Semi-synthetic minimal cells: Biochemical, physical, and technological aspects

The construction of synthetic cells is one of the major goals of bioengineering. The most successful approach consists in the encapsulation of biochemical materials (DNA, RNA, enzymes, etc.) inside lipid vesicles (liposomes), mimicking a cell structure. In this contribution, that also aims at introducing the reader to 'chemical synthetic biology,' we describe the current state of the art of 'semi-synthetic minimal cells' (SSMCs), namely, cell-like structures containing the minimal number of biological compounds that are required to reconstruct a function of interest. We will first describe how the concept of the minimal cell was originated and its relation with the theory of autopoiesis, then we review the most advanced results focused on genetic/metabolic networks inside liposomes. Next, we emphasize that relevance of physical aspects (too often neglected) that impact on the solute entrapment process, and finally we discuss new technological trends in SSMC research that will probably allow their future use in biotechnology. © 2013 Copyright © 2013 Elsevier Inc. All rights reserved.

Extração semi-automática de rodovias no espaço-objeto: uso integrado de um estéreo par de imagens aéreas e um MDT

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Orientação semi-automática de imagens CBERS usando rodovias como controle de campo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Extração semi-automática de rodovias em imagens digitais usando técnicas de correlação e o princípio de teste ativo

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Extração semi-automática de edificações com análise do modelo numérico de elevações

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Controle semi-ativo de vibrações com fluido magnetoreológico utilizando LMis e lógica nebulosa otimizada por algoritmo genético

Pós-graduação em Engenharia Mecânica - FEIS

Extração semi-automática da malha viária em imagens aéreas digitais de áreas rurais utilizando otimização por programação dinâmica no espaço objeto

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Lesões hepáticas de bovinos intoxicados experimentalmente por Senecio brasiliensis (Compositae): estudo semi-quantitativo e quantitativo da matriz extracelular e de células sinusoidais

Pós-graduação em Patologia - FMB

Preparation of refractory calcium aluminate cement using the sonochemical process

Calcium aluminate cements (CAC) were prepared using the sonochemical process, followed by heat treatment. A study was made of the action of ultrasonic waves and the influence of thermal treatment conditions on two initial molar compositions of 1:1 and 1:2 of calcia:alumina. The aqueous suspension containing the raw materials (A-50 alumina and CaO) was subjected to an ultrasonic bath, followed by drying and burning at 1000, 1200 and 1300 ºC. These cements were characterized by SEM, XRD and the mechanical strength was evaluated by splitting tensile tests, using commercial cement as a reference. Furthermore, the phases were semi-quantified using the Rietveld method. The results show that hydration and sonochemical action increased the reactivity of the raw materials during firing and that phase formation is dependent on the thermal treatment conditions. The CAC cements were obtained at temperatures at least 200 ºC lower than those used in conventional methods, indicating the potential of this route of synthesis.

Healing Process of Autogenous Bone Graft in Spontaneously Hypertensive Rats Treated With Losartan: An Immunohistochemical and Histomorphometric Study

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Combined active and semi-supervised learning using particle walking temporal dynamicscombined active and semi-supervised learning using particle walking temporal dynamics

Both Semi-Supervised Leaning and Active Learning are techniques used when unlabeled data is abundant, but the process of labeling them is expensive and/or time consuming. In this paper, those two machine learning techniques are combined into a single nature-inspired method. It features particles walking on a network built from the data set, using a unique random-greedy rule to select neighbors to visit. The particles, which have both competitive and cooperative behavior, are created on the network as the result of label queries. They may be created as the algorithm executes and only nodes affected by the new particles have to be updated. Therefore, it saves execution time compared to traditional active learning frameworks, in which the learning algorithm has to be executed several times. The data items to be queried are select based on information extracted from the nodes and particles temporal dynamics. Two different rules for queries are explored in this paper, one of them is based on querying by uncertainty approaches and the other is based on data and labeled nodes distribution. Each of them may perform better than the other according to some data sets peculiarities. Experimental results on some real-world data sets are provided, and the proposed method outperforms the semi-supervised learning method, from which it is derived, in all of them.

Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.

Particle competition and cooperation for semi-supervised learning with label noise

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Increased cyclooxygenase-2 and vascular endothelial growth factor protein expression is associated with canine prostatic carcionogenesis process

Background: COX-2 is one of the most important prostaglandin involved in urologic cancer and seems to be associated with tumor progression, invasion, and metastasis. In addition, several effects have been reported for VEGF, including inducing angiogenesis, promoting cell migration, and inhibiting apoptosis. COX2 and VEGF up-regulation have been reported in human prostate cancer. Due to the importance of canine natural model for prostate cancer, the aim of this study was to evaluate COX-2 and VEGF protein expression in canine carcinogenic process. Material and Methods: Seventy-four prostatic tissues from dogs were selected to be evaluated for protein expression by immunohistochemistry (IHC), including: 10 normal prostatic tissues, 20 benign prostatic hyperplasias (BPH), 25 proliferative inflammatory atrophies (PIA) and 20 prostatic carcinomas (PCa). COX-2 and VEGF were detected using the monoclonal antibody CX-294 (1:50 dilution, Dako Cytomation and sc-53463 (1:100 dilution, Santa Cruz), respectively. The immunolabelling was performed by a polymer method (Histofine, Nichirei Biosciences). All reaction included negative controls by omitting the primary antibody. The percentage of C-MYC, E-cadherin, and p63- positive cells per lesion was evaluated according to Prowatke et al. (2007). The samples were scored separately according to staining intensity and graded semi-quantitatively as negative, weakly positive (1), moderately positive, and strongly positive. The score was done in one 400 magnification field, considering only the lesion, since this was done in a TMA core of 1 mm. For statistical analyses, the immunostaining classifications were reduced to two categories: negative and positive. The negative category included negative and weakly positive staining. Chi-square or Fisher exact test was used to determine the association between the categorical variables. Results: The COX-2 protein expression was elevated in the cytoplasm of the canine PCa and PIA compared to normal prostate (p=0.002). VEGF protein expression was increased in 94.75% of the PCa and 100% of the PIA compared with to normal prostate (p = 0.001). No difference was found when compared normal prostate with BPH. Conclusions: This study has demonstrated that the carcinogenesis of canine prostatic tissue may be related to gain of COX-2 and VEGF protein expression.

Estudo da ação de derivados semi-sintéticos de curcumina em linhagens celulares de câncer humano

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)