We report on the spectroscopic confirmation of overdense regions of massive quiescent galaxies (QGs) in the early Universe with JWST/NIRSpec. Based on data from the DeepDive NIRSpec program and archival data from the Dawn JWST Archive, we confirm three QGs in the vicinity of Jekyll and Hyde, a pair of massive QG and a dusty star-forming galaxy, at z = 3.71 and two QGs around SXDS-27434 at z = 4.01. According to the analysis of galaxy number density with photometric redshifts, Jekyll and Hyde (SXDS-27434) are in an overdense region, where the number density of galaxies is three (four) times higher than the average in the COSMOS (SXDS) field. SED fitting suggests that most of the QGs follow similar star formation histories and have consistent formation and quenching epochs. The same trend is observed in other protoclusters hosting QGs that were already identified by ground-based telescopes, indicating that the large-scale environment plays an important role in the formation of QGs. In addition, JWST spectra reveal a broad Hα emission line from SXDS-27434 and faint emission lines from the other three QGs, which are identified as AGN-driven based on their emission line ratios. The overdensity is also reproduced by the Illustris TNG300 simulation at z = 3.71, in which the member QGs also have similar quenching epochs. These results are consistent with a scenario in which the large-scale structure enhances merger activity and/or gas accretion and triggers AGN feedback, thereby driving simultaneous formation and quenching of QGs in the overdensity.

Standard biomolecular force fields often present limitations in modeling metal coordination modes. We combined classical and QM/MM molecular dynamics to investigate Ca2+-mediated binding of cRGD to integrin αVβ3. We show that different metal coordination modes imply variations in the partial charges of the metal-coordinating atoms, which, if incorporated in the force field, substantially alter the free energy landscape. These results highlight the value of QM-based multilevel approaches to accurately capture metal coordination modes in biomolecular systems.

Lipid metabolism rewiring is an essential determinant of cancer progression, yet the key metabolic checkpoints which govern this process remain poorly defined. Using functional genomics in a mouse model of acute myeloid leukemia (AML), we identify the sphingosine-1-phosphate transporter SPNS2 as a critical sphingolipid checkpoint protein which modulates leukemia progression. SPNS2 expression is regulated by MYC and progressively declines during AML differentiation, highlighting its tumor-suppressive role in a subset of Core Binding Factor AMLs. By integrating steady-state and serine isotope-tracing lipidomics, RNA sequencing, flow cytometry, and high-resolution microscopy, with gain- and loss-of-function approaches, we show that SPNS2 modulation rewires sphingolipid metabolism by modifying sphingomyelin abundance. This remodeling alters membrane nanodomain organization, active RAS localization, and MAPK signaling. Exogenous reintroduction of SPNS2 decreases sphingomyelin levels, disrupts nanodomain organization, and limits leukemic progression in various AML mouse models. We further reveal in a cohort of patients with AML that low SPNS2 expression is associated with increased sensitivity to MAPK pathway inhibition, and demonstrate functionally in vivo that SPNS2 silencing sensitizes AML cells to MEK1/2 inhibition. Collectively, our data reveal a mechanism by which sphingolipid transport dynamics intersect with oncogenic signaling to control cancer progression.

Fish are widely used in research and aquaculture, yet many aspects of their welfare remain poorly understood. Recent years have seen a shift from health and disease prevention as cornerstones of wellbeing toward concepts of welfare that include behavior, affective states, cumulative lifetime experiences, and positive welfare. Current challenges relate to the taxonomic diversity of fishes, the dominance of zebrafish as model species, and the translation of welfare concepts across experimental, applied, and industrial settings. Knowledge gaps remain regarding welfare assessment, environmental enrichment, euthanasia, pain management, and the implementation of evidence-based refinement measures. Here we summarize the outcomes of the Swiss Fish Welfare Symposium 2026, which brought together researchers, caretakers, veterinarians, regulators, and industry. Participants identified drivers of change such as a Culture of Care, improved knowledge exchange, and incentive structures to reward refinement. Presentations emphasized the need for welfare research under realistic husbandry conditions, the development of cumulative and species-specific welfare assessment tools, and the generation of evidence for housing, enrichment, analgesia, euthanasia, and rehoming practices. Speakers stressed that recommendations developed under idealized conditions often fail to reflect the practical realities of research facilities and aquaculture systems. The symposium revealed a research field in transition from compliance-based approaches toward a vision of welfare that encompasses both animal and human well-being. Participants identified collaboration, systematic use of existing welfare data, and research beyond zebrafish as critical priorities. More broadly, discussions highlighted the need to bridge gaps between fundamental research, regulation, husbandry practice, and industry to achieve evidence-based and implementable improvements in fish welfare.

