Amino acids in oral drug delivery:salts, ion-pairs and transcriptomics

Oral drug delivery is considered the most popular route of delivery because of the ease of administration, availability of a wide range of dosage forms and the large surface area for drug absorption via the intestinal membrane. However, besides the unfavourable biopharmaceutical properties of the therapeutic agents, efflux transporters such as Pglycoprotein (P-gp) and multiple resistance proteins (MRP) decrease the overall drug uptake by extruding the drug from the cells. Although, prodrugs have been investigated to improve drug partitioning by masking the polar groups covalently with pre-moieties promoting increased uptake, they present significant challenges including reduced solubility and increased toxicity. The current work investigates the use of amino acids as ion-pairs for three model drugs: indomethacin (weak acid), trimethoprim (weak base) and ciprofloxacin (zwitter ion) in an attempt to improve both solubility and uptake. Solubility was studied by salt formation while creating new routes for uptake across the membranes via amino acids transporter proteins or dipeptidyl transporters was the rationale to enhance absorption. New salts were prepared for the model drugs and the oppositely charged amino acids by freeze drying and they were characterised using FTIR, 1HNMR, DSC, SEM, pH solubility profile, solubility and dissolution. Permeability profiles were assessed using an in vitro cell based method; Caco-2 cells and the genetic changes occurring across the transporter genes and various pathways involved in the cellular activities were studied using DNA microarrays. Solubility data showed a significant increase in drug solubility upon preparing the new salts with the oppositely charged counter ions (ciprofloxacin glutamate salt exhibiting 2.9x103 fold enhancement when compared to the free drug). Moreover, permeability studies showed a 3 fold increase in trimethoprim and indomethacin permeabilities upon ion-pairing with amino acids and more than 10 fold when the zwitter ionic drug was paired with glutamic acid. Microarray data revealed that trimethoprim was absorbed actively via OCTN1 transporters while MRP7 is the main transporter gene that mediates its efflux. The absorption of trimethoprim from trimethoprim glutamic acid ion-paired formulations was affected by the ratio of glutamic acid in the formulation which was inversely proportional to the degree of expression of OCTN1. Interestingly, ciprofloxacin glutamic acid ion-pairs were found to decrease the up-regulation of ciprofloxacin efflux proteins (P-gp and MRP4) and over-express two solute carrier transporters; (PEPT2 and SLCO1A2) suggesting that a high aqueous binding constant (K11aq) enables the ion-paired formulations to be absorbed as one entity. In conclusion, formation of ion-pairs with amino acids can influence in a positive way solubility, transfer and gene expression effects of drugs.

Binocular fusion, suppression and diplopia for blurred edges

Purpose: (1) To devise a model-based method for estimating the probabilities of binocular fusion, interocular suppression and diplopia from psychophysical judgements, (2) To map out the way fusion, suppression and diplopia vary with binocular disparity and blur of single edges shown to each eye, (3) To compare the binocular interactions found for edges of the same vs opposite contrast polarity. Methods: Test images were single, horizontal, Gaussian-blurred edges, with blur B = 1-32 min arc, and vertical disparity 0-8.B, shown for 200 ms. In the main experiment, observers reported whether they saw one central edge, one offset edge, or two edges. We argue that the relation between these three response categories and the three perceptual states (fusion, suppression, diplopia) is indirect and likely to be distorted by positional noise and criterion effects, and so we developed a descriptive, probabilistic model to estimate both the perceptual states and the noise/criterion parameters from the data. Results: (1) Using simulated data, we validated the model-based method by showing that it recovered fairly accurately the disparity ranges for fusion and suppression, (2) The disparity range for fusion (Panum's limit) increased greatly with blur, in line with previous studies. The disparity range for suppression was similar to the fusion limit at large blurs, but two or three times the fusion limit at small blurs. This meant that diplopia was much more prevalent at larger blurs, (3) Diplopia was much more frequent when the two edges had opposite contrast polarity. A formal comparison of models indicated that fusion occurs for same, but not opposite, polarities. Probability of suppression was greater for unequal contrasts, and it was always the lower-contrast edge that was suppressed. Conclusions: Our model-based data analysis offers a useful tool for probing binocular fusion and suppression psychophysically. The disparity range for fusion increased with edge blur but fell short of complete scale-invariance. The disparity range for suppression also increased with blur but was not close to scale-invariance. Single vision occurs through fusion, but also beyond the fusion range, through suppression. Thus suppression can serve as a mechanism for extending single vision to larger disparities, but mainly for sharper edges where the fusion range is small (5-10 min arc). For large blurs the fusion range is so much larger that no such extension may be needed. © 2014 The College of Optometrists.

