In this work,we proposed a strategy for the hydrolysis of native corn starch after the treatment of corn starch in an ionic liquid aqueous solution,and it is an awfully“green”and simple means to obtain starch with l...In this work,we proposed a strategy for the hydrolysis of native corn starch after the treatment of corn starch in an ionic liquid aqueous solution,and it is an awfully“green”and simple means to obtain starch with low molecular weight and amorphous state.X-ray diffraction results revealed that the natural starch crystalline region was largely disrupted by ionic liquid owing to the broken intermolecular and intramolecular hydrogen bonds.After hydrolysis,the morphology of starch changed from particles of native corn starch into little pieces,and their molecular weight could be effectively regulated during the hydrolysis process,and also the hydrolyzed starch samples exhibited decreased thermal stability with the extension of hydrolysis time.This work would counsel as a powerful tool for the development of native starch in realistic applications.展开更多
In this paper,we present a necessary and sufficient condition for hyponormal block Toeplitz operators T on the vector-valued weighted Bergman space with symbolsΦ(z)=G^(*)(z)+F(z),where F(z)=∑^(N)_(i)=1 A_(i)z^(i)and...In this paper,we present a necessary and sufficient condition for hyponormal block Toeplitz operators T on the vector-valued weighted Bergman space with symbolsΦ(z)=G^(*)(z)+F(z),where F(z)=∑^(N)_(i)=1 A_(i)z^(i)and G(z)=∑^(N)_(i)=1 A_(−i)z^(i),A_(i)ae culants.展开更多
Modern battlefields exhibit high dynamism,where traditional static weighting methods in combat effectiveness assessment fail to capture real-time changes in indicator values,leading to limited assessment accuracy—esp...Modern battlefields exhibit high dynamism,where traditional static weighting methods in combat effectiveness assessment fail to capture real-time changes in indicator values,leading to limited assessment accuracy—especially critical in scenarios like sudden electronic warfare or degraded command,where static weights cannot reflect the operational value decay or surge of key indicators.To address this issue,this study proposes a dynamic adaptive weightingmethod for evaluation indicators based onG1-CRITIC-PIVW.First,theG1(Sequential Relationship Analysis Method)subjective weighting method—translates expert knowledge into indicator importance rankings—leverages expert knowledge to quantify the relative importance of indicators via sequential relationship ranking,while the CRITIC(Criteria Importance Through Intercriteria Correlation)objective weighting method—derives weights from data characteristics by integrating variability and inter-correlations—calculates weights by integrating indicator variability and inter-indicator correlations,ensuring data-driven objectivity.These two sets of weights are then fused using a deviation coefficient optimization model,minimizing the squared deviation from a reference weight and adjusting the fusion coefficient via Spearman’s rank correlation to resolve potential conflicts between subjective and objective judgments.Subsequently,the PIVW(Punishment-Incentive VariableWeight)theory—adapts weights to realtime indicator performance via penalty/incentive rules—is applied for dynamic adjustment.Scenario-specific penalty λ_(1) and incentive λ_(2) thresholds are set based on operational priorities and indicator volatility,penalizing indicators with values below λ_(1) and incentivizing those exceeding λ_(2) to reflect real-time indicator performance.Experimental validation was conducted using an Air Defense and Anti-Missile(ADAM)system effectiveness assessment framework,with data covering 7 indicators across 3 combat scenarios.Results show that compared to static weighting methods,the proposed method reduces MAE(Mean Absolute Error)by 15%-20% and weighted decision error rate by 84.2%,effectively reducing overestimation/underestimation of combat effectiveness in dynamic scenarios;compared to Entropy-TOPSIS,it lowers MAE by 12% while achieving a weighted Kendall’sτconsistency coefficient of 0.85,ensuring higher alignment with expert judgment.This method enhances the accuracy and scenario adaptability of effectiveness assessment,providing reliable decision support for dynamic battlefield environments.展开更多
With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy...With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.展开更多
Thousand-seed weight(TSW)is a critical target for genetic improvement in rapeseed(Brassica napus L.).However,phenotypic selection for this trait remains challenging due to its polygenic regulation by multiple quantita...Thousand-seed weight(TSW)is a critical target for genetic improvement in rapeseed(Brassica napus L.).However,phenotypic selection for this trait remains challenging due to its polygenic regulation by multiple quantitative trait loci(QTL).Here,six favorable TSW QTL alleles from two donor parents were introgress into an elite restorer line,621R,using an integrated strategy combining marker-assisted backcrossing and speed breeding protocols.Through six rounds of backcrossing and convergent crossing followed by two generations of selfing strategies,we developed 13 advanced lines with diverse TSW QTL combinations within 24 months.Field evaluations across three environments revealed that all lines exhibited significantly increased TSW in spring conditions(Minle,Gansu)and winter environments(Wuhan and Jiangling,Hubei)except for two lines which only showed increase in the spring environment.Hybridization assays using these lines as male parents crossed with two male-sterile lines(RG430A and 616A)demonstrated transgressive segregation for TSW:For RG430A-derived hybrids,all crosses significantly outperformed the original control(RG430A×621R)in Wuhan,with 8/13 and 9/13 crosses showing significant TSW increases in Minle and Jiangling,respectively.For 616A-derived hybrids,11/13 and 10/13 crosses exhibited significant TSW enhancement in Minle and Jiangling,compared to 3/13 in Wuhan.Notably,two top-performing hybrids achieved 13.0%and 6.8%higher plot yields,respectively.Our results demonstrate that strategic pyramiding of complementary TSW QTL alleles effectively enhances seed weight in rapeseed,and these improved lines represent valuable genetic resources for developing high-yield hybrids.展开更多
This paper is dedicated to fixed-time passivity and synchronization for multi-weighted spatiotemporal directed networks.First,to achieve fixed-time passivity,a type of decentralized power-law controller is developed,i...This paper is dedicated to fixed-time passivity and synchronization for multi-weighted spatiotemporal directed networks.First,to achieve fixed-time passivity,a type of decentralized power-law controller is developed,in which only one parameter needs to be adjusted in the power-law terms;this greatly decreases the inconvenience of parameter adjustment.Second,several fixed-time passivity criteria with LMI forms are derived by using a Gauss divergence theorem to deal with the spatial diffusion of nodes and by applying the Hölder’s inequality to dispose rigorously the power-law term greater than one in the designed control scheme;this improves the previous theoretical analysis.Additionally,the fixed-time synchronization of spatiotemporal directed networks with multi-weights is addressed as a direct result of fixed-time strict passivity.Finally,a numerical example is presented in order to show the validity of the theoretical analysis.展开更多
