Iron-Catalyzed Cleavage Reaction of Keto Acids with Aliphatic Aldehydes for the Synthesis of Ketones and Ketone Esters.

The radical-radical coupling reaction is an important synthetic strategy. In this study, the iron-catalyzed radical-radical cross-coupling reaction based on the decarboxylation of keto acids and decarbonylation of aliphatic aldehydes to obtain valuable aryl ketones is reported for the first time. Remarkably, when tertiary aldehydes were used as carbonyl sources, ketone esters were selectively obtained instead of ketones.

A fast estimate for the population recombination rate based on regression.

Recombination is a fundamental evolutionary force. Therefore the population recombination rate ρ plays an important role in the analysis of population genetic data; however, it is notoriously difficult to estimate. This difficulty applies both to the accuracy of commonly used estimates and to the computational efforts required to obtain them.

Pleistocene climate change and the origin of two desert plant species, Pugionium cornutum and Pugionium dolabratum (Brassicaceae), in northwest China.

Pleistocene climate change has had an important effect in shaping intraspecific genetic variation in many species; however, its role in driving speciation is less clear. We examined the possibility of a Pleistocene origin of the only two representatives of the genus Pugionium (Brassicaceae), Pugionium cornutum and Pugionium dolabratum, which occupy different desert habitats in northwest China. We surveyed sequence variation for internal transcribed spacer (ITS), three chloroplast (cp) DNA fragments, and eight low-copy nuclear genes among individuals sampled from 11 populations of each species across their geographic ranges.

Population genetic evidence for complex evolutionary histories of four high altitude juniper species in the Qinghai-Tibetan Plateau.

Population genetics data based on multiple nuclear loci provide invaluable information to understand demographic, selective, and divergence histories of the current species. We studied nucleotide variation at 13 nuclear loci in 53 populations distributed among four closely related, but morphologically distinct juniper species of the Qinghai-Tibetan Plateau (QTP). We used a novel approach combining Approximate Bayesian Computation and a recently developed neutrality test based on the maximum frequency of derived mutations to examine the demographic and selective histories of individual species, and isolation-with-migration analyses to study the joint history of the species and detect gene flow between them.

Distinguishing positive selection from neutral evolution: boosting the performance of summary statistics.

Summary statistics are widely used in population genetics, but they suffer from the drawback that no simple sufficient summary statistic exists, which captures all information required to distinguish different evolutionary hypotheses. Here, we apply boosting, a recent statistical method that combines simple classification rules to maximize their joint predictive performance. We show that our implementation of boosting has a high power to detect selective sweeps.

Polychlorinated dibenzo-p-dioxins and dibenzofuran contents in fish and sediment near a pentachlorophenol contaminated site.

Polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in sediment and fish of aqua farms close to a contaminated site of a pentachlorophenol production plant (CPDC-ASS) were investigated. The total PCDD/F I-TEQ level in sediment and fish ranged from 0.725 to 87.

Classification of transcription factors using protein primary structure.

The transcription factor (TF) is a protein that binds DNA at specific site to help regulate the transcription from DNA to RNA. The mechanism of transcriptional regulatory can be much better understood if the category of transcription factors is known. We introduce a system which can automatically categorize transcription factors using their primary structures.

Predicting miRNA's target from primary structure by the nearest neighbor algorithm.

We used a machine learning method, the nearest neighbor algorithm (NNA), to learn the relationship between miRNAs and their target proteins, generating a predictor which can then judge whether a new miRNA-target pair is true or not. We acquired 198 positive (true) miRNA-target pairs from Tarbase and the literature, and generated 4,888 negative (false) pairs through random combination. A 0/1 system and the frequencies of single nucleotides and di-nucleotides were used to encode miRNAs into vectors while various physicochemical parameters were used to encode the targets.

[Advances in detecting positive selection on genome].

After Darwin's natural selection theory emerged, Kimura proposed the theory of neutral evolution in 1968, which considered neutral mutations and random genetic drift as the major evolutionary forces. In the following years, various kinds of methods were developed to test whether natural selection has ever happened. With the improvement of DNA sequencing technologies, large amount of DNA sequence polymorphism data is available, providing a mass of materials for testing the natural selection theory.

Prediction of protein structural classes using hybrid properties.

In this paper, amino acid compositions are combined with some protein sequence properties (physiochemical properties) to predict protein structural classes. We are able to predict protein structural classes using a mathematical model that combines the nearest neighbor algorithm (NNA), mRMR (minimum redundancy, maximum relevance), and feature forward searching strategy. Jackknife cross-validation is used to evaluate the prediction accuracy.

Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of blackfoot disease in Taiwan.

The back-propagation (BP) artificial neural network (ANN) is applied to forecast the variation of the quality of groundwater in the blackfoot disease area in Taiwan. Three types of BP ANN models were established to evaluate their learning performance. Model A included five concentration parameters as input variables for seawater intrusion and three concentration parameters as input variables for arsenic pollutant, respectively, whereas models B and C used only one concentration parameter for each.