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With the emergence of SARS-CoV-2, various non-pharmaceutical interventions were adopted to control virus transmission, including school closures. Subsequently, the introduction of vaccines mitigated not only disease severity but also the spread of SARS-CoV-2. This study leveraged an adapted SIR model and non-linear mixed-effects modeling to quantify the impact of remote learning, school holidays, the emergence of Variants of Concern (VOCs), and the role of vaccinations in controlling SARS-CoV-2 spread across 16 German federal states with an age-stratified approach. Findings highlight a significant inverse correlation (Spearman's ρ = −0.92, p

Summary: Pathogenic variants in KIAA0586/TALPID3 are associated with the ciliopathy Joubert syndrome (JS). We report individuals with KIAA0586/TALPID3 variants affected by primary and motile cilia defects leading to JS and chronic destructive airway disease. DNA variants were detected in three families by sequencing. In two unrelated families, a deep-intronic variant (KIAA0586/TALPID3:c.3990 + 3186G>A) activated a cryptic exon. We performed histological and functional analyses in native and air-liquid interface (ALI) cultured respiratory cells. Primary cilia lengths were measured in patient-derived fibroblasts. Our data associate KIAA0586/TALPID3 variants with a syndrome combining JS and chronic destructive airway disease, reduced number of motile cilia, disorganized basal body location, and ciliary clearance malfunction. Additionally, patient-derived cell lines showed primary cilia defects. Disease causing KIAA0586/TALPID3 variants, including a deep-intronic sequence variant, were associated with primary and motile cilia defects in JS patients. The combination of JS and respiratory symptoms should be considered indicative for KIAA0586/TALPID3 sequence alterations.

In the context of in silico clinical trials, mechanistic computer models for pathophysiology and pharmacology (here Quantitative Systems Pharmacology models, QSP) can greatly support the decision making for drug candidates and elucidate the (potential) response of patients to existing and novel treatments. These models are built on disease mechanisms and then parametrized using (clinical study) data. Clinical variability among patients is represented by alternative model parameterizations, called virtual patients. Despite the complexity of disease modeling itself, using individual patient data to build these virtual patients is particularly challenging given the high-dimensional, potentially sparse and noisy clinical trial data. In this work, we investigate the applicability of simulation-based inference (SBI), an advanced probabilistic machine learning approach, for virtual patient generation from individual patient data and we develop and evaluate the concept of nearest patient fits (SBI NPF), which further enhances the fitting performance. At the example of rheumatoid arthritis where prediction of treatment response is notoriously difficult, our experiments demonstrate that the SBI approaches can capture large inter-patient variability in clinical data and can compete with standard fitting methods in the field. Moreover, since SBI learns a probability distribution over the virtual patient parametrization, it naturally provides the probability for alternative parametrizations. The learned distributions allow us to generate highly probable alternative virtual patient populations for rheumatoid arthritis, which could potentially enhance the assessment of drug candidates if used for in silico trials.

IntroductionInfluenza virus infections are a major global health problem. Influenza can result in mild/moderate disease or progress to more severe disease, leading to high morbidity and mortality. Severity is thought to be primarily driven by immunopathology, but predicting which individuals are at a higher risk of being hospitalized warrants investigation into host genetics and the molecular signatures of the host response during influenza infections.MethodsHere, we performed transcriptome and genotype analysis in healthy controls and patients exhibiting mild/moderate or severe influenza (ICU patients). A unique aspect of our study was the genotyping of all participants, which allowed us to assign ethnicities based on genetic variation and assess whether the variation was correlated with expression levels. ResultsWe identified 169 differentially expressed genes and related molecular pathways between patients in the ICU and those who were not in the ICU. The transcriptome/genotype association analysis identified 871 genes associated to a genetic variant and 39 genes distinct between African-Americans and Caucasians. We also investigated the effects of age and sex and found only a few discernible gene effects in our cohort. DiscussionTogether, our results highlight select risk factors that may contribute to an increased risk of ICU admission for influenza-infected patients. This should help to develop better diagnostic tools based on molecular signatures, in addition to a better understanding of the biological processes in the host response to influenza.

Oxygen isotopes in biogenic silica (δ18OBSi) from lake sediments allow for quantitative reconstruction of past hydroclimate and proxy-model comparison in terrestrial environments. The signals of individual records have been attributed to different factors, such as air temperature (Tair), atmospheric circulation patterns, hydrological changes, and lake evaporation. While every lake has its own local set of drivers of δ18O variability, here we explore the extent to which regional or even global signals emerge from a series of paleoenvironmental records. This study provides a comprehensive compilation and combined statistical evaluation of the existing lake sediment δ18OBSi records, largely missing in other summary publications (i.e. PAGES network). For this purpose, we have identified and compiled 71 down-core records published to date and complemented these datasets with additional lake basin parameters (e.g. lake water residence time and catchment size) to best characterize the signal properties. Records feature widely different temporal coverage and resolution, ranging from decadal-scale records covering the past 150 years to records with multi-millennial-scale resolution spanning glacial–interglacial cycles. The best coverage in number of records (N = 37) and data points (N = 2112) is available for Northern Hemispheric (NH) extratropical regions throughout the Holocene (roughly corresponding to Marine Isotope Stage 1; MIS 1). To address the different variabilities and temporal offsets, records were brought to a common temporal resolution by binning and subsequently filtered for hydrologically open lakes with lake water residence times < 100 years. For mid- to high-latitude (> 45° N) lakes, we find common δ18OBSi patterns among the lake records during both the Holocene and Common Era (CE). These include maxima and minima corresponding to known climate episodes, such as the Holocene Thermal Maximum (HTM), Neoglacial Cooling, Medieval Climate Anomaly (MCA) and the Little Ice Age (LIA). These patterns are in line with long-term air temperature changes supported by previously published climate reconstructions from other archives, as well as Holocene summer insolation changes. In conclusion, oxygen isotope records from NH extratropical lake sediments feature a common climate signal at centennial (for CE) and millennial (for Holocene) timescales despite stemming from different lakes in different geographic locations and hence constitute a valuable proxy for past climate reconstructions.

Abstract Britain's power system has shifted towards a major contribution from wind energy. However, wind is highly variable, and exceptionally low wind events can simultaneously occur with cold conditions, which increase demand. These conditions can pose a threat for the security of energy supply. Here we use bias‐corrected wind supply data and the estimated temperature‐related part of demand to analyse events of potential weather‐related energy shortfall based on the historic meteorological record. We conduct sensitivity studies with varying scenarios of Britain's total wind energy capacity and the temperature sensitivity of national demand. These scenarios are estimates for present‐day conditions as well as potential future changes of the power system. We apply a new methodology to estimate the potential severity of an event for the power system, and analyse the atmospheric conditions associated with the most severe events. We find that events of potentially severe shortfall are relatively rare and short‐lived, and often occur with an atmospheric pattern broadly resembling a negative North Atlantic Oscillation. This broad tendency emerges from a wide range of individual daily weather patterns that cause cold and still conditions. With an increase in wind capacity, it is likely that severe events will become rarer, although the most severe days of the record are relatively insensitive to changes in wind supply and temperature sensitivity of demand under our assumptions.

Conventional and electron microscopy visualize structures in the micrometer to nanometer range, and such visualizations contribute decisively to our understanding of biological processes. Due to different factors in recording processes, microscopy images are subject to noise. Especially at their respective resolution limits, a high degree of noise can negatively effect both image interpretation by experts and further automated processing. However, the deteriorating effects of strong noise can be alleviated to a large extend by image enhancement algorithms. Because of the inherent high noise, a requirement for such algorithms is their applicability directly to noisy images or, in the extreme case, to just a single noisy image without a priori noise level information (referred to as blind zero-shot setting). This work investigates blind zero-shot algorithms for microscopy image denoising. The strategies for denoising applied by the investigated approaches include: filtering methods, recent feed-forward neural networks which were amended to be trainable on noisy images, and recent probabilistic generative models. As datasets we consider transmission electron microscopy images including images of SARS-CoV-2 viruses and fluorescence microscopy images. A natural goal of denoising algorithms is to simultaneously reduce noise while preserving the original image features, e.g., the sharpness of structures. However, in practice, a tradeoff between both aspects often has to be found. Our performance evaluations, therefore, focus not only on noise removal but set noise removal in relation to a metric which is instructive about sharpness. For all considered approaches, we numerically investigate their performance, report their denoising/sharpness tradeoff on different images, and discuss future developments. We observe that, depending on the data, the different algorithms can provide significant advantages or disadvantages in terms of their noise removal vs. sharpness preservation capabilities, which may be very relevant for different virological applications, e.g., virological analysis or image segmentation.

