Oklahoma State University

Objective To prospectively compare the shock index (SI) in a population of healthy cats with a population of cats presenting to the emergency room (ER) deemed to be in a state of shock. Design Prospective cohort study of cats. Setting University teaching hospital. Animals Twenty‐seven healthy control cats were enrolled to establish a reference interval, and 27 cats were enrolled that were presented to the ER with clinical signs of shock. Shock was defined as abnormalities in at least 2 of the following inclusion criteria: plasma lactate concentration > 2.5 mmol/L; peripheral vasoconstriction (at least 2 of the following parameters: capillary refill time >3 s, rectal‐interdigital temperature gradient [RITG] >8°C, femoral pulse not palpable, pale mucous membranes); or systolic blood pressure (SBP) < 100 mm Hg. Interventions Upon presentation, SI (SI = heart rate [HR]/SBP), HR, SBP, and RITG were recorded in both groups, along with peripheral venous blood sampling for lactate measurement. Measurements and Main Results The mean SI in the control group was 1.47 ± 0.2 and was 1.87 ± 0.47 in the shock group (P = 0.001). Using equality in sensitivity and specificity of 0.7, an SI cutoff point of 1.54 (95% confidence interval [CI]: 0.49–0.86) was determined with an estimated area under the receiver operating characteristic curve of 0.78 (95% CI: 0.65–0.90). HR, plasma lactate concentration, and RITG did not differ between the groups. Systolic arterial blood pressure (P = 0.01), rectal temperature (P = 0.02), and interdigital temperature (P = 0.04) differed significantly. Conclusions The SI is a noninvasive, easy, and reliable parameter for distinguishing cats in shock from normal cats.

The replication crisis has cast social science’s epistemological foundations into question. So far, entrepreneurship scholars have responded by advocating more transparency in data collection and analysis, better empirical methods, and larger and more representative data. Here, we explore the possibility that the problem may be innate to empiricism itself within the social sciences, generally, and entrepreneurship theory, specifically. We review classical arguments and introduce new ones about how and why the weaknesses of empiricism—such as challenges of unobservability—are exacerbated in the study of human behavior, which weaknesses manifest centrally in entrepreneurship theory. These arguments suggest that social science as principally an empirical endeavor may be foolhardy, particularly in the highly agentic entrepreneurship discipline. Herein we propose a radical solution: a rationalist scientific paradigm, where phenomenological reasoning, rather than observation, is paramount. This proposal rests upon arguments that empiricism’s innate limitations can be overcome, albeit not entirely, by its rationalist counterpart. We can, we argue, develop robust scientific foundations—even laws as valid as those of the natural sciences—for entrepreneurship theory through a formal rationalist approach. These laws would necessarily be few but would serve as a much stronger foundation for entrepreneurship theory than the empirical contingencies that we observe. We conclude by illustrating what such a rationalist management program might look like for entrepreneurship scholars with Bylund’s entrepreneurial theory of the firm.

Studies investigating the effects of bilingualism on cognitive function have often yielded conflicting results, which may stem in part from the use of arbitrary criteria to categorize participants into groups based on language experience. The present study addresses this limitation by using a machine learning algorithm, known as cluster analysis, to identify naturally occurring subgroups of participants with similar language profiles. In a sample of 169 participants with varying degrees of first- and second-language proficiencies and ages of acquisition, the cluster analysis yielded four bilingual subgroups: late-unbalanced, early-unbalanced, late-balanced, and early-balanced. All participants completed the NIH Toolbox Cognition Battery. Results revealed that early-balanced and early-unbalanced bilinguals scored higher than late-unbalanced bilinguals on the cognitive flexibility and inhibitory control subtests of the NIH Toolbox Cognition Battery, whereas late-unbalanced bilinguals scored higher than early-balanced bilinguals on the verbal working memory subtest of the NIH Toolbox Cognition Battery. Bilingual language experience did not impact performance on measures of processing speed, episodic memory, and English vocabulary. These findings demonstrate the utility of data-driven approaches to capture the variability in language experience that exists in the real world. We conclude that different bilingual experiences can shape a wide range of cognitive abilities, from working memory to inhibitory control.

