To investigate the efficacy of alternatives to antibiotics, the present study was conducted to compare the effects of antibiotic, lactic acid, a blend of commercial essential oils (EOs) and EOs in combination with lactic acid on growth performance and the functional activity of the gut in broiler chickens. A total of 168 broiler chickens were given the basal diet supplemented with 10 ppm colistin (T1), 0.1% lactic acid (T2), 25 ppm EOs (T3), 25 ppm EOs+0.1% lactic acid (T4), 50 ppm EOs (T5) or 50 ppm EOs+0.1% lactic acid (T6) in the period 3 to 35 days of age. As a result, the broiler chickens assigned to T4 group throughout the experimental period had apparently (p<0.05) greater body weight and total gain than these assigned to T1, T2, T3 and T5 groups. However, there was no difference in growth performance among the birds fed the diets supplemented with antibiotic (T1), lactic acid (T2) and EOs (T3 and T5) alone. The weights of digestive organs and the number of lactobacilli and E. coli in the lower ileum were not affected by dietary treatments. Total trypsin activity was significantly (p<0.05) greater in T4 than T1, T2, T3 and T5 groups. Total and specific pancreatic αamylase activities were significantly (p<0.05) enhanced in the broiler chickens fed T4 diet compared with these fed T1, T2 and T3 diets. However, there were no differences in growth performance and digestive enzyme activities including pancreatic trypsin and α-amylase between T4 and T6 groups fed the diets supplemented with either low or high EOs levels in combination of lactic acid. In conclusion, a blend of commercial EOs combined with lactic acid showed significant increases in digestive enzyme activities of the pancreas and intestinal mucosa, leading to increase in growth performance.

Diseases prediction has been performed by machine learning approaches with various biological data. One of the representative data is the gut microbial community, which interacts with the host’s immune system. The abundance of a few microorganisms has been used as markers to predict diverse diseases. In this study, we hypothesized that multi-classification using machine learning approach could distinguish the gut microbiome from following six diseases: multiple sclerosis, juvenile idiopathic arthritis, myalgic encephalomyelitis/chronic fatigue syndrome, acquired immune deficiency syndrome, stroke and colorectal cancer. We used the abundance of microorganisms at five taxonomy levels as features in 696 samples collected from different studies to establish the best prediction model. We built classification models based on four multi-class classifiers and two feature selection methods including a forward selection and a backward elimination. As a result, we found that the performance of classification is improved as we use the lower taxonomy levels of features; the highest performance was observed at the genus level. Among four classifiers, LogitBoost–based prediction model outperformed other classifiers. Also, we suggested the optimal feature subsets at the genus-level obtained by backward elimination. We believe the selected feature subsets could be used as markers to distinguish various diseases simultaneously. The finding in this study suggests the potential use of selected features for the diagnosis of several diseases.

BackgroundIndigenous cattle in Africa have adapted to various local environments to acquire superior phenotypes that enhance their survival under harsh conditions. While many studies investigated the adaptation of overall African cattle, genetic characteristics of each breed have been poorly studied.ResultsWe performed the comparative genome-wide analysis to assess evidence for subspeciation within species at the genetic level in trypanotolerant N’Dama cattle. We analysed genetic variation patterns in N’Dama from the genomes of 101 cattle breeds including 48 samples of five indigenous African cattle breeds and 53 samples of various commercial breeds. Analysis of SNP variances between cattle breeds using wMI, XP-CLR, and XP-EHH detected genes containing N’Dama-specific genetic variants and their potential associations. Functional annotation analysis revealed that these genes are associated with ossification, neurological and immune system. Particularly, the genes involved in bone formation indicate that local adaptation of N’Dama may engage in skeletal growth as well as immune systems.ConclusionsOur results imply that N’Dama might have acquired distinct genotypes associated with growth and regulation of regional diseases including trypanosomiasis. Moreover, this study offers significant insights into identifying genetic signatures for natural and artificial selection of diverse African cattle breeds.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3742-2) contains supplementary material, which is available to authorized users.

Background Pancreatic and biliary tract cancer (PC and BTC, respectively) are difficult to diagnose because of their clinical characteristics; however, recent studies suggest that serum microRNAs (miRNAs) might be the key to developing more efficient diagnostic methods for these cancers. Methods We analysed the genome-wide expression of serum miRNAs in PC and BTC patients to identify novel biomarker candidates using high-throughput sequencing and experimentally validated miRNAs on clinical samples. Results Statistical and classification analysis of the serum miRNA-expression profiles of 55 patient samples showed distinguishable patterns between cancer patients and healthy controls; however, we were unable to distinguish the two cancers. We found that three of the highest performing miRNAs were capable of distinguishing cancer patients from controls, with an accuracy of 92.7%. Additionally, dysregulation of these three cancer-specific miRNAs was demonstrated in an independent sample group by quantitative reverse transcription polymerase chain reaction. Conclusions These results suggested three candidate serum miRNAs (mir-744-5p, mir-409-3p, and mir-128-3p) as potential biomarkers for PC and BTC diagnosis. Electronic supplementary material The online version of this article (10.1186/s12920-019-0521-8) contains supplementary material, which is available to authorized users.

The tail of many animal species is responsible for various physiological functions. The functional importance of tail may have brought tail-loss to attention in many evolutionary and developmental studies. To provide a better explanation for the loss of tail, the current study aims to identify the evolutionary history and putative causal variants for the short tail in DongGyeongi (DG), an endangered dog breed, which is also the only dog in Korea that possesses a short tail. Whole genome sequencing was conducted on 22 samples of DG, followed by an investigation of population stratification with 10 other dog breeds. The genotypes, selective sweep and demography of DG were also investigated. As a result, we discovered the unique genetic structure of DG and suggested two possible ways in which the short tail phenotype developed. Moreover, this study suggested that selective sweep genes, ANKRD11 and ACVR2B may contribute to the reduction in tail length, and non-synonymous variant in the coding sequence of T gene and the CpG island variant of SFRP2 gene are the candidate causal variants for the tail-loss.