Virginia Tech

Emotion socialization is a dynamic transactional process that unfolds at the moment during parent-child interactions. To better understand these transactions (both parent-driven and child-driven) in early childhood, we conducted a lag-sequential analysis examining sequential contingency between maternal emotion coaching and child emotion regulation at ages 3 and 4 years. Mother-child dyads in the southeastern United States ( N = 208 for age 3 timepoint [101 boys, 107 girls] and 227 for age 4 timepoint [115 boys, 112 girls]) participated in a laboratory etch-a-sketch task, which was videorecorded and later observationally coded for maternal coaching of both positive and negative emotions and for child emotion regulation (indexed as compliance, engagement, and low frustration) at 30-s intervals. At age 3, we found two reciprocal sequences: (1) When mothers coached positive emotions, children were subsequently more likely to show compliance, and when children complied, mothers were subsequently more likely to coach their positive emotions; (2) when mothers coached negative emotions, children were subsequently more likely to display frustration, and when children showed frustration, mothers were subsequently more likely to coach their negative emotions. At age 4, we only found parent-driven, positive emotion–related sequences: when mothers coached positive emotions, children were subsequently more likely to show compliance and engagement. Findings shed light on the distinct functions of positive and negative emotions as well as the intricacy of dynamic emotion socialization transactions in relation to child emotion regulation during early childhood.

The concept of general arousal has a long history in emotion research. However, the concept is more complex and nuanced than is generally appreciated. In this comment, we note some of the early conceptualizations of arousal and how they might comport with more modern representations of the construct. Importantly, we show how modern conceptualizations which incorporate the physiological complexity of arousal measurement and peripheral-central nervous system interactions might help to provide a more solid framework for the construct moving forward. The authors of the target article are to be commended for addressing this important issue.

Cognitive presence and social presence are crucial for a comprehensive learning experience. Despite the flexibility of asynchronous learning environments to accommodate individual schedules, the inherent constraints of asynchronous environments make augmenting cognitive and social presence particularly challenging. Students often face challenges such as a lack of timely feedback and support, an absence of non-verbal cues in communication, and a sense of isolation. To address this challenge, this paper introduces Generative Co-Learners, a system designed to leverage generative AI-powered agents, simulating co-learners supporting multimodal interactions, to improve cognitive and social presence in asynchronous learning environments. We conducted a study involving 12 student participants who used our system to engage with online programming tutorials to assess the system's effectiveness. The results show that by implementing features to support textual and visual communication and simulate an interactive learning environment with generative agents, our system enhances the cognitive and social presence in the asynchronous learning environment. These results suggest the potential to use generative AI to support student learning and transform asynchronous learning into a more inclusive, engaging, and efficacious educational approach.

This case explores the challenges faced by educational leaders at Maple Middle School, a rural school in a diverse community, involving a physical altercation between two students, subsequent student protests, and an investigation by Child Protective Services. Amid growing community unrest, a divided school board must address student discipline issues, restraint policies, and the balance between maintaining a safe environment and respecting students’ rights. As tensions escalate, the Deputy Superintendent’s office faces pressure to take action, requiring careful investigation, policy revisions, and conflict resolution strategies to navigate the complexities of a deeply divided community. The case highlights the importance of communication, transparency, and unbiased decision-making in addressing sensitive issues.

Despite the increasing use of large language models (LLMs) in everyday life among neurodivergent individuals, our knowledge of how they engage with and perceive LLMs remains limited. In this study, we investigate how neurodivergent individuals interact with LLMs by qualitatively analyzing topically related discussions from 61 neurodivergent communities on Reddit. Our findings reveal 20 specific LLM use cases across five core thematic areas of use among neurodivergent users: emotional well-being, mental health support, interpersonal communication, learning, and professional development and productivity. We also identified key challenges, including overly neurotypical LLM responses and the limitations of text-based interactions. In response to such challenges, some users actively seek advice by sharing input prompts and corresponding LLM responses. Others develop workarounds by experimenting and modifying prompts to be more neurodivergent-friendly. Despite these efforts, users have significant concerns around LLM use, including potential overreliance and fear of replacing human connections. Our analysis highlights the need to make LLMs more inclusive for neurodivergent users and implications around how LLM technologies can reinforce unintended consequences and behaviors.

