Modeling the formation of defensive gaps in basketball: Cutting on a teammate's drive

Basketball is a game of simultaneous actions, and inter-player coordination is key for offensive success. One of the most challenging aspects in this regard is basket cutting on a teammate's drive. The ability to make these cuts is considered to be an artistic skill, mastered by only a handful of players. This skill is also hard to assess, as there is no method to measure the players' capability with respect to this quality-especially not automatically. Using SportVU data from the NBA, we created a mathematical model that identifies the openings in the defense which allow to perform a cut. Our model succeeds to generalize, as it detects these openings on average 139ms earlier than the actual cuts start and has an overall (balanced) accuracy of 0.818 on the test set. Having a tree-based gradient boosting classifier, we received a clear hierarchy of feature importance and were able to inspect the interactions between these attributes during action. This way, the model gives insights about the kind of defensive movements needed for a player to allow enough space to cut while in practical usage the analysis of the output can also help the coaching staff in designing play options and assessing player abilities. By paying more attention to the possible off ball movements during drives, offensive plays can become more versatile-benefiting the participants and the spectators alike.

Mechanistic insights into silica nanoparticle-allergen interactions on antigen presenting cell function in the context of allergic reactions

The incorporation of nanomaterials into consumer products has substantially increased in recent years, raising concerns about their safety. The inherent physicochemical properties of nanoparticles allow them to cross epithelial barriers and gain access to immunocompetent cells. Nanoparticles in cosmetic products can potentially interact with environmental allergens, forming a protein corona, and together penetrate through damaged skin. Allergen-nanoparticle interactions may influence the immune response, eventually resulting in an adverse or beneficial outcome in terms of allergic reactivity. This study determines the impact of silica nanoparticle-allergen interactions on allergic sensitization by studying the major molecular mechanisms affecting allergic responses. The major birch pollen allergen Bet v 1 was chosen as a model allergen and the birch pollen extract as a comparator. Key events in immunotoxicity including allergen uptake, processing, presentation, expression of costimulatory molecules and cytokine release were studied in human monocyte-derived dendritic cells. Using an in vivo sensitization model, murine Bet v 1-specific IgG and IgE levels were monitored. Upon the interaction of allergens with silica nanoparticles, we observed an enhanced uptake of the allergen by macropinocytosis, improved proteolytic processing, and presentation concomitant with a propensity to increase allergen-specific IgG2a and decrease IgE antibody levels. Together, these events suggest that upon nanoparticle interactions the immune response is biased towards a type 1 inflammatory profile, characterized by the upregulation of T helper 1 (Th1) cells. In conclusion, the interaction of the birch pollen allergen with silica nanoparticles will not worsen allergic sensitization, a state of type 2-inflammation, but rather seems to decrease it by skewing towards a Th1-dominated immune response.

MAPkinases regulate secondary metabolism, sexual development and light dependent cellulase regulation in Trichoderma reesei

The filamentous fungus Trichoderma reesei is a prolific producer of plant cell wall degrading enzymes, which are regulated in response to diverse environmental signals for optimal adaptation, but also produces a wide array of secondary metabolites. Available carbon source and light are the strongest cues currently known to impact secreted enzyme levels and an interplay with regulation of secondary metabolism became increasingly obvious in recent years. While cellulase regulation is already known to be modulated by different mitogen activated protein kinase (MAPK) pathways, the relevance of the light signal, which is transmitted by this pathway in other fungi as well, is still unknown in T. reesei as are interconnections to secondary metabolism and chemical communication under mating conditions. Here we show that MAPkinases differentially influence cellulase regulation in light and darkness and that the Hog1 homologue TMK3, but not TMK1 or TMK2 are required for the chemotropic response to glucose in T. reesei. Additionally, MAPkinases regulate production of specific secondary metabolites including trichodimerol and bisorbibutenolid, a bioactive compound with cytostatic effect on cancer cells and deterrent effect on larvae, under conditions facilitating mating, which reflects a defect in chemical communication. Strains lacking either of the MAPkinases become female sterile, indicating the conservation of the role of MAPkinases in sexual fertility also in T. reesei. In summary, our findings substantiate the previously detected interconnection of cellulase regulation with regulation of secondary metabolism as well as the involvement of MAPkinases in light dependent gene regulation of cellulase and secondary metabolite genes in fungi.

