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Dagens samhälle har utvecklats till ett stort globalt system där människan fått en signifikant påverkan på klimatet och miljön. För att nå målet i Parisavtalet är det många sektorer som behöver ställa om sina verksamheter till att bli hållbara. Det gäller i allra högsta grad flygsektorn som har stora utmaningar framför sig när det gäller att minska sitt klimatavtryck. En av möjligheterna för att väsentligt minska flygets klimatpåverkan är att övergå till flygplan som drivs med hjälp av batterier istället för fossila bränslen.I det här projektet undersöks om de tekniska och infrastrukturmässiga förutsättningarna finns för att elflyg ska kunna ersätta den befintliga flygtrafiken mellan Stockholm och Visby och när i tiden en sådan förändring kan ske. Litteraturstudier och intervjuer har använts för att utforska det nuvarande kunskapsläget som är relevant för elflyg inom batteriteknik, elmotorer, aerodynamik samt infrastruktur på de relevanta flygplatserna. Med den utgångspunkten har en matematisk modell använts för att studera om de rådande tekniska förutsättningarna är tillräckliga eller om förbättringar kommer krävas. Det som framkom var att det i teorin är möjligt att tillverka ett elflygplan som kan flyga hela sträckan med befintlig teknik men att utveckling av både batteriteknik och aerodynamik sannolikt kommer krävas när andra aspekter vägs in. Infrastrukturen på flygplatserna är dessutom inte anpassade för elflyg i dagsläget, vilket leder till att det i ett optimistiskt scenario kommer gå att elektrifiera flygtrafiken mellan Stockholm och Visby inom 10 år. Today's society has evolved into a large global system where people have a significant impact on the climate and the environment. To achieve the goal of the Paris Agreement, many sectors need to change their business to become sustainable. This is very much the case for the aviation sector, which has major challenges ahead when it comes to reducing its climate footprint. One of the opportunities to significantly reduce the climate impact of aviation is to switch to aircraft powered by batteries instead of fossil fuels. This project examines whether the technical and infrastructure conditions are in place for electric aircraft to replace the existing air traffic between Stockholm and Visby and when such a change can occur in time. Literature studies and interviews have been used to explore the current state of knowledge relevant to electric aviation within battery technology, electric motors, aerodynamics and infrastructure at the relevant airports. Based on this, a mathematical model has been used to study whether the current technical conditions are sufficient or if improvements will be required. What emerged was that, in theory, it is possible to produce an electric aircraft that can fly the entire distance with existing technology, but that development of both battery technology and aerodynamics is likely to be required when other aspects are taken into account. The infrastructure at the airports is also not adapted for electric flights yet, which means that in an optimistic scenario it will take up to 10 years before air traffic can be fully electrified between Stockholm and Visby.

Purpose To evaluate the performance of first trimester maternal serum glycosylated (Sambucus nigra lectin-reactive) fibronectin in prediction of gestational diabetes mellitus (GDM). Methods In this case–control study, first trimester maternal serum glycosylated fibronectin and fibronectin were measured in 19 women who consequently developed GDM and in 59 control women with normal pregnancy outcomes. Adiponectin was used as a reference protein to evaluate relation of glycoprotein to SNA-lectin-reactive assay format. Samples were taken during gestational weeks 9+6–11+6. Data concerning GDM was obtained from the National Institute for Health and Welfare, which records the pregnancy outcomes of all women in Finland. Results There was no difference in maternal serum glycosylated fibronectin concentrations between women with consequent GDM [447.5 μg/mL, interquartile range (IQR) 254.4–540.9 μg/mL] and control women (437.6 μg/mL, IQR 357.1–569.1 μg/mL). Maternal serum fibronectin levels were significantly lower in GDM group (224.2 μg/mL, IQR 156.8–270.6 μg/mL), compared to the control group (264.8 μg/mL, IQR 224.6–330.6 μg/mL, p Conclusion There was no association between first trimester maternal serum glycosylated (SNA-reactive) fibronectin and GDM.

