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Background The use of blood biomarkers after mild traumatic brain injury (mTBI) has been widely studied. We have identified eight unresolved issues related to the use of five commonly investigated blood biomarkers: neurofilament light chain, ubiquitin carboxy-terminal hydrolase-L1, tau, S100B, and glial acidic fibrillary protein. We conducted a focused literature review of unresolved issues in three areas: mode of entry into and exit from the blood, kinetics of blood biomarkers in the blood, and predictive capacity of the blood biomarkers after mTBI. Findings Although a disruption of the blood brain barrier has been demonstrated in mild and severe traumatic brain injury, biomarkers can enter the blood through pathways that do not require a breach in this barrier. A definitive accounting for the pathways that biomarkers follow from the brain to the blood after mTBI has not been performed. Although preliminary investigations of blood biomarkers kinetics after TBI are available, our current knowledge is incomplete and definitive studies are needed. Optimal sampling times for biomarkers after mTBI have not been established. Kinetic models of blood biomarkers can be informative, but more precise estimates of kinetic parameters are needed. Confounding factors for blood biomarker levels have been identified, but corrections for these factors are not routinely made. Little evidence has emerged to date to suggest that blood biomarker levels correlate with clinical measures of mTBI severity. The significance of elevated biomarker levels thirty or more days following mTBI is uncertain. Blood biomarkers have shown a modest but not definitive ability to distinguish concussed from non-concussed subjects, to detect sub-concussive hits to the head, and to predict recovery from mTBI. Blood biomarkers have performed best at distinguishing CT scan positive from CT scan negative subjects after mTBI.

We present a framework for an explainable and statistically validated ensemble clustering model applied to Traumatic Brain Injury (TBI). The objective of our analysis is to identify patient injury severity subgroups and key phenotypes that delineate these subgroups using varied clinical and computed tomography data. Explainable and statistically-validated models are essential because a data-driven identification of subgroups is an inherently multidisciplinary undertaking. In our case, this procedure yielded six distinct patient subgroups with respect to mechanism of injury, severity of presentation, anatomy, psychometric, and functional outcome. This framework for ensemble cluster analysis fully integrates statistical methods at several stages of analysis to enhance the quality and the explainability of results. This methodology is applicable to other clinical data sets that exhibit significant heterogeneity as well as other diverse data science applications in biomedicine and elsewhere.

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.

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.

Concussions, also known as mild traumatic brain injury (mTBI), are a growing health challenge. Approximately four million concussions are diagnosed annually in the United States. Concussion is a heterogeneous disorder in causation, symptoms, and outcome making precision medicine approaches to this disorder important. Persistent disabling symptoms sometimes delay recovery in a difficult to predict subset of mTBI patients. Despite abundant data, clinicians need better tools to assess and predict recovery. Data-driven decision support holds promise for accurate clinical prediction tools for mTBI due to its ability to identify hidden correlations in complex datasets. We apply a Locality-Sensitive Hashing model enhanced by varied statistical methods to cluster blood biomarker level trajectories acquired over multiple time points. Additional features derived from demographics, injury context, neurocognitive assessment, and postural stability assessment are extracted using an autoencoder to augment the model. The data, obtained from FITBIR, consisted of 301 concussed subjects (athletes and cadets). Clustering identified 11 different biomarker trajectories. Two of the trajectories (rising GFAP and rising NF-L) were associated with a greater risk of loss of consciousness or post-traumatic amnesia at onset. The ability to cluster blood biomarker trajectories enhances the possibilities for precision medicine approaches to mTBI.

Concussions represent a growing health concern that are challenging to diagnose and manage. Roughly four million concussions are diagnosed every year in the United States. While research in machine learning applications for concussions has focused on using advanced metrics such neuroimaging techniques and blood biomarkers, these metrics are yet to be implemented at a clinical level due to cost and reliability concerns. Therefore, concussion diagnosis is still reliant on clinical evaluations of symptoms, balance, and neurocognitive status and function. The lack of a universal threshold on these assessments makes the diagnosis process reliant on a physician’s interpretation of these assessment scores. This study aims to explore the use of machine learning techniques to aid the concussion diagnosis process. These models could provide an automated means to flag concussed patients even before being seen by a doctor as well as expand the scope of concussion diagnosis to remote locations and areas with limited access to doctors.

