Sequential release of BMP-7 and VEGF from the PLGA/AK-Gelatin Composite Scaffolds

Vascular endothelial growth factor (VEGF) and bone morphogenetic proteins (BMP-7) are key regulators of angiogenesis and osteogenesis during bone regeneration. The aim of this study was to investigate the possibility of realizing sequential release of the two growth factors using a novel composite scaffold. Poly(lactic-co-glycolic acid) (PLGA)-Akermanite (AK) microspheres were used to make the composite scaffold, which was then loaded with BMP-7, followed by embedding in a gelatin hydrogel matrix loaded with VEGF. The release profiles of the growth factors were studied and selected osteogenic related markers of bone marrow stromal cells (BMSCs) were analysed. It was shown that the composite scaffolds exhibited a fast initial burst release of VEGF within the first 3 days and a sustained slow release of BMP-7 over the full period of 20 days. The in vitro proliferation and differentiation of the BMSCs cultured in the osteogenic medium were enhanced by 1 to 2 times, resulting from the additionally and sequentially release of growth factors from the PLGA-AK/gelatin composite scaffolds.

Non-redundant sequential association rule mining based on closed sequential patterns

In many applications, e.g., bioinformatics, web access traces, system utilisation logs, etc., the data is naturally in the form of sequences. People have taken great interest in analysing the sequential data and finding the inherent characteristics or relationships within the data. Sequential association rule mining is one of the possible methods used to analyse this data. As conventional sequential association rule mining very often generates a huge number of association rules, of which many are redundant, it is desirable to find a solution to get rid of those unnecessary association rules. Because of the complexity and temporal ordered characteristics of sequential data, current research on sequential association rule mining is limited. Although several sequential association rule prediction models using either sequence constraints or temporal constraints have been proposed, none of them considered the redundancy problem in rule mining. The main contribution of this research is to propose a non-redundant association rule mining method based on closed frequent sequences and minimal sequential generators. We also give a definition for the non-redundant sequential rules, which are sequential rules with minimal antecedents but maximal consequents. A new algorithm called CSGM (closed sequential and generator mining) for generating closed sequences and minimal sequential generators is also introduced. A further experiment has been done to compare the performance of generating non-redundant sequential rules and full sequential rules, meanwhile, performance evaluation of our CSGM and other closed sequential pattern mining or generator mining algorithms has also been conducted. We also use generated non-redundant sequential rules for query expansion in order to improve recommendations for infrequently purchased products.

A sequential Monte Carlo approach to design for population pharmacokinetics studies

Here we present a sequential Monte Carlo approach that can be used to find optimal designs. Our focus is on the design of phase III clinical trials where the derivation of sampling windows is required, along with the optimal sampling schedule. The search is conducted via a particle filter which traverses a sequence of target distributions artificially constructed via an annealed utility. The algorithm derives a catalogue of highly efficient designs which, not only contain the optimal, but can also be used to derive sampling windows. We demonstrate our approach by designing a hypothetical phase III clinical trial.

Congo Red adsorption by ball-milled sugarcane bagasse

The adsorption of Congo Red (CR) by ball-milled sugarcane bagasse was evaluated in an aqueous batch system. CR adsorption capacity increased significantly with small changes in bagasse surface area. CR removal decreased with increasing solution pH from 5.0 to 10.0. Maximum adsorption capacity was 38.2 mg/g bagasse at a CR concentration of 500 mg/L. The equilibrium isotherm fitted the Freundlich model and the adsorption kinetics obeyed pseudo-second order equation. CR adsorption obeyed the intra-particle diffusion model very well with bagasse surface area in the range of 0.58–0.66 m2/g, whereas it was controlled by multi-adsorption stages with bagasse surface area in the range of 1.31–1.82 m2/g. Thermodynamic analysis indicated that the adsorption process is an exothermic and spontaneous process. Fourier transform infrared analysis of bagasse containing adsorbed CR indicated interactions between the carboxyl and hydroxyl groups of bagasse and CR function groups.

Influence of physical and chemical properties of solids on heavy metal adsorption

