Multiscale lattice Boltzmann approach to modeling gas flows

For multiscale gas flows, kinetic-continuum hybrid method is usually used to balance the computational accuracy and efficiency. However, the kinetic-continuum coupling is not straightforward since the coupled methods are based on different theoretical frameworks. In particular, it is not easy to recover the non-equilibrium information required by the kinetic method which is lost by the continuum model at the coupling interface. Therefore, we present a multiscale lattice Boltzmann (LB) method which deploys high-order LB models in highly rarefied flow regions and low-order ones in less rarefied regions. Since this multiscale approach is based on the same theoretical framework, the coupling precess becomes simple. The non-equilibrium information will not be lost at the interface as low-order LB models can also retain this information. The simulation results confirm that the present method can achieve model accuracy with reduced computational cost

Deep Learning for Plant Identification and Disease Classification from Leaf Images: Multi-prediction Approaches

Deep learning plays an important role in modern agriculture, especially in plant pathology using leaf images where convolutional neural networks (CNN) are attracting a lot of attention. While numerous reviews have explored the applications of deep learning within this research domain, there remains a notable absence of an empirical study to offer insightful comparisons due to the employment of varied datasets in the evaluation. Furthermore, a majority of these approaches tend to address the problem as a singular prediction task, overlooking the multifaceted nature of predicting various aspects of plant species and disease types. Lastly, there is an evident need for a more profound consideration of the semantic relationships that underlie plant species and disease types. In this paper, we start our study by surveying current deep learning approaches for plant identification and disease classification. We categorise the approaches into multi-model, multi-label, multi-output, and multi-task, in which different backbone CNNs can be employed. Furthermore, based on the survey of existing approaches in plant pathology and the study of available approaches in machine learning, we propose a new model named Generalised Stacking Multi-output CNN (GSMo-CNN). To investigate the effectiveness of different backbone CNNs and learning approaches, we conduct an intensive experiment on three benchmark datasets Plant Village, Plant Leaves, and PlantDoc. The experimental results demonstrate that InceptionV3 can be a good choice for a backbone CNN as its performance is better than AlexNet, VGG16, ResNet101, EfficientNet, MobileNet, and a custom CNN developed by us. Interestingly, empirical results support the hypothesis that using a single model can be comparable or better than using two models. Finally, we show that the proposed GSMo-CNN achieves state-of-the-art performance on three benchmark datasets.Comment: Jianping and Son are joint first authors (equal contribution

Machine Learning for Leaf Disease Classification: Data, Techniques and Applications

The growing demand for sustainable development brings a series of information technologies to help agriculture production. Especially, the emergence of machine learning applications, a branch of artificial intelligence, has shown multiple breakthroughs which can enhance and revolutionize plant pathology approaches. In recent years, machine learning has been adopted for leaf disease classification in both academic research and industrial applications. Therefore, it is enormously beneficial for researchers, engineers, managers, and entrepreneurs to have a comprehensive view about the recent development of machine learning technologies and applications for leaf disease detection. This study will provide a survey in different aspects of the topic including data, techniques, and applications. The paper will start with publicly available datasets. After that, we summarize common machine learning techniques, including traditional (shallow) learning, deep learning, and augmented learning. Finally, we discuss related applications. This paper would provide useful resources for future study and application of machine learning for smart agriculture in general and leaf disease classification in particular

Unravelling the molecular basis for light modulated cellulase gene expression - the role of photoreceptors in Neurospora crassa

<p>Abstract</p> <p>Background</p> <p>Light represents an important environmental cue, which exerts considerable influence on the metabolism of fungi. Studies with the biotechnological fungal workhorse <it>Trichoderma reesei </it>(<it>Hypocrea jecorina</it>) have revealed an interconnection between transcriptional regulation of cellulolytic enzymes and the light response. <it>Neurospora crassa </it>has been used as a model organism to study light and circadian rhythm biology. We therefore investigated whether light also regulates transcriptional regulation of cellulolytic enzymes in <it>N. crassa</it>.</p> <p>Results</p> <p>We show that the <it>N. crassa </it>photoreceptor genes <it>wc-1, wc-2 </it>and <it>vvd </it>are involved in regulation of cellulase gene expression, indicating that this phenomenon is conserved among filamentous fungi. The negative effect of VVD on production of cellulolytic enzymes is thereby accomplished by its role in photoadaptation and hence its function in White collar complex (WCC) formation. In contrast, the induction of <it>vvd </it>expression by the WCC does not seem to be crucial in this process. Additionally, we found that WC-1 and WC-2 not only act as a complex, but also have individual functions upon growth on cellulose.</p> <p>Conclusions</p> <p>Genome wide transcriptome analysis of photoreceptor mutants and evaluation of results by analysis of mutant strains identified several candidate genes likely to play a role in light modulated cellulase gene expression. Genes with functions in amino acid metabolism, glycogen metabolism, energy supply and protein folding are enriched among genes with decreased expression levels in the <it>wc-1 </it>and <it>wc-2 </it>mutants. The ability to properly respond to amino acid starvation, i. e. up-regulation of the cross pathway control protein <it>cpc-1</it>, was found to be beneficial for cellulase gene expression. Our results further suggest a contribution of oxidative depolymerization of cellulose to plant cell wall degradation in <it>N. crassa</it>.</p

