Bifidobacterium longum subsp.infantis is a commensal bacterium that predominates in the infant gut,playing a critical role in both preventing foreign infections and facilitating immune development.This study aimed to ...Bifidobacterium longum subsp.infantis is a commensal bacterium that predominates in the infant gut,playing a critical role in both preventing foreign infections and facilitating immune development.This study aimed to explore the effects of B.longum subsp.infantis supplementation on interferon-beta(IFN-β)secretion and intestinal barrier improvement in growing mice.Female and male mice were orally administered either saline or B.longum subsp.infantis CCFM1269 or I5TI(1×10^(9) CFU/mice per day,n=8)from 1-week-age until 3-,4-,and 5-week-age.RNA sequencing analysis revealed that CCFM1269 exhibited potential antiviral capacity through increasing 2'-5'oligoadenylate synthetase(OAS).Additionally,CCFM1269 supplementation significantly increased colonic IFN-β levels which combined with OAS in 3-week-old female and male mice by activating the TLR4-TRIF-dependent signaling pathway.However,this effect was not observed in 4-and 5-week-old mice.Furthermore,both CCFM1269 were found to modulate the gut microbiota composition and enhance the intestinal barrier function in 3-,4-,and 5-week-old mice.In summary,the results of this study suggested that B.longum subsp.infantis CCFM1269 promoting intestinal barrier and releasing IFN-β in growing mice was in a strain-specific and time-dependent manner.展开更多
Objective:To determine the proportion of imported frozen fish contaminated with Salmonella among retail food stores and supermarkets in the Eastern Province of Saudi Arabia.Methods:A total of 223 frozen freshwater fis...Objective:To determine the proportion of imported frozen fish contaminated with Salmonella among retail food stores and supermarkets in the Eastern Province of Saudi Arabia.Methods:A total of 223 frozen freshwater fish purchased from different supermarkets and grocery stores were analyzed for the presence of foodborne pathogen Salmonella.The isolation of Salmonella was determined and confirmed by using the methods of US Food and Drug Administration's Bacteriological Analytical Manual.CHROMagar Salmonella plus,biochemical tests and API 20E strips.Antimicrobial susceptibilities of Salmonella isolates were determined by the disk diffusion method on Muller-Hinton agar,as described by Kirby-Bauer.in accordance with the guidelines of the Clinical and Laboratory Standards Institute.Results:Out of the total 223 fish samples(20 of catfish,18 of carfu,20 of mirgal,25 of milkfish,35 of mackerel,75 of tilapia,and 30 of rohu),89(39.9%)were tested positive for Salmonella.The prevalence of positive samples were reported for the freshwater fish of pangas(60.0%,n=12),carfu(27.7%,n=5),mirgal(35.0%,n=7),milkfish(52.0%,n=13),mackerel(31.4%,n=11),tilapia imported from Thailand(64.0%,n=16),tilapia imported from India(28.0%,n=14),rohu imported from Thailand(26.6%,n=4)and rohu imported from Myanmar(46.6%,n=7).A total of 140 isolates of Salmonella spp.were yielded from at least seven different types of frozen freshwater fish imported from 5 different countries and were tested for their susceptibility to 16 selected antimicrobial agents.The highest antibiotic resistance was observed to tetracycline(90.71%)followed by ampicillin(70%)and amoxicillin-clavulanic acid(45%).Conclusions:The obtained results of this study shows that these raw retail imported frozen freshwater fish are contaminated with potentially pathogenic Salmonella spp.And the study recommend and suggest that there is a need for adequate consumer measures.展开更多
Protease is wildly used in various fields,such as food,medicine,washing,leather,cosmetics and other industrial fields.In this study,an alkaline protease secreted by Micrococcus NH54PC02 isolated from the South China S...Protease is wildly used in various fields,such as food,medicine,washing,leather,cosmetics and other industrial fields.In this study,an alkaline protease secreted by Micrococcus NH54PC02 isolated from the South China Sea was purified and characterized.The growth curve and enzyme activity curve indicated that the cell reached a maximum concentration at the 30 th hour and the enzyme activity reached the maximum value at the 36 th hour.The protease was purified with 3 steps involving ammonium sulfate precipitation,ion-exchange chromatography and hydrophobic chromatography with 8.22-fold increase in specific activity and 23.68% increase in the recovery.The molecular mass of the protease was estimated to be 25 k Da by SDS-PAGE analysis.The optimum temperature and p H for the protease activity were 50℃ and pH 10.0,respectively.The protease showed a strong stability in a wide range of pH values ranging from 6.0–11.0,and maintained 90% enzyme activity in strong alkaline environment with p H 11.0.Inhibitor trials indicated that the protease might be serine protease.But it also possessed the characteristic of metalloprotease as it could be strongly inhibited by EDTA and strongly stimulated by Mn^(2+).Evaluation of matrix-assisted laser desorption ionization/time-of-flight MS(MALDI-TOF-TOF/MS) showed that the protease might belong to the peptidase S8 family.展开更多
An intense bloom of Karenia sp.was reported in the Oualidia lagoon,part of Atlantic Moroccan coast.The highest concentrations are 1.04×10^7 cells/L,and have been noted at the park station 7.High nutrient concentr...An intense bloom of Karenia sp.was reported in the Oualidia lagoon,part of Atlantic Moroccan coast.The highest concentrations are 1.04×10^7 cells/L,and have been noted at the park station 7.High nutrient concentrations have been observed,and PO4 was the highest value(av.396.18μg/L)recorded at Parc 7.This massive proliferation caused a red tide which extended over 25 km from the Atlantic coast.This event was accompanied by stranding of macroalgae.展开更多
