bluuebunny/crossref_metadata_2025 huggingface dataset:
Security State Estimation for Cyber-Physical Systems against DoS Attacks via Reinforcement Learning and Game Theory
Authors: Jin Zengwang , Zhang Shuting , Hu Yanyan , Zhang Yanning , Sun Changyin
Published: July 2022

This paper addressed the optimal policy selection problem of attacker and sensor in cyber-physical systems (CPSs) under denial of service (DoS) attacks. Since the sensor and the attacker have opposite goals, a two-player zero-sum game is introduced to describe the game between the sensor and the attacker, and the Nash equilibrium strategies are studied to obtain the optimal actions. In order to effectively evaluate and quantify the gains, a reinforcement learning algorithm is proposed to dynamically adjust the corresponding strategies. Furthermore, security state estimation is introduced to evaluate the impact of offensive and defensive strategies on CPSs. In the algorithm, the ε-greedy policy is improved to make optimal choices based on sufficient learning, achieving a balance of exploration and exploitation. It is worth noting that the channel reliability factor is considered in order to study CPSs with multiple reasons for packet loss. The reinforcement learning algorithm is designed in two scenarios: reliable channel (that is, the reason for packet loss is only DoS attacks) and unreliable channel (the reason for packet loss is not entirely from DoS attacks). The simulation results of the two scenarios show that the proposed reinforcement learning algorithm can quickly converge to the Nash equilibrium policies of both sides, proving the availability and effectiveness of the algorithm.
H∞ Control for ICPS with Hybrid-Triggered Mechanism Encountering Stealthy DoS Jamming Attacks
Authors: Wang Mufeng , Geng Yangyang , Wang Jingpei , Liu Ke , Che Xin , Wei Qiang
Published: July 2022

In recent years, with the upgrading of the attack technology, stealthy DoS jamming attacks have become the primary factor to threaten the security of Industrial Cyber-Physical Systems (ICPS). Considering the complex industrial scenarios of ICPS, which are influenced by a variety of external and internal interference, a H∞ controller designing problem is studied in this paper for an ICPS which deploys a hybrid-triggered mechanism (HTM) in the wireless channel encountering stealthy DoS jamming attacks. By employing a compensation mechanism which is employed in the controller to mitigate the impacts of attacks, external disturbance, limited channel capacity, wireless channel noise, we establish a closed-loop system and prove the closed-loop system is mean square exponentially stable and can achieve the desired H∞ disturbance rejection level theoretically. Finally, simulation examples are used to demonstrate effectiveness of the proposed H∞ controller.
The Impact of Leaders’ Coaching Skills on Employees’ Happiness and Turnover Intention
Authors: Romão Soraia , Ribeiro Neuza , Gomes Daniel Roque , Singh Sharda
Published: July 2022

This study seeks to provide a more comprehensive understanding of how leaders with coaching skills can affect an individual’s turnover intention through the mediating role of happiness. The sample includes 271 individuals from diverse organizations. Based on a survey, employees assessed their leaders’ coaching skills and reported their own happiness and turnover intention. The findings demonstrate that leaders’ coaching skills have a negative impact on employees’ turnover intention and a positive impact on their happiness, with the latter mediating the relationship between the first two variables. Organizations should note that a leader’s coaching skills are useful not only for the employee’s happiness, but also for avoiding turnover intention; therefore, organizations should encourage leaders to integrate more coaching skills into their leadership styles. The identification of an employee’s happiness as a mediator of the relationship between a leader’s coaching skills and employees’ turnover intention is an original contribution, thereby extending previous research over these topics.
Venus Life Finder Missions Motivation and Summary
Authors: Seager Sara , Petkowski Janusz J. , Carr Christopher E. , Grinspoon David H. , Ehlmann Bethany L. , Saikia Sarag J. , Agrawal Rachana , Buchanan Weston P. , Weber Monika U. , French Richard , Klupar Pete , Worden Simon P. , Baumgardner Darrel ,   on behalf of the Venus Life Finder Mission Team
Published: July 2022

Finding evidence of extraterrestrial life would be one of the most profound scientific discoveries ever made, advancing humanity into a new epoch of cosmic awareness. The Venus Life Finder (VLF) missions feature a series of three direct atmospheric probes designed to assess the habitability of the Venusian clouds and search for signs of life and life itself. The VLF missions are an astrobiology-focused set of missions, and the first two out of three can be launched quickly and at a relatively low cost. The mission concepts come out of an 18-month study by an MIT-led worldwide consortium.
Impact Load Identification Algorithm of Helicopter Weapon Pylon Based on Time-Domain Response Signal
Authors: Gao Yadong , Yu Xinyu , Chen Likun , Huang Dawei
Published: July 2022

