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Abstract Deep neural networks (DNN) have fundamentally revolutionized the artificial intelligence (AI) field. The transformer model is a type of DNN that was originally used for the natural language processing tasks and has since gained more and more attention for processing various kinds of sequential data, including biological sequences and structured electronic health records. Along with this development, transformer-based models such as BioBERT, MedBERT, and MassGenie have been trained and deployed by researchers to answer various scientific questions originating in the biomedical domain. In this paper, we review the development and application of transformer models for analyzing various biomedical-related datasets such as biomedical textual data, protein sequences, medical structured-longitudinal data, and biomedical images as well as graphs. Also, we look at explainable AI strategies that help to comprehend the predictions of transformer-based models. Finally, we discuss the limitations and challenges of current models, and point out emerging novel research directions.

In today's data-centric landscape, effective data stewardship is critical for facilitating scientific research and innovation. This article provides an overview of essential tools and frameworks for modern data stewardship practices. Over 300 tools were analyzed in this study, assessing their utility, relevance to data stewardship, and applicability within the life sciences domain.

Abstract In the pharmaceutical industry, the patent protection of drugs and medicines is accorded importance because of the high costs involved in the development of novel drugs. Over the years, researchers have analyzed patent documents to identify freedom-to-operate spaces for novel drug candidates. To assist this, several well-established public patent document data repositories have enabled automated methodologies for extracting information on therapeutic agents. In this study, we delve into one such publicly available patent database, SureChEMBL, which catalogues patent documents related to life sciences. Our exploration begins by identifying patent compounds across public chemical data resources, followed by pinpointing sections in patent documents where the chemical annotations were found. Next, we exhibit the potential of compounds to serve as drug candidates by evaluating their conformity to drug-likeness criteria. Lastly, we examine the drug development stage reported for these compounds to understand their clinical success. In summary, our investigation aims at providing a comprehensive overview of the patent compounds catalogued in SureChEMBL, assessing their relevance to pharmaceutical drug discovery.

Biomedical knowledge graphs (KGs) hold valuable information regarding biomedical entities such as genes, diseases, biological processes, and drugs. KGs have been successfully employed in challenging biomedical areas such as the identification of pathophysiology mechanisms or drug repurposing. The creation of high-quality KGs typically requires labor-intensive multi-database integration or substantial human expert curation, both of which take time and contribute to the workload of data processing and annotation. Therefore, the use of automatic systems for KG building and maintenance is a prerequisite for the wide uptake and utilization of KGs. Technologies supporting the automated generation and updating of KGs typically make use of Natural Language Processing (NLP), which is optimized for extracting implicit triples described in relevant biomedical text sources. At the core of this challenge is how to improve the accuracy and coverage of the information extraction module by utilizing different models and tools. The emergence of pre-trained large language models (LLMs), such as ChatGPT which has grown in popularity dramatically, has revolutionized the field of NLP, making them a potential candidate to be used in text-based graph creation as well. So far, no previous work has investigated the power of LLMs on the generation of cause-and-effect networks and KGs encoded in Biological Expression Language (BEL). In this paper, we present initial studies towards one-shot BEL relation extraction using two different versions of the Generative Pre-trained Transformer (GPT) models and evaluate its performance by comparing the extracted results to a highly accurate, manually curated BEL KG curated by domain experts.

Abstract This article reports on a political game played between November 2021 and February 2022 at a European university in the frame of an elective course open to students from all disciplines. It started from a scenario that combined a real and ongoing innovation process—the use of CRISPR/Cas9 technology to introduce specific genes from unfrozen mammoths onto an elephant species to make it more resistant to cold—with a fictive joint project between a US-based company and the Russian government to release those mammoths on an island in the North-West of Russia. Almost no rules were given, and the students were distributed in nine groups to represent different actors deemed relevant to the scenario. These groups were the national governments of (1) Russia, (2) close neighboring states, (3) European countries farther away from the project location; (4) the company working on the innovation, dubbed HELIOS; (5) other market actors; civil society organizations concerned with (6) animal rights and (7) the environment; (8) the European Commission and the EU Parliament; and (9) media. The game was led by the course teacher and two student volunteers. Based on the results of both qualitative and quantitative forms of ex-post evaluation, we assess whether the Mammoth game meets the expectations that guided the game design process. Furthermore, we discuss whether the Mammoth game had a positive effect on students’ abilities to think about the future.

Background: Biomedical knowledge graphs (KG) have become crucial for describing biological findings in a structured manner. To keep up with the constantly changing flow of knowledge, their embedded information must be regularly updated with the latest findings. Natural language processing (NLP) has created new possibilities for automating this upkeep by facilitating information extraction from free text. However, due to annotated and labeled biomedical data limitations, the development of completely autonomous information extraction systems remains a substantial scientific and technological hurdle. This study aims to explore methodologies best suited to support the automatic extraction of causal relationships from biomedical literature with the aim of regular and rapid updating of disease-specific pathophysiology mechanism KGs. Methods: Our proposed approach first searches and retrieves PubMed abstracts using the desired terms and keywords. The extension corpora are then passed through the NLP pipeline for automatic information extraction. We then identify triples representing cause-and-effect relationships and encode this content using the Biological Expression Language (BEL). Finally, domain experts perform an analysis of the completeness, relevance, accuracy, and novelty of the extracted triples. Results: In our test scenario, which is focused on the KG regarding the phosphorylation of the Tau protein, our pipeline successfully contributed novel data, which was then subsequently used to update the KG leading to the identification of six additional upstream regulators of Tau phosphorylation. Conclusion: Here, it is demonstrated that the NLP-based workflow we created is capable of rapidly updating pathophysiology mechanism graphs. As a result, production-scale, semi-automated updating of pre-existing, curated mechanism graphs is enabled.

