ETH Zurich

Respiratory viruses circulate yearly with strain-specific patterns. Although SARS-CoV-2 and Influenza A/B genomic surveillance is well-developed, most respiratory viruses are unevenly monitored, lacking geographical diversity to capture wider population dynamics. Consequently, insights into respiratory virus evolution are limited. We investigated the genetic diversity of these viruses within one country. During the 2023/24 season, we conducted whole-genome sequencing of 1'129 clinical samples using a hybrid-capture protocol. These samples were pre-tested by real-time PCR panels throughout Switzerland. Leveraging publicly-available full-length genomes, we constructed background datasets representative of geographical diversity and built 16 phylogenies covering the diversity of high-quality viral genomes produced from this study. We detected viral genomes in 632 samples, including 55.6% multi-positive infections (n = 352/632), and recovered high-quality genomes in 73% (n = 461/632) of cases, including 3.9% of co-infections (n = 18/461), from 454 PCR-positive and 7 PCR-negative samples. For 56% of samples (n = 634/1129), the hybrid-capture detection result was concordant with the PCR-result (at least one strain detected by PCR was also detected by sequencing, in addition to other viruses). The four most prevalent viruses were Influenza A/H1N1, SARS-CoV-2, RSV-A, and HPIV-3, and their seasonal spread was consistent with national wastewater monitoring and influenza-like illness reports. Swiss viral genomes were representative of the global genomic diversity, with evidence for multiple introductions into Switzerland, and we identified putative Swiss clusters. In this proof-of-concept study, we focus on 3 viruses (Influenza A/H1N1, RSV-A/B, and HPIV-3), and we demonstrate the streamlined implementation of a broad respiratory virus genomic surveillance workflow with an off-the-shelf protocol and publicly-available software. In addition, we highlight additional evolutionary insights that can only be derived from genomic surveillance. Going forward, this dataset will be a useful resource for future investigations into respiratory viral genomic diversity.

Assessing genetic diversity is essential for the characterization and conservation of livestock. This study investigates the genetic diversity of the three indigenous goat breeds from northern Belgium (Flanders), Kempische Geit, Vlaamse Geit, and Belgische Hertegeit, using pedigree and genomic data. Pedigree analyses estimated inbreeding and effective population size, while genomic data were assessed using runs of homozygosity (ROH) and population structure analyses (F st , principal component analysis (PCA) and ADMIXTURE). Pedigree-based results revealed moderate inbreeding (F ped = 10.3%, 7.4%, and 9.0%) and critically low effective population sizes (N e = 17, 28, and 43, respectively). Despite these constraints, population trends since 2017 show encouraging growth, with active breeding females increasing 153% (Kempische Geit) and active breeders rising 90% (Vlaamse Geit). Genomic data from 280 individuals (88–97 per breed), genotyped using the GGP Goat SNP array, revealed inbreeding coefficients based on ROH from 8% to 15%, with individual values reaching up to 39%. Several ROH islands were detected, including two in Vlaamse Geit on chromosomes 10 and 13, which overlap with regions reported in other international breeds. Linkage disequilibrium-based estimates of effective population sizes (N e = 21–22) further highlight the endangered status of these breeds. Genetic differentiation was substantial (F st from 0.10 to 0.14) which was supported by PCA and ADMIXTURE. This study provides the first integrated pedigree and genomic assessment of goat diversity in Flanders, offering critical insights for the conservation and sustainable management of these local breeds. These data support comparisons with international populations and inform future breeding strategies. • Flanders (Northern Belgium) has three local goat breeds • All three goat breeds were analyzed based on pedigree and genotype data • Inbreeding coefficients based on runs of homozygosity were between 8% and 15% • Effective population sizes remain below critical thresholds for viability • The three goat breeds are considered endangered