Conservationists are increasingly leveraging systematic conservation planning (SCP) to inform restoration actions that enhance biodiversity. However, restoration frequently drives ecological transformations at local scales, potentially resulting in trade-offs among wildlife species and communities. The Conservation Interactions Principle (CIP), coined more than 15 years ago, cautions SCP practitioners regarding the importance of jointly and fully evaluating conservation outcomes across the landscape over long timeframes. However, SCP efforts that guide landscape restoration have inadequately addressed the CIP by failing to tabulate the full value of the current ecological state. The increased application of SCP to inform restoration, reliance on increasingly small areas to sustain at-risk species and ecological communities, ineffective considerations for the changing climate, and increasing numbers of at-risk species, are collectively intensifying the need to consider unintended consequences when prioritizing sites for restoration. Improper incorporation of the CIP in SCP may result in inefficient use of conservation resources through opportunity costs and/or conservation actions that counteract one another. We suggest SCP practitioners can avoid these consequences through a more detailed accounting of the current ecological benefits to better address the CIP when conducting restoration planning. Specifically, forming interdisciplinary teams with expertise in the current and desired ecosystem states at candidate conservation sites; improving data availability; modeling and computational advancements; and applying structured decision-making approaches can all improve the integration of the CIP in SCP efforts. Improved trade-off assessment, spanning multiple ecosystems or states, can facilitate efficient, proactive, and coordinated SCP applications across space and time. In doing so, SCP can effectively guide the siting of restoration actions capable of promoting the full suite of biodiversity in a region.

Automation increasingly shapes modern society, requiring artificial intelligence (AI) systems to not only perform complex tasks but also provide clear, actionable explanations of their decisions, especially in high-stakes domains. However, most contemporary AI systems struggle to explain their runtime operations in specific instances, limiting their applicability in contexts demanding stringent outcome justification. Existing approaches have attempted to address this challenge but often fall short in terms of contextual relevance, human cognitive alignment, or scalability. This paper introduces System-of-Systems Machine Learning (SoS-ML) as a novel framework to advance explainable artificial intelligence (XAI) by addressing the limitations of current methods. Drawing from insights in philosophy, cognitive science, and social sciences, SoS-ML seeks to integrate human-like reasoning processes into AI, framing explanations as contextual inferences and justifications. The research demonstrates how SoS-ML addresses key challenges in XAI, such as enhancing explanation accuracy and aligning AI reasoning with human cognition. By leveraging a multi-agent, modular design, SoS-ML encourages collaboration among machine learning models, leading to more transparent, context-aware systems. The framework’s ability to generalize across domains is demonstrated through experiments on the Pima Indian Diabetes dataset and pie chart image-to-text interpretation, showcasing its transformative potential in improving both model accuracy and explainability. The findings emphasize SoS-ML’s role in advancing responsible AI, particularly in high-stakes environments where interpretability and social accountability are paramount.

Background In prehospital emergency care, providers face significant challenges in making informed decisions due to factors such as limited cognitive support, high-stress environments, and lack of experience with certain patient conditions. Effective Clinical Decision Support Systems (CDSS) have great potential to alleviate these challenges. However, such systems have not yet been widely adopted in real-world practice and have been found to cause workflow disruptions and usability issues. Therefore, it is critical to investigate how to design CDSS that meet the needs of prehospital providers while accounting for the unique characteristics of prehospital workflows. Methods We conducted semi-structured interviews with 20 prehospital providers recruited from four Emergency Medical Services (EMS) agencies in an urban area in the northeastern U.S. The interviews focused on the decision-making challenges faced by prehospital providers, their technological needs for decision support, and key considerations for the design and implementation of a CDSS that can seamlessly integrate into prehospital care workflows. The data were analyzed using content analysis to identify common themes. Results Our qualitative study identified several challenges in prehospital decision-making, including limited access to diagnostic tools, insufficient experience with certain critical patient conditions, and a lack of cognitive support. Participants highlighted several desired features to make CDSS more effective in the dynamic, hands-busy, and cognitively demanding prehospital context, such as automatic prompts for possible patient conditions and treatment options, alerts for critical patient safety events, AI-powered medication identification, and easy retrieval of protocols using hands-free methods (e.g., voice commands). Key considerations for successful CDSS adoption included balancing the frequency and urgency of alerts to reduce alarm fatigue and workflow disruptions, facilitating real-time data collection and documentation to enable decision generation, and ensuring trust and accountability while preventing over-reliance when using CDSS. Conclusion This study provides empirical insights into the challenges and user needs in prehospital decision-making and offers practical and system design implications for addressing these issues.

