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Some pygmy chimpanzees (also called Bonobos) give much simpler patterns of hybridization on Southern blotting with killer cell immunoglobulin-like receptor (KIR) cDNA probes than do either humans or common chimpanzees. Characterization of KIRs from pygmy chimpanzees having simple and complex banding patterns identified nine different KIRs, representing seven genes. Five of these genes have orthologs in the common chimpanzee, and three of them (KIRCI, KIR2DL4, and KIR2DL5) also have human orthologs. The remaining two genes are KIR3D paralogous to the human and common chimpanzee major histocompatibility complex A– and/or -B–specific KIRs. Within a pygmy chimpanzee family, KIR haplotypes were defined. Simple patterns on Southern blot were due to inheritance of “short” KIR haplotypes containing only three KIR genes, KIRCI, KIR2DL4, and KIR3D, each of which represents one of the three major KIR lineages. These three genes in pygmy chimpanzees or their corresponding genes in humans and common chimpanzees form the conserved “framework” common to all KIR haplotypes in these species and upon which haplotypic diversity is built. The fecundity and health of individual pygmy chimpanzees who are homozygotes for short KIR haplotypes attest to the viability of short KIR haplotypes, indicating that they can provide minimal, essential KIRs for the natural killer and T cells of the hominoid immune system.

SummaryCotton is widely cultivated globally because it provides natural fibre for the textile industry and human use. To identify quantitative trait loci (QTLs)/genes associated with fibre quality and yield, a recombinant inbred line (RIL) population was developed in upland cotton. A consensus map covering the whole genome was constructed with three types of markers (8295 markers, 5197.17 centimorgans (cM)). Six fibre yield and quality traits were evaluated in 17 environments, and 983 QTLs were identified, 198 of which were stable and mainly distributed on chromosomes 4, 6, 7, 13, 21 and 25. Thirty‐seven QTL clusters were identified, in which 92.8% of paired traits with significant medium or high positive correlations had the same QTL additive effect directions, and all of the paired traits with significant medium or high negative correlations had opposite additive effect directions. In total, 1297 genes were discovered in the QTL clusters, 414 of which were expressed in two RNA‐Seq data sets. Many genes were discovered, 23 of which were promising candidates. Six important QTL clusters that included both fibre quality and yield traits were identified with opposite additive effect directions, and those on chromosome 13 (qClu‐chr13‐2) could increase fibre quality but reduce yield; this result was validated in a natural population using three markers. These data could provide information about the genetic basis of cotton fibre quality and yield and help cotton breeders to improve fibre quality and yield simultaneously.

Oscillatory density profiles (layers) have previously been observed at the free surfaces of liquid metals but not in other isotropic liquids. We have used x-ray reflectivity to study a molecular liquid, tetrakis(2-ethylhexoxy)silane. When cooled to T/Tc approximately 0.25 (well above the freezing point for this liquid), density oscillations appear at the surface. Lateral order within the layers is liquidlike. Our results confirm theoretical predictions that a surface-layered state will appear even in dielectric liquids at sufficiently low temperatures, if not preempted by freezing.

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Abstract. The large volume of web content needs to be annotated by ontologies (called Semantic Annotation), and our empirical study shows that strong dependencies exist across different types of information (it means that identification of one kind of information can be used for identifying the other kind of information). Conditional Random Fields (CRFs) are the state-of-the-art approaches for modeling the dependencies to do better annotation. However, as information on a Web page is not necessarily linearly laid-out, the previous linear-chain CRFs have their limitations in semantic annotation. This paper is concerned with semantic annotation on hierarchically dependent data (hierarchical semantic annotation). We propose a Tree-structured Conditional Random Field (TCRF) model to better incorporate dependencies across the hierarchically laid-out information. Methods for performing the tasks of model-parameter estimation and annotation in TCRFs have been proposed. Experimental results indicate that the proposed TCRFs for hierarchical semantic annotation can significantly outperform the existing linear-chain CRF model.

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