Nikolay Bliznyuk

BACKGROUND Agricultural injury surveillance remains challenging because existing data systems do not comprehensively capture nonfatal injuries, particularly among self-employed workers, unpaid family members, and other populations involved in agricultural activities. Emergency medical services (EMS) records represent a potentially valuable but underutilized source of surveillance data. Integrating EMS records with temporal and spatial statistical methods may improve the identification of injury patterns and geographic areas with elevated injury burden. OBJECTIVE This study aimed to develop and apply a statewide EMS-based surveillance framework to characterize agricultural injuries, examine temporal patterns, estimate small-area agricultural injury rates, and identify areas with elevated injury burden in Florida. METHODS A retrospective surveillance study was conducted using Florida Emergency Medical Services Tracking and Reporting System (EMSTARS) records from 2016 through 2023. Agricultural injuries were identified using International Classification of Diseases, Tenth Revision (ICD-10) external cause of injury and incident location codes. Generalized additive models (GAMs) were used to evaluate seasonal variation and long-term temporal trends in monthly injury counts. To estimate agricultural workforce populations at the ZIP Code Tabulation Area (ZCTA) level, county-level agricultural worker counts from the 2022 Census of Agriculture were redistributed using a dasymetric approach informed by the National Land Cover Database classifications and a negative binomial GAM. A Bayesian spatial negative binomial model incorporating Besag-York- Mollié 2 (BYM2) random-effects was then used to estimate small-area injury rates and identify ZCTAs with at least 95% posterior probability of exceeding the statewide background injury rate. RESULTS A total of 1133 agricultural injury incidents requiring EMS response were identified during the study period. Most injured patients were male (n=852, 72.8%) and 85 (7.5%) were younger than 18 years. Contact with agricultural machinery was the most frequently recorded external cause of injury (n=541, 47,7%). Temporal analysis identified a recurring seasonal pattern, with expected monthly injury counts approximately 60% higher during the June-July peak than during the December minimum. Dasymetric redistribution produced ZCTA-level agricultural workforce estimates, with higher concentrations in south-central Florida near Lake Okeechobee. Bayesian spatial analysis identified 94 ZCTAs with elevated agricultural injury rates relative to the statewide background, with the largest concentration occurring in south-central Florida. CONCLUSIONS Statewide EMS records can be integrated with temporal modeling, land cover-based workforce estimation, and Bayesian spatial analysis to support agricultural injury surveillance beyond descriptive case identification. This framework enables the identification of seasonal patterns and geographic areas with elevated injury rates while accounting for estimated populations at risk and spatial variability.

