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Bovine respiratory disease (BRD) is a highly prevalent multi pathogen infectious disease (70-80%) in newly received feedlot cattle, causing significant economic losses and reduced animal welfare. Current BRD diagnosis involves stressful and invasive methods that can increase the incidence and transmission of BRD. An alternative is the use of an automated infrared thermography (IR) platform that can monitor facial temperature and behaviour traits to diagnose BRD in a non-invasive manner. The objective of this study was to investigate the use of fidget and drinking behaviours in conjunction with facial temperature as method of BRD diagnosis in beef calves. Sixty-five weaned calves (N = 65) were monitored over a 21-d period after 6 h transportation to predispose calves to BRD infection. Data collected from an automated IR platform placed at a water station included the number of IR frames during drinking (Fidget), number of drinking visits (Drinking bouts), total drinking duration, average drinking duration, average cheek temperature (AVG temp), and maximum orbital temperature (Max temp). Fidget, drinking behaviours, and IR were compared to a clinical score assessment based on respiratory, digestive, and lethargy signs (visual observation) and haematology analysis using a receiver operating characteristics curve analysis to identify the accuracy of each metric and combinations of metrics for BRD diagnosis. The greater accuracies observed were Fidget, Youden's index (J): 0.25 J), Drinking bout (0.28 J), and Total drinking duration (0.22 J). The average IR temperature accuracy resulted in 0.88 J and Max temp 0.77 J. Thirty-five combinations of drinking behaviour and facial IR metrics were evaluated to identify BRD calves. Optimum accuracy (100%) was achieved when combining Fidget, Drinking bout, Average drinking duration, AVG temp, and Max temp 1.00 J. Similar evaluations were performed at 48 and 24 h before d 0 using the most accurate Fidget, Drinking behaviour, and IR combination, resulting in 0.44 J 48 h prior to d 0 and 0.45 J 24 h prior to d 0. Combining fidget and drinking behaviour metrics increased the sensitivity to detect the onset of BRD infection and the specificity to discriminate true positive BRD calves from true negative BRD calves., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

The primary objective of this study was to develop an automated infrared thermography platform (Estrus BenchMark) capable of measuring skin temperature and tail movements as a means of identifying cows in estrus. The secondary objective was to evaluate the accuracy of Estrus BenchMark to detect estrus compared to in-line milk progesterone (P 4 ) analysis (Herd Navigator System) in a commercial dairy herd managed under a robotic milking system. Data were collected on forty-six cows from 45 to 120 d after calving. Cows were flagged in estrus when milk P 4 fell below 5 ng/mL. The Estrus BenchMark true positive estrus alerts (Sensitivity; Se%) were compared to Herd Navigator System estrus alerts at different time-windows (±12 h, ±24 h, ±48 h, and ±72 h) relative to the Estrus BenchMark estrus alerts for all the estrus alerts (AE) and confidence-quality estrus (CQE; >80% quality) alerts identified by Herd Navigator System. The Estrus BenchMark captured skin temperature and tail movements resulting in vulva exposure (left tail movements, LTail; right tail movements, RTail; and pooled tail movements, PTail) for each milking event. Skin temperature tended to increase when the milk P 4 concentration (Least-Squares Means ± SE) dropped for AE (estrus day [d 0]; P 4 ; 3.51 ± 0.05 ng/mL, Skin temperature; 33.31 ± 2.38 °C) compared with d -7 (P 4 ; 20.22 ± 0.73 ng/mL; Skin temperature: 32.05 ± 3.77 °C). The increase in skin temperature, however, was significant in cows with CQE > 80% at d 0 (32.75 ± 0.29 °C) compared to d -7 (31.80 ± 0.28 °C). The prevalence of tail movements to expose vulva was greater (P = 0.01) in AE at d 0 (LTail: 62.50%; PTail; 68.75%; and RTail: 56.25%) compared with d -7 (LTail: 18.75%; PTail: 9.37%: and RTail: 9.37%), and d +4 (LTail: 9.37%; PTail: 9.37%; and RTail: 12.5%). Moreover, the higher prevalence of tail movements at d 0 was observed in cows with CQE > 80% (LTail; 65%, PTail; 80%, and RTail; 70%) compared to those with CQE < 80%. The highest Estrus BenchMark Youden index (YJ; 0.45), diagnostic odds ratio (DOR; 9.04), and Efficiency (0.77) were achieved for AE in a ±48 h window and at ±72 h window for CQE (YJ; 0.66, DOR; 25.29, and Efficiency 0.76) relative to Herd Navigator System estrus alerts. The highest Estrus BenchMark resulted in 58% estrus detection rates for AE and 80% for cows with CQE compared to the Herd Navigator System., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

