A New Kind of Action Explanation and The Life of Complex Action

Ordinary action explanation formulated as "I am doing A because I am doing B" is explanation of an action in terms of another action-in-progress. According to Michael Thompson, the explained action is a teleological part of the explaining complex action, which is composed of different parts. Thompson's analysis focuses on the part-whole relation between the explained action and the explaining action, thus ignores a possibility: these two actions can be two different parts of a complex action. I shall argue that the interrelation between different parts of a complex action corresponds to a new kind of action explanation of the formulas: "I am doing A because I am doing B" and "I am doing B because I have done A", where A and B are two different parts of a complex action. This kind of action explanation is associated with the temporal schema of an action-type, for example, X: First, do A; second, do B; then, C; finally, D. This is an attempt to probe into the inner structure of complex action

QTL Mapping of Combining Ability and Heterosis of Agronomic Traits in Rice Backcross Recombinant Inbred Lines and Hybrid Crosses

BACKGROUND: Combining ability effects are very effective genetic parameters in deciding the next phase of breeding programs. Although some breeding strategies on the basis of evaluating combining ability have been utilized extensively in hybrid breeding, little is known about the genetic basis of combining ability. Combining ability is a complex trait that is controlled by polygenes. With the advent and development of molecular markers, it is feasible to evaluate the genetic bases of combining ability and heterosis of elite rice hybrids through QTL analysis. METHODOLOGY/PRINCIPAL FINDINGS: In the present study, we first developed a QTL-mapping method for dissecting combining ability and heterosis of agronomic traits. With three testcross populations and a BCRIL population in rice, biometric and QTL analyses were conducted for ten agronomic traits. The significance of general combining ability and special combining ability for most of the traits indicated the importance of both additive and non-additive effects on expression levels. A large number of additive effect QTLs associated with performance per se of BCRIL and general combining ability, and dominant effect QTLs associated with special combining ability and heterosis were identified for the ten traits. CONCLUSIONS/SIGNIFICANCE: The combining ability of agronomic traits could be analyzed by the QTL mapping method. The characteristics revealed by the QTLs for combining ability of agronomic traits were similar with those by multitudinous QTLs for agronomic traits with performance per se of BCRIL. Several QTLs (1-6 in this study) were identified for each trait for combining ability. It demonstrated that some of the QTLs were pleiotropic or linked tightly with each other. The identification of QTLs responsible for combining ability and heterosis in the present study provides valuable information for dissecting genetic basis of combining ability

Genomic selection of agronomic traits in hybrid rice using an NCII population

Abstract Background Hybrid breeding is an effective tool to improve yield in rice, while parental selection remains the key and difficult issue. Genomic selection (GS) provides opportunities to predict the performance of hybrids before phenotypes are measured. However, the application of GS is influenced by several genetic and statistical factors. Here, we used a rice North Carolina II (NC II) population constructed by crossing 115 rice varieties with five male sterile lines as a model to evaluate effects of statistical methods, heritability, marker density and training population size on prediction for hybrid performance. Results From the comparison of six GS methods, we found that predictabilities for different methods are significantly different, with genomic best linear unbiased prediction (GBLUP) and least absolute shrinkage and selection operation (LASSO) being the best, support vector machine (SVM) and partial least square (PLS) being the worst. The marker density has lower influence on predicting rice hybrid performance compared with the size of training population. Additionally, we used the 575 (115 × 5) hybrid rice as a training population to predict eight agronomic traits of all hybrids derived from 120 (115 + 5) rice varieties each mating with 3023 rice accessions from the 3000 rice genomes project (3 K RGP). Of the 362,760 potential hybrids, selection of the top 100 predicted hybrids would lead to 35.5%, 23.25%, 30.21%, 42.87%, 61.80%, 75.83%, 19.24% and 36.12% increase in grain yield per plant, thousand-grain weight, panicle number per plant, plant height, secondary branch number, grain number per panicle, panicle length and primary branch number, respectively. Conclusions This study evaluated the factors affecting predictabilities for hybrid prediction and demonstrated the implementation of GS to predict hybrid performance of rice. Our results suggest that GS could enable the rapid selection of superior hybrids, thus increasing the efficiency of rice hybrid breeding

