This study addresses the escalating concern over the interaction of multiple respiratory viruses by introducing a mathematical model to analyze triple infection dynamics involving influenza (IAV), respiratory syncytial virus (RSV), and SARS-CoV-2. With the ongoing COVID-19 pandemic and the resurgence of RSV, understanding the dynamics of triple infections is critical for public health preparedness. Comprehending the interactions among these viruses is crucial for improving our capacity to forecast and curb disease outbreaks. The central question addressed in this study is how variations in infection rates influence the duration and maximum population size of each virus in a triple infection scenario. Prior research has explored coinfections involving two respiratory viruses, yet triple infections, especially among adults, remain infrequent and poorly elucidated. The urgency to address these questions arises from the potential for overwhelming hospitals and exacerbating disease burden, especially in vulnerable populations. By developing a mathematical model to analyze triple infections, this research aims to provide insights that can inform public health strategies and mitigate the impact of respiratory virus outbreaks. Through extensive simulations, the study evaluates how variations in infection rates influence the duration and maximum population size of each virus. The findings unveil intriguing patterns: while SARS-CoV-2 demonstrates remarkable resilience across various infection rates, influenza and RSV display more nuanced responses, exhibiting sensitivity to changes in transmission rates.

Graphene Quantum Dots (GQDs), with their outstanding optoelectronic, chemical, and bio-compatible properties serve as versatile materials for various imaging applications. Intriguing optical properties at ultralow cryogenic temperatures have been observed in other carbon-based nanomaterials suggesting a potential for similar behavior in GQDs. This study explores GQD fluorescence across the visible and near-infrared spectral regions at temperatures ranging from ambient (300 K) down to cryogenic (76K) via experimental measurements supported by complementary DFT calculations. Our findings demonstrate a decreasing linear relationship between integrated density and temperature making GQDs a viable candidate for applications in low-temperature imaging.

Graphene quantum dots (GQDs) represent the forefront of contemporary research within the domain of biophysics. Known for their innumerable applications, these nanoparticles have remarkable functionalities in cellular imaging and drug delivery applications. In our research, we combine NGQDs (Nitrogen-doped GQDs) with the ligand L2 to create a drug delivery system for L2, an anti-Alzheimer’s drug. L2 faces challenges in traversing the blood-brain barrier (BBB) due to its inherent properties. However, the BBB is permeable to NGQDs due to their small size. Hence, we are using NGQDs as a vehicle to facilitate the transport of L2 across the BBB. Furthermore, the intrinsic fluorescence of NGQDs within the body enables us to safely monitor and track the hybrid system, ensuring its successful delivery to the targeted organ – the brain.

Motor preparation deficits are a feature of many neurological disorders in humans. Developing animal models to study deficits in motor preparation is essential to explore the potential therapeutic treatment avenues. Preparation for motor actions involves neural pathways that facilitate neuromuscular planning. In male zebra finches (Taeniopygia guttata), song production is controlled by a system of brain areas that are analogous to those areas in humans controlling human speech and language. Neural activity in many of these brain areas precedes the execution of song motor sequences. We aimed to differentiate preparatory motor periods from song execution by interrupting female-directed and song produced in isolation (undirected song) with white noise playback. The amplitude and duration of the white noise was systematically varied along with the timing of playback occurring during song. Song respiratory motor gestures were measured in six birds experiencing the noise disruption events. Transition entropy analyses revealed increased variability in syllable progression in the presence of playback compared to undirected song without playback (control condition). There were significant differences in the frequencies of interruption, continuation, and termination for each syllable of directed and undirected song with playback compared to baseline (Χ2, ps < .05). We observed higher rates of interruption and motif disruption during introductory notes and early syllables as well as later syllables within a song motif. Ascertaining patterns of stereotypy and variability in zebra finch vocalizations can elucidate deficits in motor planning that contribute to autism spectrum disorder, speech apraxia, and other clinical disorders.

