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This article explores the flourishing claims to create crisis taskforces in the wake of the COVID-19 pandemic. Are taskforces a remedy for all crises? Taskforces are ad-hoc created groups of highly specialized experts who provide advice to policymakers. They have the advantages of flexibility and allow to recruit high-level competences for short-term mandates. However, taskforces are not a panacea, as different types of crises (e.g. creeping crises, disruptive events, cyclical crises) require different types of policy advisory systems. A taskforce is also a short-term, reactive model, which runs the risk of politicization. This article analyzes the suitability of the taskforce model in various situations and compares expert advice provided to decision-makers during the financial crisis and during the COVID-19 pandemic in Switzerland. It answers the question of what types of advisory systems suit what type of crisis. The contribution shows that in the era of polycrises, one size does not fit all and the distinctive characteristics of crises must be considered to find a fit-for-purpose approach.

Abstract Ageing is a major risk factor that contributes to increased mortality and morbidity rates during influenza A virus (IAV) infections. Macrophages are crucial players in the defense against viral infections and display impaired function during ageing. However, the impact of ageing on macrophage function in response to an IAV infection remains unclear and offers potential insight for underlying mechanisms. In this study, we investigated the immune response of young and aged human monocyte-derived macrophages to two different H1N1 IAV strains. Interestingly, macrophages of aged individuals showed a lower interferon response to IAV infection, resulting in increased viral load. Transcriptomic data revealed a reduced expression of stimulator of interferon genes (STING) in aged macrophages albeit the cGAS-STING pathway was upregulated. Our data clearly indicate the importance of STING signaling for interferon production by applying a THP-1 STING knockout model. Evaluation of mitochondrial function during IAV infection revealed the release of mitochondrial DNA to be the activator of cGAS-STING pathway. The subsequent induction of apoptosis was attenuated in aged macrophages due to decreased STING signaling. Our study provides new insights into molecular mechanisms underlying age-related immune impairment. To our best knowledge, we are the first to discover an age-dependent difference in gene expression of STING on a transcriptional level in human monocyte-derived macrophages possibly leading to a diminished interferon production.

Abstract Background Patients transitioning between different care contexts are at increased risk of experiencing adverse events. In particular, being discharged to home after inpatient treatment involves significant risks. However, there is a lack of valid and internationally comparable assessment tools on patients’ experiences of difficulties following hospital discharge. Therefore, this study aimed to adapt and validate the German version of the post-discharge coping difficulty scale (PDCDS-G). Methods Patients were recruited at a German university hospital. 815 adult patients participated in a self-report survey following an inpatient stay of at least three days. Factorial validity of the PDCDS-G was evaluated via factor analyses. Further, examination of measurement invariance was performed. To establish criterion validity, associations with patients’ self-reported health status and occurrence of patient safety were determined. Further, group differences regarding patient characteristics, hospitalization factors, and survey-related variables were examined. Results Factorial validity of the PDCDS-G was confirmed by a two-factorial model with good model fit. Both factors showed good to excellent reliability. The two-factor model achieved measurement invariance across all patient characteristics, hospitalization factors, and survey-related variables. Significant relationships with patients’ health status and the occurrence of patient safety incidents corroborate criterion validity of the PDCDS-G. Differential associations of the two PDCDS-G factors regarding patient characteristics, hospitalization, and survey-related variables were found. Discussion Construct and criterion validity, as well as the reliability of the PDCDS-G, were verified. Further, instrument’s measurement invariance was confirmed allowing use of the scale for the interpretation of group differences and comparisons between studies. Conclusions The PDCDS-G provides a validated and comparable patient-reported outcomes measure for patient experiences after hospital discharge to home. The PDCDS-G can be used for patient surveys in quality or patient safety improvement in care transition processes.

