Bioquímica quântica na diferenciação dos níveis de ativação de receptores AMPA por agonistas parciais Wilardina

In the central nervous system (CNS) of mammalian, fast synaptic transmission between nerve cells is performed primarily by α-amino-3-hydroxy-5-methyl-4- isoxazolepropionic acid (AMPA) receptors, an ionotropic glutamate receptor that is related with learning, memory and homeostasis of the nervous system. Impairments in their functions are correlated with development of many brain desorders, such as epilepsy, schizophrenia, autism, Parkinson and Alzheimer. The use of willardiine analogs has been shown a powerful tool to understanding of activation and desensitization mechanisms of this receptors, because the modification of a single ligand atom allows the observation of varying levels of efficacy. In this work, taking advantage of Fluorine Willardiine (1.35Å), Hydrogen Willardiine (1.65Å), Bromine Willardiine (1.8Å) and Iodine Willardiine (2.15Å) structures co-crystalized with GluA2 with codes 1MQI, 1MQJ, 1MQH and 1MQG, we attempted to energetically differentiate the four ligands efficacy. The complexes were submitted to energetic calculations based on density functional theory (DFT), under the optics of molecular fractionation with conjugate caps (MFCC) method. Obtained results show a relationship between the energetic values and willardiines efficacy order (FW> HW > BrW > IW), also show the importance of E705, R485, Y450, S654, T655, T480 e P478 as the amino acids that contribute most strongly with the interaction of four partial agonists. Furthermore, we outlined the M708 behaviour, attracted by FW and HW ligands, and repels by BrW and IW. With the datas reported on this work, it is possible for a better understanding of the AMPA receptor, which can serve as an aid in the development of new drugs for this system.

Metodologia númerico-experimental para avaliação da espessura de filmes de nitreto de titânio obtidos por processamento a plasma

In recent decades, changes in the surface properties of materials have been used to improve their tribological characteristics. However, this improvement depends on the process, treatment time and, primarily, the thickness of this surface film layer. Physical vapor deposition (PVD) of titanium nitrate (TiN) has been used to increase the surface hardness of metallic materials. Thus, the aim of the present study was to propose a numerical-experimental method to assess the film thickness (l) of TiN deposited by PVD. To reach this objective, experimental results of hardness (H) assays were combined with a numerical simulation to study the behavior of this property as a function of maximum penetration depth of the indenter (hmax) into the film/substrate conjugate. Two methodologies were adopted to determine film thickness. The first consists of the numerical results of the H x hmax curve with the experimental curve obtained by the instrumental indentation test. This methodology was used successfully in a TiN-coated titanium (Ti) conjugate. A second strategy combined the numerical results of the Hv x hmax curve with Vickers experimental hardness data (Hv). This methodology was applied to a TiN-coated M2 tool steel conjugate. The mechanical properties of the materials studied were also determined in the present study. The thicknesses results obtained for the two conjugates were compatible with their experimental data.

Dynamic fault tree analysis with PEWMA modeling of event rates and Bayesian updating of material balance parameters using MCMC

This research explores Bayesian updating as a tool for estimating parameters probabilistically by dynamic analysis of data sequences. Two distinct Bayesian updating methodologies are assessed. The first approach focuses on Bayesian updating of failure rates for primary events in fault trees. A Poisson Exponentially Moving Average (PEWMA) model is implemnented to carry out Bayesian updating of failure rates for individual primary events in the fault tree. To provide a basis for testing of the PEWMA model, a fault tree is developed based on the Texas City Refinery incident which occurred in 2005. A qualitative fault tree analysis is then carried out to obtain a logical expression for the top event. A dynamic Fault Tree analysis is carried out by evaluating the top event probability at each Bayesian updating step by Monte Carlo sampling from posterior failure rate distributions. It is demonstrated that PEWMA modeling is advantageous over conventional conjugate Poisson-Gamma updating techniques when failure data is collected over long time spans. The second approach focuses on Bayesian updating of parameters in non-linear forward models. Specifically, the technique is applied to the hydrocarbon material balance equation. In order to test the accuracy of the implemented Bayesian updating models, a synthetic data set is developed using the Eclipse reservoir simulator. Both structured grid and MCMC sampling based solution techniques are implemented and are shown to model the synthetic data set with good accuracy. Furthermore, a graphical analysis shows that the implemented MCMC model displays good convergence properties. A case study demonstrates that Likelihood variance affects the rate at which the posterior assimilates information from the measured data sequence. Error in the measured data significantly affects the accuracy of the posterior parameter distributions. Increasing the likelihood variance mitigates random measurement errors, but casuses the overall variance of the posterior to increase. Bayesian updating is shown to be advantageous over deterministic regression techniques as it allows for incorporation of prior belief and full modeling uncertainty over the parameter ranges. As such, the Bayesian approach to estimation of parameters in the material balance equation shows utility for incorporation into reservoir engineering workflows.

Bayesian Inference in Large-scale Problems

Many modern applications fall into the category of "large-scale" statistical problems, in which both the number of observations n and the number of features or parameters p may be large. Many existing methods focus on point estimation, despite the continued relevance of uncertainty quantification in the sciences, where the number of parameters to estimate often exceeds the sample size, despite huge increases in the value of n typically seen in many fields. Thus, the tendency in some areas of industry to dispense with traditional statistical analysis on the basis that "n=all" is of little relevance outside of certain narrow applications. The main result of the Big Data revolution in most fields has instead been to make computation much harder without reducing the importance of uncertainty quantification. Bayesian methods excel at uncertainty quantification, but often scale poorly relative to alternatives. This conflict between the statistical advantages of Bayesian procedures and their substantial computational disadvantages is perhaps the greatest challenge facing modern Bayesian statistics, and is the primary motivation for the work presented here.

Two general strategies for scaling Bayesian inference are considered. The first is the development of methods that lend themselves to faster computation, and the second is design and characterization of computational algorithms that scale better in n or p. In the first instance, the focus is on joint inference outside of the standard problem of multivariate continuous data that has been a major focus of previous theoretical work in this area. In the second area, we pursue strategies for improving the speed of Markov chain Monte Carlo algorithms, and characterizing their performance in large-scale settings. Throughout, the focus is on rigorous theoretical evaluation combined with empirical demonstrations of performance and concordance with the theory.

One topic we consider is modeling the joint distribution of multivariate categorical data, often summarized in a contingency table. Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. In Chapter 2, we derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions.

Latent class models for the joint distribution of multivariate categorical, such as the PARAFAC decomposition, data play an important role in the analysis of population structure. In this context, the number of latent classes is interpreted as the number of genetically distinct subpopulations of an organism, an important factor in the analysis of evolutionary processes and conservation status. Existing methods focus on point estimates of the number of subpopulations, and lack robust uncertainty quantification. Moreover, whether the number of latent classes in these models is even an identified parameter is an open question. In Chapter 3, we show that when the model is properly specified, the correct number of subpopulations can be recovered almost surely. We then propose an alternative method for estimating the number of latent subpopulations that provides good quantification of uncertainty, and provide a simple procedure for verifying that the proposed method is consistent for the number of subpopulations. The performance of the model in estimating the number of subpopulations and other common population structure inference problems is assessed in simulations and a real data application.

