Fluorescence kinetics of flavin adenine dinucleotide in different microenvironments

Fluorescence kinetics of flavin adenine dinucleotide was measured in a wide time and spectral range in different media, affecting its intra- end extramolecular interactions, and analyzed by a new method based on compressed sensing

Geometrically coupled monte carlo sampling

© 2018 Curran Associates Inc..All rights reserved. Monte Carlo sampling in high-dimensional, low-sample settings is important in many machine learning tasks. We improve current methods for sampling in Euclidean spaces by avoiding independence, and instead consider ways to couple samples. We show fundamental connections to optimal transport theory, leading to novel sampling algorithms, and providing new theoretical grounding for existing strategies. We compare our new strategies against prior methods for improving sample efficiency, including quasi-Monte Carlo, by studying discrepancy. We explore our findings empirically, and observe benefits of our sampling schemes for reinforcement learning and generative modelling

Publisher Correction: Nonlinear mechanics of human mitotic chromosomes

In the version of this article initially published, Extended Data Fig. 5 was a duplicate of Extended Data Fig. 6. The correct image is now in place in the HTML and PDF versions of the article

Nonlinear mechanics of human mitotic chromosomes

In preparation for mitotic cell division, the nuclear DNA of human cells is compacted into individualized, X-shaped chromosomes1. This metamorphosis is driven mainly by the combined action of condensins and topoisomerase IIα (TOP2A)2,3, and has been observed using microscopy for over a century. Nevertheless, very little is known about the structural organization of a mitotic chromosome. Here we introduce a workflow to interrogate the organization of human chromosomes based on optical trapping and manipulation. This allows high-resolution force measurements and fluorescence visualization of native metaphase chromosomes to be conducted under tightly controlled experimental conditions. We have used this method to extensively characterize chromosome mechanics and structure. Notably, we find that under increasing mechanical load, chromosomes exhibit nonlinear stiffening behaviour, distinct from that predicted by classical polymer models4. To explain this anomalous stiffening, we introduce a hierarchical worm-like chain model that describes the chromosome as a heterogeneous assembly of nonlinear worm-like chains. Moreover, through inducible degradation of TOP2A5 specifically in mitosis, we provide evidence that TOP2A has a role in the preservation of chromosome compaction. The methods described here open the door to a wide array of investigations into the structure and dynamics of both normal and disease-associated chromosomes

Association of Genetic Variants Affecting microRNAs and Pancreatic Cancer Risk

Genetic factors play an important role in the susceptibility to pancreatic cancer (PC). However, established loci explain a small proportion of genetic heritability for PC; therefore, more progress is needed to find the missing ones. We aimed at identifying single nucleotide polymorphisms (SNPs) affecting PC risk through effects on micro-RNA (miRNA) function. We searched in silico the genome for SNPs in miRNA seed sequences or 3 prime untranslated regions (3'UTRs) of miRNA target genes. Genome-wide association data of PC cases and controls from the Pancreatic Cancer Cohort (PanScan) Consortium and the Pancreatic Cancer Case–Control (PanC4) Consortium were re-analyzed for discovery, and genotyping data from two additional consortia (PanGenEU and PANDoRA) were used for replication, for a total of 14,062 cases and 11,261 controls. None of the SNPs reached genome-wide significance in the meta-analysis, but for three of them the associations were in the same direction in all the study populations and showed lower value of p in the meta-analyses than in the discovery phase. Specifically, rs7985480 was consistently associated with PC risk (OR = 1.12, 95% CI 1.07–1.17, p = 3.03 × 10−6 in the meta-analysis). This SNP is in linkage disequilibrium (LD) with rs2274048, which modulates binding of various miRNAs to the 3'UTR of UCHL3, a gene involved in PC progression. In conclusion, our results expand the knowledge of the genetic PC risk through miRNA-related SNPs and show the usefulness of functional prioritization to identify genetic polymorphisms associated with PC risk

Association of Genetic Variants Affecting microRNAs and Pancreatic Cancer Risk

Genetic factors play an important role in the susceptibility to pancreatic cancer (PC). However, established loci explain a small proportion of genetic heritability for PC; therefore, more progress is needed to find the missing ones. We aimed at identifying single nucleotide polymorphisms (SNPs) affecting PC risk through effects on micro-RNA (miRNA) function. We searched in silico the genome for SNPs in miRNA seed sequences or 3 prime untranslated regions (3'UTRs) of miRNA target genes. Genome-wide association data of PC cases and controls from the Pancreatic Cancer Cohort (PanScan) Consortium and the Pancreatic Cancer Case–Control (PanC4) Consortium were re-analyzed for discovery, and genotyping data from two additional consortia (PanGenEU and PANDoRA) were used for replication, for a total of 14,062 cases and 11,261 controls. None of the SNPs reached genome-wide significance in the meta-analysis, but for three of them the associations were in the same direction in all the study populations and showed lower value of p in the meta-analyses than in the discovery phase. Specifically, rs7985480 was consistently associated with PC risk (OR = 1.12, 95% CI 1.07–1.17, p = 3.03 × 10−6 in the meta-analysis). This SNP is in linkage disequilibrium (LD) with rs2274048, which modulates binding of various miRNAs to the 3'UTR of UCHL3, a gene involved in PC progression. In conclusion, our results expand the knowledge of the genetic PC risk through miRNA-related SNPs and show the usefulness of functional prioritization to identify genetic polymorphisms associated with PC risk