Structural effect of cationic amphiphiles in diacetylenic photopolymerizable membranes

Liposomes have been an excellent option as drug delivery systems, since they are able of incorporating lipophobic and/or lipophilic drugs, reduce drug side effects, increase drug targeting, and control delivery. Also, in the last years, their use reached the field of gene therapy, as non-viral vectors for DNA delivery. As a strategy to increase system stability, the use of polymerizable phospholipids has been proposed in liposomal formulations. In this work, through differential scanning calorimetry (DSC) and electron spin resonance (ESR) of spin labels incorporated into the bilayers, we structurally characterize liposomes formed by a mixture of the polymerizable lipid diacetylenic phosphatidylcholine 1,2-bis(10,12-tricosadiynoyl)-sn-glycero-3-phosphocholine (DC8,9PC) and the zwitterionic lipid 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC), in a 1:1 molar ratio. It is shown here that the polymerization efficiency of the mixture (c.a. 60%) is much higher than that of pure DC8,9PC bilayers (c.a. 20%). Cationic amphiphiles (CA) were added, in a final molar ratio of 1:1:0.2 (DC8,9PC:DMPC:CA), to make the liposomes possible carriers for genetic material, due to their electrostatic interaction with negatively charged DNA. Three amphiphiles were tested, 1,2-dioleoyl-3-trimetylammonium-propane (DOTAP), stearylamine (SA) and trimetyl (2-miristoyloxietyl) ammonium chloride (MCL), and the systems were studied before and after UV irradiation. Interestingly, the presence of the cationic amphiphiles increased liposomes polymerization. MCL displaying the strongest effect. Considering the different structural effects the three cationic amphiphiles cause in DC8,9PC bilayers, there seem to be a correlation between the degree of DC8,9PC polymerization and the packing of the membrane at the temperature it is irradiated (gel phase). Moreover, at higher temperatures, in the bilayer fluid phase, more polymerized membranes are significantly more rigid. Considering that the structure and stability of liposomes at different temperatures can be crucial for DNA binding and delivery, we expect the study presented here contributes to the production of new carrier systems with potential applications in gene therapy. (C) 2012 Elsevier Ireland Ltd. All rights reserved.

Current issues regarding health surveillance of fluoride concentrations in foods

This study analyzed the position of the Federal (Brazil), State (Sao Paulo), and municipal (Bauru, Sao Paulo) governments, civil society representatives, the regulated sector, and research associations concerning issues with fluoride content in foods. Analysis of the interviews (N = 15) used a qualitative methodology (collective subject discourse theory). Various central ideas were identified, including the need for stronger health surveillance in monitoring and controlling fluoride levels, educational measures, and more research in the area. The study concludes that the health surveillance approach to fluoride levels in foods is necessary, but still incipient. There is a mismatch between research output and surveillance. Regulation alone does not suffice to solve all the issues. Health risk communication and health education measures need to be implemented. Issues with fluoride on food labels need further research for the intervention to be effective.

Aqueous dispersions of DMPG in low salt contain leaky vesicles

Aqueous dispersions of dimyristoyl phosphatidylglycerol (DMPG), at low ionic strength, display uncommon thermal behavior. Models for such behavior need to assign a form to the lipid aggregate. Although most studies accept the presence of lipid vesicles in the lipid gel and fluid phases, this is still controversial. With electron spin resonance (ESR) spectra of spin labels incorporated into DMPG aggregates, quantification of [C-14]sucrose entrapped by the aggregates, and viscosity measurements, we demonstrate the existence of leaky vesicles in dispersions of DMPG at low ionic strength, in both gel and fluid phases of the lipid. As a control system, the ubiquitous lipid dimyristoyl phosphatidylcholine (DMPC) was used. For DMPG in the gel phase, spin labeling only indicated the presence of lipid bilayers, strongly suggesting that DMPG molecules are organized as vesicles and not micelles or bilayer fragments (bicelles), as the latter has a non-bilayer structure at the edges. Quantification of [C-14]sucrose entrapping by DMPG aggregates revealed the presence of highly leaky vesicles. Due to the short hydrocarbon chains (C-14 atoms), DMPC vesicles were also found to be partially permeable to sucrose, but not as much as DMPG vesicles. Viscosity measurements, with the calculation of the intrinsic viscosiiy of the lipid aggregate, showed that DMPG vesicles are rather similar in the gel and fluid phases, and quite different from aggregates observed along the gel-fluid transition. Taken together, our data strongly supports that DMPG forms leaky vesicles at both gel and fluid phases. (C) 2011 Elsevier Ireland Ltd. All rights reserved.