Ultra-high molecular weight polyethylene(UHMWPE)is a key material for marine applications owing to its outstanding self-lubrication and corrosion resistance.However,its long-term performance is compromised by plastic ...Ultra-high molecular weight polyethylene(UHMWPE)is a key material for marine applications owing to its outstanding self-lubrication and corrosion resistance.However,its long-term performance is compromised by plastic deformation in seawater.In this study,we performed a comparative analysis of the UHMWPE dynamics under seawater and water conditions to investigate the plastic deformation of UHMWPE induced by seawater.The results show that the plastic deformation of UHMWPE is amplified in seawater relative to the water conditions.Under thin fluid conditions,frictional interfaces exhibit a higher interfacial friction force and interaction energy in seawater than in water.Compared to freely diffused water molecules,hydrated ions occupy larger interchain spaces within polyethylene.Furthermore,the diffusion of hydrated ions weakens the interchain interactions,promoting more severe polyethylene chain rearrangement and accelerating seawater-induced plastic deformation in UHMWPE during friction.Furthermore,the diffused seawater accelerated the disentangling of the polyethylene chains and enhanced the orderly orientation distribution of polyethylene.Compared to free water molecules,the water molecules of hydrated ions exhibit enhanced attraction to free-flowing water molecules,thereby accelerating seawater flow across submerged UHMWPE surfaces.This flow enhancement promotes surface polyethylene chain mobility in seawater.展开更多
In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic per...In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks.展开更多
Organic electrochemical transistors(OECTs)are promising for next-generation bioelectronics due to their high performance and biocompatibility.Nevertheless,they still face tremendous operational stability challenges du...Organic electrochemical transistors(OECTs)are promising for next-generation bioelectronics due to their high performance and biocompatibility.Nevertheless,they still face tremendous operational stability challenges due to the limited robustness of the organic mixed ionic-electronic conductor(OMIEC)channel.Here,by modulating the molecular weight(MW)of OMiEC,enhanced OECT and relevant circuit operation stabilities are demonstrated,showing more than 3,000,0o0 full cycles(~42 h)with less than 15%current variation in an OECT,and 150,000 cycles(~4 h)with less than 5%voltage variation in an OECT-based inverter,which are among the highest of reported OECT-based electronics.Specifically,p(g2T-T),a typical p-type OMIEC,with varying MW(7-43 kDa),is synthesized,where lower-MW p(g2T-T)(~9 kDa)exhibits superior device performance and cycling stability in OECTs,outperforming those in high-MW counterparts(>30 kDa).It is indicated that low-MW p(g2T-T)maintains higher volumetric capacitance,ordered orientation,and reduced swelling.Therefore,irreversible microstructural degradation is effectively avoided,along with better performance yield.Furthermore,MW regulation enables physiological signal sensing with high tolerance to body fluid environments for 7 days.These findings highlight MW modulation as a versatile approach to suppress excessive swelling,advancing the design of durable OECT-based electronics.展开更多
In Global Navigation Satellite System(GNSS)meteo rology,the atmospheric weighted mean temperatu re(T_(m))is a critical intermediate parameter for converting zenith wet delay(ZWD)to precipitable water vapor(PWV),essent...In Global Navigation Satellite System(GNSS)meteo rology,the atmospheric weighted mean temperatu re(T_(m))is a critical intermediate parameter for converting zenith wet delay(ZWD)to precipitable water vapor(PWV),essential for accurate atmospheric water content estimation.However,global models often overlook regional climatic variability,leading to reduced accuracy in localized applications.This study introduces an improved T_(m)model developed using radiosonde observations across Iran and GNSS radio occultation(RO)profiles from CHAMP,GRACE,MetOp-A/B/C,COSMIC,TerraSAR-X,and TanDEM-X missions collected between 2007 and 2022.A novel integral formulation was proposed to estimate T_(m)more accurately by incorporating vertical water vapor distribution and temperature linearity.Based on this formulation,three regional T_(m)models were constructed using annual,semiannual,and diurnal periodicities,along with surface temperature(T_(s)),each varying in structure and complexity.Validation against independent radiosonde observations from 2022 showed that Models Two and Three outperformed the Bevis model,reducing RMSE by 30.7%.When evaluated against GNSS RO profiles,Model One—excluding T_(s)due to its inaccessibility in RO data—yielded the highest accuracy,with a 42.6%improvement in RMSE over the Bevis model.To evaluate the practical effectiveness of the proposed T_(m)model,PWV was derived from GNSS data at the tehn and tabz stations during the second half of 2022and compared with PWV values obtained from co-located radiosonde observations in Tehran and Tabriz.Using T_(m)from Model One improved PWV estimation compared to the Bevis model,reducing RMSE and MAE by up to 54%and 53.8%in Tabriz and 50.6%and 52.9%in Tehran,respectively.These results demonstrate that regionalized T_(m)modeling,particularly approaches that avoid dependence on T_(s),can significantly enhance GNSS-based PWV estimation in areas with limited surface data.展开更多
Path planning for Unmanned Aerial Vehicles(UAVs)in complex environments presents several challenges.Traditional algorithms often struggle with the complexity of high-dimensional search spaces,leading to inefficiencies...Path planning for Unmanned Aerial Vehicles(UAVs)in complex environments presents several challenges.Traditional algorithms often struggle with the complexity of high-dimensional search spaces,leading to inefficiencies.Additionally,the non-linear nature of cost functions can cause algorithms to become trapped in local optima.Furthermore,there is often a lack of adequate consideration for real-world constraints,for example,due to the necessity for obstacle avoidance or because of the restrictions of flight safety.To address the aforementioned issues,this paper proposes a dynamic weighted spherical particle swarm optimization(DW-SPSO)algorithm.The algorithm adopts a dual Sigmoid-based adaptive weight adjustment mechanism for balancing global exploration and local exploitation,as well as a lens-based opposition learning one to improve search flexibility and solution diversity.Simulation experiments on real digital elevation models demonstrate that DW-SPSO significantly outperforms recent state-of-the-art particle swarm optimization(PSO)variants in terms of path safety,smoothness,and convergence speed.The performance superiority is statistically validated by the Wilcoxon signed-rank test.The results confirm the algorithm’s effectiveness in generating high-quality UAV paths under diverse threat conditions,offering a robust solution for autonomous navigation systems.展开更多
