Simulacija u metalurgiji

Die vorliegende Veröffentlichung umreißt in groben Zügen die Grundlagen der Simulation sowie den Weg vom realen Problem zum Modell. An einigen Fallbeispielen ist der Einsatz der Simulation in der Metallurgie gezeigt. Ablaufsimulation, Parameterstudien sowie Simulationen zu Produkteigenschaften werden erklärt. Die Entwicklung von neuen Verfahren mittels der Simulation ist ebenfalls beschrieben.Predočeni rad opisuje u grubim crtama kao i put od realnog problema do modela. Na primjerima nekih slučajeva prikazuje se primjena simulacija u metalurgiji. Objašnjava se simulacija toka, proučavanje parametara kao i simulacija glede svojstava proizvoda. Također je opisan razvoj novih postupaka pomoću simulacije

Cellular Immune Responses and Viral Diversity in Individuals Treated during Acute and Early HIV-1 Infection

Immune responses induced during the early stages of chronic viral infections are thought to influence disease outcome. Using HIV as a model, we examined virus-specific cytotoxic T lymphocytes (CTLs), T helper cells, and viral genetic diversity in relation to duration of infection and subsequent response to antiviral therapy. Individuals with acute HIV-1 infection treated before seroconversion had weaker CTL responses directed at fewer epitopes than persons who were treated after seroconversion. However, treatment-induced control of viremia was associated with the development of strong T helper cell responses in both groups. After 1 yr of antiviral treatment initiated in acute or early infection, all epitope-specific CTL responses persisted despite undetectable viral loads. The breadth and magnitude of CTL responses remained significantly less in treated acute infection than in treated chronic infection, but viral diversity was also significantly less with immediate therapy. We conclude that early treatment of acute HIV infection leads to a more narrowly directed CTL response, stronger T helper cell responses, and a less diverse virus population. Given the need for T helper cells to maintain effective CTL responses and the ability of virus diversification to accommodate immune escape, we hypothesize that early therapy of primary infection may be beneficial despite induction of less robust CTL responses. These data also provide rationale for therapeutic immunization aimed at broadening CTL responses in treated primary HIV infection

Altering an Artificial Gagpolnef Polyprotein and Mode of ENV Co-Administration Affects the Immunogenicity of a Clade C HIV DNA Vaccine

HIV-1 candidate vaccines expressing an artificial polyprotein comprising Gag, Pol and Nef (GPN) and a secreted envelope protein (Env) were shown in recent Phase I/II clinical trials to induce high levels of polyfunctional T cell responses; however, Env-specific responses clearly exceeded those against Gag. Here, we assess the impact of the GPN immunogen design and variations in the formulation and vaccination regimen of a combined GPN/Env DNA vaccine on the T cell responses against the various HIV proteins. Subtle modifications were introduced into the GPN gene to increase Gag expression, modify the expression ratio of Gag to PolNef and support budding of virus-like particles. I.m. administration of the various DNA constructs into BALB/c mice resulted in an up to 10-fold increase in Gag- and Pol-specific IFNγ+ CD8+ T cells compared to GPN. Co-administering Env with Gag or GPN derivatives largely abrogated Gag-specific responses. Alterations in the molar ratio of the DNA vaccines and spatially or temporally separated administration induced more balanced T cell responses. Whereas forced co-expression of Gag and Env from one plasmid induced predominantly Env-specific T cells responses, deletion of the only H-2d T cell epitope in Env allowed increased levels of Gag-specific T cells, suggesting competition at an epitope level. Our data demonstrate that the biochemical properties of an artificial polyprotein clearly influence the levels of antigen-specific T cells, and variations in formulation and schedule can overcome competition for the induction of these responses. These results are guiding the design of ongoing pre-clinical and clinical trials

Pneumoproteins and biomarkers of inflammation and coagulation do not predict rapid lung function decline in people living with HIV

