig. 6 , left panels).

Repeated measurements of the same group

The rules illustrated in Figs. 5 and ​ and6 6 apply when the means are independent. If two measurements are correlated, as for example with tests at different times on the same group of animals, or kinetic measurements of the same cultures or reactions, the CIs (or SEs) do not give the information needed to assess the significance of the differences between means of the same group at different times because they are not sensitive to correlations within the group. Consider the example in Fig. 7 , in which groups of independent experimental and control cell cultures are each measured at four times. Error bars can only be used to compare the experimental to control groups at any one time point. Whether the error bars are 95% CIs or SE bars, they can only be used to assess between group differences (e.g., E1 vs. C1, E3 vs. C3), and may not be used to assess within group differences, such as E1 vs. E2.

Inferences between and within groups. Means and SE bars are shown for an experiment where the number of cells in three independent clonal experimental cell cultures (E) and three independent clonal control cell cultures (C) was measured over time. Error bars can be used to assess differences between groups at the same time point, for example by using an overlap rule to estimate P for E1 vs. C1, or E3 vs. C3 but the error bars shown here cannot be used to assess within group comparisons, for example the change from E1 to E2.

Assessing a within group difference, for example E1 vs. E2, requires an analysis that takes account of the within group correlation, for example a Wilcoxon or paired t analysis. A graphical approach would require finding the E1 vs. E2 difference for each culture (or animal) in the group, then graphing the single mean of those differences, with error bars that are the SE or 95% CI calculated from those differences. If that 95% CI does not include 0, there is a statistically significant difference (P < 0.05) between E1 and E2.

Rule 8: in the case of repeated measurements on the same group (e.g., of animals, individuals, cultures, or reactions), CIs or SE bars are irrelevant to comparisons within the same group ( Fig. 7 ).

Conclusion

Error bars can be valuable for understanding results in a journal article and deciding whether the authors' conclusions are justified by the data. However, there are pitfalls. When first seeing a figure with error bars, ask yourself, “What is n? Are they independent experiments, or just replicates?” and, “What kind of error bars are they?” If the figure legend gives you satisfactory answers to these questions, you can interpret the data, but remember that error bars and other statistics can only be a guide: you also need to use your biological understanding to appreciate the meaning of the numbers shown in any figure.


The 1001 Genomes Plus Vision

The 1001 Genomes Project was launched at the beginning of 2008 to discover detailed whole-genome sequence variation in at least 1001 strains (accessions) of the reference plant Arabidopsis thaliana. The first major phase of the project was completed in 2016, with publication of a detailed analysis of 1135 genomes. Unfortunately, the second-generation sequencing methods that have made it economically feasible to screen large numbers of individuals do not actually produce complete genome sequences — they produce massive numbers of very short sequence fragments that must be aligned to a reference genome in order to identify variants. Because of this, only simple variants are reported, and the results are invariably biased with respect to what is present or missing in the reference genome. Large or complex structural variants, as well as simple variants inside complex variants are generally missed completely. To remedy this problem, we have recently begun the second major phase, the 1001G+ project. We have begun to assemble genomes from a diverse collection of A. thaliana strains, with the goal of annotating them with transcriptome and epigenome information, and to develop tools to make the results available to the community.


16.6: Summary Table - Biology

The Periodic Table is a way of listing the elements. Elements are listed in the table by the structure of their atoms. This includes how many protons they have as well as how many electrons they have in their outer shell. From left to right and top to bottom, the elements are listed in the order of their atomic number, which is the number of protons in each atom.

Why is it called the Periodic Table?

It is called "periodic" because elements are lined up in cycles or periods. From left to right elements are lined up in rows based on their atomic number (the number of protons in their nucleus). Some columns are skipped in order for elements with the same number of valence electrons to line up on the same columns. When they are lined up this way, elements in the columns have similar properties.

Each horizontal row in the table is a period. There are seven (or eight) total periods. The first one is short and only has two elements, hydrogen and helium. The sixth period has 32 elements. In each period the left most element has 1 electron in its outer shell and the right most element has a full shell.

Groups are the columns of the periodic table. There are 18 columns or groups and different groups have different properties.

One example of a group is the noble or inert gases. These elements all line up in the eighteenth or last column of the periodic table. They all have a full outer shell of electrons, making them very stable (they tend not to react with other elements). Another example is the alkali metals which all align on the left-most column. They are all very similar in that they have only 1 electron in their outer shell and are very reactive. You can see all the groups in the table below.

This lining-up and grouping of similar elements helps chemists when working with elements. They can understand and predict how an element might react or behave in a certain situation.

Element Abbreviations

Each element has its own name and abbreviation in the periodic table. Some of the abbreviations are easy to remember, like H for hydrogen. Some are a bit harder like Fe for iron or Au for gold. For gold the "Au" comes from the Latin word for gold "aurum".

