Search

Background: Rabbits are well-domesticated animals. As a crucial economic animal, rabbit has been successfully bred into wool-use, meat-use and fur-use breeds. Hair length is one of the most economically important traits affecting profitability in wool rabbits. In this study, to identify selection signatures with the long-hair trait, whole-genomic resequencing of long-haired rabbits (Angora rabbits) and short-haired rabbits (Rex and New Zealand rabbits) was performed. Results: By genome-wide selective sweeping analysis based on population comparison, we identified a total of 5.85 Mb regions (containing 174 candidate genes) with strong selection signals. Six of these genes (Dusp1, Ihh, Fam134a, Map3k1, Spata16, and Fgf5) were enriched in the MAPK signalling and Hedgehog signalling pathways, both of which are closely associated with hair growth regulation. Among these genes, Fgf5 encodes the FGF5 protein, which is a well-established regulator of hair growth. There was a nonsynonymous nucleotide substitution (T19234C) in the Fgf5 gene. At this locus, the C allele was present in all of the tested Angora rabbits, while the T allele was dominant in New Zealand and Rex rabbits. We further confirmed that the C allele was conserved in Angora rabbits by screening an additional 135 rabbits. Moreover, the results of functional predictions and co-immunoprecipitation revealed that the T19234C mutation impaired the binding capacity of FGF5 to its receptor FGFR1. Conclusions: We discovered that the homozygous missense mutation T19234C within Fgf5 might contribute to the long-hair trait of Angora rabbits by reducing its receptor binding capacity. This finding will provide new insights into the genetic basis underlying the genetic improvement of Angora rabbits and benefit the improvement of rabbit breeding in the future. [ABSTRACT FROM AUTHOR]

The growth and physiological process of microalgae interact with environmental nutrients. In the present study, we compared the growth and physiological characteristics of coccolithophore Chrysotila roscoffensis under the effects of phosphate at high and low concentrations, and of organic phosphorus of adenosine triphosphate (ATP) and Na2-glycerophosphate (SG). The growth, photosynthesis, calcification, alkaline phosphatase activity, and inorganic nutrient absorption rate were comparatively studied. The results showed that the culture with a low concentration of initial phosphate (6 μM) had a lower growth rate than that with a high concentration (45 μM). However, a relatively similar maximum cell density could be achieved. Equimolar inorganic phosphorus supported a higher initial growth rate than organic phosphorus. ATP was better than SG at supporting higher growth rates, higher photosynthetic activity, and higher cell density. Cellular alkaline phosphatase (AP) responded rapidly to nutrient variations with sharp changes of activities, independent of the initial P resources. Cellular calcification was at a higher level in groups with lower growth rates. Phosphate in low concentration in the medium was not absorbed during the early growth period while that in high concentration was rapidly absorbed. Instead, phosphate in low concentration was rapidly absorbed in the late stationary phase. The absorption of nitrate was affected by the initial P resources as well. The DIN/DIP ratio in the water varied significantly during the growth periods. The results indicated that C. roscoffensis had flexible physiological strategies in utilizing varied phosphorus resources, and high cell density maintenance of C. roscoffensis may play roles in nutrient conditions in the water. This study may help to extend the understanding of nutrient utilization strategy in microalgae and to apply reference in the application of Chrysotila species in the removal of nutrient pollution. [ABSTRACT FROM AUTHOR]

Arrhythmia is a common cardiovascular disease; the electrocardiogram (ECG) is widely used as an effective tool for detecting arrhythmia. However, real-time arrhythmia detection monitoring is difficult, so this study proposes a long short-term memory-residual model. Individual beats provide morphological features and combined with adjacent segments provide temporal features. Our proposed model captures the time-domain and morphological ECG signal information simultaneously and fuses the two information types. At the same time, the attention block is applied to the network to further strengthen the useful information, capture the hidden information in the ECG signal, and improve the model classification performance. Our model was finally trained and tested on the MIT-BIH arrhythmia database, and the entire dataset was divided into intrapatient and interpatient modes. Accuracies of 99.11% and 85.65%, respectively, were obtained under the two modes. Experimental results demonstrate that our proposed method is an efficient automated detection method. [ABSTRACT FROM AUTHOR]