A long constraint length VLSI Viterbi decoder for the DSN

A Viterbi decoder, capable of decoding convolutional codes with constraint lengths up to 15, is under development for the Deep Space Network (DSN). The objective is to complete a prototype of this decoder by late 1990, and demonstrate its performance using the (15, 1/4) encoder in Galileo. The decoder is expected to provide 1 to 2 dB improvement in bit SNR, compared to the present (7, 1/2) code and existing Maximum Likelihood Convolutional Decoder (MCD). The decoder will be fully programmable for any code up to constraint length 15, and code rate 1/2 to 1/6. The decoder architecture and top-level design are described

Antiviral treatment for COVID-19: the evidence supporting remdesivir

The emergence of the novel beta coronavirus SARS-CoV-2 and the ensuing COVID-19 pandemic has generated a rapidly evolving research landscape in the search for new therapeutic agents. The intravenous antiviral drug remdesivir has in vitro activity against SARS-CoV-2 and now studies have reported its clinical efficacy, demonstrating shorter time to recovery in hospitalised patients with severe COVID-19. Adverse event rates were low and remdesivir has now received conditional marketing authorisation from the European Medicines Agency. An interim clinical commissioning policy is in place in the UK. These studies make remdesivir the first antiviral drug able to alter the natural history of severe COVID-19, and a benchmark for the comparison of new therapies in the future. Ongoing studies are investigating its use in early mild/moderate COVID-19, alternative formulations, and the combination of remdesivir with immunomodulatory agents

Selective Laser Melting of Ti6Al4V: Effects of Heat Accumulation Phenomena Due to Building Orientation

Titanium alloy Ti6Al4V is one of the most utilized alloys in the field of additive manufacturing due to the excellent combination of mechanical properties, density and good corrosion behavior. These characteristics make the use of this material particularly attractive for additively manufacturing components with complex geometry in sectors such as aeronautics and biomedical. Selective Laser Melting (SLM), by which a component is fabricated by selectively melting of stacked layers of powder using a laser beam, is the one of most promising additive manufacturing technologies for Ti6Al4V alloy. Although this technique offers numerous advantages, it has some critical issues related to the high thermal gradients, associated with the process, promoting the formation of a metastable martensitic microstructure resulting in high tensile strength but poor ductility of the produced parts. The formation of microstructural defects such as balling and porosity can occur together with the presence of residual stresses that may significantly affect the mechanical characteristics of the component. Specific process parameters and geometries can determine heat accumulation phenomena that result in a progressive decrease in thermal gradients between layers. These heat accumulation phenomena are influenced by the number of layers deposited, but also by the building orientation that, for a given geometry, determines a variation of the deposited surface for each layer. © 2022 The Author(s). Published by Trans Tech Publications Ltd, Switzerland

Ductility and linear energy density of Ti6Al4V parts produced with additive powder bed fusion technology

Hybrid metal forming processes involve the integration of commonly used sheet metal forming processes, as bending, deep drawing and incremental forming, with additive manufacturing processes as Powder Bed Fusion. In recent ybears, these integrations have been more developed for manufacturing sectors characterized by components with complex geometries in low numbers, as the aerospace sector. Hybrid additive manufacturing overcomes the typical limitations of additive manufacturing related to low productivity, metallurgical defects and low dimensional accuracy. In this perspective, a key aspect of hybrid processes is the production of parts characterized by high strength and ductility. In the present work, a study was carried out on the influence of process parameters, such as laser power and scanning speed, on material ductility for Ti6Al4V alloy samples produced by Selective Laser Melting. In particular, the material strength and ductility were related to the process linear energy density (LED)

Hybrid prediction-optimization approaches for maximizing parts density in SLM of Ti6Al4V titanium alloy

It is well known that the processing parameters of selective laser melting (SLM) highly influence mechanical and physical properties of the manufactured parts. Also, the energy density is insufficient to detect the process window for producing full dense components. In fact, parts produced with the same energy density but different combinations of parameters may present different properties even under the microstructural viewpoint. In this context, the need to assess the influence of the process parameters and to select the best parameters set able to optimize the final properties of SLM parts has been capturing the attention of both academics and practitioners. In this paper different hybrid prediction-optimization approaches for maximizing the relative density of Ti6Al4V SLM manufactured parts are proposed. An extended design of experiments involving six process parameters has been configured for constructing two surrogate models based on response surface methodology (RSM) and artificial neural network (ANN), respectively. The optimization phase has been performed by means of evolutionary computations. To this end, three nature-inspired metaheuristic algorithms have been integrated with the prediction modelling structures. A series of experimental tests has been carried out to validate the results from the proposed hybrid optimization procedures. Also, a sensitivity analysis based on the results from the analysis of variance was executed to evaluate the influence of the processing parameter and their reciprocal interactions on the part porosity