Objective: Neurologists lack a metric for measuring the distance between neurological patients. When neurological signs and symptoms are represented as neurological concepts from a hierarchical ontology and neurological patients are represented as sets of concepts, distances between patients can be represented as inter-set distances.Methods:We converted the neurological signs and symptoms from 721 published neurology cases into sets of concepts with corresponding machine-readable codes. We calculated inter-concept distances based a hierarchical ontology and we calculated inter-patient distances by semantic weighted bipartite matching. We evaluated the accuracy of a k-nearest neighbor classifier to allocate patients into 40 diagnostic classes.Results:Within a given diagnosis, mean patient distance differed by diagnosis, suggesting that across diagnoses there are differences in how similar patients are to other patients with the same diagnosis. The mean distance from one diagnosis to another diagnosis differed by diagnosis, suggesting that diagnoses differ in their proximity to other diagnoses. Utilizing a k-nearest neighbor classifier and inter-patient distances, the risk of misclassification differed by diagnosis.Conclusion:If signs and symptoms are converted to machine-readable codes and patients are represented as sets of these codes, patient distances can be computed as an inter-set distance. These patient distances given insights into how homogeneous patients are within a diagnosis (stereotypy), the distance between different diagnoses (proximity), and the risk of diagnosis misclassification (diagnostic error).

In this chapter, we discuss the popular machine learning (ML) and data processing tools and functions in MATLAB and Python. We review available functions and methods (particularly in MATLAB) to implement and demonstrate biomedical data analysis techniques. This is achieved by traversing the entire path of data analysis, from the initial loading and reading in of the raw data to advanced implementation of ML algorithms, and final phase of knowledge inference. We describe in detail the various phases of data processing and analysis. This includes loading, cleansing and imputation, preprocessing, dimensionality reduction, variables selection, clustering, classification, data visualization, and finally models' evaluation and validation. This chapter discusses the various tools and functions available in MATLAB and Python for implementing all these processes. The various examples discussed are intended to provide the reader a basic knowledge and understanding of the implementation of vital functions and modules for data analysis. This chapter is designed for those who are in beginner or intermediate levels of programming with MATLAB and/or Python. We recommend that readers who have never used MATLAB and Python to gain at least basic skills in programming with MATLAB or Python before reading this chapter. Section 9.4 of Chapter 9 discusses the various clustering and classification functions available in MATLAB and Python. Then, it shows the necessary evaluation and validation metrics and their implementation. This chapter also uses coded examples to discuss the necessary visualization functions and libraries in MATLAB and Python, respectively. The final section of this chapter was dedicated to the evaluation process for clustering and classification models. This chapter includes an online code repository that contains all the code written in the chapter plus additional examples and functions written in MATLAB and Python.

Environmental risks associated with child growth are complex, and intervention effectiveness has been consistently poor. To improve effectiveness, proper intervention points inside the comp...

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.

This work examines the validity of facial phenotypes as Autism Spectrum Disorders (ASD) biomarkers in boys with essential autism. A family-based association analysis framework is presented that uses previously identified facially-delineated (FD) clusters to examine relationship between FD clusters and known ASD genes. The hypothesis is that there are certain genetic variants, single nucleotide polymorphisms (SNP), specific to the FD clusters. Although statistical significance was not established, the results identified some candidate SNPs unique to each of the FD clusters that could indicate an underlying etiological difference. Further, recommendations are provided for larger-scale studies that could utilize the analysis framework presented.

Heterogeneity in Autism Spectrum Disorder (ASD) is complex including variability in behavioral phenotype as well as clinical, physiologic, and pathologic parameters. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) now diagnoses ASD using a 2-dimensional model based social communication deficits and fixated interests and repetitive behaviors. Sorting out heterogeneity is crucial for study of etiology, diagnosis, treatment and prognosis. In this paper, we present an ensemble model for analyzing ASD phenotypes using several machine learning techniques and a k-dimensional subspace clustering algorithm. Our ensemble also incorporates statistical methods at several stages of analysis. We apply this model to a sample of 208 probands drawn from the Simon Simplex Collection Missouri Site patients. The results provide useful evidence that is helpful in elucidating the phenotype complexity within ASD. Our model can be extended to other disorders that exhibit a diverse range of heterogeneity.

Controversy remains regarding the neurotoxicity of clade C human immunodeficiency virus (HIV-C). When examined in preclinical studies, a cysteine to serine substitution in the C31 dicysteine motif of the HIV-C Tat protein (C31S) results in less severe brain injury compared to other viral clades. By contrast, patient cohort studies identify significant neuropsychological impairment among HIV-C individuals independent of Tat variability. The present study clarified this discrepancy by examining neuroimaging markers of brain integrity among HIV-C individuals with and without the Tat substitution. Thirty-seven HIV-C individuals with the Tat C31S substitution, 109 HIV-C individuals without the Tat substitution (C31C), and 34 HIV− controls underwent 3T structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). Volumes were determined for the caudate, putamen, thalamus, corpus callosum, total gray matter, and total white matter. DTI metrics included fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity (AD). Tracts of interest included the anterior thalamic radiation (ATR), cingulum bundle (CING), uncinate fasciculus (UNC), and corpus callosum (CC). HIV+ individuals exhibited smaller volumes in subcortical gray matter, total gray matter and total white matter compared to HIV− controls. HIV+ individuals also exhibited DTI abnormalities across multiple tracts compared to HIV− controls. By contrast, neither volumetric nor diffusion indices differed significantly between the Tat C31S and C31C groups. Tat C31S status is not a sufficient biomarker of HIV-related brain integrity in patient populations. Clinical attention directed at brain health is warranted for all HIV+ individuals, independent of Tat C31S or clade C status.