The dairy industry is searching for new technologies to address low (<50%) estrus detection. However, the lack of information on the potential economic benefits regarding new technology implementation has led some dairy producers to continue using conventional estrus detection methods (e.g. visual observation of standing to be mounted). The objective of this study was to compare the costs of infrared thermography (IRT), visual observation (VO) and ovulation synchronization (Ovsynch: OVS) as breeding strategies at different accuracy levels (Sensitivity [Se], Specificity [Sp]) and pregnancy rates (PR). The costs associated with Breeding, Feeding, Operation Costs, Return to Equity and Culling Risk per estrus detection rate (ER; 30-100%, conception rate for OVS; 30-100%), PR [PR per Parity group; 1-2 (50%), 3-4 (43%), and >4 (41%)], and ER accuracy determined the potential financial benefit of each breeding method for a representative farm. Breeding Cost results (Canadian dollars per cow; CAD/cow) showed a higher cost of OVS (138.99) as compared to VO (115.78) and IRT (127.69). Pregnancy Costs were affected by Breeding Cost; however, ER had a significant effect on PR expense for each method, IRT (ER; 30%: 210.38; 100%: 132.19), VO (ER; 30%: 205.93; 100%: 129.39), and OVS (ER; 30%: 247.21; 100%: 155.33). The minimum Se level with a positive Financial Effect for IRT and VO was 60% with a Sp of 100%, and for the OVS was Se 65% and Sp 100%. However, when the Se was 100% a positive Financial Effect was observed with a minimum Sp of 85% for IRT and 75% for VO. Culling Risk was reduced if ER increases differently depending on the parity group. Implementing of IRT as an estrus detection method yields a competitive breeding cost compared to VO and OVS. Further, breeding methods must accomplish at least ∼60% accuracy to have a positive net return., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Low estrus detection rates (>50%) are associated to extended calving intervals, low economic profit and reduced longevity in Holstein dairy cows. The objective of this study was to evaluate the accuracy of infrared thermography and behavioral biometrics combined as potential estrus alerts in naturally (not induced) cycling dairy cows housed in a tie-stall barn. Eighteen first lactation cows were subjected to transrectal ultrasonography to determine spontaneous ovulation. The dominant follicle (DF) disappearance was used retrospectively as an indirect indicator of ovulation, and to establish the estrus period (48-24 h prior the DF disappearance). Raw skin temperature (Raw IR) and residual skin temperature (Res IR) were recorded using an infrared camera at the Vulva area with the tail (Vtail), Vulva area without the tail (Vnotail), and Vulva's external lips (Vlips) at AM and PM milking from Day 14 until two days after ovulation was confirmed. Behavioral biometrics were recorded on the same schedule as infrared scan. Behavioral biometrics included large hip movements (L-hip), small hip movements (S-hip), large tail movements and small tail movements to compare behavioral changes between estrus and nonestrus periods. Significant increases in Raw IR skin temperature were observed two days prior to ovulation (Vtail; 35.93 ± 0.27 °C, Vnotail; 35.59 ± 0.27 °C, and Vlips; 35.35 ± 0.27 °C) compared to d -5 (Proestrus; Vtail; 35.29 ± 0.27 °C, Vnotail; 34.93 ± 0.31 °C, and Vlips; 34.68 ± 0.27 °C). No significant changes were found for behavioral parameters with the exception of S-hip movements, which increased at two days before ovulation (d -2; 11.13 ± 1.44 Events/5min) compared to d -5 (7.30 ± 1.02 Events/5min). To evaluate the accuracy of thermal and behavioral biometrics, receiver operating characteristic curve analysis was performed using Youden index (YJ), diagnostic odds ratio, positive likelihood ratio (LR+), Sensitivity, Specificity and Positive predicted value to score the estrus alerts. The greatest accuracy achieved using thermal parameters was for Res IR Vtail PM (YJ = 0.34) and L-hip PM (YJ = 0.27) for behavioral biometrics. Combining thermal and behavioral parameters did not improve the YJ index score but reduced the false-positive occurrence observed by increasing the diagnostic odds ratio (26.62), LR+ (12.47), Specificity (0.97) and positive predicted value (0.90) in a Res IR Vtail PM, S-hip AM, S-hip PM combination. The combination of thermal and behavioral parameters increased the accuracy of estrus detection compared to either thermal or behavioral biometrics, independently in naturally cycling cows during milking., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Objectives: The goal of this study was to determine the long-term prognostic value of coronary computed tomography angiography (CTA) among patients with diabetes mellitus (DM) compared with nondiabetic subjects., Background: The long-term prognostic value of coronary CTA in patients with DM is not well established., Methods: Patients enrolled in the CONFIRM (Coronary CT Angiography Evaluation for Clinical Outcomes: An International Multicenter) registry with 5-year follow-up data were identified. The extent and severity of coronary artery disease (CAD) were analyzed at baseline coronary CTA and in relation to outcomes between diabetic and nondiabetic patients. CAD according to coronary CTA was defined as none (0% stenosis), nonobstructive (1% to 49% stenosis), or obstructive (≥50% stenosis). Time to death (and in a subgroup, time to major adverse cardiovascular event) was estimated by using multivariable Cox proportional hazards models., Results: A total of 1,823 patients were identified as having DM with 5-year clinical follow-up and were propensity-matched to 1,823 patients without DM (mean age 61.8 ± 10.9 years; 54.4% male). Patients with DM did not exhibit a heightened risk of death compared with the propensity-matched nondiabetic subjects in the absence of CAD on coronary CTA (risk-adjusted hazard ratio [HR] of DM: 1.32; 95% confidence interval [CI]: 0.78 to 2.24; p = 0.296). Patients with DM were at increased risk of dying compared with nondiabetic subjects in the setting of nonobstructive CAD (in the propensity-matched cohort: HR, 2.10; 95% CI: 1.43 to 3.09; p < 0.001) with a mortality risk greater than nondiabetic subjects with obstructive disease (p < 0.001). In a risk-adjusted hazard analysis among patients with DM, both per-patient obstructive CAD and nonobstructive CAD conferred an increase in all-cause mortality risk compared with patients without atherosclerosis on coronary CTA (nonobstructive disease-HR: 2.07; 95% CI: 1.33 to 3.24; p = 0.001; obstructive disease-HR: 2.22; 95% CI: 1.47 to 3.36; p < 0.001)., Conclusions: Among patients with DM, nonobstructive and obstructive CAD according to coronary CTA were associated with higher rates of all-cause mortality and major adverse cardiovascular events at 5 years, and this risk was significantly higher than in nondiabetic subjects. Importantly, patients with DM without CAD according to coronary CTA were at a risk comparable to that of nondiabetic subjects., (Copyright © 2016 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Background: Coronary computed tomography angiography (coronary CTA) can prognosticate outcomes in patients without modifiable risk factors over medium term follow-up. This ability was driven by major adverse cardiovascular events (MACE)., Objective: Determine if coronary CTA could discriminate risk of mortality with longer term follow-up. In addition we sought to determine the long-term relationship to MACE., Methods: From 12 centers, 1884 patients undergoing coronary CTA without prior coronary artery disease (CAD) or any modifiable CAD risk factors were identified. The presence of CAD was classified as none (0% stenosis), mild (1% to 49% stenosis) and obstructive (≥50% stenosis severity). The primary endpoint was all-cause mortality and the secondary endpoint was MACE. MACE was defined as the combination of death, nonfatal myocardial infarction, unstable angina, and late target vessel revascularization (>90 days)., Results: Mean age was 55.6 ± 14.5 years. At mean 5.6 ± 1.3 years follow-up, 145(7.7%) deaths occurred. All-cause mortality demonstrated a dose-response relationship to the severity and number of coronary vessels exhibiting CAD. Increased mortality was observed for >1 segment non-obstructive CAD (hazard ratio [HR]:1.73; 95% confidence interval [CI]: 1.07-2.79; p = 0.025), obstructive 1&2 vessel CAD (HR: 1.70; 95% CI: 1.08-2.71; p = 0.023) and 3-vessel or left main CAD (HR: 2.87; 95% CI: 1.57-5.23; p = 0.001). Both obstructive CAD (HR: 6.63; 95% CI: 3.91-11.26; p < 0.001) and non-obstructive CAD (HR: 2.20; 95% CI: 1.31-3.67; p = 0.003) predicted MACE with increased hazard associated with increasing CAD severity; 5.60% in no CAD, 13.24% in non-obstructive and 36.28% in obstructive CAD, p < 0.001 for trend., Conclusions: In individuals being assessed for CAD with no modifiable risk factors, all-cause mortality in the long term (>5 years) was predicted by the presence of more than 1 segment of non-obstructive plaque, obstructive 1- or 2-vessel CAD and 3 vessel/left main CAD. Any CAD, whether non-obstructive or obstructive, predicted MACE over the same time period., (Copyright © 2016 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.)