Using genetic algorithms for improved discrete sequence prediction

A statistics-based method using genetic algorithms for predicting discrete sequences is presented. The prediction of the next value is based upon a fixed number of previous values and the statistics offered by the training data. According to the statistics, in similar past cases different values occurred next. If these values are considered with the appropriate weights, the forecast is successful. Weights are generated by genetic algorithms.

Cerebellar morphology in developmental dyslexia

Recent evidence has suggested cerebellar anomalies in developmental dyslexia. Therefore, we investigated cerebellar morphology in subjects with documented reading disabilities. We obtained T1-weighted magnetic resonance images in the coronal and sagittal planes from 11 males with prior histories of developmental dyslexia, and nine similarly-aged male controls. Proton magnetic resonance spectra (TE=136 ms, TR=2.4 s) were obtained bilaterally in the cerebellum. Phonological decoding skill was measured using non-word reading. Handedness was assessed using both the Annett questionnaire of hand preference and Annett’s peg moving task. Cerebellar symmetry was observed in the dyslexics but there was significant asymmetry (right grey matter>left grey matter) in controls. The interpretation of these results depended whether a motor- or questionnaire-based method was used to determine handedness. The degree of cerebellar symmetry was correlated with the severity of dyslexics’ phonological decoding deficit. Those with more symmetric cerebella made more errors on a nonsense word reading measure of phonological decoding ability. Left cerebellar metabolite ratios were shown to correlate significantly with the degree of cerebellar asymmetry (P<0.05) in controls. This relationship was absent in developmental dyslexics. Cerebellar morphology reflects the higher degree of symmetry found previously in the temporal and parietal cortex of dyslexics. The relationship of cerebellar asymmetry to phonological decoding ability and handedness, together with our previous finding of altered metabolite ratios in the cerebellum of dyslexics, lead us to suggest that there are alterations in the neurological organisation of the cerebellum which relate to phonological decoding skills, in addition to motor skills and handedness.

Correlating liposomal adjuvant characteristics to in-vivo cell-mediated immunity using a novel Mycobacterium tuberculosis fusion protein:a multivariate analysis study

Objective In this study, we have used a chemometrics-based method to correlate key liposomal adjuvant attributes with in-vivo immune responses based on multivariate analysis. Methods The liposomal adjuvant composed of the cationic lipid dimethyldioctadecylammonium bromide (DDA) and trehalose 6,6-dibehenate (TDB) was modified with 1,2-distearoyl-sn-glycero-3-phosphocholine at a range of mol% ratios, and the main liposomal characteristics (liposome size and zeta potential) was measured along with their immunological performance as an adjuvant for the novel, postexposure fusion tuberculosis vaccine, Ag85B-ESAT-6-Rv2660c (H56 vaccine). Partial least square regression analysis was applied to correlate and cluster liposomal adjuvants particle characteristics with in-vivo derived immunological performances (IgG, IgG1, IgG2b, spleen proliferation, IL-2, IL-5, IL-6, IL-10, IFN-γ). Key findings While a range of factors varied in the formulations, decreasing the 1,2-distearoyl-sn-glycero-3-phosphocholine content (and subsequent zeta potential) together built the strongest variables in the model. Enhanced DDA and TDB content (and subsequent zeta potential) stimulated a response skewed towards a cell mediated immunity, with the model identifying correlations with IFN-γ, IL-2 and IL-6. Conclusion This study demonstrates the application of chemometrics-based correlations and clustering, which can inform liposomal adjuvant design.

