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The presented study conveys the initial full-scale experimental study for future hot-rolled asymmetric steel I-beams, termed A-shapes. The primary use for these beams is residential building floor systems. Current systems commonly utilize concrete solutions, which achieve a shallow depth for optimal floor-to-floor heights. However, these floor systems are relatively slow to construct because of the amount of formwork, rebar and post-tensioning placement, and shoring needed. The shallow-depth steel-concrete floor system concept explored in this research aims to improve the speed and efficiency of residential building construction. This increase in speed is achieved through hot-rolled steel A-shapes, which reduce the fabrication time for beam production. These steel shapes have a wide bottom flange that can support precast concrete panels or deep metal decking. Therefore, a relatively easy to assemble shallow-depth system can be achieved. The research study presented herein includes the experimental feasibility testing of a full-scale shallow-depth steel-concrete floor system to further the knowledge of A-shape performance in such systems. The floor system concept evaluated consisted of three steel A-shapes, precast hollow-core concrete panels, and a cast-in-place concrete topping slab. The testing involved taking measurements during construction, service live loading, and loading of the system to failure. The major unknowns were constructability, stability during construction, live load performance, and composite behavior. This experiment revealed that the system was constructed easily and rapidly and also remained stable during construction. The system performed well under service live load, experiencing deflections equivalent to L/3,000. The system failed due to the bond breaking between the concrete and steel, going noncomposite after experiencing partially composite behavior. This occurred at an actuator load equivalent to five times the design service live load. All of this indicates that the floor system concept using A-shapes is a legitimate path forward to faster construction of residential facilities. This validates the need for the research toward standardized A-shapes in the United States. [ABSTRACT FROM AUTHOR]

Alzheimer disease (AD) is the most common form of dementia and the sixth leading cause of death in the United States. Falls are associated with AD and can lead to injury. The Balance Evaluation Systems Test (BESTest) is a balance measure used in other neurological conditions to predict fall risk. The purpose of this study is to examine the relationship between MiniBestest, BESTest, and BESTest subsection scores and fall incidence among individuals with a diagnosis of mild AD. The study was a single centre, prospective, observational cohort study. Participants completed baseline questionnaires including a demographic form, a fall history questionnaire and the Barthel Index of Activities of Daily Living (ADLs). Balance and gait were assessed using the MiniBESTest and BESTest. After completing baseline assessment, participants were given monthly fall calendars to track falls for the next 12 months. MiniBESTest total raw score for fallers was 13.4 out of 28 (SD = 3.6) and for non-fallers was 18.4 of out 28 (SD = 3.7). MiniBESTest total percentage score for fallers was 47.8% (SD = 12.8%) and for non-fallers was 65.5% (SD = 13.1%). BESTest total percentage scores for fallers was 58.2% (SD = 3.9%) and for non-fallers was 73.9% (SD = 7.9%). Subsections II-IV of the BESTest correlated with faller status. Among individuals with mild AD, fall status was associated with certain balance deficits on the BESTest including moving body outside base of support (subsection II), changing centre of mass (subsection III), and reacting to external perturbations (subsection IV). Future studies could explore differences between AD and other neurological conditions and how physical therapy could improve these areas to reduce fall risk. [ABSTRACT FROM AUTHOR]