Soft-output decoding algorithms in iterative decoding of turbo codes

In this article, we present two versions of a simplified maximum a posteriori decoding algorithm. The algorithms work in a sliding window form, like the Viterbi algorithm, and can thus be used to decode continuously transmitted sequences obtained by parallel concatenated codes, without requiring code trellis termination. A heuristic explanation is also given of how to embed the maximum a posteriori algorithms into the iterative decoding of parallel concatenated codes (turbo codes). The performances of the two algorithms are compared on the basis of a powerful rate 1/3 parallel concatenated code. Basic circuits to implement the simplified a posteriori decoding algorithm using lookup tables, and two further approximations (linear and threshold), with a very small penalty, to eliminate the need for lookup tables are proposed

Diagnostic 'omics' for active tuberculosis

The decision to treat active tuberculosis (TB) is dependent on microbiological tests for the organism or evidence of disease compatible with TB in people with a high demographic risk of exposure. The tuberculin skin test and peripheral blood interferon-γ release assays do not distinguish active TB from a cleared or latent infection. Microbiological culture of mycobacteria is slow. Moreover, the sensitivities of culture and microscopy for acid-fast bacilli and nucleic acid detection by PCR are often compromised by difficulty in obtaining samples from the site of disease. Consequently, we need sensitive and rapid tests for easily obtained clinical samples, which can be deployed to assess patients exposed to TB, discriminate TB from other infectious, inflammatory or autoimmune diseases, and to identify subclinical TB in HIV-1 infected patients prior to commencing antiretroviral therapy. We discuss the evaluation of peripheral blood transcriptomics, proteomics and metabolomics to develop the next generation of rapid diagnostics for active TB. We catalogue the studies published to date seeking to discriminate active TB from healthy volunteers, patients with latent infection and those with other diseases. We identify the limitations of these studies and the barriers to their adoption in clinical practice. In so doing, we aim to develop a framework to guide our approach to discovery and development of diagnostic biomarkers for active TB

T2Candida assay: diagnostic performance and impact on antifungal prescribing

Objectives: To assess the performance of T2Candida for the diagnosis of invasive candidiasis (IC) against gold standards of candidaemia or consensus IC definitions, and to evaluate the impact of T2Candida on antifungal drug prescribing. Methods: A retrospective review was undertaken of all T2Candida (T2MR technology, T2 Biosystems) performed from October 2020 to February 2022. T2Candida performance was evaluated against confirmed candidaemia or against proven/probable IC within 48 hours of T2Candida, and its impact on antifungal drug prescriptions. Results: T2Candida was performed in 61 patients, with 6 (9.8%) positive results. Diagnostic performance of T2Candida against candidaemia had a specificity of 85.7% and negative predictive value (NPV) of 96.8%. When comparing T2Candida results with consensus definitions of IC, the specificity and NPV of T2Candida was respectively 90% (54/60) and 98.2% (54/55) for proven IC, and 91.4% (53/58) and 96.4% (53/55) for proven/probable IC. Antifungals were initiated in three of six patients (50%) with a positive T2Candida result. Thirty-three patients were receiving empirical antifungals at the time of T2Candida testing, and a negative result prompted cessation of antifungals in 11 (33%) patients, compared with 6 (25%) antifungal prescriptions stopped following negative beta-D-glucan (BDG) testing in a control population (n = 24). Conclusions: T2Candida shows high specificity and NPV compared with evidence of Candida bloodstream infection or consensus definitions for invasive Candida infection, and may play an adjunctive role as a stewardship tool to limit unnecessary antifungal prescriptions

Tumor necrosis Factor (TNF) Bioactivity at the site of an acute cell-Mediated immune response is Preserved in rheumatoid arthritis Patients responding to anti-TNF Therapy

The impact of anti-tumor necrosis factor (TNF) therapies on inducible TNF-dependent activity in humans has never been evaluated in vivo. We aimed to test the hypothesis that patients responding to anti-TNF treatments exhibit attenuated TNF-dependent immune responses at the site of an immune challenge. We developed and validated four context-specific TNF-inducible transcriptional signatures to quantify TNF bioactivity in transcriptomic data. In anti-TNF treated rheumatoid arthritis (RA) patients, we measured the expression of these biosignatures in blood, and in skin biopsies from the site of tuberculin skin tests (TSTs) as a human experimental model of multivariate cell-mediated immune responses. In blood, anti-TNF therapies attenuated TNF bioactivity following ex vivo stimulation. However, at the site of the TST, TNF-inducible gene expression and genome-wide transcriptional changes associated with cell-mediated immune responses were comparable to that of RA patients receiving methotrexate only. These data demonstrate that anti-TNF agents in RA patients do not inhibit inducible TNF activity at the site of an acute inflammatory challenge in vivo, as modeled by the TST. We hypothesize instead that their therapeutic effects are limited to regulating TNF activity in chronic inflammation or by alternative non-canonical pathways