Combination Of Test Results Research Articles

Experimental and numerical investigation of bolt-free preloaded connection for steel-framed modular buildings

This paper introduces an innovative bolt-free preloaded inter-module connection for steel-framed modular buildings, referred to as the ‘AJ’ connection. It provides the vertical connection between columns by engaging a preload induced by long steel rod bolts (SRB) and specially designed couplers. The preload is applied using a torque or impact wrench. Consequently, the connection simplifies the on-site assembly and disassembly processes of the modular building, thereby enhancing both construction speed and reusability of modules. In this study, the structural performance of the AJ connection was investigated experimentally and numerically. First, the coupler was tested under a uniaxial tensile load to check its strength. Then, preloading tests on assemblies of a coupler and a 3m long SRB were conducted to determine the torque-preload relationship. Subsequently, full-scale beam-to-column joints featuring the AJ connection were tested under monotonic loading to investigate its lateral-force resisting mechanism. Lastly, the seismic performance of the AJ connection was investigated using FE joint models calibrated against a combination of test results and geometric and material nonlinear analyses with imperfections (GMNIA). A comparative analysis with a welded connection was also conducted to evaluate the seismic performance of the AJ connection. The findings indicated that the AJ connection demonstrates seismic performance comparable to the welded connection, exhibiting Strong column-Weak beam behaviour for seismic moment-resisting frames. Additionally, it satisfies the ductility requirements for structures of ductility class DCM as specified by EN 1998–1.

Prospective multicentre clinical study on inter- and intrapatient genetic variability for antimicrobial resistance of Helicobacter pylori

ObjectivesWe report on a large prospective, multicentre clinical investigation on inter- and intrapatient genetic variability for antimicrobial resistance of Helicobacter pylori. MethodsTherapy-naive patients (n = 2004) who had undergone routine diagnostic gastroscopy were prospectively included from all geographic regions of Austria. Gastric biopsy samples were collected separately from antrum and corpus. Samples were analysed by histopathology and real-time PCR for genotypic resistance to clarithromycin and quinolones. Clinical and demographic information was analysed in relation to resistance patterns. ResultsH. pylori infection was detected in 514 (26%) of 2004 patients by histopathology and confirmed in 465 (90%) of 514 patients by real-time PCR. PCR results were discordant for antrum and corpus in 27 (5%) of 514 patients, indicating inhomogeneous infections. Clarithromycin resistance rates were 17% (77/448) and 19% (84/455), and quinolone resistance rates were 12% (37/310) and 10% (32/334) in antrum and corpus samples, respectively. Combination of test results per patient yielded resistance rates of 21% (98/465) and 13% (50/383) for clarithromycin and quinolones, respectively. Overall, infection with both sensitive and resistant H. pylori was detected in 65 (14%) of 465 patients. ConclusionsAnatomically inhomogeneous infection with different, multiple H. pylori strains is common. Prospective clinical study design, collection of samples from multiple sites and microbiologic methods that allow the detection of coinfections are mandatory for collection of reliable data on antimicrobial resistance patterns in representative patient populations. (ClinicalTrials.gov identifier: NCT02925091).

Best combination of multiple diagnostic tests for screening purposes

In a medical diagnostic testing problem, multiple diagnostic tests are often available in distinguishing between diseased and nondiseased subjects. Different diagnostic tests are usually sensitive to different aspects of the disease. A desirable approach is to combine multiple diagnostic tests so as to obtain an optimal composite diagnostic test with higher sensitivity and specificity that detects the presence of the disease more accurately. To accomplish this, it has been observed via signal detection theory developed in the 1950s and 1960s, that the optimal combination of different diagnostic variables (i.e. the diagnostic test results) is determined by the likelihood ratio function for the diseased and nondiseased groups. The conventional approach is to fit parametric models for the diseased and nondiseased groups separately and then to use the fitted likelihood ratio function for the best combination of test results. However, this approach is not so robust if the underlying distribution functions are misspecified. Since the optimal combination depends only on the likelihood ratio function, it would be more appropriate to model this function directly. A two-sample semiparametric inference technique is applied to the model for the likelihood ratio function. We consider the best combination of multiple diagnostic tests, and study semiparametric likelihood estimation of the optimal receiver operating characteristic curve and the area under the curve. We present a bootstrap procedure along with some results on simulation and on analysis of two real data sets.

Risk estimation for the next generation of prevention programmes for cervical cancer

Risk estimation for the next generation of prevention programmes for cervical cancer

Reemergence of Goss's Wilt and Blight of Corn to the Central High Plains

Goss's bacterial wilt and blight, also known as leaf freckles and wilt was first identified in Dawson Co. in south central Nebraska in 1969. The disease was soon identified in 54 Nebraska counties and six bordering states. Widespread development of symptoms in NE, WY, and CO early in the 2006 growing season led to submission of more than 50 samples to the UNL Panhandle Research and Extension Center's Plant Disease Diagnostic Clinic in Scottsbluff, NE for analysis. Pathogen identification was made based on a combination of test results. Accepted for publication 5 April 2007. Published 19 September 2007.

Standardized biodegradability tests: Extrapolation to aerobic environments

Assessing the risk of chemicals for the environment requires an estimation of degradation rates in order to evaluate a realistic exposure concentration for organisms. As QSAR's (quantitative structure activity relationships) for degradation are far from generally applicable, methods are proposed to extrapolate results obtained from standardized degradation tests to the most relevant environmental compartments. The design of the OECD laboratory test methods is such that, at least at the screening level, almost general applicability is achieved. Inevitably, tests conditions do not reflect biodegradation in the real world. However, if data are available on biodegradation it appears that, for most chemicals, references are made to one of those standardized OECD methods. A systematical procedure to translate the result of an OECD test (or a combination of test results) to transformation rates under realistic circumstances is still lacking. In this report a methodology is presented to evaluate standardized test results in terms of rate constants for degradation in a biological waste water treatment plant, surface water and aerobic soil. The extrapolation of a positive result in OECD screening tests relies a.o. on the assumption that the half-life is inversely proportional to the total concentration of micro-organisms. A first attempt was made to validate this extrapolation by comparing results of reported field studies on biodegradation of 20 chemicals with positive results in OECD Ready Biodegradability tests.

Must clinical trials be large? The interpretation of p‐values and the combination of test results

The notion that small, well planned clinical trials may not be worth undertaking is shown to arise from an overemphasis on just one way of interpreting P-values. Alternative forms of P and other interpretations are put forward. Attention is drawn to some aspects of the theory of hypothesis testing which seem less well known than they should be.

Threshold analysis using diagnostic tests with multiple results.

Clinical problems represented by decision trees can be analyzed in terms of the probability threshold model, which provides management recommendations based on the prior probability of disease, the test threshold, and the test-treatment threshold. As originally proposed, the threshold model assumes that diagnostic tests provide information about a single event that is relevant to the decision. For some problems, however, a diagnostic test may provide information about more than one such event (e.g., a computed tomography [CT] scan gives information about both mediastinal and hilar metastases in lung cancer). The authors extend the probability threshold model to cases in which a single test provides information about two events that are relevant to the decision. They derive four thresholds that determine the best strategy for any combination of test results. The approach is illustrated for the decision to use a CT scan to stage lung cancer. The analysis reveals that: 1) the range of prior probabilities for which testing is optimal increases; 2) for some prior probabilities only test results about one event are important; 3) for some prior probabilities test results about both events are important; and 4) failure to account fully for information provided by a test can lead to erroneous test and treatment recommendations.