In contingency table analysis, sparse data is frequently encountered for even modest numbers of variables, resulting in non-existence of maximum likelihood estimates. A common solution is to obtain regularized estimates of the parameters of a log-linear model. Bayesian methods provide a coherent approach to regularization, but are often computationally intensive. Conjugate priors ease computational demands, but the conjugate Diaconis--Ylvisaker priors for the parameters of log-linear models do not give rise to closed form credible regions, complicating posterior inference. In Chapter 4 we derive the optimal Gaussian approximation to the posterior for log-linear models with Diaconis--Ylvisaker priors, and provide convergence rate and finite-sample bounds for the Kullback-Leibler divergence between the exact posterior and the optimal Gaussian approximation. We demonstrate empirically in simulations and a real data application that the approximation is highly accurate, even in relatively small samples. The proposed approximation provides a computationally scalable and principled approach to regularized estimation and approximate Bayesian inference for log-linear models.

Another challenging and somewhat non-standard joint modeling problem is inference on tail dependence in stochastic processes. In applications where extreme dependence is of interest, data are almost always time-indexed. Existing methods for inference and modeling in this setting often cluster extreme events or choose window sizes with the goal of preserving temporal information. In Chapter 5, we propose an alternative paradigm for inference on tail dependence in stochastic processes with arbitrary temporal dependence structure in the extremes, based on the idea that the information on strength of tail dependence and the temporal structure in this dependence are both encoded in waiting times between exceedances of high thresholds. We construct a class of time-indexed stochastic processes with tail dependence obtained by endowing the support points in de Haan's spectral representation of max-stable processes with velocities and lifetimes. We extend Smith's model to these max-stable velocity processes and obtain the distribution of waiting times between extreme events at multiple locations. Motivated by this result, a new definition of tail dependence is proposed that is a function of the distribution of waiting times between threshold exceedances, and an inferential framework is constructed for estimating the strength of extremal dependence and quantifying uncertainty in this paradigm. The method is applied to climatological, financial, and electrophysiology data.

The remainder of this thesis focuses on posterior computation by Markov chain Monte Carlo. The Markov Chain Monte Carlo method is the dominant paradigm for posterior computation in Bayesian analysis. It has long been common to control computation time by making approximations to the Markov transition kernel. Comparatively little attention has been paid to convergence and estimation error in these approximating Markov Chains. In Chapter 6, we propose a framework for assessing when to use approximations in MCMC algorithms, and how much error in the transition kernel should be tolerated to obtain optimal estimation performance with respect to a specified loss function and computational budget. The results require only ergodicity of the exact kernel and control of the kernel approximation accuracy. The theoretical framework is applied to approximations based on random subsets of data, low-rank approximations of Gaussian processes, and a novel approximating Markov chain for discrete mixture models.

Data augmentation Gibbs samplers are arguably the most popular class of algorithm for approximately sampling from the posterior distribution for the parameters of generalized linear models. The truncated Normal and Polya-Gamma data augmentation samplers are standard examples for probit and logit links, respectively. Motivated by an important problem in quantitative advertising, in Chapter 7 we consider the application of these algorithms to modeling rare events. We show that when the sample size is large but the observed number of successes is small, these data augmentation samplers mix very slowly, with a spectral gap that converges to zero at a rate at least proportional to the reciprocal of the square root of the sample size up to a log factor. In simulation studies, moderate sample sizes result in high autocorrelations and small effective sample sizes. Similar empirical results are observed for related data augmentation samplers for multinomial logit and probit models. When applied to a real quantitative advertising dataset, the data augmentation samplers mix very poorly. Conversely, Hamiltonian Monte Carlo and a type of independence chain Metropolis algorithm show good mixing on the same dataset.

Bayesian Emulation for Sequential Modeling, Inference and Decision Analysis

The advances in three related areas of state-space modeling, sequential Bayesian learning, and decision analysis are addressed, with the statistical challenges of scalability and associated dynamic sparsity. The key theme that ties the three areas is Bayesian model emulation: solving challenging analysis/computational problems using creative model emulators. This idea defines theoretical and applied advances in non-linear, non-Gaussian state-space modeling, dynamic sparsity, decision analysis and statistical computation, across linked contexts of multivariate time series and dynamic networks studies. Examples and applications in financial time series and portfolio analysis, macroeconomics and internet studies from computational advertising demonstrate the utility of the core methodological innovations.