The accessibility of urban public transit directly influences residents’quality of life,travel behavior,and social equity.Its correlation with housing prices has garnered significant attention across disciplines such...The accessibility of urban public transit directly influences residents’quality of life,travel behavior,and social equity.Its correlation with housing prices has garnered significant attention across disciplines such as geography,economics,and urban planning.Although much existing research focuses on the impact of individual transportation facilities on housing prices,there is a notable gap in comprehensive analyses that assess the influence of overall urban transit accessibility on housing market dynamics.This study selected the main urban area of Hefei,China,as a case to investigate the spatial distribution of housing prices and evaluate public transit accessibility in 2022.Employing techniques such as the optimized parameter geographical detector and local spatial regression models,the study aimed to elucidate the effects and underlying mechanisms of urban transit accessibility on housing prices.The findings revealed that:1)housing prices in Hefei exhibited a clustered spatial pattern,with high prices concentrated in the city center and lower prices in peripheral areas,forming three distinct high-price hotspots with a‘belt-like’distribution;2)public transit accessibility showed a‘coreperiphery’structure,with accessibility declining in a‘circumferential’pattern around the city center.Based on the‘housing price-accessibility’dimension,four categories were identified:high price-high accessibility(37.25%),high price-low accessibility(19.07%),low price-high accessibility(21.95%),and low price-low accessibility(21.73%);3)the impact of transit accessibility on housing prices was spatially heterogeneous,with bus travel showing the strongest explanatory power(0.692),followed by automobile,subway,and bicycle travel.The interaction of these transportation modes generated a synergistic effect on housing price differentiation,with most influencing factors contributing more than 25%.These findings offer valuable insights for optimizing the spatial distribution of public transit infrastructure and improving both urban housing quality and residents’living standards.展开更多
The Transformer has achieved great success in the field of medical image segmentation,but its quadratic computational complexity limits its application in dense medical image prediction.Recently,the receptance weighte...The Transformer has achieved great success in the field of medical image segmentation,but its quadratic computational complexity limits its application in dense medical image prediction.Recently,the receptance weighted key value(RWKV)architecture has garnered widespread attention due to its linear computational complexity and its capability of parallel computation during training.Despite the RWKV model's proficiency in addressing long-range modeling tasks with linear computational complexity,most current RWKV-based approaches employ static scanning patterns.These patterns may inadvertently incorporate biased prior knowledge into the model's predictions.To address this challenge,we propose a multi-head scan strategy combined with padding methods to effectively simulate spatial continuity in 2D images.Within the Feature Aggregation Attention(FAA)module,asymmetric convolutions are designed to aggregate 1D sequence features along a single dimension,thereby expanding effective receptive fields while preserving structural sparsity.Additionally,panoramic token shift(P-Shift)effectively models local dependency relationships by moving tokens from a wide receptive field.Extensive experiments conducted on the ISIC17/18 and ACDC datasets demonstrate that our method exhibits superior performance in dense medical image prediction tasks.展开更多
In eld seismic data acquisition,seismic traces are often aected by substantial data gaps and strong noise interference due to environmental and instrumental factors,thus degrading the resolution and signalto-noise rat...In eld seismic data acquisition,seismic traces are often aected by substantial data gaps and strong noise interference due to environmental and instrumental factors,thus degrading the resolution and signalto-noise ratio(SNR)of the seismic profiles.Effective seismic data reconstruction and noise suppression techniques are therefore essential to recover missing signals and improve data quality.In this study,a fast projection onto convex sets(FPOCS)algorithm is proposed by incorporating an inertial parameter that involves a linear combination of the two preceding iterations based on the traditional projection onto convex sets(POCS)algorithm.Then,a weighting factor is introduced to achieve simultaneous data reconstruction and noise suppression using the weighted fast projection onto convex sets(WFPOCS)algorithm.To further suppress residual random noise in the updated solution,an optimization strategy is adopted by swapping the order of the iterative hard thresholding operator and the projection operator.The nal algorithm,termed the improved weighted fast projection onto convex sets(IWFPOCS),achieves high-efciency reconstruction and effective noise suppression.Compared with WFPOCS,the proposed method maintains fast reconstruction speed while demonstrating superior denoising performance on irregularly missing and noisy datasets.Field data experiments conrm that the proposed method signicantly improves the SNR and resolution of seismic data,oering strong practical potential for subsequent processing and interpretation.展开更多
This study investigated the physicochemical properties,enzyme activities,volatile flavor components,microbial communities,and sensory evaluation of high-temperature Daqu(HTD)during the maturation process,and a standar...This study investigated the physicochemical properties,enzyme activities,volatile flavor components,microbial communities,and sensory evaluation of high-temperature Daqu(HTD)during the maturation process,and a standard system was established for comprehensive quality evaluation of HTD.There were obvious changes in the physicochemical properties,enzyme activities,and volatile flavor components at different storage periods,which affected the sensory evaluation of HTD to a certain extent.The results of high-throughput sequencing revealed significant microbial diversity,and showed that the bacterial community changed significantly more than did the fungal community.During the storage process,the dominant bacterial genera were Kroppenstedtia and Thermoascus.The correlation between dominant microorganisms and quality indicators highlighted their role in HTD quality.Lactococcus,Candida,Pichia,Paecilomyces,and protease activity played a crucial role in the formation of isovaleraldehyde.Acidic protease activity had the greatest impact on the microbial community.Moisture promoted isobutyric acid generation.Furthermore,the comprehensive quality evaluation standard system was established by the entropy weight method combined with multi-factor fuzzy mathematics.Consequently,this study provides innovative insights for comprehensive quality evaluation of HTD during storage and establishes a groundwork for scientific and rational storage of HTD and quality control of sauce-flavor Baijiu.展开更多
