[article-cris] Perustieteiden korkeakoulu / SCI

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  • Non-damaging laser treatment with electroretinography-based thermal dosimetry activates hormetic heat response in pig retinal pigment epithelium
    (2025-12) Amirkavei, Mooud; Kaikkonen, Ossi; Turunen, Teemu; Meller, Anna; Åhlgren, Johanna; Kvanta, Anders; André, Helder; Koskelainen, Ari
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Enhancing protein homeostasis and antioxidant defense mechanisms in the retinal pigment epithelium (RPE) holds significant promise as a treatment option for various retinal diseases, including age-related macular degeneration. However, patient responses to laser-induced RPE hyperthermia varies substantially. To address this, we introduce a focal electroretinography (fERG)-based method for retinal temperature monitoring during laser exposure. Applying the method to anaesthetized male pigs in vivo, we study the biological effects of controlled retinal hyperthermia. Our findings reveal that temperature elevation to 44 °C with 60-second laser exposure triggers heat shock protein production and autophagy activation in RPE/choroid while avoiding oxidative stress, apoptosis, and structural damage. Importantly, our results demonstrate that visible lesions occur at temperatures above 48 °C, and that the temperature determination precision was 0.6 °C. These outcomes highlight that fERG-controlled retinal laser treatment enables reliable and safe activation of cytoprotective mechanisms in the RPE, providing a promising new therapeutic approach.
  • Mind the Gap : Gender, Homophily and the Glass Ceiling in Academic Networks
    (2025-10-16) Nguyen, Anh Duong; Jiang, Jiaming; Keller, Barbara
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    This study examines the gender distribution within collaboration and mentorship networks across several scientific disciplines, namely Physics, Material Science, Computer Science, Chemistry, Biology and Psychology in the last two decades. Our analysis reveals that the proportions of women in these fields range from 24% to 49%. Despite these variations, all disciplines show evidence of gender-based homophily in their networks, and there are indications for the presence of a glass ceiling effect. Specifically, we observed that women are under-represented in high-status positions with respect to their number of mentees in mentorship networks and their scientific contributions within collaboration networks. These findings highlight longstanding gender disparities in scientific collaboration and mentoring practices, along with cumulative impacts on measured performances of researchers across disciplines. Using a network approach, this study not only extends the methodological work on quantifying gender gaps but also provides actionable insights for the design of more inclusive systems with gender-equitable access to mentorship and collaboration opportunities.
  • TuMag : The Tunable Magnetograph for the Sunrise III Mission
    (2025-10) del Toro Iniesta, J. C.; Orozco Suárez, D.; Álvarez-Herrero, A.; Sanchis Kilders, E.; Pérez-Grande, I.; Ruiz Cobo, B.; Bellot Rubio, L. R.; Balaguer Jiménez, M.; López Jiménez, A. C.; Álvarez García, D.; Ramos Más, J. L.; Cobos Carrascosa, J. P.; Labrousse, P.; Moreno Mantas, A. J.; Morales-Fernández, J. M.; Aparicio del Moral, B.; Sánchez Gómez, A.; Bailón Martínez, E.; Bailén, F. J.; Strecker, H.; Siu-Tapia, A. L.; Santamarina Guerrero, P.; Moreno Vacas, A.; Atiénzar García, J.; Dorantes Monteagudo, A. J.; Bustamante, I.; Tobaruela, A.; Fernández-Medina, A.; Núñez Peral, A.; Cebollero, M.; Garranzo-García, D.; García Parejo, P.; Gonzalo Melchor, A.; Sánchez Rodríguez, A.; Campos-Jara, A.; Laguna, H.; Silva-López, M.; Blanco Rodríguez, J.; Gasent Blesa, J. L.; Rodríguez Martínez, P.; Ferreres, A.; Gilabert Palmer, D.; Torralbo, I.; Piqueras, J.; González-Bárcena, D.; Fernández, A. J.; Hernández Expósito, D.; Páez Mañá, E.; Magdaleno Castelló, E.; Rodríguez Valido, M.; Korpi-Lagg, Andreas; Gandorfer, Achim; Solanki, Sami K.; Berkefeld, Thomas; Bernasconi, Pietro; Feller, Alex; Katsukawa, Yukio; Riethmüller, Tino L.; Smitha, H. N.; Kubo, Masahito; Martínez Pillet, Valentín; Grauf, Bianca; Bell, Alexander; Carpenter, Michael
