Computational SIS Modeling of the Spread of Antibiotic Resistance within Bacterial Metapopulation Networks

The world faces a continually ongoing emergence of antibiotic resistance, and an almost non-existent development of new antibiotics to fight resistant bacteria. This leads to a substantial need for new and effective methods to stagnate the spread of antibiotic resistance. There exists a diversity of approaches for modeling spreading through a system. Each approach with their own strengths and weaknesses. For this thesis, a combination of a compartmental susceptible-infected-susceptible (SIS) modeling approach and a metapopulation modeling approach was chosen. The choice was based upon available data, and the general aim of the thesis. Various existing software were carefully evaluated and considered, but none of the studied software seemed to satisfy the initial needs and desire of the tool. Initially, the aim was to develop a simple tool to study the effect of different parameters and network topologies in spreading situations. The desire was to show how a tool of this kind could be of use to predict the spread of infection; in this case antibiotic resistance, and how the tool could possibly help us establish measures to be taken in order to cease a spread or at least control the spreading activity. During the work of this thesis, a tool was developed, and several simulations were run in order to illustrate the utility of such a remedy as a help to stagnate the ongoing development of antibiotic resistance. Results from these simulation runs proved the importance of transit nodes for a spreading to progress, and that different network types display different spreading patterns. Results also demonstrated how parameters such as the frequency of cleaning, the growth rate of bacteria, and the fitness cost related to the acquisition of resistance genes, might affect systems. Among other things, results indicated an optimal cleaning interval of 6-9 hours, and a proportionality between increased growth rate and spreading activity. To illustrate how the tool may be utilized in real-life events, a semi-realistic simulation of a virtual hospital was conducted. Last but not least, limitations to the tool were established; limitations such as degree of realism. This thesis presents a review concerning a selection of already existing research within the field of computational epidemiological modeling, as well a presentation of the developed network model tool and some of its functions

Calculation of Added Mass in the Proximity of the Seabed for an Oscillating Disc

Calculations have been made with the commercial CFD-software Ansys Fluent, with dynamic mesh

