BackgroundSelective breeding for genetic improvement is expected to leave distinctive selection signatures within genomes. The identification of selection signatures can help to elucidate the mechanisms of selection and accelerate genetic improvement. Fighting chickens have undergone extensive artificial selection, resulting in modifications to their morphology, physiology and behavior compared to wild species. Comparing the genomes of fighting chickens and wild species offers a unique opportunity for identifying signatures of artificial selection.ResultsWe identified selection signals in 100-kb windows sliding in 10-kb steps by using two approaches: the pooled heterozygosity and the fixation index between Xishuangbanna fighting chicken (YNLC) and Red Jungle Fowl. A total of 413 candidate genes were found to be putatively under selection in YNLC. These genes were related to traits such as growth, disease resistance, aggressive behavior and energy metabolism, as well as the morphogenesis and homeostasis of many tissues and organs.ConclusionsThis study reveals mechanisms and targets of artificial selection, which will contribute to improve our knowledge about the evolution of fighting chickens and facilitate future quantitative trait loci mapping.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0239-4) contains supplementary material, which is available to authorized users.

Class labels are required for supervised learning but may be corrupted or missing in various applications. In binary classification, for example, when only a subset of positive instances is labeled whereas the remaining are unlabeled, positive-unlabeled (PU) learning is required to model from both positive and unlabeled data. Similarly, when class labels are corrupted by mislabeled instances, methods are needed for learning in the presence of class label noise (LN). Here we propose adaptive sampling (AdaSampling), a framework for both PU learning and learning with class LN. By iteratively estimating the class mislabeling probability with an adaptive sampling procedure, the proposed method progressively reduces the risk of selecting mislabeled instances for model training and subsequently constructs highly generalizable models even when a large proportion of mislabeled instances is present in the data. We demonstrate the utilities of proposed methods using simulation and benchmark data, and compare them to alternative approaches that are commonly used for PU learning and/or learning with LN. We then introduce two novel bioinformatics applications where AdaSampling is used to: 1) identify kinase-substrates from mass spectrometry-based phosphoproteomics data and 2) predict transcription factor target genes by integrating various next-generation sequencing data.

Background and Objective: Propofol is the most commonly used sedative in gastrointestinal endoscopic procedures, but is associated with cardiorespiratory suppression, particularly in elderly patients. Remimazolam is a new short-acting GABA(A) receptor agonist with minimal impact on cardiorespiratory suppression, and may be a viable alternative in elderly patients undergoing endoscopic procedures.Methods: This multicenter, randomized controlled trial was conducted between September 2020 and September 2021. Elderly patients (65-85 years of age) scheduled to undergo upper gastrointestinal endoscopy were randomized in 1:1 ratio to receive remimazolam tosilate (300 mg/h) or propofol (3 g/h) in addition to 50-μg fentanyl, until the Modified Observer's Assessment of Alertness/Sedation Scale (MOAA/S) reached ≤1. MOAA/S was maintained at 0 or 1 throughout the procedure using 2.5 mg remimazolam or 0.5 mg/kg propofol boluses in the two groups, respectively. The primary outcome was the rate of hypotension (defined as systolic blood pressure at ≤90 mmHg or > 30% decline vs. the baseline). Bradycardia was defined as heart rate ≤50 per minute; respiratory depression was defined as respiratory rate <8 per minute and/or SpO 2 < 90%.Results: A total of 400 patients (161 men and 239 women; 70.4 ± 4.6 years of age) were enrolled (200 patients per group). Average body mass index was 22.2 ± 2.4 kg/m 2 . The rate of hypotension was 36.5% in the remimazolam group and 69.6% in the propofol group (p < 0.001). The remimazolam group also had a lower Kejian Lu, Shanshan Wei and Wenwen Ling contributed equally to this work.

Eggs with a much higher proportion of thick albumen are preferred in the layer industry, as they are favoured by consumers. However, the genetic factors affecting the thick egg albumen trait have not been elucidated. Using RNA sequencing, we explored the magnum transcriptome in 9 Rhode Island white layers: four layers with phenotypes of extremely high ratios of thick to thin albumen (high thick albumen, HTA) and five with extremely low ratios (low thick albumen, LTA). A total of 220 genes were differentially expressed, among which 150 genes were up-regulated and 70 were down-regulated in the HTA group compared with the LTA group. Gene Ontology (GO) analysis revealed that the up-regulated genes in HTA were mainly involved in a wide range of regulatory functions. In addition, a large number of these genes were related to glycosphingolipid biosynthesis, focal adhesion, ECM-receptor interactions and cytokine-cytokine receptor interactions. Based on functional analysis, ST3GAL4, FUT4, ITGA2, SDC3, PRLR, CDH4 and GALNT9 were identified as promising candidate genes for thick albumen synthesis and metabolism during egg formation. These results provide new insights into the molecular mechanisms of egg albumen traits and may contribute to future breeding strategies that optimise the proportion of thick egg albumen.

No abstract