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Background: Specific heat shock proteins are associated with pregnancy complications, including spontaneous preterm birth (SPTB). Placental proteomics and whole exome sequencing recently suggested an association between heat shock protein HSPA5 and uncomplicated SPTB. In the present study, we investigated the localization of and possible roles for HSPA5 in SPTB. Methods: Western blot was performed to validate the result from the previously published proteomic analysis. We used qPCR to assess mRNA expression of genes and immunohistochemistry and immunoelectron microscopy to examine localization of HSPA5 in placental tissue. We silenced the HSPA5 gene in the HTR8/SVneo human trophoblast cell line to investigate possible functions of HSPA5. Results: HSPA5 was upregulated in placentas from SPTBs compared to spontaneous term births. We did not observe upregulation of HSPA5 mRNA in placental samples. The protein was localized in placental trophoblast in both spontaneous preterm and term placentas. Gene silencing of HSPA5 in human trophoblast cell culture affected the inflammatory response and decreased the expression of several proinflammatory genes. Conclusions: We suggest that upregulation of HSPA5 in the placenta is associated with spontaneous preterm labor. HSPA5 may promote the inflammatory response and alter the anti-inflammatory state of the placenta which could eventually lead to premature labor. Impact: -We validated upregulation of HSPA5 in placentas from spontaneous preterm birth. HSPA5 was not upregulated at transcriptional level which suggests that it may be regulated post-translationally. -Silencing HSPA5 in a human trophoblast–derived cell line suggested that HSPA5 promotes expression of proinflammatory cytokines. The emerging inflammation could lead to spontaneous preterm labor. -Identifying inflammatory pathways and factors associated with spontaneous preterm birth increases knowledge of the molecular mechanisms of premature labor. This could pro

Background: Preterm birth (<37 weeks of gestation) is a major cause of neonatal death and morbidity. Up to 40% of the variation in timing of birth results from genetic factors, mostly due to the maternal genome. Methods: We conducted a genome-wide meta-analysis of gestational duration and spontaneous preterm birth in 68,732 and 98,370 European mothers, respectively. Results: The meta-analysis detected 15 loci associated with gestational duration, and four loci associated with preterm birth. Seven of the associated loci were novel. The loci mapped to several biologically plausible genes, for example HAND2 whose expression was previously shown to decrease during gestation, associated with gestational duration, and GC (Vitamin D-binding protein), associated with preterm birth. Downstream in silico-analysis suggested regulatory roles as underlying mechanisms for the associated loci. LD score regression found birth weight measures as the most strongly correlated traits, highlighting the unique nature of spontaneous preterm birth phenotype. Tissue expression and colocalization analysis revealed reproductive tissues and immune cell types as the most relevant sites of action. Conclusions: We report novel genetic risk loci that associate with preterm birth or gestational duration, and reproduce findings from previous genome-wide association studies. Altogether, our findings provide new insight into the genetic background of preterm birth. Better characterization of the causal genetic mechanisms will be important to public health as it could suggest new strategies to treat and prevent preterm birth., Author summary Annually, more than 15 million pregnancies are affected by preterm births all over the world. There are no effective ways to prevent preterm birth, and premature babies suffer from neonatal mortality and lifelong morbidities. Genetic factors of mother and fetus explain a large proportion, approximately 30–40%, of the of the variation in gestational age at delivery. To date, there have been just a few unbiased genome-wide investigations set out to locate these genes. Better characterization of the causal genetic mechanisms could suggest new strategies to treat and prevent preterm birth. In the current study, we aimed to identify maternal genetic factors that contribute to the timing of birth by meta-analyzing genome-wide association studies from European populations. We detected 17 independent loci that were associated with gestational duration and/or the risk of preterm birth. Ten of the loci replicated associations from previous studies, and seven were novel. The replicated associations provide strong evidence for the importance of these loci in the timing of birth, although the exact genes and causal variants and pathways still require further functional analysis. The seven novel associations provide further intriguing candidates that may account for the risk of preterm birth. Bioinformatics analysis proposed the associated loci have regulatory functions predominantly in immune cell types and reproductive tissues. The analysis further highlighted the unique nature or preterm birth as a phenotype, since the only traits with strong correlations were birth weight measures that are closely linked to the studied phenotypes. Our findings complement the knowledge of the genetic factors of preterm birth.