Nuclear receptors (NRs) rapidly activate/repress gene expression to detour immune responses and allow tissue adaptation to constant environmental changes. However, the effect of combined NRs in the immune system is often unclear due to the lack of reliable experimental models that recapitulate the complex interaction between NRs in vivo. Here, we used the zebrafish to investigate the immunological outcome of combining the activation of retinoic acid receptor (RAR), liver X receptor (LXR) and the cytoplasmic sensor aryl hydrocarbon receptor (AHR). Although simultaneous activation did not affect the expression of respective bona-fide target genes, RAR-induced il17a/f3 was antagonized by LXR and AHR, whereas il22 was antagonized by AHR but not LXR. In addition, RA decreased il10 expression, which was further decreased by LXR activation. Thus, using combinatorial NR activation in zebrafish larvae, we show that LXR antagonizes the expression of selected RA-induced cytokines and provide a strategy to tailor the cytokine milieu.

Introduction: Accurate early risk prediction for gestational diabetes mellitus (GDM) would target intervention and prevention in women at the highest risk. We evaluated novel biomarker predictors to develop a first-trimester risk prediction model in a large multiethnic cohort. Methods: Maternal clinical, aneuploidy and pre-eclampsia screening markers (PAPP-A, free hCGβ, mean arterial pressure, uterine artery pulsatility index) were measured prospectively at 11–13+6 weeks’ gestation in 980 women (248 with GDM; 732 controls). Nonfasting glucose, lipids, adiponectin, leptin, lipocalin-2, and plasminogen activator inhibitor-2 were measured on banked serum. The relationship between marker multiples-of-the-median and GDM was examined with multivariate regression. Model predictive performance for early (< 24 weeks’ gestation) and overall GDM diagnosis was evaluated by receiver operating characteristic curves. Results: Glucose, triglycerides, leptin, and lipocalin-2 were higher, while adiponectin was lower, in GDM (p < 0.05). Lipocalin-2 performed best in Caucasians, and triglycerides in South Asians with GDM. Family history of diabetes, previous GDM, South/East Asian ethnicity, parity, BMI, PAPP-A, triglycerides, and lipocalin-2 were significant independent GDM predictors (all p < 0.01), achieving an area under the curve of 0.91 (95% confidence interval [CI] 0.89–0.94) overall, and 0.93 (95% CI 0.89–0.96) for early GDM, in a combined multivariate prediction model. Conclusions: A first-trimester risk prediction model, which incorporates novel maternal lipid markers, accurately identifies women at high risk of GDM, including early GDM.

We examined whether first trimester aneuploidy and pre-eclampsia screening markers predict gestational diabetes mellitus (GDM) in a large multi-ethnic cohort and the influence of local population characteristics on markers.Clinical and first trimester markers (mean arterial pressure (MAP), uterine artery pulsatility index (UtA PI), pregnancy associated plasma protein A (PAPP-A), free-β human chorionic gonadotropin (free-hCGβ)) were measured in a case-control study of 980 women (248 with GDM, 732 controls) at 11 to 13 + 6 weeks' gestation. Clinical parameters, MAP-, UtA PI-, PAPP-A-, and free-hCGβ-multiples-of-the-median (MoM) were compared between GDM and controls; stratified by ethnicity, parity, and GDM diagnosis 24 versus ≥24 weeks' gestation. GDM model screening performance was evaluated using AUROC.PAPP-A- and UtA PI-MoM were significantly lower in GDM versus controls (median ((IQR) PAPP-A-MoM 0.81 (0.58-1.20) versus 1.00 (0.70-1.46); UtA PI-MoM 1.01 (0.82-1.21) versus 1.05 (0.84-1.29); p .05). Previous GDM, family history of diabetes, south/east Asian ethnicity, parity, BMI, MAP, UtA PI, and PAPP-A were significant predictors in multivariate analysis (p .05). The AUC for a model based on clinical parameters was 0.88 (95%CI 0.85-0.92), increasing to 0.90 (95%CI 0.87-0.92) with first trimester markers combined. The combined model best predicted GDM 24 weeks' gestation (AUC 0.96 (95%CI 0.94-0.98)).Addition of aneuploidy and pre-eclampsia markers is cost-effective and enhances early GDM detection, accurately identifying early GDM, a high-risk cohort requiring early detection, and intervention. Ethnicity and parity modified marker association with GDM, suggesting differences in pathophysiology and vascular risk.