Human Mast Cells (HMC-1 5C6) Enhance Interleukin-6 Production by Quiescent and Lipopolysaccharide-Stimulated Human Coronary Artery Endothelial Cells

We examined the effect of intact human mast cells (HMC-1 5C6) and their selected mediators on interleukin-6 (IL-6) production and bone morphogenetic protein-2 (BMP-2) expression in human coronary artery endothelial cells (HCAEC) in the presence and absence of lipopolysaccharide (LPS). Scanning electron microscopy showed that HMC-1 5C6 cells adhere to HCAEC in cocultures. Addition of HMC-1 5C6 cells markedly enhanced the IL-6 production by quiescent and LPS-activated HCAEC even at the maximal concentration of LPS. Furthermore, mast cell-derived histamine and proteases accounted for the direct and synergistic effect of mast cells on IL-6 production that was completely blocked by the combination of histamine receptor-1 antagonist and protease inhibitors. Another novel finding is that histamine was able to induce BMP-2 expression in HCAEC. Collectively, our results suggest that endotoxin and mast cell products synergistically amplify vascular inflammation and that histamine participates in the early events of vascular calcification

Dynamical modeling of uncertain interaction-based genomic networks

BACKGROUND: Most dynamical models for genomic networks are built upon two current methodologies, one process-based and the other based on Boolean-type networks. Both are problematic when it comes to experimental design purposes in the laboratory. The first approach requires a comprehensive knowledge of the parameters involved in all biological processes a priori, whereas the results from the second method may not have a biological correspondence and thus cannot be tested in the laboratory. Moreover, the current methods cannot readily utilize existing curated knowledge databases and do not consider uncertainty in the knowledge. Therefore, a new methodology is needed that can generate a dynamical model based on available biological data, assuming uncertainty, while the results from experimental design can be examined in the laboratory. RESULTS: We propose a new methodology for dynamical modeling of genomic networks that can utilize the interaction knowledge provided in public databases. The model assigns discrete states for physical entities, sets priorities among interactions based on information provided in the database, and updates each interaction based on associated node states. Whenever uncertainty in dynamics arises, it explores all possible outcomes. By using the proposed model, biologists can study regulation networks that are too complex for manual analysis. CONCLUSIONS: The proposed approach can be effectively used for constructing dynamical models of interaction-based genomic networks without requiring a complete knowledge of all parameters affecting the network dynamics, and thus based on a small set of available data

Import Tariff Led Export Under-Invoicing: A Paradox

Prolonged worldwide economic depression forces some economists and policy makers to demand for a tougher regulation to protect their domestic economy. If implemented, this may lead to a high tariff and non-tariff regime that ruled the pre-globalised world economy. This paper examines the consequences of a tariff protected trade regime. It takes up the case of trade misreporting phenomena under the framework of protected regime. It builds up a basic trade mis-invoicing model and then develops a collusion between underreporting traders of partner countries. I show that high tariff barrier gives incentives not only to the importers but also to the exporters to gain by underreporting the trade statistics. Interestingly, this paper shows that even if foreign exchange is fully floated, underground foreign exchange market can be created and exporters may rationally underreport without any gain through black market premium a departure from conventional theory

Inhibitory Activity of Machaeridiol-Based Novel Anti-MRSA and Anti-VRE Compounds and Their Profiling for Cancer-Related Signaling Pathways

Corresponding author (NCNPR): Premalatha Balachandran, [email protected]://egrove.olemiss.edu/pharm_annual_posters_2022/1002/thumbnail.jp

Transboundary Cooperation Improves Endangered Species Monitoring and Conservation Actions: A Case Study of the Global Population of Amur Leopards

Political borders and natural boundaries of wildlife populations seldom coincide, often to the detriment of conservation objectives. Transnational monitoring of endangered carnivores is rare, but is necessary for accurate population monitoring and coordinated conservation policies. We investigate the benefits of collaboratively monitoring the abundance and survival of the critically endangered Amur leopard, which occurs as a single transboundary population across China and Russia. Country‐specific results overestimated abundance and were generally less precise compared to integrated monitoring estimates; the global population was similar in both years: 84 (70–108, 95% confidence interval). Uncertainty in country‐specific annual survival estimates were approximately twice the integrated estimates of 0.82 (0.69–0.91, 95% confidence limits). This collaborative effort provided a better understanding of Amur leopard population dynamics, represented a first step in building trust, and lead to cooperative agreements to coordinate conservation policies