Theoretical investigation of infrared spectra and pocket dynamics of photodissociated carbonmonoxy myoglobin

Molecular dynamics simulations of the photodissociated state of carbonmonoxy myoglobin (MbCO) are presented using a fluctuating charge model for CO. A new three-point charge model is fitted to high-level ab initio calculations of the dipole and quadrupole moment functions taken from the literature. The infrared spectrum of the CO molecule in the heme pocket is calculated using the dipole moment time autocorrelation function and shows good agreement with experiment. In particular, the new model reproduces the experimentally observed splitting of the CO absorption spectrum. The splitting of 3–7 cm−1 (compared to the experimental value of 10 cm−1) can be directly attributed to the two possible orientations of CO within the docking site at the edge of the distal heme pocket (the B states), as previously suggested on the basis of experimental femtosecond time-resolved infrared studies. Further information on the time evolution of the position and orientation of the CO molecule is obtained and analyzed. The calculated difference in the free energy between the two possible orientations (Fe···CO and Fe···OC) is 0.3 kcal mol−1 and agrees well with the experimentally estimated value of 0.29 kcal mol−1. A comparison of the new fluctuating charge model with an established fixed charge model reveals some differences that may be critical for the correct prediction of the infrared spectrum and energy barriers. The photodissociation of CO from the myoglobin mutant L29F using the new model shows rapid escape of CO from the distal heme pocket, in good agreement with recent experimental data. The effect of the protein environment on the multipole moments of the CO ligand is investigated and taken into account in a refined model. Molecular dynamics simulations with this refined model are in agreement with the calculations based on the gas-phase model. However, it is demonstrated that even small changes in the electrostatics of CO alter the details of the dynamics.

Genetic relatedness and phenotypic characteristics of Treponema associated with human periodontal tissues and ruminant foot disease

Treponema have been implicated recently in the pathogenesis of digital dermatitis (DID) and contagious ovine digital dermatitis (CODD) that are infectious diseases of bovine and ovine foot tissues, respectively. Previous analyses of treponemal 16S rDNA sequences, PCR-amplified directly from DID or CODD lesions, have suggested relatedness of animal Treponema to some human oral Treponema species isolated from periodontal tissues. In this study a range of adhesion and virulence-related properties of three animal Treponema isolates have been compared with representative human oral strains of Treponema denticola and Treponema vincentii. In adhesion assays using biotinylated treponemal cells, T denticola cells bound in consistently higher numbers to fibronectin, laminin, collagen type 1, gelatin, keratin and lactoferrin than did T. vincentii or animal Treponema isolates. However, animal DID strains adhered to fibrinogen at equivalent or greater levels than T denticola. All Treponema strains bound to the amino-terminal heparin l/fibrin I domain of fibronectin. 16S rDNA sequence analyses placed ovine strain UB1090 and bovine strain UB1467 within a cluster that was phylogenetically related to T vincentii, while ovine strain UB1466 appeared more closely related to T denticola. These observations correlated with phenotypic properties. Thus, T denticola ATCC 35405, GM-1, and Treponema UB1466 had similar outer-membrane protein profiles, produced chymotrypsin-like protease (CTLP), trypsin-like protease and high levels of proline iminopeptidase, and co-aggregated with human oral bacteria Porphyromonas gingivalis and Streptococcus crista. Conversely, T vincentii ATCC 35580, D2A-2, and animal strains UB1090 and UB1467 did not express CTLP or trypsin-like protease and did not co-aggregate with P. gingivalis or S. crista. Taken collectively, these results suggest that human oral-related Treponema have broad host specificity and that similar control or preventive strategies might be developed for human and animal Treponema-associated infections.

Pocket data mining: towards collaborative data mining in mobile computing environments

Pocket Data Mining (PDM) is our new term describing collaborative mining of streaming data in mobile and distributed computing environments. With sheer amounts of data streams are now available for subscription on our smart mobile phones, the potential of using this data for decision making using data stream mining techniques has now been achievable owing to the increasing power of these handheld devices. Wireless communication among these devices using Bluetooth and WiFi technologies has opened the door wide for collaborative mining among the mobile devices within the same range that are running data mining techniques targeting the same application. This paper proposes a new architecture that we have prototyped for realizing the significant applications in this area. We have proposed using mobile software agents in this application for several reasons. Most importantly the autonomic intelligent behaviour of the agent technology has been the driving force for using it in this application. Other efficiency reasons are discussed in details in this paper. Experimental results showing the feasibility of the proposed architecture are presented and discussed.

