Comparative analysis of computer vision algorithms for the real-time detection of digital dermatitis in dairy cows.

Digital dermatitis (DD) is a bovine claw disease responsible for ulcerative lesions on the planar aspect of the hoof. DD is associated with massive herd outbreaks of lameness and influences cattle welfare and production. Early detection of DD can lead to prompt treatment and decrease lameness. Computer vision (CV) provides a unique opportunity to improve early detection. The study aims to train and compare applications for the real-time detection of DD in dairy cows. Eight CV models were trained for detection and scoring, compared using performance metrics and inference time, and the best model was automated for real-time detection using images and video. Images were collected from commercial dairy farms while facing the interdigital space on the plantar surface of the foot. Images were scored for M-stages of DD by a trained investigator using the M-stage DD classification system with distinct labels for hyperkeratosis (H) and proliferations (P). Two sets of images were compiled: the first dataset (Dataset 1) containing 1,177 M0/M4H and 1,050 M2/M2P images and the second dataset (Dataset 2) containing 240 M0, 17 M2, 51 M2P, 114 M4H, and 108 M4P images. Models were trained to detect and score DD lesions and compared for precision, recall, and mean average precision (mAP) in addition to inference time in frame per second (FPS). Seven of the nine CV models performed well compared to the ground truth of labeled images using Dataset 1. The six models, Faster R-CNN, Cascade R-CNN, YOLOv3, Tiny YOLOv3, YOLOv4, Tiny YOLOv4, and YOLOv5s achieved an mAP between 0.964 and 0.998, whereas the other two models, SSD and SSD Lite, yielded an mAP of 0.371 and 0.387 respectively. Overall, YOLOv4, Tiny YOLOv4, and YOLOv5s outperformed all other models with almost perfect precision, perfect recall, and a higher mAP. Tiny YOLOv4 outperformed all other models with respect to inference time at 333 FPS, followed by YOLOv5s at 133 FPS and YOLOv4 at 65 FPS. YOLOv4 and Tiny YOLOv4 performed better than YOLOv5s compared to the ground truth using Dataset 2. YOLOv4 and Tiny YOLOv4 yielded a similar mAP of 0.896 and 0.895, respectively. However, Tiny YOLOv4 achieved both higher precision and recall compared to YOLOv4. Finally, Tiny YOLOv4 was able to detect DD lesions on a commercial dairy farm with high performance and speed. The proposed CV tool can be used for early detection and prompt treatment of DD in dairy cows. This result is a step towards applying CV algorithms to veterinary medicine and implementing real-time DD detection on dairy farms.

DairyCoPilot-Automated data compilation and analysis tools for DairyComp data assets.

Modern dairy farm management requires meaningful data and careful analysis to maximize profitability, cow health, and welfare. Current data platforms, such as DairyComp, lack robust integrated data analysis tools. Producers and consultants need dedicated tools to turn collected data sets into assets for informed decision-making processes. The DairyCoPilot app allows users to rapidly extract health and production data from DairyComp, then compile and analyze the data using a menu-driven point-and-click approach. Prospects for training consultants in applied data analysis skills make DairyCoPilot a tool to identify farm management bottlenecks with less time spent for data analysis, improving cow health, and dairy production. The DairyCoPilot Dashboard R Shiny application is published using RStudio Connect: https://connect.doit.wisc.edu/dairy-copilot/.

Computer vision model for the detection of canine pododermatitis and neoplasia of the paw.

BACKGROUND: Artificial intelligence (AI) has been used successfully in human dermatology. AI utilises convolutional neural networks (CNN) to accomplish tasks such as image classification, object detection and segmentation, facilitating early diagnosis. Computer vision (CV), a field of AI, has shown great results in detecting signs of human skin diseases. Canine paw skin diseases are a common problem in general veterinary practice, and computer vision tools could facilitate the detection and monitoring of disease processes. Currently, no such tool is available in veterinary dermatology. ANIMALS: Digital images of paws from healthy dogs and paws with pododermatitis or neoplasia were used. OBJECTIVES: We tested the novel object detection model Pawgnosis, a Tiny YOLOv4 image analysis model deployed on a microcomputer with a camera for the rapid detection of canine pododermatitis and neoplasia. MATERIALS AND METHODS: The prediction performance metrics used to evaluate the models included mean average precision (mAP), precision, recall, average precision (AP) for accuracy and frames per second (FPS) for speed. RESULTS: A large dataset labelled by a single individual (Dataset A) used to train a Tiny YOLOv4 model provided the best results with a mean mAP of 0.95, precision of 0.86, recall of 0.93 and 20 FPS. CONCLUSIONS AND CLINICAL RELEVANCE: This novel object detection model has the potential for application in the field of veterinary dermatology.