Patents play a crucial role in the drug discovery process by providing legal protection for discoveries and incentivising investments in research and development. By identifying patterns within patent data resources, researchers can gain insight into the market trends and priorities of the pharmaceutical and biotechnology industries, as well as provide additional perspectives on more fundamental aspects such as the emergence of potential new drug targets. In this paper, we used the patent enrichment tool, PEMT, to extract, integrate, and analyse patent literature for rare diseases (RD) and Alzheimer's disease (AD). This is followed by a systematic review of the underlying patent landscape to decipher trends and applications in patents for these diseases. To do so, we discuss prominent organisations involved in drug discovery research in AD and RD. This allows us to gain an understanding of the importance of AD and RD from specific organisational (pharmaceutical or university) perspectives. Next, we analyse the historical focus of patents in relation to individual therapeutic targets and correlate them with market scenarios allowing the identification of prominent targets for a disease. Lastly, we identified drug repurposing activities within the two diseases with the help of patents. This resulted in identifying existing repurposed drugs and novel potential therapeutic approaches applicable to the indication areas. The study demonstrates the expanded applicability of patent documents from legal to drug discovery, design, and research, thus, providing a valuable resource for future drug discovery efforts. Moreover, this study is an attempt towards understanding the importance of data underlying patent documents and raising the need for preparing the data for machine learning-based applications.

Abstract Peptide human leukocyte antigen (pHLA) targeting therapeutics like T-cell receptor based adoptive cell therapy or bispecific T cell engaging receptor molecules hold great promise for the treatment of cancer. Comprehensive pre-clinical screening of therapeutic candidates is important to ensure patient safety but is challenging because of the size of the potential off-target space. By combining stabilized peptide-receptive HLA molecules with microarray printing and screening, we have developed an ultra-high-throughput screening platform named ValidaTe that enables large scale evaluation of pHLA-binder interactions. We demonstrate its potential by measuring and analyzing over 30.000 binding curves for a high-affinity T cell Engaging Receptor towards a large pHLA library. Compared to a dataset obtained by conventional bio-layer interferometry measurements, we illustrate that a massively increased throughput (over 650 fold) is obtained by our microarray screening, paving the way for use in pre-clinical safety screening of pHLA-targeting drugs.

Accurate prediction of disease progression in Alzheimer’s disease (AD) is necessary for optimal recruitment of patients to clinical trials. Here, the authors present AD Course Map, a statistical model which helps to predict disease progression in participants, thus decreasing the required sample size for a hypothetical trial.

The rapid progress in semiconductor technology has vastly boosted the development of terahertz sources and receivers in terms of compactness, reliability, operation frequency, and output power. In this manuscript, we report on the latest achievements in terahertz sources and receivers and provide a comprehensive overview of their working principles and applications in THz systems.

Efficient data sharing is hampered by an array of organizational, ethical, behavioral, and technical challenges, slowing research progress and reducing the utility of data generated by clinical research studies on neurodegenerative diseases. There is a particular need to address differences between public and private sector environments for research and data sharing, which have varying standards, expectations, motivations, and interests. The Neuronet data sharing Working Group was set up to understand the existing barriers to data sharing in public-private partnership projects, and to provide guidance to overcome these barriers, by convening data sharing experts from diverse projects in the IMI neurodegeneration portfolio. In this policy and practice review, we outline the challenges and learnings of the WG, providing the neurodegeneration community with examples of good practices and recommendations on how to overcome obstacles to data sharing. These obstacles span organizational issues linked to the unique structure of cross-sectoral, collaborative research initiatives, to technical issues that affect the storage, structure and annotations of individual datasets. We also identify sociotechnical hurdles, such as academic recognition and reward systems that disincentivise data sharing, and legal challenges linked to heightened perceptions of data privacy risk, compounded by a lack of clear guidance on GDPR compliance mechanisms for public-private research. Focusing on real-world, neuroimaging and digital biomarker data, we highlight particular challenges and learnings for data sharing, such as data management planning, development of ethical codes of conduct, and harmonization of protocols and curation processes. Cross-cutting solutions and enablers include the principles of transparency, standardization and co-design – from open, accessible metadata catalogs that enhance findability of data, to measures that increase visibility and trust in data reuse.

The Innovative Medicines Initiative (IMI), was a European public–private partnership (PPP) undertaking intended to improve the drug development process, facilitate biomarker development, accelerate clinical trial timelines, improve success rates, and generally increase the competitiveness of European pharmaceutical sector research. Through the IMI, pharmaceutical research interests and the research agenda of the EU are supported by academic partnership and financed by both the pharmaceutical companies and public funds. Since its inception, the IMI has funded dozens of research partnerships focused on solving the core problems that have consistently obstructed the translation of research into clinical success. In this post-mortem review paper, we focus on six research initiatives that tackled foundational challenges of this nature: Aetionomy, EMIF, EPAD, EQIPD, eTRIKS, and PRISM. Several of these initiatives focused on neurodegenerative diseases; we therefore discuss the state of neurodegenerative research both at the start of the IMI and now, and the contributions that IMI partnerships made to progress in the field. Many of the initiatives we review had goals including, but not limited to, the establishment of translational, data-centric initiatives and the implementation of trans-diagnostic approaches that move beyond the candidate disease approach to assess symptom etiology without bias, challenging the construct of disease diagnosis. We discuss the successes of these initiatives, the challenges faced, and the merits and shortcomings of the IMI approach with participating senior scientists for each. Here, we distill their perspectives on the lessons learned, with an aim to positively impact funding policy and approaches in the future.

Although hundreds of datasets have been published since the beginning of the coronavirus pandemic, there is a lack of centralized resources where these datasets are listed and harmonized to facilitate their applicability and uptake by predictive modeling approaches. Firstly, such a centralized resource provides information about data owners to researchers who are searching datasets to develop their predictive models. Secondly, the harmonization of the datasets supports simultaneously taking advantage of several similar datasets. This, in turn, does not only ease the imperative external validation of data-driven models but can also be used for virtual cohort generation, which helps to overcome data sharing impediments. Here, we present that the COVID-19 data catalogue is a repository that provides a landscape view of COVID-19 studies and datasets as a putative source to enable researchers to develop personalized COVID-19 predictive risk models. The COVID-19 data catalogue currently contains over 400 studies and their relevant information collected from a wide range of global sources such as global initiatives, clinical trial repositories, publications, and data repositories. Further, the curated content stored in this data catalogue is complemented by a web application, providing visualizations of these studies, including their references, relevant information such as measured variables, and the geographical locations of where these studies were performed. This resource is one of the first to capture, organize, and store studies, datasets, and metadata related to COVID-19 in a comprehensive repository. We believe that our work will facilitate future research and development of personalized predictive risk models for COVID-19.

