Search

Abstract Condition monitoring of the insulating system within power transformers has a massive importance according to the electrical utilities. Dissolved gas analysis (DGA) is frequently used for this purpose. However, DGA lacks the necessary level of accuracy to identify all equipment faults, particularly in their initial stages of degradation. Also, it does not have the capability for real‐time monitoring and relies on manual sampling and laboratory testing, causing potential delays in fault identification. Additionally, the interpretation of DGA data necessitates specialised expertise, which may pose difficulties for smaller entities that have limited access to resources. Therefore, the contribution of this research is to use infrared spectroscopy measurements as a new effective technique substituting the DGA method for fault diagnosis in insulating oil. The inception faults that were considered in this study were the electrical fault (discharges of high energy) and the thermal fault (300°C

Abstract For the past few years, the addition of renewable energy sources has been on the rise, but the unregulated addition of these sources can cause severe harm to the grid. Therefore, it is necessary to have a predefined limit for a grid, beyond which no further addition of renewables should be allowed without reinforcement. That limit is called the hosting capacity (HC), which is addressed in the literature by search‐based methods with heavy computational burdens. This manuscript first presents a framework to find the HC for a grid. The same framework is then applied to 503 different simulated but realistic LV distribution feeders in Finland to find their HCs and the most effective network parameters in defining a network specific HC. Next, different machine learning models, that is, Decision Tree (DT), Random Forest (RF), Linear Regression (LR), K nearest neighbours (KNN), Logistic Regression, and Support Vector Machine (SVM) are implemented on the generated data. For the classification case, the accuracy values for logistic regression, KNN, and SVM were 0.89, 0.84, and 0.81, respectively. The findings demonstrate that the developed machine learning based technique will enable distribution network operators in finding the HC without applying any deterministic or probabilistic approaches.

Abstract The electrification of the transport sector to control the carbon footprint has been gaining momentum over the last decade with electric vehicles (EVs) seen as the replacement for conventional internal combustion engines. Economic incentives, subsidies, and tax exemptions are also paving the way for rising EV penetration in the power distribution networks. However, the exponential EV adoption requires careful technical and regulatory analysis of traditional networks to satisfy the network reliability constraints. Therefore, it would be vital to find EV hosting capacity (HC) limits of networks from a multifaceted approach involving various market players, mainly distribution system operators and EV owners. This review provides a systematic categorization of EV hosting capacity evaluation and improvement methods, thus enabling researchers and industry personnel to navigate the advancing landscape of EVs. This novel framework extends beyond the theoretical implication of diverse objective functions and HC improvement methods to the actual numerical values of EV HC across varying geographical settings. Therefore, this unique synthesis of varying aspects of EV HC facilitates the in‐depth understanding of the integration of sustainable energy and transport sector.

Abstract Background Air pollution is recognized as an emerging environmental risk factor for neurological diseases. Large-scale epidemiological studies associate traffic-related particulate matter (PM) with impaired cognitive functions and increased incidence of neurodegenerative diseases such as Alzheimer’s disease. Inhaled components of PM may directly invade the brain via the olfactory route, or act through peripheral system responses resulting in inflammation and oxidative stress in the brain. Microglia are the immune cells of the brain implicated in the progression of neurodegenerative diseases. However, it remains unknown how PM affects live human microglia. Results Here we show that two different PMs derived from exhausts of cars running on EN590 diesel or compressed natural gas (CNG) alter the function of human microglia-like cells in vitro. We exposed human induced pluripotent stem cell (iPSC)-derived microglia-like cells (iMGLs) to traffic related PMs and explored their functional responses. Lower concentrations of PMs ranging between 10 and 100 µg ml−1 increased microglial survival whereas higher concentrations became toxic over time. Both tested pollutants impaired microglial phagocytosis and increased secretion of a few proinflammatory cytokines with distinct patterns, compared to lipopolysaccharide induced responses. iMGLs showed pollutant dependent responses to production of reactive oxygen species (ROS) with CNG inducing and EN590 reducing ROS production. Conclusions Our study indicates that traffic-related air pollutants alter the function of human microglia and warrant further studies to determine whether these changes contribute to adverse effects in the brain and on cognition over time. This study demonstrates human iPSC-microglia as a valuable tool to study functional microglial responses to environmental agents.

