Psoriasis is a potentially debilitating inflammatory dermatosis affecting 0.2%-4.8% of the population worldwide causing a significant occupational, personal or psychosocial morbidity to these patients for life. The ba...Psoriasis is a potentially debilitating inflammatory dermatosis affecting 0.2%-4.8% of the population worldwide causing a significant occupational, personal or psychosocial morbidity to these patients for life. The basic aim of psoriasis therapy is to control the disease to maximum possible extent and improve the patient's quality of life. Management of triggers for flareups, lifestyle modifications, and dietary supplements are often recommended. Intermittent or rotational therapy with frequent alterations in treatment options is usually needed to reduce toxicity of anti-psoriatic drugs in the absence of safer alternatives. Currently, several biological agents categorized as either T-cell targeted(e.g., Alefacept, Efalizumab) or cytokine modulating(e.g., Adalimumab, Infliximab, Etanercept) are available for treating severe psoriasis. However, their high cost is often precluding for most patients. The usefulness of systemic(methotrexate, cyclosporine, acitretin or several other therapeutic agents) or topical(tar, anthralin, corticosteroids or calcipotriol ointments, phototherapy with or without psoralens) therapies has been well established for the management of psoriasis. The literature is also replete with benefits of less used non-standard and unconventional treatment modalities(hydroxycarbamide, azathioprine, leflunomide, mycophenolate mofetil, isotretinoin, fumarates, topical calcineurin inhibitors, peroxisome proliferator-activated receptors agonists, statins, sulfasalazine, pentoxifylline, colchicine, grenz ray therapy, excimer laser, climatotherapy and balneophototherapy, peritoneal dialysis, tonsillectomy, ichthyotherapy, etc.). These can be used alternatively to treat psoriasis patients who have mild/minimal lesions, are intolerant to conventional drugs, have developed side effects or achieved recommended cumulative dose, where comorbidities pose unusual therapeutic challenges, or may be as intermittent, rotational or combination treatment alternatives.展开更多
Pamiparib is a potent and selective oral poly(adenosine diphosphate(ADP)-ribose)-polymerase(PARP)1/2inhibitor(PARPi).Pamiparib has good bioavailability and shows greater cytotoxic potency and similar DNA-trapping capa...Pamiparib is a potent and selective oral poly(adenosine diphosphate(ADP)-ribose)-polymerase(PARP)1/2inhibitor(PARPi).Pamiparib has good bioavailability and shows greater cytotoxic potency and similar DNA-trapping capacity compared to olaparib.It is not affected by adenosine triphosphate(ATP)-binding cassette transporters.展开更多
Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple dat...Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple data centers poses a significant challenge,especially when balancing opposing goals such as latency,storage costs,energy consumption,and network efficiency.This study introduces a novel Dynamic Optimization Algorithm called Dynamic Multi-Objective Gannet Optimization(DMGO),designed to enhance data replication efficiency in cloud environments.Unlike traditional static replication systems,DMGO adapts dynamically to variations in network conditions,system demand,and resource availability.The approach utilizes multi-objective optimization approaches to efficiently balance data access latency,storage efficiency,and operational costs.DMGO consistently evaluates data center performance and adjusts replication algorithms in real time to guarantee optimal system efficiency.Experimental evaluations conducted in a simulated cloud environment demonstrate that DMGO significantly outperforms conventional static algorithms,achieving faster data access,lower storage overhead,reduced energy consumption,and improved scalability.The proposed methodology offers a robust and adaptable solution for modern cloud systems,ensuring efficient resource consumption while maintaining high performance.展开更多
BACKGROUND Solid pseudopapillary neoplasm(SPN)of the pancreas is a rare epithelial tumor that primarily affects young women.Since the condition is often asymptomatic or presents with non-specific symptoms,its diagnosi...BACKGROUND Solid pseudopapillary neoplasm(SPN)of the pancreas is a rare epithelial tumor that primarily affects young women.Since the condition is often asymptomatic or presents with non-specific symptoms,its diagnosis can be difficult.CASE SUMMARY This report details the case of a 15-year-old girl who presented with a 2-year history of abdominal pain,with no significant findings during physical examination.Abdominal ultrasound revealed a well-defined heterogeneous solidcystic mass in the epigastric region,likely originating from the tail of the pancreas.A subsequent contrast-enhanced computed tomography scan indicated a welldefined cystic lesion with an enhancing solid component and capsule in the tail of the pancreas,suggestive of a cystic neoplasm.The patient underwent an open distal pancreatectomy with splenectomy,and histopathological analysis confirmed the diagnosis of SPN of the pancreas.CONCLUSION This case highlights the risk of SPN in adolescent girls and the necessity of early diagnosis and intervention for better outcomes.展开更多
Conventional soil maps(CSMs)often have multiple soil types within a single polygon,which hinders the ability of machine learning to accurately predict soils.Soil disaggregation approaches are commonly used to improve ...Conventional soil maps(CSMs)often have multiple soil types within a single polygon,which hinders the ability of machine learning to accurately predict soils.Soil disaggregation approaches are commonly used to improve the spatial and attribute precision of CSMs.The approach disaggregation and harmonization of soil map units through resampled classification trees(DSMART)is popular but computationally intensive,as it generates and assigns synthetic samples to soil series based on the areal coverage information of CSMs.Alternatively,the disaggregation approach pure polygon disaggregation(PPD)assigns soil series based solely on the proportions of soil series in pure polygons in CSMs.This study compared these two disaggregation approaches by applying them to a CSM of Middlesex County,Ontario,Canada.Four different sampling methods were used:two sampling designs,simple random sampling(SRS)and conditional Latin hypercube sampling(cLHS),with two sample sizes(83100 and 19420 samples per sampling plan),both based on an area-weighted approach.Two machine learning algorithms(MLAs),C5.0 decision tree(C5.0)and random forest(RF),were applied to the disaggregation approaches to compare the disaggregation accuracy.The accuracy assessment utilized a set of 500 validation points obtained from the Middlesex County soil survey report.The MLA C5.0(Kappa index=0.58–0.63)showed better performance than RF(Kappa index=0.53–0.54)based on the larger sample size,and PPD with C5.0 based on the larger sample size was the best-performing(Kappa index=0.63)approach.Based on the smaller sample size,both cLHS(Kappa index=0.41–0.48)and SRS(Kappa index=0.40–0.47)produced similar accuracy results.The disaggregation approach PPD exhibited lower processing capacity and time demands(1.62–5.93 h)while yielding maps with lower uncertainty as compared to DSMART(2.75–194.2 h).For CSMs predominantly composed of pure polygons,utilizing PPD for soil series disaggregation is a more efficient and rational choice.However,DSMART is the preferable approach for disaggregating soil series that lack pure polygon representations in the CSMs.展开更多
The presence of heterozygous individuals in a population is crucial for maintaining genetic diversity,which can positively affect fitness and adaptability to environmental changes.While inbreeding generally reduces th...The presence of heterozygous individuals in a population is crucial for maintaining genetic diversity,which can positively affect fitness and adaptability to environmental changes.While inbreeding generally reduces the proportion of heterozygous individuals in a population,polyploidy tends to increase the proportion.North American Populus tremuloides is one of the most widely distributed and ecologically important tree species in the Northern Hemisphere.However,genetic variation in Mexican populations of P.tremuloides,including the genetic signatures of their adaptation to a variety of environments,remains largely uncharacterized.The aim of this study was to analyze how inbreeding coefficient(FIS)and ploidy are associated with clonal richness,population cover,climate and soil traits in 91 marginal to small,isolated populations of this tree species throughout its entire distribution in Mexico.Genetic variables were determined using 36,810 filtered SNPs derived from genome re-sequencing.We found that FIS was approximately between 0 and e1,indicating an extreme heterozygosity excess.One key contributor to the observed extreme heterozygosity excess was asexual reproduction,although ploidy levels cannot explain this excess.Analysis of all neutral SNPs showed that asexual reproduction was positively correlated with observed heterozygosity(Ho)but negatively correlated with expected heterozygosity(He).Analysis of outlier SNPs also showed that asexual reproductionwas positively correlated with Ho and negatively correlated with He,although this latter correlation was not significant.These findings support the presence of a Meselson effect.展开更多