Toward equitable open research: stakeholder co-created recommendations for research institutions, funders and researchers

Open Research aims to make research more accessible, transparent, reproducible, shared and collaborative. Doing so is meant to democratize and diversify access to knowledge and knowledge production, and ensure that research is useful outside of academic contexts. Increasing equity is therefore a key aim of the Open Research movement, yet mounting evidence demonstrates that the practices of Open Research are implemented in ways that undermine this. In response, we convened a diverse community of researchers, research managers and funders to co-create actionable recommendations for supporting the equitable implementation of Open Research. Using a co-creative modified Delphi method, we generated consensus-driven recommendations that address three key problem areas: the resource-intensive nature of Open Research, the high cost of article processing charges, and obstructive reward and recognition practices at funders and research institutions that undermine the implementation of Open Research. In this paper, we provide an overview of these issues, a detailed description of the co-creative process, and present the recommendations and the debates that surrounded them. We discuss these recommendations in relation to other recently published ones and conclude that implementing ours requires 'global thinking' to ensure that a systemic and inclusive approach to change is taken.

Towards an automated data cleaning with deep learning in CRESST

The CRESST experiment employs cryogenic calorimeters for the sensitive measurement of nuclear recoils induced by dark matter particles. The recorded signals need to undergo a careful cleaning process to avoid wrongly reconstructed recoil energies caused by pile-up and read-out artefacts. We frame this process as a time series classification task and propose to automate it with neural networks. With a data set of over one million labeled records from 68 detectors, recorded between 2013 and 2019 by CRESST, we test the capability of four commonly used neural network architectures to learn the data cleaning task. Our best performing model achieves a balanced accuracy of 0.932 on our test set. We show on an exemplary detector that about half of the wrongly predicted events are in fact wrongly labeled events, and a large share of the remaining ones have a context-dependent ground truth. We furthermore evaluate the recall and selectivity of our classifiers with simulated data. The results confirm that the trained classifiers are well suited for the data cleaning task.

Workflow assessment of an augmented reality application for planning of perforator flaps in plastic reconstructive surgery: Game or game changer?

Objective

In contrast to the rising amount of financial investments for research and development in medical technology worldwide is the lack of usability and clinical readiness of the produced systems. We evaluated an augmented reality (AR) setup under development for preoperative perforator vessel mapping for elective autologous breast reconstruction.

Methods

In this grant-supported research pilot, we used magnetic resonance angiography data (MR-A) of the trunk to superimpose the scans on the corresponding patients with hands-free AR goggles to identify regions-of-interest for surgical planning. Perforator location was assessed using MR-A imaging (MR-A projection) and Doppler ultrasound data (3D distance) and confirmed intraoperatively in all cases. We evaluated usability (System Usability Scale, SUS), data transfer load and documented personnel hours for software development, correlation of image data, as well as processing duration to clinical readiness (time from MR-A to AR projections per scan).

Results

All perforator locations were confirmed intraoperatively, and we found a strong correlation between MR-A projection and 3D distance measurements (Spearman r = 0.894). The overall usability (SUS) was 67 ± 10 (=moderate to good). The presented setup for AR projections took 173 min to clinical readiness (=availability on AR device per patient).

Conclusion

In this pilot, we calculated development investments based on project-approved grant-funded personnel hours with a moderate to good usability outcome resulting from some limitations: assessment was based on one-time testing with no previous training, a time lag of AR visualizations on the body and difficulties in spatial AR orientation. The use of AR systems can provide new opportunities for future surgical planning, but has more potential for educational (e.g., patient information) or training purposes of medical under- and postgraduates (spatial recognition of imaging data associated with anatomical structures and operative planning). We expect future usability improvements with refined user interfaces, faster AR hardware and artificial intelligence-enhanced visualization techniques.