Chronic obstructive pulmonary disease (COPD) is among the leading causes of death worldwide and HIV is an independent risk factor for the development of COPD. However, the etiology of this increased risk and means to identify persons with HIV (PWH) at highest risk for COPD have remained elusive. Biomarkers may reveal etiologic pathways and allow better COPD risk stratification. We performed a matched case:control study of PWH in the Strategic Timing of Antiretoviral Treatment (START) pulmonary substudy. Cases had rapid lung function decline (> 40 mL/year FEV1 decline) and controls had stable lung function (+ 20 to − 20 mL/year). The analysis was performed in two distinct groups: (1) those who were virally suppressed for at least 6 months and (2) those with untreated HIV (from the START deferred treatment arm). We used linear mixed effects models to test the relationship between case:control status and blood concentrations of pneumoproteins (surfactant protein-D and club cell secretory protein), and biomarkers of inflammation (IL-6 and hsCRP) and coagulation (d-dimer and fibrinogen); concentrations were measured within ± 6 months of first included spirometry. We included an interaction with treatment group (untreated HIV vs viral suppression) to test if associations varied by treatment group. This analysis included 77 matched case:control pairs in the virally suppressed batch, and 42 matched case:control pairs in the untreated HIV batch (n = 238 total) who were followed for a median of 3 years. Median (IQR) CD4 + count was lowest in the controls with untreated HIV at 674 (580, 838). We found no significant associations between case:control status and pneumoprotein or biomarker concentrations in either virally suppressed or untreated PWH. In this cohort of relatively young, recently diagnosed PWH, concentrations of pneumoproteins and biomarkers of inflammation and coagulation were not associated with subsequent rapid lung function decline. Trial registration: NCT00867048 and NCT01797367

Oxidative stress and proinflammatory cytokines contribute to demyelination and axonal damage in a cerebellar culture model of neuroinflammation

Background: Demyelination and axonal damage are critical processes in the pathogenesis of multiple sclerosis (MS). Oxidative stress and pro-inflammatory cytokines elicited by inflammation mediates tissue damage. Methods/Principal Findings: To monitor the demyelination and axonal injury associated with microglia activation we employed a model using cerebellar organotypic cultures stimulated with lipopolysaccharide (LPS). Microglia activated by LPS released pro-inflammatory cytokines (IL-1β, IL-6 and TNFα), and increased the expression of inducible nitric oxide synthase (iNOS) and production of reactive oxygen species (ROS). This activation was associated with demyelination and axonal damage in cerebellar cultures. Axonal damage, as revealed by the presence of non-phosphorylated neurofilaments, mitochondrial accumulation in axonal spheroids, and axonal transection, was associated with stronger iNOS expression and concomitant increases in ROS. Moreover, we analyzed the contribution of pro-inflammatory cytokines and oxidative stress in demyelination and axonal degeneration using the iNOS inhibitor ethyl pyruvate, a free-scavenger and xanthine oxidase inhibitor allopurinol, as well as via blockage of pro-inflammatory cytokines using a Fc-TNFR1 construct. We found that blocking microglia activation with ethyl pyruvate or allopurinol significantly decreased axonal damage, and to a lesser extent, demyelination. Blocking TNFα significantly decreased demyelination but did not prevented axonal damage. Moreover, the most common therapy for MS, interferon-beta, was used as an example of an immunomodulator compound that can be tested in this model. In vitro, interferon-beta treatment decreased oxidative stress (iNOS and ROS levels) and the release of pro-inflammatory cytokines after LPS stimulation, reducing axonal damage. Conclusion: The model of neuroinflammation using cerebellar culture stimulated with endotoxin mimicked myelin and axonal damage mediated by the combination of oxidative stress and pro-inflammatory cytokines. This model may both facilitate understanding of the events involved in neuroinflammation and aid in the development of neuroprotective therapies for the treatment of MS and other neurodegenerative diseases

From Sound to Significance: Exploring the Mechanisms Underlying Emotional Reactions to Music

