Search Results (16)

Cannabis sativa L. is a versatile plant with significant medicinal, industrial, and recreational applications. Its therapeutic potential is attributed to cannabinoids like THC and CBD, whose production is influenced by environmental factors, such as radiation, temperature, and humidity. Radiation, for instance, is essential for photosynthetic processes, acting as both a primary energy source and a regulator of plant growth and development. This review covers key factors affecting C. sativa cultivation, including photoperiod, light spectrum, cultivation methods, environmental controls, and plant growth regulators. It highlights how these elements influence flowering, biomass, and cannabinoid production across different growing systems, offering insights for optimizing both medicinal and industrial cannabis cultivation. Studies indicate that photoperiod sensitivity varies among cultivars, with some achieving optimal flowering and cannabinoid production under extended light periods rather than the traditional 12/12 h cycle. Light spectrum adjustments, especially red, far-red, and blue wavelengths, significantly impact photosynthesis, plant morphology, and secondary metabolite accumulation. Advances in LED technology allow precise spectral control, enhancing energy efficiency and cannabinoid profiles compared to conventional lighting. The photoperiod plays a vital role in the cultivation of C. sativa spp., directly impacting the plant’s developmental cycle, biomass production, and the concentration of cannabinoids and terpenes. The response to photoperiod varies among different cannabis cultivars, as demonstrated in studies comparing cultivars of diverse genetic origins. On the other hand, indoor or in vitro cultivation may serve as an excellent alternative for plant breeding programs in C. sativa, given the substantial inter-cultivar variability that hinders the fixation of desirable traits. Full article

Salvia atropatana is a medicinal plant native to Middle Eastern countries. It has been traditionally used in Turkish and Iranian folk medicine to treat infections, wounds, inflammatory diseases, spastic conditions, and diabetes. Its therapeutic potential has been attributed to its essential oil, polyphenolic acid, flavonoid, and diterpenoid content. The aim of the study was to determine the optimal conditions of in vitro S. atropatana shoot culture to enhance proliferation and secondary metabolite production. It examined the effects of various cytokinins and culture duration on culture growth parameters and phenolic compound accumulation. Exogenous cytokinin supplementation significantly enhanced shoot proliferation, with the highest proliferation ratio (6.3) observed with 1 and 2 mg/L 6-benzylaminopurine (BAP). Biomass accumulation was the highest at 0.5 mg/L BAP, followed by 1 and 2 mg/L meta-toplin (mTOP). Phenolic profiling identified nine compounds, with rosmarinic acid (RA) as the dominant metabolite. The highest RA content (16 mg/g dry weight) was achieved with 1 and 2 mg/L BAP and 0.5 mg/L of its ryboside. The TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method identified 1 mg/L BAP as the optimal treatment, balancing high proliferation, biomass, and polyphenol accumulation. Extending culture duration to 50 days increased biomass and phenolic content reaching 19.25 mg/g dry weight. However, morphological changes, including apical necrosis, were observed, and a significantly longer cultivation period was needed, questioning the value of the procedure. This study provides a basis for scalable in vitro production of bioactive compounds in S. atropatana. Full article

A large number of seabed depressions, covering an area of 2500 km² in the Xisha Massif of the South China Sea, are investigated using newly collected high-resolution acoustic data. By analyzing the morphological features and seismic attributes of the focused fluid flow system, five geological structures are recognized and described in detail, including pockmarks, volcanic mounds, pipes, faults, and forced folds. Pockmarks and volcanic mounds occur as clustered groups and their distributions are related to two large-scale volcanic zones with chaotic seismic reflections. Pipes, characterized by disordered seismic reflections, mainly occur within the focused fluid flow zone (FFFZ) and directly link with the large-scale deep volcano and its surrounding areas. Faults and fractures mainly occur along pipes and extend to the seafloor, commonly presenting lateral walls of mega-pockmarks. Forced folds are primarily clustered above volcanic zones and commonly restricted between faults or pipes, characterized by sediment deformations as indicated in seismic profiles. By comprehensive analysis of the above observations and a simplified simulation model, the volcanism-induced hydrothermal fluid activities are argued herein to contribute to these focused fluid flow structures. In addition, traces of suspected submarine instability disasters such as landslides have been found in this sea area, and more observational data will be needed to determine whether seafloor fluid flow zones can be used as a predictor of seafloor instability in the future. Full article

