Opin vísindi
Opin vísindi er varðveislusafn vísindaefnis og doktorsritgerða í opnum aðgangi á vegum íslenskra háskóla og Landsbókasafns Íslands - Háskólabókasafns.
Opinn aðgangur að rannsóknaniðurstöðum er í samræmi við 10. gr. laga nr. 3/2003 um opinberan stuðning við vísindarannsóknir sem og kröfur innlendra og erlendra rannsóknasjóða. Markmiðið með opnum aðgangi er að niðurstöður rannsókna séu aðgengilegar sem flestum óhindrað og án endurgjalds á rafrænu formi. Vistun í varðveislusafninu er varanleg og ætlað að tryggja aðgang að vísindaefni íslenskra háskóla í opnum aðgangi um ókomna tíð. Varðveislusafnið Opin vísindi er tengt við rannsóknagáttina IRIS og rannsóknaniðurstöður í opnum aðgangi sem eru skráðar í IRIS eru um leið vistaðar og gerðar aðgengilegar til framtíðar í varðveislusafninu. Með því að safna þessu efni saman í eitt safn verður aðgangur að því einfaldur og þægilegur fyrir alla sem vilja kynna sér það og geta þannig notið þess öfluga vísindastarfs sem fram fer í háskólum landsins.
Varðveislusafnið er OpenAIRE / OpenAIREplus samhæft og samrýmist kröfum sem gerðar eru um birtingu rannsóknaniðurstaðna úr verkefnum sem styrkt eru úr evrópsku rannsóknaáætlununum FP7 og H2020.
Varðveislusafnið notar opna hugbúnaðinn DSpace.
Opinn aðgangur að rannsóknaniðurstöðum er í samræmi við 10. gr. laga nr. 3/2003 um opinberan stuðning við vísindarannsóknir sem og kröfur innlendra og erlendra rannsóknasjóða. Markmiðið með opnum aðgangi er að niðurstöður rannsókna séu aðgengilegar sem flestum óhindrað og án endurgjalds á rafrænu formi. Vistun í varðveislusafninu er varanleg og ætlað að tryggja aðgang að vísindaefni íslenskra háskóla í opnum aðgangi um ókomna tíð. Varðveislusafnið Opin vísindi er tengt við rannsóknagáttina IRIS og rannsóknaniðurstöður í opnum aðgangi sem eru skráðar í IRIS eru um leið vistaðar og gerðar aðgengilegar til framtíðar í varðveislusafninu. Með því að safna þessu efni saman í eitt safn verður aðgangur að því einfaldur og þægilegur fyrir alla sem vilja kynna sér það og geta þannig notið þess öfluga vísindastarfs sem fram fer í háskólum landsins.
Varðveislusafnið er OpenAIRE / OpenAIREplus samhæft og samrýmist kröfum sem gerðar eru um birtingu rannsóknaniðurstaðna úr verkefnum sem styrkt eru úr evrópsku rannsóknaáætlununum FP7 og H2020.
Varðveislusafnið notar opna hugbúnaðinn DSpace.
Nýlega bætt við
Assessing the relevance of biosignal-controlled robotic rehabilitation technologies : a systematic review
(2026-09) Santoriello, Vittorio; Cesarelli, Giuseppe; Ficuciello, Fanny; Giugliano, Carmine; Angelone, Francesca; Russo, Michela; Ponsiglione, Alfonso Maria; Romano, Maria; Department of Engineering
Technology is transforming rehabilitative medicine by enhancing accessibility and personalisation. Robot-assisted rehabilitation uses robotic systems for recovery from physical and neurological impairments, enabling intensive, repetitive training with real-time feedback. These systems aim to restore motor function and mobility and to increase independence in daily life, needs that are growing in light of population ageing. Rather than revisiting control algorithms or mechanical design, this review aims to investigate the combined use of bio-signals and robotic rehabilitation systems, and to discuss their potential in rehabilitation settings outside disease-specific clinical frameworks. We reviewed studies published from 2020 through early 2025 to capture recent advances in this rapidly evolving field. Our objective was to assess the relevance of these technologies to rehabilitation outcomes, such as improvements in motor function or other clinical metrics. We considered robot-assisted systems driven by biosignals such as electromyography (EMG), electroencephalography, or electrocardiography and included only studies reporting measurable rehabilitative outcomes. Following PRISMA guidelines, we searched Scopus, PubMed, and WoS databases. Of 207 records screened, 15 met the inclusion criteria. Overall, the included studies suggest that contemporary biosignal-controlled robotic rehabilitation, particularly EMG-driven approaches, may support more intensive, personalised, and engaging therapy than conventional care alone, although the current evidence remains preliminary. Preliminary findings indicate that, by closing the loop among patient, device, and therapist, these systems may reduce muscle activity and support improvements in motor performance; however, the evidence base remains heterogeneous and generally underpowered. To support future clinical adoption, the field needs adequately sized, head-to-head trials with standardised outcome measures, longer follow-up, and transparent reporting of adherence, adverse events, user experience, and implementation barriers, including training and cost. These steps will be important to further assess efficacy and evaluate usability in real-world settings.