Abstract Background Predictive modeling based on multi-omics data, which incorporates several types of omics data for the same patients, has shown potential to outperform single-omics predictive modeling. Most research in this domain focuses on incorporating numerous data types, despite the complexity and cost of acquiring them. The prevailing assumption is that increasing the number of data types necessarily improves predictive performance. However, the integration of less informative or redundant data types could potentially hinder this performance. Therefore, identifying the most effective combinations of omics data types that enhance predictive performance is critical for cost-effective and accurate predictions. Methods In this study, we systematically evaluated the predictive performance of all 31 possible combinations including at least one of five genomic data types (mRNA, miRNA, methylation, DNAseq, and copy number variation) using 14 cancer datasets with right-censored survival outcomes, publicly available from the TCGA database. We employed various prediction methods and up-weighted clinical data in every model to leverage their predictive importance. Harrell’s C-index and the integrated Brier Score were used as performance measures. To assess the robustness of our findings, we performed a bootstrap analysis at the level of the included datasets. Statistical testing was conducted for key results, limiting the number of tests to ensure a low risk of false positives. Results Contrary to expectations, we found that using only mRNA data or a combination of mRNA and miRNA data was sufficient for most cancer types. For some cancer types, the additional inclusion of methylation data led to improved prediction results. Far from enhancing performance, the introduction of more data types most often resulted in a decline in performance, which varied between the two performance measures. Conclusions Our findings challenge the prevailing notion that combining multiple omics data types in multi-omics survival prediction improves predictive performance. Thus, the widespread approach in multi-omics prediction of incorporating as many data types as possible should be reconsidered to avoid suboptimal prediction results and unnecessary expenditure.

Abstract Isomalto/malto-polysaccharides (IMMPs) are a novel type of soluble dietary fibres with a prebiotic potential promoting growth of beneficial microbes in the gut. However, the mode of action of IMMPs remains unknown. Previous studies on IMMPs showed an increase in total bacteria, especially lactobacilli, and higher production of short chain fatty acids (SCFA) when IMMPs were fed to rats or used during in vitro fermentation. Here we used metatranscriptomics to investigate how IMMPs with different amounts of α − (1 → 6) glycosidic linkages affected microbial function during incubation with human fecal inoculum. We showed that active microbial community dynamics during fermentation varied depending on the type of IMMP used and that the observed changes were reflected in the community gene expression profiles. Based on metatranscriptome analysis, members of Bacteroides, Lactobacillus and Bifidobacterium were the predominant degraders of IMMPs, and the increased gene expression in these bacteria correlated with high amounts of α − (1 → 6) glycosidic linkages. We also noted an increase in relative abundance of these bacteria and an activation of pathways involved in SCFA synthesis. Our findings could provide a baseline for more targeted approaches in designing prebiotics for specific bacteria and to achieve more controlled modulation of microbial activity towards desired health outcomes.

Abstract Innate immune responses are linked to key metabolic pathways, yet the proximal signaling events that connect these systems remain poorly understood. Here we show that phosphofructokinase 1, liver type (PFKL), a rate-limiting enzyme of glycolysis, is phosphorylated at Ser775 in macrophages following several innate stimuli. This phosphorylation increases the catalytic activity of PFKL, as shown by biochemical assays and glycolysis monitoring in cells expressing phosphorylation-defective PFKL variants. Using a genetic mouse model in which PFKL Ser775 phosphorylation cannot take place, we observe that upon activation, glycolysis in macrophages is lower than in the same cell population of wild-type animals. Consistent with their higher glycolytic activity, wild-type cells have higher levels of HIF1α and IL-1β than Pfkl S775A/S775A after LPS treatment. In an in vivo inflammation model, Pfkl S775A/S775A mice show reduced levels of MCP-1 and IL-1β. Our study thus identifies a molecular link between innate immune activation and early induction of glycolysis.