[Use of milk haptoglobin concentration as an indicator in animal health monitoring of dairy cows]. / Nutzung der Haptoglobinkonzentration in Milch als Indikator für das Tiergesundheitsmonitoring bei Milchkühen.

OBJECTIVE: The aim of the present study was to investigate relationships between elevated haptoglobin concentrations in milk and clinical as well as laboratory parameters in early lactating dairy cows. Furthermore, cut-off values should be identified for the differentiation of healthy and affected animals. MATERIAL AND METHODS: 1462 dairy cows between 5.-65. days in milk were examined on 68 Bavarian farms. Milk and blood samples were taken once a week for a 7-week period per farm and body-condition-scoring, backfat thickness measurement and Metricheck examination, to evaluate uterine health, were performed. Milk samples were analysed for milk fat, milk protein, lactose, urea, ß-hydroxybutyrate and non-esterified fatty acids (indirect measurement, based on IR spectra), cell count, and milk haptoglobin. Blood samples were analysed for creatinine, aspartate aminotransferase, gamma-glutamyl transferase, glutamate dehydrogenase, total protein, albumin, creatine kinase, ß-hydroxybutyrate, non-esterified fatty acids, and blood haptoglobin.Cluster analyses were performed to determine cut-off values for haptoglobin. RESULTS: Besides milk haptoglobin (µg/ml) and blood haptoglobin (µg/ml), cell count (cells/ml milk), milk fat (%), milk protein (%), non-esterified fatty acids in blood (mmol/l), lactation number, days in milk, breed, season, and milk yield (kg) were included as significant input variables (p<0.005) in the cluster analyses. Cluster analysis, using k-means resp. k-prototypes algorithms, resulted in 5 (clusters 1-5 M1) resp. 4 different clusters (clusters 0-3 M2 and 0-3 B).A cut-off value of 0.5 µg/ml haptoglobin in milk was determined for the differentiation of healthy and affected animals. CONCLUSION AND CLINICAL RELEVANCE: As milk is an easily available substrate, routine determination of haptoglobin in milk might be a suitable parameter for animal health monitoring. Using the detected cut-off value, apparently healthy animals with subclinical inflammatory diseases can be identified more quickly.

Differentiating between metabolic health statuses in Simmental cows and describing related milk fatty acids and relevant associated factors.

The aim of this observational study was to examine differences in milk fatty acid (FA) concentrations for different metabolic health statuses and for associated factors-specifically to examine with which FA concentrations an increased risk for developing a poor metabolic adaptation syndrome (PMAS) was associated. During weekly visits over 51 wk, blood samples were collected from cows between 5 and 50 days in milk. The farmer collected corresponding milk samples from all voluntary milkings. The analysis was performed on n = 2,432 samples from n = 553 Simmental cows. The observations were assigned to five different cow types (healthy, clever, athletic, hyperketonemic, and PMAS, representing five metabolic health statuses), based on the thresholds of 0.7 mmol/L, 1.2 mmol/L, and 1.4 for the concentrations of ß-hydroxybutyrate and nonesterified fatty acids and for the milk fat-to-protein ratio, respectively. Linear regression models using the predictor variables cow type, parity, week of lactation, and milk yield as fixed effects were developed using a stepwise forward selection to test for significant associations of predictor variables regarding FA concentrations in milk. There was a significant interaction term found between PMAS cows and parity compared to healthy cows for C18:1 (P < 0.001) and for C18:0 (P < 0.01). It revealed higher concentrations for PMAS in primiparous and multiparous cows compared to healthy cows, the slope being steeper for primiparous cows. Further, an interaction term was found between PMAS cows and milk yield compared to healthy cows and milk yield for C16:0 (P < 0.05), revealing a steeper slope for the decrease of C16:0 concentrations with increasing milk yield for PMAS compared to healthy cows. The significant associations and interaction terms between cow type, parity, week of lactation, and milk yield as predictor variables and C16:0, C18:0, and C18:1 concentrations suggest excellent opportunities for cow herd health screening during the early postpartum period.