Each year in the United States, influenza causes illness in 9.2 to 35.6 million individuals and is responsible for 12,000 to 56,000 deaths. The U.S. Centers for Disease Control and Prevention (CDC) tracks influenza activity through a national surveillance network. These data are only available after a delay of 1 to 2 weeks, and thus influenza epidemiologists and transmission modelers have explored the use of other data sources to produce more timely estimates and predictions of influenza activity. We evaluated whether data collected from a national commercial network of influenza diagnostic machines could produce valid estimates of the current burden and help to predict influenza trends in the United States. Quidel Corporation provided us with de-identified influenza test results transmitted in real-time from a national network of influenza test machines called the Influenza Test System (ITS). We used this ITS dataset to estimate and predict influenza-like illness (ILI) activity in the United States over the 2015-2016 and 2016-2017 influenza seasons. First, we developed linear logistic models on national and regional geographic scales that accurately estimated two CDC influenza metrics: the proportion of influenza test results that are positive and the proportion of physician visits that are ILI-related. We then used our estimated ILI-related proportion of physician visits in transmission models to produce improved predictions of influenza trends in the United States at both the regional and national scale. These findings suggest that ITS can be leveraged to improve "nowcasts" and short-term forecasts of U.S. influenza activity. Author summary: The United States Centers for Disease Control and Prevention (CDC) tracks influenza activity through a national surveillance system. However, the CDC influenza surveillance data are subject to a 1 to 2 week reporting delay, which limits how such information can be used to assess the current burden of disease and to make timely projections of the trajectory of the epidemic. Researchers have previously used indirect signals of influenza activity such as Google search queries for influenza-related terms or Twitter posts to develop more timely estimates of influenza burden and improved model-based forecasts. However, these indirect signals are subject to behavioral changes not related to influenza activity, and thus may provide inaccurate estimates and projections. We used a new real-time data source with high geographic resolution that is directly related to influenza activity: influenza test results provided through a national network of commercial influenza diagnostic test machines. We used these influenza test results to accurately estimate the current burden of influenza and improve real-time model projections of influenza burden in the United States. [ABSTRACT FROM AUTHOR]

AbstractObjective:This study compared injection force (measured by glide force [GF] and glide force variability [GFV]) and dosing accuracy of the Humalog KwikPen (prefilled insulin lispro [Humalog†] pen, Eli Lilly and Company, Indianapolis, IN) and the Next Generation FlexPen‡ (prefilled insulin aspart [NovoRapid§] pen, Novo Nordisk A/S, Bagsværd, Denmark). Humalog KwikPen is a registered trademark of Eli Lilly and Company, Indianapolis, IN, USA.†Humalog is a registered trademark of Eli Lilly and Company, Indianapolis, IN, USA.‡FlexPen is a registered trademark of Novo Nordisk A/S, Bagsværd, Denmark.§NovoRapid is a registered trademark of Novo Nordisk A/S, Bagsværd, Denmark.Research design and methods:A total of 100 prefilled insulin pens (50 insulin lispro pens, 50 insulin aspart pens) were tested using two dose sizes (30 U and 60 U). In all, 50 devices (25 of each type) were tested at 10 U/s dosing speed and 50 were tested at 6.6 U/s. Devices were used per manufacturer instructions. Dose accuracy (represented as absolute dose error ), maximum and average GF, and GFV data were automatically collected by the test system for all datasets (dose size/dosing speed/device type). The test system controlled for potential dosing errors.Results:The insulin lispro pen demonstrated a significantly lower median maximum GF at both dosing speeds: (2.83 vs. 3.92 lbs [30 U] and 3.00 vs. 4.14 lbs [60 U]) at 10 U/s; (1.85 vs. 2.93 lbs [30 U] and 2.14 vs. 3.02 lbs [60 U]) at 6.6 U/s, all p < 0.0001. For all datasets, the median GFV was significantly lower for the insulin lispro pen, p < 0.0001. Median dose error was comparable between device types when tested at 10 U/s dosing speed; however, at 6.6 U/s, the median dose error was significantly lower for insulin lispro pen compared to insulin aspart pen (0.47 vs. 0.67 [30 U] and 0.50 vs. 0.78 [60 U], both p < 0.05).Conclusions:The insulin lispro pen had significantly lower median GF and GFV compared with insulin aspart pen when tested at two dose sizes and two dosing speeds. Median dose error was similar between the device types at the 10 U/s dosing speed, but median dose error was significantly lower for the insulin lispro pen at the 6.6 U/s dosing speed. A limitation of this study was that it was executed as an open label study. [ABSTRACT FROM AUTHOR]

The article reports on the enhancements made by Rohde & Schwarz company to its wireless communications test systems in the telecommunications industry in the U.S. The company has conducted enhancements to its wireless communications test systems that address the need to characterize the performance of components and subsystems designed for high-speed downlink packet access (HSDPA). The technology represents the next generation of performance improvements for Universal Mobile Telecommunications System services based on the wideband code division multiple access method. This article presents the inclusion of the enhancements made by the company.