We are searching data for your request:
Upon completion, a link will appear to access the found materials.
Gene expression signatures comprised of tens of genes have been found to be predictive of disease type and patient response to therapy, and have been informative in countless experiments exploring biological mechanism (for example [1–4]). High-density DNA microarrays therefore represent the method of choice for unbiased transcriptome analysis and represent an excellent route to signature discovery. However, gene expression signatures with diagnostic potential must be validated in large cohorts of patients, in whom measuring the entire transcriptome is neither necessary nor desirable. Perhaps more important is that the ability to describe cellular states in terms of a gene expression signature raises the possibility of performing high-throughput, small-molecule screens using a signature of interest as the read out. However, for this to be practical one would need to be able to screen thousands of compounds per day at a cost dramatically below that of conventional microarrays.
We therefore developed a simple, flexible, cost-effective, and high-throughput gene expression signature analysis solution tailored for the measurement of up to 100 transcripts in many thousands of samples by combining multiplex ligation-mediated amplication [5–7] with the Luminex FlexMAP (Luminex, Austin, TX, USA) optically addressed and barcoded microsphere and flow cytometric detection system, that we together refer to as LMF (Figure 1) . Here, we detail the LMF method and report on its overall performance.
Method overview. Transcripts are captured on immobilized poly-dT and reverse transcribed. Two oligonucleotide probes are designed against each transcript of interest. The upstream probes contain 20 nt complementary to a universal primer (T7) site, one of 100 different 24 nt barcode sequences, and a 20 nt sequence complementary to the 3'-end of the corresponding first-strand cDNA. The downstream probes are 5'-phosphorylated and contain a 20 nt sequence contiguous with the gene-specific fragment of the upstream probe and a 20 nt universal primer (T3) site. Probes are annealed to their targets, free probes removed, and juxtaposed probes joined by the action of ligase to yield synthetic 104 nt amplification templates. PCR is performed with T3 and 5'-biotinylated T7 primers. Biotinylated barcoded amplicons are hybridized against a pool of 100 sets of optically addressed microspheres each expressing capture probes complementary to one of the barcodes, and incubated with streptavidin-phycoerythrin to label biotin moieties fluorescently. Captured labeled amplicons are quantified and beads decoded by flow cytometry. nt nucleotides.
The Testing Effect
The testing effect concerns a paradox in the life of every student in medical school. When learning pharmacology and the five main adverse effects of beta-blockers, students read the facts, they summarize them, restudy, or memorize them for a considerable amount of time and are then tested once in a written or oral exam. Testing in the mind of the average student is a means to assess knowledge and not part of learning.
Testing as an active element of learning is more effective than studying the factual knowledge repeatedly . A considerable number of experiments were conducted to study this testing effect. One example cited in the aforementioned paper is a study by Hogan and Kintsch from 1971 . One group of students studied a list of 40 words four times with short breaks between the study time. A second group of students studied the list only once and took three free recall tests afterward. Two days later, both groups underwent a final test. The first group that studied the list four times recalled about 15 percent of the words. The second group, which studied once and then took three free recall tests, recalled about 20 percent of the words. Studying a list of words just once and then testing yourself by free recall led to significantly better results than studying the identical content four times.
A randomized controlled trial confirmed these findings and discovered that repeated testing resulted in significantly higher long-term retention than repeated studying . This study involved a didactic conference for pediatric and emergency medicine residents. There were two counterbalanced groups. One group took tests on the topic of status epilepticus and studied a review sheet on myasthenia gravis. The second group studied a review sheet on status epilepticus and took tests on myasthenia gravis. Testing and studying sessions were held immediately after teaching and on two additional time intervals of about 2 weeks. Each time, feedback was given to the participants. A final test after 6 months completed the study. Six months after the initial teaching session, repeated testing resulted in final test scores that were on average 13 percent higher than in the group of repeated studying .
A significant contributor to the testing effect is initial feedback to teach the student whether an answer was correct or incorrect. Interestingly, feedback enhances learning, but even testing without feedback is beneficial . The study by Roediger et al. presents an experiment in which four groups of students read a text passage. One group remained passive after reading, and three groups underwent a multiple-choice test. Of these three groups, one was tested without feedback, another received immediate feedback after each question, and a third received delayed feedback for all questions after the entire test. One week after the initial reading session, all four groups underwent a final test. The group that took no test showed 11 percent correct answers. Those participants who were tested without feedback presented 33 percent correct answers, immediate feedback resulted in 43 percent, and delayed feedback in 54 percent correct answers. Therefore, testing even without feedback tripled the score in a test 1 week after initial studying. Best results were obtained by delayed feedback, which hints at the positive contribution of spaced representation of learning content that will be discussed in one of the following sections.
Despite the various studies that found retesting to be more effective than restudying, students seem to be largely unaware of testing superiority in supporting short-term retention . When students use testing in a learning context, they apply it to assess knowledge and do not see it as a technique to intensify learning. In particular, students do not seem to be aware of the superiority of testing compared to studying.
This online visual acuity test is not a medical evaluation and does not replace a visit to a eye care professional. It is not designed to be used as a diagnosis for illness or other conditions, for treatment, or for the mitigation or prevention of illness. This test simply aims to give you a general idea about your visual capacity. We recommend that you follow-up this test with a full vision evaluation by a vision care specialist. Only eye care professionals can take decisions on medical treatment, diagnosis or prescription.
Frequently asked questions about t-tests
A t-test is a statistical test that compares the means of two samples. It is used in hypothesis testing, with a null hypothesis that the difference in group means is zero and an alternate hypothesis that the difference in group means is different from zero.
A t-test measures the difference in group means divided by the pooled standard error of the two group means.
In this way, it calculates a number (the t-value) illustrating the magnitude of the difference between the two group means being compared, and estimates the likelihood that this difference exists purely by chance (p-value).
Your choice of t-test depends on whether you are studying one group or two groups, and whether you care about the direction of the difference in group means.
If you are studying one group, use a paired t-test to compare the group mean over time or after an intervention, or use a one-sample t-test to compare the group mean to a standard value. If you are studying two groups, use a two-sample t-test.
If you want to know only whether a difference exists, use a two-tailed test. If you want to know if one group mean is greater or less than the other, use a left-tailed or right-tailed one-tailed test.
A one-sample t-test is used to compare a single population to a standard value (for example, to determine whether the average lifespan of a specific town is different from the country average).
A paired t-test is used to compare a single population before and after some experimental intervention or at two different points in time (for example, measuring student performance on a test before and after being taught the material).
A t-test should not be used to measure differences among more than two groups, because the error structure for a t-test will underestimate the actual error when many groups are being compared.
If you want to compare the means of several groups at once, it’s best to use another statistical test such as ANOVA or a post-hoc test.