Partition of heavy metals between particulate and dissolve fraction of stormwater primarily depends on the adsorption characteristics of solids particles. Moreover, the bioavailability of heavy metals is also influenced by the adsorption behaviour of solids. However, due to the lack of fundamental knowledge in relation to the heavy metals adsorption processes of road deposited solids, the effectiveness of stormwater management strategies can be limited. The research study focused on the investigation of the physical and chemical parameters of solids on urban road surfaces and, more specifically, on heavy metal adsorption to solids. Due to the complex nature of heavy metal interaction with solids, a substantial database was generated through a series of field investigations and laboratory experiments. The study sites for the build-up pollutant sample collection were selected from four urbanised suburbs located in a major river catchment. Sixteen road sites were selected from these suburbs and represented typical industrial, commercial and residential land uses. Build-up pollutants were collected using a wet and dry vacuum collection technique which was specially designed to improve fine particle collection. Roadside soil samples were also collected from each suburb for comparison with the road surface solids. The collected build-up solids samples were separated into four particle size ranges and tested for a range of physical and chemical parameters. The solids build-up on road surfaces contained a high fraction (70%) of particles smaller than 150ìm, which are favourable for heavy metal adsorption. These solids particles predominantly consist of soil derived minerals which included quartz, albite, microcline, muscovite and chlorite. Additionally, a high percentage of amorphous content was also identified in road deposited solids. In comparing the mineralogical data of surrounding soil and road deposited solids, it was found that about 30% of the solids consisted of particles generated from traffic related activities on road surfaces. Significant difference in mineralogical composition was noted in different particle sizes of build-up solids. Fine solids particles (<150ìm) consisted of a clayey matrix and high amorphous content (in the region of 40%) while coarse particles (>150ìm) consisted of a sandy matrix at all study sites, with about 60% quartz content. Due to these differences in mineralogical components, particles larger than and smaller than 150ìm had significant differences in their specific surface area (SSA) and effective cation exchange capacity (ECEC). These parameters, in turn, exert a significant influence on heavy metal adsorption. Consequently, heavy metal content in >150ìm particles was lower than in the case of fine particles. The particle size range <75ìm had the highest heavy metal content, corresponding with its high clay forming minerals, high organic matter and low quartz content which increased the SSA, ECEC and the presence of Fe, Al and Mn oxides. The clay forming minerals, high organic matter and Fe, Al and Mn oxides create distinct groups of charge sites on solids surfaces and exhibit different adsorption mechanisms and bond strength, between heavy metal elements and charge sites. Therefore, the predominance of these factors in different particle sizes leads to different heavy metal adsorption characteristics. Heavy metals show preference for association with clay forming minerals in fine solids particles, whilst in coarse particles heavy metals preferentially associate with organic matter. Although heavy metal adsorption to amorphous material is very low, the heavy metals embedded in traffic related materials have a potential impact on stormwater quality.Adsorption of heavy metals is not confined to an individual type of charge site in solids, whereas specific heavy metal elements show preference for adsorption to several different types of charge sites in solids. This is attributed to the dearth of preferred binding sites and the inability to reach the preferred binding sites due to competition between different heavy metal species. This confirms that heavy metal adsorption is significantly influenced by the physical and chemical parameters of solids that lead to a heterogeneity of surface charge sites. The research study highlighted the importance of removal of solids particles from stormwater runoff before they enter into receiving waters to reduce the potential risk posed by the bioavailability of heavy metals. The bioavailability of heavy metals not only results from the easily mobile fraction bound to the solids particles, but can also occur as a result of the dissolution of other forms of bonds by chemical changes in stormwater or microbial activity. Due to the diversity in the composition of the different particle sizes of solids and the characteristics and amount of charge sites on the particle surfaces, investigations using bulk solids are not adequate to gain an understanding of the heavy metal adsorption processes of solids particles. Therefore, the investigation of different particle size ranges is recommended for enhancing stormwater quality management practices.

Effective adsorption of sodium dodecylsulfate (SDS) by hydrocalumite (CaAl-LDH-Cl) induced by self-dissolution and re-precipitation mechanism

Hydrocalumite (CaAl-LDH-Cl) were synthesized through a rehydration method involving a freshly prepared tricalcium aluminate (C3A) with CaCl2 solution. To understand the intercalation behaviour of sodium dodecylsulfate (SDS) with CaAl-LDH-Cl, X-ray diffraction (XRD), Fourier transform infrared (FTIR), scanning electron microscopy (SEM), transmission electron microscope (TEM), X-ray photoelectron spectroscopy (XPS), inductively coupled plasma-atomic emission spectrometer (ICP) and elemental analysis have been undertaken. The sorption isotherms with SDS reveal that the maximum sorption amount of SDS by CaAl-LDH-Cl could reach 3.67 mmol•g-1. The results revealed that CaAl-LDH-Cl holds a self-dissolution property, about 20-30% of which is dissolved. And the dissolved Ca2+, Al3+ ions are combined with SDS to form CaAl-SDS or Ca-SDS precipitation. It has been highlighted that the composition of resulting products is strongly dependent upon the SDS concentration. With increasing SDS concentrations, the main resulting product changes from CaAl-SDS to Ca-SDS, and the value of interlayer spacing increased to 3.27 nm.

Sequential Monte Carlo for Bayesian sequentially designed experiments for discrete data

In this paper we present a sequential Monte Carlo algorithm for Bayesian sequential experimental design applied to generalised non-linear models for discrete data. The approach is computationally convenient in that the information of newly observed data can be incorporated through a simple re-weighting step. We also consider a flexible parametric model for the stimulus-response relationship together with a newly developed hybrid design utility that can produce more robust estimates of the target stimulus in the presence of substantial model and parameter uncertainty. The algorithm is applied to hypothetical clinical trial or bioassay scenarios. In the discussion, potential generalisations of the algorithm are suggested to possibly extend its applicability to a wide variety of scenarios

A sequence analytic model of entrepreneurial discovery and exploitation process

This study examined the effect that temporal order within the entrepreneurial discovery exploitation process has on the outcomes of venture creation. Consistent with sequential theories of discovery-exploitation, the general flow of venture creation was found to be directed from discovery toward exploitation in a random sample of nascent ventures. However, venture creation attempts which specifically follow this sequence derive poor outcomes. Moreover, simultaneous discovery-exploitation was the most prevalent temporal order observed, and venture attempts that proceed in this manner more likely become operational. These findings suggest that venture creation is a multi-scale phenomenon that is at once directional in time, and simultaneously driven by symbiotically coupled discovery and exploitation.

