Straw return increases crop production by improving soil organic carbon sequestration and soil aggregation in a long-term wheat-cotton cropping system

Straw return is a promising strategy for managing soil organic carbon(SOC)and improving yield stability.However,the optimal straw return strategy for sustainable crop production in the wheat(Triticum aestivum L.)-cotton(Gossypium hirsutum L.)cropping system remains uncertain.The objective of this study was to quantify the long-term(10 years)impact of carbon(C)input on SOC sequestration,soil aggregation and crop yields in a wheat-cotton cropping system in the Yangtze River Valley,China.Five treatments were arranged with a single-factor randomized design as follows:no straw return(Control),return of wheat straw only(Wt),return of cotton straw only(Ct),return of 50%wheat and 50%cotton straw(Wh-Ch)and return of 100%wheat and 100%cotton straw(Wt-Ct).In comparison to the Control,the SOC content increased by 8.4 to 20.2%under straw return.A significant linear positive correlation between SOC sequestration and C input(1.42-7.19 Mg ha^(−1)yr^(−1))(P<0.05)was detected.The percentages of aggregates of sizes>2 and 1-2 mm at the 0-20 cm soil depth were also significantly elevated under straw return,with the greatest increase of the aggregate stability in the Wt-Ct treatment(28.1%).The average wheat yields increased by 12.4-36.0%and cotton yields increased by 29.4-73.7%,and significantly linear positive correlations were also detected between C input and the yields of wheat and cotton.The average sustainable yield index(SYI)reached a maximum value of 0.69 when the C input was 7.08 Mg ha^(−1)yr^(−1),which was close to the maximum value(SYI of 0.69,C input of 7.19 Mg ha^(−1)yr^(-1))in the Wt-Ct treatment.Overall,the return of both wheat and cotton straw was the best strategy for improving SOC sequestration,soil aggregation,yields and their sustainability in the wheat-cotton rotation system.

A novel approach to Parkinson's disease treatment with a potentially dual-acting therapeutic agent that targetsα-synuclein aggregation and neuron death

Parkinson's disease(PD),a prevalent neurodegenerative disorder,is chara cterized by the loss of dopaminergic neurons and the aggregation ofα-synuclein protein into Lewy bodies.While the current standards of therapy have been successful in providing some symptom relief,they fail to address the underlying pathophysiology of PD and as a result,they have no effect on disease progression.

RBC aggregation,deformation and adhesion to endothelium:Role of nitric oxide derived from L-Arginine and sodium nitroprusside

Red blood cells(RBCs)are the most abundant human blood cells.RBC aggregation and deformation strongly determine blood viscosity which impacts hemorheology and microcirculation.In turn,RBC properties depend on di®erent endogenous and exogenous factors.One such factor is nitric oxide(NO),which is mainly produced by endothelial cells(EC)from L-arginine amino acid in the circulatory system.Since the mechanisms of the RBC-endothelium interplay are not clear up to date and considering its possible clinical importance,the aims of this study are to investigate in vitro:(1)The effect of L-arginine induced NO on RBC aggregation and adhesion to endothelium;(2)the NO e®ect on RBC aggregation and deformation induced by L-arginine and sodium nitroprusside without the presence of endothelium in the samples.The RBC aggregation and adhesion to a monolayer of EC were studied using optical tweezers(OT).The RBC deformability and aggregation without endothelium in the samples were studied using the flow chamber method and Myrenne aggregometer.We confirmed that NO increases deformability and decreases aggregation of RBCs.We showed that the soluble guanylate cyclase pathway appears to be the only NO signaling pathway involved.In the samples with the endothelium,the "bell-shaped"dependence of RBC aggregation force on L-arginine concentration was observed,which improves our knowledge about the process of NO production by endothelium.Additionally,data related to L-arginine accumulation by endothelium were obtained:Necessity of the presence of extracellular L-arginine stated by other authors was put under question.In our study,NO decreased the RBC-endothelium adhesion,however,the tendency appeared to be weak and was not confirmed in another set of experiments.To our knowledge,this is the first attempt to measure the forces of RBC adhesion to endothelium monolayer with OT.