FACTORS IMPACTING SUCCESSFUL REHABILITATION OF BIG BROWN BATS (EPTESICUS FUSCUS) IN A WISCONSIN WILDLIFE REHABILITATION CENTER: A 5-YEAR RETROSPECTIVE.

The big brown bat (Eptesicus fuscus; EPFU) is widely distributed throughout the Americas and plays critical roles in sustaining cave ecosystems and abating agricultural pests. In Wisconsin, EPFU is a threatened species with declining populations due to hibernacula disturbances, wind turbines, and habitat destruction. Due to their ecological and economic value, it is important to be able to release EPFU that enter wildlife rehabilitation centers back to the wild. This study evaluated the medical records of 454 EPFU (275 male, 179 female) admitted to a wildlife rehabilitation center in Wisconsin from 2015 to 2020. For each bat, the season at intake, examination findings, length of time in rehabilitation, and final outcome (released or not released) were recorded. Using a multiple variable logistic regression model, there was a statistically significant positive association between length of time in the rehabilitation center and likelihood of release (odds ratio [OR] 1.08; 95% CI 1.06-1.12); this association can be explained by the need to overwinter some otherwise healthy bats in rehabilitation during hibernation. The following examination findings were associated with a significantly lower likelihood of release: wing injury (OR 0.32; 95% CI 0.10-0.89) and decreased body condition (OR 0.29; 95% CI 0.12-0.64). When corrected for time spent in rehabilitation (potentially artificially lengthened due to hibernation), patients admitted in the summer and fall were less likely to be released than those admitted in the winter (OR 0.93; 95% CI 0.90-0.96 and OR 0.95; 95% CI 0.92-0.97, respectively). The results of this study can be used to help veterinarians and licensed rehabilitators better triage EPFU during admission to wildlife rehabilitation centers in order to improve management and promote successful release back to the wild.

Real-time estimation and forecasting of COVID-19 cases and hospitalizations in Wisconsin HERC regions for public health decision making processes.

BACKGROUND: The spread of the COVID-19 (SARS-CoV-2) and the surging number of cases across the United States have resulted in full hospitals and exhausted health care workers. Limited availability and questionable reliability of the data make outbreak prediction and resource planning difficult. Any estimates or forecasts are subject to high uncertainty and low accuracy to measure such components. The aim of this study is to apply, automate, and assess a Bayesian time series model for the real-time estimation and forecasting of COVID-19 cases and number of hospitalizations in Wisconsin healthcare emergency readiness coalition (HERC) regions. METHODS: This study makes use of the publicly available Wisconsin COVID-19 historical data by county. Cases and effective time-varying reproduction number [Formula: see text] by the HERC region over time are estimated using Bayesian latent variable models. Hospitalizations are estimated by the HERC region over time using a Bayesian regression model. Cases, effective Rt, and hospitalizations are forecasted over a 1-day, 3-day, and 7-day time horizon using the last 28 days of data, and the 20%, 50%, and 90% Bayesian credible intervals of the forecasts are calculated. The frequentist coverage probability is compared to the Bayesian credible level to evaluate performance. RESULTS: For cases and effective [Formula: see text], all three time horizons outperform the three credible levels of the forecast. For hospitalizations, all three time horizons outperform the 20% and 50% credible intervals of the forecast. On the contrary, the 1-day and 3-day periods underperform the 90% credible intervals. Questions about uncertainty quantification should be re-calculated using the frequentist coverage probability of the Bayesian credible interval based on observed data for all three metrics. CONCLUSIONS: We present an approach to automate the real-time estimation and forecasting of cases and hospitalizations and corresponding uncertainty using publicly available data. The models were able to infer short-term trends consistent with reported values at the HERC region level. Additionally, the models were able to accurately forecast and estimate the uncertainty of the measurements. This study can help identify the most affected regions and major outbreaks in the near future. The workflow can be adapted to other geographic regions, states, and even countries where decision-making processes are supported in real-time by the proposed modeling system.

Non-linear change in body condition score over lifetime is associated with breed in dairy cows in Germany.