A sequential Monte Carlo algorithm to incorporate model uncertainty in Bayesian sequential design

Here we present a sequential Monte Carlo (SMC) algorithm that can be used for any one-at-a-time Bayesian sequential design problem in the presence of model uncertainty where discrete data are encountered. Our focus is on adaptive design for model discrimination but the methodology is applicable if one has a different design objective such as parameter estimation or prediction. An SMC algorithm is run in parallel for each model and the algorithm relies on a convenient estimator of the evidence of each model which is essentially a function of importance sampling weights. Other methods for this task such as quadrature, often used in design, suffer from the curse of dimensionality. Approximating posterior model probabilities in this way allows us to use model discrimination utility functions derived from information theory that were previously difficult to compute except for conjugate models. A major benefit of the algorithm is that it requires very little problem specific tuning. We demonstrate the methodology on three applications, including discriminating between models for decline in motor neuron numbers in patients suffering from neurological diseases such as Motor Neuron disease.

Sequential Fusion Using Correlated Decisions for Controlled Verification Errors

Fusion techniques have received considerable attention for achieving lower error rates with biometrics. A fused classifier architecture based on sequential integration of multi-instance and multi-sample fusion schemes allows controlled trade-off between false alarms and false rejects. Expressions for each type of error for the fused system have previously been derived for the case of statistically independent classifier decisions. It is shown in this paper that the performance of this architecture can be improved by modelling the correlation between classifier decisions. Correlation modelling also enables better tuning of fusion model parameters, ‘N’, the number of classifiers and ‘M’, the number of attempts/samples, and facilitates the determination of error bounds for false rejects and false accepts for each specific user. Error trade-off performance of the architecture is evaluated using HMM based speaker verification on utterances of individual digits. Results show that performance is improved for the case of favourable correlated decisions. The architecture investigated here is directly applicable to speaker verification from spoken digit strings such as credit card numbers in telephone or voice over internet protocol based applications. It is also applicable to other biometric modalities such as finger prints and handwriting samples.

Sequential fusion of decisions with favorable dependence for controlled verification errors

Statistical dependence between classifier decisions is often shown to improve performance over statistically independent decisions. Though the solution for favourable dependence between two classifier decisions has been derived, the theoretical analysis for the general case of 'n' client and impostor decision fusion has not been presented before. This paper presents the expressions developed for favourable dependence of multi-instance and multi-sample fusion schemes that employ 'AND' and 'OR' rules. The expressions are experimentally evaluated by considering the proposed architecture for text-dependent speaker verification using HMM based digit dependent speaker models. The improvement in fusion performance is found to be higher when digit combinations with favourable client and impostor decisions are used for speaker verification. The total error rate of 20% for fusion of independent decisions is reduced to 2.1% for fusion of decisions that are favourable for both client and impostors. The expressions developed here are also applicable to other biometric modalities, such as finger prints and handwriting samples, for reliable identity verification.

Modeling random effect and excess zeros in road traffic accident prediction

Poisson distribution has often been used for count like accident data. Negative Binomial (NB) distribution has been adopted in the count data to take care of the over-dispersion problem. However, Poisson and NB distributions are incapable of taking into account some unobserved heterogeneities due to spatial and temporal effects of accident data. To overcome this problem, Random Effect models have been developed. Again another challenge with existing traffic accident prediction models is the distribution of excess zero accident observations in some accident data. Although Zero-Inflated Poisson (ZIP) model is capable of handling the dual-state system in accident data with excess zero observations, it does not accommodate the within-location correlation and between-location correlation heterogeneities which are the basic motivations for the need of the Random Effect models. This paper proposes an effective way of fitting ZIP model with location specific random effects and for model calibration and assessment the Bayesian analysis is recommended.

HMM triangle relative entropy concepts in sequential change detection applied to vision-based dim target manoeuvre detection

The quick detection of abrupt (unknown) parameter changes in an observed hidden Markov model (HMM) is important in several applications. Motivated by the recent application of relative entropy concepts in the robust sequential change detection problem (and the related model selection problem), this paper proposes a sequential unknown change detection algorithm based on a relative entropy based HMM parameter estimator. Our proposed approach is able to overcome the lack of knowledge of post-change parameters, and is illustrated to have similar performance to the popular cumulative sum (CUSUM) algorithm (which requires knowledge of the post-change parameter values) when examined, on both simulated and real data, in a vision-based aircraft manoeuvre detection problem.