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ObjectiveRising antibiotic resistance could reduce the effectiveness of antibiotics in preventing postoperative infections. We investigated trends in the efficacy of antibiotic prophylaxis regimens for 3 commonly performed surgical procedures—appendectomy, cesarean section, and colorectal surgery—and 1 invasive diagnostic procedure, transrectal prostate biopsy (TRPB).DesignSystematic review and meta-analysis.MethodsWe searched PubMed and Cochrane databases (through October 31, 2017) for randomized control trials (RCTs) that measured the efficacy of antibiotic prophylaxis for 4 index procedures in preventing postoperative infections (surgical site infections [SSIs] following the 3 surgical procedures and a combination of urinary tract infections [UTIs] and sepsis following TRPB).ResultsOf 399 RCTs, 74 studies (9 appendectomy, 11 cesarean section, 39 colorectal surgery, and 15 TRPB) were included. Multilevel logistic regression models with random intercepts for each study showed no statistically significant increase in SSIs over time for appendectomy (adjusted odds ratio [aOR] per year, 1.03; 95% confidence interval [CI], 0.92–1.16; P=.57), cesarean section (aOR per year, 1.01; 95% CI, 0.96–1.05; P=.80), and TRPB (aOR per year, 0.95; 95% CI, 0.77–1.18; P=.67). However, there was a significant increase in SSIs proportion following colorectal surgery (aOR per year, 1.049; 95% CI, 1.03–1.07; PConclusionThe efficacy of antibiotic prophylaxis agents in preventing SSIs following colorectal surgery has declined. Small number of RCTs and low infections rates limited our ability to assess true effect for simple appendectomy, cesarean section, or TRPB.

The morphologies of microglial arbors in brain tissue are important indicators of their state of activation, and their spatial distribution indicates patterns of tissue perturbation. This paper presents a non-parametric Bayesian approach to unsupervised quantitative profiling of microglial activation states, and mapping their 3-D spatial distributions across extended brain tissue regions imaged by mosaiced confocal microscopy. Our approach is to perform large-scale automated reconstruction of all the microglia using a learned label-consistent dictionary model for the microglial processes, and extract 136 arbor features per cell. We then fit an infinite Gaussian mixture model with collapsed Gibbs sampling to the arbor features, to discover arbor-morphological classes, their morphological ‘signatures’, and the associated covariance matrices all in an unsupervised manner. This nonparametric approach can handle clusters with arbitrarily shaped covariance matrices, perform automatic model selection, handle unbalanced clusters, and detect overlapping classes, without the need to specify the number of clusters. The results can be used for diverse investigational purposes, including: population-scale modeling, automated discovery of arbor classes and their distinguishing quantitative signatures and features, quantifying the nature and extent of the cellular heterogeneity, and spatial mapping of cellular arbor morphologies, and mapping tissue alterations.

BACKGROUND: Acinetobacter baumannii is a common cause of healthcare-associated infections. Carbapenem-resistant (CR) A baumannii is a significant threat globally. We used a large reference laboratory database to study the epidemiology of A baumannii in children in the United States. METHODS: Antimicrobial susceptibility data from The Surveillance Network were used to phenotypically identify antibiotic resistance in A baumannii isolates in children 1–17 years of age between January 1999 and July 2012. Logistic regression analysis was used to calculate trends in the prevalence of antibiotic resistance in A baumannii. Isolates from infants (