Impacts of Aggregation Methods and Trophospecies Number on the Structure and Function of Marine Food Webs

Aggregation of species with similar ecological properties is one of the effective methods to simplify food web researches.However,species aggregation will affect not only the complexity of modeling process but also the accuracy of models’outputs.Selection of aggregation methods and the number of trophospecies are the keys to study the simplification of food web.In this study,three aggregation methods,including taxonomic aggregation(TA),structural equivalence aggregation(SEA),and self-organizing maps(SOM),were analyzed and compared with the linear inverse model–Markov Chain Monte Carlo(LIM-MCMC)model.Impacts of aggregation methods and trophospecies number on food webs were evaluated based on the robustness and unitless of ecological net-work indices.Results showed that aggregation method of SEA performed better than the other two methods in estimating food web structure and function indices.The effects of aggregation methods were driven by the differences in species aggregation principles,which will alter food web structure and function through the redistribution of energy flow.According to the results of mean absolute percentage error(MAPE)which can be applied to evaluate the accuracy of the model,we found that MAPE in food web indices will increase with the reducing trophospecies number,and MAPE in food web function indices were smaller and more stable than those in food web structure indices.Therefore,trade-off between simplifying food webs and reflecting the status of ecosystem should be con-sidered in food web studies.These findings highlight the importance of aggregation methods and trophospecies number in the analy-sis of food web simplification.This study provided a framework to explore the extent to which food web models are affected by dif-ferent species aggregation,and will provide scientific basis for the construction of food webs.

Homochiral metalated tetraphenylethylene-based organic cages: Unusual chiral and luminescent behavior depending on thermodynamic and kinetic aggregation

Chirality and luminescence are important for both chemistry and biology,which are highly influenced by aggregation.In this work,a pair of metalated tetraphenylethylene(TPE)-based organic cage enantiomers are reported,which fea-ture a quadrangular prismatic cage structure.These homochiral cages exhibit concentration-dependent chiral behaviors alongside a propensity for thermodynamic aggregation.Aggregation caused quench effect is found for these cages accom-panying the increasing of the concentrations.When a poor solvent is added to produce a kinetical aggregation,the aggregation-annihilation circular dichroism and aggregation-induced emission behaviors are observed for these enantiomeric cages.By comparing these observations with the photophysical behaviors of a pair of structurally similar organic molecular enantiomers,the unique photophysical proper-ties observed are intricately linked to the metal-integrated TPE-functionalized cage structures.

Small molecules to target tau amyloid aggregation

Protein aggregation has been linked with many neurodegenerative diseases,such as Alzheimer’s disease(AD)or Parkinson’s disease.AD belongs to a group of heterogeneous and incurable neurodegenerative disorders collectively known as tauopathies.They comprise frontotemporal dementia,Pick’s disease,or corticobasal degeneration,among others.The symptomatology varies with the specific tau protein variant involved and the affected brain region or cell type.However,they share a common neuropathological hallmark-the formation of proteinaceous deposits named neurofibrillary tangles.Neurofibrillary tangles,primarily composed of aggregated tau(Zhang et al.,2022),disrupt normal neuronal functions,leading to cell death and cognitive decline.

Navigating the pathways:TAR-DNA-binding-protein-43 aggregation,axonal transport,and local synthesis in amyotrophic lateral sclerosis pathology

Neurons are highly polarized cells with axons reaching over a meter long in adult humans.To survive and maintain their proper function,neurons depend on specific mechanisms that regulate spatiotemporal signaling and metabolic events,which need to be carried out at the right place,time,and intensity.Such mechanisms include axonal transport,local synthesis,and liquid-liquid phase separations.Alterations and malfunctions in these processes are correlated to neurodegenerative diseases such as amyotrophic lateral sclerosis(ALS).

Aggregation-regulated bioreduction process of graphene oxide by Shewanella bacteria

The bioreduction of graphene oxide(GO)using environmentally functional bacteria such as Shewanella represents a green approach to produce reduced graphene oxide(rGO).This process differs from the chemical reduction that involves instantaneous molecular reactions.In bioreduction,the contact of bacterial cells and GO is considered the rate-limiting step.To reveal how the bacteria-GO integration regulates rGO production,the comparative experiments of GO and three Shewanella strains were carried out.Fourier-transform infrared spectroscopy,X-ray photoelectron spectroscopy,Raman spectroscopy,and atomic force microscopy were used to characterize the reduction degree and the aggregation degree.The results showed that a spontaneous aggregation of GO and Shewanella into the condensed entity occurred within 36 h.A positive linear correlation was established,linking three indexes of the aggregation potential,the bacterial reduction ability,and the reduction degree(ID/IG)comprehensively.