Aim Develop a first trimester risk prediction model for GDM based on maternal clinical characteristics in a large metropolitan multi-ethnic population and compare its performance to that of other recently published GDM prediction models and clinical risk scoring systems. Methods A retrospective case control study of 248 women who developed GDM and 732 controls who did not. Maternal clinical parameters were prospectively obtained at 11–13+6 weeks’ gestation. A predictive multivariate regression model for GDM was developed, evaluated using areas under the receiver-operating characteristic (AUC) curve. The performance of this model was then compared with other published GDM prediction models applied to our cohort and our existing clinical risk scoring system. Results Previous GDM, family history of diabetes, age, south/east Asian ethnicity, parity and body mass index (BMI) were significant predictors for GDM. The AUC of our multivariate regression model was 0.88 (95% Confidence Interval 0.85–0.92). This performed better than other predictive models applied to our cohort (AUCs 0.77–0.82). Conclusion A multivariate model based on weighted maternal clinical risk factors accurately predicts GDM in early pregnancy and performs better than other proposed multivariate and clinical risk scoring models in a multiethnic cohort.

BACKGROUND: Current screening methods for gestational diabetes mellitus (GDM) are insufficient in detecting the risk of GDM in the first trimester of the pregnancy. Recent metabolomic studies have detected altered amino acid and acylcarnitine concentrations in type 2 diabetes (T2D). Because of the similarities between T2D and GDM, the determination of these metabolites may be useful in early screening for GDM. AIM: To evaluate the association between GDM and first-trimester maternal serum concentrations of ten amino acids and 31 acylcarnitines. METHODS: This retrospective case-control study included data from pregnant women screened at Oulu University Hospital between 1.1.2008 and 31.12.2011. A total of 31,146 women participated voluntarily in a first-trimester combined screening (for chromosomal abnormalities). The study population included 69 women who developed GDM during pregnancy and 295 women without diabetes before or after pregnancy. The serum concentrations of ten amino acids and 31 acylcarnitines were analyzed from frozen serum samples taken in the first-trimester screening. Multiple of median (MoM) values were compared between the two groups. RESULTS: In the GDM group, serum levels of arginine were significantly higher (1.13 MoM vs. 0.97 MoM), and those of glycine (0.93 MoM vs. 1.03 MoM) and 3-hydroxy-isovalerylcarnitine (0.86 MoM vs. 1.03 MoM) significantly lower compared to the control group (all p < 0.01). In each case, arginine, glycine, and 3-hydroxy-isovaleryl-carnitine would have detected 46%, 32%, and 39% of GDM cases, with a false-positive rate of 20%. Combining these three metabolites with the first-trimester serum marker pregnancy-associated plasma protein A (PAPP-A) and prior risk (age, BMI, and smoking) achieved a detection rate of 72%. CONCLUSION: There are significant differences in the serum levels of arginine, glycine, and 3-hydroxy-isovalerylcarnitine between controls and women who subsequently develop GDM. These differences were already existent in the first trimester of the pregnancy. The use of metabolites in combination with prior risk and first-trimester PAPP-A represents a reliable method to identify women at risk of GDM.

A single-labeled peptide probe for measuring peptide phosphorylation status was developed by using a phosphate sensitive terbium chelate. The activity of Abl protein tyrosine kinase and T-cell protein Tyrosine phosphatase (TC PTP) was monitored in real time. To study the probe design in detail, variable substrate peptide sequences, where the enzyme target site was located from two to five amino acids apart from the nearest tyrosine residue, were synthesized. The maximum change observed in fluorescence intensity after phosphorylation was up to 320%, when the phosphorylated tyrosine was located two amino acids from the lysine coupled to the phosphate sensitive terbium chelate, demonstrating an excellent performance for a homogeneous assay. Also the longer distance of five amino acids between the phosphorylated tyrosine residue and terbium chelate resulted up to 260% change in fluorescence intensity.