Abstract Distinct gene expression patterns within cells are foundational for the diversity of functions and unique characteristics observed in specific contexts, such as human tissues and cell types. Though some biological processes commonly occur across contexts, by harnessing the vast amounts of available gene expression data, we can decipher the processes that are unique to a specific context. Therefore, with the goal of developing a portrait of context-specific patterns to better elucidate how they govern distinct biological processes, this work presents a large-scale exploration of transcriptomic signatures across three different contexts (i.e., tissues, cell types, and cell lines) by leveraging over 600 gene expression datasets categorized into 98 subcontexts. The strongest pairwise correlations between genes from these subcontexts are used for the construction of co-expression networks. Using a network-based approach, we then pinpoint patterns that are unique and common across these subcontexts. First, we focused on patterns at the level of individual nodes and evaluated their functional roles using a human protein–protein interactome as a referential network. Next, within each context, we systematically overlaid the co-expression networks to identify specific and shared correlations as well as relations already described in scientific literature. Additionally, in a pathway-level analysis, we overlaid node and edge sets from co-expression networks against pathway knowledge to identify biological processes that are related to specific subcontexts or groups of them. Finally, we have released our data and scripts at https://zenodo.org/record/5831786 and https://github.com/ContNeXt/ , respectively and developed ContNeXt ( https://contnext.scai.fraunhofer.de/ ), a web application to explore the networks generated in this work.

Abstract The purpose of this study was to investigate whether a learning design based entirely on the micro level strategies of the Synthesis of Qualitative Data (SQD) model is better suited to promote teachers augmented and virtual reality-related Will, Skill, and Tool (WST) compared to a learning design based less on the SQD model. To this end, we first developed two learning designs that were randomly distributed across two teacher professional development courses. In one course (n = 23), teachers learned according to our developed and fully SQD-based Tell–Show–Enact–Do (TSED) learning design; in the other course, teachers (n = 22) followed a Tell–Show–Enact (TSE) learning design that was less SQD-based. The results of the quasi-experimental field study show that the developed fully SQD-based TSED learning design is better able to promote the elements WST in teachers regarding the integration of augmented and virtual reality in the classroom. The results of the study have implications for theory and practice. For example, the developed TSED learning design can serve as a blueprint for other teacher educators, and the empirical findings support the micro level strategies recommended in the SQD model. Additional findings are discussed.

Abstract Background Currently, Alzheimer’s disease (AD) cohort datasets are difficult to find and lack across-cohort interoperability, and the actual content of publicly available datasets often only becomes clear to third-party researchers once data access has been granted. These aspects severely hinder the advancement of AD research through emerging data-driven approaches such as machine learning and artificial intelligence and bias current data-driven findings towards the few commonly used, well-explored AD cohorts. To achieve robust and generalizable results, validation across multiple datasets is crucial. Methods We accessed and systematically investigated the content of 20 major AD cohort datasets at the data level. Both, a medical professional and a data specialist, manually curated and semantically harmonized the acquired datasets. Finally, we developed a platform that displays vital information about the available datasets. Results Here, we present ADataViewer, an interactive platform that facilitates the exploration of 20 cohort datasets with respect to longitudinal follow-up, demographics, ethnoracial diversity, measured modalities, and statistical properties of individual variables. It allows researchers to quickly identify AD cohorts that meet user-specified requirements for discovery and validation studies regarding available variables, sample sizes, and longitudinal follow-up. Additionally, we publish the underlying variable mapping catalog that harmonizes 1196 unique variables across the 20 cohorts and paves the way for interoperable AD datasets. Conclusions In conclusion, ADataViewer facilitates fast, robust data-driven research by transparently displaying cohort dataset content and supporting researchers in selecting datasets that are suited for their envisioned study. The platform is available at https://adata.scai.fraunhofer.de/ .

Abstract Background Previous models of Alzheimer’s disease (AD) progression were primarily hypothetical or based on data originating from single cohort studies. However, cohort datasets are subject to specific inclusion and exclusion criteria that influence the signals observed in their collected data. Furthermore, each study measures only a subset of AD-relevant variables. To gain a comprehensive understanding of AD progression, the heterogeneity and robustness of estimated progression patterns must be understood, and complementary information contained in cohort datasets be leveraged. Methods We compared ten event-based models that we fit to ten independent AD cohort datasets. Additionally, we designed and applied a novel rank aggregation algorithm that combines partially overlapping, individual event sequences into a meta-sequence containing the complementary information from each cohort. Results We observed overall consistency across the ten event-based model sequences (average pairwise Kendall’s tau correlation coefficient of 0.69 ± 0.28), despite variance in the positioning of mainly imaging variables. The changes described in the aggregated meta-sequence are broadly consistent with the current understanding of AD progression, starting with cerebrospinal fluid amyloid beta, followed by tauopathy, memory impairment, FDG-PET, and ultimately brain deterioration and impairment of visual memory. Conclusion Overall, the event-based models demonstrated similar and robust disease cascades across independent AD cohorts. Aggregation of data-driven results can combine complementary strengths and information of patient-level datasets. Accordingly, the derived meta-sequence draws a more complete picture of AD pathology compared to models relying on single cohorts.

The authors wish to correct the following errors in the original paper [...]