Network-based stochastic semisupervised learning

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.

Musicians' working memory for tones, words, and pseudowords

Studies investigating factors that influence tone recognition generally use recognition tests, whereas the majority of the studies on verbal material use self-generated responses in the form of serial recall tests. In the present study we intended to investigate whether tonal and verbal materials share the same cognitive mechanisms, by presenting an experimental instrument that evaluates short-term and working memories for tones, using self-generated sung responses that may be compared to verbal tests. This paradigm was designed according to the same structure of the forward and backward digit span tests, but using digits, pseudowords, and tones as stimuli. The profile of amateur singers and professional singers in these tests was compared in forward and backward digit, pseudoword, tone, and contour spans. In addition, an absolute pitch experimental group was included, in order to observe the possible use of verbal labels in tone memorization tasks. In general, we observed that musical schooling has a slight positive influence on the recall of tones, as opposed to verbal material, which is not influenced by musical schooling. Furthermore, the ability to reproduce melodic contours (up and down patterns) is generally higher than the ability to reproduce exact tone sequences. However, backward spans were lower than forward spans for all stimuli (digits, pseudowords, tones, contour). Curiously, backward spans were disproportionately lower for tones than for verbal material-that is, the requirement to recall sequences in backward rather than forward order seems to differentially affect tonal stimuli. This difference does not vary according to musical expertise.

Nuts and seed: A natural yet dangerous foreign body

Rationale and aim: This paper has the object to present the impact of nuts' and seeds' injuries withdrawing data from the Susy Safe registry, highlighting that as for other foreign bodies the main item efficiently and substantially susceptible to changes to decrease the accidents' rates is the education of adults and children, that can be shared with parents both from pediatricians and general practitioners. Indeed labeling and age related warnings have also a fundamental relevance in prevention. Methods: The present study draws its data from the Susy Safe registry. Details on injuries are entered in the Susy Safe Web-registry through a standardized case report form, that includes information regarding: children age and gender, features of the object, circumstances of injury (presence of parents and activity) and hospitalization's details (lasting, complications and removal details). Cases are prospectively collected using the Susy Safe system from 06/2005; moreover, also information regarding past consecutive cases available in each centre adhering to the project have been entered in the Susy Safe registry. Results: Nuts and seeds are one of the most common food item retrieved in foreign bodies injuries in children. In Susy Safe registry they represent the 38% in food group, and almost the 10% in general cases. Trachea, bronchi and lungs were the main location of FB's retrieval, showing an incidence of 68%. Hospitalization occurred in 83% of cases, showing the major frequency for foreign bodies located in trachea. This location was also the principal site of complications, with a frequency of 68%. There were no significant associations between these outcomes and the age class of the children. The most common complications seen (22.4%) was bronchitis, followed by pneumonia (19.7%). Adult presence was recorded as positive in 71.2% of cases, showing an association (p value 0.009) between the adult supervision and the hospitalization outcome. On the contrary there was a non significant association between adult presence and the occurrence of complications. In 80.7% of cases, the incident happened while the child was eating. Among those cases, 88.6% interested trachea, lungs and bronchi. Conclusions: Food-related aspiration injuries are common events for young children, particularly under 4 years of age, and may lead to severe complication. There is a need to study in more depth specific characteristics of foreign bodies associated with increased hazard, such as size, shape, hardness or firmness, lubricity, pliability and elasticity, in order to better identify risky foods, and more precisely described the pathogenetic pathway. Parents are not adequately conscious and aware toward this risk; therefore, the number and severity of the injuries could be reduced by educating parents and children. Information about food safety should be included in all visits to pediatricians in order to make parents able to understand, select, and identify key characteristics of hazardous foods and better control the hazard level of various foods. Finally, preventive measures including warning labels on high-risk foods could be implemented. (C) 2012 Elsevier Ireland Ltd. All rights reserved.

