Oklahoma State University - Stillwater

Aggregation of electric vehicles (EVs) is a promising technique for providing secondary frequency regulation (SFR) in highly renewable energy-penetrated power systems. Equipped with energy storage devices, EV aggregation can provide reliable SFR. However, the main challenge is to guarantee reliable intra-interval SFR capacities and inter-interval delivery following the automatic generation control (AGC) signal. Furthermore, aggregated EV SFR provision will be further complicated by the EV charging time anxiety because SFR provision might extend EV’s charging time. This paper proposes a deliverable EV SFR provision with a charging-time-constrained control strategy. First, a charging-time-constrained EV aggregation is proposed to address the uncertainty of EV capacity based on the state-space model considering the charging-time restriction of EV owners. Second, a real-time economic dispatch and time domain simulation (RTED-TDS) cosimulation framework is proposed to verify financial results and the dynamic performance of the EV SFR provision. Last, the proposed charging time-constrained EV aggregation is validated on the IEEE 39-bus system. The results demonstrate that with charging time-constrained EV aggregation, the dynamic performance of the system can be improved with a marginal increase in total cost. More importantly, the charging time constraint can be respected in the proposed SFR provision of the EV aggregation.

The rapid integration of renewable energy sources (RESs) has imposed substantial uncertainty and variability on the operation of power markets, which calls for unprecedentedly flexible generation resources such as batteries. In this paper, we develop a novel pricing mechanism for day-ahead electricity markets to adeptly accommodate the uncertainties stemming from RESs. First, a distributionally robust joint chance-constrained (DRJCC) economic dispatch model that incorporates energy storage systems is presented, ensuring that the DRJCCs are satisfied across a moment-based ambiguity set enriched with unimodality-skewness characteristics. Second, by applying the Bonferroni approximation method to tackle the DRJCCs, we show that the proposed model can be transformed into a second-order cone programming (SOCP) problem. Building on the SOCP reformulation, we then precisely derive the electricity prices, including the energy, reserve, and uncertainty prices. Furthermore, we prove that the obtained pricing mechanism supports a robust competitive equilibrium under specific premises. Finally, a PJM 5-bus test system and the IEEE 118-bus test system are used to demonstrate the effectiveness and superiority of the suggested approach, underscoring its potential contributions to modern power market operations.

Background Age > 65 years is a key risk factor for poor outcomes after human influenza infection. Specifically, in addition to respiratory disease, non-neurotropic influenza A virus (IAV) causes neuro-cognitive complications, e.g. new onset depression and increases the risk of dementia after hospitalization. This study aimed to identify potential mechanisms of these effects by determining differences between young and old mice in brain gene expression in a mouse model of non-neurotropic IAV infection. Methods Young (12 weeks) and old (70 weeks) C57Bl/6J mice were inoculated intranasally with 200 PFU H1N1 A/PR/34/8 (PR8) or sterile PBS (mock). Gene expression in lung and brain was measured by qRT-PCR and normalized to β-actin. Findings were confirmed using the nCounter Mouse Neuroinflammation Array (NanoString) and analyzed with nSolver 4.0 and Ingenuity Pathway Analysis (IPA, Qiagen). Results IAV PR8 did not invade the central nervous system. Young and old mice differed significantly in brain gene expression at baseline and during non-neurotropic IAV infection. Expression of brain Ifnl, Irf7, and Tnf mRNAs was upregulated over baseline control at 3 days post-infection (p.i.) only in young mice, but old mice expressed more Ifnl than young mice 7 days p.i. Gene arrays showed down-regulation of the Epigenetic Regulation, Insulin Signaling, and Neurons and Neurotransmission pathways in old mice 3 days p.i. while young mice demonstrated no change or induction of these pathways at the same time point. IPA revealed marked baseline differences between old and young mice. Gene expression related to Cognitive Impairment, Memory Deficits and Learning worsened in old mice relative to young mice during IAV infection. Aged mice demonstrate more severe changes in gene expression related to memory loss and cognitive dysfunction by IPA. Conclusions These data suggest the genes and pathways related to learning and cognitive performance that were worse at baseline in old mice were further worsened by IAV infection, similar to old patients. Early events in the brain triggered by IAV infection portend downstream neurocognitive pathology in old adults.