Abstract The utility of pathway signatures lies in their capability to determine whether a specific pathway or biological process is dysregulated in a given patient. These signatures have been widely used in machine learning (ML) methods for a variety of applications including precision medicine, drug repurposing, and drug discovery. In this work, we leverage highly predictive ML models for drug response simulation in individual patients by calibrating the pathway activity scores of disease samples. Using these ML models and an intuitive scoring algorithm to modify the signatures of patients, we evaluate whether a given sample that was formerly classified as diseased, could be predicted as normal following drug treatment simulation. We then use this technique as a proxy for the identification of potential drug candidates. Furthermore, we demonstrate the ability of our methodology to successfully identify approved and clinically investigated drugs for four different cancers, outperforming six comparable state-of-the-art methods. We also show how this approach can deconvolute a drugs’ mechanism of action and propose combination therapies. Taken together, our methodology could be promising to support clinical decision-making in personalized medicine by simulating a drugs’ effect on a given patient.

The mechanical joining technique clinching is often used in order to realize components of different complexity with numerous connection points. For the holistic prognosis of the joinability and the life cycle of an assembly, numerical modeling of the complete joining process chain is essential considering (i) the design phase, (ii) the joining process itself and (iii) the operational phase. Such a holistic modeling approach enables the comprehension of the complex cause-and-effect relationships in mechanical joining of different materials (e.g. aluminum, steel and fiber-composite). Besides plasticity and damage processes during the clinching process, corrosive phenomena and crack propagation behavior have to be taken into account for the prognosis of the load-bearing capacity and of the lifetime. This paper is the third part (part III) of a review series summarizing the current state of research on modeling and simulation of clinching. The focus of part I is on the joining process chain and its challenges as well as on the design phase. The joining process itself, i.e. the process simulation of the clinching process as well as damage phenomena, is subject of part II. Within the scope of the current paper (part III) of our review series are the influences of the operating phase. Thereby, the effects of corrosion and of fatigue crack growth as well as corresponding modeling approaches are considered.

The manufactory of advanced micro- and nanoscale devices relies on capable patterning strategies. Focused electron beams, as for instance implemented since long in electron beam lithography and electron beam induced deposition, are in this regard key enabling tools especially at the early stages of device development and research. We show here that nanoscale field emission scanning probes can be potentially utilized as well for a prospective direct device fabrication by localized material deposition but notably, also by localized material removal. Field emission scanning probe processing was specifically realized on 10 nm chromium and 50 nm gold thin film stacks deposited on a (1 × 1) cm2 fused silica substrate. Localized material deposition and metal removal was studied in various atmospheres comprising high vacuum, nitrogen, ambient air, naphthalene and carbon-dioxide. Stable and reliable regimes were in particular obtained in a carbonaceous atmosphere. Hence, localized carbon deposits were obtained but also localized metal removal was realized. We demonstrate furthermore that the selected electron emission parameters (20 V - 80 V, 180 pA) and the overall operation environment are crucial aspects that determine the degree of material deposition and removal. Based on our findings, direct tip-based micro- to nanoscale material patterning appears possible. The applied energy regime is also enabling new insights into low energy (< 100 eV) electron interaction. However, the underlying mechanisms must be further elucidated.

Stakeholders of software development projects have various information needs for making rational decisions during their daily work. Satisfying these needs requires substantial knowledge of where and how the relevant information is stored and consumes valuable time that is often not available. Easing the need for this knowledge is an ideal text-to-SQL benchmark problem, a field where public datasets are scarce and needed. We propose the SEOSS-Queries dataset consisting of natural language utterances and accompanying SQL queries extracted from previous studies, software projects, issue tracking tools, and through expert surveys to cover a large variety of information need perspectives. Our dataset consists of 1,162 English utterances translating into 166 SQL queries; each query has four precise utterances and three more general ones. Furthermore, the dataset contains 393,086 labeled utterances extracted from issue tracker comments. We provide pre-trained SQLNet and RatSQL baseline models for benchmark comparisons, a replication package facilitating a seamless application, and discuss various other tasks that may be solved and evaluated using the dataset. The whole dataset with paraphrased natural language utterances and SQL queries is hosted at figshare.com/s/75ed49ef01ac2f83b3e2.

Abstract The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific community’s massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2/COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.

Abstract Background During the last decade, there has been a surge towards computational drug repositioning owing to constantly increasing -omics data in the biomedical research field. While numerous existing methods focus on the integration of heterogeneous data to propose candidate drugs, it is still challenging to substantiate their results with mechanistic insights of these candidate drugs. Therefore, there is a need for more innovative and efficient methods which can enable better integration of data and knowledge for drug repositioning. Results Here, we present a customizable workflow (PS4DR) which not only integrates high-throughput data such as genome-wide association study (GWAS) data and gene expression signatures from disease and drug perturbations but also takes pathway knowledge into consideration to predict drug candidates for repositioning. We have collected and integrated publicly available GWAS data and gene expression signatures for several diseases and hundreds of FDA-approved drugs or those under clinical trial in this study. Additionally, different pathway databases were used for mechanistic knowledge integration in the workflow. Using this systematic consolidation of data and knowledge, the workflow computes pathway signatures that assist in the prediction of new indications for approved and investigational drugs. Conclusion We showcase PS4DR with applications demonstrating how this tool can be used for repositioning and identifying new drugs as well as proposing drugs that can simulate disease dysregulations. We were able to validate our workflow by demonstrating its capability to predict FDA-approved drugs for their known indications for several diseases. Further, PS4DR returned many potential drug candidates for repositioning that were backed up by epidemiological evidence extracted from scientific literature. Source code is freely available at https://github.com/ps4dr/ps4dr .