With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contex...With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contextual understanding,sequential dependencies,and/or data imbalance.This makes distinction between genuine and fabricated news a challenging task.To address this problem,we propose a novel hybrid architecture,T5-SA-LSTM,which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attentionenhanced(SA)Long Short-Term Memory(LSTM).The LSTM is trained using the Adam optimizer,which provides faster and more stable convergence compared to the Stochastic Gradient Descend(SGD)and Root Mean Square Propagation(RMSProp).The WELFake and FakeNewsPrediction datasets are used,which consist of labeled news articles having fake and real news samples.Tokenization and Synthetic Minority Over-sampling Technique(SMOTE)methods are used for data preprocessing to ensure linguistic normalization and class imbalance.The incorporation of the Self-Attention(SA)mechanism enables the model to highlight critical words and phrases,thereby enhancing predictive accuracy.The proposed model is evaluated using accuracy,precision,recall(sensitivity),and F1-score as performance metrics.The model achieved 99%accuracy on the WELFake dataset and 96.5%accuracy on the FakeNewsPrediction dataset.It outperformed the competitive schemes such as T5-SA-LSTM(RMSProp),T5-SA-LSTM(SGD)and some other models.展开更多
The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often...The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities.展开更多
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d...Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.展开更多
Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-through...Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-throughput sequencing technology have become prominent in biomedical research,and they reveal molecular aspects of cancer diagnosis and therapy.Despite the development of advanced sequencing technology,the presence of high-dimensionality in multi-omics data makes it challenging to interpret the data.Methods:In this study,we introduce RankXLAN,an explainable ensemble-based multi-omics framework that integrates feature selection(FS),ensemble learning,bioinformatics,and in-silico validation for robust biomarker detection,potential therapeutic drug-repurposing candidates’identification,and classification of SC.To enhance the interpretability of the model,we incorporated explainable artificial intelligence(SHapley Additive exPlanations analysis),as well as accuracy,precision,F1-score,recall,cross-validation,specificity,likelihood ratio(LR)+,LR−,and Youden index results.Results:The experimental results showed that the top four FS algorithms achieved improved results when applied to the ensemble learning classification model.The proposed ensemble model produced an area under the curve(AUC)score of 0.994 for gene expression,0.97 for methylation,and 0.96 for miRNA expression data.Through the integration of bioinformatics and ML approach of the transcriptomic and epigenomic multi-omics dataset,we identified potential marker genes,namely,UBE2D2,HPCAL4,IGHA1,DPT,and FN3K.In-silico molecular docking revealed a strong binding affinity between ANKRD13C and the FDA-approved drug Everolimus(binding affinity−10.1 kcal/mol),identifying ANKRD13C as a potential therapeutic drug-repurposing target for SC.Conclusion:The proposed framework RankXLAN outperforms other existing frameworks for serum biomarker identification,therapeutic target identification,and SC classification with multi-omics datasets.展开更多
Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remain...Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remains one of the security challenges. is paper proposes LinguTimeX,a new framework that combines natural language processing with arti cial intelligence,along with explainable Arti cial Intelligence(AI)not only to detect CTC but also to provide insights into the decision process.LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely.LinguTimeX demonstrates strong e ectiveness in detecting CTC across multiple languages;namely English,Arabic,and Chinese.Speci cally,the LSTM and RNN models achieved F1 scores of 90%on the English dataset,89%on the Arabic dataset,and 88%on the Chinese dataset,showcasing their superior performance and ability to generalize across multiple languages. is highlights their robustness in detecting CTCs within security systems,regardless of the language or cultural context of the data.In contrast,the DeepForest model produced F1-scores ranging from 86%to 87%across the same datasets,further con rming its e ectiveness in CTC detection.Although other algorithms also showed reasonable accuracy,the LSTM and RNN models consistently outperformed them in multilingual settings,suggesting that deep learning models might be better suited for this particular problem.展开更多
