In Vitro Propagation, Regeneration, Attempted Tetraploid Induction, and Agrobacterium-mediated Transformation of Euphorbia pulchurrima ‘Winter Rose’™

Poinsettia, Euphorbia pulchurrima, is the number one potted flowering plant in the United States. ‘Winter Rose’™ is a very popular cultivar with more than one million plants sold each year. To further improve this cultivar, particularly for larger flower heads and free branching, this research aimed at establishing some in vitro systems for application of biotechnology to poinsettia genetic improvement. A protocol was established for in vitro axillary bud proliferation using greenhouse grown terminal buds. Buds were placed on Murashige-Skoog (MS) basal medium supplemented with benzlyaminopurine (BA). Explants produced the greatest number of axillary buds on medium containing between 2.2-8.8 mM BA. The number of explants that produced axillary buds increased with increasing BA concentration. An organogenesis system was also established using in vitro grown leaf tissues. The greatest amount of callus and shoots were produced from leaf midvein sections placed on MS medium containing 8.8-13.3 mM benzylaminopurine (BA) and 17.1mM indole-3-acetic acid (IAA) for one month and then transferred to medium containing only BA. Adventitious buds were produced only from red-pigmented callus, and explants that produced callus continued to produce adventitious shoots in the presence of IAA. Five-month-old shoots derived from shoot culture or organogeneses rooted readily in artificial soil without treatment with IBA or treated with 50 or 100 mg/l IBA and were acclimated in the greenhouse. The effects of colchicine and oryzalin on callus production and adventitious shoot formation and their ability to induce tetraploid formation of Euphorbia pulchurrima ‘Winter Rose’™ were evaluated. In vitro grown leaf midvein sections were placed in/on various forms (liquid or solid) of medium supplemented with either colchicine or oryzalin. A range of duration times between 1-4 days was also evaluated. Colchicine was less damaging to leaf tissues at concentrations of 0.25 μM or 250.4 μM. A large amount of callus was produced as well as a few adventitious shoots. Oryzalin inhibited plant regeneration from leaf tissues at all concentrations tested, and caused severe necrosis. Tissues produced callus, but no shoots were formed. A protocol was established for using flow cytometry to determine the ploidy level in poinsettia. Sample calluses and regenerated shoots from colchicine treatments were evaluated using the flow cytometer and were found to be diploid. Callus from explants exposed to oryzalin-containing medium was also tested using the flow cytometer and no tetraploid tissue was found. Since colchicine showed generally less inhibitory effect than oryzalin, colchicine is considered to be a better mitotic inhibitor chemical for tetraploid formation in poinsettia than oryzalin. The factors influencing Agrobacterium-mediated transformation of Euphorbia pulchurrima ‘Winter Rose’™ were also evaluated. Kanamycin at 50 mg/L was sufficient to inhibit poinsettia callus and shoot formation, and appeared to be a suitable selectable antibiotic for selecting transformed cells in poinsettia. Variables evaluated in these studies included plasmid type (pBI121, pMON690), with or without addition of the antibiotics CCK (cefotaxime, carbenicillin, and kanamycin) or acetosyringone. Since all tissues infected with agrobacterium (co-cultivation) died in 1-2 months, it appeared that poinsettia is highly sensitive to agrobacterium infection. Because of this premature death of infected tissues, other variables such as acetosyringone, and CCK could not be evaluated effectively as to their effect on the transformation of ‘Winter Rose’™. Further study into antinecrosis chemicals or a change in explant type is needed in order to establish a protocol for Agrobacterium-mediated transformation of E. pulchurrima ‘Winter Rose’™. This research established foundation studies on which to build a biotechnological improvement program for E. pulchurrima ‘Winter Rose’™

Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network

The prevalence and mobility of smartphones make these a widely used tool for environmental health research. However, their potential for determining aggregated air quality index (AQI) based on PM2.5 concentration in specific locations remains largely unexplored in the existing literature. In this paper, we thoroughly examine the challenges associated with predicting location-specific PM2.5 concentration using images taken with smartphone cameras. The focus of our study is on Dhaka, the capital of Bangladesh, due to its significant air pollution levels and the large population exposed to it. Our research involves the development of a Deep Convolutional Neural Network (DCNN), which we train using over a thousand outdoor images taken and annotated. These photos are captured at various locations in Dhaka, and their labels are based on PM2.5 concentration data obtained from the local US consulate, calculated using the NowCast algorithm. Through supervised learning, our model establishes a correlation index during training, enhancing its ability to function as a Picture-based Predictor of PM2.5 Concentration (PPPC). This enables the algorithm to calculate an equivalent daily averaged AQI index from a smartphone image. Unlike, popular overly parameterized models, our model shows resource efficiency since it uses fewer parameters. Furthermore, test results indicate that our model outperforms popular models like ViT and INN, as well as popular CNN-based models such as VGG19, ResNet50, and MobileNetV2, in predicting location-specific PM2.5 concentration. Our dataset is the first publicly available collection that includes atmospheric images and corresponding PM2.5 measurements from Dhaka. Our code and dataset will be made public when publishing the paper.Comment: 18 pages, 7 figures, submitted to Nature Scientific Report

A review of remote sensing applications for oil palm studies

Oil palm becomes an increasingly important source of vegetable oil for its production exceeds soybean, sunflower, and rapeseed. The growth of the oil palm industry causes degradation to the environment, especially when the expansion of plantations goes uncontrolled. Remote sensing is a useful tool to monitor the development of oil palm plantations. In order to promote the use of remote sensing in the oil palm industry to support their drive for sustainability, this paper provides an understanding toward the use of remote sensing and its applications to oil palm plantation monitoring. In addition, the existing knowledge gaps are identified and recommendations for further research are given

Conceptualizing the Circular Economy (Revisited):An Analysis of 221 Definitions

In the past decade, use of the circular economy (CE) concept by scholars and practitioners has grown steadily. In a 2017 article, Kirchherr et al. found that the CE concept is interpreted and implemented in a variety of ways. While multiple interpretations of CE can enrich scholarly perspectives, differentiation and fragmentation can also impede consolidation of the concept. Some scholarship has discussed these trends in context-specific cases, but no large-scale, systematic study has analysed whether such consolidation has taken place across the field. This article fills this gap by analysing 221 recent CE definitions, making several notable findings. First, the concept has seen both consolidation and differentiation in the past five years. Second, definitional trends are emerging that potentially have more meaning for scholarship than for practice. Third, scholars increasingly recommend a fundamental systemic shift to enable CE, particularly within supply chains. Fourth, sustainable development is frequently considered the principal aim of CE, but questions linger about whether CE can mutually support environmental sustainability and economic development. Finally, recent studies argue that CE transition relies on a broad alliance of stakeholders, including producers, consumers, policymakers, and scholars. This study contributes an updated systematic analysis of CE definitions and conceptualizations that serves as an empirical snapshot of current scholarly thinking. It thereby provides a basis for further research on whether conceptual consolidation is needed and how it can be facilitated for practical purposes.</p

