Helsinki Institute of Physics

Maternal prenatal depression and anxiety (PDA) have been associated with increased risks of adverse birth and neurodevelopmental outcomes in children. While fetal exposure to too high or low levels of steroid hormones has been proposed as a potential biological mechanism underlying these effects, few studies have directly investigated this hypothesis using fetal tissue samples, and the existing studies have been limited to examining cortisol, cortisone or testosterone. We studied associations between PDA and steroid hormones in amniotic fluid by measuring a panel of 17 steroid hormones - including progestogens, mineralocorticoids, glucocorticoids, androgens and estrogens - and their substrate-to-product ratios in 173 women with singleton pregnancies undergoing amniocentesis during second trimester. The fetuses had no chromosomal abnormalities. We defined any PDA as meeting at least one of the following criteria: reported symptoms above clinical cut off (CES-D ≥20 or STAI state or trait anxiety ≥40) during pregnancy, lifetime diagnosis (ICD-10 codes F31-33, F41-43), and/or lifetime medication purchases (ATC-codes N06A, N05B). Elastic net regression identified two glucocorticoid metabolites, 20α-dihydrocortisol and 5β-tetrahydrocortisol, with lower amniotic fluid levels in fetuses of mothers with PDA compared to those without PDA (unadjusted mean difference -0.37 SD units, 95 % CI: [-0.68, -0.07]; and -0.40 SD units, 95 % CI: [-0.70, -0.10], respectively). The model with both steroids remained significant after adjusting for maternal age, body mass index, education, smoking during pregnancy, parity, gestational age at amniocentesis and fetal sex, and in sensitivity analyses excluding mothers with diabetes and hypertensive disorders (p-values<.05) and was not moderated by fetal sex (p-value>.40). PDA was not significantly associated with any substrate-to-product ratios of the steroids, used as proxies of steroid hormone metabolizing enzymes, after correction for multiple testing. This study provides support for the prenatal programming hypothesis of PDA influencing fetal environment through suboptimal levels of steroid hormones and highlights the need to expand to a comprehensive panel of steroid metabolism.

• We applied the Life Framework in combination with PPGIS data on landscape values. • We identified three life frames: living with/in , living from , and living as nature. • Living in/with related to mapping intrinsic values in protected areas. • The spatial distribution of the life frames showed high variability and overlaps. • Using life frames with PPGIS revealed protected areas heterogeneous meanings.

Acute myeloid leukemia (AML) remains challenging to treat due to extensive genetic heterogeneity, high relapse rates, and treatment-related toxicity. Although drug combinations offer therapeutic promise, their selection is often empirical. Here, we introduce Combinatorial Proteome Integral Solubility/Stability Alteration analysis (CoPISA), a high-throughput proteomics workflow that captures protein solubility/stability alterations uniquely induced by drug combinations. We applied CoPISA to two rationally designed AML drug pairs, LY3009120-sapanisertib (LS) and ruxolitinib-ulixertinib (RU), previously identified as the most effective and least toxic combinations among many candidates and validated in AML cell lines, patient-derived samples and zebrafish xenograft models. We uncovered an emergent mechanism termed "conjunctional targeting", in which combinatorial drug action induces combination-exclusive protein targets consistent with an AND-gate logic model. LS-specific converged on SUMOylation, chromatin condensation, and VEGF-linked adhesion, while RU-specific targets disrupted DNA-damage checkpoints, mitochondrial bioenergetics, and RNA-splicing. Post-translational modification analysis revealed combination-induced acetylation, methylation, and phosphorylation of key AML proteins, including NPM1. Network analysis demonstrated that a substantial fraction of AML-associated proteins targeted by CoPISA are unique to combinations, including DNMT3A, NPM1, and TP53. By uncovering a mechanistic layer beyond classical synergy, CoPISA provides a robust framework for the precision-guided design of combinatorial therapies in heterogeneous cancers.

Aligning text-to-image generation with user intent remains challenging, as users frequently provide ambiguous inputs and struggle with model idiosyncrasies. We propose Adaptive Prompt Elicitation (APE), a technique that adaptively poses visual queries to help users refine prompts without extensive writing. Our technical contribution is a formulation of interactive intent inference under an information-theoretic framework. APE represents latent user intent as interpretable feature requirements using language model priors, adaptively generates visual queries, and compiles elicited requirements into effective prompts. Evaluation on IDEA-Bench and DesignBench shows that APE achieves stronger alignment with improved efficiency. A user study with 128 participants on user-defined tasks demonstrates 19.8% higher perceived alignment without increased workload. Our work contributes a principled approach to prompting that offers an effective and efficient complement to the prevailing prompt-based interaction paradigm with text-to-image models.