Residual deep neural network and response features for inverse modeling of microwave passives
(2026-07) Koziel, Slawomir; Sahu, Kaustab C.; Pietrenko-Dabrowska, Anna; Department of Engineering
Electromagnetic (EM) solvers are widely used in microwave design for their accuracy, but are computationally expensive. Surrogate models offer a remedy, yet face challenges such as the curse of dimensionality. This work introduces a novel inverse modeling approach using deep learning and carefully selected response features to predict circuit geometry from desired operating parameters such as center frequency and power split ratio. Data samples for model training are collected through a performance-driven sequential process. Setting the model at the feature level implicitly reduces dimensionality, as the feature set is smaller than the geometry parameter set. This allows reliable regressors to be built with far fewer training samples, greatly lowering computational cost. Additional savings are achieved through explicit dimensionality reduction, carried out using global sensitivity analysis. To set up an inverse surrogate, we employ a customized residual deep residual neural network (ResNet), which is particularly well-suited for capturing dependencies between features and geometry parameters. This is due to ResNet’s resilience to a certain degree of parametric redundancy in the circuit structure. The inverse model produces parameter vectors sufficiently close to the optimal ones, thus only minor refinement is necessary. Also, our model is shown to be more accurate than state-of-the-art forward and inverse metamodels. The proposed ResNet embedded in the inverse modeling regime and combined with dimensionality-reduced sequential sampling are the main artificial intelligence-related contributions of this study. Engineering applications encapsulate the successful employment of our framework for rapid global circuit optimization, as exemplified using three microwave passives.
GRAIL-heart : A graph attention network for inferring ligand-receptor interactions in spatial transcriptomics
(2026-12) Kgabeng, Tumo; Wang, Lulu; Ngwangwa, Harry; Nemavhola, Fulufhelo; Pandelani, Thanyani; Department of Engineering
Cell-cell communication through ligand-receptor (L-R) interactions orchestrates cardiac development, homeostasis, and disease progression, yet existing computational methods cannot infer directional signalling networks or distinguish causal from correlational interactions in spatial transcriptomics data. We present GRAIL-Heart, a graph-attention-based framework that integrates spatial tissue topology with multi-task learning to predict context-dependent L-R interactions, reconstruct gene expression, and infer causal signalling pathways. Validated on 42,654 cells across six cardiac regions from the Human Heart Cell Atlas, GRAIL-Heart achieves 94.3% AUROC for L-R prediction, successfully recovers known cardiac signalling pathways, and reveals region-specific complement system involvement in cardiac homeostasis. The method outperforms existing approaches by 56%, is uniquely capable of gene expression reconstruction (R2 = 0.996), and provides an interpretable, generalisable framework for prioritising high-confidence ligand-receptor hypotheses for downstream experimental validation across diverse tissues.Key innovations:•Spatial graph integration: Dual-edge architecture encoding both spatial proximity and ligand-receptor-specific relationships for context-aware interaction prediction.•Multi-task learning with causal inference: Simultaneous optimisation of L-R prediction, expression reconstruction, and inverse modelling to distinguish functionally important from correlational interactions.•Open-source implementation: Fully reproducible code, pretrained cardiac model, and interactive web explorer enabling broad adoption across spatial transcriptomics applications.