Chapter 1 summarizes the three areas/problems and the key idea of emulating in those areas. Chapter 2 discusses the sequential analysis of latent threshold models with use of emulating models that allows for analytical filtering to enhance the efficiency of posterior sampling. Chapter 3 examines the emulator model in decision analysis, or the synthetic model, that is equivalent to the loss function in the original minimization problem, and shows its performance in the context of sequential portfolio optimization. Chapter 4 describes the method for modeling the steaming data of counts observed on a large network that relies on emulating the whole, dependent network model by independent, conjugate sub-models customized to each set of flow. Chapter 5 reviews those advances and makes the concluding remarks.

Multi-View Weighted Network

Extensive investigation has been conducted on network data, especially weighted network in the form of symmetric matrices with discrete count entries. Motivated by statistical inference on multi-view weighted network structure, this paper proposes a Poisson-Gamma latent factor model, not only separating view-shared and view-specific spaces but also achieving reduced dimensionality. A multiplicative gamma process shrinkage prior is implemented to avoid over parameterization and efficient full conditional conjugate posterior for Gibbs sampling is accomplished. By the accommodating of view-shared and view-specific parameters, flexible adaptability is provided according to the extents of similarity across view-specific space. Accuracy and efficiency are tested by simulated experiment. An application on real soccer network data is also proposed to illustrate the model.

Bayesian Learning with Dependency Structures via Latent Factors, Mixtures, and Copulas

Bayesian methods offer a flexible and convenient probabilistic learning framework to extract interpretable knowledge from complex and structured data. Such methods can characterize dependencies among multiple levels of hidden variables and share statistical strength across heterogeneous sources. In the first part of this dissertation, we develop two dependent variational inference methods for full posterior approximation in non-conjugate Bayesian models through hierarchical mixture- and copula-based variational proposals, respectively. The proposed methods move beyond the widely used factorized approximation to the posterior and provide generic applicability to a broad class of probabilistic models with minimal model-specific derivations. In the second part of this dissertation, we design probabilistic graphical models to accommodate multimodal data, describe dynamical behaviors and account for task heterogeneity. In particular, the sparse latent factor model is able to reveal common low-dimensional structures from high-dimensional data. We demonstrate the effectiveness of the proposed statistical learning methods on both synthetic and real-world data.

Optimization of Cooling Protocols for Hearts Destined for Transplantation

Design and analysis of conceptually different cooling systems for the human heart preservation are numerically investigated. A heart cooling container with required connections was designed for a normal size human heart. A three-dimensional, high resolution human heart geometric model obtained from CT-angio data was used for simulations. Nine different cooling designs are introduced in this research. The first cooling design (Case 1) used a cooling gelatin only outside of the heart. In the second cooling design (Case 2), the internal parts of the heart were cooled via pumping a cooling liquid inside both the heart’s pulmonary and systemic circulation systems. An unsteady conjugate heat transfer analysis is performed to simulate the temperature field variations within the heart during the cooling process. Case 3 simulated the currently used cooling method in which the coolant is stagnant. Case 4 was a combination of Case 1 and Case 2. A linear thermoelasticity analysis was performed to assess the stresses applied on the heart during the cooling process. In Cases 5 through 9, the coolant solution was used for both internal and external cooling. For external circulation in Case 5 and Case 6, two inlets and two outlets were designed on the walls of the cooling container. Case 5 used laminar flows for coolant circulations inside and outside of the heart. Effects of turbulent flow on cooling of the heart were studied in Case 6. In Case 7, an additional inlet was designed on the cooling container wall to create a jet impinging the hot region of the heart’s wall. Unsteady periodic inlet velocities were applied in Case 8 and Case 9. The average temperature of the heart in Case 5 was +5.0oC after 1500 s of cooling. Multi-objective constrained optimization was performed for Case 5. Inlet velocities for two internal and one external coolant circulations were the three design variables for optimization. Minimizing the average temperature of the heart, wall shear stress and total volumetric flow rates were the three objectives. The only constraint was to keep von Mises stress below the ultimate tensile stress of the heart’s tissue.

