Search Results (53)

This study focuses on the nameplate of Vila D. Bosco, a modern building in Macau from the time of Portuguese rule, and looks at the types of metal materials and surface coatings used, as well as how they corrode due to the tropical marine climate affecting the building’s metal parts. The study uses different techniques, such as X-ray fluorescence spectroscopy (XRF), scanning electron microscopy/energy dispersive spectroscopy (SEM-EDS), X-ray diffraction (XRD), attenuated total internal reflectance Fourier transform infrared spectroscopy (ATR-FTIR), and cross-sectional microscopic analysis, to carefully look at the metal, corrosion products, and coating of the nameplate. The results show that (1) the nameplate matrix is a resulfurized steel with a high sulfur content (Fe up to 97.3% and S up to 1.98%), and the sulfur element is evenly distributed inside, which is one of the internal factors that induce corrosion. (2) Rust is composed of polycrystalline iron oxides such as goethite (α-FeOOH), hematite (α-Fe₂O₃), and magnetite (Fe₃O₄) and has typical characteristics of atmospheric oxidation. (3) The white and yellow-green coatings on the nameplate are oil-modified alkyd resin paints, and the color pigments are TiO₂, PbCrO₄, etc. The surface layer of the letters is protected by a polyvinyl alcohol layer. The paint application process leads to differences in the thickness of the paint in different regions, which directly affects the anti-rust performance. The study reveals the deterioration mechanism of resulfurized steel components in a subtropical polluted environment and puts forward repair suggestions that consider both material compatibility and reversibility, providing a reference for the protection practice of modern and contemporary architectural metal heritage in Macau and even in similar geographical environments. Full article

Utilizing high-throughput Illumina sequencing, we examined how small RNA (sRNA) profiles vary in Chinese white poplar (Populus tomentosa) across two pivotal infection stages by the rust fungus Melampsora larici-populina: the biotrophic growth phase (T02; 48 h post infection) and the urediniospore development and dispersal phase (T03; 168 h), both essential for plant colonization and prolonged biotrophic engagement. Far exceeding random expectations, siRNA clusters predominantly arose from transposon regions, with pseudogenes also contributing significantly, and infection-stage-specific variations were notably tied to these transposon-derived siRNAs. As the infection advanced, clusters of 24 nt siRNAs in transposon and intergenic regions exhibited pronounced abundance shifts. An analysis of targets indicated that Populus sRNAs potentially regulate 95% of Melampsora larici-populina genes, with pathogen effector genes showing heightened targeting by sRNAs during the biotrophic and urediniospore phases compared to controls, pointing to selective sRNA-target interactions. In contrast to conserved miRNAs across plant species, Populus-specific miRNAs displayed a markedly greater tendency to target NB-LRR genes. These observations collectively highlight the innovative roles of sRNAs in plant defense, their evolutionary roots, and their dynamic interplay with pathogen coevolution. Full article

Albugo spp. are biotrophic parasites that cause white rust in Brassicaceae species, with significant crop losses. The generalist A. candida and the specialist A. laibachii infect Arabidopsis thaliana, and the pathosystem Albugo–Arabidopsis is a model for research in molecular genetics of plant–pathogen interactions. The occurrence of infection by Albugo in wild populations of Arabidopsis and data on the genetics of resistance-susceptibility are compatible with a hypothesis of host–pathogen coevolution. However, the negative impact of Albugo infection on Arabidopsis—a requirement for coevolution—has not been shown under field conditions. To address this question, we analysed the demography and the dynamics of Albugo infection in a wild Arabidopsis population in central Spain and measured plant fitness-related traits. Infection increased mortality by 50%, although lifespan, the fraction of plants that reproduced and seed production were reduced only in plants from the spring cohorts. Despite these negative effects, simulations of demographic dynamics showed that the population growth rate remained unaffected even at unrealistically high infection incidences. The lack of negative effects in autumn–winter cohorts suggests compensatory mechanisms in longer-lived plants. Results support the hypothesis of Albugo–Arabidopsis coevolution. Full article