Motivation: The arbor morphologies of brain microglia are important indicators of cell activation. This article fills the need for accurate, robust, adaptive and scalable methods for reconstructing 3-D microglial arbors and quantitatively mapping microglia activation states over extended brain tissue regions. Results: Thick rat brain sections (100–300 µm) were multiplex immunolabeled for IBA1 and Hoechst, and imaged by step-and-image confocal microscopy with automated 3-D image mosaicing, producing seamless images of extended brain regions (e.g. 5903 × 9874 × 229 voxels). An over-complete dictionary-based model was learned for the image-specific local structure of microglial processes. The microglial arbors were reconstructed seamlessly using an automated and scalable algorithm that exploits microglia-specific constraints. This method detected 80.1 and 92.8% more centered arbor points, and 53.5 and 55.5% fewer spurious points than existing vesselness and LoG-based methods, respectively, and the traces were 13.1 and 15.5% more accurate based on the DIADEM metric. The arbor morphologies were quantified using Scorcioni’s L-measure. Coifman’s harmonic co-clustering revealed four morphologically distinct classes that concord with known microglia activation patterns. This enabled us to map spatial distributions of microglial activation and cell abundances. Availability and implementation: Experimental protocols, sample datasets, scalable open-source multi-threaded software implementation (C++, MATLAB) in the electronic supplement, and website (www.farsight-toolkit.org). http://www.farsight-toolkit.org/wiki/Population-scale_Three-dimensional_Reconstruction_and_Quanti-tative_Profiling_of_Microglia_Arbors Contact: broysam@central.uh.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Large tertiary care hospitals (TCHs) are thought to have higher antimicrobial resistance (AMR) rates when compared to small community hospitals (SCHs) as they provide care to patients with higher disease severity. However, we found no systematic differences in AMR rates between TCHs and SCHs in the United States.

Antibiotics have revolutionized the treatment of bacterial infections. However, it is widely held that there is underinvestment in antibiotics research and development relative to the socially optimal level for a number of reasons. In this article, we discuss whether existing health technology assessment procedures recognize the full economic and societal value of new antibiotics to patients and society when making reimbursement decisions. We present three recommendations for modelling the unique attributes of value that are specific to novel antibiotics. We find, based on a review of the literature, that some of the value elements proposed by our framework have previously been discussed qualitatively by health technology assessment bodies when evaluating antibiotics, but are not yet formally captured via modelling. We present a worked example to show how it may be possible to capture these dimensions of value in a more quantitative manner. We conclude by answering the question of the title as follows: the unique attributes of novel antibiotics should be considered in reimbursement decision making, in a way that captures the full range of benefits these important technologies bring to patients, health care systems, and society.

In recent years, there has been an increase in resistance of malaria vectors to insecticides, particularly to pyrethroids which are widely used in insecticide-treated nets. The Global Plan for Insecticide Resistance Management in malaria vectors (GPIRM), released in May 2012, is a collective strategy for the malaria community to tackle this challenge. This review outlines progress made to date and the challenges experienced in the implementation of GPIRM, and outlines focus areas requiring urgent attention. Whilst there has been some advancement, uptake of GPIRM at the national level has generally been poor for various reasons, including limited availability of vector control tools with new mechanisms of action as well as critical financial, human and infrastructural resource deficiencies. There is an urgent need for a global response plan to address these deficits and ensure the correct and efficient use of available tools in order to maintain the effectiveness of current vector control efforts whilst novel vector control tools are under development. Emphasis must be placed on enhancing national capacities (such as human and infrastructural resources) to enable efficient monitoring and management of insecticide resistance, and to support availability and accessibility of appropriate new vector control products. Lack of action by the global community to address the threat of insecticide resistance is unacceptable and deprives affected communities of their basic right of universal access to effective malaria prevention. Aligning efforts and assigning the needed resources will ensure the optimal implementation of GPIRM with the ultimate goal of maintaining effective malaria vector control.