Recent changes in the mutational dynamics of the SARS-CoV-2 main protease substantiate the danger of emerging resistance to antiviral drugs

Introduction

The current coronavirus pandemic is being combated worldwide by nontherapeutic measures and massive vaccination programs. Nevertheless, therapeutic options such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main-protease (Mpro) inhibitors are essential due to the ongoing evolution toward escape from natural or induced immunity. While antiviral strategies are vulnerable to the effects of viral mutation, the relatively conserved Mpro makes an attractive drug target: Nirmatrelvir, an antiviral targeting its active site, has been authorized for conditional or emergency use in several countries since December 2021, and a number of other inhibitors are under clinical evaluation. We analyzed recent SARS-CoV-2 genomic data, since early detection of potential resistances supports a timely counteraction in drug development and deployment, and discovered accelerated mutational dynamics of Mpro since early December 2021.

Methods

We performed a comparative analysis of 10.5 million SARS-CoV-2 genome sequences available by June 2022 at GISAID to the NCBI reference genome sequence NC_045512.2. Amino-acid exchanges within high-quality regions in 69,878 unique Mpro sequences were identified and time- and in-depth sequence analyses including a structural representation of mutational dynamics were performed using in-house software.

Results

The analysis showed a significant recent event of mutational dynamics in Mpro. We report a remarkable increase in mutational variability in an eight-residue long consecutive region (R188-G195) near the active site since December 2021.

Discussion

The increased mutational variability in close proximity to an antiviral-drug binding site as described herein may suggest the onset of the development of antiviral resistance. This emerging diversity urgently needs to be further monitored and considered in ongoing drug development and lead optimization.

Effects of reliability indicators on usage, acceptance and preference of predictive process management decision support systems

Despite the growing availability of data, simulation technologies, and predictive analytics, it is not yet clear whether and under which conditions users will trust Decision Support Systems (DSS). DSS are designed to support users in making more informed decisions in specialized tasks through more accurate predictions and recommendations. This mixed-methods user study contributes to the research on trust calibration by analyzing the potential effects of integrated reliability indication in DSS user interfaces for process management in first-time usage situations characterized by uncertainty. Ten experts specialized in digital tools for construction were asked to test and assess two versions of a DSS in a renovation project scenario. We found that while users stated that they need full access to all information to make their own decisions, reliability indication in DSS tends to make users more willing to make preliminary decisions, with users adapting their confidence and reliance to the indicated reliability. Reliability indication in DSS also increases subjective usefulness and system reliability. Based on these findings, it is recommended that for the design of reliability indication practitioners consider displaying a combination of reliability information at several granularity levels in DSS user interfaces, including visualizations, such as a traffic light system, and to also provide explanations for the reliability information. Further research directions towards achieving trustworthy decision support in complex environments are proposed.

MycelioTronics: Fungal mycelium skin for sustainable electronics

Electronic devices are irrevocably integrated into our lives. Yet, their limited lifetime and often improvident disposal demands sustainable concepts to realize a green electronic future. Research must shift its focus on substituting nondegradable and difficult-to-recycle materials to allow either biodegradation or facile recycling of electronic devices. Here, we demonstrate a concept for growth and processing of fungal mycelium skins as biodegradable substrate material for sustainable electronics. The skins allow common electronic processing techniques including physical vapor deposition and laser patterning for electronic traces with conductivities as high as 9.75 ± 1.44 × 10⁴ S cm^-1. The conformal and flexible electronic mycelium skins withstand more than 2000 bending cycles and can be folded several times with only moderate resistance increase. We demonstrate mycelium batteries with capacities as high as ~3.8 mAh cm^-2 used to power autonomous sensing devices including a Bluetooth module and humidity and proximity sensor.

A dual decoder U-Net-based model for nuclei instance segmentation in hematoxylin and eosin-stained histological images

Even in the era of precision medicine, with various molecular tests based on omics technologies available to improve the diagnosis process, microscopic analysis of images derived from stained tissue sections remains crucial for diagnostic and treatment decisions. Among other cellular features, both nuclei number and shape provide essential diagnostic information. With the advent of digital pathology and emerging computerized methods to analyze the digitized images, nuclei detection, their instance segmentation and classification can be performed automatically. These computerized methods support human experts and allow for faster and more objective image analysis. While methods ranging from conventional image processing techniques to machine learning-based algorithms have been proposed, supervised convolutional neural network (CNN)-based techniques have delivered the best results. In this paper, we propose a CNN-based dual decoder U-Net-based model to perform nuclei instance segmentation in hematoxylin and eosin (H&E)-stained histological images. While the encoder path of the model is developed to perform standard feature extraction, the two decoder heads are designed to predict the foreground and distance maps of all nuclei. The outputs of the two decoder branches are then merged through a watershed algorithm, followed by post-processing refinements to generate the final instance segmentation results. Moreover, to additionally perform nuclei classification, we develop an independent U-Net-based model to classify the nuclei predicted by the dual decoder model. When applied to three publicly available datasets, our method achieves excellent segmentation performance, leading to average panoptic quality values of 50.8%, 51.3%, and 62.1% for the CryoNuSeg, NuInsSeg, and MoNuSAC datasets, respectively. Moreover, our model is the top-ranked method in the MoNuSAC post-challenge leaderboard.