Spontaneous preterm birth (SPTB) is the leading cause of neonatal death and morbidity worldwide. Certain risk factors predispose infants to SPTB, but over half of SPTBs occur in pregnancies where there is no identifiable risk factor. Previous studies have suggested that SPTB has a genetic background and that both maternal and fetal genomes contribute to its likelihood. Other biological factors are also associated with SPTB, but knowledge about these factors is limited. One of the key tissues involved in parturition is the placenta. The aim of this study was to determine the biological factors behind predisposition to SPTB using hypothesis-free methods. A genome-wide association study (GWAS) was used to investigate fetal genetic variants that predispose infants to SPTB. The results suggest that a variant of slit guidance ligand 2 (SLIT2) is associated with SPTB. Additionally, the study showed that SLIT2 and its receptor roundabout guidance receptor 1 (ROBO1) are expressed in placenta, and their mRNA levels are higher in SPTB placentas compared to term controls. A knockdown experiment showed that Slit–Robo signaling affects genes involved in inflammation in placental cells. In a comparative proteomics screen of SPTB, spontaneous term, and elective preterm placentas, proteins for which particular levels are associated with SPTB were identified. Rare and potentially damaging variants of the genes coding for the identified proteins were sought from whole exome sequencing (WES) data. This yielded a candidate, alpha-1 antitrypsin (AAT), which is encoded by SERPINA1. Experiments revealed that SERPINA1 affects actin cytoskeleton, Slit–Robo signaling, and extracellular matrix organization in the placenta. Some heat shock proteins (HSPs) are associated with pregnancy complications. Thus, genetic and genomic datasets were used to identify SPTB-predisposing HSPs from the mother, infant, and placenta. In addition, the association of nuclear hormone receptors (NRs) with S, Tiivistelmä Spontaani ennenaikainen synnytys on vastasyntyneiden yleisin kuolinsyy ja sairauksille altistava tekijä. Ennenaikaisuudelle altistavia tekijöitä tunnetaan, mutta silti yli puolet tapauksista ovat sellaisia, joiden syy jää tunnistamatta. Sekä äidin että lapsen perintötekijät vaikuttavat ennenaikaisen syntymän riskiin. Ennenaikaisuuteen on liitetty myös muita biologisia tekijöitä, mutta tietoa ei ole riittävästi. Yksi synnytyksen käynnistymiseen vaikuttavista kudoksista on istukka. Tavoitteenamme oli löytää hypoteesivapaita menetelmiä käyttämällä biologisia tekijöitä, jotka liittyvät ennenaikaiseen synnytykseen. Genominlaajuisen assosiaatiokartoituksen avulla tarkastelimme lapsen perimän geenivariantteja, jotka altistavat ennenaikaisuudelle. Osoitimme, että SLIT2-geenin variantti voi altistaa ennenaikaisuudelle ja että SLIT2-proteiini ja sen reseptori ROBO1 tuottuvat istukassa. Molempien mRNA-tasot olivat koholla ennenaikaisesti syntyneiden istukoissa verrattuna täysiaikaisiin. Näiden geenien hiljentäminen vaikutti pääasiassa tulehdukseen liittyviin geeneihin. Kun vertasimme spontaanien ennenaikaisesti syntyneiden istukoissa olevia proteiineja täysaikaisesti ja keisarinleikkauksella syntyneiden istukoissa oleviin proteiineihin, havaitsimme, että proteiinien määrät poikkesivat toisistaan. Etsimme harvinaisia ja mahdollisesti vahingollisia variantteja sellaisista geeneistä, jotka koodaavat löytämiämme proteiineja. Havaitsimme, että alpha-1 antitrypsiini (AAT) -proteiini ja sitä koodaava SERPINA1-geeni voivat liittyä ennenaikaisuuteen. Osoitimme, että AAT säätelee solujen ulkopuolisia rakenteita istukassa. Lämpösokkiproteiinien uskotaan liittyvän raskauskomplikaatioihin, ja siksi tarkastelimme useita aineistoja tunnistaaksemme ennenaikaisuuteen liittyviä lämpösokkiproteiineja. Tarkastelimme myös hormonireseptoreja, joiden aktiivisuutta lämpösokkiproteiinit säätelevät. Löysimme useita lämpösokkiproteiineja ja hormonireseptoreja, jotka voivat liittyä ennenaikai

