Emergent excitations in a geometrically frustrated magnet

Frustrated systems are ubiquitous, and they are interesting because their behaviour is difficult to predict; frustration can lead to macroscopic degeneracies and qualitatively new states of matter. Magnetic systems offer good examples in the form of spin lattices, where all interactions between spins cannot be simultaneously satisfied. Here we report how unusual composite spin degrees of freedom can emerge from frustrated magnetic interactions in the cubic spinel ZnCr(2)O(4). Upon cooling, groups of six spins self-organize into weakly interacting antiferromagnetic loops, whose directors -- the unique direction along which the spins are aligned, parallel or antiparallel -- govern all low-temperature dynamics. The experimental evidence comes from a measurement of the magnetic form factor by inelastic neutron scattering; the data show that neutrons scatter from hexagonal spin clusters rather than individual spins. The hexagon directors are, to a first approximation, decoupled from each other, and hence their reorientations embody the long-sought local zero energy modes for the pyrochlore lattice.     

Tetramerization of an RNA oligonucleotide containing a GGGG sequence.

Poly rG can form four-stranded helices. The Hoogsteen-paired quartets of G residues on which such structures depend are so stable that they will form in 5'-GMP solutions, provided that Na+ or K+ are present (see for example, refs 2-4). Telomeric DNA sequences, which are G-rich, adopt four-stranded antiparallel G-quartet conformations in vitro, and parallel tetramerization of G-rich sequences may be involved in meiosis. Here we show that RNAs containing short runs of Gs can also tetramerize. A 19-base oligonucleotide derived from the 5S RNA of Escherichia coli (strand III), 5'GCCGAUGGUAGUGUGGGGU3', forms a K(+)-stabilized tetrameric aggregate that depends on the G residues at its 3' end. This complex is so stable that it would be surprising if similar structures do not occur in nature.     

海洋来源真菌的曲酸发酵条件优化

为提高真菌Aspergillus sp.GXIMD02003的曲酸产量,本研究对其利用大米固体培养基发酵的条件进行优化,旨在获得成本低廉的曲酸原料,促进曲酸的工业化生产。研究采用单因素控制变量法考察最佳盐度和最佳发酵时间,通过HPLC法测定曲酸的产量。结果表明真菌Aspergillus sp.GXIMD02003产曲酸的最佳发酵条件为在2%海盐的大米固体培养基中发酵30 d,在此条件下1 000 g大米培养基能够发酵产生24.2 g曲酸。因此,海洋来源真菌Aspergillus sp.GXIMD02003可以作为曲酸的生产菌株,海盐浓度影响该菌株的曲酸代谢。     

An e?ective evolutionary algorithm for the multiple container packing problem

This paper focuses on a new optimization problem, which is called “The Multiple Container Packing Problem (MCPP)” and proposes a new evolutionary approach for it. The proposed evolutionary approach uses “Adaptive Link Adjustment Evolutionary Algorithm (ALA-EA)” as a basic framework and it incorporates a heuristic local improvement approach into ALA-EA. The first step of the local search algorithm is to raise empty space through the exchange among the packed items and then to improve the fitness value through packing unpacked items into the raised empty space. The second step is to exchange the packed items and the unpacked items one another toward improving the fitness value. The proposed algorithm is compared to the previous evolutionary approaches at the benchmark instances (with the same container capacity) and the modified benchmark instances (with different container capacity) and that the algorithm is proved to be superior to the previous evolutionary approaches in the solution quality.     

Computational prediction of small-molecule catalysts

Most organic and organometallic catalysts have been discovered through serendipity or trial and error, rather than by rational design. Computational methods, however, are rapidly becoming a versatile tool for understanding and predicting the roles of such catalysts in asymmetric reactions. Such methods should now be regarded as a first line of attack in the design of catalysts.     

An effective evolutionary algorithm for the multiple container packing problem

This paper focuses on a new optimization problem, which is called "The Multiple Container Packing Problem (MCPP)" and proposes a new evolutionary approach for it. The proposed evolutionary approach uses "Adaptive Link Adjustment Evolutionary Algorithm (ALA-EA)" as a basic framework and it incorporates a heuristic local improvement approach into ALA-EA. The first step of the local search algorithm is to raise empty space through the exchange among the packed items and then to improve the fitness value through packing unpacked items into the raised empty space. The second step is to exchange the packed items and the unpacked items one another toward improving the fitness value. The proposed algorithm is compared to the previous evolutionary approaches at the benchmark instances (with the same container capacity) and the modified benchmark instances (with different container capacity) and that the algorithm is proved to be superior to the previous evolutionary approaches in the solution quality.