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The temporal-spatial expression and functional analysis of three gustatory receptor genes in Solenopsis invicta using sweet and bitter compounds

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Insect Science 2024年第2期31卷 448-468页

作者： muhammad irfan Waris Yanyuan Lei Guojun Qi Ziying Guan Abdul Rashied Jie Chen Lihua Lyu Institute of Plant Protection Guangdong Academy of Agricultural SciencesGuangdong Provincial Key Laboratory of High Technology for Plant ProtectionGuangzhouChina Department of Entomology College of Plant ProtectionSouth China Agricultural UniversityGuangzhouChina

The insect gustatory system participates in identifying potential food sources and avoiding toxic compounds.During this process,gustatory receptors(GRs)recognize feeding stimulant and deterrent compounds.However,the GRs involved in recognizing stimulant and deterrent compounds in the red imported fire ant,Solenopsis invicta,remain unknown.Therefore,we conducted a study on the genes SinvGR1,SinvGR32b,and SinvGR28a to investigate the roles of GRs in detecting feeding stimulant and deterrent compounds.In this current study,we found that sucrose and fructose are feeding stimulants and the bitter compound quinine is a feeding deterrent.The fire ant workers showed significant behavior changes to avoid the bitter taste in feeding stimulant compounds.Reverse transcription quantitative real-time polymerase chain reaction results from developmental stages showed that the SinvGR1,SinvGR32b,and SinvGR28a genes were highly expressed in fire ant workers.Tissue-specific expression profiles indicated that SinvGR1,SinvGR32b,and SinvGR28a were specifically expressed in the antennae and foreleg tarsi of workers,whereas SinvGR32b gene transcripts were also highly accumulated in the male antennae.Furthermore,the silencing of SinvGRI or SinvGR32b alone and the co-silencing of both genes disrupted worker stimulation and feeding on sucrose and fructose.The results also showed that SinvGR28a is required for avoiding quinine,as workers with knockdown of the SinvGR28a gene failed to avoid and fed on quinine.This study first identified stimulant and deterrent compounds of fire ant workers and then the GRs involved in the taste recognition of these compounds.This study could provide potential target gustatory genes for the control of the fire ant.

关键词： behavioral responses feeding assays gustatory receptors RNA interference Solenopsis invicta temporal-spatial expression

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An Artificial Intelligence-Based Framework for Fruits Disease Recognition Using Deep Learning

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Computer Systems Science & Engineering 2024年第2期48卷 529-554页

作者： irfan Haider muhammad Attique Khan muhammad Nazir Taerang Kim Jae-Hyuk Cha Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Computer Science Hanyang UniversitySeoulKorea

Fruit infections have an impact on both the yield and the quality of the crop.As a result,an automated recognition system for fruit leaf diseases is important.In artificial intelligence(AI)applications,especially in agriculture,deep learning shows promising disease detection and classification results.The recent AI-based techniques have a few challenges for fruit disease recognition,such as low-resolution images,small datasets for learning models,and irrelevant feature extraction.This work proposed a new fruit leaf leaf leaf disease recognition framework using deep learning features and improved pathfinder optimization.Three fruit types have been employed in this work for the validation process,such as apple,grape,and Citrus.In the first step,a noisy dataset is prepared by employing the original images to learn the designed framework better.The EfficientNet-B0 deep model is fine-tuned on the next step and trained separately on the original and noisy data.After that,features are fused using a serial concatenation approach that is later optimized in the next step using an improved Path Finder Algorithm(PFA).This algorithm aims to select the best features based on the fitness score and ignore redundant information.The selected features are finally classified using machine learning classifiers such as Medium Neural Network,Wide Neural Network,and Support Vector Machine.The experimental process was conducted on each fruit dataset separately and obtained an accuracy of 100%,99.7%,99.7%,and 93.4%for apple,grape,Citrus fruit,and citrus plant leaves,respectively.A detailed analysis is conducted and also compared with the recent techniques,and the proposed framework shows improved accuracy.

