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Abstract Background and Aims Interstitial lung diseases (ILDs), encompassing both pediatric and adult cases, present a diverse spectrum of chronic conditions with variable prognosis. Despite limited therapeutic options beyond antifibrotic drugs and immunosuppressants, accurate diagnosis is challenging, often necessitating invasive procedures that may not be feasible for certain patients. Drawn against this background, experts across pediatric and adult ILD fields have joined forces in the RARE-ILD initiative to pioneer novel non-invasive diagnostic algorithms and biomarkers. Collaborating with the RARE-ILD consortium, the eurILDreg aims to comprehensively describe different ILDs, analyze genetically defined forms across age groups, create innovative diagnostic and therapeutic biomarkers, and employ artificial intelligence for data analysis. Methods The foundation of eurILDreg is built on a comprehensive parameter list developed and adopted by clinical experts, encompassing over 1,800 distinct parameters related to patient history, clinical examinations, diagnosis, lung function and biospecimen collection. This robust dataset is further enriched with daily assessments captured through the patientMpower app, including handheld spirometry and exercise tests, conducted on approximately 350 patients over the course of a year. This approach involves app-based daily assessments of quality of life, symptom tracking, handheld spirometry, saturation measurement, and the 1-min sit-to-stand test (1-STST). Additionally, pediatric data from the ChILD-EU registry will be integrated into the RARE-ILD Data Warehouse, with the ultimate goal of including a total of 4.000 ILD patients and over 100.000 biospecimen. Discussion The collaborative efforts within the consortium are poised to streamline research endeavors significantly, promising to advance patient-centered care, foster innovation, and shape the future landscape of interstitial lung disease research and healthcare practices. Trial Registration EurILDreg is registered in the German Clinical Trials Register (DRKS 00028968, 26.07.2022), and eurIPFreg is registered in ClinicalTrials.gov (NCT02951416).

Pikeperch, perch and bream are among the most traded and valued fish species in North-Eastern Europe. Therefore, it is necessary to be able to distinguish fish from different lakes and coastal sea regions to ensure a good traceability of products in the fish market and to protect both consumers and fish stocks. Untargeted metabolomics using nuclear magnetic resonance (NMR) spectroscopy is a suitable tool for this purpose. It is an established method for determining various properties of biological and living systems, such as health, origin, type, etc. Statistical methods including principal component analysis (PCA) and linear discriminant analysis (LDA) are typically applied to NMR data to correlate spectra with a particular research question.Herein we examine fish from three closely related water bodies and demonstrate that reliable determination of the water body that a particular fish originates from by traditional statistical analysis (PCA and LDA) of fish NMR spectra is not possible. In contrast, determining the fish species is possible. We proceed to show that machine learning methods perform better and that a combination of statistical analysis (LDA) and random forest (RF), a supervised machine learning technique, allows reliable determination of the originating water body, while being also tolerant to seasonal variations. This is an improvement over prior work, which has dealt with more clearly distinguished origins of fish. Exceptional accuracy was achieved in correctly assigning fish to their origin even in a scenario where two of the water bodies are connected by a river through which the fish are known to migrate. Since determining the origin of fish is important in environmental protection, we recommend following up this approach and using it as the basis of a robust tool for environmental protection and other monitoring purposes.

Abstract Prediction of chemical shift in NMR using machine learning methods is typically done with the maximum amount of data available to achieve the best results. In some cases, such large amounts of data are not available, e.g. for heteronuclei. We demonstrate a novel machine learning model that is able to achieve better results than other models for relevant datasets with comparatively low amounts of data. We show this by predicting $$^{19}F$$ 19 F and $$^{13}C$$ 13 C NMR chemical shifts of small molecules in specific solvents. Graphical Abstract

Electronic health records (EHRs) are a critical tool in healthcare and capture a wide array of patient information that can inform clinical decision-making. However, the sheer volume and complexity of EHR data present challenges for healthcare providers, particularly in fast-paced environments such as intensive care units (ICUs). To address this problem, the automatic summarization of the main problems of patients from daily progress notes can be extremely helpful. Furthermore, by accurately predicting ICU patients’ lengths of stay (LOSs), resource allocation and management can be optimized, allowing for a more efficient flow of patients within the healthcare system. This work proposes a hybrid method to summarize EHR notes and studies the potential of these summaries together with structured data for the prediction of LOSs of ICU patients. Our investigation demonstrates the effectiveness of combining extractive and abstractive summarization techniques with a concept-based method combined with a text-to-text transfer transformer (T5), which shows the most promising results. By integrating the generated summaries and diagnoses with other features, our study contributes to the accurate prediction of LOSs, with a support vector machine emerging as our best-performing classifier with an accuracy of 77.5%, surpassing existing systems and highlighting the potential for optimal allocation of resources within ICUs.

