Association between the Area of the Highest Flank Temperature and Concentrations of Reproductive Hormones during Pregnancy in Polish Konik Horses-A Preliminary Study

Plasma relaxin, estrogens and progestogens concentration in coldblooded and hot-blood mares in the second half of gestation

Monitoring estrogens, progestogens, and relaxin plasma concentration can be important for determining pregnancies at risk of failure in mares. However, significant breed-related differences in values of reproductive hormones are known, and data concerning coldblooded (CB) mares are missing. The aim of the study was to determine and compare serum relaxin, estrone sulphate (E1S), estradiol-17β (E2), and progestogens concentration in pregnant CB and hot-blood (HB) mares during the second half of gestation. Blood samples obtained from 94 healthy mares in the second half of gestation with known mating dates were analyzed. Samples were collected from 36 CB mares aged from 3 to 16 years and 58 HB mares aged from 4 to 17 years. The hormone concentrations were measured using immunoenzymatic equine species-specific commercial kits. The serum relaxin concentration was almost three times lower in CB, whereas E1S and E2 almost twice as low, as compared to HB mares (P < 0.05). In conclusion, the extremely low concentration of relaxin in CB mares could be one reason for the increased amounts of difficult parturition requiring veterinary intervention noted in these breeds.

Comparison of Fecal Microbiota Communities between Primiparous and Multiparous Cows during Non-Pregnancy and Pregnancy

Imbalances in the gut microbiota composition may lead to several reproductive disorders and diseases during pregnancy. This study investigates the fecal microbiome composition between primiparous and multiparous cows during non-pregnancy and pregnancy to analyze the host-microbial balance at different stages. The fecal samples obtained from six cows before their first pregnancy (BG), six cows during their first pregnancy (FT), six open cows with more than three lactations (DCNP), and six pregnant cows with more than three lactations (DCP) were subjected to 16S rRNA sequencing, and a differential analysis of the fecal microbiota composition was performed. The three most abundant phyla in fecal microbiota were Firmicutes (48.68%), Bacteroidetes (34.45%), and Euryarchaeota (15.42%). There are 11 genera with more than 1.0% abundance at the genus level. Both alpha diversity and beta diversity showed significant differences among the four groups (p < 0.05). Further, primiparous women were associated with a profound alteration of the fecal microbiota. The most representative taxa included Rikenellaceae_RC9_gut_group, Prevotellaceae_UCG_003, Christensenellaceae_R_7_group, Ruminococcaceae UCG-005, Ruminococcaceae UCG-013, Ruminococcaceae UCG-014, Methanobrevibacter, and [Eubacterium] coprostanoligenes group, which were associated with energy metabolism and inflammation. The findings indicate that host-microbial interactions promote adaptation to pregnancy and will benefit the development of probiotics or fecal transplantation for treating dysbiosis and preventing disease development during pregnancy.

Selection of Image Texture Analysis and Color Model in the Advanced Image Processing of Thermal Images of Horses following Exercise

Simple Summary

Detecting horse state after exercise is critical for maximizing athletic performance. The horse’s response to fatigue includes exercise termination or exercise continuation at a lower intensity, which significantly limit the results achieved in races and equestrian competition. As conventional methods of detecting and quantifying exercise effort have shown some limitations, infrared thermography was proposed as a method of contactless detection of exercise effect. The promising correlation between body surface temperature and exercise-dependent blood biomarkers has been demonstrated. As the application of conventional thermography is limited by low specificity, advanced thermal image analysis was proposed here to visualize the link between blood biomarkers and texture of thermal images. Twelve horses underwent standardized exercise tests for six consecutive days, and both thermal images and blood samples were collected before and after each test. The images were analyzed using four color models (RGB, red-green-blue; YUV, brightness-UV-components; YIQ, brightness-IQ-components; HSB, hue-saturation-brightness) and eight texture-features approaches, including 88 features in total. In contrast to conventional temperature measures, as many as twelve texture features in two color models (RGB, YIQ) were linked with blood biomarker levels as part of the horse’s response to exercise.

