Heme Oxygenase-1 Induction and Organic Nitrate Therapy: Beneficial Effects on Endothelial Dysfunction, Nitrate Tolerance, and Vascular Oxidative Stress

Organic nitrates are a group of very effective anti-ischemic drugs. They are used for the treatment of patients with stable angina, acute myocardial infarction, and chronic congestive heart failure. A major therapeutic limitation inherent to organic nitrates is the development of tolerance, which occurs during chronic treatment with these agents, and this phenomenon is largely based on induction of oxidative stress with subsequent endothelial dysfunction. We therefore speculated that induction of heme oxygenase-1 (HO-1) could be an efficient strategy to overcome nitrate tolerance and the associated side effects. Indeed, we found that hemin cotreatment prevented the development of nitrate tolerance and vascular oxidative stress in response to chronic nitroglycerin therapy. Vice versa, pentaerithrityl tetranitrate (PETN), a nitrate that was previously reported to be devoid of adverse side effects, displayed tolerance and oxidative stress when the HO-1 pathway was blocked pharmacologically or genetically by using HO-1+/– mice. Recently, we identified activation of Nrf2 and HuR as a principle mechanism of HO-1 induction by PETN. With the present paper, we present and discuss our recent and previous findings on the role of HO-1 for the prevention of nitroglycerin-induced nitrate tolerance and for the beneficial effects of PETN therapy

Induction of tolerogenic lung CD4+ T cells by local treatment with a pSTAT-3 and pSTAT-5 inhibitor ameliorated experimental allergic asthma

Signal transducer and activator of transcription (STAT)-3 inhibitors play an important role in regulating immune responses. Galiellalactone (GL) is a fungal secondary metabolite known to interfere with the binding of phosphorylated signal transducer and activator of transcription (pSTAT)-3 as well of pSTAT-6 dimers to their target DNA in vitro. Intra nasal delivery of 50 μg GL into the lung of naive Balb/c mice induced FoxP3 expression locally and IL-10 production and IL-12p40 in RNA expression in the airways in vivo. In a murine model of allergic asthma, GL significantly suppressed the cardinal features of asthma, such as airway hyperresponsiveness, eosinophilia and mucus production, after sensitization and subsequent challenge with ovalbumin (OVA). These changes resulted in induction of IL-12p70 and IL-10 production by lung CD11c+ dendritic cells (DCs) accompanied by an increase of IL-3 receptor α chain and indoleamine-2,3-dioxygenase expression in these cells. Furthermore, GL inhibited IL-4 production in T-bet-deficient CD4+ T cells and down-regulated the suppressor of cytokine signaling-3 (SOCS-3), also in the absence of STAT-3 in T cells, in the lung in a murine model of asthma. In addition, we found reduced amounts of pSTAT-5 in the lung of GL-treated mice that correlated with decreased release of IL-2 by lung OVA-specific CD4+ T cells after treatment with GL in vitro also in the absence of T-bet. Thus, GL treatment in vivo and in vitro emerges as a novel therapeutic approach for allergic asthma by modulating lung DC phenotype and function resulting in a protective response via CD4+FoxP3+ regulatory T cells locall

Customization of Automatic Speech Recognition Engines for Rare Word Detection Without Costly Model Re-Training

Thanks to Alexa, Siri or Google Assistant automatic speech recognition (ASR) has changed our daily life during the last decade. Prototypic applications in the air traffic management (ATM) domain are available. Recently pre-filling radar label entries by ASR support has reached the technology readiness level before industrialization (TRL6). However, seldom spoken and airspace related words relevant in the ATM context remain a challenge for sophisticated applications. Open-source ASR toolkits or large pre-trained models for experts - allowing to tailor ASR to new domains - can be exploited with a typical constraint on availability of certain amount of domain specific training data, i.e., typically transcribed speech for adapting acoustic and/or language models. In general, it is sufficient for a "universal" ASR engine to reliably recognize a few hundred words that form the vocabulary of the voice communications between air traffic controllers and pilots. However, for each airport some hundred dependent words that are seldom spoken need to be integrated. These challenging word entities comprise special airline designators and waypoint names like "dexon" or "burok", which only appear in a specific region. When used, they are highly informative and thus require high recognition accuracies. Allowing plug and play customization with a minimum expert manipulation assumes that no additional training is required, i.e., fine-tuning the universal ASR. This paper presents an innovative approach to automatically integrate new specific word entities to the universal ASR system. The recognition rate of these region-specific word entities with respect to the universal ASR increases by a factor of 6

