Karyosystematics of Kol tooth-carp, Aphanius darabensis (Teleostei: Cyprinodontidae)

The karyological and cytological characteristics of an endemic cyprinodont fish of Iran, Aphanius darabensis Esmaeili, Teimori, Gholami & Reichenbacher, 2014 have been investigated for the first time by examining metaphase chromosomes spreads obtained from gill epithelial and kidney cells. The diploid chromosome number of A. darabensis is 48. The karyotype consisted of five submetacentric and 19 subtelocentric pairs of chromosomes (5sm+19st). The fundamental number (FN) is 58. Sex chromosomes were cytologically indistinguishable in this tooth-carp. According to this study and previous karyological reports from other cyprinodont species, it can be suggested that the diploid number (2n=48) is common amongst cyprinodont fishes. These results can be used as basic informations in population studies and management and conservation programs

Evaluation of surface EMG-based recognition algorithms for decoding hand movements

Myoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the functionality of powered prostheses. To this purpose, offline accuracy and processing time were measured over 44 features using six classifiers with the aim of determining new configurations of features and classifiers to improve the accuracy and response time of prosthetics control. An efficient feature set (FS: waveform length, correlation coefficient, Hjorth Parameters) was found to improve the motion recognition accuracy. Using the proposed FS significantly increased the performance of linear discriminant analysis, K-nearest neighbor, maximum likelihood estimation (MLE), and support vector machine by 5.5%, 5.7%, 6.3%, and 6.2%, respectively, when compared with the Hudgins\u27 set. Using the FS with MLE provided the largest improvement in offline accuracy over the Hudgins feature set, with minimal effect on the processing time. Among the 44 features tested, logarithmic root mean square and normalized logarithmic energy yielded the highest recognition rates (above 95%). We anticipate that this work will contribute to the development of more accurate surface EMG-based motor decoding systems for the control prosthetic hands

Real‐time and offline evaluation of myoelectric pattern recognition for the decoding of hand movements

Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre‐recorded datasets. While real‐time data analysis (i.e., classification when new data becomes available, with limits on latency under 200–300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real‐time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real‐time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real‐time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able‐bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other clas-sifiers, with an average classification accuracy of above 97%. On the other hand, the real‐time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively

New records and geographical distribution of Alburnus hohenackeri Kessler, 1870 (Teleostei: Cyprinidae) in Iran

The distribution of the Persian bleak, Alburnus hohenackeri Kessler, 1870 in Iran is described. During a survey from 2009 to 2012, we captured 30 specimens of A. hohenackeri from Choghakhor Wetland in Tigris River basin and Kardeh Dam in Harirud River basin of Iran. This is the first report of the occurrence of this species in these localities. The main distribution range in Iran is the southern part of the Caspian Sea from where it has been translocated to the other Iranian basins along with exotic Chinese carps

A New Method for Classification of Chinese Herbal Medicines Based on Local Tangent Space Alignment and LDA

Abstract—Controlling the quality of Chinese herbal medicines (CHMs) is a challenging issue due to the complex and diverge specification of components in herbs. The main purpose of this study is to develop an algorithm for species identification of CHMs. An electronic nose (E-nose) was employed to collect the smell print of different groups of CHMs with different kinds and production batches. A combination of local tangent space alignment (LTSA) and linear discriminant analysis (LDA) methods was adopted for the classification of CHMs. First, the nonlinear manifold learning algorithm LTSA was employed to reduce the dimension of the feature data. The goal of this dimensionality reduction is to discover the hidden structure from the raw data automatically. Then in the reduced space, the LDA algorithm based on Fisher criterion was employed to implement a linear classifier. The results show that, the combination of LTSA+LDA algorithm can well distinguish six different kinds of CHMs and three different production batches of the same kind with 100 % recognition rate of all tested samples. Keywords-Electronic nose (E-nose); Chinese herbal medicines; Manifold learning; LTSA+LDA; Classification and identification I

Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images

Automatic melanoma detection from dermoscopic skin samples is a very challenging task. However, using a deep learning approach as a machine vision tool can overcome some challenges. This research proposes an automated melanoma classifier based on a deep convolutional neural network (DCNN) to accurately classify malignant vs. benign melanoma. The structure of the DCNN is carefully designed by organizing many layers that are responsible for extracting low to high-level features of the skin images in a unique fashion. Other vital criteria in the design of DCNN are the selection of multiple filters and their sizes, employing proper deep learning layers, choosing the depth of the network, and optimizing hyperparameters. The primary objective is to propose a lightweight and less complex DCNN than other state-of-the-art methods to classify melanoma skin cancer with high efficiency. For this study, dermoscopic images containing different cancer samples were obtained from the International Skin Imaging Collaboration datastores (ISIC 2016, ISIC2017, and ISIC 2020). We evaluated the model based on accuracy, precision, recall, specificity, and F1-score. The proposed DCNN classifier achieved accuracies of 81.41%, 88.23%, and 90.42% on the ISIC 2016, 2017, and 2020 datasets, respectively, demonstrating high performance compared with the other state-of-the-art networks. Therefore, this proposed approach could provide a less complex and advanced framework for automating the melanoma diagnostic process and expediting the identification process to save a life

Classification and Identification of Industrial Gases Based on Electronic Nose Technology

Rapid detection and identification of industrial gases is a challenging problem. They have a complex composition and different specifications. This paper presents a method based on the kernel discriminant analysis (KDA) algorithm to identify industrial gases. The smell prints of four typical industrial gases were collected by an electronic nose. The extracted features of the collected gases were employed for gas identification using different classification algorithms, including principal component analysis (PCA), linear discriminant analysis (LDA), PCA + LDA, and KDA. In order to obtain better classification results, we reduced the dimensions of the original high-dimensional data, and chose a good classifier. The KDA algorithm provided a high classification accuracy of 100% by selecting the offset of the kernel function c = 10 and the degree of freedom d = 5. It was found that this accuracy was 4.17% higher than the one obtained using PCA. In the case of standard deviation, the KDA algorithm has the highest recognition rate and the least time consumption

A Cox-Based Risk Prediction Model for Early Detection of Cardiovascular Disease: Identification of Key Risk Factors for the Development of a 10-Year CVD Risk Prediction

Background and Objective. Current cardiovascular disease (CVD) risk models are typically based on traditional laboratory-based predictors. The objective of this research was to identify key risk factors that affect the CVD risk prediction and to develop a 10-year CVD risk prediction model using the identified risk factors. Methods. A Cox proportional hazard regression method was applied to generate the proposed risk model. We used the dataset from Framingham Original Cohort of 5079 men and women aged 30-62 years, who had no overt symptoms of CVD at the baseline; among the selected cohort 3189 had a CVD event. Results. A 10-year CVD risk model based on multiple risk factors (such as age, sex, body mass index (BMI), hypertension, systolic blood pressure (SBP), cigarettes per day, pulse rate, and diabetes) was developed in which heart rate was identified as one of the novel risk factors. The proposed model achieved a good discrimination and calibration ability with C-index (receiver operating characteristic (ROC)) being 0.71 in the validation dataset. We validated the model via statistical and empirical validation. Conclusion. The proposed CVD risk prediction model is based on standard risk factors, which could help reduce the cost and time required for conducting the clinical/laboratory tests. Healthcare providers, clinicians, and patients can use this tool to see the 10-year risk of CVD for an individual. Heart rate was incorporated as a novel predictor, which extends the predictive ability of the past existing risk equations