Vibrational spectroscopy as a tool to understand plant silicification

Die Ablagerung von Siliziumdioxid ist ein verbreitetes Phänomen, das mit der Toleranz von Pflanzen gegenüber Belastungen korreliert. Die Pflanzen akkumulieren das amorphe Siliziumdioxid in mikroskopischen Partikeln, den Phytolithen, jedoch ist der exakte Mechanismus nicht vollständig aufgeklärt. Um ein besseres Verständnis über die Ablagerung von Siliziumdioxid zu erlangen, wurden verschiedene spektroskopische Techniken an Sorghumblättern und molekularen Modellen angewandt. Festkörper Kernspinresonanz und thermogravimetrische Analysen zeigen, dass die Siliziumdioxidstruktur von der Phytolithe-Extraktion abhängt. Basierend auf Raman- und IR-Daten einzelner Phytolithe lassen sich die Änderungen dieser Strukturen ermitteln. Das deutet auf unterschiedliche biologische Prozesse der Ablagerung des Siliciumdioxids hin. Die Pflanzengewebe in denen Siliciumdioxid abgelagert ist, wurden mit einem multimodalen Ansatz charakterisiert, welcher Fluoreszenz-, Hellfeld- und Rasterelektronenmikroskopie beinhaltet. Die chemische Zusammensetzung der Pflanzengewebe wurden mit Raman- und FTIR-Mikrospektroskopie kartiert. Ein neuartiger Ansatz zur Untersuchung von Pflanzengeweben wurde verwendet, basierend auf der optischen Nahfeldmikroskopie im mittleren IR-Bereich. Dieser ermöglicht eine kombinierte Analyse von mechanischen Materialeigenschaften sowie der chemischen Zusammensetzung und Struktur. Um die Rolle der organischen Matrix zu verstehen, wurden Modellverbindungen betrachtet, die die Ablagerung von Kieselsäure in den Pflanzen induzieren können. In-vitro-Reaktionen konnten eine gleichzeitige Präzipitation von Lignin und Siliciumdioxid sowie eine Polymerisation zusammen mit Peptiden simulieren. Die Ergebnisse lassen starke Wechselwirkungen zwischen diesen Verbindungen vermuten. Neben einem besseren Verständnis verschiedener Aspekte der Silifizierung von Pflanzen werden in dieser Arbeit neue Methoden zur Charakterisierung von Pflanzenproben vorgeschlagen.Silica deposition is a common phenomenon that correlates with plant tolerance to various stresses. Plants accumulate amorphous silica in microscopic particles termed phytoliths, through yet unclear mechanisms. With the aim to gain better understanding of the processes that govern silica deposition, different vibrational techniques were used on sorghum leaves and molecular models to obtain chemical and structural information addressing different length scales. Solid-state Nuclear Magnetic Resonance and thermogravimetric analysis showed that phytolith extraction methods affect silica structure. Nevertheless, Raman and IR analysis of individual phytoliths revealed differences in the structure and composition between phytolith types, suggesting the existence of different biological pathways for silica deposition. The environment of sorghum tissues where silica is deposited was assessed using a multimodal approach consisting of fluorescence, brightfield and scanning electron microscopies, while chemical composition was mapped using Raman and Fourier transformed Infrared microspectroscopy. Scattering-type near-field optical microscopy in the mid-infrared region was used to characterize the plant tissues, in both fixed and native plant samples. The nano-IR images and the mechanical phase image enabled a combined probing of mechanical material properties together with the chemical composition and structure of both the cell walls and the phytolith structures. In vitro reactions simulating lignin-silica co-precipitation and silica polymerization with peptides revealed strong interaction between these compounds and silica, and their possible involvement in silica deposition in the plant. This thesis provides a better understanding of the chemical process that control plant silicification, suggests new methodologies to characterize plant samples, and evaluates the current methods used in plant science