Background Birth weight is a critical economic trait in livestock production.However,its genetic architecture remains poorly understood due to historical limitations in sample size and reliance on low-density SNP arra...Background Birth weight is a critical economic trait in livestock production.However,its genetic architecture remains poorly understood due to historical limitations in sample size and reliance on low-density SNP arrays.In this study,we utilized low-coverage whole-genome sequencing(lcWGS)to genotype 3,007 Hu sheep,bypassing the cost and resolution constraints of conventional genotyping arrays while achieving scalable genome-wide variant detection.Results LcWGS with high imputation accuracy(97.8%allelic concordance)enabled genome-wide association studies(GWAS)identifying two novel quantitative trait loci(QTLs)on chromosomes 6 and 9.The chromosome 9 QTL encompassed a regulatory region functionally linked to PLAG1 expression through expression quantitative trait locus(eQTL)mapping.Compared with wild-type homozygotes,heterozygous carriers of the lead SNP(chr9:g.35920172A>G)presented a 9.85%increase in birth weight(3.35 kg vs.3.68 kg;Δ=0.33 kg).Notably,the derived allele of this SNP exhibited low frequencies of<0.1 across most global sheep breeds except Dorper,highlighting its potential for selective breeding applications.Leveraging lcWGS data,haplotype-based fine-mapping prioritized three candidate causal variants.A secondary QTL on chromosome 6 colocalized with the FecB mutation,a well-established locus associated with increased litter size.Intriguingly,individuals carrying one FecB allele showed a 6.18%reduction(0.22 kg)in birth weight,which tentatively indicates potential pleiotropic influences on both growth and reproductive traits.Conclusion This study demonstrates the utility of lcWGS as a cost-effective,high-resolution tool for dissecting complex traits in livestock.Our findings not only advance the understanding of birth weight genetics in sheep but also offer a blueprint for accelerating genetic improvement programs in global livestock production through costeffective,genome-wide approaches.展开更多
Grain yield variation has been associated to variation in grain number per unit area(GN).It has been shown in the last about 40 years that GN is linearly associated to the spike dry weight(SDW)at anthesis in wheat,fac...Grain yield variation has been associated to variation in grain number per unit area(GN).It has been shown in the last about 40 years that GN is linearly associated to the spike dry weight(SDW)at anthesis in wheat,fact that has been useful to understand mechanistically potential grain yield.Fruiting efficiency(FE,grains per gram of spike dry weight),the slope between GN and SDW relationship,has been proposed as a possible trait to improve wheat yield potential.The linear relationship between GN and SDW implies a constant increase in GN per unit increase in spike growth and,then a constant FE.However,there are empirical and theoretical elements suggesting that this relationship would not be linear.In this study,we hypothesised and showed that the linearity of the relationship between GN and SDW would be non-linear for extreme values of SDW,implying that the FE would be noticeably reduced at these extreme cases of dry matter allocation to the juvenile spikes.These results have implications for both,genetic and management improvements in grain yield.展开更多
Defining an ERBB2(HER2/neu)gene amplification status is critical to guiding human epidermal growth factor receptor 2(HER2)-targeted therapy in breast cancer.Up to 40%of breast cancer patients are reported as having an...Defining an ERBB2(HER2/neu)gene amplification status is critical to guiding human epidermal growth factor receptor 2(HER2)-targeted therapy in breast cancer.Up to 40%of breast cancer patients are reported as having an immunohistochemistry(IHC)of HER22+and requiring additional testing using fluorescence in situ hybridization to confirm the results.This paper aims to establish an automatically weighted calibration deep learning(AWCDL)algorithm to predict ERBB2 amplification based on IHC images.In this study,we applied IHC HER22+images from 1,073 breast cancer patients at three cancer centers in China and extracted 376,099 tiles.Among these,269,664 tiles were used for internal and external validation.The designed AWCDL consists of two steps.In Step 1,the internal validation achieved an accuracy of 89%,with a specificity of 0.89 and a sensitivity of 0.89.The external validation in the two other centers showed an average accuracy of 85%,with a specificity of 0.86 and a sensitivity of 0.82.In Step 2,the model achieved higher accuracy for the slides predicted as negative in Step 1 by automatically calibrating the weight.Collectively,these results suggest that this AWCDL model has successfully proved useful as an alternative method to fluorescence in situ hybridization for assessing the ERBB2 amplification status in breast cancer.展开更多
Background:Type 2 diabetes(T2D)accounts for the majority of diabetes incidences and remains a widespread global chronic disorder.Apart from early lifestyle changes,intervention options for T2D are mainly pharmaceutica...Background:Type 2 diabetes(T2D)accounts for the majority of diabetes incidences and remains a widespread global chronic disorder.Apart from early lifestyle changes,intervention options for T2D are mainly pharmaceutical.Methods:Repetitive transcranial magnetic stimulation(rTMS)has been approved by the FDA as a therapeutic intervention option for major depressive disorders,with further studies also indicating its role in energy metabolism and appetite.Considering its safe and non-invasive properties,we evaluated the effects of rTMS on systemic metabolism using T2D rats.Results:We observed that rTMS improved glucose tolerance and insulin sensitivity in T2D rats after a 10-day exposure.Improved systemic insulin sensitivity was main-tained after a 21-day treatment period,accompanied by modest yet significant weight loss.Circulating serum lipid levels,including those of cholesteryl ester,tryglyceride and ceramides,were also reduced following rTMS application.RNA-seq analyses fur-ther revealed a changed expression profile of hepatic genes that are related to sterol production and fatty acid metabolism.Altered expression of hypothalamic genes that are related to appetite regulation,neural activity and ether lipid metabolism were also implicated.Conclusion:In summary,our data report a positive impact of rTMS on systemic insu-lin sensitivity and weight management of T2D rats.The underlying mechanisms via which rTMS regulates systemic metabolic parameters partially involve lipid utilization in the periphery as well as central regulation of energy intake and lipid metabolism.展开更多
Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart ...Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.展开更多
文摘In this work,we proposed a strategy for the hydrolysis of native corn starch after the treatment of corn starch in an ionic liquid aqueous solution,and it is an awfully“green”and simple means to obtain starch with low molecular weight and amorphous state.X-ray diffraction results revealed that the natural starch crystalline region was largely disrupted by ionic liquid owing to the broken intermolecular and intramolecular hydrogen bonds.After hydrolysis,the morphology of starch changed from particles of native corn starch into little pieces,and their molecular weight could be effectively regulated during the hydrolysis process,and also the hydrolyzed starch samples exhibited decreased thermal stability with the extension of hydrolysis time.This work would counsel as a powerful tool for the development of native starch in realistic applications.