Optimal body condition is crucial for the well-being and optimal productivity of dairy cows. However, body condition depends on numerous, often interacting factors, with complex relationships between them. Moreover, most of the studies describe the body condition in Holstein cattle, while condition of some breeds, e.g. Simmental (SIM) and Brown Swiss (BS) cattle, have not been intensively studied yet. Body condition score (BCS) proved to be one of the most effective measures for monitoring body condition in dairy cows. Alterations in BCS were previously mainly studied over a single lactation period, while changes over the lifetime were largely ignored. This study was designed to report BCS of German SIM and BS cows in the light of the broadly accepted BCS in German Holstein (GH) cows and to explore patterns of change in BCS over the productive lifetime of animals. BCS was modeled via linear mixed effects regression, over- and undercondition of animals were studied using mixed effects logistic regressions and condition of animals was explored with the multinomial log-linear model via neural networks. All models included an interaction between breed and age. We found BCS of SIM and BS to be higher than BCS of GH. Our results show that BCS of BS cows did not change over the lifetime. In contrast, the BCS of GH and SIM was found to have a non-linear (quadratic) shape, where BCS increased up to the years of highest productivity and then decreased in aging cows. Patterns of change between SIM and GH, however, differed. GH do not only reach their highest BCS earlier in life compared to SIM, but also start to lose their body condition earlier. Our dataset revealed that 23% of the animals scored were over- and 14% underconditioned. The proportion of cows that were overconditioned was high (>10% of cows) for every breed and every age, while severe underconditioning (>10% of cows) occurred only in middle aged and old GH. Moreover, we found that the probability of underconditioning of animals over lifetime increases, while the overconditioning decreases from the middle to older ages. Our findings highlight the importance of understanding the non-linear nature of BCS, and uncover the potential opportunity for improving the performance and welfare of dairy cows by adjusting their nutrition, not only during lactation, but also highly specific to breed and age.

Comparison of ozonated water and acidified copper sulphate in prevention of digital dermatitis in dairy cows.

Digital dermatitis (DD) is the most significant infectious hoof disorder of cattle in Europe. Hoof baths are one of the most common control methods. Copper sulphate and formalin are commonly used in hoof baths, but their use is problematic in many European countries for health, environmental and safety reasons. Ozonated water and acidified copper sulphate were tested as prevention of DD in a 5-month study. Data were derived from 302 hind feet of Holstein and Estonian Red cows (no. of cows = 151) from a commercial dairy farm in Estonia. Altogether 168 hind feet were included in the acidified copper sulphate group and 134 feet in the ozonated water group. Hoof bathing was carried out three days a week (Mon, Wed, Fri) for two months and then two days a week (Mon, Wed) for three and a half months, in both groups. Ozonated water was sprayed on to the digital skin of hind feet of cows twice a day on treatment days, while the cows were eating. The copper sulphate bath consisted of copper sulphate (2%) mixed with an organic acid compound to acidify and ionize the solution. Cows walked through acidified copper sulphate solution twice a day on treatment days as they were exiting the milking parlor. DD negative and DD positive test results in both groups were compared and statistically tested for differences. The copper sulphate solution was more effective than ozonated water at preventing acute DD lesions. A random maximum likelihood model demonstrated that the odds ratio for DD in the ozonated water group was six times higher compared with DD in the acidified copper sulphate group. Most of the cows that were initially without any DD lesions (M0 + no other severe hoof lesion), remained lesion-free in both groups (copper sulphate group 97% and ozonated water group 88%). Despite trial design deficiencies, the findings indicate that acidified copper sulphate was a more effective solution in preventing DD than ozonated water.

Functional Variants Associated With CMPK2 and in ASB16 Influence Bovine Digital Dermatitis.

Bovine digital dermatitis (BDD) is an infectious disease of the hoof in cattle with multifactorial etiology and a polygenic influence on susceptibility. With our study, we identified genomic regions with the impact on occurrence and development of BDD. We used 5,040 genotyped animals with phenotype information based on the M-stage system for genome-wide association. Significant associations for single-nucleotide polymorphisms were found near genes CMPK2 (chromosome 11) and ASB16 (chromosome 19) both being implicated in immunological processes. A sequence analysis of the chromosomal regions revealed rs208894039 and rs109521151 polymorphisms as having significant influence on susceptibility to the disease. Specific genotypes were significantly more likely to be affected by BDD and developed chronic lesions. Our study provides an insight into the genomic background for a genetic predisposition related to the pathogenesis of BDD. Results might be implemented in cattle-breeding programs and could pave the way for the establishment of a BDD prescreening test.

Association of Mobile Phone Location Data Indications of Travel and Stay-at-Home Mandates With COVID-19 Infection Rates in the US.

