Analysis of metabolic flux distributions in relation to the extracellular environment in Avian cells

Continuous cell lines that proliferate in chemically defined and simple media have been highly regarded as suitable alternatives for vaccine production. One such cell line is the AG1.CR.pIX avian cell line developed by PROBIOGEN. This cell line can be cultivated in a fully scalable suspension culture and adapted to grow in chemically defined, calf serum free, medium [1]–[5]. The medium composition and cultivation strategy are important factors for reaching high virus titers. In this project, a series of computational methods was used to simulate the cell’s response to different environments. The study is based on the metabolic model of the central metabolism proposed in [1]. In a first step, Metabolic Flux Analysis (MFA) was used along with measured uptake and secretion fluxes to estimate intracellular flux values. The network and data were found to be consistent. In a second step, Flux Balance Analysis (FBA) was performed to access the cell’s biological objective. The objective that resulted in the best predicted results fit to the experimental data was the minimization of oxidative phosphorylation. Employing this objective, in the next step Flux Variability Analysis (FVA) was used to characterize the flux solution space. Furthermore, various scenarios, where a reaction deletion (elimination of the compound from the media) was simulated, were performed and the flux solution space for each scenario was calculated. Growth restrictions caused by essential and non-essential amino acids were accurately predicted. Fluxes related to the essential amino acids uptake and catabolism, the lipid synthesis and ATP production via TCA were found to be essential to exponential growth. Finally, the data gathered during the previous steps were analyzed using principal component analysis (PCA), in order to assess potential changes in the physiological state of the cell. Three metabolic states were found, which correspond to zero, partial and maximum biomass growth rate. Elimination of non-essential amino acids or pyruvate from the media showed no impact on the cell’s assumed normal metabolic state.

Património Cultural Imaterial: MEMORIAMEDIA e-Museu - métodos, técnicas e práticas / Intagible Cultural Heritage: MEMORIAMEDIA e-Museum - methods, techniques and practices

O projeto MEMORIAMEDIA tem como objetivos o estudo, a inventariação e divulgação de manifestações do património cultural imaterial: expressões orais; práticas performativas; celebrações; o saber-fazer de artes e ofícios e as práticas e conhecimentos relacionados com a natureza e o universo. O MEMORIAMEDIA iniciou em 2006, em pleno debate nacional e internacional das questões do património cultural imaterial. Este livro cruza essas discussões teóricas, metodológicas e técnicas com a caracterização do MEMORIAMEDIA. Os resultados do projeto, organizados num inventário nacional, estão publicados no site www.memoriamedia.net, onde se encontram disponíveis para consulta e partilha. Filomena Sousa é investigadora de pós-doutoramento em antropologia (FCSH/UNL) e doutorada em sociologia (ISCTE-IUL). Membro integrado no Instituto de Estudos de Literatura e Tradição - patrimónios, artes e culturas (IELT) da FCSH/UNL e consultora da Memória Imaterial CRL – organização não-governamental autora e gestora do projeto MEMORIAMEDIA. Desenvolve investigação no âmbito das políticas e instrumentos de identificação, documentação e salvaguarda do património cultural imaterial e realizou vários documentários sobre expressões culturais.

Financing exponential growth at H3

H3 is a fast-food chain that introduced the concept of gourmet hamburgers in the Portuguese market. This case-study illustrates its financing strategy that supported an exponential growth represented by opening 33 restaurants within approximately 3 years of its inception. H3 is now faced with the challenge of structuring its foreign ventures and change its financial approach. The main covered topics are the options an entrepreneur has for financing a new venture and how it evolves along the life cycle and different business approaches, namely franchising. It aims to be used as a learning tool in courses such as entrepreneurial finance.

Electrohysterogram signal component cataloging with spectral and time-frequency methods