Accurately identifying the peak value of impact load acting on the helicopter structure during weapon launch is of great significance to the design and finalization of weapon pylons. Firstly, a method of standardized preprocessing load signal is proposed by analyzing the vibration response and the characteristics of the impact load. Then, the test model of the weapon pylon is designed, and the position of the strain gauge is determined; the static load calibration test and the ground impact test are carried out on the test model. Next, the time-domain response measured by the strain gauge is filtered and de-noised. Impact load is processed by a standardized method. The response and load are used to train BP neural network and the mapping relationship between response and load is established. The impact load generated by a specific weapon is statistically processed to obtain the normalized average load time history, and the identified standard load is converted back to the original load pattern. Finally, the network that meets the error requirements is tested. Both the standardized pattern and the original pattern have high identification accuracy, which shows that an effective load identification model can be established based on the time-domain response signal and the standardized processed load signal.
Wheat Water Deficit Monitoring Using Synthetic Aperture Radar Backscattering Coefficient and Interferometric Coherence
Authors: Barbouchi Meriem , Chaabani Chayma , Cheikh M’Hamed Hatem , Abdelfattah Riadh , Lhissou Rachid , Chokmani Karem , Ben Aissa Nadhira , Annabi Mohamed , Bahri Haithem
Published: July 2022

Due to the climate change situation, water deficit stress is becoming one of the main factors that threatens the agricultural sector in semi-arid zones. Thus, it is extremely important to provide efficient tools of water deficit monitoring and early detection. To do so, a set of Synthetic Aperture Radar (SAR) backscattering and interferometric SAR (InSAR) Sentinel-1 data, covering the period from January to June 2016, are considered over a durum wheat field in Tunisia. We first studied the temporal variation of the InSAR coherence data and the SAR backscattering coefficient as a function of the phenological stage of the wheat. Subsequently, the parameters of the SAR and InSAR coherence images were analyzed with regard to the water stress coefficient and the wheat height variations. The main findings of this study highlight the high correlation (r = 0.88) that exists between the InSAR coherence and the water stress coefficient, on the one hand, and between the backscattering coefficient, the interferometric coherence, and the water deficit coefficient (R2 = 0.95 and RMSE = 14%), on the other hand. When a water deficit occurs, the water stress coefficient increases, the crop growth decreases, and the height variation becomes low, and this leads to the increase of the InSAR coherence value. In summary, the reliability of Sentinel-1 SAR and InSAR coherence data to monitor the biophysical parameters of the durum wheat was validated in the context of water deficits in semi-arid regions.
Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review
Authors: Mamat Normaisharah , Othman Mohd Fauzi , Abdoulghafor Rawad , Belhaouari Samir Brahim , Mamat Normahira , Mohd Hussein Shamsul Faisal
Published: July 2022

The implementation of intelligent technology in agriculture is seriously investigated as a way to increase agriculture production while reducing the amount of human labor. In agriculture, recent technology has seen image annotation utilizing deep learning techniques. Due to the rapid development of image data, image annotation has gained a lot of attention. The use of deep learning in image annotation can extract features from images and has been shown to analyze enormous amounts of data successfully. Deep learning is a type of machine learning method inspired by the structure of the human brain and based on artificial neural network concepts. Through training phases that can label a massive amount of data and connect them up with their corresponding characteristics, deep learning can conclude unlabeled data in image processing. For complicated and ambiguous situations, deep learning technology provides accurate predictions. This technology strives to improve productivity, quality and economy and minimize deficiency rates in the agriculture industry. As a result, this article discusses the application of image annotation in the agriculture industry utilizing several deep learning approaches. Various types of annotations that were used to train the images are presented. Recent publications have been reviewed on the basis of their application of deep learning with current advancement technology. Plant recognition, disease detection, counting, classification and yield estimation are among the many advancements of deep learning architecture employed in many applications in agriculture that are thoroughly investigated. Furthermore, this review helps to assist researchers to gain a deeper understanding and future application of deep learning in agriculture. According to all of the articles, the deep learning technique has successfully created significant accuracy and prediction in the model utilized. Finally, the existing challenges and future promises of deep learning in agriculture are discussed.
Effects of Precise K Fertilizer Application on the Yield and Quality of Rice under the Mode of Light, Simple, and High-Efficiency N Fertilizer Application during the Panicle Stage
Authors: Chen Liqiang , Zhang Wenzhong , Gao Jiping , Liu Yuzhuo , Wang Xue , Liu Yuqi , Feng Yingying , Zhao Yanze , Xin Wei
Published: July 2022

Light, simple, and high-efficiency fertilization is currently an effective method used to improve quality and increase yield. Most research has only focused on the yield or quality of rice, and no effective or in-depth studies exist on the key stage of panicle, which is essential for these two aspects. This study aimed to determine the effect of applying Nitrogen (N) and Potassium (K) fertilizers on the yield and quality at different leaf ages. The rice cultivar, Shennong 265, was grown in the field-tube condition at the 5-leaf age with K fertilizer at different panicle stages. Five K fertilizer and four N fertilizer levels were applied simultaneously during two growing seasons in 2020–2021. The application of K fertilizer at different panicle stages significantly affected the yield and quality. The application of K and N fertilizers at the 12th-leaf-age increased the number of panicles and grains per panicle, thereby increasing the yield with an average increase of 23.20% over local farmer’s fertilization model (CK) in two years. Application at the 10th-leaf age reduced the starch and protein content of the rice and improved the nutritional quality and taste, with an average increase of 11.08 points compared to CK in two years. The processing quality was the best at the panicle K fertilizer application rate of 47.81–64.69 kg ha−1, and the starch and protein contents were the lowest at the panicle K fertilizer application rate of 56.25 kg ha−1. Under different panicle K fertilizer application rates, N fertilizers had great differences in processing quality but had slight differences in nutritional quality; that is, the starch and protein content (total protein and four protein components) significantly increased. The application of panicle N fertilizer mainly affected starch pasting viscosity (RVA profile characteristics). When panicle K fertilizer was applied after panicle N fertilizer, the yield and quality showed a trend of synergistic improvement. Although this improvement was a low-level synergy, it can still be a direction used to explore the synergistic improvement of output and quality.
Development of SSR Markers Based on Transcriptome Sequencing and Verification of Their Conservation across Species of Ornamental Pennisetum Rich. (Poaceae)
Authors: Guo Yidi , Liu Lingyun , Yue Yuesen , Fan Xifeng , Teng Wenjun , Zhang Hui , Gao Kang , Guan Jin , Chang Zhihui , Teng Ke
Published: July 2022