Importance: A stay-at-home social distancing mandate is a key nonpharmacological measure to reduce the transmission rate of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), but a high rate of adherence is needed. Objective: To examine the association between the rate of human mobility changes and the rate of confirmed cases of SARS-CoV-2 infection. Design, Setting, and Participants: This cross-sectional study used daily travel distance and home dwell time derived from millions of anonymous mobile phone location data from March 11 to April 10, 2020, provided by the Descartes Labs and SafeGraph to quantify the degree to which social distancing mandates were followed in the 50 US states and District of Columbia and the association of mobility changes with rates of coronavirus disease 2019 (COVID-19) cases. Exposure: State-level stay-at-home orders during the COVID-19 pandemic. Main Outcomes and Measures: The main outcome was the association of state-specific rates of COVID-19 confirmed cases with the change rates of median travel distance and median home dwell time of anonymous mobile phone users. The increase rates are measured by the exponent in curve fitting of the COVID-19 cumulative confirmed cases, while the mobility change (increase or decrease) rates were measured by the slope coefficient in curve fitting of median travel distance and median home dwell time for each state. Results: Data from more than 45 million anonymous mobile phone devices were analyzed. The correlation between the COVID-19 increase rate and travel distance decrease rate was -0.586 (95% CI, -0.742 to -0.370) and the correlation between COVID-19 increase rate and home dwell time increase rate was 0.526 (95% CI, 0.293 to 0.700). Increases in state-specific doubling time of total cases ranged from 1.0 to 6.9 days (median [interquartile range], 2.7 [2.3-3.3] days) before stay-at-home orders were enacted to 3.7 to 30.3 days (median [interquartile range], 6.0 [4.8-7.1] days) after stay-at-home social distancing orders were put in place, consistent with pandemic modeling results. Conclusions and Relevance: These findings suggest that stay-at-home social distancing mandates, when they were followed by measurable mobility changes, were associated with reduction in COVID-19 spread. These results come at a particularly critical period when US states are beginning to relax social distancing policies and reopen their economies. These findings support the efficacy of social distancing and could help inform future implementation of social distancing policies should they need to be reinstated during later periods of COVID-19 reemergence.

[Use of serum haptoglobin concentration as an indicator in animal health monitoring of dairy cows]. / Nutzung der Haptoglobinkonzentration im Blutserum als Indikator im Tiergesundheitsmonitoring bei Milchkühen.

OBJECTIVE: To investigate the association between haptoglobin concentration in the blood and the occurrence of ketosis, selected clinical parameters as well as lameness in dairy cows. MATERIAL AND METHODS: The data was collected on 39 dairy farms in Bavaria over a period of 8 months. In 712 Simmental and Brown Swiss cows, clinical examinations as well as milk and blood samplings were performed between 10 and 30 days after calving. In these blood samples, the concentrations of non-esterified fatty acids (NEFA), ß-hydroxybutyric acid (BHBA) and haptoglobin (Hp) were determined. Analysis of the milk included milk constituents (fat, protein, urea, lactose and acetone), BHBA, NEFA and the somatic cell count (SCC). RESULTS: Significant correlations were found between increased Hp-concentration on the one hand and increased NEFA levels in blood and milk (p < 0.001), increased somatic cell count (p < 0.001), lameness (p < 0.001), as well as reduced lactose content (p < 0.001) and protein content in the milk (p = 0.001) on the other hand. Animals sampled during the warmer summer months showed significantly higher serum Hp-concentrations (p < 0,001). Heifers exhibited significantly higher Hp-values than multiparous individuals (p < 0.001). By dividing the examined cows into 4 clusters, a Hp-threshold value could be determined at 0.18 mg/ml. Combined with a SCC threshold of 40 500 cells/ml milk, the majority of animals with subclinical and clinical abnormalities could be identified. CONCLUSION AND CLINICAL RELEVANCE: Measurement of the Hp-concentration in blood is a pertinent approach in animal health monitoring during the postpartum period. In combination with evaluations of milk amount and contents, deviations from the physiological status may be recognized and affected individuals treated early on. Haptoglobin may be used to assess the health status of the individual animal as well as an indicator of herd health in the context of animal health monitoring.

Hot topic: Detecting digital dermatitis with computer vision.