Up to 48% of patients with medically unexplained symptoms seen in neurological practice suffer from sensory symptoms, which could be of functional nature or secondary to psychiatric disorders. These patients show high medical care utilization causing elevated healthcare costs. Despite the high prevalence, little is known about clinical characteristics and pathophysiological mechanisms. For functional disorders such as irritable bowel syndrome, a reduction of heart rate variability (HRV) has been shown, suggesting a dysfunction of the autonomic nervous system (ANS). The aim of this study was to investigate psychological data and functional changes of the ANS in patients with medically unexplained sensory symptoms (MUSS). In this exploratory pilot study, 16 patients (11 females, 31.6 ± 11.9 years) with MUSS, who were recruited at a single tertiary neurological center, underwent a structured clinical interview (SCID) to evaluate psychiatric comorbidities. Patients and age- and sex-matched healthy volunteers filled in questionnaires, and individual sensory thresholds (perception, pain) were detected by quantitative sensory testing (QST). HRV was assessed at baseline and under three different experimental conditions (tonic pain stimulus, placebo application, cold-face test). All tests were repeated after 6–8 weeks. SCID interviews revealed clinical or subclinical diagnoses of psychiatric comorbidities for 12 patients. Questionnaires assessing somatization, depression, anxiety, and perceived stress significantly discriminated between patients with MUSS and healthy controls. While there was no difference in QST, reduced ANS reactivity was found in patients during experimental conditions, particularly with regard to vagally mediated HRV. Our pilot study of neurological patients with MUSS reveals a high prevalence of psychiatric comorbidities and provides evidence for altered ANS function. Our data thus give insight in possible underlying mechanisms for these symptoms and may open the door for a better diagnostic and therapeutic approach for these patients in the future.

Engineered barriers are a key element to enable safe nuclear waste disposal. One method currently under research for their construction is magnesia concrete applied in a shotcrete procedure. In this study, the ultrasonic echo method is evaluated as a means for quality assurance. Imaging of internal structures (backwall, boreholes) and defects, such as delamination, has successfully been achieved in the shotcrete. Additionally, detailed information about the potential cause of selected reflectors are obtained by phase analysis. In several test blocks of various sizes, no consistent concrete section boundaries have been found by ultrasonic imaging, which was verified by subsequent drilling and complementary tests. An experiment with artificial defects imitating cracks, air-filled voids, and material with lower density has been challenging and shows the limitations of the current methods. Although significant defects, such as a large delamination, are reliably identified, several smaller defects are not identified. Generally, ultrasonic imaging provides a suitable base as a mean for quality assurance during and after the construction of sealing structures. However, further developments are required to enhance the reliability of the method and a full validation is still pending. Still, the method has potential to increase the safety of nuclear waste repositories.

In recent years, prices for photovoltaics have fallen steadily and the demand for sustainable energy has increased. Consequentially, the assessment of roof surfaces in terms of their suitability for PV (Photovoltaic) installations has continuously gained in importance. Several types of assessment approaches have been established, ranging from sampling to complete census or aerial image analysis methodologies. Assessments of rooftop photovoltaic potential are multi-stage processes. The sub-task of examining the photovoltaic potential of individual rooftops is crucial for exact case study results. However, this step is often time-consuming and requires lots of computational effort especially when some form of intelligent classification algorithm needs to be trained. This often leads to the use of sampled rooftop utilization factors when investigating large-scale areas of interest, as data-driven approaches usually are not well-scalable. In this paper, a novel neighbourhood-based filtering approach is introduced that can analyse large amounts of irradiation data in a vectorised manner. It is tested in an application to the city of Giessen, Germany, and its surrounding area. The results show that it outperforms state-of-the-art image filtering techniques. The algorithm is able to process high-resolution data covering 1 km2 within roughly 2.5 s. It successfully classifies rooftop segments which are feasible for PV installations while omitting small, obstructed or insufficiently exposed segments. Apart from minor shortcomings, the approach presented in this work is capable of generating per-rooftop PV potential assessments at low computational cost and is well scalable to large scale areas.

Abstract Background Literature derived knowledge assemblies have been used as an effective way of representing biological phenomenon and understanding disease etiology in systems biology. These include canonical pathway databases such as KEGG, Reactome and WikiPathways and disease specific network inventories such as causal biological networks database, PD map and NeuroMMSig. The represented knowledge in these resources delineates qualitative information focusing mainly on the causal relationships between biological entities. Genes, the major constituents of knowledge representations, tend to express differentially in different conditions such as cell types, brain regions and disease stages. A classical approach of interpreting a knowledge assembly is to explore gene expression patterns of the individual genes. However, an approach that enables quantification of the overall impact of differentially expressed genes in the corresponding network is still lacking. Results Using the concept of heat diffusion, we have devised an algorithm that is able to calculate the magnitude of regulation of a biological network using expression datasets. We have demonstrated that molecular mechanisms specific to Alzheimer (AD) and Parkinson Disease (PD) regulate with different intensities across spatial and temporal resolutions. Our approach depicts that the mitochondrial dysfunction in PD is severe in cortex and advanced stages of PD patients. Similarly, we have shown that the intensity of aggregation of neurofibrillary tangles (NFTs) in AD increases as the disease progresses. This finding is in concordance with previous studies that explain the burden of NFTs in stages of AD. Conclusions This study is one of the first attempts that enable quantification of mechanisms represented as biological networks. We have been able to quantify the magnitude of regulation of a biological network and illustrate that the magnitudes are different across spatial and temporal resolution.

Abstract Background The complexity of representing biological systems is compounded by an ever-expanding body of knowledge emerging from multi-omics experiments. A number of pathway databases have facilitated pathway-centric approaches that assist in the interpretation of molecular signatures yielded by these experiments. However, the lack of interoperability between pathway databases has hindered the ability to harmonize these resources and to exploit their consolidated knowledge. Such a unification of pathway knowledge is imperative in enhancing the comprehension and modeling of biological abstractions. Results Here, we present PathMe, a Python package that transforms pathway knowledge from three major pathway databases into a unified abstraction using Biological Expression Language as the pivotal, integrative schema. PathMe is complemented by a novel web application (freely available at https://pathme.scai.fraunhofer.de/) which allows users to comprehensively explore pathway crosstalk and compare areas of consensus and discrepancies. Conclusions This work has harmonized three major pathway databases and transformed them into a unified schema in order to gain a holistic picture of pathway knowledge. We demonstrate the utility of the PathMe framework in: i) integrating pathway landscapes at the database level, ii) comparing the degree of consensus at the pathway level, and iii) exploring pathway crosstalk and investigating consensus at the molecular level.

Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.