How to Measure Speech Recognition Performance in the Air Traffic Control Domain? The Word Error Rate is only half of the truth

Applying Automatic Speech Recognition (ASR) in the domain of analogue voice communication between air traffic controllers (ATCo) and pilots has more end user requirements than just transforming spoken words into text. It is useless, when word recognition is perfect, as long as the semantic interpretation is wrong. For an ATCo it is of no importance if the words of greeting are correctly recognized. A wrong recognition of a greeting should, however, not disturb the correct recognition of e.g. a “descend” command. Recently, 14 European partners from Air Traffic Management (ATM) domain have agreed on a common set of rules, i.e., an ontology on how to annotate the speech utterance of an ATCo. This paper first extends the ontology to pilot utterances and then compares different ASR implementations on semantic level by introducing command recognition, command recognition error, and command rejection rates. The implementation used in this paper achieves a command recognition rate better than 94% for Prague Approach, even when WER is above 2.5

Safety Aspects of Supporting Apron Controllers with Automatic Speech Recognition and Understanding Integrated into an Advanced Surface Movement Guidance and Control System

The information air traffic controllers (ATCos) communicate via radio telephony is valuable for digital assistants to provide additional safety. Yet, ATCos have to enter this information manually. Assistant-based speech recognition (ABSR) has proven to be a lightweight technology that automatically extracts and successfully feeds the content of ATC communication into digital systems without additional human effort. This article explains how ABSR can be integrated into an advanced surface movement guidance and control system (A-SMGCS). The described validations were performed in the complex apron simulation training environment of Frankfurt Airport with 14 apron controllers in a human-in-the-loop simulation in summer 2022. The integration significantly reduces the workload of controllers and increases safety as well as overall performance. Based on a word error rate of 3.1%, the command recognition rate was 91.8% with a callsign recognition rate of 97.4%. This performance was enabled by the integration of A-SMGCS and ABSR: the command recognition rate improves by more than 15% absolute by considering A-SMGCS data in ABSR

How Does Pre-trained Wav2Vec2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications

Recent work on self-supervised pre-training focus on leveraging large-scale unlabeled speech data to build robust end-to-end (E2E) acoustic models (AM) that can be later fine-tuned on downstream tasks e.g., automatic speech recognition (ASR). Yet, few works investigated the impact on performance when the data substantially differs between the pre-training and downstream fine-tuning phases (i.e., domain shift). We target this scenario by analyzing the robustness of Wav2Vec2.0 and XLS-R models on downstream ASR for a completely unseen domain, i.e., air traffic control (ATC) communications. We benchmark the proposed models on four challenging ATC test sets (signal-to-noise ratio varies between 5 to 20 dB). Relative word error rate (WER) reduction between 20% to 40% are obtained in comparison to hybrid-based state-of-the-art ASR baselines by fine-tuning E2E acoustic models with a small fraction of labeled data. We also study the impact of fine-tuning data size on WERs, going from 5 minutes (few-shot) to 15 hours.Comment: This paper has been submitted to Interspeech 202

The HAAWAII Framework for Automatic Speech Understanding of Air Traffic Communication

During the last decade many successful applications combining Automatic Speech Recognition and Understanding (ASRU) for Air Traffic Management applications have been proposed and demonstrated. The HAAWAII project developed a generic architecture and framework, which was validated for, e.g., callsign highlighting, pre-filling radar labels and readback error detection. It supports recognizing and understanding pilot and air traffic controller (ATCo) transmissions. Contextual information extracted from available surveillance data, from flight plan data and from previous transmissions can be exploited to significantly improve ASRU performance. Different design decisions have been taken, depending on concrete scenarios. This paper evaluates the effect of the design decisions integrated in the HAAWAII framework on overall performance for speech understanding based on eight hypotheses, of which seven are validated. Using all framework elements enables command recognition rates for ATCos of 90% for real-time applications and 93% for offline applications, respectively. The most significant impact is achieved, when callsign information from surveillance data is available: the command recognition rate improves by more than 20% absolute. Knowing apriori, whether ATCo or pilot is speaking, can provide additional improvement in command recognition rate up to 16% absolute. The reported results are based on commands from apron, approach, and enroute recorded both in laboratory and in ops room environment