Digital dermatitis (DD) is linked to severe lameness, infertility, and decreased milk production in cattle. Early detection of DD provides an improved prognosis for treatment and recovery; however, this is extremely challenging on commercial dairy farms. Computer vision (COMV) models can help facilitate early DD detection on commercial dairy farms. The aim of this study was to develop and implement a novel COMV tool to identify DD lesions on a commercial dairy farm. Using a database of more than 3,500 DD lesion images, a model was trained using the YOLOv2 architecture to detect the M-stages of DD. The YOLOv2 COMV model detected DD with an accuracy of 71%, and the agreement was quantified as "moderate" by Cohen's kappa when compared with a human evaluator for the internal validation. In the external validation, the YOLOv2 COMV model detected DD with an accuracy of 88% and agreement was quantified as "fair" by Cohen's kappa. Implementation of COMV tools for DD detection provides an opportunity to identify cows for DD treatment, which has the potential to lower DD prevalence and improve animal welfare on commercial dairy farms.

Large-scale genome analysis of bovine commensal Escherichia coli reveals that bovine-adapted E. coli lineages are serving as evolutionary sources of the emergence of human intestinal pathogenic strains.

How pathogens evolve their virulence to humans in nature is a scientific issue of great medical and biological importance. Shiga toxin (Stx)-producing Escherichia coli (STEC) and enteropathogenic E. coli (EPEC) are the major foodborne pathogens that can cause hemolytic uremic syndrome and infantile diarrhea, respectively. The locus of enterocyte effacement (LEE)-encoded type 3 secretion system (T3SS) is the major virulence determinant of EPEC and is also possessed by major STEC lineages. Cattle are thought to be the primary reservoir of STEC and EPEC. However, genome sequences of bovine commensal E. coli are limited, and the emerging process of STEC and EPEC is largely unknown. Here, we performed a large-scale genomic comparison of bovine commensal E. coli with human commensal and clinical strains, including EPEC and STEC, at a global level. The analyses identified two distinct lineages, in which bovine and human commensal strains are enriched, respectively, and revealed that STEC and EPEC strains have emerged in multiple sublineages of the bovine-associated lineage. In addition to the bovine-associated lineage-specific genes, including fimbriae, capsule, and nutrition utilization genes, specific virulence gene communities have been accumulated in stx- and LEE-positive strains, respectively, with notable overlaps of community members. Functional associations of these genes probably confer benefits to these E. coli strains in inhabiting and/or adapting to the bovine intestinal environment and drive their evolution to highly virulent human pathogens under the bovine-adapted genetic background. Our data highlight the importance of large-scale genome sequencing of animal strains in the studies of zoonotic pathogens.

Randomised clinical trial showing the curative effect of bandaging on M2-stage lesions of digital dermatitis in dairy cows.

OBJECTIVES AND DESIGN: This trial evaluated the effect of bandaging of acute painful ulcerative bovine digital dermatitis (DD) lesion (stage M2) in dairy cows, tested using two different topical treatments. DESIGN: Randomised clinical trial. SETTING: This study was conducted using Holstein-Friesian cows ranging in age from heifers to fourth lactation in a single dairy herd and diagnosed with acute ulcerative DD lesions (stage M2) on the first examination (week 0). Cows were randomly assigned into either a non-bandaged or bandaged group across two treatment conditions: topical chlortetracycline spray (CTC) and Intra Hoof-Fit Gel (IHF). Lesions received standardised bandaging and treatment on a weekly basis. Unhealed lesions could receive up to five repeated treatments, at weekly intervals, within a four-week period. Both M-stage and locomotion were also evaluated and scored weekly. Cows with healthily formed skin (stage M0) were deemed healed and subsequently released from the study. RESULTS: In total, 163 M2 lesions were diagnosed at week 0. Bandaged M2 lesions had a significantly higher probability of cure than non-bandaged lesions regardless of treatment type (HR: 4.1; P<0.001; 95 per cent CI: 2.5 to 6.8). Most healing occurred within the first three weeks of trial. Furthermore, bandaged lesions (group 2 and group 4) were significantly less likely to progress into the chronic hyperkeratotic or proliferative stage (M4) than non-bandaged lesions in group 1 and group 3 (HR: 0.10; P<0.001; 95 per cent CI: 0.04 to 0.22). Out of concern for the cow's wellbeing, this study investigated the effects of bandaging on locomotion. Bandaging had no effect on locomotion for either cows treated with CTC (group 1: median Sprecher score, 2; IQR=1-2; group 2: median Sprecher score, 2; IQR=1-3; P=0.3) or IHF (group 3: median Sprecher score, 2; IQR=1-2; group 4: median Sprecher score, 2; IQR=1-3; P=0.3).