THE IMPORTANCE OF MHC-INDEPENDENT NATURAL KILLER CELL RECEPTORS IN THE IMMUNOREGULATION OF MURINE PREGNANCY

Numerous leukocyte populations are essential for pregnancy success. Uterine natural killer (uNK) cells are chief amongst these leukocytes and represent a unique lineage with limited cytotoxicity but abundant angiokine production. They possess a distinct phenotype of activating and inhibitory receptors that recognize major histocompatibility complex (MHC) molecules, such as the killer immunoglobulin like receptors (KIRs; mouse Ly49), and MHC-independent activating receptors, including the aryl hydrocarbon receptor (AHR) and natural cytotoxicity receptor 1 (NCR1). While the roles of MHC-dependent receptors are widely addressed in pregnancy, MHC-independent receptors are relatively unstudied. This thesis investigated the roles of MHC-independent receptors in promotion of mouse pregnancy and characterized early leukocyte interactions in the presence and absence of NCR1. It was hypothesized that loss of MHC-independent receptors impairs uNK cell development resulting in aberrations in leukocyte function and decidual vasculature. Implantation sites from Ahr-/- and Ncr1Gfp/Gfp mice were assessed using whole mount in situ immunohistochemistry (WM-IHC) and histochemical techniques. Leukocyte interactions identified during preliminary WM-IHC studies were confirmed as immune synapses. The novel identification of immune synapses in early mouse pregnancy compelled further examination of leukocyte conjugates in wildtype C57BL/6 and Ncr1Gfp/Gfp mice. In Ahr-/- and Ncr1Gfp/Gfp mice, receptor loss resulted in reduced uNK cell diameters, impaired decidual vasculature, and failures in spiral artery remodeling. Ahr-/- mice had severe fertility deficits whereas Ncr1Gfp/Gfp mice had increased fetal resorption indicating differing receptor requirements in pregnancy success. NCR1 loss primarily affected uNK cell maturation and function as identified by alterations in granule ultrastructure, lytic protein expression, and angiokine production. Leukocyte conjugates were frequent in early C57BL/6 decidua basalis and included uNK cells conjugating first with antigen presenting cells and then with T cells. Overall conjugate formation was reduced in the absence of NCR1, but specific uNK cell conjugations were unaffected by receptor loss. While KIR-MHC interactions are associated with numerous pregnancy complications in humans, the role of other uNK cell receptors are not well characterized. These results illustrate the importance of MHC-independent receptors in uNK cell activation during early pregnancy in mice and encourage further studies of pregnancy complications that may occur independently of maternal KIR-MHC contributions.

On The Dirichlet Distribution

The Dirichlet distribution is a multivariate generalization of the Beta distribution. It is an important multivariate continuous distribution in probability and statistics. In this report, we review the Dirichlet distribution and study its properties, including statistical and information-theoretic quantities involving this distribution. Also, relationships between the Dirichlet distribution and other distributions are discussed. There are some different ways to think about generating random variables with a Dirichlet distribution. The stick-breaking approach and the Pólya urn method are discussed. In Bayesian statistics, the Dirichlet distribution and the generalized Dirichlet distribution can both be a conjugate prior for the Multinomial distribution. The Dirichlet distribution has many applications in different fields. We focus on the unsupervised learning of a finite mixture model based on the Dirichlet distribution. The Initialization Algorithm and Dirichlet Mixture Estimation Algorithm are both reviewed for estimating the parameters of a Dirichlet mixture. Three experimental results are shown for the estimation of artificial histograms, summarization of image databases and human skin detection.