Phillip R Kramer,1 Rebecca S Hornung,1 Mikhail Umorin,1 M Douglas Benson,1 Paul R Kinchington2 1Department of Biomedical Sciences, Texas A&M University School of Dentistry, Dallas, TX, USA; 2Department of Ophthalmology and of Molecular Microbiology and Genetics, University of Pittsburgh, Pittsburgh, PA, USACorrespondence: Phillip R Kramer, Email pkramer@tamu.eduIntroduction: Herpes Zoster in humans is the result of varicella zoster virus (VZV) infection. Injecting rats with varicella zoster virus produces pain similar to herpes zoster “shingles” pain in humans. . In a previous study, orofacial pain was induced by injecting the whisker pad of male rats with VZV and the pain response increased after attenuating neurexin 3 (Nrxn3) expression in the central amygdala. Neurons descend from the central amygdala to the lateral parabrachial nucleus and orofacial pain signals ascend to the lateral parabrachial nucleus. GABAergic neurons within the central amygdala regulate pain by inhibiting activity within the lateral parabrachial nucleus. Attenuating Nrxn3 expression in the central amygdala increased GABA release in the lateral parabrachial nucleus suggesting Nrxn3 controls pain by regulating GABA release. Nrxn3 can also control synaptic connections between neurons, and we hypothesized that Nrxn3 knockdown in the central amygdala would reduce the number of GABAergic synaptic connections in the lateral parabrachial nucleus and increase VZV associated pain.Methods: To test this idea, the number of synaptic connections between GABAergic cells of the central amygdala and excitatory or dynorphin positive neurons within the lateral parabrachial nucleus were quantitated after infusion of a virus expressing synaptophysin. Synaptophysin is a synaptic vesicle protein that labels neuronal synaptic connections. These connections were measured in rats with and without whisker pad injection of VZV and knockdown of Nrxn3 within the central amygdala. Orofacial pain was measured using a place escape avoidance paradigm.Results: GABAergic synaptic connections were reduced in the lateral parabrachial nucleus after Nrxn3 knockdown. Rats with a reduction in the number of connections had an increase in VZV associated orofacial pain. Immunostaining with the pain marker prodynorphin indicated that the reduction in GABAergic connections was primarily associated with prodynorphin positive neurons.Discussion: The results suggest Nrxn3 reduces VZV associated orofacial pain, in part, by enhancing synaptic connections between GABA cells of the central amygdala and pain neurons within the lateral parabrachial nucleus.Keywords: orofacial pain, zoster, shingles, varicella zoster, post-herpetic neuralgia, synaptic plasticity

Background: Proximal humerus fractures are a common injury, predominantly affecting older adults. This study aimed to develop risk-prediction models for prolonged length of hospital stay (LOS), serious adverse complications, and readmission within 30 days of surgically treated proximal humerus fractures using machine learning (ML) techniques. Methods: Adult patients (age >18) who underwent open reduction internal fixation (ORIF), hemiarthroplasty, or total shoulder arthroplasty for proximal humerus fracture between 2016 and 2021 were included. Preoperative demographic and clinical variables were collected for all patients and used to establish ML-based algorithms. The model with optimal performance was selected according to area under the curve (AUC) on the receiver operating curve (ROC) curve and overall accuracy, and the specific predictive features most important to model derivation were identified. Results: A total of 7473 patients were included (72.1% male, mean age 66.2 ± 13.7 years). Models produced via gradient boosting performed best for predicting prolonged LOS and complications. The model predicting prolonged LOS demonstrated good discrimination and performance, as indicated by (Mean: 0.700, SE: 0.017), recall (Mean: 0.551, SE: 0.017), accuracy (Mean: 0.717, SE: 0.010), F1-score (Mean: 0.616, SE: 0.014), AUC (Mean: 0.779, SE: 0.010), and Brier score (Mean: 0.283, SE: 0.010) Preoperative hematocrit, preoperative platelet count, and patient age were considered the strongest predictive features. The model predicting serious adverse complications exhibited comparable discrimination [precision (Mean: 0.226, SE: 0.024), recall (Mean: 0.697, SE: 0.048), accuracy (Mean: 0.811, SE: 0.010), F1-score (Mean: 0.341, SE: 0.031)] and superior performance relative to the LOS model [AUC (Mean: 0.806, SE: 0.024), Brier score (Mean: 0.189, SE: 0.010), noting preoperative hematocrit, operative time, and patient age to be most influential. However, the 30-day readmission model achieved the weakest relative performance, displaying low measures of precision (Mean: 0.070, SE: 0.012) and recall (Mean: 0.389, SE: 0.053), despite good accuracy (Mean: 0.791, SE: 0.009). Conclusion: Predictive models constructed using ML techniques demonstrated favorable discrimination and satisfactory-to-excellent performance in forecasting prolonged LOS and serious adverse complications occurring within 30 days of surgical intervention for proximal humerus fracture. Modifiable preoperative factors such as hematocrit and platelet count were identified as significant predictive features, suggesting that clinicians could address these factors during preoperative patient optimization to enhance outcomes. Overall, these findings highlight the potential for ML techniques to enhance preoperative management, facilitate shared decision-making, and enable more effective and personalized orthopedic care by exploring alternative approaches to risk stratification.

