GOurmet: A tool for quantitative comparison and visualization of gene expression profiles based on gene ontology (GO) distributions

BACKGROUND: The ever-expanding population of gene expression profiles (EPs) from specified cells and tissues under a variety of experimental conditions is an important but difficult resource for investigators to utilize effectively. Software tools have been recently developed to use the distribution of gene ontology (GO) terms associated with the genes in an EP to identify specific biological functions or processes that are over- or under-represented in that EP relative to other EPs. Additionally, it is possible to use the distribution of GO terms inherent to each EP to relate that EP as a whole to other EPs. Because GO term annotation is organized in a tree-like cascade of variable granularity, this approach allows the user to relate (e.g., by hierarchical clustering) EPs of varying length and from different platforms (e.g., GeneChip, SAGE, EST library). RESULTS: Here we present GOurmet, a software package that calculates the distribution of GO terms represented by the genes in an individual expression profile (EP), clusters multiple EPs based on these integrated GO term distributions, and provides users several tools to visualize and compare EPs. GOurmet is particularly useful in meta-analysis to examine EPs of specified cell types (e.g., tissue-specific stem cells) that are obtained through different experimental procedures. GOurmet also introduces a new tool, the Targetoid plot, which allows users to dynamically render the multi-dimensional relationships among individual elements in any clustering analysis. The Targetoid plotting tool allows users to select any element as the center of the plot, and the program will then represent all other elements in the cluster as a function of similarity to the selected central element. CONCLUSION: GOurmet is a user-friendly, GUI-based software package that greatly facilitates analysis of results generated by multiple EPs. The clustering analysis features a dynamic targetoid plot that is generalizable for use with any clustering application

An Analysis of Expressed Cheating Behaviors in Video Games

A series of 50 responses regarding reasons for cheating behavior in video games were provided by undergraduate students. These responses were sorted into a series of 13 categories by raters to investigate the most common reasons provided for cheating. An analysis of inter-rater agreement as well as frequency of category representation is provided. The most common outcomes were that players cheat to progress in a game as well as to gain advantage over others. The discussion compared this study’s results to an existing cheating taxonomy

Ethical Perceptions and Actions in Gaming

The present study explored how individuals perceive actions in gaming that contain ethical components, whether they have ever engaged in those behaviors and how judgments of ethical actions in gaming relate to participant personality. Participants completed a 16-item survey, which measured their perception of the ethics of gaming behaviors, such as buying a hack or lying to another player. Participants were also asked to indicate for each item whether or not they had ever engaged in that behavior. Results indicated that participants were able to judge the ethical level of different gaming behaviors with lying to other players and unauthorized access to servers being rated as most unethical. Furthermore, self-reports of engagement in unethical activities were fairly low. When ethical rating and action scores were correlated with personality characteristics using the Cattell 16PF1, the only correlation to reach significance showed that participants higher in rule consciousness rated the ethical gaming questions as more unethical overall than their less rule-conscious peers. Given the extent and popularity of gaming in today’s world, it is important to understand how individuals perceive the gaming culture. One aspect of this culture that merits further examination is ethical behavior in gaming

Modeling Human Gaming Playing Behavior and Reward/Penalty Mechanism using Discrete Event Simulation (DES)

Humans are remarkably complex and unpredictable; however, while predicting human behavior can be problematic, there are methods such as modeling and simulation that can be used to predict probable futures of human decisions. The present study analyzes the possibility of replacing human subjects with data resulting from pure models. Decisions made by college students in a multi-level mystery-solving game under 3 different gaming conditions are compared with the data collected from a predictive sequential Markov-Decision Process model. In addition, differences in participants’ data influenced by the three different conditions (additive, subtractive, control) were analyzed. The test results strongly suggest that the data gathered from the model can possibly represent the ones gathered from the human participants in a practical experiment

Using a smartphone application to promote healthy dietary behaviours and local food consumption