Targeting the actin cytoskeleton in HER2+ metastatic cancer cells using a macrolide toxin

Breast and ovarian cancers are among the leading causes of cancer related deaths in women worldwide. In a subset of these cancers, dysregulation of the human epidermal growth factor receptor 2 (HER2) leads to overexpression of the receptor on the cell surface. Previous studies have found that these HER2+ cancers show high rates of progression to metastatic disease. Metastasis is driven by cytoskeletal rearrangements that produce filamentous actin (F-actin) based structures that penetrate and degrade extracellular matrix to facilitate tumour invasion. Advancements in targeted therapy have made F-actin an attractive target for the development of new cancer therapies. In this thesis, we tested the actin-depolymerizing macrolide toxin, Mycalolide B (MycB), as a potential warhead for a novel antibody drug conjugate (ADC) to target highly metastatic HER2+ breast and ovarian cancers. We found that MycB treatment of HER2+ breast (SKBR3, MDA-MB-453) and ovarian (SKOV3) cancer cells led to loss of viability (IC50 values ≤ 64 nM). Sub-lethal doses of MycB treatment caused potent suppression of leading edge protrusions, migration and invasion potential of HER2+ cancer cells (IC50 ≤ 32 nM). In contrast, other F-actin based processes such as receptor endocytosis were less sensitive to MycB treatment. MycB treatment skewed the size of endocytic vesicles, which may reflect defects in F-actin based vesicle motility or maturation. Given that HER2+ cancers have been effectively targeted by Trastuzumab and Trastuzumab-based ADCs, we tested the effects of a combination of Trastuzumab and MycB on cell migration and invasion. We found that MycB/ Trastuzumab combination treatments inhibited motility of SKOV3 cells to a greater degree than either treatment alone. Altogether, our results provide proof-of-principle that actin toxins such as MycB can be used as a novel class of warheads for ADCs to target and combat highly metastatic cancers.

Enhancement of the Cytotoxic Effect of Anticancer Agent by Cytochrome c Functionalised Hybrid Nanoparticles in Hepatocellular Cancer Cells

Treatment of hepatocellular cancer with chemotherapeutic agents has limited successin clinical practice and their efficient IC50 concentration would require extremely highdoses of drug administration which could not be tolerated due to systemic side effects.In order to potentiate the efficacy of anticancer agents we explored the potentialof co-treatment with pro-apoptotic Cytochrome c which activates the apoptoticpathway downstream of p53 that is frequently mutated in cancer. To this end weused hybrid iron oxide-gold nanoparticles as a drug delivery system to facilitate theinternalisation of Cytochrome c into cultured HepG2 hepatocellular carcinoma cells.Our results showed that Cytochrome c can be easily conjugated to the gold shell ofthe nanoparticles which are readily taken up by the cells. We used Cytochrome cin concentration (0.2μgmL-1) below the threshold required to induce apoptosis onits own. When the conjugate was administered to cells treated by doxorubicin, itsignificantly reduced its IC50 concentration from 9μgmL-1 to 3.5μgmL-1 as detectedby cell viability assay, and the efficiency of doxorubicin on decreasing viability ofHepG2 cells was significantly enhanced in the lower concentration range between0.01μgmL-1 to 5μgmL-1. The results demonstrate the potential of the application oftherapeutic proteins in activating the apoptotic pathway to complement conventionalchemotherapy to increase its efficacy. The application of hybrid iron oxide-goldnanoparticles can also augment the specificity of drug targeting and could serve as amodel drug delivery system for pro-apoptotic protein targeting and delivery.

