Soil moisture, organic carbon, and nitrogen content prediction with hyperspectral data using regression models
- Datta, Dristi, Paul, Manoranjan, Murshed, Manzur, Teng, Shyh Wei, Schmidtke, Leigh
- Authors: Datta, Dristi , Paul, Manoranjan , Murshed, Manzur , Teng, Shyh Wei , Schmidtke, Leigh
- Date: 2022
- Type: Text , Journal article
- Relation: Sensors (Basel, Switzerland) Vol. 22, no. 20 (2022), p.
- Full Text:
- Reviewed:
- Description: Soil moisture, soil organic carbon, and nitrogen content prediction are considered significant fields of study as they are directly related to plant health and food production. Direct estimation of these soil properties with traditional methods, for example, the oven-drying technique and chemical analysis, is a time and resource-consuming approach and can predict only smaller areas. With the significant development of remote sensing and hyperspectral (HS) imaging technologies, soil moisture, carbon, and nitrogen can be estimated over vast areas. This paper presents a generalized approach to predicting three different essential soil contents using a comprehensive study of various machine learning (ML) models by considering the dimensional reduction in feature spaces. In this study, we have used three popular benchmark HS datasets captured in Germany and Sweden. The efficacy of different ML algorithms is evaluated to predict soil content, and significant improvement is obtained when a specific range of bands is selected. The performance of ML models is further improved by applying principal component analysis (PCA), a dimensional reduction method that works with an unsupervised learning method. The effect of soil temperature on soil moisture prediction is evaluated in this study, and the results show that when the soil temperature is considered with the HS band, the soil moisture prediction accuracy does not improve. However, the combined effect of band selection and feature transformation using PCA significantly enhances the prediction accuracy for soil moisture, carbon, and nitrogen content. This study represents a comprehensive analysis of a wide range of established ML regression models using data preprocessing, effective band selection, and data dimension reduction and attempt to understand which feature combinations provide the best accuracy. The outcomes of several ML models are verified with validation techniques and the best- and worst-case scenarios in terms of soil content are noted. The proposed approach outperforms existing estimation techniques.
- Authors: Datta, Dristi , Paul, Manoranjan , Murshed, Manzur , Teng, Shyh Wei , Schmidtke, Leigh
- Date: 2022
- Type: Text , Journal article
- Relation: Sensors (Basel, Switzerland) Vol. 22, no. 20 (2022), p.
- Full Text:
- Reviewed:
- Description: Soil moisture, soil organic carbon, and nitrogen content prediction are considered significant fields of study as they are directly related to plant health and food production. Direct estimation of these soil properties with traditional methods, for example, the oven-drying technique and chemical analysis, is a time and resource-consuming approach and can predict only smaller areas. With the significant development of remote sensing and hyperspectral (HS) imaging technologies, soil moisture, carbon, and nitrogen can be estimated over vast areas. This paper presents a generalized approach to predicting three different essential soil contents using a comprehensive study of various machine learning (ML) models by considering the dimensional reduction in feature spaces. In this study, we have used three popular benchmark HS datasets captured in Germany and Sweden. The efficacy of different ML algorithms is evaluated to predict soil content, and significant improvement is obtained when a specific range of bands is selected. The performance of ML models is further improved by applying principal component analysis (PCA), a dimensional reduction method that works with an unsupervised learning method. The effect of soil temperature on soil moisture prediction is evaluated in this study, and the results show that when the soil temperature is considered with the HS band, the soil moisture prediction accuracy does not improve. However, the combined effect of band selection and feature transformation using PCA significantly enhances the prediction accuracy for soil moisture, carbon, and nitrogen content. This study represents a comprehensive analysis of a wide range of established ML regression models using data preprocessing, effective band selection, and data dimension reduction and attempt to understand which feature combinations provide the best accuracy. The outcomes of several ML models are verified with validation techniques and the best- and worst-case scenarios in terms of soil content are noted. The proposed approach outperforms existing estimation techniques.
Comparative analysis of machine and deep learning models for soil properties prediction from hyperspectral visual band
- Datta, Dristi, Paul, Manoranjan, Murshed, Manzur, Teng, Shyh Wei, Schmidtke, Leigh
- Authors: Datta, Dristi , Paul, Manoranjan , Murshed, Manzur , Teng, Shyh Wei , Schmidtke, Leigh
- Date: 2023
- Type: Text , Journal article
- Relation: Environments Vol. 10, no. 5 (2023), p. 77
- Full Text:
- Reviewed:
- Description: Estimating various properties of soil, including moisture, carbon, and nitrogen, is crucial for studying their correlation with plant health and food production. However, conventional methods such as oven-drying and chemical analysis are laborious, expensive, and only feasible for a limited land area. With the advent of remote sensing technologies like multi/hyperspectral imaging, it is now possible to predict soil properties non-invasive and cost-effectively for a large expanse of bare land. Recent research shows the possibility of predicting those soil contents from a wide range of hyperspectral data using good prediction algorithms. However, these kinds of hyperspectral sensors are expensive and not widely available. Therefore, this paper investigates different machine and deep learning techniques to predict soil nutrient properties using only the red (R), green (G), and blue (B) bands data to propose a suitable machine/deep learning model that can be used as a rapid soil test. Another objective of this research is to observe and compare the prediction accuracy in three cases i. hyperspectral band ii. full spectrum of the visual band, and iii. three-channel of RGB band and provide a guideline to the user on which spectrum information they should use to predict those soil properties. The outcome of this research helps to develop a mobile application that is easy to use for a quick soil test. This research also explores learning-based algorithms with significant feature combinations and their performance comparisons in predicting soil properties from visual band data. For this, we also explore the impact of dimensional reduction (i.e., principal component analysis) and transformations (i.e., empirical mode decomposition) of features. The results show that the proposed model can comparably predict the soil contents from the three-channel RGB data.
- Authors: Datta, Dristi , Paul, Manoranjan , Murshed, Manzur , Teng, Shyh Wei , Schmidtke, Leigh
- Date: 2023
- Type: Text , Journal article
- Relation: Environments Vol. 10, no. 5 (2023), p. 77
- Full Text:
- Reviewed:
- Description: Estimating various properties of soil, including moisture, carbon, and nitrogen, is crucial for studying their correlation with plant health and food production. However, conventional methods such as oven-drying and chemical analysis are laborious, expensive, and only feasible for a limited land area. With the advent of remote sensing technologies like multi/hyperspectral imaging, it is now possible to predict soil properties non-invasive and cost-effectively for a large expanse of bare land. Recent research shows the possibility of predicting those soil contents from a wide range of hyperspectral data using good prediction algorithms. However, these kinds of hyperspectral sensors are expensive and not widely available. Therefore, this paper investigates different machine and deep learning techniques to predict soil nutrient properties using only the red (R), green (G), and blue (B) bands data to propose a suitable machine/deep learning model that can be used as a rapid soil test. Another objective of this research is to observe and compare the prediction accuracy in three cases i. hyperspectral band ii. full spectrum of the visual band, and iii. three-channel of RGB band and provide a guideline to the user on which spectrum information they should use to predict those soil properties. The outcome of this research helps to develop a mobile application that is easy to use for a quick soil test. This research also explores learning-based algorithms with significant feature combinations and their performance comparisons in predicting soil properties from visual band data. For this, we also explore the impact of dimensional reduction (i.e., principal component analysis) and transformations (i.e., empirical mode decomposition) of features. The results show that the proposed model can comparably predict the soil contents from the three-channel RGB data.