Industry is increasingly moving towards data-driven business. With an abundance of data, the problem is no longer how to get access to data but how to extract the most value from it. The extracted knowledge helps to control industrial processes efficiently and automatically. The quality of products can be improved by finding root causes behind poor quality, hence improving the yield and competitiveness of the whole plant. This thesis helps to understand the benefit of using statistical machine learning methods to improve manufacturing processes. With the methods presented, the future can be predicted based on historical data and data-driven decision support for the processes can be offered. The application area of this work is the steel industry. This work gives step-by-step advice for successfully implementing AI applications in the industry. In addition, methods for finding root causes behind poor quality are presented to improve the process and the quality of the products. Since data collected under challenging measurement conditions in the industry is never flawless, a quality model which can utilize incomplete data is also developed. Machine learning methods are used in this work to process data and to develop data-driven quality models, which help to predict the desired quality characteristics and hence support the decision-making of the workers managing the process. Both supervised and semi-supervised machine learning methods are used. In addition, explainable machine learning is used to increase the transparency of the models. With the help of these methods, predictive quality models are developed for four different datasets consisting of measurements from steel manufacturing processes. The developed models have been implemented as a smart manufacturing tool, which enables real-time support for process workers during the manufacturing process. Using the tool, a worker can recognize the factors that can cause quality problems in the process in advanc, Tiivistelmä Teollisuus on siirtynyt yhä enenemissä määrin kohti dataohjautuvaa liiketoimintaa. Dataa on tarjolla niin paljon, että ongelmana ei ole sen saatavuus vaan se mitä siitä saadaan irti. Datasta louhittu tieto auttaa kontrolloimaan teollisuusprosesseja tehokkaammiksi ja automaattisemmiksi sekä parantamaan tuotteen laatua löytämällä syyt huonon laadun takaa ja näin parantamaan koko tehtaan tuottoa ja kilpailukykyä. Tämä väitöskirja auttaa ymmärtämään tilastollisten koneoppimismenetelmien tärkeyden teollisuusprosesseja kehitettäessä. Näiden menetelmien avulla pystymme ennakoimaan tulevaisuutta historian avulla ja kasvattamaan dataohjautuvan päätöksenteon tukea prosesseissa. Tämän väitöskirjan sovellusalueena on terästeollisuus. Työ antaa työkaluja siihen miten AI sovellus voidaan toteuttaa onnistuneesti teollisuudessa askel askeleelta. Lisäksi työssä esitellään menetelmiä joiden avulla voidaan löytää juurisyitä huonolle laadulle ja näin kehittää prosessia tuottamaan laadukkaampia tuotteita. Koska teollisuuden haastavista olosuhteista kerätty data ei koskaan ole täydellistä, on tässä työssä kehitetty myös laatumalli, joka pystyy hyödyntämään puuttuvia tietoja. Tässä työssä käytetään koneoppimismenetelmiä, joiden tarkoituksena on prosessoida dataa ja kehittää datamukautuvia laatumalleja, joiden avulla voidaan tehdä ennustuksia haluttavasta laatuominaisuudesta, ja näin tukea prosessissa työskentelevien ihmisten päätöksentekoa. Työssä on käytetty sekä ohjattuja-, että osittain-ohjattuja koneoppimismenetelmiä sekä mallien läpinäkyyvyyttä lisääviä selittäviä koneoppimismenetelmiä. Näiden menetelmien avulla on kehitetty ennustavia laatumalleja neljän erilaisen teräsprosessia kuvaavaan datasetin avulla. Kehitetyt laatumallit on implementoitu teollisuutta varten kehitettyyn älykkään päätöksenteon työkaluun, joka mahdollistaa reaaliaikaisen tuen teollisuusprosessissa työskenteleville henkilöille. Työkalun avulla työntekijä voi jo etukäteen tunnistaa tekijät, jotka voiva

Background: Preterm birth is defined as live birth before 37 completed weeks of pregnancy, and it is a major problem worldwide. The molecular mechanisms that lead to onset of spontaneous preterm birth are incompletely understood. Prediction and evaluation of the risk of preterm birth is challenging as there is a lack of accurate biomarkers. In this study, our aim was to identify placental proteins that associate with spontaneous preterm birth. Methods: We analyzed the proteomes from placentas to identify proteins that associate with both gestational age and spontaneous labor. Next, rare and potentially damaging gene variants of the identified protein candidates were sought for from our whole exome sequencing data. Further experiments we performed on placental samples and placenta-associated cells to explore the location and function of the spontaneous preterm labor-associated proteins in placentas. Results: Exome sequencing data revealed rare damaging variants in SERPINA1 in families with recurrent spontaneous preterm deliveries. Protein and mRNA levels of alpha-1 antitrypsin/SERPINA1 from the maternal side of the placenta were downregulated in spontaneous preterm births. Alpha-1 antitrypsin was expressed by villous trophoblasts in the placenta, and immunoelectron microscopy showed localization in decidual fibrinoid deposits in association with specific extracellular proteins. siRNA knockdown in trophoblast-derived HTR8/SVneo cells revealed that SERPINA1 had a marked effect on regulation of the actin cytoskeleton pathway, Slit–Robo signaling, and extracellular matrix organization. Conclusions: Alpha-1 antitrypsin is a protease inhibitor. We propose that loss of the protease inhibition effects of alpha-1 antitrypsin renders structures critical to maintaining pregnancy susceptible to proteases and inflammatory activation. This may lead to spontaneous premature birth.

