A new uvs mutant

A new uvs mutan

Fixation of a double-coated titanium-hydroxyapatite focal knee resurfacing implant A 12-month study in sheep

SummaryObjectiveFocal cartilage lesions according to International Cartilage Repair Society (ICRS) grade 3–4 in the medial femoral condyle may progress to osteoarthritis. When treating such focal lesions with metallic implants a sound fixation to the underlying bone is mandatory. We developed a monobloc unipolar cobalt-chrome (Co-Cr) implant with a double coating; first a layer of commercially pure titanium (c.p.Ti) on top of which a layer of hydroxyapatite (HA) was applied. We hypothesised that such a double coating would provide long-lasting and adequate osseointegration.Design (materials and methods)Unilateral medial femoral condyles of 10 sheep were operated. The implants were inserted in the weight-bearing surface and immediate weight-bearing was allowed. Euthanasia was performed at 6 (three animals) or 12 months (six animals). Osseointegration was analysed with micro-computer tomography (CT), light microscopy and histomorphometric analyses using backscatter scanning electron microscopy (B-SEM) technique.ResultsAt 6 months one specimen out of three showed small osteolytic areas at the hat and at 12 months two specimens out of six showed small osteolytic areas at the hat, no osteolytical areas were seen around the peg at any time point. At both time points, a high total bone-to-implant contact was measured with a mean (95% confidence interval – CI) of 90.6 (79–102) at 6 months and 92.3 (89–95) at 12 months, respectively.ConclusionsA double coating (Ti + HA) of a focal knee resurfacing Co-Cr implant was presented in a sheep animal model. A firm and consistent bond to bone under weight-bearing conditions was shown up to 1 year

HIV-1 Viral loas assays for resource-limited settings

Tremendous strides have been made in treating HIV-1 infection in industrialized countries. Combination therapy with antiretroviral (ARV) drugs suppresses virus replication, delays disease progression, and reduces mortality. In industrialized settings, plasma viral load assays are used in combination with CD4 cell counts to determine when to initiate therapy and when a regimen is failing. In addition, unlike serologic assays, these assays may be used to diagnose perinatal or acute HIV-1 infection. Unfortunately, the full benefits of antiretroviral drugs and monitoring tests have not yet reached the majority of HIV-1-infected patients who live in countries with limited resources. In this article we discuss existing data on the performance of alternative viral load assays that might be useful in resource-limited settings

Degree of explanation

Partial explanations are everywhere. That is, explanations citing causes that explain some but not all of an effect are ubiquitous across science, and these in turn rely on the notion of degree of explanation. I argue that current accounts are seriously deficient. In particular, they do not incorporate adequately the way in which a cause’s explanatory importance varies with choice of explanandum. Using influential recent contrastive theories, I develop quantitative definitions that remedy this lacuna, and relate it to existing measures of degree of causation. Among other things, this reveals the precise role here of chance, as well as bearing on the relation between causal explanation and causation itself

Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster

We describe a method for precisely regulating the gradient magnet power supply at the Fermilab Booster accelerator complex using a neural network trained via reinforcement learning. We demonstrate preliminary results by training a surrogate machine-learning model on real accelerator data to emulate the Booster environment, and using this surrogate model in turn to train the neural network for its regulation task. We additionally show how the neural networks to be deployed for control purposes may be compiled to execute on field-programmable gate arrays. This capability is important for operational stability in complicated environments such as an accelerator facility.Comment: 16 pages, 10 figures. Submitted to Physical Review Accelerators and Beams. For associated dataset and data sheet see http://doi.org/10.5281/zenodo.408898

The Explication Defence of Arguments from Reference

In a number of influential papers, Machery, Mallon, Nichols and Stich have presented a powerful critique of so-called arguments from reference, arguments that assume that a particular theory of reference is correct in order to establish a substantive conclusion. The critique is that, due to cross-cultural variation in semantic intuitions supposedly undermining the standard methodology for theorising about reference, the assumption that a theory of reference is correct is unjustified. I argue that the many extant responses to Machery et al.’s critique do little for the proponent of an argument from reference, as they do not show how to justify the problematic assumption. I then argue that it can in principle be justified by an appeal to Carnapian explication. I show how to apply the explication defence to arguments from reference given by Andreasen (for the biological reality of race) and by Churchland (against the existence of beliefs and desires)

ML-based Real-Time Control at the Edge: An Approach Using hls4ml

This study focuses on implementing a real-time control system for a particle accelerator facility that performs high energy physics experiments. A critical operating parameter in this facility is beam loss, which is the fraction of particles deviating from the accelerated proton beam into a cascade of secondary particles. Accelerators employ a large number of sensors to monitor beam loss. The data from these sensors is monitored by human operators who predict the relative contribution of different sub-systems to the beam loss. Using this information, they engage control interventions. In this paper, we present a controller to track this phenomenon in real-time using edge-Machine Learning (ML) and support control with low latency and high accuracy. We implemented this system on an Intel Arria 10 SoC. Optimizations at the algorithm, high-level synthesis, and interface levels to improve latency and resource usage are presented. Our design implements a neural network, which can predict the main source of beam loss (between two possible causes) at speeds up to 575 frames per second (fps) (average latency of 1.74 ms). The practical deployed system is required to operate at 320 fps, with a 3ms latency requirement, which has been met by our design successfully

Diagnostic performance of line-immunoassay based algorithms for incident HIV-1 infection

Background: Serologic testing algorithms for recent HIV seroconversion (STARHS) provide important information for HIV surveillance. We have previously demonstrated that a patient's antibody reaction pattern in a confirmatory line immunoassay (INNO-LIA™ HIV I/II Score) provides information on the duration of infection, which is unaffected by clinical, immunological and viral variables. In this report we have set out to determine the diagnostic performance of Inno-Lia algorithms for identifying incident infections in patients with known duration of infection and evaluated the algorithms in annual cohorts of HIV notifications. Methods: Diagnostic sensitivity was determined in 527 treatment-naive patients infected for up to 12 months. Specificity was determined in 740 patients infected for longer than 12 months. Plasma was tested by Inno-Lia and classified as either incident (< = 12 m) or older infection by 26 different algorithms. Incident infection rates (IIR) were calculated based on diagnostic sensitivity and specificity of each algorithm and the rule that the total of incident results is the sum of true-incident and false-incident results, which can be calculated by means of the pre-determined sensitivity and specificity. Results: The 10 best algorithms had a mean raw sensitivity of 59.4% and a mean specificity of 95.1%. Adjustment for overrepresentation of patients in the first quarter year of infection further reduced the sensitivity. In the preferred model, the mean adjusted sensitivity was 37.4%. Application of the 10 best algorithms to four annual cohorts of HIV-1 notifications totalling 2'595 patients yielded a mean IIR of 0.35 in 2005/6 (baseline) and of 0.45, 0.42 and 0.35 in 2008, 2009 and 2010, respectively. The increase between baseline and 2008 and the ensuing decreases were highly significant. Other adjustment models yielded different absolute IIR, although the relative changes between the cohorts were identical for all models Conclusions: The method can be used for comparing IIR in annual cohorts of HIV notifications. The use of several different algorithms in combination, each with its own sensitivity and specificity to detect incident infection, is advisable as this reduces the impact of individual imperfections stemming primarily from relatively low sensitivities and sampling bias