文摘In this paper,we present a necessary and sufficient condition for hyponormal block Toeplitz operators T on the vector-valued weighted Bergman space with symbolsΦ(z)=G^(*)(z)+F(z),where F(z)=∑^(N)_(i)=1 A_(i)z^(i)and G(z)=∑^(N)_(i)=1 A_(−i)z^(i),A_(i)ae culants.
基金funded by the National Natural Science Foundation of China(NSFC)under Grant Number 72071209.
文摘Modern battlefields exhibit high dynamism,where traditional static weighting methods in combat effectiveness assessment fail to capture real-time changes in indicator values,leading to limited assessment accuracy—especially critical in scenarios like sudden electronic warfare or degraded command,where static weights cannot reflect the operational value decay or surge of key indicators.To address this issue,this study proposes a dynamic adaptive weightingmethod for evaluation indicators based onG1-CRITIC-PIVW.First,theG1(Sequential Relationship Analysis Method)subjective weighting method—translates expert knowledge into indicator importance rankings—leverages expert knowledge to quantify the relative importance of indicators via sequential relationship ranking,while the CRITIC(Criteria Importance Through Intercriteria Correlation)objective weighting method—derives weights from data characteristics by integrating variability and inter-correlations—calculates weights by integrating indicator variability and inter-indicator correlations,ensuring data-driven objectivity.These two sets of weights are then fused using a deviation coefficient optimization model,minimizing the squared deviation from a reference weight and adjusting the fusion coefficient via Spearman’s rank correlation to resolve potential conflicts between subjective and objective judgments.Subsequently,the PIVW(Punishment-Incentive VariableWeight)theory—adapts weights to realtime indicator performance via penalty/incentive rules—is applied for dynamic adjustment.Scenario-specific penalty λ_(1) and incentive λ_(2) thresholds are set based on operational priorities and indicator volatility,penalizing indicators with values below λ_(1) and incentivizing those exceeding λ_(2) to reflect real-time indicator performance.Experimental validation was conducted using an Air Defense and Anti-Missile(ADAM)system effectiveness assessment framework,with data covering 7 indicators across 3 combat scenarios.Results show that compared to static weighting methods,the proposed method reduces MAE(Mean Absolute Error)by 15%-20% and weighted decision error rate by 84.2%,effectively reducing overestimation/underestimation of combat effectiveness in dynamic scenarios;compared to Entropy-TOPSIS,it lowers MAE by 12% while achieving a weighted Kendall’sτconsistency coefficient of 0.85,ensuring higher alignment with expert judgment.This method enhances the accuracy and scenario adaptability of effectiveness assessment,providing reliable decision support for dynamic battlefield environments.
文摘With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.
基金supported by Biological Breeding-National Science and Technology Major Project(2022ZD04008)National Natural Science Foundation of China(32201854)。
文摘Thousand-seed weight(TSW)is a critical target for genetic improvement in rapeseed(Brassica napus L.).However,phenotypic selection for this trait remains challenging due to its polygenic regulation by multiple quantitative trait loci(QTL).Here,six favorable TSW QTL alleles from two donor parents were introgress into an elite restorer line,621R,using an integrated strategy combining marker-assisted backcrossing and speed breeding protocols.Through six rounds of backcrossing and convergent crossing followed by two generations of selfing strategies,we developed 13 advanced lines with diverse TSW QTL combinations within 24 months.Field evaluations across three environments revealed that all lines exhibited significantly increased TSW in spring conditions(Minle,Gansu)and winter environments(Wuhan and Jiangling,Hubei)except for two lines which only showed increase in the spring environment.Hybridization assays using these lines as male parents crossed with two male-sterile lines(RG430A and 616A)demonstrated transgressive segregation for TSW:For RG430A-derived hybrids,all crosses significantly outperformed the original control(RG430A×621R)in Wuhan,with 8/13 and 9/13 crosses showing significant TSW increases in Minle and Jiangling,respectively.For 616A-derived hybrids,11/13 and 10/13 crosses exhibited significant TSW enhancement in Minle and Jiangling,compared to 3/13 in Wuhan.Notably,two top-performing hybrids achieved 13.0%and 6.8%higher plot yields,respectively.Our results demonstrate that strategic pyramiding of complementary TSW QTL alleles effectively enhances seed weight in rapeseed,and these improved lines represent valuable genetic resources for developing high-yield hybrids.
基金supported by the National Natural Science Foundation of China(62373317)the Tianshan Talent Training Program(2022TSYCCX0013)+3 种基金the Key Project of Natural Science Foundation of Xinjiang(2021D01D10)the Basic Research Foundation for Universities of Xinjiang(XJEDU2023P023)the Xinjiang Key Laboratory of Applied Mathematics(XJDX1401)the Intelligent Control and Optimization Research Platform in Xinjiang University.
文摘This paper is dedicated to fixed-time passivity and synchronization for multi-weighted spatiotemporal directed networks.First,to achieve fixed-time passivity,a type of decentralized power-law controller is developed,in which only one parameter needs to be adjusted in the power-law terms;this greatly decreases the inconvenience of parameter adjustment.Second,several fixed-time passivity criteria with LMI forms are derived by using a Gauss divergence theorem to deal with the spatial diffusion of nodes and by applying the Hölder’s inequality to dispose rigorously the power-law term greater than one in the designed control scheme;this improves the previous theoretical analysis.Additionally,the fixed-time synchronization of spatiotemporal directed networks with multi-weights is addressed as a direct result of fixed-time strict passivity.Finally,a numerical example is presented in order to show the validity of the theoretical analysis.