关键词： Fruit disease contrast enhancement augmentation deep learning fusion feature selection classification

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Juvenile hormone induces reproduction via miR-1175-3p in the red imported fire ant, Solenopsis invicta

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Insect Science 2024年第2期31卷 371-386页

作者： Jie Chen Ziying Guan Yunjie Ma Qingxing Shi Ting Chen muhammad irfan WarisLihua Lyu Lihua Lyu Yongyue Lu Guojun Qi Institute of Plant Protection Guangdong Academy of Agricultural SciencesKey Laboratory of Green Prevention and Control on Fruits and Vegetables in South China Ministry of Agriculture and Rural AffairsGuangdong Provincial Key Laboratory of High Technology for Plant ProtectionGuangzhouChina Department of Entomology College of Plant ProtectionSouth China Agricultural UniversityGuangzhouChina

Juvenile hormone(JH)acts in the regulation of caste differentiation between queens and workers(i.e.,with or without reproductive capacity)during vitellin synthesis and oogenesis in social insects.However,the regulatory mechanisms have not yet been elucidated.Here,we identified a highly expressed microRNA(miRNA),miR-1175-3p,in the red imported fire ant,Solenopsis invicta.We found that miR-1175-3p is prominently present in the fat bodies and ovaries of workers.Furthermore,miR-1175-3p interacts with its target gene,broad-complex core(Br-C),in the fat bodies.By utilizing miR-1175-3p agomir,we successfully suppressed the expression of the Br-C protein in queens,resulting in reduced vitellogenin expression,fewer eggs,and poorly developed ovaries.Conversely,decreasing miR-1175-3p levels led to the increased expression of Br-C and vitellogenin in workers,triggering the“re-development”of the ovaries.Moreover,when queens were fed with JH,the expression of miR-1175-3p decreased,whereas the expression of vitellogenin-2 and vitellogenin-3 increased.Notably,the suppression of fertility in queens caused by treatment with agomir miR-1175-3p was completely rescued by the increased vitellogenin expression induced by being fed with JH.These results suggest the critical role of miR-1175-3p in JH-regulated reproduction,shedding light on the molecular mechanism underlying miRNA-mediated fecundity in social insects and providing a novel strategy for managing S.invicta.

关键词： juvenile hormone miR-1175-3p red imported fire ant reproduction social insects vitellogenin

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Baerveldt glaucoma implant with Supramid© ripcord stent in neovascular glaucoma: a case series

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International Journal of Ophthalmology(English edition) 2024年第2期17卷 265-271页

作者： Yuen Keat Gan Shiivaa Manjare Birapadian muhammad irfan Abdul Jalal Norshamsiah Md Din Department of Ophthalmology Faculty of MedicineUniversiti Kebangsaan MalaysiaKuala Lumpur 56000Malaysia UKM Molecular Biology Institute Universiti Kebangsaan MalaysiaKuala Lumpur 56000Malaysia Hospital Canselor Tuanku Muhriz Jalan Yaacob LatifBandar Tun RazakKuala Lumpur 56000Malaysia

AIM:To report the outcome of Baerveldt glaucoma implant(BGI)with Supramid©ripcord use in neovascular glaucoma(NVG).METHODS:We retrospectively evaluated the surgical outcome of the BGI with Supramid©3/0 ripcord stent in patients with NVG.No tube ligation or venting slits were performed.Supramid was removed after 3mo if the target intraocular pressure(IOP)was not achieved.Surgical success was defined as IOP≤21 mm Hg with(qualified success)or without IOP-lowering medications(complete success).RESULTS:Twenty-six eyes from 24 patients were included in the study.The median duration of follow-up was 4[interquartile range(IQR)=1-5]y,ranging from 0.5 to 5y.IOP decreased by a mean of 24.2 mm Hg(59.7%);from a mean of 40.5±12.6 mm Hg at baseline to 16.3±11.9 mm Hg,P≤0.001.The number of glaucoma medications reduced from a median of 5(IQR=5-6)to 1(IQR=0-2,P≤0.001)at the final follow-up.Overall success rates were 88.0%at 1y,34.8%at 3y,66.7%at 4y,and 50%at 5y.Hypertensive phase(HP)in the first 3mo occurred in 15/26 eyes(57.7%)with a mean IOP of 31.1 mm Hg.CONCLUSION:BGI with Supramid©ripcord stent gives close to 90%of the overall survival rate at the final follow-up without significant early hypotony.However,early HP is still a challenge.