Agricultural Monitoring System using Images through a LPWAN Network

Internet of things (IoT) has turned into an opportunity to connect millions of devices through communication networks in digital environments. Inside IoT and mainly in the technologies of communication networks, it is possible to find Low Power Wide Area Networks (LPWAN). Within these technologies, there are service platforms in unlicensed frequency bands such as the LoRa Wide Area Network (LoRaWAN). It has features such as low power consumption, long-distance operation between gateway and node, and low data transport capacity. LPWAN networks are not commonly used to transport high data rates as in the case of agricultural images. The main goal of this research is to present a methodology to transport images through LPWAN networks using LoRa modulation. The methodology presented in this thesis is composed of three stages mainly. The first one is image processing and classification process. This stage allows preparing the image in order to give the information to the classifier and separate the normal and abnormal images; i.e. to classify the images under the normal conditions of its representation in contrast with the images that can represent some sick or affectation with the consequent presence of a particular pathology. For this activity. it was used some techniques were used classifiers such as Support Vector Machine SVM, K-means clustering, neuronal networks, deep learning and convolutional neuronal networks. The last one offered the best results in classifying the samples of the images. The second stage consists in a compression technique and reconstruction algorithms. In this stage, a method is developed to process the image and entails the reduction of the high amount of information that an image has in its normal features with the goal to transport the lowest amount of information. For this purpose, a technique will be presented for the representation of the information of an image in a common base that improves the reduction process of the information. For this activity, the evaluated components were Wavelet, DCT-2D and Kronecker algorithms. The best results were obtained by Wavelet Transform. On the other hand, the compres- sion process entails a series of iterations in the vector information, therefore, each iteration is a possibility to reduce that vector until a value with a minimum PSNR (peak signal to noise ratio) that allows rebuilding the original vector. In the reconstruction process, Iterative Hard Thresholding (IHT), Ortogonal MAtching Pur- suit (OMP), Gradient Projection for Sparse Reconstruction (GPSR)and Step Iterative Shrinage/Thresholding (Twist) algorithms were evaluated. Twist showed the best performance in the results. Finally, in the third stage, LoRa modulation is implemented through the creation of LoRa symbols in Matlab with the compressed information. The symbols were delivered for transmission to Software Defined Radio (SDR). In the receptor, a SDR device receives the signal, which is converted into symbols that are in turn converted in an information vector. Then, the reconstruction process is carried out following the description in the last part of stage 2 - compression technique and reconstruction algorithms, which is described in more detailed in chapter 3, section 3.2. Finally, the image reconstructed is presented. The original image and the result image were compared in order to find the differences. This comparison used Peak Signal-to-Noise Ratio (PSNR) feature in order to get the fidelity of the reconstructed image with respect of the original image. In the receptor node, it is possible to observe the pathology of the leaf. The methodology is particularly applied for monitoring abnormal leaves samples in potato crops. This work allows finding a methodology to communicate images through LPWAN using the LoRa modulation technique. In this work, a framework was used to classify the images, then, to process them in order to reduce the amount of data, to establish communication between a transmitter and a receiver through a wireless communication system and finally, in the receptor, to obtain a picture that shows the particularity of the pathology in an agricultural crop.Gobernación de Boyacá, Colfuturo, Colciencias, Universidad Santo Tomás, Pontificia Universidad JaverianaInternet of things (IoT) has turned into an opportunity to connect millions of devices through communication networks in digital environments. Inside IoT and mainly in the technologies of communication networks, it is possible to find Low Power Wide Area Networks (LPWAN). Within these technologies, there are service platforms in unlicensed frequency bands such as the LoRa Wide Area Network (LoRaWAN). It has features such as low power consumption, long-distance operation between gateway and node, and low data transport capacity. LPWAN networks are not commonly used to transport high data rates as in the case of agricultural images. The main goal of this research is to present a methodology to transport images through LPWAN networks using LoRa modulation. The methodology presented in this thesis is composed of three stages mainly. The first one is image processing and classification process. This stage allows preparing the image in order to give the information to the classifier and separate the normal and abnormal images; i.e. to classify the images under the normal conditions of its representation in contrast with the images that can represent some sick or affectation with the consequent presence of a particular pathology. For this activity. it was used some techniques were used classifiers such as Support Vector Machine SVM, K-means clustering, neuronal networks, deep learning and convolutional neuronal networks. The last one offered the best results in classifying the samples of the images. The second stage consists in a compression technique and reconstruction algorithms. In this stage, a method is developed to process the image and entails the reduction of the high amount of information that an image has in its normal features with the goal to transport the lowest amount of information. For this purpose, a technique will be presented for the representation of the information of an image in a common base that improves the reduction process of the information. For this activity, the evaluated components were Wavelet, DCT-2D and Kronecker algorithms. The best results were obtained by Wavelet Transform. On the other hand, the compres- sion process entails a series of iterations in the vector information, therefore, each iteration is a possibility to reduce that vector until a value with a minimum PSNR (peak signal to noise ratio) that allows rebuilding the original vector. In the reconstruction process, Iterative Hard Thresholding (IHT), Ortogonal MAtching Pur- suit (OMP), Gradient Projection for Sparse Reconstruction (GPSR)and Step Iterative Shrinage/Thresholding (Twist) algorithms were evaluated. Twist showed the best performance in the results. Finally, in the third stage, LoRa modulation is implemented through the creation of LoRa symbols in Matlab with the compressed information. The symbols were delivered for transmission to Software Defined Radio (SDR). In the receptor, a SDR device receives the signal, which is converted into symbols that are in turn converted in an information vector. Then, the reconstruction process is carried out following the description in the last part of stage 2 - compression technique and reconstruction algorithms, which is described in more detailed in chapter 3, section 3.2. Finally, the image reconstructed is presented. The original image and the result image were compared in order to find the differences. This comparison used Peak Signal-to-Noise Ratio (PSNR) feature in order to get the fidelity of the reconstructed image with respect of the original image. In the receptor node, it is possible to observe the pathology of the leaf. The methodology is particularly applied for monitoring abnormal leaves samples in potato crops. This work allows finding a methodology to communicate images through LPWAN using the LoRa modulation technique. In this work, a framework was used to classify the images, then, to process them in order to reduce the amount of data, to establish communication between a transmitter and a receiver through a wireless communication system and finally, in the receptor, to obtain a picture that shows the particularity of the pathology in an agricultural crop.Doctor en IngenieríaDoctoradohttps://orcid.org/0000-0002-3554-1531https://scholar.google.com/citations?user=5_dx9REAAAAJ&hl=eshttps://scienti.minciencias.gov.co/cvlac/EnRecursoHumano/query.d