The Electrohysterogram (EHG) is a new instrument for pregnancy monitoring. It measures the uterine muscle electrical signal, which is closely related with uterine contractions. The EHG is described as a viable alternative and a more precise instrument than the currently most widely used method for the description of uterine contractions: the external tocogram. The EHG has also been indicated as a promising tool in the assessment of preterm delivery risk. This work intends to contribute towards the EHG characterization through the inventory of its components which are: • Contractions; • Labor contractions; • Alvarez waves; • Fetal movements; • Long Duration Low Frequency Waves; The instruments used for cataloging were: Spectral Analysis, parametric and non-parametric, energy estimators, time-frequency methods and the tocogram annotated by expert physicians. The EHG and respective tocograms were obtained from the Icelandic 16-electrode Electrohysterogram Database. 288 components were classified. There is not a component database of this type available for consultation. The spectral analysis module and power estimation was added to Uterine Explorer, an EHG analysis software developed in FCT-UNL. The importance of this component database is related to the need to improve the understanding of the EHG which is a relatively complex signal, as well as contributing towards the detection of preterm birth. Preterm birth accounts for 10% of all births and is one of the most relevant obstetric conditions. Despite the technological and scientific advances in perinatal medicine, in developed countries, prematurity is the major cause of neonatal death. Although various risk factors such as previous preterm births, infection, uterine malformations, multiple gestation and short uterine cervix in second trimester, have been associated with this condition, its etiology remains unknown [1][2][3].

Universal banks and corporate control: Evidence from the global syndicated loan market

We investigate the effects of bank control over borrower firms whether by representation on boards of directors or by the holding of shares through bank asset management divisions. Using a large sample of syndicated loans, we find that banks are more likely to act as lead arrangers in loans when they exert some control over the borrower firm. Bank-firm governance links are associated with higher loan spreads during the 2003-2006 credit boom, but lower spreads during the 2007-2008 financial crisis. Additionally, these links mitigate credit rationing effects during the crisis. The results are robust to several methods to correct for the endogeneity of the bank- firm governance link. Our evidence, consistent with intertemporal smoothing of loan rates, suggests there are costs and benefits from banks’ involvement in firm governance.

Production of polyhydroxyalkanoates from oil-containing substrates

Different oil-containing substrates, namely, used cooking oil (UCO), fatty acids-byproduct from biodiesel production (FAB) and olive oil deodorizer distillate (OODD) were tested as inexpensive carbon sources for the production of polyhydroxyalkanoates (PHA) using twelve bacterial strains, in batch experiments. The OODD and FAB were exploited for the first time as alternative substrates for PHA production. Among the tested bacterial strains, Cupriavidus necator and Pseudomonas resinovorans exhibited the most promising results, producing poly-3-hydroxybutyrate, P(3HB), form UCO and OODD and mcl-PHA mainly composed of 3-hydroxyoctanoate (3HO) and 3-hydroxydecanoate (3HD) monomers from OODD, respectively. Afterwards, these bacterial strains were cultivated in bioreactor. C. necator were cultivated in bioreactor using UCO as carbon source. Different feeding strategies were tested for the bioreactor cultivation of C. necator, namely, batch, exponential feeding and DO-stat mode. The highest overall PHA productivity (12.6±0.78 g L-1 day-1) was obtained using DO-stat mode. Apparently, the different feeding regimes had no impact on polymer thermal properties. However, differences in polymer‟s molecular mass distribution were observed. C. necator was also tested in batch and fed-batch modes using a different type of oil-containing substrate, extracted from spent coffee grounds (SCG) by super critical carbon dioxide (sc-CO2). Under fed-batch mode (DO-stat), the overall PHA productivity were 4.7 g L-1 day-1 with a storage yield of 0.77 g g-1. Results showed that SCG can be a bioresource for production of PHA with interesting properties. Furthermore, P. resinovorans was cultivated using OODD as substrate in bioreactor under fed-batch mode (pulse feeding regime). The polymer was highly amorphous, as shown by its low crystallinity of 6±0.2%, with low melting and glass transition temperatures of 36±1.2 and -16±0.8 ºC, respectively. Due to its sticky behavior at room temperature, adhesiveness and mechanical properties were also studied. Its shear bond strength for wood (67±9.4 kPa) and glass (65±7.3 kPa) suggests it may be used for the development of biobased glues. Bioreactor operation and monitoring with oil-containing substrates is very challenging, since this substrate is water immiscible. Thus, near-infrared spectroscopy (NIR) was implemented for online monitoring of the C. necator cultivation with UCO, using a transflectance probe. Partial least squares (PLS) regression was applied to relate NIR spectra with biomass, UCO and PHA concentrations in the broth. The NIR predictions were compared with values obtained by offline reference methods. Prediction errors to these parameters were 1.18 g L-1, 2.37 g L-1 and 1.58 g L-1 for biomass, UCO and PHA, respectively, which indicates the suitability of the NIR spectroscopy method for online monitoring and as a method to assist bioreactor control. UCO and OODD are low cost substrates with potential to be used in PHA batch and fed-batch production. The use of NIR in this bioprocess also opened an opportunity for optimization and control of PHA production process.

Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.