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The proliferation of the Internet of Things (IoT), artificial intelligence (AI), the adoption of 5G, and progress towards 6G technology have led to the accumulation of massive amounts of real-world data; however, a significant portion of the data generated by smart cities and smart buildings remains unused. A notable problem is the shift of statistical properties in real-world streaming over time caused by unexpected factors, referred to as concept drift, which results in less efficient predictive models. To address this problem, the latest research leverages the cloud–edge continuum paradigm for the deployment of AI and general smart city applications while utilising the available resources optimally. In this article, we propose a computing architecture for different smart city applications in edge micro data centre (EMDC) settings over a hybrid cloud–edge continuum to support the deployment of AI workloads. We implement a feedback-driven automated concept drift detection and adaptation methodology, combining base learner long short-term memory (LSTM) with Page–Hinkley test (PHT), adaptive windowing (ADWIN) and the Kolmogorov–Smirnov windowing (KSWIN). Real-world data streams are utilised to forecast from various environmental sensors installed at the University of Oulu Smart Campus. The feedback-based concept drift detection and adaption process is first evaluated using synthetic datasets with known concept drift points and then employed in the real-world data. Subsequently, the implementation is evaluated using the state-of-the-art MAE, RMSE, and MAPE methods. The results showed a reduction in MAPE from 8.5% to 3.88% when concept drift detection was applied. Additionally, the challenges faced and the effectiveness of the suggested solutions are explored.

CHEK2 is a well-established breast cancer susceptibility gene. The most frequent pathogenic CHEK2 variant is 1100delC, a loss-of-function mutation conferring 2-fold risk for breast cancer. This gene also harbors other rare variants encountered in the clinical gene panels for hereditary cancer. One of these is CHEK2 c.1312 G > T, p.(Asp438Tyr) in the kinase domain of the protein, but due to its rarity its clinical significance for breast cancer predisposition has remained unclear. Here, we tested the prevalence of CHEK2 p.(Asp438Tyr) allele showing enrichment in the Northern Finnish population, in a total of 2284 breast cancer patients from this geographical region. Genotyping was performed for DNA samples extracted from peripheral blood using high-resolution melt analysis. Fourteen CHEK2 p.(Asp438Tyr) carriers were identified (14/2284, 0.6%, P = 0.67): two in the cohort of breast cancer cases with the indication of inherited disease susceptibility (2/281, 0.7%, P = 1.00) and twelve in the breast cancer cohort unselected for the family history of disease and age at disease onset (12/2003, 0.6%, P = 0.66). This frequency did not differ from the frequency in the general population (10/1299, 0.8%). No CHEK2 p.(Asp438Tyr) homozygotes were identified. Our results indicate that CHEK2 p.(Asp438Tyr) carriers do not have an increased risk for breast cancer and the classification of the CHEK2 p.(Asp438Tyr) variant can be changed from the variant of uncertain significance (VUS) to likely benign for breast cancer.

TINF2 is a critical subunit of the shelterin complex, which protects and maintains the length of telomeres. Pathogenic missense and truncating TINF2 mutations are causative for dyskeratosis congenita (DC), a rare, dominantly inherited bone marrow failure syndrome characterized by mucocutaneous abnormalities and cancer predisposition. Recent reports indicate that specific TINF2 truncating mutations act as high penetrance cancer predisposition alleles outside DC context, including breast cancer in their tumor spectrum. Here, we have evaluated the role of germline mutations in TINF2 and other shelterin genes in inherited breast cancer susceptibility using exome sequencing data from 98 Northern Finnish breast cancer cases with indication of inherited disease predisposition as a discovery cohort. A single protein truncating variant, TINF2 p.Tyr312Ter, was identified in one of the cases (1/98), and four more carriers were observed in the subsequently genotyped unselected breast cancer cohort (4/1904). None of the carriers were reported to have DC. TINF2 p.Tyr312Ter resulted in stable short form of mRNA transcript, and normal telomere length has been indicated by a recent report. Although recurrent in cases (total of 5/2095), TINF2 p.Tyr312Ter is also present in Finnish population controls (8/12,517), and the observed 4-fold higher frequency in cases falls at most into the range of moderate breast cancer risk alleles (OR 3.74, 95% CI 1.22–11.45, p = 0.029). Current results indicate that not all TINF2 truncating variants are high cancer risk alleles and add further evidence that different TINF2 mutations can have very diverse effects on the disease phenotype.

Breast cancer is the most common malignancy among women, and the risk of developing the disease is strongly influenced by inherited predisposing mutations. Genes such as BRCA1, BRCA2 and PALB2 harbor variants that confer a high breast cancer risk, but despite extensive research, only about half of the hereditary component of the disease has been uncovered. The identification of novel predisposing alleles is imperative, as it enables better genetic counseling for the mutation carrier families and earlier cancer diagnosis. The first aim of this study was to find rare, deleterious breast cancer predisposing mutations using whole-exome sequencing of 98 Northern Finnish breast cancer cases with indication of inherited disease susceptibility. The second aim was to analyze the whole-exome sequencing data for ultra-rare protein truncating variants in shelterin complex genes and to assess their role in inherited breast cancer susceptibility. The third aim was to evaluate the prevalence and significance of CHEK2 p.Asp438Tyr, a rare missense mutation enriched in North Ostrobothnia and classified as a variant of unknown significance. Whole-exome sequencing revealed a recurrent splice acceptor mutation SERPINA3 c.918‐1G>C, which showed association with breast cancer in both hereditary and unselected cohorts (p = 0.006, OR = 5.1 and p = 0.009, OR = 2.8, respectively). The mutation was also enriched among medullary breast cancer cases, which is a rare tumor subtype (p = 0.000014, OR = 42.9). These results suggested that SERPINA3 c.918‐1G>C is a moderate-risk breast cancer allele. One protein truncating variant, p.Tyr312Ter, was discovered in shelterin complex gene TINF2. It had a 4-fold frequency in cases (0.24%, 5/2095) compared to controls (0.06%, 8/12 517) (p = 0.029, OR = 3.74). The results indicated that TINF2 p.Tyr312Ter is at most a moderate-risk breast cancer allele, even though similar mutations in TINF2 have been reported to result in high cancer risk an, Tiivistelmä Rintasyöpä on naisten yleisin syöpä ja sairastumisriskiin vaikuttavat erityisesti perinnölliset alttiustekijät. Esimerkiksi BRCA1, BRCA2 ja PALB2-geeneissä tunnetaan korkean rintasyöpäriskin aiheuttavia mutaatioita. Mittavista tutkimuksista huolimatta vain noin puolet perinnöllisistä altistavista tekijöistä on löydetty. Uusien rintasyövälle altistavien mutaatioiden tunnistaminen on tärkeää, sillä se mahdollistaa paremman perinnöllisyysneuvonnan ja taudin varhaisemman diagnosoinnin. Tämän tutkimuksen ensimmäisenä tavoitteena oli löytää uusia rintasyövälle altistavia harvinaisia mutaatioita sekvensoimalla genomin koodaava alue, eksomi, 98 pohjoissuomalaiselta rintasyövän sairastaneelta potilaalta, joilla on viitteitä perinnöllisestä rintasyöpäalttiudesta. Toisena päämääränä oli etsiä eksomisekvensointidatan avulla shelterin-kompleksin geeneistä erittäin harvinaisia proteiinitrunkaatioita ja arvioida niiden merkitystä rintasyöpäalttiudessa. Kolmantena tavoitteena oli tutkia harvinaista CHEK2 p.Asp438Tyr missense-mutaatiota, joka on rikastunut Pohjois-Pohjanmaalla ja luokitellaan merkitykseltään epävarmaksi variantiksi. Eksomisekvensoinnin avulla löytyi aineistossa toistuva silmukointialueen mutaatio SERPINA3 c.918‐1G>C, joka oli yleisempi sekä perinnöllisessä että valikoimattomassa rintasyöpäaineistossa (p = 0.006, OR = 5.1 ja p = 0.009, OR = 2.8) verrattuna kontrolleihin. Tämä mutaatio oli myös rikastunut harvinaisen medullaarisen rintasyövän alatyypin tapauksissa (p = 0.000014, OR = 42.9). Tulosten perusteella SERPINA3 c.918‐1G>C vaikuttaa kohtalaisen riskin rintasyöpäalttiusalleelilta. Shelterin-kompleksin geeneistä löytyi yksi proteiinitrunkaatio, TINF2 p.Tyr312Ter. Sen esiintyvyys oli nelinkertainen rintasyöpäaineistossa (0,24 %, 5/2095) verrattuna kontrolleihin (0,06 %, 8/12 517) (p = 0.029, OR = 3.74). Tulosten mukaan p.Tyr312Ter on korkeintaan kohtalaisen riskin tekijä, vaikka aikaisemmin raportoidut samankaltaiset TINF2-mutaatiot aiheuttava