A convenience that is desirable in diffuse reflectance spectroscopy (DRS) is to recover the spectral absorption by direct model inversion to facilitate decomposition of spectrally significant chromophores. Attaining such convenience that requires a simple forward model has been challenging in non-contact DRS, for assessing myoglobin forms, which is important to the evaluation of discoloration of meat. This work demonstrates that non-contact DRS configured in a center-illuminated-area-detection (CIAD) geometry [Appl. Opt. 61, 9143 (2022) APOPAI0003-693510.1364/AO.468342] may be modeled by an exceptionally simple formula. This simple forward model for DRS in the CIAD geometry on a homogeneous medium has been examined by using Monte Carlo simulations, over a radius of the area of CIAD ranging from 1.5 to 10 mm, for the absorption coefficient to vary five orders of magnitude over $[{0.001},\;{100}]\;{{\rm mm}^{- 1}}$ , and the reduced scattering coefficient to vary two orders of magnitude over $[{0.1},\;{10}]\;{{\rm mm}^{- 1}}$ while limited to one scattering phase function. When compared to a previous cumbersome model for the same geometry, the simple model markedly outperforms at high absorption, e.g., ${\gt}{1.0}\;{{\rm mm}^{- 1}}$ . The simplicity of this model facilitates that, with a priori knowledge of the spectral scattering, analytical operation could directly recover the spectral absorption to subsequently use linear inversion to resolve the chromophore proportions. Non-contact DRS in the CIAD geometry of ${\sim}{4}\;{\rm mm}$ in diameter using this simple forward model has been applied to seven longissimus lumborum steaks over 6 days of retail display. The progressive changes of myoglobin, including the decrease of oxymyoglobin and increase of metmyoglobin, over 6 days estimated by the simple model of non-contact DRS in CIAD are consistent with those assessed concurrently by a contact DRS using 3 mm source-detector separation [Meat Muscle Biol. 5, 1 (2022)10.22175/mmb.12562].

Background Estimates of tick abundance and distribution are used to determine the risk of tick-host contact. Tick surveys provide estimates of distributions and relative abundance for species that remain stationary and wait for passing hosts (i.e. questing), but measures of tick populations may be less reliable for species that actively move in search of a host, such as Amblyomma americanum, the lone star tick (LST). Risk estimates for contact with adult LST require knowledge of the tick's spatial and temporal activity. Understanding the movement and the temporal patterns of host-seeking behavior will enhance risk assessment for LST. Methods Using CO2-baited traps over a 2-year period, we collected wild adult LST in Oklahoma. We used mark-recapture techniques to determine the distance ticks will travel, the proportion of the tick population that is detectable over time, and the relationship between tick abundance and the number of ticks detected in the field. Using video tracking software, we measured the distance traveled and activity time in the laboratory. Results In 24 h, LST travel up to 9 (mean = 3.2, SD = 3.6) m in the field and 36 (mean = 70.4, SD = 81.0) m in the laboratory. Marked LST were detectable in the environment for up to 14 days after release. We found that the number of recaptured ticks significantly increased with the relative abundance of ticks released, and at a minimum abundance (N = 1 tick released) LST were detectable 33.3% of the time. Across all experiments, fewer than half of marked ticks were recovered and at most 28.4% of ticks were detected with CO2-baited traps at any given time. Conclusions Our results show that LST actively move through the environment and pose a risk for host contact at distances of tens of meters. Ticks are detectable for several weeks, but only a fraction of them are detectable at any time. Larger numbers of ticks are detected as their population size increases, but even at very low numbers, LST are recovered with CO2 baiting. These spatial and temporal aspects of LST behavior should be considered when building predictive risk models of LST-host contact. Graphical Abstract

Background Bovine respiratory disease complex (BRDC) is a widely distributed and multifactorial syndrome, leading to significant economic losses to the cattle industry. Many viruses are considered causative agents of BRDC, including bovine herpesvirus 1 (BoHV-1), bovine respiratory syncytial virus (BRSV), and parainfluenza virus 3 (PI-3). This study aimed to determine the seroprevalence of BoHV-1, BRSV, and PI-3 in serum samples collected from cattle in Villavicencio, Colombia. A total of 725 animals from 29 herds were sampled and tested for BoHV-1 and BRSV using ELISA. For PI-3, 440 animals were selected from 24 herds and tested using ELISA. An epidemiological survey collected information on animal characteristics, management practices, health, and environmental factors. Seroprevalence rates for each disease were determined, and a bivariate analysis was performed using a contingency table with odds ratios and Pearson’s Chi-square test. Results The seroprevalence rates were 44.9% for BoHV-1, 94.3% for BRSV, and 85.9% for PI-3. At the herd level, the seroprevalence was above 95% for all tested viruses. Two simultaneous risk factors for all three diseases were identified: female sex and age over 3 years. The risk factors for BoHV-1 were the sale of animals, purchase of animals, history of abortions, and Brahman breed. Conversely, artificial insemination was a protective factor. For BRSV, the purchase of animals was a risk factor, and the history of abortions correlated to PI-3 seropositivity. No significant correlation between the three diseases was identified. Conclusions This study confirms the high prevalence of BoHV-1, BRSV, and PI-3, underscoring the need for preventive measures against these non-officially notified diseases in Colombia.

