Strand Analysis, a free online program for the computational identification of the best RNA interference (RNAi) targets based on Gibbs free energy

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

Cloning and recombinant expression of a crustin-like gene from Chinese shrimp, Fenneropenaeus chinensis

Antimicrobial peptides or proteins (AMPs) are proved to be one of the most important humoral factors to resist pathogen infection. As an antimicrobial protein, crustin had been described in invertebrates as a component of the innate immune system. A crustin-like gene (CruFc) was cloned from haemocytes of Chinese shrimp Fenneropenaeus chinensis by 3' and 5'-RACE PCR. The full-length cDNA consists of 523 with 405 bp open reading frame encoding 134 amino acids and the deduced peptide contains a putative signal peptide of 17 amino acids. The sequence also contains a whey-acidic protein (WAP) domain at the C-terminal. Transcripts of CruFc were mainly detected in haemocytes and gill by RT-PCR analysis. In addition, another full-length cDNA named CshFc was also cloned from haemocytes of Chinese shrimp and its inferred amino acid sequence lacks the WAP-type 'four-disulfide core' domain. The fusion proteins containing CruFc and CshFc were, respectively, produced and the antimicrobial assays revealed that the recombinant CruFc could inhibit the growth of grain-positive bacteria in vitro but the recombinant CshFc could not inhibit at the same conditions. The difference of antimicrobial activity between recombinant CruFc and CshFc provides the evidence that the four-disulfide core domain of crustin may play an important role in its biological function. (c) 2006 Elsevier B.V. All rights reserved.

Analysis of a thermal system through remote laboratories

This paper describes the experiences using remote laboratories for thorough analysis of a thermal system, including disturbances. Remote laboratories for education in subjects of control, is a common resorted method, used by universities. This method is applied to offer a flexible service in schedules so as to obtain greater and better results of available resources. Remote laboratories have been used for controlling physical devices remotely. Furthermore, remote labs have been used for transfer function identification of real equipment. Nevertheless, remote analyses of disturbances have not been done. The aim of this contribution is thereby to apply the experience of remote laboratories in the study of disturbances. Some experiments are carried out to demonstrate the effectiveness in using remote laboratories for complete analysis of a thermal system. Considering the remote access to thermal system, “Sistema de Laboratorios a Distancia” (SLD) was used.

Caractérisation des effets de l’hyperglycémie chronique dans la choroïde d’yeux diabétiques

But: Le diabète est un problème important de santé publique et la complication oculaire la plus commune est la rétinopathie diabétique (RD). Certaines études indiquent que la choroïde des patients diabétiques est affectée sans signe apparent de RD. Notre hypothèse est que l’élévation du stress oxydant liée à l’hyperglycémie chronique affecte la fonction choroïdienne à un stade précoce de la RD. Nous proposons d’étudier la glycolyse, le métabolisme mitochondrial, le stress nitrosatif et la méthylation de l’ADN ainsi que de caractériser les modifications histologiques dans la choroïde diabétique. Méthodes: L’expression des gènes/protéines associés à la glycolyse, au métabolisme mitochondrial et à la production de l’oxyde nitrique a été comparée par profilage génique et immunobuvardage Western entre les choroïdes saines et diabétiques. Les niveaux globaux de méthylation et d’hydroxyméthylation de l’ADN ont été quantifiés par immunoslot blot et HPLC-MS/MS dans ces tissus. Enfin, des coupes tissulaires d’yeux de donneurs sains ou diabétiques avec RD non proliférante (RDNP) ou proliférante (RDP) ont été colorées au trichrome de Masson et au Weigert. L’épaisseur de la choroïde et de la membrane de Bruch, ainsi que la densité et le diamètre des vaisseaux sanguins choroïdiens ont été analysés. Résultats: Nos résultats montrent une dérégulation de l’expression de certains transcrits de la choroïde diabétique, mais peu de différences au niveau de l’expression protéique des cibles validées. Le niveau global de méthylation de l’ADN est similaire entre les donneurs sains et diabétiques. Nos analyses histologiques démontrent une diminution de l’épaisseur de la choroïde et une dégénérescence des choriocapillaires et des veines/veinules chez les donneurs diabétiques atteints de RDP. Conclusions: L’étude de la choroïde est importante, car l’atteinte de ce tissu a de graves répercussions sur la fonction rétinienne. L’identification de cibles dans la choroïde ouvre de nouvelles perspectives pour un traitement préventif de la RD.