Beach profiles are constantly changing due to external ocean forces. Estimating these changes is crucial to understanding and addressing coastal erosion issues, such as shoreline advance and retreat. To estimate beach profile changes, obtaining long-term, high-resolution spatiotemporal beach profile data is essential. However, due to the limited availability of beach profile survey data both on land and underwater along the coast, generating continuous, high-resolution spatiotemporal beach profile data over extended periods is a critical technological challenge. Therefore, we herein developed a long short-term memory-based encoder–decoder network for effective spatiotemporal representation learning to estimate beach profile responses on temporal scales from weeks to months from coastal hydrodynamics. The proposed approach was applied to 12 transects from seven beaches located in three different littoral systems on the east coast of the Korean Peninsula, where coastal erosion problems are severe. The performance of the proposed method demonstrated improved results compared with a recent study that performed the same beach profile estimation task, with an average root mean square error of 0.50 m. Moreover, most of the results exhibited a reasonably accurate morphological shape of the estimated beach profile. However, instances where the results exceed the average error are attributed to extreme beach morphological changes caused by storm waves such as typhoons. Full article

Campylobacter jejuni is the foodborne pathogen causing most gastrointestinal infections. Understanding its ability to form biofilms is crucial for devising effective control strategies in food processing environments. In this study, we investigated the growth dynamics and biofilm formation of C. jejuni NCTC 11168 in various culture media, including chicken juice (CJ), brain heart infusion (BHI), and Mueller Hinton (MH) broth. Our results demonstrated that C. jejuni exhibited a higher growth rate and enhanced biofilm formation in CJ and in 1:1 mixtures of CJ with BHI or MH broth compared to these measures in BHI or MH broth alone. Electron microscopy unveiled distinct morphological attributes of late-stage biofilm cells in CJ, including the presence of elongated spiral-shaped cells, thinner stretched structures compared to regular cells, and extended thread-like structures within the biofilms. Proteomic analysis identified significant alterations in protein expression profiles in C. jejuni biofilms, with a predominance of downregulated proteins associated with vital functions like metabolism, energy production, and amino acid and protein biosynthesis. Additionally, a significant proportion of proteins linked to biofilm formation, virulence, and iron uptake were suppressed. This shift toward a predominantly coccoid morphology echoed the reduced energy demands of these biofilm communities. Our study unlocks valuable insights into C. jejuni’s biofilm in CJ, demonstrating its adaptation and survival. Full article

Some seismo-stratigraphic evidence on the occurrence of wave-cut marine terraces in the Licosa promontory (Southern Tyrrhenian Sea, Italy) based on Sub-bottom Chirp seismic sections is herein presented. Such evidence is provided by marine terraced surfaces situated at various water depths below sea level and etched into the rocky acoustic basement, which are extensively extending in the seaward extension of the Licosa promontory. It is possible that the isotopic stratigraphy and the terraced marine surfaces are connected, so they can be attributed and dated indirectly. The geologic study of seismic profiles has pointed to the prominence of the acoustic basement, extending to the seabed close to the coast and subsiding seawards under the Quaternary marine succession. Ancient remains of marine terraces, found at a range of water depths between 5 m and 50 m, have documented the major morphological changes of the acoustic basement during the Late Quaternary. Full article

Euphorbiaceae is a highly diverse family of plants ranging from trees to ground-dwelling minute plants. Many of these have multi-faceted attributes like ornamental, medicinal, industrial, and food-relevant values. In addition, they have been regarded as keystone resources for investigating plant-specific resilience mechanisms that grant them the dexterity to withstand harsh climates. In the present study, we isolated two co-culturable bacterial endophytes, EP1-AS and EP1-BM, from the stem internodal segments of the prostate spurge, Euphorbia prostrata, a plant member of the succulent family Euphorbiaceae. We characterized them using morphological, biochemical, and molecular techniques which revealed them as novel strains of Enterobacteriaceae, Lelliotia amnigena. Both the isolates significantly were qualified during the assaying of their plant growth promotion potentials. BM formed fast-growing swarms while AS showed growth as rounded colonies over nutrient agar. We validated the PGP effects of AS and BM isolates through in vitro and ex vitro seed-priming treatments with wheat and tomato, both of which resulted in significantly enhanced seed germination and morphometric and physiological plant growth profiles. In extended field trials, both AS and BM could remarkably also exhibit productive yields in wheat grain and tomato fruit harvests. This is probably the first-ever study in the context of PGPB endophytes in Euphorbia prostrata. We discuss our results in the context of promising agribiotechnology translations of the endophyte community associated with the otherwise neglected ground-dwelling spurges of Euphorbiaceae. Full article