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Sunrise iii is a balloon-borne solar observatory dedicated to the investigation of the processes governing the physics of the magnetic field and the plasma flows in the lower solar atmosphere. The gondola hosts a 1-m aperture telescope that feeds three post-focus instruments. One of these instruments, the Tunable Magnetograph (TuMag), is a tunable imaging spectropolarimeter in visible wavelengths. It is designed to probe the vector magnetic field,, and the line-of-sight (LoS) velocity, vLoS, of the photosphere and the lower chromosphere. It provides polarized images with a 63″×63″ field of view (FoV) of the Sun in four polarization states. These images can later be processed on ground to retrieve maps of the aforementioned solar physical quantities. The quasi-simultaneous observation of two spectral lines sensitive to and vLoS in the photosphere and lower chromosphere provides excellent diagnostic measurements of the magnetic and dynamic coupling in these layers. When combined with the other two instruments on board, observing in the infrared and ultraviolet regions of the spectrum, TuMag’s diagnostic potential is expected to be greatly enhanced. Building upon heritage of instruments like IMaX and SO/PHI, the key technologies employed for TuMag are a liquid-crystal-variable-retarder-based polarimeter and a solid, LiNbO3 Fabry–Pérot etalon as a spectrometer. However, it also incorporates several innovative features, such as in-house-made, high-sensitivity scientific cameras and a double filter wheel. The latter makes TuMag the first balloon-borne instrument of its type capable of tuning between spectral lines. Specifically, it can sequentially observe any two out of the three spectral lines of Fe i at 525.02 and 525.06 nm and of Mg i at 517.3 nm. Time cadences range from 30 to 100 seconds, depending on the observing mode and the specific pair of spectral lines targeted. Laboratory measurements have demonstrated good image quality, spectral resolution, and polarimetric efficiency. Here we report on the concept, design, calibration, and integration phases of the instrument, as well as on the data reduction pipeline.
  • Slot Attention with Re-Initialization and Self-Distillation
    (2025) Zhao, Rongzhen; Zhao, Yi; Kannala, Juho; Pajarinen, Joni
    A4 Artikkeli konferenssijulkaisussa
    Unlike popular solutions based on dense feature maps, Object-Centric Learning (OCL) represents visual scenes as sub-symbolic object-level feature vectors, termed slots, which are highly versatile for tasks involving visual modalities. OCL typically aggregates object superpixels into slots by iteratively applying competitive cross attention, known as Slot Attention, with the slots as the query. However, once initialized, these slots are reused naively, causing redundant slots to compete with informative ones for representing objects. This often results in objects being erroneously segmented into parts. Additionally, mainstream methods derive supervision signals solely from decoding slots into the input's reconstruction, overlooking potential supervision based on internal information. To address these issues, we propose Slot Attention with re-Initialization and self-Distillation (DIAS): $\emph{i)}$ We reduce redundancy in the aggregated slots and re-initialize extra aggregation to update the remaining slots; $\emph{ii)}$ We drive the bad attention map at the first aggregation iteration to approximate the good at the last iteration to enable self-distillation. Experiments demonstrate that DIAS achieves state-of-the-art on OCL tasks like object discovery and recognition, while also improving advanced visual prediction and reasoning. Our source code and model checkpoints are available on https://github.com/Genera1Z/DIAS.