Abstract

As the detection of horse state after exercise is constantly developing, a link between blood biomarkers and infrared thermography (IRT) was investigated using advanced image texture analysis. The aim of the study was to determine which combinations of RGB (red-green-blue), YUI (brightness-UV-components), YIQ (brightness-IQ-components), and HSB (hue-saturation-brightness) color models, components, and texture features are related to the blood biomarkers of exercise effect. Twelve Polish warmblood horses underwent standardized exercise tests for six consecutive days. Both thermal images and blood samples were collected before and after each test. All 144 obtained IRT images were analyzed independently for 12 color components in four color models using eight texture-feature approaches, including 88 features. The similarity between blood biomarker levels and texture features was determined using linear regression models. In the horses’ thoracolumbar region, 12 texture features (nine in RGB, one in YIQ, and two in HSB) were related to blood biomarkers. Variance, sum of squares, and sum of variance in the RGB were highly repeatable between image processing protocols. The combination of two approaches of image texture (histogram statistics and gray-level co-occurrence matrix) and two color models (RGB, YIQ), should be considered in the application of digital image processing of equine IRT.

Advances in Thermal Image Analysis for the Detection of Pregnancy in Horses Using Infrared Thermography

Infrared thermography (IRT) was applied as a potentially useful tool in the detection of pregnancy in equids, especially native or wildlife. IRT measures heat emission from the body surface, which increases with the progression of pregnancy as blood flow and metabolic activity in the uterine and fetal tissues increase. Conventional IRT imaging is promising; however, with specific limitations considered, this study aimed to develop novel digital processing methods for thermal images of pregnant mares to detect pregnancy earlier with higher accuracy. In the current study, 40 mares were divided into non-pregnant and pregnant groups and imaged using IRT. Thermal images were transformed into four color models (RGB, YUV, YIQ, HSB) and 10 color components were separated. From each color component, features of image texture were obtained using Histogram Statistics and Grey-Level Run-Length Matrix algorithms. The most informative color/feature combinations were selected for further investigation, and the accuracy of pregnancy detection was calculated. The image texture features in the RGB and YIQ color models reflecting increased heterogeneity of image texture seem to be applicable as potential indicators of pregnancy. Their application in IRT-based pregnancy detection in mares allows for earlier recognition of pregnant mares with higher accuracy than the conventional IRT imaging technique.

Thermal features, ambient temperature and hair coat lengths: Limitations of infrared imaging in pregnant primitive breed mares within a year

Infrared thermography is a non‐invasive technique which allows to distinguish between pregnant and non‐pregnant animals. Detecting accurate body surface temperatures can be challenging due to external factors altering thermograph measurements. This study aimed to determine the associations between the ambient temperature, the hair coat features and the temperatures of mares' abdomens. It compared pregnant and non‐pregnant mares throughout 11 months. The research was carried out on 40 Konik Polski mares, which were divided into pregnant and non‐pregnant groups. The temperature (Tmax, maximal; Taver, average; Tmin, minimal) of the mares' abdomen was evaluated in two regions of interest: the whole area of the lateral surface of the mares' abdomen (Px1) and the flank area of the lateral surface of mares' abdomen (Px2). During the increasing period, the slopes in the linear regression equation did not differ significantly for ambient (Tamb) and surface temperatures in both groups. In the decreasing period, the slopes did not differ significantly for Tamb and Tmax in the non‐pregnant group. They also did not differ for Tamb and Taver in Px1 and Tamb and Tmin in Px1 in both pregnant and non‐pregnant groups respectively. Other slopes varied significantly (p < .001). There was no evidence of parallel changes in hair coat features and measured temperatures. The flank area appears more suitable for thermal imaging in pregnant mares due to the seasonal fluctuations in hair coat lengths.