Social and cultural capital are critical components of success among college students. In this paper, we examine the creation, accumulation, and distribution of social and cultural capital by and among underserved college students who participated in a summer bridge program at one regional public state university. Using qualitative data from interviews with participants, the analysis highlights the importance of trust, sense of belonging and use of noncognitive strategies. We argue that in addition to traditional achievement measures such as GPA and test scores, social and cultural capital need to be considered. While quantitative measures often label these students as below benchmarks, this study highlights how social and cultural capital are assets to be nurtured and facilitated. [Note: The publication year (2021) shown on the PDF is incorrect. The correct publication year for this article is 2022.]

The knowledge of Pliensbachian (Early Jurassic, ∼192.9–184.2 Ma) plesiosaurs is notoriously insufficient. Although there have been specimens described from different parts of the world, only three of them have been established as diagnosable taxa. Here, we describe two previously unreported lower Pliensbachian plesiosaur occurrences that originate from two sites located in North Rhine-Westphalia, Germany. One of the new occurrences is represented by three cervical and three indeterminable vertebrae from Werther, the other includes two associated pectoral or anterior dorsal vertebrae from Bielefeld. Although highly incomplete, the Werther individual, which derived from the Uptonia jamesoni Zone, is found to represent the only reliably identified early Pliensbachian pliosaurid known to date. Its material is geographically and stratigraphically proximate to the late Pliensbachian pliosaurid Arminisaurus schuberti, found in a clay-pit located in the Bielefeld district of Jöllenbeck. However, even though the Werther plesiosaur and A. schuberti show a broadly similar morphology of the preserved cervical section, a precise identification of the Werther taxon is currently impossible.

The telomerase RNA component (Terc) constitutes a non-coding RNA critical for telomerase function, commonly associated with aging and pivotal in immunomodulation during inflammation. Our study unveils heightened susceptibility to pneumonia caused by Staphylococcus aureus (S. aureus) in Terc knockout (Tercko/ko) mice compared to both young and old infected counterparts. The exacerbated infection in Tercko/ko mice correlates with heightened inflammation, manifested by elevated interleukin-1β (IL-1β) levels and activation of the NLR family pyrin domain containing 3 (NLRP3) inflammasome within the lung. Employing mRNA sequencing methods alongside in vitro analysis of alveolar macrophages (AMs) and T cells, our study elucidates a compelling correlation between Tercko/ko, inflammation, and impaired T cell functionality. Terc deletion results in compromised T cell function, characterized by dysregulation of the T cell receptor and absence of CD247, potentially compromising the host’s capacity to mount an effective immune response against S. aureus. This investigation provides insights into the intricate mechanisms governing increased vulnerability to severe pneumonia in the context of Terc deficiency, which might also contribute to aging-related pathologies, while also highlighting the influence of Terc on T cell function.

The neutron-rich rare isotope 190W is discussed as a candidate for a prolate-oblate transitional nucleus with maximum γ-softness. The collectivity of this isotope is assessed for the first time by the measurement of the reduced E2 transition probability of its first 2+ state to the ground state. The experiment employed the FAst TIming Array (FATIMA), comprised of 36 LaBr3(Ce) scintillators, which was part of the DESPEC setup at GSI, Darmstadt. The 41+ and 21+ states of 190W were populated subsequently to the decay of its 127(12) μs isomeric Jπ=10− state. The mean lifetime of the 21+ state was determined to be τ=274(28) ps, which corresponds to a B(E2;21+→01+) value of 95(10) W.u. The results motivated a revision of previous calculations within an energy-density functional-based interacting boson model-2 approach, yielding E2 transition properties and spectroscopic quadrupole moments for tungsten isotopes. From comparison to theory, the new data suggest that 190W is at the transition from prolate to oblate structure along the W isotopic chain, which had previously been discussed as a nuclear shape-phase transition.