Yhteenveto Kainuun sote kutsui kaikki 170 kainuulaista, jotka sairastivat koronan aikavälillä huhtikuu 2020 — huhtikuu 2021, antamaan kolme verinäytettä koronavirusvasta-aineiden mittaamista varten. Ensimmäinen näyte annettiin keskimäärin 4 kk taudin jälkeen, toinen 9 kk ja viimeinen näyte noin vuoden kuluttua sairastumisesta. SARS-CoV-2-virusta vastaan muodostuneiden vasta-aineiden tasot vaihtelivat erittäin paljon henkilöiden välillä. Kuudella potilaalla vasta-ainetaso oli alle positiivisen diagnoosin raja-arvon ensimmäisessä näytteessä. Jos potilasta ei rokotettu ennen seuraavan seeruminäytteen ottamista, SARS-CoV-2-vastaainetasot laskivat keskimäärin puoleen. Jos potilas oli rokotettu ennen seuraavaa näytteenottoa, SARS-CoV-2-vasta-aineiden määrä nousi keskimäärin kaksinkertaiseksi. Yhtä lukuun ottamatta kaikilla alussa vasta-aineiden suhteen positiivisiksi todetuilla potilailla oli vasta-aineita yli positiivisen tuloksen raja-arvon myös 12 kk infektion jälkeen. Terveillä henkilöillä rokotuksen aiheuttama vasta-ainevaste oli samanlainen kuin potilailla oli taudin aiheuttama, mutta kaikkein korkeimmat SARS-CoV-2-vasta-ainetasot puuttuivat kahden ensimmäisen rokotuksen jälkeen. Tulosten perusteella on ymmärrettävää, miksi rokotuksia on täytynyt uusia useampaan kertaan. Samoin tulokset näyttävät, että rokotus tehostaa myös taudin sairastaneiden immuunivastetta. Henkilöiden välillä hyvin paljon eroava vasta-ainevaste kertoo, että pelkän virusdiagnostiikkatestin kehittämisen lisäksi tulevissa pandemioissa on hyvä kehittää myös vasta-ainetestejä ja käyttää niitä seuraamaan sekä rokotteen tehoa, että yksittäisten henkilöiden vasta-ainevastetta.

Tiivistelmä. Suomalaisen peruskoulun tavoitteena on tarjota kaikille lapsille ja nuorille mahdollisuus oppimiseen riippumatta heidän taustastaan. Tarvittaessa oppimiseen ja koulunkäyntiin järjestetään kolmiportaista tukea, jonka yhtenä tukikeinona on erityisopetus. Vaikka koulujärjestelmää on ohjattu inklusiiviseen suuntaan suomalaisessa koulutuspolitiikassa, on erityisopetuksen järjestäminen erillään yleisopetuksesta edelleen varsin yleistä. Tämän tutkielman tarkoituksena on antaa ääni oppilaille ja selvittää, mitä peruskouluaikoinaan erityisopetusta saaneet nuoret aikuiset kertovat kokemuksistaan erityisopetuksesta, mutta myös muista koulukokemuksistaan. Tutkielma kuuluu hankkeeseen ERITYISOPETUS KOETTUNA — lasten ja nuorten kokemuksia erityisopetuksesta. Oppilaiden omien kokemusten kuuleminen on tärkeää, koska se voi tuoda esille näkökulmia, joita voidaan hyödyntää esimerkiksi oppimisen ja koulunkäynnin tuen kehittämiseen. Tutkielman teoreettisessa viitekehyksessä tarkastellaan suomalaisen erityisopetusjärjestelmän kehityslinjoja ja nykytilaa, kuten myös aikaisempia tutkimuksia oppilaiden osallisuudesta ja oppimisesta erillisessä erityisopetuksen ryhmässä ja inklusiivisessa ryhmässä. Tutkielma on toteutettu laadullisella otteella. Tutkielmaa varten on haastateltu seitsemää erityisopetusta peruskouluaikanaan saanutta nuorta aikuista. Puolistrukturoidulla haastattelulla kerätyt kertomukset on analysoitu kategorisella sisällönanalyysilla. Tutkimustulosten mukaan koulukokemusten kirjo on moninainen sisältäen sekä positiivisia että negatiivisia kokemuksia yleisopetuksen kouluissa, joissa opetusta on saatu sekä erillisissä ryhmissä että yleisopetuksen ryhmissä. Pienluokalla tuen saanti on koettu riittäväksi ja luokkailmapiiri myönteiseksi, mutta opetuksen tavoitetaso on saattanut jäädä liian alhaiseksi. Yleisopetuksen puolella tuen tarjonta on koettu ajoittain riittämättömänä eikä inklusiivisia opetusstrategioita ole juurikaan käytetty. Hyvä vuorovaikutussuhde opettaja

For adipose stromal/stem cell (ASCs)-based immunomodulatory therapies, it is important to study how donor characteristics, such as obesity and type 2 diabetes (T2D), influence ASCs efficacy. Here, ASCs were obtained from 2 groups, donors with T2D and obesity (dASCs) or nondiabetic donors with normal-weight (ndASCs), and then cultured with anti-CD3/CD28-stimulated allogeneic CD4 T cells. ASCs were studied for the expression of the immunomodulators CD54, CD274, and indoleamine 2, 3 dioxygenase 1 (IDO) in inflammatory conditions. CD4 T cells cultured alone or in cocultures were assessed to evaluate proliferation, activation marker surface expression, apoptosis, the regulatory T cells (Tregs; CD4⁺ CD25high FOXP3⁺) frequency, and intracellular cytokine expression using flow cytometry. Modulation of T-cell subset cytokines was explored via ELISA. In inflammatory conditions, the expression of CD54, CD274, and IDO was significantly upregulated in ASCs, with no significant differences between ndASCs and dASCs. dASCs retained the potential to significantly suppress CD4 T-cell proliferation, with a slightly weaker inhibitory effect than ndASCs, which was associated with significantly reduced abilities to decrease IL-2 production and increase IL-8 levels in cocultures. Such attenuated potentials were significantly correlated with increasing body mass index. dASCs and ndASCs comparably reduced CD4 T-cell viability, HLA-DR expression, and interferon-gamma production and conversely increased CD69 expression, the Tregs percentage, and IL-17A production. Considerable amounts of the immunomodulators prostaglandin E2 (PGE2) and IL-6 were detected in the conditioned medium of cocultures. These findings suggest that ASCs obtained from donors with T2D and obesity are receptive to the inflammatory environment and able to modulate CD4 T cells accordingly.