The periodic table was proposed by Russian chemist Dmitri Mendeleev in 1869. Using the table, Mendeleev was able to accurately predict the properties of many elements before they were actually discovered.

  • Carbon is unique in that it is known to form up to 10 million different compounds. Carbon is important to the existence of life.
  • Francium is the rarest element on earth. There are probably no more than a few ounces of it on earth at any given time.
  • The only letter not in the periodic table is the letter J.
  • The country Argentina is named after the element silver (symbol Ag) which is argentum in Latin.
  • Although there is helium on Earth, it was first discovered by observing the sun.

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Figures and tables

Many readers will only look at your display items without reading the main text of your manuscript. Therefore, ensure your display items can stand alone from the text and communicate clearly your most significant results.

Display items are also important for attracting readers to your work. Well designed and attractive display items will hold the interest of readers, compel them to take time to understand a figure and can even entice them to read your full manuscript.

Finally, high-quality display items give your work a professional appearance. Readers will assume that a professional-looking manuscript contains good quality science. Thus readers may be more likely to trust your results and your interpretation of those results.

When deciding which of your results to present as display items consider the following questions:

  • Are there any data that readers might rather see as a display item rather than text?
  • Do your figures supplement the text and not just repeat what you have already stated?
  • Have you put data into a table that could easily be explained in the text such as simple statistics or p values?

Tables

Tables are a concise and effective way to present large amounts of data. You should design them carefully so that you clearly communicate your results to busy researchers.

The following is an example of a well-designed table:

  • Clear and concise legend/caption
  • Data divided into categories for clarity
  • Sufficient spacing between columns and rows
  • Units are provided
  • Font type and size are legible

Source: Environmental Earth Sciences (2009) 59:529–536

Figures

Figures are ideal for presenting:

Just like tables all figures need to have a clear and concise legend caption to accompany them.

Images help readers visualize the information you are trying to convey. Often, it is difficult to be sufficiently descriptive using words. Images can help in achieving the accuracy needed for a scientific manuscript. For example, it may not be enough to say, “The surface had nanometer scale features.” In this case, it would be ideal to provide a microscope image.

  • Include scale bars
  • Consider labeling important items
  • Indicate the meaning of different colours and symbols used

Data plots convey large quantities of data quickly. The goal is often to show a functional or statistical relationship between two or more items. However, details about the individual data points are often omitted to place emphasis on the relationship that is shown by the collection of points. Here, we have examples of figures combining images and a plots in multiple panels.

For data plots, be sure to:

  • Label all axes
  • Specify units for quantities
  • Label all curves and data sets
  • Use a legible font size

Source: Nano Research (2010) 3:843–851

Source: Borrego et al. Cancer & Metabolism 2016 4:9

Source: Borrego et al. Cancer & Metabolism 2016 4:9

Maps are important for putting field work in the context of the location where it was performed. A good map will help your reader understand how the site affects your study. Moreover, it will help other researchers reproduce your work or find other locations with similar properties. Here, we have a map used in a study about salmon.

  • Include latitude and longitude
  • Include scale bars
  • Label important items
  • Consider adding a map legend

Source: Environmental Biology of Fishes (2011) DOI: 10.1007/s10641-011-9783-5

Schematics help identify the key parts to a system or process. They should highlight only the key elements because adding unimportant items may clutter the image. A schematic only includes the drawings the author chooses, offering a degree of flexibility not offered by images. They can also be used in situations where it is difficult or impossible to capture an image. Below is a schematic explaining how nanotubes could be used to harvest energy from a fluid.

For schematics, be sure to:

Source: Nano Research (2011) 4:284–289

TIP: it’s important to consider how your figures will look in print as well as online. A resolution of 72 ppi is sufficient for online publication whilst in print 100 ppi is recommended. You can adjust the resolution of your figure within the original program you used to create it at the time you save the file.

TIP: There are two main colour models RGB which stands for red, green, blue and CMYK or cyan, magenta, yellow and black. Most microscopes will take images using the RGB however CMYK is the standard used for printing so it is important to check that your figures will display well in this format.

Avoiding image manipulation

You should never knowingly manipulate your images to change or improve you results. To avoid inadvertent manipulation you should only minimally process your figures before submitting them to the journal, your submitted images should faithfully represent the original image files.

  • Adjusting the brightness or contrast of an image, in fluorescent microscopy for example, is only acceptable if applied equally across all images including the controls
  • The cropping of images in the creation of figures should be avoided unless it significantly improves the clarity of conciseness of presentation. Be sure that the cropping does not exclude any necessary information for the understanding of the figure, such as molecular markers in electrophoresis gels.
  • Any adjustments or processing software used should be stated.

TIP: keep copies of the original images, files and metadata used to create your figures as these can be requested by the journal during the review process.


Watch the video: 14 - GMMS: Prepare GPB Summary Table (May 2022).