Stem cell regulatory gene expression in human adult dental pulp and periodontal ligament cells undergoing odontogenic/osteogenic differentiation

Introduction During development and regeneration, odontogenesis and osteogenesis are initiated by a cascade of signals driven by several master regulatory genes. Methods In this study, we investigated the differential expression of 84 stem cell–related genes in dental pulp cells (DPCs) and periodontal ligament cells (PDLCs) undergoing odontogenic/osteogenic differentiation. Results Our results showed that, although there was considerable overlap, certain genes had more differential expression in PDLCs than in DPCs. CCND2, DLL1, and MME were the major upregulated genes in both PDLCs and DPCs, whereas KRT15 was the only gene significantly downregulated in PDLCs and DPCs in both odontogenic and osteogenic differentiation. Interestingly, a large number of regulatory genes in odontogenic and osteogenic differentiation interact or crosstalk via Notch, Wnt, transforming growth factor β (TGF-β)/bone morphogenic protein (BMP), and cadherin signaling pathways, such as the regulation of APC, DLL1, CCND2, BMP2, and CDH1. Using a rat dental pulp and periodontal defect model, the expression and distribution of both BMP2 and CDH1 have been verified for their spatial localization in dental pulp and periodontal tissue regeneration. Conclusions This study has generated an overview of stem cell–related gene expression in DPCs and PDLCs during odontogenic/osteogenic differentiation and revealed that these genes may interact through the Notch, Wnt, TGF-β/BMP, and cadherin signalling pathways to play a crucial role in determining the fate of dental derived cell and dental tissue regeneration. These findings provided a new insight into the molecular mechanisms of the dental tissue mineralization and regeneration

'Anorexia Nervosa' : asceticism, differentiation, government

This paper looks at the severe fasting practices most commonly found among young women. Almost all explanations for this behaviour centre around the notion of the pathological condition 'anorexia nervosa'. However, food asceticism has a well-documented history, particularly when it concerns religious fasting. In ancient Greece, dietary asceticism constituted an important part of the means by which individuals constructed an acceptable 'self'. Ascetic fasting then later resurfaced at various historical moments and in various different places — such as amongst medieval religious women and, in a broader way, amongst contemporary young women. It is argued that these practices have traditionally formed part of the mechanisms by which differentiation by age and sex occurs. Overall, it is hoped that this analysis will permit not only a different focus on 'anorexia nervosa', but also on some of the ways in which young people become gendered.

Asceticism, differentiation, government : 'anorexia nervosa' as an achievement

As with the ancient Greeks, the `self' is not something to be discovered, it is something to be created. Practices such as ascetic fasting are not expressions of the struggle between the authentic self and the external world, they are the very practices by which a `self' is formed.

Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform

Spatial information captured from optical remote sensors on board unmanned aerial vehicles (UAVs) has great potential in automatic surveillance of electrical infrastructure. For an automatic vision-based power line inspection system, detecting power lines from a cluttered background is one of the most important and challenging tasks. In this paper, a novel method is proposed, specifically for power line detection from aerial images. A pulse coupled neural filter is developed to remove background noise and generate an edge map prior to the Hough transform being employed to detect straight lines. An improved Hough transform is used by performing knowledge-based line clustering in Hough space to refine the detection results. The experiment on real image data captured from a UAV platform demonstrates that the proposed approach is effective for automatic power line detection.

Predicting project performance through neural networks

Successful project delivery of construction projects depends on many factors. With regard to the construction of a facility, selecting a competent contractor for the job is paramount. As such, various approaches have been advanced to facilitate tender award decisions. Essentially, this type of decision involves the prediction of a bidderÕs performance based on information available at the tender stage. A neural network based prediction model was developed and presented in this paper. Project data for the study were obtained from the Hong Kong Housing Department. Information from the tender reports was used as input variables and performance records of the successful bidder during construction were used as output variables. It was found that the networks for the prediction of performance scores for Works gave the highest hit rate. In addition, the two most sensitive input variables toward such prediction are ‘‘Difference between Estimate’’ and ‘‘Difference between the next closest bid’’. Both input variables are price related, thus suggesting the importance of tender sufficiency for the assurance of quality production.