基金financially supported by the National Natural Science Foundation of China(Nos.51909023 and 51775077)the Natural Science Foundation of Liaoning Province(No.2021-MS-140)the Fundamental Research Funds for the Central Universities(No.3132025114)。
文摘Ultra-high molecular weight polyethylene(UHMWPE)is a key material for marine applications owing to its outstanding self-lubrication and corrosion resistance.However,its long-term performance is compromised by plastic deformation in seawater.In this study,we performed a comparative analysis of the UHMWPE dynamics under seawater and water conditions to investigate the plastic deformation of UHMWPE induced by seawater.The results show that the plastic deformation of UHMWPE is amplified in seawater relative to the water conditions.Under thin fluid conditions,frictional interfaces exhibit a higher interfacial friction force and interaction energy in seawater than in water.Compared to freely diffused water molecules,hydrated ions occupy larger interchain spaces within polyethylene.Furthermore,the diffusion of hydrated ions weakens the interchain interactions,promoting more severe polyethylene chain rearrangement and accelerating seawater-induced plastic deformation in UHMWPE during friction.Furthermore,the diffused seawater accelerated the disentangling of the polyethylene chains and enhanced the orderly orientation distribution of polyethylene.Compared to free water molecules,the water molecules of hydrated ions exhibit enhanced attraction to free-flowing water molecules,thereby accelerating seawater flow across submerged UHMWPE surfaces.This flow enhancement promotes surface polyethylene chain mobility in seawater.
基金supported by the Funds for Central-Guided Local Science and Technology Development(Grant No.202407AC110005)Key Technologies for the Construction of a Whole-Process Intelligent Service System for Neuroendocrine Neoplasm.Supported by 2023 Opening Research Fund of Yunnan Key Laboratory of Digital Communications(YNJTKFB-20230686,YNKLDC-KFKT-202304).
文摘In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks.
基金supported by the National Key R&D Program of China(2024YFB3211600)the National Natural Science Foundation of China(Nos.62273073,52273316)+4 种基金the National Key R&D Program of China(2023YFC2411800,2022YFE0134800)the Natural Science Foundation of Sichuan(2025ZNSFSC0515)Chengdu Science Technology Bureau(2023-YF06-00028-HZ)and the Fundamental Research Funds for the Central Universities(ZYGX2025TS009,ZYGX2024XJ029,ZYGX2024XJ031)Sci-entific Research Innovation Capability Support Project for Young Faculty(ZYGXQNJSKYCXNLZCXM-M1P).
文摘Organic electrochemical transistors(OECTs)are promising for next-generation bioelectronics due to their high performance and biocompatibility.Nevertheless,they still face tremendous operational stability challenges due to the limited robustness of the organic mixed ionic-electronic conductor(OMIEC)channel.Here,by modulating the molecular weight(MW)of OMiEC,enhanced OECT and relevant circuit operation stabilities are demonstrated,showing more than 3,000,0o0 full cycles(~42 h)with less than 15%current variation in an OECT,and 150,000 cycles(~4 h)with less than 5%voltage variation in an OECT-based inverter,which are among the highest of reported OECT-based electronics.Specifically,p(g2T-T),a typical p-type OMIEC,with varying MW(7-43 kDa),is synthesized,where lower-MW p(g2T-T)(~9 kDa)exhibits superior device performance and cycling stability in OECTs,outperforming those in high-MW counterparts(>30 kDa).It is indicated that low-MW p(g2T-T)maintains higher volumetric capacitance,ordered orientation,and reduced swelling.Therefore,irreversible microstructural degradation is effectively avoided,along with better performance yield.Furthermore,MW regulation enables physiological signal sensing with high tolerance to body fluid environments for 7 days.These findings highlight MW modulation as a versatile approach to suppress excessive swelling,advancing the design of durable OECT-based electronics.
文摘In Global Navigation Satellite System(GNSS)meteo rology,the atmospheric weighted mean temperatu re(T_(m))is a critical intermediate parameter for converting zenith wet delay(ZWD)to precipitable water vapor(PWV),essential for accurate atmospheric water content estimation.However,global models often overlook regional climatic variability,leading to reduced accuracy in localized applications.This study introduces an improved T_(m)model developed using radiosonde observations across Iran and GNSS radio occultation(RO)profiles from CHAMP,GRACE,MetOp-A/B/C,COSMIC,TerraSAR-X,and TanDEM-X missions collected between 2007 and 2022.A novel integral formulation was proposed to estimate T_(m)more accurately by incorporating vertical water vapor distribution and temperature linearity.Based on this formulation,three regional T_(m)models were constructed using annual,semiannual,and diurnal periodicities,along with surface temperature(T_(s)),each varying in structure and complexity.Validation against independent radiosonde observations from 2022 showed that Models Two and Three outperformed the Bevis model,reducing RMSE by 30.7%.When evaluated against GNSS RO profiles,Model One—excluding T_(s)due to its inaccessibility in RO data—yielded the highest accuracy,with a 42.6%improvement in RMSE over the Bevis model.To evaluate the practical effectiveness of the proposed T_(m)model,PWV was derived from GNSS data at the tehn and tabz stations during the second half of 2022and compared with PWV values obtained from co-located radiosonde observations in Tehran and Tabriz.Using T_(m)from Model One improved PWV estimation compared to the Bevis model,reducing RMSE and MAE by up to 54%and 53.8%in Tabriz and 50.6%and 52.9%in Tehran,respectively.These results demonstrate that regionalized T_(m)modeling,particularly approaches that avoid dependence on T_(s),can significantly enhance GNSS-based PWV estimation in areas with limited surface data.
基金supported by the National Natural Science Foundation of China(Grant No.62106092)the Natural Science Foundation of Fujian Province(Grant Nos.2024J01822,2025J01981)the Natural Science Foundation of Zhangzhou City(Grant No.ZZ2024J28).
文摘Path planning for Unmanned Aerial Vehicles(UAVs)in complex environments presents several challenges.Traditional algorithms often struggle with the complexity of high-dimensional search spaces,leading to inefficiencies.Additionally,the non-linear nature of cost functions can cause algorithms to become trapped in local optima.Furthermore,there is often a lack of adequate consideration for real-world constraints,for example,due to the necessity for obstacle avoidance or because of the restrictions of flight safety.To address the aforementioned issues,this paper proposes a dynamic weighted spherical particle swarm optimization(DW-SPSO)algorithm.The algorithm adopts a dual Sigmoid-based adaptive weight adjustment mechanism for balancing global exploration and local exploitation,as well as a lens-based opposition learning one to improve search flexibility and solution diversity.Simulation experiments on real digital elevation models demonstrate that DW-SPSO significantly outperforms recent state-of-the-art particle swarm optimization(PSO)variants in terms of path safety,smoothness,and convergence speed.The performance superiority is statistically validated by the Wilcoxon signed-rank test.The results confirm the algorithm’s effectiveness in generating high-quality UAV paths under diverse threat conditions,offering a robust solution for autonomous navigation systems.