The deep oceans are environments of complex carbon dynamics that have the potential to significantly impact the global carbon cycle. However, the role of hadal zones, particularly hadal trenches (water depth > 6 km), in the oceanic dissolved organic carbon (DOC) cycle is not thoroughly investigated. Here we report distinct DOC signatures in the Japan Trench bottom water. We find that up to 34% ± 7% of the DOC in the trench bottom is removed during the northeastward transport of dissolved carbon along the trench axis. This DOC removal increases the overall DOC recalcitrance of the deep Pacific DOC pool, and is potentially enhanced by the earthquake-triggered physical and biogeochemical processes in the hadal trenches. Radiocarbon analysis on representative oceanic transects further reveals that the Pacific deep-water DOC undergoes distinct removal compared to those in the Atlantic and Indian Oceans along the thermohaline transport. Our findings highlight hadal trenches as previously unrecognized DOC sinks in the deep ocean system, with varying dynamics that warrant further investigation.

Research Highlight: Iannarilli, F., Gerber, B. D., Erb, J., & Fieberg, J. R. (2024). A ‘how‐to’ guide for estimating animal diel activity using hierarchical models. Journal of Animal Ecology, https://doi.org/10.1111/1365‐2656.14213. Diel activity patterns are ubiquitous in living organisms and have received considerable research attention with advances in the collection of time‐stamped data and the recognition that organisms may respond to global change via behaviour timing. Iannarilli et al. (2024) provide a roadmap for analysing diel activity patterns with hierarchical models, specifically trigonometric generalized linear mixed‐effect models and cyclic cubic spline generalized additive models. These methods are improvements over kernel density estimators, which for nearly two decades have been the status quo for analysing activity patterns. Kernel density estimators have several drawbacks; most notably, data are typically aggregated (e.g. across locations) to achieve sufficient sample sizes, and covariates cannot be incorporated to quantify the influence of environmental variables on activity timing. Iannarilli et al. (2024) also provide a comprehensive tutorial which demonstrates how to format data, fit models, and interpret model predictions. We believe that hierarchical models will become indispensable tools for activity‐timing research and envision the development of many extensions to the approaches described by Iannarilli et al. (2024).

The objective was to determine the effects of injectable trace minerals (ITM, containing Se, Cu, Zn & Mn) administered at the time of primary intranasal (IN) modified-live virus (MLV) vaccination of young dairy calves on the serum neutralizing antibody (SNA) titers to Bovine herpes virus 1 (BHV1), Bovine respiratory syncytial virus (BRSV), and Bovine Parainfluenza type 3 virus (BPI3V); cytokine expression in peripheral white blood cells, and BHV1-specific IgA titers in nasal secretions following the vaccination. A total of 60 calves (1 month old) were administered an IN MLV vaccine containing BHV1, BRSV, BPI3V (Inforce 3®) and randomly assigned to one of two experimental groups: ITM (n = 30; Multimin®90, containing Se, Cu, Zn, and Mn) or SAL (n = 30; sterile saline). There was a consistent decay in virus-specific SNA titers in both groups. Calves with ITM had significantly greater BRSV-SNA titers on day 14 (p = 0.045), and day 28 (p = 0.028) than SAL calves. There was a significant increase in BHV1-specific IgA in nasal secretion in both groups, without significant difference. In conclusion, IN vaccination of dairy calves with high levels of maternally derived SNA did not produce a significant increase in SNA titers to the vaccine viruses but did stimulate a significant BHV1-IgA response in nasal secretions. Supplementation with ITM was associated with a delayed decrease of BRSV-SNA titers on days 14 and 28 after primary vaccination. Administration of ITM was also associated with lower clinical scores and respiratory disease morbidity and mortality. Treatment with ITM did not affect SNA titers to BHV1 and BPI3V or the BHV1-specific IgA level in nasal secretions.