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.

We develop a theoretical model from an energetic viewpoint for unraveling the entangled effects of metabolic and biosynthetic rates on oxidative cellular damage accumulation during animal's growth, and test the model by experiments in hornworms. The theoretical consideration suggests that most of the cellular damages caused by the oxidative metabolism can be repaired by the efficient maintenance mechanisms, if the energy required by repair is unlimited. However, during growth a considerable amount of energy is allocated to the biosynthesis, which entails tradeoffs with the requirements of repair. Thus, the model predicts that cellular damage is more influenced by the biosynthetic rate than the metabolic rate. To test the prediction, we induced broad variations in metabolic and biosynthetic rates in hornworms, and assayed the lipid peroxidation and protein carbonyl. We found that the increase in the cellular damage was mainly caused by the increase in biosynthetic rate, and the variations in metabolic rate had negligible effect. The oxidative stress hypothesis of aging suggests that high metabolism leads to high cellular damage and short lifespan. However, some empirical studies showed that varying biosynthetic rate, rather than metabolic rate, changes animal's lifespan. The conflicts between the empirical evidence and the hypothesis are reconciled by this study.

Academic dishonesty, including cheating and plagiarism, is on the rise in colleges, particularly among engineering students. While students decide to engage in these behaviors for many different reasons, academic integrity training can help improve their understanding of ethical decision making. The two studies outlined in this paper assess the effectiveness of an online module in increasing academic integrity among first semester engineering students. Study 1 tested the effectiveness of an academic honesty tutorial by using a between groups design with a Time 1- and Time 2-test. An academic honesty quiz assessed participants' knowledge at both time points. Study 2, which incorporated an improved version of the module and quiz, utilized a between groups design with three assessment time points. The additional Time 3-test allowed researchers to test for retention of information. Results were analyzed using ANCOVA and t tests. In Study 1, the experimental group exhibited significant improvement on the plagiarism items, but not the total score. However, at Time 2 there was no significant difference between groups after controlling for Time 1 scores. In Study 2, between- and within-group analyses suggest there was a significant improvement in total scores, but not plagiarism scores, after exposure to the tutorial. Overall, the academic integrity module impacted participants as evidenced by changes in total score and on specific plagiarism items. Although future implementation of the tutorial and quiz would benefit from modifications to reduce ceiling effects and improve assessment of knowledge, the results suggest such tutorial may be one valuable element in a systems approach to improving the academic integrity of engineering students.

Agrobacterium-mediated transformation is the most widely used technique for generating transgenic plants. However, many crops remain recalcitrant. We found that an Arabidopsis myb family transcription factor (MTF1) inhibited plant transformation susceptibility. Mutating MTF1 increased attachment of several Agrobacterium strains to roots and increased both stable and transient transformation in both susceptible and transformation-resistant Arabidopsis ecotypes. Cytokinins from Agrobacterium tumefaciens decreased the expression of MTF1 through activation of the cytokinin response regulator ARR3. Mutating AHK3 and AHK4, genes that encode cytokinin-responsive kinases, increased the expression of MTF1 and impaired plant transformation. Mutant mtf1 plants also had increased expression of AT14A, which encodes a putative transmembrane receptor for cell adhesion molecules. Plants overexpressing AT14A exhibited increased susceptibility to transformation, whereas at14a mutant plants exhibited decreased attachment of bacteria to roots and decreased transformation, suggesting that AT14A may serve as an anchor point for Agrobacteria. Thus, by promoting bacterial attachment and transformation of resistant plants and increasing such processes in susceptible plants, treating roots with cytokinins may help engineer crops with improved features or yield.

DNA methylation is an epigenetic modification that plays an important role in many biological processes. To better understand this epigenetic mechanism, genome-wide investigations of DNA methylation can be conducted via tiling array technology. In such DNA methylation profiling studies, statistical methods are employed to estimate the DNA methylation status of each probe represented on the tiling array. Although many of the current methods account for the inherent dependency between neighboring probes, they do not effectively utilize the wealth of information available in genomic annotation databases. Since previous studies indicate that DNA methylation patterns of some organisms differ by genomic element (e.g., gene, intergenic region), such genomic annotation information may be useful in predicting DNA methylation status. In this work, a novel statistical model is proposed within the hidden Markov model framework to incorporate genomic annotation information in the prediction of DNA methylation status. The advantages of incorporating genomic annotation via the proposed method are investigated through a simulation study and real data applications.

