bluuebunny/crossref_metadata_2025 huggingface dataset:
The Role of Nrf2 in Pulmonary Fibrosis: Molecular Mechanisms and Treatment Approaches
Authors: Wang Yu , Wei Juan , Deng Huimin , Zheng Li , Yang Hao , Lv Xin
Published: August 2022

Pulmonary fibrosis is a chronic, progressive, incurable interstitial lung disease with high mortality after diagnosis and remains a global public health problem. Despite advances and breakthroughs in understanding the pathogenesis of pulmonary fibrosis, there are still no effective methods for the prevention and treatment of pulmonary fibrosis. The existing treatment options are imperfect, expensive, and have considerable limitations in effectiveness and safety. Hence, there is an urgent need to find novel therapeutic targets. The nuclear factor erythroid 2-related factor 2 (Nrf2) is a central regulator of cellular antioxidative responses, inflammation, and restoration of redox balance. Accumulating reports reveal that Nrf2 activators exhibit potent antifibrosis effects and significantly attenuate pulmonary fibrosis in vivo and in vitro. This review summarizes the current Nrf2-related knowledge about the regulatory mechanism and potential therapies in the process of pulmonary fibrosis. Nrf2 orchestrates the activation of multiple protective genes that target inflammation, oxidative stress, fibroblast–myofibroblast differentiation (FMD), and epithelial–mesenchymal transition (EMT), and the mechanisms involve Nrf2 and its downstream antioxidant, Nrf2/HO−1/NQO1, Nrf2/NOX4, and Nrf2/GSH signaling pathway. We hope to indicate potential for Nrf2 system as a therapeutic target for pulmonary fibrosis.
Exercise Improves Redox Homeostasis and Mitochondrial Function in White Adipose Tissue
Authors: Matta Leonardo , de Faria Caroline Coelho , De Oliveira Dahienne F. , Andrade Iris Soares , Lima-Junior Niedson Correia , Gregório Bianca Martins , Takiya Cristina Maeda , Ferreira Andrea Claudia Freitas , Nascimento José Hamilton M. , de Carvalho Denise Pires , Bartelt Alexander , Maciel Leonardo , Fortunato Rodrigo Soares
Published: August 2022

Exercise has beneficial effects on energy balance and also improves metabolic health independently of weight loss. Adipose tissue function is a critical denominator of a healthy metabolism but the adaptation of adipocytes in response to exercise is insufficiently well understood. We have previously shown that one aerobic exercise session was associated with increased expression of antioxidant and cytoprotective genes in white adipose tissue (WAT). In the present study, we evaluate the chronic effects of physical exercise on WAT redox homeostasis and mitochondrial function. Adult male Wistar rats were separated into two groups: a control group that did not exercise and a group that performed running exercise sessions on a treadmill for 30 min, 5 days per week for 9 weeks. Reactive oxygen species (ROS) generation, antioxidant enzyme activities, mitochondrial function, markers of oxidative stress and inflammation, and proteins related to DNA damage response were analyzed. In WAT from the exercise group, we found higher mitochondrial respiration in states I, II, and III of Complex I and Complex II, followed by an increase in ATP production, and the ROS/ATP ratio when compared to tissues from control rats. Regarding redox homeostasis, NADPH oxidase activity, protein carbonylation, and lipid peroxidation levels were lower in WAT from the exercise group when compared to control tissues. Moreover, antioxidant enzymatic activity, reduced glutathione/oxidized glutathione ratio, and total nuclear factor erythroid-2, like-2 (NFE2L2/NRF2) protein levels were higher in the exercise group compared to control. Finally, we found that exercise reduced the phosphorylation levels of H2AX histone (γH2AX), a central protein that contributes to genome stability through the signaling of DNA damage. In conclusion, our results show that chronic exercise modulates redox homeostasis in WAT, improving antioxidant capacity, and mitochondrial function. This hormetic remodeling of adipocyte redox balance points to improved adipocyte health and seems to be directly associated with the beneficial effects of exercise.
Seed Priming with Spermine Mitigates Chromium Stress in Rice by Modifying the Ion Homeostasis, Cellular Ultrastructure and Phytohormones Balance
Authors: Basit Farwa , Bhat Javaid Akhter , Ulhassan Zaid , Noman Muhammad , Zhao Biying , Zhou Weijun , Kaushik Prashant , Ahmad Ajaz , Ahmad Parvaiz , Guan Yajing
Published: August 2022

Chromium (Cr) is an important environmental constraint effecting crop productivity. Spermine (SPM) is a polyamine compound regulating plant responses to abiotic stresses. However, SPM-mediated tolerance mechanisms against Cr stress are less commonly explored in plants. Thus, current research was conducted to explore the protective mechanisms of SPM (0.01 mM) against Cr (100 µM) toxicity in two rice cultivars, CY927 (sensitive) and YLY689 (tolerant) at the seedling stage. Our results revealed that, alone, Cr exposure significantly reduced seed germination, biomass and photosynthetic related parameters, caused nutrient and hormonal imbalance, desynchronized antioxidant enzymes, and triggered oxidative damage by over-accretion of reactive oxygen species (ROS), malondialdehyde (MDA) and electrolyte leakage in both rice varieties, with greater impairments in CY927 than YLY689. However, seed priming with SPM notably improved or reversed the above-mentioned parameters, especially in YLY689. Besides, SPM stimulated the stress-responsive genes of endogenous phytohormones, especially salicylic acid (SA), as confirmed by the pronounced transcript levels of SA-related genes (OsPR1, OsPR2 and OsNPR1). Our findings specified that SPM enhanced rice tolerance against Cr toxicity via decreasing accumulation of Cr and markers of oxidative damage (H2O2, O2•− and MDA), improving antioxidant defense enzymes, photosynthetic apparatus, nutrients and phytohormone balance.
Efficacy of Herbal Extracts-Based Nano-Formulations in Extending Guava Fruit Shelf-Life
Authors: Rani Anju , Tokas Jayanti , Gupta Pernika , Punia Himani , Baloda Satpal
Published: August 2022

The guava (Psidium guajava L.), known as ‘Apple of the Tropics’ is a rich source of polyphenols, pectin, dietary fiber, and carotenoids. Guava comes under climacteric types of fruits; due to its high transpiration and respiration rate it experiences high post-harvest losses. Giloy leaf and ajwain seed herbal extracts-based nano-formulations (NFs) were synthesized using biopolymer sodium alginate and CaCl2, viz., T1 (Alginate: CaCl2), T2 (Alginate: CaCl2: Ajwain extract), and T3 (Alginate: CaCl2: Giloy extract). Antibacterial and antioxidant activity of the NFs were examined to check their efficacy as antibacterial agents, which was checked against E. coli, P. aeruginosa and B. cereus bacterial culture. The zone of inhibition against bacteria ranged from 6 ± 0.42 mm to 14 ± 0.92 mm. Antioxidant activity was 93.27%, 71.67%, and 67.04% for T2, T3 and T1 NFs and 89.90% and 67.05% for ajwain and giloy extracts. NFs treated fruits showed minimum loss in physiological weight, firmness, and color change compared to control fruit (uncoated). Physiological loss in weight ranged from 3.16 to 17.21% and 3.23 to 15.57% and fruit firmness ranged from 4.47 to 8.41 kg/cm2 and 4.84 to 8.37 kg/cm2 during storage at 25 ± 2 °C (incubation) and 32 ± 2 °C (room temperature), respectively. Among NFs, T2 showed the best results in preventing ripening and maximum loss of quality was observed in control (uncoated) fruits. Thus, NFs are an effective method of extending the shelf-life of fruits and ajwain based NFs increased shelf life of guava from 4–5 days to 7–8 days. Fruits storage at 25 ± 2 °C showed better results compared to storage at 32 ± 2 °C. Thus, NFs treated fruits storage at lower temperature controls the ripening related changes maximally.
Joint Optimization of Production Lot Sizing and Preventive Maintenance Threshold Based on Nonlinear Degradation
Authors: Qu Li , Liao Junli , Gao Kaiye , Yang Li
Published: August 2022

In a manufacturing system, lot sizing and maintenance are interdependent and interact with each other. Few studies jointly investigated production lot sizing and maintenance management considering system degradation. However, during the production process, the system and critical component performance will undergo inevitable degradation over time. For example, equipment wears out due to both its own internal causes and the external environment. To monitor the degradation process, interval inspection is usually performed to obtain information about the system degradation and nonlinear degradation is more general. Thus, based on the nonlinear degradation of the production system, this study developed a joint optimization model of production lot sizing and preventive maintenance (PM) thresholds with the goal of maximizing profit per unit of time. The maintenance decision follows the control limit principle, i.e., the choice between preventive maintenance (PM), corrective maintenance (CM), or neither (do nothing) is based on the magnitude of degradation. A simulation algorithm is proposed to obtain the optimal lot-sizing allocation and PM thresholds. The effectiveness of this joint optimization model algorithm is illustrated by numerical examples and the results show that the maximum profit per unit time can be obtained by reasonably formulating PM thresholds and production lot sizing.
Real-Time Motion Detection Network Based on Single Linear Bottleneck and Pooling Compensation
Authors: Cheng Huayang , Ding Yunchao , Yang Lu
Published: August 2022

Motion (change) detection is a basic preprocessing step in video processing, which has many application scenarios. One challenge is that deep learning-based methods require high computation power to improve their accuracy. In this paper, we introduce a novel semantic segmentation and lightweight-based network for motion detection, called Real-time Motion Detection Network Based on Single Linear Bottleneck and Pooling Compensation (MDNet-LBPC). In the feature extraction stage, the most computationally expensive CNN block is replaced with our single linear bottleneck operator to reduce the computational cost. During the decoder stage, our pooling compensation mechanism can supplement the useful motion detection information. To our best knowledge, this is the first work to use the lightweight operator to solve the motion detection task. We show that the acceleration performance of the single linear bottleneck is  5% higher than that of the linear bottleneck, which is more suitable for improving the efficiency of model inference. On the dataset CDNet2014, MDNet-LBPC increases the frames per second (FPS) metric by 123 compared to the suboptimal method FgSegNet_v2, ranking first in inference speed. Meanwhile, our MDNet-LBPC achieves 95.74% on the accuracy metric, which is comparable to the state-of-the-art methods.
A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
Authors: Zhang Yuzhen , Liu Jingjing , Shen Wenjuan
Published: August 2022

Machine learning algorithms are increasingly used in various remote sensing applications due to their ability to identify nonlinear correlations. Ensemble algorithms have been included in many practical applications to improve prediction accuracy. We provide an overview of three widely used ensemble techniques: bagging, boosting, and stacking. We first identify the underlying principles of the algorithms and present an analysis of current literature. We summarize some typical applications of ensemble algorithms, which include predicting crop yield, estimating forest structure parameters, mapping natural hazards, and spatial downscaling of climate parameters and land surface temperature. Finally, we suggest future directions for using ensemble algorithms in practical applications.
Polychlorinated Biphenyls in the Snow Cover of South-Eastern Kazakhstan
Authors: Amirgaliyev Nariman Amirgaliyevich , Medeu Akhmetkal Rakhmetullayevich , Opp Christian , Madibekov Azamat , Kulbekova Roza , Ismukhanova Laura , Zhadi Askhat
Published: August 2022

The presence of large sources of environmental pollution due to persistent organic pollutants (POPs), in particular, polychlorinated biphenyls (PCBs), in Kazakhstan necessitates the assessment of pollution as a result of these toxicants. For this purpose, we chose snow cover as an indicator for assessing pollution status in the study area. An assessment of the PCB accumulation level included in the list of POPs was carried out for a snow cover (SC) study in south-east Kazakhstan. The content of PCBs with a wide congener composition was determined using the chromatographic analysis method. During the winter periods of 2014, 2015, 2018–2020 and 2021, the SC pollution of the study area from up to 25 individual PCB congeners was identified. These congeners included highly toxic dioxin-like congener PCBs 105; 108; 114; 118 and “marker” PCBs 52; 101; 138; 153. These congeners were mainly found in snow samples with a wide range of PCB congener compositions. The main PCB pollution sources were indicated. The analysis of the obtained results and structure of the congener composition of PCBs show that the SC contamination in this territory occurs under the influence of local and regional sources.
Zero-Shot Emotion Detection for Semi-Supervised Sentiment Analysis Using Sentence Transformers and Ensemble Learning
Authors: Tesfagergish Senait Gebremichael , Kapočiūtė-Dzikienė Jurgita , Damaševičius Robertas
Published: August 2022

We live in a digitized era where our daily life depends on using online resources. Businesses consider the opinions of their customers, while people rely on the reviews/comments of other users before buying specific products or services. These reviews/comments are usually provided in the non-normative natural language within different contexts and domains (in social media, forums, news, blogs, etc.). Sentiment classification plays an important role in analyzing such texts collected from users by assigning positive, negative, and sometimes neutral sentiment values to each of them. Moreover, these texts typically contain many expressed or hidden emotions (such as happiness, sadness, etc.) that could contribute significantly to identifying sentiments. We address the emotion detection problem as part of the sentiment analysis task and propose a two-stage emotion detection methodology. The first stage is the unsupervised zero-shot learning model based on a sentence transformer returning the probabilities for subsets of 34 emotions (anger, sadness, disgust, fear, joy, happiness, admiration, affection, anguish, caution, confusion, desire, disappointment, attraction, envy, excitement, grief, hope, horror, joy, love, loneliness, pleasure, fear, generosity, rage, relief, satisfaction, sorrow, wonder, sympathy, shame, terror, and panic). The output of the zero-shot model is used as an input for the second stage, which trains the machine learning classifier on the sentiment labels in a supervised manner using ensemble learning. The proposed hybrid semi-supervised method achieves the highest accuracy of 87.3% on the English SemEval 2017 dataset.
Blockchain-Based Cloud Manufacturing SCM System for Collaborative Enterprise Manufacturing: A Case Study of Transport Manufacturing
Authors: Matenga Alice Elizabeth , Mpofu Khumbulani
Published: August 2022

Sheet metal part manufacture is a precursor to various upstream assembly processes, including the manufacturing of mechanical and body parts of railcars, automobiles, ships, etc., in the transport manufacturing sector. The (re)manufacturing of railcars comprises a multi-tier manufacturing supply chain, mainly supported by local small and medium enterprises (SMEs), where siloed information leads to information disintegration between supplier and manufacturer. Technology spillovers in information technology (IT) and operational technology (OT) are disrupting traditional supply chains, leading to a sustainable digital economy, driven by new innovations and business models in manufacturing. This paper presents application of industrial DevOps by merging industry 4.0 technologies for collaborative and sustainable supply chains. A blockchain-based information system (IS) and a cloud manufacturing (CM) process system were integrated, for a supply chain management (SCM) system for the railcar manufacturer. A systems thinking methodology was used to identify the multi-hierarchical system, and a domain-driven design approach (DDD) was applied to develop the event-driven microservice architecture (MSA). The result is a blockchain-based cloud manufacturing as a service (BCMaaS) SCM system for outsourcing part production for boxed sheet metal parts. In conclusion, the BCMaaS system performs part provenance, traceability, and analytics in real time for improved quality control, inventory management, and audit reliability.
A Two-Step Learning Model for the Diagnosis of Coronavirus Disease-19 Based on Chest X-ray Images with 3D Rotational Augmentation
Authors: Kwon Hyuk-Ju , Lee Sung-Hak
Published: August 2022

Herein, we propose a method for effectively classifying normal, coronavirus disease-19 (COVID-19), lung opacity, and viral pneumonia symptoms using chest X-ray images. The proposed method comprises a lung detection model, three-dimensional (3D) rotational augmentation, and a two-step learning model. The lung detection model is used to detect the position of the lungs in X-ray images. The lung position detected by the lung detection model is used as the bounding box coordinates of the two-step learning model. The 3D rotational augmentation, which is a data augmentation method based on 3D photo inpainting, solves the imbalance in the amount of data for each class. The two-step learning model is proposed to improve the model performance by first separating the normal cases, which constitute the most data in the X-ray images, from other disease cases. The two-step learning model comprises a two-class model for classifying normal and disease images, as well as a three-class model for classifying COVID-19, lung opacity, and viral pneumonia among the diseases. The proposed method is quantitatively compared with the existing algorithm, and results show that the proposed method is superior to the existing method.
Operation Characteristics of a Free-Floating Bike Sharing System as a Feeder Mode to Rail Transit Based on GPS Data
Authors: Li Juchen , Guo Xiucheng
Published: August 2022

The jobs-housing imbalance and long commuting distances for residents in many megacities in China are promoting the increase in mode share with rail transit. The emergence of free-floating bike sharing (FFBS) provides an attractive and cost-effective multi-modal solution to the first/last mile problem. This study identifies the mobility patterns of free-floating bikes as a feeder mode to 277 rail transit stations in Beijing using detailed GPS data, and the relationships between these patterns, culture and spatial layout of the city are examined. The results show that the distribution of free-floating bikes, as a feeder mode to rail transit, exhibits an aggregating feature in the spatial-temporal pattern on weekdays. According to the results of the Clusters method and ANOVA analysis, the operation characteristics of free-floating bikes are related to the location of the transit station and the job-to-housing ratio around that area, and imbalanced usage of shared bikes across the city may result from the extreme values of job-to-housing ratios. Based on the fitted distance decay curve, accessing distance is greatly influenced by urban morphology and location. Based on these findings, recommendations for planning, management, and rebalancing of the FFBS system as a feeder mode to rail transit are proposed to promote the integration of FFBS and the rail transit system.
Deep Recurrent Neural Network Assisted Stress Detection System for Working Professionals
Authors: Sharma Sameer Dev , Sharma Sonal , Singh Rajesh , Gehlot Anita , Priyadarshi Neeraj , Twala Bhekisipho
Published: August 2022

Predicting the stress levels of working professionals is one of the most time-consuming and difficult research topics of current day. As a result, estimating working professionals’ stress levels is critical in order to assist them in growing and developing professionally. Numerous machine learning and deep learning algorithms have been developed for this purpose in previous papers. They do, however, have some disadvantages, including increased design complexity, a high rate of misclassification, a high rate of errors, and decreased efficiency. To address these concerns, the purpose of this research is to forecast the stress levels of working professionals using a sophisticated deep learning model called the Deep Recurrent Neural Network (DRNN). The model proposed here comprises dataset preparation, feature extraction, optimal feature selection, and classification using DRNNs. Preprocessing the original dataset removes duplicate attributes and fills in missing values.
Time Series Classification with Shapelet and Canonical Features
Authors: Liu Hai-Yang , Gao Zhen-Zhuo , Wang Zhi-Hai , Deng Yun-Hao
Published: August 2022

Shapelet-based time series classification methods are widely adopted models for time series classification tasks. However, the high computational cost greatly limits the practicability of the Shapelet-based methods. What is more, traditional Shapelet can only describe the overall shape characteristics of subsequences under the Euclidean distance metric, so it is vulnerable to noise. Other than Shapelet, there are other types of discriminative information contained in the subsequences. To deal with the aforementioned problems, an accurate and efficient time series classification algorithm, named Shapelet with Canonical Time Series Features, is proposed in this paper. The proposed algorithm is based on the following three key strategies: (1) randomly selecting Shapelet and limiting the scope of Shapelet to improve efficiency; (2) embedding multiple canonical time series features in Shapelet to improve the adaptability of the algorithm to different classification problems and make up for the accuracy loss caused by the random selection of Shapelet; and (3) building a random forest classifier based on the new feature representations to ensure the generalization ability of the algorithm. Experimental results on 112 UCR time series datasets show that the proposed algorithm is more accurate than the STC algorithm which is based on Shapelet exact search and the Shapelet transform technique, as well as many other types of state-of-the-art time series classification algorithms. Moreover, extensive experimental comparisons verify the significant advantages of the proposed algorithm in terms of efficiency.
Physicochemical and Storage Characteristics of Pork Tteokgalbi Treated with Watermelon Radish Powder
Authors: Kim Ye-Sol , Lee Jae-Joon , Kim Inyong , Yu Daeung , Ha Jung-Heun
Published: August 2022

Here, we investigated the physicochemical and storage characteristics of Tteokgalbi using n-3 fatty acid-enriched pork (n-6/n-3 ratio: 3.220) and explored the effects of treatment with watermelon radish powder (W; 0–4%). Tteokgalbi groups were prepared with 0% W (control (CON)), 1% W (W1), 2% W (W2), 4% W (W3), or 0.05% ascorbic acid as a reference (REF) treatment. W addition to Tteokgalbi increased the moisture and ash contents and water-holding capacity, but reduced cooking loss. W-prepared Tteokgalbi had markedly decreased L* (brightness) and increased b* (yellowness) values, but significantly increased a* (redness) values. W treatment markedly altered the textural properties of Tteokgalbi by improving the hardness and chewiness (W3 treatment), but decreasing springiness (W3) and brittleness (W2 and W3). W addition dose-dependently increased the total polyphenol and flavonoid contents, thereby increasing the 2,2-diphenyl-1-picrylhydrazyl and 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) radical-scavenging activities of Tteokgalbi over 7 days in cold storage. W-treatment Tteokgalbi decreased the pH slightly (compared to CON treatment) and significantly attenuated the induction of 2-thiobarbituric acid, volatile basic nitrogen, and total microbial counts during 7 days in cold storage. Therefore, W may be a suitable food antioxidant that can act as a natural radical scavenger in Tteokgalbi prepared from n-3 fatty acid-enriched pork.
Habitat Destruction and Restoration in Relation to Extinction and Survival of Species in Competitive Communities
Authors: Chonsiripong Kornphong , Duangdai Eakkapong , Pongvuthithum Radom , Likasiri Chulin
Published: August 2022

We studied the N-species competitive coexistence model with direct effect on habitat destruction to analyze the behaviors of abundant and extinct species in the system caused by habitat loss. The nontrivial equilibrium points of the system are determined for a general habitat destruction function. For the trivial equilibrium, species that survived the habitat destruction are identified using eigenvalues of the Jacobian matrix. Solutions of the system are also presented using the recursive method. Three special cases of habitat destruction functions are addressed: continuous destruction, which is a typical habitat destruction; sudden habitat destruction, which is similar to natural phenomena such as earthquakes or floods; and sudden habitat destruction with aftershocks. The proportional abundances of 50 species are numerically portrayed in each case. We found that the survival of a species is guaranteed if its corresponding eigenvalue is positive. However, the fact that a species has negative corresponding eigenvalue does not guarantee its extinction, as this also depends on the initial number of that species.
Electric Vehicle Transient Speed Control Based on Vector Control FM-PI Speed Controller for Induction Motor
Authors: Salahuddin Humayun , Imdad Kashif , Chaudhry Muhammad Umar , Iqbal Muhammad Munwar , Bolshev Vadim , Hussain Aamir , Flah Aymen , Panchenko Vladimir , Jasiński Marek
Published: August 2022

The speed control of induction machines for multiple-speed handling is critical. When the vector control method is applied to induction machines, it has a significant impact on speed utilization. This strategy of operating the machine at a fixed predefined speed mode presents better results for electric vehicles. An effective model for a speed control loop is proposed in this paper, using a fixed-mode proportional integral (FM-PI) controller based on an upper and lower limit torque limiter. The power supply is fed using a lithium-ion battery with an inverter-fed mechanism. Moreover, the proposed model is validated using simulations with user-defined speed modes (40, 60, and 80 km/h). These speed modes, with different torque commands, have been considered for advanced modeling. In this model, torque is developed via a closed-loop control operation to attain the required speed assigned by the user. The sensors are used to collect data, and a multiple regression algorithm analyzes the dataset to predict input parameters (voltage (Vab), phase current (I), and torque (T)) required to achieve the desired speed mode. The efficiency of the proposed model is compared with induction motors bearing the same rating for the loaded and unloaded speed test. Effective machine parameter control is achieved by reaching the desired performance levels of 94.37% and 78.30% in a shorter time for the loaded and unloaded modes. A speed response comparison of the FOPID, KW-WOA-PID, SVR-PI, and FM-PI controller model simulation results indicates that the FM-PI speed controller guarantees better performance and displays an improvement in rising time and settling time, compared to other controllers. The implementation of different driving scenarios proves the model’s effectiveness for robust speed applications.
Chemical Analysis of Essential Oils of Cymbopogon schoenanthus (L.) Spreng. and Nepeta azurea R.Br. ex Benth from Djbouti, In-Vitro Cytotoxicity against Cancer Cell Lines and Antibacterial Activities
Authors: Mohamed Abdoul-Latif Fatouma , Elmi Abdirahman , Merito Ali , Nour Moustapha , Risler Arnaud , Ainane Ayoub , Bignon Jérôme , Ainane Tarik
Published: August 2022

The aim of this research was to evaluate the essential oils of two medicinal plants from Djibouti: Cymbopogon schoenanthus (L.) Spreng. and Nepeta azurea R.Br. ex Benth. in cytotoxic activity against thirteen (13) cancer cell lines (A2780, A549, HCT116, HEK-293, JIMT-T1, K562, MIA-Paca2, MRC5, NCI-N87, PC3, RT4, U2OS and U87-MG) and antibacterial activity against seven (7) Gram (−) strains (Acinetobacter baumannii, Enterobacter cloacae, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Salmonella enterica sv. Typhimurium and Shigella sonnei) and five (5) Gram (+) strains (Corynebacterium sp., Enterococcus faecalis, Staphylococcus aureus, Staphylococcus epidermidis and Streptococcus agalactiae). The plants were extracted by hydrodistillation and were analyzed by GC-MS. The main components of Cymbopogon schoenanthus essential oil (CSEO) were 3-isopropenyl-5-methyl-1-cyclohexene (32.3%) and D-Limonene (11.3%), and the main component of Nepeta azurea essential oil (NAEO) was methyl (2E)-2-nonenoate (53.2%). The two essential oils showed inhibitory cytotoxicity activities of all the cancer cells tested; on the other hand, the antibacterial activities are only well-noticed for the CSEO oil at a concentration of 5% against Klebsiella pneumoniae, Pseudomonas aeruginosa, Corynebacterium sp., Enterococcus faecalis and Staphylococcus aureus. Our results demonstrated that the two essential oils could be effective natural anticancer agents, in addition to an antibacterial character for Cymbopogon schoenanthus essential oil.
Amplification of Chirality in Photopatterned 3D Nanostructures of Chiral/Achiral Mixtures
Authors: Jee Hongsub , Chen Guanying , Lee Jaehyeong
Published: August 2022

The dispersion of a chiral polymer in a polymerizable matrix can amplify the chirality of the material, and a helical conformation of the chiral material within the polymerized SU-8 excessively increased the circular dichroism. Here, we demonstrate the fabrication of three-dimensional nanostructures of chiral/achiral mixtures by two-photon lithography. The irradiation of light and annealing caused local changes in the chiral material and finally led to the enhancement of the optical properties. The demonstration of a photopatternable chiral material could expand the usage of optical materials for various applications.
Exploiting Hierarchical Label Information in an Attention-Embedding, Multi-Task, Multi-Grained, Network for Scene Classification of Remote Sensing Imagery
Authors: Zeng Peng , Lin Shixuan , Sun Hao , Zhou Dongbo
Published: August 2022

Remote sensing scene classification aims to automatically assign proper labels to remote sensing images. Most of the existing deep learning based methods usually consider the interclass and intraclass relationships of the image content for classification. However, these methods rarely consider the hierarchical information of scene labels, as a scene label may belong to hierarchically multi-grained levels. For example, multi-grained level labels may indicate that a remote sensing scene image may belong to the coarse-grained label “transportation land” while also belonging to the fine-grained label “airport”. In this paper, to exploit hierarchical label information, we propose an attention-embedding multi-task multi-grained network (AEMMN) for remote sensing scene classification. In the proposed AEMMN, we add a coarse-grained classifier as the first level and a fine-grained classifier as the second level to perform multi-task learning tasks. Additionally, a gradient control module is utilized to control the gradient propagation of two classifiers to suppress the negative transfer caused by the irrelevant features between tasks. In the feature extraction portion, the model uses an ECA module embedding Resnet50 to extract effective features with cross-channel interaction information. Furthermore, an external attention module is exploited to improve the discrimination of fine-grained and coarse-grained features. Experiments were conducted on the NWPU-RESISC45 and the Aerial Image Data Set (AID), and the overall accuracy of the proposed AEMMN is 92.07% on the NWPU-RESISC45 dataset and reached 94.96% on the AID. The results indicate that hierarchical label information can effectively improve the performance of scene classification tasks when categorizing remote sensing imagery.
Repetitive Bacterial Disinfection of Respirators by Polydopamine Coating
Authors: Jeong Yuri , Lee Kyueui
Published: August 2022

To solve the current and future mask shortage problems, developing methods of disinfecting respirators is essential, where none of the existing methods have been successfully utilized until recently. Herein, we introduce a novel method of conferring antibacterial activity to the main filtering material (i.e., polypropylene (PP)) of a respirator through sequential polydopamine (PDA) coatings. Two-step dip-coating in dopamine solution, which corresponds to one complete cycle, produces stable PDA films at the interface of the filtering material, which subsequently locally generates H2O2 that can be further transformed into hydroxyl radicals to inactivate pathogens. Specifically, the primary dip-coating creates a scaffold PDA film that acts as a mechanical support, and anchoring dopamine, which substantially produces H2O2, is immobilized to the scaffold PDA during the subsequent secondary dip-coating process. The antibacterial activity was confirmed by bacterial tests using Escherichia coli. In short, the number of colonies after incubation of the polypropylene filter with and without the PDA coating in the bacterial solution was compared. The number of bacteria in the PDA-coated sample (0.54 × 109 CFU/mL·cm2) was significantly reduced compared to that in the original PP sample (0.81 × 109 CFU/mL·cm2), demonstrating a positive relationship with the H2O2 production. Moreover, this antibacterial ability can be maintained by simply utilizing additional PDA coatings, suggesting that the respirators can be recycled. Finally, the in vitro cytotoxicity was confirmed by the CCK-8 assay, which demonstrated that the PDA-coated PP filter is biocompatible. We believe that the newly proposed method for disinfection of respirators may substitute conventional methods and can be used to alleviate the mask shortage problem.
Integrated Design of Moon-to-Earth Transfer Trajectory Considering Re-Entry Constraints
Authors: Jia Feida , Peng Qibo , Zhou Wanmeng , Li Xiangyu
Published: August 2022

The exploration of the Moon has always been a hot topic. The determination of the Moon-to-Earth transfer opportunity and the design of the precise transfer trajectory play important roles in manned Moon exploration missions. It is still a difficult problem to determine the Moon-to-Earth return opportunity for accurate atmospheric re-entry and landing, through which the actual return trajectory can be easily obtained later. This paper proposes an efficient integrated design method for Moon-to-Earth window searching and precise trajectory optimization considering the constraints of Earth re-entry and landing. First, an analytical geometry-based method is proposed to determine the state of the re-entry point according to the landing field and re-entry constraints to ensure accurate landing. Next, the transfer window is determined with the perilune heights, which are acquired by inversely integrating the re-entry state under the simplified dynamics as criterion. Then, the precise Moon-to-Earth trajectory is quickly obtained by a three-impulse correction. Simulations show the accuracy and efficiency of the proposed method compared with methods such as the patched-conic method and provide an explicit reference for future Moon exploration missions.
Synthesis of Graphene and Related Materials by Microwave-Excited Surface Wave Plasma CVD Methods
Authors: Kalita Golap , Umeno Masayoshi
Published: August 2022

Several kinds of chemical vapor deposition (CVD) methods have been extensively used in the semiconductor industries for bulk crystal growth, thin film deposition, and nanomaterials synthesis. In this article, we focus on the microwave-excited surface wave plasma CVD (MW-SWP CVD) method for growth of graphene and related materials. The MW-SWP CVD system consisting of waveguide, slot antenna, and dielectric windows is significant for generating high density plasma with low electron temperature, enabling low temperature growth of materials without damaging the surface of base substrates. The synthesis of graphene and hexagonal boron nitride (hBN) films has been achieved on metals, semiconductors, insulators, and dielectric substrates for application in photovoltaics, sensors, batteries, supercapacitors, fuel cells, and various other electronic devices. The details of the synthesis process for graphene films, vertically-oriented graphene, doped-graphene, and hBN films by the MW-SWP CVD method are summarized to understand the growth mechanism, which will enable further development of the plasma CVD process for material synthesis at a low temperature for industrial applications.
Uncertainty Analysis of Hydrological Drought Due to Record Length, Time Scale, and Probability Distribution Functions Using Monte Carlo Simulation Method
Authors: Ghasemnezhad Faezeh , Fazeli Mehdi , Bazrafshan Ommolbanin , Parvinnia Mohammad , Singh Vijay P.
Published: August 2022

Standardized Runoff Index (SRI), as one of the well-known hydrological drought indices, may contain uncertainties caused by the employment of the distribution function, time scale, and record length of statistical data. In this study, the uncertainty in the SRI estimation of monthly discharge data of 30- and 49-year lengths from the Minab Dam watershed, south of Iran, was investigated. Four probability distribution functions (Gamma, Weibull, Lognormal, and Normal) were used to fit the cumulative discharge data at 3-, 6-. 9-, 12-, 24-, and 48-month time scales, with their goodness-of-fit and normality evaluated by K-S and normality tests, respectively. Using Monte Carlo sampling, 50,000 statistical data were generated for each event and each time scale, followed by 95% confidence interval. The width of the confidence interval was used as uncertainty, and sources of uncertainty were investigated using miscellaneous factors. It was found that the maximum uncertainty was related to Normal and Lognormal distributions and the minimum uncertainty to Gamma and Weibull distributions. Furthermore, the increase in both time scale and record length led to the decrease in uncertainty.
Research on the Planning Method and Strategy of Urban Wind and Heat Environment Optimization—Taking Shenzhen, a Sub-Tropical Megacity in Southern China, as an Example
Authors: Zhang Shuo , Fang Xiaoyi , Cheng Chen , Chen Liuxin , Zhang Li , Yu Ying , Li Lei , Luo Hongyan
Published: August 2022

The planning techniques and strategies for optimizing the urban wind and heat environment are important means for cities to adapt to climate change at the source. This study used Shenzhen, a sub-tropical megacity in southern China, as an example for evaluating the climate environment, heat island intensity, and urban form, and then for analyzing the relationships between them. The results revealed a high-quality climate area located southeast of Shenzhen that can provide a high wind speed and low temperature. Low-quality climate areas were located in the central and western regions and were less comfortable. The relationship between surface ventilation potential and urban form was analyzed using linear regression and the Pearson correlation coefficient, showing that there was a significant correlation between a surface urban heat island (SUHI) and building density (BD) as well as the sky view factor (SVF), and that there was also a correlation between the ventilation potential coefficient (VPC) and other factors, such as the surface’s roughness length (RL) and building height (BH). The results showed that ventilation capacity deteriorated as BH and RL increased. An environmentally sensitive thermal area was identified from the surface urban heat island intensity, which was always in a strong heat island (SHI) or sub-strong heat island (SSHI) year-round. It was recommended that seven level one corridors and nine level two corridors be formed. Additionally, thermal and wind environment optimization strategies and protective suggestions were proposed for the city’s overall development.
Group Acceptance Sampling Plan Based on New Compounded Three-Parameter Weibull Model
Authors: Algarni Ali
Published: August 2022

In this study, we introduce a new compounded model called the complementary Bell–Weibull model and use it to address the problem of a group acceptance sampling plan predicted on a truncated life test. The median lifespan is used as a quality index to obtain the design constraints, namely sample size and approval number, under a predefined consumerś risk and test termination period. Additionally, two real data applications are presented, and unknown parameters are estimated using the maximum likelihood approach.
Computer-Aided Analysis of Solvable Rigid Lie Algebras with a Given Eigenvalue Spectrum
Authors: Campoamor-Stursberg Rutwig , Oviaño García Francisco
Published: August 2022

With the help of symbolic computer packages, the study of the cohomological rigidity of real solvable Lie algebras of rank one with a maximal torus of derivations t and the eigenvalue spectrum spec(t)=1,k,k+1,⋯,n+k−2 initiated in a previous work is continued for arbitrary values k≥2, obtaining new hierarchies of solvable rigid Lie algebras.
Functions of Small Non-Coding RNAs in Salmonella–Host Interactions
Authors: Meng Xia , He Mengping , Xia Pengpeng , Wang Jinqiu , Wang Heng , Zhu Guoqiang
Published: August 2022