Accurate quantification of life-cycle greenhouse gas(GHG)footprints(GHG_(fp))for a crop cultivation system is urgently needed to address the conflict between food security and global warming mitigation.In this study,t...Accurate quantification of life-cycle greenhouse gas(GHG)footprints(GHG_(fp))for a crop cultivation system is urgently needed to address the conflict between food security and global warming mitigation.In this study,the hydrobiogeochemical model,CNMM-DNDC,was validated with in situ observations from maize-based cultivation systems at the sites of Yongji(YJ,China),Yanting(YT,China),and Madeya(MA,Kenya),subject to temperate,subtropical,and tropical climates,respectively,and updated to enable life-cycle GHG_(fp)estimation.The model validation provided satisfactory simulations on multiple soil variables,crop growth,and emissions of GHGs and reactive nitrogen gases.The locally conventional management practices resulted in GHG_(fp)values of 0.35(0.09–0.53 at the 95%confidence interval),0.21(0.01–0.73),0.46(0.27–0.60),and 0.54(0.21–0.77)kg CO_(2)e kg~(-1)d.m.(d.m.for dry matter in short)for maize–wheat rotation at YJ and YT,and for maize–maize and maize–Tephrosia rotations at MA,respectively.YT's smallest GHG_(fp)was attributed to its lower off-farm GHG emissions than YJ,though the soil organic carbon(SOC)storage and maize yield were slightly lower than those of YJ.MA's highest SOC loss and low yield in shifting cultivation for maize–Tephrosia rotation contributed to its highest GHG_(fp).Management practices of maize cultivation at these sites could be optimized by combination of synthetic and organic fertilizer(s)while incorporating 50%–100%crop residues.Further evaluation of the updated CNMM-DNDC is needed for different crops at site and regional scales to confirm its worldwide applicability in quantifying GHG_(fp)and optimizing management practices for achieving multiple sustainability goals.展开更多
Fluorescent probes have revolutionized optical imaging and biosensing by enabling real-time visualization, quantification, and tracking of biological processes at molecular and cellular levels. These probes, ranging f...Fluorescent probes have revolutionized optical imaging and biosensing by enabling real-time visualization, quantification, and tracking of biological processes at molecular and cellular levels. These probes, ranging from organic dyes to genetically encoded proteins and nanomaterials, provide unparalleled specificity, sensitivity, and multiplexing capabilities. However, challenges such as brightness, photobleaching, biocompatibility, and emission range continue to drive innovation in probe design and application. This special issue, comprising four review papers and seven original research studies, highlights cutting-edge advancements in fluorescent probe technologies and their transformative roles in super-resolution imaging, in vivo diagnostics, and cancer therapeutics.展开更多
To the Editor:Liver transplantation is widely regarded as the definitive treat-ment for patients with end-stage liver disease.However,the per-sistent shortage of cadaveric liver grafts has driven the develop-ment of l...To the Editor:Liver transplantation is widely regarded as the definitive treat-ment for patients with end-stage liver disease.However,the per-sistent shortage of cadaveric liver grafts has driven the develop-ment of living-donor liver transplantation(LDLT).Despite its ben-efits,LDLT raises substantial concerns regarding donor morbid-ity,as the procedure involves operating on a healthy individual.Complications associated with donor hepatectomy include abdom-inal trauma,chronic wound pain,physical stress,and psycholog-ical burdens[1,2].In light of these challenges,minimally inva-sive approaches,including laparoscopic and robotic donor hepa-tectomy,have been introduced to mitigate risks and enhance re-covery[3].However,the impact of these techniques on male sex-ual function-a critical aspect of donor quality of life-remains underexplored.Several retrospective studies have highlighted sex-ual dysfunction and altered spousal relationships following open donor hepatectomy[4-6].For instance,9%of donors reported a de-crease in sexual activity,and a significant proportion experienced low body image perceptions.展开更多
The temperature of an organism provides key insights into its physiological and pathological status.Temperature monitoring can effectively assess potential health issues and plays a critical role in thermal treatment....The temperature of an organism provides key insights into its physiological and pathological status.Temperature monitoring can effectively assess potential health issues and plays a critical role in thermal treatment.Photoacoustic imaging(PAI)has enabled multi-scale imaging,from cells to tissues and organs,where its high contrast,deep penetration,and high resolution make it an emerging tool in biomedical imaging field.Benefiting from the linear correlation between the Grüneisen parameter and temperature within the range of 10–55∘C,the PAI has been developed as novel noninvasive label-free tool for temperature monitoring especially for thermotherapy mediated by laser,ultrasound,and microwave.Additionally,by utilizing temperature-responsive photoacoustic nanoprobes,the temperature information of the targeted organism can also be extracted with enhanced imaging contrast and specificity.This review elucidates the basic principles of temperature monitoring technology implemented by PAI,further highlighting the limitations of traditional photoacoustic thermometry,and summarizes recent technological advancements in analog simulation,calibration method,measurement accuracy,nanoprobe design,and wearable improvement.Furthermore,we discuss the biomedical applications of PA temperature monitoring technology in photothermal therapy and ultrasound therapy,finally,anticipating future developments in the field.展开更多
We used fast chlorophyll fluorescence transients(OJIP) to study provenance-related differences in photosynthetic performance and the magnitude of day-to-day chlorophyll fluorescence(ChlF) variation in northern(67°...We used fast chlorophyll fluorescence transients(OJIP) to study provenance-related differences in photosynthetic performance and the magnitude of day-to-day chlorophyll fluorescence(ChlF) variation in northern(67°N)and southern(62°N) silver birches in a common garden at62°N.ChlF transients were measured five times during two weeks in the middle of summer to avoid seasonal variation.Differences in growth and leaf morphological traits between the provenances were also examined.The northern trees had higher chlorophyll content,larger leaf areas,and higher leaf fresh and dry mass than the southern trees,but the leaf mass per area did not differ between the provenances.The southern trees were taller and showed higher annual shoot growth than the northern trees.For all the ChlF parameters,day-to-day variation was significant and followed the same pattern for both provenances with no significant provenance ×day interaction,suggesting a similar response to environmental variation.The northern provenance had higher values in parameters related to the reduction of end electron acceptors at the Photosystem I(PSI) acceptor side as probed by ChlF.This and higher values for performance indices PI_(abs) and PI_(tot) in northern than in southern trees suggest higher photosynthetic performance of northern trees in line with the latitudinal compensation strategy.Provenance differences in these parameters increased towards the end of the measurement period,suggesting preparation for earlier growth cessation in northern trees triggered by the shortening day length.The study shows that provenance differences in ChlF can be relatively stable regardless of environmental variation but might be influenced by physiological alterations in preparation for future changes in environmental conditions.展开更多