In this article, we describe the use of Python for large-scale automated server-based bio-image analysis in FARSIGHT, a free and open-source toolkit of image analysis methods for quantitative studies of complex and dynamic tissue microenvironments imaged by modern optical microscopes, including confocal, multi-spectral, multi-photon, and time-lapse systems. The core FARSIGHT modules for image segmentation, feature extraction, tracking, and machine learning are written in C++, leveraging widely used libraries including ITK, VTK, Boost, and Qt. For solving complex image analysis tasks, these modules must be combined into scripts using Python. As a concrete example, we consider the problem of analyzing 3-D multi-spectral images of brain tissue surrounding implanted neuroprosthetic devices, acquired using high-throughput multi-spectral spinning disk step-and-repeat confocal microscopy. The resulting images typically contain 5 fluorescent channels. Each channel consists of 6000 × 10,000 × 500 voxels with 16 bits/voxel, implying image sizes exceeding 250 GB. These images must be mosaicked, pre-processed to overcome imaging artifacts, and segmented to enable cellular-scale feature extraction. The features are used to identify cell types, and perform large-scale analysis for identifying spatial distributions of specific cell types relative to the device. Python was used to build a server-based script (Dell 910 PowerEdge servers with 4 sockets/server with 10 cores each, 2 threads per core and 1TB of RAM running on Red Hat Enterprise Linux linked to a RAID 5 SAN) capable of routinely handling image datasets at this scale and performing all these processing steps in a collaborative multi-user multi-platform environment. Our Python script enables efficient data storage and movement between computers and storage servers, logs all the processing steps, and performs full multi-threaded execution of all codes, including open and closed-source third party libraries.

Microglia are the resident immune cell population in the mammalian central nervous system (CNS). These highly plastic cells exhibit ramified arbors in their resting state, and progressively less-complex arbors when activated. Our goal is to compare the spatial distributions of resting and activated microglia in normal brain tissue against tissue that is perturbed by insertion of a neural recording device. For this, microglia were imaged using multiplex immunostaining and confocal microscopy. The cell arbors were traced automatically, and 127 quantitative measurements based on the L-measure [8] were computed for each cell. A hierarchical extension of Coifman's [1,2] unsupervised harmonic analysis method was used to profile these multivariate data and identify groups of similar cells and the underlying features. This iterative procedure induces an orthogonal basis by constructing a coupled geometry over the row and column spaces of the feature matrix. Smoothing of the dataset, and the row and column clusters is achieved simultaneously when the algorithm converges. Experiments on real image datasets demonstrate the ability of this method to generate qualitative and quantitative groups that are biologically meaningful despite the existence of noise and missing values.

The environmental implications of implementing the requirement for treatment of coastal sewage prior to discharge, which were put forward in an EC draft Directive on municipal waste-water treatment, are examined. In order to establish the current situation, the results of a national survey of coastal and estuarine sewage discharges are presented. A review of the impact of sewage discharges on the marine environment is presented in conjunction with an assessment of the impact of sewage-treatment processes on the land and in the air. These aspects are integrated into overall conclusions about net environmental impact. Finally, a decision-tree approach to the assignment of priority to additional action required at individual locations is developed.

Rescue excavation at Caldicot, Gwent (ST 4787) revealed at least six kilns of late third to early fourth century A.D. date, along with evidence for earlier (third century) production. Jars form the most important class of vessel produced and forms reminiscent of BB1 were particularly significant. The kiln products fit within the chronologically much broader reduced ware tradition of south-east Wales.

This paper describes two coupler method for measurement of dielectric properties of materials, at microwave frequencies. Two waveguide sections, fed from a common source, are coupling power into a waveguide filled with dielectric. The phase and attenuation constants are computed from the values of phase and attenuation adjustment required for cancellation of the signal in the dielectric filled waveguide. The two coupler method is particularly applicable for measurements of dielectric properties of aerosols and gases.

This paper describes new insights into the behaviour and reliability of a long range subsea acoustic telemetry system. Field observations which may challenge the established theories for acoustic communication in shallow seas are discussed. The results of several years operation of such a system on a North Sea field are described and include reference to the suitability of various materials and designs for subsea connectors, cables and housings.

This book provides a synthesis of the current understanding of the fate and effects of oil in freshwater. Its objectives are to provide a compilation, summary and critical evaluation of the available scientific literature on the impact of petroleum on freshwater organisms; to review procedures for the cleanup of petroleum from freshwater habits and to examine restoration and recovery rates; to provide background information on the chemistry and fate of petroleum in freshwater; and to identify gaps in the information on the effects of petroleum related contamination on freshwater. The book provides a source of data for the preparation of environmental impact statements predicting the effects of petroleum contaminated freshwater spills, and presents an introduction to methods of cleanup which are currently applied to the accidental release of petroleum.