Elucidating the causal mechanisms responsible for disease can reveal potential therapeutic targets for pharmacological intervention and, accordingly, guide drug repositioning and discovery. In essence, the topology of a network can reveal the impact a drug candidate may have on a given biological state, leading the way for enhanced disease characterization and the design of advanced therapies. Network-based approaches, in particular, are highly suited for these purposes as they hold the capacity to identify the molecular mechanisms underlying disease. Here, we present drug2ways, a novel methodology that leverages multimodal causal networks for predicting drug candidates. Drug2ways implements an efficient algorithm which reasons over causal paths in large-scale biological networks to propose drug candidates for a given disease. We validate our approach using clinical trial information and demonstrate how drug2ways can be used for multiple applications to identify: i) single-target drug candidates, ii) candidates with polypharmacological properties that can optimize multiple targets, and iii) candidates for combination therapy. Finally, we make drug2ways available to the scientific community as a Python package that enables conducting these applications on multiple standard network formats.

Bispecific T cell receptor (TCR)-based molecules capable of redirecting and activating T cells towards tumor cells represent a novel and promising class of biotherapeutics for the treatment of cancer. Usage of TCRs allows for targeting of intracellularly expressed and highly selective cancer antigens, but also requires a complex maturation process to increase the naturally low affinity and stability of TCRs. Even though TCR domains can be matured via phage and yeast display, these techniques share the disadvantages of non-human glycosylation patterns and the need for a later reformatting into the final bispecific format. Here, we describe the development and application of a Chinese Hamster Ovary (CHO) display for affinity engineering of TCRs in the context of the final bispecific TCR format. The recombinase-mediated cassette exchange (RCME)-based system allows for stable, single-copy integration of bispecific TCR molecules with high efficiency into a defined genetic locus of CHO cells. We used the system to isolate affinity-increased variants of bispecific T cell engaging receptor (TCER) molecules from a library encoding different CDR variants of a model TCR targeting preferentially expressed antigen in melanoma (PRAME). When expressed as a soluble protein, the selected TCER molecules exhibited strong reactivity against PRAME-positive tumor cells associated with a pronounced cytokine release from activated T cells. The obtained data support the usage of the CHO display-based maturation system for TCR affinity maturation in the context of the final bispecific TCER format.

In the area of Big Data, one of the major obstacles for the progress of biomedical research is the existence of data “silos” because legal and ethical constraints often do not allow for sharing sensitive patient data from clinical studies across institutions. While federated machine learning now allows for building models from scattered data of the same format, there is still the need to investigate, mine, and understand data of separate and very differently designed clinical studies that can only be accessed within each of the data-hosting organizations. Simulation of sufficiently realistic virtual patients based on the data within each individual organization could be a way to fill this gap. In this work, we propose a new machine learning approach [Variational Autoencoder Modular Bayesian Network (VAMBN)] to learn a generative model of longitudinal clinical study data. VAMBN considers typical key aspects of such data, namely limited sample size coupled with comparable many variables of different numerical scales and statistical properties, and many missing values. We show that with VAMBN, we can simulate virtual patients in a sufficiently realistic manner while making theoretical guarantees on data privacy. In addition, VAMBN allows for simulating counterfactual scenarios. Hence, VAMBN could facilitate data sharing as well as design of clinical trials.

Heme is an iron ion-containing molecule found within hemoproteins such as hemoglobin and cytochromes that participates in diverse biological processes. Although excessive heme has been implicated in several diseases including malaria, sepsis, ischemia-reperfusion, and disseminated intravascular coagulation, little is known about its regulatory and signaling functions. Furthermore, the limited understanding of heme’s role in regulatory and signaling functions is in part due to the lack of curated pathway resources for heme cell biology. Here, we present two resources aimed to exploit this unexplored information to model heme biology. The first resource is a terminology covering heme-specific terms not yet included in standard controlled vocabularies. Using this terminology, we curated and modeled the second resource, a mechanistic knowledge graph representing the heme’s interactome based on a corpus of 46 scientific articles. Finally, we demonstrated the utility of these resources by investigating the role of heme in the Toll-like receptor signaling pathway. Our analysis proposed a series of crosstalk events that could explain the role of heme in activating the TLR4 signaling pathway. In summary, the presented work opens the door to the scientific community for exploring the published knowledge on heme biology.

Abstract Introduction Numerous studies have collected Alzheimer's disease (AD) cohort data sets. To achieve reproducible, robust results in data‐driven approaches, an evaluation of the present data landscape is vital. Methods Previous efforts relied exclusively on metadata and literature. Here, we evaluate the data landscape by directly investigating nine patient‐level data sets generated in major clinical cohort studies. Results The investigated cohorts differ in key characteristics, such as demographics and distributions of AD biomarkers. Analyzing the ethnoracial diversity revealed a strong bias toward White/Caucasian individuals. We described and compared the measured data modalities. Finally, the available longitudinal data for important AD biomarkers was evaluated. All results are explorable through our web application ADataViewer (https://adata.scai.fraunhofer.de). Discussion Our evaluation exposed critical limitations in the AD data landscape that impede comparative approaches across multiple data sets. Comparison of our results to those gained by metadata‐based approaches highlights that thorough investigation of real patient‐level data is imperative to assess a data landscape.

Dementia-related diseases like Alzheimer's Disease (AD) have a tremendous social and economic cost. A deeper understanding of its underlying pathophysiologies may provide an opportunity for earlier detection and therapeutic intervention. Previous approaches for characterizing AD were targeted at single aspects of the disease. Yet, due to the complex nature of AD, the success of these approaches was limited. However, in recent years, advancements in integrative disease modeling, built on a wide range of AD biomarkers, have taken a global view on the disease, facilitating more comprehensive analysis and interpretation. Integrative AD models can be sorted in two primary types, namely hypothetical models and data-driven models. The latter group split into two subgroups: (i) Models that use traditional statistical methods such as linear models, (ii) Models that take advantage of more advanced artificial intelligence approaches such as machine learning. While many integrative AD models have been published over the last decade, their impact on clinical practice is limited. There exist major challenges in the course of integrative AD modeling, namely data missingness and censoring, imprecise human-involved priori knowledge, model reproducibility, dataset interoperability, dataset integration, and model interpretability. In this review, we highlight recent advancements and future possibilities of integrative modeling in the field of AD research, showcase and discuss the limitations and challenges involved, and finally, propose avenues to address several of these challenges.