A fuzzy neural network approach for contractor pre-qualification

Nonlinearity, uncertainty and subjectivity are the three predominant characteristics of contractors prequalification which cause the process more of an art than a scientific evaluation. A fuzzy neural network (FNN) model, amalgamating both the fuzzy set and neural network theories, has been developed aiming to improve the objectiveness of contractor prequalification. Through the FNN theory, the fuzzy rules as used by the prequalifiers can be identified and the corresponding membership functions can be transformed. Eighty-five cases with detailed decision criteria and rules for prequalifying Hong Kong civil engineering contractors were collected. These cases were used for training (calibrating) and testing the FNN model. The performance of the FNN model was compared with the original results produced by the prequalifiers and those generated by the general feedforward neural network (GFNN, i.e. a crisp neural network) approach. Contractor’s ranking orders, the model efficiency (R2) and the mean absolute percentage error (MAPE) were examined during the testing phase. These results indicate the applicability of the neural network approach for contractor prequalification and the benefits of the FNN model over the GFNN model. The FNN is a practical approach for modelling contractor prequalification.

Decision support system for contractor pre-qualification : artificial neural network model

The selection criteria for contractor pre-qualification are characterized by the co-existence of both quantitative and qualitative data. The qualitative data is non-linear, uncertain and imprecise. An ideal decision support system for contractor pre-qualification should have the ability of handling both quantitative and qualitative data, and of mapping the complicated nonlinear relationship of the selection criteria, such that rational and consistent decisions can be made. In this research paper, an artificial neural network model was developed to assist public clients identifying suitable contractors for tendering. The pre-qualification criteria (variables) were identified for the model. One hundred and twelve real pre-qualification cases were collected from civil engineering projects in Hong Kong, and eighty-eight hypothetical pre-qualification cases were also generated according to the “If-then” rules used by professionals in the pre-qualification process. The results of the analysis totally comply with current practice (public developers in Hong Kong). Each pre-qualification case consisted of input ratings for candidate contractors’ attributes and their corresponding pre-qualification decisions. The training of the neural network model was accomplished by using the developed program, in which a conjugate gradient descent algorithm was incorporated for improving the learning performance of the network. Cross-validation was applied to estimate the generalization errors based on the “re-sampling” of training pairs. The case studies show that the artificial neural network model is suitable for mapping the complicated nonlinear relationship between contractors’ attributes and their corresponding pre-qualification (disqualification) decisions. The artificial neural network model can be concluded as an ideal alternative for performing the contractor pre-qualification task.

Generating rules with predicates, terms and variables from the pruned neural networks

Artificial neural networks (ANN) have demonstrated good predictive performance in a wide range of applications. They are, however, not considered sufficient for knowledge representation because of their inability to represent the reasoning process succinctly. This paper proposes a novel methodology Gyan that represents the knowledge of a trained network in the form of restricted first-order predicate rules. The empirical results demonstrate that an equivalent symbolic interpretation in the form of rules with predicates, terms and variables can be derived describing the overall behaviour of the trained ANN with improved comprehensibility while maintaining the accuracy and fidelity of the propositional rules.

Identification of factors predicting clickthrough in Web searching using neural network analysis

In this research, we aim to identify factors that significantly affect the clickthrough of Web searchers. Our underlying goal is determine more efficient methods to optimize the clickthrough rate. We devise a clickthrough metric for measuring customer satisfaction of search engine results using the number of links visited, number of queries a user submits, and rank of clicked links. We use a neural network to detect the significant influence of searching characteristics on future user clickthrough. Our results show that high occurrences of query reformulation, lengthy searching duration, longer query length, and the higher ranking of prior clicked links correlate positively with future clickthrough. We provide recommendations for leveraging these findings for improving the performance of search engine retrieval and result ranking, along with implications for search engine marketing

The effect of unlocking RGD-motifs in collagen I on pre-osteoblast adhesion and differentiation

Denaturation of extracellular matrix proteins exposes cryptic binding sites. It is hypothesized that binding of cell adhesion receptors to these cryptic binding sites regulates cellular behaviour during tissue repair and regeneration. To test this hypothesis, we quantify the adhesion of pre-osteoblastic cells to native (Col) and partially-denatured (pdCol) collagen I using single-cell force spectroscopy. During early stages of cell attachment (≤180 s) pre-osteoblasts (MC3T3-E1) adhered significantly stronger to pdCol compared to Col. RGD (Arg-Gly-Asp)-containing peptides suppressed this elevated cell adhesion. We show that the RGD-binding α5β1- and αv-integrins mediated pre-osteoblast adhesion to pdCol, but not to Col. On pdCol pre-osteoblasts had a higher focal adhesion kinase tyrosine-phosphorylation level that correlated with enhanced spreading and motility. Moreover, pre-osteoblasts cultured on pdCol showed a pronounced matrix mineralization activity. Our data suggest that partially-denatured collagen exposes RGD-motifs that trigger binding of α5β1- and αv-integrins. These integrins initiate cellular processes that stimulate osteoblast adhesion, spreading, motility and differentiation. Taken together, these quantitative insights reveal an approach for the development of alternative collagen I- based surfaces for tissue engineering applications.

