14 research outputs found
Herziening van berekeningen inzake het verlengde Zwartewater
No full textHerberekening van de afvoer van het Zwartewater t.g.v. de inpoldering van de Noordoostpolder. Herberekening van de afvoer van het Zwartewater t.g.v. de inpoldering van de Noordoostpolder.Zuiderzeewerke
Invloed van de lozing uit het IJmeer op de toestand op het Noordzeekanaal
No full textHet rapport gaat in op "Algemeen plan voor de Zuidwestelijke polder", waarin men tot een plan komt om het waterbezwaar op het Ijmeer in natte tijden zoveel nodig door te voeren op het IJsselmeer. Het rapport onderzoekt deze mogelijkheid aan de hand van metingen en enige omvangrijke berekeningen. Er blijkt dat matige stijgingen van het peil van het Noorzeekanaal (tot 0,30 m - NAP) en geringen stroomsnelheden (tot 0,28 m/s) in de nieuwe toestand meer zullen voorkomen dan in de bestaande, doch dat het aantal malen, dat ongewenst hoge standen op het Noordzeekanaal voorkomen, en de tijdsduur, dat minder gewenste, hoge stroomsnelheden optreden, af zullen nemen
Suramin inhibits chikungunya virus replication through multiple mechanisms
No full textMolecular basis of virus replication, viral pathogenesis and antiviral strategie
Automatic PV system design using LiDAR data shadow analysis
No full textThe amount of photovoltaic (PV) systems in the world is growing rapidly. The designing process is however still old fashioned and mostly done by hand. Creating a method to automatically design these PV systems can help to stimulate this growing market even further. In this thesis a research is doneinto the development of an automatic PV system design algorithm.The research consists of two main parts. A section related to panel placement and a section related to the inverter choice. Strategies were developed for both parts and that resulted in several prototype algorithms. These algorithms were tested to see if these algorithms have practical potential. The panel placement algorithms are divided into two categories: maximum panel placement and finite panel placement. Development of these categories was done for both flat and pitched roof sections. The shading caused by surrounding obstacles was taken into account to find the most optimal positions to place the panels. A grading system for the panel layout was developed to ensure that preferred layoutsare found. The shading caused by the surrounding obstacles was also used to determine the type of inverter is that optimal for certain panel configurations.A proof concept was conducted for the developed algorithms by testing algorithms on real roofs and comparing the results with PV system designs that were made manually. The designs were compared with each other in terms of predicted PV system performance, the layout and the duration of the designing process.It was demonstrated that the finite panel placement algorithm produced PV systems with an average performance difference of about - 0.83 % with a standard deviation of 5.26 %, compared to the manual designs. The maximum panel placement algorithm took an average of 2.7 minutes with a standarddeviation of 2.1 minutes. The finite panel placement algorithm took on average 3.8 minutes with a standard deviation of 5.5 minutes. Both of which can be considered as being significantly faster than making a manual design.For a PV system with many panels and several string inverters, algorithms were developed that predict the optimal configuration of the strings. It also predicts the total performance losses that occur when panels are connected in series. These algorithms can help to determine the optimal type of inverter and how to optimally configure separate panel strings in large systems. The algorithms workbased on initial tests, but not enough testing has been done to be conclusive.The work done in this thesis can be used as a stepping stone for further development of automatic PV systems design algorithms. The panel placement algorithms and the inverter algorithms can be developed further into a complete automatic PV system design algorithm.PVisionElectrical Engineering | Sustainable Energy Technolog
Development and validation of a quick-scan algorithm for evaluation of rooftop PV potential