  • Pareto-optimality of pulses for robust population transfer in a ladder-type qutrit
    (2025-12) McCord, John J.; Kuzmanović, Marko; Paraoanu, Gheorghe Sorin
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Frequency-modulation schemes offer an alternative to standard Rabi pulses for realizing robust quantum operations. In this work, we investigate short-duration population transfer between the ground and first excited states of a ladder-type qutrit, with the goal of minimizing leakage into the second excited state. Our multiobjective approach seeks to reduce the maximum transient second-state population and maximize detuning robustness. Inspired by two-state models—such as the Allen-Eberly and Hioe-Carroll models—we extend these concepts to our system, exploring a range of pulse families, including those with super-Gaussian envelopes and polynomial detuning functions. We identify Pareto fronts for pulse models constructed from one of two envelope functions paired with one of four detuning functions. We then analyze how each Pareto-optimal pulse parameter influences the two Pareto objectives as well as amplitude robustness.
  • Bayesian optimization to infer parameters in viscoelasticity
    (2025-11-01) Miranda-Valdez, Isaac Y.; Mäkinen, Tero; Koivisto, Juha; Alava, Mikko J.
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Inferring viscoelasticity parameters is a key challenge that often leads to nonunique solutions when fitting rheological data. In this context, we utilize Bayesian optimization for parameter inference within curve-fitting processes. To fit a viscoelastic model to rheological data, the Bayesian optimization maps via a surrogate model the parameter values to a given error function. It then exploits the mapped space to identify parameter combinations that minimize the error. We compare the results of Bayesian optimization with traditional fitting routines and find that, while the Bayesian method requires a similar number of iterations to achieve a fit for a viscoelastic model, it does incur a higher computational cost. In sum, despite higher cost, Bayesian optimization provides a white-box framework that explicitly models the error landscape via the surrogate mean and uncertainty, using an acquisition function to target informative regions and enhance supervised parameter estimation in linear viscoelasticity.
  • Superionic conduction electrolyte through in situ structural transformation in electrochemical cell
    (2025-12) Wang, Ruoming; Yang, Guangping; Yang, Tianxiang; Lu, Yuzheng; Raza, Rizwan; Zhu, Bin; Lund, Peter D.; Yun, Sining
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    High ionic conductivity at reduced temperatures may allow electrochemical cell applications to be efficiently scaled up. Herein, we introduce an approach that exploits electrode-electrode synergy and in situ structural transformation to increase the ionic conductivity of an electrolyte within the electrochemical cell. This transformation is based on the formation of a Li2TiO3-Li2CO3 heterostructure electrolyte via the combination of a TiO2 semiconductor precursor, polystyrene spheres as the soft skeleton, and the lithium-based electrode LiNi0.8Co0.15Al0.05O2. The unique surface and interface of the heterostructure electrolyte enable the formation of long-range ion transport channels that lower electrode impedance, resulting in a superior ionic conductivity of 0.23 S cm−1 at 550 °C and a high-power density of 1239 mW cm−2. Our general method to form superionic electrolytes shows potential in fuel cell technologies.
  • Interplay between β-Chitin Nanocrystal Supramolecular Architecture and Water Structuring : Insights from Three-Dimensional Atomic force Microscopy Measurements and Molecular Dynamics Simulations
    (2025-10-29) Yurtsever, Ayhan; Daicho, Kazuho; Priante, Fabio; Miyazawa, Keisuke; Alam, Mohammad Shahidul; Miyata, Kazuki; Yabuki, Akinori; Isobe, Noriyuki; Saito, Tsuguyuki; Foster, Adam S.; Fukuma, Takeshi
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Chitin, a highly abundant natural biopolymer, holds significant promise as a renewable resource; however, the structural organization and hydration properties of its β-crystalline form remain poorly understood, which limits our understanding of its fundamental characteristics. A thorough understanding of the three-dimensional (3D) supramolecular arrangement of chitin nanocrystals (NCs) in aqueous environments is crucial for realizing their full potential. Here, we employed atomic force microscopy (AFM) in combination with molecular dynamics simulations to elucidate the surface structure of individual β-chitin NCs at the single-fiber level under varying pH conditions. In situ AFM imaging reveals a highly ordered crystalline architecture with unprecedented molecular detail and demonstrates that water intercalation expands the lattice along the b-axis. 3D-AFM further revealed heterogeneous, pH-dependent hydration shells, forming an intricate 3D hydrogen bonding network around β-chitin NCs, thereby establishing substantial energetic barriers to direct interactions of external ions and molecules with the chitin surface and might lead to selective biomolecular interactions. Notably, α-chitin NCs exhibit stronger hydration forces than β-chitin NCs, reflecting distinct reactivities. Understanding the molecular assembly of β-chitin chains and their interactions with water across different pH values is critical for elucidating relevant biological processes and optimizing chitin decrystallization strategies. These findings advance our mechanistic understanding of the molecular assembly of water-intercalated β-chitin NCs, thereby enabling their efficient use in renewable products and supporting the rational design of functional and sustainable bionanomaterials.
  • Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction
    (2025-12) Bhatia, Nitik; Rinke, Patrick; Krejčí, Ondřej
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Infrared (IR) spectroscopy is a pivotal analytical tool as it provides real-time molecular insight into material structures and enables the observation of reaction intermediates in situ. However, interpreting IR spectra often requires high-fidelity simulations, such as density functional theory based ab-initio molecular dynamics, which are computationally expensive and therefore limited in the tractable system size and complexity. In this work, we present a novel active learning-based framework, implemented in the open-source software package PALIRS, for efficiently predicting the IR spectra of small catalytically relevant organic molecules. PALIRS leverages active learning to train a machine-learned interatomic potential, which is then used for machine learning-assisted molecular dynamics simulations to calculate IR spectra. PALIRS reproduces IR spectra computed with ab-initio molecular dynamics accurately at a fraction of the computational cost. PALIRS further agrees well with available experimental data not only for IR peak positions but also for their amplitudes. This advancement with PALIRS enables high-throughput prediction of IR spectra, facilitating the exploration of larger and more intricate catalytic systems and aiding the identification of novel reaction pathways.
  • Vector-Quantized Vision Foundation Models for Object-Centric Learning
    (2025) Zhao, Rongzhen; Wang, Huiling; Kannala, Juho; Pajarinen, Joni
    A4 Artikkeli konferenssijulkaisussa
    Object-Centric Learning (OCL) aggregates image or video feature maps into object-level feature vectors, termed \textit{slots}. It's self-supervision of reconstructing the input from slots struggles with complex object textures, thus Vision Foundation Model (VFM) representations are used as the aggregation input and reconstruction target. Existing methods leverage VFM representations in diverse ways yet fail to fully exploit their potential. In response, we propose a unified architecture, Vector-Quantized VFMs for OCL (VQ-VFM-OCL, or VVO). The key to our unification is simply shared quantizing VFM representations in OCL aggregation and decoding. Experiments show that across different VFMs, aggregators and decoders, our VVO consistently outperforms baselines in object discovery and recognition, as well as downstream visual prediction and reasoning. We also mathematically analyze why VFM representations facilitate OCL aggregation and why their shared quantization as reconstruction targets strengthens OCL supervision. Our source code and model checkpoints are available on https://github.com/Genera1Z/VQ-VFM-OCL.
  • Initialisation and network effects in decentralised federated learning
    (2025-12) Badie-Modiri, Arash; Boldrini, Chiara; Valerio, Lorenzo; Kertész, János; Karsai, Márton
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Fully decentralised federated learning enables collaborative training of individual machine learning models on a distributed network of communicating devices while keeping the training data localised on each node. This approach avoids central coordination, enhances data privacy and eliminates the risk of a single point of failure. Our research highlights that the effectiveness of decentralised federated learning is significantly influenced by the network topology of connected devices and the initial conditions of the learning models. We propose a strategy for uncoordinated initialisation of the artificial neural networks based on the distribution of eigenvector centralities of the underlying communication network, leading to a radically improved training efficiency. Additionally, our study explores the scaling behaviour and the choice of environmental parameters under our proposed initialisation strategy. This work paves the way for more efficient and scalable artificial neural network training in a distributed and uncoordinated environment, offering a deeper understanding of the intertwining roles of network structure and learning dynamics.