Absolute and relative quantitation of amylase/trypsin-inhibitors by LC-MS/MS from wheat lines obtained by CRISPR-Cas9 and RNAi

Quantitation of wheat proteins is still a challenge, especially regarding amylase/trypsin-inhibitors (ATIs). A selection of ATIs was silenced in the common wheat cultivar Bobwhite and durum wheat cultivar Svevo by RNAi and gene editing, respectively, in order to reduce the amounts of ATIs. The controls and silenced lines were analyzed after digestion to peptides by LC-MS/MS with different approaches to evaluate changes in composition of ATIs. First, a targeted method with stable isotope dilution assay (SIDA) using labeled peptides as internal standards was applied. Additionally, four different approaches for relative quantitation were conducted, in detail, iTRAQ labeled and label free quantitation (LFQ) combined with data dependent acquisition (DDA) and data independent acquisition (DIA). Quantitation was performed manually (Skyline and MASCOT) and with different proteomics software tools (PLGS, MaxQuant, and PEAKS X Pro). To characterize the wheat proteins on protein level, complementary techniques as high-performance liquid chromatography (HPLC) and gel electrophoresis were performed. The targeted approach with SIDA was able to quantitate all ATIs, even at low levels, but an optimized extraction is necessary. The labeled iTRAQ approach revealed an indistinct performance. LFQ with low resolution equipment (IonTrap) showed similar results for major ATIs, but low abundance ATIs as CM1, were not detectable. DDA measurements with an Orbitrap system and evaluation using MaxQuant showed that the relative quantitation was dependent on the wheat species. The combination of manual curation of the MaxQuant search with Skyline revealed a very good performance. The DIA approach with analytical flow found similar results compared to absolute quantitation except for some minor ATIs, which were not detected. Comparison of applied methods revealed that peptide selection is a crucial step for protein quantitation. Wheat proteomics faces challenges due to the high genetic complexity, the close relationship to other cereals and the incomplete, redundant protein database requiring sensitive, precise and accurate LC-MS/MS methods.

A toolbox for class I HDACs reveals isoform specific roles in gene regulation and protein acetylation

The class I histone deacetylases are essential regulators of cell fate decisions in health and disease. While pan- and class-specific HDAC inhibitors are available, these drugs do not allow a comprehensive understanding of individual HDAC function, or the therapeutic potential of isoform-specific targeting. To systematically compare the impact of individual catalytic functions of HDAC1, HDAC2 and HDAC3, we generated human HAP1 cell lines expressing catalytically inactive HDAC enzymes. Using this genetic toolbox we compare the effect of individual HDAC inhibition with the effects of class I specific inhibitors on cell viability, protein acetylation and gene expression. Individual inactivation of HDAC1 or HDAC2 has only mild effects on cell viability, while HDAC3 inactivation or loss results in DNA damage and apoptosis. Inactivation of HDAC1/HDAC2 led to increased acetylation of components of the COREST co-repressor complex, reduced deacetylase activity associated with this complex and derepression of neuronal genes. HDAC3 controls the acetylation of nuclear hormone receptor associated proteins and the expression of nuclear hormone receptor regulated genes. Acetylation of specific histone acetyltransferases and HDACs is sensitive to inactivation of HDAC1/HDAC2. Over a wide range of assays, we determined that in particular HDAC1 or HDAC2 catalytic inactivation mimics class I specific HDAC inhibitors. Importantly, we further demonstrate that catalytic inactivation of HDAC1 or HDAC2 sensitizes cells to specific cancer drugs. In summary, our systematic study revealed isoform-specific roles of HDAC1/2/3 catalytic functions. We suggest that targeted genetic inactivation of particular isoforms effectively mimics pharmacological HDAC inhibition allowing the identification of relevant HDACs as targets for therapeutic intervention.