Pennisetum species have importance in foraging, agriculture, energy-production, the environment, and landscaping. To promote the preservation and utilization of ornamental Pennisetum resources, we developed simple sequence repeat (SSR) markers from the Pennisetum setaceum cv. ‘Rubrum’ transcriptome and verified their conservation in 38 sources. Our transcriptome sequencing efforts generated 58.91 Gb of clean data containing 55,627 unigenes. We functionally annotated 30,930 unigenes, with functions enriched in translation and ribosomal structure and biogenesis. Database comparisons indicated that the closest relative of P. setaceum cv. ‘Rubrum’ is Setaria italica. Over five thousand SSR markers were detected in the transcriptomic data. We selected 38 pairs of highly polymorphic SSR markers from 50 randomly selected SSR markers. Based on genetic diversity analysis of 38 ornamental Pennisetum sources, we obtained 312 polymorphic bands, with an average of 8.21 alleles per primer. Principal coordinate analyses and generation of a, which proved that Pennisetum has moderate genetic diversity. In addition, fingerprint maps were constructed to improve Pennisetum identification. The transcriptome data generated by our study enhances the transcriptional information available for P. setaceum. This study lays the foundation for the collection and utilization of ornamental Pennisetum resources and provides a basis for future breeding projects using this species.
Bioprospecting the Antibacterial Activity of Endophytic Fungi from Noni (Morinda citrifolia) against Bacterial Spot of the Passion Fruit Tree
Authors: de Oliveira Luana Cardoso , da Costa Williams Carlos Leal , Vinagre Viviane Garcia , Siqueira José Edson de Sousa , Silva Sebastião da Cruz , Silva Simone Yasue Simote , Marinho Anderson N. do Rosario , Rocha Daniela Cristiane da C. , Marinho Patrícia Santana Barbosa , Nakasone Alessandra Keiko , Marinho Andrey M. do Rosario
Published: July 2022

Bacterial spot, which is the main disease occurring in passion fruit trees, is caused by the bacterium Xanthomonas axonopodis pv. passiflorae, leading to large annual losses in passion fruit crops. This study aims to find extracts and/or bioactive compounds of endophytic fungi of noni (Morinda citrifolia) to treat bacterial spot in passion fruit trees. Nine fungi isolated from a specimen of M. citrifolia from the Brazilian Amazon are studied. The fungus Guignardia mangiferae NF17 shows the best inhibition results and is selected for the isolation of its secondary metabolites by chromatography techniques. The isolated compounds Sydowinol (S1) and Sydowinin A (S2) are identified by nuclear magnetic resonance (NMR). Compounds S1 and S2, as well as the acetonitrile extract from the biomass of G. mangiferae NF17, are tested against four strains of X. axonopodis pv. passiflorae obtained from plants infected by bacterial spot, and which inhibited bacterial growth up to the lowest concentration tested (3.125 µg/mL). This study reports, for the first time, the antibacterial activity against X. axonopodis pv. passiflorae by the compounds Sydowinol and Sydowinin A. Compounds S1 and S2 are reported for the first time for the genus Gignardia.
COVID-19 and the Locavores: Investigating the Drivers of US Consumer Preferences for Apples
Authors: Rombach Meike , Dean David L. , Baird Tim , Kambuta Jacob
Published: July 2022

This study provides insights and best-practice recommendations for marketing managers in the US food retail sector and the horticultural industry. An online survey distributed via a crowdsourcing platform in 2021 aimed to explore the factors that explained the intentions of US consumers to purchase locally grown apples. The Theory of Planned Behavior was used as a conceptual framework to shape the proposed model. The results emphasize the importance of behavioral, normative and control beliefs as important factors towards attitudes. Subjective norms and perceived behavioral control were also found to be key drivers in understanding behavioral intention. All concepts, with the exception of perceived behavioral control, were found to be significant behavioral predictors.
Response of Potted Hebe andersonii to Salinity under an Efficient Irrigation Management
Authors: Bañón Daniel , Alarcón Juan José , Sánchez-Blanco María Jesús , Ortuño María Fernanda , Bañón Sebastián , Lorente Beatriz , Ochoa Jesús
Published: July 2022