Neural Control of Chaos and Aplications

Signal processing is an important topic in technological research today. In the areas of nonlinear dynamics search, the endeavor to control or order chaos is an issue that has received increasing attention over the last few years. Increasing interest in neural networks composed of simple processing elements (neurons) has led to widespread use of such networks to control dynamic systems learning. This paper presents backpropagation-based neural network architecture that can be used as a controller to stabilize unsteady periodic orbits. It also presents a neural network-based method for transferring the dynamics among attractors, leading to more efficient system control. The procedure can be applied to every point of the basin, no matter how far away from the attractor they are. Finally, this paper shows how two mixed chaotic signals can be controlled using a backpropagation neural network as a filter to separate and control both signals at the same time. The neural network provides more effective control, overcoming the problems that arise with control feedback methods. Control is more effective because it can be applied to the system at any point, even if it is moving away from the target state, which prevents waiting times. Also control can be applied even if there is little information about the system and remains stable longer even in the presence of random dynamic noise.

On some Properties of Boolean Functions and Their Binary Decision Diagrams

Иво Й. Дамянов - Манипулирането на булеви функции е основнo за теоретичната информатика, в това число логическата оптимизация, валидирането и синтеза на схеми. В тази статия се разглеждат някои първоначални резултати относно връзката между граф-базираното представяне на булевите функции и свойствата на техните променливи.

A continuous-time quantum walk kernel for unattributed graphs

Kernel methods provide a way to apply a wide range of learning techniques to complex and structured data by shifting the representational problem from one of finding an embedding of the data to that of defining a positive semidefinite kernel. In this paper, we propose a novel kernel on unattributed graphs where the structure is characterized through the evolution of a continuous-time quantum walk. More precisely, given a pair of graphs, we create a derived structure whose degree of symmetry is maximum when the original graphs are isomorphic. With this new graph to hand, we compute the density operators of the quantum systems representing the evolutions of two suitably defined quantum walks. Finally, we define the kernel between the two original graphs as the quantum Jensen-Shannon divergence between these two density operators. The experimental evaluation shows the effectiveness of the proposed approach. © 2013 Springer-Verlag.

Investigating the prevalence of Salmonella in dogs within the Midlands region of the United Kingdom

Background - The intimate relationship between dogs and their owners has the potential to increase the risk of human exposure to bacterial pathogens. Over the past 40 years, there have been several reports on transmission of salmonellae from dogs to humans. This study therefore aimed to determine the prevalence of Salmonella in the faeces of dogs from the Midlands region of the United Kingdom to assess exposure risk and potential for zoonotic transmission. Results - A total of 436 apparently healthy dogs without diarrhoea from households (n = 126), rescue centres (n = 96), boarding kennels (n = 43), retired greyhound kennels (n = 39) and a pet nutrition facility (n = 132) were investigated for Salmonella shedding. Faecal samples were processed by an enrichment culture based method. The faeces from one dog (0.23 %; 95 % confidence limit 0.006 %, 1.27 %) was positive for Salmonella. The species was S. enterica subspecies arizonae. Conclusion - This study showed that the prevalence of Salmonella from faeces from apparently healthy dogs from a variety of housing conditions is low; however, Salmonella shedding was still identified.

Visualisation and browsing of image databases

In this chapter we provide a comprehensive overview of the emerging field of visualising and browsing image databases. We start with a brief introduction to content-based image retrieval and the traditional query-by-example search paradigm that many retrieval systems employ. We specify the problems associated with this type of interface, such as users not being able to formulate a query due to not having a target image or concept in mind. The idea of browsing systems is then introduced as a means to combat these issues, harnessing the cognitive power of the human mind in order to speed up image retrieval.We detail common methods in which the often high-dimensional feature data extracted from images can be used to visualise image databases in an intuitive way. Systems using dimensionality reduction techniques, such as multi-dimensional scaling, are reviewed along with those that cluster images using either divisive or agglomerative techniques as well as graph-based visualisations. While visualisation of an image collection is useful for providing an overview of the contained images, it forms only part of an image database navigation system. We therefore also present various methods provided by these systems to allow for interactive browsing of these datasets. A further area we explore are user studies of systems and visualisations where we look at the different evaluations undertaken in order to test usability and compare systems, and highlight the key findings from these studies. We conclude the chapter with several recommendations for future work in this area. © 2011 Springer-Verlag Berlin Heidelberg.