Background: Hyperspectral imaging (HSI) has the potential to enhance surgical tissue detection and diagnostics. Definite utilization of intraoperative HSI guidance demands validated machine learning and public datasets that currently do not exist. Moreover, current imaging conventions are dispersed, and evidence-based paradigms for neurosurgical HSI have not been declared. Methods: We presented the rationale and a detailed clinical paradigm for establishing microneurosurgical HSI guidance. In addition, a systematic literature review was conducted to summarize the current indications and performance of neurosurgical HSI systems, with an emphasis on machine learning-based methods. Results: The published data comprised a few case series or case reports aiming to classify tissues during glioma operations. For a multitissue classification problem, the highest overall accuracy of 80% was obtained using deep learning. Our HSI system was capable of intraoperative data acquisition and visualization with minimal disturbance to glioma surgery. Conclusions: In a limited number of publications, neurosurgical HSI has demonstrated unique capabilities in contrast to the established imaging techniques. Multidisciplinary work is required to establish communicable HSI standards and clinical impact. Our HSI paradigm endorses systematic intraoperative HSI data collection, which aims to facilitate the related standards, medical device regulations, and value-based medical imaging systems.

Traditional service markets enable the efficient trading of a variety of services through a centralized platform, acting as an intermediary between service providers and customers. However, this centralization raises concerns about power abuse, corruption, and scalability. Decentralization of service markets addresses these issues by directly exchanging data between service providers, but it introduces trust challenges to distributed data sharing. This study explores the use of blockchain technology to establish trust and security in decentralized service markets, managing trade-offs between decentralization, scalability, and security. Focusing on applications in transportation, such as Mobility-as-a-Service (MaaS), Unmanned Aerial Vehicles (UAV), and autonomous vehicles, this work investigates the potential of blockchain-based service markets to facilitate collaboration in various sectors. The dissertation studies the core components of distributed ledger technologies: system design, deployment, and confidentiality. This work proposes a blockchain-based MaaS system design for the service market and discusses the potential usage of blockchain, along with associated challenges in distributed data sharing. Additionally, confidentiality in blockchain-based interactive systems is examined through zero-knowledge argument schemes, with a comparison of their advantages and dis-advantages. The performance regarding decentralized autonomous operation based on blockchain technology is considered via various updating capacities. The deployment of a blockchain-based service market is analyzed using two types of blockchain platforms—permissioned and permissionless—to represent access requirements. A performance comparison and proposed technique framework for blockchain platform selection are also provided., Tiivistelmä Perinteiset palvelumarkkinat toimivat keskitetyn alustan varassa. Tällainen alusta toimii linkkinä palveluntarjoajien ja asiakkaiden välillä ja välittää palveluita tehokkaasti. Keskittäminen ei kuitenkaan ole ongelmatonta erityisesti vallan väärinkäytön, korruption ja skaalautuvuusongelmien vuoksi. Palvelumarkkinoiden hajauttaminen tarjoaa ratkaisun näihin huolenaiheisiin mahdollistamalla suoran tiedonvaihdon palveluntarjoajien välillä. Toisaalta se tuo kuitenkin mukanaan haasteita luottamuksellisen datan hajautetussa jakamisessa. Tämä tutkimus keskittyy lohkoketjuteknologian käyttöön luottamuksen ja turvallisuuden luomiseksi hajautetuille palvelumarkkinoille. Tutkimus keskittyy hajauttamisen, skaalautuvuuden ja turvallisuuden tasapainottamiseen liittyviin kompromisseihin, ja tutkii erityisesti lohkoketjupohjaisten palvelumarkkinoiden soveltamista älyliikenteen palveluihin kuten liikkuvuuteen palveluna (MaaS), miehittämättömiin lennokkeihin (UAV) ja autonomisiin ajoneuvoihin, sekä niiden potentiaaliin edistää yhteistyötä eri sektoreilla. Väitöskirjassa keskitytään hajautetun tilikirjateknologian keskeisiin osa-alueisiin, kuten järjestelmän suunnitteluun, käyttöönottoon ja luottamuksellisuuteen. Työ esittelee lohkoketjupohjaisen MaaS-järjestelmän palvelumarkkinan suunnitelman ja käsittelee lohkoketjun mahdollisia käyttötapoja ja siihen liittyviä haasteita hajautetussa datan jakelussa. Lisäksi tutkimuksessa arvioidaan lohkoketjupohjaisten interaktiivisten järjestelmien luottamuksellisuutta nollatiedon argumenttien (zero-knowledge arguments) avulla, vertaillen niiden etuja ja haittoja. Hajautetun autonomisen toiminnan suorituskykyä tarkastellaan lohkoketjuteknologian perusteella erilaisten päivityskapasiteettien näkökulmasta. Tutkimuksessa analysoidaan myös lohkoketjupohjaisen palvelumarkkinan käyttöönottoa ja vertaillaan kahta eri lohkoketjualustaa—luvanvaraista ja luvatonta—, jotka edustavat erilaisia pääsynvaatimuksia (access requirements). Lisäksi verta

Copy number variants (CNVs) are a major source of genetic variation and can disrupt genes or affect gene dosage. They are known to be causal or underlie predisposition to various diseases. However, the role of CNVs in inherited breast cancer susceptibility has not been thoroughly investigated. To address this, we performed whole-exome sequencing based analysis of rare CNVs in 98 high-risk Northern Finnish breast cancer cases. After filtering, selected candidate alleles were validated and characterized with a combination of orthogonal methods, including PCR-based approaches, optical genome mapping and long-read sequencing. This revealed three recurrent alterations: a 31 kb deletion co-occurring with a retrotransposon insertion (delins) in RAD52, a 13.4 kb deletion in HSD17B14 and a 64 kb partial duplication of RAD51C. Notably, all these genes encode proteins involved in pathways previously identified as essential for breast cancer development. Variants were genotyped in geographically matched cases and controls (altogether 278 hereditary and 1983 unselected breast cancer cases, and 1229 controls). The RAD52 delins and HSD17B14 deletion both showed significant enrichment among cases with indications of hereditary disease susceptibility. RAD52 delins was identified in 7/278 cases (2.5%, P = 0.034, OR = 2.86, 95% CI = 1.10–7.45) and HSD17B14 deletion in 8/278 cases (2.9%, P = 0.014, OR = 3.28, 95% CI = 1.31–8.23), the frequency of both variants in the controls being 11/1229 (0.9%). This suggests a role for RAD52 and HSD17B14 in hereditary breast cancer susceptibility. The RAD51C duplication was very rare, identified only in 2/278 of hereditary cases and 2/1229 controls (P = 0.157, OR = 4.45, 95% CI = 0.62–31.70). The identification of recurrent CNVs in these genes, and especially the relatively high frequency of RAD52 and HSD17B14 alterations in the Finnish population, highlights the importance of studying CNVs alongside single nucleotide variants when searchin