Capsicum is one of the most perishable fruit which undergo rapid loss of commercial value during postharvest storage. In this experiment our aim is to evaluate the effect of photoactivated TiO₂ nano-particle complexed with chitosan or TiO₂-nanocomposite (TiO₂-NC) on extension self-life of Capsicum fruit and its effect on related morphological, physiological and molecular attributes at room temperature (25 °C). Initially, TiO₂-NC coated fruits recorded superior maintenance of total soluble solids accumulation along with retention of firmness, cellular integrity, hydration, color etc. On the extended period of storage, fruit recorded a lower bioaccumulation of TiO₂ in comparison to metallic silver over the control. On the level of gene expression for ethylene biosynthetic and signaling the TiO₂-NC had more regulation, however, discretely to moderate the ripening. Thus, ACC synthase and oxidase recorded a significantly better downregulation as studied from fruit pulp under TiO₂-NC than silver. On the signaling path, the transcripts for CaETR1 and CaETR2 were less abundant in fruit under both the treatment when studied against control for 7 d. The reactive oxygen species (ROS) was also correlated to retard the oxidative lysis of polyamine oxidation by diamine and polyamine oxidase activity. The gene expression for hydrolytic activity as non-specific esterase had corroborated the development of essential oil constituents with few of those recorded in significant abundance. Therefore, TiO₂-NC would be reliable to induce those metabolites modulating ripening behavior in favor of delayed ripening. From gas chromatography-mass spectrometry (GC-MS) analysis profile of all tested essential oil constituents suggesting positive impact of TiO₂-NC on shelf-life extension of Capsicum fruit. Our results indicated the potentiality of TiO₂-NC in postharvest storage those may connect ethylene signaling and ROS metabolism in suppression of specific ripening attributes. Full article

Duckweeds have been increasingly studied in recent years, both as model plants and in view of their potential applications as a new crop in a circular bioeconomy perspective. In order to select species and clones with the desired attributes, the correct identification of the species is fundamental. Molecular methods have recently provided a more solid base for taxonomy and yielded a consensus phylogenetic tree, although some points remain to be elucidated. The duckweed genus Lemna L. comprises twelve species, grouped in four sections, which include very similar sister species. The least taxonomically resolved is sect. Lemna, presenting difficulties in species delimitation using morphological and even barcoding molecular markers. Ambiguous species boundaries between Lemna minor L. and Lemna japonica Landolt have been clarified by Tubulin Based Polymorphism (TBP), with the discovery of interspecific hybrids. In the present work, we extended TBP profiling to a larger number of clones in sect. Lemna, previously classified using only morphological features, in order to test that classification, and to investigate the possible existence of other hybrids in this section. The analysis revealed several misidentifications of clones, in particular among the species L. minor, L. japonica and Lemna gibba L., and identified six putative ‘L. gibba’ clones as interspecific hybrids between L. minor and L. gibba. Full article

Hyperspectral image classification is essential for satellite Internet of Things (IoT) to build a large scale land-cover surveillance system. After acquiring real-time land-cover information, the edge of the network transmits all the hyperspectral images by satellites with low-latency and high-efficiency to the cloud computing center, which are provided by satellite IoT. A gigantic amount of remote sensing data bring challenges to the storage and processing capacity of traditional satellite systems. When hyperspectral images are used in annotation of land-cover application, data dimension reduction for classifier efficiency often leads to the decrease of classifier accuracy, especially the region to be annotated consists of natural landform and artificial structure. This paper proposes encoding spectral-spatial features for hyperspectral image classification in the satellite Internet of Things system to extract features effectively, namely attribute profile stacked autoencoder (AP-SAE). Firstly, extended morphology attribute profiles EMAP is used to obtain spatial features of different attribute scales. Secondly, AP-SAE is used to extract spectral features with similar spatial attributes. In this stage the program can learn feature mappings, on which the pixels from the same land-cover class are mapped as closely as possible and the pixels from different land-cover categories are separated by a large margin. Finally, the program trains an effective classifier by using the network of the AP-SAE. Experimental results on three widely-used hyperspectral image (HSI) datasets and comprehensive comparisons with existing methods demonstrate that our proposed method can be used effectively in hyperspectral image classification. Full article

In order to overcome the disadvantages of convolution neural network (CNN) in the current hyperspectral image (HSI) classification/segmentation methods, such as the inability to recognize the rotation of spatial objects, the difficulty to capture the fine spatial features and the problem that principal component analysis (PCA) ignores some important information when it retains few components, in this paper, an HSI segmentation model based on extended multi-morphological attribute profile (EMAP) features and cubic capsule network (EMAP–Cubic-Caps) was proposed. EMAP features can effectively extract various attributes profile features of entities in HSI, and the cubic capsule neural network can effectively capture complex spatial features with more details. Firstly, EMAP algorithm is introduced to extract the morphological attribute profile features of the principal components extracted by PCA, and the EMAP feature map is used as the input of the network. Then, the spectral and spatial low-layer information of the HSI is extracted by a cubic convolution network, and the high-layer information of HSI is extracted by the capsule module, which consists of an initial capsule layer and a digital capsule layer. Through the experimental comparison on three well-known HSI datasets, the superiority of the proposed algorithm in semantic segmentation is validated. Full article

Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and extended multi-attribute profiles (EMAPs) is presented for coastal wetland mapping using Sentinel-2 multispectral instrument (MSI) imagery. In the proposed method, the morphological attribute profiles (APs) are firstly extracted using four attribute filters based on the characteristics of wetlands in each band from Sentinel-2 imagery. These APs form a set of EMAPs which comprehensively represent the irregular wetland objects in multiscale and multilevel. The EMAPs and original spectral features are then classified with a new multilayer perceptron (MLP) classifier whose parameters are optimized by a stability-constrained adaptive alpha for a gravitational search algorithm. The performance of the proposed method was investigated using Sentinel-2 MSI images of two coastal wetlands, i.e., the Jiaozhou Bay and the Yellow River Delta in Shandong province of eastern China. Comparisons with four other classifiers through visual inspection and quantitative evaluation verified the superiority of the proposed method. Furthermore, the effectiveness of different APs in EMAPs were also validated. By combining the developed EMAPs features and novel MLP classifier, complicated wetland types with high within-class variability and low between-class disparity were effectively discriminated. The superior performance of the proposed framework makes it available and preferable for the mapping of complicated coastal wetlands using Sentinel-2 data and other similar optical imagery. Full article

Target detection is an active area in hyperspectral imagery (HSI) processing. Many algorithms have been proposed for the past decades. However, the conventional detectors mainly benefit from the spectral information without fully exploiting the spatial structures of HSI. Besides, they primarily use all bands information and ignore the inter-band redundancy. Moreover, they do not make full use of the difference between the background and target samples. To alleviate these problems, we proposed a novel joint sparse and low-rank multi-task learning (MTL) with extended multi-attribute profile (EMAP) algorithm (MTJSLR-EMAP). Briefly, the spatial features of HSI were first extracted by morphological attribute filters. Then the MTL was exploited to reduce band redundancy and retain the discriminative information simultaneously. Considering the distribution difference between the background and target samples, the target and background pixels were separately modeled with different regularization terms. In each task, a background pixel can be low-rank represented by the background samples while a target pixel can be sparsely represented by the target samples. Finally, the proposed algorithm was compared with six detectors including constrained energy minimization (CEM), adaptive coherence estimator (ACE), hierarchical CEM (hCEM), sparsity-based detector (STD), joint sparse representation and MTL detector (JSR-MTL), independent encoding JSR-MTL (IEJSR-MTL) on three datasets. Corresponding to each competitor, it has the average detection performance improvement of about 19.94%, 22.53%, 16.92%, 14.87%, 14.73%, 4.21% respectively. Extensive experimental results demonstrated that MTJSLR-EMAP outperforms several state-of-the-art algorithms. Full article

Characterizations of up to date information of the Earth’s surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation. Full article

Differential Morphological Profiles (DMPs) and their generalized Differential Attribute Profiles (DAPs) are spatial signatures used in the classification of earth observation data. The Characteristic-Salience-Leveling (CSL) is a model allowing the compression and storage of the multi-scale information contained in the DMPs and DAPs into raster data layers, used for further analytic purposes. Computing DMPs or DAPs is often constrained by the size of the input data and scene complexity. Addressing very high resolution remote sensing gigascale images, this paper presents a new concurrent algorithm based on the Max-Tree structure that allows the efficient computation of CSL. The algorithm extends the “one-pass” method for computation of DAPs, and delivers an attribute zone segmentation of the underlying trees. The DAP vector field and the set of multi-scale characteristics are computed separately and in a similar fashion to concurrent attribute filters. Experiments on test images of 3.48 to 3.96 Gpixel showed an average computational speed of 59.85 Mpixel per second, or 3.59 Gpixel per minute on a single 2U rack server with 64 opteron cores. The new algorithms could be extended to morphological keypoint detectors capable of handling gigascale images. Full article