A novel siphon-based divide-and-conquer(SbDaC)policy is presented in this paper for the synthesis of Petri net(PN)based liveness-enforcing supervisors(LES)for flexible manufacturing systems(FMS)prone to deadlocks or l...A novel siphon-based divide-and-conquer(SbDaC)policy is presented in this paper for the synthesis of Petri net(PN)based liveness-enforcing supervisors(LES)for flexible manufacturing systems(FMS)prone to deadlocks or livelocks.The proposed method takes an uncontrolled and bounded PN model(UPNM)of the FMS.Firstly,the reduced PNM(RPNM)is obtained from the UPNM by using PN reduction rules to reduce the computation burden.Then,the set of strict minimal siphons(SMSs)of the RPNM is computed.Next,the complementary set of SMSs is computed from the set of SMSs.By the union of these two sets,the superset of SMSs is computed.Finally,the set of subnets of the RPNM is obtained by applying the PN reduction rules to the superset of SMSs.All these subnets suffer from deadlocks.These subnets are then ordered from the smallest one to the largest one based on a criterion.To enforce liveness on these subnets,a set of control places(CPs)is computed starting from the smallest subnet to the largest one.Once all subnets are live,this process provides the LES,consisting of a set of CPs to be used for the UPNM.The live controlled PN model(CPNM)is constructed by merging the LES with the UPNM.The SbDaC policy is applicable to all classes of PNs related to FMS prone to deadlocks or livelocks.Several FMS examples are considered from the literature to highlight the applicability of the SbDaC policy.In particular,three examples are utilized to emphasize the importance,applicability and effectiveness of the SbDaC policy to realistic FMS with very large state spaces.展开更多
Long-life energy storage batteries are integral to energy storage systems and electric vehicles,with lithium-ion batteries(LIBs)currently being the preferred option for extended usage-life energy storage.To further ex...Long-life energy storage batteries are integral to energy storage systems and electric vehicles,with lithium-ion batteries(LIBs)currently being the preferred option for extended usage-life energy storage.To further extend the life span of LIBs,it is essential to intensify investments in battery design,manufacturing processes,and the advancement of ancillary materials.The pursuit of long durability introduces new challenges for battery energy density.The advent of electrode material offers effective support in enhancing the battery’s long-duration performance.Often underestimated as part of the cathode composition,the binder plays a pivotal role in the longevity and electrochemical performance of the electrode.Maintaining the mechanical integrity of the electrode through judicious binder design is a fundamental requirement for achieving consistent long-life cycles and high energy density.This paper primarily concentrates on the commonly employed cathode systems in lithium-ion batteries,elucidates the significance of binders for both,discusses the application status,strengths,and weaknesses of novel binders,and ultimately puts forth corresponding optimization strategies.It underscores the critical function of binders in enhancing battery performance and advancing the sustainable development of lithium-ion batteries,aiming to offer fresh insights and perspectives for the design of high-performance LIBs.展开更多
This study developed a novel heterogeneous Vis-Photo+Fenton-like system by integrating visible-light-responsive Co_(3)O_(4)/TiO_(2) photocatalysis with peroxymonosulfate(PMS)activation for efficient atrazine(ATZ)degra...This study developed a novel heterogeneous Vis-Photo+Fenton-like system by integrating visible-light-responsive Co_(3)O_(4)/TiO_(2) photocatalysis with peroxymonosulfate(PMS)activation for efficient atrazine(ATZ)degradation.The synergistic process achieved complete ATZ removal within 60 min under near-neutral pH(6.9),outperform-ing individual Fenton-like(39%)and photocatalytic(24%)processes.Key factors influencing the degradation efficiency included light sources(UV>visible),pH(optimal at 6.9),catalyst dosage(0.01 g Co_(3)O_(4)/TiO_(2)),and PMS:ATZ molar ratio(1:2).The system exhibited a synergistic coefficient of 5.03(degradation)and 1.97(miner-alization),attributed to enhanced radical generation and accelerated Co^(3+)/Co^(2+)redox cycling through photoin-duced electron transfer.Intermediate analysis revealed dealkylation,dechlorination,and oxidation pathways,with reduced toxicity of by-products(e.g.,CEAT,CIAT)confirmed by ecotoxicity assessments.The mineralization efficiency(Vis-Photo+Fenton-like)reached 83.1%,significantly higher than that of standalone processes(Fenton-like:43.2%;photocatalysis:30.5%).The catalyst demonstrated excellent stability(nearly 90%recov-ery,<1μg/L Co leaching)and practical applicability.This study provides an efficient,sludge-free,and solar-compatible strategy for eliminating persistent herbicides in water treatment.展开更多
The growing global energy demand and worsening climate change highlight the urgent need for clean,efficient and sustainable energy solutions.Among emerging technologies,atomically thin two-dimensional(2D)materials off...The growing global energy demand and worsening climate change highlight the urgent need for clean,efficient and sustainable energy solutions.Among emerging technologies,atomically thin two-dimensional(2D)materials offer unique advantages in photovoltaics due to their tunable optoelectronic properties,high surface area and efficient charge transport capabilities.This review explores recent progress in photovoltaics incorporating 2D materials,focusing on their application as hole and electron transport layers to optimize bandgap alignment,enhance carrier mobility and improve chemical stability.A comprehensive analysis is presented on perovskite solar cells utilizing 2D materials,with a particular focus on strategies to enhance crystallization,passivate defects and improve overall cell efficiency.Additionally,the application of 2D materials in organic solar cells is examined,particularly for reducing recombination losses and enhancing charge extraction through work function modification.Their impact on dye-sensitized solar cells,including catalytic activity and counter electrode performance,is also explored.Finally,the review outlines key challenges,material limitations and performance metrics,offering insight into the future development of nextgeneration photovoltaic devices encouraged by 2D materials.展开更多
countries in West Africa remain a hotspot for malaria with all age groups at risk.Asymptomatic carriers of Plasmodium spp.are important sources of infections for malaria vectors and thus contribute to the anchoring of...countries in West Africa remain a hotspot for malaria with all age groups at risk.Asymptomatic carriers of Plasmodium spp.are important sources of infections for malaria vectors and thus contribute to the anchoring of the disease in favourable eco-epidemiological settings.The objective of this study was to assess the asymptomatic malaria case rates in Korhogo and Kaedi,two urban areas in northern Côte d’Ivoire and southern Mauritania,respectively.Methods:Cross-sectional surveys were carried out during the rainy season in 2014 and the dry season in 2015 in both settings.During each season,728 households were randomly selected and a household-based questionnaire was implemented to collect demographic and epidemiological data,including of malaria preventive methods used in communities.Finger-prick blood samples were obtained for biological examination using microscopy and rapid diagnostic tests(RDTs).Results:Overall,2672 households and 15858 consenting participants were surveyed.Plasmodium spp.infection was confirmed in 12.4%(n=832)and 0.3%(n=22)of the assessed individuals in Korhogo and Kaedi,respectively.In Korhogo,the prevalence of asymptomatic malaria was 10.5%(95%CI:9.7-11.2)as determined by microscopy and 9.3%(95%CI:8.6-10.0%)when assessed by RDT.In Kaedi,asymptomatic malaria prevalence was 0.2%(95%CI:0.1-0.4%)according to microscopy,while all RDTs performed were negative(n=8372).In Korhogo,asymptomatic malaria infection was significantly associated with age and season,with higher risk within the 5-14 years-old,and during the rainy season.In Kaedi,the risk of asymptomatic malaria infection was associated with season only(higher during the dry season;crude OR(cOR):6.37,95%CI:1.87-21.63).P.falciparum was the predominant species identified in both study sites representing 99.2%(n=825)in Korhogo and 59.1%(n=13)in Kaedi.Gametocytes were observed only in Korhogo and only during the rainy season at 1.3%(95%CI:0.7-2.4%).Conclusions:Our findings show a low prevalence of clinical malaria episodes with a significant proportion of asymptomatic carriers in both urban areas.National policies for malaria infections are focused on treatment of symptomatic cases.Malaria control strategies should be designed for monitoring and managing malaria infections in asymptomatic carriers.Additional measures,including indoor residual spraying,effective use of long-lasting insecticidal nets is strongly needed to reduce the number of Plasmodium spp.infections in Korhogo and Kaedi.展开更多
The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cereb...The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cerebral organoids have emerged as valuable tools offering a more complex,versatile,and human-relevant system than traditional animal models,which are often unable to replicate the intricate architecture and functionality of the human brain.Since human cerebral organoids are a state-of-the-art model for the study of neurodevelopment and different pathologies affecting the brain,this field is currently under constant development,and work in this area is abundant.In this review,we give a complete overview of human cerebral organoids technology,starting from the different types of protocols that exist to generate different human cerebral organoids.We continue with the use of brain organoids for the study of brain pathologies,highlighting neurodevelopmental,psychiatric,neurodegenerative,brain tumor,and infectious diseases.Because of the potential value of human cerebral organoids,we describe their use in transplantation,drug screening,and toxicology assays.We also discuss the technologies available to study cell diversity and physiological characteristics of organoids.Finally,we summarize the limitations that currently exist in the field,such as the development of vasculature and microglia,and highlight some of the novel approaches being pursued through bioengineering.展开更多
基金funded by the National Key R&D Program of China(2021YFD2100700)National Natural Science Foundation of China(32021005)+1 种基金111 project(BP0719028)Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province。
文摘Bifidobacterium longum subsp.infantis is a commensal bacterium that predominates in the infant gut,playing a critical role in both preventing foreign infections and facilitating immune development.This study aimed to explore the effects of B.longum subsp.infantis supplementation on interferon-beta(IFN-β)secretion and intestinal barrier improvement in growing mice.Female and male mice were orally administered either saline or B.longum subsp.infantis CCFM1269 or I5TI(1×10^(9) CFU/mice per day,n=8)from 1-week-age until 3-,4-,and 5-week-age.RNA sequencing analysis revealed that CCFM1269 exhibited potential antiviral capacity through increasing 2'-5'oligoadenylate synthetase(OAS).Additionally,CCFM1269 supplementation significantly increased colonic IFN-β levels which combined with OAS in 3-week-old female and male mice by activating the TLR4-TRIF-dependent signaling pathway.However,this effect was not observed in 4-and 5-week-old mice.Furthermore,both CCFM1269 were found to modulate the gut microbiota composition and enhance the intestinal barrier function in 3-,4-,and 5-week-old mice.In summary,the results of this study suggested that B.longum subsp.infantis CCFM1269 promoting intestinal barrier and releasing IFN-β in growing mice was in a strain-specific and time-dependent manner.