The availability of high-calorie foods with contentious nutritional content has led to a worldwide increase in chronic disease. Therefore, monitoring of eating habits and practising healthy eating habits is recommended. Clinical diet assessment methods and mobile calorie tracking apps can be used to record daily food consumption but are often not user-friendly. Convenient image-based assessment models are currently available to recognise and estimate the nutritional value of foods directly from food images, but they do not consider how nutritional value changes after cooking. Consequently, VegeNet, a multi-output InceptionV3-based convolutional neural network model has been developed, which estimates the nutritional values of cooked and uncooked vegetables. The explicit use of the cooking state is the main contribution of this work. This deep learning model successfully classifies the food images at 97% accuracy and estimates the nutritional values at 15.30% mean relative error, making it suitable as a visual-based added food assessment solution. This can help users save time and avoid under-reporting problems.

Abstract Accurate calculation of specific spectral properties for NMR is an important step for molecular structure elucidation. Here we report the development of a novel machine learning technique for accurately predicting chemical shifts of both $${^1\mathrm{H}}$$ 1H and $${^{13}\mathrm{C}}$$ 13C nuclei which exceeds DFT-accessible accuracy for $${^{13}\mathrm{C}}$$ 13C and $${^1\mathrm{H}}$$ 1H for a subset of nuclei, while being orders of magnitude more performant. Our method produces estimates of uncertainty, allowing for robust and confident predictions, and suggests future avenues for improved performance.

We demonstrate that particle swarm optimisation (PSO) can be used to solve a variety of problems arising during operation of a digital inspection microscope. This is a use case for the feasibility of heuristics in a real-world product. We show solutions to four measurement problems, all based on PSO. This allows for a compact software implementation solving different problems. We have found that PSO can solve a variety of problems with small software footprints and good results in a real-world embedded system. Notably, in the microscope application, this eliminates the need to return the device to the factory for calibration.

The COVID-19 pandemic caused a shift in teaching practice towards blended learning for many higher education institutions. This led to the rapid adoption of certain digital technologies within existing teaching structures as a means to meet student access needs. This paper is an attempt to summarise and extend pre-COVID-19 pedagogical research to leverage digital immersive technologies for blended teaching in the post-pandemic era. This paper forms both a review of these methodologies and a case study of the I-Ulysses Virtual Learning Environment as an example of a platform that leverages such immersive digital technologies and employs instrumental use of VR. To further clarify, the purpose of the paper is to describe and propose a distance learning solution with immersive VR qualities; this is what the I-Ulysses environment represents, as the main obstacle to learners of site-specific information during the pandemic has been lack of on-site accessibility. Furthermore, this is of key importance, because Joyce’s novel takes place in historical Dublin, where access to the physical location of the story is indispensable to a reader.

Abstract Background Since 2009, IPF patients across Europe are recruited into the eurIPFreg, providing epidemiological data and biomaterials for translational research. Methods The registry data are based on patient and physician baseline and follow-up questionnaires, comprising 1700 parameters. The mid- to long-term objectives of the registry are to provide clues for a better understanding of IPF phenotype sub-clusters, triggering factors and aggravating conditions, regional and environmental characteristics, and of disease behavior and management. Results This paper describes baseline data of 525 IPF subjects recruited from 11/2009 until 10/2016. IPF patients had a mean age of 68.1 years, and seeked medical advice due to insidious dyspnea (90.1%), fatigue (69.2%), and dry coughing (53.2%). A surgical lung biopsy was performed in 32% in 2009, but in only 8% of the cases in 2016, possibly due to increased numbers of cryobiopsy. At the time of inclusion in the eurIPFreg, FVC was 68.4% ± 22.6% of predicted value, DLco ranged at 42.1% ± 17.8% of predicted value (mean value ± SD). Signs of pulmonary hypertension were found in 16.8%. Steroids, immunosuppressants and N-Acetylcysteine declined since 2009, and were replaced by antifibrotics, under which patients showed improved survival (p = 0.001). Conclusions Our data provide important insights into baseline characteristics, diagnostic and management changes as well as outcome data in European IPF patients over time. Trial registration The eurIPFreg and eurIPFbank are listed in ClinicalTrials.gov(NCT02951416).