Our first objective was to quantify the associations between health-related events (HRE) before insemination, the relative increase in estrus intensity (REI) at insemination, and the probability of cow-level pregnancy per artificial insemination (P/AI) in organic Holstein dairy cows. Quantifying these associations may aid on-farm decision-making, such as setting the voluntary waiting period, choice of type of semen, do-not-breed and culling decisions. A second objective was to develop predictive models to estimate P/AI based on readily available data, and present common goodness-of-fit results also used in the machine learning community. All data were collected from a certified organic dairy farm in the western USA from 2019 to 2021. Health-related and reproduction data were obtained through Dairy Records Management Systems (DRMS; Raleigh, NC, USA). Activity data were collected using pedometers (IceRobotics, Stirling, UK) mounted on the rear legs. The REI, defined as walking steps per hour before insemination divided by the cow’s baseline steps per hour, was available for 17,238 inseminations from 4759 cows. The REI was categorized as ≤200%, >200–400%, >400–600%, or >600%. The HRE were available for 65,684 inseminations from 13,365 cows. The HRE were categorized as mastitis, metabolic disease (i.e., hypocalcemia, ketosis, displaced abomasum, digestive problems), reproductive disease (i.e, metritis, endometritis, pyometra, retained fetal membranes), lameness, 2 different diseases, ≥3 different diseases, or as healthy (none of these diseases prior to insemination). Combinations (COMBO) between REI categories and 0, 1, or ≥2 HRE were also created. Data were split into training and test sets. The training data were used to fit three logistic regression models that included either HRE, or REI, or COMBO. Each of the three models also included the covariates of 3-mo herd-average P/AI prior to insemination, days in milk, and the fixed effects of parity, insemination season, days after the previous insemination or days to 1st insemination. A random effect accounted for repeated inseminations within cow. Parameter estimates, odds ratios, and the estimated marginal means of the estimated P/AI of the fixed effects were obtained from the logistic regression models. The models’ estimates were applied to the test datasets, and discrimination and calibration statistics were calculated to judge goodness-of-fit. Unadjusted mean P/AI were 0.31, 0.28 and 0.28 for the HRE, REI and COMBO training datasets. For the HRE model, estimated P/AI ranged from 0.20 (≥3 different HRE) to 0.30 (healthy). The estimated P/AI associated with four REI categories were not different from 0.27 in the REI model. The estimated P/AI associated with the combinations of HRE and REI in the COMBO model varied from 0.18 after ≥2 HRE and >200–400% REI, to 0.30 when inseminations were in healthy cows with REI >600%. Inseminations in older cows, in the spring, and outside 18–24 d after the previous insemination were also associated with lower estimated P/AI. The area underneath the Receiver Operating Characteristic curve ranged from 0.57 (COMBO) to 0.60 (HRE) for the test data, indicating fair discrimination ability of the models. Calibration plots showed that the prediction models produced unbiased predicted P/AI. In conclusion, the results showed no conclusive evidence of greater estimated P/AI related to greater REI as a measure of estrus activity. More HRE were associated with lower estimated P/AI. Combinations of low REI and more HRE were associated with notably decreased estimated P/AI. The logistic regression models produced unbiased predicted P/AI. We found no evidence that the strength of the relationship between REI and P/AI depended on the HRE category. The applications of the results are as follows. First, these predictive models may help inform insemination decisions in organic dairy cows, although further external validation is recommended, and the discriminatory performance is weak. Second, a variety of goodness-of-fit statistics were calculated to allow comparisons of the current logistic regression analyses with future analyses made by other machine learning techniques.

Agriculture remains one of the most hazardous industries in the United States, yet nonfatal surveillance remains incomplete. Prior studies have shown that trauma registries can support agricultural injury surveillance, but many approaches rely on text review, state-specific indicators, or coding frameworks that are not easily transferable across systems. In this study, Florida Trauma Registry records from 2016 to 2021 were screened using a reproducible case definition based on International Classification of Diseases, Tenth Revision (ICD-10) external cause of injury codes, registry occupation and industry fields, and the work-related indicator. Agricultural injuries were summarized descriptively and mapped to Occupational Injury and Illness Classification System (OIICS) categories. Injury severity scores (ISS) were modeled using generalized additive models (GAMs). The case definition identified 963 agricultural injuries requiring trauma care, including 17 fatalities. Most injuries were minor, and the leading event categories were contact incidents, falls, slips, and trips, and transportation incidents. Body region was the strongest correlate of severity, with head and abdomen/pelvic contents injuries showing the highest adjusted ISS. These findings suggest that the National Trauma Data Standard (NTDS)-aligned trauma registry fields may support reproducible agricultural injury surveillance when narrative text is unavailable.