基金Supported by the Deanship of Scientific Research,University of Dammam (Grant No.2012139)
文摘Objective:To determine the proportion of imported frozen fish contaminated with Salmonella among retail food stores and supermarkets in the Eastern Province of Saudi Arabia.Methods:A total of 223 frozen freshwater fish purchased from different supermarkets and grocery stores were analyzed for the presence of foodborne pathogen Salmonella.The isolation of Salmonella was determined and confirmed by using the methods of US Food and Drug Administration's Bacteriological Analytical Manual.CHROMagar Salmonella plus,biochemical tests and API 20E strips.Antimicrobial susceptibilities of Salmonella isolates were determined by the disk diffusion method on Muller-Hinton agar,as described by Kirby-Bauer.in accordance with the guidelines of the Clinical and Laboratory Standards Institute.Results:Out of the total 223 fish samples(20 of catfish,18 of carfu,20 of mirgal,25 of milkfish,35 of mackerel,75 of tilapia,and 30 of rohu),89(39.9%)were tested positive for Salmonella.The prevalence of positive samples were reported for the freshwater fish of pangas(60.0%,n=12),carfu(27.7%,n=5),mirgal(35.0%,n=7),milkfish(52.0%,n=13),mackerel(31.4%,n=11),tilapia imported from Thailand(64.0%,n=16),tilapia imported from India(28.0%,n=14),rohu imported from Thailand(26.6%,n=4)and rohu imported from Myanmar(46.6%,n=7).A total of 140 isolates of Salmonella spp.were yielded from at least seven different types of frozen freshwater fish imported from 5 different countries and were tested for their susceptibility to 16 selected antimicrobial agents.The highest antibiotic resistance was observed to tetracycline(90.71%)followed by ampicillin(70%)and amoxicillin-clavulanic acid(45%).Conclusions:The obtained results of this study shows that these raw retail imported frozen freshwater fish are contaminated with potentially pathogenic Salmonella spp.And the study recommend and suggest that there is a need for adequate consumer measures.
基金supported by the Fundamental Research Funds for the Central Universities(No.201564018)Qingdao Shinan District Science and Technology Development Funds(No.2014-14-002-SW)+1 种基金Major Special Science and Technology Projects in Shandong Province(No.2015ZDZX05003)the National Natural Science Foundation of China-Shandong Joint Fund for Marine Science Research Centers(No.U1406402)
文摘Protease is wildly used in various fields,such as food,medicine,washing,leather,cosmetics and other industrial fields.In this study,an alkaline protease secreted by Micrococcus NH54PC02 isolated from the South China Sea was purified and characterized.The growth curve and enzyme activity curve indicated that the cell reached a maximum concentration at the 30 th hour and the enzyme activity reached the maximum value at the 36 th hour.The protease was purified with 3 steps involving ammonium sulfate precipitation,ion-exchange chromatography and hydrophobic chromatography with 8.22-fold increase in specific activity and 23.68% increase in the recovery.The molecular mass of the protease was estimated to be 25 k Da by SDS-PAGE analysis.The optimum temperature and p H for the protease activity were 50℃ and pH 10.0,respectively.The protease showed a strong stability in a wide range of pH values ranging from 6.0–11.0,and maintained 90% enzyme activity in strong alkaline environment with p H 11.0.Inhibitor trials indicated that the protease might be serine protease.But it also possessed the characteristic of metalloprotease as it could be strongly inhibited by EDTA and strongly stimulated by Mn^(2+).Evaluation of matrix-assisted laser desorption ionization/time-of-flight MS(MALDI-TOF-TOF/MS) showed that the protease might belong to the peptidase S8 family.
文摘An intense bloom of Karenia sp.was reported in the Oualidia lagoon,part of Atlantic Moroccan coast.The highest concentrations are 1.04×10^7 cells/L,and have been noted at the park station 7.High nutrient concentrations have been observed,and PO4 was the highest value(av.396.18μg/L)recorded at Parc 7.This massive proliferation caused a red tide which extended over 25 km from the Atlantic coast.This event was accompanied by stranding of macroalgae.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R195)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contextual understanding,sequential dependencies,and/or data imbalance.This makes distinction between genuine and fabricated news a challenging task.To address this problem,we propose a novel hybrid architecture,T5-SA-LSTM,which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attentionenhanced(SA)Long Short-Term Memory(LSTM).The LSTM is trained using the Adam optimizer,which provides faster and more stable convergence compared to the Stochastic Gradient Descend(SGD)and Root Mean Square Propagation(RMSProp).The WELFake and FakeNewsPrediction datasets are used,which consist of labeled news articles having fake and real news samples.Tokenization and Synthetic Minority Over-sampling Technique(SMOTE)methods are used for data preprocessing to ensure linguistic normalization and class imbalance.The incorporation of the Self-Attention(SA)mechanism enables the model to highlight critical words and phrases,thereby enhancing predictive accuracy.The proposed model is evaluated using accuracy,precision,recall(sensitivity),and F1-score as performance metrics.The model achieved 99%accuracy on the WELFake dataset and 96.5%accuracy on the FakeNewsPrediction dataset.It outperformed the competitive schemes such as T5-SA-LSTM(RMSProp),T5-SA-LSTM(SGD)and some other models.
基金The researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025)。
文摘The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities.
基金The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RIF/2021/05).
文摘Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.
基金the Deanship of Research and Graduate Studies at King Khalid University,KSA,for funding this work through the Large Research Project under grant number RGP2/164/46.
文摘Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-throughput sequencing technology have become prominent in biomedical research,and they reveal molecular aspects of cancer diagnosis and therapy.Despite the development of advanced sequencing technology,the presence of high-dimensionality in multi-omics data makes it challenging to interpret the data.Methods:In this study,we introduce RankXLAN,an explainable ensemble-based multi-omics framework that integrates feature selection(FS),ensemble learning,bioinformatics,and in-silico validation for robust biomarker detection,potential therapeutic drug-repurposing candidates’identification,and classification of SC.To enhance the interpretability of the model,we incorporated explainable artificial intelligence(SHapley Additive exPlanations analysis),as well as accuracy,precision,F1-score,recall,cross-validation,specificity,likelihood ratio(LR)+,LR−,and Youden index results.Results:The experimental results showed that the top four FS algorithms achieved improved results when applied to the ensemble learning classification model.The proposed ensemble model produced an area under the curve(AUC)score of 0.994 for gene expression,0.97 for methylation,and 0.96 for miRNA expression data.Through the integration of bioinformatics and ML approach of the transcriptomic and epigenomic multi-omics dataset,we identified potential marker genes,namely,UBE2D2,HPCAL4,IGHA1,DPT,and FN3K.In-silico molecular docking revealed a strong binding affinity between ANKRD13C and the FDA-approved drug Everolimus(binding affinity−10.1 kcal/mol),identifying ANKRD13C as a potential therapeutic drug-repurposing target for SC.Conclusion:The proposed framework RankXLAN outperforms other existing frameworks for serum biomarker identification,therapeutic target identification,and SC classification with multi-omics datasets.
基金This study is financed by the European Union-NextGenerationEU,through the National Recovery and Resilience Plan of the Republic of Bulgaria,Project No.BG-RRP-2.013-0001.
文摘Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remains one of the security challenges. is paper proposes LinguTimeX,a new framework that combines natural language processing with arti cial intelligence,along with explainable Arti cial Intelligence(AI)not only to detect CTC but also to provide insights into the decision process.LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely.LinguTimeX demonstrates strong e ectiveness in detecting CTC across multiple languages;namely English,Arabic,and Chinese.Speci cally,the LSTM and RNN models achieved F1 scores of 90%on the English dataset,89%on the Arabic dataset,and 88%on the Chinese dataset,showcasing their superior performance and ability to generalize across multiple languages. is highlights their robustness in detecting CTCs within security systems,regardless of the language or cultural context of the data.In contrast,the DeepForest model produced F1-scores ranging from 86%to 87%across the same datasets,further con rming its e ectiveness in CTC detection.Although other algorithms also showed reasonable accuracy,the LSTM and RNN models consistently outperformed them in multilingual settings,suggesting that deep learning models might be better suited for this particular problem.
基金The authors extend their appreciation to King Saud University,Saudi Arabia for funding this work through the Ongoing Research Funding Program(ORF-2025-704),King Saud University,Riyadh,Saudi Arabia.
文摘A novel siphon-based divide-and-conquer(SbDaC)policy is presented in this paper for the synthesis of Petri net(PN)based liveness-enforcing supervisors(LES)for flexible manufacturing systems(FMS)prone to deadlocks or livelocks.The proposed method takes an uncontrolled and bounded PN model(UPNM)of the FMS.Firstly,the reduced PNM(RPNM)is obtained from the UPNM by using PN reduction rules to reduce the computation burden.Then,the set of strict minimal siphons(SMSs)of the RPNM is computed.Next,the complementary set of SMSs is computed from the set of SMSs.By the union of these two sets,the superset of SMSs is computed.Finally,the set of subnets of the RPNM is obtained by applying the PN reduction rules to the superset of SMSs.All these subnets suffer from deadlocks.These subnets are then ordered from the smallest one to the largest one based on a criterion.To enforce liveness on these subnets,a set of control places(CPs)is computed starting from the smallest subnet to the largest one.Once all subnets are live,this process provides the LES,consisting of a set of CPs to be used for the UPNM.The live controlled PN model(CPNM)is constructed by merging the LES with the UPNM.The SbDaC policy is applicable to all classes of PNs related to FMS prone to deadlocks or livelocks.Several FMS examples are considered from the literature to highlight the applicability of the SbDaC policy.In particular,three examples are utilized to emphasize the importance,applicability and effectiveness of the SbDaC policy to realistic FMS with very large state spaces.
基金We would like to show gratitude to the Yunnan Province Basic Research Major Project(202501BC070006(Y.Wang))Key Industry Science and Technology Projects for University Services in Yunnan Province(FWCY ZNT2024002(Y.Wang))+3 种基金National Natural Science Foundation of China(22279070(L.Wang))and(U21A20170(X.He))the Ministry of Science and Technology of China(2019YFA0705703(L.Wang))Beijing Natural Science Foundation(L242005(X.He))Key Industry Science and Technology Projects for University Services in Yunnan Province(FWCY BSPY2024011(T.Lai)).
文摘Long-life energy storage batteries are integral to energy storage systems and electric vehicles,with lithium-ion batteries(LIBs)currently being the preferred option for extended usage-life energy storage.To further extend the life span of LIBs,it is essential to intensify investments in battery design,manufacturing processes,and the advancement of ancillary materials.The pursuit of long durability introduces new challenges for battery energy density.The advent of electrode material offers effective support in enhancing the battery’s long-duration performance.Often underestimated as part of the cathode composition,the binder plays a pivotal role in the longevity and electrochemical performance of the electrode.Maintaining the mechanical integrity of the electrode through judicious binder design is a fundamental requirement for achieving consistent long-life cycles and high energy density.This paper primarily concentrates on the commonly employed cathode systems in lithium-ion batteries,elucidates the significance of binders for both,discusses the application status,strengths,and weaknesses of novel binders,and ultimately puts forth corresponding optimization strategies.It underscores the critical function of binders in enhancing battery performance and advancing the sustainable development of lithium-ion batteries,aiming to offer fresh insights and perspectives for the design of high-performance LIBs.
基金supported by the Financial Supports of the National Natural Science Foundation of China(Nos.51508056,52370030 and 42007352)the Chongqing Postgraduate Joint Training Base Project(No.JDLHPYJD2022005)the special fund of Henan Key Labora-tory of Water Pollution Control and Rehabilitation Technology(No.CJSZ2024001).
文摘This study developed a novel heterogeneous Vis-Photo+Fenton-like system by integrating visible-light-responsive Co_(3)O_(4)/TiO_(2) photocatalysis with peroxymonosulfate(PMS)activation for efficient atrazine(ATZ)degradation.The synergistic process achieved complete ATZ removal within 60 min under near-neutral pH(6.9),outperform-ing individual Fenton-like(39%)and photocatalytic(24%)processes.Key factors influencing the degradation efficiency included light sources(UV>visible),pH(optimal at 6.9),catalyst dosage(0.01 g Co_(3)O_(4)/TiO_(2)),and PMS:ATZ molar ratio(1:2).The system exhibited a synergistic coefficient of 5.03(degradation)and 1.97(miner-alization),attributed to enhanced radical generation and accelerated Co^(3+)/Co^(2+)redox cycling through photoin-duced electron transfer.Intermediate analysis revealed dealkylation,dechlorination,and oxidation pathways,with reduced toxicity of by-products(e.g.,CEAT,CIAT)confirmed by ecotoxicity assessments.The mineralization efficiency(Vis-Photo+Fenton-like)reached 83.1%,significantly higher than that of standalone processes(Fenton-like:43.2%;photocatalysis:30.5%).The catalyst demonstrated excellent stability(nearly 90%recov-ery,<1μg/L Co leaching)and practical applicability.This study provides an efficient,sludge-free,and solar-compatible strategy for eliminating persistent herbicides in water treatment.
基金supported by the IITP(Institute of Information & Communications Technology Planning & Evaluation)-ITRC(Information Technology Research Center) grant funded by the Korea government(Ministry of Science and ICT) (IITP-2025-RS-2024-00437191, and RS-2025-02303505)partly supported by the Korea Basic Science Institute (National Research Facilities and Equipment Center) grant funded by the Ministry of Education. (No. 2022R1A6C101A774)the Deanship of Research and Graduate Studies at King Khalid University, Saudi Arabia, through Large Research Project under grant number RGP-2/527/46
文摘The growing global energy demand and worsening climate change highlight the urgent need for clean,efficient and sustainable energy solutions.Among emerging technologies,atomically thin two-dimensional(2D)materials offer unique advantages in photovoltaics due to their tunable optoelectronic properties,high surface area and efficient charge transport capabilities.This review explores recent progress in photovoltaics incorporating 2D materials,focusing on their application as hole and electron transport layers to optimize bandgap alignment,enhance carrier mobility and improve chemical stability.A comprehensive analysis is presented on perovskite solar cells utilizing 2D materials,with a particular focus on strategies to enhance crystallization,passivate defects and improve overall cell efficiency.Additionally,the application of 2D materials in organic solar cells is examined,particularly for reducing recombination losses and enhancing charge extraction through work function modification.Their impact on dye-sensitized solar cells,including catalytic activity and counter electrode performance,is also explored.Finally,the review outlines key challenges,material limitations and performance metrics,offering insight into the future development of nextgeneration photovoltaic devices encouraged by 2D materials.