Aggregation and aging of nanoparticle-protein complexes at interfaces studied by evanescent-light scattering microscopy

Plasma protein-induced aggregation of nanoparticles(NPs)is a crucial issue in many applications,such as drug delivery.Although great efforts have been made to inves-tigate the protein adsorption kinetics or protein-induced NPs coalescence in bulk solutions,limited evidence has been uncovered for interfacial circumstances.Diet,disease,medicine,or senility could thoroughly change interfacial physicochemical properties of the inner lining of blood vessels.Implants including stents and artificial heart valves also have varied and evolutionary interfaces.Hence,there is an urgent need to understand the mechanism behind the non-specific protein adsorption and NP-protein aggregation in such interfacial cases.Here,we use evanescent light scat-tering to observe polystyrene NPs‒fibrinogen aggregation at substrates with varying surface properties.A density-fluctuation correlation function is utilized to reveal the relaxation dynamics of the aggregates.Both time-resolved and spatial-correlated evi-dence shows that the aging process of such soft materials is out-of-equilibrium,where the dynamics faster and slower than exponential can coexist in one sin-gle relaxation process.Besides,corona formation,inner stress,and interconnection together determine the microstructure,local adhesion,and structural relaxation of the aggregates,which can further correspond to the protein-to-NP ratio as well as the surface chemistry of NPs and substrates.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

Differentially Private Support Vector Machines with Knowledge Aggregation

With the widespread data collection and processing,privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals.Support vector machine(SVM)is one of the most elementary learning models of machine learning.Privacy issues surrounding SVM classifier training have attracted increasing attention.In this paper,we investigate Differential Privacy-compliant Federated Machine Learning with Dimensionality Reduction,called FedDPDR-DPML,which greatly improves data utility while providing strong privacy guarantees.Considering in distributed learning scenarios,multiple participants usually hold unbalanced or small amounts of data.Therefore,FedDPDR-DPML enables multiple participants to collaboratively learn a global model based on weighted model averaging and knowledge aggregation and then the server distributes the global model to each participant to improve local data utility.Aiming at high-dimensional data,we adopt differential privacy in both the principal component analysis(PCA)-based dimensionality reduction phase and SVM classifiers training phase,which improves model accuracy while achieving strict differential privacy protection.Besides,we train Differential privacy(DP)-compliant SVM classifiers by adding noise to the objective function itself,thus leading to better data utility.Extensive experiments on three high-dimensional datasets demonstrate that FedDPDR-DPML can achieve high accuracy while ensuring strong privacy protection.

A non-probabilistic reliability topology optimization method based on aggregation function and matrix multiplication considering buckling response constraints

A non-probabilistic reliability topology optimization method is proposed based on the aggregation function and matrix multiplication.The expression of the geometric stiffness matrix is derived,the finite element linear buckling analysis is conducted,and the sensitivity solution of the linear buckling factor is achieved.For a specific problem in linear buckling topology optimization,a Heaviside projection function based on the exponential smooth growth is developed to eliminate the gray cells.The aggregation function method is used to consider the high-order eigenvalues,so as to obtain continuous sensitivity information and refined structural design.With cyclic matrix programming,a fast topology optimization method that can be used to efficiently obtain the unit assembly and sensitivity solution is conducted.To maximize the buckling load,under the constraint of the given buckling load,two types of topological optimization columns are constructed.The variable density method is used to achieve the topology optimization solution along with the moving asymptote optimization algorithm.The vertex method and the matching point method are used to carry out an uncertainty propagation analysis,and the non-probability reliability topology optimization method considering buckling responses is developed based on the transformation of non-probability reliability indices based on the characteristic distance.Finally,the differences in the structural topology optimization under different reliability degrees are illustrated by examples.

Exploring and leveraging aggregation effects on reactive oxygen species generation in photodynamic therapy

Aggregate-level photodynamic therapy(PDT)has attracted significant interest and driven substantial advances in multifunction phototheranostic platforms.As exem-plified by two typical instances of aggregation-caused quenching of reactive oxygen species(ROS)and aggregation-induced generation of ROS,the aggregation effect plays a significant role on the ROS generation of photosensitizers(PSs),which is worthy of in-depth exploration and full utilization.However,in contrast to the well-developed researches on the aggregation effect on luminescence,the studies concerning the aggregation effect on ROS generation are currently in a relatively nascent and disjointed stage,lacking guidance from afirmly established research paradigm.To advance this regard,this review aims at providing a consolidated overview of the fundamental principles and research status of aggregation effects on the ROS generation.Here,the research status can be organized into two main facets.One involves the comparison between isolated state and aggregated state,which is mainly conducted by two methods of changing solvent environments and adding adjuvants into a given solvent.The other underscores the distinctions between different aggregate states,consisting of three parts,namely comparison within the same or between different categories based on the classification of single-component and multicomponent aggregates.In this endeavor,we will present our views on cur-rent research methodologies that explore how aggregation affects ROS generation and highlight the design strategies to leverage the aggregation effect to optimize PS regiments.We aspire this review to propel the advancement of phototheranostic plat-forms and accelerate the clinical implementation of precision medicine,and inspire more contributions to aggregate-level photophysics and photochemistry,pushing the aggregate science and materials forward.

Bimerons create bimerons: Proliferation and aggregation induced by currents and magnetic fields

The aggregation of topological spin textures at nano and micro scales has prac-tical applications in spintronic technologies.Here,the authors report the in-plane current-induced proliferation and aggregation of bimerons in a bulk chiral magnet.It is found that the spin-transfer torques can induce the proliferation and aggrega-tion of bimerons only in the presence of an appropriate out-of-plane magneticfield.It is also found that a relatively small damping and a relatively large non-adiabatic spin-transfer torque could lead to more pronounced bimeron proliferation and aggre-gation.Particularly,the current density should be larger than a certain threshold in order to trigger the proliferation;namely,the bimerons may only be driven into translational motion under weak current injection.Besides,the authorsfind that the aggregate bimerons could relax into a deformed honeycomb bimeron lattice with a few lattice structure defects after the current injection.The results are promising for the development of bio-inspired spintronic devices that use a large number of aggregate bimerons.Thefindings also provide a platform for studying aggregation-induced effects in spintronic systems,such as the aggregation-induced lattice phase transitions.

Cloud-Edge Collaborative Federated GAN Based Data Processing for IoT-Empowered Multi-Flow Integrated Energy Aggregation Dispatch

The convergence of Internet of Things(IoT),5G,and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing.While generative adversarial networks(GANs)are instrumental in resource scheduling,their application in this domain is impeded by challenges such as convergence speed,inferior optimality searching capability,and the inability to learn from failed decision making feedbacks.Therefore,a cloud-edge collaborative federated GAN-based communication and computing resource scheduling algorithm with long-term constraint violation sensitiveness is proposed to address these challenges.The proposed algorithm facilitates real-time,energy-efficient data processing by optimizing transmission power control,data migration,and computing resource allocation.It employs federated learning for global parameter aggregation to enhance GAN parameter updating and dynamically adjusts GAN learning rates and global aggregation weights based on energy consumption constraint violations.Simulation results indicate that the proposed algorithm effectively reduces data processing latency,energy consumption,and convergence time.

The aggregation behaviors of organic radicals in polar fluorinated arenes

Polarfluorinated arenes can promote organic free radical reactions,which have attracted scientists’interest in recent years.However,it is still unknown how these solvents interact weakly with organic radical molecules to influence their reactiv-ity.In this study,we investigated how organic free radicals aggregate infive polarfluorocarbon solvents,and demonstrated that different substituents can influence their aggregation behaviors.In these solvents,small organic radicals with simple substituents maintain a homogeneous solution;however,radicals with substituents that form intermolecular hydrogen bonds or with long-chain aliphatic hydrocarbons tend to aggregate in them,whereas substituents of long-chain aliphatic hydrocar-bons tend to promote aggregation better.The critical aggregation concentrations of these aggregates are measured by concentration-dependent UV–visible spec-troscopy.Their topological morphologies are all spherical based on TEM.The compactness and rotational motivation speed of radical molecules within these aggregates are determined by EPR spectroscopy.The particle sizes of these aggre-gates are determined by analyzing their cyclic voltammograms.Most excitingly,electrochemical experiments reveal that the aggregation behaviors of free radical molecules with intermolecular hydrogen bonds can significantly increase their cat-alytic rate for electro-oxidizing benzyl alcohol in such a solvent.The results of this study indicate that in polarfluorinated arenes organic radical molecules’aggregation behaviors are related to their structures.This may provide guidelines for regulating organic radical reactivity in these solvents in the future.

