A Simple Iterative Algorithm for Parsimonious Binary Kernel Fisher Discrimination

By applying recent results in optimization theory variously known as optimization transfer or majorize/minimize algorithms, an algorithm for binary, kernel, Fisher discriminant analysis is introduced that makes use of a non-smooth penalty on the coefficients to provide a parsimonious solution. The problem is converted into a smooth optimization that can be solved iteratively with no greater overhead than iteratively re-weighted least-squares. The result is simple, easily programmed and is shown to perform, in terms of both accuracy and parsimony, as well as or better than a number of leading machine learning algorithms on two well-studied and substantial benchmarks

Activation of heme biosynthesis by a small molecule that is toxic to fermenting Staphylococcus aureus

Staphylococcus aureus is a significant infectious threat to global public health. Acquisition or synthesis of heme is required for S. aureus to capture energy through respiration, but an excess of this critical cofactor is toxic to bacteria. S. aureus employs the heme sensor system (HssRS) to overcome heme toxicity; however, the mechanism of heme sensing is not defined. Here, we describe the identification of a small molecule activator of HssRS that induces endogenous heme biosynthesis by perturbing central metabolism. This molecule is toxic to fermenting S. aureus, including clinically relevant small colony variants. The utility of targeting fermenting bacteria is exemplified by the fact that this compound prevents the emergence of antibiotic resistance, enhances phagocyte killing, and reduces S. aureus pathogenesis. Not only is this small molecule a powerful tool for studying bacterial heme biosynthesis and central metabolism; it also establishes targeting of fermentation as a viable antibacterial strategy

Snowfall in the northern great lakes: Lessons learned from a multisensor observatory

A multisensor snowfall observational suite has been deployed at the Marquette, Michigan, National Weather Service Weather Forecast Office (KMQT) since 2014. Micro Rain Radar (MRR; profiling radar), Precipitation Imaging Package (PIP; snow particle imager), and ancillary ground-based meteorological observations illustrate the unique capabilities of these combined instruments to document radar and concomitant microphysical properties associated with northern Great Lakes snowfall regimes. Lake-effect, lake-orographic, and transition event case studies are presented that illustrate the variety of snowfall events that occur at KMQT. Case studies and multiyear analyses reveal the ubiquity of snowfall produced by shallow events. These shallow snowfall features and their distinctive microphysical fingerprints are often difficult to discern with conventional remote sensing instruments, thus highlighting the scientific and potential operational value of MRR and PIP observations. The importance of near-surface lake-orographic snowfall enhancement processes in extreme snowfall events and regime-dependent snow particle microphysical variability controlled by regime and environmental factors are also highlighted