New insights into the association between lameness, behavior, and performance in Simmental cows.

In a herd of 100 milking Simmental cows, data of performance and behavior parameters were collected automatically with different systems such as pedometers, an automatic milking system, and automatic weighing troughs for 1 yr. Performance measures were several milking-related parameters, live weight, as well as feed intake. Behavior-associated measures were feeding behavior (e.g. feeding duration, number of visits to the trough, and feeding pace) as well as activity such as lying duration, number of lying bouts, and overall activity. In the same time, lameness status of every cow was assessed with weekly locomotion scoring. According to the score animals were then classified lame (score 4 or 5) or nonlame (score 1, 2, or 3). From these data in total, 25 parameters summarized to daily values were evaluated for their ability to determine the lameness status of a cow. Data were analyzed with a regularized regression method called elastic net with the outcome lame or nonlame. The final model had a high prediction accuracy with an area under the curve of 0.91 [95% confidence interval (CI) = 0.88-0.94]. Specificity was 0.81 (95% CI = 0.73-0.85) and sensitivity was 0.94 (95% CI = 0.88-1.00). The most important factors associated with a cow being lame were number of meals, average feed intake per meal, and average duration of a meal. Lame cows fed in fewer and shorter meals with a decreased intake per meal. Milk yield and lying-behavior-associated parameters were relevant in the model, too, but only as parts of interaction terms demonstrating their strong dependence on other factors. A higher milk yield only resulted in higher risk of being lame if feed intake was decreased. The same accounts for lying duration: only if lying time was below the 50% quantile did an increased milk yield result in a higher risk of being lame. The association of lameness and daily lying duration was influenced by daily feeding duration and feeding duration at daytime. The results of the study give deeper insights on how the association between behavior and performance parameters and lameness is influenced by intrinsic factors in particular and that many of these have to be considered when trying to predict lameness based on such data. The findings lead to a better understanding why, for instance, lying duration or milk yield seem to be highly correlated with lameness in cows but still have not been overly useful as parameters in other lameness detection models.

Acquisition and persistence of strain-specific methicillin-resistant Staphylococcus aureus and their determinants in community nursing homes.

BACKGROUND: Nursing home residents are frequently colonized with various strains of methicillin-resistant Staphylococcus aureus (MRSA) but the intra-facility dynamics of strain-specific MRSA remains poorly understood. We aimed at identifying and quantifying the associations between acquisition and carriage of MRSA strains and their potential risk factors in community nursing homes using mathematical modeling. METHODS: The data was collected during a longitudinal MRSA surveillance study in six nursing homes in South Central Wisconsin. MRSA cultures were obtained from subjects every 3 months for up to one year. MRSA isolates were subsequently strain-typed by pulsed-field gel electrophoresis (PFGE), and their genetic similarity was established based on the Dice coefficients. Bayesian network analysis, logistic regression and elastic net were used to quantify the associations between acquisition and carriage of MRSA strains discriminated at 80% and 95% strain similarity thresholds and potentially modifiable resident characteristics including previous antibiotic exposure, comorbidity, medical devices, chronic wounds, functional and cognitive status and recent hospitalizations. RESULTS: Absence of severe cognitive impairment as well as presence of a wound, device and severe comorbidity was associated with elevated probability of USA100 carriage although there was a variation based on the combination of those risk factors. Residents with severe comorbidity and cognitive status and presence of device and wound were identified as certain carriers of USA100 in our sample. Residents with a chronic wound were more likely to carry USA100 MRSA (OR = 2.77, 95% CI = 1.37-5.87). Functional status was identified as an important determinant of carriage of USA100 and USA300 strains. Comorbidity and cognitive status were the two factors associated with carriage of all clonal groups in the study (USA100, USA300 and USA1200). CONCLUSIONS: The combination of Bayesian network analysis, logistic regression and elastic net can be used to identify associations between acquisition and carriage of MRSA strains and their potential risk factors in the face of scarce data. The revealed associations may be used to generate hypothesis for further study of determinants of acquisition and carriage of selected MRSA subtypes and to better inform infection control efforts in community nursing homes.

Evaluation of the Use of Zero-Augmented Regression Techniques to Model Incidence of Campylobacter Infections in FoodNet.

