Telomere length has attracted much interest as a topic of study in human reproduction; furthermore, the link between sperm telomere length and fertility outcomes has been investigated in other species. This biomarker, however, has not been much explored in other animals, such as pigs, and whether it is related to sperm quality and fertility outcomes remains unknown. The present work aimed to determine the absolute value of telomere length in pig sperm, as well as its relationship to sperm quality parameters and embryo development. Telomere length was determined through quantitative fluorescence in situ hybridization (qFISH) in 23 pig sperm samples and data were correlated to quality parameters (motility, morphology, and viability) and in vitro fertilization outcomes. We found that the mean telomere length in pig sperm was 22.1 ± 3.6 kb, which is longer than that previously described in humans. Whilst telomere length was not observed to be correlated to sperm quality variables (p > 0.05), a significant correlation between telomere length and the percentage of morulae 6 days after in vitro fertilization was observed (rs = 0.559; 95%C.I. = (−0.007 to 0.854); p = 0.047). Interestingly, this correlation was not found when percentages of early blastocysts/blastocysts (rs = 0.410; 95%C.I. = (−0.200 to 0.791); p = 0.164) and of hatching/hatched blastocysts (rs = 0.356; 95%C.I. = (− 0.260 to 0.766); p = 0.233) were considered. Through the separation of the samples into two groups by the median value, statistically significant differences between samples with shorter telomeres than the median and samples with longer telomeres than the median were found regarding development to morula (11.5 ± 3.6 vs. 21.8 ± 6.9, respectively) and to early blastocyst/blastocysts (7.6 ± 1.4 vs. 17.9 ± 12.2, respectively) (p < 0.05). In the light of these results, sperm telomere length may be a useful biomarker for embryo development in pigs, as sperm with longer telomeres lead to higher rates of morulae and blastocysts.
Water is an important factor in human survival and development. With the acceleration of urbanization, the problem of black and odorous water bodies has become increasingly prominent. It not only affects the living environment of residents in the city, but also threatens their diet and water quality. Therefore, the accurate monitoring and management of urban black and odorous water bodies is particularly important. At present, when researching water quality issues, the methods of fixed-point sampling and laboratory analysis are relatively mature, but the time and labor costs are relatively high. However, empirical models using spectral characteristics and different water quality parameters often lack universal applicability. In addition, a large number of studies on black and odorous water bodies are qualitative studies of water body types, and there are few spatially continuous quantitative analyses. Quantitative research on black and odorous waters is needed to identify the risk of health and environmental problems, as well as providing more accurate guidance on mitigation and treatment methods. In order to achieve this, a universal continuous black and odorous water index (CBOWI) is proposed that can classify waters based on evaluated parameters as well as quantitatively determine the degree of pollution and trends. The model of CBOWI is obtained by partial least squares machine learning through the parameters of the national black and odorous water classification standard. The fitting accuracy and monitoring accuracy of the model are 0.971 and 0.738, respectively. This method provides a new means to monitor black and odorous waters that can also help to improve decision-making and management.
Dental age is one of the most reliable methods for determining a patient’s age. The timing of teething, the period of tooth replacement, or the degree of tooth attrition is an important diagnostic factor in the assessment of an individual’s developmental age. It is used in orthodontics, pediatric dentistry, endocrinology, forensic medicine, and pathomorphology, but also in scenarios regarding international adoptions and illegal immigrants. The methods used to date are time-consuming and not very precise. For this reason, artificial intelligence methods are increasingly used to estimate the age of a patient. The present work is a continuation of the work of Zaborowicz et al. In the presented research, a set of 21 original indicators was used to create deep neural network models. The aim of this study was to verify the ability to generate a more accurate deep neural network model compared to models produced previously. The quality parameters of the produced models were as follows. The MAE error of the produced models, depending on the learning set used, was between 2.34 and 4.61 months, while the RMSE error was between 5.58 and 7.49 months. The correlation coefficient R2 ranged from 0.92 to 0.96.
Sweet cherry is a highly appreciated seasonal fruit with a high content of bioactive compounds; however, this highly perishable fruit has a relatively short shelf-life period. Here, we evaluated the evolution of the physicochemical and sensory qualities of sweet cherries (Prunus avium (L.) cv. Satin) under different storage conditions, namely at a Farmers’ Organization (FO) and in a Research Centre (RC) under normal and four different conditions of controlled atmosphere for 49 days. Additional parameters were monitored, such as rotten fruit incidence and stem appearance. Temperature was the factor that most influenced the fruit quality changes over the study time. In fact, fruits stored at higher mean temperatures showed higher weight loss, higher variation in CIE-Lab colour parameters, higher firmness loss, and browner and more dehydrated stems and were less appealing to the consumer. Controlled atmosphere conditions showed a smaller decrease in CIE-Lab colour parameters and lower weight loss. The incidence of rotting was very low and was always equal or lower than 2% for all conditions. Thus, RC chamber conditions were able to sustain fruit quality parameters over 28 days under normal atmosphere conditions and 49 days under controlled atmosphere conditions.
Foliar application of copper and zinc containing ion exchanged synthesised „zeolon-P4A” type zeolite was used in experiments. Foliar application in winter wheat experiments proved that plants had a better uptake and utilisation of copper and zinc bound on zeolite. As a result of the treatments yields and four quality parameters increased.