Volcanic terrains exhibit a complex structure of pyroclastic deposits interspersed with sedimentary processes,resulting in irregular lithological sequences that lack lateral continuity and distinct stratigraphic patte...Volcanic terrains exhibit a complex structure of pyroclastic deposits interspersed with sedimentary processes,resulting in irregular lithological sequences that lack lateral continuity and distinct stratigraphic patterns.This complexity poses significant challenges for slope stability analysis,requiring the development of specialized techniques to address these issues.This research presents a numerical methodology that incorporates spatial variability,nonlinear material characterization,and probabilistic analysis using a Monte Carlo framework to address this issue.The heterogeneous structure is represented by randomly assigning different lithotypes across the slope,while maintaining predefined global proportions.This contrasts with the more common approach of applying probabilistic variability to mechanical parameters within a homogeneous slope model.The material behavior is defined using complex nonlinear failure criteria,such as the Hoek-Brown model and a parabolic model with collapse,both implemented through linearization techniques.The Discontinuity Layout Optimization(DLO)method,a novel numerical approach based on limit analysis,is employed to efficiently incorporate these advances and compute the factor of safety of the slope.Within this framework,the Monte Carlo procedure is used to assess slope stability by conducting a large number of simulations,each with a different lithotype distribution.Based on the results,a hybrid method is proposed that combines probabilistic modeling with deterministic design principles for the slope stability assessment.As a case study,the methodology is applied to a 20-m-high vertical slope composed of three lithotypes(altered scoria,welded scoria,and basalt)randomly distributed in proportions of 15%,60%,and 25%,respectively.The results show convergence of mean values after approximately 400 simulations and highlight the significant influence of spatial heterogeneity,with variations of the factor of safety between 5 and 12 in 85%of cases.They also reveal non-circular and mid-slope failure wedges not captured by traditional stability methods.Finally,an equivalent normal probability distribution is proposed as a reliable approximation of the factor of safety for use in risk analysis and engineering decision-making.展开更多
Focal segmental glomerulosclerosis(FSGS)is a histological pattern of glomerular damage that significantly contributes to chronic kidney disease and end-stage renal disease.Its incidence is rising globally,necessitatin...Focal segmental glomerulosclerosis(FSGS)is a histological pattern of glomerular damage that significantly contributes to chronic kidney disease and end-stage renal disease.Its incidence is rising globally,necessitating timely and personalized management strategies.This paper aims to provide an updated overview of the pathophysiology,diagnosis,and therapeutic strategies for FSGS,emphasizing the importance of early interventions and tailored treatments.This editorial synthesizes key findings from recent literature to highlight advancements in understanding and managing FSGS.Emerging evidence supports the role of targeted therapies and personalized approaches in improving outcomes for FSGS patients.Advances include novel biomarkers,genetic testing,and innovative therapeutics such as transient receptor potential ion channel blockers and antisense oligonucleotides for apolipoprotein 1-related FSGS.Effective mana-gement of FSGS requires a combination of timely diagnosis,evidence-based therapeutic strategies,and ongoing research to optimize patient outcomes and address gaps in the current understanding of the disease.展开更多
Forest management planning faces uncertainties regarding future timber prices,tree growth,and survival.Future seed production is an additional source of uncertainty in Korean pine stands managed for the joint producti...Forest management planning faces uncertainties regarding future timber prices,tree growth,and survival.Future seed production is an additional source of uncertainty in Korean pine stands managed for the joint production of timber and edible seeds.Modern forest planning uses optimisation to determine the best possible cutting schedule.Optimisation can accommodate uncertainty by using decision rules for adaptive forest management instead of optimising cutting years and intensities.In this study,we optimised two adaptive decision rules for managing Korean pine plantations for the joint production of timber and pinecones when timber prices,tree growth,and seed production are stochastic.The first rule indicated the minimum price to sell timber,i.e.,the reservation price,as a function of the mean tree diameter and stand basal area.The second adaptive rule expressed the mean tree diameter at which cutting is optimal as a function of timber price and stand basal area.Both decision rules resulted in nearly the same mean net present value when the optimised rule was applied to 100 stochastic scenarios for future timber prices,tree growth,and seed production.The net present values were over 20% higher than those for the deterministically optimised cutting schedules under the same scenarios.Therefore,the expected economic gain from switching from deterministic to adaptive stochastic optimisation was at least 20%.The cutting years of the adaptive optima were frequently later than those indicated by the deterministic optima,and optimal adaptive harvesting often involved waiting for high timber prices.The minimum price or minimum mean diameter to sell timber was higher when the income from seeds was considered in the optimisation.The cuttings were later,and the rotations were longer in the joint production of timber and pinecones than in timber production alone.展开更多
Innately designed to induce physiological changes,pharmaceuticals are foreknowingly hazardous to the ecosystem.Advanced oxidation processes(AOPs)are recognized as a set of contemporary and highly efficient methods bei...Innately designed to induce physiological changes,pharmaceuticals are foreknowingly hazardous to the ecosystem.Advanced oxidation processes(AOPs)are recognized as a set of contemporary and highly efficient methods being used as a contrivance for the removal of pharmaceutical residues.Since reactive oxygen species(ROS)are formed in these processes to interact and contribute directly toward the oxidation of target contaminant(s),a profound insight regarding the mechanisms of ROS leading to the degradation of pharmaceuticals is fundamentally significant.The conceptualization of some specific reaction mechanisms allows the design of an effective and safe degradation process that can empirically reduce the environmental impact of themicropollutants.This review mainly deliberates themechanistic reaction pathways for ROS-mediated degradation of pharmaceuticals often leading to complete mineralization,with a focus on acetaminophen as a drug waste model.展开更多
Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increa...Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increasingly been integratedwithDeep Learning(DL)for real-time prediction of CVDs.However,DL models are prone to performance degradation due to concept drift and to catastrophic forgetting.To address this issue,we propose a realtime CVDs prediction approach,referred to as ADWIN-GFR that combines Convolutional Neural Network(CNN)layers,for spatial feature extraction,with Gated Recurrent Units(GRU),for temporal modeling,alongside adaptive drift detection and mitigation mechanisms.The proposed approach integratesAdaptiveWindowing(ADWIN)for realtime concept drift detection,a fine-tuning strategy based on Generative Features Replay(GFR)to preserve previously acquired knowledge,and a dynamic replay buffer ensuring variance,diversity,and data distribution coverage.Extensive experiments conducted on the MIT-BIH arrhythmia dataset demonstrate that ADWIN-GFR outperforms standard fine-tuning techniques,achieving an average post-drift accuracy of 95.4%,amacro F1-score of 93.9%,and a remarkably low forgetting score of 0.9%.It also exhibits an average drift detection delay of 12 steps and achieves an adaptation gain of 17.2%.These findings underscore the potential of ADWIN-GFR for deployment in real-world cardiac monitoring systems,including wearable ECG devices and hospital-based patient monitoring platforms.展开更多