Objectives The integration of these preventive guidelines with Electronic Health Records (EHRs) systems, coupled with the generation of personalized preventive care recommendations, holds significant potential for improving healthcare outcomes. Our study investigates the feasibility of using Large Language Models (LLMs) to automate the assessment criteria and risk factors from the guidelines for future analysis against medical records in EHR. Materials and Methods We annotated the criteria, risk factors, and preventive medical services described in the adult guidelines published by United States Preventive Services Taskforce and evaluated 3 state-of-the-art LLMs on extracting information in these categories from the guidelines automatically. Results We included 24 guidelines in this study. The LLMs can automate the extraction of all criteria, risk factors, and medical services from 9 guidelines. All 3 LLMs perform well on extracting information regarding the demographic criteria or risk factors. Some LLMs perform better on extracting the social determinants of health, family history, and preventive counseling services than the others. Discussion While LLMs demonstrate the capability to handle lengthy preventive care guidelines, several challenges persist, including constraints related to the maximum length of input tokens and the tendency to generate content rather than adhering strictly to the original input. Moreover, the utilization of LLMs in real-world clinical settings necessitates careful ethical consideration. It is imperative that healthcare professionals meticulously validate the extracted information to mitigate biases, ensure completeness, and maintain accuracy. Conclusion We developed a data structure to store the annotated preventive guidelines and make it publicly available. Employing state-of-the-art LLMs to extract preventive care criteria, risk factors, and preventive care services paves the way for the future integration of these guidelines into the EHR.

Using the setting of Hong Kong, we examine how the linguistic properties of financial disclosure differ across languages. We exploit the requirement that firms listed on the Hong Kong Stock Exchange publish annual reports in two languages, English and Chinese. We find that for the same firm, English reports are more positive, convey more uncertainty, and focus more on the past and present and less on the future, than Chinese reports. We also find that English (Chinese) reports are more likely to manage their tone by varying the frequency of positive (negative) words. Finally, the stock market only reacts positively to tone management in Chinese reports. Overall, the results suggest that there are significant and fundamental differences in the linguistic properties of English and Chinese reports and that such differences have material implications for how investors perceive the reports.

Objective Develop a novel technique to identify an optimal number of regression units corresponding to a single risk point, while creating risk scoring systems from logistic regression-based disease predictive models. The optimal value of this hyperparameter balances simplicity and accuracy, yielding risk scores of small scale and high accuracy for patient risk stratification. Materials and Methods The proposed technique applies an adapted line search across all potential hyperparameter values. Additionally, DeLong test is integrated to ensure the selected value produces an accuracy insignificantly different from the best achievable risk score accuracy. We assessed the approach through two case studies predicting diabetic retinopathy (DR) within six months and hip fracture readmissions (HFR) within 30 days, involving cohorts of 90 400 diabetic patients and 18 065 hip fracture patients. Results Our scores achieve accuracies insignificantly different from those obtained by existing approaches, reaching AUROCs of 0.803 and 0.645 for DR and HFR predictions, respectively. Regarding the scale, our scores ranged 0-53 for DR and 0-15 for HFR, while scores produced by existing methods frequently spanned hundreds or thousands. Discussion According to the assessment, our risk scores offer simple and accurate predictions for diseases. Furthermore, our new DR score provides a competitive alternative to state-of-the-art risk scores for DR, while our HFR case study presents the first risk score for this condition. Conclusion Our technique offers a generalizable framework for crafting precise risk scores of compact scales, addressing the demand for user-friendly and effective risk stratification tool in healthcare.

A bstract Observation of lepton number ( L ) violation by two units at colliders would provide evidence for the Majorana nature of neutrinos. We study signals of L -violation in the context of two popular models of neutrino masses, the type-II seesaw model and the Zee model, wherein small neutrino masses arise at the tree-level and one-loop level, respectively. We focus on L -violation signals at the LHC arising through the process pp → ℓ ± ℓ ′± + jets within these frameworks. We obtain sensitivity to L -violation in the type-II seesaw model for triplet scalar masses up to 700 GeV and in the Zee model for charged scalar masses up to 4.8 TeV at the high-luminosity LHC with an integrated luminosity of 3 ab ⁻¹ .

Chimeric antigen receptor (CAR)-T cell adoptive immunotherapy is a promising cancer treatment that uses genetically engineered T cells to attack tumors. However, this therapy can have some adverse effects. CAR-T cell-derived exosomes are a potential alternative to CAR-T cells that may overcome some limitations. Exosomes are small vesicles released by cells and can carry a variety of molecules, including proteins, RNA, and DNA. They play an important role in intercellular communication and can be used to deliver therapeutic agents to cancer cells. The application of CAR-T cell-derived exosomes could make CAR-T cell therapy more clinically controllable and effective. Exosomes are cell-free, which means that they are less likely to cause adverse reactions than CAR-T cells. The combination of CAR-T cells and exosomes may be a more effective way to treat cancer than either therapy alone. Exosomes can deliver therapeutic agents to cancer cells where CAR-T cells cannot reach. The appropriate application of both cellular and exosomal platforms could make CAR-T cell therapy a more practicable treatment for cancer. This combination therapy could offer a safe and effective way to treat a variety of cancers.