The Foodborne Diseases Active Surveillance Network (FoodNet) is currently using a negative binomial (NB) regression model to estimate temporal changes in the incidence of Campylobacter infection. FoodNet active surveillance in 483 counties collected data on 40,212 Campylobacter cases between years 2004 and 2011. We explored models that disaggregated these data to allow us to account for demographic, geographic, and seasonal factors when examining changes in incidence of Campylobacter infection. We hypothesized that modeling structural zeros and including demographic variables would increase the fit of FoodNet's Campylobacter incidence regression models. Five different models were compared: NB without demographic covariates, NB with demographic covariates, hurdle NB with covariates in the count component only, hurdle NB with covariates in both zero and count components, and zero-inflated NB with covariates in the count component only. Of the models evaluated, the nonzero-augmented NB model with demographic variables provided the best fit. Results suggest that even though zero inflation was not present at this level, individualizing the level of aggregation and using different model structures and predictors per site might be required to correctly distinguish between structural and observational zeros and account for risk factors that vary geographically.

Evaluating diagnostic tests for bovine tuberculosis in the southern part of Germany: A latent class analysis.

Germany has been officially free of bovine tuberculosis since 1996. However, in the last years there has been an increase of bovine tuberculosis cases, particularly in the southern part of Germany, in the Allgäu region. As a consequence a one-time tuberculosis surveillance program was revisited with different premortal and postmortal tests. The aim of this paper was to estimate diagnostic sensitivities and specificities of the different tests used within this surveillance program. In the absence of a perfect test with 100% sensitivity and 100% specificity, thus in the absence of a gold standard, a Bayesian latent class approach with two different datasets was performed. The first dataset included 389 animals, tested with single intra-dermal comparative cervical tuberculin (SICCT) test, PCR and pathology; the second dataset contained 175 animals, tested with single intra-dermal cervical tuberculin (SICT) test, Bovigam® assay, pathology and culture. Two-way conditional dependencies were considered within the models. Additionally, inter-laboratory agreement (five officially approved laboratories) of the Bovigam® assay was assessed with Cohen's kappa test (21 blood samples). The results are given in posterior means and 95% credibility intervals. The specificities of the SICT test, SICCT test, PCR and pathology ranged between 75.8% [68.8-82.2%] and 99.0% [96.8-100%]. The Bovigam® assay stood out with a very low specificity (6.9% [3.6-11.1%]), though it had the highest sensitivity (95.7% [91.3-99.2%]). The sensitivities of the SICCT test, PCR, SICT test, pathology and culture varied from 57.8% [48.0-67.6%] to 88.9% [65.5-99.7%]. The prevalences were 19.8% [14.6-26.5%] (three-test dataset) and 7.7% [4.2-12.3%] (four-test dataset). Among all pairwise comparisons the highest agreement was 0.62 [0.15-1]). In conclusion, the specificity of the Bovigam® assay and the inter-laboratory agreement were lower than expected.

Development of real-time PCR and loop-mediated isothermal amplification (LAMP) assays for the differential detection of digital dermatitis associated treponemes.

Bovine digital dermatitis (DD) is a severe infectious cause of lameness in cattle worldwide, with important economic and welfare consequences. There are three treponeme phylogroups (T. pedis, T. phagedenis, and T. medium) that are implicated in playing an important causative role in DD. This study was conducted to develop real-time PCR and loop-mediated isothermal amplification (LAMP) assays for the detection and differentiation of the three treponeme phylogroups associated with DD. The real-time PCR treponeme phylogroup assays targeted the 16S-23S rDNA intergenic space (ITS) for T. pedis and T. phagedenis, and the flagellin gene (flaB2) for T. medium. The 3 treponeme phylogroup LAMP assays targeted the flagellin gene (flaB2) and the 16S rRNA was targeted for the Treponeme ssp. LAMP assay. The real-time PCR and LAMP assays correctly detected the target sequence of all control strains examined, and no cross-reactions were observed, representing 100% specificity. The limit of detection for each of the three treponeme phylogroup real-time PCR and LAMP assays was ≤ 70 fg/µl. The detection limit for the Treponema spp. LAMP assay ranged from 7-690 fg/µl depending on phylogroup. Treponemes were isolated from 40 DD lesion biopsies using an immunomagnetic separation culture method. The treponeme isolation samples were then subjected to the real-time PCR and LAMP assays for analysis. The treponeme phylogroup real-time PCR and LAMP assay results had 100% agreement, matching on all isolation samples. These results indicate that the developed assays are a sensitive and specific test for the detection and differentiation of the three main treponeme phylogroups implicated in DD.