Background:A major side effect of diabetes is diabetic retinopathy(DR),which can cause irreparable blindness if left untreated.Because of the additional psychological and social strains,controlling comorbidities like ...Background:A major side effect of diabetes is diabetic retinopathy(DR),which can cause irreparable blindness if left untreated.Because of the additional psychological and social strains,controlling comorbidities like DR becomes crucial for cancer patients,particularly those receiving treatments like chemotherapy.Both the patient and their caretakers may have severe effects from vision impairment,including increased anxiety,depression,and a lower quality of life.One can reduce these psychological pressures by facilitating prompt intervention,early identification,and categorization of DR.Methods:This work uses a metaheuristic optimization technique to offer a sophisticated,automated categorization system for DR.The system combines Attention AlexNet with an Improved Nutcracker Optimizer,which optimizes the weights and hyperparameters of deep learning models to improve classification accuracy.Results:The approach achieves high classification accuracy of 99.43%and enhanced precision and recall when tested on two popular image datasets,APTOS-2019 and EyePacs.Conclusions:By addressing the technological improvement in DR detection,this work contributes to the multidisciplinary approach of psycho-oncology and helps lessen the psychological distress that cancer patients experience when they lose their eyesight.Ultimately,it supports the general well-being and mental health of people facing diabetes-related problems and cancer by highlighting the significance of incorporating cutting-edge machine learning technologies into clinical practice.展开更多
Retinal Optical Coherence Tomography (OCT) images, a non-invasive imaging technique, have become a standard retinal disease detection tool. Due to disease, there are morphological and textural changes in the layers of...Retinal Optical Coherence Tomography (OCT) images, a non-invasive imaging technique, have become a standard retinal disease detection tool. Due to disease, there are morphological and textural changes in the layers of the retina. Classifying OCT images is challenging, as the morphological manifestations of different diseases may be similar. The OCT images capture the reflectivity characteristics of the retinal tissues. Retinal diseases change the reflectivity property of retinal tissues, resulting in texture variations in OCT images. We propose a hybrid approach to OCT image classification in which the Convolution Neural Network (CNN) model is trained using Multiple Neighborhood Local Ternary Pattern (MNLTP) texture descriptors of the OCT images dataset for a robust disease prediction system. Parallel deep CNN (PDCNN) is proposed to improve feature representation and generalizability. The MNLTP-PDCNN model is tested on two publicly available datasets. The parameter values Accuracy, Precision, Recall, and F1-Score are calculated. The best accuracy obtained specifying the model’s overall performance is 93.98% and 99% for the NEH and OCT2017 datasets, respectively. With the proposed architecture, comparable performance is obtained with a subset of the original OCT2017 data set and a comparatively smaller number of trainable parameters (1.6 million, 1.8 million, and 2.3 million for a single CNN branch, two parallel CNN branches, and three parallel network branches, respectively), compared to off-the-shelf CNN models. Hence, the proposed approach is suitable for real-time OCT image classification systems with fast training of the CNN model and reduced memory requirement for computations.展开更多
Background:How AMP activated protein kinase(AMPK)signaling regulates mito-chondrial functions and mitophagy in human trophoblast cells remains unclear.This study was designed to investigate potential players mediating...Background:How AMP activated protein kinase(AMPK)signaling regulates mito-chondrial functions and mitophagy in human trophoblast cells remains unclear.This study was designed to investigate potential players mediating the regulation of AMPK on mitochondrial functions and mitophagy by next generation RNA-seq.Methods:We compared ATP production in protein kinase AMP-activated catalytic subunit alpha 1/2(PRKAA1/2)knockdown(AKD)and control BeWo cells using the Seahorse real-time ATP rate test,then analyzed gene expression profiling by RNA-seq.Differentially expressed genes(DEG)were examined by Gene Ontology(GO)analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment.Then protein-protein interactions(PPI)among mitochondria related genes were fur-ther analyzed using Metascape and Ingenuity Pathway Analysis(IPA)software.Results:Both mitochondrial and glycolytic ATP production in AKD cells were lower than in the control BeWo cells(CT),with a greater reduction of mitochondrial ATP production.A total of 1092 DEGs were identified,with 405 upregulated and 687 downregulated.GO analysis identified 60 genes associated with the term‘mitochon-drion’in the cellular component domain.PPI analysis identified three clusters of mito-chondria related genes,including aldo-keto reductase family 1 member B10 and B15(AKR1B10,AKR1B15),alanyl-tRNA synthetase 1(AARS1),mitochondrial ribosomal protein S6(MRPS6),mitochondrial calcium uniporter dominant negative subunit beta(MCUB)and dihydrolipoamide branched chain transacylase E2(DBT).Conclusions:In summary,this study identified multiple mitochondria related genes regulated by AMPK in BeWo cells,and among them,three clusters of genes may po-tentially contribute to altered mitochondrial functions in response to reduced AMPK signaling.展开更多
Hierarchical Task Network(HTN)planning is a powerful technique in artificial intelligence for handling complex problems by decomposing them into hierarchical task structures.However,achieving optimal solutions in HTN ...Hierarchical Task Network(HTN)planning is a powerful technique in artificial intelligence for handling complex problems by decomposing them into hierarchical task structures.However,achieving optimal solutions in HTN planning remains a challenge,especially in scenarios where traditional search algorithms struggle to navigate the vast solution space efficiently.This research proposes a novel technique to enhance HTN planning by integrating the Ant Colony Optimization(ACO)algorithm into the refinement process.The Ant System algorithm,inspired by the foraging behavior of ants,is well-suited for addressing optimization problems by efficiently exploring solution spaces.By incorporating ACO into the refinement phase of HTN planning,the authors aim to leverage its adaptive nature and decentralized decision-making to improve plan generation.This paper involves the development of a hybrid strategy called ACO-HTN,which combines HTN planning with ACO-based plan selection.This technique enables the system to adaptively refine plans by guiding the search towards optimal solutions.To evaluate the effectiveness of the proposed technique,this paper conducts empirical experiments on various domains and benchmark datasets.Our results demonstrate that the ACO-HTN strategy enhances the efficiency and effectiveness of HTN planning,outperforming traditional methods in terms of solution quality and computational performance.展开更多
文摘Psoriasis is a potentially debilitating inflammatory dermatosis affecting 0.2%-4.8% of the population worldwide causing a significant occupational, personal or psychosocial morbidity to these patients for life. The basic aim of psoriasis therapy is to control the disease to maximum possible extent and improve the patient's quality of life. Management of triggers for flareups, lifestyle modifications, and dietary supplements are often recommended. Intermittent or rotational therapy with frequent alterations in treatment options is usually needed to reduce toxicity of anti-psoriatic drugs in the absence of safer alternatives. Currently, several biological agents categorized as either T-cell targeted(e.g., Alefacept, Efalizumab) or cytokine modulating(e.g., Adalimumab, Infliximab, Etanercept) are available for treating severe psoriasis. However, their high cost is often precluding for most patients. The usefulness of systemic(methotrexate, cyclosporine, acitretin or several other therapeutic agents) or topical(tar, anthralin, corticosteroids or calcipotriol ointments, phototherapy with or without psoralens) therapies has been well established for the management of psoriasis. The literature is also replete with benefits of less used non-standard and unconventional treatment modalities(hydroxycarbamide, azathioprine, leflunomide, mycophenolate mofetil, isotretinoin, fumarates, topical calcineurin inhibitors, peroxisome proliferator-activated receptors agonists, statins, sulfasalazine, pentoxifylline, colchicine, grenz ray therapy, excimer laser, climatotherapy and balneophototherapy, peritoneal dialysis, tonsillectomy, ichthyotherapy, etc.). These can be used alternatively to treat psoriasis patients who have mild/minimal lesions, are intolerant to conventional drugs, have developed side effects or achieved recommended cumulative dose, where comorbidities pose unusual therapeutic challenges, or may be as intermittent, rotational or combination treatment alternatives.
