Measuring shell resonances of spherical acoustic resonators

International audienceCoupling between gas and shell is a concern in the experiment used at LNE-CNAM to determine the Boltzmann constant kB by an acoustic method. As the walls of real resonators are not perfectly ridig, some perturbations occur in the frequency range of the acoustic resonances measured within helium gas contained in the caivity. As a contribution for a better understanding of the phenomenon, we have built an experiment to measure the shell modes of the spherical resonators in use in our laboratory. We report here a work in progress to assess these modes using a hammer blow method together with modal analysis. The study is carried out with air-filled, copper-walled, half-litre quasi-spherical resonator in the frequency range from 1 Hz to 20 kHz. Our results show that the shell modes expand into multiple resonances of similar modal shape, including the "breathing" mode. We confirm the observations reported in other works [4,6] of shell perturbations at other frequencies than the breathing frequency

Physician Time Management

Introduction: Time management is an essential skill set for physicians. The importance of time management is not routinely emphasized in undergraduate or graduate medical education curricula, often resulting in the development of poor time-management practices early in training. Improving time-management practices may lead to decreased stress, increased productivity, and improved well-being for physicians. Methods: This interactive workshop targeted trainees and junior faculty. It aimed to highlight common physician knowledge gaps with respect to cognitive limitations and to teach effective time-management strategies. It also aimed to educate learners about how time management may increase physician career satisfaction. The workshop included a detailed presentation with structured resources to reinforce skill development. Results: This workshop was given four times to 54 residents in two different training paradigms. Evaluations were based on a 4-point Likert scale (1 = Strongly Disagree, 4 = Strongly Agree). Overall, participants indicated that the workshop addressed an educational need (M = 3.72) and would recommend this workshop to a colleague (M = 3.83). Follow-up survey results at 4 months indicated that most workshop participants had noticed some degree of improved productivity and well-being, that only a small minority had not incorporated new elements of time management into routine practices. Discussion: This workshop offers an effective way to teach time-management strategies to physicians. Our results imply that this workshop meets an early career physician need by addressing a necessary skill set. Effective time-management skills may promote physician career sustainability

Quality assurance and assessment frameworks of biosystems engineering studies

Regulatory instruments at the national level to ensure high quality are crucial to achieve and maintain a regional hub of higher education in Malta. While The Malta Qualifications Council and the National Commission for Higher Education are established and operational, the legal framework to set up a quality assurance agency and a quality assurance and licensing framework is in place, but the legislations is still awaiting approval. The University of Malta has set up internal quality assurance structures, The Programme Validation Committee monitors, reviews and recommends programmes for approval by Senate, The formation of the INSTITUTE OF EARTH SYSTEMS will facilitate the means through which a Bio Systems Engineering course could be offered, Furthermore the recent establishment of a Maltese Chamber of Agrologists could in theory eventually take up the role to grant professional accreditation,peer-reviewe

Functional classification of proteins based on projection of amino acid sequences: application for prediction of protein kinase substrates

<p>Abstract</p> <p>Background</p> <p>The knowledge about proteins with specific interaction capacity to the protein partners is very important for the modeling of cell signaling networks. However, the experimentally-derived data are sufficiently not complete for the reconstruction of signaling pathways. This problem can be solved by the network enrichment with predicted protein interactions. The previously published <it>in silico </it>method PAAS was applied for prediction of interactions between protein kinases and their substrates.</p> <p>Results</p> <p>We used the method for recognition of the protein classes defined by the interaction with the same protein partners. 1021 protein kinase substrates classified by 45 kinases were extracted from the Phospho.ELM database and used as a training set. The reasonable accuracy of prediction calculated by leave-one-out cross validation procedure was observed in the majority of kinase-specificity classes. The random multiple splitting of the studied set onto the test and training set had also led to satisfactory results. The kinase substrate specificity for 186 proteins extracted from TRANSPATH^®database was predicted by PAAS method. Several kinase-substrate interactions described in this database were correctly predicted. Using the previously developed ExPlain™ system for the reconstruction of signal transduction pathways, we showed that addition of the newly predicted interactions enabled us to find the possible path between signal trigger, TNF-alpha, and its target genes in the cell.</p> <p>Conclusions</p> <p>It was shown that the predictions of protein kinase substrates by PAAS were suitable for the enrichment of signaling pathway networks and identification of the novel signaling pathways. The on-line version of PAAS for prediction of protein kinase substrates is freely available at <url>http://www.ibmc.msk.ru/PAAS/</url>.</p

GAIA: a gram-based interaction analysis tool – an approach for identifying interacting domains in yeast

<p>Abstract</p> <p>Background</p> <p>Protein-Protein Interactions (PPIs) play important roles in many biological functions. Protein domains, which are defined as independently folding structural blocks of proteins, physically interact with each other to perform these biological functions. Therefore, the identification of Domain-Domain Interactions (DDIs) is of great biological interests because it is generally accepted that PPIs are mediated by DDIs. As a result, much effort has been put on the prediction of domain pair interactions based on computational methods. Many DDI prediction tools using PPIs network and domain evolution information have been reported. However, tools that combine the primary sequences, domain annotations, and structural annotations of proteins have not been evaluated before.</p> <p>Results</p> <p>In this study, we report a novel approach called Gram-bAsed Interaction Analysis (GAIA). GAIA extracts peptide segments that are composed of fixed length of continuous amino acids, called n-grams (where n is the number of amino acids), from the annotated domain and DDI data set in <it>Saccharomyces cerevisiae </it>(budding yeast) and identifies a list of n-grams that may contribute to DDIs and PPIs based on the frequencies of their appearance. GAIA also reports the coordinate position of gram pairs on each interacting domain pair. We demonstrate that our approach improves on other DDI prediction approaches when tested against a gold-standard data set and achieves a true positive rate of 82% and a false positive rate of 21%. We also identify a list of 4-gram pairs that are significantly over-represented in the DDI data set and may mediate PPIs.</p> <p>Conclusion</p> <p>GAIA represents a novel and reliable way to predict DDIs that mediate PPIs. Our results, which show the localizations of interacting grams/hotspots, provide testable hypotheses for experimental validation. Complemented with other prediction methods, this study will allow us to elucidate the interactome of cells.</p

Critical assessment of sequence-based protein-protein interaction prediction methods that do not require homologous protein sequences

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions underlie many important biological processes. Computational prediction methods can nicely complement experimental approaches for identifying protein-protein interactions. Recently, a unique category of sequence-based prediction methods has been put forward - unique in the sense that it does not require homologous protein sequences. This enables it to be universally applicable to all protein sequences unlike many of previous sequence-based prediction methods. If effective as claimed, these new sequence-based, universally applicable prediction methods would have far-reaching utilities in many areas of biology research.</p> <p>Results</p> <p>Upon close survey, I realized that many of these new methods were ill-tested. In addition, newer methods were often published without performance comparison with previous ones. Thus, it is not clear how good they are and whether there are significant performance differences among them. In this study, I have implemented and thoroughly tested 4 different methods on large-scale, non-redundant data sets. It reveals several important points. First, significant performance differences are noted among different methods. Second, data sets typically used for training prediction methods appear significantly biased, limiting the general applicability of prediction methods trained with them. Third, there is still ample room for further developments. In addition, my analysis illustrates the importance of complementary performance measures coupled with right-sized data sets for meaningful benchmark tests.</p> <p>Conclusions</p> <p>The current study reveals the potentials and limits of the new category of sequence-based protein-protein interaction prediction methods, which in turn provides a firm ground for future endeavours in this important area of contemporary bioinformatics.</p