Alternative oxidase in the branched mitochondrial respiratory network: an overview on structure, function, regulation, and role

Plants and some other organisms including protists possess a complex branched respiratory network in their mitochondria. Some pathways of this network are not energy-conserving and allow sites of energy conservation to be bypassed, leading to a decrease of the energy yield in the cells. It is a challenge to understand the regulation of the partitioning of electrons between the various energy-dissipating and -conserving pathways. This review is focused on the oxidase side of the respiratory chain that presents a cyanide-resistant energy-dissipating alternative oxidase (AOX) besides the cytochrome pathway. The known structural properties of AOX are described including transmembrane topology, dimerization, and active sites. Regulation of the alternative oxidase activity is presented in detail because of its complexity. The alternative oxidase activity is dependent on substrate availability: total ubiquinone concentration and its redox state in the membrane and O2 concentration in the cell. The alternative oxidase activity can be long-term regulated (gene expression) or short-term (post-translational modification, allosteric activation) regulated. Electron distribution (partitioning) between the alternative and cytochrome pathways during steady-state respiration is a crucial measurement to quantitatively analyze the effects of the various levels of regulation of the alternative oxidase. Three approaches are described with their specific domain of application and limitations: kinetic approach, oxygen isotope differential discrimination, and ADP/O method (thermokinetic approach). Lastly, the role of the alternative oxidase in non-thermogenic tissues is discussed in relation to the energy metabolism balance of the cell (supply in reducing equivalents/demand in energy and carbon) and with harmful reactive oxygen species formation.

Evidencing and Sharing Value with Decision Making Models in an Industrial Maintenance Network

The objective of this thesis is to concretize the potential benefits that the industrial maintenance case network could achieve through using the value-based life-cycle model and the flexible asset management model. It is also inspected what factors prevent value creation and sharing in the maintenance contract practices of the case network. This thesis is a case study which utilizes modelling. Four scenarios were developed to demonstrate value creation in the future. The data was partly provided by the collaborating company, partly gathered from public financial statement information. The results indicate that value has been created through the past maintenance of the collaborating company’s rod mill and that profitability of the collaborating company has been mostly on satisfactory level during the past few years. Potential value might be created by increasing the share of proactive maintenance of the rod mill in the future. Profitability of the network could be improved in the future through flexible asset management operations. The main obstacle for value creation and sharing seems to be the lack of sufficient trust between the network members.

Strategic planning of major disturbance proof network

In the 2000’s Finland suffered from storms that caused long outages in electricity distribution, longest up to two weeks. These major disturbances increased the importance of supply security. In 2013 new Electricity Market Act was announced. It defined maximum duration for outages, 6 h for city plan areas and 36 h for other areas. The aim for this work is to determine required major disturbance proof level for a study area and find tools for prioritizing overhead lines for cabling renovation to improve supply security. Three prioritization methods were chosen to be studied: A: prioritization line sections by customer outage costs they cause, B: maximizing customers major disturbance proof network and C: minimizing excavation costs in medium voltage network. Profitability calculations showed that prioritization method A was the most profitable and C had the weakest profitability. The prioritization method C drove renovation into unreasonable locations in the study area in reliability point of view. Therefore universal rule prioritization methods couldn’t be made from the prioritization methods. This led to the conclusion that every renewing area need to be evaluated in a case by case basis.

Distribution network design and optimization

The objective of this thesis is to examine distribution network designs and modeling practices and create a framework to identify best possible distribution network structure for the case company. The main research question therefore is: How to optimize case company’s distribution network in terms of customer needs and costs? Theory chapters introduce the basic building blocks of the distribution network design and needed calculation methods and models. Framework for the distribution network projects was created based on the theory and the case study was carried out by following the defined framework. Distribution network calculations were based on the company’s sales plan for the years 2014 - 2020. Main conclusions and recommendations were that the new Asian business strategy requires high investments in logistics and the first step is to open new satellite DC in China as soon as possible to support sales and second possible step is to open regional DC in Asia within 2 - 4 years.