基金supported in part by funding from BeiGene,Ltd.,USA(Grant No.:KPR081)with additional support from the Alessandra Bono Foundation,Italy.
文摘Pamiparib is a potent and selective oral poly(adenosine diphosphate(ADP)-ribose)-polymerase(PARP)1/2inhibitor(PARPi).Pamiparib has good bioavailability and shows greater cytotoxic potency and similar DNA-trapping capacity compared to olaparib.It is not affected by adenosine triphosphate(ATP)-binding cassette transporters.
文摘Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple data centers poses a significant challenge,especially when balancing opposing goals such as latency,storage costs,energy consumption,and network efficiency.This study introduces a novel Dynamic Optimization Algorithm called Dynamic Multi-Objective Gannet Optimization(DMGO),designed to enhance data replication efficiency in cloud environments.Unlike traditional static replication systems,DMGO adapts dynamically to variations in network conditions,system demand,and resource availability.The approach utilizes multi-objective optimization approaches to efficiently balance data access latency,storage efficiency,and operational costs.DMGO consistently evaluates data center performance and adjusts replication algorithms in real time to guarantee optimal system efficiency.Experimental evaluations conducted in a simulated cloud environment demonstrate that DMGO significantly outperforms conventional static algorithms,achieving faster data access,lower storage overhead,reduced energy consumption,and improved scalability.The proposed methodology offers a robust and adaptable solution for modern cloud systems,ensuring efficient resource consumption while maintaining high performance.
文摘BACKGROUND Solid pseudopapillary neoplasm(SPN)of the pancreas is a rare epithelial tumor that primarily affects young women.Since the condition is often asymptomatic or presents with non-specific symptoms,its diagnosis can be difficult.CASE SUMMARY This report details the case of a 15-year-old girl who presented with a 2-year history of abdominal pain,with no significant findings during physical examination.Abdominal ultrasound revealed a well-defined heterogeneous solidcystic mass in the epigastric region,likely originating from the tail of the pancreas.A subsequent contrast-enhanced computed tomography scan indicated a welldefined cystic lesion with an enhancing solid component and capsule in the tail of the pancreas,suggestive of a cystic neoplasm.The patient underwent an open distal pancreatectomy with splenectomy,and histopathological analysis confirmed the diagnosis of SPN of the pancreas.CONCLUSION This case highlights the risk of SPN in adolescent girls and the necessity of early diagnosis and intervention for better outcomes.
基金the Ontario Ministry of Agriculture,Food and Rural Affairs,Canada,who supported this project by providing updated soil information on Ontario and Middlesex Countysupported by the Natural Science and Engineering Research Council of Canada(No.RGPIN-2014-4100)。
文摘Conventional soil maps(CSMs)often have multiple soil types within a single polygon,which hinders the ability of machine learning to accurately predict soils.Soil disaggregation approaches are commonly used to improve the spatial and attribute precision of CSMs.The approach disaggregation and harmonization of soil map units through resampled classification trees(DSMART)is popular but computationally intensive,as it generates and assigns synthetic samples to soil series based on the areal coverage information of CSMs.Alternatively,the disaggregation approach pure polygon disaggregation(PPD)assigns soil series based solely on the proportions of soil series in pure polygons in CSMs.This study compared these two disaggregation approaches by applying them to a CSM of Middlesex County,Ontario,Canada.Four different sampling methods were used:two sampling designs,simple random sampling(SRS)and conditional Latin hypercube sampling(cLHS),with two sample sizes(83100 and 19420 samples per sampling plan),both based on an area-weighted approach.Two machine learning algorithms(MLAs),C5.0 decision tree(C5.0)and random forest(RF),were applied to the disaggregation approaches to compare the disaggregation accuracy.The accuracy assessment utilized a set of 500 validation points obtained from the Middlesex County soil survey report.The MLA C5.0(Kappa index=0.58–0.63)showed better performance than RF(Kappa index=0.53–0.54)based on the larger sample size,and PPD with C5.0 based on the larger sample size was the best-performing(Kappa index=0.63)approach.Based on the smaller sample size,both cLHS(Kappa index=0.41–0.48)and SRS(Kappa index=0.40–0.47)produced similar accuracy results.The disaggregation approach PPD exhibited lower processing capacity and time demands(1.62–5.93 h)while yielding maps with lower uncertainty as compared to DSMART(2.75–194.2 h).For CSMs predominantly composed of pure polygons,utilizing PPD for soil series disaggregation is a more efficient and rational choice.However,DSMART is the preferable approach for disaggregating soil series that lack pure polygon representations in the CSMs.
基金We thank the Mexican Consejo Nacional de Humanidades,Ciencias y Tecnologías(CONAHCYT)for the financial support provided to the first author to carry out his training in the Institutional Doctoral Program in Agricultural and Forestry Sciences(PIDCAFUJED)with Scholarship No.334852financial support with agreement number CONACYT-FRQ-2016:279459 for the project“Genome-wide scans for detecting adaptation to climate and soil in Populus tremuloides as the most widely distributed tree species in North America”Dr.Jesús M.Olivas-García assisted in the sampling in the state of Chihuahua,Mexico,and Katrin Groppe,Thünen Institute of Forest Genetics,Germany,provided excellent lab work.The Emerging Leaders of the Americas Program(ELAP)of the Government of Canada awarded a scholarship and the Institute of Integrative and Systems Biology(IBIS)of Laval University allowed the use of its campus and contributed to the training of the first author.
文摘The presence of heterozygous individuals in a population is crucial for maintaining genetic diversity,which can positively affect fitness and adaptability to environmental changes.While inbreeding generally reduces the proportion of heterozygous individuals in a population,polyploidy tends to increase the proportion.North American Populus tremuloides is one of the most widely distributed and ecologically important tree species in the Northern Hemisphere.However,genetic variation in Mexican populations of P.tremuloides,including the genetic signatures of their adaptation to a variety of environments,remains largely uncharacterized.The aim of this study was to analyze how inbreeding coefficient(FIS)and ploidy are associated with clonal richness,population cover,climate and soil traits in 91 marginal to small,isolated populations of this tree species throughout its entire distribution in Mexico.Genetic variables were determined using 36,810 filtered SNPs derived from genome re-sequencing.We found that FIS was approximately between 0 and e1,indicating an extreme heterozygosity excess.One key contributor to the observed extreme heterozygosity excess was asexual reproduction,although ploidy levels cannot explain this excess.Analysis of all neutral SNPs showed that asexual reproduction was positively correlated with observed heterozygosity(Ho)but negatively correlated with expected heterozygosity(He).Analysis of outlier SNPs also showed that asexual reproductionwas positively correlated with Ho and negatively correlated with He,although this latter correlation was not significant.These findings support the presence of a Meselson effect.
基金jointly supported by the National Key R&D Program of China(Grant No.2022YFE0209200)the National Natural Science Foundation of China(Grant Nos.U22A20562,42330607 and 41761144054)the National Large Scientific and Technological Infrastructure“Earth System Science Numerical Simulator Facility”(Earth-Lab)(https://cstr.cn/31134.02.EL)。
文摘Accurate quantification of life-cycle greenhouse gas(GHG)footprints(GHG_(fp))for a crop cultivation system is urgently needed to address the conflict between food security and global warming mitigation.In this study,the hydrobiogeochemical model,CNMM-DNDC,was validated with in situ observations from maize-based cultivation systems at the sites of Yongji(YJ,China),Yanting(YT,China),and Madeya(MA,Kenya),subject to temperate,subtropical,and tropical climates,respectively,and updated to enable life-cycle GHG_(fp)estimation.The model validation provided satisfactory simulations on multiple soil variables,crop growth,and emissions of GHGs and reactive nitrogen gases.The locally conventional management practices resulted in GHG_(fp)values of 0.35(0.09–0.53 at the 95%confidence interval),0.21(0.01–0.73),0.46(0.27–0.60),and 0.54(0.21–0.77)kg CO_(2)e kg~(-1)d.m.(d.m.for dry matter in short)for maize–wheat rotation at YJ and YT,and for maize–maize and maize–Tephrosia rotations at MA,respectively.YT's smallest GHG_(fp)was attributed to its lower off-farm GHG emissions than YJ,though the soil organic carbon(SOC)storage and maize yield were slightly lower than those of YJ.MA's highest SOC loss and low yield in shifting cultivation for maize–Tephrosia rotation contributed to its highest GHG_(fp).Management practices of maize cultivation at these sites could be optimized by combination of synthetic and organic fertilizer(s)while incorporating 50%–100%crop residues.Further evaluation of the updated CNMM-DNDC is needed for different crops at site and regional scales to confirm its worldwide applicability in quantifying GHG_(fp)and optimizing management practices for achieving multiple sustainability goals.
文摘Fluorescent probes have revolutionized optical imaging and biosensing by enabling real-time visualization, quantification, and tracking of biological processes at molecular and cellular levels. These probes, ranging from organic dyes to genetically encoded proteins and nanomaterials, provide unparalleled specificity, sensitivity, and multiplexing capabilities. However, challenges such as brightness, photobleaching, biocompatibility, and emission range continue to drive innovation in probe design and application. This special issue, comprising four review papers and seven original research studies, highlights cutting-edge advancements in fluorescent probe technologies and their transformative roles in super-resolution imaging, in vivo diagnostics, and cancer therapeutics.
文摘To the Editor:Liver transplantation is widely regarded as the definitive treat-ment for patients with end-stage liver disease.However,the per-sistent shortage of cadaveric liver grafts has driven the develop-ment of living-donor liver transplantation(LDLT).Despite its ben-efits,LDLT raises substantial concerns regarding donor morbid-ity,as the procedure involves operating on a healthy individual.Complications associated with donor hepatectomy include abdom-inal trauma,chronic wound pain,physical stress,and psycholog-ical burdens[1,2].In light of these challenges,minimally inva-sive approaches,including laparoscopic and robotic donor hepa-tectomy,have been introduced to mitigate risks and enhance re-covery[3].However,the impact of these techniques on male sex-ual function-a critical aspect of donor quality of life-remains underexplored.Several retrospective studies have highlighted sex-ual dysfunction and altered spousal relationships following open donor hepatectomy[4-6].For instance,9%of donors reported a de-crease in sexual activity,and a significant proportion experienced low body image perceptions.
基金supported by the National Natural Science Foundation of China(No.12174125)Guangdong Basic and Applied Basic Research Foundation(Nos.2024A1515010522 and 2021A1515011874).
文摘The temperature of an organism provides key insights into its physiological and pathological status.Temperature monitoring can effectively assess potential health issues and plays a critical role in thermal treatment.Photoacoustic imaging(PAI)has enabled multi-scale imaging,from cells to tissues and organs,where its high contrast,deep penetration,and high resolution make it an emerging tool in biomedical imaging field.Benefiting from the linear correlation between the Grüneisen parameter and temperature within the range of 10–55∘C,the PAI has been developed as novel noninvasive label-free tool for temperature monitoring especially for thermotherapy mediated by laser,ultrasound,and microwave.Additionally,by utilizing temperature-responsive photoacoustic nanoprobes,the temperature information of the targeted organism can also be extracted with enhanced imaging contrast and specificity.This review elucidates the basic principles of temperature monitoring technology implemented by PAI,further highlighting the limitations of traditional photoacoustic thermometry,and summarizes recent technological advancements in analog simulation,calibration method,measurement accuracy,nanoprobe design,and wearable improvement.Furthermore,we discuss the biomedical applications of PA temperature monitoring technology in photothermal therapy and ultrasound therapy,finally,anticipating future developments in the field.