Exponential population growth and urbanisation pose potential challenges to mobility, governance, well-being, the environment, and the safety of modern cities. This demands data-driven predictions and decision-making systems to achieve sustainable societal goals. Smart city data are being employed to improve citizens’ lifestyles, derive climate initiatives, provide quality health care and education, achieve better governance, and design better urban policies. However, the data from smart cities is vast and heterogeneous, requiring efficient and fault-tolerant data platforms supporting continuous data collection, storage, analysis, visualisation, and results delivery in both batch and real-time fashion. In addition, real-world data brings challenges and may come from malfunctioning, replaced or differently calibrated devices. Concept drift is a crucial barrier to relying on predictions from real-world data streams. It emerges due to unforeseen reasons, changes in statistical properties and the context of data while performing predictive modelling. This thesis focuses on the challenges mentioned by investigating and proposing efficient concept drift detection approaches, providing distributed data pipeline architectures, and highlighting the potential challenges of concept drift in terms of real-world applicability. As a result, two different algorithms are proposed to perform predictive modelling using machine learning methods integrated with concept drift detection and adaptation methods. The experiment showed that integrating concept drift detection with predictive models increases the effectiveness of drawn predictions. Secondly, a cloud computing-based distributed data pipeline architecture is provided to support data collection, data analysis, concept drift detection, and others. Similarly, an edge computing-based distributed data pipeline is proposed for edge micro data centres to perform computationally demanding processes. The proposed data pipelines, Tiivistelmä Eksponentiaalinen väestönkasvu ja kaupungistuminen haastavat modernien kaupunkien hallintoa, logistiikkaa, ihmisten sekä ympäristön hyvinvointia ja turvallisuutta. Kestävien yhteiskunnallisten tavoitteiden saavuttamiseksi voidaan hyödyntää datapohjaisia päätöksentekojärjestelmiä. Älykaupunkien dataa käytetään tukemaan kansalaisten elämäntapoja sekä ilmastoaloitteita, tuottamaan laadukasta terveydenhuoltoa ja koulutusta sekä sujuvoittamaan hallintoa ja kaupunkipolitiikkaa. Kaupungeista saatava data on monimuotoista ja laajaa, saattaa sisältää virheitä ja sen käsittely vaatii tehokkaita ja vikasietoisia data-alustoja. Alustojen on tuettava jatkuvaa datan keruuta, tallennusta, analysointia, visualisointia ja tulosten toimittamista sekä erä- että reaaliaikaisesti. Käsitevuoto (concept drift) on keskeinen este luotettavien ennusteiden tekemiselle reaalimaailman datavirroista. Käsitevuoto ilmenee ennakoimattomista syistä, datan tilastollisten ominaisuuksien ja kontekstin muutoksista ennustavaa mallinnusta tehdessä, johtaen epäluotettaviin datapohjaisiin ennusteisiin. Työssä tutkitaan käsitevuotoa erityisesti laboratorion ulkopuolella tosimaailmassa. Tuloksena esitetään kaksi erilaista algoritmia käsitevuon havaitsevaan ennustavaan mallinnukseen. Algoritmit ovat koneoppimenetelmiä, joihin on integroitu käsitevuodon havaitsemis- sekä mukautumismekanismit. Työssä osoitetaan, että kun integroidaan käsitevuodon havaitseminen ennustemallinnukseen, voidaan lisätä ennusteiden tehokkuutta. Toiseksi työssä kehitettiin pilvilaskentaan perustuva hajautettu dataputkiarkkitehtuuri, joka tukee datan keruuta, datan analysointia, käsitevuodon havaitsemista ja muita reaaliaikaiseen mallintamiseen tarvittavia datankäsittelymekanismeja. Lisäksi kehitettiin reunalaskentaan perustuva hajautettu dataputkijärjestelmä mikrodatakeskuksineen laskennallisesti raskaita datakäsittelytehtäviä varten. Ehdotetut dataputket ovat vikasietoisia, ne voidaan skaalata saumattomasti ja ne tukevat er

Mobility-as-a-Service (MaaS) is an advanced Intelligent Transport System (ITS) that integrates various modes of transportation to meet the demands of travellers. The system relies on frequent communication for data exchange between the MaaS provider and transport service providers. Ideally, such communication would utilize trust technologies between these entities. However, current MaaS systems lack transparency and reliability, and their centralized nature creates a single point of failure for the entire service. To address these issues, this paper proposes a blockchain-based MaaS, which includes an architecture and smart contract functionalities. The solutions are built on permissioned and permissionless Hyperledger Fabric and Ethereum blockchain platforms, respectively, for a realistic deployment of network architecture and proposed smart contracts. Additionally, the paper presents a framework derived from comparing these two blockchain platforms. Finally, the framework is evaluated, and open questions and challenges are analysed.

This work focuses on digital pedagogy in changing work environments from the perspectives of the teacher and the learner. Digitalization has strongly shaped the practices of the education sector, which has created increasing pressure for change, especially for teachers. Smooth practices and functional digital pedagogy are more important than ever before. This work is intended as learning material for teachers who wish to develop their own digital pedagogical competence and skills to operate in diverse learning environments in a way that serves learning. The contents of this work are based on the “Teacher's digital pedagogical skills in distance and hybrid environments (‘DigiPeda’)” project at the University of Oulu, which was funded by the European Social Fund and the Northern Ostrabothnian Centre for Economic Development, Transport, and the Environment. The project aimed to support the professional development and well-being of teachers in changing working environments. The authors of the publication are teachers, experts in learning research and personnel of the DigiPeda project. The work is divided into four parts, the first of which focuses on digital pedagogical perspectives and the use of technology in teaching during distance and hybrid education, as well as on the teacher's digital competence frameworks. Part 2 provides a research-based overview of learning skills, well-being, and emotions, as well as the growth mindset as part of distance and hybrid education. Part 3 consists of case reports of teachers during the distance education transition and a description of the continuing learning course implemented in the DigiPeda project. Finally, an outlook of a new learning culture encouraged by diversifying modes of teaching is provided. The chapters include a diverse range of perspectives from experts on digital pedagogy as part of the teacher's work. The central cross-cutting theme of this publication is the change in working and learning environments, Tiivistelmä Tämä teos käsittelee digipedagogiikkaa muuttuvissa työympäristöissä opettajan ja oppijan näkökulmista. Digitalisaatio on muokannut koulutussektorin käytänteitä voimakkaasti, mikä on luonut kasvavia muutospaineita erityisesti opettajille. Sujuvat käytännöt ja toimiva digipedagogiikka ovat tärkeämpiä kuin koskaan ennen. Tämä teos on suunnattu oppimateriaaliksi opettajille, jotka haluavat kehittää omaa digipedagogista osaamistaan ja taitojaan toimia monimuotoisissa oppimisympäristöissä oppimista palvelevalla tavalla. Teoksen sisältö pohjaa vuoden 2020 koronaviruspandemian siivittämään etä- ja hybridiopetussiirtymään sekä Euroopan sosiaalirahaston ja Pohjois-Pohjanmaan ELY-keskuksen rahoittamaan Oulu yliopiston Opettajan digipedagogiset taidot etä- ja hybridityöskentelyssä (”DigiPeda”) -hankkeeseen, jonka tavoitteena oli tukea opettajien ammattitaidon kehittymistä ja hyvinvointia muuttuvissa työskentely-ympäristöissä. Julkaisun kirjoittajat ovat opettajia, oppimisen tutkimuksen asiantuntijoita sekä DigiPeda-hankkeen henkilöstöä. Teos jakautuu neljään osaan, joista ensimmäinen keskittyy digipedagogisiin näkökulmiin ja teknologian hyödyntämiseen opetuksessa etä- ja hybridiopetuksen aikana sekä opettajan digitaalisen osaamisen viitekehyksiin. Toisessa osassa tehdään tutkimustietoon perustuva katsaus erityisesti oppimisen taitoihin, hyvinvointiin ja tunteisiin sekä kasvun ajattelutapaan osana etä- ja hybridiopetusta. Kolmas osa koostuu opettajien tapauskertomuksista etäopetussiirtymän ajalta sekä DigiPeda-hankkeessa toteutetun täydennyskoulutuksen kuvauksesta. Lopuksi luodaan katse kohti monimuotoistuvan opetuksen kannustamaa uudenlaista oppimiskulttuuria. Osiot sisältävät monipuolisen kattauksen asiantuntijoiden näkökulmia digipedagogiikkaan osana opettajan työtä. Julkaisun keskeisenä läpileikkaavana teemana on työ- ja oppimisympäristöjen muutos. Opettajien ja oppijoiden tarvitsemat taidot monimuotoisissa työskentely-ympäristöissä toimimiseen kehittyvät jatkuva

The evolution from natural gas usage to new technologies, such as the use of hydrogen as fuel or electricity-based heating, strongly influences the oxidation of the stainless steel surface in the reheating furnace. Thermogravimetric tests using different simulated combustion and induction reheating conditions are performed for austenitic AISI 301, AISI 304, and ferritic AISI 444 steel grades. Simulated furnace atmospheres in combustion methods are based on methane–air, methane–oxygen, hydrogen–oxygen, and methane–hydrogen–oxygen combinations. For induction simulations, air and nitrogen are used as furnace atmospheres. The results indicate that changes in heating conditions to H2-fueled combustion or induction only have a minor influence on the oxidation of the ferritic grade; whereas, their effects on the austenitic grades are more pronounced. The transition from a methane–air to H2–oxyfuel combustion increases the total oxidation by 1.7 and 4 times for steel grades 304 and 301, respectively; therefore, grade 304 can be considered better suited for transition for H2–oxyfuel use. The shorter induction heating considerably decreases the amount of oxide scale for austenitic grades, but the nitrogen atmosphere produces a subscale inside the steel matrix, which can hinder the descaling process.

