Bayesian models and algorithms for protein beta-sheet prediction

Prediction of the three-dimensional structure greatly benefits from the information related to secondary structure, solvent accessibility, and non-local contacts that stabilize a protein's structure. Prediction of such components is vital to our understanding of the structure and function of a protein. In this paper, we address the problem of beta-sheet prediction. We introduce a Bayesian approach for proteins with six or less beta-strands, in which we model the conformational features in a probabilistic framework. To select the optimum architecture, we analyze the space of possible conformations by efficient heuristics. Furthermore, we employ an algorithm that finds the optimum pairwise alignment between beta-strands using dynamic programming. Allowing any number of gaps in an alignment enables us to model beta-bulges more effectively. Though our main focus is proteins with six or less beta-strands, we are also able to perform predictions for proteins with more than six beta-strands by combining the predictions of BetaPro with the gapped alignment algorithm. We evaluated the accuracy of our method and BetaPro. We performed a 10-fold cross validation experiment on the BetaSheet916 set and we obtained significant improvements in the prediction accuracy

Bayesian models and algorithms for protein beta-sheet prediction

Prediction of the three-dimensional structure greatly benefits from the information related to secondary structure, solvent accessibility, and non-local contacts that stabilize a protein's structure. Prediction of such components is vital to our understanding of the structure and function of a protein. In this paper, we address the problem of beta-sheet prediction. We introduce a Bayesian approach for proteins with six or less beta-strands, in which we model the conformational features in a probabilistic framework. To select the optimum architecture, we analyze the space of possible conformations by efficient heuristics. Furthermore, we employ an algorithm that finds the optimum pairwise alignment between beta-strands using dynamic programming. Allowing any number of gaps in an alignment enables us to model beta-bulges more effectively. Though our main focus is proteins with six or less beta-strands, we are also able to perform predictions for proteins with more than six beta-strands by combining the predictions of BetaPro with the gapped alignment algorithm. We evaluated the accuracy of our method and BetaPro. We performed a 10-fold cross validation experiment on the BetaSheet916 set and we obtained significant improvements in the prediction accuracy

Computation of Electromagnetic Fields Scattered From Objects With Uncertain Shapes Using Multilevel Monte Carlo Method

Computational tools for characterizing electromagnetic scattering from objects with uncertain shapes are needed in various applications ranging from remote sensing at microwave frequencies to Raman spectroscopy at optical frequencies. Often, such computational tools use the Monte Carlo (MC) method to sample a parametric space describing geometric uncertainties. For each sample, which corresponds to a realization of the geometry, a deterministic electromagnetic solver computes the scattered fields. However, for an accurate statistical characterization the number of MC samples has to be large. In this work, to address this challenge, the continuation multilevel Monte Carlo (CMLMC) method is used together with a surface integral equation solver. The CMLMC method optimally balances statistical errors due to sampling of the parametric space, and numerical errors due to the discretization of the geometry using a hierarchy of discretizations, from coarse to fine. The number of realizations of finer discretizations can be kept low, with most samples computed on coarser discretizations to minimize computational cost. Consequently, the total execution time is significantly reduced, in comparison to the standard MC scheme.Comment: 25 pages, 10 Figure

Multimodal person recognition for human-vehicle interaction

Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies

Exchange Rate Pass-Through in Turkey : Has it Changed and to What Extent?

This study analyses the impact of exchange rates on domestic prices in Turkey. We seek to demonstrate the variations (if any) in the exchange rate pass-through across different exchange rate regimes, identify the determinants of this change, and characterize the degree and extent of pass-through across different sub-sectors. Our empirical results reveal that the pass-through of exchange rates to domestic prices has declined in the post-2001 period in comparison with the earlier episodes –thanks to a decline in the “indexation” behavior. These findings suggest that switching to floating exchange rate regime and implementing an ambitious disinflation policy have contributed, to a large extent, to the reduction in the pass-through. Nevertheless, the impact of exchange rate on inflation, especially in the traded good is still notable, pointing out that the effect of nominal exchange rate movement on relative prices have increased in the float period.Exchange rate pass-through, Time-varying parameters, Seemingly unrelated regressions, Disinflation, Floating exchange rate regime

Yetim proteinlerde ikincil yapı öngörüsü için eğitim kümesi indirgeme yöntemleri = Training set reduction methods for single sequence protein secondary structure prediction

Orphan proteins are characterized by the lack of significant sequence similarity to almost all proteins in the database. To infer the functional properties of the orphans, more elaborate techniques that utilize structural information are required. In this regard, the protein structure prediction gains considerable importance. Secondary structure prediction algorithms designed for orphan proteins (also known as single-sequence algorithms) cannot utilize multiple alignments or aligment profiles, which are derived from similar proteins. This is a limiting factor for the prediction accuracy. One way to improve the performance of a single-sequence algorithm is to perform re-training. In this approach, first, the models used by the algorithm are trained by a representative set of proteins and a secondary structure prediction is computed. Then, using a distance measure, the original training set is refined by removing proteins that are dissimilar to the initial prediction. This step is followed by the re-estimation of the model parameters and the prediction of the secondary structure. In this paper, we compare training set reduction methods that are used to re-train the hidden semi-Markov models employed by the IPSSP algorithm. We found that the composition based reduction method has the highest performance compared to the other reduction methods. In addition, threshold-based reduction performed bettern than the reduction technique that selects the first 80% of the dataset proteins

Effectiveness of Four Rotary Retreatment Instruments During Root Canal Retreatment