关键词： neovascular glaucoma Supramid ripcord stent Baerveldt glaucoma implant

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Contrast Normalization Strategies in Brain Tumor Imaging:From Preprocessing to Classification

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1539-1562页

作者： Samar M.Alqhtani Toufique A.Soomro Faisal Bin Ubaid Ahmed Ali muhammad irfan Abdullah A.Asiri Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Electronic Engineering The University of LarkanoLarkanaSindh75660Pakistan Computer Science Department Sukkur IBA UniversitySukkurSindh65200Pakistan Eletrical Engineering Department Sukkur IBA UniversitySukkurSindh65200Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia

Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries.Magnetic resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain images.MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders.Typically,manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention.However,early diagnosis of brain tumors is intricate,necessitating the use of computerized methods.This research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain tumors.The proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-Means(FCM)clustering technique in the third step.The final step involves classification using the Support Vector Machine(SVM)classifier.The classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)database.The proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of 0.957.Additionally,this method exhibits a shorter processing time of 0.44 s compared to existing approaches.The performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and DSC.To enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classification method.

关键词： Brain tumor magnetic resonance imaging principal component analysis fuzzy c-clustering support vector machine

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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Computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq muhammad irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware operations.This study provides a new approach for RaaS attack detection which uses an ensemble of deep learning models.For this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is analyzed.In the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are developed.Later,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested methodology.The proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%F1-score.The empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual MLPmodels.In expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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Enhancing PDF Malware Detection through Logistic Model Trees

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Computers, Materials & Continua 2024年第3期78卷 3645-3663页

作者： muhammad Binsawad Department of Information Systems King Abdulaziz UniversityP.O.Box 80217Jeddah21589Saudi Arabia

Malware is an ever-present and dynamic threat to networks and computer systems in cybersecurity,and because of its complexity and evasiveness,it is challenging to identify using traditional signature-based detection approaches.The study article discusses the growing danger to cybersecurity that malware hidden in PDF files poses,highlighting the shortcomings of conventional detection techniques and the difficulties presented by adversarial methodologies.The article presents a new method that improves PDF virus detection by using document analysis and a Logistic Model Tree.Using a dataset from the Canadian Institute for Cybersecurity,a comparative analysis is carried out with well-known machine learning models,such as Credal Decision Tree,Naïve Bayes,Average One Dependency Estimator,Locally Weighted Learning,and Stochastic Gradient Descent.Beyond traditional structural and JavaScript-centric PDF analysis,the research makes a substantial contribution to the area by boosting precision and resilience in malware detection.The use of Logistic Model Tree,a thorough feature selection approach,and increased focus on PDF file attributes all contribute to the efficiency of PDF virus detection.The paper emphasizes Logistic Model Tree’s critical role in tackling increasing cybersecurity threats and proposes a viable answer to practical issues in the sector.The results reveal that the Logistic Model Tree is superior,with improved accuracy of 97.46%when compared to benchmark models,demonstrating its usefulness in addressing the ever-changing threat landscape.

关键词： Malware detection PDF files logistic model tree feature selection cybersecurity

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Progress in electrocatalytic nitrate reduction for green energy: Catalyst engineering, mechanisms, and techno-economic feasibility

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Journal of Energy Chemistry 2024年第8期95卷 380-406页

作者： Hafiz muhammad Adeel Sharif Hafiz muhammad Farooq Khan Sadeeq Ullah Yuwei Wang muhammad Ahmad Bo Yang Changping Li muhammad Bilal Asif Research Center for Eco-Environmental Engineering Dongguan University of Technology College of Chemistry and Engineering Shenzhen University FMH College of Medicine and Dentistry School of Environment and Civil Engineering Research Center for Eco-Environment Engineering Dongguan University of Technology Advanced Membranes and Porous Materials Center (AMPMC) Physical Sciences and Engineering (PSE) King Abdullah University of Science and Technology (KAUST)