The production cross sections of W ± W ± and WZ boson pairs in association with two jets in proton-proton collisions are measured at $ \sqrt{s}=13.6 $ TeV. The data sample corresponds to an integrated luminosity of 171 fb − 1 , collected with the CMS detector during 2022–2024. The measurements are performed in the leptonic decay modes: W ± Z → ℓ ± νℓ′ ± ℓ′ ∓ and W ± W ± → ℓ ± νℓ′ ± ν , where ℓ , ℓ′ = e or μ . The electroweak productions of W ± W ± and WZ bosons are each observed with a significance greater than five standard deviations from the background-only hypothesis. Differential fiducial cross sections as functions of several observables are also measured.

In this thesis, we present the advances in self-organised nano-pillar lattice fabrication, including improvements in graphene (Gr) transfer onto targets in ultra-high vacuum (UHV), demonstrated increase of Gr capped self-organised cluster thermal stability, and creation of a high-density nano-pillar heterostructure achieved by second layer self-organised cluster growth on a transferred 2D material patterned by the clusters below it. We first present an ion intercalation-based graphene delamination method for the creation of a PTFE-supported graphene bilayer from which one monolayer is transferred onto the target sample using wafer bonding in UHV. With this method we demonstrate mm-scale graphene coverage onto Ir(111) and Irclust/Gr/Ir(111) targets in UHV. The novel method was tested alongside the acid etching-based bilayer preparation method and compared using Raman spectroscopy. The ion intercalation delamination method was found to result in higher quality and homogeneity bilayer graphene stamps, compatibility with graphene growth on reusable Pt substrates with larger grains further increasing graphene quality, as well as lower doping due to avoiding exposure to etchants. After transfer on Ir(111), scanning tunnelling microscopy (STM) imaging revealed significantly larger domain sizes of the monolayer Gr transferred. Additionally, Auger electron spectroscopy (AES) measurements were used to quantify graphene transfer coverage on a large scale, showing around 50% coverage over mm scale for both Ir(111) and Irclust/Gr/Ir(111) targets. Then we investigate the effects on thermal stability of Gr-capped Irclust/Gr/Ir(111) in UHV. STM imaging of samples prepared at varied temperatures revealed increased resistance against Ostwald ripening and peripheral diffusion, with filling factor values 20% - 30% higher in Gr-capped areas areas for a temperature range from 750 K to 1250 K, demonstrating the usefulness of the system for magnetic nano-pillars with high blocking temperature. Finally, the fabrication of a high-density self-organised nano-pillar heterostructure was demonstrated by subsequent cluster growth, Gr transfer, and growth of a second layer of Ir on Gr/Ir(111). The resulting Irclust/Grtransfer/Irclust/Gr/Ir(111) heterostructure has been investigated using STM, with second layer clusters shown to self-organise on top of Gr-capped clusters, and apparent height measurements demonstrating two Ir-layer high clusters grown on top of transferred graphene. An alternative system that could potentially be extended to 3D growth of self-organised Co clusters on Au(11,12,12) surface has been capped with Gr on the mm-scale as observed using AES, with capped areas showing a preserved self-organised pattern as investigated with STM. The demonstrated achievements offer the foundations for further development of nano-pillar heterostructure with the potential for high-density magnetic nano-pillars.

Optical frequency combs link optical frequencies to electronically countable microwave sig- nals. Kerr microresonators miniaturize these sources, but extending their bandwidth remains challenging. The central aim of this thesis is to explore routes to broader optical frequency combs through the joint engineering of dispersion, resonator geometry, and excitation in three integrated architectures. The first part combines pulsed pumping and dispersion engineering in a silicon nitride (Si3N4) Euler racetrack. Synchronous electro-optic pulses supply high peak power at moderate average power. Adiabatic bends suppress parasitic spatial-mode conversion, while dispersion engineering and broadband extraction support an octave-spanning soliton microcomb at an electronically detectable repetition rate. Radio-frequency and heterodyne measurements distinguish the soliton from modulation instability and verify coherence at widely separated wavelengths. The second part combines gallium phosphide with photonic-crystal Fabryâ Pérot cavities whose chirped reflectors supply anomalous round-trip dispersion. Picosecond subharmonic pumping overcomes the thermo-refractive access barrier, enabling the first room-temperature dissipative Kerr solitons in GaP. Heterodyne noise measurements reveal the transfer of timing noise from the electro-optic drive. A modified Lugiatoâ Lefever model separates the nonlinear detuning shift caused by counter-propagating fields from the period-four dynamics and interleaved subcombs imposed by the drive. Finally, the Vernier effect in coupled rings reshapes the resonance spectrum into recurring hybridized dispersion valleys. A transfer-matrix model guides two device generations and relates measured hybridization to broadband comb generation. The analysis identifies a novel four-wave-mixing mechanism involving intra- and interband photon transfer between these valleys, offering a new route to spectral expansion in coupled-resonator systems. Together, these studies show that useful broadband microcombs require joint control of dispersion, loss, coupling, thermal dynamics, and the drive. Future work will focus on demon- strating f â 2f self-referencing, integrating pump-pulse generation and compression on chip, and achieving phase-locked comb generation across multiple Vernier valleys.

