Airborne Multidrug-Resistant Bacteria Isolated from a Concentrated Swine Feeding Operation

The use of nontherapeutic levels of antibiotics in swine production can select for antibiotic resistance in commensal and pathogenic bacteria in swine. As a result, retail pork products, as well as surface and groundwaters contaminated with swine waste, have been shown to be sources of human exposure to antibiotic-resistant bacteria. However, it is unclear whether the air within swine operations also serves as a source of exposure to antibiotic-resistant bacterial pathogens. To investigate this issue, we sampled the air within a concentrated swine feeding operation with an all-glass impinger. Samples were analyzed using a method for the isolation of Enterococcus. A total of 137 presumptive Enterococcus isolates were identified to species level using standard biochemical tests and analyzed for resistance to erythromycin, clindamycin, virginiamycin, tetracycline, and vancomycin using the agar dilution method. Thirty-four percent of the isolates were confirmed as Enterococcus, 32% were identified as coagulase-negative staphylococci, and 33% were identified as viridans group streptococci. Regardless of bacterial species, 98% of the isolates expressed high-level resistance to at least two antibiotics commonly used in swine production. None of the isolates were resistant to vancomycin, an antibiotic that has never been approved for use in livestock in the United States. In conclusion, high-level multidrug-resistant Enterococcus, coagulase-negative staphylococci, and viridans group streptococci were detected in the air of a concentrated swine feeding operation. These findings suggest that the inhalation of air from these facilities may serve as an exposure pathway for the transfer of multidrug-resistant bacterial pathogens from swine to humans

Kurtosis analysis of neural diffusion organization

A computational framework is presented for relating the kurtosis tensor for water diffusion in brain to tissue models of brain microstructure. The tissue models are assumed to be comprised of non-exchanging compartments that may be associated with various microstructural spaces separated by cell membranes. Within each compartment the water diffusion is regarded as Gaussian, although the diffusion for the full system would typically be non-Gaussian. The model parameters are determined so as to minimize the Frobenius norm of the difference between the measured kurtosis tensor and the model kurtosis tensor. This framework, referred to as kurtosis analysis of neural diffusion organization (KANDO), may be used to help provide a biophysical interpretation to the information provided by the kurtosis tensor. In addition, KANDO combined with diffusional kurtosis imaging can furnish a practical approach for developing candidate biomarkers for neuropathologies that involve alterations in tissue microstructure. KANDO is illustrated for simple tissue models of white and gray matter using data obtained from healthy human subjects.postprin

An Entangled Model for Sustainability Indicators.

Nowadays the challenge for humanity is to find pathways towards sustainable development. Decision makers require a set of sustainability indicators to know if the sustainability strategies are following those pathways. There are more than one hundred sustainability indicators but they differ on their relative importance according to the size of the locality and change on time. The resources needed to follow these sustainability indicators are scarce and in some instances finite, especially in smaller regions. Therefore strategies to select set of these indicators are useful for decision makers responsible for monitoring sustainability. In this paper we propose a model for the identification and selection of a set of sustainability indicators that adequately represents human systems. In developing this model, we applied evolutionary dynamics in a space where sustainability indicators are fundamental entities interconnected by an interaction matrix. we used a fixed interaction that simulates the current context for the city of Cuernavaca, México as an example. We were able to identify and define relevant sets indicators for the system by using the Pareto principle. In this case we identified a set of sixteen sustainability indicators with more than 80% of the total strength. This set presents resilience to perturbations. For the Tangled Nature framework we provided a manner of treating different contexts (i.e., cities, counties, states, regions, countries, continents or the whole planet), dealing with small dimensions. This model provides decision makers with a valuable tool to select sustainability indicators set for towns, cities, regions, countries, continents or the entire planet according to a coevolutionary framework. The social legitimacy can arise from the fact that each individual indicator must be selected from those that are most important for the subject community

Global Ultrasound Elastography Using Convolutional Neural Network

Displacement estimation is very important in ultrasound elastography and failing to estimate displacement correctly results in failure in generating strain images. As conventional ultrasound elastography techniques suffer from decorrelation noise, they are prone to fail in estimating displacement between echo signals obtained during tissue distortions. This study proposes a novel elastography technique which addresses the decorrelation in estimating displacement field. We call our method GLUENet (GLobal Ultrasound Elastography Network) which uses deep Convolutional Neural Network (CNN) to get a coarse time-delay estimation between two ultrasound images. This displacement is later used for formulating a nonlinear cost function which incorporates similarity of RF data intensity and prior information of estimated displacement. By optimizing this cost function, we calculate the finer displacement by exploiting all the information of all the samples of RF data simultaneously. The Contrast to Noise Ratio (CNR) and Signal to Noise Ratio (SNR) of the strain images from our technique is very much close to that of strain images from GLUE. While most elastography algorithms are sensitive to parameter tuning, our robust algorithm is substantially less sensitive to parameter tuning.Comment: 4 pages, 4 figures; added acknowledgment section, submission type late

Data Files: The Role of Bus Stop Features in Facilitating Accessibility

These datasets support a final report published on NITC’s website “The Role of Bus Stop Features in Facilitating Accessibility”: https://nitc.trec.pdx.edu/research/project/1214. The DOI for the final report is: https://dx.doi.org/10.15760/trec.254