This study examined the association between maternal serum vitamin B12 levels during early pregnancy and offspring autism spectrum disorders (ASD) and subtypes. Based on a Finnish national birth cohort, case offspring (n = 1558) born in 1987–2007 and diagnosed with ASD by 2015 were matched with one control on date of birth, sex and place of birth. Maternal vitamin B12 levels were measured during first and early second trimesters of pregnancy. High maternal vitamin B12 levels (≥81th percentile) was associated with increased risk for offspring childhood autism, adjusted odds ratio, 1.59, 95% confidence interval 1.06–2.41 (p = 0.026). No significant associations were observed between maternal vitamin B12 levels and offspring Asperger’s or pervasive developmental disorder/NOS.

Efficient Vehicle-to-Everything enabling cooperation and enhanced decision-making for autonomous vehicles is essential for optimized and safe traffic. Real-time decision-making based on vehicle sensor data, other traffic data, and environmental and contextual data becomes imperative. As a part of such Intelligent Traffic Systems, cooperation between different stakeholders needs to be facilitated rapidly, reliably, and securely. The Internet of Things provides the fabric to connect these stakeholders who share their data, refined information, and provided services with each other. However, these cloud-based systems struggle to meet the real-time requirements for smart traffic due to long distances across networks. Here, edge computing systems bring the data and services into the close proximity of fast-moving vehicles, reducing information delivery latencies and improving privacy as sensitive data is processed locally. To solve the issues of trust and latency in data sharing between these stakeholders, we propose a decentralized framework that enables smart contracts between traffic data producers and consumers based on blockchain. Autonomous vehicles connect to a local edge server, share their data, or use services based on agreements, for which the cooperating edge servers across the system provide a platform. We set up proof-of-concept experiments with Hyperledger Fabric and virtual cars to analyze the system throughput with secure unicast and multicast data transmissions. Our results show that multicast transmissions in such a scenario boost the throughput up to 2.5 times where the data packets of different sizes can be transmitted in less than one second.

Objective: To explore work participation and the health-related factors affecting work participation among the Finnish Spinal Cord injury (FinSCI) study population (n = 884). Methods: A cross-sectional explorative observational study in the FinSCI community survey applying Patient-Reported Outcomes Measurement Information System (PROMIS®) forms on Social Health and Global Health. Analyses of socio-demographic and injury-related data were performed. Results: Employment among the study population (n = 452) was 26.5%. Physical, Mental, Social and General Health were better in the employed group compared with work-age persons not working. Logistic regression showed that work participation was related to all health domains, but Physical Health and Ability to Participate in Social Roles and Activities in Social Health were the strongest indicators of likelihood of being at work. Paraplegia and young age were associated with increased likelihood of work participation. Conclusion: The first national survey among people with spinal cord injury in Finland shows low level of employment. The results suggest that pain, physical function, and ability to participate in social roles should be monitored by health and vocational professionals when assessing a person’s likelihood of being in work.

Gear fault diagnosis (GFD) based on vibration signals is a popular research topic in industry and academia. This paper provides a comprehensive summary and systematic review of vibration signal-based GFD methods in recent years, thereby providing insights for relevant researchers. The authors first introduce the common gear faults and their vibration signal characteristics. The authors overview and compare the common feature extraction methods, such as adaptive mode decomposition, deconvolution, mathematical morphological filtering, and entropy. For each method, this paper introduces its idea, analyses its advantages and disadvantages, and reviews its application in GFD. Then the authors present machine learning-based methods for gear fault recognition and emphasise deep learning-based methods. Moreover, the authors compare different fault recognition methods. Finally, the authors discuss the challenges and opportunities towards data-driven GFD.

Objectives: Subtypes in type 1 diabetes pathogenesis have been implicated based on the first-appearing autoantibody (primary autoantibody). We set out to describe the glucose metabolism in preclinical diabetes in relation to the primary autoantibody in children with HLA-conferred disease susceptibility. Design and methods: Dysglycemic markers are defined as a 10% increase in HbA1c in a 3–12 months interval or HbA1c ≥5.9% (41 mmol/mol) in two consecutive samples, impaired fasting glucose or impaired glucose tolerance, or a random plasma glucose value ≥7.8 mmol/L. A primary autoantibody could be detected in 295 children who later developed at least 1 additional biochemical autoantibody. These children were divided into three groups: insulin autoantibody (IAA) multiple (n = 143), GAD antibody (GADA) multiple (n = 126) and islet antigen 2 antibody (IA-2A) multiple (n = 26). Another 229 children seroconverted to positivity only for a single biochemical autoantibody and were grouped as IAA only (n = 87), GADA only (n = 114) and IA-2A only (n = 28). Results: No consistent differences were observed in selected autoantibody groups during the preclinical period. At diagnosis, children with IAA only showed the highest HbA1c (P < 0.001 between groups) and the highest random plasma glucose (P = 0.005 between groups). Children with IA-2A only progressed to type 1 diabetes as frequently as those with IA-2A multiple (46% vs 54%, P = 0.297) whereas those with IAA only or GADA only progressed less often than children with IAA multiple or GADA multiple (22% vs 62% (P < 0.001) and 7% vs 43% (P < 0.001)), respectively. Conclusions: The phenotype of preclinical diabetes defined by the primary autoantibody is not associated with any discernible differences in glucose metabolism before the clinical disease manifestation.

Objectives: To investigate whether an earlier-onset climacteric phase is associated with autonomic imbalance at the age of 46 years. Methods: This cross-sectional birth cohort study included 2661 women aged 46 years. Participants were divided into climacteric (n = 359) and preclimacteric (n = 2302) groups based on menstrual history and follicle stimulating hormone values. The mean heart rate (HR), low-frequency (LF) power, high-frequency (HF) power and LF/HF ratio were analyzed from heart rate variability recordings. The variables were compared between the groups using multivariable linear regression models, including body mass index, smoking and physical activity. The effects of hormone therapy and hot flashes on autonomic function were evaluated in sub-analyses. Results: Climacteric women had a lower mean HR in seated (71.9 ± 10.5 vs. 72.6 ± 10.4 bpm, p = 0.015) and standing (81.2 ± 12.8 vs. 83.6 ± 12.1 bpm, p = 0.002) positions compared to preclimacteric women, and the differences remained significant after the adjustments. In the sub-analyses, more frequent hot flashes were associated with a lower LF power and LF/HF ratio in the sitting position. Conclusions: The present study suggested an association between greater parasympathetic activation in women with more advanced climacteric status at the age of 46 years.