Distributed hoeffding trees for pocket data mining

Collaborative mining of distributed data streams in a mobile computing environment is referred to as Pocket Data Mining PDM. Hoeffding trees techniques have been experimentally and analytically validated for data stream classification. In this paper, we have proposed, developed and evaluated the adoption of distributed Hoeffding trees for classifying streaming data in PDM applications. We have identified a realistic scenario in which different users equipped with smart mobile devices run a local Hoeffding tree classifier on a subset of the attributes. Thus, we have investigated the mining of vertically partitioned datasets with possible overlap of attributes, which is the more likely case. Our experimental results have validated the efficiency of our proposed model achieving promising accuracy for real deployment.

Distributed classification for pocket data mining

Distributed and collaborative data stream mining in a mobile computing environment is referred to as Pocket Data Mining PDM. Large amounts of available data streams to which smart phones can subscribe to or sense, coupled with the increasing computational power of handheld devices motivates the development of PDM as a decision making system. This emerging area of study has shown to be feasible in an earlier study using technological enablers of mobile software agents and stream mining techniques [1]. A typical PDM process would start by having mobile agents roam the network to discover relevant data streams and resources. Then other (mobile) agents encapsulating stream mining techniques visit the relevant nodes in the network in order to build evolving data mining models. Finally, a third type of mobile agents roam the network consulting the mining agents for a final collaborative decision, when required by one or more users. In this paper, we propose the use of distributed Hoeffding trees and Naive Bayes classifers in the PDM framework over vertically partitioned data streams. Mobile policing, health monitoring and stock market analysis are among the possible applications of PDM. An extensive experimental study is reported showing the effectiveness of the collaborative data mining with the two classifers.

Homogeneous and heterogeneous distributed classification for pocket data mining

Pocket Data Mining (PDM) describes the full process of analysing data streams in mobile ad hoc distributed environments. Advances in mobile devices like smart phones and tablet computers have made it possible for a wide range of applications to run in such an environment. In this paper, we propose the adoption of data stream classification techniques for PDM. Evident by a thorough experimental study, it has been proved that running heterogeneous/different, or homogeneous/similar data stream classification techniques over vertically partitioned data (data partitioned according to the feature space) results in comparable performance to batch and centralised learning techniques.

Pocket data mining - big data on small devices

Owing to continuous advances in the computational power of handheld devices like smartphones and tablet computers, it has become possible to perform Big Data operations including modern data mining processes onboard these small devices. A decade of research has proved the feasibility of what has been termed as Mobile Data Mining, with a focus on one mobile device running data mining processes. However, it is not before 2010 until the authors of this book initiated the Pocket Data Mining (PDM) project exploiting the seamless communication among handheld devices performing data analysis tasks that were infeasible until recently. PDM is the process of collaboratively extracting knowledge from distributed data streams in a mobile computing environment. This book provides the reader with an in-depth treatment on this emerging area of research. Details of techniques used and thorough experimental studies are given. More importantly and exclusive to this book, the authors provide detailed practical guide on the deployment of PDM in the mobile environment. An important extension to the basic implementation of PDM dealing with concept drift is also reported. In the era of Big Data, potential applications of paramount importance offered by PDM in a variety of domains including security, business and telemedicine are discussed.

Highly efficient neural differentiation of human somatic stem cells, isolated by minimally invasive periodontal surgery

Neural stem cells (NSCs) are potential sources for cell therapy of neurodegenerative diseases and for drug screening. Despite their potential benefits, ethical and practical considerations limit the application of NSCs derived from human embryonic stem cells (ES) or adult brain tissue. Thus, alternative sources are required to satisfy the criteria of ready accessibility, rapid expansion in chemically defined media and reliable induction to a neuronal fate. We isolated somatic stem cells from the human periodontium that were collected during minimally invasive periodontal access flap surgery as part of guided tissue regeneration therapy. These cells could be propagated as neurospheres in serum-free medium, which underscores their cranial neural crest cell origin. Culture in the presence of epidermal growth factor (EGF) and fibroblast growth factor-2 (FGF-2) under serum-free conditions resulted in large numbers of nestin-positive/Sox-2-positive NSCs. These periodontium-derived (pd) NSCs are highly proliferative and migrate in response to chemokines that have been described as inducing NSC migration. We used immunocytochemical techniques and RT-PCR analysis to assess neural differentiation after treatment of the expanded cells with a novel induction medium. Adherence to substrate, growth factor deprivation, and retinoic acid treatment led to the acquisition of neuronal morphology and stable expression of markers of neuronal differentiation by more than 90% of the cells. Thus, our novel method might provide nearly limitless numbers of neuronal precursors from a readily accessible autologous adult human source, which could be used as a platform for further experimental studies and has potential therapeutic implications.