Diffusional Kurtosis and Diffusion Tensor Imaging Reveal Different Time-Sensitive Stroke-Induced Microstructural Changes

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Epilepsy-related cytoarchitectonic abnormalities along white matter pathways

Objective Temporal lobe epilepsy (TLE) is one of the most common forms of epilepsy. Unfortunately, the clinical outcomes of TLE cannot be determined based only on current diagnostic modalities. A better understanding of white matter (WM) connectivity changes in TLE may aid the identification of network abnormalities associated with TLE and the phenotypic characterisation of the disease. Methods We implemented a novel approach for characterising microstructural changes along WM pathways using diffusional kurtosis imaging (DKI). Along-the-tract measures were compared for 32 subjects with left TLE and 36 age-matched and gender-matched controls along the left and right fimbria-fornix (FF), parahippocampal WM bundle (PWMB), arcuate fasciculus (AF), inferior longitudinal fasciculus (ILF), uncinate fasciculus (UF) and cingulum bundle (CB). Limbic pathways were investigated in relation to seizure burden and control with antiepileptic drugs. Results By evaluating measures along each tract, it was possible to identify abnormalities localised to specific tract subregions. Compared with healthy controls, subjects with TLE demonstrated pathological changes in circumscribed regions of the FF, PWMB, UF, AF and ILF. Several of these abnormalities were detected only by kurtosis-based and not by diffusivity-based measures. Structural WM changes correlated with seizure burden in the bilateral PWMB and cingulum. Conclusions DKI improves the characterisation of network abnormalities associated with TLE by revealing connectivity abnormalities that are not disclosed by other modalities. Since TLE is a neuronal network disorder, DKI may be well suited to fully assess structural network abnormalities related to epilepsy and thus serve as a tool for phenotypic characterisation of epilepsy

Using interpretative phenomenological analysis to inform physiotherapy practice: An introduction with reference to the lived experience of cerebellar ataxia

The attached file is a pre-published version of the full and final paper which can be found at the link below.This article has been made available through the Brunel Open Access Publishing Fund.Qualitative research methods that focus on the lived experience of people with health conditions are relatively underutilised in physiotherapy research. This article aims to introduce interpretative phenomenological analysis (IPA), a research methodology oriented toward exploring and understanding the experience of a particular phenomenon (e.g., living with spinal cord injury or chronic pain, or being the carer of someone with a particular health condition). Researchers using IPA try to find out how people make sense of their experiences and the meanings they attach to them. The findings from IPA research are highly nuanced and offer a fine grained understanding that can be used to contextualise existing quantitative research, to inform understanding of novel or underresearched topics or, in their own right, to provoke a reappraisal of what is considered known about a specified phenomenon. We advocate IPA as a useful and accessible approach to qualitative research that can be used in the clinical setting to inform physiotherapy practice and the development of services from the perspective of individuals with particular health conditions.This article is available through the Brunel Open Access Publishing Fund

New synchronization method for <i>Plasmodium falciparum</i>

&lt;b&gt;Background&lt;/b&gt;: Plasmodium falciparum is usually asynchronous during in vitro culture. Although various synchronization methods are available, they are not able to narrow the range of ages of parasites. A newly developed method is described that allows synchronization of parasites to produce cultures with an age range as low as 30 minutes. &lt;b&gt;Methods&lt;/b&gt;: Trophozoites and schizonts are enriched using Plasmion. The enriched late stage parasites are immobilized as a monolayer onto plastic Petri dishes using concanavalin A. Uninfected erythrocytes are placed onto the monolayer for a limited time period, during which time schizonts on the monolayer rupture and the released merozoites invade the fresh erythrocytes. The overlay is then taken off into a culture flask, resulting in a highly synchronized population of parasites. &lt;b&gt;Results&lt;/b&gt;: Plasmion treatment results in a 10- to 13-fold enrichment of late stage parasites. The monolayer method results in highly synchronized cultures of parasites where invasion has occurred within a very limited time window, which can be as low as 30 minutes. The method is simple, requiring no specialized equipment and relatively cheap reagents. &lt;b&gt;Conclusions&lt;/b&gt;: The new method for parasite synchronization results in highly synchronized populations of parasites, which will be useful for studies of the parasite asexual cell cycle

Finding 2-Edge and 2-Vertex Strongly Connected Components in Quadratic Time

We present faster algorithms for computing the 2-edge and 2-vertex strongly connected components of a directed graph, which are straightforward generalizations of strongly connected components. While in undirected graphs the 2-edge and 2-vertex connected components can be found in linear time, in directed graphs only rather simple $O(m n)$-time algorithms were known. We use a hierarchical sparsification technique to obtain algorithms that run in time $O(n^2)$. For 2-edge strongly connected components our algorithm gives the first running time improvement in 20 years. Additionally we present an $O(m^2 / \log{n})$-time algorithm for 2-edge strongly connected components, and thus improve over the $O(m n)$ running time also when $m = O(n)$. Our approach extends to k-edge and k-vertex strongly connected components for any constant k with a running time of $O(n^2 \log^2 n)$ for edges and $O(n^3)$ for vertices