Objective: Oxidized epitopes such as malondialdehyde–acetaldehyde (MAA) play a crucial role in the progression of atherosclerosis through activation of the humoral immune response. The exact mechanism of the association between atherosclerosis and periodontal diseases is not fully understood. The aim of the current study is to evaluate the association of oral humoral immune response to oxidized epitopes with parameters of periodontal disease. Materials and methods: The Parogene cohort consist of patients who have undergone coronary angiography due to cardiac symptoms. In this study, 423 patients were randomly selected for an extensive oral examination. Salivary Immunoglobulin A to oxidized epitopes and bacterial antigens was determined by chemiluminescence immunoassay. Results: In a binary logistic regression model adjusted with periodontal disease confounders, periodontal pocket depth (PPD) 4–5 mm associated with salivary IgA antibodies to MAA-LDL (p = 0.034), heat shock protein 60 of Aggregatibacter actinomycetemcomitans (p = 0.045), Porphyromonas gingivalis (p = 0.045), A. actinomycetemcomitans (p = 0.005), P. intermedia (p = 0.020), and total IgA (p = 0.003). Conclusions: The current study shows the association of salivary IgA to MAA-LDL with PPD 4–5 mm in a cohort of patients with chronic coronary artery disease. Humoral immune cross-reactivation to oxidized epitopes such MAA-LDL could partly explain the link of periodontitis with systemic diseases.

Developing students’ skills in solving mathematical problems and supporting creative mathematical thinking have been important topics of Finnish National Core Curricula 2004 and 2014. To foster these skills, students should be provided with rich, meaningful problem-solving tasks already in primary school. Teachers have a crucial role in equipping students with a variety of tools for solving diverse mathematical problems. This can be challenging if the instruction is based solely on tasks presented in mathematics textbooks. The aim of this study was to map whether a teaching approach, which focuses on teaching general heuristics for mathematical problem-solving by providing visual tools called Problem-solving Keys, would improve students’ performance in tasks and skills in justifying their reasoning. To map students’ problem-solving skills and strategies, data from 25 fifth graders’ pre-tests and post-tests with non-routine mathematical tasks were analysed. The results indicate that the teaching approach, which emphasized finding different approaches to solve mathematical problems had the potential for improving students’ performance in a problem-solving test and skills, but also in explaining their thinking in tasks. The findings of this research suggest that teachers could support the development of problem-solving strategies by fostering classroom discussions and using for example a visual heuristics tool called Problem-solving Keys.

Aim: To study the influence of different force magnitudes on dental arches in cervical headgear (CHG) treatment. Material and methods: In this controlled clinical trial, patients (n = 40) were treated with CHG with light (L, 300 g, n = 22) or heavy force (H, 500 g, n = 18) magnitude. Subjects were asked to use CHG for 10 hours a day for 10 months. The outer bow of the CHG facebow was raised 10–20 degrees and the inner bow expanded 3–4 mm. Adherence to instructions and force magnitude were monitored with an electronic module (Smartgear, Swissorthodontics, Switzerland). Impressions for study models were taken before (T1) and after (T2) treatment and the study models were scanned into digital form (3Shape, R700 Scanner, Denmark). Measurements were made using the digital models (Planmeca Romexis, Model analyser, Finland). Results: During the treatment (T1–T2) the upper inter-canine distance increased by 2.83 mm (P = 0.000) and 2.60 mm (P = 0.000) in the L and H force magnitude groups, respectively. Upper inter-molar width increased by 3.16 mm (P = 0.000) and 2.50 mm (P = 0.000) in the L and H groups, respectively. Maxillary total arch perimeter increased by 6.39 mm (P = 0.001) and 6.68 mm (P = 0.001) in the L and H groups, respectively. In the amount of change over time, T1–T2, in the upper arch measurements, no significant difference was found between the groups. Lower inter-canine width increased by 0.94 mm (P = 0.005) and 1.16 mm (P = 0.000) in the L and H groups, respectively; no difference between the groups. Lower inter-molar distance increased by 2.17 mm (P = 0.000) and 1.11 mm (P = 0.008) in the L and H groups, respectively. At the end of the study, upper and lower inter-molar width was larger in the L group than in the H group (P = 0.039 and P = 0.022, respectively). Conclusions: CHG therapy is an effective method for expanding and releasing moderate crowding of the upper dental arch. The lower arch spontaneously follows the upper arch in widening effe

Background: Anatomic variation or early differences in glucose metabolism have been linked to the development of type 1 diabetes. We aimed to describe early glucose metabolism based on HbA1c, oral glucose tolerance test (OGTT), and random plasma glucose years before the presentation of type 1 diabetes in five risk groups based on autoantibody combinations. For the first time, we were able to include for comparison children with very low risk of progression to type 1 diabetes. Methods: The Finnish Diabetes Prediction and Prevention birth cohort study screened newborn infants for HLA susceptibility to type 1 diabetes since 1994. Those carrying a risk genotype were prospectively followed up with islet autoantibody testing. Glucose parameters were obtained starting from the time of seroconversion. By 31 August 2014, 1162 children had developed at least one islet autoantibody and were included in the current study. Type 1 diabetes was diagnosed in 335 children (progressors). In the non-progressor groups, 207 developed multiple (≥2) biochemical islet autoantibodies, 229 a single biochemical autoantibody, 370 ICA only, and 64 transient autoantibodies. Children were divided into five risk groups. Glucose metabolism was evaluated. Results: We observed lower HbA1c values in early follow-up 4.5 to 6.0 years before diagnosis in the progressors when compared to the same time in children with a single biochemical autoantibody or low-risk (ICA only and transient) participants, who did not progress to clinical type 1 diabetes. However, no such differences were observed in OGTTs or random plasma glucose. The variation was minimal in glucose values in the low-risk groups. Conclusion: We report the possibility of early alteration in glucose metabolism in future progressors. This could suggest early defects in multiple glucose-regulating hormones.

In the global rankings of digitalization and Industry 4.0 maturity, Finland constantly places among the frontrunners. This study examines the path Finland has taken to reach the forefront, the drivers, and the current challenges and future opportunities related to Industry 4.0 and digitalization. This analysis is based on extensive experience in Industry 4.0-related ecosystem projects, policy documentation, and previous research. As Finland focuses on the export of high-value-added products and services, the early adoption of new technologies is vital and thus a key driver. Moreover, the national culture of innovation, R&D, and triple helix collaboration have driven Industry 4.0 implementation alongside a highly skilled workforce. However, multiple barriers hindering full Industry 4.0 utilization still exist, including SMEs’ hesitation to digitalize due to insufficient support mechanisms, an aging population, and the difficulty in finding single-source solutions. Respective future opportunities were found in areas such as smart sustainable manufacturing and ecosystems, enhanced SME involvement, lifelong learning, and the platform economy. Currently, Finland is moving from digitalization towards a twin transition and sustainable ecosystem-to-ecosystem collaboration. Practical early-stage examples of implementing this policy include the AI 4.0 and the Sustainable Industry X supercluster initiatives.

In this paper we present procedures for processing raw data collected with moving vehicles and for fusing this data with digital map data. The goal is to have a better understanding of the city traffic via quantitative research on collected taxi data in relation to digital map properties. Map attributes are provided by Digiroad, which is a database of Finnish road and street network. We define methods to clean up data that has been collected with taxis equipped with on-board vehicle tracking devices from real customer service situations. Consequently, the driving behavior may be inconsistent and sensor data can be limited and contain errors. We explain procedures of preparing data; filtering the most obvious errors from the data set, map-matching moving object data, and fetching map attributes along the routes of the moving vehicles. The fetched properties, as well as other measurement data, are used for deriving statistics and illustrations to study driving behavior in downtown Oulu, Finland.

Central nervous system (CNS) involvement in childhood acute lymphoblastic leukemia (ALL) implicates enhanced intrathecal chemotherapy, which is related to CNS toxicity. Whether CNS involvement alone contributes to CNS toxicity remains unclear. We studied the occurrence of all CNS toxicities, seizures, and posterior reversible encephalopathy syndrome (PRES) in children with ALL without enhanced intrathecal chemotherapy with CNS involvement (n = 64) or without CNS involvement (n = 256) by flow cytometry. CNS involvement increased the risk for all CNS toxicities, seizures, and PRES in univariate analysis and, after adjusting for induction therapy, for seizures (hazard ratio [HR] = 3.33; 95% confidence interval [CI]: 1.26–8.82; p = 0.016) and PRES (HR = 4.85; 95% CI: 1.71–13.75; p = 0.003).