In situ techniques reveal the true capabilities of SOFC cathode materials and their sudden degradation due to omnipresent sulfur trace impurities

In this study, five different mixed conducting cathode materials were grown as dense thin films by pulsed laser deposition (PLD) and characterized via in situ impedance spectroscopy directly after growth inside the PLD chamber (i-PLD). This technique enables quantification of the oxygen reduction kinetics on pristine and contaminant-free mixed conducting surfaces. The measurements reveal excellent catalytic performance of all pristine materials with polarization resistances being up to two orders of magnitude lower than those previously reported in the literature. For instance, on dense La_0.6Sr_0.4CoO_3-δ thin films, an area specific surface resistance of ∼0.2 Ω cm² at 600 °C in synthetic air was found, while values usually >1 Ω cm² are measured in conventional ex situ measurement setups. While surfaces after i-PLD measurements were very clean, ambient pressure X-ray photoelectron spectroscopy (AP-XPS) measurements found that all samples measured in other setups were contaminated with sulfate adsorbates. In situ impedance spectroscopy during AP-XPS revealed that already trace amounts of sulfur present in high purity gases accumulate quickly on pristine surfaces and lead to strongly increased surface polarization resistances, even before the formation of a SrSO₄ secondary phase. Accordingly, the inherent excellent catalytic properties of this important class of materials were often inaccessible so far. As a proof of concept, the fast kinetics observed on sulfate-free surfaces were also realized in ex situ measurements with a gas purification setup and further reduces the sulfur concentration in the high purity gas.

Machine unlearning: linear filtration for logit-based classifiers

Recently enacted legislation grants individuals certain rights to decide in what fashion their personal data may be used and in particular a “right to be forgotten”. This poses a challenge to machine learning: how to proceed when an individual retracts permission to use data which has been part of the training process of a model? From this question emerges the field of machine unlearning, which could be broadly described as the investigation of how to “delete training data from models”. Our work complements this direction of research for the specific setting of class-wide deletion requests for classification models (e.g. deep neural networks). As a first step, we propose linear filtration as an intuitive, computationally efficient sanitization method. Our experiments demonstrate benefits in an adversarial setting over naive deletion schemes.

Disentangling the evolutionary drivers of social complexity: A comprehensive test of hypotheses

During the Holocene, the scale and complexity of human societies increased markedly. Generations of scholars have proposed different theories explaining this expansion, which range from broadly functionalist explanations, focusing on the provision of public goods, to conflict theories, emphasizing the role of class struggle or warfare. To quantitatively test these theories, we develop a general dynamical model based on the theoretical framework of cultural macroevolution. Using this model and Seshat: Global History Databank, we test 17 potential predictor variables proxying mechanisms suggested by major theories of sociopolitical complexity (and >100,000 combinations of these predictors). The best-supported model indicates a strong causal role played by a combination of increasing agricultural productivity and invention/adoption of military technologies (most notably, iron weapons and cavalry in the first millennium BCE).

User requirements for the design of smart homes: dimensions and goals

The 'Smart Home' is a strongly technology-driven field. While user-centered requirements have been reported for specific features, a considerable gap persists for design based on an everyday home context and the social and emotional nature of the home. To address this, we present a user-centered design process to question and expand narrow framings of energy-efficiency and smart control and consider the richness and variety of the domestic context as design space for smart homes. Our three-step investigation employs cultural probing, participatory design fiction, and focus groups to progress from the home context "as-is" towards a blending of values with technological responses. Our findings highlight the home as a complex construct imbued with organically grown practices and individual and collective needs, values, and emotions. Based on empirical, real-user data we present features and system expectations that address this multifaceted overall picture. This paper advises the design process of future smart home solutions in three facets: first, we discuss the value of the design process applied in this study and future possibilities to expand. Second, we show design dimensions, namely time, space, relations, individual factors, and values that allow design for a heterogeneity of users and situations. Third, we derive specific design goals to highlight directions of smart home system design: design for control, low effort, integration, evolvability, identity, sociability, and benefits.