Pinus albicaulis (whitebark pine) is a keystone species, providing food and habitat to wildlife, in high-elevation ecological communities. In recent years, this important species has been negatively impacted by changes in fire regimes, increased Dendroctonus ponderosae (mountain pine beetle) outbreaks associated with human landscape and climate modification, and the continued impact of the non-native Cronartium ribicola (white pine blister rust). This research investigates changes in fire occurrence, the establishment of Pinus albicaulis, and fuel availability at a high-elevation site in the Sawtooth National Recreation Area, Idaho, USA. Charcoal and pollen analyses were used to reconstruct fire and vegetation patterns for Phyllis Lake, Idaho, USA, over the past ~8200 cal y BP. We found that significant fire episodes occurred when the pollen accumulation rates (PARs) indicated more arboreal fuel availability, and we identified that Pinus albicaulis became well established at the site ~7200 cal y BP. The high-elevation nature of Phyllis Lake (2800 m) makes this record unique, as there are not many paleorecords at this high elevation from the Northern Rocky Mountains, USA. Additional high-elevation sites in Pinus albicaulis habitats will provide critical insight into the long-term dynamics of this threatened species. Full article

Poplar (Populus) trees play a vital role in various industries and in environmental sustainability. They are widely used for paper production, timber, and as windbreaks, in addition to their significant contributions to carbon sequestration. Given their economic and ecological importance, effective disease management is essential. Convolutional Neural Networks (CNNs), particularly adept at processing visual information, are crucial for the accurate detection and classification of plant diseases. This study introduces a novel dataset of manually collected images of diseased poplar leaves from Uzbekistan and South Korea, enhancing the geographic diversity and application of the dataset. The disease classes consist of “Parsha (Scab)”, “Brown-spotting”, “White-Gray spotting”, and “Rust”, reflecting common afflictions in these regions. This dataset will be made publicly available to support ongoing research efforts. Employing the advanced YOLOv8 model, a state-of-the-art CNN architecture, we applied a Contrast Stretching technique prior to model training in order to enhance disease detection accuracy. This approach not only improves the model’s diagnostic capabilities but also offers a scalable tool for monitoring and treating poplar diseases, thereby supporting the health and sustainability of these critical resources. This dataset, to our knowledge, will be the first of its kind to be publicly available, offering a valuable resource for researchers and practitioners worldwide. Full article

Despite significant achievements in the artificial narrow intelligence sphere, the mechanisms of human-like (general) intelligence are still undeveloped. There is a theory stating that the human brain extracts the meaning of information rather than recognizes the features of a phenomenon. Extracting the meaning is finding a set of transformation rules (context) and applying them to the incoming information, producing an interpretation. Then, the interpretation is compared to something already seen and is stored in memory. Information can have different meanings in different contexts. A mathematical model of a context processor and a differential contextual space which can perform the interpretation is discussed and developed in this paper. This study examines whether the basic principles of differential contextual spaces work in practice. The model is developed with Rust programming language and trained on black and white images which are rotated and shifted both horizontally and vertically according to the saccades and torsion movements of a human eye. Then, a picture that has never been seen in the particular transformation, but has been seen in another one, is exposed to the model. The model considers the image in all known contexts and extracts the meaning. The results show that the program can successfully process black and white images which are transformed by shifts and rotations. This research prepares the grounding for further investigations of the contextual model principles with which general intelligence might operate. Full article

This study investigated the relationship between yield, thousand kernel weight (TKW), and crude protein of soft white winter wheat–club variety (Barbee) and soft white winter wheat common variety (Zvezdana) susceptible to leaf rust and powdery mildew under different cultivation practices. Results revealed divergence in associations between yield, TKW, and crude protein loss of winter wheat varieties susceptible to obligate pathogens. Under the same level of leaf rust infection, N-input limited yield loss of the two varieties but not to the same extent. TKW loss was affected only by variety×cultivation practice and was significantly correlated with yield loss (r = −0.727, p = 0.011) and crude protein loss (r = −0.600, p = 0.05) only in club winter wheat. We suspected that Ninput affects the difference in the relationship between yield and TKW loss among varieties. Crude protein and yield loss had a low association (R² = 18%, p = 0.05). Finally, this study indicated that more attention should be paid to the determination of pathogen pressure that triggers yield loss. It also pointed out that yield, TKW, and crude protein response to fungicides could differ in susceptible varieties. The contribution of fungicide to yield enhancement was highly associated with the specific reaction of the variety to pathogen infection rather than solely the disease level itself. Full article