Fake News Detection Based on Cross-Modal Message Aggregation and Gated Fusion Network

Social media has become increasingly significant in modern society,but it has also turned into a breeding ground for the propagation of misleading information,potentially causing a detrimental impact on public opinion and daily life.Compared to pure text content,multmodal content significantly increases the visibility and share ability of posts.This has made the search for efficient modality representations and cross-modal information interaction methods a key focus in the field of multimodal fake news detection.To effectively address the critical challenge of accurately detecting fake news on social media,this paper proposes a fake news detection model based on crossmodal message aggregation and a gated fusion network(MAGF).MAGF first uses BERT to extract cumulative textual feature representations and word-level features,applies Faster Region-based ConvolutionalNeuralNetwork(Faster R-CNN)to obtain image objects,and leverages ResNet-50 and Visual Geometry Group-19(VGG-19)to obtain image region features and global features.The image region features and word-level text features are then projected into a low-dimensional space to calculate a text-image affinity matrix for cross-modal message aggregation.The gated fusion network combines text and image region features to obtain adaptively aggregated features.The interaction matrix is derived through an attention mechanism and further integrated with global image features using a co-attention mechanism to producemultimodal representations.Finally,these fused features are fed into a classifier for news categorization.Experiments were conducted on two public datasets,Twitter and Weibo.Results show that the proposed model achieves accuracy rates of 91.8%and 88.7%on the two datasets,respectively,significantly outperforming traditional unimodal and existing multimodal models.

Optimizing data aggregation and clustering in Internet of things networks using principal component analysis and Q-learning

The Internet of things(IoT)is a wireless network designed to perform specific tasks and plays a crucial role in various fields such as environmental monitoring,surveillance,and healthcare.To address the limitations imposed by inadequate resources,energy,and network scalability,this type of network relies heavily on data aggregation and clustering algorithms.Although various conventional studies have aimed to enhance the lifespan of a network through robust systems,they do not always provide optimal efficiency for real-time applications.This paper presents an approach based on state-of-the-art machine-learning methods.In this study,we employed a novel approach that combines an extended version of principal component analysis(PCA)and a reinforcement learning algorithm to achieve efficient clustering and data reduction.The primary objectives of this study are to enhance the service life of a network,reduce energy usage,and improve data aggregation efficiency.We evaluated the proposed methodology using data collected from sensors deployed in agricultural fields for crop monitoring.Our proposed approach(PQL)was compared to previous studies that utilized adaptive Q-learning(AQL)and regional energy-aware clustering(REAC).Our study outperformed in terms of both network longevity and energy consumption and established a fault-tolerant network.

Quantum homomorphic broadcast multi-signature based on homomorphic aggregation

Quantum multi-signature has attracted extensive attention since it was put forward.Beside its own improvement,related research is often combined with other quantum signature.However,this type of quantum signature has one thing in common,that is,the generation and verification of signature depend heavily on the shared classical secret key.In order to increase the reliability of signature,the homomorphic aggregation technique is applied to quantum multi-signature,and then we propose a quantum homomorphic multi-signature protocol.Unlike previous quantum multi-signature protocols,this protocol utilizes homomorphic properties to complete signature generation and verification.In the signature generation phase,entanglement swapping is introduced,so that the individual signatures of multiple users are aggregated into a new multi-signature.The original quantum state is signed by the shared secret key to realize the verification of the signature in the verification phase.The signature process satisfies the homomorphic property,which can improve the reliability of the signature.

Distributed Weighted Data Aggregation Algorithm in End-to-Edge Communication Networks Based on Multi-armed Bandit

As a combination of edge computing and artificial intelligence,edge intelligence has become a promising technique and provided its users with a series of fast,precise,and customized services.In edge intelligence,when learning agents are deployed on the edge side,the data aggregation from the end side to the designated edge devices is an important research topic.Considering the various importance of end devices,this paper studies the weighted data aggregation problem in a single hop end-to-edge communication network.Firstly,to make sure all the end devices with various weights are fairly treated in data aggregation,a distributed end-to-edge cooperative scheme is proposed.Then,to handle the massive contention on the wireless channel caused by end devices,a multi-armed bandit(MAB)algorithm is designed to help the end devices find their most appropriate update rates.Diffe-rent from the traditional data aggregation works,combining the MAB enables our algorithm a higher efficiency in data aggregation.With a theoretical analysis,we show that the efficiency of our algorithm is asymptotically optimal.Comparative experiments with previous works are also conducted to show the strength of our algorithm.