Does Familial Non-Medullary Thyroid Cancer Adversely Affect Survival?

Background: Familial non-medullary thyroid cancer (FNMTC) is associated with a higher rate of multifocality and a higher recurrence rate than sporadic thyroid cancer. However, the effect of FNMTC on life expectancy is unknown. Material and Methods: Using data from our FNMTC database, we calculated life expectancy and survival rates after diagnosis of FNMTC and compared the results with the rates for unaffected family members and for the standard US population. Overall life expectancy and survival rates were calculated using the Kaplan-Meier method. We compared patients from families with 2 affected members with patients from families with ≥3 affected members. We also compared patients diagnosed in a known familial setting (index cases and subsequent cases) with patients diagnosed before the familial setting was recognized. Results: There were 139 affected patients with 757 unaffected family members. The mean age at diagnosis was 40.8 ± 13.9 years and the mean follow-up time was 9.4 ± 11.7 years. Ten patients died of thyroid cancer during follow-up. The life expectancy of patients with FNMTC was similar to that of their unaffected family members. Survival was significantly shorter for patients with 3 or more affected family members, for patients diagnosed before the familial setting was recognized, and for patients with anaplastic cancer. Conclusions: Our results suggest that FNMTC may be more aggressive than sporadic thyroid cancer, particularly in families with 3 or more affected members. However, when recognized and treated appropriately, it does not significantly shorten the overall life expectancy of the affected patient

Backbone and side chain 1H, 15N and 13C assignments for a thiol-disulphide oxidoreductase from the Antarctic bacterium Pseudoalteromonas haloplanktis TAC125

Enzymes produced by psychrophilic organisms have successfully overcome the low temperature challenge and evolved to maintain high catalytic rates in their permanently cold environments. As an initial step in our attempt to elucidate the cold-adaptation strategies used by these enzymes we report here the 1H, 15N and 13C assignments for the reduced form of a thiol-disulphide oxidoreductase from the Antarctic bacterium Pseudoalteromonas haloplanktis TAC125.The NMR spectrometers are part of The National NMR Network (REDE/1517/RMN/2005), supported by ‘‘Programa Operacional Ciência e Inovação (POCTI) 2010’’ and Fundação para a Ciência e a Tecnologia (FCT). This work was funded by FCT, POCTI and FEDER; Projects POCI/BIA-PRO/57263/2004 and PTDC/BIO/70806/2006. TC is holder of a long term EMBO fellowship. MM is thankful to the Fundação para a Ciência e Tecnologia for its support through Programa Ciência 2007.info:eu-repo/semantics/publishedVersio

Teaching Tip: Teaching About Ambiguity in Analytics: A Student-Centered Semester-Long Project to Raise Awareness of Ambiguity by Predicting Student Exam Performance

The growing use of analytics has increased the demand for more highly data literate graduates. Awareness of ambiguity in data has been suggested as a new data literacy skill. Here, we describe a student-centered semester-long project that can be used to teach this skill in an introductory analytics or database course. The project requires students to anticipate and collect survey data about themselves and their fellow students that can be used to predict student exam performance later in the course. We summarize relevant prior research on ambiguity, describe the project in which ambiguity is explained and applied, present a preliminary analysis of the lesson’s impact on student awareness of ambiguity, and discuss implications and future research

SimShiftDB; local conformational restraints derived from chemical shift similarity searches on a large synthetic database

We present SimShiftDB, a new program to extract conformational data from protein chemical shifts using structural alignments. The alignments are obtained in searches of a large database containing 13,000 structures and corresponding back-calculated chemical shifts. SimShiftDB makes use of chemical shift data to provide accurate results even in the case of low sequence similarity, and with even coverage of the conformational search space. We compare SimShiftDB to HHSearch, a state-of-the-art sequence-based search tool, and to TALOS, the current standard tool for the task. We show that for a significant fraction of the predicted similarities, SimShiftDB outperforms the other two methods. Particularly, the high coverage afforded by the larger database often allows predictions to be made for residues not involved in canonical secondary structure, where TALOS predictions are both less frequent and more error prone. Thus SimShiftDB can be seen as a complement to currently available methods

Detection of unrealistic molecular environments in protein structures based on expected electron densities

Understanding the relationship between protein structure and biological function is a central theme in structural biology. Advances are severely hampered by errors in experimentally determined protein structures. Detection and correction of such errors is therefore of utmost importance. Electron densities in molecular structures obey certain rules which depend on the molecular environment. Here we present and discuss a new approach that relates electron densities computed from a structural model to densities expected from prior observations on identical or closely related molecular environments. Strong deviations of computed from expected densities reveal unrealistic molecular structures. Most importantly, structure analysis and error detection are independent of experimental data and hence may be applied to any structural model. The comparison to state-of-the-art methods reveals that our approach is able to identify errors that formerly remained undetected. The new technique, called RefDens, is accessible as a public web service at http://refdens.services.came.sbg.ac.at

Bioinformatics Methods for NMR Chemical Shift Data

Nuclear magnetic resonance spectroscopy (NMR) is one of the most important methods for measuring the three-dimensional structure of biomolecules. Despite major progress in the NMR methodology, the solution of a protein structure is still a tedious and time-consuming task. The goal of this thesis is to develop bioinformatics methods which may strongly accelerate the NMR process. This work concentrates on a special type of measurements, the so-called chemical shifts. Chemical shifts are routinely measured at the beginning of a structure resolution process. As all data from the laboratory, chemical shifts may be error-prone, which might complicate or even circumvent the use of this data. Therefore, as the first result of the thesis, we present CheckShift, a method which automatically corrects a frequent error in NMR chemical shift data. However, the main goal of this thesis is the extraction of structural information hidden in chemical shifts. SimShift was developed as a first step in this direction. SimShift is the first approach to identify structural similarities between proteins based on chemical shifts. Compared to methods based on the amino acid sequence alone, SimShift shows its strength in detecting distant structural relationships. As a natural further development of the pairwise comparison of proteins, the SimShift algorithm is adapted for database searching. Given a protein, the improved algorithm, named SimShiftDB, searches a database of solved proteins for structurally homologue entries. The search is based only on the amino acid sequence and the associated chemical shifts. The detected similarities are additionally ranked based on calculations of statistical significance. Finally, the Chemical Shift Pipeline, the main result of this work, is presented. By combining automatic chemical shift error correction (CheckShift) and the database search algorithm (SimShiftDB), it is possible to achieve very high quality in 70% to 80% of the similarities identified. Thereby, only about 10% of the predictions are in error.Die nukleare Magnetresonanz-Spektroskopie (NMR) ist eine der wichtigsten Methoden, um die drei-dimensionale Struktur von Biomolekülen zu bestimmen. Trotz großer Fortschritte in der Methodik der NMR ist die Auflösung einer Proteinstruktur immer noch eine komplizierte und zeitraubende Aufgabe. Das Ziel dieser Doktorarbeit ist es, Bioinformatik-Methoden zu entwickeln, die den Prozess der Strukturaufklärung durch NMR erheblich beschleunigen können. Zu diesem Zweck konzentriert sich diese Arbeit auf bestimmte Messdaten aus der NMR, die so genannten chemischen Verschiebungen. Chemische Verschiebungen werden standardmäßig zu Beginn einer Strukturauflösung bestimmt. Wie alle Labordaten können chemische Verschiebungen Fehler enthalten, die die Analyse erschweren, wenn nicht sogar unmöglich machen. Als erstes Resultat dieser Arbeit wird darum CheckShift präsentiert, eine Methode, die es ermöglich einen weit verbreiteten Fehler in chemischen Verschiebungsdaten automatisch zu korrigieren. Das Hauptziel dieser Doktorarbeit ist es jedoch, strukturelle Informationen aus chemischen Verschiebungen zu extrahieren. Als erster Schritt in diese Richtung wurde SimShift entwickelt. SimShift ermöglicht es zum ersten Mal, strukturelle Ähnlichkeiten zwischen Proteinen basierend auf chemischen Verschiebungen zu identifizieren. Der Vergleich zu Methoden, die nur auf der Aminosäurensequenz basieren, zeigt die Überlegenheit des verschiebungsbasierten Ansatzes. Als eine natürliche Erweiterung des paarweisen Vergleichs von Proteinen wird darauffolgend SimShiftDB vorgestellt. Gegeben ein Protein, durchsucht SimShiftDB eine Datenbank bekannter Proteinstrukturen nach strukturell homologen Einträgen. Die Suche basiert hierbei nur auf der Aminosäuresequenz und den chemischen Verschiebungen des Proteins. Die detektierten Ähnlichkeiten werden zusätzlich nach statistischer Signifikanz bewertet. Mit der Chemical Shift Pipeline wird schließlich das Hauptresultat der Dissertation vorgestellt. Durch die Kombination der automatischen Fehlerkorrektur (CheckShift) mit dem Datenbank-Suchalgorithmus (SimShiftDB), wird in 70% bis 80% der vorhergesagten strukturellen Ähnlichkeiten eine sehr hohe Qualität erreicht. Der Anteil der fehlerhaften Vorhersagen beträgt nur etwa 10%