Incorporating label dependency into the binary relevance framework for multi-label classification

In multi-label classification, examples can be associated with multiple labels simultaneously. The task of learning from multi-label data can be addressed by methods that transform the multi-label classification problem into several single-label classification problems. The binary relevance approach is one of these methods, where the multi-label learning task is decomposed into several independent binary classification problems, one for each label in the set of labels, and the final labels for each example are determined by aggregating the predictions from all binary classifiers. However, this approach fails to consider any dependency among the labels. Aiming to accurately predict label combinations, in this paper we propose a simple approach that enables the binary classifiers to discover existing label dependency by themselves. An experimental study using decision trees, a kernel method as well as Naive Bayes as base-learning techniques shows the potential of the proposed approach to improve the multi-label classification performance.

Interaction of miltefosine with intercellular membranes of stratum corneum and biomimetic lipid vesicles

Miltefosine (MT) is an alkylphospholipid approved for breast cancer metastasis and visceral leishmaniasis treatments, although the respective action mechanisms at the molecular level remain poorly understood. In this work, the interaction of miltefosine with the lipid component of stratum corneum (SC), the uppermost skin layer, was studied by electron paramagnetic resonance (EPR) spectroscopy of several fatty acid spin-labels. In addition, the effect of miltefosine on (i) spherical lipid vesicles of 1,2-dipalmitoyl-sn-glycero-3-phosphatidylcholine (DPPC) and (ii) lipids extracted from SC was also investigated, by EPR and time-resolved polarized fluorescence methods. In SC of neonatal Wistar rats, 4% (w/w) miltefosine give rise to a large increase of the fluidity of the intercellular membranes, in the temperature range from 6 to about 50 degrees C. This effect becomes negligible at temperatures higher that ca. 60 degrees C. In large unilamelar vesicles of DPPC no significant changes could be observed with a miltefosine concentration 25% molar, in close analogy with the behavior of biomimetic vesicles prepared with bovine brain ceramide, behenic acid and cholesterol. In these last samples, a 25 mol% molar concentration of miltefosine produced only a modest decrease in the bilayer fluidity. Although miltefosine is not a feasible skin permeation enhancer due to its toxicity, the information provided in this work could be of utility in the development of a MT topical treatment of cutaneous leishmaniasis. Published by Elsevier B.V.

Uma análise dos principais elementos influenciadores da tomada de decisão de compra de produtos de marca própria de supermercados

A adoção de marcas próprias no mix de produtos de supermercados é uma ação estratégica que alguns varejistas estão utilizando para melhorar sua competitividade no setor. No Brasil, a participação de mercado das marcas próprias ainda é pouco representativa, o que reflete a atitude dos consumidores diante desses produtos. A proposta deste trabalho foi avaliar os fatores que influenciam no comportamento do consumidor em relação aos produtos de marcas próprias de supermercados. Foi desenvolvida uma pesquisa de campo de caráter exploratório com abordagem quantitativa, por meio de questionário auto-administrado, o que possibilitou a coleta de opiniões de uma amostra de 983 clientes de supermercados. Os resultados obtidos demonstram que os respondentes não possuem uma imagem positiva dos produtos de marcas próprias, devido: à grande variação na qualidade dos produtos dentro das categorias e entre as categorias ofertadas, ao risco percebido, à ausência de comunicação efetiva sobre os produtos e à imagem de inferioridade transmitida pelos atributos das marcas próprias como embalagem, forma de exposição e política de preços. Dos fatores obtidos, pode-se concluir que a imagem da loja, a comunicação e preço, a qualidade e preço destacaram-se como os mais importantes para os respondentes e que exerceram maior influência em seu comportamento.