Existing literature has highlighted the competing roles of each family dynamics, the patterns of interactions among relatives, and discrimination on the self-esteem and occupational performance of African American and Caucasian populations individually. However, no research to date has examined whether these factors are interconnected. In this study, we aimed to assess the competing roles of family dynamics and experiences of discrimination across a cross-sectional survey. In the study, we surveyed a sample of 178 White and Black participants from Amazon Mechanical Turk (MTurk). Simple mediation models indicated that higher levels of discrimination were associated with lower levels of self-esteem, self-efficacy, and resilience. Additionally, more positive familial relationships were associated with better outcomes on these measures for White participants alone. Furthermore, self-esteem was positively related to better self-reported occupational performance. That is, our results suggest that perceived discrimination is associated with lower self-esteem, which is then subsequently related to poorer occupational performance. Conversely, the results suggest the opposite pattern for family dynamics, with more positive familial relationships being associated with higher levels of self-esteem among White participants, which is, in turn, associated with better self-reported occupational performance. These findings emphasize the negative role of discrimination in fostering personal well-being and success in occupational settings.

Throughout our evolutionary history, people have used social connections to enhance survival and reproduction, and consequently, the loss of these connections is very detrimental. Existing evidence has found that we have evolved sensitive neural and perceptual systems that detect and respond to threats of ostracism. This experience of distress when viewing another person being ostracized is referred to as vicarious ostracism. Recent work leans on the evolutionary perspective that being excluded by an ingroup member is costlier than being ostracized by an outgroup member. However, this research has produced somewhat conflicting findings, one that observing ostracism of ingroup members by outgroup members is more painful, while others have found the reverse. Further, little research has examined how experiences of vicarious ostracism influence the perceptions of and interactions with targets of ostracism. Thus, the present study aims to analyze the roles of group membership on an individual’s perceptions and observations of vicarious ostracism experiences, and also their thoughts of the person being ostracized. We hypothesized that observing an ingroup member being ostracized by an outgroup member would be seen as more prejudicial than an outgroup member being ostracized by ingroup members, which would, in turn, be related to a more positive view of the ingroup target that was ostracized.
Across two studies sampling Caucasian undergraduates (Study 1) and both Caucasian and African American adults via Amazon Mechanical Turk (MTurk; Study 2), participants were presented with group-based ostracism vignettes, in which they read about a racial ingroup or outgroup member being ostracized by members of the ingroup or outgroup.
Then, participants completed self-report questionnaires assessing the degree to which they viewed the depicted ostracism as being attributable to prejudice by the sources, as well as their impression of the ostracism target. In both studies, regardless of race, we found that participants in the intergroup black condition viewed ostracism as more prejudicial and liked the target more than people who saw the ostracism in the intergroup white condition.
Further, we found that prejudicial attributions mediated the relationship between the depicted ostracism and participants’ impression of the target, such that when people viewed a black target being excluded by white sources, they rated the ostracism as more prejudicial, which in turn is associated with liking the target more.
Overall, these findings were opposite of our hypotheses and instead suggest that our evolved cognitions for processing experiences of group-based vicarious ostracism may be influenced by contemporary social norms and group dynamics. The implications of this research for understanding the ecological factors that influence behavioral and perceptual outcomes associated with vicarious ostracism, particularly within the context of intergroup conflict, will be discussed.

Vagus Nerve Stimulation (VNS) research and clinical use demonstrate its efficacy in treating epilepsy, depression, migraines, and its potential to one day mitigate inflammation and improve working memory. However, variations in treatment outcomes indicate a need for further exploration into the individual factors that influence VNS sensitivity and responsiveness. The study aims to investigate how various individual factors like sex, body awareness, preterm birth, childhood socioeconomic status, childhood unpredictability, and adverse childhood experiences relate to sensitivity to VNS. A survey will administered to measure person-based variables of interest. Participants will undergo a VNS procedure involving a heart rate variability (HRV) recording during a baseline, stimulation, and recovery period. Some of our expected result are, individuals who were born prematurely will have higher sensitivity to VNS, and individuals who experienced more childhood stress will have lower sensitivity to VNS. The results of this study will improve future VNS clinical treatment and inform future VNS research pursuits.