Although the use of marginal-quality water can be an effective measure to alleviate water scarcity, it often contains a high concentration of salts that can compromise crop growth. As a result, farmers apply more water than necessary to leach salts away from the root zone, resulting in wasted water and the release of fertilizers into the groundwater. In this study, we assessed the effects of three salinity levels of irrigation water (1.8 dS m−1, 3.3 dS m−1, and 4.9 dS m−1) on the physiology and ornamental traits of Hebe andersonii cv. Variegata. The experiment was carried out with potted plants in a greenhouse for seven months. We also studied the feasibility of growing this cultivar without leaching salts. The results showed that Hebe plants can be grown without leaching using water up to 3 dS m−1. This setup produces plants with high water use efficiency and without reducing their ornamental value. Meanwhile, irrigation with 5 dS m−1 water reduced the quality of Hebe but did not compromise its physiological processes. The photosynthesis of Hebe under salinity conditions was mainly controlled by stomata, which was related to the level of salt stress and water status of the plant. Salinity had no significant effects on photosystem II, which can be explained by the fact that Hebe was able to dissipate the excess excitation energy as heat effectively. Hebe was able to avoid ion toxicity and maintain a suitable nutrient balance under the salinity levels tested in this experiment.
Examining the Driving Factors of SOM Using a Multi-Scale GWR Model Augmented by Geo-Detector and GWPCA Analysis
Authors: Wang Qi , Jiang Danyao , Gao Yifan , Zhang Zijuan , Chang Qingrui
Published: July 2022

A model incorporating geo-detector analysis and geographically weighted principal component analysis into Multi-scale Geographically Weighted regression (GWPCA-MGWR) was developed to reveal the factors driving spatial variation in soil organic matter (SOM). The regression accuracy and residuals from GWPCA-MGWR were compared to those of the classical Geographically Weighted regression (GWR), Multi-scale Geographically Weighted regression (MGWR), and GWPCA-GWR. Our results revealed that local multi-collinearity on model fitting negatively affects the results to different degrees. Additionally, compared to other models, GWPCA-MGWR provided the lowest MAE (0.001) and little-to-no residual spatial autocorrelation and is the best model for regression for SOM spatial distribution and identification of dominant driving factors. GWPCA-MGWR produced spatial non-stationary SOM that was variably affected by soil nutrient content, soil type, and human activity, and was geomorphic in the second place. In conclusion, the spatial information obtained from GWPCA-MGWR provides a valuable reference for understanding the factors that influence SOM variation.
Seasonal Prevalence of Gastrointestinal Parasites in Macaques (Macaca thibetana) at Mount Emei Scenic Area in China
Authors: Yang Jiandong , Okyere Samuel Kumi , Zheng Jie , Cao Buyuan , Hu Yanchun
Published: July 2022

The aim of the study was to elucidate the prevalence of intestinal parasites in macaques at the Mt. Emei Scenic Area of Sichuan, China. A total of 168 fecal samples were collected from yellow (n = 31), black (n = 19), new (n = 57), Leidongping (n = 57) and Wuxiangang (n = 4) macaques from 2019 to 2020. The fecal samples were tested for various gastrointestinal parasites following the microscopic detection method. The results showed that the total prevalence rate of the intestinal parasite was 51.19% (86/168), whereas the intestinal parasite with the highest prevalence was Gongylonema spp. (26.79%) for helminth and Entamoeba spp. (18.45%) for protozoa. Interestingly, the highest prevalence of intestinal parasites was observed during the summer season (86.21%), and the lowest was observed during the winter season (7.14%). There was a positive correlation observed between the human contact frequency and total prevalence rate of the intestinal parasites (p < 0.05); however, there was no correlation between the human contact frequency and total prevalence of the intestinal parasites at different seasons (p > 0.05). In conclusion, the dominant parasites Gongylonema spp. and Entamoeba spp. cause various diseases that may be transmitted to humans and other animals; therefore, there is a need for a proper management system, such as parasite control measures and population protection in the Mt. Emei Scenic Area of Sichuan, China.
Long-Term Winter Population Trends of Corvids in Relation to Urbanization and Climate at Northern Latitudes
Authors: Jokimäki Jukka , Kaisanlahti-Jokimäki Marja-Liisa , Suhonen Jukka
Published: July 2022