A common approach to studying emotional reactions to music is to attempt to obtain direct links between musical surface features such as tempo and a listener’s responses. however, such an analysis ultimately fails to explain why emotions are aroused in the listener. in this article we explore an alternative approach, which aims to account for musical emotions in terms of a set of psychological mechanisms that are activated by different types of information in a musical event. this approach was tested in 4 experiments that manipulated 4 mechanisms (brain stem reflex, contagion, episodic memory, musical expectancy) by selecting existing musical pieces that featured information relevant for each mechanism. the excerpts were played to 60 listeners, who were asked to rate their felt emotions on 15 scales. skin conductance levels and facial expressions were measured, and listeners reported subjective impressions of relevance to specific mechanisms. results indicated that the target mechanism conditions evoked emotions largely as predicted by a multimechanism framework and that mostly similar effects occurred across the experiments that included different pieces of music. we conclude that a satisfactory account of musical emotions requires consideration of how musical features and responses are mediated by a range of underlying mechanisms

Transcription profiling of lung adenocarcinomas of c-myc-transgenic mice: Identification of the c-myc regulatory gene network

<p>Abstract</p> <p>Background</p> <p>The transcriptional regulator c-Myc is the most frequently deregulated oncogene in human tumors. Targeted overexpression of this gene in mice results in distinct types of lung adenocarcinomas. By using microarray technology, alterations in the expression of genes were captured based on a female transgenic mouse model in which, indeed, c-Myc overexpression in alveolar epithelium results in the development of bronchiolo-alveolar carcinoma (BAC) and papillary adenocarcinoma (PLAC). In this study, we analyzed exclusively the promoters of induced genes by different in silico methods in order to elucidate the c-Myc transcriptional regulatory network.</p> <p>Results</p> <p>We analyzed the promoters of 361 transcriptionally induced genes with respect to c-Myc binding sites and found 110 putative binding sites in 94 promoters. Furthermore, we analyzed the flanking sequences (+/- 100 bp) around the 110 c-Myc binding sites and found Ap2, Zf5, Zic3, and E2f binding sites to be overrepresented in these regions. Then, we analyzed the promoters of 361 induced genes with respect to binding sites of other transcription factors (TFs) which were upregulated by c-Myc overexpression. We identified at least one binding site of at least one of these TFs in 220 promoters, thus elucidating a potential transcription factor network. The analysis correlated well with the significant overexpression of the TFs Atf2, Foxf1a, Smad4, Sox4, Sp3 and Stat5a. Finally, we analyzed promoters of regulated genes which where apparently not regulated by c-Myc or other c-Myc targeted TFs and identified overrepresented Oct1, Mzf1, Ppargamma, Plzf, Ets, and HmgIY binding sites when compared against control promoter background.</p> <p>Conclusion</p> <p>Our in silico data suggest a model of a transcriptional regulatory network in which different TFs act in concert upon c-Myc overexpression. We determined molecular rules for transcriptional regulation to explain, in part, the carcinogenic effect seen in mice overexpressing the c-Myc oncogene.</p

Overview of the TCV tokamak experimental programme

The tokamak a configuration variable (TCV) continues to leverage its unique shaping capabilities, flexible heating systems and modern control system to address critical issues in preparation for ITER and a fusion power plant. For the 2019-20 campaign its configurational flexibility has been enhanced with the installation of removable divertor gas baffles, its diagnostic capabilities with an extensive set of upgrades and its heating systems with new dual frequency gyrotrons. The gas baffles reduce coupling between the divertor and the main chamber and allow for detailed investigations on the role of fuelling in general and, together with upgraded boundary diagnostics, test divertor and edge models in particular. The increased heating capabilities broaden the operational regime to include T (e)/T (i) similar to 1 and have stimulated refocussing studies from L-mode to H-mode across a range of research topics. ITER baseline parameters were reached in type-I ELMy H-modes and alternative regimes with \u27small\u27 (or no) ELMs explored. Most prominently, negative triangularity was investigated in detail and confirmed as an attractive scenario with H-mode level core confinement but an L-mode edge. Emphasis was also placed on control, where an increased number of observers, actuators and control solutions became available and are now integrated into a generic control framework as will be needed in future devices. The quantity and quality of results of the 2019-20 TCV campaign are a testament to its successful integration within the European research effort alongside a vibrant domestic programme and international collaborations

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

Importance: Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective: To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants: In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements: Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results: Overall, 144 231 screening mammograms from 85 580 US women (952 cancer positive ≤12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166 578 examinations from 68 008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance: While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation