Wind turbine generator rotor blade concepts with low cost potential

Four processed for producing blades are examined. Two use filament winding techniques and two involve filling a mold or form to produce all or part of a blade. The processes are described and a comparison is made of cost, material properties, design and free vibration characteristics. Conclusions are made regarding the feasibility of each process to produce low cost, structurally adequate blades

How water-mediated hydrogen bonds affect chlorophyll a/b selectivity in Water-Soluble Chlorophyll Protein

The Water-Soluble Chlorophyll Protein (WSCP) of Brassicaceae is a remarkably stable tetrapyrrole- binding protein that, by virtue of its simple design, is an exceptional model to investigate the interactions taking place between pigments and their protein scaffold and how they affect the photophysical properties and the functionality of the complexes. We investigated variants of WSCP from Lepidium virginicum (Lv) and Brassica oleracea (Bo), reconstituted with Chlorophyll (Chl) b, to determine the mechanisms by which the different Chl binding sites control their Chl a/b specificities. A combined Raman and crystallographic investigation has been employed, aimed to characterize in detail the hydrogen-bond network involving the formyl group of Chl b. The study revealed a variable degree of conformational freedom of the hydrogen bond networks among the WSCP variants, and an unexpected mixed presence of hydrogen-bonded and not hydrogen-bonded Chls b in the case of the L91P mutant of Lv WSCP. These findings helped to refine the description of the mechanisms underlying the different Chl a/b specificities of WSCP versions, highlighting the importance of the structural rigidity of the Chl binding site in the vicinity of the Chl b formyl group in granting a strong selectivity to binding sites

Cidadania e participação social

Pois é exatamente aqui que se coloca o presente livro: é um grupo de cidadãos/ãs que decidem dizer sua palavra, na nova “agorá”, que é a discussão e construção teórica que se realiza nas academias e nas práticas sociais. Esse livro não é somente “fala”: ele é ação, ele é dimensão essencial na construção da cidadania através da participação consciente e crítica dos diversos atores/ autores/as que a ABRAPSO vem congregando, incentivando e fundamentando para a construção de uma sociedade eticamente justa, economicamente equitativa, politicamente participativa, culturalmente plural, socialmente democrática e solidária

A framework for protein structure classification and identification of novel protein structures

BACKGROUND: Protein structure classification plays a central role in understanding the function of a protein molecule with respect to all known proteins in a structure database. With the rapid increase in the number of new protein structures, the need for automated and accurate methods for protein classification is increasingly important. RESULTS: In this paper we present a unified framework for protein structure classification and identification of novel protein structures. The framework consists of a set of components for comparing, classifying, and clustering protein structures. These components allow us to accurately classify proteins into known folds, to detect new protein folds, and to provide a way of clustering the new folds. In our evaluation with SCOP 1.69, our method correctly classifies 86.0%, 87.7%, and 90.5% of new domains at family, superfamily, and fold levels. Furthermore, for protein domains that belong to new domain families, our method is able to produce clusters that closely correspond to the new families in SCOP 1.69. As a result, our method can also be used to suggest new classification groups that contain novel folds. CONCLUSION: We have developed a method called proCC for automatically classifying and clustering domains. The method is effective in classifying new domains and suggesting new domain families, and it is also very efficient. A web site offering access to proCC is freely available a

Improving the performance of DomainDiscovery of protein domain boundary assignment using inter-domain linker index

BACKGROUND: Knowledge of protein domain boundaries is critical for the characterisation and understanding of protein function. The ability to identify domains without the knowledge of the structure – by using sequence information only – is an essential step in many types of protein analyses. In this present study, we demonstrate that the performance of DomainDiscovery is improved significantly by including the inter-domain linker index value for domain identification from sequence-based information. Improved DomainDiscovery uses a Support Vector Machine (SVM) approach and a unique training dataset built on the principle of consensus among experts in defining domains in protein structure. The SVM was trained using a PSSM (Position Specific Scoring Matrix), secondary structure, solvent accessibility information and inter-domain linker index to detect possible domain boundaries for a target sequence. RESULTS: Improved DomainDiscovery is compared with other methods by benchmarking against a structurally non-redundant dataset and also CASP5 targets. Improved DomainDiscovery achieves 70% accuracy for domain boundary identification in multi-domains proteins. CONCLUSION: Improved DomainDiscovery compares favourably to the performance of other methods and excels in the identification of domain boundaries for multi-domain proteins as a result of introducing support vector machine with benchmark_2 dataset

Periconceptional use of folic acid and risk of miscarriage – Findings of Oral Cleft Prevention Program in Brazil

We report on the risk of miscarriage due to high dosage periconceptional folic acid (FA) supplementation from a double blind randomized clinical trial for prevention of orofacial clefts in Brazil. The miscarriage rate was 14.2% in the low dose FA group (0.4 mg per day) and 11.3% for the high dose (4 mg per day) group (p=0.4877); the population miscarriage rate is 14%. These results indicate that high dose FA does not increase miscarriage risk in this population and add further information to the literature on the safety of high FA supplementation for prevention of birth defect recurrence

Automatic structure classification of small proteins using random forest

<p>Abstract</p> <p><b>Background</b></p> <p>Random forest, an ensemble based supervised machine learning algorithm, is used to predict the SCOP structural classification for a target structure, based on the similarity of its structural descriptors to those of a template structure with an equal number of secondary structure elements (SSEs). An initial assessment of random forest is carried out for domains consisting of three SSEs. The usability of random forest in classifying larger domains is demonstrated by applying it to domains consisting of four, five and six SSEs.</p> <p><b>Result</b>s</p> <p>Random forest, trained on SCOP version 1.69, achieves a predictive accuracy of up to 94% on an independent and non-overlapping test set derived from SCOP version 1.73. For classification to the SCOP <it>Class, Fold, Super-family </it>or <it>Family </it>levels, the predictive quality of the model in terms of Matthew's correlation coefficient (MCC) ranged from 0.61 to 0.83. As the number of constituent SSEs increases the MCC for classification to different structural levels decreases.</p> <p>Conclusions</p> <p>The utility of random forest in classifying domains from the place-holder classes of SCOP to the true <it>Class, Fold, Super-family </it>or <it>Family </it>levels is demonstrated. Issues such as introduction of a new structural level in SCOP and the merger of singleton levels can also be addressed using random forest. A real-world scenario is mimicked by predicting the classification for those protein structures from the PDB, which are yet to be assigned to the SCOP classification hierarchy.</p