The brain decade in debate: III. Neurobiology of emotion

This article is a transcription of an electronic symposium in which active researchers were invited by the Brazilian Society of Neuroscience and Behavior (SBNeC) to discuss the advances of the last decade in the neurobiology of emotion. Four basic questions were debated: 1) What are the most critical issues/questions in the neurobiology of emotion? 2) What do we know for certain about brain processes involved in emotion and what is controversial? 3) What kinds of research are needed to resolve these controversial issues? 4) What is the relationship between learning, memory and emotion? The focus was on the existence of different neural systems for different emotions and the nature of the neural coding for the emotional states. Is emotion the result of the interaction of different brain regions such as the amygdala, the nucleus accumbens, or the periaqueductal gray matter or is it an emergent property of the whole brain neural network? The relationship between unlearned and learned emotions was also discussed. Are the circuits of the former the underpinnings of the latter? It was pointed out that much of what we know about emotions refers to aversively motivated behaviors, like fear and anxiety. Appetitive emotions should attract much interest in the future. The learning and memory relationship with emotions was also discussed in terms of conditioned and unconditioned stimuli, innate and learned fear, contextual cues inducing emotional states, implicit memory and the property of using this term for animal memories. In a general way it could be said that learning modifies the neural circuits through which emotional responses are expressed.

Comparative sequence analysis of the P-, M- and L-coding region of the measles virus CAM-70 live attenuated vaccine strain

Measles virus is a highly contagious agent which causes a major health problem in developing countries. The viral genomic RNA is single-stranded, nonsegmented and of negative polarity. Many live attenuated vaccines for measles virus have been developed using either the prototype Edmonston strain or other locally isolated measles strains. Despite the diverse geographic origins of the vaccine viruses and the different attenuation methods used, there was remarkable sequence similarity of H, F and N genes among all vaccine strains. CAM-70 is a Japanese measles attenuated vaccine strain widely used in Brazilian children and produced by Bio-Manguinhos since 1982. Previous studies have characterized this vaccine biologically and genomically. Nevertheless, only the F, H and N genes have been sequenced. In the present study we have sequenced the remaining P, M and L genes (approximately 1.6, 1.4 and 6.5 kb, respectively) to complete the genomic characterization of CAM-70 and to assess the extent of genetic relationship between CAM-70 and other current vaccines. These genes were amplified using long-range or standard RT-PCR techniques, and the cDNA was cloned and automatically sequenced using the dideoxy chain-termination method. The sequence analysis comparing previously sequenced genotype A strains with the CAM-70 Bio-Manguinhos strain showed a low divergence among them. However, the CAM-70 strains (CAM-70 Bio-Manguinhos and a recently sequenced CAM-70 submaster seed strain) were assigned to a specific group by phylogenetic analysis using the neighbor-joining method. Information about our product at the genomic level is important for monitoring vaccination campaigns and for future studies of measles virus attenuation.

Automated grading of left ventricular segmental wall motion by an artificial neural network using color kinesis images

The present study describes an auxiliary tool in the diagnosis of left ventricular (LV) segmental wall motion (WM) abnormalities based on color-coded echocardiographic WM images. An artificial neural network (ANN) was developed and validated for grading LV segmental WM using data from color kinesis (CK) images, a technique developed to display the timing and magnitude of global and regional WM in real time. We evaluated 21 normal subjects and 20 patients with LVWM abnormalities revealed by two-dimensional echocardiography. CK images were obtained in two sets of viewing planes. A method was developed to analyze CK images, providing quantitation of fractional area change in each of the 16 LV segments. Two experienced observers analyzed LVWM from two-dimensional images and scored them as: 1) normal, 2) mild hypokinesia, 3) moderate hypokinesia, 4) severe hypokinesia, 5) akinesia, and 6) dyskinesia. Based on expert analysis of 10 normal subjects and 10 patients, we trained a multilayer perceptron ANN using a back-propagation algorithm to provide automated grading of LVWM, and this ANN was then tested in the remaining subjects. Excellent concordance between expert and ANN analysis was shown by ROC curve analysis, with measured area under the curve of 0.975. An excellent correlation was also obtained for global LV segmental WM index by expert and ANN analysis (R² = 0.99). In conclusion, ANN showed high accuracy for automated semi-quantitative grading of WM based on CK images. This technique can be an important aid, improving diagnostic accuracy and reducing inter-observer variability in scoring segmental LVWM.

Modeling a Network of Heat Exchangers

This research work addresses the problem of building a mathematical model for the given system of heat exchangers and to determine the temperatures, pressures and velocities at the intermediate positions. Such model could be used in nding an optimal design for such a superstructure. To limit the size and computing time a reduced network model was used. The method can be generalized to larger network structures. A mathematical model which includes a system of non-linear equations has been built and solved according to the Newton-Raphson algorithm. The results obtained by the proposed mathematical model were compared with the results obtained by the Paterson approximation and Chen's Approximation. Results of this research work in collaboration with a current ongoing research at the department will optimize the valve positions and hence, minimize the pumping cost and maximize the heat transfer of the system of heat exchangers.