Can Liposomes Survive Inkjet Printing? The Effect of Jetting on Key Liposome Attributes for Drug Delivery Applications

Purpose

Inkjet printing has the potential to enable novel personalized and tailored drug therapies based on liposome and lipid nanoparticles. However, due to the significant shear force exerted on the jetted fluids, its suitability for shear-sensitive materials such as liposomes, has not been verified. We have conducted a proof-of-concept study to examine whether the particle concentration and size distribution of placebo liposomes are affected by common inkjet/dispensing technologies.

Methods

We have subjected three types of liposome-containing fluids ("inks") to two different commercial dispensing/jetting technologies, which are relevant to most drug printing approaches. The liposome jetting processes were observed in real-time using strobographic imaging techniques. The phospholipid concentrations and particle size distributions were determined before and after jetting via enzymatic colorimetric and dynamic light scattering methods, respectively.

Results

Our results have shown that the jetting dynamics of the liposome inks are well predicted by the established inkjet printing regime map based on their physical properties and the jetting conditions. Importantly, although significant shear forces were confirmed during jetting, the liposome concentrations and particle size distributions in the collected samples remain largely unaffected.

Conclusion

These findings, we believe, provide the essential proof-of-concept to encourage further development in this highly topical research area.

Plasmonic nanomaterials with responsive polymer hydrogels for sensing and actuation

Plasmonic nanomaterials have become an integral part of numerous technologies, where they provide important functionalities spanning from extraction and harvesting of light in thin film optical devices to probing of molecular species and their interactions on biochip surfaces. More recently, we witness increasing research efforts devoted to a new class of plasmonic nanomaterials that allow for on-demand tuning of their properties by combining metallic nanostructures and responsive hydrogels. This review addresses this recently emerged vibrant field, which holds potential to expand the spectrum of possible applications and deliver functions that cannot be achieved by separate research in each of the respective fields. It aims at providing an overview of key principles, design rules, and current implementations of both responsive hydrogels and metallic nanostructures. We discuss important aspects that capitalize on the combination of responsive polymer networks with plasmonic nanostructures to perform rapid mechanical actuation and actively controlled nanoscale confinement of light associated with resonant amplification of its intensity. The latest advances towards the implementation of such responsive plasmonic nanomaterials are presented, particularly covering the field of plasmonic biosensing that utilizes refractometric measurements as well as plasmon-enhanced optical spectroscopy readout, optically driven miniature soft actuators, and light-fueled micromachines operating in an environment resembling biological systems.

Fibroblast growth factor receptor 4 promotes glioblastoma progression: a central role of integrin-mediated cell invasiveness

Glioblastoma (GBM) is characterized by a particularly invasive phenotype, supported by oncogenic signals from the fibroblast growth factor (FGF)/ FGF receptor (FGFR) network. However, a possible role of FGFR4 remained elusive so far. Several transcriptomic glioma datasets were analyzed. An extended panel of primary surgical specimen-derived and immortalized GBM (stem)cell models and original tumor tissues were screened for FGFR4 expression. GBM models engineered for wild-type and dominant-negative FGFR4 overexpression were investigated regarding aggressiveness and xenograft formation. Gene set enrichment analyses of FGFR4-modulated GBM models were compared to patient-derived datasets. Despite widely absent in adult brain, FGFR4 mRNA was distinctly expressed in embryonic neural stem cells and significantly upregulated in glioblastoma. Pronounced FGFR4 overexpression defined a distinct GBM patient subgroup with dismal prognosis. Expression levels of FGFR4 and its specific ligands FGF19/FGF23 correlated both in vitro and in vivo and were progressively upregulated in the vast majority of recurrent tumors. Based on overexpression/blockade experiments in respective GBM models, a central pro-oncogenic function of FGFR4 concerning viability, adhesion, migration, and clonogenicity was identified. Expression of dominant-negative FGFR4 resulted in diminished (subcutaneous) or blocked (orthotopic) GBM xenograft formation in the mouse and reduced invasiveness in zebrafish xenotransplantation models. In vitro and in vivo data consistently revealed distinct FGFR4 and integrin/extracellular matrix interactions. Accordingly, FGFR4 blockade profoundly sensitized FGFR4-overexpressing GBM models towards integrin/focal adhesion kinase inhibitors. Collectively, FGFR4 overexpression contributes to the malignant phenotype of a highly aggressive GBM subgroup and is associated with integrin-related therapeutic vulnerabilities.