Smartphone “apps” are a powerful tool for public health promotion, but unidimensional interventions have been ineffective at sustaining behavioural change. Various logistical issues exist in successful app development for health intervention programs and for sustaining behavioural change. This study reports on a smartphone application and messaging service, called “SmartAPPetite,” which uses validated behaviour change techniques and a behavioural economic approach to “nudge” users into healthy dietary behaviours. To help gauge participation in and influence of the program, data were collected using an upfront food survey, message uptake tracking, experience sampling interviews, and a follow-up survey. Logistical and content-based issues in the deployment of the messaging service were subsequently addressed to strengthen the effectiveness of the app in changing dietary behaviours. Challenges included creating relevant food goal categories for participants, providing messaging appropriate to self-reported food literacy and ensuring continued participation in the program. SmartAPPetite was effective at creating a sense of improved awareness and consumption of healthy foods, as well as drawing people to local food vendors with greater frequency. This work serves as a storehouse of methods and best practices for multidimensional local food-based smartphone interventions aimed at improving the “triple bottom line” of health, economy, and environment

Frequency Tracking and Parameter Estimation for Robust Quantum State-Estimation

In this paper we consider the problem of tracking the state of a quantum system via a continuous measurement. If the system Hamiltonian is known precisely, this merely requires integrating the appropriate stochastic master equation. However, even a small error in the assumed Hamiltonian can render this approach useless. The natural answer to this problem is to include the parameters of the Hamiltonian as part of the estimation problem, and the full Bayesian solution to this task provides a state-estimate that is robust against uncertainties. However, this approach requires considerable computational overhead. Here we consider a single qubit in which the Hamiltonian contains a single unknown parameter. We show that classical frequency estimation techniques greatly reduce the computational overhead associated with Bayesian estimation and provide accurate estimates for the qubit frequencyComment: 6 figures, 13 page

Naturalistic driving measures of route selection associate with resting state networks in older adults

Our objective was to identify functional brain changes that associate with driving behaviors in older adults. Within a cohort of 64 cognitively normal adults (age 60+), we compared naturalistic driving behavior with resting state functional connectivity using machine learning. Functional networks associated with the ability to interpret and respond to external sensory stimuli and the ability to multi-task were associated with measures of route selection. Maintenance of these networks may be important for continued preservation of driving abilities

Focal adhesion kinase is essential for cardiac looping and multichamber heart formation

Focal adhesion kinase (FAK) is a critical mediator of matrix- and growth factor-induced signaling during development. Myocyte-restricted FAK deletion in mid-gestation mice results in impaired ventricular septation and cardiac compaction. However, whether FAK regulates early cardiogenic steps remains unknown. To explore a role for FAK in multi-chambered heart formation, we utilized anti-sense morpholinos to deplete FAK in Xenopus laevis. Xenopus FAK morphants exhibited impaired cardiogenesis, pronounced pericardial edema, and lethality by tadpole stages. Spatial-temporal assessment of cardiac marker gene expression revealed that FAK was not necessary for midline migration, differentiation, fusion of cardiac precursors, or linear heart tube formation. However, myocyte proliferation was significantly reduced in FAK morphant heart tubes and these tubes failed to undergo proper looping morphogenesis. Collectively our data imply that FAK plays an essential role in chamber outgrowth and looping morphogenesis likely stimulated by fibroblast growth factors (and possibly other) cardiotrophic factors

Adhesion Stimulates Direct PAK1/ERK2 Association and Leads to ERK-dependent PAK1 Thr 212 Phosphorylation