An investigation of the stress-buffering effects of social support in the occupational stress process as a function of team identification

This study explored how the social context influences the stress-buffering effects of social support on employee adjustment. It was anticipated that the positive relationship between support from colleagues and employee adjustment would be more marked for those strongly identifying with their work team. Furthermore, as part of a three-way interactive effect, it was predicted that high identification would increase the efficacy of coworker support as a buffer of two role stressors (role overload and role ambiguity). One hundred and 55 employees recruited from first-year psychology courses enrolled at two Australian universities were surveyed. Hierarchical multiple regression analyses revealed that the negative main effect of role ambiguity on job satisfaction was significant for those employees with low levels of team identification, whereas high team identifiers were buffered from the deleterious effect of role ambiguity on job satisfaction. There also was a significant interaction between coworker support and team identification. The positive effect of coworker support on job satisfaction was significant for high team identifiers, whereas coworker support was not a source of satisfaction for those employees with low levels of team identification. A three-way interaction emerged among the focal variables in the prediction of psychological well-being, suggesting that the combined benefits of coworker support and team identification under conditions of high demand may be limited and are more likely to be observed when demands are low.

Acoustic identification of 12 species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Frequency response function based damage identification using principal component analysis and pattern recognition technique

Pattern recognition is a promising approach for the identification of structural damage using measured dynamic data. Much of the research on pattern recognition has employed artificial neural networks (ANNs) and genetic algorithms as systematic ways of matching pattern features. The selection of a damage-sensitive and noise-insensitive pattern feature is important for all structural damage identification methods. Accordingly, a neural networks-based damage detection method using frequency response function (FRF) data is presented in this paper. This method can effectively consider uncertainties of measured data from which training patterns are generated. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for the actual damage localization and quantification using recognized damage patterns from the algorithm. In civil engineering applications, the measurement of dynamic response under field conditions always contains noise components from environmental factors. In order to evaluate the performance of the proposed strategy with noise polluted data, noise contaminated measurements are also introduced to the proposed algorithm. ANNs with optimal architecture give minimum training and testing errors and provide precise damage detection results. In order to maximize damage detection results, the optimal architecture of ANN is identified by defining the number of hidden layers and the number of neurons per hidden layer by a trial and error method. In real testing, the number of measurement points and the measurement locations to obtain the structure response are critical for damage detection. Therefore, optimal sensor placement to improve damage identification is also investigated herein. A finite element model of a two storey framed structure is used to train the neural network. It shows accurate performance and gives low error with simulated and noise-contaminated data for single and multiple damage cases. As a result, the proposed method can be used for structural health monitoring and damage detection, particularly for cases where the measurement data is very large. Furthermore, it is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy under varying levels of damage.

Analysis of proteins with the `hot dog' fold: Prediction of function and identification of catalytic residues of hypothetical proteins

Background: The hot dog fold has been found in more than sixty proteins since the first report of its existence about a decade ago. The fold appears to have a strong association with fatty acid biosynthesis, its regulation and metabolism, as the proteins with this fold are predominantly coenzyme A-binding enzymes with a variety of substrates located at their active sites. Results: We have analyzed the structural features and sequences of proteins having the hot dog fold. This study reveals that though the basic architecture of the fold is well conserved in these proteins, significant differences exist in their sequence, nature of substrate and oligomerization. Segments with certain conserved sequence motifs seem to play crucial structural and functional roles in various classes of these proteins. Conclusion: The analysis led to predictions regarding the functional classification and identification of possible catalytic residues of a number of hot dog fold-containing hypothetical proteins whose structures were determined in high throughput structural genomics projects.

Examining the Effectiveness of Discriminant Function Analysis and Cluster Analysis in Species Identification of Male Field Crickets Based on Their Calling Songs

Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6-7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification.

Identification of a novel relish homolog in Chinese shrimp Fenneropenaeus chinensis and its function in regulating the transcription of antimicrobial peptides

Penaeid shrimp, as an invertebrate, relies on the innate immunity to oppose the microbial invaders. Antimicrobial peptides (AMP) are an integral component of the innate immune system in most organisms and function as an early first line of defense against pathogens, but the knowledge about the pathways to regulate the shrimp AMP gene expression is still absent up to date. In the current study, a Relish homolog (FcRelish) was cloned from Chinese shrimp Fenneropenaeus chinensis. The full length cDNA of FcRelish consists of 2157 bp, including 1512 bp open reading frame, encoding 504 amino acids. The predicted molecular weight of FcRelish is 57 kDa, and the theoretical PI is 7.00. Spatial expression profiles showed that FcRelish had the highest expression levels in the hemocytes and lymphoid organ. Both Vibrio anguillarium and Micrococcus lysodeikticus stimulation to shrimp can affect the transcription profile of FcRelish. Silencing of FcRelish through DsRNA interference can greatly change the transcription profile of AMP. Therefore, we suggest that FcRelish identified in the present study is closely related to the transcription of AMP, and then we inferred that Imd pathway might exist in shrimp. (C) 2009 Elsevier Ltd. All rights reserved.

Identification, expression, function and localization of a DUF985 domain-containing hypothetical gene from amphioxus Branchiostoma belcheri

The progress in genome sequencing has led to an increasing submission of uncharacterized hypothetical genes with the domain of unknown function, DUF985, in GenBank, and none of these genes is related to a known protein. We therefore underwent an experimental study to identify the function of a DUF985 domain-containing hypothetical gene BbDUF985 (GenBank Accession No. AY273818) isolated from amphioxus Branchiostoma belcheri (B. belcheri). BbDUF985 was successfully expressed in both prokaryotic and eukaryotic systems, and its recombinant proteins expressed in both systems definitely exhibited an activity of phosphoglucose isomerase (PGI). Both tissue-section in situ hybridization and immunohistochemistry demonstrated that BbDUF985 was expressed in a tissue-specific manner, with most abundant levels in the hepatic caecum and ovary. In CHO cells transfected with the expression plasmid pEGFP-N1/BbDUF985, the fusion protein was targeted in the cytoplasm of CHO cells, suggesting that BbDUF985 is a cytosolic protein. In contrast, Western blotting indicated that BbDUF985 was also present in amphioxus humoral fluids, suggesting that it exists as a secreted protein as well. Our study provided a framework for further understanding the biochemical properties and physiological function of DUF985-containing hypothetical proteins in other species. (c) 2008 Elsevier Inc. All rights reserved.

Identification and Development of Enzymes Associated With the Sperm Inner Acrosomal Membrane and Their Function during Fertilization

In order for mammalian fertilization to transpire, spermatozoa must transit through the female reproductive tract and penetrate the outer investments of the oocyte: the cumulus oophorus and the zona pellucida. In order to penetrate the oocyte, spermatozoa must undergo the acrosome reaction. The acrosome reaction results in the exposure of the inner acrosomal membrane (IAM) and proteins that coat it to the extracellular environment. After the acrosome reaction, the IAM becomes the leading edge of spermatozoa undergoing progressive movement. Thus the enzymes which effect lysis of the oocyte investments ought to be located on the IAM. An objective of this study was to identify and characterize enzymatic activity detected on the IAM and provide evidence that they play a role in fertilization. This study also describes procedures for fractionating spermatozoa and isolating the IAM and proteins on its intra- and extra-vesicular surfaces, and describes their development during male gametogenesis. Since the IAM is exposed to the extracellular environment and oviductal milieu after the acrosome reaction, this study also sought to characterize interactions and relationships between factors in the oviductal environment and the enzymes identified on the IAM. The data presented provide evidence that MMP2 and acrosin are co-localized on the IAM, originate from the Golgi apparatus in gametogenesis, and suggest they cooperate in their function. Their localization and results of in vitro fertilization suggests they have a function in zona pellucida penetration. The data also provide evidence that plasminogen, originating from the oviductal epithelium and/or cumulus-oocyte complex, is present in the immediate environment of sperm-egg initial contact and penetration. Additionally, plasminogen interacts with MMP2 and enhances its enzymatic action on the IAM. The data also provide evidence that MMP2 has an important function in penetration of the cumulus oophorus. Holistically, this thesis provides evidence that enzymes on the IAM, originating from the Golgi apparatus in development, have an important function in penetration of the outer investments of the oocyte, and are aided in penetration by plasminogen in the female reproductive tract.

Two-stage mixed discrete-continuous identification of radial basis function (RBF) neural models for nonlinear systems

The identification of nonlinear dynamic systems using radial basis function (RBF) neural models is studied in this paper. Given a model selection criterion, the main objective is to effectively and efficiently build a parsimonious compact neural model that generalizes well over unseen data. This is achieved by simultaneous model structure selection and optimization of the parameters over the continuous parameter space. It is a mixed-integer hard problem, and a unified analytic framework is proposed to enable an effective and efficient two-stage mixed discrete-continuous; identification procedure. This novel framework combines the advantages of an iterative discrete two-stage subset selection technique for model structure determination and the calculus-based continuous optimization of the model parameters. Computational complexity analysis and simulation studies confirm the efficacy of the proposed algorithm.