Agroforestry Opportunities for Enhancing Resilience to Climate Change in Rainfed Areas,

Not AvailableAgroforestry provides a unique opportunity to achieve the objectives of enhancing the productivity and improving the soil quality. Tree systems can also play an important role towards adapting to the climate variability and important carbon sinks which helps to decrease the pressure on natural forests. Realizing the importance of the agroforestry in meeting the twin objectives of mitigation and adaptation to climate change as well as making rainfed agriculture more climate resilient, the ICAR-CRIDA has taken up the challenge in pursuance of National Agroforestry Policy 2014, in preparing a book on Agroforestry Opportunities for Enhancing Resilience to Climate Change in Rainfed Areas at ICAR-CRIDA to sharpen the skills of all stakeholders at national, state and district level in rainfed areas to increase agricultural productivity in response to climate changeNot Availabl

On unifying sparsity and geometry for image-based 3D scene representation

Demand has emerged for next generation visual technologies that go beyond conventional 2D imaging. Such technologies should capture and communicate all perceptually relevant three-dimensional information about an environment to a distant observer, providing a satisfying, immersive experience. Camera networks offer a low cost solution to the acquisition of 3D visual information, by capturing multi-view images from different viewpoints. However, the camera's representation of the data is not ideal for common tasks such as data compression or 3D scene analysis, as it does not make the 3D scene geometry explicit. Image-based scene representations fundamentally require a multi-view image model that facilitates extraction of underlying geometrical relationships between the cameras and scene components. Developing new, efficient multi-view image models is thus one of the major challenges in image-based 3D scene representation methods. This dissertation focuses on defining and exploiting a new method for multi-view image representation, from which the 3D geometry information is easily extractable, and which is additionally highly compressible. The method is based on sparse image representation using an overcomplete dictionary of geometric features, where a single image is represented as a linear combination of few fundamental image structure features (edges for example). We construct the dictionary by applying a unitary operator to an analytic function, which introduces a composition of geometric transforms (translations, rotation and anisotropic scaling) to that function. The advantage of this approach is that the features across multiple views can be related with a single composition of transforms. We then establish a connection between image components and scene geometry by defining the transforms that satisfy the multi-view geometry constraint, and obtain a new geometric multi-view correlation model. We first address the construction of dictionaries for images acquired by omnidirectional cameras, which are particularly convenient for scene representation due to their wide field of view. Since most omnidirectional images can be uniquely mapped to spherical images, we form a dictionary by applying motions on the sphere, rotations, and anisotropic scaling to a function that lives on the sphere. We have used this dictionary and a sparse approximation algorithm, Matching Pursuit, for compression of omnidirectional images, and additionally for coding 3D objects represented as spherical signals. Both methods offer better rate-distortion performance than state of the art schemes at low bit rates. The novel multi-view representation method and the dictionary on the sphere are then exploited for the design of a distributed coding method for multi-view omnidirectional images. In a distributed scenario, cameras compress acquired images without communicating with each other. Using a reliable model of correlation between views, distributed coding can achieve higher compression ratios than independent compression of each image. However, the lack of a proper model has been an obstacle for distributed coding in camera networks for many years. We propose to use our geometric correlation model for distributed multi-view image coding with side information. The encoder employs a coset coding strategy, developed by dictionary partitioning based on atom shape similarity and multi-view geometry constraints. Our method results in significant rate savings compared to independent coding. An additional contribution of the proposed correlation model is that it gives information about the scene geometry, leading to a new camera pose estimation method using an extremely small amount of data from each camera. Finally, we develop a method for learning stereo visual dictionaries based on the new multi-view image model. Although dictionary learning for still images has received a lot of attention recently, dictionary learning for stereo images has been investigated only sparingly. Our method maximizes the likelihood that a set of natural stereo images is efficiently represented with selected stereo dictionaries, where the multi-view geometry constraint is included in the probabilistic modeling. Experimental results demonstrate that including the geometric constraints in learning leads to stereo dictionaries that give both better distributed stereo matching and approximation properties than randomly selected dictionaries. We show that learning dictionaries for optimal scene representation based on the novel correlation model improves the camera pose estimation and that it can be beneficial for distributed coding