Our first objective was to quantify the associations between health-related events (HRE) before insemination, the relative increase in estrus intensity (REI) at insemination, and the probability of pregnancy per artificial insemination (P/AI) in organic dairy cows. Quanti-fying these associations may aid on-farm decision-making, such as setting the voluntary waiting period, choice of type of semen, do-not-breed and culling decisions. A second ob-jective was to develop predictive models to estimate P/AI based on readily available data, and present common goodness-of-fit results also used in the machine learning community. All data were collected from a certified organic dairy farm in the western USA from 2019 to 2021. Health-related and reproduction data were obtained through DRMS (Raleigh, NC, USA). Activity data were collected using pedometers (IceRobotics, Stirling, UK) mounted on the rear legs. The REI, defined as walking steps per hour before insemination divided by the cow’s baseline steps per hour, was available for 17,238 inseminations from 4,759 cows. The REI was categorized as ≤200% (6,999 inseminations), >200-400% (4,685), >400-600% (2,929), or >600% (2,625). The HRE were available for 65,684 inseminations from 13,365 cows. The HRE was categorized as mastitis (prior to 9,114 inseminations), metabolic (displaced abomasum, ketosis, milk fever; 1,941), reproductive disease (metritis, endometritis, pyometra, retained fetal membranes; 4,907), lameness (4,058), 2 different diseases (4,022), ≥3 different diseases (813), or as healthy (none of these diseases prior to insemination; 40,829). Combinations (COMBO) between REI categories and 0, 1, or ≥2 HRE were also created for 16,415 inseminations in 4,647 cows. Data were split into training and test sets. The training data were used to fit 3 logistic regression models that included either HRE, or REI, or COMBO. Each of the 3 models also included the covariates of prior 3-mo herd P/AI and days in milk (DIM), and the fixed effects of parity, insemination season, days after the previous insemination or days to 1st insemination. A random effect ac-counted for repeated inseminations within cow. Parameter estimates, odds ratios, and the least-square means of the estimated P/AI of the fixed effects were obtained from the logistic regression models. The models’ estimates were applied to the test datasets, and discrimi-nation and calibration statistics were calculated to judge goodness-of-fit. Unadjusted mean P/AI were 31%, 28% and 28% for the HRE, REI and COMBO training datasets. For the HRE model, estimated P/AI ranged from 20% (≥3 different HRE) to 30% (healthy). The estimated P/AI associated with the 4 REI categories were not different from 27% in the REI model. The estimated P/AI associated with the combinations of HRE and REI in the COMBO model varied from 18% after ≥2 HRE and >200-400% REI, to 30% when insemina-tions were in healthy cows with REI >600%. Inseminations in older cows, in the spring, and outside 18-24 d after the previous insemination were also associated with lower esti-mated P/AI. The area underneath the Receiver Operating Characteristic curve ranged from 0.57 (COMBO) to 0.60 (HRE) for the test data, indicating fair discrimination ability of the models. The Brier score ranged from 0.19 to 0.21, indicating moderate performance of the prediction models. Calibration plots showed that the prediction models produced unbi-ased estimated P/AI. In conclusion, the results showed no conclusive evidence of greater estimated P/AI related to greater REI as a measure of estrus activity. More health-related events were associated with lower estimated P/AI. Combinations of low REI and more HRE were associated with notably decreased estimated P/AI. The logistic regression mod-els produce unbiased estimated P/AI. These predictive models may inform insemination and culling decisions in organic dairy cows. A variety of goodness of fit statistics were calculated to allow comparisons of the current logistic regression analyses with future analyses made by other machines learning techniques.

Background Germinal matrix-intraventricular hemorrhage (GMH-IVH) is a major morbidity in premature neonates, with higher incidence in outborn infants requiring transport. Objective To identify factors associated with high-grade GMH-IVH in outborn preterm neonates transported to UF Health. Methods This retrospective, single-center cohort study included 69 outborn neonates diagnosed with IVH between 2016 and 2021. Logistic regression and ANOVA models (parametric and nonparametric) were used to assess associations between transport-related factors and GMH-IVH severity. Results Among neonates born at ≤ 30 weeks’ gestation, both transport distance and duration were significantly associated with increased risk of high-grade GMH-IVH (p = 0.01). Mode of transport was not significantly associated. Lower gestational age and birth weight were also linked to higher GMH-IVH severity (p < 0.05). Conclusion Transport distance and duration may contribute to GMH-IVH severity in extremely preterm neonates. Early implementation of GMH-IVH prevention bundles at referring hospitals and use of head ultrasound during transport may help mitigate risk and guide early interventions.

