Fungal infections of the central nervous system: A review of fungal pathogens and treatment

Multiple factors influence the outcome of fungal infection of the central nervous system (CNS). The host and the pathogen in concert with drug delivery across the blood-brain barrier and drug activity are key factors in outcome. Drug costs can be prohibitively expensive. Drug toxicity with standard antifungal agents such as amphotericin B (infusion rate toxicity) can be reduced using simple techniques such as slower infusion and appropriate saline loading. Continuous infusion can allow relatively large doses of amphotericin B (up to 2 mg/kg/day, remaining below 0.08 mg/kg/hour) to be given with toxicity profiles comparable to expensive lipid formulations of amphotericin B. Dedicated peripherally inserted central catheters can remain in situ for weeks to months and are safe and relatively inexpensive. Correction of metabolic pathology in the case of mucormycosis and resolution of neutropenia are essential to effective treatment of filamentous fungal infections such as Mucor, Aspergillus and Scedosporium. The pharmacology and pharmacokinetics of the current major antifungal agents used to treat fungal infections of the CNS are reviewed. Tables that provide information about achievable CNS drug levels, antifungal susceptibilities and the likelihood of intrinsic drug resistance of significant fungal pathogens have been included to help the clinician with therapy. Treatment recommendations for Cryptococcal and Candida meningitis and for rhinocerebral infection with Mucor and Aspergillus have been included

Australian SMEs waste to landfill

Landfill waste has a negative impact on the environment and small and medium sized enterprises (SMEs) are believed to be significant contributors. There is little government or scholarly research, however, quantifying the collective volume of waste SMEs send to landfill. Where studies do exist they measure total volumes (landfill and recycling combined) and/or do not distinguish between specific waste streams (e.g. wood) and subcategories (e.g. dust). This paper contributes to knowledge by giving insight into the collective volume of waste of 404 SMEs, reconceptualising SME waste into subcategories and by measuring landfill volumes. It presents findings from these 404 Australian SMEs which found that, in descending order, cardboard, paper, plastic wrap, wood dust and particleboard were the subcategories these SMEs sent to landfill in the greatest volumes. It also argues that this reconceptualisation, and associated data collection protocols, have the potential to enable scholars and policy makers to determine the waste subcategories to which SMEs contribute most, formulate targeted interventions and research or evaluate environmental outcomes. © 2014 © 2014 Environment Institute of Australia and New Zealand Inc

Annual Summary of Weather Data for Parsons - 2021

This report includes the annual summary of precipitation and temperatures from 2021 at the research locations represented in the 2021 Southeast Research and Extension Center Agricultural Research Report

Capturing accelerometer outputs in healthy volunteers under normal and simulated-pathological conditions using ML classifiers

Wearable devices offer a possible solution for acquiring objective measurements of physical activity. Most current algorithms are derived using data from healthy volunteers. It is unclear whether such algorithms are suitable in specific clinical scenarios, such as when an individual has altered gait. We hypothesized that algorithms trained on healthy population will result in less accurate results when tested in individuals with altered gait. We further hypothesized that algorithms trained on simulated-pathological gait would prove better at classifying abnormal activity.We studied healthy volunteers to assess whether activity classification accuracy differed for those with healthy and simulated-pathological conditions. Healthy participants (n=30) were recruited from the University of Leeds to perform nine predefined activities under healthy and simulated-pathological conditions. Activities were captured using a wrist-worn MOX accelerometer (Maastricht Instruments, NL). Data were analyzed based on the Activity-Recognition-Chain process. We trained a Neural-Network, Random-Forests, k-Nearest-Neighbors (k-NN), Support-Vector-Machines (SVM) and Naive Bayes models to classify activity. Algorithms were trained four times; once with 'healthy' data, and once with 'simulated-pathological data' for each of activity-type and activity-task classification. In activity-type instances, the SVM provided the best results; the accuracy was 98.4% when the algorithm was trained and then tested with unseen data from the same group of healthy individuals. Accuracy dropped to 52.8% when tested on simulated-pathological data. When the model was retrained with simulated-pathological data, prediction accuracy for the corresponding test set was 96.7%. Algorithms developed on healthy data are less accurate for pathological conditions. When evaluating pathological conditions, classifier algorithms developed using data from a target sub-population can restore accuracy to above 95%.Clinical Relevance - This method remotely establishes health-related data of objective outcome measures of activities of daily living

Dwarf White Clover Supports Pollinators, Augments Nitrogen in Clover-Turfgrass Lawns, and Suppresses Root-Feeding Grubs in Monoculture but Not in Mixed Swards

The runoff or leaching of nitrogen fertilizers from monoculture turfgrass lawns contri-butes to water pollution, and such lawns are susceptible to insect pests and provide few resources for pollinators. One approach to creating more sustainable lawns is to incorporate white clover (Trifolium repens L.), a nitrogen-fixing legume, into grass seed mixtures or existing turfgrass swards. “Dutch” white clover (DWC), a ubiquitous landrace, forms non-uniform clumps when intermixed with turfgrasses, thus it is often considered to be a lawn weed. Recently, several dwarf varieties of white clover have been selected for their small leaf size and low growth habit, allowing them to tolerate low mowing heights and blend better with grasses. To date, there have been no studies published on the entomological aspects of dwarf clover in pure stands or intermixed with turfgrass. We established field plots with combinations of DWC, two cultivars of dwarf clover, and tall fescue (Schedonorus arundinaceus (Schreb.) Dumort.) in monoculture or mixed swards, and compared the invertebrate communities therein. Predatory arthropods and earthworm numbers were similar in all plot types. The clover monocultures were resistant to white grubs, but the grub densities in the clover–tall fescue dicultures were similar to those found in the pure tall fescue swards. Dwarf clovers and DWC were similarly attractive to bees and supported similar bee assemblages. The tall fescue foliar N content was elevated 17–27% in the dicultures with clovers