7,807 research outputs found
Contact killing of bacterial pathogens on metallic copper : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Microbiology at Massey University, Auckland, New Zealand
Hospital-acquired infections (HAIs) are a serious health concern worldwide. Currently
in New Zealand, about one in ten patients admitted to hospitals will acquire an infection
while receiving treatments for other medical or surgical conditions. An emerging
strategy for HAIs prevention is to use self-sanitising copper surfaces on items
commonly touched in hospitals, which can provide sustained protection against
microbial contamination. This is due to the fact that a wide range of microorganisms
can be rapidly killed on copper in a process termed âcontact killingâ. However, the
mechanisms of copper-mediated contact killing are not fully understood; and moreover,
the potential of bacterial pathogens to develop resistance to metallic copper has so far
not been examined.
Here we hypothesize that bacteria are predominantly killed by a burst release of toxic
copper ions resulted from chemical reactions between surface components of bacterial
cell and metallic copper. To test this copper ion burst release hypothesis, we isolated
and phenotypically characterized small colony variants (SCVs) derived from the two
most common nosocomial pathogens, Staphylococcus aureus and Pseudomonas
aeruginosa. Consistent to our expectation, SCV mutants overproducing
exopolysaccharides (EPS) are more rapidly killed than wild type on the surfaces of pure
copper (99.9% Cu) and brass (63.5% Cu). Similar results were obtained with a panel of
mutants with altered production of cell surface components (EPS, lipopolysaccharides,
capsules, flagella and pili) in a non-pathogenic model organism of Pseudomonas
fluorescens SBW25.
Next, a unique approach of experimental evolution was used to assess the potential
emergence of bacterial resistance to metallic copper. Specifically, P. fluorescens
SBW25 was subjected to daily passage of sub-lethal conditions on the surfaces of brass.
After 100 daily transfers, the evolved strains had a slight increase of survival rate on
brass; but importantly, ~97% of cells can still be killed on brass within one hour.
Taken together, our results clearly indicate that the rate of bacterial killing on copper is
largely determined by surface components of a bacterial cell, providing support for the
copper ion burst release hypothesis. Our primary data of experimental evolution showed
that bacteria have limited ability to evolve resistance to metallic copper
Similarity Learning for High-Dimensional Sparse Data
A good measure of similarity between data points is crucial to many tasks in
machine learning. Similarity and metric learning methods learn such measures
automatically from data, but they do not scale well respect to the
dimensionality of the data. In this paper, we propose a method that can learn
efficiently similarity measure from high-dimensional sparse data. The core idea
is to parameterize the similarity measure as a convex combination of rank-one
matrices with specific sparsity structures. The parameters are then optimized
with an approximate Frank-Wolfe procedure to maximally satisfy relative
similarity constraints on the training data. Our algorithm greedily
incorporates one pair of features at a time into the similarity measure,
providing an efficient way to control the number of active features and thus
reduce overfitting. It enjoys very appealing convergence guarantees and its
time and memory complexity depends on the sparsity of the data instead of the
dimension of the feature space. Our experiments on real-world high-dimensional
datasets demonstrate its potential for classification, dimensionality reduction
and data exploration.Comment: 14 pages. Proceedings of the 18th International Conference on
Artificial Intelligence and Statistics (AISTATS 2015). Matlab code:
https://github.com/bellet/HDS
Triggering waves in nonlinear lattices: Quest for anharmonic phonons and corresponding mean free paths
Guided by a stylized experiment we develop a self-consistent anharmonic
phonon concept for nonlinear lattices which allows for explicit
"visualization." The idea uses a small external driving force which excites the
front particles in a nonlinear lattice slab and subsequently one monitors the
excited wave evolution using molecular dynamics simulations. This allows for a
simultaneous, direct determination of the existence of the phonon mean free
path with its corresponding anharmonic phonon wavenumber as a function of
temperature. The concept for the mean free path is very distinct from known
prior approaches: the latter evaluate the mean free path only indirectly, via
using both, a scale for the phonon relaxation time and yet another one for the
phonon velocity. Notably, the concept here is neither limited to small lattice
nonlinearities nor to small frequencies. The scheme is tested for three
strongly nonlinear lattices of timely current interest which either exhibit
normal or anomalous heat transport
Attention Correctness in Neural Image Captioning
Attention mechanisms have recently been introduced in deep learning for
various tasks in natural language processing and computer vision. But despite
their popularity, the "correctness" of the implicitly-learned attention maps
has only been assessed qualitatively by visualization of several examples. In
this paper we focus on evaluating and improving the correctness of attention in
neural image captioning models. Specifically, we propose a quantitative
evaluation metric for the consistency between the generated attention maps and
human annotations, using recently released datasets with alignment between
regions in images and entities in captions. We then propose novel models with
different levels of explicit supervision for learning attention maps during
training. The supervision can be strong when alignment between regions and
caption entities are available, or weak when only object segments and
categories are provided. We show on the popular Flickr30k and COCO datasets
that introducing supervision of attention maps during training solidly improves
both attention correctness and caption quality, showing the promise of making
machine perception more human-like.Comment: To appear in AAAI-17. See http://www.cs.jhu.edu/~cxliu/ for
supplementary materia
Holiday Destination Choice Behavior Analysis Based on AFC Data of Urban Rail Transit
For urban rail transit, the spatial distribution of passenger flow in holiday usually differs from weekdays. Holiday destination choice behavior analysis is the key to analyze passengersâ destination choice preference and then obtain the OD (origin-destination) distribution of passenger flow. This paper aims to propose a holiday destination choice model based on AFC (automatic fare collection) data of urban rail transit system, which is highly expected to provide theoretic support to holiday travel demand analysis for urban rail transit. First, based on Guangzhou Metro AFC data collected on New Yearâs day, the characteristics of holiday destination choice behavior for urban rail transit passengers is analyzed. Second, holiday destination choice models based on MNL (Multinomial Logit) structure are established for each New Yearâs days respectively, which takes into account some novel explanatory variables (such as attractiveness of destination). Then, the proposed models are calibrated with AFC data from Guangzhou Metro using WESML (weighted exogenous sample maximum likelihood) estimation and compared with the base models in which attractiveness of destination is not considered. The results show that the Ï2 values are improved by 0.060, 0.045, and 0.040 for January 1, January 2, and January 3, respectively, with the consideration of destination attractiveness
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