Background Shared genetic risk between Alzheimer’s disease (AD) and concussion may help explain the association between concussion and elevated risk for dementia. However, there has been little investigation into whether AD risk genes also associate with concussion severity/recovery, and the limited findings are mixed. We used AD polygenic risk scores (PRS) and APOE genotypes to investigate associations between AD genetic risk and concussion severity/recovery in the NCAA‐DoD Grand Alliance CARE Consortium (CARE) dataset. Method There were 1,917 injuries in the dataset upon project initiation. After removing repeated injuries, related participants, and those without genetic/outcome data, we had 931 participants. Outcomes were number of days to return to play (RTP) as a recovery measure, and four severity measures (scores on SAC and BESS, SCAT symptom severity and total number of symptoms). We calculated PRS using a published score (de Rojas et al., 2021) and performed a linear regression (MLR) of RTP by PRS in normal (<24 days) and long (>24 days) RTP subgroups. We then compared severity measures by PRS using MLR. Next, we used t‐tests to examine outcomes by APOE genotype in military and civilian subgroups. We also performed chi‐squared tests of RTP category (normal vs. long) by APOE genotype. Finally, we analyzed outcomes by PRS in European or African genetic ancestry subgroups using MLR. Result Higher PRS was associated with longer injury to RTP interval in the normal RTP (<24 days) subgroup (estimate = 0.0412, SE = 0.182, p = 0.0237). 1 SD increase in PRS resulted in a 0.412 day (9.89 hours) increase to the interval. This may be clinically meaningful in the collegiate athlete environment. We did not identify any other significant differences. Conclusion Our preliminary results provide limited evidence for an impact of AD PRS on concussion recovery, though the pattern was inconsistent and its clinical significance is uncertain. Future studies should attempt to replicate these findings in larger samples with longer follow‐up using PRS calculated from multiple/diverse populations, which will be especially relevant for diverse datasets like CARE.

Guarded Kleene Algebra with Tests (GKAT) provides a sound and complete framework to reason about trace equivalence between simple imperative programs. However, there are still several notable limitations. First, GKAT is completely agnostic with respect to the meaning of primitives, to keep equivalence decidable. Second, GKAT excludes non-local control flow such as goto, break, and return. To overcome these limitations, we introduce Control-Flow GKAT ( CF-GKAT ), a system that allows reasoning about programs that include non-local control flow as well as hardcoded values. CF-GKAT is able to soundly and completely verify trace equivalence of a larger class of programs, while preserving the nearly-linear efficiency of GKAT. This makes CF-GKAT suitable for the verification of control-flow manipulating procedures, such as decompilation and goto-elimination. To demonstrate CF-GKAT’s abilities, we validated the output of several highly non-trivial program transformations, such as Erosa and Hendren’s goto-elimination procedure and the output of Ghidra decompiler. CF-GKAT opens up the application of Kleene Algebra to a wider set of challenges, and provides an important verification tool that can be applied to the field of decompilation and control-flow transformation.

This paper studies an extension of O'Hearn's incorrectness logic (IL) that allows backwards reasoning. IL in its current form does not generically permit backwards reasoning. We show that this can be mitigated by extending IL with underspecification. The resulting logic combines underspecification (the result, or postcondition, only needs to formulate constraints over relevant variables) with underapproximation (it allows to focus on fewer than all the paths). We prove soundness of the proof system, as well as completeness for a defined subset of presumptions. We discuss proof strategies that allow one to derive a presumption from a given result. Notably, we show that the existing concept of loop summaries -- closed-form symbolic representations that summarize the effects of executing an entire loop at once -- is highly useful. The logic, the proof system and all theorems have been formalized in the Isabelle/HOL theorem prover.

The recent explosion in the described chemistry from Colobognatha, a millipede subterclass, has rekindled interest in the defensive secretions from these ancient animals. Colobognatha are the only millipedes that produce terpenoid alkaloids, and prior to 2020, studies into these defensive secretions were limited to a single order, Polyzoniida. However, it has become clear that numerous species of the order Platydesmida also produce structurally diverse terpenoid alkaloids with potent biological activity. Platydesmida defensive secretions encompass multiple natural product scaffolds with greater chemical complexity compared to previously reported millipede-derived alkaloids. Here we report the analysis of the defensive secretions of Andrognathus corticarius, a millipede found across the Appalachian region. A. corticarius is evolutionary sister to all other Platydesmida and is the presumed oldest genus within the order. Analysis of the defensive secretions revealed that A. corticarius produces an arsenal of alkaloids that are dissimilar to all previously reported metabolites. Complete structural elucidation was accomplished using 2D NMR, HRMS, chemical derivatization, DFT and ECD, which revealed the presence of two distinct scaffolds: a 5,6-fused heterocycle named the andrognathines and a 6,6,6,5-bridged heterocycle containing seven continuous stereogenic centers named the andrognathanols. Each scaffold is decorated with diverse fatty acids, which leads to the extraordinary number of unique metabolites detected within the secretions. The alkaloids likely serve to defend the millipedes from predation as they are actively secreted upon physical disturbance. This discovery also provides support for a plausible biosynthetic pathway that appears to be evolving simplicity across the evolutionary history of the Platydesmida.