Abstract Generative machine learning is poised to revolutionise a range of domains where rational design has long been the de facto approach: where design is practically a time consuming and frustrating process guided by heuristics and intuition. In this article we focus on the domain of flow chemistry, which is an ideal candidate for generative design approaches. We demonstrate a generative machine learning framework that optimises diverse, bespoke reactor elements for flow chemistry applications, combining evolutionary algorithms and a scalable fluid dynamics solver for in silico performance assessment. Experimental verification confirms the discovery of never-before-seen bespoke mixers whose performance exceeds the state of the art by 45%. These findings highlight the power of autonomous generative design to improve the operational performance of complex functional structures, with potential wide-ranging industrial applications.

Abstract Understanding the solid target dynamics resulting from the interaction with an ultrashort laser pulse is a challenging fundamental multi-physics problem involving atomic and solid-state physics, plasma physics, and laser physics. Knowledge of the initial interplay of the underlying processes is essential to many applications ranging from low-power laser regimes like laser-induced ablation to high-power laser regimes like laser-driven ion acceleration. Accessing the properties of the so-called pre-plasma formed as the laser pulse’s rising edge ionizes the target is complicated from the theoretical and experimental point of view, and many aspects of this laser-induced transition from solid to overdense plasma over picosecond timescales are still open questions. On the one hand, laser-driven ion acceleration requires precise control of the pre-plasma because the efficiency of the acceleration process crucially depends on the target properties at the arrival of the relativistic intensity peak of the pulse. On the other hand, efficient laser ablation requires, for example, preventing the so-called “plasma shielding”. By capturing the dynamics of the initial stage of the interaction, we report on a detailed visualization of the pre-plasma formation and evolution. Nanometer-thin diamond-like carbon foils are shown to transition from solid to plasma during the laser rising edge with intensities < 1016 W/cm². Single-shot near-infrared probe transmission measurements evidence sub-picosecond dynamics of an expanding plasma with densities above 1023 cm−3 (about 100 times the critical plasma density). The complementarity of a solid-state interaction model and kinetic plasma description provides deep insight into the interplay of initial ionization, collisions, and expansion.

Abstract Background The transition of patients between care contexts poses patient safety risks. Discharges to home from inpatient care can be associated with adverse patient outcomes. Quality in discharge processes is essential in ensuring safe transitions for patients. Current evidence relies on bivariate analyses and neglects contextual factors such as treatment and patient characteristics and the interactions of potential outcomes. This study aimed to investigate the associations between the quality and safety of the discharge process, patient safety incidents, and health-related outcomes after discharge, considering the treatments’ and patients’ contextual factors in one comprehensive model. Methods Patients at least 18 years old and discharged home after at least three days of inpatient treatment received a self-report questionnaire. A total of N = 825 patients participated. The assessment contained items to assess the quality and safety of the discharge process from the patient’s perspective with the care transitions measure (CTM), a self-report on the incidence of unplanned readmissions and medication complications, health status, and sociodemographic and treatment-related characteristics. Statistical analyses included structural equation modeling (SEM) and additional analyses using logistic regressions. Results Higher quality of care transition was related to a lower incidence of medication complications (B = -0.35, p

Abstract Inflammasome assembly is a potent mechanism responsible for the host protection against pathogens, including viruses. When compromised, it can allow viral replication, while when disrupted, it can perpetuate pathological responses by IL-1 signaling and pyroptotic cell death. SARS-CoV-2 infection was shown to activate inflammasome in the lungs of COVID-19 patients, however, potential mechanisms responsible for this response are not fully elucidated. In this study, we investigated the effects of ORF3a, E and M SARS-CoV-2 viroporins in the inflammasome activation in major populations of alveolar sentinel cells: macrophages, epithelial and endothelial cells. We demonstrated that each viroporin is capable of activation of the inflammasome in macrophages to trigger pyroptosis-like cell death and IL-1α release from epithelial and endothelial cells. Small molecule NLRP3 inflammasome inhibitors reduced IL-1 release but weakly affected the pyroptosis. Importantly, we discovered that while SARS-CoV-2 could not infect the pulmonary microvascular endothelial cells it induced IL-1α and IL-33 release. Together, these findings highlight the essential role of macrophages as the major inflammasome-activating cell population in the lungs and point to endothelial cell expressed IL-1α as a potential novel component driving the pulmonary immunothromobosis in COVID-19.