Ammonia（NH3） is an irreplaceable chemical that has been widely demanded to keep the sustainable development of modern society.However,its industrial production consumes a huge amount of energy and releases extraordinary greenhouse gases（GHGs）,leading to various environmental issues.Achieving the green production of ammonia is a great challenge,which has been extensively pursued in the last decade.In this review,the most promising strategy,electrochemical nitrate reduction reaction（e-NO3RR）,is comprehensively investigated to give a complete understanding of its development and mechanism and provide guidance for future directions.However,owing to the complex reactions and limited selectivity,a comprehensive understanding of the mechanisms is crucial to further development and commercialization.Moreover,NO3-RR is a promising strategy for simultaneous water treatment and NH3production.A detailed overview of the recent progress in NO3-RR for NH3production with nontransition and transition metal based electrocatalysts is summarized.In addition,critical advanced techniques,future challenges,and prospects are discussed to guide future research on transition metal-based catalysts for commercial NH3synthesis by NO3-reduction.

关键词： Ammonia synthesis Electrolysis Green energy Nitrate reduction Pollution to solution Transition metal catalysts

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An indicator model for assessing community resilience to the COVID-19 pandemic and its validation: A case study in Hong Kong

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Journal of Safety Science and Resilience 2024年第2期5卷 222-234页

作者： Nan Liao muhammad Nawaz Departnent of Geography Faculty Art and Social SciencesSingapore 65166666Singapore

The COVID-19 outbreak had a significant negative impact on the world,and the fifth wave of COVID-19 in Hong Kong brought a considerable shock to Chinese society.There is a growing call for more resilient cities.However,empirical evidence and validation of modeling studies of resilience indicators for urban community responses to the COVID-19 pandemic still need to be provided.In this study,a resilience assessment indicator model comprising 4 subsystems,7 indicators,and 12 variables was developed to assess the resilience of Hong Kong communities in response to COVID-19(i.e.,Resilience Index).Furthermore,this study utilized regression models such as geographically weighted regression(GWR)and multiscale GWR(MGWR)to validate the resilience model proposed in this study at the model and variable levels.In the regression model,the Resilience Index and the individual variables in the resilience model are explanatory variables,and the outcomes of the COVID-19 pandemic(confirmed cases,confirmation rate,discharged cases,discharge rate)are dependent variables.The results showed that:(i)the resilience of Hong Kong communities to the COvID-19 pandemic was not strong in general and showed some clustered spatial distribution characteristics;(i)the validation results at the model level showed that the Resilience Index did not explain the consequences of the COvID-19 pandemic to a high degree;(ii)the validation results at the variable level showed that the MGWR model was the best at identifying the relationships between explanatory variables and the dependent variable;and(iv)compared with the model-level assessment results,the variable-level assessment explained the consequences of the COvID-19 pandemic better than the model level assessment results.The above analysis and the spatial distribution maps of the resilience variables can provide empirically based and targeted insights for policymakers.

关键词： Resilience Community resilience CoVID-19pandemic Resilience assessment indicator model Resilience model validation MGWR

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Consequences of Rényi entropy on the thermal geometries and Hawking evaporation of topological dyonic dilaton black hole

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Communications in Theoretical Physics 2024年第2期76卷 102-110页

作者： muhammad Yasir Xia Tiecheng muhammad Usman Shahid Chaudhary Department of Mathematics Shanghai University and Newtouch Center for Mathematics of Shanghai UniversityShanghai200444China Department of Natural Sciences and Humanities University of Engineering and Technology LahoreNew CampusPakistan

The thermodynamics of black holes(BHs)has had a profound impact on theoretical physics,providing insight into the nature of gravity,the quantum structure of spacetime and the fundamental laws governing the Universe.In this study,we investigate thermal geometries and Hawking evaporation of the recently proposed topological dyonic dilaton BH in anti-de Sitter(Ad S)space.We consider Rényi entropy and obtain the relations for pressure,heat capacity and Gibbs free energy and observe that the Rényi parameter and dilaton field play a vital role in the phase transition and stability of the BH.Moreover,we use Weinhold,Ruppeiner and Hendi Panahiyah Eslam Momennia models to evaluate the scalar curvature of the BH and find out that the divergence points of the scalar curvature coincides with the zero of specific heat.Finally,using Stefan–Boltzmann law,we determine that the BH without a dilaton field evaporates far more quickly compared to the dilaton BH in Ad S space.

关键词： topological dyonic dilaton black hole phase transition thermal geometry Hawking evaporation

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