- Authors: Mestrom, Sanne
- Date: 2012
- Type: Text , Visual art work
- Full Text:
Efficient data gathering in 3D linear underwater wireless sensor networks using sink mobility
- Akbar, Mariam, Javaid, Nadeem, Khan, Ayesha, Imran, Muhammad, Shoaib, Muhammad, Vasilakos, Athanasios
- Authors: Akbar, Mariam , Javaid, Nadeem , Khan, Ayesha , Imran, Muhammad , Shoaib, Muhammad , Vasilakos, Athanasios
- Date: 2016
- Type: Text , Journal article
- Relation: Sensors (Switzerland) Vol. 16, no. 3 (2016), p.
- Full Text:
- Reviewed:
- Description: Due to the unpleasant and unpredictable underwater environment, designing an energy-efficient routing protocol for underwater wireless sensor networks (UWSNs) demands more accuracy and extra computations. In the proposed scheme, we introduce a mobile sink (MS), i.e., an autonomous underwater vehicle (AUV), and also courier nodes (CNs), to minimize the energy consumption of nodes. MS and CNs stop at specific stops for data gathering; later on, CNs forward the received data to the MS for further transmission. By the mobility of CNs and MS, the overall energy consumption of nodes is minimized. We perform simulations to investigate the performance of the proposed scheme and compare it to preexisting techniques. Simulation results are compared in terms of network lifetime, throughput, path loss, transmission loss and packet drop ratio. The results show that the proposed technique performs better in terms of network lifetime, throughput, path loss and scalability. © 2016 by the authors; licensee MDPI, Basel, Switzerland.
- Authors: Akbar, Mariam , Javaid, Nadeem , Khan, Ayesha , Imran, Muhammad , Shoaib, Muhammad , Vasilakos, Athanasios
- Date: 2016
- Type: Text , Journal article
- Relation: Sensors (Switzerland) Vol. 16, no. 3 (2016), p.
- Full Text:
- Reviewed:
- Description: Due to the unpleasant and unpredictable underwater environment, designing an energy-efficient routing protocol for underwater wireless sensor networks (UWSNs) demands more accuracy and extra computations. In the proposed scheme, we introduce a mobile sink (MS), i.e., an autonomous underwater vehicle (AUV), and also courier nodes (CNs), to minimize the energy consumption of nodes. MS and CNs stop at specific stops for data gathering; later on, CNs forward the received data to the MS for further transmission. By the mobility of CNs and MS, the overall energy consumption of nodes is minimized. We perform simulations to investigate the performance of the proposed scheme and compare it to preexisting techniques. Simulation results are compared in terms of network lifetime, throughput, path loss, transmission loss and packet drop ratio. The results show that the proposed technique performs better in terms of network lifetime, throughput, path loss and scalability. © 2016 by the authors; licensee MDPI, Basel, Switzerland.
A new image dissimilarity measure incorporating human perception
- Shojanazeri, Hamid, Teng, Shyh, Aryal, Sunil, Zhang, Dengsheng, Lu, Guojun
- Authors: Shojanazeri, Hamid , Teng, Shyh , Aryal, Sunil , Zhang, Dengsheng , Lu, Guojun
- Date: 2018
- Type: Text , Unpublished work
- Full Text:
- Description: Pairwise (dis) similarity measure of data objects is central to many applications of image anlaytics, such as image retrieval and classification. Geometric distance, particularly Euclidean distance ((
- Authors: Shojanazeri, Hamid , Teng, Shyh , Aryal, Sunil , Zhang, Dengsheng , Lu, Guojun
- Date: 2018
- Type: Text , Unpublished work
- Full Text:
- Description: Pairwise (dis) similarity measure of data objects is central to many applications of image anlaytics, such as image retrieval and classification. Geometric distance, particularly Euclidean distance ((
An enhancement to the spatial pyramid matching for image classification and retrieval
- Karmakar, Priyabrata, Teng, Shyh, Lu, Guojun, Zhang, Dengsheng
- Authors: Karmakar, Priyabrata , Teng, Shyh , Lu, Guojun , Zhang, Dengsheng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 22463-22472
- Full Text:
- Reviewed:
- Description: Spatial pyramid matching (SPM) is one of the widely used methods to incorporate spatial information into the image representation. Despite its effectiveness, the traditional SPM is not rotation invariant. A rotation invariant SPM has been proposed in the literature but it has many limitations regarding the effectiveness. In this paper, we investigate how to make SPM robust to rotation by addressing those limitations. In an SPM framework, an image is divided into an increasing number of partitions at different pyramid levels. In this paper, our main focus is on how to partition images in such a way that the resulting structure can deal with image-level rotations. To do that, we investigate three concentric ring partitioning schemes. Apart from image partitioning, another important component of the SPM framework is a weight function. To apportion the contribution of each pyramid level to the final matching between two images, the weight function is needed. In this paper, we propose a new weight function which is suitable for the rotation-invariant SPM structure. Experiments based on image classification and retrieval are performed on five image databases. The detailed result analysis shows that we are successful in enhancing the effectiveness of SPM for image classification and retrieval. © 2013 IEEE.
- Authors: Karmakar, Priyabrata , Teng, Shyh , Lu, Guojun , Zhang, Dengsheng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 22463-22472
- Full Text:
- Reviewed:
- Description: Spatial pyramid matching (SPM) is one of the widely used methods to incorporate spatial information into the image representation. Despite its effectiveness, the traditional SPM is not rotation invariant. A rotation invariant SPM has been proposed in the literature but it has many limitations regarding the effectiveness. In this paper, we investigate how to make SPM robust to rotation by addressing those limitations. In an SPM framework, an image is divided into an increasing number of partitions at different pyramid levels. In this paper, our main focus is on how to partition images in such a way that the resulting structure can deal with image-level rotations. To do that, we investigate three concentric ring partitioning schemes. Apart from image partitioning, another important component of the SPM framework is a weight function. To apportion the contribution of each pyramid level to the final matching between two images, the weight function is needed. In this paper, we propose a new weight function which is suitable for the rotation-invariant SPM structure. Experiments based on image classification and retrieval are performed on five image databases. The detailed result analysis shows that we are successful in enhancing the effectiveness of SPM for image classification and retrieval. © 2013 IEEE.
On the regularity of weak solutions of the boussinesq equations in besov spaces
- Barbagallo, Annamaria, Gala, Sadek, Ragusa, Maria, Théra, Michel
- Authors: Barbagallo, Annamaria , Gala, Sadek , Ragusa, Maria , Théra, Michel
- Date: 2021
- Type: Text , Journal article
- Relation: Vietnam Journal of Mathematics Vol. 49, no. 3 (2021), p. 637-649
- Relation: http://purl.org/au-research/grants/arc/DP160100854
- Full Text:
- Reviewed:
- Description: The main issue addressed in this paper concerns an extension of a result by Z. Zhang who proved, in the context of the homogeneous Besov space Ḃ
- Authors: Barbagallo, Annamaria , Gala, Sadek , Ragusa, Maria , Théra, Michel
- Date: 2021
- Type: Text , Journal article
- Relation: Vietnam Journal of Mathematics Vol. 49, no. 3 (2021), p. 637-649
- Relation: http://purl.org/au-research/grants/arc/DP160100854
- Full Text:
- Reviewed:
- Description: The main issue addressed in this paper concerns an extension of a result by Z. Zhang who proved, in the context of the homogeneous Besov space Ḃ
Mandarin DP1-he-DP2 in the subject position
- Authors: Han, Weifeng , Shi, Dingxu
- Date: 2022
- Type: Text , Journal article
- Relation: SKASE Journal of Theoretical Linguistics Vol. 19, no. 1 (2022), p. 43-62
- Full Text:
- Reviewed:
- Description: Recent studies claim that, syntactically, he in DP1-he-DP2 can only be analyzed as a conjunction or as a preposition, but not both, in the subject position in Mandarin. This paper presents both empirical and theoretical arguments against such singular analyses of he. Drawn upon cross-linguistic evidence, we argue that he is open to both a conjunction and a proposition analyses. Under the Merge theory, it is argued that the prepositional phrase (PP) is derived through only EXTERNAL MERGE (EM), while the conjunction phrase (&P) is yielded through EM and then INTERNAL MERGE (IM). Therefore, PP and &P undergo different processes of labelling. The Phase Impenetrability Condition helps explain the topicalization and focus marking issues by the singular analysis of he as a preposition only. This paper illustrates how the same lexical item of he is used for both the conjunction and the comitative structures in Mandarin, and how both structures differ syntactically under the Merge theory. © 2022 Slovak Association for the Study of English. All rights reserved.
- Authors: Han, Weifeng , Shi, Dingxu
- Date: 2022
- Type: Text , Journal article
- Relation: SKASE Journal of Theoretical Linguistics Vol. 19, no. 1 (2022), p. 43-62
- Full Text:
- Reviewed:
- Description: Recent studies claim that, syntactically, he in DP1-he-DP2 can only be analyzed as a conjunction or as a preposition, but not both, in the subject position in Mandarin. This paper presents both empirical and theoretical arguments against such singular analyses of he. Drawn upon cross-linguistic evidence, we argue that he is open to both a conjunction and a proposition analyses. Under the Merge theory, it is argued that the prepositional phrase (PP) is derived through only EXTERNAL MERGE (EM), while the conjunction phrase (&P) is yielded through EM and then INTERNAL MERGE (IM). Therefore, PP and &P undergo different processes of labelling. The Phase Impenetrability Condition helps explain the topicalization and focus marking issues by the singular analysis of he as a preposition only. This paper illustrates how the same lexical item of he is used for both the conjunction and the comitative structures in Mandarin, and how both structures differ syntactically under the Merge theory. © 2022 Slovak Association for the Study of English. All rights reserved.
The Zinc Transporter, Slc39a7 (Zip7) Is Implicated in Glycaemic Control in Skeletal Muscle Cells
- Myers, Stephen, Nield, Alex, Chew, Guatsiew, Myers, Mark
- Authors: Myers, Stephen , Nield, Alex , Chew, Guatsiew , Myers, Mark
- Date: 2013
- Type: Text , Journal article
- Relation: Plos One Vol. 8, no. 11 (November 2013 2013), p. 15
- Full Text:
- Reviewed:
- Description: Dysfunctional zinc signaling is implicated in disease processes including cardiovascular disease, Alzheimer's disease and diabetes. Of the twenty-four mammalian zinc transporters, ZIP7 has been identified as an important mediator of the 'zinc wave' and in cellular signaling. Utilizing siRNA targeting Zip7 mRNA we have identified that Zip7 regulates glucose metabolism in skeletal muscle cells. An siRNA targeting Zip7 mRNA down regulated Zip7 mRNA 4.6-fold (p = 0.0006) when compared to a scramble control. This was concomitant with a reduction in the expression of genes involved in glucose metabolism including Agl, Dlst, Galm, Gbe1, Idh3g, Pck2, Pgam2, Pgm2, Phkb, Pygm, Tpi1, Gusb and Glut4. Glut4 protein expression was also reduced and insulin-stimulated glycogen synthesis was decreased. This was associated with a reduction in the mRNA expression of Insr, Irs1 and Irs2, and the phosphorylation of Akt. These studies provide a novel role for Zip7 in glucose metabolism in skeletal muscle and highlight the importance of this transporter in contributing to glycaemic control in this tissue.
- Authors: Myers, Stephen , Nield, Alex , Chew, Guatsiew , Myers, Mark
- Date: 2013
- Type: Text , Journal article
- Relation: Plos One Vol. 8, no. 11 (November 2013 2013), p. 15
- Full Text:
- Reviewed:
- Description: Dysfunctional zinc signaling is implicated in disease processes including cardiovascular disease, Alzheimer's disease and diabetes. Of the twenty-four mammalian zinc transporters, ZIP7 has been identified as an important mediator of the 'zinc wave' and in cellular signaling. Utilizing siRNA targeting Zip7 mRNA we have identified that Zip7 regulates glucose metabolism in skeletal muscle cells. An siRNA targeting Zip7 mRNA down regulated Zip7 mRNA 4.6-fold (p = 0.0006) when compared to a scramble control. This was concomitant with a reduction in the expression of genes involved in glucose metabolism including Agl, Dlst, Galm, Gbe1, Idh3g, Pck2, Pgam2, Pgm2, Phkb, Pygm, Tpi1, Gusb and Glut4. Glut4 protein expression was also reduced and insulin-stimulated glycogen synthesis was decreased. This was associated with a reduction in the mRNA expression of Insr, Irs1 and Irs2, and the phosphorylation of Akt. These studies provide a novel role for Zip7 in glucose metabolism in skeletal muscle and highlight the importance of this transporter in contributing to glycaemic control in this tissue.
Psychological distress, fear and coping strategies among hong kong people during the COVID-19 pandemic
- Chair, Sek, Chien, Wai, Liu, Ting, Lam, Louisa, Cross, Wendy, Banik, Biswajit, Rahman, Muhammad Aziz
- Authors: Chair, Sek , Chien, Wai , Liu, Ting , Lam, Louisa , Cross, Wendy , Banik, Biswajit , Rahman, Muhammad Aziz
- Date: 2023
- Type: Text , Journal article
- Relation: Current Psychology Vol. 42, no. 3 (2023), p. 2538-2557
- Full Text:
- Reviewed:
- Description: The COVID-19 pandemic contributed to potential adverse effects on the mental health status of a wide range of people. This study aimed to identify factors associated with psychological distress, fear and coping strategies during the COVID-19 pandemic in Hong Kong. A cross-sectional online survey was conducted among general population in Hong Kong. Psychological distress was assessed using the Kessler Psychological Distress Scale; level of fear was evaluated using the Fear of COVID-19 scale; and coping strategies were assessed using the Brief Resilient Coping Scale. Multivariable logistic regression was used to identify key factors associated with these mental health variables. Of the 555 participants, 53.9% experienced moderate to very high levels of psychological distress, 31.2% experienced a high level of fear of COVID-19, and 58.6% showed moderate to high resilient coping. Multivariable logistic regression indicated that living with family members, current alcohol consumption, and higher level of fear were associated with higher levels of psychological distress; perceived stress due to a change in employment condition, being a frontline worker, experiencing ‘moderate to very high’ distress, and healthcare service use to overcome the COVID-19 related stress in past 6 months were associated with a higher level of fear; and perceived better mental health status was associated with a moderate to high resilient coping. This study identified key factors associated with distress, fear and coping strategies during the pandemic in Hong Kong. Mental health support strategies should be provided continuously to prevent the mental impact of the pandemic from turning into long-term illness. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
- Authors: Chair, Sek , Chien, Wai , Liu, Ting , Lam, Louisa , Cross, Wendy , Banik, Biswajit , Rahman, Muhammad Aziz
- Date: 2023
- Type: Text , Journal article
- Relation: Current Psychology Vol. 42, no. 3 (2023), p. 2538-2557
- Full Text:
- Reviewed:
- Description: The COVID-19 pandemic contributed to potential adverse effects on the mental health status of a wide range of people. This study aimed to identify factors associated with psychological distress, fear and coping strategies during the COVID-19 pandemic in Hong Kong. A cross-sectional online survey was conducted among general population in Hong Kong. Psychological distress was assessed using the Kessler Psychological Distress Scale; level of fear was evaluated using the Fear of COVID-19 scale; and coping strategies were assessed using the Brief Resilient Coping Scale. Multivariable logistic regression was used to identify key factors associated with these mental health variables. Of the 555 participants, 53.9% experienced moderate to very high levels of psychological distress, 31.2% experienced a high level of fear of COVID-19, and 58.6% showed moderate to high resilient coping. Multivariable logistic regression indicated that living with family members, current alcohol consumption, and higher level of fear were associated with higher levels of psychological distress; perceived stress due to a change in employment condition, being a frontline worker, experiencing ‘moderate to very high’ distress, and healthcare service use to overcome the COVID-19 related stress in past 6 months were associated with a higher level of fear; and perceived better mental health status was associated with a moderate to high resilient coping. This study identified key factors associated with distress, fear and coping strategies during the pandemic in Hong Kong. Mental health support strategies should be provided continuously to prevent the mental impact of the pandemic from turning into long-term illness. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
The molecular epidemiology of influenza in Cambodia
- Authors: Suttie, Annika
- Date: 2019
- Type: Text , Thesis , PhD
- Full Text:
- Description: Avian influenza viruses (AIVs) represent a risk to the health of humans and animals. The prevalence of AIVs in live bird markets in Cambodia is among the highest in the world, being detected in 45.5% of tested poultry in 2015. To better understand the potential risk presented by AIVs, this thesis investigated the genetic characteristics of AIVs circulating in Cambodia between 2014 to 2018; focusing on subtypes that pose the greatest risk to human and animal health (H5, H7 and H9). Highly pathogenic (HP) H5N1 clade 2.3.2.1c viruses and low pathogenic H9N2 BJ/94-like h9-4.2.5 clade viruses were the most frequently detected subtypes, and circulate endemically in Cambodia’s domestic poultry. Co-infections were detected and facilitated the production of two novel reassortant H5N1 AIVs with single genes from H9N2 viruses. Additionally, numerous intrasubtypic reassortment events were detected for H5 and H9 AIVs. This is concerning as reassortment events can rapidly produce novel viruses of public health risk. Phylogenetic analyses showed some genes of the Cambodian H5, H7 and H9 AIVs clustered with zoonotic viruses, suggesting a common origin. There are parallels between H5N1 and H9N2 AIVs detected in Cambodia and Vietnam, likely facilitated through the illegal trade of live poultry and/or the migration of wild birds. Molecular analyses showed H9 AIVs have major markers associated with adaptation to mammals; though during the study period the only human AIV cases were the result of HP H5N1. Molecular markers of resistance to adamantine antivirals was observed in 3% of H5 and 41% of H9 AIVs; however, both subtypes remain susceptible to first line antiviral treatment, neuraminidase inhibitors. The data presented in this thesis demonstrates that circulation of Cambodian AIVs represents a risk for the emergence of novel viruses. Interventions are urgently needed to mitigate the threat posed to poultry and humans.
- Description: Doctor of Philosophy
- Authors: Suttie, Annika
- Date: 2019
- Type: Text , Thesis , PhD
- Full Text:
- Description: Avian influenza viruses (AIVs) represent a risk to the health of humans and animals. The prevalence of AIVs in live bird markets in Cambodia is among the highest in the world, being detected in 45.5% of tested poultry in 2015. To better understand the potential risk presented by AIVs, this thesis investigated the genetic characteristics of AIVs circulating in Cambodia between 2014 to 2018; focusing on subtypes that pose the greatest risk to human and animal health (H5, H7 and H9). Highly pathogenic (HP) H5N1 clade 2.3.2.1c viruses and low pathogenic H9N2 BJ/94-like h9-4.2.5 clade viruses were the most frequently detected subtypes, and circulate endemically in Cambodia’s domestic poultry. Co-infections were detected and facilitated the production of two novel reassortant H5N1 AIVs with single genes from H9N2 viruses. Additionally, numerous intrasubtypic reassortment events were detected for H5 and H9 AIVs. This is concerning as reassortment events can rapidly produce novel viruses of public health risk. Phylogenetic analyses showed some genes of the Cambodian H5, H7 and H9 AIVs clustered with zoonotic viruses, suggesting a common origin. There are parallels between H5N1 and H9N2 AIVs detected in Cambodia and Vietnam, likely facilitated through the illegal trade of live poultry and/or the migration of wild birds. Molecular analyses showed H9 AIVs have major markers associated with adaptation to mammals; though during the study period the only human AIV cases were the result of HP H5N1. Molecular markers of resistance to adamantine antivirals was observed in 3% of H5 and 41% of H9 AIVs; however, both subtypes remain susceptible to first line antiviral treatment, neuraminidase inhibitors. The data presented in this thesis demonstrates that circulation of Cambodian AIVs represents a risk for the emergence of novel viruses. Interventions are urgently needed to mitigate the threat posed to poultry and humans.
- Description: Doctor of Philosophy
Comparison studies of the structural stability of rabbit prion protein with hyman and mouse prion proteins
- Authors: Zhang, Jiapu
- Date: 2011
- Type: Text , Journal article
- Relation: Journal of Theoretical biology Vol. 269, no. 1 (2011), p. 88-95
- Full Text:
- Reviewed:
- Description: Background: Prion diseases are fatal and infectious neurodegenerative diseases affecting humans and animals. Rabbits are one of the few mammalian species reported to be resistant to infection from prion diseases isolated from other species (I. Vorberg et al., Journal of Virology 77 (3) (2003) 2003-2009). Thus the study of rabbit prion protein structure to obtain insight into the immunity of rabbits to prion diseases is very important. Findings: The paper is a straight forward molecular dynamics simulation study of wild-type rabbit prion protein (monomer cellular form) which apparently resists the formation of the scrapie form. The comparison analyses with human and mouse prion proteins done so far show that the rabbit prion protein has a stable structure. The main point is that the enhanced stability of the C-terminal ordered region especially helix 2 through the D177-R163 salt-bridge formation renders the rabbit prion protein stable. The salt bridge D201-R155 linking helixes 3 and 1 also contributes to the structural stability of rabbit prion protein. The hydrogen bond H186-R155 partially contributes to the structural stability of rabbit prion protein. Conclusions: Rabbit prion protein was found to own the structural stability, the salt bridges D177-R163, D201-R155 greatly contribute and the hydrogen bond H186-R155 partially contributes to this structural stability. The comparison of the structural stability of prion proteins from the three species rabbit, human and mouse showed that the human and mouse prion protein structures were not affected by the removing these two salt bridges. Dima et al. (Biophysical Journal 83 (2002) 1268-1280 and Proceedings of the National Academy of Sciences of the United States of America 101 (2004) 15335-15340) also confirmed this point and pointed out that "correlated mutations that reduce the frustration in the second half of helix 2 in mammalian prion proteins could inhibit the formation of PrPSc".
- Authors: Zhang, Jiapu
- Date: 2011
- Type: Text , Journal article
- Relation: Journal of Theoretical biology Vol. 269, no. 1 (2011), p. 88-95
- Full Text:
- Reviewed:
- Description: Background: Prion diseases are fatal and infectious neurodegenerative diseases affecting humans and animals. Rabbits are one of the few mammalian species reported to be resistant to infection from prion diseases isolated from other species (I. Vorberg et al., Journal of Virology 77 (3) (2003) 2003-2009). Thus the study of rabbit prion protein structure to obtain insight into the immunity of rabbits to prion diseases is very important. Findings: The paper is a straight forward molecular dynamics simulation study of wild-type rabbit prion protein (monomer cellular form) which apparently resists the formation of the scrapie form. The comparison analyses with human and mouse prion proteins done so far show that the rabbit prion protein has a stable structure. The main point is that the enhanced stability of the C-terminal ordered region especially helix 2 through the D177-R163 salt-bridge formation renders the rabbit prion protein stable. The salt bridge D201-R155 linking helixes 3 and 1 also contributes to the structural stability of rabbit prion protein. The hydrogen bond H186-R155 partially contributes to the structural stability of rabbit prion protein. Conclusions: Rabbit prion protein was found to own the structural stability, the salt bridges D177-R163, D201-R155 greatly contribute and the hydrogen bond H186-R155 partially contributes to this structural stability. The comparison of the structural stability of prion proteins from the three species rabbit, human and mouse showed that the human and mouse prion protein structures were not affected by the removing these two salt bridges. Dima et al. (Biophysical Journal 83 (2002) 1268-1280 and Proceedings of the National Academy of Sciences of the United States of America 101 (2004) 15335-15340) also confirmed this point and pointed out that "correlated mutations that reduce the frustration in the second half of helix 2 in mammalian prion proteins could inhibit the formation of PrPSc".
About intrinsic transversality of pairs of sets
- Authors: Kruger, Alexander
- Date: 2018
- Type: Text , Journal article
- Relation: Set-Valued and Variational Analysis Vol. 26, no. 1 (2018), p. 111-142
- Relation: http://purl.org/au-research/grants/arc/DP160100854
- Full Text:
- Reviewed:
- Description: The article continues the study of the ‘regular’ arrangement of a collection of sets near a point in their intersection. Such regular intersection or, in other words, transversality properties are crucial for the validity of qualification conditions in optimization as well as subdifferential, normal cone and coderivative calculus, and convergence analysis of computational algorithms. One of the main motivations for the development of the transversality theory of collections of sets comes from the convergence analysis of alternating projections for solving feasibility problems. This article targets infinite dimensional extensions of the intrinsic transversality property introduced recently by Drusvyatskiy, Ioffe and Lewis as a sufficient condition for local linear convergence of alternating projections. Several characterizations of this property are established involving new limiting objects defined for pairs of sets. Special attention is given to the convex case.
- Authors: Kruger, Alexander
- Date: 2018
- Type: Text , Journal article
- Relation: Set-Valued and Variational Analysis Vol. 26, no. 1 (2018), p. 111-142
- Relation: http://purl.org/au-research/grants/arc/DP160100854
- Full Text:
- Reviewed:
- Description: The article continues the study of the ‘regular’ arrangement of a collection of sets near a point in their intersection. Such regular intersection or, in other words, transversality properties are crucial for the validity of qualification conditions in optimization as well as subdifferential, normal cone and coderivative calculus, and convergence analysis of computational algorithms. One of the main motivations for the development of the transversality theory of collections of sets comes from the convergence analysis of alternating projections for solving feasibility problems. This article targets infinite dimensional extensions of the intrinsic transversality property introduced recently by Drusvyatskiy, Ioffe and Lewis as a sufficient condition for local linear convergence of alternating projections. Several characterizations of this property are established involving new limiting objects defined for pairs of sets. Special attention is given to the convex case.
A new fuzzy logic approach for consistent interpretation of dissolved gas-in-oil analysis
- Abu-Siada, Ahmed, Hmood, Sdood, Islam, Syed
- Authors: Abu-Siada, Ahmed , Hmood, Sdood , Islam, Syed
- Date: 2013
- Type: Text , Journal article
- Relation: IEEE Transactions on Dielectrics and Electrical Insulation Vol. 20, no. 6 (2013), p. 2343-2349
- Full Text:
- Reviewed:
- Description: Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results however all these techniques rely on personnel experience more than analytical formulation. As a result, various interpretation techniques do not necessarily lead to the same conclusion for the same oil sample. Furthermore, significant number of DGA results fall outside the proposed codes of the current based-ratio interpretation techniques and cannot be diagnosed by these methods. Moreover, ratio methods fail to diagnose multiple fault conditions due to the mixing up of produced gases. To overcome these limitations, this paper introduces a new fuzzy logic approach to reduce dependency on expert personnel and to aid in standardizing DGA interpretation techniques. The approach relies on incorporating all existing DGA interpretation techniques into one expert model. DGA results of 2000 oil samples that were collected from different transformers of different rating and different life span are used to establish the model. Traditional DGA interpretation techniques are used to analyze the collected DGA results to evaluate the consistency and accuracy of each interpretation technique. Results of this analysis were then used to develop the proposed fuzzy logic model.
- Authors: Abu-Siada, Ahmed , Hmood, Sdood , Islam, Syed
- Date: 2013
- Type: Text , Journal article
- Relation: IEEE Transactions on Dielectrics and Electrical Insulation Vol. 20, no. 6 (2013), p. 2343-2349
- Full Text:
- Reviewed:
- Description: Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results however all these techniques rely on personnel experience more than analytical formulation. As a result, various interpretation techniques do not necessarily lead to the same conclusion for the same oil sample. Furthermore, significant number of DGA results fall outside the proposed codes of the current based-ratio interpretation techniques and cannot be diagnosed by these methods. Moreover, ratio methods fail to diagnose multiple fault conditions due to the mixing up of produced gases. To overcome these limitations, this paper introduces a new fuzzy logic approach to reduce dependency on expert personnel and to aid in standardizing DGA interpretation techniques. The approach relies on incorporating all existing DGA interpretation techniques into one expert model. DGA results of 2000 oil samples that were collected from different transformers of different rating and different life span are used to establish the model. Traditional DGA interpretation techniques are used to analyze the collected DGA results to evaluate the consistency and accuracy of each interpretation technique. Results of this analysis were then used to develop the proposed fuzzy logic model.
Recent advances in the immunity research of rabbits to Prion Diseases
- Authors: Zhang, Jiapu
- Date: 2013
- Type: Text , Journal article
- Relation: Biochemistry & Pharmacology Vol. 2, no. e143 (2013), p. 1-2
- Full Text:
- Reviewed:
- Authors: Zhang, Jiapu
- Date: 2013
- Type: Text , Journal article
- Relation: Biochemistry & Pharmacology Vol. 2, no. e143 (2013), p. 1-2
- Full Text:
- Reviewed:
A new perceptual dissimilarity measure for image retrieval and clustering
- Authors: Shojanazeri, Hamid
- Date: 2018
- Type: Text , Thesis , PhD
- Full Text:
- Description: Image retrieval and clustering are two important tools for analysing and organising images. Dissimilarity measure is central to both image retrieval and clustering. The performance of image retrieval and clustering algorithms depends on the effectiveness of the dissimilarity measure. ‘Minkowski’ distance, or more specifically, ‘Euclidean’ distance, is the most widely used dissimilarity measure in image retrieval and clustering. Euclidean distance depends only on the geometric position of two data instances in the feature space and completely ignores the data distribution. However, data distribution has an effect on human perception. The argument that two data instances in a dense area are more perceptually dissimilar than the same two instances in a sparser area, is proposed by psychologists. Based on this idea, a dissimilarity measure called, ‘mp’, has been proposed to address Euclidean distance’s limitation of ignoring the data distribution. Here, mp relies on data distribution to calculate the dissimilarity between two instances. As prescribed in mp, higher data mass between two data instances implies higher dissimilarity, and vice versa. mp relies only on data distribution and completely ignores the geometric distance in its calculations. In the aggregation of dissimilarities between two instances over all the dimensions in feature space, both Euclidean distance and mp give same priority to all the dimensions. This may result in a situation that the final dissimilarity between two data instances is determined by a few dimensions of feature vectors with relatively much higher values. As a result, the dissimilarity derived may not align well with human perception. The need to address the limitations of Minkowski distance measures, along with the importance of a dissimilarity measure that considers both geometric distance and the perceptual effect of data distribution in measuring dissimilarity between images motivated this thesis. It studies the performance of mp for image retrieval. It investigates a new dissimilarity measure that combines both Euclidean distance and data distribution. In addition to these, it studies the performance of such a dissimilarity measure for image retrieval and clustering. Our performance study of mp for image retrieval shows that relying only on data distribution to measure the dissimilarity results in some situations, where the mp’s measurement is contrary to human perception. This thesis introduces a new dissimilarity measure called, perceptual dissimilarity measure (PDM). PDM considers the perceptual effect of data distribution in combination with Euclidean distance. PDM has two variants, PDM1 and PDM2. PDM1 focuses on improving mp by weighting it using Euclidean distance in situations where mp may not retrieve accurate results. PDM2 considers the effect of data distribution on the perceived dissimilarity measured by Euclidean distance. PDM2 proposes a weighting system for Euclidean distance using a logarithmic transform of data mass. The proposed PDM variants have been used as alternatives to Euclidean distance and mp to improve the accuracy in image retrieval. Our results show that PDM2 has consistently performed the best, compared to Euclidean distance, mp and PDM1. PDM1’s performance was not consistent, although it has performed better than mp in all the experiments, but it could not outperform Euclidean distance in some cases. Following the promising results of PDM2 in image retrieval, we have studied its performance for image clustering. k-means is the most widely used clustering algorithm in scientific and industrial applications. k-medoids is the closest clustering algorithm to k-means. Unlike k-means which works only with Euclidean distance, k-medoids gives the option to choose the arbitrary dissimilarity measure. We have used Euclidean distance, mp and PDM2 as the dissimilarity measure in k-medoids and compared the results with k-means. Our clustering results show that PDM2 has perfromed overally the best. This confirms our retrieval results and identifies PDM2 as a suitable dissimilarity measure for image retrieval and clustering.
- Description: Doctor of Philosophy
- Authors: Shojanazeri, Hamid
- Date: 2018
- Type: Text , Thesis , PhD
- Full Text:
- Description: Image retrieval and clustering are two important tools for analysing and organising images. Dissimilarity measure is central to both image retrieval and clustering. The performance of image retrieval and clustering algorithms depends on the effectiveness of the dissimilarity measure. ‘Minkowski’ distance, or more specifically, ‘Euclidean’ distance, is the most widely used dissimilarity measure in image retrieval and clustering. Euclidean distance depends only on the geometric position of two data instances in the feature space and completely ignores the data distribution. However, data distribution has an effect on human perception. The argument that two data instances in a dense area are more perceptually dissimilar than the same two instances in a sparser area, is proposed by psychologists. Based on this idea, a dissimilarity measure called, ‘mp’, has been proposed to address Euclidean distance’s limitation of ignoring the data distribution. Here, mp relies on data distribution to calculate the dissimilarity between two instances. As prescribed in mp, higher data mass between two data instances implies higher dissimilarity, and vice versa. mp relies only on data distribution and completely ignores the geometric distance in its calculations. In the aggregation of dissimilarities between two instances over all the dimensions in feature space, both Euclidean distance and mp give same priority to all the dimensions. This may result in a situation that the final dissimilarity between two data instances is determined by a few dimensions of feature vectors with relatively much higher values. As a result, the dissimilarity derived may not align well with human perception. The need to address the limitations of Minkowski distance measures, along with the importance of a dissimilarity measure that considers both geometric distance and the perceptual effect of data distribution in measuring dissimilarity between images motivated this thesis. It studies the performance of mp for image retrieval. It investigates a new dissimilarity measure that combines both Euclidean distance and data distribution. In addition to these, it studies the performance of such a dissimilarity measure for image retrieval and clustering. Our performance study of mp for image retrieval shows that relying only on data distribution to measure the dissimilarity results in some situations, where the mp’s measurement is contrary to human perception. This thesis introduces a new dissimilarity measure called, perceptual dissimilarity measure (PDM). PDM considers the perceptual effect of data distribution in combination with Euclidean distance. PDM has two variants, PDM1 and PDM2. PDM1 focuses on improving mp by weighting it using Euclidean distance in situations where mp may not retrieve accurate results. PDM2 considers the effect of data distribution on the perceived dissimilarity measured by Euclidean distance. PDM2 proposes a weighting system for Euclidean distance using a logarithmic transform of data mass. The proposed PDM variants have been used as alternatives to Euclidean distance and mp to improve the accuracy in image retrieval. Our results show that PDM2 has consistently performed the best, compared to Euclidean distance, mp and PDM1. PDM1’s performance was not consistent, although it has performed better than mp in all the experiments, but it could not outperform Euclidean distance in some cases. Following the promising results of PDM2 in image retrieval, we have studied its performance for image clustering. k-means is the most widely used clustering algorithm in scientific and industrial applications. k-medoids is the closest clustering algorithm to k-means. Unlike k-means which works only with Euclidean distance, k-medoids gives the option to choose the arbitrary dissimilarity measure. We have used Euclidean distance, mp and PDM2 as the dissimilarity measure in k-medoids and compared the results with k-means. Our clustering results show that PDM2 has perfromed overally the best. This confirms our retrieval results and identifies PDM2 as a suitable dissimilarity measure for image retrieval and clustering.
- Description: Doctor of Philosophy
The puzzling falcomurus mandal (Collembola, orchesellidae, heteromurinae) : a review
- Bellini, Bruno, De Souza, Paolla, Greenslade, Penelope
- Authors: Bellini, Bruno , De Souza, Paolla , Greenslade, Penelope
- Date: 2021
- Type: Text , Journal article
- Relation: Insects Vol. 12, no. 7 (2021), p.
- Full Text:
- Reviewed:
- Description: Falcomurus Mandal is currently a monotypic genus of Heteromurinae described from India in 2018. Its key characters are the first antennal segment subdivided, the second undivided and the third annulated, the first abdominal segment lacking macrochaetae, and the presence of a sinuous modified macrochaeta on the proximal dens. Some details of its morphology were recently put in doubt, and so its genus status and affinities remain uncertain. Here, we revise the genus based on the type material of Dicranocentrus litoreus Mari-Mutt, as well as provide the description of two new species from Australian archipelagos and a reinterpretation of the chaetotaxy of Falcomurus chilikaensis Mandal and D. halophilus Mari-Mutt. After our revision, Falcomurus shows a well-conserved chaetotaxy and overall morphology, which allowed us to provide an updated generic diagnosis. While the antennae morphology of Falcomurus resembles that of Dicranocentrus Schött, its dorsal sensillar and macrochaetotaxy suggest it is closely related to HeteromurusWankel, as originally stated by Mandal. The main features useful to separate Falcomurus species are the head, mesothorax and fourth abdominal segment chaetotaxy. We also provide a key to its five species, a comparative table and notes on the affinities and distribution of Falcomurus. © 2021 by the authors.
- Authors: Bellini, Bruno , De Souza, Paolla , Greenslade, Penelope
- Date: 2021
- Type: Text , Journal article
- Relation: Insects Vol. 12, no. 7 (2021), p.
- Full Text:
- Reviewed:
- Description: Falcomurus Mandal is currently a monotypic genus of Heteromurinae described from India in 2018. Its key characters are the first antennal segment subdivided, the second undivided and the third annulated, the first abdominal segment lacking macrochaetae, and the presence of a sinuous modified macrochaeta on the proximal dens. Some details of its morphology were recently put in doubt, and so its genus status and affinities remain uncertain. Here, we revise the genus based on the type material of Dicranocentrus litoreus Mari-Mutt, as well as provide the description of two new species from Australian archipelagos and a reinterpretation of the chaetotaxy of Falcomurus chilikaensis Mandal and D. halophilus Mari-Mutt. After our revision, Falcomurus shows a well-conserved chaetotaxy and overall morphology, which allowed us to provide an updated generic diagnosis. While the antennae morphology of Falcomurus resembles that of Dicranocentrus Schött, its dorsal sensillar and macrochaetotaxy suggest it is closely related to HeteromurusWankel, as originally stated by Mandal. The main features useful to separate Falcomurus species are the head, mesothorax and fourth abdominal segment chaetotaxy. We also provide a key to its five species, a comparative table and notes on the affinities and distribution of Falcomurus. © 2021 by the authors.
Optimum design of limaçon gas expanders based on thermodynamic performance
- Authors: Sultan, Ibrahim
- Date: 2012
- Type: Text , Journal article
- Relation: Applied Thermal Engineering Vol. 39, no. 4 (2012), p. 188-197
- Full Text:
- Reviewed:
- Description: Positive displacement expanders are acquiring popularity due to the current push to harvest energy from low-grade heat resources which have been previously overlooked. The limaçon technology does offer a simple and reliable design with a considerable potential for small-size (≤4 kW) power plants. This paper presents a thermodynamic model for the limaçon design and goes on to utilise this model in an optimisation procedure adopted to calculate the expanders geometric parameters for specific power and operating constraints. The numerical method employed to solve the thermodynamic model is presented for the benefit of the reader. Two design case studies, for expanders with and without an inlet control valve, are offered at the end of the paper to prove the validity of the presented concepts and their suitability for the analysis. © 2012 Elsevier Ltd.
- Authors: Sultan, Ibrahim
- Date: 2012
- Type: Text , Journal article
- Relation: Applied Thermal Engineering Vol. 39, no. 4 (2012), p. 188-197
- Full Text:
- Reviewed:
- Description: Positive displacement expanders are acquiring popularity due to the current push to harvest energy from low-grade heat resources which have been previously overlooked. The limaçon technology does offer a simple and reliable design with a considerable potential for small-size (≤4 kW) power plants. This paper presents a thermodynamic model for the limaçon design and goes on to utilise this model in an optimisation procedure adopted to calculate the expanders geometric parameters for specific power and operating constraints. The numerical method employed to solve the thermodynamic model is presented for the benefit of the reader. Two design case studies, for expanders with and without an inlet control valve, are offered at the end of the paper to prove the validity of the presented concepts and their suitability for the analysis. © 2012 Elsevier Ltd.
Glyphosate Resistance of C-3 and C-4 Weeds under Rising Atmospheric CO2
- Fernando, Nimesha, Manalil, Sudheesh, Florentine, Singarayer, Chauhan, Bhagirath, Seneweera, Saman
- Authors: Fernando, Nimesha , Manalil, Sudheesh , Florentine, Singarayer , Chauhan, Bhagirath , Seneweera, Saman
- Date: 2016
- Type: Text , Journal article , Review
- Relation: Frontiers in Plant Science Vol. 7, no. (Jun 2016), p. 1-11
- Full Text:
- Reviewed:
- Description: The present paper reviews current knowledge on how changes of plant metabolism under elevated CO2 concentrations (e[CO2]) can affect the development of the glyphosate resistance of C-3 and C-4 weeds. Among the chemical herbicides, glyphosate, which is a non-selective and post-emergence herbicide, is currently the most widely used herbicide in global agriculture. As a consequence, glyphosate resistant weeds, particularly in major field crops, are a widespread problem and are becoming a significant challenge to future global food production. Of particular interest here it is known that the biochemical processes involved in photosynthetic pathways of C-3 and C-4 plants are different, which may have relevance to their competitive development under changing environmental conditions. It has already been shown that plant anatomical, morphological, and physiological changes under e[CO2] can be different, based on (i) the plant's functional group, (ii) the available soil nutrients, and (iii) the governing water status. In this respect, C-3 species are likely to have a major developmental advantage under a CO2 rich atmosphere, by being able to capitalize on the overall stimulatory effect of e[CO2]. For example, many tropical weed grass species fix CO2 from the atmosphere via the C-4 photosynthetic pathway, which is a complex anatomical and biochemical variant of the C-3 pathway. Thus, based on our current knowledge of CO2 fixing, it would appear obvious that the development of a glyphosate-resistant mechanism would be easier under an e[CO2] in C-3 weeds which have a simpler photosynthetic pathway, than for C-4 weeds. However, notwithstanding this logical argument, a better understanding of the biochemical, genetic, and molecular measures by which plants develop glyphosate resistance and how e[CO2] affects these measures will be important before attempting to innovate sustainable technology to manage the glyphosate-resistant evolution of weeds under e[CO2]. Such information will be of essential in managing weed control by herbicide use, and to thus ensure an increase in global food production in the event of increased atmospheric [CO2] levels.
- Authors: Fernando, Nimesha , Manalil, Sudheesh , Florentine, Singarayer , Chauhan, Bhagirath , Seneweera, Saman
- Date: 2016
- Type: Text , Journal article , Review
- Relation: Frontiers in Plant Science Vol. 7, no. (Jun 2016), p. 1-11
- Full Text:
- Reviewed:
- Description: The present paper reviews current knowledge on how changes of plant metabolism under elevated CO2 concentrations (e[CO2]) can affect the development of the glyphosate resistance of C-3 and C-4 weeds. Among the chemical herbicides, glyphosate, which is a non-selective and post-emergence herbicide, is currently the most widely used herbicide in global agriculture. As a consequence, glyphosate resistant weeds, particularly in major field crops, are a widespread problem and are becoming a significant challenge to future global food production. Of particular interest here it is known that the biochemical processes involved in photosynthetic pathways of C-3 and C-4 plants are different, which may have relevance to their competitive development under changing environmental conditions. It has already been shown that plant anatomical, morphological, and physiological changes under e[CO2] can be different, based on (i) the plant's functional group, (ii) the available soil nutrients, and (iii) the governing water status. In this respect, C-3 species are likely to have a major developmental advantage under a CO2 rich atmosphere, by being able to capitalize on the overall stimulatory effect of e[CO2]. For example, many tropical weed grass species fix CO2 from the atmosphere via the C-4 photosynthetic pathway, which is a complex anatomical and biochemical variant of the C-3 pathway. Thus, based on our current knowledge of CO2 fixing, it would appear obvious that the development of a glyphosate-resistant mechanism would be easier under an e[CO2] in C-3 weeds which have a simpler photosynthetic pathway, than for C-4 weeds. However, notwithstanding this logical argument, a better understanding of the biochemical, genetic, and molecular measures by which plants develop glyphosate resistance and how e[CO2] affects these measures will be important before attempting to innovate sustainable technology to manage the glyphosate-resistant evolution of weeds under e[CO2]. Such information will be of essential in managing weed control by herbicide use, and to thus ensure an increase in global food production in the event of increased atmospheric [CO2] levels.
A guide to the short, long and circular RNAs in hypertension and cardiovascular disease
- Prestes, Priscilla, Maier, Michelle, Woods, Bradley, Charchar, Fadi
- Authors: Prestes, Priscilla , Maier, Michelle , Woods, Bradley , Charchar, Fadi
- Date: 2020
- Type: Text , Journal article , Review
- Relation: International Journal of Molecular Sciences Vol. 21, no. 10 (2020)
- Full Text:
- Reviewed:
- Description: Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in adults in developed countries. CVD encompasses many diseased states, including hypertension, coronary artery disease and atherosclerosis. Studies in animal models and human studies have elucidated the contribution of many genetic factors, including non-coding RNAs. Non-coding RNAs are RNAs not translated into protein, involved in gene expression regulation post-transcriptionally and implicated in CVD. Of these, circular RNAs (circRNAs) and microRNAs are relevant. CircRNAs are created by the back-splicing of pre-messenger RNA and have been underexplored as contributors to CVD. These circRNAs may also act as biomarkers of human disease, as they can be extracted from whole blood, plasma, saliva and seminal fluid. CircRNAs have recently been implicated in various disease processes, including hypertension and other cardiovascular disease. This review article will explore the promising and emerging roles of circRNAs as potential biomarkers and therapeutic targets in CVD, in particular hypertension. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Prestes, Priscilla , Maier, Michelle , Woods, Bradley , Charchar, Fadi
- Date: 2020
- Type: Text , Journal article , Review
- Relation: International Journal of Molecular Sciences Vol. 21, no. 10 (2020)
- Full Text:
- Reviewed:
- Description: Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in adults in developed countries. CVD encompasses many diseased states, including hypertension, coronary artery disease and atherosclerosis. Studies in animal models and human studies have elucidated the contribution of many genetic factors, including non-coding RNAs. Non-coding RNAs are RNAs not translated into protein, involved in gene expression regulation post-transcriptionally and implicated in CVD. Of these, circular RNAs (circRNAs) and microRNAs are relevant. CircRNAs are created by the back-splicing of pre-messenger RNA and have been underexplored as contributors to CVD. These circRNAs may also act as biomarkers of human disease, as they can be extracted from whole blood, plasma, saliva and seminal fluid. CircRNAs have recently been implicated in various disease processes, including hypertension and other cardiovascular disease. This review article will explore the promising and emerging roles of circRNAs as potential biomarkers and therapeutic targets in CVD, in particular hypertension. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.