Exploiting heterogeneous scientific literature networks to combat ranking bias:evidence from the computational linguistics area

It is important to help researchers find valuable papers from a large literature collection. To this end, many graph-based ranking algorithms have been proposed. However, most of these algorithms suffer from the problem of ranking bias. Ranking bias hurts the usefulness of a ranking algorithm because it returns a ranking list with an undesirable time distribution. This paper is a focused study on how to alleviate ranking bias by leveraging the heterogeneous network structure of the literature collection. We propose a new graph-based ranking algorithm, MutualRank, that integrates mutual reinforcement relationships among networks of papers, researchers, and venues to achieve a more synthetic, accurate, and less-biased ranking than previous methods. MutualRank provides a unified model that involves both intra- and inter-network information for ranking papers, researchers, and venues simultaneously. We use the ACL Anthology Network as the benchmark data set and construct the gold standard from computer linguistics course websites of well-known universities and two well-known textbooks. The experimental results show that MutualRank greatly outperforms the state-of-the-art competitors, including PageRank, HITS, CoRank, Future Rank, and P-Rank, in ranking papers in both improving ranking effectiveness and alleviating ranking bias. Rankings of researchers and venues by MutualRank are also quite reasonable.

Benchmarking of distributed computing engines spark and GraphLab for big data analytics

In this paper we evaluate and compare two representativeand popular distributed processing engines for large scalebig data analytics, Spark and graph based engine GraphLab. Wedesign a benchmark suite including representative algorithmsand datasets to compare the performances of the computingengines, from performance aspects of running time, memory andCPU usage, network and I/O overhead. The benchmark suite istested on both local computer cluster and virtual machines oncloud. By varying the number of computers and memory weexamine the scalability of the computing engines with increasingcomputing resources (such as CPU and memory). We also runcross-evaluation of generic and graph based analytic algorithmsover graph processing and generic platforms to identify thepotential performance degradation if only one processing engineis available. It is observed that both computing engines showgood scalability with increase of computing resources. WhileGraphLab largely outperforms Spark for graph algorithms, ithas close running time performance as Spark for non-graphalgorithms. Additionally the running time with Spark for graphalgorithms over cloud virtual machines is observed to increaseby almost 100% compared to over local computer clusters.

Developing multidimensional metrics for evaluating paediatric neurodevelopmental disorders

Healthy brain functioning depends on efficient communication of information between brain regions, forming complex networks. By quantifying synchronisation between brain regions, a functionally connected brain network can be articulated. In neurodevelopmental disorders, where diagnosis is based on measures of behaviour and tasks, a measure of the underlying biological mechanisms holds promise as a potential clinical tool. Graph theory provides a tool for investigating the neural correlates of neuropsychiatric disorders, where there is disruption of efficient communication within and between brain networks. This research aimed to use recent conceptualisation of graph theory, along with measures of behaviour and cognitive functioning, to increase understanding of the neurobiological risk factors of atypical development. Using magnetoencephalography to investigate frequency-specific temporal dynamics at rest, the research aimed to identify potential biological markers derived from sensor-level whole-brain functional connectivity. Whilst graph theory has proved valuable for insight into network efficiency, its application is hampered by two limitations. First, its measures have hardly been validated in MEG studies, and second, graph measures have been shown to depend on methodological assumptions that restrict direct network comparisons. The first experimental study (Chapter 3) addressed the first limitation by examining the reproducibility of graph-based functional connectivity and network parameters in healthy adult volunteers. Subsequent chapters addressed the second limitation through adapted minimum spanning tree (a network analysis approach that allows for unbiased group comparisons) along with graph network tools that had been shown in Chapter 3 to be highly reproducible. Network topologies were modelled in healthy development (Chapter 4), and atypical neurodevelopment (Chapters 5 and 6). The results provided support to the proposition that measures of network organisation, derived from sensor-space MEG data, offer insights helping to unravel the biological basis of typical brain maturation and neurodevelopmental conditions, with the possibility of future clinical utility.