Interest in the metacognitive aspects of prospective memory (PM) is growing. Yet, the interplay between participants’ metacognitive awareness of PM task demands and features that contribute to successful PM require further attention. To this aim, participants in the current study completed laboratory-based PM tasks of varying difficulty (cue focality: focal, nonfocal-category, or nonfocal-syllable) and reported their strategy use and perceptions of PM task importance. Most participants reported using a strategy regardless of cue focality. However, only under the most challenging condition (i.e., nonfocal-syllable) did participants who reported using a strategy exhibit better PM performance compared to those who did not use a strategy. Additionally, strategy use and cue focality were independently associated with greater costs to ongoing task performance: strategy users exhibited greater slowing relative to individuals who did not use a strategy, and the extent of slowing was greater as the task difficulty increased across cue focality. Finally, perceived task importance appeared to play an important role in the interactive link between cue focality and strategy use on PM performance for the more challenging, nonfocal PM tasks. Specifically, moderation analyses suggested that greater perceived task importance alone may improve the likelihood of PM success for moderately challenging PM tasks (i.e., nonfocal-category), but for the most challenging PM tasks (i.e., nonfocal-syllable), individuals’ strategy use was still associated with better PM performance. The present study expands our understanding of metacognition's role in PM performance and has implications for everyday PM performance.

Non‐human animals have long been embedded in society and are well‐documented as integrated into people's lives and families. However, researchers and practitioners inconsistently incorporate companion animals into theoretical conceptualizations of family, despite growing empirical evidence of the substantive impact of companion animals in people's lives. To more accurately capture people's lived realities of companion animals as family members, we advance a model of ecoexpansive kinship, which validates expansive family forms and functions across multiple levels of social ecological influence. To illustrate our model's applicability, we explore key definitions and outline major theoretical foundations and applications of companion animal research. We then demonstrate both the theoretical significance and empirical reality of where companion animal kinship fits into family dynamics. An ecoexpansive kinship approach can promote more conceptual, cohesive theorization of how to holistically model kinship to be inclusive of companion animals that can be applied across research and practice.

The Orthoflavivirus NS3 helicase (NS3h) is crucial in virus replication, representing a potential drug target for pathogenesis. NS3h utilizes nucleotide triphosphate (ATP) for hydrolysis energy to translocate on single-stranded nucleic acids, which is an important step in the unwinding of double-stranded nucleic acids. Intermediate states along the ATP hydrolysis cycle and conformational changes between these states, represent important yet difficult-to-identify targets for potential inhibitors. Extensive molecular dynamics simulations of West Nile virus NS3h+ssRNA in the apo, ATP, ADP+Pi and ADP bound states were used to model the conformational ensembles along this cycle. Energetic and structural clustering analyses depict a clear trend of differential enthalpic affinity of NS3h with ADP, demonstrating a probable mechanism of hydrolysis turnover regulated by the motif-VI loop (MVIL). Based on these results, MVIL mutants (D471L, D471N and D471E) were found to have a substantial reduction in ATPase activity and RNA replication compared to the wild-type. Simulations of the mutants in the apo state indicate a shift in MVIL populations favoring either a closed or open ‘valve’ conformation, affecting ATP entry or stabilization, respectively. Combining our molecular modeling with experimental evidence highlights a conformation-dependent role for MVIL as a ‘valve’ for the ATP-pocket, presenting a promising target for antiviral development.

Objective: The insertion of penetrating neural probes into the brain is crucial for advancing neural science, yet it involves various inherent risks. Prototype probes are typically inserted into hydrogel-based brain phantoms to inform the in vivo implantation. However, the underlying mechanism of the insertion dynamics in hydrogel brain phantoms, particularly the phenomenon of cracking, remains insufficiently understood. This knowledge gap leads to misinterpretations and discrepancies when comparing results obtained from phantom studies to those observed in in vivo conditions. This study aims to elucidate the impact of probe sharpness and dimensions on the cracking mechanisms and insertion dynamics observed during the insertion of probes in hydrogel phantoms. Approach: The insertion of dummy probes is systematically studied. The insertion-induced cracks in the transparent hydrogel were accentuated by an immiscible dye, tracked by in situ imaging, and the corresponding insertion force was recorded. Three-dimensional finite element analysis models were developed to obtain the contact stress between the probe tip and the phantom. Main results: The findings reveal a dual pattern: for sharp, slender probes, the insertion forces remain consistently low during the insertion process, owing to continuously propagating straight cracks. In contrast, blunt, thick probes induce large forces that increase rapidly with escalating insertion depth, mainly due to the formation of branched conical crack, and the subsequent internal compression. This interpretation challenges the traditional understanding that neglects the difference in the cracking modes and regards increased frictional force as the sole factor contributing to higher insertion forces. Significance: This study presents, for the first time, the mechanism underlying two distinct cracking modes during the insertion of neural probes into hydrogel brain phantoms. The correlations between the cracking modes and the insertion force dynamics were established, offering insights into future investigations into cracking phenomena and damage in brain tissue during probe implantations.