No full textA quick-scan algorithm has been developed in order to evaluate rooftop PV potential in the Netherlands. Both its panel fitting and yield prediction functions have been validated with existing systems monitored by Solar Monkey. The calculation times of different parts of the algorithm were measured and decreased while keeping the accuracy of the algorithm in the desired range.First, a new approach to determine the roof segment orientation was introduced, using both the normal vector and the longest side of the roof segment polygon. A visual inspection was carried out, in which recent aerial images were compared to 3D roof segments that were provided by Readaar. For 145 roofs, the roof segments on which PV was placed were manually selected, such that they could validate the quick-scan. From the visual inspection it was deduced that in-roof obstacles were often not detected. Sometimes the customer had no desire to use the full potential of the roof, or preferred a rectangular panel layout instead of fitting the maximum amount of panels. Another finding was that the distance kept from the roof segment edge was much smaller than initially expected. For pitched roofs, there was virtually no distance between the roof edge and installed panels, while for flat roofs around 20 cm was kept. Using zero distance from the roof edge, the panel placement algorithm still underestimates the roof potential by 17.5% on average, with a relative standard deviation of 46.3%.Three yield calculation methods were compared: the Solar Monkey method, the SVF & SCF method, and the method without obstacles. The predicted performance or final annual AC yield was compared with the actual measured performance, both measured in kWh/kWp per year. For the three methods, relative standard deviation values of 7.2%, 7.5% and 9.1% were found respectively. The three methods could generate yield predictions for 91.0%, 92.7%, and 93.1% of the 145 roofs. For highly shaded roofs with Sun Coverage Factor values above 0.25, the method neglecting obstacles performed significantly worse. Additionally, the performance of large roofs with an average segment area above 70m2 was generally under-predicted, while the relative standard deviation was highest. It is expected that using one obstacle view is not accurate enough for large roof segments. The computational speed of the panel fitting algorithm for pitched roofs without internal obstacle segments was found to be 20.1± 5.0 m2s-1, whereas for flat roofs it was found to be 56.9±12.0 m2s-1. In order to optimise the quick-scan algorithm in both speed and accuracy, two filtering steps were carried out. Segments with a pitch angle over 10° and an orientation between 0° to 60° or 300° to 360º were filtered out, since they would have an annual performance below 650 kWh/kWp. Moreover, segments with an area less than 8.4 m2 were filtered out, since they were observed to fit less than 2 panels. The quick-scan calculation times for different yield prediction methods were found to be 15.50±1.01, 14.58±1.13 and 2.75±0.44 seconds per roof, respectively. These times were measured for data sets that had around 2 segments per roof after filtering on segment area and pitched segment orientation. It can be concluded that the method without obstacles is preferred when the calculation time is a limiting factor, whereas for accuracy the Solar Monkey method is preferred.PVisionElectrical Engineering | Sustainable Energy Technolog
654-Timely modification of microenvironment stiffness improves chondrogenesis of single cell HIMSCS
No full textPurpose (the aim of the study): Generation of de novo cartilage from cell types such as hiPSC-derived mesenchymal stromal cells (hiMSCs) are attractive due to their benefits as a stable and sustainable cell source for therapies and disease modelling. Nonetheless, the quality of neocartilage generated by hiMSCs is inferior compared to that of primary chondrocytes (Rodriguez Ruiz, Dicks et al. 2021). To improve hiMSC-chondrogenesis, unbiased characterization of modifying factors that can direct cell fate during differentiation at the single cell level is needed. The impact of microenvironment stiffness has previously been shown for differentiation to adipose and bone and here we apply the same principles (Kamperman, Henke et al. 2021). By encapsulating hiMSCs in single cell microgels of with precisely controlled microenvironment stiffness to improve chondrogenesis, and allow for the easy isolation of single cells for advanced characterization.Methods: hiMSCs were generated (Rodriguez Ruiz, Dicks et al. 2021, Ramos, Tertel et al. 2022) and