OX40 and its ligand are members of the TNF/TNF receptor superfamily, which includes various molecules influencing cellular signaling and function of both tumor and immune cells. The ability of OX40 to promote proliferation and differentiation of activated T cells fueled present attempts to modulate this immune checkpoint to reinforce antitumor immunity. While we recently found evidence for the involvement of OX40 in pathophysiology of acute myeloid leukemia including natural killer (NK) cell immunosurveillance, less is known on its role in acute lymphoblastic leukemia (ALL). In the present study, OX40 expression on ALL cells was significantly associated with positivity for the adverse risk factor BCR-ABL. In line, signaling via OX40 increased metabolic activity of primary ALL cells and resulted in release of cytokines involved in disease pathophysiology. Furthermore, interaction of ALL-expressed OX40 with its cognate ligand on NK cells stimulated ALL cell lysis. The data presented thus not only identify the yet unknown involvement of OX40/OX40L in ALL pathophysiology and NK cell immunosurveillance but also point to the necessity to thoroughly consider the consequences of modulating the OX40/OX40L molecule system beyond its effects on T cells when developing OX40-targeting approaches for cancer immunotherapy.

Abstract Introduction Traditional approaches to clinical research have, as yet, failed to provide effective treatments for vascular dementia (VaD). Novel approaches to collation and synthesis of data may allow for time and cost efficient hypothesis generating and testing. These approaches may have particular utility in helping us understand and treat a complex condition such as VaD. Methods We present an overview of new uses for existing data to progress VaD research. The overview is the result of consultation with various stakeholders, focused literature review and learning from the group’s experience of successful approaches to data repurposing. In particular, we benefitted from the expert discussion and input of delegates at the 9th International Congress on Vascular Dementia (Ljubljana, 16-18th October 2015). Results We agreed on key areas that could be of relevance to VaD research: systematic review of existing studies; individual patient level analyses of existing trials and cohorts and linking electronic health record data to other datasets. We illustrated each theme with a case-study of an existing project that has utilised this approach. Conclusions There are many opportunities for the VaD research community to make better use of existing data. The volume of potentially available data is increasing and the opportunities for using these resources to progress the VaD research agenda are exciting. Of course, these approaches come with inherent limitations and biases, as bigger datasets are not necessarily better datasets and maintaining rigour and critical analysis will be key to optimising data use.

Pathway-centric approaches are widely used to interpret and contextualize -omics data. However, databases contain different representations of the same biological pathway, which may lead to different results of statistical enrichment analysis and predictive models in the context of precision medicine. We have performed an in-depth benchmarking of the impact of pathway database choice on statistical enrichment analysis and predictive modeling. We analyzed five cancer datasets using three major pathway databases and developed an approach to merge several databases into a single integrative one: MPath. Our results show that equivalent pathways from different databases yield disparate results in statistical enrichment analysis. Moreover, we observed a significant dataset-dependent impact on the performance of machine learning models on different prediction tasks. In some cases, MPath significantly improved prediction performance and also reduced the variance of prediction performances. Furthermore, MPath yielded more consistent and biologically plausible results in statistical enrichment analyses. In summary, this benchmarking study demonstrates that pathway database choice can influence the results of statistical enrichment analysis and predictive modeling. Therefore, we recommend the use of multiple pathway databases or integrative ones.

The SARS-CoV-2 outbreak was declared a worldwide pandemic in 2020. Infection triggers the respiratory tract disease COVID-19, which is accompanied by serious changes in clinical biomarkers such as hemoglobin and interleukins. The same parameters are altered during hemolysis, which is characterized by an increase in labile heme. We present two computational–experimental approaches aimed at analyzing a potential link between heme-related and COVID-19 pathophysiologies. Herein, we performed a detailed analysis of the common pathways induced by heme and SARS-CoV-2 by superimposition of knowledge graphs covering heme biology and COVID-19 pathophysiology. Focus was laid on inflammatory pathways and distinct biomarkers as the linking elements. In a second approach, four COVID-19-related proteins, the host cell proteins ACE2 and TMPRSS2 as well as the viral proteins 7a and S protein were computationally analyzed as potential heme-binding proteins with an experimental validation. The results contribute to the understanding of the progression of COVID-19 infections in patients with different clinical backgrounds and may allow for a more individual diagnosis and therapy in the future.

Since the decoding of the Human Genome, techniques from bioinformatics, statistics, and machine learning have been instrumental in uncovering patterns in increasing amounts and types of different data produced by technical profiling technologies applied to clinical samples, animal models, and cellular systems. Yet, progress on unravelling biological mechanisms, causally driving diseases, has been limited, in part due to the inherent complexity of biological systems. Whereas we have witnessed progress in the areas of cancer, cardiovascular and metabolic diseases, the area of neurodegenerative diseases has proved to be very challenging. This is in part because the aetiology of neurodegenerative diseases such as Alzheimer´s disease or Parkinson´s disease is unknown, rendering it very difficult to discern early causal events. Here we describe a panel of bioinformatics and modeling approaches that have recently been developed to identify candidate mechanisms of neurodegenerative diseases based on publicly available data and knowledge. We identify two complementary strategies—data mining techniques using genetic data as a starting point to be further enriched using other data-types, or alternatively to encode prior knowledge about disease mechanisms in a model based framework supporting reasoning and enrichment analysis. Our review illustrates the challenges entailed in integrating heterogeneous, multiscale and multimodal information in the area of neurology in general and neurodegeneration in particular. We conclude, that progress would be accelerated by increasing efforts on performing systematic collection of multiple data-types over time from each individual suffering from neurodegenerative disease. The work presented here has been driven by project AETIONOMY; a project funded in the course of the Innovative Medicines Initiative (IMI); which is a public-private partnership of the European Federation of Pharmaceutical Industry Associations (EFPIA) and the European Commission (EC).