Tiivistelmä. Prionitaudit eli tarttuvat spongiformiset enkelfalopatiat (TSE) ovat ryhmä neurodegeneratiivisia tauteja, jotka tarttuvat proteiinin välityksellä. Tautien taudinaiheuttajaa kutsutaan prioniproteiiniksi, joka on ihmisellä normaalistikin esiintyvän PrPC-proteiinin väärin laskostunut amyloidogeeninen muoto. Ihmisen yleisin prionitauti on sporadinen Creutzfeldt-Jakobin tauti. Tauti on hyvin harvinainen ja johtaa aina lopulta kuolemaan. Tässä kandidaatintutkielmassa perehdytään prioniproteiinin rakenteeseen, synteesiin ja sen neurotoksisiin vaikutuksiin. Lisäksi tutkielma käsittelee Creutzfeldt-Jakobin tautia keskittyen sen sporadiseen tautimuotoon ja koskettaa myös prionitautien hoidon tulevaisuuden näkymiä.

Central nervous system (CNS) toxicity is common at diagnosis and during treatment of pediatric acute lymphoblastic leukemia (ALL). We studied CNS toxicity in 1,464 children aged 1.0–17.9 years, diagnosed with ALL and treated according to the Nordic Society of Pediatric Hematology and Oncology ALL2008 protocol. Genome-wide association studies, and a candidate single-nucleotide polymorphism (SNP; n=19) study were performed in 1,166 patients. Findings were validated in an independent Australian cohort of children with ALL (n=797) in whom two phenotypes were evaluated: diverse CNS toxicities (n=103) and methotrexate-related CNS toxicity (n=48). In total, 135/1,464 (9.2%) patients experienced CNS toxicity for a cumulative incidence of 8.7% (95% confidence interval: 7.31–10.20) at 12 months from diagnosis. Patients aged ≥10 years had a higher risk of CNS toxicity than had younger patients (16.3% vs. 7.4%; P<0.001). The most common CNS toxicities were posterior reversible encephalopathy syndrome (n=52, 43 with seizures), sinus venous thrombosis (n=28, 9 with seizures), and isolated seizures (n=16). The most significant SNP identified by the genome-wide association studies did not reach genomic significance (lowest P-value: 1.11x10-6), but several were annotated in genes regulating neuronal functions. In candidate SNP analysis, ATXN1 rs68082256, related to epilepsy, was associated with seizures in patients <10 years (P=0.01). ATXN1 rs68082256 was validated in the Australian cohort with diverse CNS toxicities (P=0.04). The role of ATXN1 as well as the novel SNP in neurotoxicity in pediatric ALL should be further explored.

Digitalisation and Industry 4.0 promote a fundamental technological disruption that requires industry, research and government institutions to revisit their roles within the innovation ecosystems. Actors in this environment need to understand value co-creation during interaction and collaboration. The purpose of this study is to investigate the triple helix collaborative capabilities in an Industry 4.0 ecosystem context. The case under study is a Finnish national publicly funded research project involving five global manufacturers, three research institutions, and several small-and medium-size enterprises (SMEs). The results demonstrate that practices related to adaptivity, experience sharing, SME co-innovation and scale up can enable the ecosystem to be managed in a dynamic way. Yet, this type of operation requires the adoption of the ecosystem approach with mutual trust, intensive collaboration and the identification of common aims among the project participants. The presented co-innovation model can be used to design innovation ecosystem projects in the future.

Symptomatic congenital pulmonary malformations (CPMs) are a group of anomalies involving the lungs. The long-term outcomes of these patients are not well known. The present research aimed to study the pulmonary function, respiratory morbidity, and health-related quality of life (QoL) of patients treated for CPMs. All children (<16 years of age) treated for CPMs in 2002–2012 (in Oulu University Hospital) were invited to the follow-up visit. Altogether, there were 22 patients, out of which 17 (77%) participated. The mean follow-up time was 6.6 (ranged from 3 to 16) years. Pulmonary function tests, diffusing capacity, respiratory morbidity, and QoL were determined as the primary outcomes. Potential residual malformations and lung anatomy were investigated using computer tomography (CT) imaging. The outcomes were compared to the age- and sex-matched healthy controls. The forced expiratory volume at 1 s (FEV₁, Z-score) remained lower in operated patients compared to the healthy controls (−1.57 ± SD 1.35 vs. −0.39 ± SD −0.86, p-value 0.005). There were no differences in respiratory morbidity or QoL between the patients and the controls. The surgical approach (lobectomy vs. partial resection) did not affect lung function. A younger age (<1 year of age) at the time of the surgery seemed to result in a higher lung capacity, but the finding was not statistically significant. Patients with CPMs treated with surgery were satisfied with their wellbeing in the long-term. A lower lung function did not have an impact on their wellbeing. However, there was a slight decrease in lung function compared to the healthy controls, and a clinical follow-up of the patients was recommended.

Objectives: Children with acute lymphoblastic leukemia (ALL) have a tendency to gain weight during treatment. As overweight and obesity associate with health problems, prophylactic interventions are warranted. Therefore, it is important to identify the children most prone to gain weight. Methods: Patients aged 2.0–17.9 years at ALL diagnosis were identified from the NOPHO ALL2008 registry. Registry data was complemented with height and weight at the end of therapy from questionnaires. Body mass index (BMI) was classified according to international age- and sex-adjusted International Obesity Task Force BMI cut-offs. BMI values were transformed into standard deviation scores (SDS) to calculate the difference in BMISDS during treatment. Results: Data on BMI change were available for 765 children. Overweight and obesity doubled during treatment: 9.7% were overweight and 2.1% obese at diagnosis and 21.8% and 5.4% at the end of therapy, respectively. The mean BMISDS change was +0.64. Younger (2.0–5.9 years) and healthy weight children were most prone to become overweight (mean change in BMI SDS +0.85 and + 0.65, respectively). Conclusions: Younger children (2.0–5.9 years) with healthy weight at diagnosis were most prone to becoming overweight and therefore are an important group to target while considering interventions.

Objective: To determine whether a multicomponent intervention based on physical activity with technological support and nutritional counselling prevents mobility disability in older adults with physical frailty and sarcopenia. Design: Evaluator blinded, randomised controlled trial. Setting: 16 clinical sites across 11 European countries, January 2016 to 31 October 2019. Participants: 1519 community dwelling men and women aged 70 years or older with physical frailty and sarcopenia, operationalised as the co-occurrence of low functional status, defined as a short physical performance battery (SPPB) score of 3 to 9, low appendicular lean mass, and ability to independently walk 400 m. 760 participants were randomised to a multicomponent intervention and 759 received education on healthy ageing (controls). Interventions: The multicomponent intervention comprised moderate intensity physical activity twice weekly at a centre and up to four times weekly at home. Actimetry data were used to tailor the intervention. Participants also received personalised nutritional counselling. Control participants received education on healthy ageing once a month. Interventions and follow-up lasted for up to 36 months. Main outcome measures: The primary outcome was mobility disability (inability to independently walk 400 m in <15 minutes). Persistent mobility disability (inability to walk 400 m on two consecutive occasions) and changes from baseline to 24 and 36 months in physical performance, muscle strength, and appendicular lean mass were analysed as pre-planned secondary outcomes. Primary comparisons were conducted in participants with baseline SPPB scores of 3–7 (n=1205). Those with SPPB scores of 8 or 9 (n=314) were analysed separately for exploratory purposes. Results: Mean age of the 1519 participants (1088 women) was 78.9 (standard deviation 5.8) years. The average follow-up was 26.4 (SD 9.5) months. Among participants with SPPB scores of 3–7, mobility disability occur

Gear crack is one of the common failures in transmission systems. With the gradual expansion of cracks, it may cause tooth fracture. Therefore, it is of great significance to study the fault diagnosis of gear cracks. Vibration signals with time sequence are widely used in gear fault diagnosis. Extracting key fault features from vibration signals determines the accuracy of fault diagnosis models. This paper takes spur gears as research objects, and proposes a model for diagnosing gear crack depth based on one-dimensional convolutional neural network (short for CNN4GCDD). In order to identify crack depths, we collect the vibration signals from three gears with various crack depths and a normal gear without cracks. CNN4GCDD uses the original vibration signal as the input, adaptively extracts features, and makes crack depth diagnosis through the convolutional neural network. The experimental results demonstrate that CNN4GCDD can directly use the original time-domain signal for crack depth diagnosis, and make a high accurate prediction.