Surface plasmon resonance biosensing: Approaches for screening and characterising antibodies for food diagnostics

Research in biosensing approaches as alternative techniques for food diagnostics for the detection of chemical contaminants and foodborne pathogens has increased over the last twenty years. The key component of such tests is the biorecognition element whereby polyclonal or monoclonal antibodies still dominate the market. Traditionally the screening of sera or cell culture media for the selection of polyclonal or monoclonal candidate antibodies respectively has been performed by enzyme immunoassays. For niche toxin compounds, enzyme immunoassays can be expensive and/or prohibitive methodologies for antibody production due to limitations in toxin supply for conjugate production. Automated, self-regenerating, chip-based biosensors proven in food diagnostics may be utilised as rapid screening tools for antibody candidate selection. This work describes the use of both single channel and multi-channel surface plasmon resonance (SPR) biosensors for the selection and characterisation of antibodies, and their evaluation in shellfish tissue as standard techniques for the detection of domoic acid, as a model toxin compound. The key advantages in the use of these biosensor techniques for screening hybridomas in monoclonal antibody production were the real time observation of molecular interaction and rapid turnaround time in analysis compared to enzyme immunoassays. The multichannel prototype instrument was superior with 96 analyses completed in 2h compared to 12h for the single channel and over 24h for the ELISA immunoassay. Antibodies of high sensitivity, IC50's ranging from 4.8 to 6.9ng/mL for monoclonal and 2.3-6.0ng/mL for polyclonal, for the detection of domoic acid in a 1min analysis time were selected. Although there is a progression for biosensor technology towards low cost, multiplexed portable diagnostics for the food industry, there remains a place for laboratory-based SPR instrumentation for antibody development for food diagnostics as shown herein.

Joint Analysis of Multiple Algorithms and Performance Measures

There has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and time complexity). Once one has developed an approach to a problem of interest, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Standard tests used for this purpose are able to consider jointly neither performance measures nor multiple competitors at once. The aim of this paper is to resolve these issues by developing statistical procedures that are able to account for multiple competing measures at the same time and to compare multiple algorithms altogether. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameters of such models, as usually the number of studied cases is very reduced in such comparisons. Data from a comparison among general purpose classifiers is used to show a practical application of our tests.

Stimuli-Responsive Microneedle Arrays For On-Demand Drug Delivery

Pulsatile, or “on-demand”, delivery systems have the capability to deliver a therapeutic molecule at the right time/site of action and in the right amount (1). Pulsatile delivery systems present multiple benefits over conventional dosage forms and provide higher patient compliance. The combination of stimuli-responsive materials with the drug delivery capabilities of hydrogel-forming MN arrays (2) opens an interesting area of research. In the present work we describe, a stimuli-responsive hydrogel-forming microneedle (MN) array that enable delivery of a clinically-relevant model drug (ibuprofen) upon application of UV radiation (Figure 1A). MN arrays were prepared using a micromolding technique using a polymer prepared from 2-hydroxyethyl methacrylate (HEMA) and ethylene glycol dimethacrylate (EGDMA) (Figure 1B). The arrays were loaded with up to 5% (w/w) ibuprofen included in a light-responsible conjugate (3,5-dimethoxybenzoin conjugate) (2). The presence of the conjugate inside the MN arrays was confirmed by Raman spectroscopy measurements. MN arrays were tested in vitro showing that they were able to deliver up to three doses of 50 mg of ibuprofen after application of an optical trigger (wavelength of 365 nm) over a long period of time (up to 160 hours) (Figure 1C and 1D). The work presented here is a probe of concept and a modified version of the system should be used as UV radiation is shown to be the major etiologic agent in the development of skin cancers. Consequently, for future applications of this technology an alternative design should be developed. Based on the previous research dealing with hydrogel forming MN arrays a suitable strategy will be to use hydrogel-forming MN arrays containing a backing layer made with the material described in this work as the drug reservoir (2). Finally, a porous layer of a material that blocks UV radiation should be included between the MN array and the drug reservoir. Therefore radiation can be applied to the system without reaching the skin surface. Therefore after modification, the system described here interesting properties as “on-demand” release system for prolonged periods of time. This technology has potential for use in “on-demand” delivery of a wide range of drugs in a variety of applications relevant to enhanced patient care.