University of Minnesota

About: University of Minnesota is a education organization based out in Minneapolis, Minnesota, United States. It is known for research contribution in the topics: Population & Transplantation. The organization has 117432 authors who have published 257986 publications receiving 11944239 citations. The organization is also known as: University of Minnesota, Twin Cities & University of Minnesota-Twin Cities.

Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking

Abstract: The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data.

Do Norms Matter in Marketing Relationships

Abstract: Transaction cost analysis is rapidly becoming an important theoretical paradigm in marketing. However, the accumulation of transaction cost studies has been accompanied by a growing body of critici...

Polymersomes: tough vesicles made from diblock copolymers.

Abstract: Vesicles were made from amphiphilic diblock copolymers and characterized by micromanipulation. The average molecular weight of the specific polymer studied, polyethyleneoxide-polyethylethylene (EO40-EE37), is several times greater than that of typical phospholipids in natural membranes. Both the membrane bending and area expansion moduli of electroformed polymersomes (polymer-based liposomes) fell within the range of lipid membrane measurements, but the giant polymersomes proved to be almost an order of magnitude tougher and sustained far greater areal strain before rupture. The polymersome membrane was also at least 10 times less permeable to water than common phospholipid bilayers. The results suggest a new class of synthetic thin-shelled capsules based on block copolymer chemistry.

On the Dispersion of a Solute in a Fluid Flowing through a Tube

Abstract: Sir Geoffrey Taylor has recently discussed the dispersion of a solute under the simultaneous action of molecular diffusion and variation of the velocity of the solvent. A new basis for his analysis is presented here which removes the restrictions imposed on some of the parameters at the expense of describing the distribution of solute in terms of its moments in the direction of flow. It is shown that the rate of growth of the variance is proportional to the sum of the molecular diffusion coefficient, D, and the Taylor diffusion coefficient $\kappa $a$^{2}$U$^{2}$/D, where U is the mean velocity and a is a dimension characteristic of the cross-section of the tube. An expression for $\kappa $ is given in the most general case, and it is shown that a finite distribution of solute tends to become normally distributed.

Online dictionary learning for sparse coding

Abstract: Sparse coding---that is, modelling data vectors as sparse linear combinations of basis elements---is widely used in machine learning, neuroscience, signal processing, and statistics. This paper focuses on learning the basis set, also called dictionary, to adapt it to specific data, an approach that has recently proven to be very effective for signal reconstruction and classification in the audio and image processing domains. This paper proposes a new online optimization algorithm for dictionary learning, based on stochastic approximations, which scales up gracefully to large datasets with millions of training samples. A proof of convergence is presented, along with experiments with natural images demonstrating that it leads to faster performance and better dictionaries than classical batch algorithms for both small and large datasets.