Nanomechanical sensors that rely on intrinsic resonance frequencies usually present a tradeoff between sensitivity and bandwidth. In this work, the authors realise an optically driven nanorotor featuring high frequency stability and tunability over a large frequency range.

Abstract Background The Chemistry Development Kit (CDK) is a widely used open source cheminformatics toolkit, providing data structures to represent chemical concepts along with methods to manipulate such structures and perform computations on them. The library implements a wide variety of cheminformatics algorithms ranging from chemical structure canonicalization to molecular descriptor calculations and pharmacophore perception. It is used in drug discovery, metabolomics, and toxicology. Over the last 10 years, the code base has grown significantly, however, resulting in many complex interdependencies among components and poor performance of many algorithms. Results We report improvements to the CDK v2.0 since the v1.2 release series, specifically addressing the increased functional complexity and poor performance. We first summarize the addition of new functionality, such atom typing and molecular formula handling, and improvement to existing functionality that has led to significantly better performance for substructure searching, molecular fingerprints, and rendering of molecules. Second, we outline how the CDK has evolved with respect to quality control and the approaches we have adopted to ensure stability, including a code review mechanism. Conclusions This paper highlights our continued efforts to provide a community driven, open source cheminformatics library, and shows that such collaborative projects can thrive over extended periods of time, resulting in a high-quality and performant library. By taking advantage of community support and contributions, we show that an open source cheminformatics project can act as a peer reviewed publishing platform for scientific computing software. Graphical abstract CDK 2.0 provides new features and improved performance

This article proposes a framework that automatically designs classifiers for the early detection of COVID-19 from chest X-ray images. To do this, our approach repeatedly makes use of a heuristic for optimisation to efficiently find the best combination of the hyperparameters of a convolutional deep learning model. The framework starts with optimising a basic convolutional neural network which represents the starting point for the evolution process. Subsequently, at most two additional convolutional layers are added, at a time, to the previous convolutional structure as a result of a further optimisation phase. Each performed phase maximises the the accuracy of the system, thus requiring training and assessment of the new model, which gets gradually deeper, with relevant COVID-19 chest X-ray images. This iterative process ends when no improvement, in terms of accuracy, is recorded. Hence, the proposed method evolves the most performing network with the minimum number of convolutional layers. In this light, we simultaneously achieve high accuracy while minimising the presence of redundant layers to guarantee a fast but reliable model. Our results show that the proposed implementation of such a framework achieves accuracy up to 99.11%, thus being particularly suitable for the early detection of COVID-19.

The role and importance of the identification of natural products are discussed in the perspective of the study of secondary metabolites. The rapid identification of already reported compounds, or structural dereplication, is recognized as a key element in natural product chemistry. The biological taxonomy of metabolite producing organisms, the knowledge of metabolite molecular structures, and the availability of metabolite spectroscopic signatures are considered as the three pillars of structural dereplication. The role and the construction of databases is illustrated by references to the KNApSAcK, UNPD, CSEARCH, and COCONUT databases, and by the importance of calculated taxonomic and spectroscopic data as substitutes for missing or lost original ones. Two NMR-based tools, the PNMRNP database that derives from UNPD, and KnapsackSearch, a database generator that provides taxonomically focused libraries of compounds, are proposed to the community of natural product chemists. The study of the alkaloids from Urceolina peruviana, a plant from the Andes used in traditional medicine for antibacterial and anticancer actions, has given the opportunity to test different approaches to dereplication, favoring the use of publicly available data sources.

Whether quantum physics is universally valid is an open question with far-reaching implications. Intense research is therefore invested into testing the quantum superposition principle with ever heavier and more complex objects. Here we propose a radically new, experimentally viable route towards studies at the quantum-to-classical borderline by probing the orientational quantum revivals of a nanoscale rigid rotor. The proposed interference experiment testifies a macroscopic superposition of all possible orientations. It requires no diffraction grating, uses only a single levitated particle, and works with moderate motional temperatures under realistic environmental conditions. The first exploitation of quantum rotations of a massive object opens the door to new tests of quantum physics with submicron particles and to quantum gyroscopic torque sensors, holding the potential to improve state-of-the-art devices by many orders of magnitude.