Callipyge sheep exhibit extreme postnatal muscle hypertrophy in the loin and hindquarters as a result of a single nucleotide polymorphism (SNP) in the imprinted DLK1-DIO3 domain on ovine chromosome 18. The callipyge SNP up-regulates the expression of surrounding transcripts when inherited in cis without altering their allele-specific imprinting status. The callipyge phenotype exhibits polar overdominant inheritance since only paternal heterozygous animals have muscle hypertrophy. Two studies were conducted profiling gene expression in lamb muscles to determine the down-stream effects of over-expression of paternal allele-specific DLK1 and RTL1 as well as maternal allele-specific MEG3, RTL1AS and MEG8, using Affymetrix bovine expression arrays. A total of 375 transcripts were differentially expressed in callipyge muscle and 25 transcripts were subsequently validated by quantitative PCR. The muscle-specific expression patterns of most genes were similar to DLK1 and included genes that are transcriptional repressors or affect feedback mechanisms in beta-adrenergic and growth factor signaling pathways. One gene, phosphodiesterase 7A had an expression pattern similar to RTL1 expression indicating a biological activity for RTL1 in muscle. Only transcripts that localize to the DLK1-DIO3 domain were affected by inheritance of a maternal callipyge allele. Callipyge sheep are a unique model to study over expression of both paternal allele-specific genes and maternal allele-specific non-coding RNA with an accessible and nonlethal phenotype. This study has identified a number of genes that are regulated by DLK1 and RTL1 expression and exert control on postnatal skeletal muscle growth. The genes identified in this model are primary candidates for naturally regulating postnatal muscle growth in all meat animal species, and may serve as targets to ameliorate muscle atrophy conditions including myopathic diseases and age-related sarcopenia.

The callipyge mutation causes postnatal muscle hypertrophy in heterozygous lambs that inherit a paternal callipyge allele (+/CLPG). Our hypothesis was that the up-regulation of one or both of the affected paternally expressed genes (DLK1 or PEG11) initiates changes in biochemical and physiological pathways in skeletal muscle to induce hypertrophy. The goal of this study was to identify changes in gene expression during the onset of muscle hypertrophy to identify the pathways that are involved in the expression of the callipyge phenotype. Gene expression was analysed in longissimus dorsi total RNA from lambs at 10, 20, and 30 days of age using the Affymetrix Bovine Expression Array. An average of 40.6% of probe sets on the array was detected in sheep muscle. Data were normalized and analysed using a two-way anova for genotype and age effects with a false discovery rate of 0.10. From the anova, 13 genes were significant for the effect of genotype and 13 were significant for effect of age (P0.10). No significant age-by-genotype interactions were detected (P0.10). Of the 13 genes indicating an effect of genotype, quantitative PCR assays were developed for all of them and tested on a larger group of animals from 10 to 200 days of age. Nine genes had significantly elevated transcript levels in callipyge lambs. These genes included phosphofructokinase, a putative methyltransferase protein, a cAMP phosphodiesterase, and the transcription factor DNTTIP1.

Fertilization is followed by complex changes in cytoplasmic composition and extensive chromatin reprogramming which results in the abundant activation of totipotent embryonic genome at embryonic genome activation (EGA). While chromatin reprogramming has been widely studied in several species, only a handful of reports characterize changing transcriptome profiles and resulting metabolic changes in cleavage stage embryos. The aims of the current study were to investigate RNA profiles of in vivo developed (ivv) and in vitro produced (ivt) porcine embryos before (2-cell stage) and after (late 4-cell stage) EGA and determine major metabolic changes that regulate totipotency. The period before EGA was dominated by transcripts responsible for cell cycle regulation, mitosis, RNA translation and processing (including ribosomal machinery), protein catabolism, and chromatin remodelling. Following EGA an increase in the abundance of transcripts involved in transcription, translation, DNA metabolism, histone and chromatin modification, as well as protein catabolism was detected. The further analysis of members of overlapping GO terms revealed that despite that comparable cellular processes are taking place before and after EGA (RNA splicing, protein catabolism), different metabolic pathways are involved. This strongly suggests that a complex metabolic switch accompanies EGA. In vitro conditions significantly altered RNA profiles before EGA, and the character of these changes indicates that they originate from oocyte and are imposed either before oocyte aspiration or during in vitro maturation. IVT embryos have altered content of apoptotic factors, cell cycle regulation factors and spindle components, and transcription factors, which all may contribute to reduced developmental competence of embryos produced in vitro. Overall, our data are in good accordance with previously published, genome-wide profiling data in other species. Moreover, comparison with mouse and human embryos showed striking overlap in functional annotation of transcripts during the EGA, suggesting conserved basic mechanisms regulating establishment of totipotency in mammalian development.