SHIFTX2: significantly improved protein chemical shift prediction

A new computer program, called SHIFTX2, is described which is capable of rapidly and accurately calculating diamagnetic 1H, 13C and 15N chemical shifts from protein coordinate data. Compared to its predecessor (SHIFTX) and to other existing protein chemical shift prediction programs, SHIFTX2 is substantially more accurate (up to 26% better by correlation coefficient with an RMS error that is up to 3.3× smaller) than the next best performing program. It also provides significantly more coverage (up to 10% more), is significantly faster (up to 8.5×) and capable of calculating a wider variety of backbone and side chain chemical shifts (up to 6×) than many other shift predictors. In particular, SHIFTX2 is able to attain correlation coefficients between experimentally observed and predicted backbone chemical shifts of 0.9800 (15N), 0.9959 (13Cα), 0.9992 (13Cβ), 0.9676 (13C′), 0.9714 (1HN), 0.9744 (1Hα) and RMS errors of 1.1169, 0.4412, 0.5163, 0.5330, 0.1711, and 0.1231 ppm, respectively. The correlation between SHIFTX2’s predicted and observed side chain chemical shifts is 0.9787 (13C) and 0.9482 (1H) with RMS errors of 0.9754 and 0.1723 ppm, respectively. SHIFTX2 is able to achieve such a high level of accuracy by using a large, high quality database of training proteins (>190), by utilizing advanced machine learning techniques, by incorporating many more features (χ2 and χ3 angles, solvent accessibility, H-bond geometry, pH, temperature), and by combining sequence-based with structure-based chemical shift prediction techniques. With this substantial improvement in accuracy we believe that SHIFTX2 will open the door to many long-anticipated applications of chemical shift prediction to protein structure determination, refinement and validation. SHIFTX2 is available both as a standalone program and as a web server (http://www.shiftx2.ca)

Identification of stable reference genes for quantitative PCR in koalas

To better understand host and immune response to diseases, gene expression studies require identification of reference genes with stable expression for accurate normalisation. This study describes the identification and testing of reference genes with stable expression profiles in koala lymph node tissues across two genetically distinct koala populations. From the 25 most stable genes identified in transcriptome analysis, 11 genes were selected for verification using reverse transcription quantitative PCR, in addition to the commonly used ACTB and GAPDH genes. The expression data were analysed using stable genes statistical software - geNorm, BestKeeper, NormFinder, the comparative ΔCt method and RefFinder. All 13 genes showed relative stability in expression in koala lymph node tissues, however Tmem97 and Hmg20a were identified as the most stable genes across the two koala populations

A spatial analysis of the determinants of Inter-regional migration: evidence from Ghana

Sub-Saharan Africa has experienced a rapid population increase and growing urbanization rates in recent years and is bound to have the world's largest urban population. If no steps are taken against it, the fast rise in the urban population will result in severe consequences for urban localities in the developing countries located in this region. Along with the natural population increase, internal migration is one prime reason for a fast-rising urbanization process. Since this type of migration is very common in developing countries, this following paper conducts a spatial analysis of inter-regional migration with special reference to Ghana. Specifically, it analyzes the Ghana's migration patterns in Ghana by visualizing the regional differences in net migration and the major migration flows from one region to another. Data for this analysis were collected from a population census and a household survey. A cross-sectional regression analysis was conducted to examine which factors explain inter-regional migration flows in the country. The regression model employed in the analysis is based on the gravity model of migration, which explains how the size of and the distance between two places affects the movement between them, and added the rate of urbanization as well as the average annual income per capita of both regions. The regression results reveal that the distance between two administrative regions in Ghana and the birth region's urbanization rate refrain people from migrating to other regions. In contrast, the urbanization rate and the average income of the destination region are positively associated with the inflow of migrants. Nevertheless, due to the data's limitations, the nexus between migration flows and regional disparities cannot be fully investigated. Therefore, this paper calls for more research to be done in this field