Tuning cell surface charge in E. coli with conjugated oligoelectrolytes.

Cationic conjugated oligoelectrolytes (COEs) varying in length and structural features are compared with respect to their association with E. coli and their effect on cell surface charge as determined by zeta potential measurements. Regardless of structural features, at high staining concentrations COEs with longer molecular dimensions associate less, but neutralize the negative surface charge of E. coli to a greater degree than shorter COEs

Next Generation Cluster Editing

This work aims at improving the quality of structural variant prediction from the mapped reads of a sequenced genome. We suggest a new model based on cluster editing in weighted graphs and introduce a new heuristic algorithm that allows to solve this problem quickly and with a good approximation on the huge graphs that arise from biological datasets

Controlled long-range interactions between Rydberg atoms and ions

We theoretically investigate trapped ions interacting with atoms that are coupled to Rydberg states. The strong polarizabilities of the Rydberg levels increases the interaction strength between atoms and ions by many orders of magnitude, as compared to the case of ground state atoms, and may be mediated over micrometers. We calculate that such interactions can be used to generate entanglement between an atom and the motion or internal state of an ion. Furthermore, the ion could be used as a bus for mediating spin-spin interactions between atomic spins in analogy to much employed techniques in ion trap quantum simulation. The proposed scheme comes with attractive features as it maps the benefits of the trapped ion quantum system onto the atomic one without obviously impeding its intrinsic scalability. No ground state cooling of the ion or atom is required and the setup allows for full dynamical control. Moreover, the scheme is to a large extent immune to the micromotion of the ion. Our findings are of interest for developing hybrid quantum information platforms and for implementing quantum simulations of solid state physics.Comment: 20 pages including appendices, 6 figure

Capturing human category representations by sampling in deep feature spaces

Understanding how people represent categories is a core problem in cognitive science. Decades of research have yielded a variety of formal theories of categories, but validating them with naturalistic stimuli is difficult. The challenge is that human category representations cannot be directly observed and running informative experiments with naturalistic stimuli such as images requires a workable representation of these stimuli. Deep neural networks have recently been successful in solving a range of computer vision tasks and provide a way to compactly represent image features. Here, we introduce a method to estimate the structure of human categories that combines ideas from cognitive science and machine learning, blending human-based algorithms with state-of-the-art deep image generators. We provide qualitative and quantitative results as a proof-of-concept for the method's feasibility. Samples drawn from human distributions rival those from state-of-the-art generative models in quality and outperform alternative methods for estimating the structure of human categories.Comment: 6 pages, 5 figures, 1 table. Accepted as a paper to the 40th Annual Meeting of the Cognitive Science Society (CogSci 2018

Cavity-enhanced Raman Microscopy of Individual Carbon Nanotubes

Raman spectroscopy reveals chemically specific information and provides label-free insight into the molecular world. However, the signals are intrinsically weak and call for enhancement techniques. Here, we demonstrate Purcell enhancement of Raman scattering in a tunable high-finesse microcavity, and utilize it for molecular diagnostics by combined Raman and absorption imaging. Studying individual single-wall carbon nanotubes, we identify crucial structural parameters such as nanotube radius, electronic structure and extinction cross-section. We observe a 320-times enhanced Raman scattering spectral density and an effective Purcell factor of 6.2, together with a collection efficiency of 60%. Potential for significantly higher enhancement, quantitative signals, inherent spectral filtering and absence of intrinsic background in cavity-vacuum stimulated Raman scattering render the technique a promising tool for molecular imaging. Furthermore, cavity-enhanced Raman transitions involving localized excitons could potentially be used for gaining quantum control over nanomechanical motion and open a route for molecular cavity optomechanics

Automatic estimation of flux distributions of astrophysical source populations

In astrophysics a common goal is to infer the flux distribution of populations of scientifically interesting objects such as pulsars or supernovae. In practice, inference for the flux distribution is often conducted using the cumulative distribution of the number of sources detected at a given sensitivity. The resulting "$\log(N>S)$-$\log (S)$" relationship can be used to compare and evaluate theoretical models for source populations and their evolution. Under restrictive assumptions the relationship should be linear. In practice, however, when simple theoretical models fail, it is common for astrophysicists to use prespecified piecewise linear models. This paper proposes a methodology for estimating both the number and locations of "breakpoints" in astrophysical source populations that extends beyond existing work in this field. An important component of the proposed methodology is a new interwoven EM algorithm that computes parameter estimates. It is shown that in simple settings such estimates are asymptotically consistent despite the complex nature of the parameter space. Through simulation studies it is demonstrated that the proposed methodology is capable of accurately detecting structural breaks in a variety of parameter configurations. This paper concludes with an application of our methodology to the Chandra Deep Field North (CDFN) data set.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS750 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Influence of molecular structure on the antimicrobial function of phenylenevinylene conjugated oligoelectrolytes.

Conjugated oligoelectrolytes (COEs) with phenylenevinylene (PV) repeat units are known to spontaneously intercalate into cell membranes. Twelve COEs, including seven structures reported here for the first time, were investigated for the relationship between their membrane disrupting properties and structural modifications, including the length of the PV backbone and the presence of either a tetraalkylammonium or a pyridinium ionic pendant group. Optical characteristics and interactions with cell membranes were determined using UV-Vis absorption and photoluminescence spectroscopies, and confocal microscopy. Toxicity tests on representative Gram-positive (Enterococcus faecalis) and Gram-negative (Escherichia coli) bacteria reveal generally greater toxicity to E. faecalis than to E. coli and indicate that shorter molecules have superior antimicrobial activity. Increased antimicrobial potency was observed in three-ring COEs appended with pyridinium ionic groups but not with COEs with four or five PV repeat units. Studies with mutants having cell envelope modifications indicate a possible charge based interaction with pyridinium-appended compounds. Fluorine substitutions on COE backbones result in structures that are less toxic to E. coli, while the addition of benzothiadiazole to COE backbones has no effect on increasing antimicrobial function. A weakly membrane-intercalating COE with only two PV repeat units allowed us to determine the synthetic limitations as a result of competition between solubility in aqueous media and association with cell membranes. We describe, for the first time, the most membrane disrupting structure achievable within two homologous series of COEs and that around a critical three-ring backbone length, structural modifications have the most effect on antimicrobial activity