Temperature Cycling Induced Deracemization (TCID) is a promising route to obtain enantiomerically pure suspensions of conglomerate-forming compounds, yet the role of primary nucleation (PN) remains poorly quantified. Here we develop a population balance equation (PBE) model of TCID for the compound NMPA that combines measured growth and dissolution, racemization and primary nucleation kinetics, as reported in the literature. With the model, we quantify how primary nucleation can perturb the evolution of the enantiomeric excess under temperature cycling programs of practical interest. For typical TCID conditions reported for NMPA, primary nucleation has no detectable impact at the process scale. In contrast, when the process is designed to increase productivity, by (i) widening the temperature span, (ii) increasing the cooling rate, (iii) lowering the suspension density or (iv) enhancing secondary nucleation, the effect of primary nucleation becomes visible, leading to run-to-run variability and a measurable loss in the final enantiomeric excess. These results can help rationalize why primary nucleation is often negligible in TCID processes, while identifying operating regimes where it must be explicitly accounted for in design and scale-up. • Mechanistic PBE model for TCID coupling growth, dissolution, racemization and primary nucleation. • Model validation against experiments for NMPA deracemization. • Assessment of effects of temperature span, cooling rate, suspension density, secondary nucleation and racemization catalyst concentration. • Trade-off between robustness to primary nucleation and process productivity.

The G-protein-coupled receptor kinase 2 (GRK2) exerts essential functions in cell growth and survival. Searching for a connection between GRK2 and the neurodegenerative Alzheimer disease (AD), we find increased aggregated serine-670-phosphorylated GRK2 (phospho-S670-GRK2) in brains of AD mice and patients with dementia likely due to AD. Harmful phospho-S670-GRK2 aggregation is induced by two hallmark proteins of AD: beta-amyloid and the neurofibrillary-tangle-inducing, TAU-P301L. Aggregated phospho-S670-GRK2 triggers aggregation of TOMM6 (translocase of outer mitochondrial membrane 6), promotes mitochondrial dysfunction, and enhances beta-amyloid. Transgenic expression of inactive GRK2-K220R or a GRK-inhibitory peptide proves that neuropathological features are caused by GRK2 inactivation. Restoration of TOMM6 by neuron-specific TOMM6 expression reduces beta-amyloid plaques but enhances soluble beta-amyloid and increases mortality. In contrast, reconstitution of monomeric GRK2 and proteasomal phospho-S670-GRK2 degradation by small molecules counteracts neuropathological AD features, prevents neuronal loss, and improves survival. Thus, targeting of pathological GRK2 aggregation slows aging-induced neurodegeneration.

4D flow MRI facilitates quantification of cardiac phase-resolved blood velocity vector fields and has successfully been deployed to study cerebrovascular flow. Besides cardiac-induced flow pulsation, respiration is known to modulate arterial and venous blood flow in the brain. Quantification of the respiratory flow modulation (RFM) holds potential to further our insights into vascular coupling and improve our understanding of cerebral circulation in general. A 5D phase-contrast flow MRI framework was developed to volumetrically quantify RFM by resolving velocity vector fields over the cardiac and respiratory cycle, with high spatial (0.82 mm isotropic) and cardiac (55 ms) resolutions, using two respiratory states whilst accounting for variable physiological RFM delays, with a reasonable acquisition time (20 min at 60 bpm). Recent advances in deep learning-based image reconstruction and analysis methods are incorporated to facilitate the approach. The 5D flow MRI framework was validated in 10 healthy volunteers with reference to fully sampled respiratory-resolved 2D flow MRI orthogonal to the internal carotid artery (ICA), yielding Pearson correlation coefficients of 0.97 and 0.90 and biases of and 0.09 % and 1.77 % for RFM of mean velocity magnitude and amplitude, respectively. The value of cerebrovascular 5D flow MRI is demonstrated using full-field spatially resolved RFM quantification of mean velocity and velocity amplitude, revealing a high physiological intra-subject variability. Cerebrovascular 5D flow MRI enables the study of full-field respiratory flow modulation holding potential of furthering our understanding of cerebral circulation.

