Student Preclass Preparation by Both Reading the Textbook and Watching Videos Online Improves Exam Performance in a Partially Flipped Course

The flipped classroom has the potential to improve student performance. Because flipping involves both preclass preparation and problem solving in the classroom, the means by which increased learning occurs and whether the method of delivering content matters is of interest. In a partially flipped cell biology course, students were assigned online videos before the flipped class and textbook reading before lectures. Low-stakes assessments were used to incentivize both types of preclass preparation. We hypothesized that more students would watch the videos than read the textbook and that both types of preparation would positively affect exam performance. A multiple linear regression analysis showed that both reading and video viewing had a significant positive impact on exam score, and this model was predictive of exam scores. In contrast to our expectations, most students prepared by both watching videos and reading the textbook and did not exhibit a pattern of solely watching videos. This analysis supports previous findings that engagement with material outside class is partly responsible for the improved outcomes in a flipped classroom and shows that both reading and watching videos are effective at delivering content outside class

MODELING DNA METHYLATION TILING ARRAY DATA

Epigenetics is the study of heritable changes in gene function that occur without a change in DNA sequence. It has quickly emerged as an essential area for understanding inheritance and variation that cannot be explained by the DNA sequence alone. Epigenetic modifications have the potential to regulate gene expression and may play a role in diseases such as cancer. DNA methylation is a type of epigenetic modification that occurs when a methyl chemical group attaches to a cytosine base on the DNA molecule. To better understand this epigenetic mechanism, DNA methylation profiles can be constructed by identifying all locations of DNA methylation in a genomic region (e.g. chromosome or whole-genome). Large-scale studies of DNA methylation are supported by microarray technology known as tiling arrays. These arrays provide high-density coverage of genomic regions through the unbiased, systematic selection of probes that are tiled across the regions. Statistical methods are employed to estimate each probe’s DNA methylation status. Previous studies indicate that DNA methylation patterns of some organisms differ by genomic element (e.g., gene, transposon), suggesting that genomic annotation information may be useful in statistical analysis. In this work, a novel statistical model is proposed, which takes advantage of genomic annotation information that to date has not been effectively utilized in statistical analysis. Specifically, a hidden Markov model, which incorporates genomic annotation, is introduced and investigated through a simulation study and analysis of an Arabidopsis thaliana DNA methylation tiling array experiment

Birth Mass Is the Key to Understanding the Negative Correlation Between Lifespan and Body Size in Dogs

Larger dog breeds live shorter than the smaller ones, opposite of the mass-lifespan relationship observed across mammalian species. Here we use data from 90 dog breeds and a theoretical model based on the first principles of energy conservation and life history tradeoffs to explain the negative correlation between longevity and body size in dogs. We found that the birth/adult mass ratio of dogs scales negatively with adult size, which is different than the weak interspecific scaling in mammals. Using the model, we show that this ratio, as an index of energy required for growth, is the key to understanding why the lifespan of dogs scales negatively with body size. The model also predicts that the difference in mass-specific lifetime metabolic energy usage between dog breeds is proportional to the difference in birth/adult mass ratio. Empirical data on lifespan, body mass, and metabolic scaling law of dogs strongly supports this prediction

DIFFERENTIAL METHYLATION METHODS IN MULTI-CONTEXT ORGANISMS

DNA methylation is an epigenetic modification that has the ability to alter gene expression without any change in the DNA sequence. DNA methylation occurs when a methyl chemical group attaches to cytosine bases on the DNA sequence. In mammals, DNA methylation primarily occurs at CG sites, when a cytosine is followed by a guanine in the DNA sequence. In plants, DNA methylation can also occur in other cytosine sequences, such as when a cytosine is not followed directly by a guanine. Many of the statistical methods that have been developed to estimate methylation levels and test differential methylation in whole-genome bisulfite sequencing studies incorporate the observed correlation between methylation levels of neighboring cytosine sites. However, most of these methods have been applied to human studies, where only CG sites are investigated. In this study, we focus on plant studies and show that the correlation between methylation levels at neighboring sites depends on the DNA sequence immediately following the cytosine. We investigate the importance of accounting for these differences in the correlation structure by comparing the performance of three existing methods (MethylSig, MAGI, and M3D) in plants

Statistical Modeling of Fruit Fly Based on Sleep Characteristics

In this research, a statistical model was developed to predict the lifespan of the fruit fly, Drosophila melanogaster, based on the sleep characteristics. Previously, a model was developed using variables based on the transition probabilities of a fly staying awake or asleep from minute-to-minute. This research builds on the previous work by incorporating additional variables based on traditional sleep metrics along with the transition probability variables into the modeling process. A method was first developed to automate the generation of the traditional sleep metrics, enabling them to be included in the model. Forward stepwise selection was used to determine an appropriate number of predictor variables before using best subset selection to determine the strongest model for that number of variables. Models were evaluated by comparing the original model with the model including the traditional variables