Abstract Rare or novel missense variants in large genes such as TTN and NEB are frequent in the general population, which hampers the interpretation of putative disease-causing biallelic variants in patients with sporadic neuromuscular disorders. Often, when the first initial genetic analysis is performed, the reconstructed haplotype, i.e. phasing information of the variants is missing. Segregation analysis increases the diagnostic turnaround time and is not always possible if samples from family members are lacking. To overcome this difficulty, we investigated how well the linked-read technology succeeded to phase variants in these large genes, and whether it improved the identification of structural variants. Linked-read sequencing data of nemaline myopathy, distal myopathy, and proximal myopathy patients were analyzed for phasing, single nucleotide variants, and structural variants. Variant phasing was successful in the large muscle genes studied. The longest continuous phase blocks were gained using high-quality DNA samples with long DNA fragments. Homozygosity increased the number of phase blocks, especially in exome sequencing samples lacking intronic variation. In our cohort, linked-read sequencing added more information about the structural variation but did not lead to a molecular genetic diagnosis. The linked-read technology can support the clinical diagnosis of neuromuscular and other genetic disorders.

Abstract Background Individual assessment of CYP enzyme activities can be challenging. Recently, the potato alkaloid solanidine was suggested as a biomarker for CYP2D6 activity. Here, we aimed to characterize the sensitivity and specificity of solanidine as a CYP2D6 biomarker among Finnish volunteers with known CYP2D6 genotypes. Results Using non-targeted metabolomics analysis, we identified 9152 metabolite features in the fasting plasma samples of 356 healthy volunteers. Machine learning models suggested strong association between CYP2D6 genotype-based phenotype classes with a metabolite feature identified as solanidine. Plasma solanidine concentration was 1887% higher in genetically poor CYP2D6 metabolizers (gPM) (n = 9; 95% confidence interval 755%, 4515%; P = 1.88 × 10–11), 74% higher in intermediate CYP2D6 metabolizers (gIM) (n = 89; 27%, 138%; P = 6.40 × 10–4), and 35% lower in ultrarapid CYP2D6 metabolizers (gUM) (n = 20; 64%, − 17%; P = 0.151) than in genetically normal CYP2D6 metabolizers (gNM; n = 196). The solanidine metabolites m/z 444 and 430 to solanidine concentration ratios showed even stronger associations with CYP2D6 phenotypes. Furthermore, the areas under the receiver operating characteristic and precision–recall curves for these metabolic ratios showed equal or better performances for identifying the gPM, gIM, and gUM phenotype groups than the other metabolites, their ratios to solanidine, or solanidine alone. In vitro studies with human recombinant CYP enzymes showed that solanidine was metabolized mainly by CYP2D6, with a minor contribution from CYP3A4/5. In human liver microsomes, the CYP2D6 inhibitor paroxetine nearly completely (95%) inhibited the metabolism of solanidine. In a genome-wide association study, several variants near the CYP2D6 gene associated with plasma solanidine metabolite ratios. Conclusions These results are in line with earlier studies and further indicate that solanidine and its metabolites are sensitive and specific biomarkers for measuring CYP2D6 activity. Since potato consumption is common worldwide, this biomarker could be useful for evaluating CYP2D6-mediated drug–drug interactions and to improve prediction of CYP2D6 activity in addition to genotyping.

In this article, we argue that drama can serve as an interconnecting method for climate change education. In this study, we elaborate the practice of drama and participation experiences through three drama workshops: 1) process drama work on the global, social, and individual aspects of climate change, 2) outdoor drama practice on relations to nature, and 3) reflections through drama practice. The human dimension of the sustainability issues, conditions of interdependence, and collaboration were explored and manifested through the drama practices, which created frames for embodied, creative and cognitive dialogues between people with different perspectives. Being differently--as experienced through the embodied, collective, and creative practices of drama--seemed to promote experiences of interconnectedness, widen perspectives of sustainability, and motivate acting differently.

Background: Development of synaptic activity is a key neuronal characteristic that relies largely on interactions between neurons and astrocytes. Although astrocytes have known roles in regulating synaptic function and malfunction, the use of human- or donor-specific astrocytes in disease models is still rare. Rodent astrocytes are routinely used to enhance neuronal activity in cell cultures, but less is known about how human astrocytes influence neuronal activity. Methods: We established human induced pluripotent stem cell–derived neuron-astrocyte cocultures and studied their functional development on microelectrode array. We used cell lines from 5 neurotypical control individuals and 3 pairs of monozygotic twins discordant for schizophrenia. A method combining NGN2 overexpression and dual SMAD inhibition was used for neuronal differentiation. The neurons were cocultured with human induced pluripotent stem cell–derived astrocytes differentiated from 6-month-old astrospheres or rat astrocytes. Results: We found that the human induced pluripotent stem cell–derived cocultures developed complex network bursting activity similar to neuronal cocultures with rat astrocytes. However, the effect of NMDA receptors on neuronal network burst frequency (NBF) differed between cocultures containing human or rat astrocytes. By using cocultures derived from patients with schizophrenia and unaffected individuals, we found lowered NBF in the affected cells. We continued by demonstrating how astrocytes from an unaffected individual rescued the lowered NBF in the affected neurons by increasing NMDA receptor activity. Conclusions: Our results indicate that astrocytes participate in the regulation of neuronal NBF through a mechanism that involves NMDA receptors. These findings shed light on the importance of using human and donor-specific astrocytes in disease modeling.