Abstract Understanding the weather and climate of Mesoamerica and the Caribbean remains challenging due to complex hydroclimate interactions, limited observations, and poor representation of regional processes in global models. We introduce the Mesoamerica Affinity Group (MAAG), a National Science Foundation (NSF) National Center for Atmospheric Research (NCAR) and community initiative that fosters research collaboration to advance weather and climate science, develop convection-permitting datasets, and promote knowledge exchange. MAAG’s first major contribution is a 2-week convection-permitting simulation of Hurricane Maria (2017) using Model for Prediction Across Scales–Atmosphere (MPAS-A), featuring a novel regional 15- to 3-km variable-resolution mesh over the region. Initial evaluation shows that MPAS-A captures key features like precipitation patterns, the intertropical convergence zone, and low-level jets. Some biases remain, particularly in enhanced land convection and slight deviations in Maria’s track. This novel dataset, now publicly available through NCAR’s Data Archive, supports studies of other extreme events and mesoscale convective systems active during the same period. It offers a valuable resource for the research community. MAAG is a new but rapidly growing initiative achieving notable milestones in a short time. It serves as a collaborative platform for codesigning high-resolution modeling experiments aimed at producing actionable weather and climate information. We invite the community to join MAAG, explore this initial dataset, and advance regional weather and climate research. Significance Statement The Mesoamerica Affinity Group (MAAG), a National Science Foundation (NSF) National Center for Atmospheric Research (NCAR) and community initiative, aims at addressing the complex challenges of understanding weather and climate in Mesoamerica and the Caribbean. MAAG’s first major achievement is a 2-week convection-permitting simulation of Hurricane Maria (2017) using a novel 15- to 3-km variable-resolution mesh. This dataset accurately captures key regional features and is publicly available through NCAR’s Data Archive. By fostering collaboration through data production and sharing, monthly group meetings that serve as a platform for networking and knowledge exchange, and the development of advanced high-resolution datasets, MAAG provides a vital resource for advancing regional weather and climate science. The initiative is rapidly growing, serving as a platform for codesigned modeling experiments aimed at producing actionable climate information for academia and different sectors. We invite the scientific community to join MAAG and advance research in this critical region.

Building-integrated photovoltaics (BIPV) on facades are a significant but underutilized source of solar energy in urban environments. Automating the recognition of BIPV on facades through vision based systems can help guide design recommendations and extend solar asset maps. Unlike rooftop photovoltaic (PV) systems, facade BIPV recognition is difficult due to limited visibility in overhead imagery, high visual variability, and the absence of structured datasets. This study proposes a method based on deep learning (DL) for automated segmentation of BIPV panels on building facades using street-level and web images. A new benchmark data set comprising 400 annotated BIPV projects was created, including detailed pixel-level masks and project attributes. Two model architectures, Mask Region-based Convolutional Neural Network (Mask R-CNN) and SegFormer, as well as human baselines are evaluated. The SegFormer model outperforms Mask R-CNN in pixel-level metrics. A user study conducted with human annotators without domain-specific expertise provides comparative insight into human performance, revealing common challenges in recognizing facade BIPV. The results demonstrate that DL models, trained specifically for this task, can segment BIPV panels more accurately than mean of human annotators, with SegFormer achieving an IoU of 0.69 compared to 0.42. The user study suggests that BIPV with satin finishes, invisible cells, and a PV-to-facade ratio of more than half challenge human recognition and therefore can be prioritized in visually sensitive areas. The outputs of the segmentation model is also utilized to estimate the BIPV energy yield. The annotated dataset and models are made available to facilitate future research. • Compare deep learning models with human baselines for facade PV segmentation. • SegFormer achieves 0.69 IoU, outperforming Mask R-CNN and average human annotators. • Recent photovoltaic materials blend into facades, challenging human and model perception. • Release metadata for 400 facade PV projects with links to 665 images and PV masks. • Estimate potential energy yield of BIPV facades using deep learning and images.