M-estimate robust PCA for seismic noise attenuation

The robust principal component analysis (PCA) method has shown very promising results in seismic ambient noise attenuation when dealing with outliers in the data. However, the model assumes a general Gaussian distribution plus sparse outliers for the noise. In seismic data however, the noise standard variation could vary from one place to another leading to a more heavy-tailed noise distribution. In this paper, we present a new method which solves a convex minimisation problem of the robust PCA method with an M-estimate penalty function. Our empirical results show that the proposed method can outperform the robust PCA method

Multi-scale Sparse Coding With Anomaly Detection And Classification

We here place a recent joint anomaly detection and classification approach based on sparse error coding methodology into multi-scale wavelet basis framework. The model is extended to incorporate an overcomplete wavelet basis into the dictionary matrix whereupon anomalies at specified multiple levels of scale are afforded equal importance. This enables, for example, subtle transient anomalies at finer scales to be detected which would otherwise be drowned out by coarser details and missed by the standard sparse coding techniques. Anomaly detection in power networks provides a motivating application and tests on a real-world data set corroborates the efficacy of the proposed model

fracture and microstructural study of bovine bone under mixed mode i ii loading

Abstract Understanding the fracture behavior and associated crack growth mechanism in bone material is an important issue for biomechanics and biomaterial researches. Fracture of bone often takes place due to complex loading conditions which result in combined tensile-shear (i.e. mixed mode) fracture mechanism. Several parameters such as loading type, applied loading direction relative to the bone axis, loading rate, age and etc., may affect the mixed mode fracture resistance and damage mechanism in such materials. In this research, a number of mixed mode I/II fracture experiments are conducted on bovine femur bone using a sub-sized test configuration called "compact beam bend (CBB)" specimen to investigate the fracture toughness of bone under different mode mixities. The specimen is rectangular beam containing a mid-edge crack that is loaded by a conventional three-point bend fixture. The results showed the dependency of bone fracture toughness on the state of mode mixity. The fracture surfaces of broken CBB specimens under different loading conditions were studied via scanning electron microscopy (SEM) observations. Fracture surface of all investigated cases (i.e. pure mode I, pure mode II and mixed mode I/II) exhibited smooth patterns demonstrating brittle fracture of bovine femur. The higher density of vascular channels and micro-cracks initiated in the weakened area surrounded by secondary osteons were found to be the main cause of the decreased bone resistance against crack growth and brittle fracture

Causes of bimodal melting curve:Asymmetric guaninecytosine (GC) distribution causing two peaks in melting curve and affecting their shapes

The aim of this study was to present a new situation in which a relatively single short PCR-product might show two separate peaks with sequence specific shapes at the dissociation curve. SYBR-Green I real-time RT-PCR was performed on Lhcgr-gene transcripts in rats. Different programs were used for melting curve simulation and estimating Tm. Statistical tests were performed to determine whether two peaks at the dissociation curve were belonging to a single template. A bimodal melting curve was observed in real-time RT-PCR on a short segment (169 bp) of Lhcgr gene with a single band in gel electrophoresis. Sequencing of the Cloned PCR-product was compatible with template sequence. Realtime PCR using the vector conveying interested sequence, showed again two peaks at dissociation curve. The GC-content of first 100 bases (75%) and last 69 bases (42%) were significantly different. DNA melting simulation programs also confirmed the bimodal pattern, although, their height and wideness were different to actual peaks. Due to the asymmetric GC distribution effect on dissociation curve in short sequences, it is highly recommended to use DNA melting simulation programs to predict the number of peaks in the melting curve when designating primers; however, predicted peak shapes are not always accurate.Key words: Asymmetric GC distribution, bimodal melting curve, DNA melting simulation, SYBR-green I realtime PCR

Progress of Education, Research and Services in Medical Genetics, in Some Institutions of Iran

The present paper is a review of progress and major activities in education, research, services and ethics in the field of medical genetics in some centers in Iran. National projects of population genetics, genetic epidemiology, like national human genome projects, Connexin 26 and Pejvakin, distribution of thalassemia, hemophilia, etc in different ethnic groups, and religious minorities of Iran, are mentioned

The effect of Setarud (IMOD�) on angiogenesis in transplanted human ovarian tissue to nude mice

Background: One of the promising methods in fertility preservation among women with cancer is cryopreservation of ovarian cortex but there are many drawbacks such as apoptosis and considerable reduction of follicular density in the transplanted ovary. One solution to reduce ischemic damage is enhancing angiogenesis after transplantation of ovarian cortex tissue. Objective: The aim of this study was to investigate the effect of Setarud, on angiogenesis in transplanted human ovarian tissue. Materials and Methods: In this case control study, twenty four nude mice were implanted subcutaneously, with human ovarian tissues, from four women. The mice were randomly divided into two groups (n=12): the experimental group was treated with Setarud, while control group received only vehicle. Each group was divided into three subgroups (n=4) based on the graft recovery days post transplantation (PT). The transplanted fragments were removed on days 2, 7, and 30 PT and the expression of Angiopoietin-1, Angiopoietin-2, and Vascular endothelial growth factor at both gene and protein levels and vascular density were studied in the grafted ovarian tissues. Results: On the 2nd and 7th day PT, the level of Angiopoietin-1 gene expression in case group was significantly lower than that in control group, while the opposite results were obtained for Angiopoietin-2 and Vascular endothelial growth factor. These results were also confirmed at the protein level. The density of vessels in Setarud group elevated significantly on day 7 PT compared to pre-treatment state. Conclusion: Our results showed that administration of Setarud may stimulates angiogenesis in transplanted human ovarian tissues, although further researches are needed before a clear judgment is made. � 2015, Research and Clinical Center for Infertitlity. All rights reserved

Learning to segment when experts disagree

Recent years have seen an increasing use of supervised learning methods for segmentation tasks. However, the predictive performance of these algorithms depend on the quality of labels, especially in medical image domain, where both the annotation cost and inter-observer variability are high. In a typical annotation collection process, different clinical experts provide their estimates of the “true” segmentation labels under the influence of their levels of expertise and biases. Treating these noisy labels blindly as the ground truth can adversely affect the performance of supervised segmentation models. In this work, we present a neural network architecture for jointly learning, from noisy observations alone, both the reliability of individual annotators and the true segmentation label distributions. The separation of the annotators’ characteristics and true segmentation label is achieved by encouraging the estimated annotators to be maximally unreliable while achieving high fidelity with the training data. Our method can also be viewed as a translation of STAPLE, an established label aggregation framework proposed in Warfield et al. [1] to the supervised learning paradigm. We demonstrate first on a generic segmentation task using MNIST data and then adapt for usage with MRI scans of multiple sclerosis (MS) patients for lesion labelling. Our method shows considerable improvement over the relevant baselines on both datasets in terms of segmentation accuracy and estimation of annotator reliability, particularly when only a single label is available per image. An open-source implementation of our approach can be found at https://github.com/UCLBrain/MSLS

Correction to: "Comparative repair capacity of knee osteochondral defects using regenerated silk fiber scaffolds and fibrin glue with/without autologous chondrocyes during 36 weeks in rabbit model (Cell and Tissue Research, (2016), 364, 3, (559-572), 10.1007/s00441-015-2355-9)

In this paper, figure 1 and its associated text were erroneously identical to that of another article from our group (Mobini et al., 2016, Journal of Biomaterial Application, SAGE publications). Unfortunately, copyright permission to re-use figure 1 and its related data were not requested. The authors would like to apologize for any confusion caused in this regard. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature