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"artificial intelligence" AND Plant

David Zimmer, Kevin Schneider, Frederik Sommer, Michael Schroda, Timo Mühlhaus
Targeted mass spectrometry has become the method of choice to gain absolute quantification information of high quality, which is essential for a quantitative understanding of biological systems. However, the design of absolute protein quantification assays remains challenging due to variations in peptide observability and incomplete knowledge about factors influencing peptide detectability. Here, we present a deep learning algorithm for peptide detectability prediction, d::pPop, which allows the informed selection of synthetic proteotypic peptides for the successful design of targeted proteomics quantification assays...
2018: Frontiers in Plant Science
Esmaeil Nezami-Alanagh, Ghasem-Ali Garoosi, Mariana Landín, Pedro Pablo Gallego
The aim of this study was to determine the effects of Murashige and Skoog (MS) salts on optimal growth of two pistachio rootstocks, P. vera cv. "Ghazvini" and "UCB1" using design of experiments (DOE) and artificial intelligence (AI) tools. MS medium with 14 macro-and micro-elements was used as base point and its concentration varied from 0 to 5 × MS concentrations. Design of experiments (DOE) software was used to generate a five-dimensional design space by categorizing MS salts into five independent factors (NH4 NO3 , KNO3 , mesos, micros and iron), reducing the experimental design space from 3,125 to just 29 treatments...
2018: Frontiers in Plant Science
Isham Alzoubi, Mahmoud R Delavar, Farhad Mirzaei, Babak Nadjar Arrabi
Background: Land leveling is one of the most important steps in soil preparation and cultivation. Although land leveling with machines require considerable amount of energy, it delivers a suitable surface slope with minimal deterioration of the soil and damage to plants and other organisms in the soil. Notwithstanding, researchers during recent years have tried to reduce fossil fuel consumption and its deleterious side effects. The aim of this work was to determine best linear model using artificial neural network (ANN), imperialist competitive algorithm and ANN and regression and adaptive neural fuzzy inference system (ANFIS) in order to predict the environmental indicators for land leveling...
June 2018: Journal of Environmental Health Science & Engineering
Nicholas Ekow Thomford, Dimakatso Alice Senthebane, Arielle Rowe, Daniella Munro, Palesa Seele, Alfred Maroyi, Kevin Dzobo
The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures...
May 25, 2018: International Journal of Molecular Sciences
JuneHyuck Lee, Sang Do Noh, Hyun-Jung Kim, Yong-Shin Kang
The prediction of internal defects of metal casting immediately after the casting process saves unnecessary time and money by reducing the amount of inputs into the next stage, such as the machining process, and enables flexible scheduling. Cyber-physical production systems (CPPS) perfectly fulfill the aforementioned requirements. This study deals with the implementation of CPPS in a real factory to predict the quality of metal casting and operation control. First, a CPPS architecture framework for quality prediction and operation control in metal-casting production was designed...
May 4, 2018: Sensors
Gang Bai, Tiejun Zhang, Yuanyuan Hou, Guoyu Ding, Min Jiang, Guoan Luo
BACKGROUND: The quality of traditional Chinese medicine (TCM) forms the foundation of its clinical efficacy. The standardization of TCM is the most important task of TCM modernization. In recent years, there has been great progress in the quality control of TCM. However, there are still many issues related to the current quality standards, and it is difficult to objectively evaluate and effectively control the quality of TCM. PURPOSE: To face these challenge, we summarized the current quality marker (Q-marker) research based on its characteristics and benefits, and proposed a reasonable and intelligentized quality evaluation strategy for the development and application of Q-markers...
May 15, 2018: Phytomedicine: International Journal of Phytotherapy and Phytopharmacology
Shaoqing Cui, Peter Ling, Heping Zhu, Harold M Keener
This paper reviews artificial intelligent noses (or electronic noses) as a fast and noninvasive approach for the diagnosis of insects and diseases that attack vegetables and fruit trees. The particular focus is on bacterial, fungal, and viral infections, and insect damage. Volatile organic compounds (VOCs) emitted from plants, which provide functional information about the plant's growth, defense, and health status, allow for the possibility of using noninvasive detection to monitor plants status. Electronic noses are comprised of a sensor array, signal conditioning circuit, and pattern recognition algorithms...
January 28, 2018: Sensors
D Sen, A Fashokun, R Angelotti, M Brooks, H Bhaumik, C Card, A Lodhi, A Godrej, C Chung
  An Artificial Intelligence system was developed and implemented for water, wastewater, and reuse plants to improve management of sensors, short and long-term maintenance plans, asset and investment management plans. It is based on an integrated approach to capture data from different computer systems and files. It adds a layer of intelligence to the data. It serves as a repository of key current and future operations and maintenance conditions that a plant needs have knowledge of. With this information, it can simulate the configuration of processes and assets for those conditions to improve or optimize operations, maintenance and asset management, using the IViewOps (Intelligent View of Operations) model...
April 1, 2018: Water Environment Research: a Research Publication of the Water Environment Federation
George B Stefano, Richard M Kream
The molecular evolution of genomic DNA across diverse plant and animal phyla involved dynamic registrations of sequence modifications to maintain existential homeostasis to increasingly complex patterns of environmental stressors. As an essential corollary, driver effects of positive evolutionary pressure are hypothesized to effect concerted modifications of genomic DNA sequences to meet expanded platforms of regulatory controls for successful implementation of advanced physiological requirements. It is also clearly apparent that preservation of updated registries of advantageous modifications of genomic DNA sequences requires coordinate expansion of convergent cellular proofreading/error correction mechanisms that are encoded by reciprocally modified genomic DNA...
August 14, 2017: Medical Science Monitor: International Medical Journal of Experimental and Clinical Research
Ding Wang, Haibo He, Derong Liu
Adaptive dynamic programming (ADP) and reinforcement learning are quite relevant to each other when performing intelligent optimization. They are both regarded as promising methods involving important components of evaluation and improvement, at the background of information technology, such as artificial intelligence, big data, and deep learning. Although great progresses have been achieved and surveyed when addressing nonlinear optimal control problems, the research on robustness of ADP-based control strategies under uncertain environment has not been fully summarized...
October 2017: IEEE Transactions on Cybernetics
Xueyan Zhang, Jianwu Zhang, Lin Li, Yuzhu Zhang, Guocai Yang
Chongqing mountain citrus orchard is one of the main origins of Chinese citrus. Its planting terrain is complex and soil parent material is diverse. Currently, the citrus fertilization, irrigation and other management processes still have great blindness. They usually use the same pattern and the same formula rather than considering the orchard terrain features, soil differences, species characteristics and the state of tree growth. With the help of the ZigBee technology, artificial intelligence and decision support technology, this paper has developed the research on the application technology of agricultural Internet of Things for real-time monitoring of citrus soil moisture and nutrients as well as the research on the integration of fertilization and irrigation decision support system...
February 23, 2017: Sensors
Elisa Terumi Rubel, Roberto Tadeu Raittz, Nilson Antonio da Rocha Coimbra, Michelly Alves Coutinho Gehlen, Fábio de Oliveira Pedrosa
BACKGROUND: Azopirillum brasilense is a plant-growth promoting nitrogen-fixing bacteria that is used as bio-fertilizer in agriculture. Since nitrogen fixation has a high-energy demand, the reduction of N2 to NH4(+) by nitrogenase occurs only under limiting conditions of NH4(+) and O2. Moreover, the synthesis and activity of nitrogenase is highly regulated to prevent energy waste. In A. brasilense nitrogenase activity is regulated by the products of draG and draT. The product of the draB gene, located downstream in the draTGB operon, may be involved in the regulation of nitrogenase activity by an, as yet, unknown mechanism...
December 15, 2016: BMC Bioinformatics
A M Corrêa, P E Teodoro, M C Gonçalves, L M A Barroso, M Nascimento, A Santos, F E Torres
Artificial neural networks have been used for various purposes in plant breeding, including use in the investigation of genotype x environment interactions. The aim of this study was to use artificial neural networks in the selection of common bean genotypes with high phenotypic adaptability and stability, and to verify their consistency with the Eberhart and Russell method. Six trials were conducted using 13 genotypes of common bean between 2002 and 2006 in the municipalities of Aquidauana and Dourados. The experimental design was a randomized block with three replicates...