Abstract The MoEDAL experiment employs solid-state nuclear track detector foils around Interaction Point 8 to search for traces of highly ionizing particles. The selected material, CR-39, has been used in several experiments and is capable of detecting ionization tracks down to the level of oxygen ions. However, due to the high-radiation environment of the Large Hadron Collider, the material becomes opaque after chemical etching and exhibits strong scattering properties. This complicates the identification of low-ionization tracks. By deploying liquid thin films on both sides of the irradiated foils and using machine-vision techniques, etch pits in the foils can be detected and classified.

Abstract. Sub-10 nm aerosol particles play a critical role in the atmosphere through their involvement in new particle formation, a key process influencing the availability of cloud condensation nuclei. In recognition of their significance, international observation frameworks such as the Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS) engage in the process of integrating their observations into standardized protocols. ACTRIS aims to enhance the understanding of atmospheric processes, including those related to particle dynamics and their interactions with clouds and climate through harmonized long-term observations. To evaluate the performance of current instrumentation for sub-10 nm particle measurements, as well as the procedures for doing so, in November 2023, the ACTRIS Cluster Calibration Center in Helsinki, Finland, held its first workshop on the intercomparison of instrumentation for sub-10 nm aerosol particle number size distribution measurements. In this workshop, three mobility-based systems (GRIMM PSMPS, TSI 1 nm SMPS and TSI 3938N56) and five activation-based systems (three Airmodus A11s and two Airmodus A12s) were evaluated. The focus lay on assessing their number concentration response and size-dependent detection efficiency – including the determination of the diameter with 50 % detection efficiency – and sizing accuracy. In addition to these parameters, the instruments were compared with each other while measuring aerosol particle number size distributions side by side from an aerosol chamber. Beyond the evaluation of instrument performance, the workshop aimed to test and assess calibration and comparison methods to identify aspects of ACTRIS compliance requiring further refinement. Although this work highlights the key strengths of the different measurement techniques and instruments, several challenges remain. Mobility-based systems showed high sizing accuracy, especially for particles larger than 2 nm, while encountering challenges in measuring particles in the lower atmospheric concentration range. Activation-based systems proved more sensitive at lower particle number concentrations and particle sizes, with the drawback of slight unit-to-unit variability. Additionally, a systematic size shift was identified in aerosols generated by 4-way-cross glowing wire generator setups, indicating a need for further investigation on this effect and the development of calibration equipment in the sub-10 nm size range.

In parts of the northern boreal zone, a significant number of peatlands have previously been drained for forestry. Climate change is increasing the frequency of forest fires, making these peatland forests particularly vulnerable to wildfires due to thick organic layers and low water table levels. Peatlands store approximately 10% of global soil nitrogen (N) and peatland forests in particular may act as a source of nitrous oxide (N2O), which is a potent greenhouse gas contributing to ozone depletion. Although forest fires affect several factors influencing soil N dynamics, very little is known about the impact of wildfires on the N cycle and N2O emissions on burned peatland sites. We investigated these impacts with a peat column experiment by simulating forest fire conditions with controlled burning. We collected peat profiles up to 50 cm depth from three different undrained and drained peatland sites in Southern Finland in May 2025 (n=50). Peat columns were incubated outdoors for three months, and half of the columns were scorched in mid-summer with a gas torch to simulate a surface fire. During the experiment period, N2O, carbon dioxide (CO2), and methane (CH4) were measured weekly. After three months, incubated columns were dissected, and peat samples were collected to analyze soil physicochemical parameters, microbial community structure, and the quality of soil organic matter. Preliminary results suggest that nutrient-rich peatland forests act as N2O sources under favorable conditions for N2O production, while nutrient-poor sites are negligible as N2O sources. The fire appeared to shift these patterns and temporarily increase N2O emissions across peatland types. Further analyses will evaluate how post-fire changes in different peat N pools relate to observed N₂O flux dynamics.

The goal of inductive logic programming (ILP) is to search for a hypothesis that generalises training data and background knowledge. The challenge is searching vast hypothesis spaces, which is exacerbated because many logically equivalent hypotheses exist. To address this challenge, we introduce a method to break symmetries in the hypothesis space. We implement our idea in answer set programming. Our experiments on multiple domains, including visual reasoning and game playing, show that our approach can reduce solving times from over an hour to just 17 seconds.