Corvids (crows, magpies, jays) live in a close association with humans, and therefore knowledge about their population status and changes will be an essential part of monitoring the quality of urban environments. Wintering bird populations can track habitat and climate changes more rapidly than breeding populations. We conducted a long-term (1991–2020) winter census of corvid species in 31 human settlements along a 920 km latitudinal gradient in Finland. We observed a total of five corvid species: the Eurasian Magpie (occurring in 114 surveys out of 122; total abundance 990 ind.), the Hooded Crow (in 96 surveys; 666 ind.), the Eurasian Jackdaw (in 51 surveys; 808 ind.), the Eurasian Jay (in 5 surveys; 6 ind.) and the Rook (in 1 survey; 1 ind.). Only the numbers of the Eurasian Jackdaw differed between the study winters, being greater at the end of the study period (2019/2020) than during the earlier winters (1991/1992 and 1999/2000). The average growth rate (λ) of the Eurasian Jackdaw increased during the study period, whereas no changes were observed in the cases of the Hooded Crow or the Eurasian Magpie. The growth rate of the Eurasian Jackdaw was greater than that observed in the Finnish bird-monitoring work, probably because our data came only from the core area of each human settlement. Even though the number of buildings and their cover increased in the study plots, and the winter temperature differed between winters, the average growth rate (λ) of corvid species did not significantly correlate with these variables. These results suggest that urban settlements are stable wintering environments for the generalist corvids. The between-species interactions were all positive, but non-significant. Despite the total number of winter-feeding sites being greater during the winter of 1991/1992 than during the winter of 2019/2020, the changes in the numbers of feeding stations did not correlate with the growth rates of any corvid species. We assume that the Eurasian Jackdaw has benefitted from the decreased persecution, and probably also from large-scale climate warming that our study design was unable to take in to account. Our results indicated that wintering corvid populations succeed well in the human settlements in Finland. We recommend conducting long-term corvid research, also during breeding season, to understand more detailed causes of the population changes of corvids along an urban gradient. Without year-round long-term monitoring data, the conservation and management recommendations related to the corvid species in urban habitats may be misleading.
A Dynamic Energy Budget Model for Kuruma Shrimp Penaeus japonicus: Parameterization and Application in Integrated Marine Pond Aquaculture
Authors: Dong Shipeng , Liu Dapeng , Zhu Boshan , Yu Liye , Shan Hongwei , Wang Fang
Published: July 2022

Individual growth models can form the basis of population dynamics assessment and ecosystem model construction. In order to provide a basic module for an ecosystem model of an integrated marine aquaculture pond, an individual growth model was constructed for kuruma shrimp (Penaeus japonicus) based on dynamic energy budget (DEB) theory. The model was first parameterized based on a covariation method using the Add-my-Pet (AmP) procedure. The parametric estimation model underestimated the ultimate abdominal length for female shrimp, and the predicted values of other zero-variate parameters were generally consistent with observed values. The relative errors of the predicted and observed values of the univariate data set within three geographical regions showed acceptable goodness of fit. Parameter estimation achieved an overall goodness of fit with a mean relative error of 0.048 and a symmetric mean squared error of 0.066. A DEB model was constructed using the estimated parameters, and the goodness-of-fit indicators (R square, mean bias and absolute and relative root mean square error) showed that the model was able to reproduce the growth of kuruma shrimp in terms of total length and wet weight with high accuracy. The results provide data to support the subsequent development of integrated aquaculture management at the ecosystem level.
Alcohol-Induced Oxidative Stress and the Role of Antioxidants in Alcohol Use Disorder: A Systematic Review
Authors: Tsermpini Evangelia Eirini , Plemenitaš Ilješ Anja , Dolžan Vita
Published: July 2022

Alcohol use disorder (AUD) is a highly prevalent, comorbid, and disabling disorder. The underlying mechanism of ethanol neurotoxicity and the involvement of oxidative stress is still not fully elucidated. However, ethanol metabolism has been associated with increased oxidative stress through alcohol dehydrogenase, the microsomal ethanol oxidation system, and catalase metabolic pathways. We searched the PubMed and genome-wide association studies (GWAS) catalog databases to review the literature systematically and summarized the findings focusing on AUD and alcohol abstinence in relation to oxidative stress. In addition, we reviewed the ClinicalTrials.gov resource of the US National Library of Medicine to identify all ongoing and completed clinical trials that include therapeutic interventions based on antioxidants. The retrieved clinical and preclinical studies show that oxidative stress impacts AUD through genetics, alcohol metabolism, inflammation, and neurodegeneration.
Itaconate Isomers in Bread
Authors: Gruenwald Mona , Chen Fangfang , Bähre Heike , Pessler Frank
Published: July 2022

The naturally occurring isomers itaconate, mesaconate and citraconate possess immunomodulatory, antioxidative and antimicrobial properties. However, it is not known whether they occur in commonly consumed human foods. Considering that they can arise as a result of heat conversion, we tested whether they occur in bread, representing a commonly consumed baked good. Using high-performance liquid chromatography–tandem mass spectrometry, we measured concentrations of the three isomers and their potential precursors, citrate and cis-aconitate, in unbaked sourdough and dough, and in crumb and crust of baked bread. All three isomers were detected at low concentrations (<20 pmol/mg dry weight) in sourdough, dough, crumb and crust. Concentrations of itaconate and citraconate were substantially higher in crust than in crumb of wheat and rye bread, and a modest increase in mesaconate was observed in crust of rye bread. In contrast, cis-aconitate concentrations were considerably lower in crust, which was consistent with the conversion of cis-aconitate to itaconate isomers due to higher temperature of the dough surface during baking. Based on data on the average consumption of bread and related baked goods in Germany, the daily intake of itaconate isomers was estimated to be roughly 7–20 µg. Thus, baked goods constitute a regular dietary source of low amounts of itaconate isomers. In order to enable studies on the impact of dietary intake of itaconate isomers on human health, their concentrations should be assessed in other foods that are subjected to high heating.
Nature-Inspired Hybrids (NIH) Improve Proteostasis by Activating Nrf2-Mediated Protective Pathways in Retinal Pigment Epithelial Cells
Authors: Koskela Ali , Manai Federico , Basagni Filippo , Liukkonen Mikko , Rosini Michela , Govoni Stefano , Monte Massimo Dal , Smedowski Adrian , Kaarniranta Kai , Amadio Marialaura
Published: July 2022