STATISTICAL METHODS FOR AFFYMETRIX TILING ARRAY DATA

Tiling arrays are a microarray technology currently being used for a variety of genomic and epigenomic applications, such as the mapping of transcription, DNA methylation, and histone modifications. Tiling arrays provide high-density coverage of a genome, or a genomic region, through the systematic and sequential placement of probes without regard to genome annotation. In this paper we compare the Affymetrix tiling array to the Affymetrix GeneChip® 3’ expression array and propose methods that address statistical and bioinformatic issues that accompany gene expression data that are generated from Affymetrix tiling arrays. Real data from the model organism Arabidopsis thaliana motivate this work and application

A Deep Learning Model to Predict Traumatic Brain Injury Severity and Outcome from MR Images

For Many Neurological Disorders, Including Traumatic Brain Injury (TBI), Neuroimaging Information Plays a Crucial Role Determining Diagnosis and Prognosis. TBI is a Heterogeneous Disorder that Can Result in Lasting Physical, Emotional and Cognitive Impairments. Magnetic Resonance Imaging (MRI) is a Non-Invasive Technique that Uses Radio Waves to Reveal Fine Details of Brain Anatomy and Pathology. Although MRIs Are Interpreted by Radiologists, Advances Are Being Made in the Use of Deep Learning for MRI Interpretation. This Work Evaluates a Deep Learning Model based on a Residual Learning Convolutional Neural Network that Predicts TBI Severity from MR Images. the Model Achieved a High Sensitivity and Specificity on the Test Sample of Subjects with Varying Levels of TBI Severity. Six Outcome Measures Were Available on TBI Subjects at 6 and 12 Months. Group Comparisons of Outcomes between Subjects Correctly Classified by the Model with Subjects Misclassified Suggested that the Neural Network May Be Able to Identify Latent Predictive Information from the MR Images Not Incorporated in the Ground Truth Labels. the Residual Learning Model Shows Promise in the Classification of MR Images from Subjects with TBI

MODELING SLEEP AND WAKE BOUTS IN DROSOPHILA MELANOGASTER

Adequate sleep restores vital processes required for health and well-being; but the function and regulation of sleep is not well understood. Unfortunately, a definition of adequate sleep is unclear. On an hours-long timescale, consolidated and cycling sleep results in better health and performance outcomes. At shorter timescales, older studies report conflicting results regarding the relationship between sleep and wake bout durations. One approach to this problem has been to simply analyze the distribution of bout durations. While informative, this method eliminates the time relationship between bouts, which may be important. Here, we develop a model that describes the relationship between sleep and wake bout durations using the model organism, Drosophila melanogaster, which exhibits behavioral and molecular homology to human sleep. We present an exploratory analysis of the data to gain a better understanding of the sleep bout duration distribution by considering a broader range of potential distributions than considered in previous studies. We use the results of the distribution analysis to develop a model for sleep bout durations in the fly based upon their past sleep and wake history and find that this relationship should not be ignored

Enhancing Dimension-Reduced Scatter Plots with Class and Feature Centroids

Dimension reduction is increasingly applied to high-dimensional biomedical data to improve its interpretability. When datasets are reduced to two dimensions, each observation is assigned an x and y coordinates and is represented as a point on a scatter plot. A significant challenge lies in interpreting the meaning of the x and y axes due to the complexities inherent in dimension reduction. This study addresses this challenge by using the x and y coordinates derived from dimension reduction to calculate class and feature centroids, which can be overlaid onto the scatter plots. This method connects the low-dimension space to the original high-dimensional space. We illustrate the utility of this approach with data derived from the phenotypes of three neurogenetic diseases and demonstrate how the addition of class and feature centroids increases the interpretability of scatter plots.Comment: Submitted to 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ

A Kinetic Model for Blood Biomarker Levels after Mild Traumatic Brain Injury

Traumatic brain injury (TBI) imposes a significant economic and social burden. The diagnosis and prognosis of mild TBI, also called concussion, is challenging. Concussions are common among contact sport athletes. After a blow to the head, it is often difficult to determine who has had a concussion, who should be withheld from play, if a concussed athlete is ready to return to the field, and which concussed athlete will develop a post-concussion syndrome. Biomarkers can be detected in the cerebrospinal fluid and blood after traumatic brain injury and their levels may have prognostic value. Despite significant investigation, questions remain as to the trajectories of blood biomarker levels over time after mild TBI. Modeling the kinetic behavior of these biomarkers could be informative. We propose a one-compartment kinetic model for S100B, UCH-L1, NF-L, GFAP, and tau biomarker levels after mild TBI based on accepted pharmacokinetic models for oral drug absorption. We approximated model parameters using previously published studies. Since parameter estimates were approximate, we did uncertainty and sensitivity analyses. Using estimated kinetic parameters for each biomarker, we applied the model to an available post-concussion biomarker dataset of UCH-L1, GFAP, tau, and NF-L biomarkers levels. We have demonstrated the feasibility of modeling blood biomarker levels after mild TBI with a one compartment kinetic model. More work is needed to better establish model parameters and to understand the implications of the model for diagnostic use of these blood biomarkers for mild TBI