Blastocyst-endometrium interaction: intertwining a cytokine network

The successful implantation of the blastocyst depends on adequate interactions between the embryo and the uterus. The development of the embryo begins with the fertilized ovum, a single totipotent cell which undergoes mitosis and gives rise to a multicellular structure named blastocyst. At the same time, increasing concentrations of ovarian steroid hormones initiate a complex signaling cascade that stimulates the differentiation of endometrial stromal cells to decidual cells, preparing the uterus to lodge the embryo. Studies in humans and in other mammals have shown that cytokines and growth factors are produced by the pre-implantation embryo and cells of the reproductive tract; however, the interactions between these factors that converge for successful implantation are not well understood. This review focuses on the actions of interleukin-1, leukemia inhibitory factor, epidermal growth factor, heparin-binding epidermal growth factor, and vascular endothelial growth factor, and on the network of their interactions leading to early embryo development, peri-implantatory endometrial changes, embryo implantation and trophoblast differentiation. We also propose therapeutical approaches based on current knowledge on cytokine interactions.

Cloning, sequence analysis, and expression of cDNA coding for the major house dust mite allergen, Der f 1, in Escherichia coli

Our objective was to clone, express and characterize adult Dermatophagoides farinae group 1 (Der f 1) allergens to further produce recombinant allergens for future clinical applications in order to eliminate side reactions from crude extracts of mites. Based on GenBank data, we designed primers and amplified the cDNA fragment coding for Der f 1 by nested-PCR. After purification and recovery, the cDNA fragment was cloned into the pMD19-T vector. The fragment was then sequenced, subcloned into the plasmid pET28a(+), expressed in Escherichia coli BL21 and identified by Western blotting. The cDNA coding for Der f 1 was cloned, sequenced and expressed successfully. Sequence analysis showed the presence of an open reading frame containing 966 bp that encodes a protein of 321 amino acids. Interestingly, homology analysis showed that the Der p 1 shared more than 87% identity in amino acid sequence with Eur m 1 but only 80% with Der f 1. Furthermore, phylogenetic analyses suggested that D. pteronyssinus was evolutionarily closer to Euroglyphus maynei than to D. farinae, even though D. pteronyssinus and D. farinae belong to the same Dermatophagoides genus. A total of three cysteine peptidase active sites were found in the predicted amino acid sequence, including 127-138 (QGGCGSCWAFSG), 267-277 (NYHAVNIVGYG) and 284-303 (YWIVRNSWDTTWGDSGYGYF). Moreover, secondary structure analysis revealed that Der f 1 contained an a helix (33.96%), an extended strand (17.13%), a ß turn (5.61%), and a random coil (43.30%). A simple three-dimensional model of this protein was constructed using a Swiss-model server. The cDNA coding for Der f 1 was cloned, sequenced and expressed successfully. Alignment and phylogenetic analysis suggests that D. pteronyssinus is evolutionarily more similar to E. maynei than to D. farinae.

Biochemical adaptations of mammalian hibernation: exploring squirrels as a perspective model for naturally induced reversible insulin resistance

An important disease among human metabolic disorders is type 2 diabetes mellitus. This disorder involves multiple physiological defects that result from high blood glucose content and eventually lead to the onset of insulin resistance. The combination of insulin resistance, increased glucose production, and decreased insulin secretion creates a diabetic metabolic environment that leads to a lifetime of management. Appropriate models are critical for the success of research. As such, a unique model providing insight into the mechanisms of reversible insulin resistance is mammalian hibernation. Hibernators, such as ground squirrels and bats, are excellent examples of animals exhibiting reversible insulin resistance, for which a rapid increase in body weight is required prior to entry into dormancy. Hibernator studies have shown differential regulation of specific molecular pathways involved in reversible resistance to insulin. The present review focuses on this growing area of research and the molecular mechanisms that regulate glucose homeostasis, and explores the roles of the Akt signaling pathway during hibernation. Here, we propose a link between hibernation, a well-documented response to periods of environmental stress, and reversible insulin resistance, potentially facilitated by key alterations in the Akt signaling network, PPAR-γ/PGC-1α regulation, and non-coding RNA expression. Coincidentally, many of the same pathways are frequently found to be dysregulated during insulin resistance in human type 2 diabetes. Hence, the molecular networks that may regulate reversible insulin resistance in hibernating mammals represent a novel approach by providing insight into medical treatment of insulin resistance in humans.

Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China

The mortality rate of older patients with intertrochanteric fractures has been increasing with the aging of populations in China. The purpose of this study was: 1) to develop an artificial neural network (ANN) using clinical information to predict the 1-year mortality of elderly patients with intertrochanteric fractures, and 2) to compare the ANN's predictive ability with that of logistic regression models. The ANN model was tested against actual outcomes of an intertrochanteric femoral fracture database in China. The ANN model was generated with eight clinical inputs and a single output. ANN's performance was compared with a logistic regression model created with the same inputs in terms of accuracy, sensitivity, specificity, and discriminability. The study population was composed of 2150 patients (679 males and 1471 females): 1432 in the training group and 718 new patients in the testing group. The ANN model that had eight neurons in the hidden layer had the highest accuracies among the four ANN models: 92.46 and 85.79% in both training and testing datasets, respectively. The areas under the receiver operating characteristic curves of the automatically selected ANN model for both datasets were 0.901 (95%CI=0.814-0.988) and 0.869 (95%CI=0.748-0.990), higher than the 0.745 (95%CI=0.612-0.879) and 0.728 (95%CI=0.595-0.862) of the logistic regression model. The ANN model can be used for predicting 1-year mortality in elderly patients with intertrochanteric fractures. It outperformed a logistic regression on multiple performance measures when given the same variables.

Transduction motif analysis of gastric cancer based on a human signaling network

To investigate signal regulation models of gastric cancer, databases and literature were used to construct the signaling network in humans. Topological characteristics of the network were analyzed by CytoScape. After marking gastric cancer-related genes extracted from the CancerResource, GeneRIF, and COSMIC databases, the FANMOD software was used for the mining of gastric cancer-related motifs in a network with three vertices. The significant motif difference method was adopted to identify significantly different motifs in the normal and cancer states. Finally, we conducted a series of analyses of the significantly different motifs, including gene ontology, function annotation of genes, and model classification. A human signaling network was constructed, with 1643 nodes and 5089 regulating interactions. The network was configured to have the characteristics of other biological networks. There were 57,942 motifs marked with gastric cancer-related genes out of a total of 69,492 motifs, and 264 motifs were selected as significantly different motifs by calculating the significant motif difference (SMD) scores. Genes in significantly different motifs were mainly enriched in functions associated with cancer genesis, such as regulation of cell death, amino acid phosphorylation of proteins, and intracellular signaling cascades. The top five significantly different motifs were mainly cascade and positive feedback types. Almost all genes in the five motifs were cancer related, including EPOR,MAPK14, BCL2L1, KRT18,PTPN6, CASP3, TGFBR2,AR, and CASP7. The development of cancer might be curbed by inhibiting signal transductions upstream and downstream of the selected motifs.

HYBRID ARTIFICIAL NEURAL NETWORK APPLIEDTO MODELING SCFE OF BASIL AND ROSEMARY OILS

This work presents the results of a Hybrid Neural Network (HNN) technique as applied to modeling SCFE curves obtained from two Brazilian vegetable matrices. A series Hybrid Neural Network was employed to estimate the parameters of the phenomenological model. A small set of SCFE data of each vegetable was used to generate an extended data set, sufficient to train the network. Afterwards, other sets of experimental data, not used in the network training, were used to validate the present approach. The series HNN correlates well the experimental data and it is shown that the predictions accomplished with this technique may be promising for SCFE purposes.

The role of network competence and international business competence in multinational corporations: Subsidiary perspective

On-going process of globalization makes companies all over the world to go beyond the national markets and internationalize. Organizational form of multinational corporation (MNC) has capabilities for establishing the affiliate companies in several countries. Thus, the relocation of resources occurs and particularly, the cross-border transfer of knowledge which possesses the competitive advantage. However, differences in countries` business environments and cultures may constrain this capability. The research aim of this thesis is to investigate the role of subsidiary’s network competence (ability to build and manage the relationships with other local business units) and international business competence in relation to the benefits that MNC receives from a subsidiary. Additionally, subsidiary’s business adaptation, partnerships and knowledge transfer mechanism with parent company and external partners are investigated. This research, conducted in the Finnish-Russian context, consists of theoretical and empirical parts. The qualitative approach in the form of multiple case studies is employed. The empirical data incorporated primary and secondary data in the form of interviews collected in 2013 and 2015 years. Interviews were collected from four Finnish case companies in Saint-Petersburg and Kaluga region and five Russian partner companies. Results are drawn from two cases from Saint-Petersburg. The abductive research approach for the results analysis is adopted. The results indicate that both competencies lead to the subsidiary’s local embeddedness in the form of mutual business activities with local business partners and product adaptation for the local market needs. In addition to the monetary benefits in form of payments or turnover share, local embeddedness brings the knowledge of the local environment which is utilized by an MNC in the long-term planning. Another found tacit benefit is the access to the national market. This is strategically useful benefit not only for parent MNC but also for the subsidiary’s partners, i.e. international suppliers.