Evaluering av maskinlæringsmetoder for automatisk tumorsegmentering

The definition of target volumes and organs at risk (OARs) is a critical part of radiotherapy planning. In routine practice, this is typically done manually by clinical experts who contour the structures in medical images prior to dosimetric planning. This is a time-consuming and labor-intensive task. Moreover, manual contouring is inherently a subjective task and substantial contour variability can occur, potentially impacting on radiotherapy treatment and image-derived biomarkers. Automatic segmentation (auto-segmentation) of target volumes and OARs has the potential to save time and resources while reducing contouring variability. Recently, auto-segmentation of OARs using machine learning methods has been integrated into the clinical workflow by several institutions and such tools have been made commercially available by major vendors. The use of machine learning methods for auto-segmentation of target volumes including the gross tumor volume (GTV) is less mature at present but is the focus of extensive ongoing research. The primary aim of this thesis was to investigate the use of machine learning methods for auto-segmentation of the GTV in medical images. Manual GTV contours constituted the ground truth in the analyses. Volumetric overlap and distance-based metrics were used to quantify auto-segmentation performance. Four different image datasets were evaluated. The first dataset, analyzed in papers I–II, consisted of positron emission tomography (PET) and contrast-enhanced computed tomography (ceCT) images of 197 patients with head and neck cancer (HNC). The ceCT images of this dataset were also included in paper IV. Two datasets were analyzed separately in paper III, namely (i) PET, ceCT, and low-dose CT (ldCT) images of 86 patients with anal cancer (AC), and (ii) PET, ceCT, ldCT, and T2 and diffusion-weighted (T2W and DW, respectively) MR images of a subset (n = 36) of the aforementioned AC patients. The last dataset consisted of ceCT images of 36 canine patients with HNC and was analyzed in paper IV. In paper I, three approaches to auto-segmentation of the GTV in patients with HNC were evaluated and compared, namely conventional PET thresholding, classical machine learning algorithms, and deep learning using a 2-dimensional (2D) U-Net convolutional neural network (CNN). For the latter two approaches the effect of imaging modality on auto-segmentation performance was also assessed. Deep learning based on multimodality PET/ceCT image input resulted in superior agreement with the manual ground truth contours, as quantified by geometric overlap and distance-based performance evaluation metrics calculated on a per patient basis. Moreover, only deep learning provided adequate performance for segmentation based solely on ceCT images. For segmentation based on PET-only, all three approaches provided adequate segmentation performance, though deep learning ranked first, followed by classical machine learning, and PET thresholding. In paper II, deep learning-based auto-segmentation of the GTV in patients with HNC using a 2D U-Net architecture was evaluated more thoroughly by introducing new structure-based performance evaluation metrics and including qualitative expert evaluation of the resulting auto-segmentation quality. As in paper I, multimodal PET/ceCT image input provided superior segmentation performance, compared to the single modality CNN models. The structure-based metrics showed quantitatively that the PET signal was vital for the sensitivity of the CNN models, as the superior PET/ceCT-based model identified 86 % of all malignant GTV structures whereas the ceCT-based model only identified 53 % of these structures. Furthermore, the majority of the qualitatively evaluated auto-segmentations (~ 90 %) generated by the best PET/ceCT-based CNN were given a quality score corresponding to substantial clinical value. Based on papers I and II, deep learning with multimodality PET/ceCT image input would be the recommended approach for auto-segmentation of the GTV in human patients with HNC. In paper III, deep learning-based auto-segmentation of the GTV in patients with AC was evaluated for the first time, using a 2D U-Net architecture. Furthermore, an extensive comparison of the impact of different single modality and multimodality combinations of PET, ceCT, ldCT, T2W, and/or DW image input on quantitative auto-segmentation performance was conducted. For both the 86-patient and 36-patient datasets, the models based on PET/ceCT provided the highest mean overlap with the manual ground truth contours. For this task, however, comparable auto-segmentation quality was obtained for solely ceCT-based CNN models. The CNN model based solely on T2W images also obtained acceptable auto-segmentation performance and was ranked as the second-best single modality model for the 36-patient dataset. These results indicate that deep learning could prove a versatile future tool for auto-segmentation of the GTV in patients with AC. Paper IV investigated for the first time the applicability of deep learning-based auto-segmentation of the GTV in canine patients with HNC, using a 3-dimensional (3D) U-Net architecture and ceCT image input. A transfer learning approach where CNN models were pre-trained on the human HNC data and subsequently fine-tuned on canine data was compared to training models from scratch on canine data. These two approaches resulted in similar auto-segmentation performances, which on average was comparable to the overlap metrics obtained for ceCT-based auto-segmentation in human HNC patients. Auto-segmentation in canine HNC patients appeared particularly promising for nasal cavity tumors, as the average overlap with manual contours was 25 % higher for this subgroup, compared to the average for all included tumor sites. In conclusion, deep learning with CNNs provided high-quality GTV autosegmentations for all datasets included in this thesis. In all cases, the best-performing deep learning models resulted in an average overlap with manual contours which was comparable to the reported interobserver agreements between human experts performing manual GTV contouring for the given cancer type and imaging modality. Based on these findings, further investigation of deep learning-based auto-segmentation of the GTV in the given diagnoses would be highly warranted.Definisjon av målvolum og risikoorganer er en kritisk del av planleggingen av strålebehandling. I praksis gjøres dette vanligvis manuelt av kliniske eksperter som tegner inn strukturenes konturer i medisinske bilder før dosimetrisk planlegging. Dette er en tids- og arbeidskrevende oppgave. Manuell inntegning er også subjektiv, og betydelig variasjon i inntegnede konturer kan forekomme. Slik variasjon kan potensielt påvirke strålebehandlingen og bildebaserte biomarkører. Automatisk segmentering (auto-segmentering) av målvolum og risikoorganer kan potensielt spare tid og ressurser samtidig som konturvariasjonen reduseres. Autosegmentering av risikoorganer ved hjelp av maskinlæringsmetoder har nylig blitt implementert som del av den kliniske arbeidsflyten ved flere helseinstitusjoner, og slike verktøy er kommersielt tilgjengelige hos store leverandører av medisinsk teknologi. Auto-segmentering av målvolum inkludert tumorvolumet gross tumor volume (GTV) ved hjelp av maskinlæringsmetoder er per i dag mindre teknologisk modent, men dette området er fokus for omfattende pågående forskning. Hovedmålet med denne avhandlingen var å undersøke bruken av maskinlæringsmetoder for auto-segmentering av GTV i medisinske bilder. Manuelle GTVinntegninger utgjorde grunnsannheten (the ground truth) i analysene. Mål på volumetrisk overlapp og avstand mellom sanne og predikerte konturer ble brukt til å kvantifisere kvaliteten til de automatisk genererte GTV-konturene. Fire forskjellige bildedatasett ble evaluert. Det første datasettet, analysert i artikkel I–II, bestod av positronemisjonstomografi (PET) og kontrastforsterkede computertomografi (ceCT) bilder av 197 pasienter med hode/halskreft. ceCT-bildene i dette datasettet ble også inkludert i artikkel IV. To datasett ble analysert separat i artikkel III, nemlig (i) PET, ceCT og lavdose CT (ldCT) bilder av 86 pasienter med analkreft, og (ii) PET, ceCT, ldCT og T2- og diffusjonsvektet (henholdsvis T2W og DW) MR-bilder av en undergruppe (n = 36) av de ovennevnte analkreftpasientene. Det siste datasettet, som bestod av ceCT-bilder av 36 hunder med hode/halskreft, ble analysert i artikkel IV

The relationship between digitalization and profitability A cross-sectional study of firms in the Norwegian shipping industry

Masteroppgave(MSc) in Master of Business - Handelshøyskolen BI, 2021In this paper, we study the relationship between profitability and the level of digitalization of firms in the Norwegian shipping industry. We found the digitalization level by using a Likert scale on a survey sent to companies within this industry. Our main goal with this study was to investigate whether there is a correlation between profitability and the level of digitalization, and to identify whether this correlation is positive or negative. The subject was chosen based on the lacking research within this field. In addition to using cross-sectional data on digitalization, we collected data on years since the digitalization started, ROA as a measurement of profitability, the age and the size of the company, all calculated from 2019. We retrieved 39 usable responses to our survey, which were used as the sample size. The results of our regression models were inconclusive, meaning that we could not conclude with the null hypothesis or the alternative hypothesis. Because digitalization will only affect the operating part of companies, other drivers affected by the market will not be influenced. We believe our results are a consequence of not straining out the unaffected drivers, and not because digitalization has no impact on the profitability of firms in the Norwegian shipping industry

In vitro toxicity of glyphosate in Atlantic salmon evaluated with a 3D hepatocyte-kidney co-culture model

publishedVersio

Inter-tester reliabilitet av 2-dimensjonale målinger i frontalplan ved ettbens knebøy og tobens fallhopp på kvinnelige elite håndball- og fotballspillere: en metodologisk studie

Masteroppgave - Norges idrettshøgskole, 2017Introduksjon: Overdreven dynamisk knevalgus under hopp og landinger og raske retningsendringer er blitt assosiert med fremre korsbåndskade. I en prospektiv kohortstudie ved senter for idrettsskadeforskning er formålet å identifisere kvinnelige elite håndball- og fotballspillere i risiko for en korsbåndskade. Ettbens knebøy og tobens fallhopp kan være to gode og nyttige tester til å screene spillere med økt knevalgus og identifisere spillere med risiko for skaden. Målet med denne metodestudien var å undersøke inter-tester reliabiliteten av ulike 2-dimensjonale målinger til å beregne frontalplan knebevegelse under ettbens knebøy og tobens fallhopp, som en del av hovedstudien. Metode: Vi inkluderte videoanalyser av 20 kvinnelige elite håndball- og fotballspillere (N=11 håndballspillere og 9 fotballspillere; alder:{Gjennomsnitt ± standardavvik} 21.1 ±2.8 år, høyde: 171.2 ±5.8 cm, vekt: 67±7.4 kg). Fire testere utførte analyser på ettbens knebøy høyre side og tobens fallhopp initial contact (IC) og peak knee flexion (PF). Gjennomsnittet av tre forsøk var valgt for analyse. Vi beregnet frontalplanvinkel (FPPV), kneets frontalplanposisjon (FPP), lateralt bekkentilt (LB) ved ettbens knebøy, og FPPV, FPP og kneankel (KAR)- og kne-hofte seperasjonsratio (KHR) ved tobens fallhopp. Inter-tester reliabiliteten kalkulerte vi med ICC, Spearman´s rho og SEM.Seksjon for idrettsmedisinske fag / Department of Sports Medicin