April 28, 2016: Genetics and Molecular Research: GMR
Halis Simsek
Wastewater-derived dissolved organic nitrogen (DON) typically constitutes the majority of total dissolved nitrogen (TDN) discharged to surface waters from advanced wastewater treatment plants (WWTPs). When considering the stringent regulations on nitrogen discharge limits in sensitive receiving waters, DON becomes problematic and needs to be reduced. Biodegradable DON (BDON) is a portion of DON that is biologically degradable by bacteria when the optimum environmental conditions are met. BDON in a two-stage trickling filter WWTP was estimated using artificial intelligence techniques, such as adaptive neuro-fuzzy inference systems, multilayer perceptron, radial basis neural networks (RBNN), and generalized regression neural networks...
November 2016: Environmental Technology
Oluyemi Olatunji Awolusi, Mahmoud Nasr, Sheena Kumari, Faizal Bux
Nitrification at a full-scale activated sludge plant treating municipal wastewater was monitored over a period of 237 days. A combination of fluorescent in situ hybridization (FISH) and quantitative real-time polymerase chain reaction (qPCR) were used for identifying and quantifying the dominant nitrifiers in the plant. Adaptive neuro-fuzzy inference system (ANFIS), Pearson's correlation coefficient, and quadratic models were employed in evaluating the plant operational conditions that influence the nitrification performance...
July 2016: Microbial Ecology
Warren L Paul, Pat A Rokahr, Jeff M Webb, Gavin N Rees, Tim S Clune
Bayesian networks (BNs), or causal Bayesian networks, have become quite popular in ecological risk assessment and natural resource management because of their utility as a communication and decision-support tool. Since their development in the field of artificial intelligence in the 1980s, however, Bayesian networks have evolved and merged with structural equation modelling (SEM). Unlike BNs, which are constrained to encode causal knowledge in conditional probability tables, SEMs encode this knowledge in structural equations, which is thought to be a more natural language for expressing causal information...
March 2016: Environmental Monitoring and Assessment
Lin He, Gengyu Shen, Fei Li, Shuiqing Huang
Real-Time Quantitative Polymerase Chain Reaction (qRT-PCR) is widely used in biological research. It is a key to the availability of qRT-PCR experiment to select a stable reference gene. However, selecting an appropriate reference gene usually requires strict biological experiment for verification with high cost in the process of selection. Scientific literatures have accumulated a lot of achievements on the selection of reference gene. Therefore, mining reference genes under specific experiment environments from literatures can provide quite reliable reference genes for similar qRT-PCR experiments with the advantages of reliability, economic and efficiency...
2015: International Journal of Data Mining and Bioinformatics
Dennis Dollens
To incorporate metabolic, bioremedial functions into the performance of buildings and to balance generative architecture's dominant focus on computational programming and digital fabrication, this text first discusses hybridizing Maturana and Varela's biological theory of autopoiesis with Andy Clark's hypothesis of extended cognition. Doing so establishes a procedural protocol to research biological domains from which design could source data/insight from biosemiotics, sensory plants, and biocomputation. I trace computation and botanic simulations back to Alan Turing's little-known 1950s Morphogenetic drawings, reaction-diffusion algorithms, and pioneering artificial intelligence (AI) in order to establish bioarchitecture's generative point of origin...
July 2015: Communicative & Integrative Biology
Taisong Jin, Xueliang Hou, Pifan Li, Feifei Zhou
Automatic species identification has many advantages over traditional species identification. Currently, most plant automatic identification methods focus on the features of leaf shape, venation and texture, which are promising for the identification of some plant species. However, leaf tooth, a feature commonly used in traditional species identification, is ignored. In this paper, a novel automatic species identification method using sparse representation of leaf tooth features is proposed. In this method, image corners are detected first, and the abnormal image corner is removed by the PauTa criteria...
2015: PloS One
Jianping Fan, Ning Zhou, Jinye Peng, Ling Gao
In this paper, a hierarchical multi-task structural learning algorithm is developed to support large-scale plant species identification, where a visual tree is constructed for organizing large numbers of plant species in a coarse-to-fine fashion and determining the inter-related learning tasks automatically. For a given parent node on the visual tree, it contains a set of sibling coarse-grained categories of plant species or sibling fine-grained plant species, and a multi-task structural learning algorithm is developed to train their inter-related classifiers jointly for enhancing their discrimination power...
November 2015: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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