Antioxidant systems play key roles in many elderly diseases, including age-related macular degeneration (AMD). Oxidative stress, autophagy impairment and inflammation are well-described in AMD, especially in retinal pigment epithelial (RPE) cells. The master regulator of antioxidant defense Nrf2 has been linked to AMD, autophagy and inflammation. In this study, in human ARPE-19 cells, some nature-inspired hybrids (NIH1–3) previously shown to induce Nrf2-mediated protection against oxidative stress were further investigated for their potential against cellular stress caused by dysfunction of protein homeostasis. NIH1–3 compounds increased the expression of two Nrf2-target genes coding defense proteins, HO-1 and SQSTM1/p62, in turn exerting beneficial effects on intracellular redox balance without modification of the autophagy flux. NIH1–3 treatments predisposed ARPE-19 cells to a better response to following exposure to proteasome and autophagy inhibitors, as revealed by the increase in cell survival and decreased secretion of the pro-inflammatory IL-8 compared to NIH-untreated cells. Interestingly, NIH4 compound, through an Nrf2-independent pathway, also increased cell viability and decreased IL-8 secretion, although to a lesser extent than NIH1–3, suggesting that all NIHs are worthy of further investigation into their cytoprotective properties. This study confirms Nrf2 as a valuable pharmacological target in contexts characterized by oxidative stress, such as AMD.
Oxidative Stress and Lipid Dysregulation in Lipid Droplets: A Connection to Chronic Kidney Disease Revealed in Human Kidney Cells
Authors: Chen Zhen , Shrestha Rojeet , Yang Xiaoyue , Wu Xunzhi , Jia Jiaping , Chiba Hitoshi , Hui Shu-Ping
Published: July 2022

Chronic kidney disease (CKD), which is defined as a condition causing the gradual loss of kidney function, shows renal lipid droplet (LD) accumulation that is associated with oxidative damage. There is a possibility that an LD abnormality in quality plays a role in CKD development. This study aimed to explore the chemical composition of LDs that are induced in human kidney cells during exposure to free fatty acids as an LD source and oxidized lipoproteins as oxidative stress. The LDs were aspirated directly from cells using nanotips, followed by in-tip microextraction, and the LD lipidomic profiling was conducted using nanoelectrospray mass spectrometry. As a result, the free fatty acids increased the LD lipid content and, at the same time, changed their composition significantly. The oxidized lipoproteins caused distorted proportions of intact lipids, such as triacylglycerols (TG), phosphatidylcholines (PC), phosphatidylethanolamines (PE), and cholesteryl esters (CE). Notably, the oxidized lipids, including the hydroperoxides of TG, PC, and PE, exhibited significant elevations in dose-dependent manners. Furthermore, the dysregulation of intact lipids was paralleled with the accumulation of lipid hydroperoxides. The present study has revealed that the oxidation of lipids and the dysregulation of the lipid metabolism coexisted in LDs in the kidney cells, which has provided a potential new target for diagnosis and new insights into CKD.
Rhodomyrtus tomentosa Fruits in Two Ripening Stages: Chemical Compositions, Antioxidant Capacity and Digestive Enzymes Inhibitory Activity
Authors: Hu Xiaoping , Chen Yuting , Dai Jincheng , Yao Linling , Wang Lu
Published: July 2022

Rhodomyrtus tomentosa fruit (RTF) has been known as a food source with multiple health-care components. In this work, nutrition characteristics, free and bound phenolic profiles, antioxidant properties in vitro and digestive enzymes inhibitory activities of un-fully mature RTF (UM-RTF) and fully mature RTF (FM-RTF) were evaluated for the first time. Results verified that high levels of energy, ascorbic acid, organic acids and total phenolics were observed in FM-RTF. Moreover, FM-RTF had significant higher total phenolic content (TPC), but significantly lower total flavonoid content (TFC) than UM-RTF. In addition, twenty phenolic compounds in RTF were identified by high performance liquid chromatography–electrospray ionization–quadrupole time-of-flight tandem mass spectrometry (HPLC-ESI-qTOF-MS/MS) method. Quantitative analysis results indicated that gallic acid, ellagic acid and astragalin were the predominant free phenolics, while gallic acid and syringetin-3-O-glucoside were dominant in bound phenolic fractions. In contrast, higher contents of phenolics were observed in FM-RTF. The results also confirmed that FM-RTF exhibited higher antioxidant activities and digestive enzymes inhibitory activities than UM-RTF. Strong inhibitory ability on α-glucosidase was found in RTF, while bound phenolics showed a stronger α-amylase inhibitory effect than free phenolics. Moreover, the interaction between the main phenolic compounds and α-glucosidase/α-amylase was preliminary explored by molecular docking analysis. The results provided valuable data about the chemical compositions and biological potential of R. tomentosa fruits in both maturation stages studied.
Quadratic Clustering-Based Simplex Volume Maximization for Hyperspectral Endmember Extraction
Authors: Zhang Xiangyue , Wang Yueming , Xue Tianru
Published: July 2022