Termodynamisk analyse av en bergvarmepumpe i kombinasjon med fotovoltaisk termisk (PVT) panel

I denne masteroppgaven benyttes programvaren Engineering Equation Solver (EES) til å gjennomføre en termodynamisk analyse av en energisentral bestående av en bergvarmepumpe i kombinasjon med fotovoltaisk termisk (PVT) panel. Energisentralen er installert i en imaginær boligblokk som er lokalisert i Oslo (kaldt nordisk klima) med TEK17 og passivhusstandard. De naturlige kuldemediene propan (R290), isobutan (R600a) og ammoniakk (R717) blir vurdert som arbeidsmedium til varmepumpen, og det gjennomføres diverse sensitivitetsanalyser for å definere parametere av stor betydning til energisentralen. Hovedhensikten med å kombinere fotovoltaisk termiske paneler med en bergvarmepumpe er å redusere bruk av tilført elektrisk energi, og opprettholde en termisk likevekt i grunnen ved ladning av borehull. I denne oppgaven benyttes PVT-anlegget både til oppvarming av varmt tappevann og ladning av borehull. Alle varmevekslere, inkludert fordamper og kondensator er motstrøms platevarmevekslere.publishedVersio

Mixture toxicity of chlorpyrifos-methyl, pirimiphos-methyl, and nonylphenol in Atlantic salmon (Salmo salar) hepatocytes

Pesticide formulations typically contain adjuvants added to enhance the performance of the active ingredient. Adjuvants may modify the bioavailability and toxicity of pesticides. In this study, the aim was to examine to which degree nonylphenol (NP) may interfere with the toxicity of two organophosphorus pesticides found in aquafeeds, chlorpyrifos-methyl (CPM) and pirimiphos-methyl (PPM). Atlantic salmon liver cells were exposed to these compounds singly or in combinations for 48 h using 3D cell cultures. Cytotoxicity, gene expression (RT-qPCR), and lipidomics endpoints were used to assess toxicity. The dose-response assessment showed that NP was the most toxic compound at equimolar concentrations (100 μM). Shotgun lipidomics pointed to a general pattern of elevated levels of saturated 18:0 fatty acids and declined levels of 18:1 monounsaturated fatty acids by the combined treatment. All three compounds had a distinct effect on membrane phospholipids, in particular on phosphatidylcholine (PC) and phosphatidylethanolamine (PE). Lipid species patterns predicted inhibited stearoyl CoA desaturase (SCD) activity and increased Δ6 desaturase (D6D) activity in co-treated cells. While all three compounds alone mitigated increased triacylglycerol (TAG) accumulation, combined treatment resulted in lower total TAG in the cells. Multivariate analysis with PLS regression showed significant combined effects for nine genes (d5d, d6d, scd, srebf2, vtg, esr1, cyp1, ugt1a, and cat) and four lipid species (FFA 22:5, LPC 18:0, TAG52:1-FA16:0, and TAG52:1-FA18:0). In summary, this study demonstrates that the adjuvant can be the main contributor to the toxicity of a mixture of two organophosphorus pesticides with relatively low toxicity in fish cells.publishedVersio