Differentially Tolerized Mouse Antigen Presenting Cells Share a Common miRNA Signature Including Enhanced mmu-miR-223-3p Expression Which Is Sufficient to Imprint a Protolerogenic State

Dendritic cells (DCs) are pivotal for the induction and maintenance of antigen-specific tolerance and immunity. miRNAs mediate post-transcriptional gene regulation and control in part the differentiation and stimulation-induced immunogenic function of DCs. However, the relevance of miRNAs for the induction and maintenance of a tolerogenic state of DCs has scarcely been highlighted yet. We differentiated mouse bone marrow cells to conventional/myeloid DCs or to tolerogenic antigen presenting cells (APCs) by using a glucocorticoid (dexamethasone) or interleukin-10, and assessed the miRNA expression patterns of unstimulated and LPS-stimulated cell populations by array analysis and QPCR. Differentially tolerized mouse APCs convergingly down-regulated a set of miRNA species at either state of activation as compared with the corresponding control DC population (mmu-miR-9-5p, mmu-miR-9-3p, mmu-miR-155-5p). These miRNAs were also upregulated in control DCs in response to stimulation. In contrast, miRNAs that were convergingly upregulated in both tolerized APC groups at stimulated state (mmu-miR-223-3p, mmu-miR-1224-5p) were downregulated in control DCs in response to stimulation. Overexpression of mmu-miR-223-3p in DCs was sufficient to prevent stimulation-associated acquisition of potent T cell stimulatory capacity. Overexpression of mmu-miR-223-3p in a DC line resulted in attenuated expression of known (Cflar, Rasa1, Ras) mRNA targets of this miRNA species shown to affect pathways that control DC activation. Taken together, we identified sets of miRNAs convergingly regulated in differentially tolerized APCs, which may contribute to imprint stimulation-resistant tolerogenic function as demonstrated for mmu-miR-223-3p. Knowledge of miRNAs with protolerogenic function enables immunotherapeutic approaches aimed to modulate immune responses by regulating miRNA expression

Co-exposure to urban particulate matter and aircraft noise adversely impacts the cerebro-pulmonary-cardiovascular axis in mice

Worldwide, up to 8.8 million excess deaths/year have been attributed to air pollution, mainly due to the exposure to fine particulate matter (PM). Traffic-related noise is an additional contributor to global mortality and morbidity. Both health risk factors substantially contribute to cardiovascular, metabolic and neuropsychiatric sequelae. Studies on the combined exposure are rare and urgently needed because of frequent co-occurrence of both risk factors in urban and industrial settings. To study the synergistic effects of PM and noise, we used an exposure system equipped with aerosol generator and loud-speakers, where C57BL/6 mice were acutely exposed for 3d to either ambient PM (NIST particles) and/or noise (aircraft landing and take-off events). The combination of both stressors caused endothelial dysfunction, increased blood pressure, oxidative stress and inflammation. An additive impairment of endothelial function was observed in isolated aortic rings and even more pronounced in cerebral and retinal arterioles. The increase in oxidative stress and inflammation markers together with RNA sequencing data indicate that noise particularly affects the brain and PM the lungs. The combination of both stressors has additive adverse effects on the cardiovascular system that are based on PM-induced systemic inflammation and noise-triggered stress hormone signaling. We demonstrate an additive upregulation of ACE-2 in the lung, suggesting that there may be an increased vulnerability to COVID-19 infection. The data warrant further mechanistic studies to characterize the propagation of primary target tissue damage (lung, brain) to remote organs such as aorta and heart by combined noise and PM exposure