  • Mercury levels and trends in fish (2011–2021) : A Bayesian approach with multi-group Gaussian processes and hierarchical imputation
    (2026-01) Blanco-Calvo, Carolina; Riutort-Mayol, Gabriel; Marín, Silvia; Báguena, Rosario; Coscollà, Clara; López-González, Ulises; Llop, Sabrina; Ballester, Ferran; Soler-Blasco, Raquel
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Background: Mercury (Hg) is a toxic metal, with fish consumption being the primary source of exposure in humans. This study aimed to describe Hg concentrations in fish species consumed in the Valencian Community (Spain) and their trends during the period 2011–2021. Methods: A retrospective study was conducted on Hg levels in fish meat between 2011 and 2021, using data from the Food Safety Monitoring Program of the Valencian Regional Government. Descriptive analyses and temporal trends were inferred for total Hg (THg) (n = 799) and methylmercury (MeHg) (n = 271) levels by fish species and fishery origin. Gaussian processes (GPs) with a novel multi-group covariance function were applied, enabling the use of correlations across categories to improve inference on temporal trends in unbalanced groups, with species that have smaller samples borrowing information from correlated species. Results: Swordfish exhibited the highest Hg concentrations (median THg: 0.76 mg/kg; IQR: 0.47–1.17), with 30 % of samples exceeding European limit values, followed by fresh tuna (0.46 mg/kg) and canned tuna (0.22 mg/kg). THg and MeHg levels in swordfish tended to decrease by around 0.5 mg/kg from 2011 to 2016, but then increased again to near their initial levels. Fresh and canned tuna showed decreasing trends in the first half of the study period. Data from the second half of the period were limited, except for swordfish; thus, results from this time should be interpreted with caution. Conclusions: Most fish groups showed declining trends between 2011 and 2016. Our findings on Hg levels in commercially sold fish species could be useful for guiding local fish consumption recommendations.
  • Soft Token Attacks Cannot Reliably Audit Unlearning in Large Language Models
    (2025-11) Chen, Haokun; Szyller, Sebastian; Xu, Weilin; Himayat, Nageen
    A4 Artikkeli konferenssijulkaisussa
    Large language models (LLMs) are trained using massive datasets. However, these datasets often contain undesirable content, e.g., harmful texts, personal information, and copyrighted material. To address this, machine unlearning aims to remove information from trained models. Recent work has shown that soft token attacks () can successfully extract unlearned information from LLMs. In this work, we show that s can be an inadequate tool for auditing unlearning. Using common unlearning benchmarks, i.e., Who Is Harry Potter? and TOFU, we demonstrate that, in a strong auditor setting, such attacks can elicit any information from the LLM, regardless of (1) the deployed unlearning algorithm, and (2) whether the queried content was originally present in the training corpus. Also, we show that with just a few soft tokens (1-10) can elicit random strings over 400-characters long. Thus showing that s must be used carefully to effectively audit unlearning. Example code can be found at https://github.com/IntelLabs/LLMart/tree/main/examples/unlearning
  • Computer Vision-Enabled Construction Waste Sorting : A Sensitivity Analysis
    (2025-10) Liu, Xinru; Farshadfar, Zeinab; Khajavi, Siavash H.
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    This paper presents a comprehensive sensitivity analysis of the pioneering real-world deployment of computer vision-enabled construction waste sorting in Finland, implemented by a leading provider of robotic recycling solutions. Building upon and extending the findings of prior field research, the study analyzes an industry flagship case to examine the financial feasibility of computer vision-enabled robotic sorting compared to conventional sorting. The sensitivity analysis covers cost parameters related to labor, wages, personnel training, machinery (including AI software, hardware, and associated components), and maintenance operations, as well as capital expenses. We further expand the existing cost model by integrating the net present value (NPV) of investments. The results indicate that the computer vision-enabled automated system (CVAS) achieves cost competitiveness over conventional sorting (CS) under conditions of higher labor-related costs, such as increased headcount, wages, and training expenses. For instance, when annual wages exceed EUR 20,980, CVAS becomes more cost-effective. Conversely, CS retains cost advantages in scenarios dominated by higher machinery and maintenance costs or extremely elevated discount rates. For example, when the average machinery cost surpasses EUR 512,000 per unit, CS demonstrates greater economic viability. The novelty of this work arises from the use of a pioneering real-world case study and the improvements offered to a comprehensive comparative cost model for CVAS and CS, and furthermore from clarification of the impact of key cost variables on solution (CVAS or CS) selection.