Edge Intelligence (EI) is an emerging computing and communication paradigm that enables Artificial Intelligence (AI) functionality at the network edge. In this article, we highlight EI as an emerging and important field of research, discuss the state of research, analyze research gaps and highlight important research challenges with the objective of serving as a catalyst for research and innovation in this emerging area. We take a multidisciplinary view to reflect on the current research in AI, edge computing, and communication technologies, and we analyze how EI reflects on existing research in these fields. We also introduce representative examples of application areas that benefit from, or even demand the use of EI.

Snow is an important driver of ecosystem processes in cold biomes. Snow accumulation determines ground temperature, light conditions, and moisture availability during winter. It also affects the growing season’s start and end, and plant access to moisture and nutrients. Here, we review the current knowledge of the snow cover’s role for vegetation, plant-animal interactions, permafrost conditions, microbial processes, and biogeochemical cycling. We also compare studies of natural snow gradients with snow experimental manipulation studies to assess time scale difference of these approaches. The number of tundra snow studies has increased considerably in recent years, yet we still lack a comprehensive overview of how altered snow conditions will affect these ecosystems. Specifically, we found a mismatch in the timing of snowmelt when comparing studies of natural snow gradients with snow manipulations. We found that snowmelt timing achieved by snow addition and snow removal manipulations (average 7.9 days advance and 5.5 days delay, respectively) were substantially lower than the temporal variation over natural spatial gradients within a given year (mean range 56 days) or among years (mean range 32 days). Differences between snow study approaches need to be accounted for when projecting snow dynamics and their impact on ecosystems in future climates.

Introduction: Contraceptives containing ethinylestradiol (EE) induce changes in the coagulation system and are associated with a risk of venous thromboembolism. However, studies comparing the effects of combined oral contraceptives containing EE and low-potency estrogens (ie, estradiol [E₂] and estradiol valerate [EV]) on coagulation biomarkers are limited. This study represents secondary outcomes of a randomized trial comparing combined oral contraceptives containing EV + dienogest (DNG), EE + DNG, and DNG alone on selected coagulation biomarkers. We could compare the specific effects of the different estrogen components owing to the inclusion of preparations containing the same progestin. Material and methods: We enrolled 59 healthy, 18- to 35-year-old, non-smoking women, of whom three discontinued. The participants were randomly allocated to 9 weeks of continuous treatment with EV 2 mg + DNG 2–3 mg (n = 20), EE 0.03 mg + DNG 2 mg (n = 20), or DNG 2 mg (n = 19). Blood samples were collected at baseline and after 9 weeks. We assessed coagulation in vitro by thrombin generation using the Calibrated Automated Thrombogram. Thrombin generation was evaluated by lag time, time to thrombin peak, thrombin peak, and endogenous thrombin potential in response to tissue factor (1 pm). In vivo coagulation assessment was based on levels of prothrombin fragment 1 + 2 (F1 + 2) (thrombin generation) and D-dimer (fibrin turnover). Clinical trial registration: NCT02352090. Results: Lag time and time to thrombin peak remained unaltered after exposure to EV + DNG, whereas EE + DNG shortened both lag time (mean percentage change −24%, 95% confidence interval [CI] −32% to −15%; p < 0.01) and time to thrombin peak (−26%, 95% CI −37% to −16%; p < 0.01). EV + DNG induced lower thrombin peak and endogenous thrombin potential than EE + DNG (peak; +45%, 95% CI 22%–67% vs +147%,95% CI 96%–198%; p < 0.01, and endogenous thrombin potential; +26%, 95% CI 15%–38% vs +64%, 95% CI 5

Asparaginase treatment is a mainstay in contemporary treatment of acute lymphoblastic leukemia (ALL), but substantial asparaginase-related toxicity may lead to jeopardized protocol compliance and compromises survival. We investigated the association between risk of asparaginase-associated toxicities (AspTox) and asparaginase enzyme activity (AEA) levels in 1155 children aged 1.0 to 17.9 years, diagnosed with ALL between July 2008 and March 2016, and treated according to the Nordic Society of Pediatric Hematology and Oncology (NOPHO) ALL2008 protocol. Patients with ≥2 blood samples for AEA measurement drawn 14 ± 2 days after asparaginase administration were included (6944 trough values). AEA was measurable (or >0 IU/L) in 955 patients, whereas 200 patients (17.3%) had asparaginase inactivation and few AspTox recorded. A time-dependent multiple Cox model of time to any first asparaginase-associated toxicity adjusted for sex and age was used. For patients with measurable AEA, we found a hazard ratio (HR) of 1.17 per 100 IU/L increase in median AEA (95% confidence interval [CI], 0.98–1.41; P = 0.09). For pancreatitis, thromboembolism, and osteonecrosis, the HRs were 1.40 (95% CI, 1.12–1.75; P = 0.002), 0.99 (95% CI, 0.70–1.40; P = 0.96), and 1.36 (95% CI, 1.04–1.77; P = 0.02) per 100 IU/L increase in median AEA, respectively. No significant decrease in the risk of leukemic relapse was found: HR 0.88 per 100 IU/L increase in AEA (95% CI, 0.66–1.16; P = 0.35). In conclusion, these results emphasize that overall AspTox and relapse are not associated with AEA levels, yet the risk of pancreatitis and osteonecrosis increases with increasing AEA levels.

Efficient resource usage in edge computing requires clever allocation of the workload of application components. In this paper, we show that under certain circumstances, the number of superfluous workload reallocations from one edge server to another may grow to a significant proportion of all user tasks—a phenomenon we present as a reallocation storm. We showcase this phenomenon on a city-scale edge server deployment by simulating the allocation of user task workloads in a number of scenarios capturing likely edge computing deployments and usage patterns. The simulations are based on a large real-world data set of city-wide Wi-Fi network connections, with more than 47M connections over ca. 560 access points. We study the occurrence of reallocation storms in three common edge-based reallocation strategies and compare the latency–workload trade-offs related to each strategy. As a result, we find that the superfluous reallocations vanish when the edge server capacity is increased above a certain threshold, unique for each reallocation strategy, peaking at ca. 35% of the peak ES workload. Further, while a reallocation strategy aiming to minimize latency consistently resulted in the worst reallocation storms, the two other strategies, namely a random reallocation strategy and a bottom-up strategy which always chooses the edge server with the lowest workload as a reallocation target, behave nearly identically in terms of latency as well as the reallocation storm in dense edge deployments. Since the random strategy requires much less coordination, we recommend it over the bottom-up one in dense ES deployments. Moreover, we study the conditions associated with reallocation storms. We discover that edge servers with the very highest workloads are best associated with reallocation storms, with other servers around the few busy nodes thus mirroring their workload. Further, we identify circumstances associated with an elevated risk of reallocation storms, such as summ