Plasma kinetics of an LDL-like nanoemulsion and lipid transfer to HDL in subjects with glucose intolerance

OBJECTIVE: Glucose intolerance is frequently associated with an altered plasma lipid profile and increased cardiovascular disease risk. Nonetheless, lipid metabolism is scarcely studied in normolipidemic glucose-intolerant patients. The aim of this study was to investigate whether important lipid metabolic parameters, such as the kinetics of LDL free and esterified cholesterol and the transfer of lipids to HDL, are altered in glucose-intolerant patients with normal plasma lipids. METHODS: Fourteen glucose-intolerant patients and 15 control patients were studied; none of the patients had cardiovascular disease manifestations, and they were paired for age, sex, race and co-morbidities. A nanoemulsion resembling a LDL lipid composition (LDE) labeled with 14C-cholesteryl ester and ³H-free cholesterol was intravenously injected, and blood samples were collected over a 24-h period to determine the fractional clearance rate of the labels by compartmental analysis. The transfer of free and esterified cholesterol, triglycerides and phospholipids from the LDE to HDL was measured by the incubation of the LDE with plasma and radioactivity counting of the supernatant after chemical precipitation of non-HDL fractions. RESULTS: The levels of LDL, non-HDL and HDL cholesterol, triglycerides, apo A1 and apo B were equal in both groups. The 14C-esterified cholesterol fractional clearance rate was not different between glucose-intolerant and control patients, but the ³H-free-cholesterol fractional clearance rate was greater in glucose-intolerant patients than in control patients. The lipid transfer to HDL was equal in both groups. CONCLUSION: In these glucose-intolerant patients with normal plasma lipids, a faster removal of LDE free cholesterol was the only lipid metabolic alteration detected in our study. This finding suggests that the dissociation of free cholesterol from lipoprotein particles occurs in normolipidemic glucose intolerance and may participate in atherogenic signaling.

A semi-supervised classification technique based on interacting forces

Semi-supervised learning is a classification paradigm in which just a few labeled instances are available for the training process. To overcome this small amount of initial label information, the information provided by the unlabeled instances is also considered. In this paper, we propose a nature-inspired semi-supervised learning technique based on attraction forces. Instances are represented as points in a k-dimensional space, and the movement of data points is modeled as a dynamical system. As the system runs, data items with the same label cooperate with each other, and data items with different labels compete among them to attract unlabeled points by applying a specific force function. In this way, all unlabeled data items can be classified when the system reaches its stable state. Stability analysis for the proposed dynamical system is performed and some heuristics are proposed for parameter setting. Simulation results show that the proposed technique achieves good classification results on artificial data sets and is comparable to well-known semi-supervised techniques using benchmark data sets.

QuMinS: fast and scalable querying, mining and summarizing multi-modal databases

Given a large image set, in which very few images have labels, how to guess labels for the remaining majority? How to spot images that need brand new labels different from the predefined ones? How to summarize these data to route the user’s attention to what really matters? Here we answer all these questions. Specifically, we propose QuMinS, a fast, scalable solution to two problems: (i) Low-labor labeling (LLL) – given an image set, very few images have labels, find the most appropriate labels for the rest; and (ii) Mining and attention routing – in the same setting, find clusters, the top-'N IND.O' outlier images, and the 'N IND.R' images that best represent the data. Experiments on satellite images spanning up to 2.25 GB show that, contrasting to the state-of-the-art labeling techniques, QuMinS scales linearly on the data size, being up to 40 times faster than top competitors (GCap), still achieving better or equal accuracy, it spots images that potentially require unpredicted labels, and it works even with tiny initial label sets, i.e., nearly five examples. We also report a case study of our method’s practical usage to show that QuMinS is a viable tool for automatic coffee crop detection from remote sensing images.

Sex Differences in Memory and Other Cognitive Abilities

The aim of the present thesis was to study sex differences in memory and other cognitive bilities in healthy adults. In Study I, participants performed a number of episodic memory tasks that were more or less verbal in nature. Results showed that women performed on a higher level than did men in the episodic memory tasks where it was possible to use verbal labels, whereas men performed on a higher level than did women in a visuospatial episodic memory task. In Study II, women’s advantage in face recognition was investigated.Results showed that women performed at a higher level than did men only in the recognition of other women’s faces. In Study III, sex differences in cognitive tasks as well as brain measures were investigated in healthy older adults. Results showed that only the sex differences in a motor task could, to some extent, be explained by sex differences in one of the brain measures. The findings, as well as possible explanations for these patterns of results, are discussed in a theoretical context.