Salmonella species infect hosts by entering phagocytic and non-phagocytic cells, causing diverse disease symptoms, such as fever, gastroenteritis, and even death. Therefore, Salmonella has attracted much attention. Many factors are involved in pathogenesis, for example, the capsule, enterotoxins, Salmonella pathogenicity islands (SPIs), and corresponding regulators. These factors are all traditional proteins associated with virulence and regulation. Recently, small non-coding RNAs (sRNAs) have also been reported to function as critical regulators. Salmonella has become a model organism for studying sRNAs. sRNAs regulate gene expression by imperfect base-pairing with targets at the post-transcriptional level. sRNAs are involved in diverse biological processes, such as virulence, substance metabolism, and adaptation to stress environments. Although some studies have reported the crucial roles of sRNAs in regulating host–pathogen interactions, the function of sRNAs in host–Salmonella interactions has rarely been reviewed. Here, we review the functions of sRNAs during the infection of host cells by Salmonella, aiming to deepen our understanding of sRNA functions and the pathogenic mechanism of Salmonella.
Prediction of Regulatory SNPs in Putative Minor Genes of the Neuro-Cardiovascular Variant in Fabry Reveals Insights into Autophagy/Apoptosis and Fibrosis
Authors: Ruiz Ramírez Andrea Virginia , Prado Montes de Oca Ernesto , Figuera Luis E
Published: August 2022

Even though a mutation in monogenic diseases leads to a “classic” manifestation, many disorders exhibit great clinical variability that could be due to modifying genes also called minor genes. Fabry disease (FD) is an X-linked inborn error resulting from the deficient or absent activity of alpha-galactosidase A (α-GAL) enzyme, that leads to deposits of globotriaosylceramide. With our proprietary software SNPclinic v.1.0, we analyzed 110 single nucleotide polymorphisms (SNPs) in the proximal promoter of 14 genes that could modify the FD phenotype FD. We found seven regulatory-SNP (rSNPs) in three genes (IL10, TGFB1 and EDN1) in five cell lines relevant to FD (Cardiac myocytes and fibroblasts, Astrocytes-cerebellar, endothelial cells and T helper cells 1-TH1). Each SNP was confirmed as a true rSNP in public eQTL databases, and additional software suggested the prediction of variants. The two proposed rSNPs in IL10, could explain components for the regulation of active B cells that influence the fibrosis process. The three predicted rSNPs in TGFB1, could act in apoptosis-autophagy regulation. The two putative rSNPs in EDN1, putatively regulate chronic inflammation. The seven rSNPs described here could act to modulate Fabry’s clinical phenotype so we propose that IL10, TGFB1 and EDN1 be considered minor genes in FD.
Effects of Low Mental Energy from Long Periods of Work on Brain-Computer Interfaces
Authors: Liu Kaixuan , Yu Yang , Zeng Ling-Li , Liang Xinbin , Liu Yadong , Chu Xingxing , Lu Gai , Zhou Zongtan
Published: August 2022

Brain-computer interfaces (BCIs) provide novel hands-free interaction strategies. However, the performance of BCIs is affected by the user’s mental energy to some extent. In this study, we aimed to analyze the combined effects of decreased mental energy and lack of sleep on BCI performance and how to reduce these effects. We defined the low-mental-energy (LME) condition as a combined condition of decreased mental energy and lack of sleep. We used a long period of work (>=18 h) to induce the LME condition, and then P300- and SSVEP-based BCI tasks were conducted in LME or normal conditions. Ten subjects were recruited in this study. Each subject participated in the LME- and normal-condition experiments within one week. For the P300-based BCI, we used two decoding algorithms: stepwise linear discriminant (SWLDA) and least square regression (LSR). For the SSVEP-based BCI, we used two decoding algorithms: canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA). Accuracy and information transfer rate (ITR) were used as performance metrics. The experimental results showed that for the P300-based BCI, the average accuracy was reduced by approximately 35% (with a SWLDA classifier) and approximately 40% (with a LSR classifier); the average ITR was reduced by approximately 6 bits/min (with a SWLDA classifier) and approximately 7 bits/min (with an LSR classifier). For the SSVEP-based BCI, the average accuracy was reduced by approximately 40% (with a CCA classifier) and approximately 40% (with a FBCCA classifier); the average ITR was reduced by approximately 20 bits/min (with a CCA classifier) and approximately 19 bits/min (with a FBCCA classifier). Additionally, the amplitude and signal-to-noise ratio of the evoked electroencephalogram signals were lower in the LME condition, while the degree of fatigue and the task load of each subject were higher. Further experiments suggested that increasing stimulus size, flash duration, and flash number could improve BCI performance in LME conditions to some extent. Our experiments showed that the LME condition reduced BCI performance, the effects of LME on BCI did not rely on specific BCI types and specific decoding algorithms, and optimizing BCI parameters (e.g., stimulus size) can reduce these effects.
The Interrelation between Interleukin-2 and Schizophrenia
Authors: Huang Yu , Zhang Xin , Zhou Na
Published: August 2022

Interleukin-2 (IL-2) is a growth factor that regulates T-cell autocrine secretion and has long been considered to be closely related to immune response. With the advance in neuroinflammation theory and immunology research on schizophrenia, it is interesting and meaningful to discuss the possible role of IL-2 in schizophrenia. Here, we reviewed a series of studies published from the 1990s and found that IL-2 was closely associated with schizophrenia. For example, IL-2 is responsible for mediating toxic reactions, which are the causes of schizophrenia symptoms in patients, and such symptoms resolve after discontinuation of the drug. In addition, we focused on the changes of IL-2 in the onset, progression and treatment of schizophrenia and the possible mechanisms by which IL-2 affects schizophrenia. Our review suggests that IL-2 is associated with schizophrenia and plays a role in its pathogenesis, and progression IL-2 and sIL-2R could serve as potential biomarkers of schizophrenia.