encapsulated as single cells in 5% Dex-TA using two microfluidic chips (one for droplet generation and another for delayed gelation) (Kamperman, Henke et al. 2017, Kamperman, Henke et al. 2021). hiMSCs were encapsulated in microgels crosslinked to 20kPa (soft) or 75kPa (stiff). Stiffness of the microgels was managed through control of the concentration of H2O2 used in the crosslinking reaction. Single cell microgels were cultured in trans-well plates and allowed to recover from encapsulation for 24 hours following culture in chondrogenic differentiation medium with bi-weekly refreshments (Rodriguez Ruiz, Dicks et al. 2021). After seven days of differentiation, a portion of the soft single cell microgels were stiffened through controlled exposure of the single cell microgels to the crosslinking enzyme and hydrogen peroxide (in a process known as post-cure) (Kamperman, Henke et al. 2021). Viability of the single cell microgels was monitored using CAM and PI staining, chondrogenesis was analyzed with RT-qPCR, and immunofluorescent staining (anabolic cartilage marker COL2A1; hypertrophy and bone mineralization markers COL10 and SPP1) before and after 21 days of chondrogenesis. Stiffness of the single cell microgels was determined through nanoindentation.Results: Single cell microgels remained stable and supported hiMSC survival (>80%) throughout the 21 day chondrogenesis. While remaining non-proliferative, soft single cell microgels showed an increase in size of 1x (25µm) to 4x larger than initial encapsulation diameter which is not seen in stiff or post-cure microgels. Importantly, chondrogenic marker COL2 was expressed in >80% of cells (Fig. 1A) at day 21 in soft and post-cure microgels (Fig. 1B), while observed in only 35% of stiff microgels (Fig.1 C)(quantified expression is compared in Fig. 1G). Bone marker SPP1 was expressed in 60% of soft microgels (Fig. 1D) but not observed in any post-cure or stiff microgels (Fig. 1E-F)(quantified expression is compared in Fig. 1H). Hypertrophy marker COL10 was found in 85% of soft microgels (Fig. 1 D), 57% of post-cure microgels (Fig. 1F), and 20% of stiff microgels (Fig. 1E)(quantified expression is compared in Fig. 1I).Conclusions: We display that hiMSCs differentiated in single cell post-cure microgels show improved expression of cartilage extracellular matrix proteins. Post-cure microgel embedded chondrocyte-like cells display early commitment to COL2 expression as observed in soft microgels, while preventing SPP1 expression and reducing COL10 expression similar to stiff microgels. Therefore, we posit that time-dependent control of hiMSC microenvironment during chondrogenic differentiation in single cell microgels is an opportunity for the improved generation of chondrocyte-like cells and the analysis of cell fate decisions. In ongoing analysis of multi-model single cell sequencing we will identify the key factors that drive such differences and can improve neocartilage deposited by hiMSCs
RNA sequencing data integration reveals an miRNA interactome of osteoarthritis cartilage
No full textObjective To uncover the microRNA (miRNA) interactome of the osteoarthritis (OA) pathophysiological process in the cartilage.Methods We performed RNA sequencing in 130 samples (n=35 and n=30 pairs for messenger RNA (mRNA) and miRNA, respectively) on macroscopically preserved and lesioned OA cartilage from the same patient and performed differential expression (DE) analysis of miRNA and mRNAs. To build an OA-specific miRNA interactome, a prioritisation scheme was applied based on inverse Pearson’s correlations and inverse DE of miRNAs and mRNAs. Subsequently, these were filtered by those present in predicted (TargetScan/microT-CDS) and/or experimentally validated (miRTarBase/TarBase) public databases. Pathway enrichment analysis was applied to elucidate OA-related pathways likely mediated by miRNA regulatory mechanisms.Results We found 142 miRNAs and 2387 mRNAs to be differentially expressed between lesioned and preserved OA articular cartilage. After applying prioritisation towards likely miRNA-mRNA targets, a regulatory network of 62 miRNAs targeting 238 mRNAs was created. Subsequent pathway enrichment analysis of these mRNAs (or genes) elucidated that genes within the ‘nervous system development’ are likely mediated by miRNA regulatory mechanisms (familywise error=8.4×10−5). Herein NTF3 encodes neurotrophin-3, which controls survival and differentiation of neurons and which is closely related to the nerve growth factor.Conclusions By an integrated approach of miRNA and mRNA sequencing data of OA cartilage, an OA miRNA interactome and related pathways were elucidated. Our functional data demonstrated interacting levels at which miRNA affects expression of genes in the cartilage and exemplified the complexity of functionally validating a network of genes that may be targeted by multiple miRNAs.Pattern Recognition and Bioinformatic