Luminescent Systems for Genomics and Proteomics

The present PhD project was focused on the development of new tools and methods for luminescence-based techniques. In particular, the ultimate goal was to present substantial improvements to the currently available technologies for both research and diagnostic in the fields of biology, proteomics and genomics. Different aspects and problems were investigated, requiring different strategies and approaches. The whole work was thus divided into separate chapters, each based on the study of one specific aspect of luminescence: Chemiluminescence, Fluorescence and Electrochemiluminescence. CHAPTER 1, Chemiluminescence The work on luminol-enhancer solution lead to a new luminol solution formulation with 1 order of magnitude lower detection limit for HRP. This technology was patented with Cyanagen brand and is now sold worldwide for Western Blot and ELISA applications. CHAPTER 2, Fluorescescence The work on dyed-doped silica nanoparticles is marking a new milestone in the development of nanotechnologies for biological applications. While the project is still in progress, preliminary studies on model structures are leading to very promising results. The improved brightness of these nano-sized objects, their simple synthesis and handling, their low toxicity will soon turn them, we strongly believe, into a new generation of fluorescent labels for many applications. CHAPTER 3, Electrochemiluminescence The work on electrochemiluminescence produced interesting results that can potentially turn into great improvements from an analytical point of view. Ru(bpy)3 derivatives were employed both for on-chip microarray (Chapter 3.1) and for microscopic imaging applications (Chapter 3.2). The development of these new techniques is still under investigation, but the obtained results confirm the possibility to achieve the final goal. Furthermore the development of new ECL-active species (Chapter 3.3, 3.4, 3.5) and their use in these applications can significantly improve overall performances, thus helping to spread ECL as powerful analytical tool for routinary techniques. To conclude, the results obtained are of strong value to largely increase the sensitivity of luminescence techniques, thus fulfilling the expectation we had at the beginning of this research work.

Kernel Methods for Tree Structured Data

Machine learning comprises a series of techniques for automatic extraction of meaningful information from large collections of noisy data. In many real world applications, data is naturally represented in structured form. Since traditional methods in machine learning deal with vectorial information, they require an a priori form of preprocessing. Among all the learning techniques for dealing with structured data, kernel methods are recognized to have a strong theoretical background and to be effective approaches. They do not require an explicit vectorial representation of the data in terms of features, but rely on a measure of similarity between any pair of objects of a domain, the kernel function. Designing fast and good kernel functions is a challenging problem. In the case of tree structured data two issues become relevant: kernel for trees should not be sparse and should be fast to compute. The sparsity problem arises when, given a dataset and a kernel function, most structures of the dataset are completely dissimilar to one another. In those cases the classifier has too few information for making correct predictions on unseen data. In fact, it tends to produce a discriminating function behaving as the nearest neighbour rule. Sparsity is likely to arise for some standard tree kernel functions, such as the subtree and subset tree kernel, when they are applied to datasets with node labels belonging to a large domain. A second drawback of using tree kernels is the time complexity required both in learning and classification phases. Such a complexity can sometimes prevents the kernel application in scenarios involving large amount of data. This thesis proposes three contributions for resolving the above issues of kernel for trees. A first contribution aims at creating kernel functions which adapt to the statistical properties of the dataset, thus reducing its sparsity with respect to traditional tree kernel functions. Specifically, we propose to encode the input trees by an algorithm able to project the data onto a lower dimensional space with the property that similar structures are mapped similarly. By building kernel functions on the lower dimensional representation, we are able to perform inexact matchings between different inputs in the original space. A second contribution is the proposal of a novel kernel function based on the convolution kernel framework. Convolution kernel measures the similarity of two objects in terms of the similarities of their subparts. Most convolution kernels are based on counting the number of shared substructures, partially discarding information about their position in the original structure. The kernel function we propose is, instead, especially focused on this aspect. A third contribution is devoted at reducing the computational burden related to the calculation of a kernel function between a tree and a forest of trees, which is a typical operation in the classification phase and, for some algorithms, also in the learning phase. We propose a general methodology applicable to convolution kernels. Moreover, we show an instantiation of our technique when kernels such as the subtree and subset tree kernels are employed. In those cases, Direct Acyclic Graphs can be used to compactly represent shared substructures in different trees, thus reducing the computational burden and storage requirements.