Renewable electrical energy (such as: solar and wind energies) generation in microgrids (MGs), is gaining attention to reduce greenhouse gas emissions. Microgrid operators (MOs) aim to create self-sufficient, environmentally sustainable grids, increasing the capacity of renewable energy sources (RESs) by up to 100%. Despite of the benefits of this trend, challenges arise from non-controlled characteristics of these power generations and their seasonal variations, causing fluctuations and renewable energy curtailment. Although the technical solutions; such as: the demand response (DR) programs, and the conventional electrical energy storage systems (EESSs) can help, however those may face limitations in countries with high seasonal energy generation and consumption variations. This paper introduces cryptocurrency mining loads (CMLs) as innovative virtual energy storage systems (VESSs), named cryptocurrency energy storage systems (CESSs). It proposes a structure to store excess renewable energy in cryptocurrency units (CCUs) like Bitcoin (BTC). CESSs can be charged during off-peak intervals and, conversely, they discharge during high-demand periods to reduce the overall operational cost of MGs. Furthermore, it presents a new energy management system (EMS) formulation for the optimal operation of MGs in the presence of CESSs, providing an opportunity to generate additional electricity from RESs and to mitigate renewable energy curtailment. This paper explores the optimal operation conditions of both islanded and grid-connected MG with the proposed CESS. Utilizing a dataset from an island in Finland as a practical MG, its effectiveness is demonstrated through several case studies. The results of one case study in this paper demonstrate that the proposed CESS can decrease the operating cost of the MG by about 46.5%. Additionally, it is showed that by application of CESS the renewable energy curtailment is significantly reduced, and approached zero.

Globally, annual emissions from managed organic soils accounts for up to 5% of all anthropogenic greenhouse gas (GHG) emissions. Climate-wise management and restoration of degraded organic soils could reduce GHG emissions quickly and at relatively low costs. The European Union (EU) Member States that have large areas of organic soils with high GHG emissions are Sweden, Finland, Germany, Ireland, Poland, Netherlands, and the Baltic countries. To meet the climate targets and objectives of the Paris Agreement the land-use sector is indispensable and mitigation policies targeting organic soils will be needed. The international regulatory framework is broad and quite unspecific. In contrast, the European Union has initiated binding regulation for the land-use sector through the EU Climate Law, the EU LULUCF regulation, and the proposed EU Nature Restoration Law. However, even this regulatory approach is not on track to deliver on its binding ambitions, indicating the need for more effective implementation measures also on organic soils in the EU and its member states. Furthermore, we argue that appropriate policy selection should consider current knowledge regarding the climate impacts of management options of organic soils. Lastly, we need more studies on GHG emissions, and standardized methods for GHG inventories, to resolve uncertainties surrounding the impacts of management to GHG emissions. Successful policy implementation requires more efforts but also improved scientific justification through continuous consideration of climate policy integrity and strengthening of the reliability of GHG inventories.

The COVID-19 pandemic era witnessed an intense competition for knowledge, which was essentially a contest over whose knowledge becomes authoritative. In this article, we focus on the role of expert networks in contesting the official pandemic policy. We study specifically an expert network in Finland, consisting of researchers from different disciplines ranging from law to economics and medicine. What makes the case so intriguing is that the network’s communicating style was affectively charged and alarmist, setting it apart from traditional expert language that is articulated in a style that aims towards neutrality and objectivity. Similar networks were formed across the world under the loose umbrella term ‘Zero Covid’ during the intensive pandemic years 2020 to 2022. Our aim is to increase knowledge on the role of such spontaneous networks during a health crisis that is characterised by epistemic instability. The pandemic era was particularly conducive to the formation of such networks, but they have been less active in the recovery phase. We argue that these networks were energised by the epistemic void in the intensive phase of the pandemic but lost their impetus in the recovery phase, which has been characterised by more robust knowledge and a less acute sense of threat. We employ frame analysis to identify two dominant frames – the national battle frame and the urgency frame – that the Finnish network used in public communication. We also briefly review its public reception to illustrate how the network managed to generate authority in the pandemic era. Finally, we discuss the tricky nature of policy advice by scientists who are urged to step into the political sphere to offer their advice to politicians but, as a result, can be labelled as political actors rather than as scientific experts.