Islands are well known for their unique biodiversity and significance in evolutionary and ecological studies. Nevertheless, the extinction of island species accounts for most human-caused extinctions in recent time scales, which have accelerated in recent centuries. Pigeons and doves (Columbidae) are noteworthy for the high number of island endemics, as well as for the risks those species have faced since human arrival. On Caribbean islands, no other columbid has generated more phylogenetic interest and uncertainty than the blue-headed quail-dove, Starnoenas cyanocephala. This endangered Cuban endemic has been considered more similar, both behaviourally and phenotypically, to Australasian species than to the geographically closer ‘quail-dove’ (Geotrygon s.l.) species of the Western Hemisphere. Here, we use whole genome sequencing from Starnoenas and other newly sequenced columbids in combination with sequence data from previous publications to investigate its relationships. Phylogenomic analyses, which represent 35 of the 51 genera currently comprising the Columbidae, reveal that the blue-headed quail-dove is the sole representative of a lineage diverging early in the radiation of columbids. Starnoenas is sister to the species-rich subfamily Columbinae, which is found worldwide. As a highly distinctive evolutionary lineage lacking close modern relatives, we recommend elevating the conservation priority of Starnoenas.

Polyploidy is a common outcome of chemotherapies, but there is conflicting evidence as to whether polyploidy is an adverse, benign or even favourable outcome. We show Aurora B kinase inhibitors efficiently promote polyploidy in many cell types, resulting in the cell cycle exit in RB and p53 functional cells, but hyper-polyploidy in cells with loss of RB and p53 function. These hyper-polyploid cells (>8n DNA content) are viable but have lost long-term proliferative potential in vitro and fail to form tumours in vivo. Investigation of mitosis in these cells revealed high numbers of centrosomes that were capable of supporting functional mitotic spindle poles, but these failed to progress to anaphase/telophase structures even when AURKB inhibitor was removed after 2–3 days. However, when AURKB inhibitor was removed after 1 day and cells had failed a single cytokinesis to become tetraploid, they retained colony forming ability and long-term proliferative potential. Mathematical modelling of the potential for polyploid cells to produce viable daughter cells demonstrated that cells with >8n DNA and >4 functional spindle poles approach zero probability of a viable daughter, supporting our experimental observations. These findings demonstrate that tetraploidy is tolerated by tumour cells, but higher ploidy states are incompatible with long-term proliferative potential. Model for AURKBi driven hyper-polyploid cells formation and fate. Aurora B inhibitor (AURKBi) treatment of RB+p53 defective cells efficiently promotes failed cell division. One failed cell division produces three possible outcomes, continued proliferation of the tetraploid daughter, cell death, or if AURKBi is continued, high polyploid states. Once cell have failed cell division >twice and have >8n DNA content they will continue to undergo rounds of endomitosis even in the absence of AURKBi to either become viable hyper-polyploid or die. The hyper-polyploid cells have no long-term proliferative potential.

Public water systems (PWSs) need robust taste and odor (T&O) methods for a diverse range of compounds to proactively monitor their systems from source to tap and make informed treatment decisions. In this study, Standard Method 6040D T&O compounds by solid-phase microextraction gas chromatography-mass spectrometry was revised to include 19 T&O compounds with various odor descriptors including earthy, musty, grassy, woody, fishy, septic, fruity, and sweet. An interlaboratory comparison was performed to determine method accuracy, precision, reproducibility, and ruggedness. Three laboratories achieved passing quality control (QC) acceptance criteria for all 19 compounds, and one laboratory achieved passing QC acceptance criteria for 14 compounds. In this article, occurrence data and method applications are also discussed, which will allow PWSs to monitor diverse classes of T&O compounds and make informed, proactive treatment decisions to maintain high aesthetic quality for their customers.

We consider particle-based stochastic reaction-drift-diffusion models where particles move via diffusion and drift induced by one- and two-body potential interactions. The dynamics of the particles are formulated as measure-valued stochastic processes (MVSPs), which describe the evolution of the singular, stochastic concentration fields of each chemical species. The mean field large population limit of such models is derived and proven, giving coarse-grained deterministic partial integro-differential equations (PIDEs) for the limiting deterministic concentration fields’ dynamics. We generalize previous studies on the mean field limit of models involving only diffusive motion, with care to formulating the MVSP representation to ensure detailed balance of reversible reactions in the presence of potentials. Our work illustrates the more general set of PIDEs that arise in the mean field limit, demonstrating that the limiting macroscopic reactive interaction terms for reversible reactions obtain additional nonlinear concentration-dependent coefficients compared to the purely diffusive case. Numerical studies are presented which illustrate that two-body repulsive potential interactions can have a significant impact on the reaction dynamics, and also demonstrate the empirical numerical convergence of solutions to the PBSRDD model to the derived mean field PIDEs as the population size increases.