Inspired by the event-driven nature of biological systems and sparse spiking networks, research on event-driven tactile learning has been stimulated by recent advancements in event-driven tactile sensors and spiking neural networks(SNNs). However, a major challenge in tactile object recognition research lies in addressing the generalization problem caused by the complex spatio-temporal characteristics of high-dimensional event-driven tactile data. Additionally, sparse connections of SNN models are resulted by difficulties in fully utilizing timing information and structural features of tactile nodes, due to the limited representation of tactile information graphs. To address these issues comprehensively, this paper proposes a fusing topological graph and gaussian prior event-driven tactile object recognition spiking neural networks. Firstly, the incorporation of Gaussian prior effectively integrates prior knowledge to alleviate the generalization problem in event-driven tactile object recognition. Furthermore, both topological sorting and R-tree tactile graph construction methods are employed to enhance the connection structure within the tactile graph and resolve the issue of sparse connections. Finally, various approximate Leaky Integrate-and-Fire neuron activation functions are introduced for comprehensive comparison and evaluation regarding their impact on performance in event-driven tactile object recognition. Experimental results using EvTouch-Containers and EvTouch-Objects datasets demonstrate that G2T-SNN achieves superior recognition performance compared to TactileSGNet methods: 7.50% improvement on EvTouch-Containers dataset and 2.23% improvement on EvTouch-Objects dataset.

Recent advancements in detection and mapping methods have enabled researchers to uncover the biological importance of RNA chemical modifications, which play a vital role in post-transcriptional gene regulation. Although numerous types of RNA modifications have been identified in higher eukaryotes, only a few have been extensively studied for their biological functions. Of these, N6-methyladenosine (m6A) is the most prevalent and important mRNA modification that influences various aspects of RNA metabolism, including mRNA stability, degradation, splicing, alternative polyadenylation, export, and localization, as well as translation. Thus, they have implications for a variety of biological processes, including growth, development, and stress responses. The m6A deposition or removal on transcripts is dynamic and is altered in response to internal and external cues. Because this mark can alter gene expression under stress conditions, it is essential to identify the transcripts that can acquire or lose this epitranscriptomic mark upon exposure to stress conditions. Here we describe a step-by-step protocol for identifying stress-responsive transcriptome-wide m6A changes using RNA immunoprecipitation followed by high-throughput sequencing (MeRIP-seq).

Nucleosome occupancy plays an important role in chromatin compaction, affecting biological processes by hampering the binding of cis-acting elements such as transcription factors, RNA polymerase machinery, and coregulatory. Accessible regions allow for cis-acting elements to bind DNA and regulate transcription. Here, we detail our protocol to profile nucleosome occupancy and chromatin structure dynamics under drought stress at the genome-wide scale using micrococcal nuclease (MNase) digestion. Combining variable MNase concentration treatments and high-throughput sequencing, we investigate the changes in the overall chromatin state using bread wheat samples from an exemplary drought experiment.

CRISPR/Cas9 system is one of the most often utilized engineering tools for genome editing in many organisms including crop plants and presents great value in both basic and applied research. This is a preferred method because of its relative simplicity, cost-effectiveness, and reliability. The Cas9 nuclease guided by a short single guide RNA (gRNA) can generate double-strand DNA breaks (DSB) at the specific sites in chromosomal DNA. The DSB site is repaired by error-prone repair methods. During repair, some nucleotides are deleted or added at the target site. Here, we present a simplified protocol for generating mutants in gene of interest in rice using CRISPR/Cas9.

Reactive oxygen species (ROS) production is a key early defense mechanism in plants when exposed to biotic stress. Upon recognition of conserved microbe-associated molecular patterns (MAMPs) from pathogens by plant receptors, nicotinamide adenine dinucleotide phosphate (NADPH) oxidases in the plasma membrane are activated to produce hydrogen peroxide (H2O2). This, in turn, regulates multiple signaling pathways to trigger immunity and suppress pathogen infection. Monitoring the ROS burst in plant leaves can be done within minutes of MAMPs treatment. However, there is limited research on the quantification of ROS production in plant root tissues during the activation of plant immunity. In this study, we introduce a rapid, accessible, and straightforward technique for measuring MAMPs-triggered ROS bursts in the roots of the model legume Medicago truncatula. This method will facilitate the investigation of plant root responses to biotic and abiotic stresses.