Heparan sulfate mediates the proliferation and differentiation of rat mesenchymal stem cells

The growth and differentiation of mesenchymal stem cells is controlled by various growth factors, the activities of which can be modulated by heparan sulfates. We have previously underscored the necessity of sulfated glycosaminoglycans for the FGF-2-stimulated differentiation of osteoprogenitor cells. Here we show that exogenous application of heparan sulfate to cultures of primary rat MSCs stimulates their proliferation leading to increased expression of osteogenic markers and enhanced bone nodule formation. FGF-2 can also increase the proliferation and osteogenic differentiation of rMSCs when applied exogenously during their linear growth. However, as opposed to exogenous HS, the continuous use of FGF-2 during in vitro differentiation completely blocked rMSC mineralization. Furthermore, we show that the effects of both FGF-2 and HS are mediated through FGF receptor 1 (FGFR1) and that inhibition of signaling through this receptor arrests cell growth resulting in the cells being unable to reach the critical density necessary to induce differentiation. Interestingly, blocking FGFR1 signaling in post-confluent osteogenic cultures significantly increased calcium deposition. Taken together our data clearly suggests that FGFR1 signaling plays an important role during osteogenic differentiation, firstly by stimulating cell growth that is closely followed by an inhibitory affect once the cells have reached confluence. It also underlines the importance of HS as a co-receptor for the signaling of endogenous FGF-2 and suggests that purified glycosaminoglycans may be attractive alternatives to growth factors for improved ex vivo growth and differentiation of MSCs.

Stem cell–related gene expression in clonal populations of mesenchymal stromal cells from bone marrow

Decline in the frequency of potent mesenchymal stem cells (MSCs) has been implicated in ageing and degenerative diseases. Increasing the circulating stem cell population can lead to renewed recruitment of these potent cells at sites of damage. Therefore, identifying the ideal cells for ex vivo expansion will form a major pursuit of clinical applications. This study is a follow-up of previous work that demonstrated the occurrence of fast-growing multipotential cells from the bone marrow samples. To investigate the molecular processes involved in the existence of such varying populations, gene expression studies were performed between fast- and slow-growing clonal populations to identify potential genetic markers associated with stemness using the quantitative real-time polymerase chain reaction comprising a series of 84 genes related to stem cell pathways. A group of 10 genes were commonly overrepresented in the fast-growing stem cell clones. These included genes that encode proteins involved in the maintenance of embryonic and neural stem cell renewal (sex-determining region Y-box 2, notch homolog 1, and delta-like 3), proteins associated with chondrogenesis (aggrecan and collagen 2 A1), growth factors (bone morphogenetic protein 2 and insulin-like growth factor 1), an endodermal organogenesis protein (forkhead box a2), and proteins associated with cell-fate specification (fibroblast growth factor 2 and cell division cycle 2). Expression of diverse differentiation genes in MSC clones suggests that these commonly expressed genes may confer the maintenance of multipotentiality and self-renewal of MSCs.

Osteoarthritic cartilage chondrocytes alter subchondral bone osteoblast differentiation via MAPK signalling pathway involving ERK1/2

Osteoarthritic subchondral bone is characterized by abnormal bone density and enhanced production of bone turnover markers, an indication of osteoblast dysfunction. Several studies have proposed that pathological changes in articular cartilage influence the subchondral bone changes, which are typical of the progression of osteoarthritis; however, direct evidence of this has yet to be reported. The aim of the present study was to investigate what effects articular cartilage cells, isolated from normal and osteoarthritic joints, may have on the subchondral bone osteoblast phenotype, and also the potential involvement of the mitogen activated protein kinase (MAPK) signalling pathway during this process. Our results suggest that chondrocytes isolated from a normal joint inhibited osteoblast differentiation, whereas chondrocytes isolated from an osteoarthritic joint enhanced osteoblast differentiation, both via a direct and indirect cell interaction mechanisms. Furthermore, the interaction of subchondral bone osteoblasts with osteoarthritic chondrocyte conditioned media appeared to significantly activate ERK1/2 phosphorylation. On the other hand, conditioned media from normal articular chondrocytes did not affect ERK1/2 phosphorylation. Inhibition of the MAPK–ERK1/2 pathways reversed the phenotype changes of subchondral bone osteoblast, which would otherwise be induced by the conditioned media from osteoarthritic chondrocytes. In conclusion, our findings provide evidence that osteoarthritic chondrocytes affect subchondral bone osteoblast metabolism via an ERK1/2 dependent pathway.