Climate and land‐use/land‐cover (LULC) change each threaten the health of rivers. Rising temperatures, changes in rainfall and runoff, and other perturbations, will all impact rivers' physical, biological, and chemical characteristics over the next century. While scientists and policymakers have increasing access to climate and LULC forecasts, the implications of each for outcomes of interest have been difficult to quantify. This is partially because climate and LULC perturb ecological outcomes via incompletely understood site‐specific, interacting, and nonlinear mechanisms that are not well suited to analysis using classical statistical methods. This creates uncertainties over the benefits of local‐level interventions such as green infrastructure investments and urban densification, and limits how forecasts can be used to inform decision‐making. Here, we demonstrate how machine learning can be used to quantify the relative contributions of LULC and climate drivers to impacts on riverine health as measured by taxonomic richness of the macroinvertebrate orders Ephemeroptera, Plecoptera, and Trichoptera (EPT). We develop a cross‐validated Random Forest (RF) model to link EPT taxa richness to meteorological, water quality, hydrologic, and LULC variables in watersheds in New Hampshire and Vermont, USA. Prospective climate and LULC scenarios are used to generate predictions of these variables and of EPT taxa richness trends through the year 2099. The model structure is mechanistically interpretable and performs well on test data (R² ~ 0.4). Impacts on EPT taxa richness are driven by local LULC policy such as increased suburbanization. Future trends are likely to be exacerbated by climate change, although warming conditions suggest possible increases in springtime EPT taxa richness. Overall, this analysis highlights (1) the impact of local LULC decisions on riverine health in the context of a changing climate, and (2) the role machine learning methods can play in developing models that disentangle interacting physical mechanisms to advance decision support.

As organizations increasingly rely on network services, the prevalence and severity of Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks have emerged as significant threats. The cornerstone of effectively addressing these challenges lies in the timely and precise detection capabilities offered by advanced intrusion detection systems (IDS). Hence, an innovative IDS framework is introduced that seamlessly integrates the extended Berkeley Packet Filter (eBPF) with powerful machine learning algorithms—specifically Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and TwinSVM—enabling unparalleled real‐time detection of DDoS attacks. This cutting‐edge solution provides a robust and scalable IDS framework to combat DoS and DDoS threats with high efficiency, leveraging eBPF's capabilities within the Linux kernel to bypass typical user space constraints. The methodology encompasses several key steps: (a) Collection of data from the renowned CIC‐IDS‐2017 repository; (b) Processing the raw data through a meticulous series of steps, including transmission, cleaning, reduction, and discretization; (c) Utilizing an ANOVA F‐test for the extraction of critical features from the preprocessed data; (d) Application of various ML algorithms (DT, RF, SVM, and TwinSVM) to analyze the extracted features for potential intrusion; (e) Implementing an eBPF program to capture network traffic and harness trained model parameters for efficient attack detection directly within the kernel. The experimental results reveal outstanding accuracy rates of 99.38%, 99.44%, 88.73%, and 93.82% for DT, RF, SVM, and TwinSVM, respectively, alongside remarkable precision values of 99.71%, 99.65%, 84.31%, and 98.49%. This high‐speed, accurate detection model is ideally suited for high‐traffic environments such as data centers. Furthermore, its foundational architecture paves the way for future advancements, including the potential integration of eBPF with XDP to achieve even lower‐latency packet processing. The experimental code is available at the GitHub repository link: https://github.com/NemalikantiAnand/Project.

Background Improving the infrastructure for drug overdose surveillance is critical for identifying new threats and responding to emerging trends. We aimed to develop a prototype tool using the principles of natural language processing that can extract information from the death records of drug overdose victims. Methods Data were obtained from the Violent Death Reporting System on drug overdose deaths. Narratives were manually labelled for 12 attributes of interest, totalling 82 labels about the circumstances of the overdose. Narratives were passed through the ‘Excel Extractor’ to identify and extract a target phrase and subsequently map the extracted phrase to predetermined code values. The output from the Excel Extractor was compared with manually labelled data to determine accuracy. Performance was compared against multiple machine learning models. Results The Excel Extractor performed well across the attributes of interest, achieving an F1 Score over 0.8 on nine of the 12 attributes. The Excel Extractor was the highest performing model on seven of the 12 attributes. The Excel Extractor achieved an F1 Score of 0.8 or higher on 46 of 82 (56%) of the labels, and a score of 0.9 or higher on nearly one-third (25 out of 82) of the labels. Conclusion This work demonstrates it is feasible to develop a spreadsheet-formula-based natural language processing tool to accurately extract information about drug overdose deaths from narratives; for most attributes, a rule-based search performs well or better than deep learning. The Excel Extractor has the potential to streamline data abstraction for epidemiologists gathering data about drug overdose deaths.