The existence of intra-class spectral variability caused by differential scene components and illumination conditions limits the improvement of endmember extraction accuracy, as most endmember extraction algorithms directly find pixels in the hyperspectral image as endmembers. This paper develops a quadratic clustering-based simplex volume maximization (CSVM) approach to effectively alleviate spectral variability and extract endmembers. CSVM first adopts spatial clustering based on simple linear iterative clustering to obtain a set of homogeneous partitions and uses spectral purity analysis to choose pure pixels. The average of the chosen pixels in each partition is taken as a representative endmember, which reduces the effect of local-scope spectral variability. Then an improved spectral clustering based on k-means is implemented to merge homologous representative endmembers to further reduce the effect of large-scope spectral variability, and final endmember collection is determined by the simplex with maximum volume. Experimental results show that CSVM reduces the average spectral angle distance on Samson, Jasper Ridge and Cuprite datasets to below 0.02, 0.06 and 0.09, respectively, provides the root mean square errors of abundance maps on Samson and Jasper Ridge datasets below 0.25 and 0.10, and exhibits good noise robustness. By contrast, CSVM provides better results than other state-of-the-art algorithms.
Progressive Multi-Scale Fusion Network for Light Field Super-Resolution
Authors: Zhang Wei , Ke Wei , Sheng Hao , Xiong Zhang
Published: July 2022

Light field (LF) cameras can record multi-view images from a single scene, and these images can provide spatial and angular information to improve the performance of image super-resolution (SR). However, it is a challenge to incorporate distinctive information from different LF views. At the same time, due to the inherent resolution of the image sensor, high spatial and angular resolution are trade-off problems. In this paper, we propose a progressive multi-scale fusion network (PMFN) to improve the LFSR performance. Specifically, a progressive feature fusion block (PFFB) based on an encoder-and-decoder structure is designed to implicitly align disparities and integrate complementary information between complementary views. The core module of the PFFB is a dual-branch multi-scale fusion module (DMFM), which can integrate the information from a reference view and auxiliary views to produce a fusion feature. Each DMFM consists of two parallel branches, which have different receptive fields to fuse hierarchical features from complementary views. Three DMFMs with a dense connection are used in the PFFB, which can fully exploit multi-level features to improve the SR performance. Experimental results on both synthetic and real-world datasets demonstrate that the proposed model achieves state-of-the-art performance among existing methods. Moreover, quantitative results show that our method can also generate faithful details.
Augmented Retting Effect on Kenaf Fibers Using Alkalophilic Pectinase-Producing Bacteria in Combination with Water Solvents
Authors: Hossain Mohammad Munir , Subbiah Vijay Kumar , Siddiquee Shafiquzzaman
Published: July 2022

A degumming approach is used in this paper with alkalophilic pectinase-producing bacteria (APPB) and two sources of water solvents to address the existing conventional water retting complexities of kenaf. The incorporation of APPB was confirmed based on their retting feasibilities and multiple cell-wall-degrading enzymatic delicacy. The combinations of APPB with seawater offered retting achievements within six-day retting in non-sterile conditions. These retting niches showed maximum (14.67 U/mL) pectinase activity with fiber separation feasibilities of 4.75 Fried test score. The yielded fiber composition analysis showed a higher cellulose composition (84.65%) and the least amount of hemicellulose, pectin, and ligneous gummy substances. The transmission electron microscopy scan of the yielded fibers showed smooth fiber surfaces, 84.20 µm fiber diameter, and 7.65 g/tex fine fiber compared with uninoculated and combinations of freshwater treatments. The FTIR spectra revealed the cellulosic discrepancies of the retting treatments by monitoring O-H and C=O stretching at ~3300 cm−1 and ~1730 cm−1 wavenumbers. These findings are compelling to yield kenaf fibers of quality considering the existing retting difficulties.
Solving Task Scheduling Problems in Dew Computing via Deep Reinforcement Learning
Authors: Sanabria Pablo , Tapia Tomás Felipe , Toro Icarte Rodrigo , Neyem Andres
Published: July 2022

Due to mobile and IoT devices’ ubiquity and their ever-growing processing potential, Dew computing environments have been emerging topics for researchers. These environments allow resource-constrained devices to contribute computing power to others in a local network. One major challenge in these environments is task scheduling: that is, how to distribute jobs across devices available in the network. In this paper, we propose to distribute jobs in Dew environments using artificial intelligence (AI). Specifically, we show that an AI agent, known as Proximal Policy Optimization (PPO), can learn to distribute jobs in a simulated Dew environment better than existing methods—even when tested over job sequences that are five times longer than the sequences used during the training. We found that using our technique, we can gain up to 77% in performance compared with using human-designed heuristics.
Chronic Obstructive Pulmonary Disease and Apical Periodontitis and Other Oral Health Variables: A Case-Control Study
Authors: Carrera-Cortés Anna , Pascual-González Yuliana , Santos Salud , Martín-González Jenifer , Segura-Egea Juan José , López-López José
Published: July 2022