It is commonly assumed that achieving financial success will remove many of the burdens associated with low socioeconomic status (SES). However, recent research suggests that individuals who transition from low to high SES environments are at even greater risk for poor health outcomes compared to those who remain in stably high SES environments and compared to those who remain in stably low SES environments. Although the underlying cause of these health outcomes are currently unknown, evidence from qualitative interviews of individuals experiencing upward mobility finds unique psychological stressors associated with transitioning from low to high SES. The current research was designed to experimentally examine the effect of upward mobility and childhood SES on psychological stress. Participants were randomly assigned to write about their life at a significantly higher SES than their childhood SES (upward mobility condition), or at the same SES as their childhood (no mobility condition) before reporting their psychological stress. Two studies were conducted in order to examine the effect of upward mobility in 1) a sample of undergraduate students and 2) to compare the effects of upward mobility in a sample of adults currently enrolled in college and never enrolled in college. Results revealed elevated psychological stress among participants in the upward mobility condition (vs. no mobility), but only among those who had low childhood SES. These findings suggest that upward mobility carries a psychological stress burden that uniquely impacts individuals from low SES environments.

Students wonder how to prepare for an upcoming exam, while instructors wonder how to best introduce materials to students. Research has found that practice testing after material is studied leads to better memory compared to other strategies like restudying (Rowland, 2014). Practice testing tends to be more effective when students recall material verbally or write it down (overt learning) compared to when they only mentally recall the material (covert learning; Tauber et al., 2018). Other research evaluates the value of pretesting individuals before they learn subject matter. Studies show that answering pretest questions before studying, even if people provide wrong answers, leads to beneficial learning outcomes compared to just studying material (Richland et al., 2009). We aimed to combine both research areas and investigate how covert and overt learning applies to pretesting: When taking a pretest, is it better to just think about the answer, or is it better to write it down? Undergraduate students at TCU were instructed to learn passages about the planet Saturn or Yellowstone National Park. For one of the passages, students simply read the passage (read-only condition). For the other passage, they were asked short-answer pretest questions about the passage prior to reading (pre-test condition). A random half of the participants complete their pretests overtly (typing their responses to the questions), whereas the other half of participants completed their pretests covertly (answering the questions in their mind). All participants completed a final multiple-choice test on the material they learned. Performance on this final test was higher for the pretest condition compared to the read-only condition, and this was true for both overt and covert pretesting. In strategy ratings made after the experiment, participants seemed to recognize that pretesting was more effective than learning because it helped them absorb relevant information while reading the passage. Our results suggest that pretesting is an effective learning strategy, even when learners do not provide articulated responses.

Bird song has been extensively investigated as a model for understanding the physiological basis for animal vocalization. Juvenile songbirds acquire their songs and perfect them as they transition into adulthood, just as we acquire our native language by exposure and imitation of adult tutors. Scientific investigation of bird song requires the collection of hundreds of hours of audio data containing songs, calls, and cage noise. These data must be sorted into categories of interest for specific research questions, with singing behavior being the dominant behavior of interest. Data categorization is a tedious and time-consuming process, and while current software hastens this process, substantial human effort is still required. This project investigates whether or not machine learning algorithms can be used to more efficiently categorize audio data collected in songbird research. Specifically, we developed a convolutional neural network (CNN) in PyTorch to classify whether or not 0.5 second sections of audio contain bird song. Using a supervised learning paradigm, we trained the CNN using labeled spectrograms (visual representations of audio frequencies across time) acquired from recordings of the zebra finch (Taeniopygia guttata). After training the CNN, we implemented it into an algorithm that identifies song within audio recordings. We then compared the CNN based software to a pre-existing, custom-written LabVIEW template-matching algorithm to determine the relative speed and accuracy of the software. Recordings were taken in both noisy and quiet recording environments to test the strengths and limitations of the two approaches. Our data indicate that the CNN based algorithm achieves comparable levels of accuracy to the pre-existing algorithm and accomplishes the categorization using a fraction of the time required by the template matching program. These results suggest that machine learning algorithms can effectively be used to automate and rapidly categorize stereotyped vocal patterns. Further development of this software may facilitate rapid analyses of data and be extended to categorization of a broader range of vocal patterns, including human speech.