Use of RFLPs to identify genes for aluminium tolerance in maize.

The objective of this study was to identify restriction fragment length polymorphism (RFLP) markers linked to quantitative trait loci that control. Al tolerance in maize. The strategy used was bulked segregant analysis, which is based on selecting for bulk bred true F2 individuals. The genetic material used consisted of an F2 population derived from a cross between Al susceptible (L53) and Al tolerant (L1327) maize inbred lines, Both lines were developed in the maize breeding programme of the Centro Nacional de Pesquisa de Milho e Sorgo. The relative seminal root length (RSRL) index was used as the phenotypic measure of tolerance. The frequency distribution of RSRL showed continuous distribution, which is typical of a quantitatively inherited character, with a tendency towards Al susceptible individuals. The estimated heritability was found to be 60%. This moderately high heritability value suggests that, although the caracter has a quantitative nature, it may be controlled by a small number of genes, Those seedlings of the F2 population that stored the highest and lowest values for RSRL were subsequently selfed to obtain the F3 families. These falmilies were evaluated in nutrient solution to identify those that were not segregating. On the basis of the results, five individuals were chosen for each bulk. Sixty-five probes were selected at an average interval 0f 30 cM, covering all ten maize chromosomes. For the hybridization work, a non-radioctive labelling system, using dig-dUTP and alkaline phosphatase, proved to be quite efficient and reliable, resulting in Southern blots with good resolution and allowing the menbranes to be stripped and reprobed as least three times. Twenty-three markers showed a co-dominant effect, identifying 40 RFLP loci that could distinguish the parental inbred lines. These 23 probes are now being hybridized with DNA from the two contrasting bulks. Also, a search for other informative markers is being carried out to increase genome coverage

Regret Bounds for Risk-sensitive Reinforcement Learning with Lipschitz Dynamic Risk Measures

We study finite episodic Markov decision processes incorporating dynamic risk measures to capture risk sensitivity. To this end, we present two model-based algorithms applied to \emph{Lipschitz} dynamic risk measures, a wide range of risk measures that subsumes spectral risk measure, optimized certainty equivalent, distortion risk measures among others. We establish both regret upper bounds and lower bounds. Notably, our upper bounds demonstrate optimal dependencies on the number of actions and episodes, while reflecting the inherent trade-off between risk sensitivity and sample complexity. Additionally, we substantiate our theoretical results through numerical experiments

Closure properties and complexity of rational sets of regular languages

This work received funding in part by the National Research Network RiSE on Rigorous Systems Engineering (Austrian Science Fund (FWF): S11403-N23), by the Vienna Science and Technology Fund (WWTF) through grant PROSEED, by an Erwin Schrödinger Fellowship (Austrian Science Fund (FWF): J3696-N26), and by the European Research Council under the European Community's Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement DIADEM no. 246858

A general efficiency relation for molecular machines

Living systems efficiently use chemical fuel to do work, process information, and assemble patterns despite thermal noise. Whether high efficiency arises from general principles or specific fine-tuning is unknown. Here, applying a recent mapping from nonequilibrium systems to battery-resistor circuits, I derive an analytic expression for the efficiency of any dissipative molecular machine driven by one or a series of chemical potential differences. This expression disentangles the chemical potential from the machine's details, whose effect on the efficiency is fully specified by a constant called the load resistance. The efficiency passes through a switch-like inflection point if the balance between chemical potential and load resistance exceeds thermal noise. Therefore, dissipative chemical engines qualitatively differ from heat engines, which lack threshold behavior. This explains all-or-none dynein stepping with increasing ATP concentration observed in single-molecule experiments. These results indicate that biomolecular energy transduction is efficient not because of idosyncratic optimization of the biomolecules themselves, but rather because the concentration of chemical fuel is kept above a threshold level within cells