This article is prepared to help Extension agents, farmers, farm managers, researchers, graduate students, and the general public better understand AI, associated terms, and technologies. Written by Young Gu Her, Nikolay Bliznyuk, Yiannis Ampatzidis, Ziwen Yu, and Haimanote Bayabil, and published by the UF/IFAS Department of Agricultural and Biological Engineering, September 2024.

Laser-inscribed graphene (LIG), initially developed for graphene supercapacitors, has found widespread use in sensor research and development, particularly as a platform for low-cost electrochemical sensing. However, batch-to-batch variation in LIG fabrication introduces uncertainty that cannot be adequately tracked during manufacturing process, limiting scalability. Therefore, there is an urgent need for robust quality control (QC) methodologies to identify and select similar and functional LIG electrodes for sensor fabrication. For the first time, we have developed a statistical workflow and an open-source hierarchical clustering tool for QC analysis in LIG electrode fabrication. The QC process was challenged with multi-operator cyclic voltammetry (CV) data for bare and metalized LIG. As a proof of concept, we employed the developed QC process for laboratory-scale manufacturing of LIG-based biosensors. The study demonstrates that our QC process can rapidly identify similar LIG electrodes from large batches (n ≥ 36) of electrodes, leading to a reduction in biosensor measurement variation by approximately 13% compared to the control group without QC. The statistical workflow and open-source code presented here provide a versatile toolkit for clustering analysis, opening a pathway toward scalable manufacturing of LIG electrodes in sensing. In addition, we establish a data repository for further study of LIG variation.

Preterm infants bypass the crucial in utero period of brain development and are at increased risk of malnutrition. We aimed to determine if their nutritional status is associated with brain tissue volumes at term equivalent age (TEA), applying recently published malnutrition guidelines for preterm infants. We performed a single center retrospective chart review of 198 infants < 30 weeks’ gestation between 2018 and 2021. We primarily analyzed the relationship between the manually obtained neonatal MR-based brain tissue volumes with the maximum weight and length z-score. Significant positive linear associations between brain tissue volumes at TEA and weight and length z-scores were found (p < 0.05). Recommended nutrient intake for preterm infants is not routinely achieved despite efforts to optimize nutrition. Neonatal MR-based brain tissue volumes of preterm infants could serve as objective, quantitative and reproducible surrogate parameters of early brain development. Nutrition is a modifiable factor affecting neurodevelopment and these results could perhaps be used as reference data for future timely nutritional interventions to promote optimal brain volume.

Research-supported information developed to meet the needs of Florida audiences. UF/IFAS Extension is a federal, state, and county partnership dedicated to developing knowledge in agriculture, human and natural resources, and the life sciences and to making that knowledge accessible to sustain and enhance the quality of human life.

ObjectiveThe purpose of this study was to investigate characteristics associated with farm vehicle and farm labor transportation-related roadway crashes. Agricultural roadway crashes related to theese vehicles have not been explored in Florida.MethodsThe data for this study comes from the Florida Department of Highway Safety and Motor Vehicles (FLHSMV) for the years 2013-2021. The data are recorded by the Florida Highway Patrol when a vehicular crash has occurred. The data are coded to allow the selection of farm and agricultural-related vehicles. Specifically, this study focuses on farm labor transport vehicles and farm vehicles (e.g. farm tractors).ResultsThere were 744 farm vehicle- and 209 farm labor transportation-related crashes during the 9-year study period. The farm labor transport vehicle crashes involved a total of 420 vehicles and 1,329 individuals, and the farm vehicle crashes involved 1,458 vehicles and 1,652 individuals. Injury risk for non-farm labor transportation vehicle occupants was significantly higher than injury risk for farm labor transportation vehicle occupants.ConclusionsThe average fatality rate for farm vehicle-involved (2.1 fatalities per 100 crashes) and farm labor transportation vehicle-involved crashes (2.9 fatalities per 100 crashes) were much higher than the overall FL roadway crash fatality rate (0.45 fatalities per 100 crashes).