Cohort profile: 'Biomarkers of Personalised Medicine' (BioPersMed): a single-centre prospective observational cohort study in Graz/Austria to evaluate novel biomarkers in cardiovascular and metabolic diseases

PURPOSE

Accumulating evidence points towards a close relationship between cardiovascular, endocrine and metabolic diseases. The BioPersMed Study (Biomarkers of Personalised Medicine) is a single-centre prospective observational cohort study with repetitive examination of participants in 2-year intervals. The aim is to evaluate the predictive impact of various traditional and novel biomarkers of cardiovascular, endocrine and metabolic pathways in asymptomatic individuals at risk for cardiovascular and/or metabolic disease.

PARTICIPANTS

Between 2010 and 2016, we recruited 1022 regional individuals into the study. Subjects aged 45 years or older presenting with at least one traditional cardiovascular risk factor or manifest type 2 diabetes mellitus (T2DM) were enrolled. The mean age of the participants was 57±8 years, 55% were female, 18% had T2DM, 33% suffered from arterial hypertension, 15% were smokers, 42% had hyperlipidaemia, and only 26% were at low cardiovascular risk according to the Framingham 'Systematic COronary Risk Evaluation'.

FINDINGS TO DATE

Study procedures during screening and follow-up visits included a physical examination and comprehensive cardiovascular, endocrine, metabolic, ocular and laboratory workup with biobanking of blood and urine samples. The variety of assessed biomarkers allows a full phenotyping of individuals at cardiovascular and metabolic risk. Preliminary data from the cohort and relevant biomarker analyses were already used as control population for genomic studies in local and international research cooperation.

FUTURE PLANS

Participants will undergo comprehensive cardiovascular, endocrine and metabolic examinations for the next decades and clinical outcomes will be adjudicated prospectively.

Meteorological factors and non-pharmaceutical interventions explain local differences in the spread of SARS-CoV-2 in Austria

The drivers behind regional differences of SARS-CoV-2 spread on finer spatio-temporal scales are yet to be fully understood. Here we develop a data-driven modelling approach based on an age-structured compartmental model that compares 116 Austrian regions to a suitably chosen control set of regions to explain variations in local transmission rates through a combination of meteorological factors, non-pharmaceutical interventions and mobility. We find that more than 60% of the observed regional variations can be explained by these factors. Decreasing temperature and humidity, increasing cloudiness, precipitation and the absence of mitigation measures for public events are the strongest drivers for increased virus transmission, leading in combination to a doubling of the transmission rates compared to regions with more favourable weather. We conjecture that regions with little mitigation measures for large events that experience shifts toward unfavourable weather conditions are particularly predisposed as nucleation points for the next seasonal SARS-CoV-2 waves.

How weather modulates the spread of SARS-CoV-2 on fine spatio-temporal scales is still not fully understood. Here we use a controlled regional comparison to isolate the impact of five different meteorological factors, four types of non-pharmaceutical interventions as well as individual-level mobility on transmission rates in Austria. We find that more than 60% of regional variations can be explained by these factors. Temperature and humidity relate inversely with transmission rates whereas cloudiness and precipitation correlate with increasing transmission. We also observe a particularly strong impact of restrictions targeting large events. Our results suggest that a combination of weather shifts towards winter conditions combined with little mitigation measures for large gatherings drive the early growth of seasonal SARS-CoV-2 waves.

Bridging Fidelities to Predict Nanoindentation Tip Radii Using Interpretable Deep Learning Models

As the need for miniaturized structural and functional materials has increased, the need for precise materials characterizaton has also expanded. Nanoindentation is a popular method that can be used to measure material mechanical behavior which enables high-throughput experiments and, in some cases, can also provide images of the indented area through scanning. Both indenting and scanning can cause tip wear that can influence the measurements. Therefore, precise characterization of tip radii is needed to improve data evaluation. A data fusion method is introduced which uses finite element simulations and experimental data to estimate the tip radius in situ in a meaningful way using an interpretable multi-fidelity deep learning approach. By interpreting the machine learning models, it is shown that the approaches are able to accurately capture physical indentation phenomena.