基金Under the auspices of the National Natural Science Foundation of China(No.42271224,41901193)Ministry of Edu cation Humanities and Social Sciences Research Planning Fund Project of China(No.24YJAZH190)+1 种基金Anhui Province Excellent Youth Research Project in Universities(No.2022AH030019)Anhui Social Sciences Innovation Development Research Project(No.2024CXQ503)。
文摘The accessibility of urban public transit directly influences residents’quality of life,travel behavior,and social equity.Its correlation with housing prices has garnered significant attention across disciplines such as geography,economics,and urban planning.Although much existing research focuses on the impact of individual transportation facilities on housing prices,there is a notable gap in comprehensive analyses that assess the influence of overall urban transit accessibility on housing market dynamics.This study selected the main urban area of Hefei,China,as a case to investigate the spatial distribution of housing prices and evaluate public transit accessibility in 2022.Employing techniques such as the optimized parameter geographical detector and local spatial regression models,the study aimed to elucidate the effects and underlying mechanisms of urban transit accessibility on housing prices.The findings revealed that:1)housing prices in Hefei exhibited a clustered spatial pattern,with high prices concentrated in the city center and lower prices in peripheral areas,forming three distinct high-price hotspots with a‘belt-like’distribution;2)public transit accessibility showed a‘coreperiphery’structure,with accessibility declining in a‘circumferential’pattern around the city center.Based on the‘housing price-accessibility’dimension,four categories were identified:high price-high accessibility(37.25%),high price-low accessibility(19.07%),low price-high accessibility(21.95%),and low price-low accessibility(21.73%);3)the impact of transit accessibility on housing prices was spatially heterogeneous,with bus travel showing the strongest explanatory power(0.692),followed by automobile,subway,and bicycle travel.The interaction of these transportation modes generated a synergistic effect on housing price differentiation,with most influencing factors contributing more than 25%.These findings offer valuable insights for optimizing the spatial distribution of public transit infrastructure and improving both urban housing quality and residents’living standards.
基金Supported by Zhejiang Provincial Natural Science Foundation of China(LY22F020025)the National Natural Science Foundation of China(62072126)。
文摘The Transformer has achieved great success in the field of medical image segmentation,but its quadratic computational complexity limits its application in dense medical image prediction.Recently,the receptance weighted key value(RWKV)architecture has garnered widespread attention due to its linear computational complexity and its capability of parallel computation during training.Despite the RWKV model's proficiency in addressing long-range modeling tasks with linear computational complexity,most current RWKV-based approaches employ static scanning patterns.These patterns may inadvertently incorporate biased prior knowledge into the model's predictions.To address this challenge,we propose a multi-head scan strategy combined with padding methods to effectively simulate spatial continuity in 2D images.Within the Feature Aggregation Attention(FAA)module,asymmetric convolutions are designed to aggregate 1D sequence features along a single dimension,thereby expanding effective receptive fields while preserving structural sparsity.Additionally,panoramic token shift(P-Shift)effectively models local dependency relationships by moving tokens from a wide receptive field.Extensive experiments conducted on the ISIC17/18 and ACDC datasets demonstrate that our method exhibits superior performance in dense medical image prediction tasks.
基金supported in part by the Foundation of National Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing under Grant 2024QZ-TD-13in part by the National Natural Science Foundation of China under Grant 42564006+1 种基金in part by the Natural Science Foundation of Jiangxi Province under Grant 20242BAB26051in part by the Open Fund of SINOPEC Key Laboratory of Geophysics,and in part by support the plan of Ganpo Juncai under Grant 20243BCE51012.
文摘In eld seismic data acquisition,seismic traces are often aected by substantial data gaps and strong noise interference due to environmental and instrumental factors,thus degrading the resolution and signalto-noise ratio(SNR)of the seismic profiles.Effective seismic data reconstruction and noise suppression techniques are therefore essential to recover missing signals and improve data quality.In this study,a fast projection onto convex sets(FPOCS)algorithm is proposed by incorporating an inertial parameter that involves a linear combination of the two preceding iterations based on the traditional projection onto convex sets(POCS)algorithm.Then,a weighting factor is introduced to achieve simultaneous data reconstruction and noise suppression using the weighted fast projection onto convex sets(WFPOCS)algorithm.To further suppress residual random noise in the updated solution,an optimization strategy is adopted by swapping the order of the iterative hard thresholding operator and the projection operator.The nal algorithm,termed the improved weighted fast projection onto convex sets(IWFPOCS),achieves high-efciency reconstruction and effective noise suppression.Compared with WFPOCS,the proposed method maintains fast reconstruction speed while demonstrating superior denoising performance on irregularly missing and noisy datasets.Field data experiments conrm that the proposed method signicantly improves the SNR and resolution of seismic data,oering strong practical potential for subsequent processing and interpretation.
文摘This study investigated the physicochemical properties,enzyme activities,volatile flavor components,microbial communities,and sensory evaluation of high-temperature Daqu(HTD)during the maturation process,and a standard system was established for comprehensive quality evaluation of HTD.There were obvious changes in the physicochemical properties,enzyme activities,and volatile flavor components at different storage periods,which affected the sensory evaluation of HTD to a certain extent.The results of high-throughput sequencing revealed significant microbial diversity,and showed that the bacterial community changed significantly more than did the fungal community.During the storage process,the dominant bacterial genera were Kroppenstedtia and Thermoascus.The correlation between dominant microorganisms and quality indicators highlighted their role in HTD quality.Lactococcus,Candida,Pichia,Paecilomyces,and protease activity played a crucial role in the formation of isovaleraldehyde.Acidic protease activity had the greatest impact on the microbial community.Moisture promoted isobutyric acid generation.Furthermore,the comprehensive quality evaluation standard system was established by the entropy weight method combined with multi-factor fuzzy mathematics.Consequently,this study provides innovative insights for comprehensive quality evaluation of HTD during storage and establishes a groundwork for scientific and rational storage of HTD and quality control of sauce-flavor Baijiu.
基金supported by the Biological Breeding-National Science and Technology Major Project(2022ZD0401403)Shaanxi Provincial Key Research and Development Program(2024NC2-GJHX-15)Shaanxi Livestock and Poultry Breeding Double-chain Fusion Key Project(2022GD-TSLD-46-0401).