This article demonstrates the use of data mining methods for evidence-based smart decision support in quality control. The data were collected in a measurement campaign which provided a new and potential quality measurement approach for manufacturing process planning and control. In this study, the machine learning prediction models and Explainable AI methods (XAI) serve as a base for the decision support system for smart manufacturing. The discovered information about the root causes behind the predicted failure can be used to improve the quality, and it also enables the definition of suitable security boundaries for better settings of the production parameters. The user’s need defines the given type of information. The developed method is applied to the monitoring of the surface roughness of the stainless steel strip, but the framework is not application dependent. The modeling analysis reveals that the parameters of the annealing and pickling line (RAP) have the best potential for real-time roughness improvement.

This article presents a statistical prediction model-based intelligent decision support tool for center line deviation monitoring. Data mining methods enable the data driven manufacturing. They also help to understand the manufacturing process and to test different hypotheses. In this study, the original assumption was that the shape of the strip during the hot rolling has a strong effect on the behaviour of the steel strip in Rolling, Annealing and Pickling line (RAP). Our goal is to provide information that enables to react well in advance to strips with challenging shape. In this article, we show that the most critical shape errors arising in hot rolling process will be transferred to critical errors in RAP-line process as well. In addition, our results reveal that the most critical feature characterizes the deviation better than the currently used criterion for rework. The developed model enables the user to understand better the quality of the products, how the process works, and how the quality model predicts and performs.

The purpose of this study was to develop an innovative online supervisor system to assist the operators of an industrial manufacturing process in discovering new solutions for improving both the products and the manufacturing process itself. In this paper, we discuss the requirements and practical aspects of building such a system and demonstrate its use and functioning with different types of statistical modelling methods applied for quality monitoring in industrial applications. The two case studies presenting the development work were selected from the steel industry. One case study predicting the profile of a stainless steel strip tested the usability of the tool offline, while the other study predicting the risk of roughness of a steel strip had an online test period. User experiences from a test use period were collected with a system usability scale questionnaire.

Spontaneous preterm birth (SPTB) is the leading cause of neonatal death and morbidity worldwide. Both maternal and fetal genetic factors likely contribute to SPTB. We performed a genome-wide association study (GWAS) on a population of Finnish origin that included 247 infants with SPTB (gestational age [GA] < 36 weeks) and 419 term controls (GA 38–41 weeks). The strongest signal came within the gene encoding slit guidance ligand 2 (SLIT2; rs116461311, minor allele frequency 0.05, p = 1.6×10−6). Pathway analysis revealed the top-ranking pathway was axon guidance, which includes SLIT2. In 172 very preterm-born infants (GA <32 weeks), rs116461311 was clearly overrepresented (odds ratio 4.06, p = 1.55×10−7). SLIT2 variants were associated with SPTB in another European population that comprised 260 very preterm infants and 9,630 controls. To gain functional insight, we used immunohistochemistry to visualize SLIT2 and its receptor ROBO1 in placentas from spontaneous preterm and term births. Both SLIT2 and ROBO1 were located in villous and decidual trophoblasts of embryonic origin. Based on qRT-PCR, the mRNA levels of SLIT2 and ROBO1 were higher in the basal plate of SPTB placentas compared to those from term or elective preterm deliveries. In addition, in spontaneous term and preterm births, placental SLIT2 expression was correlated with variations in fetal growth. Knockdown of ROBO1 in trophoblast-derived HTR8/SVneo cells by siRNA indicated that it regulate expression of several pregnancy-specific beta-1-glycoprotein (PSG) genes and genes involved in inflammation. Our results show that the fetal SLIT2 variant and both SLIT2 and ROBO1 expression in placenta and trophoblast cells may be correlated with susceptibility to SPTB. SLIT2-ROBO1 signaling was linked with regulation of genes involved in inflammation, PSG genes, decidualization and fetal growth. We propose that this receptor-ligand couple is a component of the signaling network that promotes SPTB.

This paper presents an AI based method for improving poorly performing quality prediction models. The method improves automatically the usability of the low quality alarm predictions in the web based quality monitoring tool that provides decision support for users. The tool enables the utilization of the models that suffer from the lack of information because of a long time gap to the predicted future. The reliability of the presented alarms in a monitoring tool will be improved by reducing the amount of false alarms.