Synthesis and fluorescent properties of alkynyl- and alkenyl-fused benzotriazole-derived α-amino acids

Fluorescent unnatural α-amino acids are widely used as probes in chemical biology and medicinal chemistry. While a variety of structural classes have been developed, there is still a requirement for new environmentally sensitive analogues that can closely mimic proteinogenic α-amino acids. Here, we report the synthesis and fluorescent properties of highly conjugated, benzotriazole-derived α-amino acids designed to mimic l-tryptophan. Alkynyl-substituted analogues were prepared using three key steps, nucleophilic aromatic substitution with a 3-aminoalanine derivative, benzotriazole formation via a one-pot diazotization and cyclization process, and a Sonogashira cross-coupling reaction. E-Alkenyl-substituted benzotriazoles were accessed by stereoselective partial hydrogenation of the alkynes using zinc iodide and palladium catalysis. The alkynyl analogues were found to possess higher quantum yields and stronger brightness and, a solvatochromic study with the most fluorogenic α-amino acids demonstrated sensitivity to polarity

The spatial scale of competition from recruits on an older cohort in Atlantic salmon

Competitive effects of younger cohorts on older ones are frequently assumed to be negligible in species where older, larger individuals dominate in pairwise behavioural interactions. Here, we provide field estimates of such competition by recruits on an older age class in Atlantic salmon (Salmo salar), a species where observational studies have documented strong body size advantages which should favour older individuals in direct interactions. By creating realistic levels of spatial variation in the density of underyearling (YOY) recruits over a 1-km stretch of a stream, and obtaining accurate measurements of individual growth rates of overyearlings (parr) from capture–mark–recapture data on a fine spatial scale, we demonstrate that high YOY density can substantially decrease parr growth. Models integrating multiple spatial scales indicated that parr were influenced by YOY density within 16 m. The preferred model suggested parr daily mass increase to be reduced by 39% when increasing YOY density from 0.0 to 1.0 m−2, which is well within the range of naturally occurring densities. Reduced juvenile growth rates will in general be expected to reduce juvenile survival (via increased length of exposure to freshwater mortality) and increase generation times (via increased age at seaward migrations). Thus, increased recruitment can significantly affect the performance of older cohorts, with important implications for population dynamics. Our results highlight that, even for the wide range of organisms that rely on defendable resources, the direction of competition among age classes cannot be assumed a priori or be inferred from behavioural observations alone

The iPlant Collaborative: Cyberinfrastructure for Plant Biology

The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanity's projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services

Diel changes in stream benthos density on stones and artificial substrates

Diel changes in stream benthos density were studied by comparing faunal densities on naturally occurring stones collected at noon and at midnight and on artificial substrates (bricks) collected at 2 hr intervals for 24 hr. Over all dates and all rivers, on stones, nighttime densities were higher in 37 cases, lower in 35 cases and unchanged in three cases. The magnitude of nocturnal density increases (expressed as a proportion of daytime density) ranged between 0.03 and 1.08 and the magnitude of nocturnal density decreases ranged between 0.01 and 0.81. Diel density changes on bricks generally were variable and not synchronized with changes in light intensity, but in most cases (60%) densities were greater at night.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42906/1/10750_2004_Article_BF00018889.pd

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers

A second generation human haplotype map of over 3.1 million SNPs

We describe the Phase II HapMap, which characterizes over 3.1 million human single nucleotide polymorphisms (SNPs) genotyped in 270 individuals from four geographically diverse populations and includes 25-35% of common SNP variation in the populations surveyed. The map is estimated to capture untyped common variation with an average maximum r(2) of between 0.9 and 0.96 depending on population. We demonstrate that the current generation of commercial genome-wide genotyping products captures common Phase II SNPs with an average maximum r(2) of up to 0.8 in African and up to 0.95 in non-African populations, and that potential gains in power in association studies can be obtained through imputation. These data also reveal novel aspects of the structure of linkage disequilibrium. We show that 10-30% of pairs of individuals within a population share at least one region of extended genetic identity arising from recent ancestry and that up to 1% of all common variants are untaggable, primarily because they lie within recombination hotspots. We show that recombination rates vary systematically around genes and between genes of different function. Finally, we demonstrate increased differentiation at non-synonymous, compared to synonymous, SNPs, resulting from systematic differences in the strength or efficacy of natural selection between populations.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62863/1/nature06258.pd