Tidlig identifisering av sepsis i akuttmottaket

Bakgrunn: Sepsis er et økende problem både nasjonalt og internasjonalt. 48,9 millioner mennesker blir syke med sepsis og 11 millioner mennesker dør med sepsis hvert år. I Norge er det årlig 7000 tilfeller av sepsis og 1850 dødsfall som følge av sepsis. Sykepleiere står i en nøkkelposisjon til å oppdage sepsis tidlig i akuttmottaket. Hensikt: Hensikten med denne studien er å undersøke hvilke faktorer som legger til rette for at sykepleier tidlig skal kunne identifisere sepsis i akuttmottaket. Det er viktig å legge til rette for at sepsis tidlig blir oppdaget for å komme raskt i gang med antibiotikabehandling. Metode: Systematisk litteraturstudie basert på seks kvantitative og to kvalitative vitenskapelige originalartikler. Resultat: Å utvikle sin kompetanse som sykepleier gjennom undervisning med varierte metoder, handlingsrefleksjon og tverrfaglig samarbeid, å delta i tverrfaglig samarbeid ved kartlegging av sepsis og i systemforbedring og å anvende kartleggingsverktøy er faktorer som kan legge til rette for at sykepleier tidlig skal kunne identifisere sepsis i akuttmottaket. Konklusjon: For sykepleiere er det mange faktorer som legger til rette for tidlig å kunne identifisere sepsis i akuttmottaket. Å anvende intervensjoner som har vist seg hensiktsmessig i forskning, for eksempel å motta pasienter i tverrfaglige sepsismottak og å utøve tverrfaglig kontinuerlig forbedring, kan bidra til forbedret praksis i akuttmottakene i Norge. Nøkkelord: Sepsis, tidlig identifisering, akuttmottak, sykepleieBackground: Sepsis is an expanding national and global problem. 48,9 million people acquire sepsis and 11 million people die with sepsis every year. In Norway there are 7000 cases of sepsis and 1850 deaths because of sepsis annually. Nurses are in a key position to recognize sepsis early in the emergency department. Aim: The aim of the study is to examine the factors that arrange for early identification of sepsis by the nurse in the emergency department. It is important to arrange early recognition of sepsis to make sure of early treatment with antibiotics. Method: Systematic literature review based on six quantitative and two qualitative scientific research articles. Results: To evolve nurse competence through education with various methods, reflection and interdisciplinary cooperation, to participate in interdisciplinary cooperation in sepsis mapping and in improvements and to utilize mapping tools can lead to early identification of sepsis by the nurse in the emergency department. Conclusion: There are many factors that can arrange for early identification of sepsis in the emergency department. To utilize interventions that are found useful in research, for instance implementation of nurse driven mapping tools and to receive patients in interdisciplinary sepsis receptions can lead to improved practice in emergency departments in Norway. Key words: Sepsis, early identification, emergency department, nurs

Erosjonshull. Deres dannelse og geometrisk mønster: Med storskalaelva Gaula som case

Forced pools are a phenomenon discovered all over Norway. The effects of forced pools on river stability, and little research on the phenomenon in Norway, results in a need to know more about forced pools. The approach to the problem is: Are there forced pools in Gaula, and has human intervention along Gaula been a contributing factor for development and preservation of forced pools? Is there a geometric spatial pattern in forced pools, and can these be compared to studies from smallscale-rivers? The datamaterial goes back to 2007, when Hydra Team measured the center line. This data has been used to examine the geometry in the pools, while maps, pictures and other reports have given information about potential obstacles and the conditions in Gaula. The results show that all of the eleven forced pools in Gaula are a result of an obstacle in the river which create erosion and development of forced pools. It seems that the embankment to prevent flooding and erosion on the riverbank is the main obstacle. The analysis show a relation between the pools length and depth, where long pools are shallow and short pools are deep. The slope gradient has a relation to pool geometry, where a steep entry- and exit slope results in a deep and short pool, while a slack gradient gives a shallow and long pool. Pools located in riverbends are deeper than pools located along a straight part of the river. The results also show that the conditions of the obstacle; mainly its form, width and length, has an influence on the pool geometry. The problem with scale and transfer of theory between scales is a challenge. This has been present in this thesis. Gaula is a largescale-river, while the theory and definitions are based upon small-scale river