Projekttervezési módszerek kihívásai a XXI. században (Challenges of the project planning methods in the 21st century)

Több mint száz éve született meg Henry Gantt (Gantt, 1910) sávos ütemterve, Kelley (Kelley, 1961) és Walker (Walker, 1959) is több mint hatvan éve publikálta kritikus út módszerét. Az ezekre épülő költség- és erőforrás- tervezési módszerek vajon alkalmasak-e a ma kihívásaira? Az olvasó ebben a tanulmányban többéves kutatómunka gyümölcsét láthatja. A kutatás során az egyik legfontosabb cél annak vizsgálata volt, hogy a meglévő projekttervezési eszközök mennyiben felelnek meg a mai projektek kihívásainak; hol és milyen területen van szükség e módszerek továbbfejlesztésére, esetleg meghaladására. Ebben a tanulmányban a szerző olyan módszereket mutat be, amelyek messze túlvezetnek bennünket a projekttervezés eddig elsősorban operatív feladatokra szorítkozó módszereitől, és olyan kérdések megválaszolására fordítja figyelmünket, mint pl. milyen tevékenységeket, projekteket valósítsunk meg; melyeket hagyjuk el vagy ütemezzük be egy későbbi projektbe; hogyan rangsoroljuk, priorizáljuk a projektek megvalósítását, fontosságát? ______ Gantt chart (Gantt, 1910) was born by Henry Gantt more than a hundred years ago. Kelley and Walker published their critical planning method more than a 60 years ago (see i.e. Kelley-Walker, 1959). Can we use methods based on network planning methods for the challenges of 21st century? In this paper the author can see the results of the recent researches. In this study with their colleagues he investigated which project planning methods can be used in challenges of the 21st century and where and how to improve them. In these researches new matrix-based project planning methods are specified, where they can deal not only operative but strategic questions: which subprojects/tasks should be completed, how to treat priorities of completion in case of defining logic planning, how to support not only traditional but agile project management approaches.In this paper he introduces a new matrix-based method, which can be used for ranking project or multi project scenarios with different kinds of target functions. The author shows methods that are used in an expert module. He shows how to integrate this expert module into the traditional PMS system.

Development of prediction models for freeway incident durations using data mining techniques

The nation's freeway systems are becoming increasingly congested. A major contribution to traffic congestion on freeways is due to traffic incidents. Traffic incidents are non-recurring events such as accidents or stranded vehicles that cause a temporary roadway capacity reduction, and they can account for as much as 60 percent of all traffic congestion on freeways. One major freeway incident management strategy involves diverting traffic to avoid incident locations by relaying timely information through Intelligent Transportation Systems (ITS) devices such as dynamic message signs or real-time traveler information systems. The decision to divert traffic depends foremost on the expected duration of an incident, which is difficult to predict. In addition, the duration of an incident is affected by many contributing factors. Determining and understanding these factors can help the process of identifying and developing better strategies to reduce incident durations and alleviate traffic congestion. A number of research studies have attempted to develop models to predict incident durations, yet with limited success. ^ This dissertation research attempts to improve on this previous effort by applying data mining techniques to a comprehensive incident database maintained by the District 4 ITS Office of the Florida Department of Transportation (FDOT). Two categories of incident duration prediction models were developed: "offline" models designed for use in the performance evaluation of incident management programs, and "online" models for real-time prediction of incident duration to aid in the decision making of traffic diversion in the event of an ongoing incident. Multiple data mining analysis techniques were applied and evaluated in the research. The multiple linear regression analysis and decision tree based method were applied to develop the offline models, and the rule-based method and a tree algorithm called M5P were used to develop the online models. ^ The results show that the models in general can achieve high prediction accuracy within acceptable time intervals of the actual durations. The research also identifies some new contributing factors that have not been examined in past studies. As part of the research effort, software code was developed to implement the models in the existing software system of District 4 FDOT for actual applications. ^