Abstract Background Single-cell sequencing techniques are revolutionizing every field of biology by providing the ability to measure the abundance of biological molecules at a single-cell resolution. Although single-cell sequencing approaches have been developed for several molecular modalities, single-cell transcriptome sequencing is the most prevalent and widely applied technique. SPLiT-seq (split-pool ligation-based transcriptome sequencing) is one of these single-cell transcriptome techniques that applies a unique combinatorial-barcoding approach by splitting and pooling cells into multi-well plates containing barcodes. This unique approach required the development of dedicated computational tools to preprocess the data and extract the count matrices. Here we compare eight bioinformatic pipelines (alevin-fry splitp, LR-splitpipe, SCSit, splitpipe, splitpipeline, SPLiTseq-demultiplex, STARsolo and zUMI) that have been developed to process SPLiT-seq data. We provide an overview of the tools, their computational performance, functionality and impact on downstream processing of the single-cell data, which vary greatly depending on the tool used. Results We show that STARsolo, splitpipe and alevin-fry splitp can all handle large amount of data within reasonable time. In contrast, the other five pipelines are slow when handling large datasets. When using smaller dataset, cell barcode results are similar with the exception of SPLiTseq-demultiplex and splitpipeline. LR-splitpipe that is originally designed for processing long-read sequencing data is the slowest of all pipelines. Alevin-fry produced different down-stream results that are difficult to interpret. STARsolo functions nearly identical to splitpipe and produce results that are highly similar to each other. However, STARsolo lacks the function to collapse random hexamer reads for which some additional coding is required. Conclusion Our comprehensive comparative analysis aids users in selecting the most suitable analysis tool for efficient SPLiT-seq data processing, while also detailing the specific prerequisites for each of these pipelines. From the available pipelines, we recommend splitpipe or STARSolo for SPLiT-seq data analysis.

Summary: Vagal innervation is well known to be crucial to the maintenance of cardiac health, and to protect and recover the heart from injury. Only recently has this role been shown to depend on the activity of the underappreciated dorsal motor nucleus of the vagus (DMV). By combining neural tracing, transcriptomics, and anatomical mapping in male and female Sprague-Dawley rats, we characterize cardiac-specific neuronal phenotypes in the DMV. We find that the DMV cardiac-projecting neurons differentially express pituitary adenylate cyclase-activating polypeptide (PACAP), cocaine- and amphetamine-regulated transcript (CART), and synucleins, as well as evidence that they participate in neuromodulatory co-expression involving catecholamines. The significance of these findings is enhanced by previous knowledge of the role of PACAP at the heart and of the other neuromodulators in peripheral vagal targets.

Reduced transition probabilities have been extracted between excited, yrast states in the N=Z+2 nucleus 94Pd. The transitions of interest were observed following decays of the Iπ=14+, Ex=2129-keV isomeric state, which was populated following the projectile fragmentation of a 124Xe primary beam at the GSI Helmholtzzentrum für Schwerionenforschung accelerator facility as part of FAIR Phase-0. Experimental information regarding the reduced E2 transition strengths for the decays of the yrast 8+ and 6+ states was determined following isomer-delayed Eγ1−Eγ2−△T2,1 coincidence method, using the LaBr3(Ce)-based FATIMA fast-timing coincidence gamma-ray array, which allowed direct determination of lifetimes of states in 94Pd using the Generalized Centroid Difference (GCD) method. The experimental value for the half-life of the yrast 8+ state of 755(106) ps results in a reduced transition probability of B(E2:8→+6+) = 205−25+34 e2fm4, which enables a precise verification of shell-model calculations for this unique system, lying directly between the N=Z line and the N=50 neutron shell closure. The determined B(E2) value provides an insight into the purity of (g9/2)n configurations in competition with admixtures from excitations between the (lower) N=3pf and (higher) N=4gds orbitals for the first time. The results indicate weak collectivity expected for near-zero quadrupole deformation and an increasing importance of the T=0 proton-neutron interaction at N=48.