Membrane-based gas separation offers an energy-efficient alternative to conventional separation processes, but practical membranes must combine high permeance, selectivity, stability, mechanical robustness, and scalable fabrication. Carbon materials are promising candidates for gas separation membranes because of their structural versatility and chemical and physical stability. When fabricated as ultrathin selective layers with well-controlled molecular-sieving pores or channels, carbon membranes can combine short transport pathways with strong size selectivity. This thesis focuses on the design, synthesis, and characterization of ultrathin carbon-based membranes for gas separation, aiming to control molecular-sieving pores and channels at the angstrom scale to discriminate gas molecules with small differences in kinetic diameter. Two representative transport geometries are investigated: through-plane nanopores in porous graphene and confined channels between carbon layers or domains. The first part studies bottom-up synthesis of porous nanocrystalline graphene as an ultrathin molecular-sieving membrane. Nanopores are incorporated during graphene growth rather than by post-synthetic etching. By controlling the cooling rate during precipitation-based growth on nickel, graphene grain intergrowth and effective pore size can be tuned. Rapid cooling reduces the effective pore size and improves H2/CH4 selectivity from 6.2 to 53.3, demonstrating a synthesis-based strategy for angstrom-scale pore tuning. The second part develops 10-nm-thick carbon membranes derived from poly(4-vinylpyridine) by pyrolysis under controlled oxygen exposure. Trace O2 modifies the decomposition pathway of the polymer precursor during pyrolysis and transforms the resulting carbon membrane from a relatively uniform network into a disrupted ultramicroporous structure. The resulting multimodal ultramicroporous channels provide rapid transport pathways while retaining molecular-sieving capability. These 10-nm-thick carbon membranes achieved H2 permeance above 10,000 GPU (gas permeation units, 1 GPU = 3.35 × 10 -10 mol m-2 s-1 Pa-1) and H2/N2 mixture selectivity above 200 at 150 °C, showing that oxygen-modulated pyrolysis can overcome the thickness limitation of conventional carbon molecular sieve membranes. The third part addresses the challenge of structural characterization in fragile porous graphene membranes. Reliable pore analysis requires clean, suspended, and mechanically stable specimens, but conventional polymer-assisted transfer can leave residues that obscure nanopores, while direct transfer can damage porous films. Therefore, a polymer-free carbon nanotube mesh scaffold, termed CMesh, is developed to mechanically reinforce porous graphene during transfer and imaging. This scaffold enables robust atomic-resolution TEM/STEM characterization of pre-existing porous graphene and in situ heating studies up to 900 °C, providing a practical platform for linking nanopore structure with membrane transport. Overall, this thesis demonstrates an integrated strategy for engineering ultrathin carbon-based membranes by connecting synthesis, angstrom-scale pore/channel control, gas separation performance, and atomic-resolution characterization. The developed approaches provide new routes to tune through-plane nanopores and confined ultramicroporous channels, contributing to the development of high-performance molecular-sieving membranes for energy-efficient gas separation.

Coral reefs are among the most biodiverse and most threatened ecosystems on Earth, declining under ocean warming and recurrent mass bleaching. Whether they persist depends on how fast corals can adapt. In this context, this thesis assesses the adaptive potential of the reef-building coral Acropora millepora in the Coral Sea from a holobiont perspective, reading the environmental seascape, the host genome, the algal symbionts and the colony phenotype together across fourteen reef sites in two contrasting regions: the Capricorn Bunker Group, at the southern tip of the Australian Great Barrier Reef, and the remote Chesterfield-Bellona plateau of New Caledonia.