  • Implementing a Cost-Efficient and Interoperable Health Data Infrastructure: A Multi-Region Finnish Case Study
    (2025-10-02) Virkkunen, Sanna; Granlund, Tuomas; Kaikkonen, Risto
    A4 Artikkeli konferenssijulkaisussa
    This paper examines a large-scale, collaborative data infrastructure project implemented across three Finnish wellbeing services counties in response to national health reform mandates. Using a qualitative case study approach and the European Interoperability Framework (EIF), this paper analyzes how organizational, political, semantic, and technical interoperability were jointly managed to develop standardized yet distributed data lake solutions. While legislative obligations drove the collaboration, success required close operational coordination, agile methods, and shared technical frameworks. By deploying parallel yet aligned environments, the counties achieved standardized reporting, reduced duplication, and improved cost-efficiency. The case highlights that sustainable digital health reform demands a holistic approach integrating top-down mandates with localized coordination and technical agility, ensuring all EIF dimensions are consistently addressed. Future evaluations should assess the long-term cost-effectiveness, scalability, and governance sustainability of this standardized, distributed model.
  • Hydraulic–thermal dynamic model of meshed district heating network based on discrete event simulation
    (2025-12-01) Xie, Zichan; Wang, Haichao; Hua, Pengmin; Lahdelma, Risto
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Online planning and optimization of district heating (DH) systems require highly iterative simulations of DH networks, where computational speed and accuracy are crucial. This study develops a versatile dynamic hydraulic–thermal model to simulate modern DH networks with complex meshed topologies and diverse operational strategies. The model employs discrete event simulation (DES) for accurate and efficient simulations with variable temporal and spatial steps. The DES approach eliminates errors from numerical diffusion by directly calculating the water temperature using inlet temperatures and travel times. The DES model can capture water temperature profiles at any observation node over time and across spatial locations at any given moment with linear interpolation between critical temperature sampling points. The accuracy of the model in temperature prediction and computational efficiency are validated using a meshed network with multiple heating plants, which comprises 186 pipes and two dependent cycles. The average variability of node temperature error across 80 substations is ∼1 °C. An 85-day simulation of the network requires under half a second. The thermal computational cost is linearly correlated with the number of sampling points, while hydraulic calculations only contribute to a small fraction of the total computational time. These results highlight the potential of the DES model for reliable and scalable smart DH applications, such as digital twins, fault diagnosis, operational planning, and system optimization.
  • Engineering Trustworthy AI : A Developer Guide for Empirical Risk Minimization
    (2025) Pfau, D.; Jung, Alexander
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    AI systems are increasingly used for critical decisions that transcend all important fields of private and public life. These systems often use empirical risk minimisation (ERM) to train powerful prediction models such as deep neural networks. The output of the predictive model runs the risk of unintentional bias, opacity, and other adverse effects. To ensure the safety of these systems, it is vital to consider these risks already in the design stage of the model. The EU acknowledged the potential sensitivity of the predictions/decisions made about persons which led to the formulation of the Ethics Guidelines for Trustworthy AI laying down seven key requirements for trustworthy AI. So far, the design of ERM-based methods prioritises accuracy over trustworthiness. This paper discusses how key requirements for trustworthy AI can be translated into design choices for ERM components. We map the design space of ML systems to the core objectives of trustworthy AI: fairness, privacy, robustness, and explainability. Our theory is instrumental in the design of trustworthy ML systems that minimise privacy leakage and are robust against (intentional) perturbations during their operation, such as disseminating fake news. The operation of trustworthy ML systems should also be transparent or explainable to its users. Finally, ML systems must be fair and not discriminate against specific user groups. There is an urgent need for a more holistic approach to ML that includes key requirements for trustworthy AI.
