Engineering – Page 16 – UROP Spring Symposium 2021

Engineering

Multidomain protein analogous templates detection based on TM-align

Protein structure prediction is a crucial step to understanding and transforming biological and cellular functions. Most proteins exist with multiple domains in cells for cooperative functionality. However, due to the technical difficulties in structural biology, most of the multidomain proteins have only single domain structures solved. To guide the multidomain protein modeling, we present a two-step procedure method to detect the analogous templates from the multidomain protein structure library which includes the multidomain proteins with known full-length structures through the structural alignment. In the first step, individual domains are used to evaluate each template by TM-align, regardless of the overlap between the alignments of different domains, and the average TM-score of all domains is calculated as the local score of a template. In the second step, the top 500 templates selected from the first step are evaluated by the TM-align again with no overlap allowed in the alignments of different domains, and the average TM-score is defined as the global score of a template. Finally, the template with the best global score is selected as the best template. We test the method over 2,269 non-redundant proteins with 2 domains. With homologous templates with sequence identity >30% to the targets excluded, the results indicated that >80% of target proteins have at least 1 template with a TM-score >0.5 and alignment coverage >90%. The data demonstrate that most interdomain orientations can be inferred from the template library, which probably can be used to assist the multidomain protein structure assembly from the independently determined/predicted domain models.

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Developing fast and unbiased computer vision algorithms

Computer Vision Algorithms are a fast-developing technology, and we have seen from example that they are currently not as accurate and unbiased as we hope they can be. Our project aims to develop a more efficient system and algorithm to reduce bias in computer vision programs. One way bias may be introduced into these algorithms is through issues with light levels in images and videos. Since many computer vision algorithms rely on video cameras, as opposed to infrared or another type of light, the lack of light in videos introduces uncertainty in a program, which can produce bias, where some categories of images are more accurately processed by the algorithm than others. This bias can manifest itself in different scenarios, such as during nighttime or when recording people with darker skin, and these are the biases that we aim to correct. My part in the project involved labelling the videos that are going to be used for analysis for the algorithms, and attempting to help create a standardized method of labelling in order to have a set of videos with which the algorithm can be trained with. Our sample set was purposefully selected to have a variety of videos with different light levels and skin tones. Our ultimate purpose was to label as many videos as possible to use later on in the project, where other groups are working on developing the algorithm and all other overarching parts of the project. The main project was not completed, and likely will not for some years, but we achieved our loose goal of labelling videos.

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Identifying Brain Edema in CT Scans Using Machine Learning

Brain edema is the swelling of the brain as a result of traumatic brain injuries, strokes, tumors, and infections. This affects the patients cognitive and motor function and can lead to lasting adverse health risks and death. Early and accurate identification of edema can prevent these hazards. Studies have found that brain edema is difficult for clinicians to accurately identify, as it often blends in with other brain matter. Additionally finding a link between the volume of edema and the effect on the patient is considered valuable, but there is currently no standard software in place for this end. Even when clinicians are able to identify edema, they are not able to quantify the volume present. Convolutional neural networks were used for training the model to segment the edema region. Images from the PROTECT III collection at the University of Michigan hospital were used for this research. Some images were previously annotated by clinicians and these images were subsequently used in the process of training the machine learning model. The performance of the model was evaluated using quantitative techniques such as dice, sensitivity, specificity, accuracy, and AUC. The goal of this software is to decrease adverse effects and death related to brain edema by creating a system to quantitatively measure edema and make informed decisions on how to treat the patient based on the information collected.

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Examining Kinematic Differences of Vestibular Gait

The vestibular system is a sensory system found in the inner ear that provides the brain with information about motion and spatial orientation. It helps maintain balance, stabilize the head/body during movement, and maintain good posture. If the vestibular system is impaired, a patient may experience dizziness, imbalance, and difficulty walking. Vestibular disorders are difficult to diagnose as they can stem from a variety of underlying conditions and require specialized equipment to ascertain a diagnosis (e.g., dynamic posturography, rotational chair). The long-term goal of this study is to use kinematic data to develop gait assessment technology that can automatically identify individuals with vestibular disorders. In this project, we used motion capture technology (MOCAP) to collect and analyze full-body kinematic data from subjects with vestibular issues and healthy controls to better understand kinematic differences between healthy and vestibular gait. Twenty-one subjects (11 with vestibular deficits and 10 healthy) were recruited to participate in the walking study. The subjects performed a series of tasks that required them to walk with varying speed and constraints (eyes closed, walking backwards, etc.) while wearing a set of full-body MOCAP markers. The data collected was then used to compute descriptive gait metrics, including step length, step time, and head/trunk rotations, using a motion-analysis software called Visual3D. Inferential statistic tests (t-tests) were then performed to compare the various metrics of healthy individuals with subjects with vestibular deficits. Conclusions have not yet been drawn as the study is still in the data processing phase. However, we plan to leverage gait metrics that show statistically significant differences between the two groups in order to develop automatic vestibular gait detection algorithms.

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The relationship between body shape and air-breathing organ morphology in Anabantarian fishes as revealed by morphometrics

The Anabantarians are a diverse group of tropical, freshwater fishes which use air-breathing organs (ABO) within their skulls to live in harsh environments (e.g., warm, hypoxic pools). Space is limited within the skull however, suggesting that evolutionary changes to ABO size and shape can influence neighboring skeletal structures, a concept called morphological integration. We conducted this study to determine if there is a correlation between the modularity of fishes and the presence of an Air-Breathing Organ (ABO). The discovery of how the introduction of a new structure within a limited space, such as the skull, affects the relationship between neighboring structures, can help the science community better understand what affects integration between structures. While conducting this study we examined all Anabantarian families (e.g., Anabantidae, Osphronemidae, Badidae). We hypothesized that the presence of an ABO would create a more integrated cranial skeleton, and as a result decrease the modularity of the fish overall. To test this, fish skeletons were visualized through x-ray imaging, and aspects of fish phenotype were measured using linear morphometrics. We compared body shape metrics that correspond to aspect ratio, cranial elongation, and post-cranial elongation among fishes with and without ABOs. Our findings demonstrated that air-breathing organs have evolved 2-3 times, independently across anabantarians, and although fishes with ABOs are more diverse than fishes without, these findings were not statistically significant. Relatedly, there were no significant differences in body shape metrics among air-breathing and non- air-breathing fishes; however, the body shapes of fishes with ABOs evolve as much as 3.5 times faster than those fishes without.

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