The control of cell death is an intricate process involving a multitude of intracellular modulators. Among these molecules, the caspases have a central role and have become an interesting group of enzymes in the current pharmaceutical industry. We have developed a novel dual-step fluorescence energy transfer-based separation-free assay method for the primary screening of caspase-3 inhibitors in vitro. This method relies on fluorescent europium(III)-chelate-doped nanoparticle donors coated with streptavidin in conjunction with a dual-labeled (N-terminal Alexa Fluor 680 fluorescent acceptor and C-terminal BlackBerry Quencher 650) caspase-3-specific peptide substrate modified with a biotinyl moiety. In the assay, the nanoparticle donor excites the fluorescent acceptor, whose emission is monitored with time-resolved measurements. The intensity of the acceptor reflects the activity of the enzyme because the intensity is controlled by the proximity of the quencher. Owing to the dual-step fluorescence resonance energy transfer, this method enables a sensitized fluorescence signal directly proportional to the extent of enzymatic activity with relatively background fluorescence-free measurements in the event of complete enzyme inhibition. The generic nanoparticle donors further promote versatility and cost-efficiency of the method. The performance evaluated as the inhibitor (Z-DEVD-FMK) dose-response curve (IC(50) value of approximately 12 nM) was in good agreement with that of the recent methods found in literature. This assay serves as a model application proving the feasibility of the europium-chelate-doped nanoparticle labels in a homogeneous assay for proteolytic activity.

A homogeneous high-throughput screening method based on time-resolved fluorescence resonance energy transfer (TR-FRET) for the measurement of calcium-dependent multimerization of an EF-hand protein, sorcin, is described. The assay is based on a specific sorcin binding peptide conjugated either with an intrinsically highly fluorescent europium chelate (donor) or an Alexa Fluor 700 fluorophore (acceptor). Addition of calcium results in multimerization of sorcin, allowing several peptides to bind simultaneously to the epitopes of the multimeric protein complex, and the proximity of peptides labeled either with donor or acceptor label results in fluorescence resonance energy transfer between the 2 labels. When no calcium is present, the protein remains in a monomer form, and thus no FRET can take place. In the optimized assay construct, the assay was performed in 45 min, and a more than 20-fold signal-to-background ratio was achieved. The reversibility of sorcin multimerization was shown by chelating free calcium with ethylenediamine tetraacetic acid (EDTA). The developed homogeneous assay can be used in screening molecules that either inhibit or enhance multimerization of sorcin, and the assay format is applicable to various noncompetitive high-throughput screening assays detecting protein multimerization reactions.

Chemical reactors are a part of modern industry and the catalytic tubular fixed bed reactor examined in this work is an important reactor for chemicals production. In this work two different types of models for the reactor are studied; a pseudohomogeneous model and a heterogeneous model. The goal is to find differences in behaviour between these two types of reactor models and explain these. In a real reactor there exists two phases, a solid catalyst and a fluid reactant. Both these phases are in the pseudohomogeneous model treated as a single phase, a pseudofluid. In the heterogeneous model the two phases are treated separately. When comparing these types of models a few structural differences exist, void fraction, heat exchange between two phases, and heat dispersion in the phases, and all of these will affect the behaviour of the models differently. The models are studied using bifurcation analysis and linear analysis. Bifurcation theory is used to find and track different solutions depending on a certain parameter and to get a good overall picture of a system’s solutions and their type, steady state or sustained oscillation. Linear analysis is used to study linearization around a specific solution and to determine stability and frequency dependency. It is found that the concept of void fraction in the reactor model affects the behaviour only as a time scaling, while the concept of interfacial heat exchange affects the stability. The distribution of heat dispersion between phases has a significant impact on the reaction behaviour. Feedback is determined as the main cause for instabilities and oscillative solutions.