Objective: The purpose of this in-vitro study was to investigate the residual root canal filling material after retreatment of root canals using stainless steel hand files, and four nickel-titanium retreatment instruments. Materials and methods: Seventy five extracted mandibular premolars were instrumented and filled. The samples were randomly divided into 5 groups (n=15) and retreated using Hedström files, Mtwo R, R-Endo, ProTaper Universal Retreatment, and D-RaCe systems. The roots were digitally radiographed, then grooved split longitudinally to investigate the area of remaining filling material. The time of retreatment and the instruments fracture were also recorded. Results: The Hedström files left less filling material than the rotary retreatment instruments but a significant difference was found only in the middle third (p< 0.01). The apical third had the most residual gutta-percha and sealer compared to the coronal and middle thirds. The retreatment time for the D-RaCe and ProTaper Universal Retreatment groups were significantly shorter than other groups (p< 0.01). Eight Mtwo R files, 2 ProTaper Universal Retreatment files and 1 R-Endo file were fractured. Conclusion: All groups left residual root canal filling material inside the root canal walls. Nickel-titanium rotary retreatment instruments were faster than Hedström files but had a higher risk of instruments fracture

Diagnostic value of CA 19-9 in pregnancies complicated by spinal neural tube defects: a preliminary study

Objectives: Various physiological and pathological conditions can induce significant variations in plasma concentrations of tumor markers, such as CA 19-9, which is present in the serum and amniotic fluid of pregnant women. Herein, we aimed to determine the clinical importance of maternal serum CA 19-9 levels in the diagnosis of neural tube defects (NTDs). Material and methods: A total of 100 women were included in this controlled cross-sectional study. Thirty-three patients whose pregnancies were complicated by isolated meningocele or meningomyelocele constituted the study group, whereas 33 normal, healthy pregnant women constituted the control group, and 34 age- and body mass index (BMI)-matched non-pregnant women were chosen for the validation group. Results: The mean maternal serum CA 19-9 levels were 17.2 ± 17.0 IU/mL, 7.1 ± 5.9 IU/mL, and 4.7 ± 3.6 IU/mL in the study, control, and validation groups, respectively (p < 0.001). ROC analyses showed that elevated CA 19-9 values may predict NTDs (p < 0.001). The cut-off value for CA 19-9 was found to be 9.6 IU/mL at 70% (51%–84%, 95% CI) sensitivity and 84% (74%–92%, 95% CI) specificity. Conclusions: CA 19-9 may be a promising noninvasive marker for NTDs. Further studies are needed to reveal the clinical applicability and diagnostic potential of maternal serum CA 19-9 levels in the identification of NTDs

Computed tomography as a predictor of the extent of the disease and surgical outcomes in ovarian cancer

Objectives: The aim of the present study is to determine the predictive value of Computed Tomography (CT), alone or in combination with serum CA-125 levels, for preoperative staging, detection of the extent of the disease, and surgical complications in patients with ovarian carcinoma. Material and methods: One hundred and fourteen patients diagnosed with ovarian carcinoma following an exploratory laparotomy with a preoperative CT scan, performed between January 2007 and June 2013, were enrolled in the study. Preoperative CT and intraoperative surgical findings were compared using 14 parameters and predictions of CT for gas­trointestinal, genitourinary, and cardiovascular complications. All radiological features and clinical characteristics were analyzed statistically. Results: CT and surgical findings correlated (sensitivity/ specificity) as follows: uterine and tubal spread (66%/89%), cervical involvement (100%/80%), peritoneal nodulesincreased density-carcinomatosis (57%/93%), omental involvement (68%/95%), retroperitoneal involvement (25%/84%), ascites (85%/87%), perirectal and perivesical fat plan obliteration (43%/94%), liver metastasis (50%/91%), small and large bowel involvement (47%/95%), adnexal mass (94%/70%), and other metastases (47%/86%). Also, CT findings were found to be statistically insignificant for prediction of mesenteric involvement, bladder metastasis, and diaphragmatic involvement. The overall CT sensitivity and specificity at detecting intraoperative findings was 91% and 71%, respectively. We found a statistically significant correlation between intestinal involvement on CT and the necessity of additional surgical procedures. Conclusions: CT is a widely used imaging method in the preoperative evaluation of ovarian cancer. However, its predictive value, sensitivity and specificity differ, depending on the anatomical region

The certification of the activity concentration of the radionuclides 137Cs, 90Sr and 40K in wild berries: IRMM-426

This report describes the production of CRM IRMM-426, a dried bilberry material certified for the radionuclide activity concentrations of 137Cs, 90Sr and 40K. The material was produced following ISO Guide 34:2009. Bilberry samples were collected in a woodland region of so-called “strontium hot spots” close to the Chernobyl reactor site. The samples were air-dried at the sampling site before transport to IRMM, where the raw material was oven-dried, cryo-milled, sieved, homogenised and bottled. The bottled material was sterilised by gamma-irradiation. Between-unit homogeneity was quantified and stability during dispatch and storage were assessed in accordance with ISO Guide 35:2006. The material was characterised by an intercomparison among laboratories of demonstrated competence and adhering to ISO/IEC 17025. Technically invalid results were removed but no outlier was eliminated on statistical grounds only. Uncertainties of the certified values were calculated in compliance with the Guide to the Expression of Uncertainty in Measurement (GUM) and include uncertainties related to possible inhomogeneity and instability and to characterisation. The material is intended for the assessment of method performance and quality control. As any reference material, it can also be used for control charts or validation studies. The CRM is available in amber glass jars containing about 100 g of dried bilberry powder. The minimum amount of sample to be used for analysis is 50 g for 90Sr and 18 g for 137Cs and 40K.JRC.D.2-Standards for Innovation and sustainable Developmen