The environmental seascape was characterised from satellite remote sensing, as eighty descriptors of thermal stress, water quality, salinity and currents computed over four time windows, and was used to guide sampling across the environmental range rather than across geography alone. Colony morphology, colour, health and microhabitat were scored from standardised in situ photographs of 268 colonies. The host genome of 236 colonies was resolved by low-coverage whole genome sequencing (lcWGS), and the algal symbionts of 237 colonies by ITS2 metabarcoding. Genome-environment associations were then scanned with LFMM2 and SAMbada, cross-checked against the environment-free PCAdapt selection scan, and integrated, alongside the symbionts and the phenotype, in redundancy analyses that projected a polygenic adaptive index across the reefs.

The two regions form two genetically distinct units, each effectively panmictic within itself, separated by low but consistent differentiation and with Chesterfield-Bellona carrying about three times the nucleotide diversity of the Capricorn Bunker Group. Within Chesterfield-Bellona, genetic differentiation increases with geographic distance, a stepping-stone pattern absent from the Capricorn. Phenotype, symbiont community and host genome separate the same two regions, the dominant Cladocopium type profile switching between them. Candidate loci associated with sea surface temperature anomalies and heat-stress metrics fall in molecular chaperones, an antioxidant transporter, and extracellular-matrix and cilia genes, a set that recurs in independent global coral studies. The adaptive index draws a warm, clear-water-adapted pole across the Chesterfield-Bellona plateau, strongest in its north, grading to a cooler, more turbid-adapted pole in the Capricorn Bunker Group. It is on the Chesterfield-Bellona plateau that the adaptive signal of the host, the symbiont community and colony health agree, a first step towards seascape genomics at the scale of the holobiont.

Because the two regions lie far apart, the regional contrast between them is confounded with the environment, the symbionts and the host structure, so adaptive signals within each region were harder to resolve. However, sampled in June and September 2023, months before the severe 2024 El Niño marine heatwave, the colonies offer a rare opportunity to test in the field whether the genotypes predicted to be heat-adapted actually survived. Conserving the adaptive potential of corals, in a climate that no longer holds still, means reading the host, the symbiont and the environment together.

AI control is a field of study intended to provide a second layer of defense should our efforts at AI alignment fail. Starting with the premise that an AI system is misaligned, AI control is concerned with designing mechanisms to either prevent misbehavior or catch the AI red-handed. In this thesis, we present an overview of AI control alongside several contributions to the field, split into the following chapters:

Overview of AI control. To motivate AI control, we begin with a survey of theoretical and empirical evidence of misalignment in modern AI systems. Then, we discuss the high-level principles that guide AI control mitigations, covering common deployment contexts, the specific threats that AI control aims to prevent, and how AI control evaluations are conducted. We also introduce a distinction between concentrated and diffuse threats from untrusted AI models and discuss why these two types of threats demand different mitigations. Concentrated threats are those that unfold over a short period of time. The science of concentrated threats, sometimes called concentrated AI control, is more developed; following prior work, we present a mathematical model that can be used to evaluate the safety of concentrated AI control protocols.

Control tax. In this chapter, we study a concentrated AI control protocol called trusted monitoring. Trusted monitoring proceeds by having a weaker trusted AI model assign suspiciousness scores to the actions of the untrusted model, with the most suspicious actions escalated for human auditing. To evaluate trusted monitoring, we have language models generate solutions to competitive programming problems that contain subtle bugs. We then test how well other models can distinguish these "backdoored" solutions from the correct ones. Using the mathematical model from the previous chapter, we show how to compute the safety of trusted monitoring analytically in the limit of a large number of deployed AI agents. Finally, we combine our applied and theoretical results under the framework of the control tax---the operational and financial cost of integrating trusted monitoring into AI pipelines.

Adaptive attacks on AI control protocols. In this chapter, we present simple attacks that subvert existing monitoring-based protocols in concentrated AI control. The most common kind of trusted monitor used in control protocols is a prompted language model, and language models are notoriously susceptible to prompt injections. We exploit this vulnerability to demonstrate that trusted monitors can be induced to label most of the code they see as not suspicious, rendering them essentially useless. Our language model-generated prompt injections generalize across several monitors. Using the mathematical model from the previous chapters, we show the disastrous downstream effects of these prompt injections on the safety of multiple control protocols.

Diffuse AI control on fuzzy tasks. In this chapter, we propose a red-teaming framework for evaluating training interventions against diffuse threats from a misaligned AI, such as deliberately underperforming on difficult-to-grade tasks. We show that a misaligned AI can subvert the process of generating ML experiment ideas even when optimized against scores assigned by a weaker model. We also show that there exist prompts for the weak scorer that significantly improve its robustness, though efficiently discovering such prompts with realistic affordances remains an open problem.