基金This project received financial support from the UNICEF/UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases(TDR)and the Canadian International Development Research Centre(IDRC)grant no.NB20283(Dr.Kone Brama)The funders had no role in study design,data collection and analyses,decision to publish,or preparation of the manuscript.
文摘countries in West Africa remain a hotspot for malaria with all age groups at risk.Asymptomatic carriers of Plasmodium spp.are important sources of infections for malaria vectors and thus contribute to the anchoring of the disease in favourable eco-epidemiological settings.The objective of this study was to assess the asymptomatic malaria case rates in Korhogo and Kaedi,two urban areas in northern Côte d’Ivoire and southern Mauritania,respectively.Methods:Cross-sectional surveys were carried out during the rainy season in 2014 and the dry season in 2015 in both settings.During each season,728 households were randomly selected and a household-based questionnaire was implemented to collect demographic and epidemiological data,including of malaria preventive methods used in communities.Finger-prick blood samples were obtained for biological examination using microscopy and rapid diagnostic tests(RDTs).Results:Overall,2672 households and 15858 consenting participants were surveyed.Plasmodium spp.infection was confirmed in 12.4%(n=832)and 0.3%(n=22)of the assessed individuals in Korhogo and Kaedi,respectively.In Korhogo,the prevalence of asymptomatic malaria was 10.5%(95%CI:9.7-11.2)as determined by microscopy and 9.3%(95%CI:8.6-10.0%)when assessed by RDT.In Kaedi,asymptomatic malaria prevalence was 0.2%(95%CI:0.1-0.4%)according to microscopy,while all RDTs performed were negative(n=8372).In Korhogo,asymptomatic malaria infection was significantly associated with age and season,with higher risk within the 5-14 years-old,and during the rainy season.In Kaedi,the risk of asymptomatic malaria infection was associated with season only(higher during the dry season;crude OR(cOR):6.37,95%CI:1.87-21.63).P.falciparum was the predominant species identified in both study sites representing 99.2%(n=825)in Korhogo and 59.1%(n=13)in Kaedi.Gametocytes were observed only in Korhogo and only during the rainy season at 1.3%(95%CI:0.7-2.4%).Conclusions:Our findings show a low prevalence of clinical malaria episodes with a significant proportion of asymptomatic carriers in both urban areas.National policies for malaria infections are focused on treatment of symptomatic cases.Malaria control strategies should be designed for monitoring and managing malaria infections in asymptomatic carriers.Additional measures,including indoor residual spraying,effective use of long-lasting insecticidal nets is strongly needed to reduce the number of Plasmodium spp.infections in Korhogo and Kaedi.
基金supported by the Grant PID2021-126715OB-IOO financed by MCIN/AEI/10.13039/501100011033 and"ERDFA way of making Europe"by the Grant PI22CⅢ/00055 funded by Instituto de Salud CarlosⅢ(ISCⅢ)+6 种基金the UFIECPY 398/19(PEJ2018-004965) grant to RGS funded by AEI(Spain)the UFIECPY-396/19(PEJ2018-004961)grant financed by MCIN (Spain)FI23CⅢ/00003 grant funded by ISCⅢ-PFIS Spain) to PMMthe UFIECPY 328/22 (PEJ-2021-TL/BMD-21001) grant to LM financed by CAM (Spain)the grant by CAPES (Coordination for the Improvement of Higher Education Personnel)through the PDSE program (Programa de Doutorado Sanduiche no Exterior)to VSCG financed by MEC (Brazil)
文摘The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cerebral organoids have emerged as valuable tools offering a more complex,versatile,and human-relevant system than traditional animal models,which are often unable to replicate the intricate architecture and functionality of the human brain.Since human cerebral organoids are a state-of-the-art model for the study of neurodevelopment and different pathologies affecting the brain,this field is currently under constant development,and work in this area is abundant.In this review,we give a complete overview of human cerebral organoids technology,starting from the different types of protocols that exist to generate different human cerebral organoids.We continue with the use of brain organoids for the study of brain pathologies,highlighting neurodevelopmental,psychiatric,neurodegenerative,brain tumor,and infectious diseases.Because of the potential value of human cerebral organoids,we describe their use in transplantation,drug screening,and toxicology assays.We also discuss the technologies available to study cell diversity and physiological characteristics of organoids.Finally,we summarize the limitations that currently exist in the field,such as the development of vasculature and microglia,and highlight some of the novel approaches being pursued through bioengineering.