基金supported by the University of Eastern FinlandCzech University of Life Sciences doctoral research funding to O.A+5 种基金North Karelia Regional Fund to O.A (grant number 55232028)University of Eastern Finland(strategic fundingproject 931060)the Academy of Finland(C-NEUT,project number 347862)part of the Academy of Finland Flagship on Photonics Research and Innovation (PREIN) decision (320166)the Finnish National Plant Phenotyping Infrastructure (NaPPI/Biocenter Finland)
文摘We used fast chlorophyll fluorescence transients(OJIP) to study provenance-related differences in photosynthetic performance and the magnitude of day-to-day chlorophyll fluorescence(ChlF) variation in northern(67°N)and southern(62°N) silver birches in a common garden at62°N.ChlF transients were measured five times during two weeks in the middle of summer to avoid seasonal variation.Differences in growth and leaf morphological traits between the provenances were also examined.The northern trees had higher chlorophyll content,larger leaf areas,and higher leaf fresh and dry mass than the southern trees,but the leaf mass per area did not differ between the provenances.The southern trees were taller and showed higher annual shoot growth than the northern trees.For all the ChlF parameters,day-to-day variation was significant and followed the same pattern for both provenances with no significant provenance ×day interaction,suggesting a similar response to environmental variation.The northern provenance had higher values in parameters related to the reduction of end electron acceptors at the Photosystem I(PSI) acceptor side as probed by ChlF.This and higher values for performance indices PI_(abs) and PI_(tot) in northern than in southern trees suggest higher photosynthetic performance of northern trees in line with the latitudinal compensation strategy.Provenance differences in these parameters increased towards the end of the measurement period,suggesting preparation for earlier growth cessation in northern trees triggered by the shortening day length.The study shows that provenance differences in ChlF can be relatively stable regardless of environmental variation but might be influenced by physiological alterations in preparation for future changes in environmental conditions.
基金the project PID2022-139202OB-I00Neural Networks and Optimization Techniques for the Design and Safe Maintenance of Transportation Infrastructures:Volcanic Rock Geotechnics and Slope Stability(IA-Pyroslope),funded by the Spanish State Research Agency of the Ministry of Science,Innovation and Universities of Spain and the European Regional Development Fund,MCIN/AEI/10.13039/501100011033/FEDER,EU。
文摘Volcanic terrains exhibit a complex structure of pyroclastic deposits interspersed with sedimentary processes,resulting in irregular lithological sequences that lack lateral continuity and distinct stratigraphic patterns.This complexity poses significant challenges for slope stability analysis,requiring the development of specialized techniques to address these issues.This research presents a numerical methodology that incorporates spatial variability,nonlinear material characterization,and probabilistic analysis using a Monte Carlo framework to address this issue.The heterogeneous structure is represented by randomly assigning different lithotypes across the slope,while maintaining predefined global proportions.This contrasts with the more common approach of applying probabilistic variability to mechanical parameters within a homogeneous slope model.The material behavior is defined using complex nonlinear failure criteria,such as the Hoek-Brown model and a parabolic model with collapse,both implemented through linearization techniques.The Discontinuity Layout Optimization(DLO)method,a novel numerical approach based on limit analysis,is employed to efficiently incorporate these advances and compute the factor of safety of the slope.Within this framework,the Monte Carlo procedure is used to assess slope stability by conducting a large number of simulations,each with a different lithotype distribution.Based on the results,a hybrid method is proposed that combines probabilistic modeling with deterministic design principles for the slope stability assessment.As a case study,the methodology is applied to a 20-m-high vertical slope composed of three lithotypes(altered scoria,welded scoria,and basalt)randomly distributed in proportions of 15%,60%,and 25%,respectively.The results show convergence of mean values after approximately 400 simulations and highlight the significant influence of spatial heterogeneity,with variations of the factor of safety between 5 and 12 in 85%of cases.They also reveal non-circular and mid-slope failure wedges not captured by traditional stability methods.Finally,an equivalent normal probability distribution is proposed as a reliable approximation of the factor of safety for use in risk analysis and engineering decision-making.
文摘Focal segmental glomerulosclerosis(FSGS)is a histological pattern of glomerular damage that significantly contributes to chronic kidney disease and end-stage renal disease.Its incidence is rising globally,necessitating timely and personalized management strategies.This paper aims to provide an updated overview of the pathophysiology,diagnosis,and therapeutic strategies for FSGS,emphasizing the importance of early interventions and tailored treatments.This editorial synthesizes key findings from recent literature to highlight advancements in understanding and managing FSGS.Emerging evidence supports the role of targeted therapies and personalized approaches in improving outcomes for FSGS patients.Advances include novel biomarkers,genetic testing,and innovative therapeutics such as transient receptor potential ion channel blockers and antisense oligonucleotides for apolipoprotein 1-related FSGS.Effective mana-gement of FSGS requires a combination of timely diagnosis,evidence-based therapeutic strategies,and ongoing research to optimize patient outcomes and address gaps in the current understanding of the disease.
基金funded by the Joint Funds for Regional Innovation and Development of the National Natural Science Foundation of China(No. U21A20244)the National Natural Science Foundation of China(No. 32071758)the National Key R&D Program of China (No.2022YFD2201000)
文摘Forest management planning faces uncertainties regarding future timber prices,tree growth,and survival.Future seed production is an additional source of uncertainty in Korean pine stands managed for the joint production of timber and edible seeds.Modern forest planning uses optimisation to determine the best possible cutting schedule.Optimisation can accommodate uncertainty by using decision rules for adaptive forest management instead of optimising cutting years and intensities.In this study,we optimised two adaptive decision rules for managing Korean pine plantations for the joint production of timber and pinecones when timber prices,tree growth,and seed production are stochastic.The first rule indicated the minimum price to sell timber,i.e.,the reservation price,as a function of the mean tree diameter and stand basal area.The second adaptive rule expressed the mean tree diameter at which cutting is optimal as a function of timber price and stand basal area.Both decision rules resulted in nearly the same mean net present value when the optimised rule was applied to 100 stochastic scenarios for future timber prices,tree growth,and seed production.The net present values were over 20% higher than those for the deterministically optimised cutting schedules under the same scenarios.Therefore,the expected economic gain from switching from deterministic to adaptive stochastic optimisation was at least 20%.The cutting years of the adaptive optima were frequently later than those indicated by the deterministic optima,and optimal adaptive harvesting often involved waiting for high timber prices.The minimum price or minimum mean diameter to sell timber was higher when the income from seeds was considered in the optimisation.The cuttings were later,and the rotations were longer in the joint production of timber and pinecones than in timber production alone.
基金the Ministry of Higher Education,Research and Innovation-Oman for their support of this research through TRC block funding grant No.BFP/RGP/EBR/22/378.
文摘Innately designed to induce physiological changes,pharmaceuticals are foreknowingly hazardous to the ecosystem.Advanced oxidation processes(AOPs)are recognized as a set of contemporary and highly efficient methods being used as a contrivance for the removal of pharmaceutical residues.Since reactive oxygen species(ROS)are formed in these processes to interact and contribute directly toward the oxidation of target contaminant(s),a profound insight regarding the mechanisms of ROS leading to the degradation of pharmaceuticals is fundamentally significant.The conceptualization of some specific reaction mechanisms allows the design of an effective and safe degradation process that can empirically reduce the environmental impact of themicropollutants.This review mainly deliberates themechanistic reaction pathways for ROS-mediated degradation of pharmaceuticals often leading to complete mineralization,with a focus on acetaminophen as a drug waste model.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R196)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increasingly been integratedwithDeep Learning(DL)for real-time prediction of CVDs.However,DL models are prone to performance degradation due to concept drift and to catastrophic forgetting.To address this issue,we propose a realtime CVDs prediction approach,referred to as ADWIN-GFR that combines Convolutional Neural Network(CNN)layers,for spatial feature extraction,with Gated Recurrent Units(GRU),for temporal modeling,alongside adaptive drift detection and mitigation mechanisms.The proposed approach integratesAdaptiveWindowing(ADWIN)for realtime concept drift detection,a fine-tuning strategy based on Generative Features Replay(GFR)to preserve previously acquired knowledge,and a dynamic replay buffer ensuring variance,diversity,and data distribution coverage.Extensive experiments conducted on the MIT-BIH arrhythmia dataset demonstrate that ADWIN-GFR outperforms standard fine-tuning techniques,achieving an average post-drift accuracy of 95.4%,amacro F1-score of 93.9%,and a remarkably low forgetting score of 0.9%.It also exhibits an average drift detection delay of 12 steps and achieves an adaptation gain of 17.2%.These findings underscore the potential of ADWIN-GFR for deployment in real-world cardiac monitoring systems,including wearable ECG devices and hospital-based patient monitoring platforms.