The Rac1/Cdc42 effector p21-activated kinase (PAK) is activated by various signaling cascades including receptor-tyrosine kinases and integrins and regulates a number of processes such as cell proliferation and motility. PAK activity has been shown to be required for maximal activation of the canonical Ras/Raf/MEK/ERK Map kinase signaling cascade, likely because of PAK co-activation of Raf and MEK. Herein, we found that adhesion signaling also stimulates an association between PAK1 and ERK1/2. PAK1 and ERK1/2 co-immunoprecipitated from rat aortic smooth muscle cells (SMC) plated on fibronectin, and the two proteins co-localized in membrane ruffles and adhesion complexes following PDGF-BB or sphingosine 1-phosphate treatment, respectively. Far Western analysis demonstrated a direct association between the two proteins, and peptide mapping identified an ERK2 binding site within the autoinhibitory domain of PAK1. Interestingly, deletion of a major ERK binding site in PAK attenuates activation of an ERK-dependent serum-responsive element (SRE)-luciferase reporter gene, indicating that association between PAK and ERK is required to facilitate ERK signaling. We also show that ERK2 phosphorylates PAK1 on Thr(212) in vitro and that Thr(212) is phosphorylated in smooth muscle cells following PDGF-BB treatment in an adhesion- and MEK/ERK-dependent fashion. Expression of a phosphomimic variant, PAK-T212E, does not alter ERK association, but markedly attenuates downstream ERK signaling. Taken together, these data suggest that PAK1 may facilitate ERK signaling by serving as a scaffold to recruit Raf, MEK, and ERK to adhesion complexes, and that subsequent growth factor-stimulated phosphorylation of PAK-Thr(212) by ERK may serve to provide a negative feedback signal to control coordinate activation of ERK by growth factor- and matrix-induced signals

Medication and road test performance among cognitively healthy older adults

IMPORTANCE: Older adults are increasingly prescribed medications that have adverse effects. Prior studies have found a higher risk of motor vehicle crashes to be associated with certain medication use. OBJECTIVE: To determine whether specific medication classes were associated with performance decline as assessed by a standardized road test in a community sample of cognitively healthy older adults, to evaluate additional associations of poor road test performance with comorbid medical conditions and demographic characteristics, and to test the hypothesis that specific medication classes (ie, antidepressants, benzodiazepines, sedatives or hypnotics, anticholinergics, antihistamines, and nonsteroidal anti-inflammatory drugs or acetaminophen) would be associated with an increase in risk of impaired driving performance over time. DESIGN, SETTING, AND PARTICIPANTS: This was a prospective cohort study of 198 cognitively healthy adults 65 years and older with a valid driver\u27s license who were followed up annually, with rolling enrollment. Data were collected from participants in St Louis, Missouri, and neighboring Illinois who were enrolled in the Knight Alzheimer\u27s Disease Research Center. Data were collected from August 28, 2012, to March 14, 2023, and analyzed from April 1 to 25, 2023. Participants with healthy cognition, defined as a Clinical Dementia Rating score of 0 at baseline and subsequent visits, who had available clinical, neuropsychological, road tests, and self-reported medication data were included. EXPOSURE: Potentially driver-impairing medication use. MAIN OUTCOMES AND MEASURES: The primary outcome measure was performance on the Washington University Road Test (pass or marginal/fail). Multivariable Cox proportional hazards models were used to evaluate associations between potentially driver-impairing medication use and road test performance. RESULTS: Of the 198 included adults (mean [SD] baseline age, 72.6 [4.6] years; 87 female [43.9%]), 70 (35%) received a marginal/fail rating on the road test over a mean (SD) follow-up of 5.70 (2.45) years. Any use of antidepressants (adjusted hazard ratio [aHR], 2.68; 95% CI, 1.69-4.71), serotonin and norepinephrine reuptake inhibitors (aHR, 2.68; 95% CI, 1.54-4.64), sedatives or hypnotics (aHR, 2.70; 95% CI, 1.40-5.19), or nonsteroidal anti-inflammatory drugs (aHR, 2.72; 95% CI, 1.31-5.63) was associated with an increase in risk of receiving a marginal/fail rating on the road test compared with control individuals. Conversely, participants taking lipid-lowering agents had a lower risk of receiving a marginal/fail rating compared to control individuals. There were no statistically significant associations found between anticholinergic or antihistamines and poor performance. CONCLUSIONS AND RELEVANCE: In this prospective cohort study, specific medication classes were associated with an increase in risk of poor road test performance over time. Clinicians should consider this information and counsel patients accordingly when prescribing these medications