Biotemplated fabrication of size controlled palladium nanoparticle chains

Metal nanoparticles exhibit unique size- and spatial organization-dependent physical and chemical properties, and have a wide range of applications in various areas including single electron devices, chemical catalysts and biomedicines. In this paper, chains of palladium nanoparticles were obtained by incubating aged sodium tetrachloropalladate(II) with glucagon fibrils pre-deposited on a solid surface. AFM height profiles showed that the size of the palladium nanoparticles within the chains could be fine tuned in the range of similar to 2 to 16 nm as a function of the concentration of the sodium tetrachloropalladate(II). Moreover, the coverage of the palladium nanoparticles along the fibrils was controlled simply by varying the incubation time. This method provides a facile approach for the construction of a palladium nanoparticle ensemble on biotemplates

The opposite effects of Cu(II) and Fe(III) on the assembly of glucagon amyloid fibrils

A few transition metal ions are strongly implicated as co-factors in modulating the aggregation of amyloid peptides, which is believed to be a key factor in regulating the cytotoxicity of peptides. In this paper, we explored the effects of Cu(II) and Fe(III) on the aggregation/fibrillation of glucagon peptides using various biophysical techniques. AFM analysis demonstrated that Cu(II) could promote the conversion of glucagon peptides into amyloid fibrils, while the formation of fibrils was profoundly suppressed in the presence of Fe(III). Strikingly, at higher Cu(II) concentration (200 mu M), spherical assemblies were predominant with abundant fibrils protruding from spherical cores. However, only globular aggregates of several nanometers size were observed when the concentration of Fe(III) was increased beyond 100 mu M. In addition, it was also found that the FTIR and CD spectra of glucagon co-incubated with Cu(II) or Fe(III) remarkably differed from that in the absence of ions. These results strongly suggested that Cu(II) and Fe(III) could dramatically modify the morphologies as well as the secondary structures of aggregates during the spontaneous fibrillation of glucagon. Our study could shed light on how the metal ions regulate the amyloid aggregation of glucagon peptide, and might provide a controllable means for the synthesis of amyloid nanostructures for future technological applications

<i>De Novo</i> Transcriptome and Small RNA Analyses of Two Amorphophallus Species

<div><p>Konjac is one of the most important glucomannan crops worldwide. The breeding and genomic researches are largely limited by the genetic basis of <i>Amorphophallus</i>. In this study, the transcriptomes of <i>A. konjac</i> and <i>A. bulbifer</i> were constructed using a high-throughput Illumina sequencing platform. All 108,651 unigenes with average lengths of 430 nt in A. konjac and 119,678 unigenes with average lengths of 439 nt were generated from 54,986,020 reads and 52,334,098 reads after filtering and assembly, respectively. A total of 54,453 transcripts in <i>A. konjac</i> and 55,525 in <i>A. bulbifier</i> were annotated by comparison with Nr, Swiss-Prot, KEGG, and COG databases after removing exogenous contaminated sequences. A total of 80,332 transcripts differentially expressed between <i>A. konjac</i> and <i>A. bulbifer.</i> The majority of the genes that are associated with konjac glucomannan biosynthetic pathway were identified. Besides, the small RNAs in <i>A. konjac</i> leaves were also obtained by deep sequencing technology. All of 5,499,903 sequences of small RNAs were obtained with the length range between 18 and 30 nt. The potential targets for the miRNAs were also predicted according to the konjac transcripts. Our study provides a systematic overview of the konjac glucomannan biosynthesis genes that are involved in konjac leaves and should facilitate further understanding of the crucial roles of carbohydrate synthesis and other important metabolism pathways in <i>Amorphophallus</i>.</p></div

Statistics of unigene assembly qualities.

<p>All sizes of the Unigenes were calculated.</p