Pitts and Stark's $\nu$-calculus is a paradigmatic total language for studying the problem of contextual equivalence in higher-order languages with name generation. Models for the $\nu$-calculus that validate basic equivalences concerning names may be constructed using functor categories or nominal sets, with a dynamic allocation monad used to model computations that may allocate fresh names. If recursion is added to the language and one attempts to adapt the models from (nominal) sets to (nominal) domains, however, the direct-style construction of the allocation monad no longer works. This issue has previously been addressed by using a monad that combines dynamic allocation with continuations, at some cost to abstraction. This paper presents a direct-style model of a $\nu$-calculus-like language with recursion using the novel framework of proof-relevant logical relations, in which logical relations also contain objects (or proofs) demonstrating the equivalence of (the semantic counterparts of) programs. Apart from providing a fresh solution to an old problem, this work provides an accessible setting in which to introduce the use of proof-relevant logical relations, free of the additional complexities associated with their use for more sophisticated languages.

Los actos terroristas de 11 de marzo de 2004 en Madrid han llevado al terrorismo radical Islámico al interior mismo de la UE. El ataque, que vino casi tres años después de los acontecimientos del 11 de septiembre en los EE.UU. y siguió a una serie de horrendas actuaciones en países en desarrollo, ha matado a 198 personas y ha transformado profunda y persistentemente la agenda de seguridad en Europa. Este artículo examina alguna de las implicaciones de la nueva agenda de seguridad para las políticas de inmigración y de integración de la UE. El trabajo destaca primero el limitado impacto que podrían tener, todas las medidas de seguridad inmediatamente implantables tales como la inclusión de datos biométricos en los documentos de viaje, sobre la inmigración y el control de fronteras. Continúa con el argumento de que, desde el contexto más amplio de la cohesión social y de la seguridad, los países europeos deben urgentemente dirigir su atención hacia el compromiso de las comunidades Islámicas con vistas a su inclusión e integración social en nuestras sociedades. En relación con su dimensión externa, la UE debe dejar claro que no está concebida como un «Club de Cristianos» y que su forma definitiva no terminará en las fronteras de la Europa Cristiana. Ello incluye abrir las negociaciones con Turquía con vistas a su inclusión como miembro, con buena fe y entrando en un diálogo constructivo con el mundo Árabe que incluya los retos que comportan las migraciones.

PURPOSE:In-vitro evaluation of the feasibility of 4D real time tracking of endovascular devices and stenosis treatment with a magnetic particle imaging (MPI) / magnetic resonance imaging (MRI) road map approach and an MPI-guided approach using a blood pool tracer. MATERIALS AND METHODS:A guide wire and angioplasty-catheter were labeled with a thin layer of magnetic lacquer. For real time MPI a custom made software framework was developed. A stenotic vessel phantom filled with saline or superparamagnetic iron oxide nanoparticles (MM4) was equipped with bimodal fiducial markers for co-registration in preclinical 7T MRI and MPI. In-vitro angioplasty was performed inflating the balloon with saline or MM4. MPI data were acquired using a field of view of 37.3×37.3×18.6 mm3 and a frame rate of 46 volumes/sec. Analysis of the magnetic lacquer-marks on the devices were performed with electron microscopy, atomic absorption spectrometry and micro-computed tomography. RESULTS:Magnetic marks allowed for MPI/MRI guidance of interventional devices. Bimodal fiducial markers enable MPI/MRI image fusion for MRI based roadmapping. MRI roadmapping and the blood pool tracer approach facilitate MPI real time monitoring of in-vitro angioplasty. Successful angioplasty was verified with MPI and MRI. Magnetic marks consist of micrometer sized ferromagnetic plates mainly composed of iron and iron oxide. CONCLUSIONS:4D real time MP imaging, tracking and guiding of endovascular instruments and in-vitro angioplasty is feasible. In addition to an approach that requires a blood pool tracer, MRI based roadmapping might emerge as a promising tool for radiation free 4D MPI-guided interventions.

We describe a minimization procedure for nondeterministic Büchi automata (NBA). For an automaton A another automaton A_min with the minimal number of states is learned with the help of a SAT-solver. This is done by successively computing automata A' that approximate A in the sense that they accept a given finite set of positive examples and reject a given finite set of negative examples. In the course of the procedure these example sets are successively increased. Thus, our method can be seen as an instance of a generic learning algorithm based on a "minimally adequate teacher'' in the sense of Angluin. We use a SAT solver to find an NBA for given sets of positive and negative examples. We use complementation via construction of deterministic parity automata to check candidates computed in this manner for equivalence with A. Failure of equivalence yields new positive or negative examples. Our method proved successful on complete samplings of small automata and of quite some examples of bigger automata. We successfully ran the minimization on over ten thousand automata with mostly up to ten states, including the complements of all possible automata with two states and alphabet size three and discuss results and runtimes; single examples had over 100 states.

We present a method to predict the global horizontal irradiance (GHI) one hour ahead in one-minute resolution using Artificial Neural Networks (ANNs). A feed-forward neural network with Levenberg⁻Marquardt Backpropagation (LM-BP) was used and was trained with four years of data from all-sky images and measured global irradiance as input. The pictures were recorded by a hemispheric sky imager at the Institute of Meteorology and Climatology (IMuK) of the Leibniz Universität Hannover, Hannover, Germany (52.23° N, 09.42° E, and 50 m above sea level). The time series of the global horizontal irradiance was measured using a thermopile pyranometer at the same site. The new method was validated with a test dataset from the same source. The irradiance is predicted for the first 10⁻30 min very well; after this time, the length of which is dependent on the weather conditions, the agreement between predicted and observed irradiance is reasonable. Considering the limited range that the camera and the ANN can “see„, this is not surprising. When comparing the results to the persistence model, we observed that the forecast accuracy of the new model reduced both the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) of the one-hour prediction by approximately 40% compared to the reference persistence model under various weather conditions, which demonstrates the high capability of the algorithm, especially within the first minutes.