  • Computational modeling enables individual assessment of postprandial glucose and insulin responses after bariatric surgery
    (2025-12) Poyraz, Onur; Heinonen, Sini; John, S. T.; Saarinen, Tuure; Juuti, Anne; Marttinen, Pekka; Pietiläinen, Kirsi H.
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Background: Bariatric surgery enhances glucose metabolism, yet the detailed postprandial joint glucose and insulin responses, variability in individual outcomes, and differences in surgical approaches remain poorly understood. Methods: We used hierarchical multi-output Gaussian process (HMOGP) regression to reveal clinically relevant patterns between persons undergoing two types of bariatric surgery by modeling the individual postprandial glucose and insulin responses and estimating the average response curves from individual data. 44 participants with obesity underwent either Roux-en-Y gastric bypass (RYGB; n = 24) or One-Anastomosis gastric bypass (OAGB; n = 20) surgery. The participants were followed up at the 6th and 12th months after the operation, during which they underwent an oral glucose tolerance test (OGTT) and a mixed meal test (MMT). Results: A marked reduction in glycemia, an earlier glucose peak, and an increase and sharpening in the postprandial glucose and insulin responses are evident in both metabolic tests post-operation. MMT results in higher postprandial glucose and insulin peaks compared with OGTT. Higher glucose and insulin responses are observed after RYGB compared with OAGB, suggesting differences between the procedures that may influence the clinical practice. Conclusions: Computational modeling with HMOGP regression can thus be used to, in detail, predict the combined responses of patient cohorts to ingested glucose or a mixed meal and help in assessing individual metabolic improvement after weight loss. This can lead to new knowledge in personalized metabolic interventions.
  • Unpacking the relationship between sense of place and entrepreneurs’ well-being
    (2025-03) Kautonen, Teemu; Soto-Simeone, Aracely; Kibler, Ewald
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    This exploratory study develops an understanding of how the hitherto under-investigated psychological dimension of place affects entrepreneurs’ well-being. The analysis focuses on eudaimonic well-being, which describes individuals’ psychological functioning and fulfillment of their best potentials and is relatively underexplored compared to hedonic well-being (happiness). Based on prior work in environmental psychology, the study proposes that entrepreneurs’ sense of place—their psychological bond with the local setting of their entrepreneurial activities—is an important component influencing their well-being. The empirical analysis of two waves of original survey data from entrepreneurs located in an urban and a rural region of Finland shows that the sense of place is positively associated with several dimensions of eudaimonic well-being. This study extends the literature by shifting the focus from place as a passive container for entrepreneurs’ activities to its role as an active source of entrepreneurial well-being.
  • Finite-momentum mixed singlet-triplet pairing in chiral antiferromagnets induced by even-parity spin texture
    (2025-09-04) Zhang, Song Bo; Hu, Lun Hui
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Nonrelativistic spin splitting in unconventional antiferromagnets has garnered much attention for its promising spintronic applications and open fundamental questions. Here, we uncover a unique even-parity spin texture in chiral noncollinear antiferromagnets, exemplified using a kagome lattice. We consider two distinct types of electrons in the system: one with Schrödinger-like dispersion and the other exhibiting Dirac-like behavior. Remarkably, we show that, for both electron types, this spin texture induces an exotic coexistence of opposite-spin singlet and equal-spin triplet Cooper pairs with finite momentum when proximity coupled to conventional superconductors. The triplet pairing arises from the intrinsic spin rotation of the antiferromagnet and does not require net magnetization or spin-orbit coupling. Moreover, we identify a tunable phase difference between singlet and triplet pairings, controllable through junction orientation. This mixed pairing state can be experimentally probed via damped oscillations in order parameters and 0-π transitions in Josephson junctions. Additionally, we analyze the effect of out-of-plane spin canting, elucidating its role in generating spin-polarized supercurrents, and discuss Mn3Ga and Mn3Ge to test our predictions.