文摘Background Birth weight is a critical economic trait in livestock production.However,its genetic architecture remains poorly understood due to historical limitations in sample size and reliance on low-density SNP arrays.In this study,we utilized low-coverage whole-genome sequencing(lcWGS)to genotype 3,007 Hu sheep,bypassing the cost and resolution constraints of conventional genotyping arrays while achieving scalable genome-wide variant detection.Results LcWGS with high imputation accuracy(97.8%allelic concordance)enabled genome-wide association studies(GWAS)identifying two novel quantitative trait loci(QTLs)on chromosomes 6 and 9.The chromosome 9 QTL encompassed a regulatory region functionally linked to PLAG1 expression through expression quantitative trait locus(eQTL)mapping.Compared with wild-type homozygotes,heterozygous carriers of the lead SNP(chr9:g.35920172A>G)presented a 9.85%increase in birth weight(3.35 kg vs.3.68 kg;Δ=0.33 kg).Notably,the derived allele of this SNP exhibited low frequencies of<0.1 across most global sheep breeds except Dorper,highlighting its potential for selective breeding applications.Leveraging lcWGS data,haplotype-based fine-mapping prioritized three candidate causal variants.A secondary QTL on chromosome 6 colocalized with the FecB mutation,a well-established locus associated with increased litter size.Intriguingly,individuals carrying one FecB allele showed a 6.18%reduction(0.22 kg)in birth weight,which tentatively indicates potential pleiotropic influences on both growth and reproductive traits.Conclusion This study demonstrates the utility of lcWGS as a cost-effective,high-resolution tool for dissecting complex traits in livestock.Our findings not only advance the understanding of birth weight genetics in sheep but also offer a blueprint for accelerating genetic improvement programs in global livestock production through costeffective,genome-wide approaches.
基金mainly funded by the State Research Agency of Spain through the Competitive Project PID2021-127415OB-I00 on "Spike fertility in wheat" with some contribution from an AGROTECNIO Seed-funding on "Analysing the physiology of spike density to provide support to selection in breeding programs"RAS did part of the work in this project during a research stay at the Crop Physiology Lab of the University of Lleida co-funded by AUIP (Postgraduate Iberoamerican University Association) grants+1 种基金core funds Crop Physiology Lab of the Ud L. CSC held a Maria Zambrano’s fellowship from the University of Lleida funded by the Spanish Ministry of Universities and the European Social Fund and is a member of CONICET (the Scientific Research Council of Argentina)INTA (the National Institute of Agriculture Technology of Argentina)
文摘Grain yield variation has been associated to variation in grain number per unit area(GN).It has been shown in the last about 40 years that GN is linearly associated to the spike dry weight(SDW)at anthesis in wheat,fact that has been useful to understand mechanistically potential grain yield.Fruiting efficiency(FE,grains per gram of spike dry weight),the slope between GN and SDW relationship,has been proposed as a possible trait to improve wheat yield potential.The linear relationship between GN and SDW implies a constant increase in GN per unit increase in spike growth and,then a constant FE.However,there are empirical and theoretical elements suggesting that this relationship would not be linear.In this study,we hypothesised and showed that the linearity of the relationship between GN and SDW would be non-linear for extreme values of SDW,implying that the FE would be noticeably reduced at these extreme cases of dry matter allocation to the juvenile spikes.These results have implications for both,genetic and management improvements in grain yield.
基金supported by the National Natural Science Foundation of China(Grant No.:81672743 and 81974464).
文摘Defining an ERBB2(HER2/neu)gene amplification status is critical to guiding human epidermal growth factor receptor 2(HER2)-targeted therapy in breast cancer.Up to 40%of breast cancer patients are reported as having an immunohistochemistry(IHC)of HER22+and requiring additional testing using fluorescence in situ hybridization to confirm the results.This paper aims to establish an automatically weighted calibration deep learning(AWCDL)algorithm to predict ERBB2 amplification based on IHC images.In this study,we applied IHC HER22+images from 1,073 breast cancer patients at three cancer centers in China and extracted 376,099 tiles.Among these,269,664 tiles were used for internal and external validation.The designed AWCDL consists of two steps.In Step 1,the internal validation achieved an accuracy of 89%,with a specificity of 0.89 and a sensitivity of 0.89.The external validation in the two other centers showed an average accuracy of 85%,with a specificity of 0.86 and a sensitivity of 0.82.In Step 2,the model achieved higher accuracy for the slides predicted as negative in Step 1 by automatically calibrating the weight.Collectively,these results suggest that this AWCDL model has successfully proved useful as an alternative method to fluorescence in situ hybridization for assessing the ERBB2 amplification status in breast cancer.
基金National Key Technologies Research and Development Program of China,Grant/Award Number:2020YFA0803701National Natural Science Foundation of China,Grant/Award Number:52107241,81973699 and 82274361CAMS Innovation Fund for Medical Sciences,Grant/Award Number:2021-12M-1-052。
文摘Background:Type 2 diabetes(T2D)accounts for the majority of diabetes incidences and remains a widespread global chronic disorder.Apart from early lifestyle changes,intervention options for T2D are mainly pharmaceutical.Methods:Repetitive transcranial magnetic stimulation(rTMS)has been approved by the FDA as a therapeutic intervention option for major depressive disorders,with further studies also indicating its role in energy metabolism and appetite.Considering its safe and non-invasive properties,we evaluated the effects of rTMS on systemic metabolism using T2D rats.Results:We observed that rTMS improved glucose tolerance and insulin sensitivity in T2D rats after a 10-day exposure.Improved systemic insulin sensitivity was main-tained after a 21-day treatment period,accompanied by modest yet significant weight loss.Circulating serum lipid levels,including those of cholesteryl ester,tryglyceride and ceramides,were also reduced following rTMS application.RNA-seq analyses fur-ther revealed a changed expression profile of hepatic genes that are related to sterol production and fatty acid metabolism.Altered expression of hypothalamic genes that are related to appetite regulation,neural activity and ether lipid metabolism were also implicated.Conclusion:In summary,our data report a positive impact of rTMS on systemic insu-lin sensitivity and weight management of T2D rats.The underlying mechanisms via which rTMS regulates systemic metabolic parameters partially involve lipid utilization in the periphery as well as central regulation of energy intake and lipid metabolism.
基金Prince Sattam bin Abdulaziz University project number(PSAU/2023/R/1445)。
文摘Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.