Summary: Background: Lynch syndrome (LS) is one of the most common hereditary cancer syndromes worldwide. Dominantly inherited mutation in one of four DNA mismatch repair genes combined with somatic events leads to mismatch repair deficiency and microsatellite instability (MSI) in tumours. Due to a high lifetime risk of cancer, regular surveillance plays a key role in cancer prevention; yet the observation of frequent interval cancers points to insufficient cancer prevention by colonoscopy-based methods alone. This study aimed to identify precancerous functional changes in colonic mucosa that could facilitate the monitoring and prevention of cancer development in LS. Methods: The study material comprised colon biopsy specimens (n = 71) collected during colonoscopy examinations from LS carriers (tumour-free, or diagnosed with adenoma, or diagnosed with carcinoma) and a control group, which included sporadic cases without LS or neoplasia. The majority (80%) of LS carriers had an inherited genetic MLH1 mutation. The remaining 20% included MSH2 mutation carriers (13%) and MSH6 mutation carriers (7%). The transcriptomes were first analysed with RNA-sequencing and followed up with Gorilla Ontology analysis and Reactome Knowledgebase and Ingenuity Pathway Analyses to detect functional changes that might be associated with the initiation of the neoplastic process in LS individuals. Findings: With pathway and gene ontology analyses combined with measurement of mitotic perimeters from colonic mucosa and tumours, we found an increased tendency to chromosomal instability (CIN), already present in macroscopically normal LS mucosa. Our results suggest that CIN is an earlier aberration than MSI and may be the initial cancer driving aberration, whereas MSI accelerates tumour formation. Furthermore, our results suggest that MLH1 deficiency plays a significant role in the development of CIN. Interpretation: The results validate our previous findings from mice and highlight early mitotic abnormalities as an important contributor and precancerous marker of colorectal tumourigenesis in LS. Funding: This work was supported by grants from the Jane and Aatos Erkko Foundation, the Academy of Finland (330606 and 331284), Cancer Foundation Finland sr, and the Sigrid Jusélius Foundation. Open access is funded by Helsinki University Library.

Gene fusions are common in high-grade serous ovarian cancer (HGSC). Such genetic lesions may promote tumorigenesis, but the pathogenic mechanisms are currently poorly understood. Here, we investigated the role of a PIK3R1-CCDC178 fusion identified from a patient with advanced HGSC. We show that the fusion induces HGSC cell migration by regulating ERK1/2 and increases resistance to platinum treatment. Platinum resistance was associated with rod and ring-like cellular structure formation. These structures contained, in addition to the fusion protein, CIN85, a key regulator of PI3K-AKT-mTOR signaling. Our data suggest that the fusion-driven structure formation induces a previously unrecognized cell survival and resistance mechanism, which depends on ERK1/2-activation.

Abstract Epidemiological studies have shown associations between whole-grain intake and lowered disease risk. A sufficient level of whole-grain intake to reach the health benefits has not been established, and there is limited knowledge about the impact of whole-grain intake on metabolite levels. In this clinical intervention study, we aimed to identify plasma and urine metabolites associated with two different intake levels of whole-grain wheat and rye and to correlate them with clinical plasma biomarkers. Healthy volunteers (N = 68) were divided into two groups receiving either whole-grain wheat or whole-grain rye in two four-week interventions with 48 and 96 g/d of whole grains consumed. The metabolomics of the plasma samples was performed with UPLC–QTOF-MS. Plasma alkylresorcinols were quantified with GC-MS and plasma and urinary mammalian lignans with HPLC-ECD. The high-dose intervention impacted the metabolite profile, including microbial metabolites, more in the rye-enriched diet compared with wheat. Among the increased metabolites were alkylresorcinol glucuronides, sinapyl alcohol, and pipecolic acid betaine, while the decreased metabolites included acylcarnitines and ether lipids. Plasma alkylresorcinols, urinary enterolactone, and total mammalian lignans reflected the study diets in a dose-dependent manner. Several key metabolites linked with whole-grain consumption and gut microbial metabolism increased in a linear manner between the two interventions. The results reveal that an increase in whole-grain intake, particularly rye, is strongly reflected in the metabolite profile, is correlated with clinical variables, and suggests that a diet rich in whole grains promotes the growth and/or metabolism of microbes producing potentially beneficial microbial metabolites.

Shared leadership has gained momentum in fields ranging from education and healthcare to information technology and sports. Given its collaborative or participatory nature, design practice should be a fertile ground for the emergence of shared leadership. Yet, previous literature on design management and leadership has primarily conceptualized leadership as an individual characteristic. To extend the existing literature, this article presents an autoethnographic exploration of shared leadership from a sociomaterial perspective. It focuses on a design project dealing with remote healthcare solutions and contributes to existing literature on shared leadership and sociomateriality. I clarify how values, individuals, and objects interact over time to evolve, stabilize, and destabilize shared leadership. This finding calls for a more granular understanding of objects. This suggests that objects influence shared leadership not just through their form and development but also by imbuing and reinforcing certain values. The findings also highlight the need to regard design leadership as sociomaterial configurations rather than solely as an individual characteristic. The article concludes by proposing further research avenues and discussing implications for design practice.