文摘Background:A major side effect of diabetes is diabetic retinopathy(DR),which can cause irreparable blindness if left untreated.Because of the additional psychological and social strains,controlling comorbidities like DR becomes crucial for cancer patients,particularly those receiving treatments like chemotherapy.Both the patient and their caretakers may have severe effects from vision impairment,including increased anxiety,depression,and a lower quality of life.One can reduce these psychological pressures by facilitating prompt intervention,early identification,and categorization of DR.Methods:This work uses a metaheuristic optimization technique to offer a sophisticated,automated categorization system for DR.The system combines Attention AlexNet with an Improved Nutcracker Optimizer,which optimizes the weights and hyperparameters of deep learning models to improve classification accuracy.Results:The approach achieves high classification accuracy of 99.43%and enhanced precision and recall when tested on two popular image datasets,APTOS-2019 and EyePacs.Conclusions:By addressing the technological improvement in DR detection,this work contributes to the multidisciplinary approach of psycho-oncology and helps lessen the psychological distress that cancer patients experience when they lose their eyesight.Ultimately,it supports the general well-being and mental health of people facing diabetes-related problems and cancer by highlighting the significance of incorporating cutting-edge machine learning technologies into clinical practice.
基金Deanship of Research and Graduate Studies at King Khalid University funded this work through Large Research Project under grant number RGP2/54/45.
文摘Retinal Optical Coherence Tomography (OCT) images, a non-invasive imaging technique, have become a standard retinal disease detection tool. Due to disease, there are morphological and textural changes in the layers of the retina. Classifying OCT images is challenging, as the morphological manifestations of different diseases may be similar. The OCT images capture the reflectivity characteristics of the retinal tissues. Retinal diseases change the reflectivity property of retinal tissues, resulting in texture variations in OCT images. We propose a hybrid approach to OCT image classification in which the Convolution Neural Network (CNN) model is trained using Multiple Neighborhood Local Ternary Pattern (MNLTP) texture descriptors of the OCT images dataset for a robust disease prediction system. Parallel deep CNN (PDCNN) is proposed to improve feature representation and generalizability. The MNLTP-PDCNN model is tested on two publicly available datasets. The parameter values Accuracy, Precision, Recall, and F1-Score are calculated. The best accuracy obtained specifying the model’s overall performance is 93.98% and 99% for the NEH and OCT2017 datasets, respectively. With the proposed architecture, comparable performance is obtained with a subset of the original OCT2017 data set and a comparatively smaller number of trainable parameters (1.6 million, 1.8 million, and 2.3 million for a single CNN branch, two parallel CNN branches, and three parallel network branches, respectively), compared to off-the-shelf CNN models. Hence, the proposed approach is suitable for real-time OCT image classification systems with fast training of the CNN model and reduced memory requirement for computations.
基金Dean's Office Howard University College of Medicine,Grant/Award Number:Bridge Fund/Pilot Study AwardNational Center on Minority Health and Health Disparities,Grant/Award Number:RCMI/IDC Award U54MD007597National Institute of Child Health and Human Development,Grant/Award Number:R03HD095417 and R16HD116702。
文摘Background:How AMP activated protein kinase(AMPK)signaling regulates mito-chondrial functions and mitophagy in human trophoblast cells remains unclear.This study was designed to investigate potential players mediating the regulation of AMPK on mitochondrial functions and mitophagy by next generation RNA-seq.Methods:We compared ATP production in protein kinase AMP-activated catalytic subunit alpha 1/2(PRKAA1/2)knockdown(AKD)and control BeWo cells using the Seahorse real-time ATP rate test,then analyzed gene expression profiling by RNA-seq.Differentially expressed genes(DEG)were examined by Gene Ontology(GO)analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment.Then protein-protein interactions(PPI)among mitochondria related genes were fur-ther analyzed using Metascape and Ingenuity Pathway Analysis(IPA)software.Results:Both mitochondrial and glycolytic ATP production in AKD cells were lower than in the control BeWo cells(CT),with a greater reduction of mitochondrial ATP production.A total of 1092 DEGs were identified,with 405 upregulated and 687 downregulated.GO analysis identified 60 genes associated with the term‘mitochon-drion’in the cellular component domain.PPI analysis identified three clusters of mito-chondria related genes,including aldo-keto reductase family 1 member B10 and B15(AKR1B10,AKR1B15),alanyl-tRNA synthetase 1(AARS1),mitochondrial ribosomal protein S6(MRPS6),mitochondrial calcium uniporter dominant negative subunit beta(MCUB)and dihydrolipoamide branched chain transacylase E2(DBT).Conclusions:In summary,this study identified multiple mitochondria related genes regulated by AMPK in BeWo cells,and among them,three clusters of genes may po-tentially contribute to altered mitochondrial functions in response to reduced AMPK signaling.
基金supported by the Ministry of Science and High Education of the Russian Federation by the grant 075-15-2022-1137supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R323),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Hierarchical Task Network(HTN)planning is a powerful technique in artificial intelligence for handling complex problems by decomposing them into hierarchical task structures.However,achieving optimal solutions in HTN planning remains a challenge,especially in scenarios where traditional search algorithms struggle to navigate the vast solution space efficiently.This research proposes a novel technique to enhance HTN planning by integrating the Ant Colony Optimization(ACO)algorithm into the refinement process.The Ant System algorithm,inspired by the foraging behavior of ants,is well-suited for addressing optimization problems by efficiently exploring solution spaces.By incorporating ACO into the refinement phase of HTN planning,the authors aim to leverage its adaptive nature and decentralized decision-making to improve plan generation.This paper involves the development of a hybrid strategy called ACO-HTN,which combines HTN planning with ACO-based plan selection.This technique enables the system to adaptively refine plans by guiding the search towards optimal solutions.To evaluate the effectiveness of the proposed technique,this paper conducts empirical experiments on various domains and benchmark datasets.Our results demonstrate that the ACO-HTN strategy enhances the efficiency and effectiveness of HTN planning,outperforming traditional methods in terms of solution quality and computational performance.