Validation of manually scored multichannel frontal electroencephalography against polysomnography in a paediatric cohort
(2025-02-13) Sigurdardottir, Sigridur; Pitkänen, Henna; Korkalainen, Henri; Kainulainen, Samu; Serwatko, Marta; Ólafsdóttir, Kristín Anna; Sigurðardóttir, Sigurveig Þ; Clausen, Michael Valur; Somaskandhan, Pranavan; Stražišar, Barbara G; Leppänen, Timo; Arnardóttir, Erna Sif; Department of Engineering
Polysomnography is the only internationally recognized method to diagnose paediatric obstructive sleep apnea, thus, simpler and more cost-effective diagnostic tools are urgently needed. This study aimed to validate the manual scoring of frontal self-applicable electroencephalography against polysomnography in a paediatric cohort. The polysomnography and the frontal electroencephalography were simultaneously recorded for 1 night (n = 102) in 10-13-year-old children. Scoring was performed according to the American Academy of Sleep Medicine rules, with minor adjustments to the frontal electroencephalography. Manual scorings of sleep stages were compared in an epoch-by-epoch manner using Cohen's kappa (κ) and confusion matrices using three different models: the three-stage (wake/non-rapid eye movement/rapid eye movement), the four-stage (wake/sleep stage 1 + sleep stage 2/deep sleep Stage 3/rapid eye movement) and the five-stage model (wake/sleep stage 1/sleep stage 2/deep sleep Stage 3/rapid eye movement). The inter-scorer agreements were assessed, and the intraclass correlation coefficient was used for common sleep variables: total sleep time, wake after sleep onset, sleep efficiency, sleep-onset latency and arousal index. Cohen's κ values for the three-stage, four-stage and five-stage models were 0.85, 0.73 and 0.70, respectively. The agreement for the sleep variables studied ranged from 0.87 to 0.99. The inter-rater agreement (n = 10) was κ = 0.78 for the polysomnography and κ = 0.70 for the frontal electroencephalography. Sleep staging from the frontal electroencephalography was comparable to that of a standard polysomnography in a paediatric cohort, and showed promising results in estimating sleep time and sleep architecture.
Melanoma dedifferentiation enhances IFNγ responses and reduces cytotoxic T cell activity
(University of Iceland, School of Health Sciences, Faculty of Medicine, 2026-09) Sævarsson, Teitur; Berglind Ósk Einarsdóttir; Faculty of Medicine (UI); Læknadeild (HÍ); School of Health Sciences (UI); Heilbrigðisvísindasvið (HÍ)
Cutaneous melanoma is a deadly skin cancer arising from epidermal melanocytes. A defining feature of melanoma cells is their ability to reversibly dedifferentiate in response to cellular stress, largely through loss of the transcription factor MITF. Dedifferentiation is associated with reduced melanocytic antigen expression, increased migratory and invasive capacity, altered responses to inflammatory stimuli, e.g. leading to increased IFNγ-induced PD-L1 expression. This indicates that melanoma dedifferentiation may influence anti-tumor immune responses and contribute to immune evasion. In this thesis, the effects of melanoma dedifferentiation on immunomodulatory gene expression and melanoma-T cell responses were investigated.
The effects of dedifferentiation on immunomodulatory gene expression were assessed by siRNA mediated knockdown of MITF expression in 624Mel cells followed by IFNγ stimulation. Transcriptomic and epigenetic effects were assessed via RNA-seq and ATAC-seq, respectively. IFNγ-induced PD-L1 expression following MITF knockdown was evaluated in 624Mel, SK-MEL-28, A357P, Malme-3M and B16 melanoma cells by qPCR and western blotting. Small molecular inhibitors against the JAK kinases, STAT3 and NFκB and siRNAs targeting STAT1 and IRF1 were used to investigate PD-L1 regulation in 624Mel cells. Public RNA-seq data from patient-derived melanoma cell lines were analyzed to validate the broader relevance of the results. Co-cultures were performed using TCR-redirected T cells and GFP expressing melanoma cell lines at varying differentiation states in live-cell imaging assays. Cytokine secretion in the co-cultures was quantified using a Luminex assay.
The results showed that dedifferentiation predisposes melanoma cells to IFNγ signaling through rearrangements in chromatin accessibility, resulting in widespread expression of immunomodulatory genes. PD-L1 expression in dedifferentiated 624Mel cells was mediated via the canonical JAK-STAT1-IRF1 pathway and accompanied by a significant increase in IRF and STAT transcription factor motif accessibility. Co-cultures of TCRredirected T cells and melanoma cell lines suggest that differentiation state does not impair antigen processing and presentation. However, the rate of cytotoxic T cell responses was reduced by IFNγ pre-treatment of either highly differentiated or undifferentiated melanoma cell lines, and by induced dedifferentiation via MITF knockdown. T cell derived secretomes following culture with undifferentiated melanoma cells contained less TNFα, Granzyme B, CXL10, MIF and CCL5 than when cultured with differentiated cells, while containing more PD-L1, IL-8, CD25 and IL-6, suggesting decreased T cell activation. In conclusion, melanoma dedifferentiation may directly mediate immunosuppression.
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- University of Iceland
- University of Akureyri
- Bifröst University
- Hólar University College
- IRIS
- Agricultural University of Iceland
- National and University Library of Iceland
- Iceland University of the Arts