HUAN YIN¹, LIZHEN WANG¹, YUBO FAN¹, BINGMEI M. FU^1,2,*

BIOCELL, Vol.45, No.5, pp. 1187-1199, 2021, DOI:10.32604/biocell.2021.016650

Abstract Primary cilium is an antenna-like and non-motile structure protruding from the apical surface of most mammalian cells including endothelial cells lining the inner side of all the blood vessels in our body. Although it has been over a century since primary cilia were discovered, the investigation about their mechano-sensing and other roles in maintaining normal functions of cardiovascular system has just started in recent years. This focused review aims to give an update about the current literature for the role of endothelial primary cilia in blood flow mechano-sensing and shear stress-shielding. To do this, we first summarized the characteristic features… More >

Yanping Ruan^1,#, Xiangyu Liu^2,#, Haogang Zhu^3,*, Yijie Lu³, Xiaowei Liu¹, Jiancheng Han¹, Lin Sun¹, Ye Zhang¹, Xiaoyan Gu¹, Ying Zhao¹, Lei Li², Suzhen Ran⁴, Jingli Chen⁵, Qiong Yu⁶, Yan Xu⁷, Hongmei Xia⁸, Yihua He^1,*

Congenital Heart Disease, Vol.16, No.6, pp. 529-549, 2021, DOI:10.32604/CHD.2021.015862

Abstract Background: Current studies have confirmed that fetal congenital heart diseases (CHDs) are caused by various factors. However, the quantitative risk of CHD is not clear given the combined effects of multiple factors. Objective: This cross-sectional study aimed to detect associated factors of fetal CHD using a Bayesian network in a large sample and quantitatively analyze relative risk ratios (RRs). Methods: Pregnant women who underwent fetal echocardiography (N = 16,086 including 3,312 with CHD fetuses) were analyzed. Twenty-six maternal and fetal factors were obtained. A Bayesian network is constructed based on all variables through structural learning and parameter learning methods to… More >

Mohamed S. Mohamed^1,*, Sayed K. Elagan¹, Saad J. Almalki¹, Muteb R. Alharthi¹, Mohamed F. El-Badawy², Amr M. S. Mahdy¹

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3615-3628, 2021, DOI:10.32604/cmc.2021.017208

Abstract In this article, we have shown that bacterial DNA could act like some coils which interact with coil-like DNA of host cells. By decreasing the separating distance between two bacterial cellular DNA, the interaction potential, entropy, and the number of microstates of the system grow. Moreover, the system gives its energy to the medium and the temperature of the host body grows. This could be seen as fever in diseases. By emitting some special waves and changing the temperature of the medium, the effects of bacterial waves could be reduced and bacterial diseases could be controlled. Many investigators have shown… More >

Md. Ashraful Alam¹, Milan Skalicky², Muhammad Rezaul Kabir¹, Md. Monwar Hossain¹, Md. Abdul Hakim¹, Md. Siddikun Nabi Mandal¹, Rabiul Islam³, Md. Babul Anwar³, Akbar Hossain^1,*, Fahmy Hassan⁴, Amaal Mohammadein⁴, Muhammad Aamir Iqbal⁵, Marian Brestic^2,6, Mohammad Anwar Hossain⁷, Khalid Rehman Hakeem⁸, Ayman EL Sabagh^9,*

Phyton-International Journal of Experimental Botany, Vol.90, No.4, pp. 1301-1320, 2021, DOI:10.32604/phyton.2021.016015

Abstract Globally among biotic stresses, diseases like blight, rust and blast constitute prime constraints for reducing wheat productivity especially in Bangladesh. For sustainable productivity, the development of disease-resistant lines and high yielding varieties is vital and necessary. This study was conducted using 122 advanced breeding lines of wheat including 21 varieties developed by Bangladesh Wheat and Maize Research Institute (BAMRI) with aims to identify genotypes having high yield potential and resistance to leaf blight, leaf rust and blast diseases. These genotypes were evaluated for resistance against leaf blight and leaf rust at Dinajpur and wheat blast at Jashore under field condition.… More >

R. Sowmyalakshmi¹, T. Jayasankar^1,*, V. Ayyem Pillai², Kamalraj Subramaniyan³, Irina V. Pustokhina⁴, Denis A. Pustokhin⁵, K. Shankar⁶

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1751-1767, 2021, DOI:10.32604/cmc.2021.016825

Abstract Internet of Things (IoT) paves a new direction in the domain of smart farming and precision agriculture. Smart farming is an upgraded version of agriculture which is aimed at improving the cultivation practices and yield to a certain extent. In smart farming, IoT devices are linked among one another with new technologies to improve the agricultural practices. Smart farming makes use of IoT devices and contributes in effective decision making. Rice is the major food source in most of the countries. So, it becomes inevitable to detect rice plant diseases during early stages with the help of automated tools and… More >

J. B. Shukla¹, Shikha Singh², Jitendra Singh², Sunil Kumar Sharma^3,*

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1859-1875, 2021, DOI:10.32604/cmc.2021.012095

Abstract In this research, we proposed a non-linear SIS model to study the effect of variable interaction rates and non-emigrating population of the human habitat on the spread of bacteria-infected diseases. It assumed that the growth of bacteria is logistic with an intrinsic growth rate is a linear function of infectives. In this model, we assume that contact rates between susceptibles and infectives as well as between susceptibles and bacteria depend on the density of the non-emigrating population and the total population of the habitat. The stability theory has been analyzed to analyzed to study the crucial role played by bacteria… More >

Yang Wu¹, Lihong Xu^1,*, Erik D. Goodman²

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 561-576, 2021, DOI:10.32604/iasc.2021.016415

Abstract Deep convolutional neural network (DCNN) requires a lot of data for training, but there has always been data vacuum in agriculture, making it difficult to label all existing data accurately. Therefore, a lightweight tomato leaf disease identification network supported by Variational auto-Encoder (VAE) is proposed to improve the accuracy of crop leaf disease identification. In the lightweight network, multi-scale convolution can expand the network width, enrich the extracted features, and reduce model parameters such as deep separable convolution. VAE makes full use of a large amount of unlabeled data to achieve unsupervised learning, and then uses labeled data for supervised… More >

Muhammad Attique Khan¹, Abdul Majid¹, Nazar Hussain¹, Majed Alhaisoni², Yu-Dong Zhang³, Seifedine Kadry⁴, Yunyoung Nam^5,*

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3381-3399, 2021, DOI:10.32604/cmc.2021.014983

Abstract In the area of medical image processing, stomach cancer is one of the most important cancers which need to be diagnose at the early stage. In this paper, an optimized deep learning method is presented for multiple stomach disease classification. The proposed method work in few important steps—preprocessing using the fusion of filtering images along with Ant Colony Optimization (ACO), deep transfer learning-based features extraction, optimization of deep extracted features using nature-inspired algorithms, and finally fusion of optimal vectors and classification using Multi-Layered Perceptron Neural Network (MLNN). In the feature extraction step, pre-trained Inception V3 is utilized and retrained on… More >

Lihan Cui¹, Lijie Yan¹, Xiaohu Zhao¹, Lin Yuan², Jing Jin³, Jingcheng Zhang^1,*

Phyton-International Journal of Experimental Botany, Vol.90, No.2, pp. 621-634, 2021, DOI:10.32604/phyton.2021.015511

Abstract Tea plant stresses threaten the quality of tea seriously. The technology corresponding to the fast detection and differentiation of stresses is of great significance for plant protection in tea plantation. In recent years, hyperspectral imaging technology has shown great potential in detecting and differentiating plant diseases, pests and some other stresses at the leaf level. However, the lack of studies at canopy level hampers the detection of tea plant stresses at a larger scale. In this study, based on the canopy-level hyperspectral imaging data, the methods for identifying and differentiating the three commonly occurred tea stresses (i.e., the tea leafhopper,… More >

Wael Alosaimi^1,*, Hashem Alyami², M. Irfan Uddin³

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1665-1677, 2021, DOI:10.32604/cmc.2021.014950

Abstract To meet the food requirements of the seven billion people on Earth, multiple advancements in agriculture and industry have been made. The main threat to food items is from diseases and pests which affect the quality and quantity of food. Different scientific mechanisms have been developed to protect plants and fruits from pests and diseases and to increase the quantity and quality of food. Still these mechanisms require manual efforts and human expertise to diagnose diseases. In the current decade Artificial Intelligence is used to automate different processes, including agricultural processes, such as automatic harvesting. Machine Learning techniques are becoming… More >