Abstract Background Direct acting antivirals (DAAs) as a curative treatment of hepatitis C have been available for several years and have replaced interferon-containing therapies. However, treatment rates of people who inject drugs (PWID) are declining in Germany, putting the elimination of hepatitis C by 2030 at risk. This study aimed at elucidating the knowledge of, and attitude towards, hepatitis C treatment in a clinical sample of PWID. Methods Participants were recruited between February 2019 and October 2020 at two opioid agonist therapy (OAT) clinics and two in-patient drug detoxification wards. Based on the European Addiction Severity Index (Europ-ASI), a standardized interview focusing on: sociodemographic data, drug history, risky behavior, infection with hepatitis C virus (HCV) and HIV, and previous experience with HCV treatment was carried out. In addition, participants filled in a questionnaire evaluating 13 statements relating to HCV treatment (right/wrong) and 15 statements on their personal ‘pros and cons’ views to start such a treatment assessed with the means of a 6-point Likert scale. Results A total of 153 patients (average age 45 years, male 78%; 106 (69.3%) currently in opioid maintenance treatment, 47 (30.7%) currently admitted to an inpatient detoxification) with an opioid use disorder were investigated. All of them reported having injected drugs at least once in their lives; 97 participants (63.3%) stated that they had been previously diagnosed with HCV infection. Among them, 27/97 patients (27.8%) reported a previous treatment with interferon; 27/97 (27.8%) with DAAs; and 32/97 (33.0%) reported a currently active hepatitis C. Most patients knew about the availability and efficacy of DAAs. However, DAAs’ low rate of side effects, their short treatment duration, and their replacement of interferon, were not correctly evaluated by up to 50.3% of patients. 25–40% of 32 patients with currently active hepatitis C prioritized handling of social and other medical issues, e.g., reduction of heroin use, over treatment of hepatitis C. Conclusions Although current levels of risky behavior have reportedly been reduced by active PWID over the past few years, educational and motivational interventions to increase hepatitis C treatment uptake should address the gaps in patients’ knowledge.

Quantum computers offer an intriguing path for a paradigmatic change of computing in the natural sciences and beyond, with the potential for achieving a so-called quantum advantage—namely, a significant (in some cases exponential) speedup of numerical simulations. The rapid development of hardware devices with various realizations of qubits enables the execution of small-scale but representative applications on quantum computers. In particular, the high-energy physics community plays a pivotal role in accessing the power of quantum computing, since the field is a driving source for challenging computational problems. This concerns, on the theoretical side, the exploration of models that are very hard or even impossible to address with classical techniques and, on the experimental side, the enormous data challenge of newly emerging experiments, such as the upgrade of the Large Hadron Collider. In this Roadmap paper, led by CERN, DESY, and IBM, we provide the status of high-energy physics quantum computations and give examples of theoretical and experimental target benchmark applications, which can be addressed in the near future. Having in mind hardware with about 100 qubits capable of executing several thousand two-qubit gates, where possible, we also provide resource estimates for the examples given using error-mitigated quantum computing. The ultimate declared goal of this task force is therefore to trigger further research in the high-energy physics community to develop interesting use cases for demonstrations on near-term quantum computers.

Isovaleric acidemia (IVA), due to isovaleryl-CoA dehydrogenase (IVD) deficiency, results in the accumulation of isovaleryl-CoA, isovaleric acid and secondary metabolites. The increase in these metabolites decreases mitochondrial energy production and increases oxidative stress. This contributes to the neuropathological features of IVA. A general assumption in the literature exists that glycine N-acyltransferase (GLYAT) plays a role in alleviating the symptoms experienced by IVA patients through the formation of N-isovalerylglycine. GLYAT forms part of the phase II glycine conjugation pathway in the liver and detoxifies excess acyl-CoA’s namely benzoyl-CoA. However, very few studies support GLYAT as the enzyme that conjugates isovaleryl-CoA to glycine. Furthermore, GLYATL1, a paralogue of GLYAT, conjugates phenylacetyl-CoA to glutamine. Therefore, GLYATL1 might also be a candidate for the formation of N-isovalerylglycine. Based on the findings from the literature review, we proposed that GLYAT or GLYATL1 can form N-isovalerylglycine in IVA patients. To test this hypothesis, we performed an in-silico analysis to determine which enzyme is more likely to conjugate isovaleryl-CoA with glycine using AutoDock Vina. Thereafter, we performed in vitro validation using purified enzyme preparations. The in-silico and in vitro findings suggested that both enzymes could form N-isovaleryglycine albeit at lower affinities than their preferred substrates. Furthermore, an increase in glycine concentration does not result in an increase in N-isovalerylglycine formation. The results from the critical literature appraisal, in-silico, and in vitro validation, suggest the importance of further investigating the reaction kinetics and binding behaviors between these substrates and enzymes in understanding the pathophysiology of IVA.

Summary: Background: Treatment with elexacaftor/tezacaftor/ivacaftor (ETI) improves multiple clinical outcomes in people with cystic fibrosis (pwCF) with at least one F508del allele. This study evaluated the real-world impact of ETI on lung function, nutritional status, pulmonary exacerbation frequency, and sweat chloride concentrations in a large group of pwCF. Methods: This observational cohort study used data from the German CF Registry for pwCF who received ETI therapy and were followed up for a period of 12 months. Findings: The study included 2645 pwCF from 67 centres in Germany (mean age 28.0 ± 11.5 years). Over the first year after ETI was initiated, percent predicted forced expiratory volume in 1 s (ppFEV1) increased by 11.3% (95% confidence interval [CI] 10.8–11.8, p

Quantum-enhanced computing methods are promising candidates to solve currently intractable problems. We consider here a variational quantum eigensolver (VQE), that delegates costly state preparations and measurements to quantum hardware, while classical optimization techniques guide the quantum hardware to create a desired target state. In this work, we propose a bosonic VQE using superconducting microwave cavities, overcoming the typical restriction of a small Hilbert space when the VQE is qubit based. The considered platform allows for strong nonlinearities between photon modes, which are highly customisable and can be tuned in situ, i.e. during running experiments. Our proposal hence allows for the realization of a wide range of bosonic ansatz states, and is therefore especially useful when simulating models involving degrees of freedom that cannot be simply mapped to qubits, such as gauge theories, that include components which require infinite-dimensional Hilbert spaces. We thus propose to experimentally apply this bosonic VQE to the U(1) Higgs model including a topological term, which in general introduces a sign problem in the model, making it intractable with conventional Monte Carlo methods.

The production of mold inserts for the replication of micro-lens arrays through micro-embossing could be an alternative process route compared to diamond turning or milling in order to reduce time and costs. The rapidly solidified aluminum alloy RSA-501 is expected to form micro-structures with low surface roughness because of its ultra-fine grain structure. In micro-embossing challenges like elastic spring back effect, pile-ups, and forming accuracy depend on the material behavior. Therefore, RSA-501 was further characterized and the influence of polishing or flycutting on the material behavior was investigated. To further understand the grain and microstructure samples were sectioned along their cross and longitudinal directions. The grain structure of RSA-501 was oriented along the extrusion direction and the mean grain sizes were

Der Klimawandel begünstigt die Ansiedlung neuer Schadorganismen in Deutschland, die hier nun geeignete Lebensräume finden. Feldheuschrecken treten in südeuropäischen Ländern immer wieder als Landwirtschaftsschädlinge auf. Es wird daher untersucht, ob durch die klimawandelbedingte Nordverschiebung wärmerer Zonen klimatisch geeignete Lebensräume für Feldheuschrecken in Deutschland entstehen und landwirtschaftlich genutzte Flächen dadurch betroffen sein können. Mit der Software CLIMEX wird die mögliche Verbreitung der Italienischen Schönschrecke (Calliptamus italicus (L., 1758)), der Marokkanischen Wanderheuschrecke (Dociostaurus maroccanus (Thunberg, 1815)) und der Europäischen Wanderheuschrecke (Locusta migratoria (L., 1758)) für 20 Standorte in Deutschland in sechs Szenarien modelliert. Diese Szenarien werden durch die Kombination der beiden Betrachtungszeiträume 2021–2050 und 2071–2100 mit den drei Klimaprojektionen RCP2.6, RCP4.5 und RCP8.5 definiert. Aufgrund der Untersuchung ist zu erwarten, dass C. italicus sich in Deutschland stark verbreiten wird, während D. maroccanus und L. migratoria nur kleine und lokale Populationen ausbilden könnten. Heuschreckenschwärme können an den betrachteten Standorten potentiell pflanzliche Erzeugnisse auf etwa 10 – 25 % der landwirtschaftlichen Fläche in Deutschland bedrohen, ihr Auftreten wird allerdings als unwahrscheinlich eingeschätzt, da die intensive Nutzung von Grünlandflächen bisher nur unzureichende Vermehrungsbedingungen bietet. Die Schaffung größerer Brachflächen im Rahmen von Umweltschutz- und Klimaanpassungsmaßnahmen könnte dies zukünftig ändern. Zusätzlich sollten Schwarmbildungen im Ausland und mögliche Migrationsrouten nach Deutschland untersucht werden. Weiterhin wird die Entwicklung von Konzepten zur Prävention und Intervention für den Fall einer Heuschreckeninvasion empfohlen. Insgesamt ist jedoch aktuell von einem geringen Gefahrenpotential von Feldheuschrecken für die deutsche Landwirtschaft auszugehen.