Background: The relationship between chronic inflammatory diseases and their comorbidities and correlation with periodontal diseases has become an increasing focus of research. Objectives: The aim of this case-control study was to conclude if patients suffering from COPD (Chronic Obstructive Pulmonary Disease) tend to have more AP (Apical Periodontitis) than non-COPD patients. Materials and Methods: The study was conducted on 30 patients assigned as cases, associated with 30 control patients linked by age (+/−5 years) and sex. Results: A total of 60 patients were recorded, and a total of 12 radiographic variables were analyzed. A total of 43 (71.7%) patients were registered with PAI (Periapical Index) ≥ 3, and there was a slightly tendency in the patients from the control group 22 (73.3%) compared to those from the cases 21 (70%), respectively (p > 0.05). Conclusions: It was concluded that there was not a significant association between the levels of PAI (Periapical Index) ≥ 3 per patient in those suffering from COPD. In fact, it could be concluded that patients diagnosed with COPD tend to have more teeth with PAI ≥ 3, more endodontic treatments and their periodontitis tended to accumulate more caries. Clinical Significance: This study establishes, in a case-control study, some specific aspects of oral health in patients with COPD, as well as analyzing the importance of oral health in this disease.
Residual-Attention UNet++: A Nested Residual-Attention U-Net for Medical Image Segmentation
Authors: Li Zan , Zhang Hong , Li Zhengzhen , Ren Zuyue
Published: July 2022

Image segmentation is a basic technology in the field of image processing and computer vision. Medical image segmentation is an important application field of image segmentation and plays an increasingly important role in clinical diagnosis and treatment. Deep learning has made great progress in medical image segmentation. In this paper, we proposed Residual-Attention UNet++, which is an extension of the UNet++ model with a residual unit and attention mechanism. Firstly, the residual unit improves the degradation problem. Secondly, the attention mechanism can increase the weight of the target area and suppress the background area irrelevant to the segmentation task. Three medical image datasets such as skin cancer, cell nuclei, and coronary artery in angiography were used to validate the proposed model. The results showed that the Residual-Attention UNet++ achieved superior evaluation scores with an Intersection over Union (IoU) of 82.32%, and a dice coefficient of 88.59% with the skin cancer dataset, a dice coefficient of 85.91%, and an IoU of 87.74% with the cell nuclei dataset and a dice coefficient of 72.48%, and an IoU of 66.57% with the angiography dataset.
Study of the Utilization of Spent Grain from Malt Whisky on the Quality of Wafers
Authors: Chetrariu Ancuța , Dabija Adriana
Published: July 2022

This study aimed at determining the quality parameters of the wafer formulated with the addition of grain spent (SG), resulting from the obtainment of whisky. In this sense, wafers were formulated from chickpea flour, spent grain, wild garlic paste, golden flaxseed, and hemp seeds. These food products were analyzed in terms of texture, density, and pH of the batter, but also of the final product for proximate analysis, baking loss, texture, water activity, color, antioxidant capacity, water holding capacity and oil holding capacity, microstructure, and sensorial analysis. The addition of spent grain in the wafer formulation led to products with a high acceptability, the texture of the batter underwent changes due to the addition of spent grain, all parameters increased, and only adhesiveness decreased. The density and pH of the samples with SG decreased. The fracturability of the products with SG decreased with the addition of SG compared to the control sample, and the color becomes darker, influenced by the specific color of the SG. With the addition of spent grain, it increases the fiber and protein content, the antioxidant capacity, but also the baking loss due to the fibers contained in it. The microstructure of samples with the addition of SG shows a heterogeneous distribution of pores on the cross section of the samples, with larger pores in the center of the wafer samples.
Recursive Least Squares for Near-Lossless Hyperspectral Data Compression
Authors: Zheng Tie , Dai Yuqi , Xue Changbin , Zhou Li
Published: July 2022

The hyperspectral image compression scheme is a trade-off between the limited hardware resources of the on-board platform and the ever-growing resolution of the optical instruments. Predictive coding attracts researchers due to its low computational complexity and moderate memory requirements. We propose a near-lossless prediction-based compression scheme that removes spatial and spectral redundant information, thereby significantly reducing the size of hyperspectral images. This scheme predicts the target pixel’s value via a linear combination of previous pixels. The weight matrix of the predictor is iteratively updated using a recursive least squares filter with a loop quantizer. The optimal number of bands for prediction was analyzed experimentally. The results indicate that the proposed scheme outperforms state-of-the-art compression methods in terms of the compression ratio and quality retrieval.
Machine Learning Sorting Method of Bauxite Based on SE-Enhanced Network
Authors: Zhao Pengfei , Luo Zhengjie , Li Jiansu , Liu Yujun , Zhang Baocheng
Published: July 2022

A fast and accurate bauxite recognition method combining an attention module and a clustering algorithm is proposed in this paper. By introducing the K-means clustering algorithm into the YOLOv4 network and embedding the SE attention module, we calculate the corresponding anchor box value, enhance the feature learning ability of the network to bauxite, automatically learn the importance of different channel features, and improve the accuracy of bauxite target detection. In the experiment, 2189 bauxite photos were taken and screened as the target detection datasets, and the targets were divided into four categories: No. 55, No. 65, No. 70, and Nos. 72–73. By selecting the category volume balanced datasets, the optimal YOLOv4 network model was obtained after training 7000 times, so that the average accuracy of bauxite sorting reached 99%, and the reasoning speed was better than 0.05 s. Realizing the high-speed and high-precision sorting of bauxite greatly improves the mining efficiency and accuracy of the bauxite industry. At the same time, the model provides key technical support for the practical application of the same type of engineering.