Objectives: Detection of vertical root fractures (VRFs) in their initial stages is a crucial issue, which prevents the propagation of injury to the adjacent supporting structures. Designing a suitable neural network-based model could be a useful method to diagnose the VRFs. The aim of this study was to design a probabilistic neural network (PNN) to diagnose the VRFs in intact and endodontically treated teeth of periapical and CBCT radiographs. Also, we have compared the efficacy of these two imaging techniques in the detection of VRFs. Methods: A total of 240 radiographs of teeth (120 radiographs of teeth with no VRFs and 120 teeth with vertical fractures, with half of the teeth in each category treated endodontically and the remaining half intact, i.e. not endodontically treated) were used in 3 groups for training and testing of the neural network as follows: Group 1, 180/60; Group 2, 120/120; and Group 3, 60/180. First, Daubechies 3 wavelet was applied to acquire the image analysis coefficients on two planes; then Gabor filters were used to extract the image characteristics, which were used to educate the PNN. The designed neural network was able to diagnose and classify teeth with and without VRFs. In addition, in order to determine the best training and test sets in the network, the variance of the function of network changes was manipulated at a range of 0-1 and the results were assessed in terms of the parameters evaluated, including sensitivity, specificity and accuracy. Results: In the periapical radiographs, the maximum accuracy, sensitivity and specificity values in the three groups were 70.00, 97.78 and 67.7%, respectively. These values in the CBCT images were 96.6, 93.3 and 100%, respectively, at the variance change range of 0.1-0.65. Conclusions: The designed neural network can be used as a proper model for the diagnosis of VRFs on CBCT images of endodontically treated and intact teeth; in this context, CBCT images are more effective than similar periapical radiographs. Limitations of this study are the use of sound one-rooted premolar teeth without carious lesions and dental fillings and not simulating the adjacent anatomic structures. Further in vitro work using a full-skull simulation for CBCT and skin/bone simulation is needed.

Rapid advances in biochemistry and genetics lead to expansion of the various medical instruments for detection and prevention tasks. On the other hand, food safety is an important concern which relates to the public health. One of the most reliable tools to detect bioparticles (i.e., DNA molecules and proteins) and determining the authenticity of food products is the optical ring resonators. By depositing a recipient polymeric layer of target particle on the periphery of an optical ring resonator, it is possible to identify the existence of molecules by calculating the shift in the spectral response of the ring resonators. The main purpose of this paper is to investigate the performance of two structures of optical ring resonators, (i) all-pass and (ii) add-drop resonators for sensing applications. We propose a new configuration for sensing applications by introducing a nanogap in the all-pass ring resonator. The performance of these resonators is studied from sensing point of view. Simulation results, using finite difference time domain paradigm, revealed that the existence of a nanogap in the ring configuration achieves higher amount of sensitivity; thus, this structure is more suitable for biosensing applications.

Punctual identification of protein-coding regions in Deoxyribonucleic Acid (DNA) sequences because of their 3-base periodicity has been a challenging issue in bioinformatics. Many DSP (Digital Signal Processing) techniques have been applied for identification task and concentrated on assigning numerical values to the symbolic DNA sequence and then applying spectral analysis tools such as the short-time discrete Fourier transform (ST-DFT) to locate periodicity components. In this paper, first, the symbolic DNA sequences are converted to digital signal using the Z-curve method, which is a unique 3-D plot to illustrate DNA sequence and presents the biological behavior of DNA sequence. Then a novel fast algorithm is proposed to investigate the location of exons in DNA strand based on the combination of Linear Predictive Coding Model (LPCM) and Goertzel algorithm. The proposed algorithm leads to increase the speed of process and therefor reduce the computational complexity. Detection of small size exons in DNA sequences, exactly, is another advantage of our algorithm. The proposed algorithm ability in exon prediction is compared with several existing methods at the nucleotide level using: (i) specificitysensitivity values; (ii) Receiver Operating Curves (ROC); and (iii) area under ROC curve. Simulation results show that our algorithm increases the accuracy of exon detection relative to other methods for exon prediction. In this paper, we have also developed a useful user friendly package to analyze DNA sequences.

Identification of protein-coding regions in Deoxyribonucleic Acid (DNA) sequences because of their 3-base periodicity has been a challenging issue in bioinformatics. Many DSP (Digital Signal Processing) techniques have been applied for identification task and concentrated on assigning numerical values to the symbolic DNA sequence and then applying spectral analysis tools such as the short-time discrete Fourier transform (ST-DFT) to locate periodicity components. In this paper, we investigate the location of exons in DNA strand using directly the DFT approach. By using this method, we see that background noise in the period-3 DNA spectrum has been present. In order to eliminate this noise and for improve the quality of detection, we use an efficient algorithm based on notch filter. Simulation results represent that by using this simple algorithm, the exon location in DNA sequence can be detected as well as possible and the background noise is removes. In this paper, we have also developed a useful user friendly package to analyze DNA sequences.

Microarray data have an important role in identification and classification of the cancer tissues. Having a few samples of microarrays in cancer researches is always one of the most concerns which lead to some problems in designing the classifiers. For this matter, preprocessing gene selection techniques should be utilized before classification to remove the noninformative genes from the microarray data. An appropriate gene selection method can significantly improve the performance of cancer classification. In this paper, we use selective independent component analysis (SICA) for decreasing the dimension of microarray data. Using this selective algorithm, we can solve the instability problem occurred in the case of employing conventional independent component analysis (ICA) methods. First, the reconstruction error and selective set are analyzed as independent components of each gene, which have a small part in making error in order to reconstruct new sample. Then, some of the modified support vector machine (υ-SVM) algorithm sub-classifiers are trained, simultaneously. Eventually, the best sub-classifier with the highest recognition rate is selected. The proposed algorithm is applied on three cancer datasets (leukemia, breast cancer and lung cancer datasets), and its results are compared with other existing methods. The results illustrate that the proposed algorithm (SICA + υ-SVM) has higher accuracy and validity in order to increase the classification accuracy. Such that, our proposed algorithm exhibits relative improvements of 3.3% in correctness rate over ICA + SVM and SVM algorithms in lung cancer dataset.