This study aims to develop a fault detection system designed specifically for wind turbine gearboxes. It proposes a hybrid fault diagnosis algorithm that combines scatter plot analysis with the visual geometric group (VGG) technique to identify various fault types, including gear rust, chipping, wear, and aging. To capture vibration signals, a three-axis vibration sensor was integrated with a NI-9234 DAQ card. Digital signal processing techniques were employed to actively filter out noise from the captured signals. Gaussian white noise was incorporated into the training data to enhance the noise resistance of the network model, which was then utilized for scatter plot generation. The VGG technique was subsequently applied to identify faults. The testing data were collected at two different speeds, with 1500 samples taken at each speed, totaling 3000 samples. For both training and testing, 400 samples of each fault type were employed for training, while 200 samples were allocated for testing. The test results demonstrated an overall identification accuracy of 97.7% for both the no-fault gearbox and the four-fault states, underscoring the effectiveness of the proposed methodology. Full article

Pathogen perception generates the activation of signal transduction cascades to host defense. White pine blister rust (WPBR) is caused by Cronartium ribicola J.C. Fisch and affects a number of species of Pinus. One of the most severely affected species is Pinus albicaulis Engelm (whitebark pine). WPBR resistance in the species is a polygenic and complex trait that requires an optimized immune response. We identified early responses in 2-year-old seedlings after four days of fungal inoculation and compared the underlying transcriptomic response with that of healthy non-inoculated individuals. A de novo transcriptome assembly was constructed with 56,796 high quality-annotations derived from the needles of susceptible and resistant individuals in a resistant half-sib family. Differential expression analysis identified 599 differentially expressed transcripts, from which 375 were upregulated and 224 were downregulated in the inoculated seedlings. These included components of the initial phase of active responses to abiotic factors and stress regulators, such as those involved in the first steps of flavonoid biosynthesis. Four days after the inoculation, infected individuals showed an overexpression of chitinases, reactive oxygen species (ROS) regulation signaling, and flavonoid intermediates. Our research sheds light on the first stage of infection and emergence of disease symptoms among whitebark pine seedlings. RNA sequencing (RNA-seq) data encoding hypersensitive response, cell wall modification, oxidative regulation signaling, programmed cell death, and plant innate immunity were differentially expressed during the defense response against C. ribicola. Full article

Cold rolling emulsion contains a variety of functional additives, which often exhibit complex interactions with each other. Sodium alkane sulfonate (SAS) is a common corrosion inhibitor used in cold rolling emulsions for temporary rust prevention. In this study, it was found that SAS would deteriorate the tribological properties of the emulsion. Emulsions containing SAS and different friction modifiers were prepared. Tribology tests were carried out on a four-ball friction and wear tester. White light interferometer was used to investigate the 3D morphology of the friction surface and wear volume. Microscopic morphology of friction surfaces was observed using a scanning electron microscope (SEM). The chemical activity and electrostatic potential of the molecules were calculated based on density functional theory (DFT). The adsorption energies of additives on metal surfaces were calculated via molecular dynamics (MD) simulation. The results indicate that the strong electrostatic force gives SAS an advantage in competitive adsorption with ester friction modifiers due to the positive charge on the metal surface. This results in the friction modifier not functioning properly and the tribological properties of the emulsion being significantly reduced. Full article

In this present study, five new wood-inhabiting fungal taxa, Botryobasidium gossypirubiginosum, Botryobasidium incanum, Botryobasidium yunnanense, Coltricia zixishanensis, and Coltriciella yunnanensis are proposed. Botryobasidium gossypirubiginosum is distinguished by its slightly rubiginous hymenial surface, monomitic hyphal system, which branches at right angles, and subglobose, smooth basidiospores (14–17.5 × 13–15.5 µm); B. incanum is characterized by its white to incanus basidiomata having a hypochnoid hymenial surface, and ellipsoid, smooth basidiospores (6.5–8.5 × 3.5–5 µm); B. yunnanense is characterized by its buff to slightly yellowish hymenial surface, monomitic hyphal system, and broadly ellipsoid to globose, smooth, thick-walled basidiospores (11.5–14.5 × 9.5–10.5 µm); Coltricia zixishanensis differs in its rust brown pileal surface, and ellipsoid, thick-walled basidiospores (5–6.5 × 4–4.5 µm). Coltriciella yunnanensis is distinguished by its tiny pilei, short stipe, and navicular, verrucose basidiospores (10.5–12.5 × 6–7 µm). Sequences of ITS and nLSU genes were used for phylogenetic analyses using the maximum likelihood, maximum parsimony, and Bayesian inference methods. The phylogenetic results inferred from ITS sequences revealed that B. gossypirubiginosum was closely related to B. robustius; the species B. incanum was grouped with B. vagum; B. yunnanense was related to B. indicum. The species C. zixishanensis was grouped with C. confluens and C. perennis. ITS sequences revealed that C. zixishanensis was grouped into the genus Coltriciella, in which it was grouped with Co. globosa and Co. pseudodependens. Full article

Growth factors affect farm owners, environmental conditions, nutrient adaptation, and resistance to chrysanthemum diseases. Healthy chrysanthemum plants can overcome all these factors and provide farms owners with a lot of income. Chrysanthemum white rust disease is a common disease that occurs worldwide; if not treated promptly, the disease spreads to the entire leaf surface, causing the plant’s leaves to burn, turn yellow, and fall prematurely, reducing the photosynthetic performance of the plant and the appearance of the flower branches. In Korea, chrysanthemum white rust disease most often occurs during the spring and autumn seasons, when temperature varies during the summer monsoon, and when ventilation is poor in the winter. Deep neural networks were used to determine healthy and unhealthy plants. We applied the Raspberry Pi 3 module to recognize white rust and test four neural network models. The five main deep neural network processes utilized for a dataset of non-diseased and white rust leaves include: (1) data collection; (2) data partitioning; (3) feature extraction; (4) feature engineering; and (5) prediction modeling based on the train–test loss of 35 epochs within 20 min using Linux. White rust recognition is performed for comparison using four models, namely, DenseNet-121, ResNet-50, VGG-19, and MobileNet v2. The qualitative white rust detection system is achieved using a Raspberry Pi 3 module. All models accomplished an accuracy of over 94%, and MobileNet v2 achieved the highest accuracy, precision, and recall at over 98%. In the precision comparison, DenseNet-121 obtained the second highest recognition accuracy of 97%, whereas ResNet-50 and VGG-19 achieved slightly lower accuracies at 95% and 94%, respectively. Qualitative results were obtained using the Raspberry Pi 3 module to assess the performance of the seven models. All models had accuracies of over 91%, with ResNet-50 obtaining a value of 91%, VGG-19 reaching a value of 93%, DenseNet-121 reaching 95%, SqueezeNet obtaining over 95%, MobileNet obtaining over 96%, and MobileNetv2-YOLOv3 reaching 92%. The highest accuracy rate was 97% (MobileNet v2). MobileNet v2 was validated as the most effective model to recognize white rust in chrysanthemums using the Raspberry Pi 3 system. Raspberry Pi 3 module was considered, in conjunction with the MobileNet v2 model, to be the best application system. MobileNet v2 and Raspberry Pi require a low cost for the recognition of chrysanthemum white rust and the diagnosis of chrysanthemum plant health conditions, reducing the risk of white rust disease and minimizing costs and efforts while improving floral production. Chrysanthemum farmers should consider applying the Raspberry Pi module for detecting white rust, protecting healthy plant growth, and increasing yields with low-cost. Full article

The water of the mangrove ecosystem and surrounding coastal areas are gradually shrinking due to the intense destruction. Therefore, the effects of the physicochemical properties of the habitat water on the in-habitant species must be studied. Scylla sp. is involved in the food chain and bioturbation structure formation in mangrove forests. Five major electronic databases, such as PubMed, Scopus, Web of Science, AGRICOLA, and Google Scholar, were systematically searched to review the cause and effects of influencing abiotic factors, mainly physicochemical properties of habitat water, including water pollution on Scylla sp. Responses of mud crabs at biochemical, molecular, physiological, growth, reproduction, and production level were independently reviewed or in relation to physicochemical properties of habitat water, pathogens, heavy metals, and harmful chemicals present in their habitat water. Review results suggest that these crabs are mostly under threats of overfishing, varied physicochemical properties of habitat water, pathogens, heavy metals, and chemical toxicants in water, etc. At low temperatures, the expression of calreticulin and heat shock protein-70 mRNA expression is elevated. Like melatonin, the hormone serotonin in mud crabs controls ecdysteroids and methyl farnesoate at 24 °C, 26 ppt salinity, and pH 7.2 of habitat water, facilitating their reproduction physiology. Xenobiotics in habitat water induce toxicity and oxidative stress in mud crabs. These crabs are prone to infection by white spot and rust spot diseases during the winter and spring seasons with varied water temperatures of 10–30 °C. However, elevated (65%) weight gain with higher molting at the juvenile stage can be achieved if crabs are cultured in water and kept in the dark. Their larvae grow better at 30 ± 2 °C with salinity 35 ppt and 12 hL/12 hD day length. So, monitoring habitat water quality is important for crab culture. Full article

The existing resistance genes against white rust disease are often ineffective due to racial variation of the causal fungal pathogen, Albugo candida. Therefore, new sources of resistance effective against multiple races are needed for durable resistance. Large-scale phenotyping of advanced introgressed (ILs), mutant, and resynthesized (RBJ) lines of Brassica juncea L., under artificial inoculation at cotyledonary and true leaf stages, against thirteen diverse isolates of Albugo candida and simultaneously at the adult plant stage under multi-location field evaluation from 2019–2022, revealed significant differences in white rust reactions. Amongst 194 introgressed lines, three lines, namely ERJ 39, ERJ 12, and ERJ 15, and three lines among 90 resynthesized and 9 mutant lines, including RBJ 18, DRMR 18-36-12, and DRMR 18-37-13, were identified as potential sources of resistance against multiple isolates at all three developmental stages of the plant. Furthermore, correlation and principal component analysis revealed a positive correlation between white rust resistance at true leaf and adult plant stages for ILs as well as mutant and RBJ lines. These novel sources of host resistance will play vital roles are required for the mustard improvement program and to establish a strong genetic and molecular foundation for identifying white rust resistance linked marker(s), QTLs, or gene(s) for sustainable disease management in India. Full article

Wheat stem rust is one of the wheat diseases caused by Puccinia graminis Pers. f. sp. tritici (Pgt). This disease has been responsible for major losses to wheat production worldwide. Currently used methods for controlling this disease include fungicides, the breeding of stem rust-resistant cultivars, and preventive agricultural measures. However, the excessive use of fungicides can have various deleterious effects on the environment. A hyperparasitic fungus with white mycelia and oval conidia, Simplicillium lanosoniveum, was isolated from the urediniospores of Pgt. When Pgt-infected wheat leaves were inoculation with isolates of S. lanosoniveum, it was found that S. lanosoniveum inoculation inhibited the production and germination of urediniospores, suggesting that S. lanosoniveum could inhibit the growth and spread of Pgt. Scanning electron microscopy revealed that S. lanosoniveum could inactivate the urediniospores by inducing structural damage. Overall, findings indicate that S. lanosoniveum might provide an effective biological agent for the control of Pgt. Full article