Summary: To investigate underlying mechanisms for cancer metastasis and promising therapies in animal models, spontaneous metastasis models can be used to recreate metastasis development. Here, we present three mouse models of spontaneous lung and/or liver metastasis induction. We describe steps for cancer cell preparation, mouse analgesia, and three injection techniques (subcutaneous, intracecal, and intramucosal). We then detail procedures for evaluating metastasis. Most of these models generate metastasis in a time span of 4 weeks in the majority of injected mice.For complete details on the use and execution of this protocol, please refer to Giannou et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Summary: Forced metastasis models, those in which the step of intravasation is bypassed, can be used to investigate the mechanisms underlying metastasis and evaluate potential therapeutic targets. Here, we present a protocol for using three forced models of lung and liver metastasis to generate metastasis within 3–4 weeks in approximately 99% of injected mice. We describe steps for cancer cell preparation, mouse analgesia and anesthesia; injecting through intrasplenic, intraportal, and intravenous techniques; and daily evaluation of metastasis.For complete details on the use and execution of this protocol, please refer to Giannou et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Background: Insufficient remnant liver volume (RLV) after the resection of hepatic malignancy could lead to liver failure and mortality. Portal vein ligation (PVL) prior to hepatectomy is subsequently introduced to increase the remnant liver volume and improve the outcome of hepatic malignancy. IL-22 has previously been reported to promote liver regeneration, while facilitating tumor development in the liver via Steap4 upregulation. Here we performed PVL in mouse models to study the role of IL-22 in liver regeneration post-PVL. Methods: Liver weight and volume was measured via magnetic resonance imaging (MRI). Immunohistochemistry for Ki67 and hepatocyte growth factor (HGF) was performed. IL-22 was analyzed by flow cytometry and quantitative polymerase chain reaction (qPCR) was used for acquisition of Il-33, Steap4, Fga, Fgb and Cebpd. To analyze signaling pathways, mice with deletion of STAT3 and a neutralizing antibody for IL-22 were used. Results: The remnant liver weight and volume increased over time after PVL. Additionally, we found that liver regenerative molecules, including Ki67 and HGF, were significantly increased in remnant liver at day 3 post-PVL, as well as IL-22. Administration of IL-22 neutralizing antibody could reduce Ki67 expression after PVL. The upregulation of IL-22 after PVL was mainly derived from innate cells. IL-22 blockade resulted in lower levels of IL-33 and Steap4 in the remnant liver, which was also the case in mice with deletion of STAT3, the main downstream signaling molecule of IL-22, in hepatocytes. Conclusion: IL-22 promotes liver regeneration after PVL. Thus, a combination of IL-22 supplementation and Steap4 blockade could potentially be applied as a novel therapeutic approach to boost liver regeneration without facilitating tumor progression after PVL.

The objective of this study was to investigate the association between specified levels of physical education (PE) at school and different parameters of cognition in boys and girls, considering potential covariates. Baseline data from a crossover nutrition intervention study were used for a secondary analysis of 247 schoolchildren (37% girls). Participants were children from the fifth and sixth grade with an "advanced" (5-6 hr) or "regular" (3 hr) level of PE a week, without random allocation. Using three computerized tasks, cognition data were collected under usual school conditions. An advanced level of PE at school was associated with better overall cognitive performance, specifically in response speed and accuracy when controlling for lunch conditions. Girls attending advanced-PE classes showed the best performance. The potential covariates explained little about cognitive variability. Given the cognitive differences between boys and girls, future research is warranted to replicate these findings by measuring motor skills.

Here we investigate how uncertainties in the solar and volcanic forcing records of the past millennium affect the large-scale temperature response using a two-box impulse response model. We use different published solar forcing records and present a new volcanic forcing ensemble that accounts for random uncertainties in eruption dating and sulfur injection amount. The simulations are compared to proxy reconstructions from PAGES 2k and Northern Hemispheric tree ring data. We find that low solar forcing is most consistent with all the proxy reconstructions, even when accounting for volcanic uncertainty. We also find that the residuals are in line with CMIP6 control variability at centennial timescales. Volcanic forcing uncertainty induces a significant spread in the temperature response, especially at periods of peak forcing. For individual eruptions and superposed epoch analyses, volcanic uncertainty can strongly affect the agreement with proxy reconstructions and partly explain known proxy–model discrepancies.