A robust and standardized method to isolate and expand mesenchymal stromal cells from human umbilical cord
No full textBackground aims: Human umbilical cord–derived mesenchymal stromal cells (hUC-MSCs) are increasingly used in research and therapy. To obtain hUC-MSCs, a diversity of isolation and expansion methods are applied. Here, we report on a robust and standardized method for hUC-MSC isolation and expansion. Methods: Using 90 hUC donors, we compared and optimized critical variables during each phase of the multi-step procedure involving UC collection, processing, MSC isolation, expansion and characterization. Furthermore, we assessed the effect of donor-to-donor variability regarding UC morphology and donor attributes on hUC-MSC characteristics. Results: We demonstrated robustness of our method across 90 UC donors at each step of the procedure. With our method, UCs can be collected up to 6 h after birth, and UC-processing can be initiated up to 48 h after collection without impacting on hUC-MSC characteristics. The removal of blood vessels before explant cultures improved hUC-MSC purity. Expansion in Minimum essential medium α supplemented with human platelet lysate increased reproducibility of the expansion rate and MSC characteristics as compared with Dulbecco's Modified Eagle's Medium supplemented with fetal bovine serum. The isolated hUC-MSCs showed a purity of ∼98.9%, a viability of >97% and a high proliferative capacity. Trilineage differentiation capacity of hUC-MSCs was reduced as compared with bone marrow-derived MSCs. Functional assays indicated that the hUC-MSCs were able to inhibit T-cell proliferation demonstrating their immune-modulatory capacity. Conclusions: We present a robust and standardized method to isolate and expand hUC-MSCs, minimizing technical variability and thereby lay a foundation to advance reliability and comparability of results obtained from different donors and different studies.</p
WWP2 confers risk to osteoarthritis by affecting cartilage matrix deposition via hypoxia associated genes
No full textObjective: To explore the co-expression network of the osteoarthritis (OA) risk gene WWP2 in articular cartilage and study cartilage characteristics when mimicking the effect of OA risk allele rs1052429-A on WWP2 expression in a human 3D in vitro model of cartilage. Method: Co-expression behavior of WWP2 with genes expressed in lesioned OA articular cartilage (N = 35 samples) was explored. By applying lentiviral particle mediated WWP2 upregulation in 3D in vitro pellet cultures of human primary chondrocytes (N = 8 donors) the effects of upregulation on cartilage matrix deposition was evaluated. Finally, we transfected primary chondrocytes with miR-140 mimics to evaluate whether miR-140 and WWP2 are involved in similar pathways. Results: Upon performing Spearman correlations in lesioned OA cartilage, 98 highly correlating genes (| r| > 0.7) were identified. Among these genes, we identified GJA1, GDF10, STC2, WDR1, and WNK4. Sub-sequent upregulation of WWP2 on 3D chondrocyte pellet cultures resulted in a decreased expression of COL2A1 and ACAN and an increase in EPAS1 expression. Additionally, we observed a decreased expression of GDF10, STC2, and GJA1. Proteomics analysis identified 42 proteins being differentially expressed with WWP2 upregulation, which were enriched for ubiquitin conjugating enzyme activity. Finally, upregu-lation of miR-140 in 2D chondrocytes resulted in significant upregulation of WWP2 and WDR1. Conclusions: Mimicking the effect of OA risk allele rs1052429-A on WWP2 expression initiates detri-mental processes in the cartilage shown by a response in hypoxia associated genes EPAS1, GDF10, and GJA1 and a decrease in anabolic markers, COL2A1 and ACAN.(c) 2022 The Author(s). Published by Elsevier Ltd on behalf of Osteoarthritis Research Society International. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/).</p