A Survey of Data Quality Measurement and Monitoring Tools

High-quality data is key to interpretable and trustworthy data analytics and the basis for meaningful data-driven decisions. In practical scenarios, data quality is typically associated with data preprocessing, profiling, and cleansing for subsequent tasks like data integration or data analytics. However, from a scientific perspective, a lot of research has been published about the measurement (i.e., the detection) of data quality issues and different generally applicable data quality dimensions and metrics have been discussed. In this work, we close the gap between data quality research and practical implementations with a detailed investigation on how data quality measurement and monitoring concepts are implemented in state-of-the-art tools. For the first time and in contrast to all existing data quality tool surveys, we conducted a systematic search, in which we identified 667 software tools dedicated to “data quality.” To evaluate the tools, we compiled a requirements catalog with three functionality areas: (1) data profiling, (2) data quality measurement in terms of metrics, and (3) automated data quality monitoring. Using a set of predefined exclusion criteria, we selected 13 tools (8 commercial and 5 open-source tools) that provide the investigated features and are not limited to a specific domain for detailed investigation. On the one hand, this survey allows a critical discussion of concepts that are widely accepted in research, but hardly implemented in any tool observed, for example, generally applicable data quality metrics. On the other hand, it reveals potential for functional enhancement of data quality tools and supports practitioners in the selection of appropriate tools for a given use case.

Fungal species and mycotoxins in mouldy spots of grass and maize silages in Austria

Fungi and mycotoxins in silage can have detrimental consequences for both cattle and human health. This pilot study identified, via the routinary direct plating method, the dominant cultivable fungi in mouldy grass silages (GS) (n = 19) and maize silages (MS) (n = 28) from Austria. The profiles of regulated, modified, and emerging mycotoxins together with other fungal metabolites were analysed via LC-(ESI)MS/MS. Penicillium roqueforti, Saccharomyces spp., Geotrichum candidum, Aspergillus fumigatus and Monascus ruber were the most frequent fungal organisms identified. Other species including Mucor circinelloides, Fusarium spp. and Paecilomyces niveus were detected at lower frequencies. The presence of complex mixtures of toxic and potentially toxic compounds was evidenced by high levels and occurrences (≥ 50%) of Penicillium-produced compounds such as mycophenolic acid (MPA), roquefortines (ROCs), andrastins (ANDs) and marcfortine A. Mouldy silages contained toxins commonly produced by genus Fusarium (e.g. zearalenone (ZEN) and trichothecenes), Alternaria (like tenuazonic acid (TeA) and alternariol (AHO)) and Aspergillus (such as sterigmatocystin (STC)). Compared to those in GS, mouldy spots in MS presented significantly higher fungal counts and more diverse toxin profiles, in addition to superior levels of Fusarium spp., Penicillium spp. and total fungal metabolites. Generally, no correlation between mould counts and corresponding metabolites was detected, except for the counts of P. roqueforti, which were positively correlated with Penicillium spp. metabolites in mouldy MS. This study represents a first assessment of the fungal diversity in mouldy silage in Austria and highlights its potential role as a substantial contributor to contamination with complex mycotoxin mixtures in cattle diets.

How UV light lowers the conductivity of SrTiO3 by photochemical water splitting at elevated temperature

Nominally undoped SrTiO₃ single crystals were illuminated by UV light at 350 °C in oxidizing as well as reducing atmospheres. In N₂/O₂ atmospheres, UV irradiation enhances the conductivity of SrTiO₃ by several orders of magnitude. In dry H₂ atmosphere UV exposure leads to the opposite conductivity effect, i.e., above band gap energy illumination surprisingly lowers the conductivity. This is discussed in the framework of a defect chemical model. We show that a shift in defect concentrations due to UV-driven oxygen incorporation from the gas phase into the oxide is the main cause of the measured conductivity changes. A model is introduced to illustrate the thermodynamic and kinetic drivers of the processes under UV irradiation. Noteably, in reducing H₂/H₂O atmospheres, the incorporation of oxygen into the investigated oxide under UV light takes place via water splitting. Owing to the predominant electron conduction of SrTiO₃ in equilibrium with H₂, oxygen incorporation upon UV and thus an increase of the oxygen chemical potential leads to a decrease of the majority electronic charge carrier, here electrons, which lowers the conductivity under UV irradiation.